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Toward Green and Intelligent Transportation Infrastructure: Road Non-destructive Testing and Structural Health Monitoring Technologies

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 1910

Special Issue Editors


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Guest Editor
School of Transportation, Southeast University, Nanjing 211189, China
Interests: NDT technologies; structural health monitoring; advanced sensors; remote sensing; green materials
Special Issues, Collections and Topics in MDPI journals
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802, USA
Interests: non-destructive testing; ground-penetrating radar; deep learning; remote sensing technologies; digital twin
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The detection and evaluation of transportation infrastructure are critical to system safety and asset management. However, traditional destructive testing and manual evaluation methods are expensive, ineffective, and time-consuming. Recently, transportation infrastructures have been extensively tested using non-destructive (NDT) and structural health monitoring (SHM) technologies based on green and intelligent needs. These technologies include remote sensing, unmanned aerial vehicles (UAVs), ground-penetrating radar (GPR), falling weight deflectometers (FWD), seismic wave, fibre Bragg grating (FBG) sensors, etc. They have greatly improved the efficiency of detection and evaluation of transportation infrastructure, and at the same time, large quantities of detection data are in urgent need of automated processing.

Recent technological breakthroughs in artificial intelligence, machine learning, BIM, etc., have provided new ideas for the processing of big data for transportation infrastructure detection. Therefore, the aim of this Special Issue is to collect recent research advances and progress in NDT and SHM in the fields of transportation infrastructure. Additionally, papers focusing on maintenance management systems, life-cycle assessment and asset assessment of transportation infrastructures are also welcome. Topics of interest include, but are not limited to:

  1. Innovative NDT of transportation infrastructures;
  2. Recent developments in existing NDT technologies, including GPR, FWD, etc.;
  3. Remote sensing techniques for damage identification;
  4. The modeling of infrastructure performance based on NDT or multi-source data;
  5. Green and intelligent sensors and networks for road structural monitoring;
  6. Damage evaluation using artificial intelligence and deep learning;
  7. Pavement dynamic monitoring using accelerated loading testing;
  8. Unmanned aerial vehicles for road extraction and 3D modeling;
  9. Life cycle assessments on transportation infrastructures based on NDT tests.

Prof. Dr. Xingyu Gu
Dr. Zhen Liu
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • transportation infrastructures
  • road structural monitoring
  • road damage evaluation
  • pavement monitoring
  • remote sensing
  • UAV
  • intelligent sensors

Published Papers (3 papers)

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Research

22 pages, 12610 KiB  
Article
A Detection Transformer-Based Intelligent Identification Method for Multiple Types of Road Traffic Safety Facilities
by Lingxin Lu, Hui Wang, Yan Wan and Feifei Xu
Sensors 2024, 24(10), 3252; https://doi.org/10.3390/s24103252 - 20 May 2024
Viewed by 249
Abstract
Road traffic safety facilities (TSFs) are of significant importance in the management and maintenance of traffic safety. The complexity and variety of TSFs make it challenging to detect them manually, which renders the work unsustainable. To achieve the objective of automatic TSF detection, [...] Read more.
Road traffic safety facilities (TSFs) are of significant importance in the management and maintenance of traffic safety. The complexity and variety of TSFs make it challenging to detect them manually, which renders the work unsustainable. To achieve the objective of automatic TSF detection, a target detection dataset, designated TSF-CQU (TSF data collected by Chongqing University), was constructed based on images collected by a car recorder. This dataset comprises six types of TSFs and 8410 instance samples. A detection transformer with an improved denoising anchor box (DINO) was selected to construct a model that would be suitable for this scenario. For comparison purposes, Faster R-CNN (Region Convolutional Neural Network) and Yolov7 (You Only Look Once version 7) were employed. The DINO model demonstrated the highest performance on the TSF-CQU dataset, with a mean average precision (mAP) of 82.2%. All of the average precision (AP) values exceeded 0.8, except for streetlights (AP = 0.77) and rods (AP = 0.648). The DINO model exhibits minimal instances of erroneous recognition, which substantiates the efficacy of the contrastive denoising training approach. The DINO model rarely makes misjudgments, but a few missed detection. Full article
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18 pages, 5086 KiB  
Article
Automated Pavement Condition Index Assessment with Deep Learning and Image Analysis: An End-to-End Approach
by Eldor Ibragimov, Yongsoo Kim, Jung Hee Lee, Junsang Cho and Jong-Jae Lee
Sensors 2024, 24(7), 2333; https://doi.org/10.3390/s24072333 - 6 Apr 2024
Viewed by 618
Abstract
The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance [...] Read more.
The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management. Full article
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17 pages, 5660 KiB  
Article
Research on the Deformation Law of Foundation Excavation and Support Based on Fluid–Solid Coupling Theory
by Rongyu Xia, Zhizhong Zhao, Risheng Wang, Maolin Xu, Shujun Ye and Meng Xu
Sensors 2024, 24(2), 426; https://doi.org/10.3390/s24020426 - 10 Jan 2024
Viewed by 588
Abstract
To investigate the impact of underground water seepage and soil stress fields on the deformation of excavation and support structures, this study initially identified the key influencing factors on excavation deformation. Subsequently, through a finite element simulation analysis using Plaxis, this study explored [...] Read more.
To investigate the impact of underground water seepage and soil stress fields on the deformation of excavation and support structures, this study initially identified the key influencing factors on excavation deformation. Subsequently, through a finite element simulation analysis using Plaxis, this study explored the effects of critical factors, such as the excavation support form, groundwater lowering depth, permeability coefficient, excavation layer, and sequence on excavation deformation. Furthermore, a comprehensive consideration of various adverse factors was integrated to establish excavation support early warning thresholds, and optimal dewatering strategies. Finally, this study validated the simulation analysis through an on-site in situ testing with wireless sensors in the context of a physical construction site. The research results indicate that the internal support system within the excavation piles exhibited better stability compared to the external anchor support system, resulting in a 34.5% reduction in the overall deformation. Within the internal support system, the factors influencing the excavation deformation were ranked in the following order: water level (35.5%) > permeability coefficient (17.62%) > excavation layer (11.4%). High water levels, high permeability coefficients, and multi-layered soils were identified as the most unfavorable factors for excavation deformation. The maximum deformation under the coupled effect of these factors was established as the excavation support early warning threshold, and the optimal dewatering strategy involved lowering the water level at the excavation to 0.5 m below the excavation face. The on-site in situ monitoring data obtained through wireless sensors exhibited low discrepancies compared to the finite element simulation data, indicating the high precision of the finite element model for considering the fluid–structure interaction. Full article
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