Application of modern performance-based engineering and design requires building products that are thoroughly characterised in terms of their ability to perform as expected over time. The engineering community is attempting to use this information to provide predicted service-lives for various building elements. Increasingly, this service-life prediction must be based on objective measurement coupled with sound predictive models. Service-life estimates for wood products are generally based on field tests, which involve periodic assessment of representative samples in specific assemblies. These tests often take decades to complete when naturally durable species are evaluated, despite the need for more timely durability characterisation, especially for products that are new to the market. One approach to addressing this dilemma is to combine laboratory tests of key material properties with early field data from a well-characterised site to shorten the testing period without compromising reliability. Establishing the relationship between product performance at low-hazard field sites and high-hazard field sites is an important part of this process. For the purpose of this discussion paper, biodeterioration data generated during a multi-site above-ground natural durability testing program were analysed to determine the length of time required for field tests to yield useful results. Data representing the durability of replicate L-joint samples of Thuja plicata and Pseudotsuga menziesii exposed at the two sites with the most severe biodeterioration hazard were examined. The sample median or a specific product performance model were found to reliably predict the durability of T. plicata and P. menziesii L-joints after approximately half of the time required to complete an entire test. As further data become available, the feasibility of predicting the performance of replicate samples at low-hazard sites based on their performance at high-hazard sites will be considered, and laboratory tests will be examined to determine their capacity to enhance durability prediction.

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