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Structural Engineering and Mechanics Volume 68, Number 6, December25 2018 , pages 691-700 DOI: https://doi.org/10.12989/sem.2018.68.6.691 |
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Prediction of compressive strength of GGBS based concrete using RVM |
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P. K. Prasanna, A. Ramachandra Murthy and K. Srinivasu
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| Abstract | ||
| Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations. | ||
| Key Words | ||
| relevance vector machine; GGBS, concrete; compressive strength; variance | ||
| Address | ||
| P. K. Prasanna: Dept of Civil Engineering, VR Siddhartha Engineering College, Vijayawada, Acharya Nagarjuna University A.P., India A. Ramachandra Murthy: CSIR-Structural Engineering Research Centre, Chennai – 600113, India K. Srinivasu: RVR&JC College of Engineering, Guntur, A.P., India | ||