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Structural Health Monitoring in Civil Engineering: From Damage Detection to...
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Structural Health Monitoring in Civil Engineering: From Damage Detection to Safety Assessment

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

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 1947

Special Issue Editors

Prof. Dr. Wensong Zhou

E-Mail Website
Guest Editor
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China
Interests: structural health monitoring; vibration; guided wave; structural sensing
Prof. Dr. Shuang Hou

E-Mail Website
Guest Editor
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China
Interests: seismic damage monitoring; ultrasonic wave monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Around the world, the time of the massive infrastructure construction has passed in most of countries, then the needs for operation and maintenance come as follows. Structural health monitoring, as one of the most effective approaches, is able to provide essential data on the structural state of buildings. In recent years, the rapid progress in structural sensing techniques and data processing has prompted structural health monitoring become a more credible approach for assessing civil structural states.

In all aspects of structural health monitoring, the essential issue is damage detection, which provides information directly related to structural performance. Therefore, this Special Issue, “Structural Health Monitoring in Civil Engineering: From Damage Detection to Safety Assessment”, invites researchers to submit their up-to-date studies on SHM and damage detection, including but not limited to the following:

  • Vibration-based structural health monitoring;
  • Wave-based structural health monitoring;
  • Optical fiber sensing in structural health;
  • Machine vision-based structural damage monitoring;
  • Piezoelectric smart aggregate-based damage monitoring;
  • Acoustics and ultrasonic methods.

Prof. Dr. Wensong Zhou
Prof. Dr. Shuang Hou
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 health monitoring
  • damage detection
  • vibration
  • machine vision
  • piezoceramics
  • acoustics and ultrasonics

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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

20 pages, 3095 KB  
Article
Effect of Temperature Changes on the Experimental Modal Analysis of a Galvanized Steel Benchmark Structure
by Sertaç Tuhta, Varol Koç and Furkan Günday
Buildings 2026, 16(5), 1069; https://doi.org/10.3390/buildings16051069 - 8 Mar 2026
Viewed by 367
Abstract
The effect of temperature change on modal frequencies leads to erroneous results in the detection of structural damage. Therefore, quantifying the temperature dependency of modal frequencies is essential to improve the reliability of damage identification. Due to the irregular and time-dependent nature of [...] Read more.
The effect of temperature change on modal frequencies leads to erroneous results in the detection of structural damage. Therefore, quantifying the temperature dependency of modal frequencies is essential to improve the reliability of damage identification. Due to the irregular and time-dependent nature of temperature distribution, reliable correlations between air or surface temperatures and modal frequencies cannot be established. In this study, the dynamic behavior of a galvanized steel benchmark structure was investigated at two controlled temperature levels (2 °C and 32 °C) using experimental modal analysis (EMA). The structure was excited using a shaking table, while ambient vibration signals recorded at ground level were used as pre-recorded excitation input to the shaking table. Modal parameters were identified using Enhanced Frequency Domain Decomposition (EFDD). The results showed that mode shapes remained consistent across temperature levels, whereas natural frequencies decreased by an average of 2.43%. The identified dynamic parameters exhibited an approximately linear trend with temperature change. These findings highlight the importance of considering temperature effects in experimental modal analysis of galvanized steel structures to avoid false damage detection. Full article
(This article belongs to the Special Issue Structural Health Monitoring in Civil Engineering: From Damage Detection to Safety Assessment)
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16 pages, 4551 KB  
Article
Comparative Study on Internal and External Damage Imaging Using Ultrasonic Guided Waves Within a Variational Bayesian PCA Framework
by Meijie Zhao, Xiayu Gao, Biao Wu, Jingliang Liu and Wensong Zhou
Buildings 2026, 16(1), 178; https://doi.org/10.3390/buildings16010178 - 31 Dec 2025
Viewed by 456
Abstract
This study presents a comparative analysis of the novel guided-wave-based imaging method that integrates variational Bayesian principal component analysis with time-delay strategy for detecting internal and external defects in plate-like structures. The performance of the conventional delay-and-sum imaging method deteriorates when the signal-to-noise [...] Read more.
This study presents a comparative analysis of the novel guided-wave-based imaging method that integrates variational Bayesian principal component analysis with time-delay strategy for detecting internal and external defects in plate-like structures. The performance of the conventional delay-and-sum imaging method deteriorates when the signal-to-noise ratio of signals is low or when other wave packets overlap with the defect scattering signal. The imaging method based on variational Bayesian principal component analysis analyzes the principal components and corresponding singular values of the time-delayed signal array, and the maximum singular value represents the contribution of the most principal component, serving as an indicator of the coherent defect-related wave packets. Thus, the defect can be highlighted by accounting for the effect of noise and wave packet interference on the time-delayed signal array. However, when defects are located outside the sensor network, the limited information available may reduce the imaging performance. Numerical simulations and experimental studies conducted on plate-like structures demonstrate the proposed method achieves higher imaging clarity and localization accuracy for the internal defect compared with the external defect, with the former exhibiting mm-level absolute localization errors. Full article
(This article belongs to the Special Issue Structural Health Monitoring in Civil Engineering: From Damage Detection to Safety Assessment)
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21 pages, 2758 KB  
Article
A Multisectional Deformation Reconstruction Method for Heavy Haul Railway Tunnels Using Point-Line Feature Fusion Sensing Information
by Xiaokun Yan, Zheng Zhou and Yang Liu
Buildings 2025, 15(22), 4052; https://doi.org/10.3390/buildings15224052 - 10 Nov 2025
Viewed by 605
Abstract
Deformation monitoring of heavy-haul railway tunnels is essential for ensuring operational safety. However, the spatial resolution of traditional point-based sensors is often insufficient for capturing the continuous deformation fields of tunnel structures. To overcome this limitation, in this study, densely distributed strain data [...] Read more.
Deformation monitoring of heavy-haul railway tunnels is essential for ensuring operational safety. However, the spatial resolution of traditional point-based sensors is often insufficient for capturing the continuous deformation fields of tunnel structures. To overcome this limitation, in this study, densely distributed strain data that are acquired through distributed fiber-optic sensing technology are used, and a deep learning-based inversion framework that integrates high-resolution strain measurements with sparsely sampled convergence data is introduced. By employing a hybrid particle swarm optimization–random forest (PSO-RF) algorithm, a deep correlation model is constructed to establish the relationship between distributed strain profiles and discrete convergence measurements. This approach enables the prediction of cross-sectional convergence across multiple tunnel sections by using only a limited set of calibrated convergence sensors in combination with continuous strain field data, thereby effectively achieving global deformation inversion with minimal hardware deployment. The proposed method was validated through numerical simulations and field tests by using monitoring data from a heavy-haul railway tunnel. The algorithm exhibited a mean absolute error of less than 2 mm, thus demonstrating its ability to supply high-resolution deformation field data that are essential for structural health monitoring and diagnostics of tunnel infrastructures. Full article
(This article belongs to the Special Issue Structural Health Monitoring in Civil Engineering: From Damage Detection to Safety Assessment)
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