Hybrid and multiple ensemble metamodel-based evaluation for operating tunnel performance in three-dimensional spatially variable soils

Abstract

In recent years, the Random Finite Element Method (RFEM) has gained prominence in geotechnical engineering for assessing the inherent spatial variability in the mechanical properties of both natural and processed soils. Nevertheless, RFEM often demands more extensive computational resources than deterministic finite element analysis, as it is coupled with Monte-Carlo simulations (MCS). To mitigate this computational burden, metamodeling techniques have emerged as a popular approach. This paper proposes a novel and hybrid Support Vector Regression (SVR) metamodel by fusing the RFEM analysis. The metamodel can efficiently generate the original finite element method predicted quantities with limited training by utilizing input random field features, which encapsulate high-dimensional information pertaining to spatially variable soil stiffness parameters. Furthermore, based on ensemble learning, the Bagging and Adaboost algorithms were used to develop a multiple SVR (M-SVR) ensemble learning metamodel to enhance prediction reliability. Simultaneously, considering the limitation that machine learning prediction can only provide a single value, the prediction results with confidence intervals based on Bagging ensemble algorithms were also developed to quantify the uncertainty of machine learning predictions in regression analysis. The consistency between SVR and M-SVR predictions and RFEM calculations is demonstrated through a problem involving the failure probability evaluation of tunnel longitudinal performance induced by ground surface surcharge in three-dimensional spatially variable soils. The substantial improvement in efficiency with the adoption of the SVR and M-SVR, as compared to RFEM, underscores the immense potential of machine learning algorithms in conducting geotechnical reliability analyses involved with spatial variability.