Advanced Sensing Technologies, Data-Driven Analytics and Information Integration for Structural Damage Detection and Localization
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: 30 July 2026 | Viewed by 1
Special Issue Editors
Interests: structural health monitoring (SHM); vibration-based monitoring and dynamic identification; seismic engineering of buildings and bridges; masonry structures; digital twins; data fusion (on-site sensors and remote sensing); computer-vision/phase-based motion estimation for displacement; damage detection and performance assessment; satellite InSAR/DInSAR for structures and infrastructures
Special Issues, Collections and Topics in MDPI journals
Interests: nonlinear structural dynamics; computational mechanics; seismic analysis of complex structural systems; structural vibration control (active and semi-active systems); tuned mass systems and cable-supported structures; soil–structure and fluid–structure interaction; modal identification; data-based structural model identification; structural health monitoring (SHM); stability and post-critical behaviour of structures; structural reliability and maintenance management; innovative systems for seismic isolation and control
Interests: civil engineering; earthquake engineering; passive control; structural health monitoring; base isolation; energy dissipation; wooden structures; vulnerability evaluation and retrofitting; seismic risk analysis; mitigation strategies; resilience and sustainability; experimental laboratory research and activities; smart structures; nonlinear structural analysis; seismic retrofitting
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in Structural Health Monitoring (SHM) technologies and data-driven computational methods are driving a paradigm shift in the detection, localisation, and quantitative assessment of structural damage in civil engineering assets and infrastructure. Modern SHM frameworks increasingly rely on heterogeneous sensing systems—such as accelerometers, GNSS, strain gauges, fibre-optic networks, vision-based and radar systems, and distributed or remote sensing—integrated with advanced techniques for processing, fusing, and interpreting multi-source information. The synergistic use of sensing, computation, and modelling enables the extraction of valuable knowledge from indirect measurements, even under complex conditions characterised by nonlinear dynamics, non-stationary responses, or chaotic behaviours arising in realistic operational and environmental settings.
This Special Issue aims to gather contributions on advanced sensing technologies and data-driven methods for the evaluation, localisation, quantification, and long-term monitoring of structural damage. Particular attention will be devoted to analytical strategies capable of robustly distinguishing pathological anomalies (i.e., damage-induced changes or irreversible deterioration) from physiological variability, linear non-stationarity, environmental influences, and reversible effects. A further focus concerns the definition of quantitative behavioural thresholds to support decision-making, condition-based maintenance, rapid post-event damage evaluation, and resilience-oriented design.
Topics of interest include, but are not limited to, machine and deep learning, statistical identification, physics-informed models, AI-enhanced feature extraction, soliton-based representations, nonlinear dynamic interpretation, probabilistic frameworks, hybrid digital twin systems, uncertainty quantification, spectral-based signal processing, and advanced system identification. Contributions addressing information integration, multimodal data fusion, cross-technology interoperability, and protocols for combining multiple monitoring systems are particularly welcome. Studies focusing on validation through experimental campaigns, field monitoring of real structures, or post-earthquake dataset analysis are also encouraged.
This Special Issue invites original research papers, review articles, and benchmark or comparative studies that demonstrate how innovative sensing technologies and data-driven analytical frameworks can enable the next generation of structural damage detection, localisation, classification, quantification, and threshold definition—ultimately enhancing the protection, diagnosis, and resilience of the built environment.
Dr. Rocco Ditommaso
Prof. Dr. Vincenzo Gattulli
Prof. Dr. Felice Carlo Ponzo
Guest Editors
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Keywords
- structural health monitoring (SHM)
- damage detection and localization
- data-driven analytics
- machine learning and deep learning
- physics-informed models
- multisensor data fusion
- hybrid digital twin
- resilience-oriented assessment
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