NSF CAREER Project Outcomes

Project Information

Project title: CAREER: Integrating Resilience and Social Vulnerability into the Management of Civil Infrastructure Systems

Funding source: NSF Division of Civil, Mechanical, and Manufacturing Innovation (CMMI)

Abstract:

This Faculty Early Career Development (CAREER) project will advance our understanding of the influence of socioeconomic factors on differential impacts of infrastructure disruptions to different populations. The impact of the damage caused by natural hazards to critical civil infrastructure systems differs for various population groups because of social-vulnerability factors such as income, age, health status, and disability. However, such disproportionate impacts are typically not considered in existing resilience assessment methods. Consequently, infrastructure-management decisions based on the existing methods ignore the differing needs of the most vulnerable populations in communities. This project will explicitly integrate social-vulnerability factors into infrastructure resilience models so that the needs of the most vulnerable populations in communities can be adequately considered in resilience improvement decision-making processes. The award will also support an educational program to train future civil engineers to tackle the challenges faced by the built environment and provide them with the knowledge they need to be effective decision-makers. The community outreach component of the project will increase awareness of issues related to the impact of natural hazards on infrastructure systems and motivate communities to support resilience-improvement policies.

Research Data, Codes, Publications, and Tools

Figure: Spatial distribution of SV across U.S. counties. (a) vulnerability levels based on the PCA-Pareto SVI methodology combining all three themes, (b) vulnerability using the percentile ranking method.

Publication: Assessing Vulnerability to Water Service Disruptions: A Targeted Index to Inform Infrastructure Planning and Management

Abstract: Water distribution systems are critical to the stability and well-being of communities but remain highly vulnerable to disruptions from natural disasters and aging. These disruptions do not impact all populations equally, making it essential to incorporate differential vulnerability into water infrastructure management and planning. A common method for assessing such disparities is the use of a social vulnerability index (SVI). While various SVIs have been developed by researchers and government agencies, they are often generic—intended for general disaster preparedness or infrastructure challenges—and may omit key factors relevant to water service disruptions or include irrelevant ones. Consequently, these indices may not adequately identify areas critically vulnerable to water-related disruptions. This study develops a water-specific SVI to classify counties based on their vulnerability to water service interruptions. Twenty-one relevant factors were selected and grouped into three categories: health-related, socioeconomic, and housing-related vulnerabilities. Principal Component Analysis (PCA) was used to reduce dimensionality, followed by Pareto ranking to construct the index. Pareto ranking complements PCA by enabling a balanced, multi-dimensional comparison without imposing arbitrary weights, thereby preventing dominance by any single factor. For comparison, an alternative version of the index was also developed using percentile ranking. The resulting water-specific SVI was compared with FEMA’s existing SVI to highlight the importance of a tailored index. Significant differences were observed. For example, unlike FEMA’s classification, our index identifies some counties in the Southeast and Midwest as highly vulnerable due to factors such as aging infrastructure, high rental occupancy, and inadequate plumbing facilities—factors not captured by FEMA’s SVI. The proposed SVI provides a more targeted tool for policymakers, urban planners, and emergency managers to prioritize investments, allocate resources effectively, and enhance the resilience of water infrastructure systems.

Data and Codes: https://github.com/amsalman1987/NSF-CAREER-Research-Products

Figure: Hurricane Helene (Most affected counties -up to 358) – Blue line (hurricane path)

Figure: Aggregated outage intensity of 21 hurricanes (2017-2024)

Publication: Post-Hurricane Power Outage Recovery: Examining the Influence of Social Vulnerability, Economic Activity, and Critical Facilities

Abstract: Extended power outages following hurricanes can severely impact community well-being, particularly among vulnerable populations such as the elderly and individuals with medical conditions. While previous studies have investigated disparities in outage recovery, many focus on single events and rely on general social vulnerability indices, limiting their ability to identify consistent patterns or capture the specific factors that heighten vulnerability to power disruptions. Moreover, few studies have examined the influence of economic activity or the presence of critical facilities on recovery times. This study analyzes power outage recovery trajectories across 27 U.S. states affected by 21 hurricanes between 2017 and 2024. Using spatial regression analysis, we assess whether counties’ socioeconomic vulnerability, levels of economic activity, and the number of critical facilities consistently explain differences in post-hurricane power outage recovery times. A customized Social Vulnerability Index (SVI) was used to more accurately capture population vulnerability to power disruptions. Results indicate that the SVI does not have a consistent effect on recovery times, suggesting no systematic disparities based solely on socioeconomic characteristics. However, when considered independently, median income is negatively correlated with recovery time, although the effect varies with outage severity and the extent to which other factors are controlled for. Higher economic activity (measured by GDP per capita) is associated with longer recovery times, although this relationship is also dependent on the severity of outage and whether other variables are included in the model. The number of critical facilities does not show a consistent standalone effect once other county characteristics are accounted for.

Data and Codes: https://github.com/amsalman1987/NSF-CAREER-Research-Products

Education and Training

Undergraduate Students Trained

Christian Munoz, Civil & Environmental Engineering
Ian Anzigare, Civil & Environmental Engineering
Joshua Clay, Mechanical and Aerospace Engineering

Graduate Students Trained

Adam Ado Sabari, PhD student, Civil & Environmental Engineering
Muhammad Ahsan Ibrar, PhD student, Civil & Environmental Engineering

New Courses

CE 659 - Risk and Reliability Analysis

This course is offered every spring to UAH and UAB students. The course introduces probabilistic analysis concepts, as well as the techniques for characterizing and including various sources of uncertainties (loading, environment, material properties, geometric parameters, etc.) for analysis and design of civil engineering systems.

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