Strength Characteristics and Prediction of Ternary Blended Cement Building Material Using RSM and ANN

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.

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