Advances in AI-Driven Structural Health Monitoring
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 233
Special Issue Editors
Interests: structural health monitoring (SHM); damage detection; deep learning; artificial intelligence; rubber bearing
Interests: interdisciplinary application of artificial intelligence in civil engineering disaster prevention and mitigation; including bridge structural reliability calculation methods; bridge wind resistance; structural health monitoring
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) is transforming structural health monitoring (SHM) by enabling more efficient, accurate, and data-driven assessment of civil infrastructure throughout its life cycle. Buildings, bridges, tunnels, pipelines, and other assets are continuously affected by long-term deterioration processes, such as corrosion, fatigue, concrete degradation, settlement, scour, and climate-related stressors, as well as by daily operational loads.
Traditional inspection and monitoring methods often face difficulties in detecting subtle damage evolution and in converting large volumes of heterogeneous sensing data into timely maintenance decisions. Recent advances in machine learning, deep learning, computer vision, and data fusion provide new opportunities for damage detection, condition assessment, anomaly identification, and predictive maintenance based on vibration, strain, image, acoustic, and environmental data.
This Special Issue welcomes original research and review articles on AI-enabled SHM, with the aim of promoting safer, smarter, and more sustainable infrastructure systems.
Dr. Yi Zeng
Dr. Zhengliang Xiang
Dr. Yixian Li
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 250 words) can be sent to the Editorial Office for assessment.
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. Buildings 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 2600 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
- structural health monitoring (SHM)
- damage detection
- deep learning
- artificial intelligence
- machine learning
- digital twin
- life cycle management
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