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Open AccessArticle

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

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Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
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Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
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Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
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Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj 66177-15175, Iran
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Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
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Board Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj 66177-15175, Iran
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Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden
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Department of Water Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj 66177-15175, Iran
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Lead AI-ML Scientist, Department of Health, Insurance & Life Sciences, Data & Analytics, Virtusa Corporation, Irvington, NJ 07111, USA
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
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Department of Civil Engineering, Technical and Engineering College, Ale Taha University, Tehran 1488836164, Iran
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Authors to whom correspondence should be addressed.
Sustainability 2020, 12(3), 1063; https://doi.org/10.3390/su12031063
Received: 2 October 2019 / Revised: 19 December 2019 / Accepted: 23 December 2019 / Published: 3 February 2020
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. View Full-Text
Keywords: scour depth; complex piers; pile cap; machine learning algorithms; ensemble models scour depth; complex piers; pile cap; machine learning algorithms; ensemble models
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MDPI and ACS Style

Tien Bui, D.; Shirzadi, A.; Amini, A.; Shahabi, H.; Al-Ansari, N.; Hamidi, S.; Singh, S.K.; Thai Pham, B.; Ahmad, B.B.; Ghazvinei, P.T. A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers. Sustainability 2020, 12, 1063.

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