Reliability-based multi-objective optimization in tunneling alignment under uncertainty

This paper develops a framework of reliability-based multi-objective optimization (RBMO) in tunnel alignment. This study considers the two targets, the limit support pressure (LSP) and maximum ground surface deformation (MGSD), during the new tunnel’s excavation for safety and cost-saving purposes. The hybrid particle swarm optimization-neural network (PSO-NN) is used to construct the meta-model of the LSP and MGSD, based on the 100 groups of finite element numerical results of two tunnel’s excavation. The uncertainty from the soil material property and the meta-model has been considered in the RBMO as well. Through the Monte-Carlo simulation, the probability constraints in the RBMO are determined. Finally, this study entails an illustrative case to examine the superiority of the RBMO in comparison with the deterministic multi-objective optimization (DMO) and reliability-based single-objective optimization (RBSO). Through selecting the best solution of all the Pareto optimal solutions based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach, the optimized relative location of the newly built tunnel based on the RBMO is safer than that based on the RBSO under the tighter constraint for the LSP. In comparison with the RBSO, the RBMO generates a smaller LSP value with comparable MGSD value.

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