【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。更多还原

【Abstract】 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.更多还原