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

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ID: 113017
2020
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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.
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bui2020sustainabilitya Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Dieu Tien Bui;Ataollah Shirzadi;Ata Amini;Himan Shahabi;Nadhir Al-Ansari;Shahriar Hamidi;Sushant K. Singh;Binh Thai Pham;Baharin Bin Ahmad;Pezhman Taherei Ghazvinei;Tien Bui, Dieu;Shirzadi, Ataollah;Amini, Ata;Shahabi, Himan;Al-Ansari, Nadhir;Hamidi, Shahriar;Singh, Sushant K.;Thai Pham, Binh;Ahmad, Baharin Bin;Ghazvinei, Pezhman Taherei;
Journal sustainability
Year 2020
DOI
10.3390/su12031063
URL
https://www.mdpi.com/2071-1050/12/3/1063/htm
Keywords
ensemble models machine learning algorithms scour depth complex piers pile cap

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