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Risk enablers modelling for infrastructure projects using Bayesian belief network

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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


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