Nonlinear autoregressive with exogenous input neural network for structural damage detection under ambient vibration

Time-series method has become of interest in damage detection, particularly for automated and continuous structural health monitoring. In comparison to the commonly used method based on modal data, time-series method offers a straightforward application due to having no requirement for modal analysi...

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Main Author:	Umar, Sarehati
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/92426/1/SarehatiUmarPSKA2020.pdf.pdf
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id	my-utm-ep.92426
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spelling	my-utm-ep.924262021-09-28T07:34:28Z Nonlinear autoregressive with exogenous input neural network for structural damage detection under ambient vibration 2020 Umar, Sarehati TA Engineering (General). Civil engineering (General) Time-series method has become of interest in damage detection, particularly for automated and continuous structural health monitoring. In comparison to the commonly used method based on modal data, time-series method offers a straightforward application due to having no requirement for modal analysis. Sensor clustering has been proven effective in improving the ability of time-series method to detect, locate and quantify damage. However, most of the applications rely on free vibration response that can be obtained directly by impact testing, which is difficult to practice for in-service structures, or indirectly by transforming the ambient vibration response. Therefore, a reliable method that allows direct utilisation of ambient vibration response for damage detection in structures without any data transformation is proposed in this study. The implementation of the proposed response-only method involves a three-stage procedure; (i) sensor clustering, (ii) time-series modelling and (iii) damage detection. Each sensor cluster is represented by a time-series model called nonlinear autoregressive with exogenous inputs (NARX) model, which is developed via artificial neural network (ANN) using undamaged acceleration data. The model is then utilised for predicting the damaged response and the difference between prediction errors is used to extract damage sensitive feature (DSF). The existence of uncertainties is addressed through setting up a damage threshold using several sets of undamaged data. The effectiveness of the method is demonstrated through a numerical slab model and experimental structures of reinforced concrete slabs and steel arches. It is found that the proposed structural damage detection approach based on NARX neural network is superior to linear ARX model as the approach is able to detect damage under ambient vibration. The results show that the highest predicted DSF corresponds to the location of damage and its value increases relatively with the severity of damage. Better damage detection is obtained when damage threshold is integrated into the proposed approach where the precision is increased by more than 24%. Overall, the proposed method is proven applicable to identify the existence, location and relative severity of structural damage under ambient vibration. 2020 Thesis http://eprints.utm.my/id/eprint/92426/ http://eprints.utm.my/id/eprint/92426/1/SarehatiUmarPSKA2020.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:138054 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Civil Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Umar, Sarehati Nonlinear autoregressive with exogenous input neural network for structural damage detection under ambient vibration
description	Time-series method has become of interest in damage detection, particularly for automated and continuous structural health monitoring. In comparison to the commonly used method based on modal data, time-series method offers a straightforward application due to having no requirement for modal analysis. Sensor clustering has been proven effective in improving the ability of time-series method to detect, locate and quantify damage. However, most of the applications rely on free vibration response that can be obtained directly by impact testing, which is difficult to practice for in-service structures, or indirectly by transforming the ambient vibration response. Therefore, a reliable method that allows direct utilisation of ambient vibration response for damage detection in structures without any data transformation is proposed in this study. The implementation of the proposed response-only method involves a three-stage procedure; (i) sensor clustering, (ii) time-series modelling and (iii) damage detection. Each sensor cluster is represented by a time-series model called nonlinear autoregressive with exogenous inputs (NARX) model, which is developed via artificial neural network (ANN) using undamaged acceleration data. The model is then utilised for predicting the damaged response and the difference between prediction errors is used to extract damage sensitive feature (DSF). The existence of uncertainties is addressed through setting up a damage threshold using several sets of undamaged data. The effectiveness of the method is demonstrated through a numerical slab model and experimental structures of reinforced concrete slabs and steel arches. It is found that the proposed structural damage detection approach based on NARX neural network is superior to linear ARX model as the approach is able to detect damage under ambient vibration. The results show that the highest predicted DSF corresponds to the location of damage and its value increases relatively with the severity of damage. Better damage detection is obtained when damage threshold is integrated into the proposed approach where the precision is increased by more than 24%. Overall, the proposed method is proven applicable to identify the existence, location and relative severity of structural damage under ambient vibration.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Umar, Sarehati
author_facet	Umar, Sarehati
author_sort	Umar, Sarehati
title	Nonlinear autoregressive with exogenous input neural network for structural damage detection under ambient vibration
title_short	Nonlinear autoregressive with exogenous input neural network for structural damage detection under ambient vibration
title_full	Nonlinear autoregressive with exogenous input neural network for structural damage detection under ambient vibration
title_fullStr	Nonlinear autoregressive with exogenous input neural network for structural damage detection under ambient vibration
title_full_unstemmed	Nonlinear autoregressive with exogenous input neural network for structural damage detection under ambient vibration
title_sort	nonlinear autoregressive with exogenous input neural network for structural damage detection under ambient vibration
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Civil Engineering
publishDate	2020
url	http://eprints.utm.my/id/eprint/92426/1/SarehatiUmarPSKA2020.pdf.pdf
_version_	1747818597096357888

Nonlinear autoregressive with exogenous input neural network for structural damage detection under ambient vibration

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