The use of remote sensing and reanalysis weather data has not been adequately acknowledged for large-scale
built environment applications, primarily regarding engineering analyses that traditionally rely on computationally expensive numerical models. This study employs a parsimonious data-driven approach concerning service life assessment of rain-exposed wooden components, enabling mapping and assessment of related risks across Europe and Scandinavia from gridded data. We explore high resolution decay risk assessments using a neural network trained on existing numerical predictions of moisture content. The results present high resolution decay hazard maps alongside the associated spatiotemporal variations. Both spatial and temporal variations were identified as significant contributors to uncertainty in decay assessments, with their impacts varying considerably across regions in Europe. The study is part of an effort to integrate uncertainty propagation in service life prediction of wood, to enhance its resilience in outdoor applications and to optimize its potential for enduring construction solutions.
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