SHMII-9 Keynote Speakers

Keynote speakers will present during the plenary session in Grand Ballroom E on Monday, Tuesday, and Wednesday. Please check the conference schedule for specific speaker times.

Prof. Gian Paolo Cimellaro

Title: SHM Role in the Framework of Infrastructure Resilience

Upgrading critical infrastructure systems by integrating sensing, communication, computing and information technologies is becoming an urgent need to keep up with increasing natural disasters that put threat on human lives. Sensor-enabled infrastructure systems is destined to empower resilient communities with more intelligence and sustainability, and therefore enhance the operational safety of physical infrastructure. Through online and on-board monitoring, the infrastructure components incorporating appropriate analytic and predictive modelling tool not only provides real-time insight into the operational status of every system and it components, but also enables the trend prediction and timely prognosis of failure before it happens as well as early-stage diagnosis of damage in its incipiency.
The presentation will introduce the Cyber-Resilient Infrastructure framework (CRI) that encourages the incorporation of innovative techniques, such as optical fiber sensors and machine learning, for structural health monitoring of variety of infrastructure systems. It will cover both basic research as well as practical applications in structural systems. Case studies and application examples will be presented to show the resilience-related advantages gained by incorporating novel techniques in the field of disaster resilience. Lecture concludes with ongoing developments in Europe, particularly for high speed trains.

Biography: Prof. Cimellaro primary field of investigation is Earthquake Engineering with emphasis on defining Quantification of Resilience of systems. Prof. Cimellaro’s interdisciplinary research investigates representations of health system properties and processes, creating quantitative modeling solutions for a better understanding of sustainable use and resilience of systems that often challenges collaborating teams consisting of scientists, social scientists, engineers, lawyers and extension specialists across a wide spectrum of disciplines.  His major contribution has been the quantification of the concept of disaster resilience. More information is available at:

Prof. Dan M. Frangopol
Title: Extending the Life-Cycle of Civil and Marine Structures: Role of SHM

Abstract: Structural deterioration can pose tremendous risk to the functionality, serviceability, and safety of civil and marine structures, considerably limiting their service life. To extend the life-cycle of existing structures under deterioration, rational life-cycle management should be conducted accounting for various uncertainties arising from loads, resistance, and modeling. Compared to conventional inspection methods that are sometimes disruptive and costly, structural health monitoring (SHM) provides a novel and cost-efficient approach to reducing uncertainties and ultimately facilitating the decision-making process for realizing structural longevity. In this keynote lecture, accomplishments in the integration of probabilistic life-cycle management and SHM of civil and marine structures are presented.

Acknowledgements: Support from the U.S. Office of Naval Research Contracts N00014-08-1-0188, N00014-12-1-0023, and N00014-16-1-2299, the NSF Award CMMI-1537926, and the U.S. Department of Transportation Region 3 University Transportation Center Grant CIAM-UTC-REG6 is gratefully acknowledged. The opinions and conclusions presented in this keynote are those of the author and do not necessarily reflect the views of the sponsoring organizations.

Biography: Prof. Dan M. Frangopol is the inaugural holder of the Fazlur R. Khan Endowed Chair of Structural Engineering and Architecture at Lehigh University. He is recognized as a leader in the field of life-cycle engineering of civil and marine structures. His main research interests are in the application of probabilistic concepts and methods to civil and marine engineering. Dr. Frangopol is the Founding President of the International Associations for Bridge Maintenance and Safety (IABMAS) and Life-Cycle Civil Engineering (IALCCE). He is also the Founding Vice-President of the International Society for Structural Health Monitoring of Intelligent Infrastructure (ISHMII). He has authored/co-authored 3 books and over 400 articles in archival journals including 10 award-winning papers. He is the Founding Editor of the international peer-reviewed journal Structure and Infrastructure Engineering and of the Book Series Structures and Infrastructures. Dr. Frangopol is the recipient of several medals, awards, and prizes, from ASCE, IABSE, IASSAR, and other professional organizations, such as the George W. Housner Structural Control and Monitoring Medal, Lifetime Achievement Award in Education (OPAL), Newmark Medal, Alfredo Ang Award, T. Y. Lin Medal, F. R. Khan Medal, and the Croes Medal (twice), to name a few. He holds 4 honorary doctorates and 14 honorary professorships from major universities. He is a foreign member of the Academia Europaea (Academy of Europe, London), a foreign member of the Royal Academy of Belgium, an Honorary Member of the Romanian Academy, an Honorary Member of the Romanian Academy of Technical Sciences, and a Distinguished Member of ASCE.

Dr. Wolfgang R. Habel

Title: The Challenges of Introducing Scientific Results Into Practice of SHM: Reliability, Validation and Acceptance Aspects

Many structure components as part of the working infrastructure network have to be monitored for different reasons. Well-designed sensors and corresponding measurement systems are required. Both conventional and new innovative sensor solutions are used; however new solutions developed in scientific institutions have to be introduced into practice and fulfil the expectations of users and/or owners of the structure: established application methodology, optimum long-term performance, knowledge about possible drifts and creep effects, and finally verified information about the measurement uncertainty after many years of work under harsh environmental conditions.
This presentation focuses on essential – but not always considered – aspects when monitoring solutions are required. Appropriate selection of the sensor system that provides the required monitoring characteristic, which includes only those components correctly validated according to standardized procedures that consider all environmental influences etc. are discussed. Throughout this presentation, critical questions related to reliable measurement results are illustrated by means of examples. Also presented are new helpful activities in the international standardization of fiber-optic sensors, which are usefully applicable in SHM systems for civil infrastructure.

Biography: Dr. Habel received the diploma degree in Electrical Engineering in 1972 and his Ph.D. degree in Civil engineering (Dr.-Ing.) from the Technical University of Berlin. He gathered professional experience as an R&D engineer in the development of superconducting high voltage cables and automated control and sensor units for industrial building production. Since 1985, he has been developing fiber optic sensors to monitor diverse structures. He joined the German Federal Institute for Materials Research and Testing in Berlin in 1997 and was there head of the fiber-optic sensor sector until his retirement in 2014. He was leading numerous projects dealing with monitoring of diverse structures in different fields, was granted with several patents, and published numerous papers and book chapters in the field of fiber-optic sensor technology for SHM. He is still leading development of IEC standards for FOS.
From 2007, he was Vice-President, from 2014 until 2016 President of ISHMII. Since 2015, he is coordinator of a large German joint research monitoring project in high voltage engineering and has been teaching Master students at two universities in Germany in courses “Optics for NDT” and “Sensor-based monitoring”.

Professor Hongwei Huang

Title: Inspection, Recognition and Diagnosis of Structural Health for Tunnel Lining by Artificial Intelligence

As one may realize, the fast development of the construction for underground infrastructures urges the badly needs of efficient inspection and monitoring techniques for the infrastructures that are put into operation. The structural health condition may deteriorate over long time period, which threatens the safety of the assets and the publics. During the past two decades, fast mobile inspection systems have been developed over the world from MTI-200a (2018), MIMM-R (2014), TCRACK (2012), GRP-5000 (2012).  However, the image data of tunnel defects that are captured by these systems are extremely complex due to the unfavorable environmental conditions and geometrical nonlinearity of tunnel linings. Although the recognition method of cracks based on luminance difference and contrast difference in a local grids and recognition method of leakage based on water leakage infrared radiation have been proposed with certain accuracy, this paper will show that both the efficiency and accuracy have been greatly improved by using the deep learning of AI based image recognition method. Later, a novel diagnosis index named tunnel defect index for the structural defects of tunnel after the recognition is discussed with a field case application. Given the experiences on the inspection, recognition and diagnosis for tunnel lining over two decades, new practical oriented views in future studies are summarized briefly at the end of this paper.

Biography: Professor Hongwei Huang is a Full Professor and National Yangtze River Scholar Distinguished Professor of Tongji University, China. Currently, Prof. Huang is also the dean of the graduate school of Tongji University, Chair of Risk and Insurance Research Branch of China Civil Engineering Society. Prof. Huang is the Editorial Board Member of several high ranking international academic journals including Tunneling and Underground Space Technology, ASCE-ASME Journal of Risk and Uncertainty of Engineering System, and GeoRisk. He serves as core members for international academic committees including Geotechnical Safety Network (GEOSNet), Geo-Institute on Risk Management of ASCE, TC304 of the International Society for Soil Mechanics and Geotechnical Engineering (ISSMEGE), WG2 of ITA and etc. So far, he has published more than 200 journal papers in Chinese and English and more than 5 books. Based on the above achievements, he has delivered more than 12 keynotes in international conferences and chaired more than 5 international conferences.

Ben Jilk, P.E., S.E.

Title: New I-35 Bridge Instrumentation and Data Interpretation for Engineering

Biography: Ben Jilk is the Complex Analysis and Modeling Design Leader at the Minnesota Department of Transportation Bridge Office. He has been involved with the instrumentation and monitoring of the new I35W bridge from the beginning; starting with work he did as a graduate student at the University of Minnesota and subsequently followed by being technical liaison for research projects while working at MnDOT.

Presenting with Dr. Lauren Linderman, University of Minnesota

Dr. Lauren Linderman

Title: New I-35W Bridge Instrumentation and Data Interpretation for Engineering

Abstract: The I-35W Saint Anthony Falls Bridge, opened in September 2008, includes instrumentation for long-term monitoring of the structural behavior of the bridge. Data from the over 500 sensors deployed on the structure, including vibrating wire strain gages, fiber-optic strain gages, thermistors, linear potentiometers, and accelerometers, has been collected since the bridge’s opening. This large-scale, long-term deployment offers insight on monitoring of bridges and a unique data-set to investigate structural behavior. In this presentation, the monitoring system will be introduced and key results will be reviewed. The original motivation and value of the information for asset management will be discussed from a MnDOT perspective. Of particular interest are the temperature-dependent and long-term time-dependent behavior of the post-tensioned concrete bridge. A data analysis approach that leverages temperature, strain, and displacement sensors distributed throughout the structure to capture the time-dependent behavior will be described. For a structure that sees significant temperature loading and gradients, the temperature captured by the thermistors is essential to capture the temperature-dependent behavior used in evaluating time-dependent deflections and can account for significant strains. The potential impact of these analysis results on concrete bridge design practice and lessons learned for monitoring deployments will be summarized.

Biography: Dr. Linderman is an Assistant Professor in the Department of Civil, Environmental, and Geo- Engineering at the University of Minnesota. She earned her Ph.D. in Civil Engineering from the University of Illinois at Urbana-Champaign in 2013. Dr. Linderman’s research combines analytical and experiment studies in the area of smart structures for improving the long-term performance of civil infrastructure through both monitoring and vibration mitigation. Within the field of smart structures, the topics of most interest include data acquisition, wireless sensor technology, networked estimation and feedback control, modal analysis, and experimental methods. She received the NSF CAREER in 2018 on sensor selection for reliable monitoring and control of civil systems. She was named Young Engineer of the Year in 2019 by the Minnesota section of ASCE serves on the ASCE EMI Technical Committee on Structural Health Monitoring and Control.

Presenting with Ben Jilk, Minnesota Department of Transportation

Dr. Yi-Qing Ni

Title: Bayesian Machine Learning for Structural Health Monitoring of Rail Transit System

: Operational safety is the most important issue for rail transit in view of its mass transportation volume and high running speed. Developing smart rail systems by integrating sensing, communication, computing and information technologies is becoming an urgent need to satisfy the safety and reliability requirements in modern rail industry. Sensory systems have been increasingly implemented on railway infrastructure and rail vehicles for online and on-board monitoring to ensure the operational safety. In addition to innovative sensing technology, there is an urgent need to develop advanced analytic tools which enable data-driven fault diagnosis and prognosis in a real-time or near real-time manner. Probabilistic machine learning (PML) has currently emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through sensing. The PML paradigm, which is capable of describing how to represent and manipulate uncertainty in modelling and prediction, has a central role in massive data analytics. In particular, probabilistic approaches developed in the framework of Bayesian machine learning (BML) provide an efficient means to interpret the heterogeneous monitoring data with different sources of uncertainty. Not only it allows for the consideration of uncertainties inherent in monitoring data in characterization and modelling, it also enables quantification of uncertainties in prediction and forecast. This presentation outlines the applications of key BML methods, such as sparse Bayesian learning, Bayesian compressive sensing, and Gaussian process regression to fault diagnosis and prognosis of railway systems with the use of online and on-board monitoring data. Illustrative examples of using real-world data for data-driven fault diagnosis and prognosis of rail transit system in the BML context are provided.

Biography: Dr. Yi-Qing Ni is a Chair Professor of Smart Structures and Rail Transit at Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, and the Director of National Engineering Research Center on Rail Transit Electrification and Automation (The Hong Kong Polytechnic University), Hong Kong. His research areas cover structural health monitoring, smart materials and structures, and monitoring and control in rail engineering. Professor Ni has published more than 180 SCI-cited journal papers with an H-index of 37 in Web of Science Core Collection (an H-index of 49 in Google Scholar), and over 290 international conference papers. He was selected as a Highly Cited Researcher in the Field of Civil Engineering by Shanghai Ranking Consultancy and Elsevier in 2016. He received the 2017 “SHM Person of the Year Award” (selected by the editorial board of “Structural Health Monitoring: An International Journal”) during the 11th International Workshop on Structural Health Monitoring held at Stanford University in September 2017. He is a fellow of Hong Kong Institution of Engineers (HKIE), and a council member of International Society for Structural Health Monitoring of Intelligent Infrastructure (ISHMII).

Dr. Ian F.C. Smith

Title: Monitoring for Asset Management

It is estimated that the annual global expenditure of the architecture, engineering and construction industry attained $10 trillion in 2018. Construction involves the largest use of mined raw materials. To improve sustainability, we need to learn to do more with less. Placing sensors on structures can help. The vast majority of research in structural monitoring is related to defect detection and much work is focused on physical-model-free signal analysis to identify anomalies accurately and in a timely manner. While this work may be useful in areas outside of structural engineering, it provides insufficient support to managers of civil-infrastructure assets. Asset managers need to make million-dollar decisions such as retrofit versus structural replacement and if retrofit, choosing the best strategy. Such decisions require knowledge of the physical behavior of the structure. Engineers use improved physical-principles models to extrapolate into retrofit scenarios to ensure that cost estimates of alternatives are accurate. Fortunately, there is often reserve capacity (beyond safety factors) in structures such as bridges. Therefore, sensing can result in significant savings. This paper describes experience using a data-interpretation strategy that has been specially developed for infrastructure asset management. A summary of results of over twenty cases of full-scale testing confirms that most structures have significant amounts of reserve capacity. This interpretation strategy is thus a useful tool for asset managers and more generally, it has the potential to help develop less conservative design practices in a material-scarce future.

Biography: Ian F.C. Smith is a Professor of Structural Engineering at the Swiss Federal Institute of Technology (EPFL) in Lausanne, Switzerland. He received his PhD from Cambridge University, UK in 1982. His research interests are on intersections of computer science with structures and urban systems. Applications include advanced cyber-physical systems such as biomimetic structures and sensed civil infrastructure.
In 2003, he co-authored the text book Fundamentals of Computer-Aided Engineering (Wiley) and the 2nd Edition, Engineering Informatics: Fundamentals of Computer-Aided Engineering appeared in June 2013. In 2004, he was elected to the Swiss Academy of Engineering Sciences and in 2005, he received the Computing in Civil Engineering Award from the American Society of Civil Engineers (ASCE). Over fifteen years, he was Co-Editor-in-Chief of the Elsevier journal “Advanced Engineering Informatics” and among several current editorial-board memberships, he is Specialty Editor of the ASCE journal “Computing in Civil Engineering”.