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Open AccessArticle

Neural Computing Improvement Using Four Metaheuristic Optimizers in Bearing Capacity Analysis of Footings Settled on Two-Layer Soils

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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
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Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
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Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800 Bø i Telemark, Norway
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(23), 5264; https://doi.org/10.3390/app9235264
Received: 4 November 2019 / Revised: 25 November 2019 / Accepted: 27 November 2019 / Published: 3 December 2019
(This article belongs to the Special Issue Artificial Intelligence in Smart Buildings)
This study outlines the applicability of four metaheuristic algorithms, namely, whale optimization algorithm (WOA), league champion optimization (LCA), moth–flame optimization (MFO), and ant colony optimization (ACO), for performance improvement of an artificial neural network (ANN) in analyzing the bearing capacity of footings settled on two-layered soils. To this end, the models estimate the stability/failure of the system by taking into consideration soil key factors. The complexity of each network is optimized through a sensitivity analysis process. The performance of the ensembles is compared with a typical ANN to evaluate the efficiency of the applied optimizers. It was shown that the incorporation of the WOA, LCA, MFO, and ACO algorithms resulted in 14.49%, 13.41%, 18.30%, and 35.75% reductions in the prediction error of the ANN, respectively. Moreover, a ranking system is developed to compare the efficiency of the used models. The results revealed that the ACO–ANN performs most accurately, followed by the MFO–ANN, WOA–ANN, and LCA–ANN. Lastly, the outcomes demonstrated that the ACO–ANN can be a promising alternative to traditional methods used for analyzing the bearing capacity of two-layered soils. View Full-Text
Keywords: bearing capacity analysis; artificial neural network; metaheuristic algorithms bearing capacity analysis; artificial neural network; metaheuristic algorithms
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MDPI and ACS Style

Moayedi, H.; Bui, D.T.; Thi Ngo, P.T. Neural Computing Improvement Using Four Metaheuristic Optimizers in Bearing Capacity Analysis of Footings Settled on Two-Layer Soils. Appl. Sci. 2019, 9, 5264.

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