Summary Fire‐retardant coatings could be one option for providing enhanced protection to buildings during a wildfire, particularly when applied to combustible siding and in under‐eave areas. Limited studies have been conducted on their effectiveness but maintaining adequate performance after weathering has been questioned. This paper reports on a study evaluating the effect of natural weathering on the performance of intumescent‐type fire‐retardant coatings. The main concerns were (a) the reduction of ignition resistance of the coating after weathering and (b) the coating might contribute as a combustible fuel and assist the fire growth after weathering. This study evaluated the performance of 3 intumescent coatings that were exposed to natural weathering conditions for up to 12 months. A bench‐scale evaluation using a cone calorimeter was used to evaluate the performance of the coatings at 3 heat flux levels (30, 50, and 70 kW/m2). Our results showed that weathering exposure reduced the effectiveness of fire protection of intumescent coatings, but the weathered coatings did not act as additional fuels. Weathering orientation showed much less effect on the performance of intumescent coatings in comparison to other parameters. There was statistical evidence that weathering duration, heat flux level, and coating type affected the combustion properties.

Generation of firebrands from various fuels has been well-studied in the past decade. Limited details have been released about the methodology for characterizing firebrands such as the proper sample size and the measurement process. This study focuses on (1) finding the minimum required sample size to represents the characteristics of the population, and (2) proposes a framework to facilitate the tedious measurement process. To achieve these goals, several firebrand generation tests were conducted at a boundary layer wind tunnel with realistic gusty wind traces. Firebrands were generated from burning structural fuels and collected in 46 strategically located water pans. The statistical analysis showed that the minimum required sample size based on the chosen statistical parameters (standard deviation, confidence interval, and margin of error) is 1,400 for each test. To facilitate characterizing such a large sample of firebrands, an automated image processing algorithm to measure the projected area of the firebrands was developed, which can automatically detect the edges of the background sheet, rotate the photo if its tilted before cropping, detect edges of firebrands, remove erroneous particles (e.g., ash) and finally measures the projected area. To facilitate the weighing process, a Gaussian process regression was performed to predict the mass based on projected area, traveling distance and wind speed. The model can predict the firebrand mass within 5% error compared to the measurement. This framework and model can provide a probabilistic range of firebrand characteristics over the continuous range of the collection region.

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