Damage detection on railway bridges using Artificial Neural Network and train induced vibrations
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
A damage detection approach based on Artificial Neural Network (ANN), using the statistics of structural dynamic responses as the damage index, is proposed in this study for Structural Health Monitoring (SHM). Based on the sensitivity analysis, the feasibility of using the changes of variances and covariance of dynamic responses of railway bridges under moving trains as the indices for damage detection is evaluated.
A FE Model of a one-span simply supported beam bridge is built, considering both single damage case and multi-damage case. A Back-Propagation Neural Network (BPNN) is designed and trained to simulate the detection process. A series of numerical tests on the FE model with different train properties prove the validity and efficiency of the proposed approach. The results show not only that the trained ANN together with the statistics can correctly estimate the location and severity of damage in the structure, but also that the identification of the damage location is more difficult than that of the damage severity. In summary, it is concluded that the use of statistical property of structural dynamic response as damage index with the Artificial Neural Network as detection tool for damage detection is reliable and effective.
Place, publisher, year, edition, pages
Trita-BKN-Examensarbete, ISSN 1103-4297 ; 336
Damage detection; Railway Bridge; Dynamic Response; high-speed trains, Statistical Property; Artificial Neural Network (ANN)
IdentifiersURN: urn:nbn:se:kth:diva-99387ISRN: KTH/BKN/EX-336-SEOAI: oai:DiVA.org:kth-99387DiVA: diva2:542067
Subject / course
Structural Design and Bridges
Master of Science in Engineering - Urban Management