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Kernel-based models for prediction of cement compressive strength

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Abstract

This paper employs three different kernel-based models—support vector regression (SVR), relevance vector machine (RVM) and Gaussian process regression (GPR)—for the prediction of cement compressive strength. The input variables for the model are taken as C3S (%), SO3 (%), Alkali (%) and Blaine (cm2/g), while the output is 28-day cement compressive strength (N/mm2) of the cement. The hyperparameters of the SVR are obtained using two different metaheuristic optimization algorithms—particle swarm optimization (PSO) and symbiotic organism search (SOS). Trial-and-error-based approach is used for arriving at the hyperparameters of RVM and GPR. The compressive strength predicted using different kernel-based models is also compared with that obtained from ANN and fuzzy logic models reported in the literature. The performance of the different kernel-based models is benchmarked using six different error indices and residual analysis. The performance of the kernel-based models is found to be at par with ANN. The better generalization capability and excellent empirical performance of the kernel-based models overcome the disadvantages associated with ANN and provide a good tool for the prediction of the cement compressive strength.

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Acknowledgments

Authors would also like to thank and acknowledge the help received from their colleagues of Shock and Vibration Group, CSIR-SERC and Mr. Prabhat Ranjan Prem, Scientist, AML, CSIR - SERC. This paper is being published with the kind permission of the Director, CSIR-SERC, Chennai.

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Authors and Affiliations

  1. CSIR – Structural Engineering Research Centre, Chennai, India

    Mohit Verma, A. Thirumalaiselvi & J. Rajasankar

  2. Academy of Scientific and Innovative Research, Chennai, India

    Mohit Verma, A. Thirumalaiselvi & J. Rajasankar

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  1. Mohit Verma
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  2. A. Thirumalaiselvi
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  3. J. Rajasankar
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Correspondence to Mohit Verma.

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Verma, M., Thirumalaiselvi, A. & Rajasankar, J. Kernel-based models for prediction of cement compressive strength. Neural Comput & Applic 28 (Suppl 1), 1083–1100 (2017). https://doi.org/10.1007/s00521-016-2419-0

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  • DOI: https://doi.org/10.1007/s00521-016-2419-0

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