Analysis of sulfate resistance in concrete based on artificial neural networks and USBR4908-modeling

One of the available tests that can be used to evaluate concrete sulfate resistance is USBR4908. However, there are deficiencies in this test method. This study focuses on the ANN as an alternative approach to evaluate the sulfate expansion. Three types of cement combined with FA or SF, along with variable W/B were study by USBR4908. ANN model were developed by five input parameters, W/B, cement content, FA or SF, C3A, and exposure duration; output parameter is determined as expansion. Back propagation algorithm was employed for the ANN training; a Tansig function was used as the nonlinear transfer function. It was clear that the ANN models give high prediction accuracy. In addition, The engineer can avoid the use of the borderline 2.5–5% C3A content in severe sulfate environments and borderline 6–8% C3A content in moderate sulfate environments, specially with W/B ratio greater than 0.45.