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

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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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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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Correspondence to Doddy Prayogo.

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Appendix 1

Appendix 1

See Tables 7 and 8.

Table 7 Training data set
Table 8 Testing data set

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Prayogo, D., Cheng, M., Wu, Y. et al. Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams. Engineering with Computers (2019) doi:10.1007/s00366-019-00753-w

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  • Shear strength
  • RC deep beams
  • Ensemble model
  • Symbiotic organisms search
  • Support vector machine