Optimization of Mechanical Properties for High-Strength Concrete by Artificial Neural Network Modelling

After water, the next widely consumed material is concrete for construction on planet. Combinations of concrete were developed to enhance the different characteristics of concrete. An investigation into the prospective use of SCMs as a partial substitution in high-strength concrete or HSC has been carried out. In the last 3 decades, supplementary cementitious materials (SCM) like flyash (FA), metakaolin (MK), rice husk ash (RHA), Alccofine (AF), and slag (GGBFS) were extensively implemented as cement substitutes because they may substantially improve concrete durability and strength characteristics when compared with conventional Portland cement. Because different SCMs may be considered and added as admixtures, HSC can be manufactured with low water-to-binder (w/b) ratios. During the experimental phase, HSC of M40 grade has been produced by substituting cement with GGBFS and AF in different weight ratios for 7 and 28 days of curing to attain the compressive strength (CS) with the percentages of replacement of Alccofine 1203 from 5%, to 10% also GGBFS varying from 10 to 15% worked in this investigation. In addition to the experimental work, numerical modelling was done with the CS results for 7 and 28 days to evaluate the prediction of accurate results with numerical prediction for MAE and RMSE using Minitab software.