A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Risk induced contingency cost modeling for power plant projects
Abstract The current practice of expert judgment-based contingency cost allocation by owners lacks a holistic understanding of project risks, their causal relationships, and impact on project costs. This study presents an integrated fuzzy set theory and fuzzy Bayesian belief network model for a rational, realistic determination of contingency costs for infrastructure projects. The application of the model is demonstrated for real-world power plant projects in Bangladesh. The model has promising results for its ability to establish the amount of contingency costs with a maximum error of 20% between the contingency cost predicted with the model and the actual contingency cost. It has the potential to assist both the owner and contractor to set aside a realistic amount of contingency cost in the preliminary phase of a project. The approach is also equally useful for monitoring and controlling project risks, and dynamically updates the contingency cost amount during project execution.
Highlights A novel cost contingency model using fuzzy-set theory and Bayesian belief network. The model requires significantly less probabilistic data elicitation from experts. Saves data collection time and effort, and reduces the computational load on the model. Overcomes a major limitation of data-intensive cost contingency estimation models.
Risk induced contingency cost modeling for power plant projects
Abstract The current practice of expert judgment-based contingency cost allocation by owners lacks a holistic understanding of project risks, their causal relationships, and impact on project costs. This study presents an integrated fuzzy set theory and fuzzy Bayesian belief network model for a rational, realistic determination of contingency costs for infrastructure projects. The application of the model is demonstrated for real-world power plant projects in Bangladesh. The model has promising results for its ability to establish the amount of contingency costs with a maximum error of 20% between the contingency cost predicted with the model and the actual contingency cost. It has the potential to assist both the owner and contractor to set aside a realistic amount of contingency cost in the preliminary phase of a project. The approach is also equally useful for monitoring and controlling project risks, and dynamically updates the contingency cost amount during project execution.
Highlights A novel cost contingency model using fuzzy-set theory and Bayesian belief network. The model requires significantly less probabilistic data elicitation from experts. Saves data collection time and effort, and reduces the computational load on the model. Overcomes a major limitation of data-intensive cost contingency estimation models.
Risk induced contingency cost modeling for power plant projects
Islam, Muhammad Saiful (author) / Nepal, Madhav Prasad (author) / Skitmore, Martin (author) / Drogemuller, Robin (author)
2020-12-10
Article (Journal)
Electronic Resource
English
Allocation and Management of Cost Contingency in Projects
| Online Contents | 2016
Allocation and Management of Cost Contingency in Projects
| Online Contents | 2016