Probabilistic assessment of tunnel construction processes using statistical learning to include past enxperience

A Dynamic Bayesian Network (DBN) model for probabilistic estimation of tunnel construction time (and costs) is presented. It considers two main types of uncertainties: those associated with ordinary variability in the construction performance and those associated with severe delays caused by extraordinary events such as tunnel collapses or major organizational problems. A method for learning the model parameters from analysis of data from past projects is shown. The proposed DBN model is applied to a case study of a two-lane road tunnel excavated with a conventional tunneling method. The model parameters are learnt using data from one reference tunnel.     «

A Dynamic Bayesian Network (DBN) model for probabilistic estimation of tunnel construction time (and costs) is presented. It considers two main types of uncertainties: those associated with ordinary variability in the construction performance and those associated with severe delays caused by extraordinary events such as tunnel collapses or major organizational problems. A method for learning the model parameters from analysis of data from past projects is shown. The proposed DBN model is a...     »