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Applied Sciences
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Structural Health Monitoring for Bridge Structures
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Structural Health Monitoring for Bridge Structures

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

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 7442

Special Issue Editors

Dr. Yi Zhou

E-Mail Website
Guest Editor
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, 100083, China
Interests: structural health monitoring, intelligent operation & maintenance of structures, structural dynamics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guangdong Zhou

E-Mail Website
Guest Editor
College of Civil and Transportation Engineering, Hohai University, Nanjing, China
Interests: smart sensor networks; bridge health monitoring; data-driven structural condition evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) has emerged as a crucial field in ensuring the safety, reliability, and longevity of bridge structures. With the increasing complexity and aging infrastructure of bridges worldwide, the need for effective monitoring systems has become more pronounced than ever. The application of SHM techniques enables real-time assessment, the early detection of potential issues, and proactive maintenance strategies to prevent catastrophic failures.

In the past few years, Artificial Intelligence and Big Data technology have greatly promoted the development of structural health monitoring, assessment and maintenance. This Special Issue aims to bring together the latest advancements, research findings, and practical applications in the field of structural health monitoring for bridge structures. It provides a platform for researchers, engineers, and practitioners to share their innovative approaches, methodologies, and case studies for monitoring and evaluating the integrity and performance of bridge structures.

The scope of this Special Issue encompasses various aspects of SHM, including, but not limited to, the following:

  • Sensor technologies and data acquisition systems for bridge monitoring;
  • Advanced signal processing and data analysis techniques for structural health assessment;
  • Non-destructive testing and evaluation methods for bridge structures;
  • Wireless sensor networks and IoT applications in bridge monitoring;
  • Structural modeling and simulation for health monitoring and prognosis;
  • Remote sensing and imaging techniques for bridge inspection and monitoring;
  • Risk assessment and decision-making frameworks based on SHM data;
  • Case studies and practical applications of SHM in bridge structures.

Authors are invited to submit original research papers, review articles, or technical notes that address the challenges and advancements in the field of structural health monitoring for bridge structures. The papers should present novel contributions, experimental studies, or innovative applications that push the boundaries of SHM technology and its practical implementation.

We encourage submissions that demonstrate interdisciplinary approaches, theoretical developments, or practical innovations in the field. The submitted papers will undergo a rigorous peer-review process to ensure the quality, originality, and relevance to the theme of this Special Issue.

We hope that this Special Issue will serve as a valuable resource for researchers, practitioners, and policymakers in the field of bridge engineering, fostering collaborations and facilitating the exchange of knowledge and ideas to address the critical challenges in structural health monitoring of bridge structures.

Dr. Yi Zhou
Prof. Dr. Guangdong Zhou
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

  • bridge structures
  • structural health monitoring
  • sensor technology
  • condition assessment
  • damage detection
  • non-destructive testing data analytics
  • life-cycle management

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Related Special Issue

Published Papers (6 papers)

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Research

18 pages, 8165 KiB  
Article
Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
by Gunhee Kim, Junsik Shin, Jongbin Won and Jongwoong Park
Appl. Sci. 2025, 15(3), 1622; https://doi.org/10.3390/app15031622 - 5 Feb 2025
Viewed by 695
Abstract
Bearings play a crucial role in mitigating loads, maintaining stability, and transferring forces between superstructures and substructures. However, bearing failures caused by external factors can compromise structural safety. Therefore, continuous monitoring of bearing displacement is essential, yet current inspection methods are labor-intensive and [...] Read more.
Bearings play a crucial role in mitigating loads, maintaining stability, and transferring forces between superstructures and substructures. However, bearing failures caused by external factors can compromise structural safety. Therefore, continuous monitoring of bearing displacement is essential, yet current inspection methods are labor-intensive and unsuitable for long-term management. To address this, researchers have proposed systems such as Linear Variable Differential Transformers (LVDTs) and computer vision-based monitoring methods to track bearing displacement over time. However, reliance on external power sources and complex installation processes has limited their widespread application. This paper proposes an automated monitoring system integrating low-power IoT sensors, computer vision, and cloud computing. The system features an event-driven power mechanism to minimize energy consumption and utilizes vision-based displacement measurement techniques, providing both portability and efficiency. Applied in a real-world setting for nine months, the system successfully enabled the long-term monitoring of bridge bearings. The results demonstrate its effectiveness in overcoming traditional limitations and highlight its potential in supporting automated, data-driven assessments of structural stability. Full article
(This article belongs to the Special Issue Structural Health Monitoring for Bridge Structures)
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24 pages, 7699 KiB  
Article
Bridge Damage Localization Through Response Reconstruction with Multiple BP-ANNs Under Vehicular Loading
by Xuzhao Lu, Chenxi Wei, Limin Sun and Wei Zhang
Appl. Sci. 2024, 14(22), 10226; https://doi.org/10.3390/app142210226 - 7 Nov 2024
Viewed by 840
Abstract
Damage detection is a critical aspect of bridge health monitoring. While data reconstruction has been posited as a promising method for damage detection, its effectiveness in this context has rarely been empirically validated. In this study, we introduce a novel approach to pinpoint [...] Read more.
Damage detection is a critical aspect of bridge health monitoring. While data reconstruction has been posited as a promising method for damage detection, its effectiveness in this context has rarely been empirically validated. In this study, we introduce a novel approach to pinpoint potential bridge damage by reconstructing bridge inclination data. For an intact bridge, we selected reference cross-sections and trained multiple Backpropagation Artificial Neural Networks (BP-ANNs) to simulate transfer matrices for inclination between these base sections and other sections of the bridge. These BP-ANNs were then employed to reconstruct inclination data at the same cross-sections on a bridge with artificial damage. We demonstrated that damage localization is feasible through a comparison of the reconstructed and actual measured responses. The theoretical underpinnings of the transfer matrix and the damage localization method were initially elucidated through an analysis of the dynamics of a simplified vehicle–bridge interaction (VBI) system. A series of finite element models were constructed to substantiate the theoretical basis of the damage localization method. Additionally, a large-scale laboratory experiment was carried out to assess the practical effectiveness of the proposed method. The proposed method has been demonstrated to effectively pinpoint the location of potential structural damage. It successfully differentiates between areas in close proximity to the damage and those that are more distant. Compared to existing research, our method does not necessitate prior knowledge of factors such as mode shape functions, traffic conditions, or the constraint of inspecting with a single vehicle. This approach is anticipated to be more convenient for engineering applications, particularly in the development of online monitoring systems, due to its streamlined requirements and robust performance in identifying damage localization. Full article
(This article belongs to the Special Issue Structural Health Monitoring for Bridge Structures)
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20 pages, 6002 KiB  
Article
Reconstruction of High-Frequency Bridge Responses Based on Physical Characteristics of VBI System with BP-ANN
by Xuzhao Lu, Limin Sun and Ye Xia
Appl. Sci. 2024, 14(15), 6757; https://doi.org/10.3390/app14156757 - 2 Aug 2024
Cited by 3 | Viewed by 1061
Abstract
Response reconstruction is essential in bridge health monitoring for recovering missing data and evaluating service status. Previous studies have focused on reconstructing responses at specific cross-sections using data from adjacent sections. To address this challenge, time-series prediction methods have been employed for response [...] Read more.
Response reconstruction is essential in bridge health monitoring for recovering missing data and evaluating service status. Previous studies have focused on reconstructing responses at specific cross-sections using data from adjacent sections. To address this challenge, time-series prediction methods have been employed for response reconstruction. However, these methods often struggle with the inherent complexities of long-term time-varying traffic conditions, posing practical challenges. In this study, we analyzed the theoretical physical characteristics of high-frequency bridge dynamics within a simplified vehicle–bridge interaction (VBI) system. Our analysis revealed that the relationship between high-frequency bridge responses across different cross-sections is time-invariant and only dependent on the bridge’s mode shape. This relationship remains unaffected by time-varying factors such as traffic loading and environmental conditions like air temperature. Based on these physical characteristics, we propose the backpropagation artificial neural network (BP-ANN) method for response reconstruction. The validity of these physical characteristics was confirmed through finite element models, and the effectiveness of the proposed method was demonstrated using field test data from a continuous bridge. Our verification results show that the BP-ANN method enables effective utilization of short-term monitoring data for long-term bridge health monitoring, without necessitating real-time adjustments for factors such as traffic conditions or air temperature. Full article
(This article belongs to the Special Issue Structural Health Monitoring for Bridge Structures)
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13 pages, 30396 KiB  
Article
BOPVis: Bridge Monitoring Data Visualization for Operational Performance Mining
by Xiaohui Wang, Zilong Zheng, Jiaxiang You, Yuning Qin, Wentao Xia and Yi Zhou
Appl. Sci. 2024, 14(15), 6615; https://doi.org/10.3390/app14156615 - 29 Jul 2024
Cited by 1 | Viewed by 937
Abstract
Bridges are fundamental facilities in the transportation system, and their operational performance is crucial for economic and social development. Many large bridges are now equipped with structural health monitoring (SHM) systems that collect various types of real-time data. However, our user study found [...] Read more.
Bridges are fundamental facilities in the transportation system, and their operational performance is crucial for economic and social development. Many large bridges are now equipped with structural health monitoring (SHM) systems that collect various types of real-time data. However, our user study found that despite the accumulation of massive amounts of monitoring data, current analysis methods cannot efficiently process large-scale, high-dimensional data. To address this, we have developed BOPVis, a visualization system for bridge monitoring data. BOPVis allows users to intuitively locate sensors and extract corresponding data from a 3D digital model of a bridge. It also provides convenient and flexible interactions for examining trends over time and correlations across hundreds of monitoring channels. A real-world long-span suspension bridge in China is used as a case study to demonstrate the advantages of the BOPVis system for operational performance mining. Through BOPVis, the global temperature deformation behaviors of the bridge are explored and found to align with the physical mechanism documented in the SHM literature. The BOPVis system, with its interactive visualization analysis capabilities, offers a new method for analyzing bridge monitoring data. Full article
(This article belongs to the Special Issue Structural Health Monitoring for Bridge Structures)
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14 pages, 8140 KiB  
Article
Monitoring of Reinforced Concrete Corrosion: Active and Passive Bars Exposed to Climate
by Nuria Rebolledo, Julio E. Torres, Antonio Silva and Javier Sánchez
Appl. Sci. 2024, 14(11), 4665; https://doi.org/10.3390/app14114665 - 29 May 2024
Cited by 2 | Viewed by 1326
Abstract
The durability of reinforced concrete structures is a significant concern, with corrosion of reinforcement being a leading cause of reduced durability. To ensure accurate models, it is necessary to calibrate or validate them with direct measurements of the structures, specifically monitoring durability-related parameters. [...] Read more.
The durability of reinforced concrete structures is a significant concern, with corrosion of reinforcement being a leading cause of reduced durability. To ensure accurate models, it is necessary to calibrate or validate them with direct measurements of the structures, specifically monitoring durability-related parameters. The heterogeneity of structures and the dispersion of the parameters considered in models make this calibration or validation essential. To enable the predictive maintenance of structures, it is essential to monitor the parameters related to their durability. This article presents the results of the monitoring of the temperature, corrosion potential, resistivity, and corrosion rate of two structural components, a beam and a tendon, for over 10 months. The obtained values were correlated with the climate to which they were exposed. The corrosion rate can be correlated with the influence of climate, enabling real-time estimation of section loss. This is a necessary step towards the digitization of structures or the development of digital twins that incorporate the effect of corrosion. Full article
(This article belongs to the Special Issue Structural Health Monitoring for Bridge Structures)
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20 pages, 7568 KiB  
Article
Study on Corrosion Monitoring of Reinforced Concrete Based on Longitudinal Guided Ultrasonic Waves
by Ji Qian, Peiyun Zhang, Yongqiang Wu, Ruixin Jia and Jipeng Yang
Appl. Sci. 2024, 14(3), 1201; https://doi.org/10.3390/app14031201 - 31 Jan 2024
Cited by 1 | Viewed by 1411
Abstract
The corrosion of reinforced concrete (RC) is one of the most serious durability problems in civil engineering structures, and the corrosion detection of internal reinforcements is an important basis for structural durability assessment. In this paper, the appropriate frequency required to cause excitation [...] Read more.
The corrosion of reinforced concrete (RC) is one of the most serious durability problems in civil engineering structures, and the corrosion detection of internal reinforcements is an important basis for structural durability assessment. In this paper, the appropriate frequency required to cause excitation signals in the specimen is first analyzed by means of frequency dispersion curves. Subsequently, the effectiveness of five damage indexes (DIs) is discussed using random corrosion in finite elements. Finally, guided ultrasonic wave (GUW) tests are conducted on reinforcement and RC specimens at different corrosion degrees, and the test results are verified using a theoretical corrosion model. The results show that the larger the covered thickness is at the same frequency, the higher the modal order of the GUW in the frequency dispersion curve is, and the smaller the group velocity is. The SAD is the most sensitive to the corrosion state of the reinforcement compared with the other DIs, and it shows a linear increasing trend with the increase in the corrosion degree of the reinforcement. The SAD values of the RC specimens showed a three-stage change with the increase in the corrosion time, and the time until the appearance of corrosion cracks was increased with the increase in the covered thickness. It can be seen that increasing the covered thickness is an effective method to delay the time until the appearance of corrosion cracks in RC specimens. Full article
(This article belongs to the Special Issue Structural Health Monitoring for Bridge Structures)
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