Prediction of concrete mechanical properties using electrical resistivity: an ANFIS based soft computing approach

● The effects of concrete grade, specimen age, and electrical resistivity on compressive, flexural, and tensile strengths were examined experimentally. ● A lower mean deviation and a lower root mean square error (RMSE) value were obtained by the adaptive neuro-fuzzy inference system (ANFIS). ● Regression models with nonlinear and linear interaction terms were proposed to predict the mechanical properties of concrete with high R2 values greater than 0.94. ● Additional datasets were used to validate the models, which showed accuracy with an average error of less than 10% when compared to experimental results. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.