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KNOWLEDGE AND DATA INTEGRATION FOR MODELLING OF RISK

International Conference on Environmental Knowledge for Disaster Risk Management. KNOWLEDGE AND DATA INTEGRATION FOR MODELLING OF RISK. Prof. J. Durgaprasad Civil Engineering Department Gyan Ganga College of Technology Jabalpur, M.P. Plan of Presentation. 1. 3. 2.

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KNOWLEDGE AND DATA INTEGRATION FOR MODELLING OF RISK

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  1. International Conference on Environmental Knowledge for Disaster Risk Management KNOWLEDGE AND DATA INTEGRATION FOR MODELLING OF RISK Prof. J. Durgaprasad Civil Engineering Department Gyan Ganga College of Technology Jabalpur, M.P.

  2. Plan of Presentation 1 3 2 Problem Definition Case Study Methodology • Knowledge and Data Integration for Modelling of Risk

  3. Risk Analysis • In spite of the advances inscience and technology • society continues to face new hazards • New problems of hazards – with increasing complexity • risk analyst - unfamiliarity of the problem • Necessaryto develop continually Efficient methodologies and techniques • for moderating risks to be within acceptable limits

  4. Domain-specific model of human expertise - Risk analysis • Engineering Paradox:(Waterman 1986) • More competentthe domain-experts become, less able they are to describe their knowledge • Domain-Experts may not be able to express clearly about his conceptual or abstractstate of knowledge • 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

  5. Communicablemodels • Lack of correspondence between • the basis for human skilled performance and • its representation in communicable models • What is required ? • Future standard methodsand • Standard forms • for reporting datathat are suitable for electronic media storage • which facilitates the development of effective Domain-Knowledge (DN) & DSS

  6. Complexity of the Problem – Domain Knowledge • Fragmentsof domain-knowledge consists of • Conflicts • Gaps • Redundancies • Unknown interdependencies among parameters • Number of parameters to be considered • No clean-data available, which is free from inconsistencies and missing information • Contain inconsistent information • Further compounded when multiple experts provide input to the KB

  7. Domain-Knowledge for modelling • Knowledge-Base (KB) • KB gives Inputto 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

  8. Bayesian networks (BN) inRisk Assessment • BNcan be used at any stage of a risk analysis, and • may substitute both fault trees and event trees • 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

  9. Fragments of knowledge elicited from the domain-experts • Inspection for errors of consistency and completeness • Consistency errors include (i) redundancy, (ii) conflictand (iii) circularity • Completeness errors include deadends (unreachable destination), and sufficiency of Knowledge • Graph theoretic techniques Processing of Knowledge

  10. During the past 40 years, engineers have begun to make increasingly closer examinations of windstorm-induced damage • 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) • Each fragment of knowledge may be in the verbal-form that is in the form of a relationship • 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) • Fragment of knowledge, Set { fk1 }: • fk1 = { debris hazard (p2), debris potential (p3), wind speed grade (p22) } • List of 12 different fragments (fk1 to fk12) of knowledge acquired Case Study on Windstorm-induced Damage

  11. Wind speed grade (p22) Terrain exposure (p20) Percentage of glass (p10) Internal pressure due to damage to overhead doors caused by wind (p6) Representing Fragments of Knowledge Debris hazard (p2) Glass debris damage potential (p4) fk4 fk2 Debris exposure (p1) fk1 fk3 Internal pressure due to damage to glass shutters caused by debris (p5) fk2 fk1 fk4 Debris potential (p3) fk4 fk2 fk1 Debris potential (p3) fk3 Shutters (p18) fk3 fk3 fk3 Internal pressure due to damage to sliding doors and shutters caused by wind (p7) Net internal pressure (p8) fk3

  12. Relating and Building non-Directed Coherent body of Domain-Experts’ Fragmented-Knowledge Debris hazard (p2) Glass debris damage potential (p4) Shutters (p18) fk11 fk4 fk1 Wind speed grade (p22) fk11 fk11 fk4 fk4 Internal pressure due to damage to glass shutters caused by debris (p5) fk1 Percentage of glass (p10) fk1 Debris potential (p3) fk3 fk3 fk3 Internal pressure due to damage to overhead doors caused by wind (p6) fk2 fk2 fk3 Net internal pressure (p8) Debris exposure (p1) fk3 Internal pressure due to damage to sliding doors and shutters caused by wind (p7) fk3 fk5 fk6 fk5 fk6 fk5 Roof covering (p13) fk2 Overhead doors (p9) fk8 fk8 fk5 Roof covering grade (p14) fk5 fk6 Potential hazard (p11) Terrain exposure (p20) Sliding doors (p19) fk5 fk8 fk10 fk7 Prescriptive Code (p12) fk12 fk12 fk7 fk10 Roof geometry (p16) fk9 fk9 Roof damage grade (p15) fk10 Roof geometry grade (p17) Terrain exposure grade (p21) Wind speed zone (p23) fk7 fk9 fk12

  13. Defining the Problem of Windstorm-induced Risk Input Output Known Unknown

  14. Debris hazard (p2) Glass debris damage potential (p4) Processed, and Directed Coherent body of Domain-Experts’ Knowledge - BN Shutters (p18) fk4 fk1 Wind speed grade (p22) fk11 fk11 fk4 Internal pressure due to damage to glass shutters caused by debris (p5) fk1 Percentage of glass (p10) Debris potential (p3) fk3 Internal pressure due to damage to overhead doors caused by wind (p6) fk2 fk2 fk3 Net internal pressure (p8) Debris exposure (p1) Internal pressure due to damage to sliding doors and shutters caused by wind (p7) fk3 fk6 fk6 fk5 Roof covering (p13) Overhead doors (p9) fk8 fk8 Roof covering grade (p14) fk5 Potential hazard (p11) Terrain exposure (p20) Sliding doors (p19) fk5 Prescriptive Code (p12) fk12 fk7 fk10 Roof geometry (p16) fk9 fk9 Roof damage grade (p15) fk10 Roof geometry grade (p17) Terrain exposure grade (p21) Wind speed zone (p23) fk7 fk12

  15. Bayesian Networkfor Roof Damage Risk • Software packages available for supporting BNs for analysing risk: • GeNIE, (ii) Ergo, (iii) BNIF, (iv) Hugin, (v) Netica, (vi) KI and (vii) Norsys etc. • GeNIEis selected for implementing the obtained BN as shown in below:

  16. Thank You

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