Damage detection in ASCE SHM experimental benchmark problem

Piotr Omenzetter; Oliver Richard De Lautour

doi:10.13140/2.1.1231.5203

Damage detection in ASCE SHM experimental benchmark problem

Piotr Omenzetter, Oliver Richard De Lautour

Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

Abstract

Time series analysis methods are being increasingly investigated for the use of Structural Health Monitoring (SHM). In this research, Autoregressive (AR) models were used to fit the acceleration time histories obtained from the ASCE Phase II Experimental SHM Benchmark Structure in undamaged and various damaged states. The coefficients of AR models were considered to be damage sensitive features and used as inputs into an Artificial Neural Network (ANN). The ANN was trained to detect and classify damage into several states. The results show that the combination of AR models and ANNs is an efficient tool for damage detection and classification.

Original language	English
Title of host publication	Proceedings of the 2007 Meeting of Asian-Pacific Network of Centers for Earthquake Engineering Research
Pages	1-8
Number of pages	8
DOIs	https://doi.org/10.13140/2.1.1231.5203
Publication status	Published - 29 May 2007

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title = "Damage detection in ASCE SHM experimental benchmark problem",

abstract = "Time series analysis methods are being increasingly investigated for the use of Structural Health Monitoring (SHM). In this research, Autoregressive (AR) models were used to fit the acceleration time histories obtained from the ASCE Phase II Experimental SHM Benchmark Structure in undamaged and various damaged states. The coefficients of AR models were considered to be damage sensitive features and used as inputs into an Artificial Neural Network (ANN). The ANN was trained to detect and classify damage into several states. The results show that the combination of AR models and ANNs is an efficient tool for damage detection and classification.",

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ER -

Damage detection in ASCE SHM experimental benchmark problem

Abstract

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