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

This study explores the application of electrical resistivity as a non-destructive method for evaluating concrete properties in reinforced structures. It investigates correlations between surface electrical resistivity (ρ) and fundamental mechanical strengths—compressive (fc), splitting tensile (ft), and flexural (fz) across three concrete grades (M20, M30, M40). Using an Adaptive Neuro-Fuzzy Inference System (ANFIS) in MATLAB, experimental data are analysed to minimize root mean square error (RMSE). The study develops regression models incorporating nonlinear and interaction terms to predict compressive, flexural, and tensile strengths, achieving high coefficients of determination (R² values of 0.94, 0.98, and 0.98 respectively). Validation against experimental data confirms model accuracy, with errors consistently below 10%. This innovative application of ANFIS and electrical resistivity not only enhances the prediction of concrete strengths but also establishes electrical resistivity as a promising tool for non-destructive assessment, crucial for ensuring the structural integrity of concrete infrastructure.