Comparison of machine learning models for deformation prediction of supporting structures in deep foundation pit engineering
深基坑工程支护结构变形预测的机器学习模型对比

摘要

深基坑工程支护结构的变形预测是保障工程安全的重要环节。随着传统预测方法的局限性，机器学习技术在该领域的应用得到了越来越广泛的关注。本文探讨了基于机器学习的不同模型在深基坑工程支护结构变形预测中的效果与对比，重点分析了各类算法（如决策树、支持向量机、神经网络等）在处理非线性关系及复杂数据集方面的优势与挑战。通过合理选择模型，可以显著提高预测精度，为深基坑支护结构的设计与施工提供科学依据。本文的研究为深基坑工程支护结构变形预测提供了新的思路和方法，并对不同机器学习算法的适用性进行了深入探讨。

Abstract

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.