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Abstract
Due to many unforeseen factors for tunnel construction projects, the precise estimate of the tunnel productivity is a challenging task for the tunnel project planners. The current industry practice of the tunnel productivity estimate based on experts' opinions may lead to the erroneous schedule prediction. The use of simulation techniques can provide many benefits. Construction project planners can effectively plan the schedule and cost by examining multiple simulation scenarios instead of conducting costly experimentation in the field.
This thesis presents the development and implementation of a simulation-based productivity model for utility tunnel construction operations. The tunnel productivity model is an effective approach for identifying the effects of uncertainty factors and predicting the productivity under various project circumstances. The modeling concept is utilized to identify the soil characteristics for various soil conditions. The thesis is composed of three major areas of research.
The first part is Bayesian updating application into simulation in the tunneling project to update an original schedule and estimate major input parameters for a tunnel simulation model. The second part is the development of a simulation-based tunnel productivity model to accurately predict tunnel productivity by quantifying the effects of uncertainty factors. The third part is the inference of soil transitions along the tunnel path from the use of the developed productivity model. The proposed framework can be effectively utilized for identifying the soil characteristics for various soil conditions and improving the prediction of Tunnel Boring Machine (TBM) penetration rates and productivity for tunnel construction operations.