Machine Learning in Infrastructure Monitoring and Disaster Management
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 411
Special Issue Editors
Interests: structural health monitoring; disaster management; artificial intelligence; smart materials and structures; tropical island engineering; local damage monitoring method for reinforced concrete structures; health monitoring and multi-hazard protection of tropical island projects
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; hazard mitigation; wind/flood/seismic vulnerability; community resilience; catastrophic modeling
Interests: bridge retrofitting; non-destructive evaluation; composite materials; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancements in information and sensing technology have led to an exponential increase in data related to infrastructure monitoring and disaster management. This includes real-time monitoring time series, inspection images and videos, and hazard reconnaissance data. In today's era of big data, these valuable data sources serve as the new fuel driving society toward more resilient, reliable, and safer infrastructure facilities.
The vast amounts of data generated also necessitate more powerful analytical tools. Over the past few decades, advancements in machine learning (ML) techniques and hardware development have enabled significant progress in data cleansing, model training, and deployment for structural health monitoring and disaster management. ML techniques allow researchers to leverage diverse data types to assess structural conditions in real or near-real time. However, the application of ML faces several challenges in infrastructure monitoring and disaster management, such as big data collection in harsh environments, robust optimization in model training, the stability of model performance, and sensing techniques and hardware limitations.
This Special Issue delves into ML in infrastructure monitoring and disaster management, covering topics such as data collection and storage, ML applications, algorithm development, and sensing advancements. Progress in this field will lead us toward a safer and more resilient community.
Dr. Haibin Zhang
Dr. Xinzhe Yuan
Dr. Xingxing Zou
Dr. Tarutal Ghosh Mondal
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. 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
- machine learning
- infrastructure
- structural health monitoring
- smart materials and structures
- disaster management
- optimization
- resilience
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