Application Of Chimp-based ANFIS Model For Forecasting The Compressive Strength Of The Improved High-performance Concrete

In order to assess the compressive strength (CS) of high-performance concrete (HPC) prepared with fly ash and blast furnace slag, several artificial-based analytics were applied. This study, it was employed the Chimp optimizer ( CO) to identify optimal values of determinative factors of Support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), which could be adjusted to improve performance. The suggested approaches were established using 1030 tests, eight inputs (a primary component of mix designs, admixtures, aggregates, and curing age), and the CS as the forecasting objective. The outcomes were then contrasted with those found in the body of existing scientific literature. Calculation results point to the potential benefit of combining CO - SVR and CO - ANFIS study. When compared to the CO - SVR, the CO - ANFIS showed much higher R2 and lower Root means square error values. Comparing the findings shows that the created CO- ANFIS is superior to anything that has previously been published. In conclusion, the suggested CO - ANFIS analysis might be used to determine the proposed approach for estimating the CS of HPC augmented with blast furnace slag and fly ash.