Modeling the Time-Dependent Variation of Road Salt Concentrations Using Analytical and Machine-Learning Approaches to Advance Service Life Predictions for Concrete Structures

The exposure of concrete structures to environmental and climatic conditions is detrimental to their durability. In northern climates, the key contributor to their degradation is corrosion of the reinforcing steel because of chloride ions originating from de-icing salts applied on roadways during the winter season. In consequence, a key input parameter for predicting the time to the initiation of corrosion for concrete elements is the time history of the concentration of chloride ions at their surfaces. To investigate this issue, a specialized mobile monitoring station was deployed along a roadway over several winter seasons to collect data on salting operations, weather conditions, and the temporal variation of chloride ion levels on the roadway. At first, salting operations were monitored, and then exploratory and machine-learning algorithms were applied to develop relationships between weather conditions, road conditions, and chloride ion concentrations. The first proposed model is based on the simulation modeling approach, while the second is based on the machine-learning XGBoost model. The findings demonstrate that both models can predict the variation of salt concentration on the road surface as a function of time after a salting operation. By accounting for the time dependency of surface chloride in service life models, more accurate predictions of corrosion initiation time are possible, since the rate of penetration of chloride ions is highly dependent on wetting/drying cycles throughout the winter.