AUGUST 29, 2023 — Jiannan Cai, an assistant professor in the UTSA Department of Civil and Environmental Engineering and Construction Management has received the Alfred Noble Prize from the American Society of Civil Engineers (ASCE) for her paper on a new computational framework for tracking multiple workers on construction job sites.
Named for Alfred Noble, past president of the ASCE and Western Society of Engineers, the award recognizes members who have developed “a technical paper of exceptional merit.”
Cai shares the award with her co-author and Ph.D. advisor Hubo Cai of Purdue University. Their paper, “Robust Hybrid Approach of Vision-Based Tracking and Radio-Based Identification and Localization for 3D Tracking of Multiple Construction Workers,” was published in ASCE’s Journal of Computing in Civil Engineering.
“This is a prestigious award that has been presented to engineers for more than 90 years,” said Eric Brey, dean of the Margie and Bill Klesse College of Engineering and Integrated Design, where Cai is a faculty member. “In receiving this award, Dr. Cai has distinguished herself as an outstanding engineer and an amazing contributor to our program and the students who will benefit from her instruction and creativity. We cannot wait to see what is next for her.”
Hundreds, and sometimes thousands of construction workers may be involved in a large building or infrastructure project.
“Having information about real-time location and identity of construction workers and other resources is critical for efficient jobsite safety and productive management,” Cai said.
She proposed creating a hybrid framework where vision-based and radio-based tracking systems are complementary, thus leading to improved accuracy and robustness.
Vision-based tracking provides accurate location information as long as there is line of sight. Meanwhile, radio-based tracking offers reliable detection and identification but lacks location accuracy. Creating a hybrid system makes it possible to create ID-linked 3D trajectories that will improve tracking accuracy. Two indoor experiments showed that overall accuracy went from a high of 88% using the individual methods to a high of 95% using the hybrid model.
“This prestigious award is an important milestone in my academic career,” Cai said. “I deeply appreciate the recognition of my work. I am also glad to see that our field has obtained increasing recognition for great work and research done by scholars and practitioners.”
The award ceremony will take place during ASCE’s annual convention, October 18-21.
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