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Buildings
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Research in Structural Control and Monitoring
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Research in Structural Control and Monitoring

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: closed (20 December 2025) | Viewed by 2828

Special Issue Editors

Dr. Kun Liu

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Guest Editor
Key Laboratory of Structures Dynamic Behavior and Control of the Ministry of Education, School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China
Interests: structural control; structural health monitoring; inverse problem; seismic damage assessment
Dr. Yunxia Xia

E-Mail Website
Guest Editor
School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, China
Interests: structural health monitoring; signal processing; structural dynamics
Dr. Xiaohua Zhang

E-Mail Website
Guest Editor
College of Civil Engineering, Fuzhou University, Fuzhou 350108, China
Interests: structural health monitoring; sensor optimization; structural dynamics
Dr. Guibo Nie

E-Mail Website
Guest Editor
Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
Interests: structural seismic evaluation; structural dynamics; space structure; seismic performance evaluation

Special Issue Information

Dear Colleagues,

Structural control and monitoring are of great importance to maintain the safety of structures. Although the present methodologies and techniques are capable of resolving some problems, the emergence of nondestructive tests, machine learning in structural control and monitoring, image-based system evaluation, and structural assessments using multiple types of information demand the development of numerical simulation and the experimental demonstration of new methodologies and techniques.

The objective of this Special Issue is to present original research and review articles on the latest theoretical developments and experimental techniques in structural control and monitoring. Research areas may include (but are not limited to) the following topics:

  • Structural health monitoring;
  • Structural control-related techniques;
  • Nondestructive testing and evaluation;
  • Machine learning in structural control and monitoring;
  • Image-based system evaluation/identification;
  • Analytical and numerical simulation modeling of structures with damages;
  • Equipment evaluation using vibration information;
  • Advanced signal processing procedures for damage assessment;
  • Condition monitoring systems;
  • The optimal placement of sensors.

Dr. Kun Liu
Dr. Yunxia Xia
Dr. Xiaohua Zhang
Dr. Guibo Nie
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

  • structural control
  • structural health monitoring
  • sensor placement optimization
  • damage identification
  • inverse analysis
  • force identification
  • seismic damage
  • seismic performance

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

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Research

22 pages, 3418 KB  
Article
LGSTA-GNN: A Local-Global Spatiotemporal Attention Graph Neural Network for Bridge Structural Damage Detection
by Die Liu, Jianxi Yang, Jianming Li, Jingyuan Shen, Youjia Zhang, Lihua Chen and Lei Zhou
Buildings 2026, 16(2), 348; https://doi.org/10.3390/buildings16020348 - 14 Jan 2026
Cited by 2 | Viewed by 1006
Abstract
Accurate detection of structural damage is essential for ensuring the safety and reliability of bridges. However, traditional vibration-based approaches often struggle to capture rich feature representations and adequately model spatial dependencies among sensors. This study proposes a novel bridge damage detection framework, LGSTA-GNN, [...] Read more.
Accurate detection of structural damage is essential for ensuring the safety and reliability of bridges. However, traditional vibration-based approaches often struggle to capture rich feature representations and adequately model spatial dependencies among sensors. This study proposes a novel bridge damage detection framework, LGSTA-GNN, which integrates local–global spatiotemporal learning with graph neural networks. The framework first extracts multi-scale temporal–frequency features using a multi-scale feature extraction module. A local graph feature extraction module then models intrinsic spatial relationships through graph convolutions, while a global graph attention module adaptively captures inter-sensor dependencies by emphasizing structurally informative nodes. A benchmark dataset generated from a scaled bridge model under progressive damage states is used to evaluate the proposed method. Extensive experiments demonstrate that LGSTA-GNN outperforms multiple graph neural network variants and conventional deep learning techniques, achieving superior accuracy, precision, recall, and F1-score. The confusion matrix and t-SNE visualization further verify its enhanced discriminative capability and robustness. Ablation studies confirm the contribution of each module, highlighting the effectiveness of global attention in identifying subtle structural deterioration. Overall, LGSTA-GNN provides an effective and interpretable solution for intelligent bridge damage detection, with strong potential for practical structural health monitoring and real-time safety assessment. Full article
(This article belongs to the Special Issue Research in Structural Control and Monitoring)
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21 pages, 5964 KB  
Article
Research on Loosening Identification of High-Strength Bolts Based on Relaxor Piezoelectric Sensor
by Ruisheng Feng, Chao Wu, Youjia Zhang, Zijian Pan and Haiming Liu
Buildings 2025, 15(11), 1867; https://doi.org/10.3390/buildings15111867 - 28 May 2025
Cited by 3 | Viewed by 1131
Abstract
Bridges play a key and controlling role in transportation systems. Steel bridges are favored for their high strength, good seismic performance, and convenient construction. As important node connectors of steel bridges, high-strength bolts are extremely susceptible to damage such as corrosion and loosening. [...] Read more.
Bridges play a key and controlling role in transportation systems. Steel bridges are favored for their high strength, good seismic performance, and convenient construction. As important node connectors of steel bridges, high-strength bolts are extremely susceptible to damage such as corrosion and loosening. Therefore, accurate identification of bolt loosening is crucial. First, a new type of adhesive piezoelectric sensor is designed and prepared using PMN-PT piezoelectric single-crystal materials. The PMN-PT sensor and polyvinylidene fluoride (PVDF) sensor are subjected to steel plate fixed frequency load and swept frequency load tests to test the performance of the two sensors. Then, a steel plate component connected by high-strength bolts is designed. By applying exciter square wave load to the structure, the vibration response characteristics of the structure are analyzed to identify the loosening of the bolts. In addition, a piezoelectric smart washer sensor is designed to make up for the shortcomings of the adhesive piezoelectric sensor, and the effectiveness of the piezoelectric smart washer sensor is verified. Finally, a bolt loosening index is proposed to quantitatively evaluate the looseness of the bolt. The results show that the sensitivity of the PMN-PT sensor is 21 times that of the PVDF sensor. Compared with the peak stress change, the natural frequency change is used to identify the bolt loosening more effectively. Piezoelectric smart washer sensor and bolt loosening indicator can be used for bolt loosening identification. Full article
(This article belongs to the Special Issue Research in Structural Control and Monitoring)
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