Abstract
Scour is one of the major causes for bridge failures. This study established a machine learning based predictive maintenance policy for scour risk mitigation. Data used in this study was provided the French National Railway Company (SNCF). After comparing three commonly used ML algorithms, it is found that the extreme gradient boosting (XGBoost) algorithm outperforms others and achieves very encouraging results. Moreover, the trained classifier was then tested in another 40 bridge piers and the results were compared with a junior engineer's evaluation. Results show that XGBoost classifier had a slightly better performance in terms of accuracy and precision. The proposed methodology in this paper allows engineers to determine scour risk of bridge piers in an accurate yet rapid way.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.