Abstract

The process of decision-making in public procurement of construction projects during the preparation and implementation phases ought to be supported by risk identification, assessment, and management. In risk assessment one has to take into account factors that lead to risk events (background info), as well as the information about the risk symptoms (monitoring info). Typically once the risks have been assessed a decision-maker has to consider risk-management activities that minimise the risk events (mitigating factors). Finally, the decision-maker has to select best response decision(s), i.e., one that would either maximise the benefits or minimise the losses. This selection is best performed in the framework of the utility theory. Thus, a good diagnostic-decision support model (D-DSM) has to integrate the following elements: background info, risk events, monitoring info, mitigation activities, response decisions, and associated with risk events and decisions utilities. Our purpose is to demonstrate how Bayesian Belief Networks (BBNs) can be used as D-DSM to assess and manage risks, and finally select best response decisions, during the implementation phase of a large construction project. The authors use the example of a road tunnel under the Dead Vistula River in Gdansk (Poland). The D-DSM combines expert knowledge about the relationships among model components with the monitoring information. The model is able to use evidence from various sources in a mathematically rigorous manner. We demonstrate how the model may be used to estimate: the value of monitoring information (from the utility and diagnosis uncertainty perspectives) and the benefits of mitigation activities.

Citations

Authors (4)