Special sessions

Special sessions propositions are still welcome: please send an email before September 30th 2025 to the conference organisers (ewshm2026@cofrend.com) with the following information:

  • Title of the special session
  • Name, affiliation and email of the special session chairs
  • 5-10 lines presentation of the session

The final inclusion of this session in the program is subjected to a minimal number of papers, and will decided by the scientific committee, based on the quality of papers and diversity of the contributors.

 

Special Sessions:

SS1: Damage detectability and effects of environmental and operational variability in structural health monitoring

Organisers:

The dynamics of structures under environmental and operational variations (EOVs) represent a
significant challenge in the system identification and Structural Health Monitoring (SHM) fields. This
challenge is compounded by issues surrounding the successful integration of data across various time
scales, and the modeling of evolving system dynamics where the structural integrity is frequently in flux.
A burgeoning interest in SHM has catalyzed a focus on addressing the impacts of EOV on damage
diagnosis, a continuously growing topic with significant advancements in the field. To further advance
our understanding and development of methodologies in this area, this session invites contributions
that delve into the latest theoretical and practical developments aimed at identifying, modeling, and
compensating for these dynamic systems’ changes. We are particularly keen on papers that explore
the use of analytical, data-driven, and/or hybrid models that can adapt to both time and parameter
variability, and that employ data-driven models and/or physics-based models to enhance the
interpretability and efficacy of long-term structural assessments. Furthermore, research that tackles the
normalisation of dynamic features and the integration of explicit and implicit compensation strategies to
improve damage detectability under variable operational conditions is crucial. Your insights and
scholarly submissions are eagerly anticipated to enrich discussions and contribute to the evolution of
this critical area of study. This collaborative and explorative forum is expected to push forward the
boundaries of how we monitor and maintain the health of structures under continuously changing
conditions.

SS2: Economic Impact and Environmental Assessment of Structural Health Monitoring in Engineering Applications

Organisers:

Structural Health Monitoring (SHM) has a great potential to improve the safety and reducing the life
cycle costs, indeed, it improves the awareness of the structural condition without the need of human
intervention. SHM systems, however, require significant investment costs and in some cases, it may
affect the functionality of the monitored structure by increasing the weight and the complexity. To
enable a large-scale adoption of such technology, a careful cost-benefit analysis is therefore
mandatory. In addition, monitoring is energy demanding in activating transducers and acquiring,
communicating and processing data, leading to an intrinsic environmental impact. To assess
environmental consequences in adopting SHM solutions, life cycle assessment is likewise crucial.
In order to highlight the need for safe, cost effective as well as environment-friendly systems, this
special session is aimed at disseminating the tools and case studies concerning the evaluation of
economic impact and environmental assessment of SHM in different fields of engineering: aerospace,
civil, mechanical, and energy, with the purpose to: (i) highlighting the most advantageous applications
of SHM systems; (ii) proposing cost-effective Life Cycle Management strategies of smart structures;
(iii) providing structural design guidelines to fully exploit the potential of SHM, and (iv) highlight
potential environmental gains provided by the monitoring over lifetime.

SS3: Reliability and Quality Assessment of SHM systems

Organisers:

Despite intriguing features and promising breakthrough in several fields of application, many SHM systems have so far not achieved widespread industrial acceptance as a continuous monitoring technique. It is indeed of paramount importance understanding the potential effectiveness of an SHM system before transfer into routine applications. A key aspect is that there is still a lack of strategies for performance assessment that take into account the peculiarities of SHM systems. To assess the ability thereof, a variety of prerequisites and contributing factors have to be considered and need to be analysed in the way they affect the system reliability. For guided-wave based systems, e.g. it is not possible to analyse the system performance without looking into the specific structure and the applied SHM system parameters. Therefore, interdependencies of performance assessment and factors, influencing the quality, capability and reliability of an SHM system, are recently discussed and put into relation with state-of-the-art methods for performance analysis of NDE, like Probability of Detection (POD) or Receiver Operating Characteristic Curves (ROC-Curves). In this context, this Special Session aims to represent a forum for researchers and practitioners from industry, academia, and government interested in reliability and performance assessment for SHM.
This session focuses all aspects inherent to reliability and welcomes especially papers which:

  • discuss reliability aspects of any kind of SHM systems,
  • approach quality assessment for any kind of SHM systems, like ROC, POD,
  • discuss approaches on how to make the transition of POD from NDE to SHM,
  • show developments on how to enable simulation-supported quality assessment,
  • introduce new concepts for performance assessment, such as new specific analysis and procedures or artificial-intelligence supported assessment.

Moreover, case studies on defined aspects of reliability and quality assessment for specific SHM systems are very welcome.

SS4: USES of UltraSonic Embedded Sensors for the Non-Destructive Evaluation and Structural Health Monitoring of Infrastructure and Human-Built Objects

Organisers:

Recent advances have enabled the embedding of ultrasonic sensors within structures with minimal intrusiveness, opening new pathways for dense, long-term structural health monitoring (SHM). However, challenges persist due to the stringent power limitations inherent in embedded systems—particularly over decades-long lifespans in environments like concrete—which constrain data acquisition, processing, and transmission capabilities.
Mechanical waves offer powerful diagnostic capabilities due to their scalability and sensitivity to material changes. Embedding sensors directly within materials greatly improves coupling and signal quality, addressing limitations linked to poor accessibility and complex geometries. This embedded configuration is key to enabling reliable monitoring in highly attenuating or structurally complex environments.
Despite this potential, widespread SHM adoption is hindered by the lack of robustness in current systems under varying Environmental and Operational Conditions, which often lead to false alarms or missed detections. Additionally, maintaining consistent data quality and communication with isolated sensors over long durations remains a major challenge.
This session will focus on recent developments in fully embedded sensing solutions, highlighting innovations in sensor front-end design, in-situ data processing, fault detection, and the use of network redundancies to reconstruct missing or corrupted information. Particular emphasis will be placed on sensor systems that are designed from the ground up to be permanently buried within the monitored material, ensuring long-term, autonomous, and resilient structural diagnostics.

SS5: BRIDGITISE an EU network on the digitalization of bridge integrity management

Organisers:

The recently funded EU project BRIDGITISE aims to advance the digitalization of bridge management by developing innovative methods for handling bridge data. It focuses on creating and validating new technologies to improve the management of bridge information, supporting integrity-related decisions throughout the entire lifecycle. The project brings together a consortium of universities, research centers, industry partners, bridge design and assessment firms, and end-users. This Special Session will highlight ongoing research by project partners, covering topics such as cost-effective, large-scale automated data collection technologies, AI and IoT solutions tailored for bridges to process and share information, and digital decision support tools designed for comprehensive lifecycle management.

SS6: Machine learning and Bayesian methods in rotating machinery diagnostics

Organisers:

  • Szymon Gres, Department of Electronic Systems Automation & Control, Aalborg University, sg@es.aau.dk
  • Luis David Avendaño-Valencia, SDU Mechanical Engineering, University of Southern Denmark, ldav@sdu.dk

Diagnosing rotating machinery through vibration response analysis remains a complex and evolving scientific challenge. The intricate dynamics of rotating systems—compounded by friction, fluid-structure interactions, and other nonlinear effects—pose significant hurdles for advanced signal processing and machine learning techniques. These challenges are further amplified by varying operational conditions such as speed and load.
A key enabler for robust condition monitoring is the ability to distinguish early damage signatures from the inherent complexity of machine dynamics. Recent research has focused on integrating sparse, heterogeneous data with physical models, yet difficulties persist in calibrating structural properties while accounting for unmeasured excitations and unmodeled dynamics. To address these issues, modern paradigms such as physics-informed learning and reduced-order modelling are increasingly being embedded into estimation frameworks to enhance diagnostic performance.
This special session invites contributions on machine learning, Bayesian inference, and related methodologies for the identification and fault diagnosis of rotating machinery. Topics of interest include, but are not limited to:

  • Condition monitoring of rotating machinery (e.g., roller, film bearings, gears, reciprocating machine, engines, and pumps)
  • Bayesian filters for input-parameter-state estimation
  • Physics-informed and hybrid (data + physics) modelling approaches
  • Signal preprocessing for harmonic removal and component separation
  • Statistical methods for damage detection, localization, and prognosis
  • Handling disturbances and multi-source data in diagnostics
  • Machine learning techniques tailored to rotating systems

We welcome theoretical developments, numerical simulations, and experimental studies that advance the state of the art in this field. This session aims to foster interdisciplinary dialogue and showcase innovative methodologies that push the boundaries of rotating machinery diagnostics.

SS7: Damage identification with smart sensor networks: combining physics-based models with data-driven methods

Organisers:

The development of Structural Health Monitoring systems has progressed significantly over the past decade. A key factor in this development is the rise of data-driven methods and the accompanying artificial intelligence-based tools to handle the data collected by integrated smart sensor systems. The complexity of real-world systems is too high to rely on physics-based models only, such as high-fidelity numerical simulations. At the same time, interpretation of data without any physical knowledge is a stab in the dark. The solution to this dilemma is using physics informed data driven methods for Structural Health Monitoring Systems. The challenges relate to determining the data and knowledge position: how much is known, which models are available, how much data is available, what is the data quality and how is the data changing under varying operational and environmental conditions etc. The absence of run-to-failure data and limitations in knowledge on fracture mechanics from materials like composites forms another important challenge.
Contributions are welcomed that address how these challenges are tackled: which methods are used and what is their performance, when combining physics-based models with data-driven methods.

SS8: Satellite-based health monitoring for civil infrastructure

Organisers:

In the last decade, the launch of second-generation Synthetic Aperture Radar (SAR) satellites and the parallel evolution of Interferometric SAR (InSAR) processing techniques have made an unprecedented number of high-quality measurements of earth surface displacements available on large areas. Initially exploited to evaluate geophysical phenomena like earthquakes, landslides and volcanoes, these measurements are increasingly applied to monitor building and infrastructure deformations. With a sensitivity to millimetre displacements, high spatial density, and weekly revisit time, SAR satellites are now valuable for supporting structural conditions assessment. This session aims to discuss the most recent challenges and developments in InSAR applications to the health monitoring of structures. Topics cover and are not limited to advances in the processing algorithms and application case studies, including tunnels, bridges, dams, pipelines, etc.

SS9: Quantum-Enhanced Structural Health Monitoring: A New Frontier in Intelligent Sensing and Diagnostics

Organisers:

  • Dr. Vahid Yaghoubi, Department of Aerospace Structures and Materials, Faculty of Aerospace Engineering, TUDelft, v.yaghoubi@tudelft.nl

With the recent advancement of quantum computers, sensors, and the associated algorithms, a new horizon is opening for Structural Health Monitoring (SHM) systems. These developments promise to revolutionize the way we detect, localize, and predict damage in critical structures by offering enhanced sensitivity, ultra-high resolution, and fundamentally new modes of information processing. Quantum-enhanced SHM leverages principles such as quantum entanglement, superposition, and quantum interference to unlock capabilities far beyond the reach of classical sensing and computation.
This transformative approach enables precise monitoring of structural behavior under both operational and extreme conditions, laying the foundation for real-time, high-fidelity diagnostics across diverse engineering domains. When combined with quantum machine learning, these systems can extract subtle patterns from complex structural signals, enhancing predictive maintenance and decision-making in safety-critical applications. From aerospace to civil infrastructure, the integration of quantum technologies into SHM is poised to set a new standard in resilience, efficiency, and sustainability.
The objective of this special session is to bring together leading researchers and practitioners working at the intersection of quantum technology and structural health monitoring. The session will serve as a platform to share theoretical advances, experimental results, and practical implementations that demonstrate the potential of quantum-enhanced SHM.
Researchers are encouraged to contribute to the development of next-generation SHM systems that incorporate quantum sensing and computation. Topics of interest include, but are not limited to:

  • Quantum(-enhanced) Machine Learning for SHM: Quantum-inspired and quantum-enhanced algorithms for pattern recognition, anomaly detection, and system identification in complex structural datasets.
  • Hybrid Quantum-Classical systems: Integration of quantum sensing and ML subsystems with classical SHM platforms, digital twins, and embedded monitoring frameworks.
  • Quantum-Photonic Vibrometry: Development and deployment of quantum-enhanced laser vibrometers for non-contact, non-destructive structural evaluation.
  • Quantum(-enhanced) Sensors: Design and application of quantum sensors (e.g., atom interferometers, quantum-optical sensors) for high-resolution strain, vibration, or displacement measurements.

SS10: Knowledge transfer and data integration for structural health monitoring and system identification

Organisers:

This special session focuses on innovative methodologies and technologies for Structural Health Monitoring (SHM), and structural identification, with a particular focus on knowledge transfer, data integration, and data-driven approaches. Contributions addressing remote, contactless, and cost-effective monitoring solutions are encouraged, especially those capable of dealing with both short- and long-term structural behaviours.
Papers on innovative techniques for processing vibration and environmental signals, aimed at improving the diagnosis and prediction of structural behavior through data-driven, model-based, or hybrid approaches, are welcome. Topics of interest include linear and nonlinear system identification, signal processing, machine learning, transfer learning, model updating, and data analysis from static, dynamic, or remote monitoring systems. Special attention will be given to methods for damage detection, localization, and quantification, including the impact of environmental conditions on the accuracy and robustness of data-driven models. The session aims to promote interdisciplinary dialogue and foster synergy between SHM approaches. Emphasis is placed on approaches that bridge digitalization, machine learning, and large-scale data analysis to improve decision-making.

SS11: AI-enhanced Structural Health Monitoring of civil engineering structures: challenges and innovations

Organisers:

We propose a special session dedicated to the integration of Artificial Intelligence (AI), including Deep Learning and Machine Learning, with data-driven Structural Health Monitoring (SHM) for civil engineering structures. The session will explore these approaches to overcome key limitations encountered in laboratory-scale tests and field monitoring campaigns. Special emphasis will be placed on scalable solutions for networks of large-scale structures, with topics including data fusion from heterogeneous sensors, damage detection, localization and quantification, real-time decision-making, and transfer learning across structural types to address the scarcity of labeled data. The session aims to bridge theoretical advancements in AI with deployment, analysis, and decision-making challenges, promoting practical, reliable, and resilient SHM systems.