DATA ANALYSIS AND MACHINE LEARNING FOR ENHANCING RESILIENCE TO FIRE, FROM IGNITION MAPPING TO STRUCTURAL AND SYSTEMS MODELING

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Date
2023-07-19
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Johns Hopkins University
Abstract
Fire hazards pose significant threats to our communities. Mitigation of fire risk requires an understanding of a range of issues and processes at various scales, such as the occurrence of ignitions in a community, the performance of a building structure under fire, and the efficiency of prevention and protection strategies. In this thesis, we investigate several of these fire safety issues through the lens of data-driven methods. Bayesian methods and machine learning techniques are adopted and tailored to address selected fire hazards and provide contributions toward solving these challenges for a fire resilient built environment. The thesis focuses first on fire ignitions. It investigates the problems of fire following earthquakes and wildfires. These issues are studied at the scale of a city or a region. For fire following earthquakes, a hierarchical Bayesian method is developed to allow modeling with scarce data, while for wildfire ignitions, an ensemble-based machine learning model is adopted. Then, the thesis zooms in to the building scale to assess data-based methods for evaluation of structural fire performance. Surrogate models are derived based on machine learning to capture the capacity of slender steel members in fire. Finally, the thesis investigates a framework to assess system resilience under fire hazards. The framework is applied for resilience assessment of facilities subjected to fires in the process industry. The thesis applies different data-based and modeling approaches to deal with fire hazards from different perspectives and at different scales, with the aim to enhance fire safety and build a more resilient environment against fires for our community.
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Fire hazards;data-driven methods;Bayesian methods;machine learning;fire safety;resilient environment
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