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A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers
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.
Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran.
Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj , Iran. Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan.
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2020 (English)In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 12, no 3, p. 1-24, article id 1063Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Switzerland: MDPI, 2020. Vol. 12, no 3, p. 1-24, article id 1063
Keywords [en]
scour depth, complex piers, pile cap, machine learning algorithms, ensemble models
National Category
Geotechnical Engineering
Research subject
Soil Mechanics; Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-77620DOI: 10.3390/su12031063OAI: oai:DiVA.org:ltu-77620DiVA, id: diva2:1390920
Note

Validerad;2020;Nivå 2;2020-02-04 (johcin)

Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-04Bibliographically approved

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