摘要：The authors demonstrate how expert knowledge about the construction and operation phases combined with monitoring data can be utilized for the diagnosis and management of risks typical to large civil engineering projects. The methodology chosen for estimating the probabilities of risk elements is known as Bayesian Belief Networks (BBN). Using a BBN model one can keep on updating the risk event probabilities as the new evidence (monitoring information) becomes available. Furthermore, the updated probabilities estimated using the available data for the construction phase serve as background information for the subsequent phase. The integrated two-object model of construction-operation may be then used to optimize the decision making, thus minimizing the risks. To better show how the proposed approach works the authors use the example of the road tunnel constructed and operated under the Dead Vistula River in Gdansk.
其他摘要：The authors demonstrate how expert knowledge about the construction and operation phases combined with monitoring data can be utilized for the diagnosis and management of risks typical to large civil engineering projects. The methodology chosen for estimating the probabilities of risk elements is known as Bayesian Belief Networks (BBN). Using a BBN model one can keep on updating the risk event probabilities as the new evidence (monitoring information) becomes available. Furthermore, the updated probabilities estimated using the available data for the construction phase serve as background information for the subsequent phase. The integrated two-object model of construction-operation may be then used to optimize the decision making, thus minimizing the risks. To better show how the proposed approach works the authors use the example of the road tunnel constructed and operated under the Dead Vistula River in Gdansk.
