Evaluation of nonparametric tree models for predicting the scour depth of bridge piers
Abstract
R. Dalvand, M. Komasi
One of the main causes of damage to bridges, especially during flood event is the scour around the bridge. Determination of the depth of scour around the bridge piers plays a very important role in designing the bridges against this destructive factor. The complexity of the bridge scour and the effects of different parameters on its estimation more clearly reveal the necessity of using a nonlinear and comprehensive model in this field. In this present study, decision tree models, as nonparametric models, are used to estimate the scour depth. Furthermore, the statistics of different bridges and four tree methods are used. The data used to train and test decision trees including flow the velocity of upstream, the median grain size, flow depth, and the pier width, the skew of the pier to approach flow, the length of the pier, the grain size of bed material for which 84 percent is finer, a multiplying factor, input variables, and the depth of scour as output in the model. 75% of the available data is used for model training and the remaining 25% for testing. The results show that among the four models (CART, C5, QUEST, CHAID) examined, C5 model, considering the comparison of the root mean square error parameters and the coefficient of determination, is more accurate in computing the scour depth of the bridge, the amount coefficients of determination in this model is in training and testing steps are 0.92 and 0.76, as well as the mean square error values of the error is 0.56 and 0.72 respectively. Furthermore, the results reveal the QUEST model does not have a proper accuracy in scour depth estimation. Furthermore, the analysis of the models shows flow depth, the flow velocity in the upstream have the greatest effect on the scour depth.
Keywords: Scouring; sensitivity analysis; tree models
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