Hybrid Honey Badger-Based Model on Concrete with Ground Granulated Blast-Furnace Slag

Carbon emissions from Portland cement production can be reduced by adding groundgranulated blast furnace slag (GGBFS). fc is essential in the concrete mixes and construction of concrete forms. This work intends to present a sound methodology for the in-depth review of ML tactics to have better predictions of fc of concrete, including GGBFS applications. The research will propose schemes to anticipate the values of fc by using Coupled Random Forests (RF) analysis. The value of fc for the datasets collected in this research varies from 6.3 to 101.3 MPa . The investigation employed the Honey badger optimization (HBO) and Chimp optimization algorithm (COA) to enhance the efficacy of the RF approaches. This analysis will be significant in many ways, as this involves the use of COA and HBO tactics, GGBFS for estimating fc, comparison with previous research data and a full set of datasets with variation in input parameters. The anticipation of the mechanical traits of concrete employing such a method will give more accuracy and will improve the productivity of the anticipation pattern. The outcomes indicated that the merged RFHBO and RFCOA systems were able to estimate. R 2 values for RFHBO are 0.9961 in the training step, 0.9975 in the validation step, and 0.9971 in the test step. These results not only endorse the advancement of sustainable building materials but also establish a new standard for predictive modeling in concrete performance evaluation, rendering the approach beneficial for both scholarly research and practical engineering applications.