小波变换和BP神经网络模型在沉降变形监测中的应用研究
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摘要
变形预测在预报工程险情方面起着关键性的作用,针对施工中需及时、准确地预测变形的问题,本文利用小波变换原理对监测数据进行降噪处理,并采用BP神经网络分析不同训练样本下的预测效果和精度水平。实验结果表明:基于小波消噪后的BP网络模型,以连续的近期观测数据作为训练样本,对下期变形预测精度高,效果好,相对误差很小。因此,小波变换和BP神经网络模型在沉降变形监测工程中能作为预测研究与应用的参考。
Deformation prediction plays a key role in predicting the danger of engineering. It is necessary to predict the deformation timely and accurately in the construction. In this paper,the wavelet transform principle is used to reduce the noise of monitoring data,and BP neural network model is used to analyze the prediction effect and accuracy for different training samples. The experimental results show that the continuous recent observation data is used as the training sample,the prediction accuracy of the deformation prediction is high,the effect is good and the relative error is small based on the BP network model after wavelet de-noising. Therefore,this method can be used as a reference for prediction research and application in settlement deformation monitoring project.
Deformation prediction plays a key role in predicting the danger of engineering. It is necessary to predict the deformation timely and accurately in the construction. In this paper,the wavelet transform principle is used to reduce the noise of monitoring data,and BP neural network model is used to analyze the prediction effect and accuracy for different training samples. The experimental results show that the continuous recent observation data is used as the training sample,the prediction accuracy of the deformation prediction is high,the effect is good and the relative error is small based on the BP network model after wavelet de-noising. Therefore,this method can be used as a reference for prediction research and application in settlement deformation monitoring project.
引文
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[12]高隽.人工神经网络与仿真实例[M].北京:机械工业出版社,2003.
[2]罗耀海.深基坑施工监测实例[J].广东建材,2006(6):82-83.
[3]楼楠,卫建东.特殊情况下深基坑围护测斜及变形浅析[J].测绘科学,2009,34(4):42-43.
[4]张韬.深基坑变形预测模型研究及工程应用[D].长沙:中南大学,2009.
[5]郭锐.基于利用回归分析法预报上下游相关洪峰水位[J].黑龙江水利科技,2011,39(4):10-13.
[6]邓聚龙.灰色理论基础[M].武汉:华中理工大学出版社,2002.
[7]高玮.岩土工程监测位移预测的神经网络模型研究[J].岩土工程师,2002,14(1):8-12.
[8]丁德馨,毕忠伟,王卫华.开采地面沉陷预测的神经网络方法研究[J].南华大学学报(理工版),2002,16(3):1-5.
[9]胡昌华,李国华.基于MATLAB6.0X的系统分析—小波分析[M].西安:西安电子科技大学出版社,2004.
[10]贺志勇,郑伟.基于BP神经网络的深基坑变形预测[J].华南理工大学学报,2008,36(10):92-96.
[11]贾备,邬亮.基于灰色BP神经网络组合模型的基坑变形预测研究[J].隧道建设,2009,29(3):280-283.
[12]高隽.人工神经网络与仿真实例[M].北京:机械工业出版社,2003.