Damage detection in ASCE SHM experimental benchmark problem

Piotr Omenzetter, Oliver Richard De Lautour

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


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 languageEnglish
Title of host publicationProceedings of the 2007 Meeting of Asian-Pacific Network of Centers for Earthquake Engineering Research
Number of pages8
Publication statusPublished - 29 May 2007


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