Development of Bayesian Regularized Artificial Neural Network for Airborne Chlorides Estimation

Abstract

This paper suggests an artificial neural network model combining Bayesian regularization (BRANN) to estimate concentrations of airborne chlorides, which would be useful in the design of reinforced concrete structures and for estimating environmental effects on long-term structural performance. Meteorological and topographical data were collected, and airborne chlorides were measured at 19 areas all over Korea. Data were classified for the three major coasts, and then prepared for training. To investigate the relationship between each input feature and output and then construct a model for estimating airborne chlorides with only meteorological and topographical information, both the standard artificial NN (ANN) and BRANN models were examined. The 3 or 4-layered BRANN model with 64 nodes at each layer showed the best and most robust performance. This BRANN model successfully predicted airborne chloride content with reasonable values of MSE and R-square although the input data and the airborne chlorides had quite low correlation. The results showed that the BRANN was better able to solve this problem than ANN. It is expected to be broadly applicable for predicting the penetration of chlorides into concrete and even the evaluation of concrete durability.

Keywords: Bayesian regularization, Neural networks, airborne chlorides, estimation, meteorological data, topographical data