Abdullahi M. Salman

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). Although 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. The index addresses the social vulnerability dimension of water disruption risk, capturing population sensitivity to disruptions and the characteristics that limit communities’ capacity to cope with and recover from them. 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. 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, several counties in the western US ranked as low or moderate vulnerability in FEMA’s index but were identified as highly vulnerable in our analysis due to aging infrastructure, high rental occupancy, and inadequate plumbing—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.

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, 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.

Tropical cyclones have a significant impact on estuaries, resulting in deteriorating water quality, changes in phytoplankton productivity, as well as scouring and physical harm to mangroves and other vegetation. Furthermore, they have the potential to impede, pause, or even reverse ecosystem restoration and management efforts. As such, it is crucial to devise methods for detecting and measuring the severity of estuarine disturbances caused by tropical cyclones. Several statistical methods have been proposed in the past to detect and quantify disturbances, with varying levels of complexity and accuracy. Recent advancements in data collection and the quality of high-frequency data have opened up the opportunity to employ machine learning models for assessing the impact of tropical cyclones on ecosystems. This study develops a new machine learning-based model for detecting disturbances within estuaries, assessing their severity, and determining the recovery time, primarily focusing on disruptions to estuarine water quality. A Long Short-Term Memory (LSTM)-based deep learning model is developed to detect disturbances and quantify their severity while a Gaussian filter-based algorithm is developed to assess recovery time. The research utilizes data from NOAA’s National Estuarine Research Reserve System for training and validating the model. The model demonstrates an ability to distinguish between disturbances in water quality caused by tropical cyclones and those resulting from natural variability. It also assesses the extent of the disruption and the time for the estuary to revert to its pre-event state. Detecting and quantifying disturbances, as well as estimating recovery time in estuaries due to tropical cyclones, represent the initial steps in the development of predictive disturbance models. Detecting and quantifying disturbance can also aid stakeholders in comprehending the severity of tropical cyclones’ impacts on aquatic systems, allowing for the development of suitable interventions. The developed model can be applied to detect and quantify anomalies in any time series data, making it useful in various other fields.

Tornadoes are intense localized convective windstorms that are among the most devastating natural hazards that occur in the United States. Although the probability of any tornado striking o...

The projected increase in sea surface temperature due to climate change is expected to substantially intensify future hurricanes. Wooden light-frame residential buildings are particularly vulnerable to hurricane damage, and their risk is expected to increase due to heightened exposure and intensifying hurricanes. Therefore, adaptation strategies need to be planned to reduce damage to such buildings while considering the impact of climate change on hurricanes. This study investigates the effectiveness of various climate change adaptation strategies for coastal wood-frame single-story residential buildings and demonstrates how these strategies can be planned. The study considers the four Representative Concentration Pathways (RCPs) proposed by the IPCC to investigate the impact of climate change on wind hazard and losses. Additionally, three locations in the coastal United States of varying sizes, exposure, and hurricane hazard levels are considered: Harris County, Texas; Mobile County, Alabama; and Miami-Dade County, Florida. The results show that the increase in wind speeds and losses will be non-linear with time. All considered adaptation strategies decreased losses, with some able to completely counter the increasing losses even under high emission scenarios. Investigating the effectiveness of adaptive measures can guide stakeholders in allocating funds and efforts for hurricane risk management and enhancing community resilience.

Communities in US coastal regions are threatened by hurricanes more than ever, and the effect of climate change may further aggravate the risk and corresponding losses in the future. This paper investigates the potential impact of changes in sea surface temperature (SST) on hurricane wind and storm surge hazards for the Atlantic and Gulf coast regions. An empirical track model that uses SST as an input is used to account for the effect of temperature variations on hurricane intensity. The storm surge hazard is modeled using Sea, Lake, and Overland Surges from Hurricanes (SLOSH). Four projected future climate conditions based on the Intergovernmental Panel on Climate Change (IPCC) emission scenarios are adopted to capture the effect of changes in SST on future hurricane intensity. The impacts of changes in hurricane frequency and sea-level rise are also considered. The results show that the projected increase in the average SST will lead to more intense hurricanes by the end of the twenty-first century. An increase in wind speed for all the studied sites with varying degrees is observed. The most significant increase in wind speeds is observed on the northeast Atlantic coast, with some areas showing an increase of more than 60% for high return period winds under the most extreme future climate scenario. This is because a higher increase in SST is observed in such areas, which will lead to more intense hurricanes in the future. An increasing trend is also observed for the storm surge for all the study sites in the future. However, the largest increase in predicted surge heights is mainly seen in the Gulf Coast locations.

Critical facilities such as hospitals rely heavily on the functioning of complex infrastructure systems to perform efficiently. After a disaster, the functionality of these facilities is impaired because the infrastructure they rely on is impacted, and the facilities will recover as the supporting infrastructure is repaired. Infrastructure recovery planning often depends on political and economic interests, which are often conflicting and can result in a delayed or inefficient recovery. When priorities are set in the planning stages, actions can be more organized and directed, resulting in a more effective recovery, especially for critical facilities. However, existing infrastructure recovery planning approaches do not provide a systematic method for prioritizing the recovery of the functionality of critical facilities. This paper presents a framework for the postdisaster recovery planning of interdependent infrastructure systems, emphasizing the recovery of healthcare facilities. In the framework, the operation of the emergency departments of the healthcare facilities is modeled using discrete-event simulation, and a new functionality metric for the facilities is developed. The performance of the supporting infrastructure systems is quantified using a connectivity-based metric considering their interdependency using a hierarchical approach. A virtual city with healthcare facilities comprising three hospitals is used to demonstrate the proposed framework focusing on the road, power, and water infrastructure of the city. Two approaches are considered to plan the recovery of the infrastructure systems. In the first approach, recovery is planned without prioritizing any of the city’s buildings or facilities. In the second approach, the hospital network is prioritized in the recovery planning. The virtual city is used to demonstrate the proposed framework considering an earthquake scenario.

One major issue when considering the effects of climate change is to understand, qualify and quantify how the changing climate will likely impact infrastructure assets and services as it strongly depends on current and future climate variability, location, asset design life, function and condition. Expected changes to local climatic conditions may potentially lead to changes in the degradation processes of building materials, affecting the durability and service life of structures. A key question is how climate change may produce changes of vulnerability owing to physical and chemical actions affecting structural durability or changes to exposure in terms of the intensity/frequency of extreme weather events. In this context, this article focuses on the impact on structural resistance and associated challenges, considering some recent activities of members of IABSE TG6.1. Several case studies of infrastructure assets worldwide are presented and discussed in this article.

The changing climate with resulting more extreme weather events will likely impact infrastructure assets and services. This phenomenon can present direct threats to the assets as well as significant indirect effects for those relying on the services those assets deliver. Such threats are path-dependent and place-specific, as they strongly depend on current and future climate variability, location, asset design life, function and condition. One key question is how climate change is likely to increase both the probability and magnitude of extreme weather events under different scenarios of climate change. To address this issue, this paper investigates selected effects of climate change and their consequences on structural performance, in the context of evolving loading scenarios in three different continental regions: Europe, North America, and Asia. The aim is to investigate some main place-specific changes of the exposure in terms of intensity/frequency of extreme events as well as the associated challenges, considering some recent activities of members of the IABSE TG6.1. Climate change can significantly affect built infrastructure and the society by increasing the occurrence and magnitude of extreme events and increasing potential losses. Therefore, specific relationships relating hazard levels and structural vulnerability to climate change effects should be determined.

Population growth, economic development, and rapid urbanization in many areas have led to increased exposure and vulnerability of structural and infrastructure systems to hazards. Thus, developing risk-based assessment and management tools is crucial for stakeholders and the general public to make informed decisions on prehazard planning and posthazard recovery. To this end, structural risk and resilience assessment has been an ongoing research topic in the past 20 years. Recently, machine learning (ML) techniques have been shown as promising tools for advancing the risk and resilience assessment of structure and infrastructure systems. To date, however, there is a lack of a holistic review on ML progress across various branches of structural engineering; an in-depth analysis of literature that can provide a timely evaluation of risk and resilience assessment methods of the built environment, where different types of structural and infrastructure facilities are interconnected. For this reason, this study conducted a comprehensive review on ML for risk and resilience assessment in four main branches of structural engineering (buildings, bridges, pipelines, and electric power systems). To cover the crucial modules in the prevailing risk and resilience assessment frameworks, existing literature is thoroughly examined and characterized in terms of six attributes of ML, including method, task type, data source, analysis scale, event type, and topic area. Moreover, limitations and challenges are identified, and future research needs are highlighted to move forward the frontiers of ML for structural risk and resilience assessment.