Hosted by INSPIRE UTC
To Be Presented: June 16, 2021, 10:00AM-11:00 AM Central Standard Time
Speaker: Dr. Genda Chen, Missouri S&T
In this 50-minute lecture, the fundamental concepts of fiber optic sensors for both distributed and point corrosion measurements are reviewed. For the distributed monitoring of a line bridge component such as steel reinforced girders, Brillouin scattering and fiber Bragg gratings (FBG) can be coupled to measure both temperature and radial strain as an indirect indicator of corrosion process. For the point monitoring of steel structures, long period fiber gratings (LPFG) are specially designed for a direct measurement of mass loss or the loss in cross sectional area of the component. In particular, a Fe-C coated LPFG sensor is introduced for corrosion induced mass loss measurement when Fe-C materials are comparative to the parent steel component to be monitored. The sensing system operates on the principle of LPFG that is responsive to not only thermal and mechanical deformation, but also the change in refractive index of any medium surrounding the optical fiber. Fabrication process of the LPFG is demonstrated through the CO2 laser aided fiber grating system. To enable mass loss measurement, a low pressure chemical vapor deposition (LPCVD) system is introduced to synthesize a graphene/silver nanowire composite film as flexible transparent electrode for the electroplating of a thin Fe-C layer on the curve surface of a LPFG sensor. An integrated sensing package is illustrated for corrosion monitoring and simultaneous strain and temperature measurement. Two bare LPFGs, three Fe-C coated LPFG sensors are multiplexed and deployed inside three miniature, coaxial steel tubes to measure critical mass losses through the penetration of tube walls and their corresponding corrosion rates in the life cycle of an instrumented steel component. The integrated package can be utilized for in-situ deterioration detection in reinforced concrete and steel structures. Assisted by a permanent magnet in pipeline monitoring, both FBG and LPFG sensors are combined with an extrinsic Fabry-Perot interferometer (EFPI) to measure both internal and external thickness reductions without impacting the operation of the pipeline.
Dr. Genda Chen is Professor and Robert W. Abbett Distinguished Chair in Civil Engineering, Director of the INSPIRE University Transportation Center, and Director of the Center for Intelligent Infrastructure at Missouri University of Science and Technology (S&T). He received his Ph.D. degree from the State University of New York at Buffalo in 1992 and joined Missouri S&T after over three years of bridge design, inspection, and construction practices. Since 1996, Dr. Chen has authored or co-authored over 400 technical publications in structural health monitoring (SHM), structural control, computational and experimental mechanics, multi-hazards assessment and mitigation, and transportation infrastructure preservation and resiliency including over 180 journal papers, 5 book chapters, and 27 keynote and invited presentations at international conferences. He chaired the 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-9), St. Louis, Missouri, August 4-7, 2019. He received the 2019 SHM Person of the Year award, the 1998 National Science Foundation CAREER Award, the 2004 Academy of Civil Engineers Faculty Achievement Award, and the 2009, 2011, and 2013 Missouri S&T Faculty Research Awards. In 2016, he was nominated and inducted into the Academy of Civil Engineers at Missouri S&T and became an honorary member of Chi Epsilon. He is a Fellow of American Society of Civil Engineers (ASCE), Structural Engineering Institute (SEI), and the International Society for Structural Health Monitoring of Intelligent Infrastructure (ISHMII). He is a Section Editor of the Intelligent Sensors, Associate Editor of the Journal of Civil Structural Health Monitoring, Associate Editor of Advances in Bridge Engineering, Editorial Board Member of Advances in Structural Engineering, and Vice President of the U.S. Panel on Structural Control and Monitoring.
To Be Presented: March 23, 2021, 10:00AM-11:00 AM Central Standard Time
Speaker: Dr. Ruwen Qin, Stony Brook University
Advancements in sensor, Artificial Intelligence (AI), and robotic technologies have formed a foundation to enable a transformation from traditional engineering systems to complex adaptive systems. This paradigm shift will bring exciting changes to civil infrastructure systems and their builders, operators and managers. Funded by the INSPIRE University Transportation Center (UTC), Dr. Qin’s group investigated the holism of an AI-robot-inspector system for bridge inspection. Dr. Qin will discuss the need for close collaboration among the constituent components of the AI-robot-inspector system. In the workplace of bridge inspection using drones, the mobile robotic inspection platform rapidly collected big inspection video data that need to be processed prior to element-level inspections. She will illustrate how human intelligence and artificial intelligence can collaborate in creating an AI model both efficiently and effectively. Obtaining a large amount of expert-annotated data for model training is less desirable, if not unrealistic, in bridge inspection. This INSPIRE project addressed this annotation challenge by developing a semi-supervised self-learning (S3T) algorithm that utilizes a small amount of time and guidance from inspectors to help the model achieve an excellent performance. The project evaluated the improvement in job efficacy produced by the developed AI model. This presentation will conclude by introducing some of the on-going work to achieve the desired adaptability of AI models to new or revised tasks in bridge inspection as the National Bridge Inventory includes over 600,000 bridges of various types in material, shape, and age.
Dr. Ruwen Qin is an Associate Professor of Civil Engineering at Stony Brook University. She received her Ph.D. degree in Industrial Engineering and Operations Research from Pennsylvania State University - University Park. Her research focuses on creating analytics and systems methods for forming, operating, and coordinating complex adaptive systems such as cyber-physical-human systems, smart connected systems, and intelligent automation systems. Her research has been sponsored by National Science Foundation, U.S. Department of Transportation, Department of Education, state Departments of Transportation, and industries. She is a member of IEEE, INFORMS, and ASEM.