INSPIRE Archived Webinars
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Webinar Date
14 Dec 2021, 10:00 am
Abstract
In this 50-minute lecture, the fundamental concept of hyperspectral imaging is reviewed. Each hyperspectral image represents a narrow, contiguous wavelength range of an electromagnetic spectrum, which could be indicative of a chemical substance. All the images in the spectral range of a hyperspectral camera are combined to form a three-dimensional hyperspectral data cube with two spatial dimensions and one spectral dimension. A hyperspectral cube can be sampled in four ways: spatial scanning, spectral scanning, snapshot imaging, and spatio-spectral scanning. This presentation will be focused on spatial scanning from a line-scan camera. The feasibility of horizontal imaging from a synchronized hyperspectral camera and LiDAR scanner system will be explored first. The hyperspectral images are then applied into several infrastructural inspections: concrete roadway condition assessment, fresh mortar strength evaluation, chloride concentration determination in reinforced concrete, steel reinforcing bar and steel plate corrosion, and surface plant stress monitoring as an indication of gas leakage from embedded pipelines. As an example, mortar samples with a water-to-cement (w/c) ratio of 0.4-0.7 were cast and scanned during curing. Reflectance data at a wavelength range of 1920 nm to 1980 nm, associated with the O-H chemical bond, were averaged to classify different mortar types with a Support Vector Machine (SVM) algorithm and predict their compressive strength from a regression equation. After baseline and bias corrections, the reflectance intensity at 2258 nm wavelength was extracted to characterize Friedel’s salt. The possibility of steel corrosion was experimentally shown to increase with the characteristic reflectance intensity that in turn decreases linearly with the diffusion depth at a given corrosion state. For each type of mortar cubes with a constant w/c ratio, the characteristic reflectance intensity linearly increases with chloride ion Cl- concentration up to 0.8 wt.%.
Biography
Dr. Chen received his Ph.D. degree from the State University of New York at Buffalo in 1992 and joined Missouri University of Science and Technology (S&T) in 1996 after over three years of bridge design, inspection, and construction practices with Steinman Consulting Engineers in New York City. Since 1996, Dr. Chen has authored or co-authored over 400 technical publications in structural health monitoring (SHM), structural control, structural and robotic dynamics, computational and experimental mechanics, life-cycle assessment and deterioration mitigation of infrastructure, multi-hazards assessment and mitigation, 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 one patent on distributed coax cable strain/crack sensors and two patents on enamel coating of steel reinforcing bars for corrosion protection and steel-concrete bond strength. He received the 2019 international 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.
Recommended Citation
Chen, Genda, "Hyperspectral Imaging and Data Analytics for Civil Infrastructure Inspection" (2021). INSPIRE Archived Webinars. 18.
https://scholarsmine.mst.edu/inspire_webinars/18
Department(s)
Civil, Architectural and Environmental Engineering
Research Center/Lab(s)
INSPIRE - University Transportation Center
Document Type
Video - Presentation
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2021 Missouri University of Science and Technology, All rights reserved.
