Abstracts:The risk of damage of glass curtain walls in residential areas caused by impact of wind-borne debris has been given increasing importance. In this paper, by incorporating the numerical analysis of three-dimensional (3D) flight trajectories of wind-borne debris and computational fluid dynamics (CFD) simulation of the local wind environment in the context of buildings group, a novel impact damage prediction model for the glass curtain wall of urban buildings is proposed. To this end, the 3D flight trajectories and characteristics of plate-type debris are first studied by solving the 3D equation of motion of debris, of which the aerodynamic coefficients of plate-type debris is obtained by CFD numerical simulation. The flight characteristics of debris with different initial parameters are analyzed, showing that the properties of debris and initial conditions have significant effect on the flight trajectories and impact position of plate-type debris. Further, an impact damage prediction model for the glass curtain wall of an urban building is proposed. In the proposed model, the flight trajectory analysis of the wind-borne debris is embedded in the context of the local wind environment in a residential area obtained by CFD simulation. The damage caused by the impact of debris on the glass curtain wall of the buildings is then analyzed and illustrated by an example. The proposed model is of great significance for the wind damage analysis of urban building glass curtain walls considering local wind environment and real trajectories of debris.

Abstracts:As one of the principal lifeline systems, transportation networks are crucial for evacuation during extreme weather events like hurricanes, and critical network links must remain intact. The conventional evaluation measures prioritize to achieve the maximum system efficiencies, and therefore they estimate the functional criticality of a road network using measures such as travel time increase or throughput reduction caused by a link disruption. This study asks a fundamental question on equity achievement of such measures and develops a new framework to incorporate road users’ vulnerabilities in identifying critical network links. This study introduces new evaluation measures that integrate the most vulnerable zones for evacuation prioritization based on social, environmental, and economic vulnerabilities. Results show that the critical links for the vulnerable population during an evacuation are not always identified by conventional link-based measures that emphasize overall system efficiencies. Among the links selected as critical using the throughput measure, only 25% serve socially vulnerable communities and 38% serve environmentally vulnerable populations. This highlights the importance of considering road users’ vulnerability when prioritizing resources to strengthen the links since a link disruption may cause more significant consequences for vulnerable road users. Decision-making to identify critical links and minimize the impact of disruptions remains critical to distribute resources more effectively during an emergency and support the timely and safe evacuation of vulnerable populations that should be prioritized to achieve more equitable evacuation and disaster responses. An online interactive map is developed based on the results of this study to show the exact location of the critical links and other important metrics.

Abstracts:The behavior of cantilever sheet pile wall in an earthquake susceptible area was regulated by soil liquefaction, induced pore water pressure, and soil and wall properties. A dynamic analysis of cantilever sheet pile wall having infinite surcharge load was executed by using finite difference–based computer program subjected to the 1994 Northridge, 1989 Loma Gilroy, and 2001 Bhuj motions. The different liquefiable and nonliquefiable soil layers are simulated by properties of Pohang sand for UBCSAND and Mohr–Coulomb constitutive models, respectively. The ground water table was assumed at the ground surface. It was observed that the maximum bending moment can occur at any instant, not necessarily at the instant of maximum acceleration. In the liquefiable layer, the soil stiffness and shear strength decreased, causing the loss in acceleration amplification factor, and maximum amplification factor was obtained for the motion with the lowest bedrock-level acceleration. The bending moment in the cantilever sheet pile wall depended on the bedrock-level acceleration, whereas displacement depended on both the duration of the time history as well as bedrock-level acceleration. The soil was liquefied only in the case of the 1994 Northridge motion with no surcharge, and by placing the surcharge on the backfill, the liquefaction resistance increased. The bending moment, displacement, and settlement decreased with increasing distance of the surcharge load from the cantilever sheet pile wall. The present study was validated by the experimental results of cantilever walls in cohesionless dry soil.

Abstracts:This study explored the impact of Hurricane Michael within the Black community in Bay County and surrounding regions in the Florida Panhandle. It is imperative to investigate the challenges and resilience factors among historically marginalized populations to assist with empowering the community to rebuild and regain a sense of normalcy. Focus groups and interviews were conducted to expose victims’ lived experiences with accessing community, federal, and local resources after Hurricane Michael. Thematic network analysis was developed to present a visual representation of the psychosocial, environmental, and social justice issues encountered by the Black community following Hurricane Michael. A weblike depiction of the data yielded a global theme of psychosocial and psychological trauma; organizing themes based on resiliency, environmental, social justice, and mental health issues among adults and children; and basic themes regarding racism, discrimination, and ineffective assistance from local and federal agencies. The crisis intervention theory provides a framework for organizing pertinent resources within Black communities after severe natural hazards. Findings indicate the need for culturally competent counselors who understand the challenges and resilience of the Black community and to assist with rebuilding efforts.

Abstracts:This study proposes and evaluates a combined soft computing method to mitigate agricultural flood damage downstream of reservoirs, in which a fuzzy inference system and evolutionary optimization are linked. A flood damage function was developed by linking a Mamdani fuzzy inference system and Hydrological Engineering Centre—River Analysis System two-dimensional (HEC RAS-2D) model. First, an expert panel proposed the fuzzy rules of flood damage to develop the damage function. Then, this function was utilized in the reservoir operation model, in which evolutionary optimization methods were applied to optimize release from the reservoir. Finally, a decision-making system consisting of the two stages of evaluation were used for selecting the best algorithm. In the first stage, the performance of the penalty functions was considered to exclude some inappropriate algorithms. Then, the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) finalized the best solution of the reservoir management. Based on the results in the case study, either the firefly algorithm or the differential evolution algorithm is the best method to mitigate flood damage. The outputs corroborated the robustness of the developed method to mitigate potential flood damage. The maximum flood damage was reduced 40% compared with the natural flow. Moreover, average flood damage and inundation duration in the simulated period were mitigated considerably. It is recommended to apply the proposed method in flood mitigation studies in which overcoming flood damage data scarcity is a challenge.

Abstracts:This study examined the predictive ability of perceived vulnerability to disease (PVD), fear of COVID-19, and coping mechanisms on the Korean immigrants’ psychological distress level amid the pandemic. Through purposive sampling, both foreign-born and US-born Korean immigrants residing in the US above the age of 18 years were invited to an online survey. Between May and June 2020, data collection took place, which yielded the final sample of 790 participants from 42 states. An artificial neural network (ANN) was used to verify variables that predict the level of psychological distress on the participants. The model with one hidden layer holding six hidden neurons showed the best performance. The error rate was approximately 27%, and the results from the sensitivity analysis, the receiver operating characteristics (ROC) curve, showed that the area under the curve (AUC) was 0.801. The most powerful predicting variables in the neural network were resilience, PVD, and social support. Implications for practice and policy are discussed.

Abstracts:The public policy innovation and adoption processes are dynamic and complex. This is no exception for the adoption of hazard mitigation policies by localities prone to natural hazards. This paper synthesizes theories about policy innovation and adoption, and literature about hazards mitigation, and proposes a theoretical framework for understanding the factors driving hazard mitigation policy adoption at the local level. Our goal is to identify the key elements and parameters of the hazard mitigation policy adoption construct as well as the relationship between them. Using the property buyout program as an example, we present case studies in the states of North Carolina and New Jersey to illustrate a proposed theoretical framework and outline the directions for future research. The case studies show promising evidence consistent with the proposed framework, covering five categories—hazard problem, social context, institutional capacity, cross-sector collaboration, and policy diffusion. In particular, as for institutional capacity, three aspects influence the uptake of buyouts, including individual capacity [e.g., geographic information system (GIS) and technical skills], organizational capacity (e.g., reducing the negative financial impact on the tax base of buyouts and encouraging an innovative culture of flood mitigation strategies), and system capacity (e.g., cooperation among local organizations). To further validate the framework, systematic research of localities with diverse characteristics of policy adopters and nonadopters is needed.

Abstracts:In this study, our goal is to identify potentially vulnerable communities that could be subject to ongoing or compounding impacts from the pandemic and/or that may experience a slower recovery due to sociodemographic factors. For this purpose, we compiled information from multiple databases related to sociodemographic and health variables. We used a ranking-based method to integrate them and develop new combined indices. We also investigated a time-dependent correlation between vulnerability components and COVID-19 statistics to understand their time-dependent relationship. We ultimately developed pandemic vulnerability indices by combining CDC’s social vulnerability index, our newly developed composite health vulnerability index, and COVID-19 impact indices. We also considered additional assessments include expected annual loss due to natural hazards and community resilience. Potential hot spots (at the county level) were identified throughout the United States, and some general trends were noted. Counties with high COVID-19 impact indices and higher values of the pandemic vulnerability indices were primarily located in the southern United States or coastal areas in the Eastern and Southwestern United States at the beginning of the COVID-19 pandemic. Over time, the computed pandemic vulnerability indices shifted to higher values for counties in the southern and north-central United States, while values calculated for the northwestern and northeastern communities tended to decrease.