Failure mode and load prediction of steel bridge girders through 3D laser scanning and machine learning methods

Research output: Contribution to journalConference articleContributedpeer-review

Contributors

Abstract

Corrosion poses a significant threat to the longevity of steel bridges, impacting overall structural integrity. To effectively assess the structural condition of corroded steel bridges, conventional methods rely on visual inspections or single point measurements. To enhance and modernize this approach, this study introduces a novel framework integrating laser scanning data, computational models, and convolutional neural networks (CNNs). The CNN models are trained on a data set consisting of more than 1400 artificial corrosion scenarios generated by parameterizing real scan data from naturally corroded girders. This innovative method predicts the residual capacity and failure mode of corroded beam ends, achieving a low error rate of up to 3.3%. Unlike established evaluation procedures, the proposed evaluation framework directly utilizes post-processed laser scanner output, eliminating the need for feature extraction and calculations.

Details

Original languageEnglish
Pages (from-to)198-203
Number of pages6
Journal ce/papers : the online collection for conference papers in civil engineering
Volume7
Issue number3-4
Publication statusPublished - 11 Sept 2024
Peer-reviewedYes

Symposium

Title11th International Symposium on Steel Bridges 2024
Conference number11
Duration11 - 13 September 2024
Website
LocationOREA Hotel Pyramida Praha
CityPrague
CountryCzech Republic

External IDs

ORCID /0000-0003-1370-064X/work/168718056
ORCID /0000-0003-0311-1745/work/168719869

Keywords