Machine learning-based prediction of scour depth around different-shaped bridge abutments
Created by W.Langdon from
gp-bibliography.bib Revision:1.8444
- @Article{Deng:2024:PICEWM,
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author = "Yangyu Deng and Yakun Liu and Di Zhang and Ze Cao",
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title = "Machine learning-based prediction of scour depth
around different-shaped bridge abutments",
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journal = "Proceedings of the Institution of Civil Engineers -
Water Management",
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year = "2024",
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volume = "177",
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number = "5",
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pages = "308--326",
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keywords = "genetic algorithms, genetic programming, artificial
intelligence, bridge abutment, hydraulics, machine
learning, scour depth",
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ISSN = "1741-7589",
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URL = "
https://www.sciencedirect.com/science/article/pii/S1741758924000052",
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DOI = "
doi:10.1680/jwama.22.00087",
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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, 45degree 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 45degree 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
2.40percent for a vertical-wall abutment, 3.95percent
for a 45degree wing-wall abutment and 3.85percent for a
semicircular abutment",
- }
Genetic Programming entries for
Yangyu Deng
Yakun Liu
Di Zhang
Ze Cao
Citations