小波降噪及改进遗传算法的BP神经网络在基坑变形中的组合应用  

Combined Application of Wavelet Denoising and Improved Genetic Algorithm Based on BP Neural Network in Foundation Pit Deformation

作　　者：朱志成 靳海亮[1] ZHU Zhicheng;JIN Hailiang(School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454000,China)

机构地区：[1]河南理工大学测绘与国土信息工程学院,河南焦作454000

出　　处：《测绘与空间地理信息》2024年第7期169-173,共5页Geomatics & Spatial Information Technology

摘　　要：以某市人民医院基坑工程为例,针对实测数据建立实测数据结合BP神经网络预测模型,小波降噪结合BP神经网络模型和小波降噪结合改进遗传算法优化的BP神经网络模型,并利用误差分析理论对基坑变形数据预测效果评价。结果表明:对比3种模型实际处理、预测数据能力,实测数据结合BP神经网络模型预测精度在1%-4%之间,小波降噪结合BP神经网络模型预测精度1%-2%之间,小波降噪结合改进遗传算法优化的BP神经网络模型预测精度在1%以内,小波降噪结合改进遗传算法优化的BP神经网络模型的预测准确率最高。针对基坑变形监测,小波降噪结合改进遗传算法优化的BP神经网络模型具有更高预测精度,可为类似工程提供实际参考。Taking the foundation pit project of a municipal people's hospital as an example,a prediction model was established based on measured data combined with BP neural network.Wavelet denoising combined with BP neural network model and wavelet denoising combined with improved genetic algorithm optimized BP neural network model were used to evaluate the prediction effect of foundation pit deformation data using error analysis theory.The results show that comparing the actual processing and prediction data capabilities of the three models,the prediction accuracy of measured data combined with BP neural network model is between 1%and 4%,the prediction accuracy of wavelet denoising combined with BP neural network model is between 1%and 2%,the prediction accuracy of BP neural network model optimized by wavelet denoising combined with improved genetic algorithm is within 1%,and the prediction accuracy of BP neural network model optimized by wavelet denoising combined with improved genetic algorithm is the highest.For the deformation monitoring of foundation pit,the BP neural network model optimized by wavelet denoising combined with improved genetic algorithm has higher prediction accuracy,which can provide practical reference for similar projects.

关 键 词：基坑监测 组合模型 BP神经网络 小波分析 改进遗传算法 

分 类 号：P25[天文地球—测绘科学与技术] TU753[建筑科学—建筑技术科学]