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DC Field | Value | Language |
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dc.contributor.author | Singh, Ajit Pratap | - |
dc.contributor.author | Sharma, Satyendra Kumar | - |
dc.date.accessioned | 2022-02-20T06:48:10Z | - |
dc.date.available | 2022-02-20T06:48:10Z | - |
dc.date.issued | 2019-10-25 | - |
dc.identifier.uri | https://www.tandfonline.com/doi/full/10.1080/15623599.2019.1678218 | - |
dc.identifier.uri | http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/4066 | - |
dc.description.abstract | Work in the area of risk assessment of infrastructure projects is full of different techniques; study on the risk management factors is non-existent. It is required because such risks of high impact paralyze the performance of infrastructure projects. Delay and cost overruns in infrastructure projects affect the society welfare at large. Because of this, the reputation of the government is at stake. This paper is an attempt to contribute to the risk enablers modelling, by first identifying major critical success factors for risk management and then finding cause-and-effect relationships between them for developing a Bayesian belief network (BBN) model. The analysis of this model and results determine relative importance of the critical success factors on which risk management efforts must be focused first to ensure that the project achieves objectives within the stipulated time and cost. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis | en_US |
dc.subject | Civil Engineering | en_US |
dc.subject | Infrastructure projects | en_US |
dc.subject | Risk enabling factors | en_US |
dc.subject | Bayesian belief network modelling | en_US |
dc.title | Risk enablers modelling for infrastructure projects using Bayesian belief network | en_US |
dc.type | Article | en_US |
Appears in Collections: | Department of Civil Engineering |
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