作　　者： (王雪妮）; (韩国锋）;

机构地区： 杨凌职业技术学院,陕西咸阳712100

出　　处： 《隧道建设》 2017年第8期990-996,共7页

摘　　要： 为解决基坑变形预测精度低的问题,利用小波去噪和卡尔曼滤波对基坑变形序列进行去噪处理,分离趋势项及误差项,并利用支持向量机和BP神经网络分别对趋势项和误差项进行预测,以掌握基坑的变形规律及发展趋势;同时,采用重标度极差分析(R/S分析)对基坑的变形趋势进行判断,以验证变形预测的可靠性。根据实例检验,得出小波去噪的去噪效果较好,且预测结果的相对误差均值为1.03%,方差值为0.083,预测精度较高;基坑的变形序列与速率序列均具有持续增长的趋势特征,与变形预测结果一致,验证了预测思路的有效性。 The deformation prediction accuracy of foundation pit is low nowadays. The denoising is carried out for deformation sequence of foundation pit by wavelet denoising and Calman filter,the trend term and error term are separated,and the trend term and error term is predicted by support vector machine（ SVM） and BP neural network respectively. Meanwhile,the deformation trend of foundation pit is predicted by rescaled range（ R/S） analysis so as to verify the feasibility of deformation prediction results. The case study shows that： 1） For wavelet denoising method,the denoising effect is superior; the average relative error and variance of the prediction results is 1. 03% and 0. 083respectively; and the prediction accuracy is much higher. 2） The deformation sequence and deformation velocity sequence of foundation pit are prone to increasing,which coincide with the prediction results and verify the effectiveness of the prediction idea.

关 键 词： 深基坑 变形预测 去噪分析 支持向量机 分析 趋势判断