Advances in Constructional Engineering

The deformation prediction of supporting structures in deep foundation pit engineering is a crucial link to ensure engineering safety. Due to the limitations of traditional prediction methods, the application of machine learning technology in this field has attracted increasing attention. This paper explores the effects and comparisons of different machine learning-based models in predicting the deformation of supporting structures in deep foundation pit engineering, with a focus on analyzing the advantages and challenges of various algorithms (such as decision trees, support vector machines, and neural networks) in handling nonlinear relationships and complex datasets. By selecting appropriate models, the prediction accuracy can be significantly improved, providing a scientific basis for the design and construction of deep foundation pit supporting structures. The research in this paper offers new ideas and methods for the deformation prediction of supporting structures in deep foundation pit engineering and conducts in-depth discussions on the applicability of different machine learning algorithms.

Deep foundation pit engineering; Supporting structure; Deformation prediction; Machine learning; Model comparison