Machine‐learning‐based optimization framework to support recovery‐based design

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.