Predicting airborne chloride deposition in marine bridge structures using an artificial neural network model

Abstract

Chloride-induced corrosion of reinforcement is the most frequent durability problem in marine reinforced concrete (RC) structures. In particular, marine structures are intrinsically exposed to chloride ingress due to airborne chloride deposition. However, monitoring airborne chloride deposition in marine structures is difficult, since conventional on-site measurement is time-consuming and very hazardous. This study presents a prediction model for airborne chloride deposition in coastal bridges based on an artificial neural network (ANN) using local marine meteorological data. Two data sets were prepared for training: chloride deposition data in the (1) presence and (2) absence of scattered deicing salts. The proposed ANN model successfully predicted airborne chloride deposition at three different sampling sites in a coastal bridge, despite the complex relationship between airborne chloride deposition and meteorological parameters. The sampling site, such as one near a vehicle highway, was a more important factor for chloride deposition than the bridge height.