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Special Sessions

Organizer

Guided Waves in Structures for SHMProf. Wieslaw Ostachowicz and Prof. Annamaria Pau
Advancements in Smart Materials and Structures for SHM in Civil EngineeringProf. Yiska Goldfeld and Prof. Filippo Ubertini
Seismic SHM for civil structuresProf. Maria Pina Limongelli and Prof. Mehmet Celebi
Recent Advances on Data Processing Techniques for Ultrasonics-based SHM/NDEProf. Salvatore Salamone
Machine Learning and Artificial Intelligence Enabled Health Monitoring of Composite StructuresProf. Xinlin P. Qing and Prof. Yongchao Yang
Long-term damage identification in bridges under operational and environmental effects, including climate changeProf. Eloi Figueiredo, Prof. Ionut Moldovan, Prof. Luke J Prendergast, and Prof. Abdollah Malekjafarian
Wearable Sensors and Human Performance AssessmentProf. Ken Loh and Prof. Said Quqa 
Nonlinear Ultrasonics and/or Topological Acoustics based Structural Health MonitoringProf. Tribikram Kundu and Prof. Weibin Li
Acoustic Emission and Hybrid SHMProf. Victor Giurgiutiu and Prof. Zhenhua Tian
SHM enhanced Bridge Digital TwinsProf. Steffen Marx and Prof. Chongjie Kang
Human-in-the-Loop and Human-Structure Interfaces for Enhanced Structures and Machine Systems PerformanceProf. Fernando Moreu and Prof. Haeyoung Noh
Remote Satellite-based Structural Health Monitoring of Structures and InfrastructureProf. Maria Pina Limongelli and Prof. Daniel Cusson
Advances in Data Science, Artificial Intelligence, Machine Learning, and/or Computer Vision for Structural Health MonitoringProf. Mohammad Jahanshahi
SHM for Intelligent Maintenance PracticesProf. Eleni Chatzi & Prof. Dimitrios Zarouchas
Integrating Physics in Data Driven Methods for SHMProf. Fotis Kopsaftopoulos and Prof. Dimitrios Zarouchas 
SHM for Water Resources InfrastructureProf. Brian Eick, Prof. Zhen Hu, and Prof. Michael Todd
Advances in structural health monitoring of offshore energy structuresProf. Luke J Prendergast and Prof. Madhup Pandey
SHM for Space ExplorationDr. Cara Campbell Leckey, Dr. Amrita Kumar, Prof. Fu-Kuo Chang
Robotic and Machine Learning Technologies for Infrastructure Inspection Prof. Yang Wang, Prof. Genda Chen, Prof. Austin Downey
High-speed Railroad, Transportation Infrastructure, Lifelines, Structural Health Monitoring, and ManagementProf. Fernando Moreu, Prof. Hoon Sohn, Prof. Jinwoo Lee, Prof. Yi-Qing Ni, Prof. Haeyoung Noh
Probabilistic SHMProf. Daniele Zonta and Prof. Branko Glisic

Guided Waves in Structures for SHM
Organisers: Wieslaw Ostachowicz and Annamaria Pau
Key words: sensors, sensing, SHM, damage detection, signal processing

Scope of Session: There is excellent potential for model-based approaches that utilize guided waves for damage detection, localization, and size estimation. This session covers key disciplines related to guided wave propagation in both isotropic and anisotropic materials. Authors are encouraged to submit papers that explore the phenomenon of elastic wave propagation, spanning a wide range of topics including linear and nonlinear behavior, 1D, 2D, and 3D propagation, time- and frequency-domain analyses, as well as experimental and numerical approaches in complementary investigations of structures. The proposed novel techniques may contribute to the efficient application of both local and global SHM technologies. The investigations outlined above aim to develop various strategies for diagnostics (damage size estimation and damage type recognition) and prognostics. The promising combination of these techniques should lead to innovative approaches that ensure safe operation.

Advances in Data Science and Artificial Intelligence for Structural Health Monitoring
Organizers: Mohammad Jahanshahi
Keywords: data science, data analysis, data fusion, machine learning, artificial intelligence, deep learning

 Scope of Session: The recent advances in data science and artificial intelligence (AI) have led to ground-breaking innovations in the field of structural health monitoring (SHM). This special session aims to bring together researchers and practitioners to discuss the latest theoretical, computational, and experimental breakthroughs in applying AI techniques for structural identification, control, damage detection, and health monitoring. These innovative approaches can be applied to a variety of data types, including vibration signatures and non-destructive evaluation (NDE) data from advanced sensors. Topics relevant to this session include but are not limited to, machine learning-based damage assessment, deep learning, physics-informed machine learning and neural networks, generative adversarial networks (GANs), and other emerging data science and AI technologies with applications in SHM.

Advances in Computer Vision-based Structural Health Monitoring
Organizers: Mohammad Jahanshahi
Keywords: computer vision, deep learning, image and video processing, neural networks

 Scope of Session: Advances in computer vision have unlocked new capabilities for structural health monitoring (SHM), driving the next revolution in information modeling and decision-making for the health management of structural systems. This session will provide the opportunity to discuss recent theoretical, computational, and experimental advances in applying computer vision techniques to structural identification, control, damage detection, damage localization, quantification, inspections, performance assessment, response measurement, and health monitoring. Topics relevant to this session include but are not limited to, deep learning, deep reinforcement learning, active learning, computer graphics simulations, innovative imaging for structures, image and video data collection and analysis, classification, convolutional neural networks, generative adversarial networks (GANs), transformers, quantification and localization, change recognition, displacement and dynamic measurements, sensor calibration, fusion and optimization, scene reconstruction, 3D LIDAR and depth sensors, robotics integration, and inspection and monitoring using UAVs and UGVs, along with other emerging computer vision and robotic technologies.

Nonlinear Ultrasonics and/or Topological Acoustics based Structural Health Monitoring
Organizers: Tribikram Kundu and Weibin Li
Key words: nonlinear ultrasonics, topological acoustics, sensing, damage detection, SHM, NDE

Scope of Session: Papers are invited from various aspects of nonlinear ultrasonic and/or topological acoustics-based sensing techniques.  Nonlinear ultrasonic techniques such as higher harmonic generation, sub-harmonic generation, frequency modulation, nonlinear impact resonant acoustic spectroscopy, sideband peak count - Index (SPC-I), vibro-acoustics and different wave modulation techniques as sensing techniques are becoming popular. How these techniques are used for non-destructive evaluation (NDE) and structural health monitoring (SHM) will be one research focus area of this special session. The second focus area will be topological acoustics-based sensing techniques.  Papers dealing with the advantages as well as shortcomings of various nonlinear ultrasonic and/or topological acoustics-based sensing techniques, and challenges encountered while implementing these techniques using body waves and/or guided waves are of interest for this session.  Recent developments of new promising techniques that can overcome some of the existing shortcomings are of particular interest.  Objective of this session is to give the attendees a broad overview of the current technology and recent developments of nonlinear ultrasonic and topological acoustics-based sensing techniques.

Acoustic Emission and Hybrid SHM
Organizers: Dr. Zhenhua Tian and Prof. Victor Giurgiutiu
Key words: acoustic emission, AE, non-destructive evaluation, NDE, structural health monitoring, SHM, passive detection, active detection, fracture, crack growth, composite, fiber breakage, matrix cracking, damage

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 which 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 which 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.

Seismic SHM for civil structures
Organizers: Maria Pina Limongelli and Mehmet Celebi
Key words: seismic SHM, civil structures, damage identification, real-time monitoring, emergency management

Scope of Session: During the last two decades, the need for seismic structural health monitoring (S2HM) by both property owners, as well as researchers and professionals, has 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 a 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 of assessing 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. This Special Session aims to report recent advances in this field and successful applications for civil structures and infrastructures: buildings, bridges, historical structures, dams, wind turbines, and 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 the successes and limited successes of SHM.

Advancements in Smart Materials and Structures for SHM in Civil Engineering
Organizer: Yiska Goldfeld and Filippo Ubertini

Scope of Session: Advancements in smart materials and structural systems are revolutionizing structural health monitoring (SHM) in civil engineering, paving the way for a new era of intelligent infrastructure. Modern infrastructure increasingly demands durable, efficient, and cost-effective structural elements with multifunctional and self-sensory capabilities. These trends call for a new class of intelligent structures that utilize advanced processes and technologies to ensure real-time monitoring, damage detection, self-diagnosis, and predictive maintenance. This session aims to bring together researchers exploring the latest innovations in smart and multifunctional materials and structures, focusing on experimental and theoretical studies as well as practical applications. Topics include the development of smart sensors and actuators, self-monitoring structural elements, algorithmic strategies for self-sensory systems (including AI) and the integration of adaptive materials such as piezoelectric systems and self-healing composites in civil engineering structures. Through these advancements, participants will explore how novel SHM technologies are improving the safety, resilience, and sustainability of the built environment.

Machine Learning and Artificial Intelligence Enabled Health Monitoring of Composite Structures
Organizers: Xinlin P. Qing and Yongchao Yang
Key words: Sensor Network, Structural Health Monitoring, Machine Learning, Artificial Intelligence, Composite Structures.

Scope of Session: In the face of complex damage modes of composite structures and their service environments, most of current SHM methods have limitations to accurately and quantitatively monitor the damages in composite structures. With the rapid development of machine learning and artificial intelligence and their applications in SHM, it provides a great opportunity for more accurate and robust damage monitoring of composite structures in complex service environments. This session will provide a platform to discuss recent advances in leveraging machine learning and artificial intelligence techniques to enable structural identification, control, damage detection, inspection and health monitoring. Topics of interest include, but not limited to, machine learning, deep learning, active learning, transfer learning, physics-informed learning, meta learning, generative adversarial networks, and other new emerging machine learning and AI techniques for damage identification, assessment, and uncertainty quantification for composite structures.

Long-term damage identification in bridges under operational and environmental effects, including climate change
Organizers: Eloi Figueiredo, Ionut Moldovan, Luke J Prendergast, and Abdollah Malekjafarian
Key words: bridges, damage identification, machine learning, transfer learning, temperature, climate change

Scope of Session: The effects of operational and environmental variations (e.g., traffic loading, temperature, and flow characteristics of rivers) pose great challenges to structural health monitoring (SHM) of bridge assets from research to practice. In recent years, climate change has begun to pose increasing risk to the operational safety and health of bridges. Although the uncertainty associated with the magnitude of the change and the nature of how it might influence our built environment is large, the fact that our climate is changing is unequivocal. It is expected that climate change will manifest as another source of variability, resulting in changes in temperature, relative humidity, river flow, etc. Therefore, the main goal of this special session is to promote more coordinated and interdisciplinary research in the long-term vibration-based SHM of bridges affected by operational and climate variation, by proposing key developments in machine learning for damage identification under operational and environmental effects. Papers are welcomed on topics such as (but not restricted to):
-       unsupervised and supervised machine learning for damage identification,
-       transfer learning and domain adaptation,
-       hybrid data sets from numerical models and/or monitoring systems for SHM,
-       novel health monitoring algorithms,
-       effects of climate change on the damage identification process.

Remote satellite-based structural health monitoring of structures and infrastructure
Organizers: Maria Pina Limongelli and Daniel Cusson
Key words: InSAR, space-borne, radar backscatters, deformation monitoring, damage detection

Scope of Session: Satellite-borne InSAR technology provides an appealing complementary approach to traditional SHM to measure mm-accurate displacements over large geographic areas, and to follow their evolution over time. The possibility to monitor large areas (e.g., an urban centre) opens new avenues for the developing automatic alerting systems that can flag several single structures with suspected structural integrity issues within a given network. For instance, InSAR measurements can provide information relevant to displacement time history, displacement rate, and thermal deformation, that can provide useful insight into ongoing deterioration phenomena. Optical-band satellite imagery can nicely complement InSAR-based monitoring with additional information for hydraulic structures, for example, on river current speed and direction, and nearby vortex formation, which can help assess the risk of pier scouring for river bridges and marine ports. This special session will provide the venue to present and discuss theoretical developments and field applications to foster future research collaborations on the topic. It welcomes contributions on algorithms (including AI-based) to process and analyze satellite imagery and data for damage detection purposes, case studies, measurement and calculation methods, and integration of remote and local sensing data for condition assessment and decision-making.

Recent Advances on Data Processing Techniques for Ultrasonics-based SHM/NDE
Organizer: Prof. Salvatore Salamone
Key words: guided waves, acoustic emissions, signal processing, SHM, NDE, damage detection

Scope of Session: This special session aims to collect and share recent developments in data processing techniques to enhance 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.

Wearable Sensors and Human Performance Assessment
Organizers: Ken Loh and Said Quqa 
Key words: biomechanics, data visualization, digital health, human behavior, internet-of-things, physiological monitoring, prehabilitation, rehabilitation, sensing, textiles

Scope of Session: Individuals and teams are cornerstones of nearly every industry – hospitals, schools, corporate offices, schools, athletics, and the military. The successful, efficient, and effective execution of tasks – particularly physical-cognitive tasks to achieve a desired objective, such as hitting a golf ball, performing precision surgery, and putting out a building fire – require both individuals and/or the collective team to perform at their highest level. However, technologies that can measure and accurately quantify human performance are still lacking. The ones that do exist, such as commercial wearable sensors and Internet-of-Things (IoT) technologies, only provide gross information about human activity and may not be suitable for use in complex, field, or forward-deployed environments. Therefore, this special session is soliciting contributions focused on sensing the physiological and psychological conditions of human performance, as well as the interactions/interfaces between humans and the environment. Examples of specific topics of interest include: (1) wearable Internet-of-Things (IoT) technologies and feedback mechanisms; (2) bio-marker, biochemical, and bio-molecular sensing; (3) modelling of biological materials and systems; (4) static and dynamic characterization of human systems; (5) cognition and cognitive load measurements and modelling; (6) human digital twins; (7) human-structure interfaces that enhance system performance; (8) novel augmented/virtual reality and data visualization methods; (9) digital health systems and personal well-being technologies; (10) laboratory and field validation studies on human performance assessment; (11) artificial intelligence in human modelling, characterization, performance assessment, and performance enhancement; and (12) sports, military, medical, physical therapy, and biomechanical studies. 

Integrating physics in data-driven methods for SHM
Organizers: Fotis Kopsaftopoulos & Dimitrios Zarouchas
Key words: physics-informed models, data-driven models, Explainable Artificial Intelligence (XAI), machine learning methods 

Scope of Session: A successful implementation of SHM relies to a large extend on the quality of data and the damage information that this data contains. While the structure is in operation, multi-sensing techniques are usually employed (i.e., vibration-based, acousto-ultrasound, and optical-based techniques) in order to fulfill the four levels of SHM; damage existence, localization/identification, severity estimation (quantification), and remaining useful service life estimation/prediction, resulting to a vast amount of data that increases the complexity of analysis and, at the same time, reduces the efficiency and  interpretability of the SHM system. Data-driven and Machine learning (ML) algorithms have attracted the interest of the community, and although promising results have been produced, they are still facing several significant challenges and their acceptance remains under consideration by the operators of structural assets and certification bodies. To overcome this challenge and provide competent solutions, integrating physics into the data-driven/ML SHM methods, in the form of prior expert knowledge, (semi)-empirical rules, physics laws and constraints, physics informed neural networks (PINN) offers a great potential. This special session welcomes contributions that explore and propose how integrating physics into data-driven methods/ML for SHM has the potential to increase the effectiveness, robustness, reliability, and deployment of SHM systems, as well as the interpretability of the health monitoring data and the explainability of data-driven/ML algorithms. Emphasis is placed on contributions that address the integration of physics- and data-driven methods within statistical and/or probabilistic frameworks as well as highlight comparative analyses using experimental data.

 

Human-in-the-Loop and Human-Structure Interfaces for Enhanced Structures and Machine Systems Performance
Organizers: Fernando Moreu and Haeyoung Noh
Keywords: sensors, sensing, human-machine interfaces, SHM, damage, detection

Scope of Session: This session is soliciting contributions related to algorithms, theory, modeling, Internet-of-Things (IoT) technologies, implementation, evaluation, and deployment experiences of monitoring, assessing, and/or controlling human-machine performance and human-induced structural responses. Topics of interest include but are not limited to: (1) understanding and modeling of structural responses induced by humans or animals; (2) understanding the interface between humans and their built infrastructure and their decisions; (3) human-machine interfaces and theories in relation to structures; (4) human experiences and human cognition of structures; (5) analysis of everyday and/or extreme activities of humans in the structure; (6) augmented reality and virtual reality enabling human-machine interfaces; (7) enabling human-centric structural management to improve human comfort and productivity; and (8) innovative applications, laboratory studies, and field validation.

SHM for Intelligent Maintenance Practices
Organizers: Eleni Chatzi & Dimitrios Zarouchas
Key words: intelligent maintenance, data-driven/hybrid modeling, fault diagnostics, prognostics, decision-making, value of information, operational optimization  

Scope of Session: Modern societies are heavily depended upon infrastructure, transportation vehicles and energy systems for ensuring a reliable, robust, and safe operation and functionality of modern communities. Critical assets suffer continual deterioration due to both wear and slow evolving degradation processes, as well as extreme events and adverse loadings, revealing the needs for efficient maintenance practices that can anticipate the full spectrum of environmental and operational influences. For safety critical assets, the common and current practice is to impose safety factors and perform predefined inspection tasks to mitigate the risk of an unexpected failure with undesirable consequences. However, these schedules are time-consuming, expensive and in most cases unnecessary. In seeking for a paradigm shift, which further accounts for optimizing operational performance, maintenance solutions which rely on real-time diagnostics and prognostics have emerged. To realize such intelligent maintenance strategies, data related to the health state of the structure must be available, and structural health monitoring (SHM) presents a feasible way to achieve this. SHM enables the continuous collection and analysis of structural data, which can inform predictive models and improve decision-making in maintenance planning. Integrating SHM with data-driven models for fault diagnostics and prognostics allows for improved detection and understanding of asset degradation patterns, providing a foundation for real-time operational optimization. This special session focuses on the role of SHM in enabling intelligent maintenance strategies across various industries. Topics of interest include, but are not limited to, the development and application of data-driven and hybrid models for fault diagnostics and prognostics, as well as policy planning alfgorithms and dvalue of information tools for decision-making. Discussions will explore how SHM data can inform operational strategies, reduce unnecessary maintenance, and optimize the value of information for asset management. Additionally, contributions that emphasize the use of SHM for enhancing the safety, reliability, and efficiency of infrastructure, energy systems, and transportation vehicles are encouraged.

SHM for Water Resources Infrastructure
Organizers: Brian Eick, Zhen Hu, and Michael Todd
Key words: dams, locks, levees, digital twin, remote sensing, failure modelling, life cycle

Scope of Session: Water resources infrastructure is comprised of the system of locks, dams, and levees, which are crucial to our economy, public safety, and the preservation of the environment. SHM of this infrastructure presents many unique challenges such as their highly complex operational environment (which can be submerged underwater), access difficulties for sensing and data acquisition, and nonstationary loading conditions. This session seeks papers applied to water resources infrastructure that address such areas as novel sensing and remote/robotic monitoring technologies especially for submerged and/or power-denied environments, SHM uncertainty quantification in the presence of extreme environmental variability, SHM-informed life cycle management digital twins, failure modeling/prediction, and value-of-information analysis for SHM, among other relevant topics.

Advances in structural health monitoring of offshore energy structures
Organizers: Luke J Prendergast and Madhup Pandey
Key words: offshore, damage identification, machine learning, health monitoring, wind turbines, geotechnics, soil-structure interaction

Scope of Session: Offshore wind energy is growing and is a core technology leading the energy transition. Current wind farms are aging, succumbing to more extreme climate-impacts, and require expensive and challenging maintenance. Moreover, the pace of development of offshore wind turbine technology means these systems are increasingly unlike any systems previously dealt with in civil engineering maintenance management. Novel damage identification and health monitoring techniques are urgently required to facilitate continued safe operation and development of the offshore energy sector.
This special session welcomes contributions on (for example):
-       vibration-based monitoring technologies for offshore structures
-       climate impacts on offshore energy converters and mitigation strategies
-       digital twinning for offshore engineering
-       direct and indirect monitoring schemes
-       damage identification, quantification, and localization
-       fatigue assessment of structures
-       service-life extension decision-making
-       uncertainty quantification in damage detection, 
-       optimal sensor placement, 
-       novel sensing techniques including UAVs, satellites
 

SHM for Space Exploration
Organizers: Cara Campbell Leckey, Amrita Kumar, Fu-Kuo Chang
Keywords: sensors, impact detection, space vehicles, satellites, SHM, damage, detection

Scope of Session: Space programs are preparing for the next phase of human space exploration, aiming to extend human presence beyond low earth orbit and lunar missions. Safety and reliability of space habitats and transportation systems will be critical to ensure the success of space exploration.  This session is soliciting contributions related to SHM for space exploration including space flight testing, satellite structures, human exploration of space and in-space/planetary body infrastructure and habitation systems. Topics of interest include (1) SHM use for space vehicle performance in ground/flight testing, (2) SHM for On-orbit Servicing, Assembly, and Manufacturing (OSAM) operations, (3) Impact and proximity detection for satellite structures, (4) Detection of micrometeoroid and orbital debris (MMOD) threats on inflatable structures or other pressurized vessels, (5) SHM systems operation in extreme hypersonic environments (e.g., high thermal, vibrational, and acoustic environments), (6) Instrumentation and SHM systems for non-ablating structural and propulsion systems, (7) Methodologies that synthesize data from a range of extreme environment sensors into integrated vehicle health management (IVHM) systems that will support space vehicle flight exposure, component maintenance requirements, and life estimates and (8) New SHM sensors and system for astronauts to use in a habitat or in the space environment (i.e., on an extravehicular activity (EVA)) or for automated inspection, (9) SHM systems to monitor future lunar and/or Mars infrastructure.
 

Robotic and Machine Learning Technologies for Infrastructure Inspection
Organizers: Yang Wang, Genda Chen, Austin Downey
Key words: infrastructure inspection, damage detection, image analytics, robotics, machine learning, autonomy, and structural health monitoring

 Scope of Session: This session is focused on advanced robotics, control, sensing, and machine learning technologies toward infrastructure 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 robotic and sensing technologies and physics-based machine learning in overcoming contemporary challenges associated with aging physical infrastructure.  Examples include, but are not limited to:
·       Simultaneous localization and mapping - based unmanned aerial vehicles (UAVs),
·       Learning-based detection/quantification/classification of infrastructure deterioration such as cracks and corrosion,
·       UAVs for construction site monitoring towards abnormality detection,
·       Crawling robots with non-destructive evaluation for bridge inspection,
·       Control schemes to enable autonomous robotics for inspection,
·       SHM sensing technologies developed specifically to work with robotic systems, and
·       Data processing frameworks that closely integrate robotic systems into the SHM process.

SHM enhanced Bridge Digital Twins
Organizers: Steffen Marx and Chongjie Kang
Key words: bridge digital twin, digitalization, predictive bridge maintenance

Scope of Session: The condition of a structure is characterized by increasingly rapid degradation as it ages. Preventive measures against aging are most effective when implemented early. Extending the service life of complex structures, particularly bridge constructions, requires significantly more detailed information at an earlier stage than is typically available today. To bridge this gap and advance towards predictive maintenance, fundamental research is needed to develop methods for collecting, linking, and evaluating data related to geometry, materials, stress, and aging. In this context, digitization, particularly the concept of the structural health monitoring (SHM)-enhanced digital twin, takes on a transformative role. This approach enables the integration and collaborative real-time monitoring and evaluation of all data essential for the operation and maintenance of structures. This session aims to bring together researchers exploring the latest advances in SHM-enhanced bridge digital twins. Topics include, but are not limited to, the automated digitalization of bridge structures, data-driven methods for damage and mode identification, condition assessment, the integration of various SHM systems, and the incorporation of data and information into digital twins for decision-making and predictive bridge maintenance.

High-speed Railroad, Transportation Infrastructure, Lifelines, Structural Health Monitoring, and Management
Organizers: Fernando Moreu, Hoon Sohn, Jinwoo Lee, Yi-Qing Ni and Haeyoung Noh
Keywords: High Speed Railroads (HSR), transportation infrastructure, railroads, sensing, SHM, damage, detection, management

Scope of Session: This session is directed to the advancement of Structural Health Monitoring (SHM) for railroad infrastructure, railroad operations, AI and railroad engineering, algorithms, theory, modeling, with both freight and High-Speed Railroads (HSR). This session also covers multimodal sensing of highway and critical infrastructure. The session welcomes submissions on new technologies including Internet-of-Things (IoT) and their implementation, evaluation, and deployment. This session will also summarize experiences of railroad operations monitoring, assessing, and/or controlling train behavior and revenue traffic management and safety. Topics of interest include, but are not limited to: (1) understanding and modeling of railroad infrastructure responses induced by normal and unexpected operations; (2) understanding the interface between field data, modeling, simulations, and railroad management; (3) train-rail-infrastructure interfaces and theories in relation to railroad engineering; (4) practical applications on railroad management across the world, with an emphasis on railroad infrastructure including but not limited to rail, track, ballast; (5) analysis of high-speed rail data, behavior; and experience with performance, maintenance, and management; and (6) innovative applications, laboratory studies, and field validation in relation to SHM for railroads and HSR.

Probabilistic SHM
Organizers: Daniele Zonta and Branko Glisic
Key words: Bayesian inference, probabilistic methods, sensor fusion, structural reliability, risk analysis, decision making

 Scope of Session: Structural health monitoring aims to understand the condition of a structure based on sensor observations, a process which 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 decision 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.