Prediction of the depth of local scouring at a bridge pier using a gene expression programming method

摘要

Local scouring around the piers of a bridge is the one of the major reasons for bridge failure, potentially resulting in heavylosses in terms of both the economy and human life. Prediction of accurate depth of local scouring is a difficult task dueto the many factors that contribute to this process, however. The main aim of this study is thus to offer a new formula forthe prediction the local depth of scouring around the pier of a bridge using a modern fine computing modelling techniqueknown as gene expression programming (GEP), with data obtained from numerical simulations used to compareGEP performance with that of a standard non-linear regression (NLR) model. The best technique for prediction of thelocal scouring depth is then determined based on three statistical parameters: the determination coefficient (R2), meanabsolute error (MAE), and root mean squared error (RMSE). A total data set of 243 measurements, obtained by numericalsimulation in Flow-3D, for intensity of flow, ratio of pier width, ratio of flow depth, pier Froude number, and pier shapefactor is divided into training and validation (testing) datasets to achieve this. The results suggest that the formula fromthe GEP model provides better performance for predicting the local depth of scouring as compared with conventionalregression with the NLR model, with R~2= 0.901, MAE = 0.111, and RMSE = 0.142. The sensitivity analysis results furthersuggest that the ratio of the depth of flow has the greatest impact on the prediction of local scour depth as comparedto the other input parameters. The formula obtained from the GEP model gives the best predictor of depth of scouring,and, in addition, GEP offers the special feature of providing both explicit and compressed arithmetical terms to allowcalculation of such depth of scouring.