Simulation‐based methodology to identify damage indicators and safety thresholds for post‐earthquake evaluation of structures

Abstract

AbstractAfter a strong earthquake, criteria are needed to determine whether buildings are safe to reoccupy based on observable damage. This paper presents a simulation‐based methodology to identify relevant damage indicators and safety thresholds for building structures. Prior knowledge of the most relevant damage indicators and their thresholds can increase the accuracy and confidence of post‐earthquake evaluations. Current practice to translate observable damage into a tagging decision relies on qualitative guidelines based on past earthquake experience and judgment, which may be susceptible to speculation and interpretation. In addition, past experience may not be relevant to newer structural systems and to large or complex (e.g., high‐rise) buildings. To augment past observations and data from structural component tests, nonlinear dynamic analyses can be used to estimate the collapse safety of structures with simulated damage. Technologies to execute these simulations have matured over the years, although to date they have not been systematically applied to evaluate the destabilizing effects of simulated damage on collapse safety. In this paper, a methodology is presented to use numerical simulations of damage to identify and evaluate relevant damage indicators that can be quantitatively related to safety thresholds. Damage indicators are selected based on their reliability in estimating the structural safety and their sensitivity to modeling uncertainty, that is, where the preferred indicators are insensitive to variability in the structural materials and model parameters. The safety threshold for each damage indicator is selected to maximize accuracy in post‐earthquake building assessments. The methodology is demonstrated through an application study of ductile reinforced concrete frame buildings. Results show that aggregated indices of structural component damage (e.g., aggregated over the floor of a building) outperform other damage indicators based on peak or residual drifts or simpler percentages of damaged components. Subject to agreement of a number underlying assumptions, this methodology can be applied to a wider variety of structures to improve post‐earthquake evaluation guidelines.