| Abstrakt: |
This paper presents the use of data-driven models, namely Gene expression programming (GEP), M5 model tree (M5-TREE), Multivariate adaptive regression spline (MARS), and Adaptive neuro-fuzzy inference system (ANFIS) to predict bridge pier scour depth. Only 213 data sets of the live bed conditions from laboratory tests and field data measurements were considered for the present analysis. The gamma test has been performed to determine the ideal input combinations for model development. Five main non-dimensional parameters: Sediment Coarseness ratios, Froude number, flow intensity, gradation coefficient of the bed material, and shape factor, were found to be the vital input parameters for scour depth model development. The results of these 4 data-driven models were compared with the results of nine conventional empirical equations using the performance criterion correlation coefficient (R), root mean squared error (RMSE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (E), and index of agreement (Id) and graphical analysis. Based on values of the performance indices, ANFIS model was selected with R=0.986, RMSE=0.062, MAPE=6.767, E=0.975 and Id=0.987. The results also show the outperformance of ANFIS model over the other selected data driven models and conventional empirical equations. This model can also be applied to the modelling of bridge pier scour in clear water conditions and can provide insight into the efficacy of modelling approaches in hydraulic properties. [ABSTRACT FROM AUTHOR] |
