Research
Post-Disaster Reconnaissance
Our research focuses on advancing post-disaster reconnaissance and emergency response through a holistic and multidisciplinary approach. We aim to enhance the efficiency and effectiveness of disaster management by integrating cutting-edge technologies, such as drones, ground-penetrating radar, augmented reality, and artificial intelligence, to provide comprehensive situational awareness and support for search and rescue operations. We are committed to developing intelligent systems and robotic platforms that can autonomously assess and report on the structural integrity of buildings, identify potential hazards, and locate survivors in disaster-stricken areas. These technologies are designed to work in tandem with human responders, ensuring that they have the best possible information and tools to navigate complex environments and make informed decisions.
Our research also emphasizes the importance of predictive modeling and simulation to prepare for and mitigate the impacts of disasters. By leveraging advanced algorithms and machine learning, we aim to create realistic scenarios that help train first responders and improve their readiness for real-world situations. In addition to immediate response efforts, our work extends to enhancing long-term resilience and recovery strategies. We strive to integrate the lessons learned from each disaster into future preparedness plans, ensuring that communities are better equipped to withstand and recover from catastrophic events. Our ultimate goal is to save lives, reduce the impact of disasters, and build safer, more resilient communities through the continuous advancement of disaster reconnaissance and response technologies.
References
- Hu, D., Li, S., Du, J., & Cai, J. (2023). Automating building damage reconnaissance to optimize drone mission planning for disaster response. Journal of Computing in Civil Engineering, 37(3), 04023006.
- Hu, D., Chen, L., Du, J., Cai, J., & Li, S. (2022). Seeing through disaster rubble in 3D with ground-penetrating radar and interactive augmented reality for urban search and rescue. Journal of Computing in Civil Engineering, 36(5), 04022021.
- Hu, D., Chen, J., & Li, S. (2022). Reconstructing unseen spaces in collapsed structures for search and rescue via deep learning based radargram inversion. Automation in Construction, 140, 104380.
- Hu, D., Li, S., Chen, J., & Kamat, V. R. (2019). Detecting, locating, and characterizing voids in disaster rubble for search and rescue. Advanced Engineering Informatics, 42, 100974.
Robotic Solutions for Building Facility Management
References
- Hu, D., Li, S., & Wang, M. (2023). Object detection in hospital facilities: A comprehensive dataset and performance evaluation. Engineering Applications of Artificial Intelligence, 123, 106223.
- Hu, D., & Li, S. (2022). Recognizing object surface materials to adapt robotic disinfection in infrastructure facilities. Computer‐Aided Civil and Infrastructure Engineering, 37(12), 1521-1546.
- Hu, D., Zhong, H., Li, S., Tan, J., & He, Q. (2020). Segmenting areas of potential contamination for adaptive robotic disinfection in built environments. Building and environment, 184, 107226.
Subsurface Mapping
