COMPILING GEOTECHNICAL DATA TO DETERMINE THE DISTRIBUTION …€¦ · THE DISTRIBUTION AND PROPERTIES OF TOP SAND DEPOSITS IN QUADRANT K & L OF THE DUTCH SECTOR – NORTH SEA by Le

Section Geological Engineering

COMPILING GEOTECHNICAL DATA TO DETERMINE THE DISTRIBUTION AND PROPERTIES OF TOP SAND DEPOSITS

IN QUADRANT K & L OF THE DUTCH SECTOR – NORTH SEA

by Le Minh Son

A thesis submitted to International Institute for Geo-Information Science

and Earth Observation (ITC) in partial fulfillment of the requirements

for the Degree of Master of Science in Engineering Geology

February 2002

ii

ABSTRACT

Geotechnical data of the Dutch sector, North Sea collected by TNO-NITG for many years

is in the raw format. To interconnect to Geological Electronic Information Exchange

System (GEIXS) on a Web server, this data should be organised in a suitable database.

North Sea geotechnical database is established to:

convert data from the raw format to digital format,

facilitate the interchanging data among organizations through the network according to

EUMARSIN program, and

manipulate and query effectively necessary information from geotechnical data.

From the designate database, two applications are carried out as illustrations:

1. Statistical characteristics of sand deposits in Quadrant K & L, the Dutch sector, North

Sea are explored. In addition, the uncertainty of estimation in terms of the confidence

intervals and relationship between effective friction angle and other properties are also

examined.

2. Seabed surface map and thickness map of top sand deposit are established using

ordinary Kriging method. These maps are visualised in three-dimensional view using

different softwares. Besides the estimated maps, error maps are also included. Those

error maps can be used to quantify the uncertainty of interpolation.

Keywords: North Sea geotechnical database, geotechnical properties, uncertainty, geotechnical error

analysis, seabed surface, sand deposit, Kriging

iii

ACKNOWLEDGEMENTS

First of all, I would like to express my special thanks to Dr. Robert Hack, Head of

Engineering Geology Division – ITC. Without his efforts for finding financial supports, I

could not continue studying in Master of Science degree in ITC.

I am sincerely grateful to my supervisor, Ir. P.M. Maurenbrecher, who directs my research

on track, gives me the trust and freedom to conduct the research independently. My

gratitude is also expressed to Dr. Keith Turner, who gives me useful comments and

opportune advices on my results.

I am in great debt to Ir. Wolter Zigterman, who is patient to answer all of my

miscellaneous questions on soil mechanics during the lectures or coffee-breaks. Special

thanks to MSc. Senol Ozmutlu, Ir. Siefko Slob, Ir. Marco Huisman for their supports,

encouragements and guidances during the fieldwork and preparing the reports on

engineering geology mapping and slope stability.

No less significant is the help of Mr. W. Verwaal and A. Mulder in the laboratory of

Applied Earth Sciences department, TU Delft. They always try to supply the best condition

and all available facilities for my works in laboratory and in the field.

Without support from Dr. Cees Laban, Dr Jan-Diederik van Wees and Mr. Rob Versseput

in TNO – NITG, I could not complete my research on time. I am indebted. My special

thanks also go to Mrs. Ineke Theussing for her efforts to solve all my social matters.

I wish to acknowledge the tremendous help of Mr. Peter Nelemans, who continuously

supports me everything from finding the appropriate technical papers to sharing with me

tedious time during my studying in ITC and staying in the Netherlands.

Last but not least, this thesis could not be completed without the greatest support and love

of my wife and my son. My deepest gratitude is expressed to my parent, my brothers and

sister. They sacrificed themselves to give me the opportunity of studying in university and

participating in the academic life.

Le Minh Son Delft, 2002

iv

TABLE OF CONTENTS

Chapter 1 : INTRODUCTION..................................................................................................... 1 1.1. Location of the study area .................................................................................................... 1 1.2. Available data.......................................................................................................................... 3 1.3. Research requirements .......................................................................................................... 4 1.4. Methodology........................................................................................................................... 5 1.5. Thesis structure ...................................................................................................................... 5 Chapter 2 : LITERATURE REVIEW.......................................................................................... 7 2.1. Description of other geotechnical databases ..................................................................... 7

2.1.1. ASCE shallow foundation database.................................................................................... 7 2.1.2. Amsterdam INGEO-base .................................................................................................... 8 2.1.3. Geotechnical database management systems for Boston’s central Artery/Harbour

tunnel project.......................................................................................................................... 9 2.1.4. Geotechnical database for emergency vehicle access routes in Missouri ................... 10 2.1.5. UK offshore database ......................................................................................................... 12 2.1.6. DINO structure ................................................................................................................... 13

2.2. Concepts of geospatial data management system........................................................... 13 2.3. Chapter summary................................................................................................................. 14 Chapter 3 : NORTH SEA GEOTECHNICAL DATABASE ............................................... 16 3.1. Some basic concepts of database design .......................................................................... 16 3.2. Conceptual database design for the North Sea geotechnical database ........................ 18

3.2.1. Define classes ....................................................................................................................... 18 3.2.2. Define relationships............................................................................................................. 18

3.3. Implementation of the design into Microsoft Access .................................................... 19 3.4. Chapter summary................................................................................................................. 19 Chapter 4 : 3D GEOLOGICAL MODEL OF THE SUBSURFACE DEPOSITS........... 20 4.1. Using Kriging for point interpolation............................................................................... 20 4.2. Developing a model of the seabed surface ...................................................................... 21 4.3. Geological characteristics ................................................................................................... 23 4.4. Construction the stratigraphy framework for the model ............................................... 25

4.4.1. Define the stratigraphy for the model .............................................................................. 25 4.4.2. Interpolate the surfaces below the seabed ....................................................................... 26

4.5. Creating of 3D views........................................................................................................... 31 4.5.1. Surfaces viewed with Geospatial Explorer ...................................................................... 31 4.5.2. Surfaces viewed with Geo3DJViewer............................................................................... 31

Chapter 5 : STATISTICAL ANALYSIS OF GEOTECHNICAL PROPERTIES OF SAND DEPOSITS................................................................................................... 33

5.1. Introduction.......................................................................................................................... 33 5.2. Statistical characteristics of sand deposits ........................................................................ 33

5.2.1. Water content ....................................................................................................................... 33 5.2.2. Dry unit weight..................................................................................................................... 34 5.2.3. Specific gravity...................................................................................................................... 35 5.2.4. Fines ....................................................................................................................................... 36 5.2.5. Median size D50: ................................................................................................................... 39

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5.2.6. Coefficient of uniformity Cu .............................................................................................. 41 5.2.7. Coefficient of curvature Cc................................................................................................. 43 5.2.8. Effective friction angle (EFA) ........................................................................................... 45

5.3. Relationship between Effective friction angle and other geotechnical properties ..... 47 5.4. Estimate volume of top sand deposit ............................................................................... 53 5.5. Chapter summary................................................................................................................. 54 Chapter 6 : CONCLUSION AND RECOMMENDATIONS.............................................. 56 6.1. Conclusion ............................................................................................................................ 56

6.1.1. North Sea geotechnical database ....................................................................................... 56 6.1.2. 3D geological model of sand deposits.............................................................................. 56 6.1.3. Statistical characteristics of geotechnical properties....................................................... 57

6.2. Further research ................................................................................................................... 58 REFERENCES .............................................................................................................................. 60 APPENDICES ............................................................................................................................... 62 Appendix 1: Inventory of boreholes in the North Sea database......................................... 62 Appendix 2: North Sea database structure diagram.............................................................. 64 Appendix 3: Table structure of the North Sea database....................................................... 65 Appendix 4: Input forms of the North Sea database............................................................ 67 Appendix 5: Relationships in North Sea database................................................................. 69 Appendix 6: Bathymetry map of the sea floor ....................................................................... 70 Appendix 7: Holocene deposits in Quadrant K .................................................................... 71 Appendix 8: Distribution of top Pleistocene deposits.......................................................... 72 Appendix 9: Map of top surface of top Pleistocene deposits.............................................. 73 Appendix 10: Map of seabed surface......................................................................................... 74 Appendix 11: Error map of seabed surface.............................................................................. 75 Appendix 12: Map of thickness of Unit 1................................................................................. 76 Appendix 13: Map of thickness of Unit 2................................................................................. 77 Appendix 14: Map of thickness of Unit 3................................................................................. 78 Appendix 15: Map of thickness of Unit 4................................................................................. 79 Appendix 16: Map of thickness of top sand deposits ............................................................. 80 Appendix 17: Effective stress shear strength parameters....................................................... 81 Appendix 18: Effective friction angle (φ’) of sand in North Sea (Norwegian sector) using

different parameters with different formulas................................................... 81 Appendix 19: Approximate in-situ values for porosities and unit weight in natural sand

(adapted from CUR, 1996) ................................................................................. 81 Appendix 20: Representative values of geotechnical properties (after NEN 6740)........... 82 Appendix 21: Compare measured and predicted effective friction angle (φ’) of sand in

North Sea (Dutch sector, quadrant K & L)..................................................... 83

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LIST OF FIGURES Figure 1-1: Location of the study area and boreholes _________________________________2 Figure 1-2: Number of boreholes in each block_____________________________________3 Figure 2-1: Cone resistance map in INGEO-base __________________________________8 Figure 2-2: Relational database structure of Boston’s central Artery project __________________9 Figure 2-3: Diagram of geotechnical database in Missouri ____________________________ 11 Figure 2-4: Data structures for geospatial analysis _________________________________ 14 Figure 3-1: Notation for multiplicity __________________________________________ 17 Figure 3-2: Is-A relationship _______________________________________________ 18 Figure 4-1: Histogram of the depth of seabed surface ________________________________ 21 Figure 4-2: Semi-variogram model of seabed surface_________________________________ 22 Figure 4-3: Seabed surface map and standard error map of seabed surface interpolation _________ 23 Figure 4-4: (a) 3D view of seabed surface (b) Distribution of major sand banks in the southern North

Sea, after Stride et al., 1982 (Cameron et al., 1992) _______________________ 23 Figure 4-5: Simplified soil profile_____________________________________________ 26 Figure 4-6: Semi-variogram of surface of Unit 2___________________________________ 27 Figure 4-7: Histogram of bottom surface of top sand deposit (a) before and (b) after transformation__ 29 Figure 4-8: Semi-variogram of bottom surface of top sand deposit ________________________ 29 Figure 4-9: Thickness map of top sand deposit ____________________________________ 30 Figure 4-10: 3D view of top sand thickness_______________________________________ 30 Figure 4-11: 3D view by Geospatial Explorer_____________________________________ 31 Figure 4-12: 3D view by Geo3DJViewer________________________________________ 32 Figure 5-1: Distribution of water content________________________________________ 34 Figure 5-2: Distribution of dry unit weight ______________________________________ 35 Figure 5-3: Distribution of specific gravity _______________________________________ 36 Figure 5-4: Distribution of fines (before transformation) _____________________________ 38 Figure 5-5: Histogram of fines (after transformation)________________________________ 38 Figure 5-6: Distribution of D50 (before transformation) ______________________________ 40 Figure 5-7: Histogram of D50 (after transformation) ________________________________ 40 Figure 5-8: Distribution of Cu (before transformation) _______________________________ 42 Figure 5-9: Histogram of Cu (after transformation) _________________________________ 42 Figure 5-10: Distribution of Cc (before transformation) _______________________________ 44 Figure 5-11: Histogram of Cc (after transformation) _________________________________ 44 Figure 5-12: Distribution of EFA (from Direct shear test) ____________________________ 46 Figure 5-13: Distribution of EFA (from CD-Triaxial test) ___________________________ 46 Figure 5-14: Distribution of effective friction angle from both tests ________________________ 47 Figure 5-15: Flow chart of simplifying the relationship equation _________________________ 51 Figure 5-16: Compare predicted and measured EFA ________________________________ 51 Figure 5-17: Relationship between EFA, water content and fines ________________________ 53

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LIST OF TABLES Table 4-1: Descriptive statistics of seabed surface ____________________________________ 21 Table 4-2: Descriptions of Holocene formations_____________________________________ 24 Table 4-3: Descriptions of Pleistocene formations____________________________________ 24 Table 4-4: Descriptive statistics of bottom surface of top sand deposit (before transformation) _______ 28 Table 4-5: Descriptive statistics of bottom surface of top sand deposit (after transformation) ________ 29 Table 5-1: Descriptive statistics of water content ____________________________________ 34 Table 5-2: Descriptive statistics of dry unit weight ___________________________________ 35 Table 5-3: Descriptive statistics of specific gravity____________________________________ 36 Table 5-4: Descriptive statistics of fines (before transformation) __________________________ 37 Table 5-5: Descriptive statistics of fines (after transformation) ___________________________ 37 Table 5-6: Descriptive statistics of fines (after back-transformation)________________________ 37 Table 5-7: Descriptive statistics of D50 (before transformation) ___________________________ 39 Table 5-8: Descriptive statistics of D50 (after transformation) ____________________________ 39 Table 5-9: Descriptive statistics of D50 (after back-transformation) ________________________ 39 Table 5-10: Descriptive statistics of Cu (before transformation)___________________________ 41 Table 5-11: Descriptive statistics of Cu (after transformation) ___________________________ 41 Table 5-12: Descriptive statistics of Cu (after back-transformation) ________________________ 41 Table 5-13: Descriptive statistics of Cc (before transformation) ___________________________ 43 Table 5-14: Descriptive statistics of Cc (after transformation)____________________________ 43 Table 5-15: Descriptive statistics of Cc (after back-transformation) ________________________ 43 Table 5-16: Effective friction angle from Direct shear test ______________________________ 45 Table 5-17: Effective friction angle from Consolidated-Drained Triaxial test _________________ 45 Table 5-18: Descriptive statistics of effective friction angle from both tests ____________________ 45 Table 5-19: Predicted EFA from the simplified quadratic equation _______________________ 52 Table 5-20: Summary of descriptive statistics of geotechnical properties ______________________ 55

Chapter 1: INTRODUCTION

1

Chapter 1 : INTRODUCTION

The European Commission conducts through the Marine Science and Technology

Programme (MAST-III) various (marine) data management activities. One of those is

EUMARSIN program (European Marine Sediment Information Network on the Internet),

concerning marine sediment meta-databases of the Geological Surveys of the EU-countries

and Norway. EUMARSIN will provide metadata through the Geological Electronic

Information Exchange System (GEIXS) implemented on a Web server. Via GEIXS, end-

users (commercial companies, researchers, scientists…) can access an enormous amount of

marine sediment metadata.

Existing geotechnical and geophysical data, however, collected by Netherlands Institute of

Applied Geoscience TNO – National Geological Survey (TNO-NITG) is only the raw

data. To interconnect to GEIXS metadata, this raw data should be digitised, transformed

into one appropriate format and installed in a suitable geotechnical database. Geotechnical

data in an organized format will provide an economical source of information for

feasibility studies, foundation design, and for research and development activities.

To achieve these objectives, relevant data from geotechnical reports in quadrant K and L,

North Sea was collected, and stored in a North Sea geotechnical database. This database

consists of 329 boreholes in which there are 40 deep boreholes (their lengths larger than 10

m) and 289 shallow boreholes (their lengths less than 10 m).

1.1. Location of the study area

The study area, which covers quadrant K and L of the Dutch sector, North Sea (approx.

134 km × 111 km), lies northwest of the Netherlands from 3° E to 5° E, and from 53° N

to 54° N (see Figure 1-1). Each quadrant is subdivided into 18 blocks, each block covers

10’ longitude and 20’ latitude (see Figure 1-2).


2

Figure 1-1: Location of the study area and boreholes

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3

Figure 1-2: Number of boreholes in each block

1.2. Available data

Map:

Quaternary Geology (sheet Indefatigable), scale of 1:250000, published by British

Geological Survey and Rijks Geologische Dienst in 1986. This map covers only

Quadrant K of the study area and shows the distribution of Quaternary sediments. The

contour lines of water depths, depths to the base of Pleistocene sediments and three

cross sections are also presented in this map.

Seabed Sediments and Holocene (sheet Indefatigable), scale of 1:250 000, published by

British Geological Survey and Rijks Geologische Dienst in 1987. This map covers only

quadrant K of the Dutch sector, North Sea and presents the distribution of Holocene

sediments, contour lines with the interval of 10 m. The first notion from this map is

that the seabed surface is an irregular, gently undulating surface. Contour lines with the

interval of 10 m mainly run after the direction of southwest – northeast in the northern

part, a few of them follows the direction of north-south in the southern part of

K1 K2 K3

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13


4

quadrant K. Besides the majority of deposits in quadrant K is classified as sand, there

are several spots are silty sand, sandy silt or slightly gravelly sand.

The digital map of the distribution of top Pleistocene deposits in Quadrant K and L,

compiled by TNO – NITG. This map presents the distribution of Pleistocene

sediment formations in those quadrants. However there is no cross section in this map

to show the relation among those formations.

The raster map of sea floor (500 m pixel size) in Quadrant K and L, compiled from

bathymetry measurements by TNO – NITG

The raster map of the top surface of Pleistocene deposits (200 m pixel size) in

Quadrant K and L, compiled by TNO – NITG.

Reports: all geotechnical reports conducted from 1968 to 1995 are provided by TNO –

NITG. Each report given a unique number presents the location of project, coordinated

and soil description of holes (boreholes or CPT holes), geotechnical properties of soil

samples.

1.3. Research requirements

To interconnect to GEIXS in the future and to back up data in the paper format,

geotechnical data in geotechnical reports should be converted into a digital format. This

can be done by scanning the reports or entering data into an appropriate database.

Scanning the reports is the easiest way, however, scanned images are “dead/static” data

that cannot interact with the end-users to derive necessary information. In contrast, an

appropriate database can make geotechnical data “alive/dynamic”, can be updated

regularly, and therefore it is much more useful than scanned images. The structure of the

designate database should be compatible with the existing DINO structure as far as

possible. In addition, the database should be run in common software that helps the end-

users to interchange easily data with the other applications and to integrate data with GIS

package in the future.

The requirements of this research are:

1. Organize data into relevant format and enter them into an appropriate database.


5

2. Investigate the statistical characteristics of geotechnical properties of sand deposits in

the study area.

3. Explore data to understand the distribution of sand deposits in the study area based on

geostatistics.

1.4. Methodology

To fulfill the above requirements, the research is carried out in four steps:

- Design a geotechnical database that is compatible with the DINO structure as far as

possible.

- Design several graphical user interfaces to facilitate the input process; select

appropriate data from geotechnical reports and input to a designate database.

- Set up a geological framework based on the borehole information and available

geological maps and then build up a three dimensional view of the stratigraphy in the

study area.

- Analyse statistical characteristics of geotechnical (physical and mechanical) properties,

especially the uncertainty of determination of those properties in terms of the 95%

confidence interval.

Most of CPT holes in the study area were conducted using Wison equipment. This

technique resulted in the interrupted measurements. Due to the time constrains, the North

Sea database does not include CPT data.

1.5. Thesis structure

Chapter 1 introduces the location of the study area and available data related to this area.

In addition, the research task is stated and procedures applied to achieve the objectives are

described.

Chapter 2 goes through several existing geotechnical databases and existing formats used

to describe geotechnical properties. The issues in geospatial databases, integrating

geotechnical database with GIS and 3D modeling are presented as well.


6

The first step of this research presented in Chapter 3 is to organise data into an appropriate

database and implement into Microsoft Access application. Several input forms are

developed to facilitate the input process.

Chapter 4 is set forth the distribution of sand deposits in quadrant K and L. From the

designated database, a simplified soil profile is established and then a seabed surface and

layer surfaces are interpolated. The most interesting information for structural designers,

foundation engineers is the distribution and amount of top sand deposit that can be found

in this chapter. Visualization of the soil profile and top sand deposit in three dimensions

using different softwares can give the readers an overview of the stratigraphy in the study

area. The uncertainty in the interpolation is quantified using error maps that are included

together with interpolated maps.

Chapter 5 deals with statistical characteristics of geotechnical properties of sand deposits.

The uncertainty in the estimation of soil properties is described in terms of standard

deviation, coefficient of variation and confidence interval. One relationship between the

effective friction angle with other properties is examined as well in this chapter.

The process of manipulating, processing and interpreting geotechnical data is summarised

in Chapter 6. Although some results are obtained through this process, some shortcomings

of the designate database are unavoidable therefore follow-up studies are also proposed in

this chapter.

Chapter 2: LITERATURE REVIEW

7

Chapter 2 : LITERATURE REVIEW

The literature was reviewed to obtain an overview of existing geotechnical databases and to

examine contemporary issues in geospatial database management. On the basis of the

review an appropriate database for the North Sea data was set up.

2.1. Description of other geotechnical databases

Some geotechnical databases have been found in literatures. Although they are designed

for different purposes and implemented in different softwares but their core ideas and

applications show relevance to the aims of the North Sea geotechnical database.

2.1.1. ASCE shallow foundation database

Briaud et al. (1991) initiated a shallow foundation database in 1988 from the idea of ASCE

Shallow Foundation Committee. The purpose of this database is to collect, organize, and

disseminate case histories of shallow foundation behaviour. The designate database can be

profitable by:

- reducing the foundation cost;

- providing data on soil compressibility characteristics;

- preserving data on shallow foundation behaviour;

- organizing the collective experience of the profession on this subject and to make it

available for the future engineers;

- identifying types of soil for which compressibility data are not available so that research

needs can be identified;

- providing data to evaluate the degree of conservation in the design or construction of

shallow foundations.

This database using dBase IV software allows the end-users to predict the response of a

shallow foundation by using a number of design methods and to perform correlation

studies. Information of each case history consists of:

- geologic province;

- geologic units;

- soil types;


8

- consolidation state;

- structure or load types;

- field investigation techniques;

- laboratory test results;

- heave measurements;

- total settlement measurements;

- differential settlement measurements;

- estimated soil compressibility.

Unfortunately, ASCE shallow foundation committee did not explain clearly the structure

of the database. How to organise raw data into a database and the structure of the database

are questions.

2.1.2. Amsterdam INGEO-base

Up to 1989, Grondmechanica Amsterdam (the Netherlands) was holding in the archives

the measurements of 3,000 observation wells, 40,000 CPT tests and 10,000 borings. With

such a huge amount of data, storing data in paper format seemed unsuitable. To take full

profit of the existing large engineering geological data set of Amsterdam, INGEO-base

was developed using the third-party software INGRES from Relational Technology Inc.

(Herbschleb, 1990). This database consists of various information such as: ground water

levels, CPT results, borelogs and laboratory test results.

Figure 2-1: Cone resistance map in INGEO-base


9

INGEO-base is used to obtain a complete geological/geotechnical insight of the

underground. One of the objectives of INGEO-base is to make maps interactively (see

Figure 2-1). From the database, a map of cone resistance values used for geological or

geotechnical interpretations at a certain depth can be derived. This type of map is especially

useful for foundation designers. In addition, the database can be updated annually and the

accuracy of interactive maps can be improved gradually.

2.1.3. Geotechnical database management systems for Boston’s central Artery/Harbour tunnel project

To confront with the issue of an enormous amount of data, once again, a computer

database is used for Boston’s central Artery/Harbour tunnel project (Hawkees, 1991). To

manage subsurface data collected from over 3000 borings, the geotechnical database

management systems was designed. The geotechnical information in this database is

classified into several tables (see Figure 2-2):

Figure 2-2: Relational database structure of Boston’s central Artery project

- project definition: project name, contract N°, client, consultant;


10

- borehole definition: borehole N°, coordinates (easting, northing, elevation), date,

personnel and equipment;

- water level;

- sample data: sample N°, depth, standard penetration test (SPT) data, rock quality

designation (RQD) data;

- soil description;

- stratigraphy: strata name, depth.

To provide the system that is easy to use and well documented, gINT (geotechnical

Integrator), a third-party software was used. Some advantages of that database are filtering

the data, mapping database data to Computer Aided Design and Drafting (CADD) graphic

objects, developing boring location plans, building a representative subsurface model,

developing geotechnical cross sections, and so on.

2.1.4. Geotechnical database for emergency vehicle access routes in Missouri

Luna et al. (2001) designed a relational geotechnical database for current subsurface and

earthquake data for US60 corridor in Butler, Stoddard and New Madrid Counties. This

database is the first step to integrate into the larger GIS database for future development.

This database was implemented using a Microsoft Access software package. The designate

database using an object-oriented approach includes data classes such as highway structure,

borelogs, corelogs, water level observations, laboratory testing, stratigraphy, etc. Those

classes are implemented in Microsoft Access as normalized tables, which separate the data,

based on the type of analysis.

Core log table contains recovery and rock quality data.

Water observations table contains the water level observations made while the

borehole remained open.

Grain size table contains the sample depth, percent sand, silt and fines, and the percent

passing of each sieve tested.

Materials table contains data related to the stratigraphy of the soil or rock encountered

in the borehole.


11

Figure 2-3: Diagram of geotechnical database in Missouri

Physical properties table contains summary data from most common soil testing results

performed on samples (N-value, fines content, clay portion, dry unit weight, natural

water content, plasticity index, liquid limit, classification, pocket penetrometer,

unconfined compression, friction angle and compressibility).

Dynamic soil properties table contains field and laboratory data results of dynamic soil

properties such as modulus and damping ratio as they vary with strain.


12

The diagram of database shown in Figure 2-3 is very suitable for the designate North Sea

geotechnical database. However the properties such as pocket penetrometer, unconfined

compression, friction angle and compressibility should be placed in the mechanical

properties table instead of the physical properties table.

2.1.5. UK offshore database

The United Kingdom (UK) is the one of the largest users of marine sand and gravel for

construction industry purposes. The UK offshore dredging industry has a short history but

it has developed rapidly in the last decade. However it has dealt with the vital issues of

ensuring that no seabed extraction adversely affected the coastline (Evans and Giddings,

1991).

In order to manage the resource effectively in the long term, it is necessary to carry out

assessments of the quantity and quality of the resource on an on-going basis. Paper maps

are used traditionally as both storage and display medium. They work well when the

amount of data is small, the rate of data changing is slow but when the data set becomes

larger, it is necessary to prepare several special maps to avoid confusion and to facilitate

reading. In that situation, the traditional maps or plans show some shortcomings. Those

problems can be avoided by GIS which can handle essentially unlimited data and provide a

secure and easily updated database to generate maps for any specialist use (This purpose is

coincident with INGEO-base’s purpose). Moreover the most powerful feature of GIS is

the ability to analyse data in a spatial way, which provides the users with enormous power

to interrogate and evaluate the information contained in the database.

The GIS offshore database is set up using GIS software ARC/INFO and run on Sun’s

SparcStation platform. This database has been tailored with a user-friendly interface to

facilitate reporting, on screen querying, analysis and plotting. The database that stores a

huge amount of borehole information collected for many years is used to obtain the

volume of potential resource. Data structure of geological sample information is

established to conform to the existing structure of the British Geological Survey (BGS)

borehole database as far as possible.

The UK offshore GIS database enables the managing agents to exercise a sophisticated

control on coastal and offshore zone developments. Furthermore, from this offshore GIS


13

database small pockets of sediment with specific number of known attributes can be

pinpointed. In addition, areas where the aggregate quality of the seabed is seen to be poor

based on the database information can be highlighted. Although the authors did not

describe the structure of this offshore database the idea of integrating simple geotechnical

database with GIS is inherent in the system stating potential interesting applications.

2.1.6. DINO structure

DINO is a Dutch acronym for “Data and Information of the Dutch Subsurface”. It

contains geoscientific data like borehole description, lithostratigraphy, cone penetration

test, groundwater level and quality information, and geomechanical laboratory data that

have relevance to the Dutch subsurface (TNO-NITG, 2000). DINO comprises databases

on the various domains of research within the geosciences. Each well, borehole, analysis

and measurement is linked to a single location in the location database. To assure the

information in DINO to be in uniform formats, predefined structures of data such as field

names, data formats, data codes and domains are presented (refer to Rijkers et al., 1996).

Not only being as a simple geotechnical database, DINO can be considered as a metadata

or internet-based database which can provide to the end-users the quality and source of

data.

The DINO database is only used for onshore materials so far. In order to integrate

offshore data with other data sources or convert to DINO database in the future, the

designate North Sea database should follow the structure of DINO as closely as possible.

2.2. Concepts of geospatial data management system

Houlding (2000) suggests the efficient management for processing of subsurface

characterization by using three basic data types: variables, characteristics and coordinates.

- Variables: such as mineral grades, contaminant concentrations, geomechanical

properties…

- Characteristics: such as lithology, mineralogy… are observable qualities of the

subsurface that have a finite number of possible descriptive values.

- Coordinates: an orthogonal system of 3D coordinates (easting, northing, elevation) is

required to locate the values of variables and characteristics.


14

- Spatial data is organised into data structures: hole data structure, map data structure,

volume data, and grid data structure (see Figure 2-4). Hole data structure consists of

boreholes that are defined by (i) global coordinates of their collars (northing, easting,

and elevation) and (ii) survey measurements (distance, azimuth, and inclination) along

the hole axis with increasing distance.

Once geospatial data is organised into the structure as in Figure 2-4, it can be incorporated

with GIS and geostatistics packages (i) to derive useful information such as the distribution

of special features, the value of properties at a certain location or (ii) to enable three-

dimensional visualization of sediment bodies.

Figure 2-4: Data structures for geospatial analysis

2.3. Chapter summary

Although the amount of geotechnical data is rapidly increased year after year, how to

organise it in geotechnical databases is rarely mentioned in technical papers, especially for

marine geotechnical databases. Several geotechnical databases used in offshore or onshore

projects are described in this chapter.


15

All of mentioned databases are modeled by a relational database approach. The advantages

of organising geotechnical data into a relational database are to handle an enormous

amount of data, to share information, to update data quickly, to derive thematic maps

easily. Furthermore, incorporating geotechnical database with GIS and geostatistics

packages can enable to predict the spatial variation of variables, to visualise geological

features in three dimensions or to locate the potentially hazardous areas. Those advantages

help the engineers, managers and the authorities to make the decisions effectively.

Chapter 3: NORTH SEA GEOTECHNICAL DATABASE

16

Chapter 3 : NORTH SEA GEOTECHNICAL DATABASE

To extract useful information hidden inside a large data collection, the data should be well

organised. The organization of data can be the archives (in paper format) or databases (in

digital format). With data in paper format, relationships between objects are “static”, and it

is clearly difficult to update or manipulate data. Meanwhile, databases show their flexibility

in the management, update and processing data.

The first step in processing geotechnical data in the Dutch sector – North Sea is to enter

data into a relational database using an object-oriented approach.

3.1. Some basic concepts of database design

Some basic concepts of database design using the object-oriented approach are

summarized below. More information on this approach and relational database can be

found in Blaha, Premerlani (1998) and Teorey (1994).

Concept Description

Database a database is a permanent, self-descriptive repository of data that is

stored in one or more files.

Database

management

system (DBMS)

a DBMS is the software for managing a database.

Relational

database

a relational database is a database in which the data is logically

perceived as tables. A relational DBMS manages tables of data and

associated structures that increase the functionality and

performance of tables.

Objects an object is a concept, abstraction, or thing that has meaning for an

application. In geotechnical database, a borehole, a soil sample, a

project, and so on can be considered as objects.

Classes an object is an instance or occurrence of a class. A class is a

description of a group of objects with similar properties (object

attributes), common behaviour (operations and state diagrams) and

similar relationships to other objects. Class “Boreholes” consists of


17

Concept Description

a number of boreholes; class “Samples” consists of a number of

samples, and so on.

Links a link is a physical or conceptual connection between objects.

Associations an association is a description of a group of links with common

structure and common semantics. An association is denoted by a

line and an association name is shown in italic.

Multiplicity: multiplicity specifies the number of instances of one class that may relate to a

single instance of an associated class (see Figure 3-1).

Figure 3-1: Notation for multiplicity

Generalization: generalization is the relationship between a class (the superclass) and one

or more variations of the class (the subclasses). Generalization is often described in terms

of an “Is-A” relationship between a superclass and a subclass (Teorey, 1994).

Generalization specifies that all the attributes of a superclass be propagated down the

hierarchy to objects of a subclass. For example, boreholes and CPT holes have the same

properties of a “hole” such as coordinates, length of hole. But a borehole and CPT hole

have their own properties, for example, a borehole has soil sample with geotechnical

properties; CPT hole has values of cone resistances, sleeve frictions and/or pore pressures.

Aggregation: Aggregation is described as a “part-of” relationship. For example, one “Hole”

can contain sample geotechnical properties and a soil profile. In other words, geotechnical

properties and soil profile are “part-of” a hole.

The difference between aggregation and generalization is that there are no inherited

attributes in aggregation; meanwhile some attributes of subclasses are inherited from their

superclass in generalization relationship.

Class zero or more

Class zero or one

exactly one Class


18

Figure 3-2: Is-A relationship

3.2. Conceptual database design for the North Sea geotechnical database

Conceptual database design can be considered as an abstraction of the real world situation.

Firstly, the real world situation is classified into classes and then their associations are

established. The process of database design includes two steps:

Define classes

Define relationships

3.2.1. Define classes

A class is denoted by a box with the class name in the top portion of the box. The second

portion of the box may list attributes on the left side. The descriptions of attributes are

listed on the right side in italics. There are following classes in North Sea geotechnical

database: Reports, Holes, Samples, Physical Properties, Shear Strengths, Compressive

Strengths, Deformation Properties (see Appendix 3).

3.2.2. Define relationships

The relationships between classes are shown in Appendix 5. Together with relationships,

their multiplicities are also defined. One report (of Reports class) can have several holes (of

Holes class) but each hole can only belong to one report, therefore the relationship

Reports-Holes is a one-to-many relationship.

Each hole can have only one soil profile. Relationship Holes-Strata is one-to-one

relationship. Each hole could be a borehole or a CPT hole; therefore Holes class is an

Superclass

Subclass 1 Subclass 2 Subclass n …


19

aggregation of Boreholes class and CPT class. In this research, however, CPT data is not

included in the database. One borehole can have many samples therefore the Holes-

Samples relationship is a one-to-many.

Each sample has some physical properties (water content, bulk unit weight, liquid limit,

etc.) and mechanical properties (shear strength, compressive strength, deformation

properties, etc.). Those properties are “part-of” a sample. Their relationship with a sample

is “part-of” relationship.

3.3. Implementation of the design into Microsoft Access

After completing conceptual design stage, the model can be implemented into Microsoft

Access application. Each class will be expressed as a table. The associations between tables

are expressed as the relationships through primary keys, which are determined in each

table. The attribute used as a primary key for the class is marked by a symbol . The

names of attributes are coded compatibly with codes used in the DINO structure as far as

possible (see Appendix 3). Some graphical user interfaces are presented in Appendix 4.


North Sea geotechnical database is designed using the object-oriented approach and

implemented in Microsoft Access application. Geotechnical properties are classified into

classes such as: Reports, Holes, Strata, Samples, Physical Properties, Mechanical Properties

(Shear Strengths, Compressive Strengths, Deformation Properties). Names of those

properties follow the codes used in DINO structure. However, some important properties

are not coded in DINO structure such as: (1) amount of clay and silt portions, (2) D-sizes

of particles (D10, D30, D50, D60) which are necessary to calculate the coefficient of

uniformity and coefficient of curvature for sand gradation, (3) value of time when soil

samples obtain 50% or 90% consolidation (T50, T90). To the help the users easily input data

into the database, several graphical user interface (GUI) input forms are designed. The

relationships between objects are implemented through the primary keys indicated in each

table. Once data is well organised, necessary information can be derived using SQL

statements within DBMS or via GIS packages. Based on this database, geotechnical

properties of sand deposits are examined in the next two chapters.

Chapter 4: : 3D GEOLOGICAL MODEL OF THE SUBSURFACE DEPOSITS

20

Chapter 4 : 3D GEOLOGICAL MODEL OF THE SUBSURFACE DEPOSITS

The aggregate material is used for a wide variety of products including construction

aggregate, reclamation fill, etc. Especially, in the Netherlands, offshore aggregates become

of vital importance to the nation’s infrastructure. Understanding the distribution and

thickness of sand deposits in the North Sea will help to manage effectively the natural

resources.

4.1. Using Kriging for point interpolation

Soil properties vary often abruptly and at short ranges. Various interpolation methods can

be used to predict a value at an unsampled location such as Nearest Point, Trend Surface,

Moving Average, Moving Surface, and Kriging. Nowadays, Kriging is widely used for

spatial analysis. Being different from the straightforward methods – Nearest Point, Trend

Surface, Moving Average and Moving Surface, Kriging is a statistical method based on the

theory of regionalized variables. Furthermore, together with the interpolated map, Kriging

is the only interpolation method that can provide the output error map showing the

standard errors of the estimates. The error map is a mean to quantify the quality of the

estimation and to compensate for the unknown. Intensive discussions on Kriging method

can be found in Houlding (2000), Swan and Sandilands (1995), and Davis (1986).

Before using Kriging, knowledge of the spatial covariance inherent to a spatial

phenomenon is required. A notion of spatial covariance is developed from a function

known in geostatistics as the semi-variogram that is a representation of semi-variance as a

function of spatial distance/lag. Of most significance is that the standard error derived

from the semi-variance. From the standard error, different confidence intervals can be

calculated. In general, the mean value varies in a range of confidence intervals:

µ - c×SE ≤ µ ≤ µ + c×SE

where µ is the Kriging estimation and SE is the estimated error. The multiplication factors

c (critical value) for the estimated errors SE in the error map for different one-sided

confidence levels are (ITC, 2001):


21

Confidence level 90% 95% 97.5% 99% 99.5%

Critical value c 1.282 1.645 1.960 2.326 2.576

The common procedure of interpolation using Kriging as follows:

- Check whether variables normally distribute. If not, it is necessary to transform

variables.

- Calculate experimental semi-variograms.

- Approximate the experimental semi-variograms by semi-variogram models.

- Interpolate using Kriging method with a chosen semi-variogram model in the previous

step.

All interpolated maps in this chapter have the pixel size of 500 m × 500 m.

4.2. Developing a model of the seabed surface

The model of the seabed surface is

interpolated from waterdepth data of

boreholes. In the study area, water

depths increase gradually from the

southeast (near the coastline) to the

northwest. Descriptive statistics of the

seabed surface is shown in Table 4-1.

Checking the condition of normal distribution:

From Figure 4-1, the condition of normal distribution is satisfied.

Table 4-1: Descriptive statistics of seabed surface

N Mean Median SD SE Min Max Q1 Q3

Water depth (m) 329 27.36 27.00 5.591 0.308 7.00 43.00 25.3 29.0

Figure 4-1: Histogram of the depth of seabed surface


22

Semi-variogram model:

The variation of water depths in

Quadrant K & L of the Dutch

sector, North Sea is shown in

Figure 4-2. A circular model with

nugget of 0.5, sill of 29 and range

of 50000 m is used to

approximate the experimental

semi-variogram. This circular

model fits quite well the

experimental semi-variogram

Estimate the depth of seabed surface:

The result of estimation using an ordinary Kriging method is shown in Figure 4-3 and

Appendix 10. Water depths increase from the minimum value of 9.24 m in the southeast to

the maximum value of 42.5 m in the northwest of the study area. At first sight, the seabed

surface has two plain areas. The elevation of the first area ranges from 25 m to 30 m and

the second one has the elevation deeper than 35 m. This result is correspondent with the

bathymetry map of sea floor compiled by TNO-NITG (see Appendix 6). In addition, the

border of the second plain area is also coincident with Figure 4-4(b), which shows the

distribution of major sand banks in Quadrant K and L (after Cameroon et al., 1992).

The standard error of this estimation is shown in Figure 4-3, from which the minimum

error and maximum error is 0.86 m and 6.86 m respectively. The closer to the borehole

locations, the lower the standard error. The highest error of estimation occurs in the

northern part of the study area because the lack of boreholes in this part.

Figure 4-2: Semi-variogram model of seabed surface

0.0 10000.020000.0 30000.0 40000.050000.0 60000.0 70000.080000.

Distance : Point distance

0.00

10.00

20.00

30.00

40.00

50.00

60.00

Sem

iVar

: O

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Dire

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gram


23

K L

(b)(a)

-Figure 4-3: Seabed surface map and standard error map of seabed surface interpolation

Figure 4-4: (a) 3D view of seabed surface (b) Distribution of major sand banks in the southern North Sea, after Stride et al., 1982 (Cameron et al., 1992)

4.3. Geological characteristics

Due to the lack of information in Quadrant L,

only geological characteristics of Quadrant K can

be described in this section.

Error map of seabed surface Map of seabed surface


24

Holocene deposits:

Holocene deposits cover the whole Quadrant K and rest unconformably on late

Pleistocene brackish-marine clays and shelly marine sands. Holocene deposits can be

classified into three formations: Table 4-2: Descriptions of Holocene formations (British Geological Survey, 1987)

Formation Description

Bligh bank The brownish-yellow, medium grained, clean sands, locally slightly muddy with a grain size of 180 – 200 µm. The thickness varies from 1 to 10 m.

Nieuw Zeeland Gronden

This formation contains two member: Terschellingerbank member: Grey to olive-grey, slightly muddy sand. Mean grain size ranges from 130 – 220 µm in the north to 100 – 180 µm in the south. The thickness varies from 1 to 10 m. Western Mud Hole member: consists of muddy, fine or very fine sands or sandy muds, which are olive-grey in colour. The thickness varies from 2 – 5 m.

Elbow Early Holocene brackish-marine and tidal-flat deposits. This formation consists predominantly of muddy sand interbedded with clay. The colour of the sediments is grey or dark grey but near the surface it is often olive-grey. Mean grain size varies from 90 – 180 µm. Thickness is between 1 and 5 m, but in depressions it increases to a maximum of around 20 m.

Pleistocene sediments:

In Quadrant K and L, top Pleistocene deposits comprise of several formations (see Table

4-3 and Appendix 8). The map of top surface of Pleistocene deposits is attached in

Appendix 9. The surface of top Pleistocene plunges towards northwest direction with the

water depth ranges from 6.03 m to 75.76 m. Most of the study area is covered by three

formations: Twente, Brown Bank and Eem whereas the first three formations: Botney Cut,

Bolder Bank and Well cover only a small part in the northwest of Quadrant K, and Cleaver

Bank formation only in the southeast of Quadrant L, near the Dutch coast lines. Table 4-3: Descriptions of Pleistocene formations (British Geological Survey, 1986)

Formation Age Description

Botney Cut UPPER PLEISTOCENE: Late Weischelian to early Holocene

The sediments of this formation were deposited prior to the early Holocene marine transgression and can be divided into two units. The lower unit, is up to 15 m thick, comprises poorly-sorted (well-graded) gravelly, coarse sands. The upper unit is parallel-bedded, up to 35 m thick, consists of very soft, slightly sandy mud with partings of fine sand

Bolders Bank

UPPER PLEISTOCENE (Late Weischelian)

This formation has a flat or gently undulating base between 39 and 45 m below mean sea level. The till is a uniform greyish-brown, gravelly sandy clay up to 15 m thick in the west but is less than 5 m thick in the east.

Well Ground

UPPER PLEISTOCENE

The fluvioglacial deposits of the Well Ground formation underlie or pass laterally into the Bolders Bank formation. The


25

Formation Age Description

(Late Weischelian)

sediments are predominantly micaceous, very fine or fine-grained sands with intercalations of silt and clay. The formation has a maximum thickness of 5 m.

Twente UPPER PLEISTOCENE (Weischelian to early Holocene)

This formation comprises well-sorted (poorly-graded), fine-grained sands with minor intercalations of peat and silty clay. The sediments are up to 5 m thick and include lenses of fine gravel

Brown bank UPPER PLEISTOCENE (Late Eemian to early Weischelian)

This formation was deposited during the marine regression. The characteristic grey-brown, brackish-marine silty clays with silt and very fine sand laminae. The thickness is mostly less than 5 m.

Eem UPPER PLEISTOCENE (Eemian)

This formation consists of very fine, fine or medium grained, slightly gravelly, shelly marine sands. The thickness is mostly between 5 and 20 m.

Clever Bank MIDDLE PLEISTOCENE (Saalian)

This formation comprises proglacial silty clays with silt and sand laminae, and fluvioglacial, very fine to fine grained, micaceous outwash sands with interbedded silt and clay. The thickness is locally up to 15 m.

Egmond Ground

MIDDLE PLEISTOCENE (Holsteinian)

Some of the valleys of this formation penetrate through the base of the Quaternary into Tertiary. The valleys are between 0.5 and 23 km wide, mainly between 100 and 250 m deep. This formation comprises poorly-sorted (well-graded), gravelly coarse sands

4.4. Construction the stratigraphy framework for the model

4.4.1. Define the stratigraphy for the model

From the seabed surface to the depth of 80 m, the major soil type is sand material. It is

intervened by some lenses of clayey soil, silty soil or peat. The simplified soil profile in the

study area consists of five units. Each unit consists of a number of non-contiguous

polygons having the same soil properties:

- Unit 1 : Sand

- Unit 2 : Clay (lenses)

- Unit 3 : Sand

- Unit 4 : Clay (lenses)

- Unit 5 : Sand


26

Figure 4-5: Simplified soil profile

Boundaries between Unit 1 and Unit 3, Unit 3 and Unit 5 are only estimated to calculate

the thickness of Unit 2 and Unit 4. Although those boundaries do not exist but they could

be defined for practical purposes as potential concession boundaries for sand dredging

exploitation.

4.4.2. Interpolate the surfaces below the seabed

The unit surfaces in the study area can be regarded as regionalized variables. They are

continuous from place to place and hence must be spatially correlated over short distances.

The elevation of unit surfaces can be extracted from the database designed in chapter 3.

However, boreholes in the study area do not distribute very regularly and do not cover the

whole area. Most of them distribute almost linearly due to following the pipeline routes

(see Figure 1-1). For such the distribution of point samples, using Nearest Point, Trend

Surface, Moving Average or Moving Surface to interpolate is not suitable. In contrast,

Kriging is the most appropriate method due to its declustering characteristic.

The thickness maps of each unit can be derived from the surface maps. The cut-off value

of thickness maps is chosen at 0.02 m. Layers whose thickness values at a certain location

are less than the cut-off value are considered as absence at that location.

4.4.2.1. Interpolate the surface of Unit 2

Unit 1

Unit 2

Unit 4

Unit 3

Unit 5

Seabed surface


27

The same procedure of

estimation the seabed surface is

used to predict the elevation of

the surface of Unit 2. However,

Unit 2 is only encountered in 25

boreholes therefore the quality

of a semi-variogram shown in

Figure 4-6, as expected, is not so

good. The behaviour of the

elevations of Unit 2 with the

distances of boreholes is not clear.

The semi-variance of the elevation

goes up abruptly at a distance of approx. 12,000 m and falls down immediately. From

there, the variances of the elevation increase gradually together with the distances. Their

relationship can be approximated by an exponential model with the values of nugget of 0.2,

sill of 1.43 and range of 65,000 m.

From the maps of seabed surface and surface of Unit 2, thickness of Unit 1 can be derived

(see Appendix 12).

4.4.2.2. Interpolate the surface of Unit 3

Because Unit 2 only appears in some boreholes therefore the boundary between Unit 1 and

Unit 3 is undetermined at some locations (refer Figure 4-5). To predict the surface of Unit

3, Kriging is conducted for the thickness of Unit 2 instead of the surface of Unit 3 and

then, the surface of Unit 3 is derived from the surface map and thickness map of Unit 2

(refer to Appendix 13). Thickness of Unit 2 is given the value of zero at boreholes where

Unit 2 is not encountered.

From the interpolated map of thickness of Unit 2, the distribution of Unit 2 (clayey soil) is

only concentrated in the southwest of the study area with the maximum thickness of

approx. 5.2 m. Its thickness decreases gradually from the southwest to the northeast. In the

thickness map of Unit 2, a cut-off value of 0.02 m is chosen. Those values less than the

cut-off value are considered as zero and hence Unit 2 is considered not to exist at those

locations.

Figure 4-6: Semi-variogram of surface of Unit 2

0.0 20000.0 40000.0 60000.0 80000.0 100000.0 120000

Distance : Point distance

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

Sem

iVar

: O

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28

4.4.2.3. Interpolate the surface of Unit 4 and Unit 5

The interpolation of the surface of Unit 4 and Unit 5 is similar to the process applied for

Unit 2 and Unit 3. The main problem of this interpolation is the reduction of amount of

data, and consequently a low quality of estimation and high standard error. The thickness

maps of Unit 3 and Unit 4 can be referred to in Appendix 14 and Appendix 15

respectively.

In contrast to Unit 2, Unit 4 locates mainly in the eastern part of the study area and

additionally there is a small area with the thickness of 4.0 m is in the northwestern part.

This unit is not encountered in the central and the southern part. Highest value of the

thickness of Unit 4 is 13.06 m in the east of the study area

4.4.2.4. Estimate the thickness of top sand deposit

Top sand deposit mentioned in this research means the first sand layer encountered in a

soil profile from the seabed surface. It is composed by only Unit 1 at some locations, or by

Unit 1, Unit 3 and/or Unit 5 at the others.

The thickness of top sand deposit is calculated from the seabed surface and interpolated

bottom surface. The map of bottom surface is interpolated through four steps: (1) check

the condition of normal distribution, (2) calculate the experimental semi-variogram, (3)

approximate experimental semi-variogram by a model, (4) interpolate by Kriging method.

Checking the condition of normal distribution:

Table 4-4: Descriptive statistics of bottom surface of top sand deposit (before transformation)

N Mean Median SD CV Min Max Q1 Q3

[-] [°] [°] [°] [%] [°] [°] [°] [°]

40 45.84 38.65 18.17 39.6 31.9 120.10 36.3 47.55

From Figure 4-7(a), the distribution of bottom surface of top sand deposit is positively

skewed. Before interpolation by Kriging, this distribution should be transformed in order

to fulfill the requirement of normal distribution. A transform of 106×X-4 is applied (where

X is the elevation of bottom surface of top sand deposit). After applying this

transformation, the situation is improved significantly (see Figure 4-7(b).


29

Table 4-5: Descriptive statistics of bottom surface of top sand deposit (after transformation)

Figure 4-7: Histogram of bottom surface of top sand deposit (a) before and (b) after transformation

Semi-variogram of bottom surface of top sand deposit

The experimental semi-variogram is

modeled by a spherical model with the

values of nugget, sill and range shown

in Figure 4-8.

Interpolated map

From Figure 4-9, the thickness of top

sand deposit is almost regular in the

whole area. Especially in the center part,

sand deposit forms a funnel shaped

depression with its thickness exceeding

76 m from a surrounding thickness

between 20 m – 30 m. In Figure 4-10,

the depression on the seabed surface

coincides with the location of the funnel-shaped sand deposit and the location of the steep

slope in Figure 4-4(b).


[-] [°] [°] [°] [%] [°] [°] [°] [°]

40 0.4101 0.4481 0.245 0.0048 0.9657 0.1956 0.5761

Figure 4-8: Semi-variogram of bottom surface of top sand deposit

(a) (b)


30

Figure 4-9: Thickness map of top sand deposit

Figure 4-10: 3D view of top sand thickness

Error map

Due to the distribution of bottom surface of top sand deposit is transformed before

Kriging, the error map of interpolation only shows the transformed errors. Unfortunately,

those errors could not be back-transformed to obtain the real values. The readers can only

Thickness of top sand deposit Error map of transformed bottom surface

Seabed surface

Bottom surface of top sand layer


31

use this error map to visualise the relative error such as high or low error areas instead of

estimation the confidence intervals.

4.5. Creating of 3D views

To visualize the surfaces of units and thickness of top sand deposits, interpolated surfaces

are displayed by two application packages: Geospatial Explorer (demo version) and

Geo3DJViewer.

4.5.1. Surfaces viewed with Geospatial Explorer

Geospatial Explorer developed by Cyze & Associated Ltd Company enables geologists,

environmental scientists, and engineers to identify, understand, and solve complex

environmental problems. One of the advantages of Geospatial Explorer is that it can

import grid files with Arc/Info ASCII format – a common format in GIS. Users can also

incorporate Geospatial Explorer with geostatistics package GSLIB in order to simulate the

problem in three dimensions. To get more information on Geospatial Explorer, the reader

can visit the website: www.cyze.com

Figure 4-11: 3D view by Geospatial Explorer

4.5.2. Surfaces viewed with Geo3DJViewer

Geo3DJViewer, introduced in 2000, is a Java application developed by TNO-NITG.

Geo3DJViewer allows to control fully 3D visualisation of the deep subsurface mapping of

the Netherlands. The advantage of Geo3DJViewer is to show coordinates together with

View from the westView from the northeast

Unit 3

Unit 4

Unit 1 Unit 2 Unit 3

View from the southeast


32

three-dimensional views. For more information about Geo3DJViewer, the readers can

refer to the web site: http://dinolocket.nitg.tno.nl

Figure 4-12: 3D view by Geo3DJViewer

Cross section from southeast to northwest Cross section from southwest to northeast

Chapter 5: STATISTICAL ANALYSIS OF GEOTECHNICAL PROPERTIES OF SAND DEPOSITS

33

Chapter 5 : STATISTICAL ANALYSIS OF GEOTECHNICAL PROPERTIES OF SAND DEPOSITS

5.1. Introduction

This chapter goes through statistical characteristics of sand deposits in the study area and

introduces a relationship between effective friction angle and other geotechnical properties

of sand deposits. The uncertainties of geotechnical properties are presented in terms of the

standard deviation, the coefficient of variation and the confidence interval at 95%

confidence level.

5.2. Statistical characteristics of sand deposits

Soil properties often exhibit considerable spatial variation. Using statistical techniques,

geotechnical engineers can quantify the degree of spatial variation of soil properties and

obtain more meaningful estimates at unsampled locations and provide input to reliability

analyses. Given that the scatter in soil properties can be significant, geotechnical engineers

typically attempt to express a property using two numbers: (1) a best estimate, and (2) a

measure of uncertainty in the best estimate. The mean value and the standard deviation,

respectively, are used to express these two numbers (DeGroot, 1996).

Following geotechnical properties of sand deposits in North Sea will be examined using a

statistical approach: water content, dry unit weight, specific gravity, amount of clay and silt

portions, median size of sand particles, coefficient of uniformity, coefficient of curvature

and effective friction angle.

5.2.1. Water content

Water content of sand deposits in the study area scatters in a narrow range although there

are some extreme values (see Figure 5-1). The average value of water content is 22.5% and

its standard deviation is 3.3%. The interval at 95% confidence level spreads from 15.9% to

29.1%. Although sand deposits consist of several layers, there is no difference in water

content in different layers and no correlation between water content of sand deposits and

the depth of samples. Descriptive statistics of water content is shown in Table 5-1.


34

Table 5-1: Descriptive statistics of water content


[-] [%] [%] [%] [%] [%] [%] [%] [%]

1021 22.5 22.3 3.25 14 11.4 48.6 20.5 24.2

95% confidence interval: 15.9% ≤ Water content ≤ 29.1%

Figure 5-1: Distribution of water content

5.2.2. Dry unit weight

Dry unit weight of sand deposits in the study area distributes quite normally (see Figure

5-2). Although sand samples come from three units (Unit 1, Unit 3 and Unit 5, refer to

Figure 4-5) but they cannot be differentiated in Figure 5-2. The values of dry unit weight of

sand deposits are not largely fluctuating regarding to the depth.

Dry unit weight values vary in a narrow range from 10.70 kN/m³ to 18.68 kN/m³, with the

average value of 16.03 kN/m³ and the standard deviation of 0.78 kN/m³.

The range of 95% confidence level of dry unit weight is from 14.47 kN/m³ to 17.59

kN/m³.

-140.00

-120.00

-100.00

-80.00

-60.00

-40.00

-20.000.0 10.0 20.0 30.0 40.0 50.0 60.0

Water content (%)

Elev

atio

n (m

)


35

Table 5-2: Descriptive statistics of dry unit weight


[-] [kN/m³] [kN/m³] [kN/m³] [%] [kN/m³] [kN/m³] [kN/m³] [kN/m³]

963 16.03 16.03 0.78 5 10.70 18.68 15.59 16.50

95% confidence interval: 14.47 kN/m³ ≤ Dry unit weight ≤ 17.59 kN/m³

Figure 5-2: Distribution of dry unit weight

5.2.3. Specific gravity

The maximum value of the original specific gravity is 3.06, which could be considered as

an outlier because this sand sample in the corresponding report is classified as FINE

SAND, slightly silty, medium dense with shell fragments and traces of organic material.

This sample does not contain laterite gravels therefore its specific gravity could not reach

the value of 3.06. After removing the outlier value, descriptive statistics of specific gravity

is shown in Table 5-3.

In the study area the specific gravity of sand deposits is almost constant. It distributes in a

very narrow range (see Figure 5-3). The mean and median values are 2.66 and 2.65,

-140.00

-120.00

-100.00

-80.00

-60.00

-40.00

-20.0010.00 12.00 14.00 16.00 18.00 20.00

Dry unit weight (kN/m³)

Elev

atio

n (m

)


36

respectively. Standard deviation of this distribution is 0.02. The 95% confidence interval of

mean, which spreads from 2.62 to 2.70, is reasonable for sandy soils.

Table 5-3: Descriptive statistics of specific gravity


[-] [-] [-] [-] [%] [-] [-] [-] [-]

825 2.66 2.65 0.02 0.9 2.59 2.82 2.65 2.66

95% confidence interval: 2.62 ≤ Specific gravity ≤ 2.70

Figure 5-3: Distribution of specific gravity

5.2.4. Fines

Parameter “Fines” means an amount of clay and silt portions in a sand sample. In the

study area, this parameter varies in an extremely large range from 0.1% to 46%. It also

shows clearly a positively skewed distribution. However, the majority of those values are

only from 1% to 8% (from Q1 to Q3) (see Figure 5-4). The standard deviation of Fines is

9.32% that is totally higher than the mean value and hence the range of mean value with

95% confidence could not base on this standard deviation (see Table 5-4).

-140.00

-120.00

-100.00

-80.00

-60.00

-40.00

-20.002.40 2.60 2.80 3.00

Specific gravity

Elev

atio

n (m

)


37

To estimate the range of mean value with 95% confidence level, Fines is transformed by

using the α value of 0.2 for Box-Cox formula. More information on this formula can be

found in (Rock, 1988). The result of this transformed distribution is listed in Table 5-5.

After being back-transformed the value of mean and median are nearly coincident at 4.0%

(the transformed distribution is more symmetrical than the original distribution) (see

Figure 5-5). The range of mean at 95% confidence level spreads from 0.07% to 35.98%

that is coincident with the distribution of Fines in Figure 5-4.

Table 5-4: Descriptive statistics of fines (before transformation)


[-] [%] [%] [%] [%] [%] [%] [%] [%]

339 7.34 4.00 9.32 - 0.1 46.00 1.00 8.00 Table 5-5: Descriptive statistics of fines (after transformation)


[%]

339 1.57 1.60 1.83 - -1.85 5.75 0.00 2.58 Table 5-6: Descriptive statistics of fines (after back-transformation)


[-] [%] [%] [%] [%] [%] [%] [%] [%]

339 3.90 4.00 - - - - - -

95% confidence interval: 0.07% ≤ Fines ≤ 35.98%


38

Figure 5-4: Distribution of fines (before transformation)

Figure 5-5: Histogram of fines (after transformation)

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-120.00

-100.00

-80.00

-60.00

-40.00

-20.000.00 10.00 20.00 30.00 40.00 50.00

Fines (%)

Elev

atio

n (m

)


39

5.2.5. Median size D50:

The particle diameters that correspond to certain percent-passing values for a given soil are

known as the D-sizes. For example, D10 is the grain size that corresponds to 10 percent

passing. D50 is the grain size that corresponds to 50 percent passing. To classify the grain

size of cohesionless soils, median size D50 is very useful. In the study area, D50 values of

sand particles are rather uniform. It ranges from 0.06 mm to 3.5 mm with the mean value

of 0.19 mm and the standard deviation of 0.20 mm. The distribution of D50 is positively

skewed (see Figure 5-6). To transform this skewed distribution to a normal one, a

transform of 10×(D500.1 - 1) is applied (an α value of 0.1 is used in Box-Cox formula). The

descriptive statistics of median size is shown in Table 5-7. D50 at 95% confidence level

spreads from 0.04 mm to 0.33 mm. Compare this range to Figure 5-6, it is good

agreement. Sand particles in the study area can be classified as FINE TO MEDIUM

SAND according to British Standard (1981).

Table 5-7: Descriptive statistics of D50 (before transformation)


[-] [mm] [mm] [mm] [%] [mm] [mm] [mm] [mm]

322 0.19 0.18 0.20 - 0.06 3.5 0.15 0.21 Table 5-8: Descriptive statistics of D50 (after transformation)


[%]

322 -1.91 -1.87 0.43 - -3.25 1.18 -2.09 -1.69 Table 5-9: Descriptive statistics of D50 (after back-transformation)


[-] [mm] [mm] [mm] [%] [mm] [mm] [mm] [mm]

322 0.12 0.13 - - - - - -

95% confidence interval: 0.04 mm ≤ D50 ≤ 0.33 mm


40

Figure 5-6: Distribution of D50 (before transformation)

Figure 5-7: Histogram of D50 (after transformation)

-140.00

-120.00

-100.00

-80.00

-60.00

-40.00

-20.000.01 0.10 1.00 10.00

Median size (mm)

Elev

atio

n (m

)


41

5.2.6. Coefficient of uniformity Cu

Coefficient of uniformity of sand particles varies in a large range (see Figure 5-8). In order

to calculate the intervals at 95% of confidence level, a transformation of ⎟⎟⎠

⎞⎜⎜⎝

⎛ −−

−

9.11C 9.1

u is

applied. The distribution of coefficient of uniformity after transformation is shown in

Figure 5-9. The 95% confidence interval is from 1.24 to 43.49. Whereas Cu values mainly

vary from 1.51 to 2.22 (from Q1 to Q3), the right border of 95% confidence interval (43.49)

is beyond the maximum value of original data (39.00). This can be reasoned by the applied

transformation is not so good and the transformed distribution is not close to normal

distribution.

Table 5-10: Descriptive statistics of Cu (before transformation)


[-] [-] [-] [-] [%] [-] [-] [-] [-]

313 3.03 1.74 4.71 - 1.21 39.00 1.51 2.22 Table 5-11: Descriptive statistics of Cu (after transformation)


[%]

313 0.35 0.34 0.09 25 0.16 0.53 0.29 0.41 Table 5-12: Descriptive statistics of Cu (after back-transformation)


[-] [-] [-] [-] [%] [-] [-] [-] [-]

313 1.77 1.71 - - - - - -

95% confidence interval: 1.24 ≤ Cu ≤ 43.49

The range of Cu from 1.24 to 43.49 reflects that sand particles are within the range of

poorly-graded to well-graded. However, 50% of Cu values are in the range from 1.51 to

2.22 therefore they can be classified as poorly graded.


42

Figure 5-8: Distribution of Cu (before transformation)

Figure 5-9: Histogram of Cu (after transformation)

-140.00

-120.00

-100.00

-80.00

-60.00

-40.00

-20.000.00 10.00 20.00 30.00 40.00 50.00

Coef. of uniformity

Elev

atio

n (m

)


43

5.2.7. Coefficient of curvature Cc

Similar to Cu, Cc values of sand particles scatter in a large range (see Figure 5-10). In order

to calculate the intervals at 95% of confidence level, a transformation of ⎟⎟⎠

⎞⎜⎜⎝

⎛ −−

−

9.11C 9.1

c is

applied. After transformation, the distribution of coefficient of curvature is more normal

than the original one but it is still skewed (see Figure 5-11). The range of Cc at 95%

confidence level is from 0.82 to 2.54, can be classified as smooth curve.

Table 5-13: Descriptive statistics of Cc (before transformation)


[-] [-] [-] [-] [%] [-] [-] [-] [-]

313 1.53 1.10 2.02 - 0.57 26.81 1.00 1.21 Table 5-14: Descriptive statistics of Cc (after transformation)


[%]

313 0.10 0.09 0.17 - -1.01 0.53 0.00 0.16 Table 5-15: Descriptive statistics of Cc (after back-transformation)


[-] [-] [-] [-] [%] [-] [-] [-] [-]

313 1.12 1.10 - - - - - -

95% confidence interval: 0.82 ≤ Cc ≤ 2.54


44

Figure 5-10: Distribution of Cc (before transformation)

Figure 5-11: Histogram of Cc (after transformation)

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-120.00

-100.00

-80.00

-60.00

-40.00

-20.000.00 10.00 20.00 30.00

Coeff. of curvature

Elev

atio

n (m

)


45

5.2.8. Effective friction angle (EFA)

EFA in the study area are obtained from Direct shear test and Consolidated-Drained

Triaxial test (CD-Triaxial). The results of statistics show that EFA from CD-Triaxial is less

disperse than that from Direct shear test (refer to standard deviation, coefficient of

variation and confidence interval in Table 5-16, Table 5-17). In other words, CD-Triaxial is

more reliable than Direct shear test. Descriptive statistics of EFA from Direct shear test

and CD-Triaxial test are shown in Figure 5-12 and Figure 5-13.

Comparison studies have been made which show that differences obtained by different

types of apparatus are of minor importance (Pells et al., 1973) . In addition, for

cohesionless sands placed at medium densities, little difference is found between the

strength parameters given by the direct shear and triaxial tests (see Appendix 17).

The combination of EFA from both tests has a normal distribution with both the mean

and median values are 35° (see Figure 5-14). From the sea floor downward, EFA is almost

constant. There is no evidence of correlation between EFA and the depth below seabed.

The range of 95% confidence interval is from 29° to 41° (see Table 5-18). Table 5-16: Effective friction angle from Direct shear test


[-] [°] [°] [°] [%] [°] [°] [°] [°]

56 33 33 3.8 12 26 46 30 35

95% confidence interval: 26° ≤ Effective friction angle ≤ 41° Table 5-17: Effective friction angle from Consolidated-Drained Triaxial test


[-] [°] [°] [°] [%] [°] [°] [°] [°]

145 35 35 2.5 7 30 41 34 37

95% confidence interval: 30° ≤ Effective friction angle ≤ 40° Table 5-18: Descriptive statistics of effective friction angle from both tests


[-] [°] [°] [°] [%] [°] [°] [°] [°]

201 35 35 3.0 9 26 46 33 37

95% confidence interval: 29° ≤ Effective friction angle ≤ 41°


46

Figure 5-12: Distribution of EFA (from Direct shear test)

Figure 5-13: Distribution of EFA (from CD-Triaxial test)

-140.00

-120.00

-100.00

-80.00

-60.00

-40.00

-20.0020 25 30 35 40 45 50

EFA from Direct shear test (°)

Elev

atio

n (m

)

-140.00

-120.00

-100.00

-80.00

-60.00

-40.00

-20.0020 25 30 35 40 45 50

EFA from CD-Triaxial test (°)

Elev

atio

n (m

)


47

Figure 5-14: Distribution of effective friction angle from both tests

5.3. Relationship between Effective friction angle and other geotechnical properties

Effective friction angle (EFA) of sandy soils is one of the most important parameters used

for geotechnical modeling such as foundation and slope stability analysis. EFA value can

be obtained from Triaxial test or Direct shear box test. Both tests are time-consuming and

costly. In addition, EFA is sensitive to density, meanwhile, it is difficult to obtain

undisturbed sand samples so that the densities used in those tests may not correspond with

in-situ densities. On the other hand, other geotechnical properties such as water content,

bulk unit weight, grain size distribution, and so on can be obtained from simple tests

whose amount is much larger than that of EFA values in archives. If a relationship

between EFA and other geotechnical properties is established, this can help foundation

engineers and structural designers to estimate the EFA value from the other geotechnical

properties and then the bearing capacity of a foundation can be calculated.

-120.00

-100.00

-80.00

-60.00

-40.00

-20.0020 25 30 35 40 45 50

Effective Friction Angle (°)

Elev

atio

n (m

)

Direct shear CD-Triaxial


48

To examine whether the relationship between EFA and other geotechnical properties

exists, the following properties are selected: water content - WAT (%), dry unit weight –

DUW (kN/m³), fines - FIN (%), median size - D50 (mm) and coefficient of uniformity -

Cu. The confidence significance of 0.05 is chosen for a statistical analysis. Firstly, the linear

equation will be examined.

Linear relationship:

EFA = constant + c1WAT + c2DUW + c3FIN + c4D50 + c5Cu

where constant, c1, c2, c3, c4 and c5 are listed in Coefficient column in the table below

Parameter Coefficient Standard Error T statistic P-values

Constant 30.281 7.35800 4.12 0.000 WAT (c1) -0.085 0.06714 -1.27 0.209 DUW (c2) 0.855 0.39940 2.14 0.036 FIN (c3) -0.596 0.11860 -5.02 0.000 D50 (c4) -39.000 12.87000 -3.03 0.003 Cu (c5) 1.340 0.32730 4.09 0.000

R² = 35.8% R²adj = 31.1% Analysis of Variance (ANOVA)

Source Df SS MS F ratio P-value

Model 5 385.04 77.01 7.59 0.000 Residual 68 689.55 10.14 Total 73 1074.59

Although the P-value in the ANOVA table is less than 0.05 (confidence significance), the

value of R² and R²adj is too low, hence the conclusion is no statistically significant

relationship between the variables at the 95% confidence level.

Because the linear relationship is very bad, one step further to check a quadratic

relationship.

Quadratic relationship:

EFA = constant + c1WAT + c2DUW + c3FIN + c4D50 + c5Cu + c6WAT² + c7DUW² + c8FIN² +

c9D50² + c10Cu² + c11WAT×DUW + c12WAT×FIN + c13WAT×D50 + c14WAT×Cu+

c15DUW×FIN + c16DUW×D50 + c17DUW×Cu + c18FIN×D50 + c19FIN×Cu+ c20D50×Cu


49

where constant, ci (i = 1 ÷ 20) are listed in Coefficient column in the table below


Constant -24.3 195.2 -0.12 0.901 WAT (c1) 2.577 1.14 2.26 0.028 DUW (c2) -9.36 18.29 -0.51 0.611 FIN (c3) 0.71 9.506 0.07 0.941 D50 (c4) 1057.3 694.6 1.52 0.134 Cu (c5) 4.84 27.15 0.18 0.859 WAT² (c6) -0.04697 0.01367 -3.44 0.001 DUW² (c7) 0.6157 0.4735 1.300 0.199 FIN² (c8) -0.03497 0.05704 -0.61 0.542 D50² (c9) -363.4 360.2 -1.01 0.318 Cu² (c10) -0.0389 0.5631 -0.07 0.945

WAT×DUW (c11) 0.06649 0.09377 0.71 0.481

WAT×FIN (c12) 0.10745 0.09082 1.18 0.242

WAT×D50 (c13) -7.333 3.187 -2.30 0.025

WAT×Cu (c14) -0.4384 0.516 -0.85 0.399 DUW×FIN (c15) -0.1909 0.4834 -0.39 0.694

DUW×D50 (c16) -52.18 38.56 -1.35 0.182

DUW×Cu (c17) -0.247 1.659 -0.15 0.882 FIN×D50 (c18) -3.89 10.78 -0.36 0.720 FIN×Cu (c19) 0.291 0.4919 0.59 0.557

D50×Cu (c20) 37.87 69.36 0.55 0.587

R² = 62.0% R²adj = 47.9 % Analysis of Variance (ANOVA)



When the order of the relationship is increased from first order (linear) to second order

(quadratic), the coefficient of determination R² and R²adj increases remarkably from 35.8%

to 62.0% and from 31.1% to 47.9% respectively. P-value in ANOVA table is less than

0.05, therefore there is a significant relationship between the parameters at the 95 %

confidence level. In order to simplify the relationship equation, those parameters whose P-

values are greater than 0.05 (confidence significance) should be removed from the


50

relationship equation. More detailed information on the multivariate regression and the

statistical meaning of P-value is worked out in (Vardeman, 1994) and (Chatterjee et al.,

2000).

The procedure of simplifying the relationship equation shown in Figure 5-15 is a looping

process. This process is repeated until no parameter with the P-value greater than 0.05.

The first value removed from the relationship equation is a parameter FIN with P-value of

0.941. After removing this parameter out of the equation, P-values of the rest parameters

will be calculated and then the next parameter whose P-value is highest will be removed.

The final simplified relationship equation is:

EFA = - 39.98 + 2.5573 WAT + 570.7 D50 + 1.4238 Cu - 0.0355 WAT² + 0.1732 DUW²

- 7.058 WAT×D50 - 0.04116 DUW×FIN - 28.82 DUW×D50


Constant -39.98 24.3 -1.65 0.105 WAT 2.5573 0.5446 4.7 0.000 D50 570.7 216.9 2.63 0.011 Cu 1.4238 0.3007 4.74 0.000 WAT² -0.035501 0.00887 -4 0.000 DUW² 0.17324 0.0685 2.53 0.014 WAT×D50 -7.058 1.821 -3.88 0.000

DUW×FIN -0.041594 0.007972 -5.22 0.000

DUW×D50 -28.82 11.93 -2.42 0.018 R² = 53.9% R²adj = 48.2%

Analysis of Variance (ANOVA)



Compare the initial quadratic equation with simplified quadratic equation, R²adj is almost

similar but an amount of predictors in the simplified cubic equation is reduced remarkably.

Figure 5-16 shows the variation of measured and predicted EFA from the simplified

quadratic relationship.


51

Figure 5-15: Flow chart of simplifying the relationship equation

Figure 5-16: Compare predicted and measured EFA

Validation the relationship equation

Based on the simplified equation, the predicted effective friction angles are calculated and

compared with original friction angles (see Appendix 21)

Remove that parameter out of the equation

Calculate the regression equation again

Is there any parameters whose

P-value greater than 0.05 ?

No

Yes

20

25

30

35

40

45

50

20 25 30 35 40 45 50

Predicted effective friction angle (degree)

Mea

sure

d ef

fect

ive

fric

tion

angl

e (d

egre

e)


52

Because EFA values depend on five parameters hence it is impossible to visualise this

relation. In terms of three-dimensional visualisation, only two independent parameters can

be visualise with one dependent parameter. Two independent parameters whose 95%

confidence interval are largest can be chosen for the visualisation, the rest parameters will

be fixed at their mean values. From this assumption, WAT and FIN will be drawn with

EFA, the other parameters are fixed at their corresponding mean values: DUW = 16.03

kN/m³, D50 = 0.12 mm, Cu = 1.77 (see Table 5-19).

From data in the table below, a graph of EFA versus water content according to each value

of amount of clays and silts is presented in Figure 5-17. Two conclusions can be derived

from this chart:

(1) EFA increases corresponding to the increment of amount of water in soil samples. It

reaches the maximum value at an approximate water content of 24%. Beyond the value of

24%, EFA decreases. The behaviour of EFA regarding to water content is similar to the

behaviour of dry unit weight of soils during the compaction test.

(2) At a given WAT, DUW, D50, Cu, the more the amount of Fines, the less the value of

EFA. Fines will increase the cohesion of soil but reduce the interlocking forces between

particles and then reduce EFA values of cohesionless soils. Table 5-19: Predicted EFA from the simplified quadratic equation

DUW = 16.00 kN/m³, D50 = 0.12 mm, Cu = 1.77

Water content (%)

Fines (%) 16 18 20 22 24 26 28 30

0.07 38 39 40 40 41 40 40 39 0.5 38 39 40 40 40 40 40 39 1 38 39 39 40 40 40 39 39 2 37 38 39 39 39 39 39 38 5 35 36 37 37 37 37 37 36 10 32 33 33 34 34 34 33 33 15 28 29 30 31 31 31 30 29 20 25 26 27 27 27 27 27 26 25 22 23 23 24 24 24 23 23 30 18 19 20 21 21 21 20 19


53

Figure 5-17: Relationship between EFA, water content and fines

5.4. Estimate volume of top sand deposit

Volume of top sand deposit can be derived from a raster map of thickness of top sand

deposit. At each pixel, the value of thickness is derived and multiplied with the area

represented by a pixel (in this case the pixel size is 500 m × 500 m).

The estimated volume of top sand deposit is 191 × 109 m³.

15

20

25

30

35

40

45

15 20 25 30 35

Water content (%)

Effe

ctiv

e fri

ctio

n an

gle

(°)

Fines=0.07Fines=0.5Fines=1Fines=2Fines=5Fines=10Fines=15Fines=20Fines=25Fines=30Poly. (Fines=0.07)Poly. (Fines=0.5)Poly. (Fines=1)Poly. (Fines=2)Poly. (Fines=5)Poly. (Fines=10)Poly. (Fines=15)Poly. (Fines=20)Poly. (Fines=25)Poly. (Fines=30)


54


In this chapter, statistical characteristics of sand deposits in quadrant K & L of the Dutch

sector, North Sea are examined. Their geotechnical properties such as water content, dry

unit weight, specific gravity, fines, D50, coefficient of uniformity, coefficient of curvature

and effective friction angle are summarized in terms of mean, median, standard deviation,

and so on. Those results are summarised in Table 5-20. Only water content, dry unit

weight and friction angle have normal distributions with low standard deviations. The

other properties spread in large ranges and their distributions are positively skewed. The

intervals at 95% confidence level of geotechnical properties are shown in Table 5-20.

Although sand deposits in the study area originate from different units, their geotechnical

properties are similar. There is no trend or correlation with the depth of samples.

Compare dry unit weight and EFA of sand deposits in North Sea with representative

values in NEN 6740 (1991), the results are all in good agreement. However, the range of

95% confidence interval of EFA in the Dutch sector, North Sea is somewhat smaller than

the values in the Norwegian sector (see Appendix 18) (after Wu et al., 1987). A good

agreement of dry unit weights of sand deposits in Table 5-20 with the approximate in-situ

values proposed by CUR (1996) (see Appendix 19) can be found.

A relationship between EFA and other geotechnical properties is established. Although the

coefficient of determination of this relationship is not so strong, it shows the trend of

variation of EFA corresponding to water content and amount of clays and silts. This

relationship should be checked whenever the geotechnical database of North Sea is

updated.


55

Table 5-20: Summary of descriptive statistics of geotechnical properties

Water content

Dry unit weight

Specific gravity

Fines Median size

Cu Cc

Effective friction angle

Unit [%] [kN/m³] [-] [%] [mm] [-] [-] [°]

N 1021 963 826 339 322 313 313 201

Mean 22.5 16.03 2.66 7.34 3.90

0.19 0.12

3.03 1.77

1.53 1.12 35

Median 22.3 16.03 2.65 4.00 4.00

0.18 0.13

1.74 1.71

1.10 1.10 35

SD 3.3 0.78 0.02 9.32 0.20 4.71 2.02 9

Min 11.4 10.70 2.59 0.10 0.06 1.21 0.57 26

Max 48.6 18.68 3.06 46.00 3.50 39.00 26.81 46

Q1 20.5 15.59 2.65 1.00 0.15 1.51 1.00 33

Q3 24.2 16.50 2.66 8.00 0.21 2.22 1.21 37

From 15.9 14.47 2.62 0.07 0.04 1.24 0.82 29 95% confidence interval To 29.1 17.59 2.70 35.98 0.33 43.49 2.54 41

back-transformed values

Chapter 6: CONCLUSION AND RECOMMENDATIONS

56

Chapter 6 : CONCLUSION AND RECOMMENDATIONS

6.1. Conclusion

6.1.1. North Sea geotechnical database

Geotechnical data management is one of the most important issues for managers and

geotechnical engineers. How to integrate effectively geotechnical data with GIS packages

and 3D modeling packages remains unresolved despite information technology has

developed for many years.

North Sea geotechnical database is designed (i) to convert data from an analog (paper)

format to digital format primarily as a backup to secure the data against possible loss and

then (ii) to explore avenues for deriving potential useful information inside geotechnical

data collected for many years.

The criterion for database design is to be compatible as far as possible with existing

geotechnical databases such as DINO structure of TNO-NITG, AGS format of British

Geological Survey. North Sea geotechnical database is designed using the object-oriented

approach and implemented in a relational database management system MS-Access. Data

is classified into normalized tables which link to each other through predefined

relationships. Geotechnical properties are classified into classes such as: Reports, Holes,

Strata, Samples, Physical Properties, Mechanical Properties (Shear Strengths, Compressive

Strengths, Deformation Properties). Names of those properties are followed the codes

used in DINO structure. Once data is well organised, necessary information can be derived

using SQL statements within DBMS or via GIS packages.

6.1.2. 3D geological model of sand deposits

To explore the distribution of sand deposits in the study area, several surface and thickness

maps are established using Kriging interpolation. In this case, Kriging is the most suitable

method due to the lack of boreholes and their linear distribution. In addition, besides the

estimated maps, Kriging can provide the error maps that help users to quantify the

uncertainty of interpolation. The procedures of applying Kriging to interpolate the seabed

surface map and thickness maps are described step by step in Chapter 4.


57

Seabed surface map, four thickness maps of four units and one thickness map of top sand

deposit are presented in the appendices. The good agreement between the interpolated

map of seabed surface and the bathymetry map of sea floor is an illustration of the

effectiveness of the Kriging method in case of the poor quality of input data.

To provide to the users an overview of the study area, seabed surface and top sand

thickness maps are visualised in three dimensions using two software packages: Geospatial

Explorer and Geo3DJViewer. Based on the map of the thickness of top sand deposit, the

roughly estimated volume of top sand deposit is 191,457,020,000 m³.

Together with maps of seabed surface and thickness of top sand deposit, their

corresponding error maps are also presented. From the error maps readers can quantify the

uncertainty of the interpolation. The closer to borehole locations the estimated position is,

the lower the standard error value is.

6.1.3. Statistical characteristics of geotechnical properties

Descriptive statistical characteristics of following properties are examined: water content,

dry unit weight, specific gravity, median size, fines, coefficient of uniformity, coefficient of

curvature and effective friction angle. Although sand deposits are classified into three units

but geotechnical properties are not different from those units.

From statistical results, the mean, median and ranges of 95% confidence intervals for each

property are established. Those ranges can help structural and foundation engineers during

the process of foundation analysis. Values of water content, dry unit weight, specific

gravity and effective friction angle are quite uniform meanwhile the others spread in large

ranges.

Amount of clay and silt portions (Fines) in sandy soils vary from 0.07% to 35.98%,

however 50% of samples (from the first quartile to the third quartile) has Fines values

distribute only from 1.0% to 8.0%. Median size of sand particles is classified as fine to

medium (according to British Standard). Similar to Fines value, coefficient of uniformity

(Cu) varies in a large range from 1.2 to 43.5 but 50% sand samples has Cu values from 1.5

to 2.2. Besides, coefficient of curvature varies (Cc) from 0.82 to 2.54 and 50% sand samples

has Cc from 1.00 to 1.21. Therefore sand deposits in the study area can be classified as

poorly-graded FINE TO MEDIUM SAND (SP).


58

Specific gravity of sandy soils in the study area varies from 2.62 to 2.70 with the mean

value is 2.66. Water content scatters from 15.9% to 29.1% around the mean value of

22.5%. Dry unit weight with the mean value of 16.03 kN/m³ spreads from 14.47 kN/m³

to 17.59 kN/m³. Effective friction angle varies from 29° to 41° with a mean value of 35°.

A relationship between the effective friction angle and other properties is established.

Although the coefficient of determination R² is only 0.54, it shows the influence of water

content and fines on variations in effective friction angle. This correlation can be

compared with the effect of water to dry unit weight of soil samples during the

compaction.

6.2. Further research

Although this research achieves some results there are still some necessary improvements:

1. Boreholes in the study area mainly locate in the southern part that results in high error

in the interpolation in the northern part. Adding more boreholes at boundary area

could improve the accuracy of the interpolation.

2. The North Sea geotechnical database includes only properties of core samples. This

database should involve CPT data and geophysical data that can be integrated in 3D

spatial database and can improve the interpretation of the soil profile.

3. Classifying soil samples into different units is rather subjective, especially for three sand

units. It is necessary to have an objective routine to classify them.

4. Soil description composes several properties such as soil components, sample color,

structure, plasticity, bedding, and so on. In the present database, those properties are

placed only in one field “Soil description” of the database. This field violates the

principle of a relational database. Soil description should be classified into several fields

instead of one field as major component, minor component, structure, major color,

minor color, relative density and plasticity, consistency, texture, group symbol. By

doing this, the query of a certain type of soil will be more effectively.

5. The correlation of effective friction angle with other geotechnical properties should be

corrected whenever North Sea geotechnical database is updated.


59

6. To extract a value of a certain geotechnical property at a certain coordinate, 3D Kriging

should be used to interpolate. The interpolation can obtain a higher accuracy if it is

integrated with geological and geomorphological constrains.

7. To quantify the uncertainty of estimation, the error map is very useful. However if the

distribution of properties is transformed before Kriging, the error map cannot be back-

transformed and the real value of the interpolation error at a certain point is

undetermined.

REFERENCES

60

REFERENCES

1. Blaha M., Premerlani W. (1998). Object-Oriented Modeling and Design for Database Application.

Prentice-Hall Inc., New Jersey.

2. Briaud J.L. et al. (1991). Shallow Foundation Database. In: Geotechnical Engineering Congress 1991,

Vol. 1, pp. 733-741. ASCE, New York.

3. British Geological Survey, Rijks Geologische Dienst (1986). Map of Quaternary Geology,

Indefatigable sheet 53°N – 02°E, scale of 1:250000. Natural Environment Research Council,

London.

4. British Geological Survey, Rijks Geologische Dienst (1987). Map of Seabed sediments and Holocene,

Indefatigable sheet 53°N – 02°E, scale of 1:250000. Natural Environment Research Council,

London.

5. British Standard Institution (1981). Code of Practice for Site Investigation, BS 5930:1981

6. Cameron T.D.J. et al. (1992). The Geology of The Southern North Sea. Natural Environment

Research Council, London.

7. Chatterjee S., Hadi A.S., Price B. (2000). Regression Analysis By Example, 3rd ed. John Wiley &

Sons Inc.

8. Centre for Civil Engineering Research and Code (CUR) (1996). Building on Soft Soils. Balkema,

Rotterdam.

9. Davis J.C. (1986). Statistics and Data Analysis in Geology. John Wiley & Sons Inc.

10. DeGroot D.J (1996). Analyzing Spatial Variability of In Situ Soil Properties. In: Uncertainty in

The Geologic Environment: From Theory to Practice, Proceedings of Uncertainty ’96, Vol. 1. ASCE, New

York.

11. Evans A., Giddings T. (1991). The Application of a Geographical Information System to

Resource Management – The UK Offshore Sand and Gravel Case. In: Proceedings of the Seventh

Symposium on Coastal and Ocean Management, Vol.3, pp.1981-1989. ASCE, New York.

12. Hawkes M. (1991). Geotechnical Database Management Systems for Boston’s Central

Artery/Harbor Tunnel Project. In: Geotechnical Engineering Congress 1991, Vol. 1, pp. 99-109.

ASCE, New York.

13. Herbschleb J. (1990) Ingeo-base, an engineering geological database. In: 6th International IAEG

Congress, pp. 47-53. Balkema, Rotterdam.

REFERENCES

61

14. Houlding S.W. (2000). Practical Geostatistics, Modeling and Spatial Analysis. Springer-Verlag, Berlin.

15. International Institute for Aerospace Survey and Earth Sciences (ITC), (2001). ILWIS 3.0,

User’s Guide. ITC-ILWIS, Netherlands.

16. Luna R., Hertel T.P., Baker H., and Fennessey T.,. (2001). Geotechnical Database for

Emergency Vehicle Access Route in Missouri. In: Proceedings of the 80th Annual Meeting of the

Transportation Research Board, NRC, Washington, D.C. [Online]. Available from:

www.utc.umr.edu/Publications/Proceedings/2001/Geotechnical_Database.pdf

17. Nederlands Normalisatie Instituut (1991). NEN 6740, Geotechnics TGB 1990 – Basic requirements

and loads. Nederlands Normalisatie Instituut, Delft.

18. Pells P.J.N., Maurenbrecher P.M., Elges H.F.W.K. (1973). Validity of Results from the Direct

Shear Test. In: Proceedings of the 8th International Conference on Soil Mechanics and Foundation

Engineering, Vol. 1, Part 2, Moscow.

19. Rijkers R., Wassing B., de Lange G. (1996). Gegevensdefinitie geotechnische parameters LAAGEIG-

STAP (met GMP-tabellen), versie 1.3. Nederlands Instituut voor Toegepaste Geowetenschappen

TNO

20. Rock N.M.S. (1988). Lecture Notes in Earth Sciences, Vol. 18. Springer-Verlag, Berlin.

21. Swan A.R.H., Sandilands M. (1995). Geological Data Analysis. Blackwell Sciences Ltd.

22. Teorey T.J. (1994). Database Modeling & Design: The Fundatmental Principles. Morgan Kaufmann

Publishers, Inc., California.

23. TNO-NITG (2000). Dino. [Online]. Available from:

http://www.nitg.tno.nl/ned/projects_new/pdf_s/2_14eng.pdf

24. Vardeman S.B. (1994) Statistics for Engineering Problem Solving. PWS Publishing Company.

25. Wu T.H. et al. (1987). Uncertainties in evaluation of strength of marine sand. In: Journal of

Geotechnical Engineering, Vol. 113, No. 7, pp. 719-738. ASCE, New York.

APPENDICES

62

APPENDICES

Appendix 1: Inventory of boreholes in the North Sea database CPTs CPTs

Report No. Location Year Boreholes (Downhole) (Continuous)

Remarks

MK-0503 K04 1995 1 1

MK-0532 K04 1995 2

K04 Total 3 1 0

MK-0501 K05 1995 1

MK-0532 K05 1995 2

K05 Total 3 0 0

MK-0497 K06 1992 1 1

MK-1032 K06 1983 26

K06 Total 27 1 0

MK-1122 K07 1968 2

MK-1204 K07 1979 1 1

K07 Total 3 1 0

MK-1037 K08 1977 10 Shallow boreholes



MK-1146 K08 1975 2

MK-1166 K08 1977 1

K08 Total 59 0 0

MK-0502 K10 1992 1 1

K10 Total 1 1 0



MK-1117 K13 1975 1 1

MK-1150 K13 1976 1 1

MK-1165 K13 1977 1 1

K13 Total 23 3 0

MK-1148 K14 1976 1

MK-1149 K14 1974 1

MK-1158 K14 1976 1



MK-1210 K14 1968 1

MK-1212 K14 1970 3

K14 Total 122 0 0


MK-1167 K15 1977 2 1

MK-1168 K15 1975 1 1

K15 Total 27 2 0

MK-1147 K16 1975 3

K16 Total 3 0 0

APPENDICES

63

CPTs CPTs Report No. Location Year Boreholes

(Downhole) (Continuous) Remarks

MK-1171 K17 1968 1

K17 Total 1 0 0


K18 Total 17 0 0

MK-1207 L02 1969 1

L02 Total 1 0 0

MK-1055 L07 1974 10 Shallow boreholes

MK-1164 L07 1976 3 3

L07 Total 13 3 0

MK-1010 L08 1987 2 2

L08 Total 2 2 0

MK-0528 L09 1995 2 2 1

L09 Total 2 2 1

MK-1062 L10 1984 10 Only soil profiles, no soil properties

MK-1160 L10 1975 3

MK-1205 L10 1969 1

L10 Total 14 0 0

MK-1060 L11 1984 6 Shallow boreholes

L11 Total 6 0 0

MK-1206 L17 1976 2

L17 Total 2 0 0

Grand Total 329 16 1

APPENDICES

64

Appendix 2: North Sea database structure diagram

Remarks of multiplicity:

zero or more

zero or one

exactly one

Reports

Holes

CPT Samples

Physical Properties

Mechanical Properties

Shear Strength Compressive

Strength Deformation Properties

Strata

has

contain

Is-A

co

ntai

n

Is-A

contain

containcontainco

ntai

n

cont

ain

APPENDICES

65

Appendix 3: Table structure of the North Sea database

Reports Report_ID

Title Block Location From_Date To_Date Report_Date Consultant Client Datum_ID CS_ID Remarks

Report ID Title of the report Quadrant K or L Location in block Started date of investigation Completed date of investigation Date of report Name of consultant Name of client Code of datum of the coordinate system Code of the coordinate system

Holes

Hole_ID Report_ID Hole_No Hole_Type Length XCoor YCoor ZCoor

Hole ID Report ID Name of hole Type of hole (borehole or CPT hole) Length of hole Easting Northing Elevation of hole collar

Strata

Hole_ID Layer_Name Layer_No Depth Soil_ID Description

Hole ID Name of layer Layer No. Bottom depth of layer Soil type Description of layer

Samples

Sample_ID Hole_ID Sample_No Depth_T Depth_B Soil_ID Description

Sample ID Hole ID Name of sample Top depth of sample [m] Bottom depth of sample [m] Soil type Soil description

Physical Properties

Sample_ID Wat_W VM_UW_B VM_RHO_SP VM_UW_MIN VM_UW_MAX KALK_GEN ORG_STOF AT_WP AT_WL AT_WS FINES D10 D30

Sample ID Water content [%] Bulk unit weight [kN/m³] Specific gravity Minimum dry unit weight [kN/m³] Maximum dry unit weight [kN/m³] Percentage of carbonate [%] Percentage of organic matters [%] Atterberg plastic limit [%] Atterberg liquid limit [%] Atterberg shrinkage limit [%] Percentage of clay and silt portions [%] Particle diameter where there are 10 % finer [mm] Particle diameter where there are 30 % finer [mm]

APPENDICES

66

Physical Properties D50 D60

Particle diameter where there are 50 % finer [mm] Particle diameter where there are 60 % finer [mm]

Compressive Strengths

Sample_ID UCT_UCS UCT_EPS UCT_E_M PEN_UCS

Sample ID UCS value from Unconfined Compression Test [kPa] Axial strain from Unconfined Compression Test [%] Young’s modulus from Unconfined Compression Test [MPa] UCS value from Pocket Penetrometer Test [kPa]

Shear Strengths

Sample_ID DSH_C DSH_PHI DSH_C_EFF DSH_PHI_EFF TRI_UU_C TRI_UU_PHI TRI_CU_C TRI_CU_PHI TRI_CU_C_EFF TRI_CU_PHI_EFF TRI_CD_C_EFF TRI_CD_PHI_EFF VIN_H VIN_L VIN_V TRI_UU_EPS50 TRI_UU_E50 TRI_CD_EPS50 TRI_CD_E50

Sample ID Cohesion from Direct shear test [degree] Friction angle from Direct shear test [kPa] Effective cohesion from Direct shear test [degree] Effective friction angle from Direct shear test [kPa] Cohesion from Triaxial test (UU type) [kPa] Friction angle from Triaxial test (UU type) [degree] Cohesion from Triaxial test (CU type) [kPa] Friction angle from Triaxial test (CU type) [degree] Effective cohesion from Triaxial test (CU type) [kPa] Effective friction angle from Triaxial test (CU type) [degree] Effective cohesion from Triaxial test (CD type) [kPa] Effective friction angle from Triaxial test (CD type) [degree] Hand vane shear test [kPa] Lab vane shear test [kPa] Field vane shear test [kPa] Axial strain at 50% from Triaxial test (UU type) [%] Young’s modulus at 50% from Triaxial test (UU type) [MPa] Axial strain at 50% from Triaxial test (CD type) [%] Young’s modulus at 50% from Triaxial test (CD type) [MPa]

Deformation Properties

Sample_ID TERZ_OEDO_CC TERZ_OEDO_CR TERZ_OEDO_PC TERZ_OEDO_P TERZ_OEDO_DP TERZ_OEDO_OCR CASA_OEDO_C_ALPHA CASA_OEDO_CV CASA_OEDO_MV CASA_OEDO_T50 TAY_OEDO_CV TAY_OEDO_MV TAY_OEDO_T90

Sample ID Primary compression index Primary recompression index Preconsolidation pressure [kPa] Initial load [kPa] Load increment [kPa] Overconsolidation ratio Secondary compression index Coefficient of consolidation from casagrande method [mm²/sec] Coefficient of volume change from casagrande method [mm²/N] Value of t50 [sec] Coefficient of consolidation from taylor method [mm²/sec] Coefficient of volume change from taylor method [mm²/N] Value of t90 [sec]

APPENDICES

67

Appendix 4: Input forms of the North Sea database

APPENDICES

68

APPENDICES

69

Appendix 5: Relationships in North Sea database

APPENDICES

70

Appendix 6: Bathymetry map of the sea floor

Water depth

Contours of water depth

-10 m 1.4 m-15 m - -10 m-20 m - -15 m-25 m - -20 m-30 m - -25 m-35 m - -30 m-40 m - -35 m-64.8 - -40

10000 0 10000 20000 30000 Meters

QUADRANT K & L, DUTCH SECTOR, NORTH SEABATHYMETRY MAP OF SEABED SURFACE

1:1000000Scale

TNO-NITG

-35

-30

-40

-25

-20

-15

-10

-5

-45-50

0

-30

- 40

-25

-25

- 25

-25

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-30

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-25

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- 5

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-25

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- 30

- 30

-2 5-30

-30

- 25

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-25

-25

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53°0

0'

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53°1

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53°2

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53°3

0'

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54°0

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4°40'

5°00'

5°00'500000

500000

550000

550000

600000

600000

5900

000 5900000

5950

000 5950000

N

APPENDICES

71

Appendix 7: Holocene deposits in Quadrant K (after British Geological Survey, 1987)

54°N

53°N 3°E 4°E

B

B

DISTRIBUTION OF HOLOCENE DEPOSITS

CROSS SECTION B-C

54°N

53°N 3°E 4°E

MEAN GRAIN SIZE 54°N

53°N3°E 4°E

THICKNESS OF HOLOCENE DEPOSITS

APPENDICES

72

Appendix 8: Distribution of top Pleistocene deposits

TNO-NITG10000 0 10000 20000 30000 Meters

1:1000000Scale

DISTRIBUTION OF TOP PLEISTOCENE DEPOSITSQUADRANT K & L, DUTCH SECTOR, NORTH SEA

Top Pleistocene Formations

Botney Cut FormationBolders Bank FormationWell Ground FormationTwente FormationBrown Bank FormationEem FormationCleaver Bank FormationEgmond Ground FormationBorkum Riff Formation

53°0

0'

53°00'

53°1

0'

53°10'

53°2

0'

53°20'

53°3

0'

53°30'

53°4

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53°5

0'

53°50'

54°0

0'

54°00'

3°00'

3°00'

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3°20'

3°40'

3°40'

4°00'

4°00'

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4°20'

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4°40'

5°00'

5°00'500000

500000

550000

550000

600000

600000

5900

000 5900000

5950

000 5950000

N

APPENDICES

73

Appendix 9: Map of top surface of top Pleistocene deposits

-40-45

-35

-30

-50

-25

-20

-15

-5 5

-60

-65-10

-4 0

-50

-50

-30 - 15

-50

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0

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- 35

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-45

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-45

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-55

53°0

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53°00'

53°1

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53°2

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53°20'

53°3

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53°4

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53°5

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54°0

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3°00'

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5°00'

5°00'500000

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550000

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TNO-NITG

QUADRANT K & L, DUTCH SECTOR, NORTH SEAMAP OF TOP PLEISTOCENE SURFACE

1:1000000Scale

10000 0 10000 20000 30000 Meters

N

Water depth of top Pleistocene-10 m - -6 m-20 m - -10 m-30 m- -20 m-40 m - -30 m-50 m - -40 m-60 m - -50 m-70 m - - 60 m-75.8 m - -70 m

Contours of Top Pleistocene surface

APPENDICES

74

Appendix 10: Map of seabed surface

N

3D VIEW of SEABED SURFACE

Depth of seabed

< 10 m10 m - 15 m15 m - 20 m20 m - 25 m25 m - 30 m30 m - 35 m35 m - 40 m40 m - 45 m

Le Minh Son, 200210000 0 10000 20000 30000 Meters

1:1000000Scale

DEPTH OF SEABED SURFACEQUADRANT K & L, DUTCH SECTOR, NORTH SEA

53°0

0'

53°00'

53°1

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53°10'

53°2

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53°20'

53°3

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53°5

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54°0

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5°00'500000

500000

550000

550000

600000

600000

5900

000 5900000

5950

000 5950000

N

APPENDICES

75

Appendix 11: Error map of seabed surface

N

N

N

N

NNNNN

N

N

NN

NN

NNN

NNN N

NNN

NNN

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NNN N

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NNN N N N N N NNNNNNNN

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N

53°0

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N

QUADRANT K & L, DUTCH SECTOR, NORTH SEAERROR MAP OF SEABED SURFACE

1:1000000Scale

Le Minh Son, 2002

0 m - 1 m1 m - 2 m2 m - 3 m3 m - 4 m4 m - 5 m5 m - 6 m6 m - 7 m

BoreholesN

Standard error values10000 0 10000 20000 30000 Meters

APPENDICES

76

Appendix 12: Map of thickness of Unit 1

53°0

0'

53°00'

53°1

0'

53°10'

53°2

0'

53°20'

53°3

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53°30'

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THICKNESS OF UNIT 1 (SANDY SOIL)QUADRANT K & L, DUTCH SECTOR, NORTH SEA

1:1000000Scale

Thickness Range

No thickness< 5 m5 m 10 m10 m - 15 m15 m - 20 m20 m - 25 m25 m - 30 m

N

10000 0 10000 20000 30000 MetersLe Minh Son, 2002

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1:1000000Scale

THICKNESS OF UNIT 2 (CLAYEY SOIL)QUADRANT K & L, DUTCH SECTOR, NORTH SEA

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78


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THICKNESS OF UNIT 3 (SANDY SOIL)QUADRANT K & L, DUTCH SECTOR, NORTH SEA

1:1000000Scale

10000 0 10000 20000 30000 MetersLe Minh Son, 2002

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79


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THICKNESS OF UNIT 4 (CLAYEY SOIL)QUADRANT K & L, DUTCH SECTOR, NORTH SEA

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APPENDICES

80

Appendix 16: Map of thickness of top sand deposits


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THICKNESS OF TOP SAND DEPOSITQUADRANT K & L, DUTCH SECTOR, NORTH SEA

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APPENDICES

81

Appendix 17: Effective stress shear strength parameters (after Pells et al., 1973)

DRAINED TRIAXIAL TESTS DRAINED DIRECT SHEAR STRESS

Initial density

Initial moisture content

C’ φ’ Initial density

Initial moisture content

C’ φ’ No

g/cm³ % kN/m² degrees g/cm³ % kN/m² degrees

1 1.87 – 1.89 8 17 35.3 1.88 – 1.90 8 15 37.5

2 1.65 21 47 27.5 1.69 – 1.70 22.5 45 27.7

3 1.75 – 1.77 15 17 33.7 1.76 – 1.79 13.7 32 31

4 1.86 – 1.87 13.7 43 33.6 1.86 – 1.87 13.3 38 36

5 1.56 23.7 33 25.2 1.55 24.5 34 26

6 1.7 – 1.75 21 0 37 1.73 23.6 5 34.2

7 * * 0 18.4 * * 7 16

8 1.49 28 32 27.8 1.47 33 35 28.5

9 1.44 – 1.48 1.44 – 1.48 (RD = 0.67)

10 1.68 – 1.71 1.68 – 1.71 (RD = 0.71)

* Sample normally consolidated from a slurry at the liquid limit

Appendix 18: Effective friction angle (φ’) of sand in North Sea (Norwegian sector) using different parameters with different formulas (after Wu et al., 1987)

φ’ Tests

Mean Covariance

Cone penetration test 44.5 0.11

Cone penetration test + plate load test 43.1 0.05

Cone penetration test + plate load test + skirt penetration 42.8 0.05

Cone penetration test + skirt penetration 43.0 0.08

Appendix 19: Approximate in-situ values for porosities and unit weight in natural sand (adapted from CUR, 1996)

Porosity Unit weight (kN/m³) Soil type

n γsat γdry γ’

Uniform sand 0.30 – 0.50 18.5 – 21.5 13.5 – 18.5 8.5 – 11.5

Graded sand and gravel 0.25 – 0.35 21.0 – 22.5 17.5 – 20.0 11.0 – 12.5

APPENDICES

82

Appendix 20: Representative values of geotechnical properties (after NEN 6740)

APPENDICES

83

Appendix 21: Compare measured and predicted effective friction angle (φ’) of sand in North Sea (Dutch sector, quadrant K & L)

Elevation Water

content Dry unit weight

FINES D50 Cu φ’ (measured)

φ’ (predicted)No

m % kN/m³ % Mm - degree degree 1 -10.70 25.10 15.91 0.10 0.220 1.77 29 342 -23.90 22.10 16.46 1.00 0.172 1.38 39 373 -24.50 26.70 15.47 0.10 0.275 1.68 27 294 -25.30 23.50 16.03 1.00 0.189 1.28 33 355 -25.60 22.60 16.64 0.50 0.198 1.38 39 366 -25.72 25.00 14.80 1.00 0.208 1.50 35 357 -25.90 24.00 14.19 1.00 0.210 1.49 35 368 -26.60 22.00 15.33 0.80 0.211 1.50 39 369 -26.70 22.00 14.10 0.20 0.210 1.47 38 3710 -26.93 21.60 16.78 2.00 0.186 1.31 36 3611 -26.94 19.20 16.86 2.00 0.185 1.39 38 3612 -26.95 3.40 15.57 0.10 0.197 1.50 33 3213 -27.10 23.00 16.02 2.00 0.212 1.78 39 3414 -27.10 23.00 14.63 0.40 0.209 1.44 37 3615 -27.17 2.90 16.81 0.10 0.320 1.93 38 4016 -27.22 21.90 15.67 1.00 0.188 1.31 37 3617 -27.30 20.00 16.42 2.00 0.185 1.30 39 3618 -27.30 21.00 15.21 0.60 0.208 1.45 39 3719 -27.32 23.00 15.04 1.00 0.192 1.53 39 3620 -27.47 20.50 15.85 1.00 0.190 1.31 38 3621 -27.50 23.00 16.02 1.00 0.161 1.32 39 3722 -27.50 29.30 14.93 0.10 0.237 1.53 28 2923 -27.64 22.00 14.02 2.00 0.195 1.54 37 3624 -27.70 20.20 16.72 3.00 0.131 1.72 46 3925 -27.80 24.00 14.84 1.00 0.199 1.50 37 3526 -27.90 13.50 18.06 4.00 0.140 2.07 37 3827 -28.00 16.00 14.91 1.00 0.187 2.09 35 3828 -28.00 4.40 15.13 0.20 0.219 1.53 33 3529 -28.10 2.50 13.66 0.10 0.205 1.43 33 3330 -28.13 21.30 15.91 0.10 0.200 1.52 32 3731 -28.40 21.60 15.21 0.10 0.207 1.48 34 3732 -28.43 19.40 16.67 1.00 0.193 1.34 37 3733 -28.50 26.00 15.71 0.10 0.196 1.62 33 3534 -28.70 5.30 13.77 0.10 0.196 1.52 38 3435 -28.70 20.90 15.80 2.00 0.162 1.35 35 3736 -28.76 20.00 16.00 7.00 0.162 2.47 34 3537 -28.81 20.10 16.49 1.00 0.196 1.35 36 3638 -29.10 20.10 15.82 0.10 0.202 1.53 37 3739 -29.22 19.00 17.31 9.00 0.124 2.01 37 3640 -29.62 17.30 17.99 5.00 0.139 1.97 41 3941 -29.82 20.00 15.17 7.00 0.149 2.39 31 3542 -29.90 23.00 14.47 3.00 0.192 1.86 34 36

APPENDICES

84

Elevation Water

content Dry unit weight

FINES D50 Cu φ’ (measured)

φ’ (predicted)No

m % kN/m³ % Mm - degree degree 43 -29.90 24.00 14.19 30.00 0.080 11.25 34 3444 -29.90 3.60 13.90 0.10 0.201 1.50 35 3345 -30.20 19.00 16.47 4.00 0.155 1.98 38 3746 -30.20 20.20 14.98 0.10 0.209 1.52 35 3747 -30.40 30.00 14.69 3.00 0.238 1.65 26 2748 -30.49 27.70 15.27 44.00 0.059 6.42 22 2149 -31.50 21.90 16.00 6.00 0.168 1.67 35 3450 -31.90 27.00 15.35 0.10 0.244 1.52 32 3151 -32.10 21.00 15.21 0.30 0.215 1.66 40 3752 -32.70 20.80 15.65 3.00 0.130 1.32 30 3753 -32.70 23.90 15.90 5.00 0.144 1.62 34 3654 -33.60 26.80 15.30 34.00 0.080 14.67 38 3855 -34.10 19.70 16.62 2.00 0.184 2.06 35 3756 -34.60 24.10 15.79 6.00 0.120 1.64 32 3657 -35.10 23.20 15.50 6.00 0.111 1.56 34 3658 -36.10 20.40 16.69 2.00 0.182 1.81 32 3759 -38.00 28.90 14.20 12.00 0.094 1.73 35 3160 -39.68 20.00 17.00 7.00 0.152 2.33 31 3661 -39.70 22.00 16.07 4.00 0.216 1.65 34 3362 -42.50 28.00 14.69 13.00 0.104 1.88 31 3163 -44.30 19.20 16.61 3.00 0.164 1.96 36 3764 -44.90 21.70 15.69 8.00 0.142 2.15 31 3465 -45.60 21.10 16.10 15.00 0.172 8.77 40 3866 -45.80 22.00 17.30 14.00 0.169 5.24 33 3467 -47.00 23.60 15.70 5.00 0.155 1.58 34 3568 -47.00 23.60 16.42 1.00 0.199 1.54 39 3569 -51.50 21.60 15.87 3.00 0.173 1.51 37 3670 -54.50 20.40 16.69 4.00 0.166 1.80 41 3671 -55.40 21.30 15.83 4.00 0.200 2.16 36 3572 -63.50 20.70 16.32 3.00 0.181 1.77 39 3673 -64.30 19.60 16.22 3.00 0.159 2.16 38 3874 -76.60 23.00 15.61 10.00 0.151 2.77 32 33

Documents

COMPILING GEOTECHNICAL DATA TO DETERMINE THE DISTRIBUTION …€¦ · THE DISTRIBUTION AND PROPERTIES OF TOP SAND DEPOSITS IN QUADRANT K & L OF THE DUTCH SECTOR – NORTH SEA by Le