Project scheduling analysis and quantifying risk in construction delays using bayesian belief networks
By: Suresh, K. R
.
Contributor(s): Kishore, R.
Publisher: Pune NICMAR 2011Edition: Vol.26(1), Jan-Mar.Description: 16-32p.Subject(s): Construction Engineering and Management (CEM)Online resources: Click here
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NICMAR Journal of construction managementSummary: Project management techniques are widely used to plan, execute, control, and deliver construction projects. The goals of a successful project management endeavor are to finish on time, within budget and according to the specifications and quality standards. The ultimate benefit of implementing project management techniques is a satisfied customer. With higher requirements of quality, increasing demand for shorter project completion times and more efficient use of available budgets, project management professionals are facing the necessity of using analytical and quantitative tools that are more sophisticated than traditional qualitative approaches. Management of risks and uncertainties in construction projects is only possible if risks have been identified and the potential impacts have been analysed. Principles of probability theory offer the mathematical basis for modelling risks and uncertainty and the analysis of its effect. Construction schedules are affected by uncertainties in weather, productivity, design, scope, site conditions, soil properties, material delivery time, equipment efficiency, etc,.[18]. All risks in a construction project might be schedule risks because they are related to the schedule directly or indirectly. Moreover, all activities can be critical due to uncertainties, even those that are not critical according to deterministic Critical Path Method (CPM). Capturing uncertainty in projects 'needs to go beyond variability and available data'. It needs to address ambiguity and incorporate structure and knowledge. In order to measure and analyse uncertainty properly, one needs to model relations between trigger (source), risk and impacts (consequences). The duration of a task is uncertain because there is no similar experience before, so the data is incomplete and suffers from imprecision and inaccuracy. Estimation of this sort of uncertainty is mostly subjective and based on estimator judgment. Any estimation is conditionally dependent on some assumptions and conditions even if they are not mentioned explicitly. These assumptions and conditions are major sources of uncertainty and need to be addressed and handled explicitly. The most well established approach to handling uncertainty in these circumstances is Bayesian approach [9][12]. The present work is an attempt to identify the uncertainties in the construction project activity duration estimates of a construction project in Indian context and quantifying the risk involved in construction delays using Bayesian Belief Networks (BBN's).
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Project management techniques are widely used to plan, execute, control, and deliver construction projects. The goals of a successful project management endeavor are to finish on time, within budget and according to the specifications and quality standards. The ultimate benefit of implementing project management techniques is a satisfied customer. With higher requirements of quality, increasing demand for shorter project completion times and more efficient use of available budgets, project management professionals are facing the necessity of using analytical and quantitative tools that are more sophisticated than traditional qualitative approaches. Management of risks and uncertainties in construction projects is only possible if risks have been identified and the potential impacts have been analysed. Principles of probability theory offer the mathematical basis for modelling risks and uncertainty and the analysis of its effect. Construction schedules are affected by uncertainties in weather, productivity, design, scope, site conditions, soil properties, material delivery time, equipment efficiency, etc,.[18]. All risks in a construction project might be schedule risks because they are related to the schedule directly or indirectly. Moreover, all activities can be critical due to uncertainties, even those that are not critical according to deterministic Critical Path Method (CPM). Capturing uncertainty in projects 'needs to go beyond variability and available data'. It needs to address ambiguity and incorporate structure and knowledge. In order to measure and analyse uncertainty properly, one needs to model relations between trigger (source), risk and impacts (consequences). The duration of a task is uncertain because there is no similar experience before, so the data is incomplete and suffers from imprecision and inaccuracy. Estimation of this sort of uncertainty is mostly subjective and based on estimator judgment. Any estimation is conditionally dependent on some assumptions and conditions even if they are not mentioned explicitly. These assumptions and conditions are major sources of uncertainty and need to be addressed and handled explicitly. The most well established approach to handling uncertainty in these circumstances is Bayesian approach [9][12]. The present work is an attempt to identify the uncertainties in the construction project activity duration estimates of a construction project in Indian context and quantifying the risk involved in construction delays using Bayesian Belief Networks (BBN's).
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