Special Issue "Recent Advances in Structural Health Monitoring and Maintenance of Buildings"
Deadline for manuscript submissions: 20 February 2024 | Viewed by 733
Interests: smart structure; structural health monitoring; structural control; earthquake early warning
Interests: Internet of Things; structural health monitoring; digital twin; artificial intelligence; resilient infrastructure
Special Issues, Collections and Topics in MDPI journals
Interests: structural health monitoring; structural dynamics; finite element method; digital twin
Interests: structural health monitoring; earthquake early warning; earthquake engineering structural dynamics; signal processing and system identification; engineering dynamics; engineering statics
Because of the need to continuously meet operational safety and functionality goals during their long service life, structural health monitoring (SHM) of full-scale civil infrastructures is an inevitable research trend. SHM is an interdisciplinary multi-purpose subject combining signal processing, system identification, damage detection, health evaluation, and several other theories. In the past two decades, with advanced technologies such as sensing networks, data acquisition, communication, signal processing, information management, and intelligent algorithms, SHM technologies and corresponding developments applied to civil infrastructures, especially buildings, have attracted the interest of scientists and engineers. Nowadays, SHM for buildings has been successfully implemented many times. Future research is directed toward the integration of advanced sensing networks, large-scale data management, data mining, information fusion, and intelligent diagnosis.
Therefore, this Special Issue aims to collate the most recent research trends and advanced technologies in buildings SHM to track operational safety and functionality under long-term service and evaluate structural performance. We welcome papers on the following and related topics, including but not limited to:
- Structural health monitoring of buildings;
- Sensing networks and optimization;
- Signal processing;
- System identification;
- Damage detection, location, and quantification;
- Structural performance evaluation;
- Numerical modeling and model updating;
- Modal analysis;
- Artificial intelligence and data-driven approach;
- Environmental effect.
Dr. Shieh-Kung Huang
Dr. Yuguang Fu
Dr. Youqi Zhang
Dr. Ting-Yu Hsu
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 monthly 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.
- structural health monitoring
- sensing network
- system identification
- damage detection
- signal processing
- performance evaluation
- model updating
- modal analysis
- artificial intelligence
- environmental effect