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A Methodology for Estimating Seismic Retrofit Costs

Published

Author(s)

Juan F. Fung, David T. Butry, Siamak Sattar, Steven L. McCabe

Abstract

Presidential Executive Order 13717 (EO 13717), Establishing a Federal Earthquake Risk Management Standard, encourages federal agencies to ”enhance resilience...[to] future earthquakes” by evaluating and retrofitting existing federal buildings based on current existing building codes. However, while guidance on evaluation and retrofit practices is readily available, a standard approach to estimating retrofit costs does not exist. Moreover, the absence of easily obtainable estimates can make retrofits prohibitive for decision-makers. This paper develops a cost-estimating methodology for seismic retrofits that (1) captures the essential factors that drive seismic retrofit costs, such as building construction and square footage; and (2) is reproducible using data available to decision-makers. The methodology builds on FEMA 156 and 157, Typical Costs for Seismic Rehabilitation of Existing Buildings, Volumes 1 and 2. A series of regression models is fit to the data used for the FEMA reports, with the models varying in the level of data required; e.g., a decision-maker may not have information on building construction for each asset in their inventory. Thus, the trade-off from estimating retrofit costs subject to data limitations can be quantified in terms of prediction error, providing decision-makers with a set of options for estimating costs together with a measure of predictive performance. We find that a simple model, in terms of data requirements, can deliver reliable predictions.
Citation
Technical Note (NIST TN) - 1973
Report Number
1973

Citation

Fung, J. , Butry, D. , Sattar, S. and McCabe, S. (2017), A Methodology for Estimating Seismic Retrofit Costs, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.TN.1973 (Accessed October 11, 2025)

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Created September 28, 2017, Updated November 10, 2018
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