A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

Abstract

Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 < R < 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP.

Scour Detection with Monitoring Methods and Machine Learning Algorithms—A Critical Review

“…The dimensions of the flume were 20m (longitudinal), 0.7m (depth), and 0.9m (width). [110] obtained experimental local scour depth measurements of complex piers from National Hydraulic Research Institute of Malaysia and Sharif University of Technology in Iran. The laboratory models scaled existing bridges of Malesia.…”

“…The predictions obtained through reduced error pruning tree and other machine learning algorithms were significantly better than the scour depths computed with the empirical models of FDOT and HEC-18. Both [109] and [110] were able to increase the prediction power of standalone algorithms with the hybrid algorithms they proposed. [111] proposed a self-adaptive evolutionary extreme learning machine to predict scour around bridge piers.…”

Proposing a Feature Selection Approach to Predict Learners' Performance in Virtual Learning Environments (VLEs)

“…Hunt's algorithm was the basis of many decision tree algorithms such as ID3, C4.5, CART, and REPTree [13]. REPTree is a decision tree based on a splitting criterion known as the information gain ratio [18]. Equation 1 represents the formula of gain ratio [19,20].…”

Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment

“…To do this, we first considered potential classes for our conditioning factors based on previous work [78][79][80][81]. Then, we established classes to capture the ranges of factor values characteristic of our study area [29,82,83].…”