Next-Generation Structural Health Monitoring: Advanced Sensing, AI and Digital Twins
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 50
Special Issue Editors
Interests: structural health monitoring; dynamic testing; vibration-based damage detection; advanced sensors; digital twins; AI for structural diagnostics; post-earthquake assessment; bridge and tunnel monitoring; data-driven infrastructure management
Interests: mechanical and thermal measurements; MEMS sensors; inertial measurement unit; monitoring system; measurement systems; integrated navigation systems; drone; Kalman filter; IoT platforms
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Structural health monitoring (SHM) is evolving beyond traditional paradigms, driven by the convergence of advanced sensing technologies, artificial intelligence, and digital representations of physical assets.
This Special Issue aims to explore this transformation, highlighting how intelligent, data-rich systems can reshape the way civil structures are monitored and managed throughout their service life.
We invite contributions that demonstrate a strong engineering foundation, where sensor deployment, signal processing, and data interpretation are firmly anchored in structural mechanics and system behaviour. Emphasis is placed on the integration of monitoring data with digital twins, not merely as digital replicas, but as interactive, adaptive tools capable of capturing the dynamic state of structures through real-time data and predictive intelligence.
Furthermore, the role of AI and machine learning is central to this transition, offering powerful tools for pattern recognition, anomaly detection, damage classification, and model updating. The combination of physics-based insight with data-driven techniques opens the path to SHM systems that are not only automated but also context-aware and decision-oriented.
Topics of interest include, but are not limited to, the following:
- Dynamic monitoring methods and systems (e.g., operational/modal analysis, vibration-based damage detection);
- Integration of heterogeneous sensors in SHM systems (e.g., accelerometers, GNSS, fibre optics, computer vision);
- AI-driven approaches for structural diagnostics, forecasting, and risk-based decision support;
- Real-time model updating and calibration of Digital Twins using sensor data;
- Case studies of bridges, buildings, and other civil structures under operational or extreme conditions;
- Challenges in data management, long-term monitoring, and the reliability of AI-driven SHM.
This Special Issue aims to bring together researchers and practitioners from structural engineering, sensor science, and data intelligence. The goal is to foster a multidisciplinary dialogue and promote methodologies where SHM becomes a truly intelligent system—capable of supporting performance assessment, maintenance planning, and resilience under uncertainty.
Dr. Giulio Mariniello
Dr. Giorgio de Alteriis
Guest Editors
Manuscript Submission Information
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Keywords
- structural health monitoring (SHM)
- inertial sensors
- digital twin
- artificial intelligence
- dynamic monitoring
- damage detection
- modal analysis
- sensor data fusion
- machine learning
- infrastructure resilience
- civil structures
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