Development of Recursive Subspace Identification for Real-Time Structural Health Monitoring under Seismic Loading

Author:

Huang Shieh-Kung1ORCID,Chi Fu-Chung1

Affiliation:

1. Department of Civil Engineering, National Chung-Hsing University, Taichung 40227, Taiwan

Abstract

Structural health monitoring (SHM) can continuously and nondestructively evaluate the state and performance of structures using the structural responses to external loads or environmental conditions. Moreover, online or real-time SHM of civil structures provides significant advantages over periodic or manual inspection methods, especially under disaster loadings, where the consequences of failure can be severe. To achieve it, performing system identification and damage detection recursively, said recursive subspace identification (RSI), is a promising solution, and SHM based on the algorithms can evaluate damage or deterioration of civil structures, give insight into the health and performance of a structural system, and provide valuable information for decision-making on maintenance and repair. However, the time-consuming decompositions frustrate these algorithms. As a compromise, additional processing is required to implement online and real-time applications. This study demonstrates a modified algorithm that takes advantage of the projection approximation subspace tracking (PAST) algorithm and the repeated system matrices in the extended observability matrix. The modification can reduce numerical decompositions and improve important timeliness for online or real-time SHM of civil structures. Both the numerical simulation and experimental investigation have been used to verify the proposed method, and the results show its capability to determine the changes in the dynamic characteristics of a structure in either the laboratory experiment or in the field application. In the last place, the discussion and some conclusions are also drawn in this paper.

Funder

National Science and Technology Council

Publisher

Hindawi Limited

Subject

Mechanics of Materials,Building and Construction,Civil and Structural Engineering

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3