Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Advanced Structural Health Monitoring in Civil Engineering

Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Civil Engineering".

Deadline for manuscript submissions: closed (30 May 2026) | Viewed by 21051

Share This 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 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-anonymized 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

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 (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

29 pages, 51312 KB

Open AccessArticle

Digital Twin Model Reconstruction and Environmental Load Analysis of the Umbrella-Shaped Tensile Membrane Structure Based on 3D Point Clouds

by Qiu Yu, Xin Zhang, Zhiyang Jia and Chen Peng

Appl. Sci. 2026, 16(15), 7658; https://doi.org/10.3390/app16157658 - 2 Aug 2026

Viewed by 269

Abstract

Actual construction errors and accumulated damage seriously affect the spatial structural form of the tensile membrane structure, making it difficult to characterize the full-life spatial form of the physical tensile membrane structure based on the original theoretical model. There are still significant challenges in achieving the virtual–real correspondence of digital twin results based on the original theoretical design model. It is necessary to propose a high-precision digital twin model construction method that adapts to the full life cycle of the tensile membrane structure. For this reason, a refined digital twin model reconstruction and morphological deviation visualization method for the umbrella-shaped tensile membrane structure combined with 3D point clouds, computational geometry algorithms, and 3D3S finite element simulation modeling is proposed in this paper. Firstly, three-dimensional point cloud data of the umbrella-shaped membrane structure test bench were acquired, and the point cloud data were accurately registered based on the Iterative Closest Point (ICP) algorithm. Secondly, the physical membrane surface reconstruction was obtained by applying the Screened Poisson Surface Reconstruction (SPSR) algorithm. Subsequently, the 3D3S theoretical model of the umbrella-shaped tensile membrane structure was updated according to the contour of the measured membrane surface reconstruction model. Finally, global spatial geometric morphological deviation was compared among the theoretical models and the reconstructed physical model. Furthermore, different environmental load combinations were analyzed to support hazard warning of the refined digital twin model of the physical umbrella-shaped tensile membrane structure. The results show that the maximum spatial form deviation of the original theoretical model was 43.56 mm, whereas the maximum form deviation of the updated theoretical model was only 0.05 mm. The updated theoretical model accurately reflected the spatial form characteristics of the physical membrane structure and satisfied the requirement for digital twin physical–virtual consistency. In addition, the global stress distribution between the original/updated theoretical models differed markedly, and the updated theoretical model is more suitable for identifying weak areas and evaluating safety performance of the actual umbrella-shaped tensile membrane structure under different ultimate environmental loads. Full article

(This article belongs to the Special Issue Advanced Structural Health Monitoring in Civil Engineering)

► Show Figures

Figure 1

29 pages, 5473 KB

Open AccessArticle

Practical Instantaneous Cable Tension Estimation for Monitoring of Cable-Stayed Bridges

by Jungwook Seo, Changsu Shim and Jongchil Park

Appl. Sci. 2026, 16(13), 6340; https://doi.org/10.3390/app16136340 - 24 Jun 2026

Viewed by 438

Abstract

This study proposes a practical framework for estimating instantaneous stay-cable tension in cable-stayed bridges based on the first-order frequency moment (FFM). The proposed framework combines cepstrum-guided modal decomposition, FFM-based instantaneous frequency estimation, windowed cepstrum-based consistency assessment, and energy-weighted multi-modal averaging to estimate instantaneous cable tension from measured vibration responses. Unlike conventional time–frequency analysis methods that rely on local peak extraction in the time–frequency domain, the proposed approach directly estimates instantaneous frequency from the local time–frequency energy distribution, thereby improving tracking robustness while maintaining computational efficiency under operational conditions. Numerical validation demonstrates reliable instantaneous frequency tracking under noisy and non-stationary vibration conditions while maintaining low computational cost. Field validation using acceleration- and displacement-based measurements from an in-service bridge further confirms the capability of the proposed framework to capture vehicle-induced transient tension variations. The results indicate that the framework provides reliable and physically consistent cable tension information under real operational conditions. These characteristics, together with computational efficiency and compatibility with existing monitoring systems, indicate strong potential for near-real-time structural health monitoring applications. Full article

(This article belongs to the Special Issue Advanced Structural Health Monitoring in Civil Engineering)

► Show Figures

Figure 1

21 pages, 4489 KB

Open AccessArticle

Development of a Leak Detection System Based on Fiber Optic DTS Monitoring and Validation on a Full-Scale Model

by Diego Antolín-Cañada, Pedro Luis Lopez-Julian, Javier Pérez, Óscar Muñoz, Alejandro Acero-Oliete and Beniamino Russo

Appl. Sci. 2026, 16(1), 465; https://doi.org/10.3390/app16010465 - 1 Jan 2026

Viewed by 2152

Abstract

Leaks in ponds are a problem due to the loss of water resources, although the problem is greater when the ponds store livestock or agricultural waste (slurry or wastewater), in which case there is a risk of hydrogeological contamination of the environment. The proposed leak detection system is based on distributed temperature sensing (DTS) with hybrid fiber optics using the Raman effect. Using active detection techniques, i.e., applying a specific amount of electrical power to the copper wires that form part of the hybrid cable, it is possible to increase the temperature along the fiber and measure the thermal increments along it, detecting and locating the point of leakage. To validate the system, a full-scale prototype reservoir (25 m × 10 m × 3.5 m) was built, equipped with mechanisms to simulate leaks under the impermeable sheet that retains the reservoir’s contents. For environmental reasons, the tests were carried out with clean water. The results of the leak simulation showed significant differences in temperature increases due to the electrical pulse in the areas affected by the simulated leak (1 °C increase) and the areas not affected (5 °C increase). This technology, which uses hybrid fiber optics and a low-cost sensor, can be applied not only to ponds, but also to other types of infrastructure that store or retain liquids, such as dams, where it has already been tested, to measure groundwater flow, etc. Full article

(This article belongs to the Special Issue Advanced Structural Health Monitoring in Civil Engineering)

► Show Figures

Figure 1

22 pages, 6264 KB

Open AccessEditor’s ChoiceArticle

Development of Numerical Models of Degraded Pedestrian Footbridges Based on the Cable-Stayed Footbridge over the Wisłok River in Rzeszów

by Dominika Ziaja and Ewa Błazik-Borowa

Appl. Sci. 2025, 15(19), 10798; https://doi.org/10.3390/app151910798 - 8 Oct 2025

Cited by 2 | Viewed by 1211

Abstract

This article aims to perform system identification of a nearly 30-year-old cable-stayed steel footbridge over the Wisłok River in Rzeszów (Poland). The design documentation of the bridge has been lost, and since its construction, the footbridge has been subject to renovations. The structure is highly susceptible to pedestrian traffic, and before any actions are taken to improve the comfort of use, it is necessary to create and validate a numerical model and assess the force distribution in the structure. Models are often built as mappings of an ideal structure. However, real structures are not ideal. The comparison of numerical and measured data can allow for an indication of potential damage areas. Two main purposes of the article have been formulated: (1)Development of a numerical model of an old footbridge, whose components have been degraded due to long-term use. Changes, compared to the ‘original’, focused on elongation of the cables due to rheology and a decrease in their tension. (2) Demonstrate the challenges in modeling and validating this type of bridge. In the article, the result of the numerical simulation (Finite Element Method and Ansys2024 R2 was applied, the verification was made in RFEM6) for models with different boundary conditions and varied pre-tension in cables was compared with the results of static and dynamic examination of a real object. The dynamic tests showed an uneven distribution of pre-tension in cables. The ratio of the first natural frequencies of inner cables on the north side is as high as 16%. The novelty demonstrated in the article is that static tests are insufficient for proper system identification; the same value of vertical displacement can be obtained for a selected static load, with varied tension in cables. Therefore, dynamic testing is essential. Full model updating requires a multicriteria approach, which will be made in the future. Full article

(This article belongs to the Special Issue Advanced Structural Health Monitoring in Civil Engineering)

► Show Figures

Figure 1

25 pages, 3403 KB

Open AccessArticle

Local Transmissibility-Based Identification of Structural Damage Utilizing Positive Learning Strategies

by Oguz Gunes and Burcu Gunes

Appl. Sci. 2025, 15(12), 6948; https://doi.org/10.3390/app15126948 - 19 Jun 2025

Cited by 4 | Viewed by 1414

Abstract

Recent advances in sensor technology, data acquisition, and signal processing have enabled the development of data-driven structural health monitoring (SHM) strategies, offering a powerful alternative or complement to traditional model-based approaches. These approaches rely on damage-sensitive features (DSFs) extracted from vibration measurements. This study introduces an innovative, unsupervised learning framework leveraging transmissibility functions (TFs) as DSFs due to their local sensitivity to changes in dynamic behavior and their ability to operate without requiring input excitation measurements—an advantage in civil engineering applications where such data are often difficult to obtain. The novelty lies in the use of sequential sensor pairings based on structural connectivity to construct TFs that maximize damage sensitivity, combined with one-class classification algorithms for automatic damage detection and a damage index for spatial localization within sensor resolution. The method is evaluated through numerical simulations with noise-contaminated data and experimental tests on a masonry arch bridge model subjected to progressive damage. The numerical study shows detection accuracy above 90% with one-class support vector machine (OCSVM) and correct localization across all damage scenarios. Experimental findings further confirm the proposed approach’s localization capability, especially as damage severity increases, aligning well with observed damage progression. These results demonstrate the method’s practical potential for real-world SHM applications. Full article

(This article belongs to the Special Issue Advanced Structural Health Monitoring in Civil Engineering)

► Show Figures

Figure 1

26 pages, 6511 KB

Open AccessArticle

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

Cited by 5 | Viewed by 2230

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 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)

► Show Figures

Figure 1

Review

Jump to: Research

40 pages, 989 KB

Open AccessReview

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

Cited by 60 | Viewed by 11812

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 (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)

► Show Figures

Journal Menu

Journal Browser

Advanced Structural Health Monitoring in Civil Engineering

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (7 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI