Award Abstract # 2053014
Assessing Urban Post-Earthquake Community Recovery to Inform Pre-Disaster Planning

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: THE LELAND STANFORD JUNIOR UNIVERSITY
Initial Amendment Date: November 2, 2021
Latest Amendment Date: November 2, 2021
Award Number: 2053014
Award Instrument: Standard Grant
Program Manager: Giovanna Biscontin
gibiscon@nsf.gov
 (703)292-2339
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: November 1, 2021
End Date: October 31, 2025 (Estimated)
Total Intended Award Amount: $309,441.00
Total Awarded Amount to Date: $309,441.00
Funds Obligated to Date: FY 2022 = $309,441.00
History of Investigator:
  • Jack Baker (Principal Investigator)
    bakerjw@stanford.edu
Recipient Sponsored Research Office: Stanford University
450 JANE STANFORD WAY
STANFORD
CA  US  94305-2004
(650)723-2300
Sponsor Congressional District: 16
Primary Place of Performance: Stanford University
Stanford
CA  US  94305-4020
Primary Place of Performance
Congressional District:
16
Unique Entity Identifier (UEI): HJD6G4D6TJY5
Parent UEI:
NSF Program(s): DRRG-Disaster Resilience Res G
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 042E, 036E
Program Element Code(s): 198Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

Significant effort has been devoted to predicting physical damage to the built environment from disasters, but much less is understood about simulating disaster recovery, and how mitigation actions influence recovery. Further, it is well understood that there are existing socioeconomic inequalities in disaster risk and resilience. This Disaster Resilience Research Grants (DRRG) project aims to develop models to simulate housing and business operations recovery after a disaster. New insights will be gained into how a community's physical/demographic/economic characteristics interact in the recovery process. The models will be used to quantify a region's current ability to recover from disaster and evaluate the efficacy of actions that could be taken before disasters occur. Given the enormous costs of post-disaster disruption, and the significant resources committed for mitigation, insights on improving recovery will significantly benefit society, by improving disaster resilience and efficiently allocating limited societal resources to promote equity. Programs to involve underrepresented researchers, engage with city planners, and publicly release the modeling software will also contribute to the broader impacts of this work.

This project will develop a new generation of high-resolution computational simulation tools for disaster recovery simulation, to support enhanced and more equitable housing recovery. High-resolution simulations of the built, natural, and human environment (including household and company behavior) will be combined with mechanisms to quantify the benefits of resilience-enhancing policies. The housing recovery will be jointly simulated with the recovery of infrastructure and businesses, constrained by the availability of resources and affected by socioeconomic factors. The recovery process of individual households will be simulated, providing great flexibility in examining results and assessing the benefits for different groups of resilience-enhancing policies. This work will enable a holistic understanding of the community recovery process after earthquakes. The research will advance our capabilities to assess, before a disaster, the extent to which disaster preparedness interventions can reduce initial disaster consequences, speed recovery, and ensure that all groups participate in the benefits. This project's deliverables will be a modeling framework for assessing post-earthquake recovery and case-study analysis of recovery in San Francisco.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

Mongold, Emily and Baker, Jack W "Probabilistic Regional Liquefaction Hazard and Risk Analysis: A Case Study of Residential Buildings in Alameda, California" Natural Hazards Review , v.25 , 2024 https://doi.org/10.1061/NHREFO.NHENG-2078 Citation Details
Wang, Chenbo and Costa, Rodrigo and Baker, Jack W "Simulating post-disaster temporary housing needs for displaced households and out-of-town contractors" Earthquake Spectra , v.38 , 2022 https://doi.org/10.1177/87552930221112690 Citation Details
Costa, Rodrigo and Baker, Jack W "A methodology to estimate postdisaster unmet housing needs using limited data: Application to the 2017 California wildfires" Risk Analysis , v.44 , 2024 https://doi.org/10.1111/risa.14206 Citation Details
Mongold, Emily and Costa, Rodrigo and Zsarnóczay, Ádám and Baker, Jack_W "Modeling post-disaster recovery: Accounting for rental and multi-family housing" Earthquake Spectra , v.40 , 2024 https://doi.org/10.1177/87552930231222769 Citation Details
Issa, Omar and SilvaLopez, Rodrigo and Baker, Jack W. and Burton, Henry V. "Machinelearningbased optimization framework to support recoverybased design" Earthquake Engineering & Structural Dynamics , v.52 , 2023 https://doi.org/10.1002/eqe.3860 Citation Details
Costa, Rodrigo and Wang, Chenbo and Baker, Jack W. "Integrating Place Attachment into Housing Recovery Simulations to Estimate Population Losses" Natural Hazards Review , v.23 , 2022 https://doi.org/10.1061/(ASCE)NH.1527-6996.0000571 Citation Details

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page