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Buildings
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Recent Advances in Structural Health Monitoring of Buildings and Infrastructures
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Recent Advances in Structural Health Monitoring of Buildings and Infrastructures

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 8043

Special Issue Editors

Prof. Dr. Minshui Huang

E-Mail Website
Guest Editor
School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan, China
Interests: health monitoring; damage identification; dynamic analysis; condition assessment; structural optimizaiton; intelligent algorithms; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Jianfeng Gu

E-Mail Website
Guest Editor
School of Civil Engineering and Architecture, Wuhan Institute of Technology, 693 Xiongchu Ave., Wuhan, China
Interests: structural health monitoring; damage identification; structural dynamics; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the rapid development of sensing technologies and data analysis methods in recent years, the structural health monitoring (SHM) of buildings and infrastructures has become exceedingly popular in the last decade.

In this regard, this Special Issue “Recent advances in structural health monitoring of buildings and infrastructures” aims to present novel technologies and methodologies used to study buildings and infrastructures via SHM, such as sensing techniques, data processing, the machine learning of high-rise buildings, bridges, tunnels, and dams, etc. Both new methodologies and technological advancements are welcome, as well as specific laboratory or in situ experimental studies or validations.

This Special Issue provides an integrated view of the problems associated with the health monitoring of buildings under construction and the structural damage identification of in-service buildings and infrastructures.

Prof. Minshui Huang warmly invites authors to submit their papers for potential inclusion in this Special Issue on structural health monitoring of buildings and infrastructures under construction and in operation.

Prof. Dr. Minshui Huang
Dr. Jianfeng Gu
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

  • damage identification
  • inverse problems in structural engineering
  • deep learning in SHM
  • smartphone sensing, sensor network, optimal sensor placement and instrumentation design
  • environmental and operational factors in SHM
  • static load testing and vibration testing of structure
  • swarm intelligence algorithms
  • sustainable developments in SHM
  • other related topics in SHM

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

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Research

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23 pages, 7487 KiB  
Article
Bolt Loosening and Preload Loss Detection Technology Based on Machine Vision
by Zhiqiang Shang, Xi Qin, Zejun Zhang and Hongtao Jiang
Buildings 2024, 14(12), 3897; https://doi.org/10.3390/buildings14123897 - 5 Dec 2024
Viewed by 982
Abstract
Steel bridges often experience bolt loosening and even fatigue fracture due to fatigue load, forced vibration, and other factors during operation, affecting structural safety. This study proposes a high-precision bolt key point positioning and recognition method based on deep learning to address the [...] Read more.
Steel bridges often experience bolt loosening and even fatigue fracture due to fatigue load, forced vibration, and other factors during operation, affecting structural safety. This study proposes a high-precision bolt key point positioning and recognition method based on deep learning to address the high cost, low efficiency, and poor safety of current bolt loosening identification methods. Additionally, a bolt loosening angle recognition method is proposed based on digital image processing technology. Using image recognition data, the angle-preload curve is revised. The established correlation between loosening angle and pretension for commonly utilized high-strength bolts provides a benchmark for identifying loosening angles. This finding lays a theoretical foundation for defining effective detection intervals in future bolt loosening recognition systems. Consequently, it enhances the system’s ability to deliver timely warnings, facilitating swift manual inspections and repairs. Full article
(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring of Buildings and Infrastructures)
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21 pages, 4266 KiB  
Article
Deep-Learning-Based Strong Ground Motion Signal Prediction in Real Time
by Mohammad AlHamaydeh, Sara Tellab and Usman Tariq
Buildings 2024, 14(5), 1267; https://doi.org/10.3390/buildings14051267 - 1 May 2024
Viewed by 1414
Abstract
Processing ground motion signals at early stages can be advantageous for issuing public warnings, deploying first-responder teams, and other time-sensitive measures. Multiple Deep Learning (DL) models are presented herein, which can predict triaxial ground motion accelerations upon processing the first-arriving 0.5 s of [...] Read more.
Processing ground motion signals at early stages can be advantageous for issuing public warnings, deploying first-responder teams, and other time-sensitive measures. Multiple Deep Learning (DL) models are presented herein, which can predict triaxial ground motion accelerations upon processing the first-arriving 0.5 s of recorded acceleration measurements. Principal Component Analysis (PCA) and the K-means clustering algorithm were utilized to cluster 17,602 accelerograms into 3 clusters using their metadata. The accelerograms were divided into 1 million input–output pairs for training, 100,000 for validation, and 420,000 for testing. Several non-overlapping forecast horizons were explored (1, 10, 50, 100, and 200 points). Various architectures of Artificial Neural Networks (ANNs) were trained and tested, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and CNN-LSTMs. The utilized training methodology applied different aspects of supervised and unsupervised learning. The LSTM model demonstrated superior performance in terms of short-term prediction. A prediction horizon of 10 timesteps in the future with a Root Mean Squared Error (RMSE) value of 8.43 × 10−6 g was achieved. In other words, the LSTM model exhibited a performance improvement of 95% compared to the baseline benchmark, i.e., ANN. It is worth noting that all the considered models exhibited acceptable real-time performance (0.01 s) when running in testing mode. The CNN model demonstrated the fastest computational performance among all models. It predicts ground accelerations under 0.5 ms on an Intel Core i9-10900X CPU (10 cores). The models allow for the implementation of real-time structural control responses via intelligent seismic protection systems (e.g., magneto-rheological (MR) dampers). Full article
(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring of Buildings and Infrastructures)
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Review

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25 pages, 1101 KiB  
Review
The Sustainable Development of Bridges in China: Collapse Cause Analysis, Existing Management Dilemmas and Potential Solutions
by Dina Tang and Minshui Huang
Buildings 2024, 14(2), 419; https://doi.org/10.3390/buildings14020419 - 3 Feb 2024
Cited by 6 | Viewed by 5105
Abstract
The construction of sustainable bridge projects has become a global mission and challenge in the 21st century. Unfortunately, there has been a rise in bridge collapse incidents due to various factors in recent years both during the construction and service phases. These incidents [...] Read more.
The construction of sustainable bridge projects has become a global mission and challenge in the 21st century. Unfortunately, there has been a rise in bridge collapse incidents due to various factors in recent years both during the construction and service phases. These incidents have resulted in significant loss of life and property damage, exacerbating the five sustainable development issues faced by bridge engineering: natural, resource, environmental, social, and economic factors. As a result, the prevention and resolution of bridge collapse accidents have garnered attention from professionals, research institutions, and government departments, making it a prominent research area. In line with the sustainable development concept of bridge engineering, this article classifies the causes of bridge collapses into two categories: those occurring during the construction phase and those happening during the service phase; the latter includes lack of inspection, maintenance and management, external natural factors, and human factors. Furthermore, this article thoroughly examines the existing national management framework, identifying the dilemmas that hinder its effectiveness in regulating bridge collapse prevention. Finally, several effective suggestions are proposed for the prevention of bridge collapse incidents. These recommendations aim to motivate governments, project owners, designers, constructors, managers, and users to actively develop and promote high-quality sustainable bridges. Full article
(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring of Buildings and Infrastructures)
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