UCLA

Controlled rocking steel braced frames (CRSBFs) have been developed with the goal of minimizing the post-earthquake impact of primary building functions. While there has been significant research to date to demonstrate the viability of the CRSBF as a high-performance system, much less has been accomplished in the development of performance-based design and assessment methods. This research is focused on developing models, tools and techniques for practicing engineers to analyze, design and assess the performance of CRSBFs. To avoid the computational expense of nonlinear response history analyses, an approximate method is formulated to estimate the CRSBF drift demands using the primary design parameters. Additionally, a reliability-based methodology for establishing the load and resistance factors for the force-controlled (braced frame) members is formulated. A key departure from previously developed capacity design approaches is the development of an explicit link between the effect of the failure of the force-controlled components and system level performance limit states (collapse and post-earthquake structural safety). The results from a case study applied to 3-, 6- and 9-story building cases show that the effect of force-controlled components is more significant for the collapse limit state compared to post-earthquake structural safety. Also, even when the resistance to load factor ratio (ϕ/γ) is increased to 1.8, the 50-year collapse probability remained below the 1% threshold prescribed by current building codes. The effect of record-to-record and modeling uncertainty on the seismic response and performance assessment of CRSBFs is also studied. The results showed that the impact of modeling uncertainty on seismic performance increases with the building height. To enable practitioners to estimate the service life costs of potential designs, surrogate models are developed to assess earthquake-induced life cycle economic loss and environmental impacts. The effectiveness of the surrogate models is demonstrated by evaluating their accuracy on “unseen” (i.e., not used in the development of the surrogate models) designs.