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1. Well-Being and Infrastructure Disruptions during Disasters: An Empirical Analysis of Household Impact Disparities during Hurricane Harvey

   Dargin, Jennifer; Mostafavi, Ali (January 2020, ASCE Construction Research Congress 2020)

   null (Ed.)

   There are limited studies that empirically evaluate the interactions between households and infrastructure systems. As a result, the extent to which interruptions in infrastructure services influence different aspects of well-being for different subpopulations is still only vaguely understood. In order to address this knowledge gap, this study investigates multiple dimensions of well-being to derive an empirical relationship between sociodemographic factors of households and their subjective well-being impacts due to disruptions in various infrastructure services during and immediately after Hurricane Harvey. Statistical analysis driven by spearman-rank order correlations and fisher-z tests indicated significant disparities in well-being due to service disruptions among vulnerable population groups. The characterization of well-being is used to explain why and to what extent infrastructure service disruptions influence different subpopulations. The results show that disruptions in particular infrastructure systems are more likely to result in well-being impact disparities, in which racial minorities experience the greatest impact. Similarly, interruptions in services were more likely to evoke changes in social well-being and household’s connectivity to their communities. These findings present novel insights to understanding the role of infrastructure resilience in household well-being, as well as inequalities in well-being impacts across various subpopulations. The approach of the research and its findings enable a paradigm shift towards a more human-centric approach to infrastructure resilience. 

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2. Agent-Based Modeling Framework for Simulation of Societal Impacts of Infrastructure Service Disruptions during Disasters

   Esmalian, Amir; Ramaswamy, Maitreyi; Rasoulkhani, Kambiz; Mostafavi, Ali (January 2019, ASCE Computing in Civil Engineering Workshop)

   The objective of this paper is to model and examine the impacts of different levels of infrastructure service losses caused by disasters on the households’ well-being residing in a community. An agent-based simulation model was developed to capture complex mechanisms underlying households’ tolerance for the service outages, including household characteristics (e.g., sociodemographic, social capital, resources, and previous disaster experience), physical infrastructure attributes, and extreme disruptive events. The rules governing these mechanisms were determined using empirical survey data collected from the residents of Harris County affected by Hurricane Harvey as well as the existing models for power outages and service restoration times. The analysis results highlighted the spatial diffusion of service risks among households living in affected areas in disasters. The proposed simulation model will provide utility agencies with an analytical tool for prioritization of infrastructure service restoration actions to effectively mitigate the societal impacts of service losses. 

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3. Evaluating Machine Learning Models for Identifying At-Risk Highway Slope Assets

   Salunke, R; Khan, S (March 2025, American Society of Civil Engineers)

   Geographic information system (GIS) based landslide susceptibility mapping is a proven methodology for understanding and forecasting infrastructure impacts during significant weather events. While researchers worldwide have increasingly applied GIS and machine learning methods to study landslide susceptibility on hillside slopes affected by geomorphological and hydrological factors, there is a noticeable lack of focus on highway slope (HWS) failures in the literature. This research addresses this gap by comprehensively evaluating HWS failure susceptibility in central Mississippi counties. The study focused on developing an inventory of HWS susceptible to failure, susceptibility mapping, and model validation using probabilistic and statistical methods. Several supervised machine learning (ML) classification models, including artificial neural networks, were compared with random forest and logistic regression to solve the classification problem of HWS failure susceptibility mapping. Various data sources were utilized to develop causative factors, including Digital Terrain Models (DTM) created from Remote Sensing methods such as satellites, drone sensors, and terrestrial LiDAR. The failed slopes investigated in this study were from four counties in central Mississippi. The resolution used was 3 ft × 3 ft per pixel, representing an area of 9 ft2 per pixel. A ratio of 1:2 was maintained between failed and non-failed areas within the study area for developing the failure susceptibility prediction models. The causative factors considered in this study encompassed geotechnical and geomorphological attributes, such as slope, aspect, curvature, elevation, normalized vegetation difference index (NDVI), soil composition, and terrain from DTM. Hydrological factors were also incorporated, including precipitation, distance from the stream, groundwater depth, and Topographic Wetness Index (TWI). These causative factors were utilized as independent features to train the classification ML models for predicting vulnerable HWS. Based on the random forest model’s classification results of failed vs. non-failed assets on the unseen data set, the influence of the features was calculated. Among the top four influencing factors, ground elevation was the highest contributing factor, followed by distance from streams, NDVI, and precipitation. The results of this study can significantly contribute to transportation agencies by offering valuable insights to target preventative maintenance efforts and mitigate catastrophic failures caused by significant rainfall and weather events on road networks and highway slopes. The findings advocate for the integration of an AI/ML-based approach within asset management programs, enabling transportation agencies to rapidly detect at-risk infrastructure. This ML-based automated detection is especially beneficial when identifying vulnerable sites before a forecasted extreme event, providing value to infrastructure resiliency efforts. 

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4. ‘Being able to work has kept our life fairly constant:’ reconciling social practice models to assess the impact of (infra)structural inequities on household wellbeing during the COVID-19 pandemic

   https://doi.org/10.1088/2634-4505/acc272  

   Ahamed, Sonya; Schelly, Chelsea; Floress, Kristin; Lytle, William; Caggiano, Holly; Cuite, Cara L.; Wallace, Charles; Cook, Ryan; Jarvis, Rebekah; Shwom, Rachael; et al (March 2023, Environmental Research: Infrastructure and Sustainability)

   Abstract Social practice theory offers a multidisciplinary perspective on the relationship between infrastructure and wellbeing. One prominent model in practice theory framessystems of provisionas the rules, resources, and structures that enable the organization of social practices, encompassing both material and immaterial aspects of infrastructures. A second well-known model frames social practices in terms of their constituent elements: meanings, materials, and competences. Reconciling these two models, we argue that household capacity to respond to shifting systems of provision to maintain wellbeing is profoundly tied to the dynamics of privilege and inequity. To examine these dynamics, we propose a new analytical tool utilizing the Bourdieuian conceptualization of forms of capital, deepening the ability of social practice theory to address structural inequities by re-examining the question ofwhois able to access specific infrastructures. To illustrate this approach, we examine how households adapted to shifting systems of provision during the COVID-19 pandemic. Using data from 183 households in the Midwestern United States, we apply this tool to analyze adaptations to disruptions of multiple systems of provision, including work, school, food, and health, from February 2020 to August 2021. We highlight how household wellbeing during the pandemic has been impacted by forms of capital available to specific households, even as new social practices surrounding COVID-19 prevention became increasingly politicized. This research provides insight into both acute challenges and resilient social practices involving household consumption, indicating a need for policies that can address structural inequities across multiple systems of provision. 

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5. The Effects of Infrastructure Service Disruptions and Socio-Economic Vulnerability on Hurricane Recovery

   https://doi.org/10.3390/su11020516  

   Mitsova, Diana; Escaleras, Monica; Sapat, Alka; Esnard, Ann-Margaret; Lamadrid, Alberto (January 2019, Sustainability)

   null (Ed.)

   Hurricanes and extreme weather events can cause widespread damage and disruption to infrastructure services and consequently delay household and community recovery. A subset of data from a cross-sectional survey of 989 households in central and south Florida is used to examine the effects of Hurricane Irma on post-disaster recovery eight months after the landfall. Using logistic regression modeling, we find that physical damage to property, disruption of infrastructure services such as loss of electric power and cell phone/internet services and other factors (i.e., homeowner’s or renter’s insurance coverage, receiving disaster assistance and loss of income) are significant predictors of post-disaster recovery when controlling for age and race/ethnicity. 

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