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Advanced Structural Health Monitoring in Civil Engineering
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Advanced Structural Health Monitoring in Civil Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2232

Special Issue Editors

Prof. Dr. Wongi S. Na

E-Mail Website
Guest Editor
Department of Civil Engineering, Seoul National University of Science and Technology, Seoul 010811, Republic of Korea
Interests: structural health monitoring; safety evaluation; artificial intelligence; smart construction
Prof. Dr. Seungseop Jin

E-Mail Website
Guest Editor
Department of Civil & Environmental Engineering, Sejong University, Seoul, Republic of Korea
Interests: Bayesian inference; Gaussian process; SHM; system identification

Special Issue Information

Dear Colleagues,

This Special Issue aims to gather the latest research findings, innovative methodologies, and significant structural health monitoring (SHM) advancements for civil engineering structures.

Structural health monitoring has become an essential aspect of modern civil engineering, crucial in ensuring the safety, reliability, and longevity of infrastructures such as bridges, buildings, dams, and tunnels. Integrating advanced sensing technologies, data analysis techniques, and intelligent algorithms transforms SHM, enabling real-time monitoring and predictive maintenance of civil structures. As our infrastructure ages and new challenges emerge, sophisticated SHM systems become increasingly vital to prevent catastrophic failures and optimize maintenance and repair processes. This Special Issue invites original research articles, communication, and review papers that address various aspects of SHM, including but not limited to:

  • Review of SHM techniques for civil engineering structures;
  • Innovative sensing technologies and sensor development for SHM;
  • Signal processing and data analysis for SHM;
  • Case studies of SHM for civil engineering structures.

Prof. Dr. Wongi S. Na
Prof. Dr. Seungseop Jin
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. Applied Sciences 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 2400 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 (SHM)
  • sensing technology
  • data acquisition
  • machine learning
  • damage detection
  • infrastructure safety
  • real-time monitoring
  • non-destructive testing

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

Published Papers (2 papers)

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Research

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26 pages, 6511 KiB  
Article
Dynamic Response Analysis of Asphalt Pavement under Pavement-Unevenness Excitation
by Heng Liu, Xiaoge Liu, Ankang Wei and Yingchun Cai
Appl. Sci. 2024, 14(19), 8822; https://doi.org/10.3390/app14198822 - 30 Sep 2024
Viewed by 1121
Abstract
This paper investigates and analyzes the dynamic response of asphalt pavement under pavement-unevenness excitation based on an orthogonal vector function system and efficient DVP (dual variable and position) method. Firstly, starting from the pavement unevenness of the vehicle excitation source, the pavement-unevenness excitation [...] Read more.
This paper investigates and analyzes the dynamic response of asphalt pavement under pavement-unevenness excitation based on an orthogonal vector function system and efficient DVP (dual variable and position) method. Firstly, starting from the pavement unevenness of the vehicle excitation source, the pavement-unevenness excitation is established by using the filtered white-noise method, and the random load of the vehicle model is obtained by simulation. Then, based on the basic governing equation of the road-surface problem under the random load, the analytical solution of the road-surface mechanical response is obtained by using the orthogonal vector function system and DVP method. The effects of pavement-unevenness grade, vehicle speed, vehicle load, interlayer contact condition, and transverse isotropy on the mechanical response of the road surface are analyzed via the analytical results. The results show that DVP can effectively solve the dynamic response of pavements under the excitation of pavement unevenness; in addition, it can also be applied to certain situations, such as transverse isotropy of materials and interface conditions. The results show that the pavement unevenness does not affect the average stress and strain of each layer but has a significant effect on the peak value and dispersion degree. An increase in vehicle speed causes a peak in strain and a larger coefficient of variation. Poor bonding between interfaces can lead to increased stress and strain at the bottom of the surface layer. Full article
(This article belongs to the Special Issue Advanced Structural Health Monitoring in Civil Engineering)
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Review

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40 pages, 989 KiB  
Review
Structural Health Monitoring of Concrete Bridges Through Artificial Intelligence: A Narrative Review
by Vijay Prakash, Carl James Debono, Muhammad Ali Musarat, Ruben Paul Borg, Dylan Seychell, Wei Ding and Jiangpeng Shu
Appl. Sci. 2025, 15(9), 4855; https://doi.org/10.3390/app15094855 - 27 Apr 2025
Viewed by 456
Abstract
Concrete has been one of the most essential building materials for decades, valued for its durability, cost efficiency, and wide availability of required components. Over time, the number of concrete bridges has been drastically increasing, highlighting the need for timely structural health monitoring [...] Read more.
Concrete has been one of the most essential building materials for decades, valued for its durability, cost efficiency, and wide availability of required components. Over time, the number of concrete bridges has been drastically increasing, highlighting the need for timely structural health monitoring (SHM) to ensure their safety and long-term durability. Therefore, a narrative review was conducted to examine the use of Artificial Intelligence (AI)-integrated techniques in the SHM of concrete bridges for more effective monitoring. Moreover, this review also examined significant damage observed in various types of concrete bridges, with particular emphasis on concrete cracking, detection methods, and identification accuracy. Evidence points to the fact that the conventional SHM of concrete bridges relies on manual inspections that are time-consuming, error-prone, and require frequent checks, while AI-driven SHM methods have emerged as promising alternatives, especially through Machine Learning- and Deep Learning-based solutions. In addition, it was noticeable that integrating multimodal AI approaches improved the accuracy and reliability of concrete bridge assessments. Furthermore, this review is essential as it also addresses critical gaps in SHM approaches and suggests developing more accurate detection techniques, providing enhanced spatial resolution for monitoring concrete bridges. Full article
(This article belongs to the Special Issue Advanced Structural Health Monitoring in Civil Engineering)
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