A New Method for Coating Service Lifetime Prediction Under Real-time Field Environments (RIP2026-00037)

Shuzhen Liu, Ghazanfar Anwar, Qindan Huang, Qixin Zhou

The University of Akron, Marquette University, Marquette University, The University of Akron

Accurate service lifetime prediction for coatings is crucial in managing military and civil works infrastructure risks. Meanwhile, many newly developed coatings require reliable service life prediction to accurately assess their performance and cost benefits. However, conventional methods often depend heavily on correlations between indoor accelerated tests and outdoor exposure, which has still shown to provide inaccurate predictions. To address these challenges, we propose a reliability-based service life prediction framework that eliminates the modeling of the correlations between indoor property and outdoor performance, where the service life prediction uses only real-time outdoor field testing data by using pre-degraded coating samples that are generated in the lab.
In particular, the pre-degraded coating samples were prepared under accelerated degradation tests in QUV and Q-FOG chambers following the standards of ASTM D4587 and ASTM B117, respectively. Coating properties were systematically measured, and one response property quantity was selected to establish the condition rating criterion that was determined to predict the service lifetime. Based on the assessments, the color change (∆E) and the disbonded coating area were selected as the critical properties for coatings in QUV and Q-FOG, respectively. Real-time outdoor field exposures were conducted in Arizona and Florida, and the ∆E and the disbonded coating area were monitored during the outdoor exposure. A probabilistic model with incorporated environmental factors will be adopted for coating degradation prediction and the reliability analysis is conducted to predict the coating service lifetime.
This study presents a novel approach to coating service lifetime prediction that differs from previous methods, and it does not rely on the correlations between indoor testing and outdoor exposure. By using pre-degraded samples, the outdoor exposure time is significant reduced for service life prediction. The outcome of this study also offers valuable insights for enhancing coating durability and the reliable coating service life prediction can be used for optimizing coating maintenance strategies.