Amongst the main activities of this initiative will be workshops, offered by top researchers in the SHM field.
Information on upcoming conferences will be posted on this page. Stay tuned!
May 23 2022, 11:00 ET
Finite Element Model Updating with Vibration Testing Data: A Non-convex Optimization Perspective
Yang Wang is a Professor of Civil and Environmental Engineering, and an Adjunct Professor of Electrical and Computer Engineering at Georgia Tech. He joined Gerogia Tech faculty in 2007. With a B.E. and an M.S. degree in Civil Engineering awarded by Tsinghua University in Beijing, China, he received a Ph.D. degree in Civil Engineering at Stanford University in 2007, as well as an M.S. degree in Electrical Engineering. Dr. Wang’s research interests include structural health monitoring and damage detection, decentralized structural control, wireless and mobile sensors, and structural dynamics. He received an NSF Early Faculty Career Development (CAREER) Award in 2012 and a Young Investigator Award from the Air Force Office of Scientific Research (AFOSR) in 2013. Dr. Wang is the author and coauthor of over 100 journal and conference papers, and currently serves as an Associate Editor for the ASCE (American Society of Civil Engineers) Journal of Bridge Engineering.
May 23 2022, 11:45 ET
Active and Partially-Supervised Learning with Engineering Data
Nikolaos Dervilis is a Professor in the Department of Mechanical Engineering at the University of Sheffield and a member of the Dynamics Research Group (DRG). His expertise focuses on offshore wind farms, structural health monitoring (SHM), pattern recognition and machine learning, data analysis and information learning tools and nonlinear dynamics. Dr. Dervilis studied physics in the National and Kapodistrian University of Athens. Later, he obtained his MSc in Sustainable and Renewable Energy Systems from the University of Edinburgh in the Department of Electronics and Electrical Engineering. He obtained his PhD from the University of Sheffield, Mechanical Engineering Department in the field of machine learning for Structural Health Monitoring
Past Seminars and Presentation Recordings
April 25 2022, 11:00 ET
Title: Human-machine Collaborative Infrastructure Assessment
Chul-min Yeum is an Assistant Professor of Civil and Environmental Engineering at the University of Waterloo – Canada. Prof. Yeum received his Ph.D. in 2016 from Purdue University, United States. He was subsequently a postdoctoral researcher at the same university until 2018. He holds a bachelor’s and master’s degree in Civil Engineering from Korea Advanced Institute of Science, Korea. His academic background has focused on interdisciplinary research spanning the traditional boundaries between civil engineering and computer science. He continues to seek revolutionary uses of computer vision and machine (deep) learning techniques for applications where data can be used to generate actionable information and knowledge in civil engineering. In the course of his career, he has published over 40 journal and conference papers on the results of his research, with a focus on vision-based structural health monitoring and post-disaster assessment for civil infrastructure using computer vision and machine learning techniques.
April 25 2022, 11:45 ET
Title: Deep Learning-based Crack Segmentation Using Roadway Images: A Comparative Study
Wei Song is an Associate Professor in the Department of Civil, Construction and Environmental Engineering at the University of Alabama. Wei Song’s research interests include structural condition assessment, advanced experimental technique, cyber-physical system design promoting community resilience and optimal control design for nonlinear stochastic systems. Song is most focused on addressing the challenges of developing resilient and sustainable communities through civil engineering. His research approach is designed specifically to develop, assess and implement advanced techniques for next-generation structural condition assessment and retrofitting tools.
February 28 2022, 11:00 ET
Title: Wind Turbine Early Fault and Damage Diagnosis Based on Machine and Deep Learning Models
Yolanda Vidal Segui is an Associate Professor in the Department of Mathematics at the Universitat Politècnica de Catalunya (UPC) in Barcelona (Spain). She received her B.E. in Mathematics in 1999 and her Ph.D. in Applied Mathematics in 2005 from UPC. Since 2001, she has been with the Department of Mathematics and the Barcelona East School of Engineering (EEBE), at Universitat Politècnica de Catalunya, where she is currently an Associate Professor in the Control, Modeling, Identification and Applications research group (CoDAlab). She is also a Teaching Collaborator at the Open University of Catalonia, Barcelona. Her research interests include structural health monitoring (SHM), condition monitoring (CM), and fault diagnosis (FD), with an emphasis on their specific application to wind turbines. Dr. Vidal is a IEEE member and serves as an Editorial Board Member for international journals, such as Mathematics, Sensors, Energies, Frontiers in Built Environment, and Frontiers in Energy Research. Dr. Vidal is author of 50 journal articles, 17 competitive projects, 15 book chapters, 8 books, 1 invention patent, and more than 100 conference papers.
March 28 2022, 11:00 ET
Title: Advancing Human-Infrastructure Interfaces: New Frontiers and Opportunities
Fernando Moreu is an Assistant Professor in structural engineering at the Department of Civil, Construction and Environmental Engineering (CCEE) at the University of New Mexico (UNM) at Albuquerque, NM. He holds courtesy appointments in the Departments of Electrical and Computer Engineering, Mechanical Engineering, and Computer Science at UNM. He is the founder and director of the Smart Management of Infrastructure Laboratory (SMILab) at UNM. His industry experience includes ESCA Consultants, Inc. for over ten years, with experience in the design and construction of over thirty bridges in seven states in the US. His research interests include structural dynamics, structural health monitoring, wireless smart sensor networks, augmented reality, unmanned aerial systems, human-machine interfaces, and aerospace structures design, monitoring, and reusability. He received his MS and PhD degrees in structural engineering from the University of Illinois at Urbana-Champaign (2005 and 2015, respectively.)
January 5 2022, 11:00 ET
Title: Mitigating the Influences of Operational and Modeling Uncertainties in SHM
Saeed Eftekhar Azam is an Assistant Professor at the Department of Civil and Environmental Engineering in the University of New Hampshire (UNH). Prior to joining the UNH, he was a Research Assistant Professor at the University of Nebraska – Lincoln (USA). He obtained his PhD from Polytechnic University of Milan (Italy) in the field of smart structural systems and was a Postdoctoral Researcher in the University of Nebraska – Lincoln, ETH Zurich (Switzerland) and the University of Thessaly (Greece). As a researcher, he has been contributing to both theoretical and applied research and validated several novel algorithms he developed by full-scale experiments. His research contributes to resiliency of infrastructure systems via development of autonomous decision support systems robust to uncertainties. In this regard, Saeed develops smart cyber-physical infrastructure systems through online Bayesian system identification, computational modelling, Machine Learning, and large-scale experiments. The main drive behind his academic work is to evolve current probabilistic decision-making paradigms into autonomous, data-centric versions. He plans on developing multidisciplinary research projects involving data science for scalable cloud computing for infrastructure monitoring and maintenance as well as wireless network engineering for input uncertainty mitigation via smart and connected infrastructure. He has authored a monograph, 23 articles in peer-reviewed technical journals, and has contributed to over 30 conference papers and presentations coping with fundamental bottleneck barriers that prevented the transition of smart infrastructures systems from research to industrial practice. He actively contributes to the scientific community by organising sessions and short courses in symposiums, editorial activities, and professional membership.
January 31 2022, 11:00 ET
Structures as Sensors: Indirectly Monitoring Humans and Surroundings through Ambient Structural Responses
Hae Young Noh is an Associate Professor the Department of Civil and Environmental Engineering at Stanford University. Her research focuses on indirect sensing and physics-guided data analytics to enable low-cost non-intrusive monitoring of cyber-physical-human systems. She is particularly interested in developing structures to be self-, user-, and surrounding-aware to improve users’ quality of life and provide safe and sustainable built environment. The results of her work have been deployed in a number of real-world applications from trains, to the Amish community, to eldercare centers, to pig farms. Before joining Stanford, she was a faculty member at Carnegie Mellon University. She received her Ph.D. and M.S. degrees in Civil and Environmental Engineering and the second M.S. degree in Electrical Engineering at Stanford University. She earned her B.S. degree in Mechanical and Aerospace Engineering at Cornell University. She received several awards, including the Google Faculty Research Awards (2013, 2016), the Dean’s Early Career Fellowship (2018), and the NSF CAREER Award (2017).
October 25 2021, 11:00 ET
Digital Twinning of Structural Systems: Accounting for Modeling Errors
Babak Moaveni is a Professor at the Department of Civil and Environmental Engineering at Tufts University. Dr. Moaveni’s main research interests include vibration-based system and damage identification of civil structures; Bayesian inference and model updating; and uncertainty quantification and propagation in structural dynamics. He has chaired the ASCE technical committees “Structural Health Monitoring and Control”, and “Methods of Monitoring Structural Performance” and currently serves as associate editor for journals “Structural Health Monitoring”, and “Frontiers in Built Environment – Sensors”.
November 29 2021, 11:00 ET
Enhancing Computer Vision-based Structural Assessments with Synthetic Imagery
Vedhus Hoskere is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Houston (UH) and holds a joint appointment in Electrical and Computer Engineering. Dr. Hoskere received his Ph.D. in Civil Engineering in 2020, from the University of Illinois at Ubana-Champaign. At UH, Dr. Hoskere directs the Structures and Artificial Intelligence Lab. (https://sail.cive.uh.edu/). His research interests are interdisciplinary, at the intersection of structural engineering, machine learning, computer vision, and robotics. His doctoral work focused on developing artificial intelligence, machine learning and computer vision solutions for rapid and automated civil infrastructure inspection and monitoring. His research at UH looks at building on his prior experience to develop systems for autonomous infrastructure management including incorporation of physics-based modelling, autonomous robotic data acquisition, and deep learning-based data to decision frameworks. Dr. Hoskere has received awards for his research at prestigious avenues including “best poster” at SHMII-9 and “best paper” at the ASCE EMI Conference. Dr. Hoskere’s research also involves on studying damaged structures and communities in the built environment to inform the systems and frameworks his lab develops for autonomous management. As part of outreach efforts, Dr. Hoskere organizes the International Competition of Structural Health Monitoring (IC-SHM) that is designed to encourage students to explore cutting-edge interdisciplinary topics in a competitive setting.