A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Numerical study of the bridge pier scour using gene expression programming
Local scour is considered a natural phenomenon resulting from the erosive effect of flowing water. In this study, gene expression programming (GEP) was applied to predict the local scour depth around bridge piers for both live-bed and clear-water conditions. Input variables that were considered as an effective parameter on scour depth modeling include properties of sediment size, pier geometry, and upstream flow conditions. The training and testing stages of GEP models were carried out using laboratory data sets collected from different literature. Furthermore, these data sets were used to develop nonlinear regression equations to predict scour depth for both flow conditions. Testing results indicated that the GEP models predicted the scour depth with lower error and higher accuracy than those performed using new-regression models and previous empirical equations (RMSE = 0.09, MAE = 0.07, and R 2 = 0.9) for live-bed GEP model and (RMSE = 0.12, MAE = 0.08, and R 2 = 0.92) for clear-water GEP model.
Numerical study of the bridge pier scour using gene expression programming
Local scour is considered a natural phenomenon resulting from the erosive effect of flowing water. In this study, gene expression programming (GEP) was applied to predict the local scour depth around bridge piers for both live-bed and clear-water conditions. Input variables that were considered as an effective parameter on scour depth modeling include properties of sediment size, pier geometry, and upstream flow conditions. The training and testing stages of GEP models were carried out using laboratory data sets collected from different literature. Furthermore, these data sets were used to develop nonlinear regression equations to predict scour depth for both flow conditions. Testing results indicated that the GEP models predicted the scour depth with lower error and higher accuracy than those performed using new-regression models and previous empirical equations (RMSE = 0.09, MAE = 0.07, and R 2 = 0.9) for live-bed GEP model and (RMSE = 0.12, MAE = 0.08, and R 2 = 0.92) for clear-water GEP model.
Numerical study of the bridge pier scour using gene expression programming
Mohammed Saleh, Layla Ali (author) / Majeed, Sumayah Amal Al-din (author) / Alnasrawi, Fatin Abd el-kadhium M. (author)
Journal of Applied Water Engineering and Research ; 7 ; 287-294
2019-10-02
8 pages
Article (Journal)
Electronic Resource
Unknown
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