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Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams

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Abstract

This study presents a novel artificial intelligence (AI) technique based on two support vector machine (SVM) models and symbiotic organisms search (SOS) algorithm, called “optimized support vector machines with adaptive ensemble weighting” (OSVM-AEW), to predict the shear capacity of reinforced-concrete (RC) deep beams. This ensemble learning-based system combines two supervised learning models—the support vector machine (SVM) and least-squares support vector machine (LS-SVM)—with the SOS optimization algorithm as the optimizer. In OSVM-AEW, SOS is integrated to simultaneously select the optimal parameters of SVM and LS-SVM, and control the coordination process of the learning outputs. Experimental results show that OSVM-AEW achieves the greatest evaluation criteria for coefficient of correlation (0.9620), coefficient of determination (0.9254), mean absolute error (0.3854 MPa), mean absolute percentage error (7.68%), and root-mean-squared error (0.5265 MPa). This paper demonstrates the successful application of OSVM-AEW as an efficient tool for helping structural engineers in the RC deep beams design process.

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Acknowledgments

The authors gratefully acknowledge that the present research is supported by The Ministry of Research, Technology, and Higher Education of the Republic of Indonesia (No: 123.58/D2.3/KP/2018) under the “World Class Professor” (WCP) Research Grant Scheme.

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Authors and Affiliations

  1. Department of Civil Engineering, Petra Christian University, Jalan Siwalankerto 121-131, Surabaya, 60236, Indonesia

    Doddy Prayogo

  2. Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd, Taipei, 106, Taiwan, ROC

    Min-Yuan Cheng & Yu-Wei Wu

  3. Department of Construction Engineering and Management, Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh, City (VNU-HCM), 268 Ly Thuong Kiet St., Dist. 10, Ho Chi Minh City, Viet Nam

    Duc-Hoc Tran

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  1. Doddy Prayogo
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Correspondence to Doddy Prayogo.

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Appendix 1

Appendix 1

See Tables 7 and 8.

Table 7 Training data set
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Table 8 Testing data set
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Prayogo, D., Cheng, MY., Wu, YW. et al. Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams. Engineering with Computers 36, 1135–1153 (2020). https://doi.org/10.1007/s00366-019-00753-w

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