Neural Computing and Applications

, Volume 28, Supplement 1, pp 1083–1100 | Cite as

Kernel-based models for prediction of cement compressive strength

  • Mohit Verma
  • A. Thirumalaiselvi
  • J. Rajasankar
Original Article


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.


Support vector regression Relevance vector machine Gaussian process regression Cement compressive strength Particle swarm optimization Symbiotic organism search 



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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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Mohit Verma
    • 1
    • 2
  • A. Thirumalaiselvi
    • 1
    • 2
  • J. Rajasankar
    • 1
    • 2
  1. 1.CSIR – Structural Engineering Research CentreChennaiIndia
  2. 2.Academy of Scientific and Innovative ResearchChennaiIndia

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