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Extreme weather has a direct and significant impact on buildings and infrastructure, resulting in billions of dollars of damage each year. This problem continues to grow as climate patterns change and buildings are exposed to new and different hazards than what they were designed to withstand. In order to better plan for the long-range sustainment, restoration, modernization, and eventual recapitalization of these buildings, organizations with large building portfolios, such as the U.S. Department of Defense (DoD), must have an awareness of the risks that these extreme weather events present. This research aimed to develop an approach to estimate condition loss and reduction in service life for the components of a building due to extreme weather hazards, to understand the risks that may be present in certain buildings and building systems. To achieve this objective, a damage association matrix was developed that categorizes climate hazards, the damage modes that they produce, and the individual component types impacted. This damage matrix formally links state-of-the-art climate model output, which provides projections of the probability of various climate hazards with a damage effects model that quantifies the consequence on component-level condition and service life. This method is applied to an actual portfolio of buildings in a particular geographic location and with a pre-defined component inventory that comprises the building. This approach can be aggregated to the system-, facility-, and site-level thus helping support billions of dollars in recapitalization decisions related to restoration/modernization of facilities. • The BUILDER Sustainment Management System forecasts facility conditions across the U.S. Department of Defense • Lifecycle assessment tools, like BUILDER, generally do not consider vulnerability to extreme weather and climate change. • We developed a plug-in for BUILDER that evaluates the impact of future extreme weather on facility lifespans and associated costs. [ABSTRACT FROM AUTHOR]

Predicting the service life of facility equipment is a crucial aspect of facility design and maintenance that requires careful assessment of a wide range of building and environmental factors. Climate change may cause these environmental stressors, like temperature intensity, to deviate from historical trends, implying that past design assumptions may not reflect future service conditions. Therefore, additional techniques are needed to associate the effects of climate on equipment service life. This paper develops a machine learning method capable of modeling complex building and environmental factors, including climate change, on the service life of equipment such as air-handling components. A boosted decision tree model is developed to predict equipment service life based on the combined impact of building and environmental factors. The developed model outperforms other approaches, such as industry-standard approaches, Weibull models, and inspection-based techniques for estimating equipment service life. The model showed a 93.9% reduction in mean-squared-error when estimating component service life compared to the industry standard model. The developed machine learning model is then combined with climate projection models to measure the impact of predicted temperature intensity changes on the service life of air-handling units, showing an expected 12%–41% decrease in the service life of air-handling units in Yuma, Arizona, when subjected to 2060 climate projections. • Decision trees quantify the effect of climate on building equipment service life. • Capable of measuring "non-extreme" climate change impact on the built environment. • Demonstrates usefulness of regional climate projection models on building design. [ABSTRACT FROM AUTHOR]