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A Technical Framework to Map Functional Recovery Performance Objectives to Prescriptive Seismic Design Provisions for Buildings

Published

Author(s)

Dustin Cook, Siamak Sattar

Abstract

Designing buildings for improved functional recovery represents a major shift in the current design paradigm. Given the novelty of the functional recovery building performance objective, current design provisions may be ineffective at controlling the underlying damage mechanisms that influence building function and recovery. Therefore, new design provisions, developed based on a fundamental understanding of building recovery behavior, are needed to meet target performance objectives for new buildings. In this report, we present a technical framework to quantify minimum prescriptive design requirements that satisfy target functional recovery performance objectives for new buildings. At its core, the framework utilizes a machine learning algorithm, trained on the simulated performance outcomes of a set of archetype building models, to identify building characteristics that form the boundary between acceptable and unacceptable building performance. The trained algorithm serves as a decision support tool for subject matter experts in making recommendations to update building code provisions through a consensus-based process. In this report, the framework is exercised on a case study archetype design space to illustrate the framework's implementation and interpretation of results. The goal of the proposed framework is to provide a technical link between probabilistic functional recovery models and practical design implementation strategies for improving community resilience and reducing disaster impacts.
Citation
Special Publication (NIST SP) - 1321
Report Number
1321

Keywords

Functional Recovery, Seismic Design Provisions, Performance-Based Earthquake Engineering, Machine Learning

Citation

Cook, D. and Sattar, S. (2024), A Technical Framework to Map Functional Recovery Performance Objectives to Prescriptive Seismic Design Provisions for Buildings, Special Publication (NIST SP), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.SP.1321, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956820 (Accessed October 7, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created September 19, 2024