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Computers and Concrete Volume 31, Number 2, February 2023 , pages 85-95 DOI: https://doi.org/10.12989/cac.2023.31.2.085 |
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An adaptive neuro-fuzzy inference system (ANFIS) model to predict the pozzolanic activity of natural pozzolans |
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Elif Varol, Didem Benzer and Nazli Tunar Özcan
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| Abstract | ||
| Natural pozzolans are used as additives in cement to develop more durable and high-performance concrete. Pozzolanic activity index (PAI) is important for assessing the performance of a pozzolan as a binding material and has an important effect on the compressive strength, permeability, and chemical durability of concrete mixtures. However, the determining of the 28 days (short term) and 90 days (long term) PAI of concrete mixtures is a time-consuming process. In this study, to reduce extensive experimental work, it is aimed to predict the short term and long term PAIs as a function of the chemical compositions of various natural pozzolans. For this purpose, the chemical compositions of various natural pozzolans from Central Anatolia were determined with X-ray fluorescence spectroscopy. The mortar samples were prepared with the natural pozzolans and then, the short term and the long term PAIs were calculated based on compressive strength method. The effect of the natural pozzolans' chemical compositions on the short term and the long term PAIs were evaluated and the PAIs were predicted by using multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) model. The prediction model results show that both reactive SiO2 and SiO2+Al2O3+Fe2O3 contents are the most effective parameters on PAI. According to the performance of prediction models determined with metrics such as root mean squared error (RMSE) and coefficient of correlation (R2), ANFIS models are more feasible than the multiple regression model in predicting the 28 days and 90 days pozzolanic activity. Estimation of PAIs based on the chemical component of natural pozzolana with high-performance prediction models is going to make an important contribution to material engineering applications in terms of selection of favorable natural pozzolana and saving time from tedious test processes. | ||
| Key Words | ||
| adaptive neuro-fuzzy inference system (ANFIS); multiple linear regression analyses; natural pozzolana; pozzolanic activity index | ||
| Address | ||
| Elif Varol and Nazli Tunar Özcan: Department of Geological Engineering, Hacettepe University, 06800, Beytepe, Ankara, Türkiye Didem Benzer: Turkish Cement Manufacturers' Association (TÇMB), 06800, Ankara, Türkiye | ||