LAUSR

Machine learning (ML) algorithms have been gradually used in predicting tunneling-induced settlement, but there is no uniform process for establishing ML models and even obviously exists deficiency in the existing settlement prediction ML models. This study systematically demonstrates the process of application of machine learning (ML) algorithms in predicting tunneling-induced settlement. The whole process can be categorized into four phases: the selection of ML algorithms, the determination of optimum-hyper-parameters, the improvement in model robustness and sensitivity analysis. The prediction performance of five commonly used ML algorithms back-propagation (BPNN), general regression neural network (GRNN), extreme learning machine (ELM), support vector machine (SVM) and random forest (RF) was comprehensively compared. The results indicate that proposed hybrid intelligent algorithm with the integration of the meta-heuristic algorithm particle swarm optimization (PSO) and ML can effectively determine the global optimum hyper-parameters of ML algorithms. The mean prediction error of k-fold cross-validation sets defined as the fitness function of the PSO algorithm can improve the robustness of ML models. RF algorithm outperforms the remaining four ML algorithms in recognizing the evolution of tunneling-induced settlement. BPNN shows great extrapolation capability, so it is recommended to establish settlement prediction model if the existing datasets are small. Sensitivity analysis indicates the geological and geometric parameters are the most influential variables for the settlement.