Remote Sensing and Artificial Intelligence for Structural Health Monitoring

Dear Colleagues,

Structural Health Monitoring (SHM) plays a vital role in ensuring the safety, functionality, and longevity of critical infrastructure, including bridges, tunnels, dams, high-rise buildings, and other large-scale civil structures. With the increasing complexity, aging, and exposure of infrastructure systems to extreme environmental conditions and natural disasters, there is a growing demand for advanced and intelligent monitoring solutions that can provide early warnings of potential damage or failure, enable condition-based maintenance, and ultimately enhance public safety and asset management efficiency.

Recent advancements in remote sensing technologies have opened new avenues for high-resolution, real-time, and scalable SHM solutions. Technologies such as Global Navigation Satellite Systems (GNSSs), Light Detection and Ranging (LiDAR), Ground-Based Radar Interferometry (GBRI), Interferometric Synthetic Aperture Radar (InSAR),  Inertial Measurement Units (IMUs), and other in situ sensory solutions are increasingly being employed for accurate monitoring of structural displacements, deformations, vibrations, and long-term stability. These sensors offer complementary capabilities in terms of spatial and temporal resolution, sensitivity, and operating conditions, making them highly suitable for multi-sensor data fusion frameworks in SHM applications.

At the same time, the exceptional growth of data generated from these remote sensing systems necessitates the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques for effective analysis, anomaly detection, and predictive modeling. AI methods are particularly well suited for processing large-scale heterogeneous data, identifying patterns, learning the complex behavior of structures under various conditions, and enabling intelligent decision-making. The fusion of AI with remote sensing thus forms a powerful toolset for autonomous and robust SHM systems.

This Special Issue aims to showcase recent advances and novel methodologies at the intersection of remote sensing technologies and physics- and AI-driven data analytics for SHM. It invites contributions that explore the integration of GNSS, LiDAR, InSAR, IMU, and other sensing modalities, along with advanced analytic methods and machine learning and deep learning algorithms, to detect, quantify, and predict structural conditions and potential failures. By fostering interdisciplinary collaboration among geospatial scientists, civil engineers, data scientists, and system developers, this issue seeks to promote innovative research and practical solutions for the sustainable and intelligent management of infrastructure.

Suggested themes:

• Remote sensing applications in monitoring bridges, tunnels, dams, high-rise buildings, and heritage structures.
• Multi-sensor data fusion for enhanced structural deformation and damage detection.
• Spatiotemporal modeling of structural behaviors using remote sensing data.
• Physical modeling and geometric reconstruction of infrastructure from remote sensing observations.
• Machine learning and deep learning methods for anomaly detection and damage classification.
• Predictive modeling of structural health conditions using time-series remote sensing data.
• Long-term deformation and settlement analysis using remote sensing archives.
• Pattern recognition for crack detection, corrosion identification, and surface degradation.
• Real-world applications of remote sensing and AI in post-disaster damage assessment.
• Implementation of operational SHM systems using remote sensing and AI.
• Validation and benchmarking of SHM models with ground truth data.

Article Types:

• Original research articles: presenting novel algorithms, methodologies, or experimental results.
• Review articles: providing comprehensive overviews of the state of the art in relevant subfields.
• Technical notes: introducing innovative techniques or tools with preliminary demonstrations.
• Case studies: detailing practical applications and lessons learned from field deployments.

Prof. Dr. Xiangdong An
Dr. Fan Zhang
Prof. Dr. Rong Qu
Dr. Yilin Xie
Prof. Dr. Vassilis Gikas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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.

• structural health monitoring (SHM)
• multiple source fusion of GNSS, LiDAR, and InSAR
• data fusion
• machine learning
• anomaly detection
• infrastructure monitoring