灰色-小波神经网络支持下对地铁工程沉降变形的预测
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摘要
变形监测是安全化工程施工和管理的重要内容,贯穿于项目的设计、施工和运行,对监测的沉降数据进行处理,并预测沉降量,提前对工程作出安全预警,有很重要的实际意义。本文基于GM(1,1)灰色模型、小波分析和神经网络结合的相关理论,借助Matlab软件编程,建立了灰色-小波神经网络变形预测网络模型。结合工程实例,将建立的变形预测网络模型应用于累积沉降量观测数据,结果表明组合模型具有很稳定的预测效果,比单独的GM(1,1)灰色模型预测准确度高,且训练样本越多,预测越符合实际情况。
Deformation monitoring is an important part of the safety engineering construction and management,and it runs through the design,construction and operation of the project. It is of great practical significance to process the monitored settlement data,predict the settlement amount,and make early warning of the safety of the project. Based on the GM( 1,1) grey model,wavelet analysis and neural network combination of related theories,using Matlab programming,this paper establishes a gray-wavelet neural network deformation prediction network model. Combined with engineering examples,the established deformation prediction network model is applied to the accumulated settlement observation data. The results show that the combined model has a very stable forecasting effect and is more accurate than the single GM( 1,1) gray model. The more training samples,the better the fitting effect and the prediction is more in line with the actual situation.
Deformation monitoring is an important part of the safety engineering construction and management,and it runs through the design,construction and operation of the project. It is of great practical significance to process the monitored settlement data,predict the settlement amount,and make early warning of the safety of the project. Based on the GM( 1,1) grey model,wavelet analysis and neural network combination of related theories,using Matlab programming,this paper establishes a gray-wavelet neural network deformation prediction network model. Combined with engineering examples,the established deformation prediction network model is applied to the accumulated settlement observation data. The results show that the combined model has a very stable forecasting effect and is more accurate than the single GM( 1,1) gray model. The more training samples,the better the fitting effect and the prediction is more in line with the actual situation.
引文
[1]潘国荣,王穗辉.建筑物动态变形的模型辨识与预测[J].测绘学报,1999,28(4):340-344.
[2]叶俊华.基于自适应卡尔曼滤波的灰色模型改进及在变形预测中的应用研究[D].西安:长安大学,2012.
[3]侯建国,王腾军.变形监测理论与应用[M].北京:测绘出版社,2008.
[4]杜琨.变形监测数据处理的方法研究[D].长沙:中南大学,2013.
[5]王穗辉,潘国荣.基于MATLAB多变量灰色模型及其在变形预测中的应用[J].土木工程学报,2005,38(5):24-27.
[6]韩印,郑喆,赵靖,等.基于灰色-小波神经网络的有效泊位预测[J].交通运输系统工程与信息,2017,17(5):60-67.
[7]邓聚龙.灰色系统基本方法[M]. 2版.武汉:华中科技大学出版社,2005.
[8]温丽华.灰色系统理论及其应用[D].哈尔滨:哈尔滨工程大学,2003.
[9]刘思峰,党耀国,方志耕,等.灰色系统理论及其应用[M].北京:科学出版社,1999.
[10]朱登远,常晓凤.灰色预测GM(1,1)模型的Matlab实现[J].河南城建学院学报,2013,22(3):40-46.
[11]刘萍萍,马昱阳.小波神经网络的大坝变形预测研究[J].西安工业大学学报,2014,34(11):886-890.
[12]魏健.小波人工神经网络在建筑沉降预测中的应用研究[D].北京:北京交通大学,2013.
[13]蔡东健,岳建平,成微,等.基于遗传算法的小波神经网络模型及其在地面沉降监控中的应用[J].测绘通报,2010(8):34-36.
[14]陈林,黄腾,郑浩.变形预测的小波神经网络模型改进[J].测绘科学,2017,42(9):112-115.
[15]曲径.基于灰色小波神经网络的船舶交通流预测研究[J].天津航海,2010(3):33-35.
[16]刘燕芳,陈启华,丁林磊.灰色组合模型在变形预测中的应用[J].工程勘察,2013,41(1):58-60.
[17]蔡念,胡匡祜,李淑宇,等.小波神经网络及其应用[J].中国体视学与图像分析,2001,6(4):239-245.
[18]谭衢霖,魏健,胡吉平.基于小波神经网络的建筑工程沉降变形预测[J].应用基础与工程科学学报,2015,23(3):629-636.
[19]姜刚,杨志强,张贵钢.卡尔曼滤波算法的灰色理论模型在变形监测中的应用[J].测绘科学,2011,36(4):19-21.
[20]姜刚,康艳霞,杨志强,等.灰色理论模型在矿区滑坡变形预测中的应用[J].煤田地质与勘探,2011,39(3):49-51.
[2]叶俊华.基于自适应卡尔曼滤波的灰色模型改进及在变形预测中的应用研究[D].西安:长安大学,2012.
[3]侯建国,王腾军.变形监测理论与应用[M].北京:测绘出版社,2008.
[4]杜琨.变形监测数据处理的方法研究[D].长沙:中南大学,2013.
[5]王穗辉,潘国荣.基于MATLAB多变量灰色模型及其在变形预测中的应用[J].土木工程学报,2005,38(5):24-27.
[6]韩印,郑喆,赵靖,等.基于灰色-小波神经网络的有效泊位预测[J].交通运输系统工程与信息,2017,17(5):60-67.
[7]邓聚龙.灰色系统基本方法[M]. 2版.武汉:华中科技大学出版社,2005.
[8]温丽华.灰色系统理论及其应用[D].哈尔滨:哈尔滨工程大学,2003.
[9]刘思峰,党耀国,方志耕,等.灰色系统理论及其应用[M].北京:科学出版社,1999.
[10]朱登远,常晓凤.灰色预测GM(1,1)模型的Matlab实现[J].河南城建学院学报,2013,22(3):40-46.
[11]刘萍萍,马昱阳.小波神经网络的大坝变形预测研究[J].西安工业大学学报,2014,34(11):886-890.
[12]魏健.小波人工神经网络在建筑沉降预测中的应用研究[D].北京:北京交通大学,2013.
[13]蔡东健,岳建平,成微,等.基于遗传算法的小波神经网络模型及其在地面沉降监控中的应用[J].测绘通报,2010(8):34-36.
[14]陈林,黄腾,郑浩.变形预测的小波神经网络模型改进[J].测绘科学,2017,42(9):112-115.
[15]曲径.基于灰色小波神经网络的船舶交通流预测研究[J].天津航海,2010(3):33-35.
[16]刘燕芳,陈启华,丁林磊.灰色组合模型在变形预测中的应用[J].工程勘察,2013,41(1):58-60.
[17]蔡念,胡匡祜,李淑宇,等.小波神经网络及其应用[J].中国体视学与图像分析,2001,6(4):239-245.
[18]谭衢霖,魏健,胡吉平.基于小波神经网络的建筑工程沉降变形预测[J].应用基础与工程科学学报,2015,23(3):629-636.
[19]姜刚,杨志强,张贵钢.卡尔曼滤波算法的灰色理论模型在变形监测中的应用[J].测绘科学,2011,36(4):19-21.
[20]姜刚,康艳霞,杨志强,等.灰色理论模型在矿区滑坡变形预测中的应用[J].煤田地质与勘探,2011,39(3):49-51.