Prediction of sulfate expansion of PC mortar using adaptive neuro-fuzzy methodology

The prediction of sulfate attack is essential for concrete structures since it causes drastic decrements in strength and in expansion attributes of cementitious systems. In this study, the nonlinear mapping among sulfate expansion of PC mortar and some selected parameters (C3A content, C3S/C2S ratio, sulfate concentration and mineral admixture substitution level) was simulated using adaptive neuro-fuzzy system. Experimental data that had been previously collected for various levels of accounted parameters were treated in the analyses. In neuro-fuzzy inference system, Sugeno-type inference technique and linear output function were used to perform approximate reasoning of fuzzy input variables. In addition, hybrid learning algorithm, combining backpropagation learning and linear least-squares estimator, were preferred for the adaptation of free parameters. Consequently, neuro-fuzzy model was compared with results obtained using linear and nonlinear multiple regression methodologies to make comparison among different techniques. Outcomes indicated that neuro-fuzzy model exhibits superior performance.