Analysis of training parameters in the ANN learning process to mapping the concrete carbonation depth

Abstract

We propose an artificial neural network (ANN) model to predict the CO2 diffusion through the concrete to determine the carbonation depth over time, analyzing the influence of some training algorithm and the network architecture in the ANN learning process. A reliable experimental test database of the non-accelerated test with 278 results of concrete carbonation depth was created from the published literature. It was used to train, test, and validate the model. Altogether, 120 networks had been trained with different characteristics, verifying its performance. In spite of the non-linearity and complexity of the concrete carbonation phenomenon, the proposed ANN model yielded accurate prediction. Results indicate the best training algorithm and the optimum number of neurons in the hidden layer that allows faster ANN training process and generates the most accurate mapping for the concrete carbonation phenomenon. The use of ANN appears as a robust tool easily applied to the study of the concrete carbonation, aiding in decision making in engineering projects focused on durability.