顾及邻近点的改进PSO-SVM模型在基坑沉降预测的应用研究

针对SVM模型在基坑沉降预测领域存在参数选择困难和基于单点数据建模的缺点，建立顾及邻近点的PSO-SVM模型。采用PSO-SVM模型进行最优训练样本数量研究，结果表明短期样本的预测效果最优。将邻近点沉降变形值作为影响基坑沉降的因素引入到改进的PSO-SVM模型中，实例表明，在短期样本数据下顾及邻近点的PSO-SVM模型的拟合精度优于PSO-SVM模型，而在中长期样本条件下预测精度不佳。针对该缺点提出组合多尺度一维小波分解函数和柯西分布函数来改进顾及邻近点的PSO-SVM模型，实验结果表明，顾及邻近点的改进PSO-SVM模型可有效解决参数选择困难和单点建模的问题，适用于不同样本数量下的沉降变形预测，具有较高的预测精度。

Study on the Application of Improved PSO-SVM Model Considering Neighbor-Point in the Settlement Prediction of Foundation Pit

In view of the disadvantages of the SVM model,such as difficulty in parameter selection and single-point data modeling in the field of settlement prediction of foundation pit,we establish a neighbor-point PSO-SVM model.Selecting the PSO-SVM model for the optimal training sample quantity research,the results show that short-term samples have the best prediction effect.We introduce the settlement deformation value of neighbor points as a factor affecting the settlement of foundation pit into the improved PSO-SVM model.The example shows that the fitting accuracy of the PSO-SVM model considering the neighbor points under short-term sample data is better than that of the PSO-SVM model.The prediction accuracy is poor under medium and long-term sample conditions.Aiming at this shortcoming,we propose a combination of multi-scale one-dimensional wavelet decomposition function and Cauchy distribution function to improve the PSO-SVM model that takes into account the neighbor points.The experimental results show that the improved PSO-SVM model effectively solves the difficulty in parameter selection and single-point data modeling.The model is suitable for the prediction of settlement deformation under different sample sizes,and has high prediction accuracy.