In this study, steel slag (SS) and ground coal bottom ash (GCBA) were utilized to partially substitute for cement in manufacturing ternary blended cement mortar. The replacement ratios of both SS and GCBA ranged from 0% to 20%, and the total replacement ratio varied from 0 to 40%. Response-surface methodology (RSM) and an artificial neural network (ANN) were employed to establish models with which the effects of the various combined contents of SS and GCBA on the distribution of 28-day strength and 91-day strength could be identified. The results showed that the combination of SS and GCBA had a positive effect on strength at a low replacement ratio, while it had an adverse effect on strength at a high replacement ratio. At a late curing age, the pozzolanic reaction of GCBA contributes to the strength enhancement. A total of 15 out of 27 experimental data were used to establish the RSM and ANN models. Through analysis of variance (ANOVA), the models established by RSM were well-fitted with the experimental data. The ANN-trained models also exhibited a good fit with the experimental data, as indicated by an R² of >0.99. The remaining 12 out of 27 experimental data were used for the validation of the developed models, and the performances of the RSM and ANN models in prediction were compared. In conclusion, the ANN showed a better performance in strength prediction.

This study evaluates the effects of steel slag powder (SSP), fly ash (FA), and steel slag sand (SSS) on mortar compressive strength. A response surface methodology (RSM) based on central composite design (CCD) was employed to model 7-day, 28-day, and 91-day strength development, considering three quantitative variables: SSP, FA, and SSS. Statistical results confirmed the reduced cubic models were significant and predictive (R² > 0.97), with non-significant lack of fit and adequate precision. Experimental results revealed that SSP and FA negatively affected early-age strength due to dilution effects and low initial reactivity, whereas SSS slightly improved it by enhancing particle packing. At later ages, SSP exhibited nonlinear effects, where moderate dosages enhanced strength, while excessive replacement led to strength reduction. SSS showed a continuously positive contribution across all ages, particularly at 91 days. Perturbation plots, contour maps, and gradient analyses indicated that SSS played a dominant role at later stages and that maintaining a proper balance among supplementary cementitious materials (SCMs) and aggregate replacements is crucial. The developed models and response surfaces provide practical guidance for designing slag-based mortars with improved mechanical properties and enhanced sustainability.

The high demand from concrete manufacturing and applications which sourced from non-renewable source have led to a severe environmental issue. Therefore, sustai