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

Bui, Dieu Tien (author)
Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Shirzadi, Ataollah (author)
Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
Amini, Ata (author)
Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
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Shahabi, Himan (author)
Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj , Iran. Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan
Al-Ansari, Nadhir, 1947- (author)
Luleå tekniska universitet,Geoteknologi
Hamidi, Shahriar (author)
Department of Water Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran
Singh, Sushant K. (author)
Department of Health, Insurance & Life Sciences, Data & Analytics, Virtusa Corporation, Irvington, NJ, USA
Pham, Binh Thai (author)
Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
Ahmad, Baharin Bin (author)
Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia
Ghazvinei, Pezhman Taherei (author)
Department of Civil Engineering, Technical and Engineering College, Ale Taha University, Tehran, Iran
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Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran (creator_code:org_t)
2020-02-03
2020
English.
In: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:3, s. 1-24
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Geoteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Geotechnical Engineering (hsv//eng)

Keyword

scour depth
complex piers
pile cap
machine learning algorithms
ensemble models
Soil Mechanics
Geoteknik

Publication and Content Type

ref (subject category)
art (subject category)

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