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Recent Advances: Structural Health Monitoring in Civil Construction
This special issue belongs to the section “Civil Engineering“.
Special Issue Information
Dear Colleagues,
Structural health monitoring (SHM) technologies have become integral to civil engineering, ensuring the longevity and safety of infrastructure. With the increasing demands on structural integrity due to environmental and operational stresses, modern SHM methodologies are advancing to meet new performance, durability, and sustainability requirements. SHM is not only growing in field applications but also within laboratories, enabling researchers to understand and design the materials. This Special Issue aims to compile the latest research and technological advancements in SHM to promote the efficient monitoring, diagnosis, and maintenance of civil infrastructure.
Objectives and Scope:
This Special Issue will focus on the latest developments, applications, and theoretical advancements in SHM technologies, specifically tailored to civil engineering applications. This Issue will encourage contributions that focus on both experimental and computational aspects and seek papers that highlight interdisciplinary approaches. Areas of interest include, but are not limited to, the following:
Sensors and Sensing Techniques: the development of robust and durable sensors for monitoring structural health in civil structures, such as bridges, buildings, dams, and offshore platforms.
Data Analytics and AI for SHM: machine learning, deep learning, and AI-based approaches to process SHM data and predict structural performance and potential failure.
Non-Destructive Evaluation (NDE): innovations in NDE methods, including ultrasonic testing, radiographic testing, and acoustic emission, for real-time structural monitoring.
Online and Edge Monitoring: wireless sensor networks, Internet of Things (IoT), and remote sensing for continuous and automated SHM.
Emerging Technologies in SHM: applications of novel materials (e.g., smart concrete, self-sensing composites) and advanced imaging techniques for SHM.
Case Studies and Real-World Applications: practical implementations of SHM in real-world civil engineering projects, illustrating challenges and lessons learned.
Dr. Avik Kumar DAS
Dr. Giuseppe Lacidogna
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. Applied Sciences 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 2400 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
- smart sensors
- smart materials
- machine learning
- structural health monitoring
- digital twin

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