横向受荷桩承载力预测的支持向量机模型

Prediction of Bearing Capacity of Laterally Loaded Piles Based on Support Vector Machine

导出

摘要桩基作为土建工程的基础,水平向承载力是横向受荷桩承载力验算重要参数之一,为建立其与相关影响因素之间的非线性映射关系,建立了一种支持向量机(SVM)横向受荷桩承载力预测模型。该模型通过对少量样本的学习,就可以对仅知道影响因素的预测样本进行精准预测。通过支持向量机模型与BP神经网络模型的预测结果对比,验证该模型的精确度;同时采用平均相对误差和均方差两个指标评价各个模型的整体性能和稳定情况;通过置信区间验证了该模型预测结果的可靠性高,预测值全部处于置信区间90%、95%、97%之间。SVM的最大相对误差为5.41%,平均相对误差为2.81%,均方根误差为2.0278,相对于BP神经网络的预测结果,SVM结果更为精确。因此,SVM模型在横向受荷桩承载力预测方面具有可行性,为它的获取提供一种新方法。 The lateral load capacity of a single pile is an important parameter in the civil engineering design practice. To establish the nonlinear relationships among potential influencing factors, a Support Vector Machine(SVM) model for predicting the capacity of laterally loaded piles was established. By learning from a small number of samples, this model can accurately predict the prediction samples that only know the potential influencing factors. The accuracy of the model was verified by comparing the prediction results of the SVM model and BP neural network model. Both the mean relative error and the root mean square error are used to evaluate the overall performance and stability of each model. The high reliability of the predicted results was verified by the confidence interval, and all the predicted values are within the confidence interval of 90%, 95% and 97%. The Maximum Relative Error(MXAE) of SVM is 5.41%, the MRE is 2.81%, and the RMSE is 2.0278. Compared with the prediction results of BP neural network, the SVM results are more accurate. The SVM model is therefore feasible in predicting the bearing capacity of laterally loaded piles and provides a new method for its acquisition.

作者唐灵明宋宇陈学军黄翔蒋振华 TANG Lingming;SONG Yu;CHEN Xuejun;HUANG Xiang;JIANG Zhenhua(College of Civil and Architectural Engineering,Guilin University of Technology,Guilin 541004;Open Research Fund of Guangxi Karst Dynamics Laboratory,Guilin 541004;Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering,Guilin 541004;The 34th Research Institute of China CETC,Guilin 541004)

机构地区桂林理工大学土木与建筑工程学院广西师范大学广西岩土力学与工程重点实验室中国电子科技集团公司第三十四研究所

出处《土工基础》 2022年第1期57-60,77,共5页 Soil Engineering and Foundation

基金国家自然科学基金项目(41967037) 国家自然科学基金项目(41762022)。

关键词支持向量机横向受荷桩承载力预测模型 support vector machines laterally loaded pile bearing capacity prediction model

分类号 TU473 [建筑科学—结构工程]