Systematically collected and curated data sets from historical events provide a strong basis for simulating the physical and functional effects of natural hazards on the built environment. This article develops a relational database to support post-earthquake damage and recovery modeling of building portfolios. The current version of the database has been populated with information on the 3695 buildings affected by the 2014 South Napa, California, earthquake. The associated data categories include general building characteristics, site properties and shaking intensities, building damage and repair permitting (timing and type) information, and census-block-level sociodemographics. The Napa data set can be used to validate post-earthquake recovery simulation methodologies and explore the effectiveness of different modeling techniques in predicting damage. The database can be expanded to include other earthquakes and the overall framework can be adapted to other types of natural hazards (e.g. hurricanes, flooding).
Applied Technology Council (ATC) (1995) Procedures for Post-Earthquake Building Safety Evaluation Procedures (ATC-20). Redwood City, CA: ATC.
3.
BaltrusaitisTAhujaCMorencyLP (2018) Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence41: 423–443.
4.
BoatwrightJBlairJLAagaardBTWallisK (2015) The distribution of red and yellow tags in the City of Napa. Seismological Research Letters86(2A): 361–368.
5.
BooreDMStewartJPSeyhanEAtkinsonGM (2014) NGA-West2 equations for predicting PGA, PGV, and 5% damped PSA for shallow crustal earthquakes. Earthquake Spectra30(3): 1057–1085.
6.
BrocherTMAagaardBTSimpsonRWJachensRC (2006) The USGS 3D seismic velocity model for northern California. Paper presented at the 2006 fall meeting (Abstract id S51B-1266), American Geophysical Union (AGU), San Francisco, CA, 11–15December.
7.
BurtonHVKangHMilesSBNejatAYiZ (2019) A framework and case study for integrating household decision-making into post-earthquake recovery models. International Journal of Disaster Risk Reduction37: 101167.
8.
Center for Engineering Strong Motion Data (CESMD) (2018) Data for latest earthquakes. Available at: https://www.strongmotioncenter.org (accessed on January 2021).
ElliottJR (2015) Natural hazards and residential mobility: General patterns and racially unequal outcomes in the United States. Social Forces93(4): 1723–1747.
14.
Federal Emergency Management Agency (FEMA) (2003a) Multi-Hazard Loss Estimation Methodology—Earthquake Model: HAZUS MH-MR4 Technical Manual. Washington, DC: Department of Homeland Security, FEMA.
15.
Federal Emergency Management Agency (FEMA) (2003b) NHERP Recommended Provisions for Seismic Regulations for New Buildings and Other Structures (FEMA 450-1). Washington, DC: Department of Homeland Security, FEMA.
16.
Federal Emergency Management Agency (FEMA) (2012) Seismic Performance Assessment of Buildings. Redwood City, CA: Applied Technology Council (ATC).
17.
KangHBurtonHVMiaoH (2018) Replicating the recovery following the 2014 South Napa earthquake using stochastic process models. Earthquake Spectra34(3): 1247–1266.
18.
MangalathuSBurtonHV (2019) Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions. International Journal of Disaster Risk Reduction36: 101111.
19.
MangalathuSSunHNwekeCCYiZBurtonHV (2020) Classifying earthquake damage to buildings using machine learning. Earthquake Spectra36: 183–208.
20.
MansonSSchroederJVan RiperDRugglesS (2017) IPUMS National Historical Geographic Information System: Version 12.0 (Database). Minneapolis, MN: University of Minnesota, p. 39.
21.
MilesSBChangSE (2007) A simulation model of urban disaster recovery and resilience: Implementation for the 1994 Northridge earthquake. Technical report MCEER-07-0014, 7September. Buffalo, NY: Multidisciplinary Center for Earthquake Engineering Research, University at Buffalo.
22.
MySQL (2020) Open source database. Available at: https://www.mysql.com/ (accessed on January 2021).
23.
OmoyaMEroIZaker EsteghamatiMBurtonHVBrandenbergSNwekeC (2021) Relational Database for Post-Earthquake Damage and Recovery Assessment: 2014 South Napa Earthquake (DesignSafe-CI). Available at: https://doi.org/10.17603/ds2-3nvj-4127 (accessed on January 2021).
24.
RathjeEMDawsonCPadgettJEPinelliJPStanzioneDAdairAArduinoPBrandenbergSJCockerillTDeyCEstevaMHaanFLHanlonMKareemALowesLMockSMosquedaG (2017) DesignSafe: New cyberinfrastructure for natural hazards engineering. Natural Hazards Review18(3): 06017001.
25.
SeyhanEStewartJP (2014) Semi-empirical nonlinear site amplification from NGA-West2 data and simulations. Earthquake Spectra30(3): 1241–1256.
WillsCJGutierrezCIPerezFGBranumDM (2015) A next generation VS30 map for California based on geology and topography. Bulletin of the Seismological Society of America105(6): 3083–3091.
29.
Zaker EsteghamatiMLeeJMusetichMFlintMM (2020) INSSEPT: An open-source relational database of seismic performance estimation to aid with early design of buildings. Earthquake Spectra36: 2177–2197.
30.
ZhangYPeacockWG (2009) Planning for housing recovery? Lessons learned from Hurricane Andrew. Journal of the American Planning Association76(1): 5–24.
