A Methodology to Reduce Tank Inspection Frequency for U.S. Navy Surface Ships

by Brian S. Tait

B.S. in History, May 1989, U.S. Naval Academy

M.S. in Electrical Engineering, December 1995, U.S Naval Postgraduate School

A Praxis submitted to

The Faculty of

The School of Engineering and Applied Science

of The George Washington University

in partial fulfillment of the requirements

for the degree of Doctor of Engineering

January 10, 2020

Praxis directed by

Timothy Blackburn

Professorial Lecturer of Engineering Management and Systems Engineering

Amir Etemadi

Associate Professor of Engineering and Applied Science

ii

The School of Engineering and Applied Science of The George Washington University

certifies that Brian Steven Tait has passed the Final Examination for the degree of Doctor

of Engineering as of October 14, 2019. This is the final and approved form of the Praxis.

A Methodology to Reduce Tank Inspection Frequency for U.S. Navy Surface Ships

Brian S. Tait

Praxis Research Committee:

Timothy Blackburn, Professorial Lecturer of Engineering Management and

Systems Engineering, Praxis Co-Director

Amir Etemadi, Associate Professor of Engineering and Applied Science, Praxis

Co-Director

Ebrahim Malalla, Visiting Associate Professor of Engineering and Applied

Science, Committee Member

iii

© Copyright 2019 Brian S. Tait

All rights reserved

iv

Dedication

This praxis is dedicated to the engineers of the United States Navy, past, present

and future. Your engineering disciplines are indispensable in the formation and

sustainment of naval forces.

.

v

Acknowledgements

The author wishes to thank the LORD for His sovereignty and provision over all

things, especially this praxis. Thank You for answering prayer and being an ever-present

help. The author wishes to thank his wonderful wife and family for their enduring love

and support, patience and encouragement to undertake and complete such a formidable

endeavor.

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Abstract of Praxis

A Methodology to Reduce Tank Inspection Frequency for US Navy Surface Ships

The United States Navy is, by far, the largest navy in the world. With over 200

vessels that have more than 15,000 shipboard tanks to maintain, an average of nearly

2,000 tank inspections has been conducted annually since 2011 (Csapo, 2019). Twenty-

five percent of the annual Navy’s maintenance budget is spent on corrosion (Parsons,

1994). This research effort introduced a novel methodology for analyzing and integrating

factors of ultra-high and high solids (UHS/HS) tank coating types, coating age, surface

preparation method, ship class, tank type and tank criticality from existing U.S. Navy

ship tank inspection databases in order to reduce required tank inspection frequencies by

25-50%. Ways to mitigate model potential overconfidence were explored. It was also

demonstrated that significant savings were accrued by a reduction of required tank

inspections. The practical applications of this proposed methodology are: 1) tank

inspection periodicities will be optimized, 2) unnecessary tank inspections will not be

conducted, saving shipyard resources (cost and schedule) and 3) future planning efforts

will be adjusted.

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Table of Contents

Dedication……………………….…………………………………………….………….iv

Acknowledgements………………………………………………………………………..v

Abstract of Praxis………………..…………………………………………...…………...vi

List of Figures………………………………………………………...………..................xi

List of Tables …………………………………………………………….……...……..xiii

List of Acronyms……....…………………………………………………………......…xvi

Glossary of Terms……….……………….………………………………………..……xvii

Chapter 1—Introduction………………………………………………………...….…..1

1.1 Background…………………...……………………………………………………..1

1.2 Research Motivation……………….......……………………………………………2

1.3 Problem Statement………...………………………………………………………...3

1.4 Thesis Statement…………………...………………………………………………..3

1.5 Research Objectives……………...………………………………………………….3

1.6 Research Questions and Hypotheses……………...…………………………………4

1.7 Scope of Research………………………...…………………………………………5

1.8 Research Limitations………………………………………...………………………6

1.9 Organization of Praxis……………...………………………………………………..6

Chapter 2—Literature Review….…………...……………………...…………………..7

2.1 Introduction……………………..…………………………………………………...7

2.2 Overview……………..…………………………………………...…………………7

2.3 Corrosion……..…………………………………………………...…………………8

2.3.1 Corrosion Process………..………………………………………………………8

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2.3.2 Corrosion Types…….…….……………………………………………………..8

2.3.3 Corrosion Phases……..………………………………………………………….9

2.3.4 Factors Affecting Corrosion……………..……………………………………....9

2.3.5 Corrosion Rates……..………………………………………………………….11

2.3.6 Corrosion Inspections…….…………………………………..………………...12

2.3.7 Corrosion Consequences………………………………………………………..12

2.4 Corrosion Prevention……………………………………………………………….13

2.5 Coatings………………………………………………………………………….…13

2.6 Corrosion Modeling…………………………………………………………….…..14

2.7 Corrosion Risk Assessment………………...……………………………………....21

2.8 Cost of Corrosion…………………………………………………………………...22

2.9 Summary and Conclusion……………………………………………...…………...23

Chapter 3—Methodology…………………...………………………………..……..….24

3.1 Introduction……………………………………………………………..…………..24

3.2 Surface Ship Tank Inspection Data………………………………………..…….….24

3.3 Database Shaping……………………………………………………………..…….25

3.4 Database Filtering……………………………………………………………….….26

3.5 H1A and H1B Database Analysis……………………………………………….….27

3.5.1 H1A Data Set……………………………………………………………………28

3.5.2 H1B Data Set…..……..……………..………….………………….……………29

3.5.3 H1A and H1B Data Partitioning…..………..….………………………..……...30

3.5.4 H1A and H1B Model Training……..……..….………………………..……….30

3.5.5 H1A and H1B Model Testing……..……..….…………………………..……...31

ix

3.5.6 Model Classification Matrix and Optimization..……………………….......….33

3.5.7 H1A Coating Age Prediction Tool……………..……………………………...37

3.5.8 H1B Structural Score Prediction Tool…………..……………………………..37

3.6 H2 Data and Data Analysis…….…..………………………………………………38

3.7 H3 Data and Data Analysis…………………………………………...……………39

3.8 Conclusion……………………………..……………………………………..……41

Chapter 4—Results……………………...……………………………………………...42

4.1 Introduction………...……………………………………………………………….42

4.2 Models’ Assumptions and Results……..…………………………………………..42

4.2.1 Database Shaping and Filtering Results….…………………………………….48

4.3 H1A Database Composition…………..……………………………………………50

4.3.1 H1A Train Database Composition………………..…………………………….53

4.3.2 H1A Test Database Composition…………………………..…………………...56

4.4 H1B Database Composition……………...………………………………………....59

4.4.1 H1B Train Database Composition…..…………………………………………..60

4.4.2 H1B Test Database .……………………..…………………………………...…62

4.5 H1A OLR Results…………………………………………………………………..65

4.5.1 H1A Model Training and Testing Results………..……………………………..66

4.5.2 H1A Model Optimization and Classification Results………………….....……..69

4.5.3 H1A Prediction Tools…………………..…………………………………….…71

4.5.3.1 H1A Coating Score Prediction Tool #1…….…………………………….…71

4.5.3.2 H1A Coating Score Prediction Tool #2……….…………………………….72

4.6 H1B OLR Results…………………………………………………………...….…..73

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4.6.1 H1B Model Training and Testing Results……..………………………….……74

4.6.2 H1B Model Optimization…………..……………………………………….….77

4.6.3 H1B Prediction Tool………………..…………………………………...….….81

4.6.3.1 H1B Structure Score Prediction Tool………..………………………..……81

4.7 H2 Results……………………………………………………………………,.……82

4.8 H3 Results……………………………………………………….………….………83

Chapter 5—Discussion and Conclusions………………………………………..…….86

5.1 Discussion….…………………………..……………………………………..……86

5.1.1 Scope of Analysis and Framework….………..…………………………..…….87

5.1.2 H1A Discussion…….……………………..………………………………..…..87

5.2 H1B Discussion……………………………………………………………….……88

5.3 H2 Discussion………………………………………………………………..……..90

5.4 H3 Discussion………………………………………………………………..……..91

5.5 Conclusions…………………………………………………………………..…….92

5.6 Body of Knowledge…………………………………………………………..……92

5.7 Future Study…………………………………………………………………..……92

References…………………………………………………………………………..…..93

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List of Figures

Figure 1 – Coating Age Prediction Tool Sample……………………….…..……………37

Figure 2 – Structure Score Prediction Tool Sample………………………..……………38

Figure 3 – H1A Coating Age (Predictor Variable) Parallel Test Results……..…………45

Figure 4 – H1A Coating Type (Predictor Variable) Parallel Test Results…..……..……46

Figure 5 - H1B Coating Age (Predictor Variable) Parallel Test Results……..……….....47

Figure 6 – H1B Coating Score (Predictor Variable) Parallel Test Results……..………..48

Figure 7 – H1A Test Database Frequency Distribution (Graph)………………..……….51

Figure 8 – H1A Test Database Coating Age (PV) Frequency Distribution……..………52

Figure 9 – H1A Test Database Frequency Distribution (Graph)………………..……….54

Figure 10 – H1A Train Database Coating Age (PV) Frequency Distribution……..…….55

Figure 11 – H1A Test Database Frequency Distribution (Graph)……………….....……57

Figure 12 – H1A Test Database Coating Age (PV) Frequency Distribution………....…58

Figure 13 – H1B Train Database Frequency Distribution (Graph)……………….……..60

Figure 14 – H1B Train Database: Coating Age Frequency Distribution………..……….60

Figure 15 – H1B Train Database Frequency Data Base (Graph)………………..…..…..62

Figure 16 – H1B Train Database: Coating Age Frequency Distribution………...……....62

Figure 17 – H1B Test Data Base Frequency Distribution……………………...…….….64

Figure 18 - H1B Test Data: Coating Age Frequency Distribution……………...…….…64

Figure 19 – H1A OLR Results……………………………………………………….…..65

Figure 20 – H1A Coating Score Prediction Tool #1…………………………………..…72

Figure 21 – H1A Coating Score Prediction Tool #2…………………………………..…73

Figure 22 – H1B OLR Results ……………………………………………………..……74

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Figure 23 – H1B Structure Score Prediction Tool……………………………………….81

xiii

List of Tables

Table 1- Parameters Affecting Corrosion in Bulk Carriers………...………………10

Table 2 - NSTM 100 Corrosion Rates for Steels………………………….……………..11

Table 3 – Damage Inspection Techniques Matrix……………………………………….13

Table 4 – CCAMM Database Sample…………………………………………………...26

Table 5 – HA1 Database Filtering Actions………………………………………………27

Table 6 – HB1 Database Filtering Actions..…………………………………………..…27

Table 7 – H1A Database Sample Set…………………………………………………….29

Table 8 – H1B Database Sample Set…………………………………………………….30

Table 9 – Training Database Testing Sample Set………………………………….…….32

Table 10 – Test Results Sample of Testing Data Set……………………………….……33

Table 11 – H1A Design of Experiments Sample…………………………………….…..33

Table 12 – H1B Design of Experiments Sample……………………………….………..34

Table 13 – H1A Classification Matrix Example…………………………………………35

Table 14 – H1B Classification Matrix Example…………………………………………36

Table 15 – H2 Data Base Sample………………………………………………………..39

Table 16 – H3 Data Base Sample………………………………………………………..40

Table 17 – Assumption Results for H1A and H1B Models………….…………………..43

Table 18 – H1A Correlation Results………………………………………….………….44

Table 19 – H1B Correlation Results…………………………………………….……….44

Table 20 – Data Shaping and Filtering Results………………………………………….49

Table 21 - H1A Database Frequency Distribution………………………………………50

Table 22 - H1A Train Database Frequency Distribution………………...………...…….53

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Table 23 – H1A Test Data Frequency Distribution………………………...……………56

Table 24 – H1B Train Database Frequency Distribution……………………..…………59

Table 25 - H1B Train Database Frequency Distribution……………………..………….61

Table 26 – H1B Test Database Frequency Data Base…………………………..……….63

Table 27 – H1A Model Training Results………………………………………..……….67

Table 28 – H1A Model Testing Results…………………………………..……………..68

Table 29 – H1A Classification Matrix…………………………………..……………….69

Table 30 - H1A Model Training Results Classification Matrix (Pre-Optimization)..…...70

Table 31 – H1A Model Training Results Classification Matrix (Post-Optimization).......70

Table 32 – H1A Testing Data Results Classification Matrix (Pre-Optimization)…....….70

Table 33 – H1A Testing Data Results Classification Matrix (Post-Optimization)….......71

Table 34 – Model H1A Classification Performance………………………………….….71

Table 35 – H1B Model Training Results…………………………………………….…..75

Table 36 – H1B Training Result Classification Matrix (Pre-Optimization)…………..…76

Table 37 – H1B Classification Matrix………………………………………………..….78

Table 38 – H1B Training Result Classification Matrix (Pre-Optimization)…………..…78

Table 39 – H1B Training Results Classification Matrix (Post-Optimization)……….….79

Table 40 – H1B Testing Data Results Classification Matrix (Pre-Optimization)…….…79

Table 41 – H1B Testing Data Results Classification Matrix (Post-Optimization)……....80

Table 42 – Model H1B Classification Performance……………………………….…….80

Table 43 – H2 Results…………………………………………………………….….…..83

Table 44 –In-Service Tank Frequency Distribution……………………………….….…84

Table 45 – Average Tank Inspection………………………..……………………….…..84

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Table 46 – H3 Results………………………………………………………...……….…85

Table 47 - HA1 OLR Regression Results…………………………………………….….88

Table 48 – HB1 OLR Regression Results……………………………………………….89

Table 49 – H2 Results……………………………………………………………………90

Table 50 – H3 Results…………………………………………………………….......….91

xvi

List of Acronyms

ABS American Bureau of Shipping

CPA Carrier Planning Activity

CCAMM Corrosion Control Assessment and Maintenance Manual

CCIMS Corrosion Control Information Management System

HS High Solids

NAVSEA Naval Sea Systems Command

NSTM Naval Ship Technical Manual

NRL Naval Research Laboratory

SURFMEPP Surface Maintenance Engineering Planning Program

UHS Ultra High Solids

USN United States Navy

xvii

Glossary of Terms

CPA The Carrier Planning Activity is a United States Navy

organization dedicated to the planning of aircraft carrier

maintenance.

CCAMM Corrosion Control Assessment and Maintenance Manual is

a United States Navy Maintenance manual containing tank

inspection and repair requirements.

CCIMS Corrosion Control Information Management System is a

corrosion control management system maintained by the

Surface Ship Engineering Planning Program.

HS High Solids is coating that contains 60-90% solids and 250-

150 g/L Volatile Organic Compounds.

NAVSEA Naval Sea Systems Command is a United States Navy

organization supporting the design, construction, support,

sustainment and modernization of naval vessels.

NRL Naval Research Laboratory is a United States Navy

organization dedicated to conducting research for naval

applications.

SURFMEPP Surface Maintenance Engineering Planning Program is a

United States Navy organization dedicated to planning of

surface ship maintenance.

UHS Ultra High Solids is coating that contains greater than 90%

solids and less than 150 g/L Volatile Organic Compounds.

1

Chapter 1—Introduction

1.1 Background

Vessels of the United States Navy (USN) span the globe in support of the interest of

the United States. The operational tempo of keeping the ships on mission at sea has

strained naval maintenance repair periods. Given the number of the tanks, from such a

large fleet, to be maintained, and given the reduced amount of time for maintenance, it is

incumbent upon the USN technical community to conduct the right maintenance at the

right time, so that the ships can be ready for tasking when needed.

In the life of a shipboard tank, a tank coating life begins with a fresh coat of paint

which is fully adhered and fully protects all structural members. As the coating ages,

however, the coating begins to deteriorate, and gaps in the coating’s coverage begin to

appear, which results in a loss of coating integrity. In coating deficient areas, tank

structure is exposed which begins the onset of corrosion. When this occurs, the tank

coating has reached its service life. Normally, tank coatings are replaced prior to any

severe structural corrosion. In the coating refurbishment process, the aged tank coating is

stripped away exposing bare tank structure metal. The metal surface is prepared using

standardized methodologies to ensure tight coating adhesion, and then, a fresh coating is

applied. Once the coating cures, the tank is placed back into service, and the coating

aging process begins all over. This process of blasting and painting of tanks is routine

and periodically occurs in the life of a ship. Because of the number of tanks (over

15,000), the aggregate cost of maintenance and inspection is very significant.

2

The U.S. Navy regularly inspects its tanks for coating and structural degradation. The

periodicity of the tank inspections requirements and tank inspection (both coating and

structure) grading criterion are detailed in the USN Corrosion Control Assessment and

Maintenance Manual (CCAMM) revision three dated 30 June 2015. The CCAMM is a

foundational document for this research effort. The USN has been documenting tank

inspection results for decades, and results are warehoused in the Corrosion Control

Information Management System (CCIMS) which is the tank inspection results master

databank for all surface ships.

The present work effort intends to introduce a novel methodology by analyzing and

integrating the factors impacting tank coating inspection scores, structural inspection

scores, coating type, coating age, surface preparation method, tank type, and tank

criticality from the CCIMS in order to increase current tank inspection periodicity by 25-

50% with minimal risk acceptance. The practical applications of this proposed

methodology are: 1) tank inspection periodicities will be optimized, 2) unnecessary tank

inspections will not be conducted, saving shipyard resources (cost and schedule) and 3)

future planning efforts will be adjusted.

1.2 Research Motivation

With a fleetwide focus of reducing maintenance dollars and minimizing maintenance

schedules in order to keep ships at their primary mission, being at sea, every maintenance

task must be performed at the right maintenance period, when required, in order to avoid

unnecessary cost in both schedule and dollars as well as avoiding unacceptable risk.

Tank maintenance is typically a primary factor, for cost and schedule, during a ship’s

major shipyard maintenance period. Therefore, it makes sense to focus on this area for

3

any potential cost and schedule savings. Additionally, the USN technical community has

received several first-hand reports from waterfront tank inspectors that question the

reasoning for inspecting pristine tanks. These reports of unnecessarily conducted tank

inspections bring into question the validity of the current tank inspection periodicity

requirements. This occurrence has been more frequent since the introduction of high-

performance high solids and ultra-high solids (HS/UHS) paints into the Navy coatings

inventory in the 2000s. Conducting necessary inspections of tanks is a waste of precious

resources (time and material), especially given the numbers of tanks in the surface navy

inventory.

1.3 Problem Statement

Tanks aboard U.S. Navy surface ships are being inspected too frequently wasting

money and maintenance time.

1.4 Thesis Statement

An integrated methodology model factoring tank coatings deterioration, structural

degradation, coating type, surface preparation, tank type, and tank criticality will enable

increasing tank inspection periodicity by 25-50% with minimal risk acceptance.

1.5 Research Objectives

Given HS/UHS coatings, determine the impact on:

tank inspection coating and structural inspection scores

factors affecting coating and structural inspection scores

optimum tank inspection periodicities

Optimizing tanks inspection periodicities will balance cost and risk.

4

1.6 Research Questions and Hypotheses

The intent of the Research Questions is to explore the relationship between the

HS/UHS coatings with coating age, coating inspection score, and structure inspection

score, and to establish tank inspection periodicities within acceptable established risk

guidelines and recoup any cost saving associated with revised tank inspection

periodicities.

RQ1: What are the current coatings used in U.S. Navy class ships tanks, and what

are their inspection requirements? What are the different tank types and their

criticality towards the mission of the ship?

RQ2: What costs are associated with the current tank inspections?

RQ3: What are the coatings and structural inspection scores associated with high

solids and ultra-high solids tank coatings?

RQ4: What are the relationships between coating, coating age, inspection scores

(both coating and structural), coating type, surface preparation method, tank type,

tank pressure, ship class and tank criticality?

RQ5: What is the optimized UHS coating inspection periodicity for each type of

tank? What is the risk associated with each revised tank inspection periodicity?

RQ6: What are the savings implications of the optimized UHS tank coating

inspection periodicities (from RQ #5)?

There are four Research Hypotheses associated with this research effort:

H1A: Coating Type, Application Method, Tank Type, Ship Class, Tank

Criticality, Coating Age are predictors of Coating Condition Score.

5

H1B: Coating Condition Score, Ship Class, Tank Pressure, Coating Age, Tank

Criticality are predictors of Structural Condition Score.

H2: Tank inspection periodicities, where HS/UHS coatings are employed, can be

increased 25-50% using the coating and structure score predictions (developed from

H1A and H1B) with minimal increased risk of failure.

H3: There are significant savings when tank inspection periodicities increase by

25-50% (results from H2).

1.7 Scope of Research

The Scope of Research for this effort is broad because the extent and use of tank

coatings in the U.S. Navy is broad. This research effort involves 10 ship classes, 13

different tank types, over 15,000 individual tanks, 33 different coating types, etc. with an

overall total of about 25,000 unique inspection records spanning 18 years as documented

in the CCIMS.

For the HS/UHS coatings analysis portion of this present work, only tank coatings

used since 2001 were analyzed since the USN maintenance community began to

transition to HS/UHS coatings in 2000. Only the data records contained in the CCIMS

were utilized.

In the U.S. Navy, ships of the same design are organized into ship classes. A ship

class can have as few as six ships or as high as 78 ships in a ship class. The U.S. Navy

has more than 250 surface ships in the fleet. Navy ships classes have different missions,

and those missions affect the mix of tank types required for each ship class. Thirteen

different tank types represent the gamut of tanks in the surface fleet, from fuel to lube oil

to seawater to fresh water. Tanks have varying degrees of criticality codes associated

6

with their function, like fresh water being very critical (crew survival) to waste oil tanks

being not so critical. Some ship classes are designed disproportionally with more of one

type of tank than another ship class due to their mission. Thus, the range of tank types

from ship class to ship class is broad and varied.

1.8 Research Limitations

The research effort was limited to surface ships only. The research effort was limited

by the quantity and quality of data contained in the CCIMS database. Aircraft carriers

and submarines, although they have thousands of tanks as well, were not analyzed since

the CCIMS contains only surface ship data. Certainly, the methodology in this effort

could be applied toward aircraft carriers and submarines in a future study.

1.9 Organization of Praxis

The Praxis is organized as follows: Chapter 2 contains a Literature Review as it

relates to this research, Chapter 3 discusses the Methodology used in the analysis,

Chapter 4 contains the Results of the analysis, and Chapter 5 contains the Discussion,

Conclusion, how this effort added to the Body of Knowledge, and Future Study

Recommendation.

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Chapter 2—Literature Review

2.1 Introduction

The intent of this praxis Literature Review is to provide the results of a literature

search for those writings that support the practical application of this research effort. In

doing this, this research effort will have an academic basis. This research effort will be

placed in the context of existing standards and previous similar analysis efforts, and this

research effort will demonstrate the uniqueness of this effort as compared with others.

Since this applied research effort is intended to solve a specific United States Navy

(USN) coating problem, the literature review, by necessity, will include a significant

number of USN references, manuals, technical standards, technical presentations,

technical papers, and prior naval coating analysis and studies. Numerous sources outside

of the USN references were included as well for completeness, rigor, and thoroughness of

review.

2.2 Overview

The U.S. Navy has long recognized the value of potential savings given newer tank

coatings (NAVSEA, 1997). This applied research effort involves an analysis of the

impact and relationship of the degradation of existing tank coatings and tank structure in

various types of tank coating aboard USN surface ships using high-performance high

solids and ultra-high solid (HS/UHS) tank coatings aboard USN surface ships in order to

determine optimized tank inspection periodicities and potential cost and schedule

savings. The scope of this literature search is limited to the practical application of this

research effort.

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2.3 Corrosion

In this section, an overview of corrosion will be presented to include the corrosion

process, the types of corrosion, the phases of development in corrosion, the factors

influencing the development of corrosion, the rates of shipboard corrosion, and

inspection methods of corrosion.

2.3.1 Corrosion Process

The process of corrosion is a result of electrochemical reactions between materials

and substances in their environment. It involves an anode, a cathode and an electrolyte

(Gardiner and Melchers, 2003). The anode is the site of the corroding metal. The

electrolyte is the corrosive medium that enables the transfer of electrons from the anode

to the cathode. The cathode forms the electrical conductor in the cell that is not

consumed in the corrosion process (Poplar, 2013). This results in an anodic reaction

where the metal is oxidized (metal ions and free electrons released into solution), and the

cathodic reaction takes place where the electrons are reduced by an electron acceptor

(Gardiner and Melchers, 2003). Thus, a type of metal loss does occur in corrosion with

associated structural strength (Wang et al, 2003). Additionally, an oxide layer is formed,

thus helping to prevent further corrosion from occurring (American Bureau of Shipping,

2017b).

2.3.2 Corrosion Types

Corrosion is a significant factor in the marine environment. The general types of

corrosion seen in merchant ships are general (uniform), pitting (localized), stress

corrosion cracking, groove, weld metal, and microbial induced corrosion (Gardiner and

Melchers, 2001); (Rizzo et al, 2008); (American Bureau of Shipping, 2017a). General

9

corrosion is corrosion that is broadcast over a general area in a uniform fashion

(American Bureau of Shipping, 2017a). Pit corrosion is characterized as a localized

corrosion that has a pit to it (Guedes and Garbotov, 1999). Stress corrosion cracking

(SCC) is a form of localized corrosion which produces cracks in metals by simultaneous

action of a corrodent and tensile stress. (Pooplya, 2013). Lastly, microbial induced

corrosion occurs when living organisms, present in the fluid, deposit organic matter near

the metal boundary layer, and that induces corrosion (Pooplya, 2013). These types of

corrosion will occur given the proper environmental conditions.

2.3.3 Corrosion Phases

Paik et al (2003) and Gardiner and Melchers (2003) identified three phases of

corrosion developing in tanks: durability of the coating, transition (corrosion initiation),

and progress of the corrosion (corrosion of exposed metal). Coating durability is the

length of time the coating is effective. This will be discussed in detail in a subsequent

section. Transition, or corrosion initiation, is the time between the end of coating

effectiveness and the onset of measurable corrosion. Progress of corrosion phase begins

when measurable corrosion is present. In bulk carriers, the transition time has been

reported to be up to three years (Paik et al, 2003). The phases of corrosion are offered as

general background knowledge of the corrosion process experienced in tank coatings.

This present work will be examining the length of time a HS/UHS coating will last until

exposure of metal.

2.3.4 Factors Affecting Corrosion

The most significant research on the corrosion of commercial bulk carriers was

previously conducted by Guedes and Garbatov (1999), Gudze and Melchers (2008),

10

Melchers and Jiang (2006), Melchers (2006), and Gardiner (2001). They identified the

factors of the type of cargo, the time spent in ballast, the effectiveness of corrosion

protection, the environmental conditions, and the tank geometry as vital factors in the

corrosion process in bulk carriers. Gardiner and Melchers (2003) noted that factors could

be grouped into two categories: operational vs internal. Operational factors are type of

cargo, time spent in ballast, and environmental conditions (due to trade routes). Internal

conditions were due to design like geometry of tank and type of corrosion control

protection. Industry also recognized the importance of coating type and surface

preparation in the role of the corrosion process (Wheat, 2015). This complex array of

important identified factors also played a vital role in the USN corrosion research and are

factored into this praxis effort as well. In order to enhance the reliability of prediction

models for bulk carriers, Gardiner and Melchers (2003) proposed recording the

parameters listed in Table 1 for corrosion database for different shipboard operations.

Parameters affecting corrosion in bulk carriers

Table 1- Parameters Affecting Corrosion in Bulk Carriers

Operational parameters Internal (vessel-related) parameters

Cargo Type of corrosion protection

Type of ballast Member location

Operational route Corrosion Protection Systems

Cargo corrosivity

Frequency of cargo change

11

2.3.5 Corrosion Rates

Gardiner and Melchers (2003) reported that previous studies concluded that

corrosion rates were highly variable. Given the complexity of the factors affecting

corrosion, however, Gardiner (2001) identified a pattern of corrosion in bulk carriers. His

analysis provided empirically derived corrosion rates in bulk carriers (Gardiner, 2001)

from a range of 0.05 to 0.45 (exposed weather decks) mm/year depending on the

location. Likewise, the USN technical community has grouped tanks with similar

corrosion factor attributes into similar type tank groups called service codes, depending

on the content of the tank and its location. Table 2 below is the USN standards for steel

structures corrosion rates (taken from empirical data) (NAVSEA, 2017). Contents of the

tank and its location are significant to the corrosion rate per year of a particular tank. It

should also be noted the similarity in corrosion rates ranges between the two

aforementioned sources.

Table 2 - NSTM 100 Corrosion Rates for Steels

Service

Code

Type of Service Rate

Mils per Year

(mpy)

A Interior compartments, normally dry 6

B, C Interior compartments, frequently or cyclically wet

from either fresh water or seawater

15

D Seawater immersion 13

E Fresh water immersion (full) 12

F Fuels (when water is present) 13

G Contaminated fuels, oils, oily waste, waste water

(includes potentially contaminated storage tanks)

17

H CHT/VCHT 13

I Weather exposed areas or compartments 45

12

2.3.6 Corrosion Inspections

Different types of tank inspections are conducted to ascertain the condition of the

tank, its coating condition, its structural condition, and the condition of any in-place

corrosion prevention systems. They are either non-destructive or destructive type testing,

typically non-destructive. Non-destructive type testing involves a visual survey, ultra

sound in specific areas (to determine wastage), and infrared technology. Destructive

techniques involve determining if structural corrosion is present behind an apparently

intact coating surface (Martin et al, 2018). Industry and the USN use licensed inspectors

to help ensure inspection quality (American Bureau of Shipping, 2017b); (NAVSEA,

2015). American Bureau of Shipping (2017b) lists specific types of inspection types and

periodicities guidelines. U.S. Navy specific types of inspections with associated

inspections periodicities are listed in the CCAMM. Lastly, Table 3 below lists inspection

techniques that can be used while performing tank inspections to determine damage to

shipboard coating and/or structures.

2.3.7 Corrosion Consequences

The consequences of corrosion vary with the degree of severity (Rizzo et al, 2008).

Generally, corrosion in primary hull structure can lead to a significant loss of hull

strength (Wang et al, 2003). Table 3 provides a table of inspections with degradation

mechanisms (Rizzo et al, 2008).

13

Table 3 – Damage Inspection Techniques Matrix

Inspection

Technique

Damage Mechanism

Complexity in

Execution

Corrosion Fracture Mechanical

Visual X X X Low

Digital

Imaging

X X X Moderate

Pressure Test X X X Moderate

Dye Penetrant X High

Ultra Sound X X High

Magnetic

Particle

X High

2.4 Corrosion Prevention

Rizzo et al (2008) stated corrosion prevention methods were classified into two types:

active and passive. Active methods are barrier method, cathodic protection or impressed

current. Passive methods make the protective substance long-lived through design

(avoiding discontinuities or stress concentrations) or corrosion allowances (Rizzo et al

2018); (American Bureau of Shipping, 2017b); (NTSM 633 1996). Guedes and Garbatov

(2009) demonstrated that the key component to corrosion inspections is to inspect at the

right time to minimize cost.

2.5 Coatings

Coatings researched in this effort are limited to high-performance high solid and ultra-

high solid (HS/UHS) coatings that are approved for USN shipboard use. These are

14

located on the USN qualified products database (Qualified Products Database, 2019).

Thirty-three tank coatings are to be examined in this research effort. The American

Bureau of Shipping (ABS) (2017a) provides commercial recommendations for tank

coatings.

USN surface ship coating and structure coating requirements are listed in two source

documents: Naval Ship Technical Manual (NSTM) Chapter 100 (Hull Structures 2017)

and NSTM Chapter 631 (Preservation of Ships In-Service 1996). These two source

technical documents are the foundation of the Corrosion Control Assessment and

Maintenance Manual (CCAMM), the primary document for USN surface ship tank

maintenance. The CCAMM contains the requirements for tank coatings, tank inspections

periodicities, and tank risk assessments. USN technical authority over all three of these

foundational documents (NSTM 100, NSTM 631 and CCAMM) rests with Naval Sea

Systems Command (NAVSEA) Code 05P. These three documents frame any USN

surface tank technical requirements discussion regarding coatings, structure and tank

coating inspection and maintenance periodicities. This praxis research will also, by

necessity, be framed with these source documents. The American Bureau of Shipping

(2017a) contains the coatings typically used for commercial vessels.

2.6 Corrosion Modeling

The purpose of this section is to explain previous works involving corrosion modeling

and how this present work fits within the context of previous works.

Much research has been performed in order to understand and predict corrosion

progress for shipboard applications. Guedes and Garbatov (1999) proposed a non-linear

model of general corrosion wastage in a plate across all three-development phases of

15

corrosion. The conventional models of corrosion of that time assumed a constant

corrosion rate which led to a linear relationship between the materials lost versus time

(Guedes and Garbatov, 1999). Evidence of corrosion data sample reported by numerous

authors showed that a non-linear model corrosion prediction is more suitable (Guedes and

Garbatov, 1999). The significance of Guedes and Garbatov’ s research is that they

proposed a more realistic corrosion prediction model in plates across all phase of

corrosion (Guedes and Garbatov, 1999). They did not, however, factor into their work

any environmental factors (Guedes and Garbatov, 1999).

Gardiner and Melchers (2001) did, however, address the need in adding more realistic

factors in corrosion prediction modeling in bulk carriers because of the high variability of

survey thickness data. They investigated operational and internal parameters to include:

cargo, ballast, trading route, cargo corrosivity, frequency of cargo changes, and type of

corrosion protection (Gardiner and Melchers, 2001). They also proposed that these

additional parameters be factored into future corrosion modeling efforts by including

these factors into samples of future surveys of thickness measurement for inclusion in

future corrosion modeling efforts that will lead to better corrosion modeling accuracy

(Gardiner and Melchers, 2001). Factors affecting corrosion are considered in the present

work.

Gardiner and Melchers (2003) continued their work in the analysis of the factors of

immersion in seawater, exposure in enclosed atmospherics, and exposure to porous

media. Gardiner and Melchers (2003) postulated three factors impacting corrosion

modeling accuracy in bulk carriers: 1) how well the model represents corrosion

phenomena, 2) how well the model represents the uncertainty of parameters influencing

16

the corrosion phenomena, and 3) the quality of the data used to determine the empirical

corrosion model parameters. These factor into the present work. Gardiner and Melchers

(2003) and Gardiner and Melchers (2001) focused on addressing their third postulated

factor. Both Gardiner and Melchers (2001) and (2003) postulated that corrosion

prediction models for bulk carriers could be improved by the development of a corrosion

rate database that included the different operational parameters of a vessel. This database

would include data of operational factors (immersion, atmospheric, exposure to porous

media) which then could be included in corrosion modeling analysis. Melchers (2003)

then proposed a simplistic non-linear probabilistic corrosion model (for steel) that

included the aforementioned operational factors.

Wang et al (2003) conducted a survey type study of multiple smaller databases for

several types of tanks and several different types of ships. Wang et al (2003) concluded

through his analysis that: 1) corrosion wastage exhibits high variability, 2) corrosion

exhibits an increasing trend with the passage of time, and 3) corrosion wastage has an

influence on the hull girder strength. Interestingly, Gardiner and Melchers (2001) made a

similar observation of corrosion being highly variable. This research effort relates to the

work of Wang et al in that the scope will be broad like their research effort, but this

research will be of greater scope with more factors and more data points. Additionally,

this research effort will yield different research products, specifically, tank inspection

periodicities as a research product, instead of general observations that were his research

conclusions.

Paik et al (2003) developed a more complex corrosion modeling methodology for

predicting corrosion wastage as a function of time in primary structure locations (34

17

different locations in all) of single and double-hull tankers, floating storage and off load

units, and floating production storage and off load units. Approximately, 34,000 data

points were analyzed for all three phases of corrosion (Paik et al, 2003). Paik et al

concluded (2003) the following: the average annual rate of corrosion followed a Weibull

distribution but became more normal in distribution when in the severe corrosion range,

the annualized corrosion rate varied from location to location, corrosion depth could be

accurately predicted, and study findings could be used in future designs for a more

accurate corrosion allowance provision. Lastly, (Paik et al 2003) warned, for statistical

analysis of past data, to not apply corrosion models beyond that justified by the

characteristics of the database underpinning them.

In an effort to get quality inspection data for the development of a mathematical

model of coating deterioration, Melchers and Jaing (2006) developed a questionnaire that

was sent to a broad audience of tank coating surveyors. Melchers and Jaing (2006)

concluded that owners of the vessels were more pessimistic in surveying for corrosion

than expert surveyors and that good data input is vital for the development of accurate

coating deterioration modeling.

To address uncertainties in data collection and environmental complexities, Noor et al

(2007) developed and demonstrated the application of the probability distributions in

modelling the time-dependent growth of corrosion pits in vessels’ seawater ballast tanks.

In particular, Noor et al (2007) utilized the Weibull probability distribution functions to

address the high variability of corrosion wastages in seawater ballast tanks. Noor et al’s

(2007) new model demonstrated that it can be used to predict the likely variation of

corrosion depth at any point of time without having to estimate the corrosion growth rate

18

for each single defect. This simplified the corrosion modelling process, thus, making

available more data more fully useful for prediction purposes (Noor et al, 2007).

Noor et al (2013) similarly to Noor et al (2007) utilized Weibull analysis to predict

corrosion rate in structure pitting in salt water ballast tanks in bulk carriers with limited

success. Noor et al (2013) concluded the necessity of quality data in order for a more

accurate structural degradation model.

Gudze and Melchers (2008) developed a corrosion model for the prediction of

corrosion loss in the seawater ballast tanks of naval vessels. The model incorporated their

previous models (Guedes and Melchers, 2001 and 2003) which factored immersion and

atmospheric corrosions. Gudze and Melchers (2008) included the impact of the

environmental condition, namely seawater temperature, and naval operation of ballast

tanks, to model corrosion loss in this unique application. Gudze and Melchers (2008)

incorporated these two factors into their corrosion prediction model for naval units.

Guedes Soares and Garbatov (2009) produced a tank maintenance and repair model.

They conducted a probabilistic maintenance and repair analysis of two different types of

tanker deck plates subjected to general corrosion. Guedes and Garbatov (2009)

addressed the question of when to conduct maintenance and repair on corroded deck

plates. Additionally, they added a risk component in their study in the form of corrosion

tolerance. Their analysis was a Weibull analysis, which is a common methodology for

deterioration analysis (Ivosevic et al, 2017). Their analysis differs from the present work

in that the present work will be looking at more factors as well as utilizing a different

type of analysis methodology to optimize USN tank coating inspection periodicities as

previously mentioned. Guedes and Garbatov (2009) also factored repair cost in their

19

optimization which this present work will not. Guedes and Garbatove (2009) used the

term corrosion tolerance which will be utilized in this present work in presenting tank

inspection options with associated risk.

Ivosevic et al (2017) provided a probabilistic corrosion model for inner bottom plates

for fuel oil carriers. Ivosevic et al (2017) showed a logistic distribution or normal

distribution to be appropriate for the corrosion estimation model for inner bottom plates

of fuel oil carriers. Previous research efforts did not address fuel tank corrosion modeling

(Ivosevic, 2017)

In the area of tank coatings, beginning in the 1990s to the 2000s, the Naval Research

Laboratory (NRL) led the USN research effort to investigate tanks’ UHS coatings

durability for possible requirements changes in the CCAMM tank coating inspection

requirements (Lucas, 2000). Lucas (2000) proposed a USN comprehensive tank corrosion

monitoring approach. Slebodnick, (2008) concluded that UHS coating systems offered

attractive advantages and potential for a 20-year coating service life. He also reported the

critical nature that surface preparation and quality assurance has upon coating service

life. His findings were not predictive in nature and were based only on a coating failure

analysis of several limited scope case studies, and his study was not a comprehensive,

across the board, statistical regression type analysis that is proposed in this research.

The USN technical authority for tank coatings, based on previous NRL research, also

concluded that HS coatings provided promise of reaching 20 years, but more data and

more analysis were needed in order to make a definitive conclusion (Ingle, 2011). Ingle

(2011) presented NRL research that graphically compared limited tank inspection coating

results from pre-HS coatings (N=255) vs HS coatings (N=133). The graphical

20

representation demonstrated an improved longevity performance of HS coatings. Again,

no predictive, probabilistic, statistical analysis was offered to support his conclusion. By

2017, however, the USN tank coating technical community began advertising a 15-20

years UHS coating life on most types of tanks that were using the improved UHS tank

coatings (NAVSEA, 2017b), although the tank inspection periodicities requirements in

the CCAMM remained unchanged. Interestingly, the major shipbuilders of the world

(Korea, China and Japan) were long using an epoxy type HS/UHS coating for their

ballast tanks that they determined to have an expected coating life of 15 years (Eliasson

et al, 2007).

Another USN analysis effort to address extending coating inspection periodicities for

USN surface ships, given the usage of high-performance UHS coatings, was conducted

by Surface Maintenance Engineering Planning Program (SURFMEPP) (SURFMEPP,

2015). SURFMEPP (2015) conducted a limited scope Weibull type coatings analysis and

coating failure analysis. The analysis report and results, however, were not peer reviewed

nor were they accepted by the USN coatings technical warrant holder (Carrier Planning

Activity, 2016). Recently, however, the SURFMEPP methodology underwent a peer

review and was found to be faulty (Kent, 2019) as well. The SURFMEPP analysis,

although well intended, missed the mark in academic rigor and methodology, and thus, it

had to be discredited. Currently, another USN maintenance community effort, apart from

this research effort, is underway to determine tank coatings periodicities, probably using

Weibull analysis techniques.

The USN tank corrosion community recognizes that coating age, tank type, type of

coating, and surface preparation play a factor in coating deterioration (NAVSEA, 2015).

21

The relationship between coating deterioration, structural deterioration and risk

management is characterized and recognized in the CCAMM as well. The underpinnings

of the relationships of these three factors, however, is established by the expert

judgement of USN coatings and structural technical authorities, not statistical analysis.

Another aspect in the present work is to establish a regression equation between coating

and structural deteriorations, given the existing USN tank inspections data results in the

Corrosion Control Information Management System (CCIMS). This will help better

define the relationship and understanding of coating and structural degradation rates in

the USN technical community.

While these previous efforts used mathematics to predict corrosion rate, this research

effort will be utilizing a different analysis technique for predicting coating and structural

degradations than what was previously employed. This research effort will be utilizing

ordinal logistic regression since the tank inspection results in the CCIMS database are

ordinal in nature. A mathematical model will be built to predict structural corrosion

scores, given coating age, tank type, surface preparation, and tank pressure.

2.7 Corrosion Risk Assessment

In the area of risk, risk management, and risk-based inspections, Balkey et al (1990)

provides general guidelines for industrial applications. For tank applications, however,

Ifeuze and Tobins (2013) explored the role of the corrosion engineer through risk-based

inspections. In a case study, they concluded the role of risk-based inspections were valid

in determining tank inspection periodicities. Geis (2002) demonstrated the use of a risk

management tool in conducting tank inspections and maintenance. Muhammet Gul et al

(2017) explored the use of fuzzy logic in ballast tank maintenance to determine risk for

22

better safety operations. Rizzo et al (2007) considered risk-based decision procedures for

remedial actions to mitigate age related corrosion effects. USN tank risk guidance and

maintenance procedures to mitigate risk are found in the CCAMM. This research effort

will be utilizing the risk guidance found in the CCAMM. Additionally, this research

effort will employ a risk-based inspection methodology, but the scope will be much

greater than Ifeuze and Tobins (2013). Like Geis (2002), this research effort will be

utilizing a risk management tool which is in CCAMM. The risk assessment approach

used will be much simpler than Muhammet (2017) in that it will involve using a basic

lookup table in the CCAMM vice using analytical methodology. Overall, it will be

utilizing a risk-based methodology similar to others but employing the methodology to a

specifically broad issue of USN tank inspection periodicities.

2.8 Cost of Corrosion

The cost of corrosion is well documented (Parson et al, 2011); (Ingle, 2011b);

(NAVSEA, 2016). Twenty-five percent of the annual USN maintenance budget is

devoted to corrosion control (Parsons, 1994). The merit and potential savings of using

HS/UHS is and has been recognized by industry and the USN (Stein, 2015); (NAVSEA,

1997); (Ingle, 2011b); (NAVSEA 2016). Cost savings figures in the research effort are

taken directly from the USN surface navy maintenance planning organization,

SURFMEPP.

This research effort will not include:

Any tanks associated with aircraft carrier or submarines.

Any repair cost associated after a tank inspection is conducted.

23

2.9 Summary and Conclusion

Given the complex factors involved with shipboard corrosion and the complex

operational nature of USN surface ships, an academically sound and rigorous statistical

methodology is needed and required to determine and set tank coatings inspection

periodicities with associated risk. Academia, industry and the USN technical community

have identified factors affecting corrosion and have applied statistical methodologies to

predict corrosion behavior. Previous research efforts have predominately utilized Weibull

model to predict corrosion behavior (Paik et al, 2003), (Noor et al, 2007), (Noor et al,

2013), (Garbatov and Guedes, 2009). Previous research efforts have focused on many

aspects of corrosion and on trying to predict corrosion (Gardiner and Melcher, 2001),

(Gardiner and Melcher, 2003), Melcher (2003), Wang et al (2003). Given the existing

USN surface ship tank inspection data, this present work intends to conduct a much

broader analysis. Through the use of ordinal logistic regression, this present study seeks

to understand the relationship between tank coatings, surface preparation type, type of

tank, ship class, tank criticality, and coating and structural degradation in order determine

tank inspection periodicities and associated risk. This work intends to yield significant

savings for the United States Navy and for U.S. taxpayers.

24

Chapter 3—Methodology

3.1 Introduction

The intent of this chapter is to give the reader a detailed overview of how this research

was conducted and to discuss the process and procedures, step by step, from raw data to

accepting or rejecting the hypothesis.

3.2 Surface Ship Tank Inspection Data

Corrosion Control Information Management System (CCIMS) contains the United

States Navy (USN) tank inspection data repository. Tank inspectors record tank

inspection results into the CCIMS database after conducting an inspection. Tanks are

assessed using the scoring criterion provided in the Corrosion Control Assessment and

Maintenance Manual (CCAMM) (NAVSEA, 2015). Whether a coating condition

inspection or structural condition inspection, each tank inspection is given inspection

scores (ordinal data) upon inspection completion. These scores are recorded, along with

other pertinent data, in CCIMS. In the 2000s, the USN tank maintenance community

decided to consolidate tank inspection data into a single location. In 2014, the USN tank

community added a tank structural score to its inspection regiment in order to better

assess and document tank conditions (NAVSEA, 2015). Today, the CCIMS database, an

Excel spreadsheet, has over 50,000 tank inspection records, some dating back to the

1970s. A single CCIMS data record can provide up to 115 unique pieces of information

surrounding a tank inspection. Each tank inspection record contains information like:

date opened, date closed, date of inspection, the ship, tank unique identification number,

tank type, tank volume, tank area, overall coating condition, structural condition,

25

inspector’s name, coating type (if recoated), surface preparation (used), why the tank was

inspected, etc. Very specific information is also potentially available such as: square

footage and type of corrosion, repair activity doing the work, contracting organization,

etc. The database shows a mixed degree of completed fields in the records. For this

research effort, through the filtering process of record collecting, records with incomplete

fields were systematically excluded from this study.

The key fields for this present work are: Inspection Date, Ship Class, Tank Type,

Coating Type, Surface Preparation Method (for coating), Coating Inspection Score, and

Structural Inspection Score.

3.3 Database Shaping

By using the formula features in Excel, a column was created to track the time (in

years) between tank inspections for each unique tank by taking the date from one

inspection to the next inspection. When a tank was refreshed and renewed with a fresh

coat of paint, then, the coating score for that tank was reset from high coating condition

score (3 or 4) to a low coating score (1). When the tank coating condition was reset, then,

another column was added to track the age of that particular tank coating from inception

at a fresh coat of paint to subsequent inspection (with its coating inspection score) to a

following inspection (with its coating inspection score) to eventually when it was blasted

away and a new coat was applied. With this methodology, the service life of a tank

coating condition, for a particular coating type, could be tracked through the years, and

the degradation of its coating condition could be documented, monitored and tracked.

Additionally, a column was added to identify those tanks that are pressurized tanks

because the present work included an examination of the impact of pressure upon coating

26

and structural degradation. Lastly, a column was added to include the tank criticality code

taken from the Table 6-2 of the CCAMM. By USN maintenance doctrine, each tank is

assigned a tank criticality code according its mission essentialness. Tanks with a

criticality code of three are considered very mission essential while tanks with a

criticality code of one are low in mission essentiality. Table 4 displays the sample of data

base shaping.

Table 4 – CCAMM Database Sample

3.4 Database Filtering

Once the coating age, tank criticality, and tank pressure columns were set, then the

database was filtered to support the analysis of H1A and H1B. Table 5 displays actions

taken with the CCIMS database to filter the data set for H1A. Table 6 displays actions

taken with the CCIMS database to clean, shape and focus the data set for H1B. These

filtering actions formed the foundation database for H1A and H1B models.

SHIP

CLASSTANK # TANK TYPE INSPECTION #

Tank Pressure

(1 -Yes, 2,

No)

Tank Severity (1-

low, 3 high)INSP DATE

TIME BTW

INSPCOATING AGE

Coating

Score

9 665 CHT 1 No 3 16-Jan-71 0.0 0.0 1

9 665 CHT 2 No 3 12-Jun-98 27.4 27.4 4

9 666 SALT WATER BALLAST 1 No 3 16-Jan-71 0.0 0.0 1

9 666 SALT WATER BALLAST 2 No 3 12-Jun-98 27.4 27.4 2

9 666 SALT WATER BALLAST 3 No 3 03-Jul-03 5.1 32.5 4

9 667 SALT WATER BALLAST 1 No 3 16-Jan-71 0.0 0.0 1

9 667 SALT WATER BALLAST 2 No 3 03-Jul-03 32.5 32.5 4

9 668 FUEL OIL 1 No 2 16-Jan-71 0.0 0.0 1

9 668 FUEL OIL 2 No 2 03-Jul-03 32.5 32.5 1

9 669 SALT WATER BALLAST 1 No 3 16-Jan-71 0.0 0.0 1

9 669 SALT WATER BALLAST 2 No 3 12-Jun-98 27.4 27.4 3

9 669 SALT WATER BALLAST 3 No 3 03-Jul-03 5.1 32.5 4

9 670 SALT WATER BALLAST 1 No 3 16-Jan-71 0.0 0.0 1

9 670 SALT WATER BALLAST 2 No 3 12-Jun-98 27.4 27.4 2

9 670 SALT WATER BALLAST 3 No 3 03-Jul-03 5.1 32.5 4

27

Table 5 – HA1 Database Filtering Actions

Filter Action Reason Result

1. Records of Decommissioned

ships excluded

Present work studying

active fleet only

Only active ships

considered

2. Records prior to 2000

excluded

High solids/Ultra high-

solids (HS/UHS) not

implemented prior to 2000

Records of legacy paint

inspections excluded

3. Only Tanks renewed with

fresh paint coat or recoat after

2000 were included

UHS/HS were in use after

2000

Records examined in

this present work will

only be UHS/HS

coatings

4. Only Records with fields:

Coating Type, Coating Age,

Ship Class, Tank Type,

Coating Type, Surface Prep,

Coating Score were included

These factors were being

studied.

Simplified the database

field to this present

study; easier to compute

Table 6 – HB1 Database Filtering Actions

Filter Action Reason Result

1. Records of Decommissioned

ships excluded

Present work studying

active fleet only

Only active ships

considered

2. Records with fields:

Coating Score, Coating Age,

Ship Class, Tank Type,

Structural Score, Tank

Severity and Tank Pressure

were included

These factors were being

studied.

Simplified the database

field to this present

study; easier to compute

3.5 H1A and H1B Database Analysis

For this work, two different analyses were performed. The first considered the factors

(coating type, coating age, surface preparation, ship class, tank type, tank severity)

affecting coating score (degradation). The second considered the factors (coating score,

28

coating age, ship class, tank type, tank pressure and tank severity) affecting structural

score (degradation). Coating assessment score is valued at 1 to 4, 1 being pristine coating,

4 being severely corroded. Structural assessment score is valued at 1 to 5, 1 being in new

or near condition while 5 is severely degraded. The CCAMM has the complete grading

criterion for each.

Both response variables are ordinal, so an ordinal logistic regression (OLR)

methodology was chosen as the analytical methodology. Because of the type of response

variables, other regression analysis methods like ordinary least squares regression were

ruled out because the model assumptions would be violated by response variables being

ordinal. Ordinal Logistic Regression model assumptions for this work were confirmed.

Minitab 18 was chosen as the software to conduct the analysis. Predictive variables

that were labeled in text (all predictive variables except coating age) were assigned a

numeric value by using the date recode function in the Minitab drop down menu.

3.5.1 H1A Data Set

H1A: Coating Type, Application Method, Tank Type, Ship Class, Tank

Criticality, Coating Age are predictors of Coating Condition Score.

Thus, the data set for H1A was reshaped by recoding and prepared for analysis.

Table 7 shows a sample set of the recoded database for the H1A (coating degradation

analysis). Note from left to right the six independent variables followed by the single

dependent variable on the far right.

29

Table 7 – H1A Database Sample Set

COATING AGE (Years)

Recoded COATING

Type

Recoded Surface Prep

Recoded TANK TYPE

SHIP CLASS

RC Tank Criticality

Coating Score

3.98 8 8 7 1 3 1

4.04 8 7 7 1 3 1

2.28 8 7 11 1 1 1

4.04 8 7 7 1 3 1

4.76 28 8 7 1 3 2

4.76 28 8 7 1 3 2

4.04 8 7 7 1 3 1

9.67 28 8 7 1 3 3

3.86 8 7 7 1 3 1

3.5.2 H1B Data Set

H1B: Coating Condition Score, Ship Class, Tank Pressure, Coating Age, Tank

Criticality are predictors of Structural Condition Score.

Additionally, the data set for H1B was reshaped by recoding and prepared for

analysis. Table 8 shows a sample set of the recoded data base for the H1B (structural

degradation analysis). Note from left to right the six independent variables followed by

the single dependent variable on the far right.

30

Table 8 – H1B Database Sample Set

COATING AGE

Coating Score

Recoded Tank Type

RC SHIP CLASS

RC Tank Pressure

Tank Severity

Structural Score

32.61 3 10 8 1 3 5

32.61 3 3 8 1 2 5

15.18 4 10 8 2 3 5

32.61 4 11 8 1 1 3

33.43 4 1 8 1 1 5

10.56 2 2 1 1 3 1

3.13 1 6 1 1 2 1

3.13 1 6 1 1 2 1

10.99 2 9 1 1 3 1

3.5.3 H1A and H1B Data Partitioning

H1A and H1B data sets were each partitioned into a training data set and a testing

data set through the random function in Excel which assigned values between 0 and 1 to

each record file. Those values 0.80 and above were grouped into a testing data set. Those

values less than 0.80 were grouped in the training data set. Thus, each hypothesis had a

training data set and a corresponding testing data set.

3.5.4 H1A and H1B Model Training

Ordinal logistic analysis was performed for each training data set using Minitab.

The logit function was selected for each. Regression results were captured in an output

file. The model initially included all predictive factors. Based on the model’s initial

regression results, least significant factors were removed one at time, and the model

regression was conducted again. Regression results again were analyzed to determine

factors’ significance. Model results included the regression analysis output (response

information, logistic regression table, test of all slopes equal to zero, goodness-of-fit test,

31

and measures of association), coefficient of the estimation equation, and the event

probabilities. Each training result was tested for significance, goodness-of-fit, and

measures of association. A regression equation for reach model was derived as well and

subsequently used in model testing.

3.5.5 H1A and H1B Model Testing

For H1A and H1B, the OLR output event probabilities for each regression output

were inserted into each respective training database. The training data were subsequently

tested for accuracy using the following methodology. Using the regression output

response variable values and coefficients, logit values were calculated using the logit

function equation for each categorical outcome.

Calculated logit = B0 + B1X1+B2X2+…BkXk (3.1)

B0 is the categorical coefficient, and B1 and X1 are the predictor variable coefficient

and predictor value, respectively and so forth for other predictive variables.

Then, an event probability was calculated for each categorical outcome using the

corresponding calculated logit value input to the regression equation.

Calc 1 Prob =e(Calc Logit 1)/(1+e(Calc Logit 1)) (3.2)

Subsequent categorical outcome probabilities were calculated as follows:

Calc 2 Prob =e(Calc Logit 2)/(1+e(Calc Logit 2))-Calc 1 Prob (3.3)

Calc 3 Prob =e(Calc Logit 3)/(1+e(Calc Logit 3))-(Calc 1 Prob)-(Calc 2 Prob) (3.4)

Calc 4 Prob =1-(Calc 1 Prob)-(Calc 2 Prob)-(Clac 3 Prob) (3.5)

32

Table 9 – Training Database Testing Sample Set

The highest value of the calculated probability outcomes for each inspection record

was chosen as the predicted inspection score. See the record circled on the far right

portion of Table 9. The probability of score 1 is almost 86% percent, so coating score 1

is predicted. Predicted value was compared with actual coating score inspection value,

and the results were registered in an added column at the right of the training database.

This methodology was completed for each training data set to determine preliminary

model accuracy.

In order to test the trained models, a similar methodology was followed except the

output probabilities were not entered into the test database. Using the training data

regression output predictors’ values and coefficients, logit values were calculated using

the logit function equation for each categorical outcome, like before, except for the

testing data. The highest value of the calculated probability outcomes for each testing

data inspection record was chosen as the predicted inspection score. Predicted value was

compared with actual coating score inspection value, and the results were registered in an

added column at the right of the training database. This methodology was completed for

each testing data set to determine model accuracy. Table 10 shows a sample of this

testing of a testing data set.

Coating

Age

Coating

Type

Surface

Prep

Method

Tank

Type

Ship

Class

Tank

Criticalit

y

Coating

Inspection

Score

EPROB

1

EPROB

2

EPROB

3

EPROB

4

Calc 1

Logit

Calc 2

Logit

Calc 3

Logit

Calc 4

Logit

Calc 1

Prob

Calc 2

Prob

Calc 3

Prob

Calc 4

ProbPredict

Accurate

?

3.98 8 8 7 1 3 1 0.857 0.112 0.029 0.002 1.786 3.406 6.206 -22.09 0.856 0.111 0.03 0.002 1 yes

0.43 22 7 9 1 3 1 0.991 0.007 0.002 1E-04 4.67 6.29 9.09 -19.21 0.991 0.007 0.002 1E-04 1 yes

4.04 8 7 7 1 3 1 0.984 0.013 0.003 2E-04 4.082 5.702 8.502 -19.8 0.983 0.013 0.003 2E-04 1 yes

2.28 8 7 11 1 1 1 0.995 0.004 8E-04 5E-05 5.364 6.984 9.784 -18.52 0.995 0.004 9E-04 6E-05 1 yes

4.04 8 7 7 1 3 1 0.984 0.013 0.003 2E-04 4.082 5.702 8.502 -19.8 0.983 0.013 0.003 2E-04 1 yes

4.76 28 8 7 1 3 2 0.661 0.249 0.084 0.006 0.662 2.282 5.082 -23.22 0.66 0.248 0.086 0.006 1 no

4.76 28 8 7 1 3 2 0.661 0.249 0.084 0.006 0.662 2.282 5.082 -23.22 0.66 0.248 0.086 0.006 1 no

= B0 + B1X1+B2X2+…BkXk = e(Calc Logit 1)/(1+e(Calc Logit 1))

33

Table 10 – Test Results Sample of Testing Data Set

The testing of the trained models for H1A and H1B was performed in this manner.

3.5.6 Model Classification Matrix and Optimization

A classification matrix for each model was built using the training data model

results to determine how well each model classified. Models were then optimized to

address model overconfidence and to reduce the number of false positives (Type 1 errors)

while maintaining nearly the same model accuracy.

Minitab’s Design of Experiments was utilized to determine the required combinations

needed, given the number of factors (response variables logit coefficients) factors and

levels for each factor. Tables 11 and 12 are samples of the Design of Experiments factors

used for H1A and H1B. For H1A, the four responsive variable outputs (coating scores 1-

4) represented the four factors in the Design of Experiments.

Table 11 – H1A Design of Experiments Sample

COATING

AGE

Coating

Type

Surface

Prep

TANK

TYPE

SHIP

CLASS

Tank

Criticality

Coating

Insp

Score

EPROB1

from

Minitab

EPROB

2 from

Minitab

EPROB3

from

Minitab

EPROB4

from

Minitab

Calc 1

Logit

Calc 2

Logit

Calc 3

Logit

Calc 4

Logit

Calc 1

Prob

Calc 2

Prob

Calc 3

Prob

Calc 4

ProbPredict

Accurate

?

0.347222 18 8 3 1 2 1 No values 3.4 5.02 7.82 -20.48 0.968 0.026 0.006 4E-04 1 yes

3.863889 8 7 7 1 3 1 4.147 5.767 8.567 -19.73 0.984 0.012 0.003 2E-04 1 yes

0.405556 8 7 13 1 2 1 5.458 7.078 9.878 -18.42 0.996 0.003 8E-04 5E-05 1 yes

3.863889 8 7 7 1 3 1 No values 4.147 5.767 8.567 -19.73 0.984 0.012 0.003 2E-04 1 yes

4.041667 8 7 7 1 3 1 4.082 5.702 8.502 -19.8 0.983 0.013 0.003 2E-04 1 yes

4.041667 8 7 7 1 3 1 4.082 5.702 8.502 -19.8 0.983 0.013 0.003 2E-04 1 yes

0.072222 20 8 2 1 3 1 No values 3.028 4.648 7.448 -20.85 0.954 0.037 0.009 6E-04 1 yes

4.130556 8 7 6 1 2 1 4.57 6.19 8.99 -19.31 0.99 0.008 0.002 1E-04 1 yes

StdOrder RunOrder PtType BlocksCS Coeff

1

CS Coeff

2

CS Coeff

3

CS Coeff

4

1 1 1 1 22.33 24 26.8 -1.5

2 2 1 1 22.33 24 26.8 -1

3 3 1 1 22.33 24 26.8 -0.5

4 4 1 1 22.33 24 26.8 0

5 5 1 1 22.33 24 26.8 0.5

6 6 1 1 22.33 24 26.8 1

7 7 1 1 22.33 24 26.8 1.5

34

Table 12 – H1B Design of Experiments Sample

For H1A, seven levels were chosen for each factor. The range of the levels was

centered around the response variable coefficients from the OLR in order to examine the

results region around the OLR response variable coefficients. For example, the H1A OLR

response coefficient for Coating Score 1 was 23.88. CS1 (coating score) coefficient levels

were 25.33, 24.88, 24.33, 23.88, 23.33, 22.88 and 22.33. For H1A, there were 2,401

response variable logit coefficient combinations examined (47) (number of factors raised

to the number of levels).

For H1B, five factors were chosen since there are five response variable coefficients.

Five levels were chosen for each factor. Similar to H1A, the range of levels were

centered around the response variable coefficient from OLR. For H1B, there were 3,125

response variable logit coefficient combinations examined (55) (number of factors raised

to the number of levels).

Visual basic for application (VBA) code in Excel was used to automate the input of

each combination of response coefficients to execute the logit function (Equation 3.1), to

calculate probabilities (Equation 3.2), to generate a classification matrix and to populate

an overall model results spreadsheet, given all combinations of factors. Criteria for the

selection of the optimized factors were based on overall model performance balanced

StdOrder RunOrder PtType BlocksCS Coeff

1

CS Coeff

2

CS Coeff

3

CS Coeff

4

CS Coeff

5

1056 1 1 1 2.3 3.55 3.19 2.76 -1

1192 2 1 1 2.3 4.05 3.19 3.76 -0.5

190 3 1 1 1.8 2.55 3.19 3.26 1

1905 4 1 1 3.3 2.05 2.69 2.26 1

1041 5 1 1 2.3 3.55 2.69 3.76 -1

1838 6 1 1 2.8 4.05 3.69 3.26 0

2370 7 1 1 3.3 3.55 4.19 3.76 1

35

with minimizing the risk to the most mission critical tanks (tank criticality 3) from Type

1 errors from overconfident models. Model classification performance measures

included: overall accuracy, error rate average, precision average, recall average, false

positive rate (FPR) average, Cohen's kappa coefficient (k) and Mathews correlation

coefficient (MCC).

For both models, classification matrices were then obtained by using the optimized

regression coefficients. Additionally, classification matrices for each model were also

built using model testing data results to confirm intended model optimizations. Tables 13

and 14 are classification matrix examples for H1A and H1B, respectively.

Table 13 – H1A Classification Matrix Example

For Table 13, H1A predicted coating scores were classified against actual observed

inspection results. True positives (predicted value matched actual values) are shaded in

the gray diagonal. TP1 represents all tank coating inspection values predicted as coating

score 1 which when inspected were actually inspected as coating score 1. The other

model results classified in error are located on either side of the gray diagonal. The error

1 2 3 4

1 TP1 EE12 EE13 EE14

2 EE21 TP2 EE23 EE24

3 EE31 EE32 TP3 EE34

4 EE41 EE42 EE43 TP4

Predicted

H1A Model

Classification Matrix

Observed

36

cell annotation EERC means error on row R, column C. For example, EE13 (highlighted in

Table 13) represents all tank coating inspection values predicted as coating score 1, but

when inspected, the tank inspection results actually indicated a coating score of 3. Table

14 represents the H1B classification matrix. The notation in Table 14 is the same as that

which is used in Table 13. It should be noted that Table 14 has an additional row and

column since H1B has five categorical responses vice four in H1A.

Thus, for each model, the classification matrix tool was used to determine the number

of false positives (type 1 errors), false negatives (type 2 errors), true positives, and true

negatives for each categorical outcome. Model optimization focused on minimizing

errors in the pink regions of Tables 13 and 14 while maintaining overall model

performance. This pink region represented areas where model performance was

overconfident; that is, predictions were optimistic, while actual results were worse than

predicted. For example, the highlighted cell EE13 in Table 13 represents tanks predicted

to be a coating score 1 but were actually a coating score 3 when inspected. This is a false

positive situation. Type 1 errors represent the worst case scenario for this research work.

Table 14 – H1B Classification Matrix Example

1 2 3 4 5

1 TP1 EE12 EE13 EE14 EE15

2 EE21 TP2 EE23 EE24 EE25

3 EE31 EE32 TP3 EE34 EE35

4 EE41 EE42 EE43 TP4 EE45

5 EE51 EE52 EE53 EE54 TP5

H1B Model Observed

Classification Matrix

Predicted

37

3.5.7 H1A Coating Age Prediction Tool

As a product of the regression analysis of the coating score training data, by using

the regression output predictors’ values and coefficients, the coating age can be predicted

if other independent variables are known, and the desired output coating score is known.

This can be accomplished through manipulating the logit function and the regression

equation. Figure 1 shows a sample for the Coating Age Prediction Tool.

Figure 1 – Coating Age Prediction Tool Sample

3.5.8 H1B Structural Score Prediction Tool

As a product of the regression analysis of the coating score training data, by using

the regression output predictors’ values and coefficients, the structural score can be

predicted if other independent variables are known. The methodology for developing this

Predict age, given

Inputs below and Prob

entered to the right,

Enter Prob (0.XX) 0.9

AGE

Predicted

(years)

20.20

Input Factors projected minimal years to Coating Score 3

Coating Type (1-34) 27

App Method (1-8) 7

Tank Type (1-13) 10 COEFF Score

Ship Class (1-14) 11 23.8872 1.0000

Severity (1,2,3) 3 25.5316 2.0000

Coating Score (1-4) 3 28.3489 3.0000

0.0000 4.0000

COEFF 28.3489

CA -0.3626

APL -0.0422

SP -2.3174

TT -0.0660

SC 0.0503

TS -0.4537

38

tool is the same as the previous tool, manipulation of the logit function and the regression

equation. Figure 2 shows a sample for the Structural Score Prediction Tool.

Figure 2 – Structure Score Prediction Tool Sample

3.6 H2 Data and Data Analysis

A sample of the H2 database is below in Table 15. H2 is:

H2: Tank inspection periodicities, where HS/UHS coatings are employed, can be

increased 25-50% using the coating and structure score predictions (developed from

H1A and H1B) with minimal increased risk of failure.

Factors

Enter

Values

Below

Predicted

Structural

Score

Coating Age (yrs) 15 1

Coating Insp Score (1-4) 3

Tank Type (1-13) 10

Ship Class (1-10) 10

Tank Pressure (1-2) 2

Tank Criticality (1 -3) 3

1.991

3.052

3.193

3.254

05

-0.0202 CA

-0.2260 CS

-0.0487 TT

-0.0938 SC

0.7490 TP

-0.5257 TC

39

Table 15 – H2 Database Sample

The methodology used to prove H2 is systematically explained by explaining the

columns in Table 15. Each tank type is listed. Tank criticality code is listed for each tank.

The current inspection requirement periodicity for each tank (from the CCAMM) is

listed. From the H1A age prediction tool, the minimum years is derived for coating

score 3 (which is considered failure in the CCAMM). From the H1B structural score

prediction tool, the predicted structural score is given for a coating score of 3. The risk

assessment is derived using CCAMM table 6-1 given inputs (coating score 3, predicted

structure score (previous column), and tank severity (previous column)). Overall number

of inspections required in reaching minimum years (Min years/Req Inspection

Periodicity) is listed.

Net inspections equals overall inspections minus one. Percent savings is net savings

divided by overall inspections times one hundred. This methodology was used to

determine any inspection savings.

3.7 H3 Data and Data Analysis

A sample of the H3 database is below in Table 16. H3 is:

H3: There are significant savings when tank inspection periodicities increase by

25-50% (results from H2).

Tank

Type

Tank Criticality

Code

Coating

Inspection

Requirement

Periodicity

(years) per Navy

Corrosion

Manual

Minimum Years

to reach Coating

Score 3, using

H1A Coating Age

Model, (given:

coating #28, sp #

7 & tank

criticality code)

for ALL classes

with 95%

probability

Projected Structural

Score, using H1B

Structural Model

prediction tool, given

Minimum Coating Age

(years), Coating Score

#3 and Tank Criticality

Risk Assumed given

Coating Score 3 &

Projected Structure

Score per Navy

Corrosion Manual Risk

Matrix (Table 6-1)

Overall #

coating

inspections

required

during

Minimum Years

is reached (Min

Yrs/Req Insp

Periodicity)

Net Inspection

savings (Overall

Insp # less 1), if

periodicity

extended to

projected

minimal years to

Coating Score 3

Percent Insp

Savings by

extending

periodicity of

inspections (for

20 year

inspection

timeframe)

1 1 10 20.8 1 Low 2 1 50.0

2 3 4 18.1 1 Low 5 4 80.0

3 2 3 19.2 1 Low 6 5 83.3

4 3 6 17.7 1 Low 3 2 66.7

40

Table 16 – H3 Database Sample

The methodology used to prove H3 is systematically explained by explaining the

columns in Table 16. Each tank is listed. Average schedule cost per inspection for each

tank is listed (provided by Surface Maintenance Engineering Planning Program

(SURFMEPP). Average material cost per for each tank is listed (provided by

SURFMEPP). Net inspections savings (from H2) is listed. Total average schedule cost

savings in man-days (per tank) is calculated (net savings times schedule cost per

inspection). Total average material savings (per tank) is calculated (net savings times

average material cost per inspection). Total number of each type of tank in the surface

navy is listed. Aggregate schedule cost savings is listed (total average savings per tank

times the total number of that type of tank). Aggregate material cost savings is listed

(total average material savings per tank times the total number of that type of tank). The

aggregates are summed at the bottom. This is the methodology used to determine the

merit of H3.

Tank

Type

Average

Schedule Cost

per Inspection

(mandays)

Average

Material Cost

per Inspection

($ dollars)

Net Inspection

savings (from H2)

Total Ave Schedule

Cost Savings in

Mandays (per tank

type)

[Net Inspection

Savings X (Schedule

Cost/Insp)]

Total Ave Material

Cost Savings in dollars

($) (per tank type)

[Net Inspection

Savings X (Ave Mat

Cost/Insp)]

Total # of each

type of tank in

the surface

fleet

For 20 year

inspection

timeframe,

Aggregate

Schedule Cost

Savings (in

Mandays) for

each type of

tank across the

fleet (Total

Sched

Savings/per

tanks X # of

tank type in

fleet)

For 20 year

inspection

timeframe,

Aggregate

Material Cost

Savings in $

(dollars) for

each type of

tank across the

fleet (Total

Material

Savings/per

tank X # of tank

type in fleet)

1 16.55 $15,719.57 1 16.55 15719.57 344.0 5,694.0 $5,407,531.22

2 11.81 $5,966.33 4 47.23 23865.31 340.0 16,057.2 $8,114,206.52

3 11.24 $5,605.15 5 56.21 28025.73 1476.0 82,959.8 $41,365,979.90

4 20.63 $60,787.06 2 41.26 121574.12 107.0 4,415.1 $13,008,430.84

41

3.8 Conclusion

The CCIMS database was shaped, filtered and recoded. The recoded databases were

partitioned into training and testing databases. Ordinal logistic regression was principally

used to determine the merit of H1A and H1B. Both models were optimized and tested.

Predictions tools for both H1A and H1B were used as input to the development of the H2

database. H2 analysis involved simple estimating. The H2 output provided an input to H3

which was another simple estimating analysis.

42

Chapter 4—Results

4.1 Introduction

The purpose of this chapter is to present the results of the methodology presented in

the previous chapter. Hypothesis results are presented in separate sections. This work’s

hypotheses are:

H1A: Coating Type, Application Method, Tank Type, Ship Class, Tank

Criticality, Coating Age are predictors of Coating Condition Score.

H1B: Coating Condition Score, Ship Class, Tank Pressure, Coating Age, Tank

Criticality are predictors of Structural Condition Score.

H2: Tank inspection periodicities, where HS/UHS coatings are employed, can be

increased 25-50% using the coating and structure score predictions (developed from

H1A and H1B) with minimal increased risk of failure.

H3: There are significant savings when tank inspection periodicities increase by

25-50% (results from H2).

4.2 Models’ Assumptions and Results

Ordinal logistic regression model assumptions and results are presented in Table 17.

43

Table 17 – Assumption Results for H1A and H1B Models

Assumption Description Response Assumption met?

#1 Response Variable should

be measured at the ordinal

levels

Models have ordinal

Response Variables

Yes

#2 One or more predictor

variables that are

continuous, ordinal or

categorical must be used.

Models have multiple

Independent

Variables that meet

criterion

Yes

#3 There is no

multicollinearity amongst

Predictor Variables.

Correlation Matrices

results provided in

Tables 18 and 19.

Yes

#4 Proportional Odds Test of Parallelism

conducted. Figures

3-6 are provided.

Yes

Results from Tables 18 and 19 indicate no multicollinearity amongst predictor

variables. For both tables, correlation results indicated that there was enough evidence to

determine that the results were valid and that correlation coefficients were significantly

less than one, which indicates weak association amongst predictor variables and reduces

the risk of multicollinearity.

44

Table 18 – H1A Correlation Results

H1A Predictor Variables

COATING AGE

COATING Type

Surface Preparation

TANK TYPE

SHIP CLASS

Tank Criticality

COATING AGE 1

COATING Type -0.07286 1

Surface Preparation 0.261756 -0.05097 1

TANK TYPE -0.01481 0.066063 -

0.03222271 1

SHIP CLASS 0.007996 0.022524 -

0.02387041 0.22416 1

Tank Criticality -0.07854 0.159009 -0.005555 0.07628 0.0444 1

Table 19 – H1B Correlation Results

H1B Predictor Variables

COATING AGE

Coating Score

Tank Type

SHIP CLASS

Tank Pressure

Tank Criticality

COATING AGE 1

Coating Score 0.379248 1

Tank Type -0.01678 -0.02201 1

SHIP CLASS 0.020805 -0.1099 0.113923 1

Tank Pressure 0.022272 0.137278 0.091712 -0.15794 1

Tank Criticality -0.10938 0.124292 -0.0329 0.047648 0.56908 1

The proportional odds assumption, also called the assumption of parallel lines,

maintains that the impact of a predictor variable on the response variable is uniform over

all categories of response variable (UCLA, 2019). H1A and H1B have six predictor

variables each for a total of twelve predictor variables. All twelve predictor variables

45

passed the proportional odds test in demonstrating variable consistency across categorical

outcomes by virtue of having parallel lines. Figures 3 and 4 are two of the six parallel test

results for H1A. Figure 3 is an H1A continuous predictor variable and Figure 4 is H1A a

discrete predictor variable. Figures 5 and 6 are two of the six parallel test results for H1B.

Figure 5 is an H1B continuous predictor variable and Figure 6 is an H1B discrete

predictor variable. The proportional odds assumption with the ordinal logistic model was

not violated for either H1A or H1B models.

Figure 3 – H1A Coating Age (Predictor Variable) Parallel Test Results

46

Figure 4 – H1A Coating Type (Predictor Variable) Parallel Test Results

47

Figure 5 – H1B Coating Age (Predictor Variable) Parallel Test Results

48

Figure 6 – H1B Coating Score (Predictor Variable) Parallel Test Results

4.2.1 Database Shaping and Filtering Results

Table 20 shows the data shaping and filtering for H1A and H1B. Filtering of surface

ships tank inspection records began with removing decommissioned ships records which

resulted in 54,738 records remaining. Because this research effort involves analysis of

49

HS/UHS tank coatings, tanks that were coated prior to 2000 were filtered out (Table 20

filtering actions 2 and 3). Lastly, the remaining 24,311 records were filtered according to

variables associated with H1A and H1B resulting in 4,250 and 2,397 records, respectively.

Partitioning of H1A and H1B datasets represents an 80/20 split into training and test sets.

Table 20 – Data Shaping and Filtering Results

Filter Action Reason Results

Tank Inspection Record

Description

Filter Action Taken Resultant

Records

Remaining

1. All Records Minus Decommissioned vessels

records

54,736

2. All Active Vessels Tank

Inspections (54,736)

Minus Records prior to the year

2000

42,099

3. All Active Vessels Tank

Inspections since 2000 (42,099)

Include only reset tank inspection

results and any subsequent

inspections since the year 2000

24,311

3a. Tank Inspection Records

for Tanks with HS/UHS

Coatings (24,311)

Include only records that have

Hypothesis 1A analysis predictor

and response variables

4,250

3b. Hypothesis 1A Database

(4,250)

Partition data into train and test

Data Sets

3,357/893

(train/test)

4. (from #2 above) All Active

Vessels Tank Inspections

(54,736)

Include only records that have

Hypothesis 1B analysis predictor

and response variables

2,397

4a. Hypothesis 1B Database

(2,397)

Partition data into train and test

Data Sets

1,943/454

(train/test)

50

4.3 H1A Database Composition

Table 21 shows the frequency distribution for six categorical variables in the H1A

database. For example, Coating Type 1 has four records, and Tank Type 1 has 146

records.

Table 21 – H1A Database Frequency Distribution

Figure 7 shows the frequency distribution for each categorical variable in H1A in

graphical form. Figure 8 shows the coating age histogram for H1A. Given these two

figures, it is observed that the H1A dataset is not evenly distributed which means that

Coating

Type (PV)

Surface Preparation

Method (PV)

Tank Type

(PV)

Ship Class

(PV)

Tank

Criticality

(PV)

Coating

Score

(RV)

1 4 7 146 569 393 3949

2 4 10 300 1814 945 192

3 15 2 384 0 2912 99

4 93 4 43 0 10

5 4 1 60 2

6 58 21 283 0

7 24 3584 1125 9

8 1064 621 2 729

9 124 575 52

10 17 809 175

11 35 247 435

12 1 12 319

13 9 264 0

14 1 146

15 92

16 2

17 310

18 349

19 165

20 385

21 89

22 122

23 2

24 70

25 5

26 10

27 22

28 1141

29 1

30 5

31 2

32 2

33 23

Frequency Count All Data Set

Predictor and Response Variables Count

Variable

Number

51

training and testing data sets should have similar distributions. Additionally, future model

performance should reflect the impact of given distributions. It should be noted that

uneven distributions do not violate the use of the proportional odds model in ordinal

logistic regression methodology (McCullagh, 1980).

Figure 7 – H1A Database Frequency Distribution (Graph)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0 5 10 15 20 25 30 35

H1A DATA BASE COMPOSTION (VARIABLES VS FREQUENCY COUNT)

Coating Type (PV) Surface Preparation Method (PV)

Tank Type (PV) Ship Class (PV)

Tank Criticalilty (PV) Coating Score (RV)

52

Fig

ure

8 –

H1A

Dat

abas

e

Coat

ing A

ge

(PV

)

Fre

quen

cy D

istr

ibuti

on

53

4.3.1 H1A Train Database Composition

Through the 80/20 split partitioning of H1A database, the train database (80%) was

formed. Table 22 represents the frequency distribution for the six categorical variables in

the H1A train database. It is read in the same fashion as Table 21. Inspection of Tables

21 and 22 shows similar uneven distributions. The H1A training database (Table 22) is a

fair representation of the H1A database (Table 21).

Table 22 – H1A Train Database Frequency Distribution

Coating

Type (PV)

Surface Preparation

Method (PV)

Tank Type

(PV)

Ship Class

(PV)

Tank

Criticality

(PV)

Coating

Score

(RV)

1 3 5 107 451 290 3120

2 3 6 231 1436 762 154

3 12 2 307 0 2305 76

4 70 4 36 0 7

5 3 1 46 2

6 45 14 233 0

7 21 2839 903 5

8 850 486 1 573

9 104 452 43

10 13 637 148

11 28 183 330

12 1 10 260

13 7 211 0

14 1 109

15 76

16 2

17 245

18 284

19 133

20 310

21 66

22 88

23 2

24 59

25 5

26 9

27 17

28 877

29 0

30 4

31 2

32 2

33 15

Frequency Count Train Data Set

Variable

Number

Predictor and Response Variables Count

54

Figure 9 shows the frequency distribution for each categorical variable in the H1A

train database in graphical form. Figure 10 shows the coating age histogram for H1A

train database. Given these two figures, it is observed that the H1A train database is an

accurate representation of the overall H1A database, comparing Figures 7 through 10.

Figure 9 - H1A Train Database Frequency Distribution (Graph)

0

500

1000

1500

2000

2500

3000

3500

0 5 1 0 1 5 2 0 2 5 3 0 3 5

HYP 1A TRAIN DATA BASE (VARIABLES VS FREQUENCY COUNT)

Coating Type (PV) Surface Preparation (PV) Tank Type (PV)

Ship Class (PV) Tank Criticality (PV) Coating Score (RV)

55

Fig

ure

10 –

H1A

Tra

in

Dat

abas

e C

oat

ing A

ge

(PV

) F

requen

cy

Dis

trib

uti

on

56

4.3.2 H1A Test Database Composition

Through the 80/20 split partitioning of H1A database, the test database (20%) was

formed. Table 23 represents the frequency distribution for the six categorical variables in

the H1A test database. It is read in the same manner as previous similar distribution

tables. Inspection of Tables 23 and 22 show similar distributions. That is, the H1A

testing database (Table 23) is a fair representation of the H1A training database (Table

22).

Table 23 - H1A Test Data Frequency Distribution

Coating

Type (PV)

Surface Preparation

Method (PV)

Tank Type

(PV)

Ship Class

(PV)

Tank

Criticality

(PV)

Coating

Score

(RV)

1 1 2 39 118 103 829

2 1 4 69 378 183 38

3 3 0 77 0 607 23

4 23 0 7 0 3

5 1 0 14 0

6 13 7 50 0

7 3 745 222 4

8 214 135 1 156

9 20 123 9

10 4 172 27

11 7 64 105

12 0 2 59

13 2 53 0

14 0 37

15 16

16 0

17 65

18 65

19 32

20 75

21 23

22 34

23 0

24 11

25 0

26 1

27 5

28 264

29 1

30 1

31 0

32 0

33 8

Frequency Count Test Data Set

Variable

Number

Predictor and Response Variables Count

57

Figure 11 shows the frequency distribution for each categorical variable in the H1A

test database in graphical form. Figure 12 shows the coating age histogram for H1A test

database. Given these two figures, it is observed that the H1A test database is an accurate

representation of the H1A train database, comparing Figures 9 through 12.

Figure 11 – H1A Test Database Frequency Distribution (Graph)

0

100

200

300

400

500

600

700

800

900

0 5 1 0 1 5 2 0 2 5 3 0 3 5

H1A TEST DATA BASE (VARIABLES VS FREQUENCY COUNT)

Coating Type (PV) Surface Preparation Method (PV)

Tank Type (PV) Ship Class (PV)

Tank Criticalilty (PV) Coating Score (RV)

58

Fig

ure

12 –

H1A

Tes

t D

atab

ase

Coat

ing A

ge

(PV

) F

requen

cy D

istr

ibuti

on

59

4.4 H1B Database Composition

The methodology for obtaining the H1B database is detailed in section 4.2.1 of this

chapter and previously in Chapter 3 Methodology. Table 24 shows the frequency

distribution for the categorical variables in the H1B database. Like the H1A database, the

H1B database is unevenly distributed. Therefore, the same implications previously stated

for the H1A database apply to H1B database.

Table 24 - H1B Database Frequency Distribution

Figure 13 shows the frequency distribution for each categorical variable in H1B in

graphical form. Figure 14 shows the coating age histogram for H1B.

Coating

Score (PV)Tank Type (PV)

Ship Class

(PV)

Tank

Pressure

(PV)

Tank

Criticality

(PV)

Structure

Score

(RV)1 928 48 192 1656 300 1718

2 613 99 1104 741 829 112

3 535 278 3 1268 53

4 321 9 15 22

5 44 334 492

6 428 2

7 563 346

8 6 224

9 201 81

10 352 96

11 252

12 12

13 105

Frequency Count All Data Set

Predictor and Response Variables Count

Variable

Number

60

Figure 13 – H1B Database Frequency Distribution (Graph)

Figure 14 – H1B Database: Coating Age Frequency Distribution

4.4.1 H1B Train Database Composition

Through the 80/20 split partitioning of H1B database, the train database (80%) was

formed. Table 25 shows the frequency distribution for categorical variables in the H1B

61

train database. Inspection of Tables 25 and 24 shows similar uneven distributions. The

H1B training database (Table 25) is a fair representation of the H1B database (Table 24).

Table 25 – H1B Train Database Frequency Distribution

Figure 15 shows the frequency distribution for each categorical variable in the

H1B train database in graphical form. Figure 16 shows the coating age histogram for

H1B train database. Comparing Figures 13 through 16, it is observed that the H1B train

database is a fair presentation of the H1B database.

Coating

Score (PV)Tank Type (PV)

Ship Class

(PV)

Tank

Pressure

(PV)

Tank

Criticality

(PV)

Structure

Score

(RV)1 748 37 156 1333 233 1401

2 498 81 904 610 663 88

3 438 221 3 1047 44

4 259 9 13 20

5 36 278 390

6 349 2

7 468 279

8 5 179

9 168 54

10 285 75

11 196

12 10

13 78

Predictor and Response Variables Count

Variable

Number

Frequency Count Train Data Set

62

Figure 15 – H1B Train Database Frequency Data Base (Graph)

Figure 16 – H1B Train Database: Coating Age Frequency Distribution

4.4.2 H1B Test Database

Through the 80/20 split partitioning of H1B database, the test database (20%) was

formed. Table 26 shows the frequency distribution for the categorical variables in the

63

H1B test database. Inspection of Tables 26 and 25 shows similar uneven distributions.

The H1B testing database (Table 26) is a fair representation of the H1B training database

(Table 25).

Table 26 – H1B Test Database Frequency Data Base

Figure 17 shows the frequency distribution for each categorical variable in the H1B

test database in graphical form. Figure 18 shows the coating age histogram for H1B test

database. Given these two figures, it is observed that the H1B test database is not evenly

distributed as well and is also an accurate representation of the train H1B database,

comparing Figures 15 through 18.

Coating

Score (PV)Tank Type (PV)

Ship Class

(PV)

Tank

Pressure

(PV)

Tank

Criticality

(PV)

Structure

Score

(RV)1 180 11 36 323 67 317

2 115 18 200 131 166 24

3 97 57 0 221 9

4 62 0 2 2

5 8 56 102

6 79 0

7 95 67

8 1 45

9 33 27

10 67 21

11 56

12 2

13 27

Predictor and Response Variables Count

Frequency Count Test Data Set

Variable

Number

64

Figure 17 – H1B Test Data Base Frequency Distribution

Figure 18 – H1B Test Data: Coating Age Frequency Distribution

65

4.5 H1A OLR Results

Initial OLR results from a regression analysis with all predictor variables (coating age,

coating type, surface preparation method, tank type, ship class, tank criticality and tank

pressure) indicated that the predictor variable tank pressure was not significant (p > 0.05).

Tank pressure was removed as a predictor variable from the analysis, and the regression

was performed again. Figure 19 displays the final regression analysis results of H1A

using the training data set.

Figure 19 – H1A OLR Results

66

H1A OLR results indicated statistical significance for predictive variables (p-values

less than 0.05). Test of All Slope equal to Zero is good (p-value less the 0.05). Goodness

of Fit is satisfactory since P =1.0, and Measures of Association between variables is

strong, since Somers’D and Goodman-Kruskal Gamma are 0.89 (near 1.0). Overall, the

H1A OLR results indicate a good fit between data and the model.

4.5.1 H1A Model Training and Testing Results

To determine accuracy of each trained model, each dataset was tested using the

following methodology. As also described in section 3.5.4 5 of Chapter 3 Methodology,

regression output coefficients were inputted into the logit equation (3.1), which in turn

provided an input to the regression equation (3.2), which yielded output probabilities for

each categorical outcome. Table 27 and Table 28 are the results of testing the H1A

model with the training and testing datasets. Both results indicate a high degree of

accuracy (93%) for this model.

67

Co

atin

g

Age

Co

atin

g

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ace

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p

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bP

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58

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03

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72

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33

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0.4

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31

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7TS

Tab

le 2

7 -

H1A

Model

Tra

inin

g R

esult

s

68

CO

ATI

NG

AG

E

Co

atin

g

Typ

e

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yes

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66

78

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66

78

77

13

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41

yes

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61

4.1

30

55

68

76

12

14

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98

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0.9

90

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80

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41

yes

Sum

89

3

0.4

11

11

11

78

13

12

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94

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9

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33

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4.9

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6.5

57

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0.9

93

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0.4

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21

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94

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97

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1.0

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50

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30

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17

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41

yes

0.5

22

22

22

18

13

12

12

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76

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5.7

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0.9

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4.1

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2.9

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4.1

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yes

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62

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58

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31

57

71

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3.0

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4.6

76

7.4

76

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0.9

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0.1

86

11

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17

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5.6

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0.9

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1ye

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74

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5.4

94

44

41

17

12

12

13

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35

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37

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3-2

0.3

30

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20

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53

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41

yes

-0.0

66

TT

8.2

77

77

81

57

71

31

2.2

51

3.8

71

6.6

71

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0.9

05

0.0

75

0.0

19

0.0

01

1ye

s0

.05

03

3SC

0.1

94

44

41

87

71

31

5.0

56

6.6

76

9.4

76

-18

.82

0.9

94

0.0

05

0.0

01

8E-

05

1ye

s-0

.45

37

TS

Tab

le 2

8 –

H1A

Model

Tes

ting R

esult

s

69

4.5.2 H1A Model Optimization and Classification Results

Using the output testing results of the training data (Table 27), a classification

matrix was developed to better understand the classifier accuracy of the H1A model.

Table 29 represents the general classification matrix for H1A. Table 30 is the

classification matrix of the H1A trained model, given the coating score coefficients of

23.88, 25.5, 28.3 and 0 from the OLR regression results. Table 31 represents H1A

classification results using the optimized coating score coefficients of 22.33, 24, 29.3 and

0. The impact, from optimization, upon the worst type of false positives (FP - Type 1

errors), that is, to cells EE14 and EE24 for mission criticality tanks (#3), should be noted,

as FPs go to zero. Tables 30 and 33 indicate a positive outcome through the use of

optimized coating score coefficients. Additionally, Table 34 displays the impact of model

H1A optimization upon classification matrix performance parameters. Overall, the

optimization of H1A produces the desired results of decreasing false positives while

maintaining overall model performance.

Table 29 – H1A Classification Matrix

1 2 3 4

1 TP1 EE12 EE13 EE14

2 EE21 TP2 EE23 EE24

3 EE31 EE32 TP3 EE34

4 EE41 EE42 EE43 TP4

Predicted

H1A Model

Classification Matrix

Observed

70

Table 30 – H1A Model Training Results Classification Matrix (Pre-Optimization)

Table 31 – H1A Model Training Results Classification Matrix (Post-Optimization)

Table 32 – H1A Testing Data Results Classification Matrix (Pre-Optimization)

Model

Accuracy93.40%

1 2 3 4

1 3090 110 42 3 EE14 3

2 23 31 23 2 EE24 1

3 7 13 11 2

4 0 0 0 0

23.88 25.5 28.3 0

Tank Criticality (#3)

FP (Type 1 Errors)

Predicted

HA1A Model Classification Matrix (Pre-Optimization)

Observed

Coating Score Coefficients

Model

Accuracy91.70%

1 2 3 4

1 2968 54 16 0 EE14 0

2 61 23 6 0 EE24 0

3 91 77 54 7

4 0 0 0 0

22.33 24 29.3 0

Tank Criticality (#3)

FP (Type 1 Errors)

Predicted

HA1A Model Classification Matrix (Post-Optimization)

Observed

Coating Score Coefficients

Model

Accuracy93.20%

1 2 3 4

1 822 27 16 3 EE14 2

2 6 9 6 0 EE24 0

3 1 2 1 0

4 0 0 0 0

23.88 25.5 28.3 0Coating Score Coefficients

Tank Criticality (#3)

FP (Type 1 Errors)

HA1A Model Classification Matrix (Pre-Optimization)

Observed

Predicted

71

Table 33 – H1A Testing Data Results Classification Matrix (Post-Optimization)

Table 34 – Model H1A Classification Performance

4.5.3 Hypothesis 1A Prediction Tools

4.5.3.1 Hypothesis 1A Coating Score Prediction Tool #1

Through the use of the logit equation (3.1) and regression equation (3.2), for given

predictor variables, a predicted coating score can be ascertained. Figure 20 displays this

and represents the H1A coating score prediction tool. Users input the variables of coating

Model

Accuracy91.60%

1 2 3 4

1 799 17 3 1 EE14 0

2 12 2 3 0 EE24 0

3 18 19 17 2

4 0 0 0 0

22.33 24 29.3 0Coating Score Coefficients

Tank Criticality (#3)

FP (Type 1 Errors)

HA1A Model Classification Matrix (Post-Optimization)

Observed

Predicted

72

age, coating type, surface preparation method, tank type, ship class and tank type. The

automated spreadsheet yields a predicted coating score in the yellow field.

Figure 20 - H1A Coating Score Prediction Tool #1

4.5.3.2 H1A Coating Score Prediction Tool #2

A second coating prediction tool can be made by keeping predictor variables

constant and varying time and recording the response variable (coating score). Figure 21

represents this technique and is the H1A coating score prediction tool. It is intended to be

a laminated sheet for waterfront use to give tank planners a visual aid in the predicted

coating life of specified tanks.

Enter

Values

Below

Predicted

Coating Score

COATING AGE

(yrs) 12.002

COATING Type

(1-33) 28.00

Surface

Preparation (1-8) 7.00

Tank Type (1-14) 10.00

Ship Class (1-13) 10.00

Tank Criticality

(1 -3) 3.00

22.38 1Calc 1

Logit -0.89198

Calc 1

Prob 0.290702

25.5 2Calc 2

Logit 2.228022

Calc 2

Prob 0.612036

28.3 3Calc 3

Logit 5.028022

Calc 3

Prob 0.090753

0 4Calc 4

Logit -23.272

Calc 4

Prob 0.006509

-0.362584584 CA

-0.042188684 APL

-2.317391272 SP

-0.066025501 TT

0.050331542 SC

-0.453667123 TS

Coefficients

73

Figure 21 – H1A Coating Score Prediction Tool #2

4.6 H1B OLR Results

H1B OLR results shown in Figure 22. H1B predictor variables are coating age,

coating score, tank type, ship class, tank criticality and tank pressure. H1B OLR results

indicated statistical significance for predictive variables (p-values less than 0.05). Test of

All Slope equal to Zero is good (p-value less the 0.05). Goodness of Fit is satisfactory

since P =1.0 and Measures of Association between variables is moderate. Overall, the

H1B OLR results indicate a moderate fit between data and the model.

Coating Type 28, Surface Preparation 7, Tank Type 10, Ship Class 10 and

Tank Criticality 3. Every blue dot represents a quarterly inspection result.

UUnde

74

Figure 22 – H1B OLR Results

4.6.1 H1B Model Training and Testing Results

Model H1B was testing was accomplished in the same way as model H1A (see

section 4.5.1). Tables 35 and 36 display the H1B Model training and testing accuracy

results. Results indicate a modest degree of accuracy (70-72%).

75

COAT

ING

AGE

Coat

ing

Scor

e

Tank

Type

SHIP

CLAS

S

Tank

Pres

s

Tank

Criti

calit

y

Stru

ctur

e Sc

ore

EPRO

B

1 fro

m

Min

ita b

EPRO

B

2 fro

m

Min

ita b

EPRO

B

3 fro

m

Min

ita b

EPRO

B

4 fro

m

Min

ita b

EPRO

B

5 fro

m

Min

ita b

Calc

1

Logi

t

Calc

2

Logi

t

Calc

3

Logi

t

Calc

4

Logi

t

Calc

5

Logi

t

Calc

1

Prob

Calc

2

Prob

Calc

3

Prob

Calc

4

Prob

Calc

5

Prob

Pred

ictAc

cura

te

?

15.1

7778

310

82

35

0.62

20.

057

0.02

90.

013

0.27

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499

0.74

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892

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057

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no

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85

yes

12.8

0556

310

82

35

0.63

40.

057

0.02

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013

0.26

80.

547

0.79

70.

939

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0.63

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268

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32.6

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310

81

35

0.35

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034

0.01

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536

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85

noSu

m19

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8.50

5556

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82

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no

32.6

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43

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25

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033

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062

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0.37

81

no

11.3

6389

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25

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0.22

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1.02

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no2.

81

10.5

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025

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012

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052

12.8

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15

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1.64

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470.

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0.04

0.02

0.00

90.

153

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3.19

3

15.9

9167

16

81

25

0.71

20.

049

0.02

40.

011

0.20

30.

906

1.15

61.

299

1.36

5-1

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0.71

20.

048

0.02

50.

011

0.20

31

no3.

264

15.6

3611

26

81

25

0.66

60.

054

0.02

70.

012

0.24

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0.93

81.

081.

146

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665

0.05

30.

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0.01

20.

241

1no

05

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7778

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31

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35

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063

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143

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80.

062

0.03

50.

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0.44

85

yes

-0.2

26CS

19.3

2778

411

81

15

0.60

90.

058

0.03

0.01

40.

289

0.44

30.

693

0.83

50.

902

-2.3

570.

609

0.05

80.

031

0.01

40.

289

1no

-0.0

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32.6

1389

311

81

11

0.59

90.

059

0.03

0.01

40.

297

0.40

10.

651

0.79

30.

859

-2.3

990.

599

0.05

80.

031

0.01

40.

297

1ye

s-0

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SC12

.805

562

108

23

50.

684

0.05

20.

026

0.01

20.

226

0.77

31.

023

1.16

51.

232

-2.0

270.

684

0.05

10.

027

0.01

20.

226

1no

0.74

9TP

15.1

7778

311

81

15

0.68

0.05

20.

026

0.01

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229

0.75

31.

003

1.14

51.

212

-2.0

470.

680.

052

0.02

70.

012

0.22

91

no-0

.526

TS

Tab

le 3

5 –

H1B

Model

Tra

inin

g R

esult

s

76

CO

ATI

NG

AG

E

Co

atin

g

Sco

re

Tan

k

Type

SHIP

CLA

SS

Tan

k

Pre

ssu

re

Tan

k

Cri

tica

l

ity

Stru

ctu

re

Sco

re

EPR

OB

1

fro

m

Min

itab

EPR

OB

2 fr

om

Min

itab

EPR

OB

3

fro

m

Min

itab

EPR

OB

4

fro

m

Min

itab

EPR

OB

5

fro

m

Min

itab

Cal

c 1

Logi

t

Cal

c 2

Logi

t

Cal

c 3

Logi

t

Cal

c 4

Logi

t

Cal

c 5

Logi

t

Cal

c 1

Pro

b

Cal

c 2

Pro

b

Cal

c 3

Pro

b

Cal

c 4

Pro

b

Cal

c 5

Pro

bP

red

ict

Acc

ura

te?

8.51

11

08

13

50.

338

0.59

10.

729

0.79

6-2

.463

0.58

40.

060.

031

0.01

40.

311

1n

o

32.6

13

10

81

35

-0.6

01-0

.348

-0.2

1-0

.143

-3.4

020.

354

0.06

0.03

40.

016

0.53

65

yes

32.6

13

38

12

50.

265

0.51

80.

657

0.72

3-2

.535

0.56

60.

061

0.03

20.

015

0.32

71

no

15.1

84

10

82

35

0.27

40.

527

0.66

60.

732

-2.5

270.

568

0.06

10.

032

0.01

50.

325

1n

o

32.6

14

11

81

13

0.17

50.

429

0.56

70.

633

-2.6

250.

544

0.06

20.

033

0.01

50.

347

1n

o

12.8

11

10

81

35

0.25

10.

504

0.64

20.

709

-2.5

50.

562

0.06

10.

032

0.01

50.

331

no

Yes

32

0

33.4

34

18

11

50.

646

0.89

91.

037

1.10

4-2

.155

0.65

60.

055

0.02

80.

013

0.24

91

no

No

13

4

8.51

13

81

25

1.20

41.

457

1.59

61.

662

-1.5

960.

769

0.04

20.

020.

009

0.15

91

no

Sum

45

4

10.5

62

21

13

11.

116

1.36

91.

508

1.57

4-1

.684

0.75

30.

044

0.02

10.

010.

172

1ye

s%

Acc

ura

cy

0.70

5

3.13

16

11

21

1.82

32.

076

2.21

52.

281

-0.9

770.

861

0.02

80.

013

0.00

60.

093

1ye

s

3.13

16

11

21

1.82

32.

076

2.21

52.

281

-0.9

770.

861

0.02

80.

013

0.00

60.

093

1ye

s

10.9

92

91

13

10.

766

1.02

1.15

81.

224

-2.0

340.

683

0.05

20.

026

0.01

20.

227

1ye

s

29.1

42

31

12

11.

218

1.47

11.

609

1.67

6-1

.583

0.77

20.

042

0.02

0.00

90.

158

1ye

s

27.0

52

91

13

10.

442

0.69

50.

834

0.9

-2.3

580.

609

0.05

80.

030.

014

0.28

91

yes

29.1

42

11

11

11.

841

2.09

42.

232

2.29

9-0

.96

0.86

30.

027

0.01

30.

006

0.09

11

yes

20.8

64

71

23

10.

962

1.21

51.

353

1.42

-1.8

390.

723

0.04

80.

023

0.01

10.

195

1ye

s2.

81

29.1

44

71

23

10.

794

1.04

81.

186

1.25

2-2

.006

0.68

90.

052

0.02

60.

012

0.22

21

yes

3.05

42

29.1

44

71

23

10.

794

1.04

81.

186

1.25

2-2

.006

0.68

90.

052

0.02

60.

012

0.22

21

yes

3.19

23

29.1

44

71

23

10.

794

1.04

81.

186

1.25

2-2

.006

0.68

90.

052

0.02

60.

012

0.22

21

yes

3.25

84

4.45

11

11

11

52.

079

2.33

22.

472.

537

-0.7

220.

889

0.02

30.

011

0.00

50.

073

1n

o0

5

8.74

13

51

25

1.48

11.

734

1.87

21.

939

-1.3

20.

815

0.03

50.

017

0.00

70.

126

1n

o-0

.02

CA

8.74

13

51

21

1.48

11.

734

1.87

21.

939

-1.3

20.

815

0.03

50.

017

0.00

70.

126

1ye

s-0

.226

CS

6.28

21

05

23

51.

187

1.44

1.57

91.

645

-1.6

130.

766

0.04

20.

021

0.00

90.

162

1n

o-0

.049

TT

8.83

11

08

23

51.

081.

334

1.47

21.

538

-1.7

20.

747

0.04

50.

022

0.01

0.17

71

no

-0.0

94SC

8.32

21

08

23

50.

864

1.11

81.

256

1.32

2-1

.936

0.70

40.

050.

025

0.01

10.

211

no

0.74

9TP

28.0

32

10

82

35

0.46

60.

720.

858

0.92

4-2

.334

0.61

50.

058

0.03

0.01

40.

284

1n

o-0

.526

TS

Tab

le 3

6 –

H1B

Model

Tes

ting R

esult

s

77

4.6.2 H1B Model Optimization

In tank coating inspections, having an over optimistic model, that is, a model that

predicts a better coating score than actually observed when inspected (false positive),

yields the riskiest situation. Given this, in order to reduce risk, optimization involves

adjusting model classification to reduce false positives (Type 1 errors) without overly

increasing Type 2 errors.

Table 37 represents the general classification matrix for H1B. Table 38 is the

classification matrix, given the OLR coating score coefficients of 2.8, 3.05, 3.19, 3.26

and 0 from the regression results. Table 38 represents H1B classification results using

the optimized coating score coefficients of 1.8, 2.55, 3.69, 4.26, and 0. The impact upon

worst type of false positives (FP - Type 1 errors), that is, cells EE14, EE15, and EE25, for

mission criticality tanks (#3) should be noted. Tables 38 and 41 indicate a modest

outcome through the use of optimized coating score coefficients. Additionally, Table 42

displays the impact of model H1B optimization upon classification matrix performance

parameters. Overall, the optimization of H1B produces modest results of decreasing false

positives while maintaining overall model performance.

78

Table 37 – H1B Classification Matrix

Table 38 – H1B Training Result Classification Matrix (Pre-Optimization)

79

Table 39 – H1B Training Results Classification Matrix (Post-Optimization)

Table 40 – H1B Testing Data Results Classification Matrix (Pre-Optimization)

80

Table 41 – H1B Testing Data Results Classification Matrix (Post-Optimization)

Table 42 – Model H1B Classification Performance

81

4.6.3 H1B Prediction Tool

4.6.3.1 H1B Structure Score Prediction Tool

Through the use of the logit equation (3.1) and regression equation (3.2), for given

predictor variables, a predicted structural score can be ascertained. Figure 23 represents

this methodology and is the H1B coating score prediction tool. Users input the variables

of coating age, coating inspection score, tank type, ship class, tank pressure and tank

criticality. The automated spreadsheet yields a predicted structural score in the yellow

field. Given the modest accuracy of this tool, it is recommended that structural score of 3

be treated as structural score of 5 (worst case) in order to reduce potential risk from

model over optimization.

Figure 23 – H1B Structure Score Prediction Tool

Factors

Enter

Values

Below

Predicted

Structural

Score

Coating Age (yrs) 15 1

Coating Insp Score (1-4) 3

Tank Type (1-13) 10

Ship Class (1-10) 10

Tank Pressure (1-2) 2

Tank Criticality (1 -3) 3

1.991

Calc 1

Logit-0.4944

Calc 1

Prob0.3788

3.052

Calc 2

Logit0.5656

Calc 2

Prob0.2589

3.193

Calc 3

Logit0.7056

Calc 3

Prob0.0317

3.254

Calc 4

Logit0.7656

Calc 4

Prob0.0131

05

Calc 5

Logit-0.7482

Calc 5

Prob0.3174

-0.0202 CA

-0.2260 CS

-0.0487 TT

-0.0938 SC

0.7490 TP

-0.5257 TC

82

4.7 H2 Results

H2 results (Table 43) are derived from the results of H1A and H1B. The H1A coating

score prediction tool was used to determine the time in years (column 4 – Table 43) each

type of tank (column one – Table 43) would reach coating score 3, tank coating service

life according to current United States Navy (USN) maintenance standards. To ascertain

the tank structural condition at the predicted age when the coating score reaches 3, the

H1B structural prediction tool was used. The predicted coating score (H1A resultant) and

predicted structural score (H1B resultant) along with tank criticality code (column two –

Table 43) were used to derive risk using the maintenance and risk matrix Table 6-1 in the

Corrosion Control Assessment and Maintenance Manual (CCAMM). The net savings of

inspections resulted in the difference between predicted and current tank inspection

periodicities. The last column of Table 43 shows the significant savings (in percentage)

of tank inspection periodicities, over a 20 year period, by the extension of current tank

inspection periodicities. The inspection periodicities savings range from 50 to 83.3%. The

H2 inspection periodicities savings provide an input to the H3 cost savings model.

83

4.8 H3 Results

The net inspection savings results from H2 provided an input into the H3 cost model.

Other inputs into the H3 cost model included the numbers of each type of tank in the

USN surface fleet (Table 43), and the average tank inspection cost (material and man-

days) associated with each type of tank (Table 44). H3 results (Table 45) display these

Tan

k

Typ

e

Tan

k Cr

itic

alit

y

Cod

e

Coat

ing

Insp

ecti

on

Req

uir

emen

t

Per

iod

icit

y

(yea

rs)

per

Nav

y

Corr

osi

on

Man

ual

Min

imu

m Y

ears

to r

each

Co

atin

g

Sco

re 3

, usi

ng

H1A

Co

atin

g A

ge

Mo

del

, (g

iven

:

coat

ing

#28,

sp

#

7 &

tan

k

crit

ical

ity

cod

e)

for

ALL

cla

sses

wit

h 9

5%

pro

bab

ility

Pro

ject

ed S

tru

ctu

ral

Sco

re,

usi

ng

H1B

Stru

ctu

ral M

od

el

pre

dic

tio

n t

oo

l, g

iven

Min

imu

m C

oat

ing

Age

(yea

rs),

Co

atin

g Sc

ore

#3 a

nd

Tan

k Cr

itic

alit

y

Ris

k A

ssu

med

giv

en

Coat

ing

Sco

re 3

&

Pro

ject

ed S

tru

ctu

re

Sco

re p

er N

avy

Corr

osi

on

Man

ual

Ris

k

Mat

rix

(Tab

le 6

-1)

Ove

rall

#

coat

ing

insp

ecti

on

s

req

uir

ed

du

rin

g

Min

imu

m Y

ears

is r

each

ed (

Min

Yrs/

Req

Insp

Per

iod

icit

y)

Net

Insp

ecti

on

savi

ngs

(O

vera

ll

Insp

# l

ess

1), i

f

per

iod

icit

y

exte

nd

ed t

o

pro

ject

ed

min

imal

yea

rs t

o

Coat

ing

Sco

re 3

Per

cen

t In

sp

Savi

ngs

by

exte

nd

ing

per

iod

icit

y o

f

insp

ecti

on

s (f

or

20 y

ear

insp

ecti

on

tim

efra

me)

11

1020

.81

Low

21

50.0

23

418

.11

Low

54

80.0

32

317

.91

Low

65

83.3

43

617

.71

Low

32

66.7

53

617

.51

Low

32

66.7

62

1018

.61

Low

21

50.0

73

617

.11

Low

32

66.7

82

1018

.24

1Lo

w2

150

.0

93

616

.81

Low

32

66.7

103

616

.61

Low

32

66.7

111

619

1Lo

w3

266

.7

122

317

.51

Low

65

83.3

132

617

.31

Low

32

66.7

Tab

le 4

3 –

H2 R

esult

s

84

inputs as well the projected savings (calculated through estimating) over a 20 year period.

H3 results (Table 44) show a significance cost savings (in net present value) over a 20

year period of 582,420 man days and $890,242,549. The average annual savings (in net

present value) is projected to be 29,121 man-days and $44,512,127.

Table 44 – In-Service Tank

Frequency Distribution Table 45 – Average Tank Inspection

Tank Type # In-Service% of Total

In-Service

1 344 2.14

2 340 2.12

3 1476 9.19

4 107 0.67

5 199 1.24

6 3369 20.97

7 2941 18.31

8 42 0.26

9 643 4.00

10 2018 12.56

11 3878 24.14

12 156 0.97

13 552 3.44

Total 16065 100.00

Tank

Type

Average

Material

Cost ($)

Average

Manday

Cost

1 $15,719.57 16.55

2 $5,966.33 11.81

3 $5,605.15 11.24

4 $60,787.06 20.63

5 $17,648.65 14.91

6 $16,768.53 16.82

7 $48,178.56 25.04

8 $4,849.24 10.15

9 $16,260.25 15.80

10 $82,081.53 26.69

11 $14,981.51 14.86

12 $5,625.79 11.14

13 $2,230.67 10.00

Average Tank Inspection Cost

85

Tan

k

Typ

e

Ave

rage

Sch

edu

le C

ost

per

Insp

ecti

on

(man

day

s)

Ave

rage

Mat

eria

l Co

st

per

Insp

ecti

on

($ d

olla

rs)

Net

Insp

ecti

on

savi

ngs

(fr

om

H2)

Tota

l Ave

Sch

edu

le

Cost

Sav

ings

in

Man

day

s (p

er t

ank

typ

e)

[Net

Insp

ecti

on

Savi

ngs

X (

Sch

edu

le

Cost

/In

sp)]

Tota

l Ave

Mat

eria

l

Cost

Sav

ings

in d

olla

rs

($)

(per

tan

k ty

pe)

[Net

Insp

ecti

on

Savi

ngs

X (

Ave

Mat

Cost

/In

sp)]

Tota

l # o

f ea

ch

typ

e o

f ta

nk

in

the

surf

ace

flee

t

For

20 y

ear

insp

ecti

on

tim

efra

me,

Agg

rega

te

Sch

edu

le C

ost

Savi

ngs

(in

Man

day

s) f

or

each

typ

e o

f

tan

k ac

ross

th

e

flee

t (T

ota

l

Sch

ed

Savi

ngs

/per

tan

ks X

# o

f

tan

k ty

pe

in

flee

t)

For

20 y

ear

insp

ecti

on

tim

efra

me,

Agg

rega

te

Mat

eria

l Co

st

Savi

ngs

in $

(do

llars

) fo

r

each

typ

e o

f

tan

k ac

ross

th

e

flee

t (T

ota

l

Mat

eria

l

Savi

ngs

/per

tan

k X

# o

f ta

nk

typ

e in

fle

et)

116

.55

$15,

719.

571

16.5

515

719.

5734

4.0

5,69

4.0

$5,4

07,5

31.2

2

211

.81

$5,9

66.3

34

47.2

323

865.

3134

0.0

16,0

57.2

$8

,114

,206

.52

311

.24

$5,6

05.1

55

56.2

128

025.

7314

76.0

82,9

59.8

$4

1,36

5,97

9.90

420

.63

$60,

787.

062

41.2

612

1574

.12

107.

04,

415.

1

$1

3,00

8,43

0.84

514

.91

$17,

648.

652

29.8

235

297.

3019

9.0

5,93

4.0

$7,0

24,1

62.7

0

616

.82

$16,

768.

531

16.8

216

768.

5333

69.0

56,6

51.0

$5

6,49

3,16

4.65

725

.04

$48,

178.

562

50.0

896

357.

1229

41.0

147,

292.

0

$2

83,3

86,3

01.3

4

810

.15

$4,8

49.2

41

10.1

548

49.2

442

.042

6.5

$2

03,6

68.2

7

915

.80

$16,

260.

252

31.5

932

520.

5164

3.0

20,3

12.7

$2

0,91

0,68

7.60

1026

.69

$82,

081.

532

53.3

716

4163

.06

2018

.010

7,71

0.1

$331

,281

,048

.34

1114

.86

$14,

981.

512

29.7

229

963.

0238

78.0

115,

235.

6

$1

16,1

96,5

96.1

2

1211

.14

$5,6

25.7

95

55.7

228

128.

9315

6.0

8,69

2.1

$4,3

88,1

13.4

6

1310

.00

$2,2

30.6

72

20.0

044

61.3

455

2.0

11,0

40.0

$2

,462

,658

.23

582,

420.

0

$8

90,2

42,5

49.2

0

Tab

le 4

6 –

H3 R

esult

s

86

Chapter 5—Discussion and Conclusions

5.1 Discussion

This work’s hypotheses are:

H1A: Coating Type, Application Method, Tank Type, Ship Class, Tank

Criticality, Coating Age are predictors of Coating Condition Score.

H1B: Coating Condition Score, Ship Class, Tank Pressure, Coating Age, Tank

Criticality are predictors of Structural Condition Score.

H2: Tank inspection periodicities, where HS/UHS coatings are employed, can be

increased 25-50% using the coating and structure score predictions (developed from

H1A and H1B) with minimal increased risk of failure.

H3: There are significant savings when tank inspection periodicities increase by

25-50% (results from H2).

The results indicate that the use of ultra-high solids/high solids (UHS/HS) coatings

aboard United States Navy (USN) surface ships, along with other stated factors like

surface preparation method, have extended tank coating service life well beyond current

USN surface ship tank inspection periodicities. Thus, tank inspection periodicities can be

extended by 25-50% with minimal risk acceptance. Therefore, this confirms the problem

statement that unnecessary tank inspections are being conducted, incurring unnecessary

cost. The results of this work are vital to optimizing USN surface ship tank inspections

requirements, while the integrated methodology presented offers a framework for

analysis of the USN Corrosion Control Assessment and Maintenance Manual (CCAMM)

database.

87

5.1.1 Scope of Analysis and Framework

This work focused on in-service USN surface ship tank inspection data contained

in the CCAMM database. The quality and quantity of the data determined the extent of

the analysis performed. Conclusions were not drawn that could not or would not be

supported by the data and the chosen analysis methodology. The chosen analysis

methodology model’s assumptions were tested and verified. For H1A, coating inspection

scores relationship to UHS/HS coatings (along with other factors) were examined. For

H1B, structural inspection scores relationship to coating inspection scores (along with

other factors) were also examined. H2 examined the outputs of H1A and H1B to

ascertain tank inspection periodicities savings with associated risk characterization. H3

calculated and accrued savings, given revised tank inspection periodicities from H2.

5.1.2 H1A Discussion

The H1A database was partitioned into training and test sets. Frequency

distributions of variables indicate fair representation amongst the two different data sets.

The H1A model results from the Ordinal Logistics Regression (OLR) regression table

indicated that coating age, coating type, surface preparation method, tank criticality and

ship class were statistically significant factors (p-value < 0.5). Additionally, OLR

goodness of fit results, Pearson and Deviance Methods, equal to one indicates the model

fit the data set well. Also, from the OLR analysis results, the measures of association

between response variable and predictor probabilities had strong correlation (0.89).

Testing of the H1A model, with partitioned test data, indicated a strong degree of model

accuracy (93 percent). The H1A model results were optimized to reduce false positives

(Type 1 error) while maintaining overall model classification performance. Then with

88

the results listed in Table 46, based on HA1 data set OLR results, the null hypothesis

(H1Ao – coating age, coatings type, surface preparation method, tank type, ship class and

tank criticality are not predictors of coating inspection score) can be rejected, and the

present research H1A can be adopted.

Table 47 – HA1 OLR Regression Results

With this optimized and tested model, several practical predictive tools were also

developed: 1) coating score prediction tool and 2) coating score prediction graphs.

These tools are intended to be applied in waterfront tank planning and applications.

Lastly, the H1A coating age prediction tool was integrated into the H2 analysis in order

to predict when tank coatings would reach coating condition 3, the USN threshold for

coating renewal, and thus, aide in determining an optimized inspection periodicity.

5.2 H1B Discussion

The H1B database was partitioned into training and test sets. Frequency distributions

of variables indicate fair representation amongst the two different data sets. For H1B, the

results of the OLR regression table indicated that coating age, coating inspection score,

tank type, ship class, tank pressure and tank criticality were statistically significant

Ha

Confirmed

Predictor P Lower Upper

Coating Age 0.000 0.7 0.67 0.73 Yes

Coating Type 0.001 0.96 0.94 0.98 Yes

Surface Preparation

Method0.000 0.1 0.07 0.14 Yes

Tank Type 0.031 0.94 0.88 0.99 Yes

Ship Class 0.018 1.05 1.01 1.1 Yes

Tank Criticality 0.002 0.64 0.48 0.84 Yes

Odds

Ratio

95% CI

H1A Ordinal Logistic Regression Results

89

factors (p-value < 0.5). Ordinal Logistics Regression goodness of fit results indicated

that the Deviance Method equaled one, indicating the model fit the data set well.

Additionally, from the OLR analysis results, the measures of association between

response variable and predictor probabilities had moderate correlation (0.31). Testing of

the H1B model, with partitioned test data, indicated a moderate degree of model accuracy

(70 percent). The H1B model optimization results, to reduce false positives (Type 1

errors) while maintaining overall model classification performance, were moderately

successful. Then with the results listed in Table 47, based on HB1 data set OLR results,

the null hypothesis (H1Bo – coating age, coating inspection score, tank type, ship class,

tank pressure and tank criticality are not predictors of structural inspection score) can be

rejected, and the present research H1B can be adopted.

Table 48 - HB1 OLR Regression Results

With this optimized and tested model, a structure score predictive tool was developed

as well. The H1B structural score prediction tool integrated into the H2 analysis, as

well, in order to aid in characterizing risk by predicting and assessing the structural score

when optimizing tank inspection periodicities.

Ha

Confirmed

Predictor P Lower Upper

COATING AGE 0.003 0.98 0.97 0.99 Yes

Coating Score 0.000 0.8 0.72 0.88 Yes

Tank Type 0.007 0.95 0.92 0.99 Yes

Ship Class 0.000 0.91 0.88 0.95 Yes

Tank Pressure 0.000 2.11 1.61 2.77 Yes

Tank Criticality 0.000 0.59 0.49 0.71 Yes

95% CIOdds

Ratio

H1B Ordinal Logistic Regression Results

90

5.3 H2 Discussion

Previously developed H1A and H1B tools along with a risk assessment from the

maintenance decision matrix in CCAMM (Table 6-1) were utilized to assess optimal tank

inspection periodicities. Tank inspection periodicities for each tank type were developed

based on the predicted time (in years) it would take a coating to reach coating condition

3, the coating condition that requires coating replacement. The difference between

current tank inspection periodicities and predicted tank inspection periodicities (using

H1A) provided a net inspection periodicities savings.

With the H2 revised results listed in Table 48, the null hypothesis (H2o – Tank

inspection periodicities, where UHS coatings are employed, cannot be increased 25-50%

using the coating and structure score predictions, from H1A and H1B, with minimal

increased risk of failure) can be rejected.

Table 49 – H2 Results

91

5.4 H3 Discussion

The results from H2 provided an input for H3. The cost savings were derived over a

twenty-year period since the maximum projected coating life was 20 years. This allowed

sufficient time passage for cost saving accrual in the most limiting case (longest projected

longevity - 20 years). All savings were represented in net present value (2019).

Aggregate savings were the sum of the individual tank types savings. Individual tank

savings were derived from the net tank inspection savings for each tank type (from H2)

multiplied by the average cost data (material and labor) for an inspection for each type of

tank. Then with the results listed in Table 49, the null hypothesis (H2o – There are no

significant savings when tank inspection periodicities increase by 25-50% (results from

H2)) can be rejected, and the present research H3 can be adopted.

Table 50 – H3 Results

Tank

Type

Average

Schedule Cost

per Inspection

(mandays)

Average

Material Cost

per Inspection

($ dollars)

Net Inspection

savings (from H2)

Total Ave Schedule

Cost Savings in

Mandays (per tank

type)

[Net Inspection

Savings X (Schedule

Cost/Insp)]

Total Ave Material

Cost Savings in dollars

($) (per tank type)

[Net Inspection

Savings X (Ave Mat

Cost/Insp)]

Total # of each

type of tank in

the surface

fleet

For 20 year

inspection

timeframe,

Aggregate

Schedule Cost

Savings (in

Mandays) for

each type of

tank across the

fleet (Total

Sched

Savings/per

tanks X # of

tank type in

fleet)

For 20 year

inspection

timeframe,

Aggregate

Material Cost

Savings in $

(dollars) for

each type of

tank across the

fleet (Total

Material

Savings/per

tank X # of tank

type in fleet)

Ha

Confirmed

1 16.55 $15,719.57 1 16.55 15719.57 344.0 5,694.0 $5,407,531.22 Yes

2 11.81 $5,966.33 4 47.23 23865.31 340.0 16,057.2 $8,114,206.52 Yes

3 11.24 $5,605.15 5 56.21 28025.73 1476.0 82,959.8 $41,365,979.90 Yes

4 20.63 $60,787.06 2 41.26 121574.12 107.0 4,415.1 $13,008,430.84 Yes

5 14.91 $17,648.65 2 29.82 35297.30 199.0 5,934.0 $7,024,162.70 Yes

6 16.82 $16,768.53 1 16.82 16768.53 3369.0 56,651.0 $56,493,164.65 Yes

7 25.04 $48,178.56 2 50.08 96357.12 2941.0 147,292.0 $283,386,301.34 Yes

8 10.15 $4,849.24 1 10.15 4849.24 42.0 426.5 $203,668.27 Yes

9 15.80 $16,260.25 2 31.59 32520.51 643.0 20,312.7 $20,910,687.60 Yes

10 26.69 $82,081.53 2 53.37 164163.06 2018.0 107,710.1 $331,281,048.34 Yes

11 14.86 $14,981.51 2 29.72 29963.02 3878.0 115,235.6 $116,196,596.12 Yes

12 11.14 $5,625.79 5 55.72 28128.93 156.0 8,692.1 $4,388,113.46 Yes

13 10.00 $2,230.67 2 20.00 4461.34 552.0 11,040.0 $2,462,658.23 Yes

582,420.0 $890,242,549.20

Total

Savings for

20 year

timeframe

(in 2019

Dollars)

92

5.5 Conclusions

An integrated methodology of modeling (for coating and structure) presented in this

work demonstrated a significant cost savings (material and man-days) when optimizing

tank inspection periodicities, given the use of high performance UHS/HS coatings,

balancing risk and incorporating other stated factors.

5.6 Body of Knowledge

This work adds to the body of knowledge by providing proven analysis results to

optimize USN surface ship tank inspection periodicities for the USN maintenance

community. It also provides a novel methodology in doing so.

5.7 Future Study

To further this work, it is recommended that other naval communities like aircraft

carriers and submarines have tank inspection periodicities analyzed with this

methodology as well. It is also recommended to further study optimized results to

minimize the likelihood of Type 1 errors. Lastly, it is recommended that other

applications of OLR, where appropriate, be explored for potential cost savings within the

wider USN surface ship maintenance community, where inspection results are ordinal in

nature.

93

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