Failure mode and load prediction of steel bridge girders through 3D laser scanning and machine learning methods
Research output: Contribution to journal › Conference article › Contributed › peer-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 language | English |
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Pages (from-to) | 198-203 |
Number of pages | 6 |
Journal | ce/papers : the online collection for conference papers in civil engineering |
Volume | 7 |
Issue number | 3-4 |
Publication status | Published - 11 Sept 2024 |
Peer-reviewed | Yes |
Symposium
Title | 11th International Symposium on Steel Bridges 2024 |
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Conference number | 11 |
Duration | 11 - 13 September 2024 |
Website | |
Location | OREA Hotel Pyramida Praha |
City | Prague |
Country | Czech Republic |
External IDs
ORCID | /0000-0003-1370-064X/work/168718056 |
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ORCID | /0000-0003-0311-1745/work/168719869 |