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A New Approach to Damage Detection in Bridges Using Machine Learning
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
Sweco AB, Stockholm, Sweden..
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.ORCID iD: 0000-0002-2833-4585
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.ORCID iD: 0000-0002-5447-2068
2018 (English)In: EXPERIMENTAL VIBRATION ANALYSIS FOR CIVIL STRUCTURES: TESTING, SENSING, MONITORING, AND CONTROL / [ed] Conte, JP Astroza, R Benzoni, G Feltrin, G Loh, KJ Moaveni, B, SPRINGER INTERNATIONAL PUBLISHING AG , 2018, p. 73-84Conference paper, Published paper (Refereed)
Abstract [en]

At the same time that civil engineering structures are increasing in number, size and longevity, there is a conforming increasing preoccupation regarding the monitoring and maintenance of such structures. In this sense the demand for new reliable Structural Health Monitoring systems and damage detection techniques is high. A model-free damage detection approach based on Machine Learning is presented in this paper. The method performs on the collected feature measurements on a railway bridge, which for this study were gathered in a numerical experiment using a three dimensional finite element model. The first step of the approach consists in collecting the dynamic response of the structure, simulated during the passage of a train over the bridge, in both the healthy and damage states of the structure. The next step consists in the design and unsupervised training of Artificial Neural Networks that use as input accelerations and axle loads and compute a novelty index, called prediction error, based on a novelty detection approach. The distribution of the obtained prediction errors is statistically evaluated by means of a Gaussian Process and, after this process, damage indexes can be defined. Finally, the efficiency of the method is assessed in terms of Type I (false positive) and Type II (false negative) errors using Receiver Operating Characteristic curves. The promising results obtained in the case study demonstrate the capability of the presented method.

Place, publisher, year, edition, pages
SPRINGER INTERNATIONAL PUBLISHING AG , 2018. p. 73-84
Series
Lecture Notes in Civil Engineering, ISSN 2366-2557 ; 5
Keywords [en]
Structural Health monitoring, Machine Learning, Damage detection, Model-free based method, Artificial Neural Networks
National Category
Infrastructure Engineering
Identifiers
URN: urn:nbn:se:kth:diva-242272DOI: 10.1007/978-3-319-67443-8_5ISI: 000455235800005Scopus ID: 2-s2.0-85060236672ISBN: 978-3-319-67443-8 (print)ISBN: 978-3-319-67442-1 (print)OAI: oai:DiVA.org:kth-242272DiVA, id: diva2:1284921
Conference
International Conference on Experimental Vibration Analysis for Civil Engineering Structures (EVACES), JUL 12-14, 2017, Univ California San Diego, San Diego, CA
Note

QC 20190201

Available from: 2019-02-01 Created: 2019-02-01 Last updated: 2019-08-20Bibliographically approved

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Leander, JohnKaroumi, Raid

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