Neuro-fuzzy system development to estimate the compressive strength of improved high-performance concrete

High-performance concrete (HPC) outperforms regular concrete due to incorporating additional components that go beyond the typical ingredients used in standard concrete. Various artificial analytical methods were employed to assess the compressive strength (CS) of high-performance concrete containing fly ash (FA) and blast furnace slag (BFS). The primary objective of this study was to present a practical approach for a comprehensive evaluation of machine learning algorithms in predicting the CS of HPC. The study focuses on utilizing the adaptive neuro-fuzzy inference system (ANFIS) to develop models for predicting HPC characteristics. To enhance the performance of ANFIS methods, the study incorporates the arithmetic optimization algorithm (AOA) and equilibrium optimizer (EO) (abbreviated as ANAO and ANEO, respectively). Notably, this research introduces novelty through the application of the AOA and EO, the evaluation of HPC with additional components, the comparison with prior literature, and the utilization of a large dataset with multiple input variables. The results indicate that the combined ANAO and ANEO systems demonstrated strong estimation capabilities, with R 2 values of 0.9941 and 0.9975 for the training and testing components of ANAO, and 0.9878 and 0.9929 for ANEO, respectively. The results comparison of this study presented the comprehensiveness and reliability of the created ANFIS model optimized with AOA for predicting the HPC’s CS improved with FA and BFS, which could be applicable for practical usages.