A bayesian belief networks approach to risk control in construction projects

Although risk control is a key step in risk management of construction projects, very often risk measures used are based merely on personal experience and engineering judgement rather than analysis of comprehensive information relating to a specific risk. This paper deals with an approach to provide better information to derive relevant and effective risk measures for specific risks. The approach relies on developing risk models to represent interactions between risk factors and carrying out analysis to identify critical factors on which risk measures must focus. To ameliorate the problem related to the scarcity of risks information often encountered in construction projects, Bayesian Belief Networks are used and expert knowledge is elicited to augment available information. The paper describes proposed modifications to the standard methods used to develop Bayesian Belief Networks in order to deal with divergent information originated from epistemic uncertainty of risks. The capacity of the proposed approach to provide better information to support risk related decision making is verified by means of an illustrative application to risk factors involved in the construction of cross passages between tunnels tubes in soft soils.