Service Life Prediction Models to Predict Building Skin Failures
Christopher C. White, Donald L. Hunston, Adam L. Pintar
Determination of the in-service performance and failure of a building envelope material exposed to outdoor weathering has been a significant research goal for the last 100 years. Typically, estimations of the time to failure or durability of a material is established by performance in an industry standard laboratory test with serial exposure to single weathering exposure often at an extreme condition. A different approach will be discussed in this presentation. This approach begins by assuming that the change in a material due to weathering occurs via the same mechanism if the exposure is indoor or outdoor. This will be verified. Samples of the building envelope will be then exposed and the change quantified at a series of constant conditions (30C, 50% relative humidity, 0.5 W/m^2 UV light, 7.5% dynamic strain) using a custom constant condition weathering device (NIST SPHERE). This procedure establishes a rate of change for the material for this specific condition. This will be repeated at different conditions until a database of rate constants is obtained. An outdoor weather history for a specific location will be segmented into short one-hour pieces. Each hour of that weather history will be combined with the database of rate constants to generate a predictive change during that hour. This will be repeated until a long-term prediction for the building material is developed. This prediction of the property change will be validated against materials exposed and quantified in that actual exposure time and location. Once the prediction has been validated, the procedure can be repeated for any time and location where known weather conditions exist. Using this validated predictive model, the in-service performance for a material can be determined for any location with a known weather history. The validated prediction includes known uncertainty.
13th Conference on Advanced Building Skins
October 1-3, 2018
Service life prediction, materials degradation, models, modulus