Prediction of local scour depth at bridge piers under ice cover
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Abstract:
The local scour of bridge piers is intensified by the existence of ice cover.Correct calculation of local scour pit depth of bridge piers under ice cover is very important for the safety design of bridge.Many experts and scholars studies the free flow under the condition of bridge pier local scour pit depth by experimental research and artificial intelligence method.The study of bridge pier local scour problem under the condition of ice compared to the free flow conditions in terms of local scour of bridge piers research started late,and the research is relatively small,so the use of artificial intelligence method is helpful to predict the laboratory bridge pier local scour pit depth. Based on the principle of dimensional analysis,the relevant factors affecting the local scour of bridge piers were analyzed by the support vector machine (SVM) and BP neural network based on the experimental data of local scour of bridge piers under the conditions of clear water scour in the laboratory.Three-quarters of the test data were taken as the training data set of the scour pit prediction model,and one-fourth as the test data set of the scour pit prediction model.When calculating the local scour pit depth of bridge pier under the condition of open flow,the input factors of the model are:flow Froude number Fr,the ratio of water depth to pier diameter h/D,the ratio of median particle size of bed sand to pier diameter d50/D.Output factor:scour pit depth ds.The local scour pit depth of bridge piers were calculated using the 65-1 and 65-2 revisions in the Chinese Highway Engineering Hydrological Survey and Design Code (2015) and the HEC-18 formula in the American Code,and the calculated results were compared with the predicted results of SVM and BP neural network model. When predicting the local scour pit depth of bridge pier under ice sheet conditions,the input factors of the model are:flow Froude number Fr,the ratio of water depth to pier diameter h/D,the ratio of median particle size of bed sand to pier diameter d50/D,the ratio of ice cover roughness to channel bed roughness ni/nb.Output factor:scour pit depth DS,and the predicted results are compared with the test results.The correlation coefficient (r),root mean square error (δRMSE),mean absolute percentage error (δMAPE),and determination coefficient (R2) was used as the evaluation indexes of the prediction results.When predicting the local scour pit depth of the bridge pier under the condition of open flow,the rof BP neural network model and SVM model are 0.89 and 0.88,and δMAPE is 38.8% and 31%,respectively.The rand δMAPEof local scour pit depth of piers are 0.83 and 0.53 cm,respectively,and 61.2% and 189%,respectively,according to Chinese code formula and American code formula.When predicting the scour pit depth under ice sheet conditions,the predicted r values are 0.78 and 0.73,and δMAPEvalues are 43% and 46%,respectively. By integrating the test data of pier local scour under the current ice sheet and open flow conditions,the depth of the pier local scour pit was predicted by BP neural network model,SVM model,Chinese code,and American code.It is found that the study of pier local scour under open flow is enlightening,and the relationship between pier local scour depth and water depth,velocity,and pier diameter under open flow and ice cover is significant,there is a nonlinear relationship between the influence factors.When the BP neural network model and SVM model were used to predict the local scour depth of bridge piers under open flow,the accuracy is generally higher than the calculation results of Chinese code and American code.BP neural network model and SVM model showed good performance in predicting the local scour pit depth of bridge piers under open flow and ice cap,and the prediction results have high accuracy,which can provide a certain reference for the safety design of bridges.
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