Salanke, Pratik Vinod
Using risk analysis to model construction schedule delays : a bayesian belief networks approach - Vol.28(1), Jan-Mar - Pune NICMAR 2013 - 5-16p.
elays in construction projects are inevitable and could be attributed to the inherently inaccurate nature of construction schedules. Currently, no mechanisms exist to capture the uncertainty in a schedule, except experience, historical data, and professional judgment. This paper presents a new risk analysis model, based on Bayesian Belief Networks (BBN), to estimate the likelihood of schedule delay resulting from different risk factors. A web based survey was developed, based on literature, to collect data on 41 different risk factors. Construction experts in Facility Management department of North American universities were asked to complete the survey based on their perceptions of the frequencies and magnitude of delay risk factors and provide project specific data regarding schedule delays. A complete data set of 54 projects was used to develop the proposed model. The model validation was done using two case studies. The developed model provides project managers with a modeling tool for estimating the likelihood of project delay and understanding the effects of different risk factors that might be present in a construction project. The results demonstrate the benefits of BBN as a modeling tool for schedule delays and its potential application for other construction domains.
Construction Engineering and Management (CEM)
Using risk analysis to model construction schedule delays : a bayesian belief networks approach - Vol.28(1), Jan-Mar - Pune NICMAR 2013 - 5-16p.
elays in construction projects are inevitable and could be attributed to the inherently inaccurate nature of construction schedules. Currently, no mechanisms exist to capture the uncertainty in a schedule, except experience, historical data, and professional judgment. This paper presents a new risk analysis model, based on Bayesian Belief Networks (BBN), to estimate the likelihood of schedule delay resulting from different risk factors. A web based survey was developed, based on literature, to collect data on 41 different risk factors. Construction experts in Facility Management department of North American universities were asked to complete the survey based on their perceptions of the frequencies and magnitude of delay risk factors and provide project specific data regarding schedule delays. A complete data set of 54 projects was used to develop the proposed model. The model validation was done using two case studies. The developed model provides project managers with a modeling tool for estimating the likelihood of project delay and understanding the effects of different risk factors that might be present in a construction project. The results demonstrate the benefits of BBN as a modeling tool for schedule delays and its potential application for other construction domains.
Construction Engineering and Management (CEM)