Research Progress of Machine Learning in Deep Foundation Pit Deformation Prediction

Xiang Wang, Zhichao Qin, Xiaoyu Bai, Zengming Hao, Nan Yan, Jianyong Han

During deep foundation pit construction, slight improper operations may lead to excessive deformation, resulting in engineering accidents. Therefore, how to accurately predict the deformation of the deep foundation pit is of significant importance. With advancements in artificial intelligence technology, machine learning has been utilized to learn and simulate complex nonlinear relationships among various factors influencing foundation pit deformation. Prediction accuracy is significantly improved, and the dynamic trend of foundation pit deformation is accurately grasped to curb the risk of safety accidents. This paper systematically reviews the current applications of machine learning in deep foundation pit deformation prediction. The fundamental principles of machine learning models, including neural networks, support vector machines, and Bayesian networks, are elaborated in the context of their application to deep foundation pit deformation prediction. The application effects of various machine learning models in predicting deep foundation pit supporting structure deformation, surrounding surface settlement, and assessing foundation pit risks are summarized. The limitations and future development prospects of current machine learning models for deformation prediction in deep foundation pit construction are discussed. The research results offer valuable insights for the application and advancement of machine learning in the deep foundation pit deformation prediction field.