Presented: March 22, 2023, 10:00AM-11:00 AM Central Standard Time (US and Canada)
Speaker: Dr. Genda Chen, Missouri University of Science and Technology
The fundamental concept of the probability of detection in structural health monitoring is introduced. The traditional Probability of Detection (POD) method as described in the Department of Defense Handbook MIL-HDBK-1823A for nondestructive evaluation systems does not take the time dependency of data collection into account. When applied to in-situ sensors for the measurement of flaw sizes, such as fatigue-induced crack length and corrosion-induced mass loss, the validity and reliability of the traditional method is unknown. In this 50-minute lecture, the POD for in-situ sensors and their associated reliability assessment for detectable flaw sizes are evaluated using a Flaw-Size-at-Detection (FSaD) method and a Random Effects Generalization (REG) model. Although applicable to other sensors, this presentation is focused on long period fiber gratings (LPFG) corrosion sensors with thin Fe-C coatings. The FSaD method uses corrosion-induced mass losses when successfully detected from different sensors for the first time, while the REG model considers the randomness and difference between mass loss datasets from different sensors. The Fe-C coated LPFG sensors were tested in 3.5 wt.% NaCl solution until the resonant wavelength of transmission spectra no longer changed or the Fe-C coating was oxidized completely. The wavelength shift of 70% of the tested sensors ranged from 6 to 10 nm. In comparison with the FSaD method, the REG method is more robust to any departure from model assumptions since significantly more data are used in the REG method.
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 (Missouri 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 217 journal papers, 5 book chapters, and 28 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 has been granted with 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.
Present: June 13, 2023, 12:00 PM Central Time (US and Canada)
Speaker: Dr. Iris Tien, Georgia Institute of Technology
As structural inspection data is increasing, it is important to be able to translate this information into accurate and efficient updating of bridge risk assessments to support risk-based decision-making. Analytical fragility functions provide a way to quantify the risk of a structure. One method to construct seismic fragility curves is to perform a series of nonlinear dynamic analyses of the structure. However, the high computational cost in running and re-running analyses over the full finite-element model can be prohibitive to effectively update bridge risk assessments. This talk will describe new methodologies to efficiently and accurately update analytical fragility curves. The reduction in computational cost from both reducing the number of analyses required and simplifying the structural complexity are investigated. The method is applied to update calculations of seismic bridge fragilities accounting for varying levels of measured corrosion. Results comparing updated fragility curves obtained from using the proposed approach versus using the full set of dynamic analyses show that the proposed method achieves accurate, stable, and more quickly converging fragility calculations. In addition to this work on the impacts of corrosion on bridge risk, further work on integrating scour inspection data into updated assessments of bridge risk will also be discussed.
Dr. Iris Tien is a Williams Family Associate Professor in the School of Civil and Environmental Engineering at the Georgia Institute of Technology. She joined the faculty in 2014 after receiving her Ph.D. in Civil Systems Engineering from the University of California, Berkeley. Dr. Tien’s research interests are in probabilistic methods for modeling and reliability assessment of civil infrastructure systems. Her research leverages her unique interdisciplinary expertise encompassing traditional topics of civil engineering, sensing and data analytics, stochastic processes, probabilistic risk assessment, and decision making under uncertainty. Her work on interdependent infrastructure systems modeling and analysis has twice won 1st Place Paper Awards in resilient critical infrastructure. She was selected by the National Academy of Engineering to organize the session on Resilient and Reliable Infrastructure at the U.S. Frontiers of Engineering Symposium; and speak on Community Resilience at the National Academies Frontiers of Science, Engineering, and Medicine Symposium. Dr. Tien was awarded the prestigious Early Achievement Research Prize by the International Association for Structural Safety and Reliability (IASSAR), and her published work has been selected as Editor’s Choice selections in both the ASCE Journal of Infrastructure Systems and the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems.
Present: September 20, 2023, 10:00 AM Central Time (US and Canada)
Speaker: Dr. Jizhong Xiao, The City College of New York
Bridges, dams, highways, and tunnels in the U.S. are reaching their life expectancy, and thus have imperative needs for routine inspection and maintenance to ensure sustainability. It is reported that 42% of over 600,000 highway bridges in the National Bridge Inventory (NBI) have exceeded their design life of 50 years, and 42,951 bridges are rated in poor condition and classified as "structurally deficient". To inspect the structurally integrity of bridges, the inspectors also need to detect subsurface defects (i.e., delamination, voids) using NDE instruments such as GPR and impact-echo (IE) device at difficult to access components (i.e., pier, bottom side of the deck). The current practice of manual inspection hand-held NDE devices by a "spider-man" with rope access, or by using scaffolding or by using snooper truck has to block traffic, and is expensive, time-consuming, and exposes human inspectors to dangerous situations. This presentation will introduce climbing robots developed over the years at CCNY Robotics Lab that integrate the robot control and vision-based accurate positioning with NDE signal processing to detect both surface flaws and subsurface defects. The use of the robotic inspection tool will eliminate the time, hassle, and cost to layout grid lines on flat terrain, and make it possible to automatically collect NDE data with minimum human intervention. This presentation will also introduce machine learning algorithms for visual inspection to detect and measure cracks: IE data processing methods that utilizes both learning-based and classical methods to interpret the IE data and reveal subsurface objects; and DNN-based GPR data analysis software to reveal subsurface targets for better visualization.
Dr. Jizhong Xiao is a Professor in the Electrical Engineering Department at The City College of New York, the flagship campus of the City University of New York. Dr. Xiao's research interests include robotics and control, cyber-physical systems, non-destructive evaluation (NDE) of infrastructures, autonomous navigation and 3D simultaneous localization and mapping (SLAM), real-time and embedded computing, assistive technology, multi-agent systems and swarm robotics.