公路软基沉降预测的支持向量机模型

Support vector machine model of settlement prediction of road soft foundation

提出了基于支持向量机(SVM)模型对公路软基沉降进行预测的一种新方法,工程实例预测结果表明,在同样的训练均方误差下,SVM模型预测能力要优于BP神经网络模型,同时该模型能够综合利用分级加载过程中的沉降观测数据作为训练样本集,比仅依靠预压期内部分实测沉降数据的双曲线法更能反映地基土的变形趋势。因此,将建立的SVM模型应用于公路软基沉降预测能够更准确地反映实际沉降过程。

A new method based on support vector machine （SVM） model is proposed to predict settlement of road soft foundation. A case study shows that the prediction results accord well with the actual settlement measured data. The new method is also compared with BP artificial neural network model and traditional hyperbola method. The prediction results indicate that the SVM model has a better prediction ability than BP neural network model at the same training set mean-square error. Utilizing the settlement data under multi-stage loading, SVM model has a better reflection for foundation soil deformation trend compared with hyperbola method only using the data under pre-loading, Therefore, settlement prediction based on SVM model can reflect actual settlement process more correctly.