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Structural Health Monitoring and Intelligent Safety Assessment in Civil Engineering
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Structural Health Monitoring and Intelligent Safety Assessment in Civil Engineering

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

Deadline for manuscript submissions: 20 August 2025 | Viewed by 1585

Special Issue Editors

Prof. Dr. Bin Huang

E-Mail Website
Guest Editor
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
Interests: structural health monitoring; stochastic damage identification; stochastic mechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Zhifeng Wu

E-Mail Website
Guest Editor
1. School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430073, China
2. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: structural health monitoring; stochastic model updating; stochastic damage identification; safety assessment
Dr. Hui Chen

E-Mail Website
Guest Editor
College of Post and Telecommunication, Wuhan Institute of Technology, Wuhan 430074, China
Interests: structural health monitoring; stochastic model updating; stochastic damage identification

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) is an essential process that involves the continuous assessment of the conditions and performances of structures, such as bridges, buildings, and dams. By utilizing a combination of sensors and data analysis techniques, SHM systems can detect and evaluate potential issues such as cracks, vibrations, and material degradation over time. This proactive approach enables engineers to make informed decisions regarding maintenance and repair, ultimately enhancing safety and extending the lifespans of structures.

Damage detection analyzes the collected data to pinpoint specific faults, such as cracks or material degradation, and assess their severity. Advanced methodologies, including machine learning and data-driven techniques, enhance the accuracy of damage assessment, allowing for more effective decision-making.

Model updating is a key aspect of structural health monitoring and damage detection. When considering the randomness of structural modeling and measurement data, stochastic model updating becomes unavoidable. To solve stochastic model updating problems, the stochastic finite element methods can play a significant role instead of the usually used Bayesian methods. This direction is a hot topic in current structural model updating, which has very good application prospects in practical structural health monitoring.

This Special Issue, titled “Structural Health Monitoring and Intelligent Safety Assessment in Civil Engineering”, will provide an overview of the existing knowledge on new approaches for civil engineering structural monitoring. Original research, theoretical and experimental works, case studies, and comprehensive review papers are requested for possible publication. Topics relevant to this Special Issue include, but are not limited to, the following subjects:

  • New approaches for SHM;
  • New sensors and sensorial networks;
  • Monitoring strategies for construction sustainability;
  • New surrogate modeling techniques tailored to some computationally demanding problems;
  • Model updating with structural health monitoring;
  • Structural health monitoring engineering practices that accommodate uncertainties;
  • Novel uncertainty quantification techniques in model updating;
  • Machine learning and data-driven techniques;
  • Intelligent techniques for SHM;
  • Intelligent construction;
  • Finite element modeling techniques;
  • Finite element analysis;
  • Safety assessment;
  • Resilience against disasters;
  • Risk assessment;
  • Disaster prevention and reduction;
  • Building life cycle assessment.

Prof. Dr. Bin Huang
Dr. Zhifeng Wu
Dr. Hui Chen
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

  • structural health monitoring
  • damage detection
  • model updating
  • machine learning
  • intelligent techniques
  • finite element modeling
  • resilience against disasters
  • risk assessment
  • disaster prevention and reduction
  • building life cycle assessment

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

Published Papers (3 papers)

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Research

21 pages, 6022 KiB  
Article
Long-Term Performance Analysis of Steel–Concrete Composite Beams Based on Finite Element Model Updating
by Yanan Wu, Yunchong Chen, Jice Zeng, Yu Jiang and Zhibin Liu
Buildings 2025, 15(8), 1374; https://doi.org/10.3390/buildings15081374 - 20 Apr 2025
Viewed by 225
Abstract
This study introduces a Bayesian model updating approach for analyzing the long-term performance of steel–concrete composite beams (SCBs). Two nominally identical SCBs (SCB-1 and SCB-2) were designed, fabricated, and subjected to modal testing. Despite their identical design parameters, notable differences were observed in [...] Read more.
This study introduces a Bayesian model updating approach for analyzing the long-term performance of steel–concrete composite beams (SCBs). Two nominally identical SCBs (SCB-1 and SCB-2) were designed, fabricated, and subjected to modal testing. Despite their identical design parameters, notable differences were observed in their frequencies and mode shapes during testing. Initial finite element model (FEM) analyses, developed under testing conditions, revealed notable discrepancies between theoretically computed values and the results of modal testing. To resolve these differences, the FEM was updated using the Bayesian approach, integrating the dynamic test data to enhance model accuracy. The updated FEM was subsequently employed to assess the long-term performance of the SCBs, with a particular focus on the time-dependent effects of concrete shrinkage and creep in deflection calculations. The findings reveal substantial differences in the long-term deflection predictions between the updated and initial FEM. Specifically, the long-term deflection of SCB-1 increased by 13.1%, whereas that of SCB-2 decreased by 9.8%, leading to an overall difference of 25.3% between the two beams. These findings underscore the considerable impact of fabrication errors and material inhomogeneities on structural performance, highlighting the limitations of initial FEM based solely on test parameters in accurately capturing actual behavior. Consequently, the study emphasizes the critical role of model updating in accurately predicting the long-term performance of SCBs. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Intelligent Safety Assessment in Civil Engineering)
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14 pages, 4099 KiB  
Article
Critical Region Identification of Cable-Stayed Bridges Based on Eigensensitivity
by Jiajing Li, Meng Meng and Qiaoyun Wu
Buildings 2025, 15(7), 1038; https://doi.org/10.3390/buildings15071038 - 24 Mar 2025
Viewed by 167
Abstract
Conducting health monitoring on entire large-scale structures is challenging. Compared to non-critical regions, local damage in critical regions significantly impacts the overall structural performance, with even minor damage posing a threat to structural safety. Therefore, identifying the critical regions of a structure is [...] Read more.
Conducting health monitoring on entire large-scale structures is challenging. Compared to non-critical regions, local damage in critical regions significantly impacts the overall structural performance, with even minor damage posing a threat to structural safety. Therefore, identifying the critical regions of a structure is essential to enable prioritized and focused monitoring, evaluation, and management. This paper proposes a method for identifying critical regions in cable-stayed bridges based on dynamic eigensensitivity analysis. The method integrates the sensitivity of multi-order eigenvalues and eigenvectors with respect to elemental stiffness parameters, designating regions with high sensitivity values as critical. The results demonstrate that the midspan region of the main girder, the longest stay cable, and the junctions between the upper, middle, and lower bridge towers and the foundation are identified as critical regions in a cable-stayed bridge. These findings are consistent with established engineering experience. The proposed critical region identification method holds significant potential for improving the efficiency of health monitoring and assessment, as well as optimizing the allocation of manpower and material resources. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Intelligent Safety Assessment in Civil Engineering)
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26 pages, 26206 KiB  
Article
Unveiling the Influencing Factors of the Residual Life of Historical Buildings: A Study of the Wuhan Lutheran Missions Home and Agency Building
by Bo Huang, Xueqi Liu, Lanjun Liu, Zhiyong Li, Zhifeng Wu, Bin Huang and Zimo Jia
Buildings 2025, 15(2), 246; https://doi.org/10.3390/buildings15020246 - 16 Jan 2025
Viewed by 720
Abstract
The development of a city needs the accumulation of culture, and historical buildings are the most direct witness of the rise and fall of a city. Like the human body, historical buildings have a certain life cycle, but the acceleration of urbanization and [...] Read more.
The development of a city needs the accumulation of culture, and historical buildings are the most direct witness of the rise and fall of a city. Like the human body, historical buildings have a certain life cycle, but the acceleration of urbanization and unreasonable use cause an irreversible reduction in the remaining life of historical buildings. There is a notable lack of quantitative analysis regarding the residual life of historical buildings. Therefore, identifying the factors that influence their residual life is crucial for both preserving these buildings and sustaining urban culture. In order to obtain a more accurate correlation degree of influencing factors, a systematic-analysis model of influencing factors on the residual life of historical buildings based on the entropy weight method (EWM) and the grey relation analysis method (GRA) was established, so as to excavate the mechanism of the influencing factors on the residual life of historical buildings, accurately identify the main influencing factors on the residual life of historical buildings, and propose preventive measures. The results show that the structural system has the greatest influence on the residual life of historical buildings, followed by the enclosure system, and the equipment system. The research findings offer valuable insights for extending the residual life of historical buildings in the future. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Intelligent Safety Assessment in Civil Engineering)
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