Yutong Jiao

Papers

Total Citations

120

H-Index

About

Yutong Jiao’s research lies at the critical intersection of civil infrastructure and artificial intelligence, focusing on the automated condition assessment of urban water and sewer systems. Her work addresses the urgent need for reliable, non-destructive inspection of aging underground pipelines—assets that are vital to community health and safety. Jiao’s major contributions include developing automated vision systems and deep neural networks for detecting key infrastructure components, such as water pipe valves, from within inspection robots. Her most-cited paper, “Automated Vision Systems for Condition Assessment of Sewer and Water Pipelines” (2020), has garnered 79 citations, underscoring its foundational impact on the field. This work, along with her studies on valve detection using deep learning (29 and 12 citations, respectively), has paved the way for more intelligent, autonomous inspection platforms. By enabling robots to identify and assess critical pipeline features without human intervention, Jiao’s research significantly enhances the efficiency and accuracy of infrastructure maintenance. Her achievements are particularly notable for their direct application to real-world challenges, helping utilities make informed decisions about pipe replacement and rehabilitation. Jiao’s contributions are essential reading for researchers and engineers working to modernize urban water infrastructure through robotics and computer vision.