Recent Advances in Structural Health Monitoring
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".
Deadline for manuscript submissions: 15 July 2026 | Viewed by 2025
Special Issue Editors
Interests: structural health monitoring; structural dynamics; full-scale vibration test; machine learning and its application in civil engineering; operational modal analysis
Interests: structural health monitoring; smart materials and structures; piezoelectric sensors; fiber-optic sensors
Special Issues, Collections and Topics in MDPI journals
Interests: structural health monitoring; structural dynamics; full-scale vibration test; machine learning and its application in civil engineering; operational modal analysis
Interests: structural health monitoring; structural dynamics; digital twin
Special Issue Information
Dear Colleagues,
Structural health monitoring (SHM) is critical for ensuring the safety, resilience, and sustainability of civil infrastructure. Climate change and the global drive towards zero-carbon solutions further highlight the need for efficient monitoring strategies that optimize maintenance, extend service life, and reduce environmental impact. As structures become more complex and exposed to evolving risks, advancements in sensing technologies and data analytics are transforming SHM into a more intelligent and autonomous discipline. Recent developments in fiber-optic sensors, wireless networks, UAV-based inspections, and computer vision techniques have significantly improved real-time structural assessment. Meanwhile, machine learning is enabling automated damage and anomaly detection, predictive maintenance, and data-driven decision making. The integration of digital twins, data fusion techniques, and hybrid physics-based and data-driven approaches further enhances monitoring accuracy and operational efficiency.
This Special Issue invites cutting-edge research on SHM advancements, including but not limited to the following:
- Population-based SHM;
- Advanced sensing and instrumentation in SHM;
- Computer vision techniques for structural assessment;
- Machine learning applications in SHM;
- Digital twins for structural performance monitoring;
- Data fusion and integration of monitoring data;
- Decision-making frameworks for predictive maintenance and risk assessment;
- Case studies in SHM applications and lessons learnt.
Dr. Zuo Zhu
Dr. Weijie Li
Dr. Yanlong Xie
Dr. Yichen Zhu
Dr. Miaomin Wang
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
- population-based SHM
- advanced sensing and instrumentation in SHM
- computer vision techniques for structural assessment
- machine learning applications in SHM
- digital twins for structural performance monitoring
- data fusion and integration of monitoring data
- decision-making frameworks for predictive maintenance and risk assessment
- case studies in SHM applications and lessons learnt
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