ISSN 0862-5468 (Print), ISSN 1804-5847 (online) 

Ceramics-Silikáty 64, (4) 407 - 417 (2020)

Adewumi Adeshina Adewale 1, Ismail Mohammad 1, Mohd Ariffin Mohd Azreen 2, Yusuf Moruf Olalekan 3,Salami Hamza Onoruoiza 4, Owolabi Taoreed O. 5, Maslehuddin Mohammed 6
1 School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
2 School of Civil Engineering, Forensic Engineering Centre, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
3 Department of Civil Engineering, University of Hafr Al Batin, 31991 Hafr Al-Batin, Saudi Arabia
4 College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin 31991, Saudi Arabia
5 Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko, Ondo State, Nigeria
6 Center for Engineering Research, Research Institute, King Fahd University of Petroleum and Minerals, 31261 Dhahran, Saudi Arabia

Keywords: Alkaline-activated, Compressive strength, Stepwise-regression, Genetic algorithm; Natural-pozzolan and limestone powder

An experimental investigation was conducted to synthesise an alkali-activated binder using natural pozzolan and limestone powder. The effect of the mix parameters such as the binder ratio, NaOH molarity (4 - 14M), curing temperature (25 - 90 °C), sodium silicate to sodium hydroxide ratio (0.5 - 1.5), fine aggregate to binder ratio (1.4 - 2.2), alkaline activator to binder ratio (0.45 - 0.55) and curing days (1, 3, 7, 14, 28) were determined on the compressive strength of the mortar. A stepwise regression algorithm was developed to estimate the compressive strength of the mortar. Five different models (I - V) were developed using 130 experimental data sets with seven descriptors. Bayesian information criterion (BIC), Akaike's information criterion (AIC) and the sum of square error (SSE) criteria were used to fit the developed model in order to select the best model. The cubic with interactions model (V) is characterised with a high correlation coefficient (97.2%), the lowest root means square error (1.672), and the lowest mean absolute error (1.313) in comparison with the other four models (I-IV). The outcomes of this work could provide an effective and efficient way of modelling the compressive strength of environmentally friendly binders with minimal experimental stress, limit the uncertainties and errors inherent in a laboratory.

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doi: 10.13168/cs.2020.0028
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