Dr. Deniz recently attended the 2018 ASCE’s Structures Congress at Fort Worth, TX, where she co-organized and co-moderated a technical session on “Integrated Engineering and Social Science Approaches to Disaster Resilient Structures“.

Dr. Deniz co-authored two abstracts submitted to the congress and gave a presentation about her research entitled  “Direct Physical and Social Impacts of the 2016 Hurricane Matthew on the Lumberton Residential Community”:

ABSTRACT: Hurricane Mathew (2016) was a powerful and devastating tropical cyclone that caused widespread destruction and loss of life across the western Atlantic including parts of the southeastern United States. The City of Lumberton, a small and diverse community with 21,500 residents located in the rural coastal plains region of North Carolina, was among the most devastated communities due to major flooding caused by the storm, with some areas receiving 15-18 inches of rain on already saturated ground. The Center of Excellence (CoE) for Risk-Based Community Resilience Planning research team conducted a quick response field study in the City of Lumberton to understand and quantify community resilience and recovery aftermath of the major flooding. Structured surveys on structural damage and household dislocation were created and used to explore physical, economical, and social impacts of major flooding on the Lumberton community. This study focuses on the flood impacts for Lumberton households, presenting the preliminary analyses of the post-disaster data collected on structural damage to residential buildings. Multi-variate loss models have been developed to assess flooding damage to residential structures. Household specific demographic data was analyzed to identify the most important social factors on damage and dislocation patterns. This study is one of the primary studies that show interconnectivity across the direct physical and social impacts of flooding on a residential community. These loss models and outcomes from this study can be used as predictive tools for other US communities , due to the adaptability of the models and outcomes for other context and similarities in residential communities. Of particular note is that the extensive collected data set provides new insight into the level of damage that causes families to dislocate from their residence, as well as the effects that race, ethnicity, and income have on dislocation.