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Inspection and Monitoring Techniques for Bridges and Civil Structures, 2nd Edition

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

Deadline for manuscript submissions: 20 September 2025 | Viewed by 3362

Special Issue Editor

Prof. Dr. Steve C.S. Cai

E-Mail Website1 Website2
Guest Editor
1. Edwin B. and Norma S. McNeil Distinguished Professor, Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
2. School of Transportation, Southeast University, Nanjing, China
Interests: smart bridges; NDE of infrastructures; coastal structures; multi hazards; wind engineering; bridge–vehicle interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to harsh environmental loads (e.g., traffic, wind, seismic damage, scour, or thermal effects), the deterioration and degradation of infrastructure are at all times of great concern worldwide. Many bridges and other civil infrastructure in the world are estimated to be structurally deficient and ensuring the safety of bridges is an important responsibility of bridge engineers. As such, sensors have been applied to bridges, and the concept of structural health monitoring (SHM) has been introduced into bridge engineering in the last two decades. Meanwhile, different inspection technologies have also been developed recently.

This Special Issue intends to document recent developments in inspection and monitoring techniques and theories for bridges and civil structures, such as underwater inspection, non-contact inspection, robotic inspection, smart and wireless monitoring, and decentralized monitoring, to name but a few.

You are cordially invited to submit either original research or a review article to this Special Issue, and topics may include, but are not limited to, the below keywords. Please feel free to contact us with any questions.

Prof. Dr. Steve C.S. Cai
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

  • non-contact inspection
  • artificial intelligence
  • digital techniques and equipment
  • structure inspection
  • damage detection
  • computer vision-based techniques
  • structural health monitoring
  • smart sensing technology
  • structure condition assessment
  • repair, retrofitting, and rehabilitation of structures
  • innovative materials and technology for structure repair

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Related Special Issue

Published Papers (4 papers)

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Research

17 pages, 12087 KiB  
Article
Experimental and Numerical Study on Dynamic Response of High-Pier Ballastless Continuous Beam Bridge in Mountainous Area
by Wenshuo Liu, Qiong Luo, Gonglian Dai and Xin Tang
Appl. Sci. 2025, 15(8), 4341; https://doi.org/10.3390/app15084341 - 15 Apr 2025
Viewed by 249
Abstract
The dynamic performance of a ballastless track on bridges affects the vibration performance of the vehicle–bridge coupling system, which, in turn, will affect safety, the smoothness of operating trains, and passenger comfort. However, in the existing literature, few studies focus on the coupled [...] Read more.
The dynamic performance of a ballastless track on bridges affects the vibration performance of the vehicle–bridge coupling system, which, in turn, will affect safety, the smoothness of operating trains, and passenger comfort. However, in the existing literature, few studies focus on the coupled vibration response analysis of large-span continuous beam bridges for high-speed railways, especially high-pier bridges. Dynamic response tests with multiple measurement points installed on the rail, concrete slab, and bridge deck are conducted. This study investigates the dynamic characteristics of bridges with high piers under train loads. A dynamic system is built by the co-simulation platform of SIMPACK v9 and ANSYS v2022, consisting of several models, a coupling mechanism, etc. The vibration response of a train passing through the bridge at 300 km/h is analyzed, and the influence of operating speed on the motivation performance of the coupled system is further studied. The results indicate that the simulation results are validated against experimental data, showing good agreement; the train–track–continuous beam bridge coupling system meets the specification limits and has some margins for further optimization with an operating speed of 300 km/h. The refined model of train–rail–bridge coupling vibration established in this paper provides theoretical guidance for the design and application of high-speed railways. Full article
(This article belongs to the Special Issue Inspection and Monitoring Techniques for Bridges and Civil Structures, 2nd Edition)
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26 pages, 6961 KiB  
Article
Integration of Probability Maps into Machine Learning Models for Enhanced Crack Segmentation in Concrete Bridges
by Volodymyr Tyvoniuk, Roman Trach and Yuliia Trach
Appl. Sci. 2025, 15(6), 3201; https://doi.org/10.3390/app15063201 - 14 Mar 2025
Cited by 1 | Viewed by 474
Abstract
Crack segmentation in concrete bridge structures is a critical task for ensuring safety and durability. This study focuses on evaluating and improving the performance of various deep learning models for crack segmentation, including U-Net, SegNet, ENet, HRNet, FastFCN, and DeepLab V3+. A novel [...] Read more.
Crack segmentation in concrete bridge structures is a critical task for ensuring safety and durability. This study focuses on evaluating and improving the performance of various deep learning models for crack segmentation, including U-Net, SegNet, ENet, HRNet, FastFCN, and DeepLab V3+. A novel approach is proposed which integrates a probability map generated by an ensemble of classification models as an additional input channel for segmentation models. This method demonstrated significant improvements in segmentation quality, increasing the IoU by up to 25.91% and F1 score by 15.39% compared with baseline models. These improvements were achieved through the use of additional spatial information provided by the probability map, enabling the models to detect cracks more precisely. Additionally, to evaluate the relevance of this approach, the results were compared with YOLO11x-seg, the latest and largest version for segmentation. These findings highlight that integrating auxiliary data channels into neural network architectures holds promise for enhancing segmentation accuracy in real-world engineering applications. The results of this study provide valuable insights for structural engineers and researchers working on automated crack detection, contributing to the development of reliable tools for structural health monitoring. Full article
(This article belongs to the Special Issue Inspection and Monitoring Techniques for Bridges and Civil Structures, 2nd Edition)
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19 pages, 1445 KiB  
Article
Using AI-Based Tools to Quantify the Technical Condition of Bridge Structural Components
by Roman Trach, Volodymyr Tyvoniuk, Tomasz Wierzbicki, Yuliia Trach, Jan Kowalski, Sylwia Szymanek, Justyna Dzięcioł, Ihor Statnyk and Andrii Podvornyi
Appl. Sci. 2025, 15(3), 1625; https://doi.org/10.3390/app15031625 - 6 Feb 2025
Viewed by 1154
Abstract
The main task of the operation of engineering structures is to ensure the stability of structures with aggressive external influences, which have a complex probabilistic nature. The reliable functioning of bridge structures requires the development and application of modern systems for inspection and [...] Read more.
The main task of the operation of engineering structures is to ensure the stability of structures with aggressive external influences, which have a complex probabilistic nature. The reliable functioning of bridge structures requires the development and application of modern systems for inspection and assessment of the technical condition of the structure to take timely measures to ensure the safe operation of the structure in changing operating conditions. With the rapid development of AI, modern approaches are increasingly adopted, offering distinct advantages compared to classical methods. The article aims to develop an AI-based model for quantifying the technical condition of bridge structural components based on data obtained from the survey. To achieve this goal, the authors analyzed existing approaches to the inspection and assessment of bridges and studied the experience of using AI in bridge assessment. Based on the Polish Principles of Bridge Technical Condition Assessment, three datasets were formed to quantify the condition of the bridge components made from reinforced concrete: bridge deck, span structures, and piers and abutments. This study created and compared the performance of five AI-based models: XGBoost, Decision Trees, Random Forest, Support Vector Regression, and Artificial Neural Networks (ANNs). The initial comparison revealed relatively low performance across all models, with the ANN model showing a slight advantage. Subsequently, nine ANN models were optimized to achieve higher performance levels. The performance of models was conducted based on a comparison of mean absolute percentage error (MAPE) and R2 metrics. The ANN model with ReLU activation functions for hidden layers and the RMSprop optimizer achieves optimal performance at 100 epochs (MAPE = 3.5%; R2 = 0.994). The practical implementation of such a model can considerably reduce uncertainties stemming from subjective expert judgments and enhance the accuracy of assessments. Full article
(This article belongs to the Special Issue Inspection and Monitoring Techniques for Bridges and Civil Structures, 2nd Edition)
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21 pages, 10242 KiB  
Article
Nonlinear Analysis of Bridges Considering Soil–Structure Interaction and Travelling Wave Effects Under Combined Train and Near-Fault Seismic Loads
by Panagiota S. Katsimpini, George Papagiannopoulos and George Hatzigeorgiou
Appl. Sci. 2024, 14(24), 11688; https://doi.org/10.3390/app142411688 - 14 Dec 2024
Viewed by 1095
Abstract
This paper presents a comprehensive method for analyzing prestressed concrete bridges subjected to multiple concurrent dynamic loads, incorporating soil–structure interaction (SSI) and seismic wave propagation effects. The study develops a comprehensive numerical framework that simultaneously accounts for traveling seismic waves, train-induced vibrations, and [...] Read more.
This paper presents a comprehensive method for analyzing prestressed concrete bridges subjected to multiple concurrent dynamic loads, incorporating soil–structure interaction (SSI) and seismic wave propagation effects. The study develops a comprehensive numerical framework that simultaneously accounts for traveling seismic waves, train-induced vibrations, and soil–foundation dynamics. Three-dimensional finite element modeling captures the complex interaction between the bridge structure, foundation system, and surrounding soil medium. The investigation considers the spatial variability of ground motion and its influence on the bridge’s dynamic response, particularly examining how different wave velocities and coherency patterns affect structural behavior. Advanced material constitutive models based on damage mechanics theory are implemented to represent both linear and non-linear structure responses under dynamic loading conditions. The analysis reveals that traditional simplified approaches, which neglect SSI, train, and seismic loading combinations, and traveling wave effects may significantly misestimate the structural demands. The results demonstrate how wave passage effects can either amplify or attenuate the combined response depending on the relationship between seismic wave velocity, the frequency content of the ground motion recordings, and the local soil conditions. These findings could contribute to the development of more reliable design methodologies for prestressed bridges in seismically active regions with significant railway traffic. Full article
(This article belongs to the Special Issue Inspection and Monitoring Techniques for Bridges and Civil Structures, 2nd Edition)
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