Neural network prediction of concrete degradation by sulphuric acid attack

Abstract

Microbiologically induced corrosion is a leading cause of the deterioration of wastewater collection, transmission and treatment infrastructure around the world. This paper examines the feasibility of using artificial neural networks (ANNs) to predict the compressive strength of concrete and its degradation under exposure to sulphuric acid of various concentrations. A database incorporating 78 concrete mixtures performed by the authors was developed to train and test the ANN models. Data were arranged in a patterned format in such a manner that each pattern contains input variables (concrete mixture parameters) and the corresponding output vector (weight loss of concrete by H2SO4 attack and compressive strength at different ages). Results show that the ANN model I successfully predicted the weight loss of concrete specimens subjected to sulphuric acid attack, not only for mixtures used in the training process, but also for new mixtures unfamiliar to the ANN model designed within the practical range of the input parameters used in the training process. Root-mean-squared error (RMSE) and average absolute error (AAE) for ANN predictions of weight loss due to sulphuric acid attack were 0.013 and 8.45%, respectively. The ANN model II accurately predicted the compressive strength of the various concrete mixtures at different ages with RMSE and AAE of 2.35 MPa and 4.49%, respectively. A parametric study shows that both models I and II can successfully capture the sensitivity of output variables to changes in input parameters.