TY - GEN
T1 - Knowledge-based risk assessment under uncertainty in engineering projects
AU - Khokhar, R.H.
AU - Bell, David
AU - Guan, J.W.
AU - Wu, Qing
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33750604022&partnerID=8YFLogxK
U2 - 10.1007/11875581_154
DO - 10.1007/11875581_154
M3 - Conference contribution
VL - 4224
T3 - Lecture Notes in Computer Science
SP - 1296
EP - 1303
BT - Intelligent Data Engineering and Automated Learning– IDEAL 2006
ER -