1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data

Abdeljaber, Osama, Avci, Onur, Kiranyaz, Mustafa Serkan, Boashash, Boualem, Sodano, Henry and Inman, Daniel J. (2017) 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data. Neurocomputing, 275 1308-1317. doi:10.1016/j.neucom.2017.09.069


Author Abdeljaber, Osama
Avci, Onur
Kiranyaz, Mustafa Serkan
Boashash, Boualem
Sodano, Henry
Inman, Daniel J.
Title 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data
Journal name Neurocomputing   Check publisher's open access policy
ISSN 1872-8286
0925-2312
Publication date 2017-09-28
Year available 2017
Sub-type Article (original research)
DOI 10.1016/j.neucom.2017.09.069
Open Access Status Not yet assessed
Volume 275
Start page 1308
End page 1317
Total pages 10
Place of publication Amsterdam, NX Netherlands
Publisher Elsevier
Language eng
Subject 1706 Computer Science Applications
2805 Cognitive Neuroscience
1702 Artificial Intelligence
Abstract Structural damage detection has been an interdisciplinary area of interest for various engineering fields. While the available damage detection methods have been in the process of adapting machine learning concepts, most machine learning based methods extract “hand-crafted” features which are fixed and manually selected in advance. Their performance varies significantly among various patterns of data depending on the particular structure under analysis. Convolutional neural networks (CNNs), on the other hand, can fuse and simultaneously optimize two major sets of an assessment task (feature extraction and classification) into a single learning block during the training phase. This ability not only provides an improved classification performance but also yields a superior computational efficiency. 1D CNNs have recently achieved state-of-the-art performance in vibration-based structural damage detection; however, it has been reported that the training of the CNNs requires significant amount of measurements especially in large structures. In order to overcome this limitation, this paper presents an enhanced CNN-based approach that requires only two measurement sets regardless of the size of the structure. This approach is verified using the experimental data of the Phase II benchmark problem of structural health monitoring which had been introduced by IASC-ASCE Structural Health Monitoring Task Group. As a result, it is shown that the enhanced CNN-based approach successfully estimated the actual amount of damage for the nine damage scenarios of the benchmark study.
Keyword Convolutional neural networks
Infrastructure health
Neural networks
Neurocomputing
Structural damage detection
Structural damage identification
Structural health monitoring
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
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Created: Sun, 24 Dec 2017, 10:09:18 EST