TY - EJOU
AU - Dutta, Susom
AU - Murthy, A. Ramach,ra
AU - Kim, Dookie
AU - Samui, Pijush
TI - Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling
T2 - Computers, Materials \& Continua
PY - 2017
VL - 53
IS - 2
SN - 1546-2226
AB - 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.
KW - Self Compacting Concrete (SCC)
KW - Compressive Strength
KW - Extreme Learning Machine (ELM)
KW - Adaptive Neuro Fuzzy Inference System (ANFIS)
KW - Multi Adaptive Regression Spline (MARS)
DO - 10.3970/cmc.2017.053.167