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The Applications and Technologies of Structural Health Monitoring in Civil Structures

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 8671

Special Issue Editors

Dr. Yang Ding

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Guest Editor
Department of Civil Engineering, Hangzhou City University, Hangzhou 310015, China
Interests: structural health monitoring; fatigue assessment; system reliable; corrosion-fatigue; bridge engineering
Prof. Dr. Xiaowei Ye

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Guest Editor
Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
Interests: structural health monitoring; machine vision; shield tunnel; fatigue analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

After nearly three decades of development in China, structural health monitoring technology has found applications in various civil engineering fields such as bridges, tunnels, high-rise buildings, and wind turbines. This has resulted in the accumulation of a vast amount of monitoring data. Establishing a relationship between environmental load and structural performance using these data is crucial. The rapid advancement of artificial intelligence technology has made it possible to fully extract potentially useful information from this data, leading to its widespread use in structural health monitoring. However, while data-driven structural performance analysis can effectively evaluate a structure’s safety state, the lack of structural physical information can limit the model's generalization capabilities and the reliability of its results. Therefore, it is essential to integrate the prior knowledge of structural physical information with data-driven methods. This approach, known as the data–physics joint driven model, is seen as a future development trend.

Under this background, we would like to organize the Special Issue of “The Applications and Technologies of Structural Health Monitoring in Civil Structures” in Applied Sciences to collect new research and contributions in this field. The manuscripts published in this Special Issue are expected to reflect original research and technological development on topics that include, but are not limited to, the following:

  • Probabilistic model and simulation for hazard loads;New monitoring and maintenance methods for infrastructures;
  • Intelligent operation and maintenance of structures;
  • Applications of artificial intelligence (AI) in structural health monitoring;
  • Application of digital twin technology in structural health monitoring;
  • Traditional and hybrid machine learning methods;
  • Big data analysis.

Dr. Yang Ding
Prof. Dr. Xiaowei Ye
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
  • uncertainty analysis
  • randomness characterization
  • machine learning

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Published Papers (4 papers)

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Research

16 pages, 2441 KiB  
Article
Midspan Deflection Prediction of Long-Span Cable-Stayed Bridge Based on DIWPSO-SVM Algorithm
by Lilin Li, Qing He, Hua Wang and Wensheng Wang
Appl. Sci. 2025, 15(10), 5581; https://doi.org/10.3390/app15105581 - 16 May 2025
Viewed by 38
Abstract
With the increasing emphasis on the safety and longevity of large-span cable-stayed bridges, the accurate prediction of midspan deflection has become a critical aspect of structural health monitoring (SHM). This study proposes a novel hybrid model, DIWPSO-SVM, which integrates dynamic inertia weight particle [...] Read more.
With the increasing emphasis on the safety and longevity of large-span cable-stayed bridges, the accurate prediction of midspan deflection has become a critical aspect of structural health monitoring (SHM). This study proposes a novel hybrid model, DIWPSO-SVM, which integrates dynamic inertia weight particle swarm optimization (DIWPSO) with support vector machines (SVMs) to enhance the prediction accuracy of midspan deflection. The model incorporates wavelet transform to decompose deflection signals into temperature and vehicle load effects, allowing for a more detailed analysis of their individual impacts. The DIWPSO algorithm dynamically adjusts the inertia weight to balance global exploration and local exploitation, optimizing SVM parameters for improved performance. The proposed model was validated using real-world data from a long-span cable-stayed bridge, demonstrating superior prediction accuracy compared to traditional SVM and PSO-SVM models. The DIWPSO-SVM model achieved an average prediction error of 1.43 mm and a root-mean-square error (RMSE) of 2.05, significantly outperforming the original SVM model, which had an average error of 5.29 mm and an RMSE of 5.62. These results highlight the effectiveness of the DIWPSO-SVM model in providing accurate and reliable midspan deflection predictions, offering a robust tool for bridge health monitoring and maintenance decision-making. Full article
(This article belongs to the Special Issue The Applications and Technologies of Structural Health Monitoring in Civil Structures)
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16 pages, 4183 KiB  
Article
MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame
by Ruochen Qiang, Ming Sheng, Dongxu Su, Yachen Wang, Xianghong Liu and Qing Sun
Appl. Sci. 2024, 14(18), 8190; https://doi.org/10.3390/app14188190 - 12 Sep 2024
Cited by 1 | Viewed by 3674
Abstract
As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based [...] Read more.
As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based vibration acquisition. In this approach, smartphones equipped with MEMS sensors are deployed on the target structure to collect vibration data under environmental excitation. This method was applied in a dynamic field test of a novel composite substation frame. During the test, the proposed MEMS-based vibration acquisition method was conducted in parallel with traditional ultra-low-frequency vibration acquisition methods to validate the accuracy of the MEMS data. The results demonstrated that the MEMS sensors not only simplified the testing process but also provided reliable data, offering greater advantages in testing convenience compared with traditional contact methods. The modal parameters of the substation frame, including modal frequencies, damping ratios, and mode shapes, were subsequently identified using the covariance-driven stochastic subspace identification method. The experimental methodology and findings presented in this paper offer valuable insights for structural dynamic response testing and the wind-resistant design of substation frames. Full article
(This article belongs to the Special Issue The Applications and Technologies of Structural Health Monitoring in Civil Structures)
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14 pages, 7776 KiB  
Article
Research on Indirect Influence-Line Identification Methods in the Dynamic Response of Vehicles Crossing Bridges
by Yu Zhou, Yingdi Shi, Shengkui Di, Shuo Han and Jingtang Wang
Appl. Sci. 2024, 14(17), 7821; https://doi.org/10.3390/app14177821 - 3 Sep 2024
Cited by 1 | Viewed by 1143
Abstract
The bridge influence line can effectively reflect its overall structural stiffness, and it has been used in the studies of safety assessment, model updating, and the dynamic weighing of bridges. To accurately obtain the influence line of a bridge, an Empirical and Variational [...] Read more.
The bridge influence line can effectively reflect its overall structural stiffness, and it has been used in the studies of safety assessment, model updating, and the dynamic weighing of bridges. To accurately obtain the influence line of a bridge, an Empirical and Variational Mixed Modal Decomposition (E-VMD) method is used to remove the dynamic component from the vehicle-induced deflection response of a bridge, which requires the preset fundamental frequency of the structure to be used as the cutoff frequency for the intrinsic modal decomposition operation. However, the true fundamental frequency is often obtained from the picker, and the testing process requires the interruption of traffic to carry out the mode decomposition. To realize the rapid testing of the influence lines of bridges, a new method of indirectly identifying the operational modal frequency and deflection influence lines of bridge structures from the axle dynamic response is proposed as an example of cable-stayed bridge structures. Based on the energy method, an analytical solution of the first-order frequency of vertical bending is obtained for a short-tower cable-stayed bridge, which can be used as the initial base frequency to roughly measure the deflection influence line of the cable-stayed bridge. The residual difference between the deflection response and the roughly measured influence line under the excitation of the vehicle is operated by Fast Fourier Transform, from which the operational fundamental frequency identification of the bridge is realized. Using the operational fundamental frequency as the cutoff frequency and comparing the influence-line identification equations, the empirical variational mixed modal decomposition, and the Tikhonov regularization to establish a more accurate identification of the deflection influence line, the deflection influence line is finally identified. The accuracy and practicality of the proposed method are verified by real cable-stayed bridge engineering cases. The results show that the relative error between the recognized bridge fundamental frequency and the measured fundamental frequency is 0.32%, and the relative error of the recognized deflection influence line is 0.83%. The identification value of the deflection influence line has a certain precision. Full article
(This article belongs to the Special Issue The Applications and Technologies of Structural Health Monitoring in Civil Structures)
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16 pages, 4595 KiB  
Article
A General Method for Solving Differential Equations of Motion Using Physics-Informed Neural Networks
by Wenhao Zhang, Pinghe Ni, Mi Zhao and Xiuli Du
Appl. Sci. 2024, 14(17), 7694; https://doi.org/10.3390/app14177694 - 30 Aug 2024
Cited by 5 | Viewed by 2514
Abstract
The physics-informed neural network (PINN) is an effective alternative method for solving differential equations that do not require grid partitioning, making it easy to implement. In this study, using automatic differentiation techniques, the PINN method is employed to solve differential equations by embedding [...] Read more.
The physics-informed neural network (PINN) is an effective alternative method for solving differential equations that do not require grid partitioning, making it easy to implement. In this study, using automatic differentiation techniques, the PINN method is employed to solve differential equations by embedding prior physical information, such as boundary and initial conditions, into the loss function. The differential equation solution is obtained by minimizing the loss function. The PINN method is trained using the Adam algorithm, taking the differential equations of motion in structural dynamics as an example. The time sample set generated by the Sobol sequence is used as the input, while the displacement is considered the output. The initial conditions are incorporated into the loss function as penalty terms using automatic differentiation techniques. The effectiveness of the proposed method is validated through the numerical analysis of a two-degree-of-freedom system, a four-story frame structure, and a cantilever beam. The study also explores the impact of the input samples, the activation functions, the weight coefficients of the loss function, and the width and depth of the neural network on the PINN predictions. The results demonstrate that the PINN method effectively solves the differential equations of motion of damped systems. It is a general approach for solving differential equations of motion. Full article
(This article belongs to the Special Issue The Applications and Technologies of Structural Health Monitoring in Civil Structures)
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