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Nonparametric structural damage detection algorithm for ambient vibration response: utilizing artificial neural networks and self-organizing maps
Qatar University, Qatar.ORCID-id: 0000-0003-0530-9552
Qatar University, Qatar.
2016 (engelsk)Inngår i: Journal of Architectural Engineering, ISSN 1076-0431, E-ISSN 1943-5568, Vol. 22, nr 2Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This study presentes a new nonparametric structural damage detection algorithm that integrates self-organizing maps with a pattern-recognition neural network to quantify and locate structural damage. In this algorithm, self-organizing maps are used to extract a number of damage indices from the ambient vibration response of the monitored structure. The presented study is unique because it demonstrates the development of a nonparametric vibration-based damage detection algorithm that utilizes self-organizing maps to extract meaningful damage indices from ambient vibration signals in the time domain. The ability of the algorithm to identify damage was demonstrated analytically using a finite-element model of a hot-rolled steel grid structure. The algorithm successfully located the structural damage under several damage cases, including damage resulting from local stiffness loss in members and damage resulting from changes in boundary conditions. A sensitivity study was also conducted to evaluate the effects of noise on the computed damage indices. The algorithm was proved to be successful even when the signals are noise-contaminated.

sted, utgiver, år, opplag, sider
Elsevier, 2016. Vol. 22, nr 2
HSV kategori
Forskningsprogram
Teknik, Byggteknik
Identifikatorer
URN: urn:nbn:se:lnu:diva-88131DOI: 10.1061/(ASCE)AE.1943-5568.0000205OAI: oai:DiVA.org:lnu-88131DiVA, id: diva2:1344156
Tilgjengelig fra: 2019-08-20 Laget: 2019-08-20 Sist oppdatert: 2019-09-12bibliografisk kontrollert

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