A knowledge-based expert system to assess power plant project cost overrun risks

• Published in: Expert systems with applications Vol. 136; pp. 12 - 32
• Main Authors: Islam, Muhammad Saiful, Nepal, Madhav P., Skitmore, Martin, Kabir, Golam
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.12.2019; Elsevier BV
• Subjects: Belief networks; Canonical model; Constraint modelling; Construction costs; Cost overruns; Decision making; Electric power generation; Expert systems; Experts; Fuzzy logic; Fuzzy systems; Inspection; Judgments; Knowledge based systems; Knowledge bases (artificial intelligence); Power plant projects; Power plants; Project management; Rigging; Risk assessment; Soil investigations; Soil testing; Subject specialists; Task complexity; Uncertainty; Fuzzy logic; Power plant projects; Canonical model; Cost overruns; Risk assessment
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2019.06.030

•A fuzz-canonical model is proposed for risk assessment of power plant projects.•The complexity in the elicitation of probability parameters of fuzzy-BBN models is reduced.•The causal networks for cost overrun risks of power plant projects are developed.•The critical cost overrun risks in thermal power plant projects are assessed. Preventing cost overruns of such infrastructure projects as power plants is a global project management problem. The existing risk assessment methods/models have limitations to address the complicated nature of these projects, incorporate the probabilistic causal relationships of the risks and probabilistic data for risk assessment, by taking into account the domain experts’ judgments, subjectivity, and uncertainty involved in their judgments in the decision making process. A knowledge-based expert system is presented to address this issue, using a fuzzy canonical model (FCM) that integrates the fuzzy group decision-making approach (FGDMA) and the Canonical model (i.e. a modified Bayesian belief network model). The FCM overcomes: (a) the subjectivity and uncertainty involved in domain experts’ judgment, (b) significantly reduces the time and effort needed for the domain experts in eliciting conditional probabilities of the risks involved in complex risk networks, and (c) reduces the model development tasks, which also reduces the computational load on the model. This approach advances the applications of fuzzy-Bayesian models for cost overrun risks assessment in a complex and uncertain project environment by addressing the major constraints associated with such models. A case study demonstrates and tests the application of the model for cost overrun risk assessment in the construction and commissioning phase of a power plant project, confirming its ability to pinpoint the most critical risks involved ̶ in this case, the complexity of the lifting and rigging heavy equipment, inadequate work inspection and testing plan, inadequate site/soil investigation, unavailability of the resources in the local market, and the contractor's poor planning and scheduling.