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  1. Residential damage from major disasters often displaces local residents out of their homes and into temporary housing. Out-of-town contractors assisting in post-disaster housing reconstruction also need housing, creating additional pressure on the local housing stock. Communities should thus prepare for a surge in temporary housing demand to minimize the impact on the local residents and to expedite housing recovery efforts. Computational models can support recovery planning. This article introduces an agent-based simulation framework to estimate the workforce demand and the joint temporary housing needs of contractors and displaced households. The main agents are households seeking to repair their homes, local contractors, and out-of-town contractors. Out-of-town contractor agents come into the community if the labor and housing markets are favorable. The framework can be used to evaluate the resulting challenges and benefits of interventions aimed at attracting out-of-town contractors to expedite housing recovery. We present a case study on the housing recovery of the city of San Francisco after hypothetical M 6.5, M 7.2, and M 7.9 earthquakes. A shortage of contractors is shown to bottleneck the reconstruction if no out-of-town contractors are recruited. Conversely, out-of-town contractors increase the likelihood of temporary housing shortages. These results highlight the need to plan for shortages of reconstruction labor and temporary housing during recovery. 
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  2. 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.

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  3. Summary

    Regional seismic risk assessments and quantification of portfolio losses often require simulation of spatially distributed ground motions at multiple intensity measures. For a given earthquake, distributed ground motions are characterized by spatial correlation and correlation between different intensity measures, known as cross‐correlation. This study proposes a new spatial cross‐correlation model for within‐event spectral acceleration residuals that uses a combination of principal component analysis (PCA) and geostatistics. Records from 45 earthquakes are used to investigate earthquake‐to‐earthquake trends in application of PCA to spectral acceleration residuals. Based on the findings, PCA is used to determine coefficients that linearly transform cross‐correlated residuals to independent principal components. Nested semivariogram models are then fit to empirical semivariograms to quantify the spatial correlation of principal components. The resultant PCA spatial cross‐correlation model is shown to be accurate and computationally efficient. A step‐by‐step procedure and an example are presented to illustrate the use of the predictive model for rapid simulation of spatially cross‐correlated spectral accelerations at multiple periods.

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  4. Summary

    Robust estimation of collapse risk should consider the uncertainty in modeling of structures as well as variability in earthquake ground motions. In this paper, we illustrate incorporation of the uncertainty in structural model parameters in nonlinear dynamic analyses to probabilistically assess story drifts and collapse risk of buildings. Monte Carlo simulations with Latin hypercube sampling are performed on ductile and non‐ductile reinforced concrete building archetypes to quantify the influence of modeling uncertainties and how it is affected by the ductility and collapse modes of the structures. Inclusion of modeling uncertainty is shown to increase the mean annual frequency of collapse by approximately 1.8 times, as compared with analyses neglecting modeling uncertainty, for a high‐seismic site. Modeling uncertainty has a smaller effect on drift demands at levels usually considered in building codes; for the same buildings, modeling uncertainty increases the mean annual frequency of exceeding story drift ratios of 0.03 by 1.2 times. A novel method is introduced to relate drift demands at maximum considered earthquake intensities to collapse safety through a joint distribution of deformation demand and capacity. This framework 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 and capacity of selected archetype structures enable comparisons with the proposed seismic criteria for the next edition (2016) of ASCE 7. Subject to the scope of our study, the proposed drift limits are found to be unconservative, relative to the target collapse safety in ASCE 7. Copyright © 2016 John Wiley & Sons, Ltd.

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