改进的数据驱动的随机子空间算法在桥梁监测中的应用

英文篇名：Application of improved data-driven stochastic subspace algorithms in bridge monitoring
中文刊名：河北工业大学学报
英文刊名：Journal of Hebei University of Technology
作者：高俊亮 ; 王国清 ; 彭彦忠 ; 乔丽霞
英文作者：GAO Jun-liang1 ; WANG Guo-qing2 ; PENG Yan-zhong3 ; Qiao Li-xia4(1.XZJK Engineers Ltd ; Beijing 101100 ; China ; 2.Heibei Province Highway Administration Bureau ; Hebei Shijiazhuang 050031 ; China ; 3.Highway administration of Hengshui To Daming ; Hebei Hengshui 053000 ; China ; 4.Hebei University of Technology ; Tianjin 300401 ; China)
中文关键词：桥梁监测 ; 随机子空间 ; 在线识别 ; QR分解
英文关键词：bridge monitoring ; stochastic subspace ; on-line identification ; QR decomposition
出版日期：2012-12-15
机构：北京新智交科科技开发有限公司;河北省高速公路管理局;大广高速京衡段筹建处;河北工业大学;
年：2012
期：06
出版单位：河北工业大学学报

摘要

介绍了基于数据驱动的随机子空间算法的改进方法,传统的数据驱动随机子空间算法由于数据量大,处理速度慢而很难用于在线识别和监测,改进的算法在减少数据量的同时仅利用分解得到可观矩阵即可完成整个参数识别,大大提高了运算速度,满足桥梁健康监测的在线模态参数识别和动力模型修正及损伤识别,具有很高的工程应用价值.该改进算法已成功应用于大广高速公路的通用型桥梁健康检测系统.
This paper reviews the improved algorithm based on data-driven stochastic subspace.By dealing with a large number of data,the traditional algorithm based on data-driven stochastic subspace is difficult to apply to on-line system identification and monitoring.However,the improved algorithm can identify all parameters only by decomposed observablity matrix,while large amounts of data were decreased and the operation speed is greatly improved.This has high engineering application value and is applied on bridge health monitoring,dynamic model modification and damage identification.The improved algorithms has been successfully applied to the health monitoring system of general bridges of Daguang-Highway of Heibei Province.

引文

[1]常军,顾坚,孟浩.不中断运营的既有桥梁模态参数识别[J].广西大学学报:自然科学版,2010,35(1):131-135.
    [2]常军,孙利民,张启伟.随机子空间识别方法计算效率的改进[J].地震工程与工程振动,2007,27(3):88-94.
    [3]常军.随机子空间方法在桥梁模态参数识别中的应用研究[D].上海:同济大学,2004.
    [4]程云鹏.矩阵论[M].西安:西北工业大学出版社,2004.
    [5]王济,胡晓.MATLAB在震动信号处理中的应用[M].北京:中国水利水电出版社,知识产权出版社,2006.
    [6]宗周红,Bijaya Jaishi,林友勤,等.西宁北川河钢管混凝土拱桥的理论和实验模态分析[J].铁道学报,2003,25(4):89-96.