The volatile and extreme nature of fire makes structural fire engineering unique in that the load actions dictating design are intense but not geographically or seasonally bound. Simply, fire can break out anywhere, at any time, and for any number of reasons. Despite the apparent need, fire design of structures still relies on expensive fire tests, complex finite element simulations, and outdated procedures with little room for innovation. This thesis will make a case for adopting the principles of performance-based design and machine learning in structural fire engineering to simplify the process and promote the consideration of fire in all structural engineering applications.
This thesis begins with an overview of relevant topics, providing context and a frame of reference for the coming chapters. The first section of this thesis argues for the adoption of performance-based design for the structural fire design of buildings, as obtained through a comprehensive and much needed literature review. The second half of this thesis revolves around the application of performance-based design and simple machine learning in our field. An Excel file accompanies this thesis as an easy-to-use tool to encourage the consideration of fire criteria in masonry projects, focusing not on how heat affects the material-level properties but rather on how those effects accumulate to affect the final design requirements. An outline for the development of a coding-free machine learning model capable of predicting failure of unreinforced masonry structural elements exposed to elevated temperatures including its abilities and limitations, is presented. The thesis concludes with a summary of the above information and the potential for related project scopes in the future
