Kernel-based models for prediction of cement compressive strength.

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Bibliographic Details
Title:	Kernel-based models for prediction of cement compressive strength.
Authors:	Verma, Mohit mohitverma@serc.res.in, Thirumalaiselvi, A., Rajasankar, J.
Source:	Neural Computing & Applications. Dec2017 Supplement 1, Vol. 28, p1083-1100. 18p.
Subjects:	Cement testing, Compressive strength, Kernel functions, Particle swarm optimization, Support vector machines
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 CS (%), SO (%), Alkali (%) and Blaine (cm/g), while the output is 28-day cement compressive strength (N/mm) 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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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 CS (%), SO (%), Alkali (%) and Blaine (cm/g), while the output is 28-day cement compressive strength (N/mm) 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. [ABSTRACT FROM AUTHOR]
ISSN:	09410643
DOI:	10.1007/s00521-016-2419-0