A framework for determining field‐calibrated surface chloride concentration distributions for prediction of initiation of reinforcement corrosion in reinforced concrete (RC) structures exposed to hot‐arid Gulf environments

Abstract

Surface chloride concentration, C s , is a key environmental boundary parameter in chloride‐ingress modeling and corrosion‐initiation prediction for reinforced concrete structures, yet it is highly exposure‐dependent and often selected from generalized literature ranges, software defaults, or broad exposure classifications. This study develops the Field Exposure Surface Chloride Calibration with Probabilistic Updating framework (FESCAL‐PU) to convert field chloride‐depth profiles into exposure‐specific effective C s distributions. The framework combines profile‐level inverse fitting, log‐space model‐error characterization, exposure‐level probabilistic updating, robustness checks, and comparison of constant and time‐dependent C s formulations. It was applied to a Qatar database of 302 acid‐soluble chloride profiles, of which 192 satisfied the final calibration requirements. Median calibrated C s increased with exposure severity from 0.081% by cement mass for residential/industrial atmospheric exposure to 0.100% for atmospheric/near‐soil, 0.139% for underground/flooding, 0.196% for coastal atmospheric, and 2.263% for marine/splash exposure. A single global C s model was inadequate. Within the 180‐profile age‐usable subset, tested time‐dependent terms did not improve information‐criterion performance relative to the exposure‐specific constant model; because the age coverage is cross‐sectional and strongly concentrated at a few ages, this result is not interpreted as evidence of time independence. The framework converts routine field profiles into deterministic and probabilistic C s inputs with explicit conditioning, model error, and upper‐tail uncertainty for corrosion‐initiation prediction and reliability‐informed durability verification in hot‐arid Gulf environments.