Machine learning-based prediction of scour depth around different-shaped bridge abutments

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@Article{Deng:2024:PICEWM,

• author = "Yangyu Deng and Yakun Liu and Di Zhang and Ze Cao",
• title = "Machine learning-based prediction of scour depth around different-shaped bridge abutments",
• journal = "Proceedings of the Institution of Civil Engineers - Water Management",
• year = "2024",
• volume = "177",
• number = "5",
• pages = "308--326",
• keywords = "genetic algorithms, genetic programming, artificial intelligence, bridge abutment, hydraulics, machine learning, scour depth",
• ISSN = "1741-7589",
• URL = "https://www.sciencedirect.com/science/article/pii/S1741758924000052",
• DOI = "10.1680/jwama.22.00087",
• abstract = "Accurate assessment of scour depth around bridge abutments is crucial to reasonable design of abutment structures. In this study, machine learning (ML) models are implemented, including M5' model tree (M5'MT), multivariate adaptive regression spline (MARS), locally weighted polynomial regression (LWPR) and multigene genetic programming (MGGP) to predict scour depth around vertical-wall, 45 degree wing-wall and semicircular bridge abutments. Published experimental data are adopted, with four input parameters considered for the prediction of relative scour depth. The optimal input combination for each model is first determined using correlation and sensitivity analyses; results reveal that MGGP exhibits the best agreement with experimental data for vertical-wall and semicircular abutments, whereas LWPR outperforms the other models for the 45 degree wing-wall abutment. In addition, compared with the empirical equations and ML models employed in the literature, the accuracy of scour depth prediction is significantly improved with the ML models used in this study. Considering the comprehensive performance for all types of abutments in terms of accuracy, reliability and interpretability, MGGP is recommended as the representative of the implemented ML models with its mean absolute percentage error of 2.40 percent for a vertical-wall abutment, 3.95 percent for a 45 degree wing-wall abutment and 3.85 percent for a semicircular abutment",
• notes = "

  School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, PR China",

}

Genetic Programming entries for Yangyu Deng Yakun Liu Di Zhang Ze Cao

Citations