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Special Sessions

Table of Special Session Titles
Please click on the corresponding title of each Special Session to get more information.

Title
Organizer(s)
AI-Based SHM for Reliable Cyber-Physical Systems and Industry Internet-of-Things
Prof. Fu-Kuo Chang
Dr. M. Faisal Haider
Dr. Amir Nasrollahi
Digital Twin of Civil Infrastructure
Prof. Kamyab Zandi
Prof. Mani Golparvar-Fard
Human Performance Assessment and Enhancement
Prof. Ken Loh
Prof. Liming Salvino
Integration of Physical Modeling, Monitoring and Machine Learning for SHM
Prof. Eloi Figueiredo
Prof. Ionut Moldovan
Use of Optical, MEMS/NEMS and CNT/Nano sensors for structural health monitoring systems
Prof. Jayantha Epaarachchi
Advances in Machine Learning, Big Data Analytics, and/or Computer Vision for Structural Health Monitoring
Prof. Mohammad Jahanshahi
Recent Advances on Data Processing Techniques for Ultrasonic-based SHM/NDE
Prof. Salvatore Salamone
Guided Waves in Structures for SHM
Prof. Wieslaw Ostachowicz
SHM Technology in Wind Turbines
Prof. Wieslaw Ostachowicz
Assessment of the Value of Structural Health Information
Prof. Sebastian Thöns
Prof. Michael Todd
Prof. Maria Pina
Seismic SHM for civil structures
Prof. Maria Pina Limongelli
Dr. Mehmet Celebi
Nonlinear Acoustic and Ultrasonic Techniques for Structural Health Monitoring
Prof. Tribikram Kundu
Prof. Li Cheng
Distributed and Quasi-distributed Fiber-optic and Electrical Sensors, and Associated Data Analysis and Management
Prof. Branko Glisic
Prof. Daniele Zonta
Data Fusion and Machine Learning Methods for SHM/NDE of Civil Infrastructure
Prof. Jinying Zhu
Prof. Ying Zhang
Acoustic Emission and Hybrid SHM
Prof. Victor Giurgiutiu
Prof. Hanfei Mei
Physics-Enhanced Machine Learning for SHM
Dr. Erik Blasch
Prof. Fotis Kopsaftopoulos
Prof. Fu-Kuo Chang
Prognostics and Health Management of Composite Structures Towards a Condition-Based Maintenance Framework
Prof. Dimitrios Zarouchas
Prof. Theodoros Loutas
High-Rate Structural Health Monitoring and Prognostics
Prof. Yang Wang
Dr. Jacob Dodson
Damage Detection, Infrastructure Inspection, Image Analytics, Robotics, and Structural Health Monitoring
Prof. Genda Chen
Prof. Yang Wang
SHM for Heavy and Critical Equipment
Dr. Keqin Ding
SHM Standardization
Dr. Keqin Ding
Multifunctional Materials and Metamaterial Structures
Prof. Kenneth Loh
Probabilistic SHM
Prof. Daniele Zonta
Prof. Branko Glisic

Session Title

AI-Based SHM for Reliable Cyber-Physical Systems and Industry Internet-of-Things

Organizers
Fu-Kuo Chang, Aeronautics and Astronautics, Stanford University, USA
M. Faisal Haider, Aeronautics and Astronautics, Stanford University, USA
Amir Nasrollahi, Aeronautics and Astronautics, Stanford University, USA

Scope of Session
Cyber-physical systems (CPS) is a synergistic technology between physical and computational systems. The bio-inspired distributed sensors, actuators, and embedded devices are networked to sense, monitor, and control the physical world, whereas the computational world provides an autonomous solution through artificial intelligence (AI). CPSs have the ability to communicate between physical and AI-based virtual worlds like human-to-object and object-to-object interactions.  Real-time state estimation, structural diagnostics, and prognostics with distributed sensors are the key components of a reliable CPS system in SHM applications. Furthermore, a reliable CPS system can be applied to another similar CPS system for real-time diagnostic using the large-data of the sensor network. It is envisioned that CPSs will have a tremendous impact on many critical sectors such as state estimation and awareness of UAVs, self-diagnostics, and prognostics of structures, renewable energy harvesting, smart manufacturing, multi-functional materials with built-in SHM systems, transportation, autonomous driving, aerospace, smart city, etc. This special session encourages paper but not limited to the following areas to build up reliable CPS:

  • Verification and validation of AI-based systems
  • System safety and state awareness
  • Sensors, sensor network, actuators, and embedded devices for CPS   
  • Integrated analysis and predictive capabilities
  • Real-time detection, decision making, and control
  • Self-diagnostics and prognostics of AI-based SHM systems
  • Data fusion and data science applied to SHM for CPS

Keywords
SHM, diagnostics, prognostics, AI, cyber-physical systems (CPS), real-time monitoring, validation and verification, sensors, sensor network, industry internet-of-things (IIOT)

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Session Title

Digital Twin of Civil Infrastructure

Organizers
Kamyab Zandi, Chalmers University of Technology, Sweden
Mani Golparvar-Fard, University of Illinois at Urbana-Champaign, USA

Scope of Session
A digital twin of a civil infrastructure system is a digital model which mirrors and simulates an asset or a system of assets and their adjacent environment. What differentiates a digital twin from other digital models or replicas is its connection to the physical environment.  Adopting a digital twin driven approach to civil infrastructure systems can reduce construction and operation costs, increase productivity and collaboration, improve safety and resilience, and optimize asset performance and its sustainability.  These benefits have become the center of attention for academia and the industry. As such, this session invites contributions on new approaches towards the development or use of digital twins in design, construction, operation and maintenance of civil infrastructure systems. Technical areas include but are not limited to:

  • Information modeling and representation
  • Visualization (nD, VR, AR)
  • Simulation and process modeling
  • Reality capture and Scan2BIM
  • Robotics, automation, and control
  • Sensing and machine learning

Case studies and field demonstrations on the application of digital twins for construction monitoring, project controls, and operation and maintenance of civil infrastructure systems are welcome.

Keywords
digital twin, artificial intelligence, sensing, data modeling

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Session Title

Human Performance Assessment and Enhancement

Organizers
Ken Loh
, University of California San Diego, USA
Liming Salvino, University of Michigan, Ann Arbor, USA

Scope of Session
Monitoring the “human structure” and how they interact with and control artificial structures is crucial for optimizing system performance and functionality. Often, both the human operator and structure need to be treated as an integrated system and evaluated together, since the failure of any one of these could result in mission failure or poor/sub-optimal performance. This special session is soliciting contributions focused on sensing and modeling for human performance and health, as well as the interactions/interfaces between humans and artificial structural systems. Examples of specific topics of interest include Body sensor networks; Flexible electronics and sensors; Digital health; Biophysical modeling; Physiological monitoring, Bio-marker, and bio-molecular sensing; Human protective and enhancement systems; Human-machine and human-prosthetic interfaces; Prehabilitation and rehabilitation systems; Implantable sensors; In vivo sensing and applications; Noninvasive and/or noncontact sensing; Textile-based sensors; and Wearable technologies.

Keywords
digital health, human, physiological, prehabilitation, rehabilitation, sensors, textile, wearable

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Session Title

Integration of Physical Modeling, Monitoring and Machine Learning for SHM

Organizers
Eloi Figueiredo
, Lusófona University, Portugal
Ionut Moldovan, Lusófona University, Portugal

Scope of Session
Long-term structural health monitoring (SHM) has been mainly performed using two approaches: model- and data-based. A challenge in both approaches is to make the distinction between the variations of the structural response caused by damage and environmental or operational variability. Hybrid techniques for SHM that integrate model- and data-based approaches have emerged to complement the data measured by the monitoring system installed on the structure with data obtained from its numerical model, leading to both unsupervised and supervised machine learning strategies for damage identification. The hybrid approach to the SHM is still in its early development. Some of the challenges are related to the calibration of the numerical models such as producing reliable data. This Special Session intends to bring together publications involving the integration of numerical modeling, monitoring, and/or machine learning, in order to highlight the current capabilities and future trends. Besides theoretical contributions, the papers must address the practical engineering application of the techniques, using actual experimental or field monitoring data.

Keywords
machine learning, finite element, monitoring, SHM, detection, damage

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Session Title

Use of Optical, MEMS/NEMS, and CNT/Nano sensors for structural health monitoring systems

Organizer
Jayantha Epaarachchi
, University of Southern Queensland, Australia

Scope of Session
Due to the recent development of advanced sensor technologies, traditional health monitoring (SHM) technologies have significantly changed.  Therefore, many state-of-the-art sensors and related infrastructure for measuring and technology processing millions of data being used in a wider range of engineering applications.  Interestingly, advanced Optical, MEMS/MENS, and CNT/Nano sensors have been used for structural health monitoring systems in extreme environmental conditions.  A majority of newly developed sensors and related technologies are still in laboratory conditions, thus, there would be many challenges for the implementation of those sensors in real-life SHM systems. This session aims to display newly developed SHM systems and their implications in engineering applications for constructive feedback from the scientific community.

Keywords
MEMS/NEMS Sensors, CNT/Nano sensors structural health monitoring

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Session Title

Advances in Machine Learning, Big Data Analytics, and/or Computer Vision for Structural Health Monitoring

Organizer
Mohammad Jahanshahi
, Purdue University, USA

Scope of Session
The recent advances in artificial intelligence (AI) has led to ground-breaking innovations in the broad area of structural health monitoring (SHM). In particular, besides computer vision approaches, deep learning and big data analytics provide an unprecedented opportunity to complement traditional SHM. To this end, this special session will provide the opportunity to discuss recent theoretical, computational, and experimental advances in using not only computer vision but also machine learning and big data analytics solutions in general for structural identification, control, damage detection, inspection, and health monitoring. Topics relevant to this session include, but not limited to, deep learning, machine learning-based damage assessment, convolutional neural networks, generative adversarial networks, network pruning, virtual, augmented and mixed reality, innovative imaging, image/video data collection and analysis, vision-based displacement and dynamic measurements, 3D LIDAR and depth sensors, vision-based inspection using unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), robotics integration, and other new emerging machine learning or vision-based technologies.

Keywords
data analysis, machine learning, artificial intelligence, deep learning, computer vision

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Session Title

Recent Advances on Data Processing Techniques for Ultrasonic-based SHM/NDE

Organizer
Salvatore Salamone
, University of Texas-Austin, USA

Scope of Session
This special session aims to collect and share recent developments in data processing techniques to enhance the accuracy and capabilities of ultrasonic wave techniques for the SHM of complex structures. Authors are encouraged to submit papers topics that include but are not limited to 1) deep learning, 2) data mining, 3) data analytics, 4) sparse matrices for machine learning. Both theoretical contributions and practical applications are welcome.

Keywords
guided waves, acoustic emissions, signal processing, SHM, NDE, damage detection

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Session Title

Guided Waves in Structures for SHM

Organizer
Wieslaw Ostachowicz
, Polish Academy of Sciences, Poland

Scope of Session
The session covers the main disciplines which are based on guided wave propagations in both isotropic and anisotropic materials. Authors are encouraged to submit papers that include the elastic waves propagation phenomenon which spans a wide range from linear and nonlinear, 1D, 2D, and 3D, time or frequency, experimental and numerical approaches in complementary investigations of structures. The proposed novel techniques should allow performing efficiently both local and global SHM technologies. Considered above investigations are intended to develop a variety of techniques being related to diagnostics (damage size estimation and damage type recognition) and prognostics. The promising combination of investigated techniques should lead to an innovative approach to ensure safe operation.

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Session Title

SHM Technology in Wind Turbines

Organizer
Wieslaw Ostachowicz, 
Polish Academy of Sciences, Poland

Scope of Session
The session covers the main Structural Health Monitoring (SHM) topics which are focused on wind turbine structures. The research methodologies used here span a wide range of experimental and numerical approaches in complementary investigations of a rotor with blades, drive train, and support structure. The crucial issue is to assess fiber reinforced polymer materials because they are widely used for wind turbine blades. The research methodologies should span a wide range of topics from piezoelectric transducers, elastic waves propagation phenomenon, fiber Bragg gratings, structural vibrations analysis, electro-mechanical impedance method, acoustic emission, damage mechanics, 3D laser vibrometry applications, and others. The combination of proposed techniques allows performing efficient both local and global SHM of the structure. It also includes a variety of techniques being related to diagnostics (damage size estimation and damage type recognition) and prognostics. The promising combination of selected techniques should lead to an innovative approach to ensure the safe operation of the structure.

Keywords
wind turbines, sensors, sensing, SHM, damage detection, signal processing

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Session Title

Assessment of the Value of Structural Health Information

Organizers
Sebastian Thöns
, Lund University, Sweden
Michael Todd, University of California San Diego, USA
Maria Pina Limongelli, Politecnico di Milano, Italy

Scope of Session
Information gathered using Structural Health Monitoring (SHM) systems can substantially contribute to an enhanced performance of civil structures and other infrastructure, transportation, and machinery systems by providing an improved benefit generation and the reduction of operational costs and risks throughout their life cycle. However, the link of Structural Health Information (SHI) to the performance enhancement of technical systems is often missing, which prevents a systematic quantification of the value of SHI. This may lead to a lack of addressing and demonstrating the SHI systems impact and value.
The value of SHI can be quantified with the Bayesian decision analysis necessitating the modeling of decision scenarios involving (1) models of technical and SHI system life cycle performance, (2) an objective function of a decision-maker, and (3) decision variables associated to the SHI strategies and technical system performance management strategies. The value of SHI provides the basis for a targeted and optimized SHI system development optimizing the benefit generation, costs, and risks for the technical system management.
With this special session, the research efforts for quantifying and optimizing the value of SHI will be gathered and discussed. Contributors in the fields of SHM optimization, decision analysis, uncertainty modeling, and risk and reliability research are welcome.

Keywords
Value of Information, SHM, decision support

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Session Title

Seismic SHM for civil structures

Organizers
Maria Pina Limongelli
, Politecnico di Milano, Italy
Mehmet Celebi, Earthquake Science Center, USGS, USA

Scope of Session
During the last two decades, the need for seismic structural health monitoring (S2HM) both property owners, as well as researchers and professionals, have evolved. As a result, numerous monitoring systems have been installed in structures in various seismic prone countries that utilize real-time or near-real-time responses recorded during strong earthquakes to make informed decisions related to the health of structures. Data collected from S2HM systems have strategic importance both for the advancement of knowledge on the behavior and performance of structures under strong seismic actions and for the calibration of realistic and reliable numerical models that are aimed to reproduce the structural behavior and to formulate a diagnosis about possible damages. Furthermore, the possibility to assess the seismic vulnerability based on data recorded on the monitored structure opens new avenues in maintenance policies, shifting from a traditional ‘scheduled maintenance’ to a ‘condition-based maintenance’, carried out ‘on-demand' or ‘automatically’, basing on the current structural condition. The aim of this Special Session is to report recent advances in this field and successful applications for civil structures and infrastructures: buildings, bridges, historical structures, dams, wind turbines, pipelines. The session deals with theoretical and computational issues and applications and welcomes contributions that cover but are not limited to, seismic SHM algorithms for identification and damage detection, requisite strong-motion arrays and real-time monitoring systems and projects, instrumentation and measurements methods and tools, optimal sensors location, experimental tests, integration of seismic SHM in procedures for risk assessment and emergency management.
Such a session will provide a venue for the exchange of information on ongoing developments and assess successes and limited successes of SHM.

Keywords
seismic SHM, civil structures, damage identification, real-time monitoring, emergency management

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Session Title

Nonlinear Acoustic and Ultrasonic Techniques for Structural Health Monitoring

Organizers
Tribikram Kundu
, University of Arizona, USA
Li Cheng, Hong Kong Polytechnic University

Scope of Session
Papers are invited from various aspects of nonlinear acoustic and ultrasonic techniques such as higher harmonic generation, sub-harmonic generation, nonlinear resonant acoustic spectroscopy, sideband peak count (SPC), vibro-acoustics, and different wave modulation techniques. Associated sensing and wave manipulation technologies to facilitate the implementation of such techniques are also welcomed. How these techniques are used for nondestructive evaluation (NDE) and structural health monitoring (SHM) will be the focus of this special session.  Papers dealing with the difficulties and shortcomings of various nonlinear techniques and challenges encountered by the investigators in implementing nonlinear techniques using body waves and/or guided waves are of interest for this session.  Recent developments of new promising nonlinear techniques that can overcome some of the existing shortcomings and limitations in applications such as the early detection of incipient damage and material degradation are of particular interest.  The objective of this session is to give the attendees a broad overview and recent developments of nonlinear acoustic techniques.

Keywords
sensing, nonlinear ultrasonic, SHM, NDE, detection, damage

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Session Title

Distributed and Quasi-distributed Fiber-optic and Electrical Sensors, and Associated Data Analysis and Management

Organizers
Branko Glisic
, Princeton University, USA
Daniele Zonta, University of Trento, Italy

Scope of Session
Damage frequently occurs in form of strain-field anomalies. Strain-sensitive sensors installed at the location of damage have an unusual high change in their output signal and thus, can detect the damage reliably. However, it is difficult to know the exact location of damage prior to its occurrence. To address this challenge, very dense arrays of sensors could be used. Their “omnipresence” on the structure and their high sensitivity to damage, makes them very promising for reliable and accurate detection, localization, and quantification of damage. Several innovative techniques for enabling distributed and quasi-distributed arrays of sensors emerged in the last decade or so: (i) 1D distributed fiber optic sensors, (ii) 2D distributed sensing skins, paints, and sheets based on nano-technologies, large-area electronics, photonic crystals (nanospheres), conductive polymers, etc., and (iii) 2D and 3D active wave sensing techniques, embedded MEMS, and self-sensing materials. The aims of this special session are (1) to assess the state of the art of the techniques enabling dense arrays of sensors, (2) to identify challenges related to their applicability in real-life settings, and (3) to cross-fertilize the research field through an exchange of ideas. In a broader sense, the topic of the session includes data management and power harvesting techniques that can address the challenges related to execution, processing, and analysis of a large number of measurements performed by very dense arrays of sensors.

Key words
Distributed fiber optic sensors; Sensing skins, sheets, and paints; Self-sensing materials; Dense arrays of active wave-propagation sensors; MEMS; Distributed/decentralized data analysis; Wireless nodes for dense arrays of sensors; Power harvesting for dense arrays of sensors

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Session Title

Data Fusion and Machine Learning Methods for SHM/NDE of Civil Infrastructure

Organizers
Jinying Zhu, University of Nebraska-Lincoln
Ying Zhang, Georgia Institute of Technology

Session Overview
This session will present recent advances in Data Fusion and Machine Learning techniques and their applications to structural health monitoring (SHM) and nondestructive evaluation (NDE) of civil infrastructure. The increasing availability of heterogeneous data from multiple sensors provides opportunities for robust decisions in SHM/NDE by integrating the information from multiple sources. This special session will focus on experimental methods and analytical models related to data acquisition, data fusion, and machine learning techniques for SHM/NDE of civil infrastructure, which may include but are not limited to bridges, pavements, buildings, dams, nuclear power plants, etc.

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Session Title

Acoustic Emission and Hybrid SHM

Organizers
Victor Giurgiutiu
, University of South Carolina
Hanfei Mei, University of South Carolina

Scope of Session
This special session will address the topic of acoustic emission and hybrid SHM. Acoustic emission (AE) is a passive SHM technique that relies on ‘listening’ to the elastic waves generated when an incremental crack growth occurs, or impact damage happens in composites. The elastic waves associated with AE events can travel a considerable distance in metallic structures that have a low damping dissipation coefficient. AE waves also travel in composite materials, but their travel distance may be less due to the higher damping dissipation of polymer matrix composites. Hybrid SHM techniques encompass a large class of methods that aim at combining several techniques to increase the probability of damage detection. For example, one may use passive SHM to record a damaging event (such as an impact in a composite structure) and then apply active SHM to try to estimate the magnitude of the resulting damage and its severity. Or one can listen to AE events that indicate that cracks are progressing into the structure and then follow up with the active SHM technique to evaluate the crack size. Or one can use two different active SHM techniques (e.g., pitch-catch wave propagation and electromechanical impedance standing waves) to better detect the damage location and size. But these are just examples. The session is open to all innovative techniques aimed at enhancing SHM capabilities. Contributions that judiciously combine theory and experiments are highly encouraged.

Keywords
acoustic emission, AE, non-destructive evaluation, NDE, structural health monitoring, SHM, passive detection, active detection, fracture, crack growth, composite, fiber breakage, matrix cracking, damage

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Session Title

Physics-Enhanced Machine Learning for SHM

Organizers
Erik Blasch, 
Air Force Office of Scientific Research, USA
Fotis Kopsaftopoulos, Rensselaer Polytechnic Institute, USA
Fu-Kuo Chang, Stanford University, USA

Scope of Session
Recent interests in Artificial Intelligence and Machine Learning (AI/ML) promote a data-driven approach to stochastic and deterministic analysis; however, for complex and time-dependent scenarios, AI/ML techniques may not meet performance needs. Contrasted to data-driven approaches are model-based approaches based on the first-principle modeling evident in theory, simulations, and analysis. For future structural health monitoring (SHM), a coordination of model-driven and data-driven approaches are needed such as Physics-Enhanced Machine Learning (PEML). Various efforts have explored related techniques such as Dynamic Data-Driven Applications Systems (DDDAS), (Physics-Informed Machine Learning), and PINN (Physics-Informed Neural Network).  Papers are sought that utilize PEML for understanding, modeling, and control the behavior of complex systems towards structural health monitoring (SHM).  Applications include sensor systems, big computing, full-scale simulations, networked sensors, or designs towards situational, materials, and systems awareness of engineered systems. Preference is given to papers that highlight comparative analysis over real data with modeling approaches and heterogeneous sensor implementation.

Keywords
Dynamic Data, Sensing, Physics Modeling, SHM, detection, Machine Learning

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Session Title

Prognostics and Health Management of Composite Structures Towards a Condition-Based Maintenance Framework

Organizers
Dimitrios Zarouchas
, Aerospace Engineering, Delft University of Technology, Netherlands
Theodoros Loutas, Mechanical Engineering & Aeronautics, University of Patras, Greece

Scope of Session
Prognostics of the remaining useful life and decision making for the health management of light-weight composite structures based on SHM measurements are in the epicenter of research and development towards a reliable implementation of condition-based maintenance practice.  
This special session will gather the research community active in the area of damage diagnostics, prognostics and health management, address the challenges, discuss the present as well as future trends, and exchange ideas & experiences across different engineering applications. Studies in the area of prognostics of composite structures subjected to various types of loading using data-driven and physics-based models or a combination of those models are expected to be presented in this session.
Emphasis is given to the utilization of various SHM techniques, different modeling philosophies (data-driven/physics-based, hybrid models), and machine learning algorithms. Issues of optimized feature extraction towards multi-sensor data fusion, more effective diagnostic/prognostic schemes, and post-prognostics decision making for structural health management are also of potential interest.

Keywords
composite structures, diagnostics & prognostics, data-driven & physics-based models, feature extraction, post-prognostics decision making, health management

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Session Title

High-Rate Structural Health Monitoring and Prognostics

Organizers
Yang Wang
, Civil and Environmental Engineering, Georgia Institute of Technology, USA
Jacob Dodson, Airforce Research Laboratory, USA

Scope of Session
This session is focused on high-rate structural dynamics, health monitoring and prognostics. The technical grand challenges in high-rate SHM includes multi timescales of the problem, adequate sensor network and response, real-time assessment, and decision-making with quantified uncertainty and risk.  Key issues to address in such challenges include the time duration of the event, time scales of the physics, multiple sources of uncertainty, as well as limited spatiotemporal constraints for hardware execution. We welcome papers relevant to the integrated high-rate paradigm, including but are not limited to high-speed structural sensing and data acquisition, excitation input estimation, structural condition assessment, uncertainty quantification, and decision-making.

Keywords
high-rate, structural dynamics, structural health monitoring, decision-making, uncertainty quantification

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Session Title

Damage Detection, Infrastructure Inspection, Image Analytics, Robotics, and Structural Health Monitoring

Organizers
Genda Chen, 
Missouri University of Science and Technology, USA
Yang Wang, Civil and Environmental Engineering, Georgia Institute of Technology, USA

Scope of Session
This session is focused on advanced robotics and sensing technologies toward structural inspection, image analytics, abnormality detection, and infrastructure planning in the broad area of structural health monitoring (SHM). The field of robotics has been widely explored long before SHM technologies attracted significant attention. However, only in recent years have robotic prototypes been developed to a level of maturity that makes them suitable for realistic applications in SHM. This session welcomes papers that explore the use of robotics and sensing technologies in overcoming contemporary challenges associated with aging physical infrastructure.  Examples include but are not limited to vision-based unmanned aerial vehicles (UAVs) for crack detection and other surface condition of structures, UAVs for construction site monitoring towards abnormality detection, crawling robots with non-destructive evaluation for bridge inspection, among others. This session is focused on advanced robotics and sensing technologies toward structural inspection, image analytics, abnormality detection, and infrastructure planning in the broad area of structural health monitoring (SHM). The field of robotics has been widely explored long before SHM technologies attracted significant attention. However, only in recent years have robotic prototypes been developed to a level of maturity that makes them suitable for realistic applications in SHM. This session welcomes papers that explore the use of robotics and sensing technologies in overcoming contemporary challenges associated with aging physical infrastructure.  Examples include but are not limited to vision-based unmanned aerial vehicles (UAVs) for crack detection and other surface condition of structures, UAVs for construction site monitoring towards abnormality detection, crawling robots with non-destructive evaluation for bridge inspection, among others.

Keywords
damage detection, infrastructure inspection, image analytics, robotics, and structural health monitoring

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Session Title

SHM for Heavy and Critical Equipment

Organizer
Keqin Ding, 
China Special Equipment Inspection & Research Institute       

Scope of Session
Heavy and critical equipment mainly includes boiler, pressure vessel, oil pressure pipeline, crane, large-scale recreational facilities, and other equipment. It is an important infrastructure related to life safety and great danger and closely related to people's life and the national economy. Once an accident occurs, it will easily cause economic losses, casualties, and even seriously affect the political economy and social stability. With the rapid development of the global economy and the need for major engineering construction, the possession of large-scale and high-parameter equipment has increased rapidly. The service environment of heavy and critical equipment is complex and harsh, such as high temperature, high pressure, high dust, flammability, explosion, corrosion, and so on. In the course of service, the failure modes of equipment structure are various and accidents occur frequently. Most of the heavy and critical equipment inspection still stays on the traditional inspection mode of on-site, static, and off-line, which has high labor intensity and low inspection efficiency. The existing inspection and detection technology does not match the development of automation and intelligence of special equipment. It is urgent to develop remote, real-time, and online health monitoring technology to ensure the safe and reliable operation of equipment. The main objective of this session is to exchange and discuss the latest development of heavy equipment structural health monitoring, health diagnosis, damage identification, fault prediction, and health management technology.

Keywords
heavy and critical equipment, SHM, online monitoring, diagnosis, PHM

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Session Title

SHM Standardization

Organizer
Keqin Ding, 
China Special Equipment Inspection & Research Institute       

Scope of Session
With the rapid development of the world economy, technical standards have become a powerful hand to promote the close integration of science and technology and economy and enhance international competitiveness. It plays an increasingly important role in promoting the industrialization and marketization of scientific and technological innovation. In recent years, due to the development of large-scale, high parameter, and complex equipment structures, the demand for structural health monitoring is more and more urgent. However, due to the lack of structural health monitoring standards to guide the implementation, the technology and standards are seriously out of touch, which affects the wide application and promotion of a series of research results with great engineering application value. The standardization of structural health monitoring started late, and there are still some problems that can not be ignored. It is mainly manifested in the lack of a technical standard system, the need to improve the quality level, the need to explore the operation mechanism, and the lack of policy system guarantee. In order to promote the application of structural health monitoring technology and promote the sustainable development of the industry, research on standardization of structural health monitoring is urgently needed. The main objective of this session is to exchange and discuss the latest development of standardization of structural health monitoring, health diagnosis, damage identification, fault prediction, and health management technology.

Keywords
standardization, SHM, PHM, infrastructure, equipment

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Session Title

Multifunctional Materials and Metamaterial Structures

Organizers
Donghyeon Ryu,
Department of Mechanical Engineering, New Mexico Tech, USA
Nathan Salowitz, Department of Mechanical Engineering, University of Wisconsin - Milwaukee, USA
Kenneth Loh, Department of Structural Engineering, University of California San Diego, USA       

Scope of Session
Multifunctional materials and metamaterial structures are ones that have been intentionally engineered to exhibit more than one precisely defined property and with properties that greatly differ from materials commonly found in nature. Often, the encoding of desired properties is achieved through a “bottom-up” design methodology during material manufacturing. While nanotechnology has enabled the molecular assembly of a variety of new materials/structures that are then scaled up, the design and engineering of innovative multifunctional structures can Multifunctional materials and metamaterial structures are ones that have been intentionally engineered to exhibit more than one precisely defined property and with properties that greatly differ from materials commonly found in nature. Often, the encoding of desired properties is achieved through a “bottom-up” design methodology during material manufacturing. While nanotechnology has enabled the molecular assembly of a variety of new materials/structures that are then scaled up, the design and engineering of innovative multifunctional structures can occur at any length scale. As a result, this new class of material systems can exist in the form of a two- or three-dimensionally structured nanocomposite, composite, coating, and/or multi-phase material. This special session welcomes contributions that showcase the breadth of multifunctional and metamaterial material architectures, nanocomposites, field-responsive metamaterials, mechanical metamaterials, novel and additive manufacturing methods, multi-scale design, and characterization, numerical modeling, topology optimization, validation and testing, and technology demonstration, among many others. at any length scale. As a result, this new class of material systems can exist in the form of a two- or three-dimensionally structured nanocomposite, composite, coating, and/or multi-phase material. This special session welcomes contributions that showcase the breadth of multifunctional and metamaterial material architectures, nanocomposites, field-responsive metamaterials, mechanical metamaterials, novel and additive manufacturing methods, multi-scale design, and characterization, numerical modeling, topology optimization, validation and testing, and technology demonstration, among many others.

Keywords
actuation, bio-inspiration, energy dissipation, energy harvesting, metamaterial, mechanical response, nanocomposite, self-healing, sensing, stimuli-responsive

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Session Title

Probabilistic SHM

Organizers
Daniele Zonta,
Department of Civil and Environmental Engineering, University of Trento, Italy
Branko Glisic, Department of Civil and Environmental Engineering, Princeton University, USA

Scope of Session
Structural health monitoring aims to understand the condition of a structure based on sensor observations, a process that is typically affected by uncertainties in the model assumptions and in the measurements. Key questions are how to provide a reliable and robust diagnosis, properly accounting for these uncertainties, and how to rationally exploit the monitoring information to make decisions on such issues as structural maintenance, repair, and replacement. The goal of the session is to bring together researchers working on statistical data interpretation, structural risk assessment, and decision making. Contributions are invited in the fields of structural reliability, probabilistic analysis, Bayesian logic, sensor fusion, risk analysis, including economic and social aspects that affect decisions in SHM applications. Contributions proposing methodological developments and in-field applications are both welcome.

Keywords
bayesian inference, probabilistic methods, sensor fusion, structural reliability, risk analysis, decision making

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