Machine learning for prediction of wind effects on behavior of a historic truss bridge
Abstract This paper presents the behavior of a 102-year-old truss bridge under wind loading. To examine the wind-related responses of the historical bridge, state-of-the-art and traditional modeling methodologies are employed: a machine learning approach called random forest and three-dimensional fi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-11-01
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| Series: | Advances in Bridge Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43251-022-00074-x |