This present study evaluates the effect of silica modulus (Ms) and curing temperature on strengths and the microstructures of binary blended alkali-activated volcanic ash and limestone powder mortar. Mortar samples were prepared using mass ratio of combined Na2SiO3(aq)/10 M NaOH(aq) of 0.5 to 1.5 at an interval of 0.25, corresponding to Ms of 0.52, 0.72, 0.89, 1.05 and 1.18, respectively, and sole 10 M NaOH(aq). Samples were then subjected to ambient room temperature, and the oven-cured temperature was maintained from 45 to 90 °C at an interval of 15 °C for 24 h. The maximum achievable 28-day strength was 27 MPa at Ms value of 0.89 cured at 75 °C. Samples synthesised with the sole 10 M NaOH(aq) activator resulted in a binder with a low 28-day compressive strength (15 MPa) compared to combined usage of Na2SiO3(aq)/10 M NaOH(aq) activators. Results further revealed that curing at low temperatures (25 °C to 45 °C) does not favour strength development, whereas higher curing temperature positively enhanced strength development. More than 70% of the 28-day compressive strength could be achieved within 12 h of curing with the usage of combined Na2SiO3(aq)/10 M NaOH(aq). XRD, FTIR and SEM + EDX characterisations revealed that activation with combined Na2SiO3(aq)/10 M NaOH(aq) leads to the formation of anorthite (CaAl2Si2O8), gehlenite (CaO.Al2O3.SiO2) and albite (NaAlSi3O8) that improve the amorphosity, homogeneity and microstructural density of the binder compared to that of samples synthesised with sole 10 M NaOH(aq).
This paper presents the outcome of work conducted to develop models for the prediction of compressive strength (CS) of alkali-activated limestone powder and natural pozzolan mortar (AALNM) using hybrid genetic algorithm (GA) and support vector regression (SVR) algorithm, for the first time. The developed hybrid GA-SVR-CS1, GA-SVR-CS3, and GA-SVR-CS14 models are capable of estimating the one-day, three-day, and 14-day compressive strength, respectively, of AALNM up to 96.64%, 90.84%, and 93.40% degree of accuracy as measured on the basis of correlation coefficient between the measured and estimated values for a set of data that is excluded from training and testing phase of the model development. The developed hybrid GA-SVR-CS28E model estimates the 28-days compressive strength of AALNM using the 14-days strength, it performs better than hybrid GA-SVR-CS28C model, hybrid GA-SVR-CS28B model, hybrid GA-SVR-CS28A model, and hybrid GA-SVR-CS28D model that respectively estimates the 28-day compressive strength using three-day strength, one day-strength, all the descriptors and seven day-strength with performance improvement of 103.51%, 124.47%, 149.94%, and 262.08% on the basis of root mean square error. The outcome of this work will promote the use of environment-friendly concrete with excellent strength and provide effective as well as efficient ways of modeling the compressive strength of concrete.