International Conference on

Environmental Knowledge for Disaster Risk Management

KNOWLEDGE AND DATA INTEGRATION FOR MODELLING OF RISK

Prof. J. DurgaprasadCivil Engineering DepartmentGyan Ganga College of TechnologyJabalpur, M.P.

Plan of Presentation

Knowledge and Data Integration for Modelling of Risk

1Problem Definition 2Methodology 3Case Study

E In spite of the advances in science and technology» society continues to face new hazards

E New problems of hazards – with increasing complexity» risk analyst - unfamiliarity of the problem

E Necessary to develop continually Efficient methodologies and techniques » for moderating risks to be within acceptable limits

Risk Analysis

E Engineering Paradox: (Waterman 1986)» More competent the domain-experts become, less able they are to

describe their knowledge

» Domain-Experts may not be able to express clearly about his conceptual or abstract state of knowledge

E How to Resolve: (Ford K.M, Bradshaw 1993)» By not restating a coherent body of knowledge that already exists in

the minds of Domain-experts

» Rather, domain-experts are engaged in a constructive modelling process, in the context of which formal representations are newly created and shaped

Domain-specific model of human expertise - Risk analysis

E Lack of correspondence between » the basis for human skilled performance and » its representation in communicable models

E What is required ?» Future standard methods and » Standard forms

• for reporting data that are suitable for electronic media storage

• which facilitates the development of effective Domain-Knowledge (DN) & DSS

Communicable models

E Fragments of domain-knowledge consists of ● Conflicts ● Gaps ● Redundancies

E Unknown interdependencies among parameters

E Number of parameters to be considered

E No clean-data available, which is free from inconsistencies and missing information

E Contain inconsistent information

E Further compounded when multiple experts provide input to the KB

Complexity of the Problem – Domain Knowledge

E Knowledge-Base (KB)» KB gives Input to Bayesian Network (BN) systems

» Existing BN systems generally require the parameter interdependency information to be coded as part of the KB

» Requiring the developer of Decision Support System (DSS) to specify them beforehand

» Developed incrementally

Domain-Knowledge for modelling

E BN can be used at any stage of a risk analysis, and ● may substitute both fault trees and event trees

E Complexity ● as stated by Haiqin, 2004

» Building of BN considered the main difficulty &» when applying to real-world problems

● as stated by Ann Devitt et. al, 2006» Extremely difficult to build BN for complex problems» which has limited their application to real world problems

Bayesian networks (BN) in Risk Assessment

Processing of Knowledge

E Fragments of knowledge elicited from the domain-experts

● Inspection for errors of consistency and completeness

» Consistency errors include (i) redundancy, (ii) conflict and (iii) circularity

» Completeness errors include deadends (unreachable destination), and sufficiency of Knowledge

E Graph theoretic techniques

Case Study on Windstorm-induced Damage

E During the past 40 years, engineers have begun to make increasingly closer examinations of windstorm-induced damage

E Researchers and practitioners around the world have documented wind induced damage caused by extreme windstorms (Chiu et al. 1983; Dikkers et al. 1971; Eaton and Judge 1975; Mehta et al. 1975; Minor and Mehta 1979; Krishna and Pande 1975; Walker 1975; Wolde-Tensae et al. 1985)

E Each fragment of knowledge may be in the verbal-form that is in the form of a relationship

E For example: Verbal-format

● Intensity of wind speed (p22) is a major factor, since an increase in wind speed increases debris potential (p3) and results in higher intensity of debris hazard (p2)

E Fragment of knowledge, Set { fk1 }:● fk1 = { debris hazard (p2), debris potential (p3), wind speed grade (p22) }

E List of 12 different fragments (fk1 to fk12) of knowledge acquired

Representing Fragments of Knowledge

fk1

fk1

fk1Debris hazard

(p2)

Wind speed grade (p22)

Debris potential

(p3)

fk2

fk2

fk2Debris

exposure (p1)

Terrain exposure

(p20)

Debris potential

(p3)

fk3

fk3fk3

fk3

fk3

fk3

Internal pressure due to damage to glass shutters caused by debris (p5)

Internal pressure due to damage

to overhead doors caused by

wind (p6)

Internal pressure due to damage to sliding doors and shutters caused

by wind (p7)

Net internal pressure (p8)

fk4fk4

fk4Glass debris

damage potential (p4)

Percentage of glass (p10)

Shutters (p18)

Relating and Building non-Directed Coherent body of Domain-Experts’ Fragmented-Knowledge

fk8

fk8

fk8fk6

fk6 fk6

fk10

fk10

fk10

fk9

fk9 fk9

fk7

fk7fk7

fk12

fk12fk12

fk5

fk5fk5

fk5

fk5fk5

fk3

fk3fk3

fk3

fk3

fk3

fk2

fk2fk2

fk1fk1

fk1

fk11fk11fk4

fk4

fk4

Terrain exposure grade (p21)

Wind speed

zone (p23)

Debris exposure

(p1)

Debris hazard

(p2)

Internal pressure due to damage to

glass shutters caused by debris

(p5)

Internal pressure due to damage to sliding doors and shutters caused

by wind (p7)

Percentage of glass (p10)

Net internal pressure (p8)

Potential hazard (p11)

Roof covering

grade (p14)

Roof covering

(p13)

Internal pressure due to damage to

overhead doors caused by wind (p6)

Overhead doors (p9)

Sliding doors (p19)

Roof damage grade (p15)

Wind speed grade (p22)

Debris potential (p3)

Roof geometry

(p16)Roof

geometry grade (p17)

Shutters (p18)

Glass debris damage potential

(p4)

Terrain exposure (p20)

Prescriptive Code (p12)

fk11

Defining the Problem of Windstorm-induced Risk

  

 

 

 

 

 

 

   

 

 

 

 

 

 

 

 

 

  

 

 

InputOutput

Known Unknown

Processed, and Directed Coherent body of Domain-Experts’ Knowledge - BN

fk11

fk8 fk8

fk6 fk6

fk10

fk10

fk9 fk9

fk7

fk7

fk12

fk12

fk5fk5

fk5

fk3

fk3

fk3

fk2fk2

fk1

fk1

fk11fk4

fk4

Terrain exposure grade (p21)

Wind speed

zone (p23)

Debris exposure

(p1)

Debris hazard

(p2)

Internal pressure due to damage

to glass shutters caused by debris

(p5)

Internal pressure due to damage to sliding doors and shutters caused

by wind (p7)

Percentage of glass (p10)

Net internal pressure (p8)

Potential hazard (p11)

Roof covering

grade (p14)

Roof covering

(p13)

Internal pressure due to damage to overhead doors

caused by wind (p6)

Overhead doors (p9)

Sliding doors (p19)

Roof damage grade (p15)

Wind speed grade (p22)

Debris potential (p3)

Roof geometry

(p16) Roof geometry

grade (p17)

Shutters (p18)

Glass debris damage potential

(p4)

Terrain exposure (p20)

Prescriptive Code (p12)

Bayesian Network for Roof Damage Risk

E Software packages available for supporting BNs for analysing risk:(i) GeNIE, (ii) Ergo, (iii) BNIF, (iv) Hugin, (v) Netica, (vi) KI and (vii) Norsys etc.

E GeNIE is selected for implementing the obtained BN as shown in below:

Thank You