Alternative methodologies for damage-dependent fragility estimation of reinforced concrete buildings

When dealing with probabilistic seismic-risk assessments explicitly accounting for seismicity clustering and resulting damage accumulation, damage analysis must adopt state-dependent fragility relationships. Building-level, state-dependent fragilities represent the probability of reaching or exceeding a damage state (DS) threshold conditional to an initial DS due to previous seismic events (e.g., a mainshock) and the intensity of a second ground motion. Advanced methodologies for state-dependent fragility estimation have been proposed, typically relying on sequential non-linear dynamic analyses of Multi-Degree-of-Freedom (MDoF) numerical models. However, for many practical applications, lower-refinement methods may be preferred by various end-users/stakeholders, despite accepting higher uncertainties and/or some bias in the results. Research efforts are still needed to develop simplified approaches for damage-dependent fragility analysis and evaluate their accuracy compared to more refined methods. This paper contributes to this aim by investigating the trade-off between the refinement level of analysis and the result accuracy, considering alternative methodologies for state-dependent fragility estimation. This is done by analysing an archetype reinforced concrete frame structure with different seismic response analysis methods: i) non-linear static analysis, coupled with a spectrum-based approach (low-refinement level); ii) sequential non-linear dynamic analysis of equivalent inelastic Single-Degree-of-Freedom (SDoF) systems (medium-refinement level); iii) sequential non-linear dynamic analysis of advanced MDoF numerical models (high-refinement level). In line with the state-of-the-art procedures in the literature, fragility estimation is performed, whenever possible, considering energy-based approaches to effectively capture damage accumulation during ground-motion sequences. The results highlight that simplified methodologies enable a relatively accurate fragility estimation compared to the more refined approaches. Based on the characterised relative error, results from this study may facilitate calibrating suitable correction factors for adopting simplified methods in practice.