Structural damage identification with unknown external inputs based on the sparse constraint

Author:

Wei DaORCID,Li Dongsheng,Cai Enjian,Huang Jiezhong,Guo Xin

Abstract

Abstract The strength and integrity of a structure are determined not only by the quality of its materials, but also by the health of its various components. In some cases, damaged cracks inside the structure will be generated from excessive loads, which may cause catastrophic failure of structures if undetected for a long period. However, at early stages, most of the damages are minor, and therefore difficult to detect by only visual inspection. Thus, in this paper, a damage estimation algorithm based on an unscented Kalman filter (UKF) is proposed, which can identify and locate the damage parameters in real time using a limited number of sensors. Meanwhile, using this algorithm, joint force-damage estimation can be achieved, which is very applicable to the structural system with unknown external inputs. On the other hand, for most structures, the distribution of damage parameters in the space domain is sparse. Therefore, the sparsity of the damage parameter vector is introduced to UKF as an l 1-norm constraint by the pseudo measurement (PM) technique. Thus, unconstrained optimization of the damage parameter estimation is transformed into an l1 -norm constrained optimization problem. With such improvement, the process of damage parameter estimation converges faster, and the false damage parameters can be effectively restrained. Moreover, to solve the force drift problem during force identification if only acceleration data is used, the sparse constraint of the force vector is also introduced to the UKF framework by the PM technique. Finally, the performance of the proposed algorithm is validated by two case studies, including numerical simulations of a ten-story shear building and experiments of a three-story shear structure. The results indicate that the proposed algorithm can accurately identify the damage, and successfully resolve the common force drift problem.

Funder

National Natural Science Foundation of China

GuangDong Basic and Applied Basic Research Foundation

Publisher

IOP Publishing

Subject

Electrical and Electronic Engineering,Mechanics of Materials,Condensed Matter Physics,General Materials Science,Atomic and Molecular Physics, and Optics,Civil and Structural Engineering,Signal Processing

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Hybrid Perspective of Vision-Based Methods for Estimating Structural Displacements Based on Mask Region-Based Convolutional Neural Networks;ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering;2024-03-22

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