Seismic Reliability Assessment of Structures Incorporating Modeling Uncertainty and Implications for Seismic Collapse Safety

Advancements in nonlinear dynamic simulation, seismic hazard analysis, and performance-based earthquake engineering are enabling more scientific assessment of structural collapse risk and how the risk is controlled by building code design requirements. Although current collapse assessment methods carefully account for the nonlinear response of structures, most of these analyses do not explicitly capture model uncertainties associated with variability in the structural properties and response characteristics of components. Instead, modeling uncertainties are typically considered through simplified assumptions and techniques.

This dissertation focuses on modeling uncertainty in seismic performance assessment and implications on seismic collapse safety of structures. A statistical framework
assessing model parameter correlations from component tests is proposed for characterizing the modeling parameters that define dynamic response at a component level and the interactions of multiple uncertain components in structural systems. The
framework is illustrated using a dataset that is composed of over two-hundred tests
of reinforced concrete columns. Statistics of the model parameters are established
including the correlation structure of the inelastic model parameters, both within a
component and between different components. The analyses show that model parameters within structural components tend to be only mildly correlated, whereas
there are strong correlations between like parameters of different components within
a building. Uncertainty propagation methods, including Monte-Carlo simulation-
based, moment-based and surrogate (response surface and neural network) methods,
are assessed for probabilistic assessment of collapse risk. To reduce computational demand of collapse risk assessment, a Bayesian approach is proposed and demonstrated using a reinforced concrete archetype building. The results emphasize the sensitivity of collapse response to modeling uncertainties and the challenges of balancing of computational efficiency and robust uncertainty characterization.

Impacts of modeling uncertainty are evaluated for fragility functions and mean annual exceedance rates for drift limits and collapse for thirty-three reinforced concrete archetype building configurations. Modeling uncertainty is shown to have considerable impacts on collapse risk. Inclusion of modeling uncertainty is shown to increase the mean annual frequency of collapse by about 1.7 times, as compared to analyses
based on median model parameters, for a high-seismic site in California. Modeling
uncertainty has a smaller effect on drift demands at levels usually considered in building codes. A novel method is introduced to relate drift demands to collapse safety through a joint distribution of deformation demand and capacity, taking into account simulated instances of collapse and no-collapse. This method enables linking seismic performance goals specified in building codes to drift limits and other acceptance criteria. The distributions of drift demand at maximum considered earthquake level
and drift capacity of case study structures are compared with drift limits as specified
in the proposed seismic criteria for the next edition (2016) of ASCE 7.

Gokkaya, B and Baker, JW and Deierlein, GG. (2020). Seismic Reliability Assessment of Structures Incorporating Modeling Uncertainty and
Implications for Seismic Collapse Safety. Blume Earthquake Engineering Center Technical Report 201. Stanford Digital Repository. Available at: https://purl.stanford.edu/gj011qz6654