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: 31 March 2026 | Viewed by 532

Special Issue Editors


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Guest Editor
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China
Interests: structural health monitoring; vibration; guided wave; structural sensing

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Guest Editor
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China
Interests: seismic damage monitoring; ultrasonic wave monitoring
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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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Published Papers (1 paper)

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Research

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 293
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
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