Application of optimized random forest regressors in predicting the maximum principal stress of aseismic tunnel lining

Abstract

Using flexible damping technology to improve tunnel lining structure is an emerging method to resist earthquake disasters, and several methods have been explored to predict mechanical response of tunnel lining with damping layer. However, the traditional numerical methods suffer from the complex modelling and time-consuming problems. Therefore, a prediction model named the random forest regressor (RFR) is proposed based on 240 numerical simulation results of the mechanical response of tunnel lining. In addition, circle mapping (CM) is used to improve Archimedes optimization algorithm (AOA), reptile search algorithm (RSA), and Chernobyl disaster optimizer (CDO) to further improve the predictive performance of the RFR model. The performance evaluation results show that the CMRSA-RFR is the best prediction model. The damping layer thickness is the most important feature for predicting the maximum principal stress of tunnel lining containing damping layer. This study verifies the feasibility of combining numerical simulation with machine learning technology, and provides a new solution for understanding the mechanical response of aseismic tunnel with damping layer.

Keywords

maximum principal stress / aseismic tunnel lining / random forest regressor / machine learning

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