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

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

Bui, Dieu Tien (författare): 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 (författare): Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

Amini, Ata (författare): Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran

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Shahabi, Himan (författare): 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- (författare): Luleå tekniska universitet,Geoteknologi

Hamidi, Shahriar (författare): Department of Water Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

Singh, Sushant K. (författare): Department of Health, Insurance & Life Sciences, Data & Analytics, Virtusa Corporation, Irvington, NJ, USA

Pham, Binh Thai (författare): Institute of Research and Development, Duy Tan University, Da Nang, Vietnam

Ahmad, Baharin Bin (författare): Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia

Ghazvinei, Pezhman Taherei (författare): 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
Engelska.
Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:3, s. 1-24

Relaterad länk:: https://ltu.diva-por... (primary) (Raw object); visa fler...; https://www.mdpi.com...; https://urn.kb.se/re...; https://doi.org/10.3...; visa färre...

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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.

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Av författaren/redakt...: Bui, Dieu Tien; Shirzadi, Ataoll ...; Amini, Ata; Shahabi, Himan; Al-Ansari, Nadhi ...; Hamidi, Shahriar; visa fler...; Singh, Sushant K ...; Pham, Binh Thai; Ahmad, Baharin B ...; Ghazvinei, Pezhm ...; visa färre...

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

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