| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | July 28, 2016 |
| Latest Amendment Date: | September 23, 2022 |
| Award Number: | 1638311 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Daan Liang
dliang@nsf.gov (703)292-2441 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | January 1, 2017 |
| End Date: | December 31, 2022 (Estimated) |
| Total Intended Award Amount: | $2,204,202.00 |
| Total Awarded Amount to Date: | $2,106,450.00 |
| Funds Obligated to Date: |
FY 2017 = $40,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
2550 NORTHWESTERN AVE # 1100 WEST LAFAYETTE IN US 47906-1332 (765)494-1055 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
550 Stadium Mall Drive West Lafayette IN US 47907-2051 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
CRISP - Critical Resilient Int, Hurricane Harvey 2017 |
| Primary Program Source: |
01001718DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Understanding the recovery of communities after disruptions has important implications for efficiently allocating resources, better planning for disasters, and reducing time and cost of recovery. Virtually all communities are embedded in highly interdependent social and physical infrastructure. This coupling between social and physical networks can lead to complex cascading effects that cannot be understood by looking at these networks in isolation. The full implications of these interdependencies for the resilience of communities and their ability to recover after disasters are not currently understood. This research seeks an understanding of the underlying factors that lead to resilience and recovery of interdependent social and physical networks after disasters. The researchers will collect data from communities impacted by Hurricane Sandy to create and test modeling approaches for improved knowledge of both social and physical factors that lead to recovery. It will also lead to a better understanding of the interdependencies between the social and physical systems, and will identify potential tipping points where small changes in the social and physical systems significantly impact the recovery of the overall system. The findings from the study will allow governmental and emergency agencies to take actions that will accelerate system recovery and enhance its resilience. Students and underrepresented groups working on this project will gain exposure and experience working with a multi-disciplinary research team, thereby preparing them for tackling complex, systems-related challenges in their future careers. A workshop will be organized to disseminate the findings to the scientific community and various stakeholders who are involved in recovery processes.
The modeling of resilience in interdependent social and physical networks will be conducted using an interdisciplinary approach. First, the researchers will collect data pertaining to complex interdependencies that influence post-disaster recovery and decision-making. Second, the project will leverage insights gleaned from the data to identify utility functions that influence the decision-making of households, and formulate mathematical techniques based on game theory and network science for modeling and analyzing the tipping points that lead to recovery across social and physical networks. Third, the research effort will create novel state-estimation techniques using publicly available citizen data and develop multi-agent simulation models that will provide new decision-support tools for governmental agencies and emergency response organizations to model, test and predict the effects of recovery actions. The research will identify the role of network structure and function in the movement of the overall system towards better recovery states, and characterize the different events that transpire during community re-entry and recovery processes.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This NSF project developed state-of-the-art predictive modeling framework for identifying phase transitions that result in better post-disaster recovery outcomes. The project outcomes were provided based on interdisciplinary collaborations between engineering, social science, and computer science researchers. The project has significant impacts on the society and will make post-disaster recovery efficient. Figure 1 (see attachment) contains the points on collecting and analyzing novel datasets, developing new predictive household?s return behavior modeling in interdependent socio-physical networks (ISPNs).
The key outcomes from this project were:
Intellectual Merit
? Focus group interviews and three post-hurricane household mail survey datasets were gathered to model return behaviors and identify the different types of support and barriers in long-term recovery. Six focus group interviews and post-hurricane household mail survey were conducted from two counties in New Jersey for Hurricane Sandy, 2012; two post-hurricane household mail surveys were examined from fifteen counties in Texas for Hurricane Harvey, 2017. Mobile phone location data, places of interest data, and power outage were collected to infer the human mobility patterns in Hurricane Harvey, Irma, and Maria, 2017.
? Two decision models were developed to predict household return decisions. These decisions contain evacuation information, return information, social, physical, and informative support/barriers in long-term recovery, and sociodemographic variables. The results enhance understanding of the household experience during the return process and explain the influential factors of return decisions including support and barriers in long-term recovery to construct the agent-based model for post-hurricane recovery in ISPNs.
? Post-disaster repair modeling and network inference and estimation were developed to find optimal post-disaster recovery control policies in ISPNs and estimate time-delayed correlations between two events and distributed network inference complementing the data limitation. We also developed population recovery modeling to unravel universal population recovery patterns, regional differences, socio-economic inequality, and machine-learning based post-disaster mobility decisions.
? We developed predictive agent-based model for post-hurricane recovery in ISPNs to assess post-disaster recovery scenarios, identify phase transitions for fast recovery, and prevent policy scenarios of recovery failure. This framework was supported by the household return decision models, population recovery modeling, and regional differences. Through multiple scenarios, a phase diagram was depicted to find the critical transition points of the post-disaster recovery. The detailed description of agent-based model for the return behavior of post-disaster recovery can be found in https://github.com/JiaweiXue/PostDisasterSim.
Broader Impacts
? Four PhD dissertations, two MS theses, fourteen journal articles, and nine conference papers were published and will be published based on the collected data and the developed models.
? More than ten graduate students across two universities and more than two post-docs were trained in the project across engineering, social science and computational sciences.
? A seminar series called ?Urban Transformations Seminar Series? was developed by Dr. Ukkusuri in Spring 2021. More than 10 speakers from around the world were invited to present the latest research in urban systems resilience. Hazards research was an important component of this seminar series and several speakers discussed community level issues related to evacuation, citizen engagement, and metrics for measuring vulnerability using innovative datasets. Several students and researchers participated in the seminar series which was followed by a discussion session where students were able to share their work with the speakers.
? One undergraduate course and one graduate course are delivered funded by this project. An undergraduate course ?Disaster Resilience and Society? at Purdue University was developed by Dr. Ukkusuri and Dr. Lee and in Fall 2018 and Fall 2019. This course introduced students to various phases of disasters, real-world examples of natural disasters, and hands-on software programs to fulfill real-time citizen needs. Also, two graduate research assistants delivered guest lectures to the class. A graduate course ?Building Resilience? at Purdue University was developed by Dr. Lee in Fall 2018. This course introduced concepts, models, and theories related to the resilience of individuals, organizations, and societies from transdisciplinary perspectives.
? An instructional activity was delivered by Dr. Lee to provide hands-on experience of a network structure and examine how network approaches inform resilience. Students learned how to calculate basic network metrics and conduct resilience tests on a network. Several LEGO? bricks or spaghetti noodles were used to calculate network measures and test resilience.
Last Modified: 05/05/2023
Modified by: Satish Ukkusuri
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