Integrated System and Component Reliability in Seismic Collapse Safety of Structures

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Recent advancements in nonlinear computer modeling, seismic hazard analysis, and performance-based engineering are enabling better scientific assessment of building collapse under large earthquakes and other hazards. However, while existing methods do a reasonable job at characterizing the average response of buildings, they lack the resolution to evaluate how the performance is affected by inherent variability in building materials, design and construction quality assurance. This project aims to address this limitation by developing and applying new methods and data to incorporate modeling uncertainties into computational methods to simulate the structural collapse of buildings, bridges and other structures. The project entails three main tasks: (1) developing statistics on the variability in materials, design and quality assurance by collecting published test data and other supporting information and case histories, (2) developing new strategies to efficiently incorporate modeling uncertainties into nonlinear dynamic simulation methods, and (3) to implement and exercise the new approach to assess the collapse safety of a set of archetype building structures. The resulting methods will lead to fundamental advancements in quantifying collapse risk and understanding of factors that affect collapse safety of buildings. By developing methods to accurately evaluate factors that affect building collapse safety, this research will enable improvements to building codes and design and construction practices that will lead to more economical and safe buildings. The research products will also advance the state-of-art in performance based design methods that facilitate more rapid development and adoption of innovative new building technologies. To facilitate technology transfer to other researchers and engineering professionals, the PIs will publish open-source software and data to an on-line web site. The research will involve and provide research training for two graduate doctoral students and undergraduate student assistants.