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Model parameter updating can enhance the use of nonlinear structural response simulation to guide decision-making in the post-earthquake environment. Since most structures in high seismic regions are not instrumented with sensors, the response history during ground shaking is usually not available after an earthquake. Nevertheless, technologies such as Light Detection and Ranging (LiDAR) and drone-mounted imaging devices have increased the feasibility of measuring residual deformations after the shaking has subsided. It is within this context that a framework for performing nonlinear structural model parameter updating based only on residual drift measurements is proposed. The considered setting is one where a structure is subjected to a sequence of ground motions (without repairs), whereby after each event, the structural model parameters are updated using a Bayesian formulation and the measured residual drift. The methodology is demonstrated by using experimental data from a reinforced concrete bridge pier subjected to six back-to-back ground motions with significant residual drifts recorded after the third, fourth and fifth records in the sequence. The results showed that the updating procedure is able to incrementally (after each record) improve the accuracy of both the concrete and steel model parameters which also enhanced the estimates of the simulated peak and residual drifts. 

Abstract Recovery‐based design links building‐level engineering and broader community resilience objectives. However, the relationship between above‐code engineering improvements and recovery performance is highly nonlinear and varies on a building‐ and site‐specific basis, presenting a challenge to both individual owners and code developers. In addition, downtime simulations are computationally expensive and hinder exploration of the full design space. In this paper, we present an optimization framework to identify optimal above‐code design improvements to achieve building‐specific recovery objectives. We supplement the optimization with a workflow to develop surrogate models that (i) rapidly estimate recovery performance under a range of user‐defined improvements, and (ii) enable complex and informative optimization techniques that can be repeated for different stakeholder priorities. We explore the implementation of the framework using a case study office building, with a 50th percentile baseline functional recovery time of 155 days at the 475‐year ground‐motion return period. To optimally achieve a target recovery time of 21 days, we find that nonstructural component enhancements are required, and that increasing structural strength (through increase of the importance factor) can be detrimental. However, for less ambitious target recovery times, we find that the use of larger importance factors eliminates the need for nonstructural component improvements. Such results demonstrate that the relative efficacy of a given recovery‐based design strategy will depend strongly on the design criteria set by the user.