ANCRiSST-Program

PROGRAM

Itinerary for the conference

Friday, July 17, 2020	Saturday, July 18, 2020	Sunday, July 19, 2020	Monday, July 20, 2020
Welcome Reception	Technical Sessions	Technical Sessions	Site Visits and Collaboration

Technical sessions

Technical sessions will be geared towards bridging the knowledge gap and updating the participants with the state of the art in the following sessions.

Structural design, experiment and simulation

Structural health monitoring and evaluation

Smart materials and structures

real-time hybrid simulation

Acoustic and mechanical metamaterials

Signal processing and data management

Bioinspired and architectured materials

Image processing and machine learning

Robotics in inspection

Data-driven modeling

Morphing and programmable structures

Sensors and instruments

Organized sessions

OS-1: Structural Control of Civil, Mechanical, and Aerospace Systems

Organizer: Nicholas Wierschem, University of Tennessee; Lauren Linderman, University of Minnesota

Abstract: This mini-symposium is designed to serve as a gathering place for researchers and engineers to share and discuss recent progress and advances related to the use of structural control to protect civil, mechanical, and aerospace systems from excessive vibration or other unwanted dynamic responses. Of interest to this mini-symposium are experimental, numerical, and analytical work on passive, semi-active, and active control devices and methodologies. Additionally, of interest are contributions related to control algorithms, optimization of systems, distributed control systems, and wireless control systems. Contributions on recent practical engineering applications of control systems, state-of-the-art reviews, and discussions on future challenges and areas of growth are also welcome.

OS-2: Research Fronts in Hybrid Simulation

Organizer: Wei Song, University of Alabama; Amin Maghareh, Purdue University

Abstract: Hybrid simulation is a powerful technique that integrates physical experimentation with computational simulation to observe and evaluate complex engineering systems. Hybrid simulation is mainly used either when a structural system is too large or complex to evaluate using traditional techniques (high-rise buildings and long-span bridges, for example), or when the response of a physical specimen cannot be accurately predicted using the latest computational models and its behavior must be observed under realistic operational conditions.
In recent years, along with the advancement of other engineering fields, more and more researchers have realized its benefits and start the exploration of new hybrid simulation applications. These explorations have brought new capabilities as well as unique challenges to the research community. This mini-symposium offers a forum for researchers to gather, exchange, and discuss the latest research fronts, ideas, and developments in hybrid simulation. The topics of interest will cover, but not limited to, the following aspects:

Innovative hybrid simulation theories, such as new frameworks, configurations, and treatment of nonlinearities and uncertainties in hybrid simulation

Novel enabling techniques and technologies, e.g., new types of actuators and sensors, actuator control and compensation, numerical integration schemes, and high-performance computing techniques

Performance evaluation of hybrid simulations, e.g., stability analysis, performance (delay/error) assessment criteria, uncertainty quantification, and validation studies

Recent implementations and applications in hybrid simulation, especially in a multiple hazards context

Educational material to convey fundamental principles of hybrid simulation

OS-3: Machine Learning, Big Data, and Physic-Data Infusion Methods in Structural Dynamics

Organizer: Ge (Gaby) Ou, University of Utah; Wei Song, University of Alabama; Ying Wang, University of Surrey

Abstract: Recent developments in data science enable new trajectories to solve the problems in the built environment, with the aim of improving the performance of civil infrastructure during operation, intermediate stress conditions, and hazard events. The objective of this mini symposium is to collect research and application studies that leverage advanced data analytics methods as compared to, or in additional to the physics-based approach in constructing, identifying, updating, and imitating the structural dynamic models. Example topics covered in this mini-symposium include but not limited to:

Unsupervised learning/supervised learning in structural dynamics

Deep learning algorithms and their applications in structural dynamics

Damage detection and assessment

Development of structural surrogate model

Digital twin development

Structural dynamic response estimation

Infusion of data-drive and model-based methods

System identification

Physics-based or Data-based model updating

Uncertainty quantification

Applications of machine learning in natural hazards (earthquakes, hurricanes, tsunami)

Applications of machine learning techniques in structural health monitoring

OS-4: Innovations and Ideas in Passive, Semi Active, Active or Hybrid Structural Control Methodologies

Organizer: Christian Silva, Escuela Superior Politecnica de; Mariantonieta Gutierrez-Soto, University of Kentucky

Abstract: Cities play a prime role in the social and economic aspects of human life nowadays. Cities that are adopting innovative, technology-driven solutions for improving their efficiency are considered smart cities. Smart cities initiatives use data, and information technologies to provide more efficient services for citizens in the aspects of 1) monitoring and optimizing existing infrastructure; 2) increasing collaboration among different economic sectors, and; 3) encouraging innovative business models in both the private and public sectors. With such an increasing attention being given to smart cities, smart structures are needed to transform the future interaction between smart systems (e.g., autonomous vehicles, smart buildings, and so forth), and civil structures, especially when such structures are subjected to extreme events. Thus, advanced mitigation strategies that will prepare for and minimize impacts against future natural disasters in civil structures are crucial. However, technological advances have led mechanical systems and civil infrastructure to increasing levels of performance and service cycles, which in turn expose these systems to several hazards related with their day to day operation, wear and unpredictable natural disasters. Thus, effective means of controlling excessive responses are also required to have increasing capabilities and advancements. The purpose of this session is to discuss and share state-ofthe-art innovations and ideas in the field of vibration control of structures and systems. The focus here is the use of passive, semi active, active or hybrid structural control methodologies. Part of the session will be related to new or improved control devices such as mass dampers, friction-based dampers, magneto-rheological devices, cable-based devices, variable stiffness devices, base isolation systems, and other supplemental damping devices, including those that use innovative materials, nonlinear geometries, inerters, or nonlinear materials, with the ultimate goal of increasing the reliability and resilience and performance of systems and infrastructure. A great deal of interest will be placed on simulation techniques, new models and modeling approaches, developing robust algorithms, optimization methodologies, or the implementation of control devices.

OS-5: Human-Structure Interactions and Interfaces

Organizer: Fernando Moreu, University of New Mexico; Kenneth J. Loh, University of California, San Diego

Abstract: This session is soliciting contributions related to algorithms, theory, modeling, Internet-of-Things (IoT) technologies, implementation, evaluation, and deployment experiences of monitoring, assessing, or controlling human-centered monitoring of structures. Topics of interest include but are not limited to: (1) understanding the interface between humans and structures; (2) analysis of everyday and/or extreme activities of humans in the structures; (3) wearable sensors and IoT technologies for monitoring human activity, behavior, health, and mental states; (4) augmented reality and virtual reality enabling human-infrastructure interfaces; (5) improving structural performance through mitigating human effects; (6) enabling human-centric structural management to improve human comfort and productivity; and (7) innovative applications, devices, sensors, mechanisms, laboratory studies, and field validation of new human-infrastructure interfaces.

OS-6: Advances in Computer Vision and Graphics for Structural Health Monitoring

Organizer: Mohammad Jahanshahi, Purdue University

Abstract: It is generally accepted that artificial intelligence (AI) enabled computer vision will drive the next revolution in information modeling and decision making. Furthermore, due to the recent advances in sensors and computing technologies, the use of vision-based approaches provides an unprecedented opportunity to complement traditional structural health monitoring (SHM) and nondestructive evaluation (NDE) technologies, which will ultimately improve the resilience of structural systems. Moreover, vision methods are generally contactless and appropriate to be incorporated in mobile sensing robots such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), providing a transformative monitoring platform for civil structures. This mini-symposium will provide the opportunity to discuss recent theoretical, computational and experimental advances in using computer vision and machine learning approaches for structural identification, control, damage detection, and health monitoring. Topics relevant to this session include, but not limited to, deep learning, active learning, deep reinforcement learning, computer graphics, virtual reality, augmented reality, mixed reality, innovative imaging for structures, image/video data collection and analysis, damage detection, classification, convolutional neural networks, network pruning, quantification and localization, change recognition, displacement and dynamic measurements, sensor calibration, fusion and optimization, scene reconstruction, 3D LIDAR and depth sensors, activity monitoring, robotics integration, vision-based inspection using UAVs and UGVs, and other new emerging vision-based technologies.

OS-7: Advanced Computer Vision for Structural Assessment

Organizer: Chul Min Yeum, University of Waterloo

Abstract: Advances in new sensing platforms and computer vision methods revolving around artificial intelligence (AI) are enabling a new paradigm in structural monitoring and damage detection. Buildings, bridges, naval vessels, nuclear reactors, dams, oil tanks, and space structures require 2D and 3D optical inspection. Automating the monitoring and damage detection process will enhance the safety of humans and the structural integrity against environmental or operating degradation and natural disasters. For example, stereo camera systems and Lidar are able to rapidly generate high-quality 3D reconstructions of a structure. Mobile sensing platforms such as unmanned aerial and ground vehicles, providing a transformative non-contact monitoring platform by resolving the limitations of human visual inspection including consistency, accessibility, safety, and efficiency. Thus, the advantages of automating the optical data collection and analysis process include greater access to remote and difficult-to-reach regions, while enforcing consistency and saving considerable time and resources. Across hazard response, construction, and structural monitoring researchers are gaining experience with existing techniques, and building upon them to tackle realistic challenges associated with automated damage identification and classification in structures. This session will focus on the breakthroughs researchers across these disciplines are making to tackle such challenges. Potential topics relevant to this session include damage detection, classification, localization, quantification, and prediction using supervised or unsupervised learning, deep learning, active learning or reinforcement learning; scene reconstruction using structure from motion, simultaneous localization and mapping (SLAM), visual odometry, 3D LIDAR, and depth sensors; advanced sensing platform including unmanned aerial vehicle (UAV), unmanned ground vehicle (UGV), robotic integration, swarm robot, virtual reality (VR), augmented reality (AR), mixed reality (MR); and other new emerging computer vision, computer graphics, and robotics technologies.