research

The primary focus of our research is on smart and resilient civil infrastructure and urban systems. The overarching goal of our research is to provide a better understanding of infrastructure evolution as it relates to environmental, social, and technical developments to be able to identify economically efficient, environmentally friendly, and socially equitable pathways toward smart, sustainable and resilient infrastructure systems.  Specifically, we pursue research projects in the following three interrelated  areas:

1) Smart City Resilience:

We use physical sensors to collect real-time visual data such as photos, videos, and laser scanned point clouds from infrastructure assets. we also use social sensing techniques such as geocoding, temporal coding, clustering, topic detection and topic labeling, sentiment analysis, and mapping to collect and analyze the wide range of near-real-time data that is directly or indirectly shared by communities or groups of people on social media. The big data we collect from the coupled human-infrastructure systems enables us to develop intelligent, customized, searchable and tractable warnings, alerting, and evacuation orders during natural disasters to increases situational awareness of at-risk communities. The analysis would also serve as a “decision support” tool during rapidly unfolding preparedness and response phases of an emergency situation and facilitates “policy analysis” in mitigation and recovery phases when a comprehensive understanding of the vulnerabilities and resources of the affected communities are needed. 

2) Sustainability and Resilience of Infrastructure Systems: With the rapid development of new technologies, accelerated changes in behaviors of social actors, and unprecedented climatic trends, innovative methodological paradigms are required to enable understanding the evolution of infrastructure systems over the long-term. Our research will focus on creating computational models based on a system-of-systems perspective to enable exploratory assessment of the interdependencies among sociotechnical, environmental, and physical network systems with a view to identify robust pathways toward sustainable and resilient infrastructure systems. 

3) Big Data Analytics for Infrastructure and Emergency Management: As the cost of collecting real-time visual data such as photos, videos, and laser scanned point clouds decreases and the quality of such data increases, it becomes more and more critical to effectively use these data to improve infrastructure facility management practices. We collaborate with image processing and computer science experts in order to make near real-time condition assessment of infrastructure facilities. We use this information along with simulation modeling of asset-agency-user interactions and data mining techniques to identify the types of facility deformation that are likely to have the greatest long-term effects on economic, environmental, and social impacts of facilities. Identification of the most consequential facility deformations will enable disaster and infrastructure management agencies to make informed decisions about implementation of preventive maintenance on infrastructure facilities.