Special Sessions
Those interested in organizing a special session should prepare their proposal by using the template available here and send it to the Conference Chairs at rainieri@itc.cnr.it and pir3@pitt.edu. Approved sessions will be timely posted on the Conference website.
SS1 - SHM of Ageing Infrastructure in Australia and Beyond: A Focus on Practical Solutions
Andy Nguyen
University of Southern Queensland
andy.nguyen@unisq.edu.au
Jun Li
Curtin University, Perth, Australia
junli@curtin.edu.au
Tommy Chan
Queensland University of Technology
tommy.chan@qut.edu.au
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.
SS2 - Seismic Structural Health Monitoring
Maria Pina Limongelli
Politecnico di Milano, Milan, Italy
mariagiuseppina.limongelli@polimi.it
Mehmet Celebi
Earthquake Science Center, USGS, Menlo Park, CA
celebi@usgs.gov
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.
SS3 - Advances in Computer Vision and AI-Enhanced Imaging for SHM and NDT&E
Alessandro Sabato
Department of Mechanical and Industrial Engineering
University of Massachusetts Lowell
1 University Avenue, Lowell, MA, 01854
Dandeneau Hall, Room 246
Alessandro_Sabato@uml.edu
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
SS4 - Verification & Validation Approaches for Demonstrating the Value of SHM
Charikleia Stoura
Department of Civil, Environmental and Geomatic Engineering
ETH Zurich, Stefano-Franscini-Platz-5, 8093, Zurich, Switzerland
charikleia.stoura@ibk.baug.ethz.ch
Antonios Kamariotis
Zurich Insurance, Mythenquai 2, Zürich, Switzerland
antoniskam@hotmail.com
Eleni Chatzi
Department of Civil, Environmental and Geomatic Engineering
ETH Zurich, Stefano-Franscini-Platz-5, 8093, Zurich, Switzerland
chatzi@ibk.baug.ethz.ch
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
SS5 - SHM of wind turbines
Christof Devriendt
Vrije Universiteit Brussel
Christof.devriendt@vub.be
Filipe Magalhaes
Engenharia da Universidade do Porto
filipema@fe.up.pt
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.
SS6 - Acoustic Emission for Structural Health Monitoring of Civil Infrastructure
Didem Ozevin
Professor
Civil, Materials, and Environmental Engineering
University of Illinois Chicago
dozevin@uic.edu
Tomoki Shiotani
Professor
Office of Institutional Advancement and Communication, Kyoto University, Japan
shiotani.tomoki.2v@kyoto-u.jp
Els Verstrynge
Professor
Materials and Construction,
Civil Engineering Dept.,
KU Leuven, Belgium
els.verstrynge@kuleuven.be
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.
SS7 - Satellite SAR Monitoring
Daniele Zonta
University of Trento, Via Mesiano 77, 38123 Trento Italy
daniele.zonta@unitn.it
Maria Pina Limongelli
Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
mariagiuseppina.limongelli@polimi.it
Daniel Cusson
National Research Council Canada, 1200 Montreal Road, Ottawa, Canada, K1A 0R6
Daniel.Cusson@nrc-cnrc.gc.ca
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.
SS8 - Geotechnical Aspects in SHM: Tunnels, Foundations, Bridge Scour, and Landslide-Structure Interaction Monitoring
Marco Civera
Department of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin 10129, Italy
marco.civera@polito.it
Mauro Aimar
Department of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin 10129, Italy
mauro.aimar@polito.it
Paolo Borlenghi
Department of Architecture, Built Environment and Construction Engineering (DABC), Politecnico di Milano, Piazza Leonardo da Vinci, 42, 20133 Milano, Italy
paolo.borlenghi@polimi.it
Lorenzo Brezzi
Department of Civil, Environmental and Architectural Engineering (DICEA), Università degli Studi di Padova, via Ognissanti, 39, 35129 Padova, Italy
lorenzo.brezzi@unipd.it
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.
SS9 -Non-Destructive Testing and Structural Health Monitoring of Timber Structures
Carmen Amaddeo
Building Technology Department, Linnaeus University, Universitetsplatsen 1, 352 52 Växjö, Sweden
carmen.amaddeo@lnu.se
Luca Martinelli
Department of Civil and Enviromental Engineering, Politecnico of Milano, Campus Leonardo, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy
luca.martinelli@polimi.it
Andre R. Barbosa
Cecil and Sally Drinkward Professor in Structural Engineering, Oregon State University, 342 Owen Hall | Corvallis, OR 97331, USA
andre.barbosa@oregonstate.edu
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.
SS10 - Drive-by SHM of Transport Infrastructure Using Vehicle-based Sensing Methods
Abdollah Malekjafarian
University College Dublin, Dublin, Ireland
abdollah.malekjafarian@ucd.ie
Ekin Ozer
University College Dublin, Dublin, Ireland
ekin.ozer@ucd.ie
Necati Catbas
University of Central Florida, USA
catbas@ucf.edu
Furkan Lüleci
Louisiana State University, USA
Furkan.Luleci@lsu.edu
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
SS11 - Structural safety assessment via data-driven approaches and practical AI innovations for SHM
Francesca Marafini
Department of Civil and Environmental Engineering, University of Florence, via di S. Marta 3, 50139, Florence, Italy
francesca.marafini@unifi.it
Alice Cicirello
Department of Engineering, University of Cambridge, Cambridge, UK
ac685@cam.ac.uk
Michele Betti
Department of Civil and Environmental Engineering, University of Florence, via di S. Marta 3, 50139, Florence, Italy
michele.betti@unifi.it
Francesco Clementi
Department of Civil and Building Engineering and Architecture, Polytechnic University of Marche, Via Brecce Bianche 12, 60131, Ancona, Italy
francesco.clementi@univpm.it
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
SS12 - Structural health monitoring, non-destructive testing, damage assessment and modeling techniques for cables and tendons
Iván M. Díaz
Universidad Politécnica de Madrid, Spain
ivan.munoz@upm.es
Giuseppe Quaranta
Sapienza University of Rome, Italy
giuseppe.quaranta@uniroma1.it
Carlos M.C. Renedo
Universidad Politécnica de Madrid, Spain
carlos.martindelaconcha@upm.es
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.
SS13 - Experimental and Numerical Analysis of Railway Infrastructures
Matthias Baeßler
Bundesanstalt für Materialforschung und -prüfung
Division 7.2 Buildings and Structures
matthias.baessler@bam.de
Pedro Montenegro
Faculdade de Engenharia, Universidade do Porto
Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal
paires@fe.up.pt
Andreas Andersson
KTH Royal Institute of Technology
Division of Structural Engineering and Bridges
adde@kth.se
Andreia Meixedo
Faculdade de Engenharia, Universidade do Porto
Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal
ameixedo@fe.up.pt
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.
SS14 - Model updating and digital twin for SHM
Suzana Ereiz
Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Italy
suzana.ereiz@polimi.it
Ivan Duvnjak
Faculty of Civil engineering, University of Zagreb, Croatia
ivan.duvnjak@grad.unizg.hr
Javier Fernando Jiménez-Alonso
Higher Technical School of Engineering, University of Seville, Spain
jfjimenez@us.es
Michael O’Shea
Civil, Structural and Environmental Engineering, University College Cork, Ireland
michaeloshea@ucc.ie
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.
SS15 - Smart Sensors, IoT Technologies and Artificial Intelligence for Civil Infrastructure Monitoring
Yuguang Fu
School of Civil and Environmental Engineering, Nanyang Technological University
yuguang.fu@ntu.edu.sg
Yuequan Bao
School of Civil Engineering, Harbin Institute of Technology
baoyuequan@hit.edu.cn
Jian Li
School of Civil, Environmental & Architectural Engineering, University of Kansas
jianli@ku.edu
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.
SS16 - Self-sensing composites and systems for civil Structural Health Monitoring
Paolino Cassese
National Research Council of Italy (CNR), Construction Technologies Institute (ITC), Naples
cassese@itc.cnr.it
Carlo Rainieri
National Research Council of Italy (CNR), Construction Technologies Institute (ITC), Naples
rainieri@itc.cnr.it
Antonella D’Alessandro
Department of Civil and Environmental Engineering, University of Perugia, Italy
antonella.dalessandro@unipg.it
Filippo Ubertini
Department of Civil and Environmental Engineering, University of Perugia, Italy
filippo.ubertini@unipg.it
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.
SS17 - From Data to Action: Data-Driven Decision Making and Value of Information
Pier Francesco Giordano
Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milan, Italy
pierfrancesco.giordano@polimi.it
Leandro Iannacone
Division of Structural Engineering, Department of Building and Environmental Technology, Lund University, Klas Anshelms väg 14, Lund, Sweden
leandro.iannacone@kstr.lth.se
Aidan Hughes
Dynamics Research Group, School of Mechanical, Aerospace and Civil Engineering, University of Sheffield, Sheffield, S1 3JD
aidan.j.hughes@sheffield.ac.uk
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.
SS18 - Guided Ultrasonic Waves for Structural Health Monitoring
Alessandro Marzani
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136, Bologna, Italy
alessandro.marzani@unibo.it
Luca De Marchi
Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, Viale del Risorgimento 2, 40136, Bologna, Italy
l.demarchi@unibo.it
Antonio Palermo
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136, Bologna, Italy
antonio.palermo6@unibo.it
Stefano Mariani
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136, Bologna, Italy
stefano.mariani9@unibo.it
Soroosh Kamali
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136, Bologna, Italy
soroosh.kamali2@unibo.it
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.
SS19 - Multi-Domain Collaborative Swarm Autonomous Intelligence for Structural Health Monitoring
Yang XU
School of Civil Engineering, Harbin Institute of Technology, China
xyce@hit.edu.cn
Jiangpeng SHU
College of Civil Engineering and Architecture, Zhejiang University, China
jpeshu@zju.edu.cn
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.