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Structural damage detection using quantile regression

Kong fah Tee, Yuzhi Cai Orcid Logo, Hua-Peng Chen

Journal of Civil Structural Health Monitoring, Volume: 3

Swansea University Author: Yuzhi Cai Orcid Logo

Abstract

Structural health monitoring is an important emerging engineering discipline in the UK and the world. Structural failure without warning is recognised as a significant hazard in the service life of a structure. Thus there is a need to provide a clear guidance to determine the cutoff line for operati...

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Published in: Journal of Civil Structural Health Monitoring
Published: 2013
URI: https://cronfa.swan.ac.uk/Record/cronfa15290
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first_indexed 2013-08-22T01:57:36Z
last_indexed 2018-02-09T04:47:07Z
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spelling 2013-08-21T14:08:30.5514805 v2 15290 2013-07-30 Structural damage detection using quantile regression eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2013-07-30 BAF Structural health monitoring is an important emerging engineering discipline in the UK and the world. Structural failure without warning is recognised as a significant hazard in the service life of a structure. Thus there is a need to provide a clear guidance to determine the cutoff line for operation, repair and maintenance. A quantile regression approach has been proposed for structural damage detection using vibration data (accelerations). This method is based on a sequence of quantile autoregressive time series models and the differences between two distributions associated with the residual series of the undamaged and damaged structures are studied at different quantile levels. This new approach is based on the information on damages at any quantile levels, not just at a mean level that is commonly used in the literature. In addition, it does not depend on the distribution of the error term. This is a very useful feature as in practice it can be very difficult to assume a proper distribution for the error term of the model. The performance of the developed method is investigated via extensive simulation studies to detect single-damage and multi-damage scenarios with input and output measurement noise. The proposed method is further substantiated experimentally using an eight-storey steel plane frame model subjected to shaker excitation. Both numerical and experimental results have shown that the proposed method gives reasonably accurate damage identification, including both damage existence and location. Journal Article Journal of Civil Structural Health Monitoring 3 31 Structural damage detection 30 4 2013 2013-04-30 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2013-08-21T14:08:30.5514805 2013-07-30T10:24:51.1742989 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Kong fah Tee 1 Yuzhi Cai 0000-0003-3509-9787 2 Hua-Peng Chen 3
title Structural damage detection using quantile regression
spellingShingle Structural damage detection using quantile regression
Yuzhi Cai
title_short Structural damage detection using quantile regression
title_full Structural damage detection using quantile regression
title_fullStr Structural damage detection using quantile regression
title_full_unstemmed Structural damage detection using quantile regression
title_sort Structural damage detection using quantile regression
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Yuzhi Cai
author2 Kong fah Tee
Yuzhi Cai
Hua-Peng Chen
format Journal article
container_title Journal of Civil Structural Health Monitoring
container_volume 3
publishDate 2013
institution Swansea University
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 0
active_str 0
description Structural health monitoring is an important emerging engineering discipline in the UK and the world. Structural failure without warning is recognised as a significant hazard in the service life of a structure. Thus there is a need to provide a clear guidance to determine the cutoff line for operation, repair and maintenance. A quantile regression approach has been proposed for structural damage detection using vibration data (accelerations). This method is based on a sequence of quantile autoregressive time series models and the differences between two distributions associated with the residual series of the undamaged and damaged structures are studied at different quantile levels. This new approach is based on the information on damages at any quantile levels, not just at a mean level that is commonly used in the literature. In addition, it does not depend on the distribution of the error term. This is a very useful feature as in practice it can be very difficult to assume a proper distribution for the error term of the model. The performance of the developed method is investigated via extensive simulation studies to detect single-damage and multi-damage scenarios with input and output measurement noise. The proposed method is further substantiated experimentally using an eight-storey steel plane frame model subjected to shaker excitation. Both numerical and experimental results have shown that the proposed method gives reasonably accurate damage identification, including both damage existence and location.
published_date 2013-04-30T03:17:25Z
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score 11.036706

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