Change search
ReferencesLink to record
Permanent link

Direct link
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.
Series
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
Identifiers
URN: urn:nbn:se:kth:diva-189772OAI: oai:DiVA.org:kth-189772DiVA: diva2:948984
External cooperation
Politecnico di Milano
Subject / course
Structural Design and Bridges
Educational program
Degree of Master - Civil and Architectural Engineering
Supervisors
Examiners
Available from: 2016-08-31 Created: 2016-07-14 Last updated: 2016-08-31Bibliographically approved

Open Access in DiVA

fulltext(35839 kB)10 downloads
File information
File name FULLTEXT01.pdfFile size 35839 kBChecksum SHA-512
ef48432a91ace23680f6bc4fb18b06f25c55acecc43fac922c0b12ba6a1150fd770e7db6f531327ff0134a293eea431df414dd597e7d261461d9769f4c1732d3
Type fulltextMimetype application/pdf

By organisation
Structural Engineering and Bridges
Other Civil Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 10 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 28 hits
ReferencesLink to record
Permanent link

Direct link