This research aims to establish a robust procedure for accurate assessment of the collapse of steel frame structures. Understanding the causes and effects of structural collapse is critical to develop key documents such as national building codes, regional emergency response plans, and risk management strategies. This project will develop a cohesive-zone method for detailed assessment of structural collapse, and a compact plasticity-based macro beam element method for global collapse assessment of a complete frame structure. Together, these two methods will provide a systematic approach for evolution of collapse, including strength degradation and topology changes. The approach will be validated using representative testbed examples. Stochastic algorithms including Bayesian parameter estimation methods and pattern classification methods will then be developed for validating some simple nonlinear methods that are currently used to predict collapse. The research will help in identifying the key parameters and damage measures that govern the collapse capacity of a structure.
The numerical and analytical tools developed in this project will help in the development of better building code provisions that seek to prevent disproportionate collapse and in regional loss assessments that rely on accurate assessment of building collapse in a region. The research will identify new collapse resistant structural designs to enhance public safety under extreme loads. The research results will be incorporated in the graduate level courses on the topics of structural designs for extreme loads and risk assessment. Active efforts will be made to recruit students from the groups that are underrepresented in science and technology fields using the well-established institutional fellowship programs. Through their involvement in the research project, two graduate students and two undergraduate research assistants will be provided advanced training to join the Nation's highly trained workforce.
Understanding the causes and effects of structural collapse is critical to develop key documents such as national building codes, regional emergency response plans, and risk management strategies. This research has established innovative computational simulations approaches and robust procedures for probabilistic assessment of the collapse of steel frame structures. The project has explored two types of computational simulation approaches for detailed assessment of structural collapse, including developing a compact macro beam element for global collapse assessment of complete frame structures, and adapting a continuum level formulation that includes direct modeling of fracture that occurs during collapse. Together, these two methods provide a systematic approach for evolution of collapse, including strength degradation and topology changes. The proposed approaches have been validated using representative experiments of steel components and structures. The project also has developed new collapse criteria and limit-state descriptions to predict the collapse of a frame structure based on dynamic instability of the structural system instead of engineering-judgment-based rules given in terms of structural demand, or relationship between ground motion intensity and corresponding structural demand. Based on the use of the developed collapse criteria and statistical analysis of computational simulations of collapse behavior, a robust procedure has been established for probabilistic assessment of the collapse of steel frame structures. The research has identified key parameters and damage measures that govern the collapse capacity of a structure. The numerical and analytical tools and analysis procedures developed in this project will help in the development of better building code provisions that seek to prevent disproportionate collapse and in regional loss assessments that rely on accurate assessment of building collapse in a region. The research will also enable the identification of new collapse-resistant structural designs to enhance public safety under extreme loads.
