Special Sessions

SPECIAL SESSIONS

Organized by Andreas Stollwitzer, Diogo Ribeiro, Pedro Aires Montenegro, Andreas Andersson, Steven Lorenzen, Matthias Baeßler

Achieving global climate targets and establishing sustainable, resource-efficient transport systems requires a significant shift towards rail transport. In this context, the European Union is pursuing the ambitious goal of developing a comprehensive high-speed rail network by 2050. Railway bridges are key components of the rail-bound transport infrastructure. They are increasingly subjected to intensified operational demands, including higher traffic volumes, rising train speeds, and increasing axle loads of the rolling stock. These developments lead to significantly increased dynamic effects and impose new challenges on design, assessment, maintenance, and monitoring strategies. Ensuring the resilience, safety, and longevity of railway bridges under these evolving conditions is therefore a highly relevant and urgent topic within the international research and engineering community.

This Special Session aims to address these challenges by bringing together leading experts from academia, industry, and railway operators and by promoting exchanges and collaborations. Contributions are invited, but are not limited to, the following topics: dynamic behavior of railway bridges and their subsystems (bridges, viaducts, transition zones, ballasted track and fixed track, noise barriers), interaction between various components (e.g. vehicle-bridge interaction, track-bridge interaction, soil-structure interaction), structural health monitoring including data-driven approaches, drive-by monitoring, condition-based monitoring, AI-based condition assessment, predictive maintenance, resilience of the structure, train running safety, assessment of railway infrastructure under wind or earthquake actions. The Special Session welcomes papers with experimental investigations, theoretical-based applications as well as papers with reference to practical applications.

Organized by Enrique García Macías, Carlo Rainieri

Growing concerns over the risks associated with ageing infrastructures have driven the increasing deployment of dense sensor networks and advanced Structural Health Monitoring (SHM) systems, transforming the way we observe, understand, and manage civil structures. The growing availability of large-scale and multimodal monitoring data has enabled the emergence of Digital Twins as a predominant paradigm, moving beyond inspection-based maintenance and positioning digitalisation as a central framework that integrates data, models, and decision-making throughout the structural life cycle. In this context, this Special Session aims to collect recent research contributions addressing the continuum from sensing to actionable digital representations.

This Special Session brings together new research covering innovative sensing technologies and advanced methodologies for data processing, system identification, and uncertainty quantification, aiming to develop robust, interpretable, and scalable Digital Twin frameworks. Special emphasis is placed on approaches that enable the transition from raw measurements to reliable digital representations capable of real-time monitoring, prediction, and control, supporting SHM, risk-informed decision-making, and resilience under evolving environmental and operational conditions. Topics of interest include, but are not limited to:

Innovative digitalisation techniques
Intelligent management of dense sensor networks
Operational modal analysis
Management of large infrastructural systems
Structural digital twins and 6D-BIM
Model updating strategies
Surrogate modelling
Multi-physics integration
Physics-driven Digital Twins
Data fusion of multimodal monitoring data
AI agents for decision-making in SHM
Data-driven and hybrid modelling
Warning strategies and actionable SHM-driven knowledge

The session also welcomes applications to challenging scenarios such as heritage structures, long-span bridges, coastal and offshore infrastructure, and systems under extreme or non-stationary loading conditions.

Organized by Maria Giovanna Masciotta, Alberto Barontini, Giuseppe Brando

The dynamic identification of bridge structures remains a challenging task, particularly for complex systems such as cable-stayed and long-span bridges, where closely spaced modes, low-frequency content, environmental variability, and operational conditions significantly affect modal parameter estimation. These issues are further exacerbated in real-world applications, where measurement noise, non-stationary excitations, and limited instrumentation often hinder the reliability and repeatability of modal identification results.

At the same time, infrastructure managers increasingly face stringent budget constraints, which drive the need for monitoring strategies that are not only cost-efficient but also sustainable and scalable over time. This requires maximizing information gain while minimizing sensing, installation, and maintenance costs. In this context, optimal sensor placement (OSP) has emerged as a key enabler for effective structural health monitoring (SHM), aiming to balance accuracy and resource efficiency. However, its practical implementation remains limited by modelling uncertainties, time-varying system properties, scalability challenges, and the persistent gap between theoretical formulations and real-world deployment.

This special session aims to bring together recent advances in (i) output-only and input-output modal identification of bridge structures, (ii) handling of environmental and operational variability, (iii) robust identification in challenging scenarios (e.g., cable-dominated dynamics, closely spaced modes), (iv) model updating approaches informed by modal data, and (v) optimal sensor placement strategies tailored to resource-constrained monitoring systems. Contributions addressing integrated frameworks that link system identification, model updating and sensor placement, as well as real-world applications and large-scale case studies, are particularly encouraged.

The session seeks to foster discussion on bridging the gap between advanced methodologies and their practical adoption in bridge monitoring and management.

Organized by Joel Conte, Rodrigo Astroza, Andre Barbosa, Alvaro Cunha, Geert Lombaert, Babak Moaveni

This special session aims to address pressing challenges and recent advances in vibration-based system and damage identification of civil structures subjected to both operational and extreme loads. With a focus on operational excitations, natural hazards (e.g., wind, earthquakes), and anthropogenic hazards (e.g., blast loading), as well as various infrastructure systems including bridges, buildings, ports, wind turbines, industrial facilities, and dams, the session will provide a comprehensive overview of methodologies, techniques, and case studies involving both experimental and in-situ structures.

Key topics include, but are not limited to:

Model updating techniques for improving accuracy and reliability in structural dynamic analysis.
Bayesian inference methods for structural health monitoring (SHM) and damage assessment of civil infrastructure systems, including updating of nonlinear mechanics-based finite element (FE) models, treatment of epistemic and aleatoric uncertainties, and digital twin frameworks.
System identification methods for extracting modal parameters and other structural characteristics.
SHM strategies based on vibration data for real-time condition assessment and damage detection.
Damage identification approaches and algorithms for determining the location and severity of structural damage.
Damage prognosis and performance forecasting.
Applications of vibration-based techniques in civil engineering domains such as earthquake engineering, wind engineering, and the inspection and monitoring of bridges and buildings for structural integrity assessment.
Integration of advanced sensing technologies, data analytics, machine learning, and artificial intelligence for enhanced structural monitoring and damage identification.
Case studies and practical applications demonstrating the effectiveness and applicability of vibration-based approaches in real-world scenarios.
Challenges associated with the real-world implementation of SHM for civil infrastructure systems.

By bringing together researchers, practitioners, and industry experts, this special session seeks to foster interdisciplinary discussions, exchange of ideas, and collaborations toward advancing the state-of-the-art in vibration-based structural health monitoring and damage identification of civil structures, ultimately contributing to the resilience and safety of infrastructure under operational and extreme loading conditions.

Organized by Carmen Amaddeo, Luca Martinelli, Andre Barbosa, Alireza Entezami

In recent years, mass timber and timber-hybrid (timber-concrete and timber-steel) structures have garnered increasing attention within the architectural, engineering, and construction (AEC) industry due to their sustainability attributes, amidst a growing global emphasis on climate change mitigation and the reduction of global warming potential. These structural systems have earned significant interest from designers owing to their structural integrity, aesthetic appeal, and adaptable nature. Particularly noteworthy is the growing role of mass timber as the primary structural component in these systems, ranging from bridges and roof structures to electrical poles, wind turbine towers, and tall buildings.

Timber and timber-hybrid structures are typically assembled from engineered timber elements connected to one another and, in hybrid systems, to concrete or steel components via various connection systems. Owing to the inherent characteristics of timber, including its response to environmental factors such as temperature and humidity, coupled with specific demands on connections and their advantageous strength-to-weight ratio, these structures may exhibit heightened sensitivity to vibrations or present unique vibrational characteristics. Consequently, there arises a need for thorough experimental, analytical, and numerical investigations, supported by laboratory testing, field measurements, and in-situ monitoring to ensure structural integrity, serviceability, and long-term performance.

This special session seeks to address recent advances in dynamic analysis, experimental vibration analysis, and structural health monitoring of timber and timber-hybrid structures, while also exploring the integration of advanced data analytics and machine learning techniques. It offers a platform for discourse on both challenges and opportunities within this field. Participants are invited to contribute research insights, case studies, and best practices, fostering knowledge exchange and advancing the sustainability agenda within the construction sector.

Organized by Alessandro Annessi, Valentina Pasquinelli, Gloria Cosoli, Marco Arnesano, Milena Martarelli

Structural Health Monitoring (SHM) has long relied on contact-based sensors such as accelerometers, strain gauges, or electrical impedance sensors. Despite their accuracy and reliability, these methods face significant challenges regarding cost, time, installation complexity, and scalability. This is particularly valid for large-scale infrastructures or structures undergoing progressive deterioration, which is often the case in SHM applications.

To address these limitations, non-contact measurement techniques have emerged as powerful tools for rapid and non-invasive inspection to obtain meaningful high-quality information that can be exploited for the generation of early warnings, without the need for physical attachment of instrumentation. These are broadly categorized into methods investigating bulk structural responses and surface conditions. Bulk techniques, such as Laser Doppler Vibrometry (LDV), acoustic and ultrasound-based methods, analyze wave propagation to detect internal defects like cracks or voids. Conversely, vision-based techniques including Digital Image Correlation (DIC), thermal imaging, and high-speed cameras, provide high-resolution and full-field data on surface displacements and deformations.

Integrating advanced sensing technologies into digital workflows is a key aspect of modern civil engineering. Data gathered from non-contact sensors is pivotal for creating high-fidelity Digital Twins (DT) and enriching Building Information Modeling (BIM) platforms. This digital synergy is further amplified using Artificial Intelligence (AI) and Machine Learning (ML). Such data-driven strategies enable automated defect classification and predictive maintenance, moving beyond reactive repairs to proactive structural management.

The goal of this special session is thus to discuss the latest advancements regarding non-contact sensing for SHM for early defect detection as well as the exploitation of AI and ML algorithms for classification and prediction purposes, with a particular emphasis on how data-driven insights can be integrated into BIM and DT frameworks. By enabling proactive maintenance and informed decision-making, these technologies are fundamental to enhance the resilience of civil structures and infrastructures and, therefore, of their inhabitants.

Organized by Dario De Domenico, Giuseppe Quaranta, Geert Lombaert, Edwin Reynders

Prestressed concrete structures represent a significant portion of existing infrastructure stock and pose specific challenges for structural identification and damage detection. Their response is influenced by material degradation processes, particularly corrosion of prestressing tendons, which can lead to progressive prestress losses and localized damage, often without clear signatures in conventional monitoring data. In this context, the effective use of both static and dynamic measurements is essential for a reliable assessment of structural behaviour over time.

This special session focuses on the integration and interpretation of static and dynamic measurements for the identification and assessment of prestressed concrete structures. Particular attention is given to the complementary use of different data types, such as displacement, strain, and vibration measurements, to improve the detection and localization of damage and prestress-related anomalies, including those induced by corrosion.

Traditional vibration-based indicators, such as natural frequencies and displacement mode shapes, often exhibit limited sensitivity to local damage or prestress variations. Recent advances in sensing technologies provide new opportunities. High-resolution measurement techniques, including dense strain monitoring, fibre-optic sensing (e.g., Fiber Bragg Gratings), and non-contact methods, enable a more detailed characterization of structural response and enhance sensitivity to localized stiffness changes associated with degradation processes. Contributions addressing the combined use of advanced sensing technologies and conventional monitoring approaches are especially encouraged.

The session will cover topics including, but not limited to:

static and dynamic monitoring of prestressed concrete structures;
integration of heterogeneous data for structural identification;
assessment of prestress losses and time-dependent effects;
detection of corrosion-induced damage and material degradation;
high-resolution and strain-based monitoring approaches;
sensitivity to damage and environmental effects;
laboratory and full-scale applications.

The session provides a focused forum on recent advances in monitoring and identification techniques for the reliable assessment and durability evaluation of prestressed concrete structures.

Organized by Sergio Ruggieri, Andrea Nettis, Ilaria Venanzi, Matteo Castellani

The simplest and more intuitive approach for identifying damages in civil structures and infrastructures is the visual inspection, as it allows direct observation of visible degradation phenomena. Visual data constitute a rich and often underexploited source of information. Beyond their traditional use as qualitative documentation, images can be processed to extract quantitative descriptors of damage, track its evolution over time, and support objective and repeatable assessments. In this perspective, images are not only a support for inspection but can become a central element for enhancing both assessment and monitoring strategies.

In parallel, recent developments in image-based technologies enable the extraction of quantitative and objective information from visual data, such as photographs, videos, remote sensing imagery. These approaches allow for tracking continuous damage evolution as a proof for documenting the structural decay, by reduced human bias due to visual inspections. Moreover, the integration of image-based data with other information sources, e.g., sensor networks, digital twins, opens new opportunities for multi-scale and multi-source data fusion, ultimately supporting more informed decision-making in maintenance and risk mitigation.

This special session aims to exchange ideas and explore emerging trends in the use of image-based and UAV-assisted methods for assessing and monitoring the health state of structures and infrastructures. Contributions are encouraged on topics including (but not limited to): computer vision techniques, such as classification, segmentation, feature and object detection; deep learning for pattern recognition; automated inspections workflows, UAV-based image acquisition and monitoring, data fusion strategies, and scalable frameworks for large asset portfolios. Particular emphasis is given to approaches bridging the gap between research and real-world applications, ensuring robustness, interpretability, and operational feasibility.

Organized by Andrea Meoni, Yen-Fang Su, Giuseppina Uva

Smart construction materials are increasingly adopted to enable advanced structural monitoring, offering new opportunities for integrating sensing capabilities directly within civil structures and infrastructures. By embedding sensing functions into load-bearing or non-structural components, these materials allow continuous measurement of mechanical quantities such as strain, displacement, temperature and damage-related indicators, supporting a comprehensive assessment of structural performance throughout their service life.

This special session focuses on the use of smart and multifunctional construction materials for structural monitoring applications, addressing both static and dynamic response regimes. Particular attention is given to quasi-static performance assessment, long-term behavior, durability and integration strategies, together with applications where time-dependent and dynamic effects are relevant. Contributions addressing sensing principles, material-scale characterization, 3D printing and other fabrication and embedding techniques, calibration procedures and data interpretation are especially encouraged. The session also welcomes studies dealing with measurement reliability, environmental influences, and the interaction between smart materials and traditional construction materials.

A further focus is placed on the role of artificial intelligence and machine learning in processing and interpreting the data generated by smart materials. Contributions are invited on AI-based strategies for damage detection, classification and localization, sensor data fusion, anomaly identification, and the development of data-driven models supporting diagnostics and prognostics of instrumented structures.

A central aspect of the session is the presentation and discussion of real case studies, including laboratory investigations, field applications and full-scale implementations on civil infrastructures and buildings. Case studies may involve existing or newly built structures and are expected to demonstrate how smart materials support monitoring, diagnostics and decision-making under realistic operational conditions.

Overall, the session provides a multidisciplinary forum for researchers and practitioners, bridging materials science, sensing technologies, data analytics and structural engineering, and highlighting practical experiences that facilitate the transfer of intelligent construction materials from research to real-world civil engineering applications.

Organized by Marco Civera, Angelo Aloisio, Carlos Martin de la Concha Renedo, Marco Martino Rosso, Marco Domaneschi

As sustainability, eco-friendly construction practices, and circular economy principles gain prominence, timber and timber-hybrid solutions are becoming key alternatives to more common construction materials for civil structures and lightweight infrastructure, as well as for upgrading and extending the lifetimes of existing structures.

These solutions support the green transition by reducing the built environment's impact on nature while enabling efficient, lightweight, and low-carbon structural systems. However, their dynamic behaviour, long-term performance, and interaction with existing structural components still require further research, particularly in the context of structural health monitoring (SHM), damage assessment, and service-life extension.

This Special Session aims to share recent advances, opinions, and case studies on the dynamic characterisation and monitoring of timber and timber-hybrid structures, with particular emphasis on green transition strategies and the preservation, upgrading, and lifetime extension of existing structures and infrastructures.

Topics of interest include, but are not limited to:

Relevant case studies on real-life timber and timber-hybrid structures and infrastructures.
Experimental results from laboratory test rigs, replicas, scaled-down models, mock-up structures, and on-site investigations.
System Identification of new and existing timber or timber-hybrid structures.
Numerical, analytical, and theoretical studies related to timber and timber-hybrid structural applications.
Non-destructive testing techniques and their applications to assessment, monitoring, and diagnosis for such applications.
Vibration-based damage assessment approaches, including statistical pattern recognition, anomaly/outlier detection, and machine learning techniques.
Structural health monitoring strategies for performance assessment, maintenance planning, and lifetime extension.
Model calibration and updating strategies for timber and timber-hybrid structures.
Linear and nonlinear model-based simulations and analyses.
Monitoring-based approaches supporting sustainable rehabilitation and reuse of existing structures with timber and timber-hybrid retrofitting.

Organized by Marco Broccardo, Cristoforo De Martino, Giuseppe Quaranta, Gianluca Quinci, Marco Martino Rosso, Ilaria Venanzi

Artificial Intelligence (AI) is increasingly revolutionizing the field of vibration analysis by enabling advanced data interpretation, automation, and predictive capabilities that were previously difficult to achieve using traditional approaches alone. The combination of modern sensing technologies, large-scale experimental datasets, and high-performance computing has fostered the development of intelligent methodologies capable of extracting meaningful information from complex dynamic systems. AI-based approaches offer significant advantages in terms of accuracy, adaptability, computational efficiency, and real-time decision-making, supporting applications such as signal processing, modal identification, anomaly detection, response prediction, predictive maintenance, and condition assessment. In addition, hybrid physics-informed and data-driven models are opening new perspectives for reliable and interpretable vibration-based analyses in engineering practice.

This Special Session aims to provide a forum for researchers and practitioners working on innovative AI techniques applied to vibration analysis, dynamic response prediction, and condition assessment of engineering systems and infrastructures. Topics of interest include, but are not limited to, AI-enhanced signal processing, vibration response forecasting, surrogate and reduced-order modeling, digital twins, uncertainty quantification, explainable AI, transfer learning, and hybrid data-driven/physics-based approaches. The session welcomes contributions addressing both methodological developments and practical applications in civil and industrial engineering, including experimental investigations, numerical studies, benchmark applications, and real-world case studies. The Special Session aims to foster interdisciplinary discussion on emerging trends, current challenges, and future perspectives in the integration of AI with vibration-based engineering analysis and prediction.

Organized by Mehri Makki Alamdari, Chul-Woo Kim, Alireza Entezami, Qipei (Gavin) Mei

Drive-by bridge monitoring and indirect structural health monitoring are rapidly emerging as scalable and cost-effective alternatives to conventional bridge instrumentation. Instead of relying exclusively on sensors permanently installed on individual bridges, these approaches use instrumented vehicles, trains, or mobile sensing platforms to infer bridge condition from vehicle–bridge interaction responses. This paradigm is particularly attractive for network-level infrastructure assessment, where the cost, accessibility constraints, and maintenance burden of fixed sensing systems remain major barriers.

This special session will focus on recent advances in drive-by bridge monitoring, vehicle-assisted sensing, and indirect structural health monitoring for civil infrastructure, with particular attention to experimental vibration analysis and field validation. Topics of interest include, but are not limited to, vehicle–bridge interaction modelling, mobile sensing, bridge modal identification from passing vehicles, damage detection and localisation, uncertainty quantification, experimental testing and field validation, digital twins, physics-informed and data-driven methods, sensor placement, signal processing, operational and environmental variability, and fleet-scale monitoring strategies. The session aims to bring together researchers and practitioners working at the intersection of experimental vibration analysis, structural dynamics, sensing, machine learning, and bridge asset management. Particular emphasis will be placed on practical implementation challenges, including low signal-to-noise ratios, road and track roughness effects, vehicle and traffic variability, transferability across bridges, validation on real infrastructure, and integration of indirect sensing outputs into decision-making frameworks. The session aligns closely with the EVACES focus on vibration-based structural assessment, monitoring, and control of civil engineering structures, while also addressing the growing need for scalable and practically deployable SHM solutions. It will provide a dedicated forum for discussing the future of bridge health monitoring at both structural and network scales.

Organized by Enrique García-Macías, Kristof Maes, Elisa Tomassini

Operational Modal Analysis (OMA) has become a well-established tool for vibration-based monitoring, enabling the extraction of modal parameters from ambient vibration data under operational conditions as damage-sensitive features. However, the increasing adoption of dense and heterogeneous monitoring systems, continuous data acquisition, and long-term observation periods pose new challenges in terms of computational burden, automation, robustness, and reliability of modal identification. In this context, the rapid development of machine learning and artificial intelligence offers significant opportunities for advancing automated and efficient modal identification frameworks capable of handling the large volumes of information generated by densely instrumented bridge-monitoring systems.

This Special Session aims to gather recent advances in automated and AI-enhanced OMA algorithms for structures and infrastructures monitoring, with particular attention to methods that streamline the identification process, improve computational efficiency, extract novel damage-sensitive features, and reduce the need for expert supervision. Topics of interest include, but are not limited to:

Novel OMA algorithms.
Reduced-order and computationally efficient OMA approaches.
Supervised, unsupervised, and physics-informed learning for structural dynamic identification.
Automated mode clustering, stabilization diagram interpretation, and long-term modal tracking.
Uncertainty-aware and probabilistic modal identification.
Vibration-based population-based structural health monitoring (PBSHM).
AI-driven extraction of damage-sensitive features and anomaly detection.

Contributions addressing machine learning-assisted feature extraction, advanced algebraic tools, novel system identification methods, and hybrid strategies that combine data-driven algorithms with physical interpretability are also welcome.

The session seeks to promote discussion on how advanced identification algorithms can enable scalable, reliable, and computationally efficient dynamic characterization, supporting the transition from individual case studies toward continuous, long-term vibration-based monitoring of large inventories.

Organized by Alessandro Cabboi, Andrei Metrikine, Dario di Maio, Okke Bronkhorst

Damping plays a decisive role in the dynamic performance, serviceability, safety, and resilience of civil infrastructure. Yet, despite its importance, it remains one of the most difficult structural parameters to identify, interpret, and model reliably. This mini-symposium aims to bring together researchers, engineers, and practitioners working on the identification, modelling, and practical application of damping. It covers a broad range of civil infrastructure systems, including, but not limited, to high-rise buildings, long-span bridges, hydraulic and offshore structures, and slender structural elements, such as cables, masts and chimneys.

The symposium will provide a focused forum for discussing recent advances in experimental, analytical, numerical and data-driven approaches to damping identification. Contributions are invited on topics such as experimental and operational modal analysis, lab-scale and full-scale vibration monitoring, uncertainty quantification of damping estimates, identification of amplitude- and frequency-dependent damping, especially due to soil–structure and fluid–structure interaction effects, general nonlinear damping mechanisms, and the specific role of damping in structural health monitoring and digital-twin frameworks.

Particular emphasis will be placed on linking identified damping characteristics with engineering decision-making. This includes design code assumptions, vibration-control strategies, serviceability assessment, life-cycle performance, and resilience under wind, wave, seismic, traffic, and operational loading. Case studies from real structures are especially encouraged, as they provide valuable insight into the gap between theoretical damping models and observed behaviour in practice.

By spanning multiple infrastructure types and loading environments, this mini-symposium seeks to stimulate cross-disciplinary exchange and identify common challenges, best practices, and future research directions.

Organized by Pier Francesco Giordano, Leandro Iannacone, Sebastian Thön, Aidan J. Hughes

Effective decision-making in civil engineering infrastructure management requires a comprehensive understanding of structural health conditions, dynamic loading effects, and environmental influences. Traditional approaches to data collection, such as visual inspections and periodic testing, present limitations in terms of accessibility, continuity, and cost-effectiveness. Structural Health Monitoring (SHM) techniques have emerged as a promising solution, offering continuous data streams that enable proactive maintenance, risk mitigation, and emergency response strategies.

However, the integration of SHM data into decision-making processes poses challenges, including the need to quantify the Value of Information (VoI) derived from monitoring data. The VoI framework, grounded in Bayesian decision theory, provides a systematic approach to evaluate the economic and societal benefits of SHM investments and guide optimal data-collection strategies over the lifecycle of structures.

The goal of this Special Session is to present and discuss recent advances, applications, and future trends in vibration-based data-driven infrastructure management and VoI analysis. Topics of interest include, but are not limited to:

Integration of SHM data into decision support systems
Quantification of the Value of Information in SHM
Optimization of sensor placement and data collection strategies
Data and information-based decision support involving digital twin architectures and AI
VoI-driven approaches to infrastructure management and maintenance
Real-world case studies demonstrating the impact of SHM on decision-making processes
Innovations in SHM technologies and methodologies
Cyber-physical security and resilience of monitoring and decision-support systems
Emergency management through SHM-driven insights

Through presentations, discussions, and knowledge sharing, this session seeks to foster collaboration among researchers, practitioners, and industry experts in advancing the field of vibration-based monitoring and enhancing the management of civil engineering infrastructure.

Organized by Nicola Cavalagli, Sergio Pereira, Flavio Bocchi, Diana Salciarini, Filippo Ubertini

Dams are among the most critical pieces of civil infrastructure, and ensuring their long-term safety requires the timely detection, localization, and quantification of structural degradation and damage. Dam safety has traditionally relied on “static” monitoring (piezometers, extensometers, pendulums, GNSS, InSAR), which captures slow deformation and seepage trends but is less sensitive to localized stiffness loss or early-stage damage.

In this context, dynamic monitoring through structural vibrations has emerged as a powerful complementary tool. Ambient and forced vibration testing, operational modal analysis, and continuous dynamic monitoring of dam–reservoir–foundation systems allow the extraction of modal parameters, frequencies, mode shapes, and damping ratios, that are intrinsically sensitive to global and sometimes local structural changes. Variations in these dynamic features, when properly tracked over time and decoupled from environmental and operational effects such as reservoir level and temperature, could reveal the onset of damage or material degradation well before it becomes visible through static measurements alone. Moreover, the increasing availability of seismic recordings from instrumented dams is opening new opportunities for post-earthquake assessment and back-analysis of seismic dam behaviour.

This Special Session intends to bring together researchers and practitioners working across the entire SHM pipeline for dams, from data acquisition to model-based interpretation and decision making, with particular interest in combining static and dynamic monitoring as a novel strategy for advanced damage and material degradation detection and digital twinning. Theoretical, numerical and experimental contributions, as well as case studies on concrete, embankment and masonry dams, are welcome.

Topics of Interest:

Dynamic/vibration-based monitoring (ambient and forced vibration testing)
Operational modal analysis of dam–reservoir–foundation systems
Automated tracking of modal parameters over time
Environmental and operational effects removal (reservoir level, temperature)
Machine learning and statistical pattern recognition for damage detection
Numerical modelling of dams (FE, coupled hydro-mechanical, thermal-structural)
Finite element model updating and calibration (Bayesian, sensitivity-based, surrogate models)
Post-earthquake assessment methods and model validation through back-analysis of seismic recordings
Digital twin frameworks for dam behavior
Uncertainty quantification in monitoring and model-based assessment
Case studies on concrete, embankment, and masonry dams
Seismic early warning systems for embankment dams
Risk-informed decision-making and asset management

Organized by Ivan Roselli, Stefano De Santis, Marialuigia Sangirardi, Alessandro Zona

Computer-vision techniques are increasingly used for contactless structural monitoring in many engineering applications, from laboratory testing to field measurements of the structural response of bridges and buildings. Compared with consolidated approaches using contact sensors, computer-vision has a significant advantage: the possibility of monitoring multiple points for each camera without the need to access the structure and to install contact sensors. This is a crucial issue, particularly for structural parts not easily or safely accessible, as well as in case of delicate and vulnerable historical constructions, which require minimal invasiveness. Thanks to advanced algorithms, it is now possible to achieve very high spatial resolutions even with affordable video cameras, obtaining, for example, very accurate direct measurements of displacements, comparable to those from displacement transducers, without the need of stationary reference points close to the structure being monitored. Hence, computer-vision techniques, both as standalone contactless solution or as integrated components in a hybrid contact and contactless system, redefine consolidated monitoring layouts based solely on contact sensors, providing many new opportunities for structural health monitoring, many of which are still underexplored.

This Special Session seeks to explore the latest advancements in this developing field with particular emphasis on:

advancements in camera technologies, image-processing, and vision algorithms;
novel hardware and software developments enabling innovative monitoring layouts;
new applications in laboratory and field monitoring for displacement measurement, damage detection, and large-scale structural assessment;
solutions for compensation and correction of changing environmental conditions, camera motion, lighting variation, and external disturbances;
examples of integration of computer-vision techniques with traditional sensors;
uncertainty quantification, validation procedures and benchmarking against conventional sensing techniques;
practical challenges related to data-management, long-term monitoring, scalability, and deployment in real operating conditions.

Organized by Zhenkun Li, Liangliang Cheng, Zhen Peng, Said Quqa, Pier Francesco Giordano

Bridge networks are ageing worldwide, while conventional monitoring based on fixed sensors and periodic inspections remains costly to scale. Mobile sensing, crowdsensing, and indirect structural health monitoring (SHM) offer a promising alternative by exploiting responses collected from moving or passing agents, including road vehicles, trains, micromobility, and pedestrians, to assess bridge condition, modal properties, quasi-static behavior, and maintenance needs.

This special session brings together researchers and practitioners working on vibration-based, mobile, and indirect sensing approaches for bridge health monitoring, from individual structures up to regional bridge populations. Particular emphasis is placed on field validation, vehicle-bridge interaction, contact-point response, uncertainty quantification, and data fusion for network-level bridge management. Topics of interest include, but are not limited to:

Vehicle-, train-, and micromobility-assisted bridge monitoring;
Pedestrian-, smartphone-, and crowdsensing-based bridge vibration assessment;
Indirect identification of bridge modal properties, including frequencies, mode shapes, and damping ratios;
Detection, localization, and quantification of bridge damage and deterioration;
Contact-point response identification methods, instrumented-vehicle scanning, and quasi-static bridge assessment;
Vehicle-bridge, train-bridge, and human-structure interaction modelling, including time-varying and nonlinear effects;
Data fusion of mobile sensors with fixed sensors, distributed fiber optics, vision-based systems, IoT, edge devices, and remote sensing;
Machine learning, physics-informed/physics-guided AI, transfer learning, and uncertainty quantification for indirect monitoring;
Development of numerical simulations, laboratory experiments, field tests, benchmark datasets, digital platforms, and decision-support tools;
Practical challenges in sensor variability, data quality, road roughness, track irregularities, operational and environmental effects, and large-scale implementation.

Organized by Eloi Figueiredo, Ionut Moldovan, Michael H. Faber, Ole Andre Øiseth

Structural health monitoring (SHM) has increasingly been posed as a statistical pattern recognition problem, with machine learning playing a central role in transforming measured structural responses into actionable information. In parallel, recent advances in sensing technologies, data acquisition, computer vision, digital twins, and numerical simulation have opened new opportunities for observing and managing the behavior of bridges and other civil infrastructure.

Despite these advances, vibration-based SHM still faces a fundamental challenge: the reliable identification of damage under operational and environmental variability. Traffic, temperature, humidity, wind, river flow and other non-stationary effects may induce changes in structural response that are comparable to, or even larger than, those caused by damage. Climate change further amplifies this challenge by introducing long-term shifts and uncertainty in the environmental conditions that govern the infrastructure performance.

This special session aims to promote coordinated and interdisciplinary research on robust vibration-based SHM under operational, environmental and climate-related effects. Contributions are invited on methods that improve damage identification, anomaly detection, feature normalization, uncertainty quantification and decision support under changing boundary conditions. Topics of interest include, but are not limited to, machine learning, transfer learning, domain adaptation, supervised and semi-supervised learning, physics-informed and hybrid approaches, video-based SHM, numerical and experimental benchmark data sets, long-term monitoring systems, and climate-aware assessment of bridge dynamics.

The session welcomes studies based on field monitoring, laboratory experiments, numerical models, or combinations of those, with particular emphasis on methods that remain reliable when reference data sets evolve or become outdated over time.

Organized by Daniele Zonta, Maria Pina Limongelli, Enrico Tubaldi

Recent advances in radar sensing technologies and signal processing have significantly expanded the capabilities of remote monitoring for civil infrastructure. This special session focuses on ground-based and satellite-based radar techniques for Structural Health Monitoring (SHM), highlighting their complementary roles in measuring structural response across different spatial and temporal scales.

Ground-based radar systems, including interferometric and microwave Doppler radars, enable high-precision, non-contact measurements of structural displacements and vibrations over long stand-off distances. Their sensitivity to sub-millimetre movements, rapid data acquisition, and all-weather operation make them particularly suitable for monitoring bridges, towers, dams, heritage structures, and other critical infrastructure. Recent advances in radar hardware, signal processing, and operational modal analysis have further enhanced their capability to identify dynamic characteristics, detect structural changes, and support condition assessment under operational conditions.

Satellite-based monitoring, primarily through Interferometric Synthetic Aperture Radar (InSAR), has become an established tool for measuring long-term ground and structural displacements with millimetre-level accuracy over regional scales. Continuous improvements in satellite constellations, revisit frequency, imaging modes, and processing algorithms have substantially broadened its range of applications. Beyond the monitoring of slow deformation processes such as settlement, landslides, and infrastructure subsidence, recent developments are enabling the estimation of structural vibrations and modal properties from satellite radar observations. These emerging techniques offer new opportunities for dynamic structural assessment from space, complementing conventional ground-based measurements and extending monitoring capabilities to geographically distributed infrastructure.

This session aims to bring together researchers and practitioners working on radar-based monitoring technologies to present methodological innovations, experimental validations, and real-world applications. Topics include ground- and satellite-based radar sensing, vibration and modal identification, deformation monitoring, multi-scale data integration, uncertainty quantification, and data fusion approaches for the monitoring, assessment, and management of civil infrastructure.

Organized by Iván M. Díaz, Elsa Caetano, Vincent Denoël

Cable and tendon elements are key structural elements in many civil engineering structures, including cable-stayed and suspension bridges, externally prestressed bridges, long-span roofs, and transmission lines. Their structural performance and durability are critical to ensuring the safety, serviceability, and resilience of these systems. Recent failures associated to cable deterioration, corrosion, fatigue, and stress corrosion cracking have highlighted the need for reliable strategies to monitor, modelling, experimental assessment, and vibration control of these structural elements.

Attention should be devoted to grouted external tendons and stay cables, where deterioration mechanisms may remain hidden until advanced stages, potentially leading to brittle failure. In this context, non-destructive testing techniques play a key role in detecting damage, estimating cable forces, and supporting condition-based maintenance throughout the service life of structures. Advances in sensing technologies, vibration-based monitoring, numerical modelling, digital twins, artificial intelligence, and experimental methods are opening new opportunities for more accurate and reliable condition assessment. Despite these advances, there is a current need for laboratory testing under controlled damage conditions, providing benchmark datasets for the validation, calibration, and comparison of sensing technologies, structural health monitoring methodologies, and numerical models.

This special session aims to bring together researchers and practitioners to present recent advances, methodologies, and practical applications related to the monitoring, modelling, testing, assessment, and vibration control of cable and tendon systems. Contributions addressing theoretical developments, experimental investigations, field applications, and case studies are particularly welcome. The session will foster discussion on topics including, but not limited to:

Vibration-based structural health monitoring
Cable force monitoring and estimation techniques for long-term assessment and during cable replacement operations
Environmental effects (temperature, wind, live loads, etc.) and their compensation/removal strategies
Cable damage identification and localization
Acoustic sensing techniques for tendon wire break detection
Wave propagation-based methods for damage assessment
Laboratory testing under dynamic loading, fatigue, and controlled damage conditions
Fatigue resistance, fatigue performance, and crack propagation in cables and tendons
Finite element modelling and numerical approaches for cables and tendons
Data-driven and intelligent algorithms, including machine learning-based methods for cable monitoring and assessment
Vibration control of cables and tendons, including passive, semi-active, and active strategies
Applications in cable-supported structures, including cable roofs, vertical hangers, amongst others

Organized by Farshad Mirshafiei, Foad Mohajeri Nav, Mehrdad Mirshafiei

Ambient vibration testing provides a practical and non-invasive basis for identifying the dynamic properties of civil engineering structures under operational conditions. By relying on naturally occurring excitation sources such as wind, traffic, pedestrian movement, microtremors, and other environmental or operational loads, ambient vibration measurements can be used to extract modal properties without interrupting normal service or requiring controlled excitation.

This special session will focus specifically on ambient vibration-based modal identification and its use for damage quantification in civil structures. The session will emphasize methodological developments and field applications that connect modal identification results to structural damage assessment. Topics may include damage-sensitive modal features, changes in modal parameters before and after deterioration or rehabilitation, finite element model updating, inverse identification methods, statistical and data-driven damage quantification, machine learning-assisted interpretation, and the influence of environmental and operational variability on damage indicators.

The goal of the session is to discuss how ambient vibration-based modal identification can move beyond dynamic characterization and be effectively used as a quantitative tool for evaluating structural condition.

Some of the key topics of interest in this special session include:

Output-only modal identification and operational modal analysis
Automated extraction of modal frequencies, mode shapes, and damping ratios
Modal curvature, flexibility-based, and stiffness-related damage indicators
Damage detection, localization, and severity estimation using modal parameters
Quantification of stiffness loss and structural degradation from modal changes
Uncertainty and variability in identified modal properties
Finite element model updating and inverse damage identification
Data-driven and machine learning-assisted damage quantification
Before-and-after assessment of repaired or rehabilitated structures
Field case studies on modal identification and damage quantification

Organized by Ji Dang, Yasutaka Narazaki, ZhiQiang Chen

Lifecycle structural monitoring and inspection (LSMI) of civil structures, including buildings, bridges, and other lifeline infrastructure, are essential for ensuring operational safety and resilience. Recent advances in artificial intelligence (AI), computer vision (CV), unmanned vehicles, mobile robotics, and large foundation models have transformed structural inspection and monitoring processes, making them more reliable, efficient, scalable, and autonomous. As AI-based algorithms are increasingly deployed in real physical environments for more advanced inspection and monitoring tasks, their interaction with the physical world has become a critical challenge. This includes not only perception and decision-making in complex, uncertain, and dynamic environments, but also the need to interface data-driven algorithms with physical principles, structural behavior, sensor characteristics, and operational constraints.

This special session aims to provide pathways for adopting AI, CV, and mobile robotics toward reliable end-to-end automation and real-world deployment of structural inspection and monitoring tasks, and to serve as a stage for global researchers exploring the boundaries of LSMI autonomy and human roles. Emphasis will be placed on embodied and physical AI approaches that connect data-driven intelligence with physical environments and human decision-making processes. Topics of interest include, but are not limited to:

Digital twins for connecting deep learning and CV-based algorithms with the physical world.
Embodied AI that interacts with physical environments to collect information on structural conditions.
Unmanned vehicles and other mobile robots with embedded intelligence for structural inspection and monitoring in real-world environments.
Physical AI approaches that integrate DL and CV algorithms with physical principles relevant to structural inspection and monitoring.
Human-in-the-loop, guardrailing, and constraint methods and implications for robotics and unmanned vehicle control, visual analytics, and decision-making in the physical world.
Applications of foundation models, including large language models (LLMs), vision-language models (VLMs), and emerging world models, as enablers for grounding inspection and monitoring capabilities in physical environments.

Organized by Alper Kanyilmaz, Carmelo Gentile, Simone Donadello, Cecilia Clivati, Biondo Biondi, Werner Lienhart and Jingxiao Liu

The special session is dedicated to the latest advances in fiber optic sensing for structural health monitoring (SHM) of buildings and infrastructure. Optical fibers act as distributed sensors, relying on a robust and mature technology exploited for a wide range of applications, from earthquake detection to continuous measurement of strain, temperature, and vibration. When coupled to or embedded within structures such as buildings, railways, and bridges, fiber measurements can deliver real-time information on structural condition. These data can be analyzed with a range of methods, including signal processing, system identification, and artificial intelligence (AI), offering a powerful path toward intelligent SHM that detects damage as it develops and enables preventive maintenance.

A rapidly emerging frontier is the dual-use of existing telecommunication fiber networks for structural sensing. Rather than requiring dedicated installations, this paradigm exploits the millions of kilometres of optical fiber already deployed in fiber-to-the-home (FTTH) infrastructure, so the same fiber continues to carry live communications while simultaneously enabling monitoring.

Several technologies have been proposed for detecting structural vibrations, including OTDR, Distributed Acoustic Sensing (DAS), Fiber Bragg Gratings, and coherent laser interferometry. The session gathers contributions on these approaches, on the data-driven methods used to interpret the data, and on the latest in-field demonstrations, including those exploiting dual-use telecom infrastructure. By bringing together specialists from structural engineering, photonics, metrology, geophysics, and industry, the session aims to surface key challenges, share methodologies, and chart pathways from laboratory validation toward operational deployment.

The following topics are welcome:

Fiber optic technologies for structural health monitoring
Dual-use of telecommunication (FTTH) fiber networks for distributed structural and environmental sensing
Coherent laser interferometry and DAS over deployed telecom fibers
AI-powered and data-driven methods for classification of structural events detected with fiber sensing
Predictive algorithms for prompt structural health monitoring
City-scale monitoring and post-earthquake safety assessment
In-field demonstrations based on distributed optical fiber sensing

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