Knowledge-based risk assessment under uncertainty in engineering projects

R.H. Khokhar, David Bell, J.W. Guan, Qing Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we describe how an evidential-reasoner can be used as a component of risk assessment of engineering projects using a direct way of reasoning. Guan & Bell (1991) introduced this method by using the mass functions to express rule strengths. Mass functions are also used to express data strengths. The data and rule strengths are combined to get a mass distribution for each rule; i.e., the first half of our reasoning process. Then we combine the prior mass and the evidence from the different rules; i.e., the second half of the reasoning process. Finally, belief intervals are calculated to help in identifying the risks. We apply our evidential-reasoner on an engineering project and the results demonstrate the feasibility and applicability of this system in this environment.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning– IDEAL 2006
Pages1296-1303
Number of pages8
Volume4224
DOIs
Publication statusPublished - 2006

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume4224
ISSN (Electronic)1611-3349

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  • Cite this

    Khokhar, R. H., Bell, D., Guan, J. W., & Wu, Q. (2006). Knowledge-based risk assessment under uncertainty in engineering projects. In Intelligent Data Engineering and Automated Learning– IDEAL 2006 (Vol. 4224, pp. 1296-1303). (Lecture Notes in Computer Science; Vol. 4224). https://doi.org/10.1007/11875581_154