INSPIRE Archived Webinars
Structural Inspection Automation: Research Challenges and 3D Machine Vision Techniques
13 Dec 2022, 11:00 am
This presentation will start by reviewing the history of vision or imaging-based structural (surface condition or damage) inspection and discuss the notion of structural inspection automation. Besides robotic platforms for operation, this conceived automation process must feature 3-dimensional (3D) integrated structural element and damage (or any anomaly) detection, quantification, and mapping amid complex scenes. Further, the results of such a process should be readily fused with existing lifecycle 3D BIM or digital-twin models (DTMs) for ultimate decision-making.
The presentation plans to discuss that machine vision with optical sensors is the most viable approach as a core component to realizing structural inspection automation. Using an analogy of Tesla cars that rely entirely on vision sensors without using active Radar or LiDAR sensors, the presentation will then elaborate on six automation levels for structural inspection. However, our civil infrastructure stakeholders are much different from those from private automobile sectors, which have poured tremendous investment into collecting and creating semantically rich datasets for developing machine learning algorithms and AI-based software frameworks. On the other hand, large-scale semantically annotated datasets from civil engineering sectors are not expected to be available in the near future. Considering such constraints and aiming at the goal of realizing cost-effective structural inspection automation, the presentation will introduce recent efforts in the following three topics:
- Low-cost 3D structural element and damage data collection and deep learning based algorithmic benchmarking
- Human-in-the-loop based structural data collection using augmented reality headsets.
- Visual Simultaneous localization and mapping (SLAM) enabled optimal structural element and damage mapping using virtual reality-based robotic drones.
The presentation will conclude by sharing the vision and opportunities about the future of this research area.
Dr. ZhiQiang Chen is an Associate Professor of Civil Engineering at the University of Missouri, Kansas City (UMKC). Prior to joining UMKC in 2010, he received his Ph.D. in Structural Engineering from the University of California, San Diego (UCSD). He was a visiting professor at the University of California, Berkeley, in 2020; and a visiting professor at the Saitama University, Japan, in 2022. Dr. Chen’s research interests, in general, focus on Civil Systems Intelligence and Resilience. Dr. Chen has been working on multi-hazard performance and resilience computing for civil structures, climate-change effects on structural loadings, and the application and development of AI, remote sensing, and human-infrastructure interfacing technologies for disaster response and infrastructure management. Dr. Chen has been a keynote speaker for the ASCE Engineering Mechanics Institute International Conference, received the Takuji Kobori Prize from the International Association of Structural Control and Monitoring (IASCM), and was awarded a JSPS Invitational Fellowship by the Japan Society for the Promotion of Science. Dr. Chen is an Associate Editor for the ASCE Journal of Natural Hazards Review and serves as a core member in many ASCE and TRB committees.
Chen, ZhiQiang, "Structural Inspection Automation: Research Challenges and 3D Machine Vision Techniques" (2022). INSPIRE Archived Webinars. 22.
Civil, Architectural and Environmental Engineering
INSPIRE - University Transportation Center
Video - Presentation
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