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Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling

Susom Dutta1, A. Ramach,ra Murthy2, Dookie Kim3, Pijush Samui4

Undergraduate Student, School of Mechanical & Building Sciences (SMBS), VIT University, Vellore, Tamil Nadu 632014, India. Email:
Senior Scientist, Computational Structural Mechanics Group, CSIR-Structural Engineering Research Centre, Taramani, Chennai-600 113. Email :
Professor, Department of Civil Engineering, Kunsan National University, Kunsan, Jeonbuk, South Korea. Email:
Associate Professor, National Institute of Technology, Patna, India, Email:

Computers, Materials & Continua 2017, 53(2), 157-174.


In the present scenario, computational modeling has gained much importance for the prediction of the properties of concrete. This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete (SCC). Three models, namely, Extreme Learning Machine (ELM), Adaptive Neuro Fuzzy Inference System (ANFIS) and Multi Adaptive Regression Spline (MARS) have been employed in the present study for the prediction of compressive strength of self compacting concrete. The contents of cement (c), sand (s), coarse aggregate (a), fly ash (f), water/powder (w/p) ratio and superplasticizer (sp) dosage have been taken as inputs and 28 days compressive strength (fck) as output for ELM, ANFIS and MARS models. A relatively large set of data including 80 normalized data available in the literature has been taken for the study. A comparison is made between the results obtained from all the above-mentioned models and the model which provides best fit is established. The experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete.


Cite This Article

S. . Dutta, A. R. . Murthy, D. . Kim and P. . Samui, "Prediction of compressive strength of self-compacting concrete using intelligent computational modeling," Computers, Materials & Continua, vol. 53, no.2, pp. 157–174, 2017.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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