Structural Health Monitoring of Bridges: Model-free damage detection method using Machine Learning
2017 (English)Licentiate thesis, monograph (Other academic)
This is probably the most appropriate time for the development of robust and reliable structural damage detection systems as aging civil engineering structures, such as bridges, are being used past their life expectancy and beyond their original design loads. Often, when a significant damage to the structure is discovered, the deterioration has already progressed far and required repair is substantial. This is both expensive and has negative impact on the environment and traffic during replacement. For the exposed reasons the demand for efficient Structural Health Monitoring techniques is currently extremely high. This licentiate thesis presents a two-stage model-free damage detection approach based on Machine Learning. The method is applied to data gathered in a numerical experiment using a three-dimensional finite element model of a railway bridge. The initial step in this study consists in collecting the structural dynamic response that is simulated during the passage of a train, considering the bridge in both healthy and damaged conditions. The first stage of the proposed algorithm consists in the design and unsupervised training of Artificial Neural Networks that, provided with input composed of measured accelerations in previous instants, are capable of predicting future output acceleration. In the second stage the prediction errors are used to fit a Gaussian Process that enables to perform a statistical analysis of the distribution of errors. Subsequently, the concept of Damage Index is introduced and the probabilities associated with false diagnosis are studied. Following the former steps Receiver Operating Characteristic curves are generated and the threshold of the detection system can be adjusted according to the trade-off between errors. Lastly, using the Bayes’ Theorem, a simplified method for the calculation of the expected cost of the strategy is proposed and exemplified.
Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2017. , 62 p.
TRITA-BKN. Bulletin, ISSN 1103-4270 ; 149
Structural Health Monitoring, Machine Learning, Damage detection, Model-free based method, Artificial Neural Networks, Gaussian Process, Cost optimization
Other Civil Engineering Infrastructure Engineering
Research subject Civil and Architectural Engineering
IdentifiersURN: urn:nbn:se:kth:diva-205616ISBN: 978-91-7729-345-3 OAI: oai:DiVA.org:kth-205616DiVA: diva2:1089610
2017-05-30, M108, Brinellvägen 23, KTH, Stockholm, Stockholm, 13:00 (English)
Chatzi, Eleni, Professor
Karoumi, Raid, ProfessorLeander, John, ResearcherGonzalez, Ignacio, Doctor
QC 201704202017-04-202017-04-202017-04-20Bibliographically approved