Advanced Sensing and Intelligent Modeling for Structural Health Monitoring
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".
Deadline for manuscript submissions: 30 August 2026 | Viewed by 771
Special Issue Editors
Interests: deep learning; statistical analysis; risk assessment; decision theory; damage detection; computer vision; vibration analysis; signal process; time series
Interests: artificial intelligence (AI) and machine learning modeling (e.g., generative AI and agentic AI), and digital twinning; structural health monitoring; nondestructive testing and remote sensing; operation intelligence and system resilience; predictive maintenance; risk engineering and decision analytics; sustainable civil infrastructural materials; nanomaterials and multifunctional coatings for corrosion control and mitigation; water and energy systems (e.g., water and energy pipelines and networks); bridge engineering
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Special Issue Information
Dear Colleagues,
Structural Health Monitoring (SHM) is rapidly evolving from periodic inspection toward continuous, data-rich assessment powered by advances in sensing hardware and intelligent modeling. Emerging sensing modalities (e.g., distributed fiber-optic, vision-based, guided-wave, and wireless sensing) now enable higher-resolution and multi-physics measurements, while modern analytics (e.g., machine learning, physics-informed methods, and digital twins) offer new pathways to extract reliable condition information from complex, noisy, and heterogeneous data. Despite these advances, real-world SHM still faces major challenges, including environmental and operational variability, sparse or imperfect measurements, limited labeled damage data, and the need for interpretable, uncertainty-aware models that generalize across assets and operating conditions. Progress in this area is essential for safer infrastructure, more efficient maintenance, and risk-informed decision-making.
We are pleased to invite you to submit your latest research to the Special Issue “Advanced Sensing and Intelligent Modeling for Structural Health Monitoring.” This Special Issue aims to showcase state-of-the-art developments that integrate next-generation sensing with robust, explainable, and uncertainty-aware modeling to improve damage detection, diagnosis, prognosis, and decision support for civil and mechanical systems.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Advanced sensing and instrumentation for SHM (e.g., fiber-optic sensing, wireless sensing, remote sensing, vision-based monitoring, guided waves, radar/LiDAR, multi-physics sensing).
- Multi-modal data fusion and robust SHM analytics (e.g., baseline-free methods, anomaly detection, domain adaptation/transfer learning, data quality control, missing data and sensor fault handling).
- Intelligent and physics-consistent modeling for SHM (e.g., deep learning, graph learning, foundation models, physics-informed/hybrid modeling, digital twins, Bayesian inference and uncertainty quantification, interpretable and trustworthy AI).
We look forward to receiving your contributions.
Dr. Hong Pan
Dr. Zhibin Lin
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- advanced sensing
- damage detection
- machine learning
- deep learning
- physical-informed machine learning
- uncertainty quantification
- trustworthy AI
- generative AI
- digital twins
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