Risk management of tunnel construction projects

Abstract

This thesis provides tools for quantification of uncertainties in tunnel construction. First, it introduces a simple probabilistic model for the estimation of the delay due to occurrence of construction failures. The model is applied to a case study, which demonstrates, how the probabilistic estimate of construction delay can be used for assessing the risk and for making decisions.Second, advanced model including both the common variability and construction failures using Dynamic Bayesian Networks (DBNs) is presented. The model is applied to two case studies for the estimation of tunnel construction time. It is demonstrated, how observations from the tunnel construction process can be included to continuously update the prediction of excavation time. Third, an efficient algorithm for the evaluation of the proposed DBN is developed. Fourth, performance data from tunnels constructed in the past are analysed. The data motivates the development of a novel combined probability distribution to describe the excavation performance. In addition, the probability of construction failure and the delay caused by such failures is estimated using databases available in the literature. Additionally, a brief database of tunnel projects and tunnel construction failures from the Czech Republic is compiled. The statistical analysis of data presented in the thesis provides a valuable input for probabilistic prediction of construction time in infrastructure projects. The results of the case studies seem to realistically reflect the uncertainty of the construction time estimates.

This thesis provides tools for quantification of uncertainties in tunnel construction. First, it introduces a simple probabilistic model for the estimation of the delay due to occurrence of construction failures. The model is applied to a case study, which demonstrates, how the probabilistic estimate of construction delay can be used for assessing the risk and for making decisions.Second, advanced model including both the common variability and construction failures using Dynamic Bayesian Networks (DBNs) is presented. The model is applied to two case studies for the estimation of tunnel construction time. It is demonstrated, how observations from the tunnel construction process can be included to continuously update the prediction of excavation time. Third, an efficient algorithm for the evaluation of the proposed DBN is developed. Fourth, performance data from tunnels constructed in the past are analysed. The data motivates the development of a novel combined probability distribution to describe the excavation performance. In addition, the probability of construction failure and the delay caused by such failures is estimated using databases available in the literature. Additionally, a brief database of tunnel projects and tunnel construction failures from the Czech Republic is compiled. The statistical analysis of data presented in the thesis provides a valuable input for probabilistic prediction of construction time in infrastructure projects. The results of the case studies seem to realistically reflect the uncertainty of the construction time estimates.