This special session aims to capture the latest advancements in Structural Health Monitoring (SHM) with a particular emphasis on their real-world application. It will showcase innovative research and practical case studies from Australian infrastructure, where the unique challenges of a vast continent with diverse climates have driven new solutions.

The session will bring together leading researchers and industry practitioners to discuss the transition from theoretical concepts to deployable, scalable technologies. We invite contributions that demonstrate effective SHM techniques for a variety of structures, including bridges, buildings, and critical utilities, addressing challenges such as remote monitoring, data-driven decision-making, and sensor network optimisation.

The ultimate goal of the session is to foster collaboration and knowledge-sharing to ensure a safer, more resilient, and more sustainable future for aging infrastructure in Australia and beyond.

Over the past three decades, Seismic Structural Health Monitoring (S2HM) has advanced rapidly, driven by the growing demand for safe infrastructure and increased attention from researchers and practitioners. In earthquake-prone regions, numerous monitoring systems have been deployed to record real-time or near-real-time structural responses during strong seismic events. These valuable data enhance our understanding of structural performance under seismic loading and support the calibration of realistic numerical models for simulating behavior and detecting potential damage.

This Special Session aims to highlight recent developments and successful applications of seismic SHM for civil structures and infrastructure, including bridges, buildings, historical monuments, dams, wind turbines, and pipelines. Both theoretical and computational contributions, as well as case studies and practical implementations, are welcome.

Topics of interest include:

• Algorithms for structural identification and damage detection
• Strong-motion arrays and real-time monitoring systems
• Instrumentation, sensing technologies, and measurement methods
• Sensor placement strategies and experimental validation
• Integration of SHM with risk assessment and emergency management

The session provides a platform for exchanging knowledge, presenting innovative solutions, and discussing emerging challenges and future directions in seismic SHM.

Recent advances in computer vision (CV) and artificial intelligence (AI) are redefining how structural health monitoring (SHM) and nondestructive testing and evaluation (NDT&E) are conducted. Modern imaging, sensing, and learning techniques now enable automated inspection, defect detection, and condition assessment of complex structures with unprecedented accuracy and speed.

This special session invites contributions that explore innovations in computer vision and AI-enhanced vision methods for SHM and NDT&E. Topics of interest include, but are not limited to:

• Vision-based damage detection, localization, and quantification
• Computer vision techniques for structural dynamics and modal analysis
• Deep learning and data-driven approaches for automated defect recognition
• 3D reconstruction, photogrammetry, and digital twins
• UAV, robotics, or mobile vision systems for automated inspection
• Multimodal data fusion and real-time vision analytics
• Explainable and trustworthy AI for SHM applications

Submissions highlighting integration of classical vision techniques with AI, experimental validations, and real-world deployments are especially encouraged. The session aims to foster dialogue among researchers and practitioners developing next-generation vision systems for smarter, safer, and more resilient infrastructure.

This Mini-Symposium addresses the challenge of quantifying and validating the value of structural health monitoring (SHM) systems through rigorous verification, validation, and uncertainty quantification (VV&UQ) frameworks. Verification, in this context, refers to the corroboration of SHM algorithms and digital-twin models against numerical and simulation-based benchmarks, ensuring that the system is correctly implemented and behaves as intended. Validation, in turn, involves corroborating model predictions and decision metrics against experimental and in-situ monitoring data, thereby establishing credibility under real operational conditions.

The session brings together recent advances that bridge model-based, data-driven, and hybrid approaches to demonstrate the performance and trustworthiness of SHM solutions across their life cycle—from design and virtual prototyping to field deployment and decision support.

Topics of interest include, but are not limited to:

• Bayesian and decision-theoretic frameworks for model calibration and system identification
• Quantification of Value-of-Information (VoI) in SHM and its integration into maintenance and asset-management strategies
• Digital-twin and knowledge-graph architectures for managing experimental, numerical, and monitoring data, and supporting real-time model updating
• Population-based and graph-based learning methods for structural model validation
• Uncertainty propagation, model class selection, and adaptive validation in monitored systems
• Experimental benchmark campaigns and open datasets for VV&UQ in SHM

This session addresses the monitoring of onshore and offshore wind turbine structural components—foundation, tower, and blades—to detect faults or damage, identify operational deficiencies affecting structural performance, and assess slow deterioration processes such as fatigue and erosion. Proposed tools should be validated using simulated and/or experimental data, with preference for full-scale testing. They should also support asset management by informing operators about the balance between energy production and turbine wear, estimating remaining useful life, and planning maintenance.

Relevant applications include measuring environmental and operating conditions, structural loads, and response quantities such as strains, deformations, vibrational indicators, and dynamic properties (e.g., natural frequencies and damping ratios). Effective processing and fusion of raw data for diagnostic and prognostic decisions are central themes. The session welcomes contributions involving system identification, probabilistic machine learning and artificial intelligence methods, especially when deployed at the wind-farm scale. Development of optimized, cost-effective sensing strategies suitable for rapid deployment under wind-turbine constraints is also encouraged.

Acoustic emission (AE) has advanced rapidly in recent years, driven by the adoption of AI-based signal processing and real-time decision-making approaches, which enable more rapid and efficient damage detection in civil infrastructure. This special session will focus on recent advances in AE sensing, data processing, and interpretation for assessing the integrity of civil structures such as bridges, buildings, pipelines, nuclear power plants, and composite systems.

Topics include sensor development, wave propagation modelling, damage localization, AE-supported structural assessment, AI-based signal classification, and integration of AE with other SHM methods and digital twin frameworks. The session aims to bring together researchers and practitioners to share innovations, experimental work, field applications, and future directions for AE-based SHM.

Satellite-borne Synthetic Aperture Radar (SAR) offers a valuable complement to traditional SHM, enabling millimetric displacement measurements over wide geographic areas and tracking their evolution over time. The ability to monitor entire urban regions supports the development of automated alert systems that can flag structures with potential integrity issues.

InSAR data can capture displacement time histories, deformation rates, and thermal effects, providing key insight into ongoing deterioration processes. Doppler Micro-Motion SAR (MM-SAR) further enables the recording of bridge vibrations during a single satellite pass, allowing, in principle, the identification of selected modal properties. Optical satellite imagery also complements InSAR measurements, supporting applications such as hydraulic risk assessment of bridge piers and monitoring of marine port infrastructure.

This special session aims to present recent theoretical advances and field applications, fostering future research collaboration. We welcome contributions on SAR technologies, image-processing algorithms, data analysis, case studies, and methods for integrating space-borne and terrestrial data for structural condition assessment and decision-making.

Integrating geotechnical aspects into Structural Health Monitoring (SHM) systems is essential to ensure the safety and serviceability of critical infrastructures, such as tunnels and bridges. These systems face complex soil-structure interactions and environmental hazards, including scour, landslides, subsidence, and seismic events, which require advanced monitoring strategies and robust modelling techniques to predict system response under these conditions.

This Special Session focuses on interdisciplinary approaches that combine geotechnical measurements with SHM techniques for structural assessment purposes. Suitable topics include, but are not limited to:

• Tunnel monitoring
• Scour detection and experimental/numerical evaluation of scour-induced effects on water-crossing bridges
• Subsidence assessment
• Geophysical and geotechnical sensor networks
• Satellite and ground-based interferometry for settlement monitoring
• Seismic response of underground spaces
• Monitoring of land/rock-slides interacting with infrastructures

The contributions can focus on laboratory, field, and numerical investigations. Applications may involve road and rail bridges of any material (masonry, reinforced concrete, prestressed RC) and structural scheme (arch, simply supported, continuous beam bridges), as well as tunnels in various ground conditions. Consideration of climate change-driven variations in environmental hazards is also encouraged. Researchers and practitioners are invited to contribute with case studies, long-term monitoring experiences, advances in sensing equipment and methods, and innovative modelling approaches that incorporate integrated structural-geotechnical monitoring.

The use of timber in new construction is rapidly increasing due to its sustainability, favorable mechanical properties, and adaptability. Modern timber systems—including glulam, cross-laminated timber (CLT), and other engineered products—are now employed in a wide range of structural applications, from multi-story buildings to long-span roofs, bridges, and wind turbine towers. Despite these advances, the performance of timber remains strongly influenced by environmental conditions, moisture content, natural variability, and the mechanical behavior of connections. These factors affect stiffness, damping, and overall dynamic response, underlining the need for advanced evaluation methods.

Non-destructive testing (NDT) and structural health monitoring (SHM) provide essential tools for assessing the integrity, durability, and long-term performance of timber structures. These methods enable the detection of defects, identification of degradation processes, characterization of dynamic behavior, and continuous tracking of structural changes without impairing the material.

This special session invites scientific contributions focused on recent developments and applications of NDT and SHM for timber structures. Topics of interest include dynamic testing, long-term monitoring under environmental loads, detection of decay or moisture-related effects, data-driven modeling and diagnostics, digital twin frameworks, and experimental or field-based case studies. The session aims to foster rigorous discussion and advance knowledge in the monitoring and assessment of modern timber construction.

Today’s transport networks play a key role in supporting economic growth. A smart transportation system that is capable of monitoring the condition of its critical infrastructure can facilitate preventive maintenance activities and minimize disruption to service. Deterioration and damage will inevitably occur in transport infrastructure such as road pavements and road/railway bridges. Advances in structural health monitoring (SHM) have enabled the automated diagnosis of damage in infrastructure, but efficient and scalable solutions have been limited.

Recently, vehicle-based drive-by SHM approaches have received increasing attention as a practical solution due to their advantages in cost, low-maintenance, and scalability. These approaches assess infrastructure damage states using the data collected from sensors on board passing vehicles. The aim of this symposium is to bring together academic scientists and industry leaders to present their recent advances in the field of indirect structural health monitoring of civil infrastructure. It covers a broad research area from simple models to applications, numerical modelling, laboratory-scale experimental case studies and field tests.

The session will focus on, but is not limited to, the following topics:

• Bridge damage detection and health monitoring (highway and railway bridges)
• Identification of bridge modal properties
• Road pavement health monitoring methods
• Railway track health monitoring methods
• Dynamic interaction of trucks with bridges and roads
• Bridge traffic loading

Over recent years, there has been an increasing implementation of SHM systems for full-scale civil structures and infrastructures. These systems are normally designed to support stakeholders and decision-makers by providing real-time assessments of structural safety in response to both gradual and sudden changes.

In principle, insight derived from continuous data acquisition enables the identification of degradation processes, allowing for maintenance planning and timely intervention. However, several challenges remain before reliable predictive assessments can be consistently achieved. Current approaches may struggle to detect subtle variations in structural integrity, leaving the possibility of unnoticed damage or delayed maintenance actions.

This session – by bringing together researchers, practitioners, and industry experts – aims to present and discuss the most recent advancements, methodologies and best practices in the field of SHM, with particular focus on data-driven strategies and practical application of AI/ML technologies. Contributions are invited that explore innovative monitoring methods, intelligent diagnostic tools, and new frameworks capable of improving damage detection sensitivity, enhancing predictive capacity, and supporting long-term resilience of civil structures and infrastructures.

Topics of interest include, but are not limited to:

• Long- and Short-time structural monitoring
• Sensor deployment, and sensor placement optimization
• System identification methods
• SHM Data-driven approaches
• Algorithms for data processing
• Innovative application of AI methods
• Real-world practical applications

Cable and tendon elements are key structural components for civil engineering structures such as cable-stayed bridges, external post-tensioned bridges, cable roofs or transmission lines. They also serve as essential components in a wide range of devices. Thus, the mechanical characterization, numerical modeling and damage identification of cables and tendons are crucial for structure stability and safety. Indeed, due to the latest failures of cable elements, this is currently an interesting and urgent topic to be dealt with by the research community. In this regard, non-destructive testing techniques are essential for detecting cable deterioration and ensuring structural integrity throughout service life. This special session aims to share experiences and present studies on topics that include, but are not limited to, the following:

• NDT techniques for cables, mechanical and corrosion assessment
• Vibration-based structural health monitoring
• Cable force monitoring and estimation techniques
• Environmental effects (temperature, wind, live loads, etc.), analysis and their removal
• Monitoring of tensioning process, friction losses estimation
• Cable damage identification techniques
• Acoustic sensors for tendon wire breaks
• Wave propagation for damage assessment
• Lab tests including dynamic and fatigue tests
• Fatigue-resistant analysis and performance, fatigue crack propagation
• Finite element models for damage in cables and tendons
• Intelligent algorithms, Machine-Learning-based tools for cable monitoring and assessment
• Wind-induced cable vibration and their mitigation
• Specific applications such as roofs with cables, vertical cables, lick suspenders, seismic isolators, etc.

This special session aims to bring together the latest achievements, research and studies regarding the railway bridges, in terms of condition assessment and monitoring. Theoretical, experimental and computational investigations (or a combination of these) are welcome. Expected papers will cover various aspects related with cases studies on structural integrity; structural condition assessment; digital twins; model calibration and validation; structural health monitoring; new sensors and technologies; computer vision techniques; automated damage identification; remote inspection strategies; BrIM (Bridge Information Modelling); Big Data; disaster risk reduction; emergency management and intelligent asset management; degradation; damage and condition assessment; specific problems of railway bridge dynamics and train-bridge interaction; among others.

Bridge management is rapidly evolving through the integration of Structural Health Monitoring (SHM), model updating, and digital twin technologies. By linking monitoring data with numerical simulations, digital twins provide continuously updated representations of structural behaviour and support more informed decision-making. This special session focuses on advances in model updating and digital twin frameworks for SHM. Topics include vibration-based identification, finite-element model updating, uncertainty quantification, and machine-learning methods for anomaly detection and predictive maintenance. Case studies will demonstrate how data-informed digital twins enhance lifecycle management, improve reliability assessments, and optimize maintenance strategies for bridge infrastructure. Bringing together experts from engineering, modelling, and data science, the session aims to highlight cutting-edge research and practical implementations shaping the future of SHM-enabled bridge management.

Civil infrastructures serve under continuous operational and environmental stresses, sometimes facing extreme hazardous loads. They will have deterioration and damage that can disrupt their intended services and potentially compromise public safety. Recently, smart sensing/IoT technologies, accompanied with artificial intelligence (AI), has played a critical role in monitoring the integrity of civil infrastructures, supporting essential decision-making aimed at detection, diagnosis, and prognosis of infrastructure health conditions.

Emerging sensing technologies, such as wireless IoT sensors, mobile sensing, edge computing, cloud-based management, have been successfully developed. These innovations are further empowered by AI and data science. Examples agile sensing system for capturing sudden events, edge/cloud computing for structural anomaly detection, along with technologies designed to facilitate easy deployment and maintenance, among others. The objective of this special session is to foster discussions on the latest advancements in smart sensors, IoT technologies and artificial intelligence for civil infrastructure health monitoring and management. Research involving real-world smart sensing applications is especially welcome. Topics of interest include, but are not limited to, visual data analytics, edge computing, wireless sensor (networks), sensor fault diagnosis and recovery, multimodal data fusion, machine learning/deep learning for SHM applications, structural damage diagnosis and prognosis, and predictive maintenance.

The special session “Self-sensing composites and systems for civil Structural Health Monitoring” aims to collect recent findings concerning composite materials functionalized by the addition of fillers enabling piezoresistive, piezocapacitive, piezoinductive, and piezoelectric properties, useful for structural health monitoring of civil structures. The session encourages sharing and discussion about the latest advancements concerning various types of self-sensing structural materials, to catch future research directions towards smarter and safer civil infrastructures and possible applications.

Possible topics of interest are: cutting-edge solutions for functional fillers and matrices (new-generation particles, high-performance mortars/concretes/bricks, alkali-activated materials, or sustainable matrices); material durability and self-sensing stability over time; calibration of sensing properties; smart monitoring of strain, stress or environmental conditions, electromechanical modelling and algorithmic strategies for self-sensory response (including AI); innovative production procedures such as 3D printing technologies. Experimental activities involving large-scale structural members as well as practical applications on real structures are encouraged.

Effective infrastructure management depends on reliable information about structural conditions, loading effects, and environmental influences. Traditional inspections and periodic testing often lack continuity and cost-efficiency, whereas Structural Health Monitoring (SHM) provides continuous data that can support proactive maintenance, risk mitigation, and emergency response.

Yet two central challenges remain: incorporating SHM information into decision-making and understanding whether investments in monitoring truly provide added value. The Value of Information (VoI), rooted in Bayesian decision theory, offers an objective way to assess whether the benefits of SHM justify the associated costs, and to identify optimal monitoring strategies over a structure’s lifecycle.

This Special Session highlights recent advances and future directions in data-driven infrastructure management and VoI analysis. Topics include VoI quantification, integration of SHM data into decision-support tools, sensor placement optimization, VoI-driven maintenance planning, real-world applications showcasing SHM’s impact on decisions, innovations in SHM technologies, and the role of monitoring in emergency management. By gathering researchers, practitioners, and industry experts, the session aims to advance the use of SHM and strengthen informed decision-making for civil engineering infrastructure.

Guided ultrasonic waves (GUWs) offer a powerful and versatile approach to Structural Health Monitoring (SHM) in waveguide-like structures. They enable long-range inspection, full coverage of the monitored component, high sensitivity to even small defects, and seamless integration with embedded or surface-mounted sensor networks. Their ability to propagate over large distances while preserving rich diagnostic information makes GUWs particularly well suited for the continuous monitoring of a wide range of critical structures and infrastructures, including plate-like components, pipelines, pressure vessels, spherical shells, and more.

This special session aims to bring together researchers and practitioners working on all aspects of GUW-based SHM to discuss recent advances, challenges, and innovative solutions. Contributions covering theoretical, numerical, and experimental work are welcome.

Topics of interest include, but are not limited to:

• Sensor technologies for actuation and reception of guided ultrasonic waves (piezoelectric, optical, electromagnetic, etc.)
• Sensor network design using greedy / optimization and genetic-algorithm approaches that maximize the expected Value of Information per sensor cost
• Signal processing strategies, including noise mitigation, feature extraction, dispersion compensation, and Machine learning and AI-enhanced techniques for interpreting wave-based data.
• Methodological frameworks for damage detection, localization, quantification, and prognosis using GUWs
• Modelling and simulation of guided wave propagation in complex structural systems
• Statistical methods & uncertainty quantification for PoD/MAPoD
• Field applications and case studies involving real infrastructure (bridges, towers, pipelines, heritage structures, etc.)
• Integration of GUW systems into broader SHM architectures for multi-sensor fusion and decision support

The session aims to foster dialogue across different research communities and stimulate collaboration on emerging trends in guided-wave SHM.

The growing intricacy and essentiality of aging infrastructure, impacts of extreme climate-induced events, and development of resilient smart cities necessitate comprehensive, high-efficiency, and precise approaches to structural health monitoring (SHM). Existing techniques predominantly depends on stationary sensors, manual inspections, and subjective assessment, which might be inadequate for capturing dynamic, multi-scale, and ubiquitous structural degradation across vast, hazardous and extreme environments. This session aims to explore an emerging paradigm of “Air-Space-Ground-Water” multi-domain collaborative swarm autonomous intelligence for SHM, integrating distributed unmanned systems, novel sensing techniques, artificial intelligence algorithms, and multi-modal large models with intelligent agents. Emphasizing interdisciplinary advancements, this session will shed light on recent progress in advanced stereoscopic sensing, embodied AI, aerial-ground-underwater autonomous inspection systems, cross-domain group coordination, multi-modal data fusion, domain-specific large models, damage recognition and change detection, vibration measurement and dynamic identification, condition assessment and health diagnostics, maintenance decision-making and emergency response. It provides a forum to share state-of-the-art innovations and prospective discussions in academic research, industrial development, and engineering applications. By fostering collaboration among scholars from robotics, civil engineering, and computer science, this session endeavors to expedite the evolution of active, intelligent, scalable, and sustainable monitoring solutions for modern infrastructure, encompassing long-span bridges, tunnels, spatial structures, energy structures, and offshore platforms.

This special session aims to bring together recent advances in physics-based, data-driven, and hybrid methodologies for structural health monitoring (SHM) of civil infrastructure. The session focuses on integrating physical understanding of structural behaviour with modern data analytics, signal processing, and machine learning techniques to improve damage detection, localization, and assessment under complex and nonstationary operating conditions. Emphasis is placed on robust methodologies capable of handling uncertainty, environmental variability, methodologies for detecting damage initiation and extreme loading scenarios, with applications to buildings, bridges, tunnels, and other intelligent infrastructure systems.

Topics include (but are not limited to):

• Physics-based modeling and simulation for SHM
• Data-driven and machine learning approaches for SHM
• Hybrid physics-informed and data-centric monitoring frameworks
• Wavelet, time–frequency, and signal decomposition methods
• Statistical and invariant damage detection techniques
• Early-stage damage detection and localization
• Multi-sensor data fusion and decision-level integration
• SHM under seismic, wind, traffic, and extreme loading conditions
• Output-only monitoring and operational modal analysis
• Uncertainty quantification and false-alarm mitigation
• Digital twin concepts for infrastructure monitoring
• SHM applications to buildings, bridges, tunnels, and lifeline systems

Despite recent breakthroughs, many SHM systems have so far not achieved widespread industrial acceptance. It is crucial understanding the potential system effectiveness before transfer into routine applications. However, there is still a lack of strategies for performance assessment accounting the peculiarities of SHM systems. To assess the ability thereof, a variety of prerequisites and contributing factors must be considered and analysed in the way they affect the system reliability. 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 POC or ROC curves.

This Special 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.

Inland navigation structures and port infrastructure such as navigation locks, quay walls, dikes, canals, and breakwaters, are essential components of sustainable freight/logistic transport systems. Many of these assets are ageing, operate beyond their original design life, and are increasingly exposed to intensified traffic demands, environmental loading, and climate-induced effects. Despite their socio-economic importance, structural health monitoring (SHM) of inland navigation structures has received significantly less attention than bridges and buildings. The large spatial extent of these assets, their partial or full submergence, limited accessibility, and strong sensitivity to environmental variability (e.g. water levels, temperature, corrosion, and soil–structure interaction) pose distinct challenges for sensing, data interpretation, and damage identification.

This special session aims to bring together recent advances, field applications, and methodological developments in SHM of inland navigation structures and ports, with a focus on improving structural safety, resilience, and decision-making for maintenance and life-cycle management.

Contributions are invited on, but not limited to, the following topics in SHM of inland navigation structures and port infrastructure:

• Sensor technologies and sensing strategies for harsh, marine, and submerged environments
• Effects of environmental and operational variability on SHM data and damage detection
• Vibration-based, displacement-based, strain-based, and multi-sensor SHM approaches
• Data-driven, physics-based, and hybrid SHM methods for inland navigation assets
• Long-term monitoring, scalability, and network-level SHM of waterways and ports
• Integration of SHM with asset management, maintenance planning, and decision support
• Digital twins and digital twining for SHM

Structural Health Monitoring has achieved a high level of maturity in sensing technologies, data acquisition, and signal processing. Despite this progress, a critical gap remains between the extraction of monitoring indicators and their effective use in structural assessment, safety evaluation, and engineering decision making. This Special Session focuses on methodologies and applications that connect SHM data with engineering interpretation, performance assessment, and actionable decision support for existing structures.

The session aims to highlight approaches that integrate monitoring data with physics-based models, model updating strategies, and informed engineering judgment, with particular attention to uncertainty, robustness, and real-world constraints. Contributions addressing the combined use of static and dynamic information, the interpretation of SHM indicators in relation to structural performance and limit states, and the use of SHM to support risk-informed maintenance, management, and intervention strategies are especially encouraged. The objective of the session is to promote discussion on how SHM can advance beyond anomaly detection and feature extraction toward reliable engineering insight, supporting transparent assessment processes and informed decisions for the safety and management of civil infrastructure.

Suggested topics:

• Engineering interpretation of SHM indicators
• Integration of SHM data with structural models and assessment procedures
• Model updating driven by monitoring data
• Uncertainty and robustness in SHM-based evaluation
• SHM-supported maintenance and decision-making for existing structures

This SS aims to highlight innovative methodologies that integrate computational modelling and/or statistical time series analysis in AI/ML frameworks for accurate diagnostics and robust prognostics under normal, typically varying, operating conditions with emphasis on machinery and structural systems. Contributions are encouraged across diverse industrial sectors, addressing key issues such as early fault detection, fault identification and severity characterization, uncertainty quantification, and remaining useful life estimation. By fostering interdisciplinary collaboration among researchers, engineers, and industry practitioners, this SS seeks to advance the state of the art in SHM and CM (Condition Monitoring) of critical assets. The presented studies are expected to contribute to improved operational efficiency, reduced maintenance costs, extended equipment lifespan, and enhanced system resilience. Ultimately, this session aims to promote sustainable and intelligent operations through the effective integration of computational and data-centric diagnostic and prognostic frameworks.

Bridge Weigh-in-Motion (BWIM) systems have evolved from axle load estimation tools to sophisticated sensing platforms capable of supporting advanced Structural Health Monitoring (SHM) strategies. This special session focuses on recent technological developments in BWIM—including high-fidelity sensing, improved inverse algorithms, machine learning integration, uncertainty quantification, and real-time data processing—and their seamless integration with SHM frameworks.

The session aims to highlight how BWIM can move beyond traffic load monitoring to enable continuous performance assessment, damage detection, fatigue evaluation, model updating, and resilience analysis of bridge infrastructure. Contributions addressing field implementation, sensor fusion, vehicle–bridge interaction modelling, long-term monitoring case studies, and digital twin integration are particularly encouraged.

By bringing together researchers and practitioners from structural engineering, transportation engineering, structural dynamics, and data analytics, this session seeks to foster interdisciplinary dialogue and accelerate the transition from conventional load monitoring to intelligent, data-driven bridge management systems. The ultimate objective is to explore how next-generation BWIM technologies can enhance infrastructure safety, sustainability, and lifecycle performance.

Recent scientific and technological advances have enabled efficient and even autonomous assessment of bridge conditions, primarily through the implementation of intelligent inspection strategies. These strategies can help prevent failures of critical components in advance and support the identification of mitigation actions to reduce the risk of future failures. This special session aims to facilitate the exchange of knowledge and experience on the state of the art and state of practice in remote and autonomous bridge inspection, including data collection and aggregation, AI- and computer vision-based data processing, and risk analysis and operational decision-making based on remote inspection data.

Topics include, but are not limited to, the theoretical development and practical application of:

• Remote and autonomous data collection technologies.
• AI- and computer vision-based analysis of remote inspection data for condition assessment.
• Data-assimilative decision-making using multiple information modalities collected through remote and autonomous inspection approaches.
• Diagnosis and prognosis of bridges and their components based on remote inspection data.

In addition to original scientific contributions, the following categories are also welcome:

• Feasibility analyses and tools to facilitate the practical adoption of remote and autonomous inspection approaches
• Field case studies and discussions on the practical implications of scientific and technological advances in remote and autonomous inspection

Enhancing seismic resilience at the regional scale requires moving beyond single-structure assessment toward an integrated understanding of structural systems and infrastructure networks. This special session focuses on system-level and regional-level resilience of civil structures as a driving framework for advancing state-of-the-art SHM and inspection methodologies.

The session welcomes contributions on innovative sensing, data integration, and decision-support technologies that enable resilience enhancement by addressing interconnected structural components within individual structures, as well as regional bridges, lifelines, and critical infrastructure as networked systems. We also strongly encourage papers utilizing data collected from past earthquake events and other natural disasters, fostering discussion grounded in real-world observations, validation, and implementation challenges.

Topics include, but are not limited to:

• Remote, autonomous, and distributed inspection for infrastructure systems
• Regional-scale SHM data integration and post-disaster data analysis
• Advanced sensing for system-level condition assessment, such as distributed sensing, IoT-enabled monitoring, and UAV-based inspection
• SHM-informed regional decision-making before and after earthquakes
• Spatial-temporal data-driven resilience evaluation
• Pre-earthquake monitoring strategies for regional preparedness
• Rapid post-earthquake response and recovery enabled by intelligent SHM technologies