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Estimation of Clear-Water Local Scour at Pile Groups Using Genetic Expression Programming and Multivariate Adaptive Regression Splines
| 题名： | Estimation of Clear-Water Local Scour at Pile Groups Using Genetic Expression Programming and Multivariate Adaptive Regression Splines |
| 正文语种： | 英文 |
| 作者： | S. M. Bateni1; H. R. Vosoughifar2; B. Truce3; D. S. Jeng4 |
| 作者单位： | 1Assistant Professor, Dept. of Civil and Environmental Engineering and Water Resources Research Center, Univ. of Hawaii at Manoa, 2540 Dole St., Holmes 342, Honolulu, HI 96822 (corresponding author). 2Postdoctoral Scholar, Dept. of Civil and Environmental Engineering and Water Resources Research Center, Univ. of HI at Manoa, 2540 Dole St., Holmes 342, Honolulu, HI 96822. Associate Professor, Dept. of Civil Engineering, Islamic Azad Univ., South Tehran Branch. No. 223, Azarshahr St., North Iranshahr St., Karimikhan-e-Zand Ave., Tehran, Iran. 3Graduate Student, Dept. of Civil and Environmental Engineering, Univ. of Hawaii at Manoa, 2540 Dole St., Holmes 383, Honolulu, HI 96822. 4Professor, Griffith School of Engineering, Griffith Univ. Coast Campus, Southport, QLD 4222, Australia. |
| 关键词： | Genetic expression programming; Multi adaptive regression splines; Local scour; Pile groups |
| 摘要： | The physical process of scour around pile groups is complex. Due to economical and geotechnical considerations, multiple pile bridge piers have become more common in bridge designs. Various empirical models have been developed to estimate scour depth at pile groups. However, these models are mostly based on the conventional statistical regression approaches and are not able to adequately capture the highly nonlinear and complex relationship between scour depth and its influential factors. In this study, genetic expression programming (GEP) and multivariate adaptive regression splines (MARS) were utilized to estimate clear-water local scour depth at pile groups using the flow, sediment, and pile characteristics. Two combinations of data were used to train the GEP and MARS models. The first combination included dimensional variables (e.g., mean flow velocity and depth, mean grain diameter, pile diameter). The second combination contained nondimensional parameters. Results indicated that GEP and MARS can accurately estimate scour depth. Both models yielded better results when the dimensional data were used. In addition, the MARS model with a root mean square error (RMSE) of 0.0220 m and correlation coefficient (R2) of 0.902 outperformed the GEP model with an RMSE of 0.0285 m and R2 of 0.834. Performance of the GEP and MARS models was compared with that of the existing equations. The comparison showed that both models perform better than the regression-based empirical equations. Finally, a sensitivity analysis showed that pile diameter has the most significant impact on equilibrium scour depth. |
| 出版年： | 2019 |
| 期刊名称： | Journal of Waterway, Port, Coastal, and Ocean Engineering |
| 卷： | 145 |
| 期： | 1 |
| 页码： | 1-11 |
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