Performance uncertainty estimation using simplified methods of analysis

Abstract eng:
Approximate methods based on static pushover are introduced to estimate the seismic performance uncertainty of structures having uncertain plastic hinge parameters. Performance uncertainty is one of the driving forces behind modern seismic guidelines (e.g. FEMA-350) and it is arguably an essential ingredient of Performance-Based Earthquake Engineering (PBEE). We propose methodologies that use a minimum of static nonlinear analyses and are capable of accurately estimating the demand and capacity epistemic uncertainty. As a testbed, the wellknown nine-story LA9 steel frame is employed using beam-hinges with uncertain backbone properties. The properties of the backbone can be fully described by six parameters which are considered uncertain with given mean and standard deviation. Using Latin Hypercube Sampling with classic Monte Carlo simulation, the pushover curve is shown to be a powerful tool that can closely estimate the uncertainty in the seismic performance. Moment estimating techniques such as the Rosenblueth’s point estimating method and the first-order, second-moment (FOSM) method are also adopted as simple alternatives to obtain performance statistics within a few static pushover simulations. Coupled with the SPO2IDA tool, a multilinear R-µ-T relationship, such estimates can be applied at the level of the results of nonlinear dynamic analysis, allowing the evaluation of seismic capacity uncertainty even close to global dynamic instability. In this study we compare the effectiveness of Monte Carlo simulation with LHS sampling versus the moment-estimating methods and we perform a parametric study on the minimum number of Monte Carlo simulations required to sufficiently estimate the response statistics.

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