Conventional and Emerging Methods in Structural Monitoring: Bridging the Past and Future
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".
Deadline for manuscript submissions: 10 August 2026 | Viewed by 1585
Editors
Interests: pantograph-catenary interaction; structural monitoring; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent construction of bridges; intelligent operation; bridge performance improvement and reinforcement; development of novel materials; digital twins
Interests: digital twins; structural integrity; prognostic assessment; transformer; wind energy structure; bridge engineering; intelligent operation and maintenance
Interests: Bridge engineering; Wind engineering; Track engineering; Probability evaluation; Train safety assessment and prediction
Interests: structural health monitoring; computer vision; vibration measurement; smart construction; artificial intelligence
Special Issue Information
Dear Colleagues,
Structural monitoring is crucial for ensuring the safety, durability, and resilience of civil infrastructure, including bridges, buildings, dams, and tunnels. While traditional methods (e.g., strain gauges and accelerometers) have long been the backbone of structural health monitoring (SHM), rapid advancements in sensing technologies, data analytics, and automation are revolutionizing the field. Many industries still depend on traditional systems due to their reliability; however, innovative techniques (e.g., computer vision, wireless sensor networks, and digital twins) offer unprecedented precision, scalability, and cost efficiency. The rise of AI/ML, IoT, and big data analytics has enabled predictive maintenance and real-time decision-making. This Special Issue seeks to critically examine the synergies and trade-offs between conventional and emerging approaches, fostering a multidisciplinary dialogue to address pressing challenges in infrastructure resilience, sustainability, and safety.
Potential topics for this issue include, but are not limited to, the following:
- Traditional techniques: strain gauges, accelerometers, and manual visual inspections.
- Innovative solutions: computer vision, drone-based inspections, optical fiber sensors, IoT-enabled networks, and AI-driven damage detection.
- Hybrid approaches: integration of traditional systems with emerging technologies.
- Case studies: real-world applications demonstrating reliability and cost-effectiveness.
Dr. Tengjiao Jiang
Dr. Shaorui Wang
Dr. Junlin Heng
Dr. Ming Wang
Dr. Kui Luo
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-anonymized 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
- predictive maintenance
- nondestructive evaluation
- sensor technologies
- computer vision
- artificial intelligence
- deep learning
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