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Structural Health Monitoring of Bridges using Machine Learning: The influence of Temperature on the health prediction
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
Abstract [en]

A method that uses machine learning to detect and localize damage in railway bridges under various environmental conditions is proposed and validated in this work. The developed algorithm uses vertical and lateral deck accelerations as damage- sensitive features. Indeed, an Artificial Neural Network (ANN) is trained to predict deck accelerations in undamaged condition given: previous vibration data, air temperature and characteristics of the train crossing the bridge (speed, load position and load magnitude). After an appropriate training period, the comparison between ANN-predicted and measured accelerations allows to compute prediction errors. A Gaussian Process is then used to stochastically characterize prediction errors in undamaged conditions using train speed as independent variable. Recorded vibration data leading to abnormal prediction errors are flagged as damage.

The method is validated both on a simple numerical example and on data recorded on a real structure. In the latter case, an appropriate algorithm was developed with the aim of extracting vehicles characteristics from the acceleration time histories. Together with this part of the algorithm for the pre-processing of recorded accelerations, the novelty of the developed method is the addition of air temperature to the input. It allows separating between structure responses that can be flagged as damage from those only affected by environmental conditions. 

Place, publisher, year, edition, pages
2016. , 187 p.
TRITA-BKN-Examensarbete, ISSN 1103-4297
Keyword [en]
Damage detection; Structural Health Monitoring; Machine learning; Artificial Neural Network; Railway bridge; Environmental conditions.
National Category
Other Civil Engineering
URN: urn:nbn:se:kth:diva-189772OAI: diva2:948984
External cooperation
Politecnico di Milano
Subject / course
Structural Design and Bridges
Educational program
Degree of Master - Civil and Architectural Engineering
Available from: 2016-08-31 Created: 2016-07-14 Last updated: 2016-08-31Bibliographically approved

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