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The objective of this paper is to investigate the post-earthquake thermal-mechanical response of cold-formed steel (CFS) members. A 10-story cold-formed steel building (CFS-NHERI) will undergo seismic tests, followed by post-earthquake live fire tests. To support the fire test setup, computational models are developed to simulate the impact of varying post-earthquake damage levels on the fire response of the structure. As a panelized system, damage to different finish and nonstructural systems significantly affects the thermal behavior and load-bearing capacity of the CFS components. The computational models integrate the modeling capability in CUFSM and SAFIR for the elastic buckling, heat transfer, and transient structural analysis under fire. A parametric analysis considering different seismic damage levels is conducted to study the buckling and strength behavior of the CFS members under fire-induced nonuniform temperature fields. These pre-test models inform the duration and severity of the fire tests to maintain structural stability while achieving substantial thermal loading on the CFS load-bearing system. They also provide guidance for the sensor layout plan for the fire tests. This study advances methods for fire resilience of thin-walled CFS structures under multi-hazard scenarios. 

Wildfires are an essential part of a healthy ecosystem, yet the expansion of the wildland-urban interface, combined with climatic changes and other anthropogenic activities, have led to the rise of wildfire hazards in the past few decades. Managing future wildfires and their multi-dimensional impacts requires moving from traditional reactive response to deploying proactive policies, strategies, and interventional programs to reduce wildfire risk to wildland-urban interface communities. Existing risk assessment frameworks lack a unified analytical method that properly captures uncertainties and the impact of decisions across social, ecological, and technical systems, hindering effective decision-making related to risk reduction investments. In this paper, a conceptual probabilistic wildfire risk assessment framework that propagates modeling uncertainties is presented. The framework characterizes the dynamic risk through spatial probability density functions of loss, where loss can include different decision variables, such as physical, social, economic, environmental, and health impacts, depending on the stakeholder needs and jurisdiction. The proposed approach consists of a computational framework to propagate and integrate uncertainties in the fire scenarios, propagation of fire in the wildland and urban areas, damage, and loss analyses. Elements of this framework that require further research are identified, and the complexity in characterizing wildfire losses and the need for an analytical-deliberative process to include the perspectives of the spectrum of stakeholders are discussed.