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Applied Sciences
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Advances in Intelligent Bridge: Maintenance and Monitoring
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Advances in Intelligent Bridge: Maintenance and Monitoring

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

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 7057

Special Issue Editor

Dr. Jia Wang

E-Mail Website
Guest Editor
School of Transportation, Southeast University, Nanjing 211189, China
Interests: bridge engineering; advanced steel for bridges; corrosion prevention for bridges; evaluation and design of structural fatigue resistance; smart construction of bridges

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue on Advances in Intelligent Bridge: Maintenance and Monitoring.

In the landscape of infrastructure engineering, bridges stand as critical assets essential for transportation and urban development. However, as these structures age and are subjected to environmental and load stresses, their maintenance and monitoring become increasingly challenging. Intelligent technologies present opportunities to revolutionize how we approach these challenges. Intelligent bridge maintenance and monitoring encompass a range of cutting-edge technologies such as advanced sensor systems, artificial intelligence, machine learning, and big data analytics. These technologies enable real-time data collection and analysis, predictive maintenance, and informed decision-making. These approaches improve the longevity and reliability of bridge structures and also reduce bridge failure risks. In this Special Issue, we expect authors to contribute their insights, studies, and innovations in this field. Submissions may cover a range of topics including, but not limited to, the development of new sensor technologies, AI algorithms for structural health monitoring, automated inspection techniques, advanced materials for bridge repair and reinforcement, and case studies demonstrating these technologies in real-world scenarios.

Dr. Jia Wang
Guest Editor

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

  • new sensor technology
  • AI algorithm for bridge health monitoring
  • automated inspection techniques
  • advanced materials for bridge repair and reinforcement

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Published Papers (3 papers)

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Research

23 pages, 7552 KiB  
Article
A Novel Data Fusion Method to Estimate Bridge Acceleration with Surrogate Inclination Mode Shapes through Independent Component Analysis
by Xuzhao Lu, Chenxi Wei, Limin Sun, Ye Xia and Wei Zhang
Appl. Sci. 2024, 14(18), 8556; https://doi.org/10.3390/app14188556 - 23 Sep 2024
Cited by 1 | Viewed by 1276
Abstract
Data fusion is an important issue in bridge health monitoring. Through data fusion, specific unknown bridge responses can be estimated with measured responses. However, existing data fusion methods always require a precise finite element model of the bridge or partially measured target responses, [...] Read more.
Data fusion is an important issue in bridge health monitoring. Through data fusion, specific unknown bridge responses can be estimated with measured responses. However, existing data fusion methods always require a precise finite element model of the bridge or partially measured target responses, which are hard to realize in actual engineering. In this study, we propose a novel data fusion method. Measured inclinations across multiple cross-sections of the target bridge and accelerations at a subset of these sections were used to estimate accelerations at the remaining sections. Theoretical analysis of a typical vehicle-bridge interaction (VBI) system has shown parallels with the blind source separation (BSS) problem. Based on this, Independent Component Analysis (ICA) was applied to derive surrogate inclination mode shapes. This was followed by calculating surrogate displacement mode shapes through numerical integration. Finally, a surrogate inter-section transfer matrix for both measured and unmeasured accelerations was constructed, enabling the estimation of the target accelerations. This paper presents three key principles involving the relationship between the surrogate and actual inter-section transfer matrices, the integration of mode shape functions, and the consistency of transfer matrices for low- and high-frequency responses, which form the basis of the proposed method. A series of numerical simulations and a large-scale laboratory experiment were proposed to validate the proposed method. Compared to existing approaches, our proposed method stands out as a purely data-driven technique, eliminating the need for finite element analysis assessment. By incorporating the ICA algorithm and surrogate mode shapes, this study addresses the challenges associated with obtaining accurate mode shape functions from low-frequency responses. Moreover, our method does not require partial measurements of the target responses, simplifying the data collection process. The validation results demonstrate the method’s practicality and convenience for real-world engineering applications, showcasing its potential for broad adoption in the field. Full article
(This article belongs to the Special Issue Advances in Intelligent Bridge: Maintenance and Monitoring)
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18 pages, 19100 KiB  
Article
Coarse–Fine Combined Bridge Crack Detection Based on Deep Learning
by Kaifeng Ma, Mengshu Hao, Xiang Meng, Jinping Liu, Junzhen Meng and Yabing Xuan
Appl. Sci. 2024, 14(12), 5004; https://doi.org/10.3390/app14125004 - 8 Jun 2024
Viewed by 1602
Abstract
The crack detection of concrete bridges is an important link in the safety evaluation of bridge structures, and the rapid and accurate identification and detection of bridge cracks is a prerequisite for ensuring the safety and long-term stable use of bridges. To solve [...] Read more.
The crack detection of concrete bridges is an important link in the safety evaluation of bridge structures, and the rapid and accurate identification and detection of bridge cracks is a prerequisite for ensuring the safety and long-term stable use of bridges. To solve the incomplete crack detection and segmentation caused by the complex background and small proportion in the actual bridge crack images, this paper proposes a coarse–fine combined bridge crack detection method of “double detection + single segmentation” based on deep learning. To validate the effect and practicality of fine crack detection, images of old civil bridges and viaduct bridges against a complex background and images of a bridge crack against a simple background are used as datasets. You Only Look Once V5(x) (YOLOV5(x)) was preferred as the object detection network model (ODNM) to perform initial and fine detection of bridge cracks, respectively. Using U-Net as the optimal semantic segmentation network model (SSNM), the crack detection results are accurately segmented for fine crack detection. The test results showed that the initial crack detection using YOLOV5(x) was more comprehensive and preserved the original shape of bridge cracks. Second, based on the initial detection, YOLOV5(x) was adopted for fine crack detection, which can determine the location and shape of cracks more carefully and accurately. Finally, the U-Net model was used to segment the accurately detected cracks and achieved a maximum accuracy (AC) value of 98.37%. The experiment verifies the effectiveness and accuracy of this method, which not only provides a faster and more accurate method for fine detection of bridge cracks but also provides technical support for future automated detection and preventive maintenance of bridge structures and has practical value for bridge crack detection engineering. Full article
(This article belongs to the Special Issue Advances in Intelligent Bridge: Maintenance and Monitoring)
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21 pages, 6591 KiB  
Article
Design and Evaluation of Novel Submerged Floating Tunnel Models Based on Dynamic Similarity
by Hongyu Ren, Tong Guo, Zhongxiang Liu, Guoliang Zhi and Xiangyang Xu
Appl. Sci. 2024, 14(9), 3724; https://doi.org/10.3390/app14093724 - 27 Apr 2024
Cited by 2 | Viewed by 3428
Abstract
Submerged floating tunnels (SFTs), also known as the Archimedes Bridge, are new transportation structures designed for crossing deep waters. Compared with cross-sea bridges and subsea tunnels, SFTs offer superior environmental adaptability, reduced construction costs, and an enhanced spanning capacity, highlighting their significant development [...] Read more.
Submerged floating tunnels (SFTs), also known as the Archimedes Bridge, are new transportation structures designed for crossing deep waters. Compared with cross-sea bridges and subsea tunnels, SFTs offer superior environmental adaptability, reduced construction costs, and an enhanced spanning capacity, highlighting their significant development potential and research value. This paper introduces a new type of SFT scale model for hydrodynamic experiments, adhering to the criteria for geometric similarity, motion similarity, and dynamic similarity principles, including the Froude and Cauchy similarity principles. This model enables the accurate simulation of the elastic deformation of the tunnel body and complex hydrodynamic phenomena, such as fluid–structure interactions and vortex–induced vibrations. Moreover, this paper details the design methodology, fabrication process, and method for similarity evaluation, covering the mass, deflection under load, natural frequency in air, and the natural frequency of the various underwater motion freedoms of the model. The results of our experiments and numerical simulations demonstrate a close alignment, proving the reliability of the new SFT scale model. The frequency distribution observed in the white noise wave tests indicates that the SFT equipped with inclined mooring cables experiences a coupled interaction between horizontal motion, vertical motion, and rotation. Furthermore, the design methodology of this model can be applied to other types of SFTs, potentially advancing technical progress in scale modeling of SFTs and enhancing the depth of SFT research through hydrodynamic experiments. Full article
(This article belongs to the Special Issue Advances in Intelligent Bridge: Maintenance and Monitoring)
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