Analysis of ground surface settlement in anisotropic clays using extreme gradient boosting and random forest regression models

Journal of Rock Mechanics and Geotechnical Engineering

Yongqin Li¹  Zhixiong Chen²  Runhong Zhang³  Anthony T.C. Goh⁴  Wengang Zhang⁵ 

[1] Colloge of Aerospace Engineering, Chongqing University, Chongqing, 400045, China;Corresponding author.;Institute for Smart City of Chongqing University in Liyang, Chongqing University, Liyang, 213300, China;School of Civil Engineering, Chongqing University, Chongqing, 400045, China;School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore;

关键词: Anisotropic clay;    Numerical analysis;    Ground surface settlement;    Ensemble learning;   

DOI  :  

来源: DOAJ

【 摘 要 】

Excessive ground surface settlement induced by pit excavation (i.e. braced excavation) can potentially result in damage to the nearby buildings and facilities. In this paper, extensive finite element analyses have been carried out to evaluate the effects of various structural, soil and geometric properties on the maximum ground surface settlement induced by braced excavation in anisotropic clays. The anisotropic soil properties considered include the plane strain shear strength ratio (i.e. the ratio of the passive undrained shear strength to the active one) and the unloading shear modulus ratio. Other parameters considered include the support system stiffness, the excavation width to excavation depth ratio, and the wall penetration depth to excavation depth ratio. Subsequently, the maximum ground surface settlement of a total of 1479 hypothetical cases were analyzed by various machine learning algorithms including the ensemble learning methods (extreme gradient boosting (XGBoost) and random forest regression (RFR) algorithms). The prediction models developed by the XGBoost and RFR are compared with that of two conventional regression methods, and the predictive accuracy of these models are assessed. This study aims to highlight the technical feasibility and applicability of advanced ensemble learning methods in geotechnical engineering practice.

【 授权许可】

Unknown