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

Dieu Tien Bui, Ataollah Shirzadi, Ata Amini, Himan Shahabi, Nadhir Al-Ansari, Shahriar Hamidi, Sushant Singh, Binh Thai Pham, Baharin Bin Ahmad and Pezhman Taherei Ghazvinei
Additional contact information
Dieu Tien Bui: Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
Ataollah Shirzadi: Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
Ata Amini: Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj 66177-15175, Iran
Himan Shahabi: Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
Nadhir Al-Ansari: Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden
Shahriar Hamidi: Department of Water Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj 66177-15175, Iran
Binh Thai Pham: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Baharin Bin Ahmad: Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
Pezhman Taherei Ghazvinei: Department of Civil Engineering, Technical and Engineering College, Ale Taha University, Tehran 1488836164, Iran

Sustainability, 2020, vol. 12, issue 3, 1-24

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.

Keywords: scour depth; complex piers; pile cap; machine learning algorithms; ensemble models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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