Optimisation and prediction modeling of hardened concrete characteristics incorporating coal bottom Ash (CBA) via the response surface methodology (RSM) - Ku Meh K.M.F.; Mohd Zuki S.S.; Algaifi H.A.; Omar Z.; Shahidan S.; Shamsuddin S.-M.; Ihsan F. (Springer Science and Business Media B.V., 2025)
The use of Coal bottom ash (CBA) as a sand substitute in concrete has become an interesting research topic due to its potential to produce sustainable concrete. However, there is an ongoing need to optimise critical parameters in CBA concrete. Therefore, this research aims to optimise three independent variables involving CBA content, water-cement (WC) ratio, and curing ages based on the highest hardened properties, experimentally and theoretically. In particular, based on the face-centered central composite design (FC-CCD) of response surface methodology (RSM), 18 mixes of various combinations of the independent factors (WC ratio: 0.400.50, CBA replacement: 520%, and curing ages: 2856 days) were generated, and this investigation primary focused on two responses (compressive strength and water absorption). The proposed models were validated using analysis of variance (ANOVA) and other statistical parameters, and the findings suggested that both models of compressive strength and water absorption were significant and reliable, with p-values less than 0.0001 (p < 0.0001). The coefficient of determination (R2) values discovered were very high, with values of 0.99 and 0.94 for compressive strength and water absorption, respectively, indicating a significant relationship between the actual and predicted values. The results revealed that the compressive strength of CBA concrete was higher than that of the characteristic strength of the control mix (30MPa) for all levels of replacement percentage. The optimal conditions for compressive strength and minimal water absorption in CBA concrete were achieved when the lowest CBA replacement was 5%, and the WC ratio was 0.40 for 28 and 56 days. The validation findings revealed that the variation data for both models was less than 5%, indicating that the proposed equations had the potential to predict future observations. ? The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.