中铁十八局集团第三工程有限公司专栏 2025年 第卷 第07期

DOI: 10.7672 / sgjs2025070045

深基坑开挖引起的邻近建筑物变形预测研究

万达

作者简介：

万达，工程师，E⁃mail: 1245490948@ qq. com

作者单位：

中铁十八局集团第三工程有限公司，河北涿州 072750

基金项目：

∗中国铁建股份有限公司 2024 年度科技研究开发计划（ 2024⁃C1）；中铁十八局集团有限公司 2022 年度科研创新项目（C2022⁃051）

摘要：

为提高沉降预测的精度和速度，提出了一种改进的支持向量机模型用于预测沉降。引入麻雀搜索算法优化支持向量机的惩罚参数和核函数参数，提高支持向量机的预测效果。以南昌地铁 4 号线上沙沟站基坑为例，进行模型实证分析，利用改进的支持向量机进行基坑开挖引起的周边建筑物沉降预测，并与传统的支持向量机模型进行对比，经过麻雀搜索算法改进的支持向量机模型 MSE 降低了 74%,RMSE 降低了 49%,MAPE 降低了 27%,验证了改进后的支持向量机具有较好的预测精度和预测速度。对改进的支持向量机进行了泛化能力实证，验证了该模型的良好泛化能力。
地铁；深基坑；支持向量机；麻雀搜索算法；变形；预测；泛化能力

English：

An improved support vector machine model is proposed to predict settlement to improve theaccuracy and speed of settlement prediction. The sparrow search algorithm was introduced to optimize thepenalty parameters and kernel function parameters of the support vector machine to improve its predictioneffect. Taking the foundation excavation of Shangshagou Station of Nanchang Metro Line 4 as anexample, the empirical analysis of the model was carried out. The improved support vector machine wasused to predict the settlement of surrounding buildings caused by foundation excavation excavation andcompared with the traditional support vector machine model. The MSE of the support vector machinemodel improved by the sparrow search algorithm is reduced by 74%. The RMSE and the MAPE arereduced by 49% and 27%, respectively,which verifies that the improved support vector machine hasbetter prediction accuracy and prediction speed. The generalization ability of the improved support vectormachine has been verified and proven to be excellent.