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

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

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 12761

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Special Issue Editors

Prof. Dr. Xingyu Gu

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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

Dr. Zhen Liu

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Guest Editor

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:

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

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

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Keywords

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

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Published Papers (9 papers)

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Research

14 pages, 4501 KiB

Open AccessArticle

Moisture Distribution and Ice Front Identification in Freezing Soil Using an Optimized Circular Capacitance Sensor

by Xing Hu, Qiao Dong, Bin Shi, Kang Yao, Xueqin Chen and Xin Yuan

Sensors 2024, 24(22), 7392; https://doi.org/10.3390/s24227392 - 20 Nov 2024

Viewed by 449

Abstract

As the interface between frozen and unfrozen soil, the ice front is not only a spatial location concept, but also a potentially dangerous interface where the mechanical properties of soil could change abruptly. Accurately identifying its spatial position is essential for the safe and efficient execution of large-scale frozen soil engineering projects. Electrical capacitance tomography (ECT) is a promising method for the visualization of frozen soil due to its non-invasive nature, low cast, and rapid response. This paper presents the design and optimization of a mobile circular capacitance sensor (MCCS). The MCCS was used to measure frozen soil samples along the depth direction to obtain moisture distribution and three-dimensional images of the ice front. Finally, the experimental results were compared with the simulation results from COMSOL Multiphysics to analyze the deviations. It was found that the fuzzy optimization design based on multi-criteria orthogonal experiments makes the MCCS meet various performance requirements. The average permittivity distribution was proposed to reflect moisture distribution along the depth direction and showed good correlation. Three-dimensional reconstructed images could provide the precise position of the ice front. The simulation results indicate that the MCCS has a low deviation margin in identifying the position of the ice front. Full article

(This article belongs to the Special Issue Toward Green and Intelligent Transportation Infrastructure: Road Non-destructive Testing and Structural Health Monitoring Technologies)

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16 pages, 5276 KiB

Open AccessArticle

Comparative Study of Lightweight Target Detection Methods for Unmanned Aerial Vehicle-Based Road Distress Survey

by Feifei Xu, Yan Wan, Zhipeng Ning and Hui Wang

Sensors 2024, 24(18), 6159; https://doi.org/10.3390/s24186159 - 23 Sep 2024

Cited by 1 | Viewed by 1286

Abstract

Unmanned aerial vehicles (UAVs) are effective tools for identifying road anomalies with limited detection coverage due to the discrete spatial distribution of roads. Despite computational, storage, and transmission challenges, existing detection algorithms can be improved to support this task with robustness and efficiency. In this study, the K-means clustering algorithm was used to calculate the best prior anchor boxes; Faster R-CNN (region-based convolutional neural network), YOLOX-s (You Only Look Once version X-small), YOLOv5-s, YOLOv7-tiny, YOLO-MobileNet, and YOLO-RDD models were built based on image data collected by UAVs. YOLO-MobileNet has the most lightweight model but performed worst in accuracy, but greatly reduces detection accuracy. YOLO-RDD (road distress detection) performed best with a mean average precision (mAP) of 0.701 above the Intersection over Union (IoU) value of 0.5 and achieved relatively high accuracy in detecting all four types of distress. The YOLO-RDD model most successfully detected potholes with an AP of 0.790. Significant or severe distresses were better identified, and minor cracks were relatively poorly identified. The YOLO-RDD model achieved an 85% computational reduction compared to YOLOv7-tiny while maintaining high detection accuracy. Full article

(This article belongs to the Special Issue Toward Green and Intelligent Transportation Infrastructure: Road Non-destructive Testing and Structural Health Monitoring Technologies)

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16 pages, 12099 KiB

Open AccessArticle

Application of the Semi-Supervised Learning Approach for Pavement Defect Detection

by Peng Cui, Nurjihan Ala Bidzikrillah, Jiancong Xu and Yazhou Qin

Sensors 2024, 24(18), 6130; https://doi.org/10.3390/s24186130 - 23 Sep 2024

Cited by 1 | Viewed by 1037

Abstract

Road surface quality is essential for driver comfort and safety, making it crucial to monitor pavement conditions and detect defects in real time. However, the diversity of defects and the complexity of ambient conditions make it challenging to develop an effective and robust classification and detection algorithm. In this study, we adopted a semi-supervised learning approach to train ResNet-18 for image feature retrieval and then classification and detection of pavement defects. The resulting feature embedding vectors from image patches were retrieved, concatenated, and randomly sampled to model a multivariate normal distribution based on the only one-class training pavement image dataset. The calibration pavement image dataset was used to determine the defect score threshold based on the receiver operating characteristic curve, with the Mahalanobis distance employed as a metric to evaluate differences between normal and defect pavement images. Finally, a heatmap derived from the defect score map for the testing dataset was overlaid on the original pavement images to provide insight into the network’s decisions and guide measures to improve its performance. The results demonstrate that the model’s classification accuracy improved from 0.868 to 0.887 using the expanded and augmented pavement image data based on the analysis of heatmaps. Full article

(This article belongs to the Special Issue Toward Green and Intelligent Transportation Infrastructure: Road Non-destructive Testing and Structural Health Monitoring Technologies)

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22 pages, 49029 KiB

Open AccessArticle

Autonomous Crack Detection for Mountainous Roads Using UAV Inspection System

by Xinbao Chen, Chenxi Wang, Chang Liu, Xiaodong Zhu, Yaohui Zhang, Tianxiang Luo and Junhao Zhang

Sensors 2024, 24(14), 4751; https://doi.org/10.3390/s24144751 - 22 Jul 2024

Cited by 1 | Viewed by 1724

Abstract

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with diverse sensors are costly and unsuitable for mountainous roads, primarily because of the challenging terrain conditions characterized by frequent bends in the road. To address these challenges, this study proposes a customized Unmanned Aerial Vehicle (UAV) inspection system designed for automatic crack detection. This system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method (SWM) is proposed to enhance the autonomous navigation of UAV flights by generating path planning on mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the improved MRC-YOLOv8 algorithm is presented to conduct autonomous crack detection from UAV imagery in an on/offboard module. To validate the performance of our UAV inspection system, we conducted multiple experiments to evaluate its accuracy, robustness, and efficiency. The results of the experiments on automatic navigation demonstrate that our fusion method, in conjunction with SWM, effectively enables real-time route planning in GPS-denied mountainous terrains. The proposed system displays an average localization drift of 2.75% and a per-point local scanning error of 0.33 m over a distance of 1.5 km. Moreover, the experimental results on the road crack detection reveal that the MRC-YOLOv8 algorithm achieves an F1-Score of 87.4% and a mAP of 92.3%, thus surpassing other state-of-the-art models like YOLOv5s, YOLOv8n, and YOLOv9 by 1.2%, 1.3%, and 3.0% in terms of mAP, respectively. Furthermore, the parameters of the MRC-YOLOv8 algorithm indicate a volume reduction of 0.19(×10⁶) compared to the original YOLOv8 model, thus enhancing its lightweight nature. The UAV inspection system proposed in this study serves as a valuable tool and technological guidance for the routine inspection of mountainous roads. Full article

(This article belongs to the Special Issue Toward Green and Intelligent Transportation Infrastructure: Road Non-destructive Testing and Structural Health Monitoring Technologies)

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18 pages, 5911 KiB

Open AccessArticle

Simulating Two-Phase Seepage in Undisturbed Soil Based on Lattice Boltzmann Method and X-ray Computed Tomography Images

by Zhenliang Jiang, Yiqian Lin, Xian Chen, Shanghui Li, Peichen Cai and Yun Que

Sensors 2024, 24(13), 4156; https://doi.org/10.3390/s24134156 - 26 Jun 2024

Viewed by 1123

Abstract

The two-phase seepage fluid (i.e., air and water) behaviors in undisturbed granite residual soil (U-GRS) have not been comprehensively studied due to a lack of accurate and representative models of its internal pore structure. By leveraging X-ray computed tomography (CT) along with the lattice Boltzmann method (LBM) enhanced by the Shan–Chen model, this study simulates the impact of internal pore characteristics of U-GRS on the water–gas two-phase seepage flow behaviors. Our findings reveal that the fluid demonstrates a preference for larger and straighter channels for seepage, and as seepage progresses, the volume fraction of the water/gas phases exhibits an initial increase/decrease trend, eventually stabilizing. The results show the dependence of two-phase seepage velocity on porosity, while the local seepage velocity is influenced by the distribution and complexity of the pore structure. This emphasizes the need to consider pore distribution and connectivity when studying two-phase flow in undisturbed soil. It is observed that the residual gas phase persists within the pore space, primarily localized at the pore margins and dead spaces. Furthermore, the study identifies that hydrophobic walls repel adjacent fluids, thereby accelerating fluid movement, whereas hydrophilic walls attract fluids, inducing a viscous effect that decelerates fluid flow. Consequently, the two-phase flow rate is found to increase with then-enhanced hydrophobicity. The apex of the water-phase volume fraction is observed under hydrophobic wall conditions, reaching up to 96.40%, with the residual gas-phase constituting 3.60%. The hydrophilic wall retains more residual gas-phase volume fraction than the neutral wall, followed by the hydrophobic wall. Conclusively, the investigations using X-ray CT and LBM demonstrate that the pore structure characteristics and the wettability of the pore walls significantly influence the two-phase seepage process. Full article

(This article belongs to the Special Issue Toward Green and Intelligent Transportation Infrastructure: Road Non-destructive Testing and Structural Health Monitoring Technologies)

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17 pages, 9288 KiB

Open AccessArticle

Signal Denoising of Traffic Speed Deflectometer Measurement Based on Partial Swarm Optimization–Variational Mode Decomposition Method

by Chaoyang Wu, Yiyuan Duan and Hao Wang

Sensors 2024, 24(12), 3708; https://doi.org/10.3390/s24123708 - 7 Jun 2024

Cited by 1 | Viewed by 804

Abstract

To accurately identify the deflection data collected by a traffic speed deflectometer (TSD) and eliminate the noise in the measured signals, a TSD signal denoising method based on the partial swarm optimization–variational mode decomposition (PSO–VMD) method is proposed. Initially, the VMD algorithm is used for modal decomposition, calculating the correlation coefficients between each decomposed mode and the original signal for modal selection and signal reconstruction; Then, the particle swarm optimization algorithm is utilized to optimize the number of modes K and the value α for the VMD algorithm, adopting fuzzy entropy as the affinity function to circumvent effects from sequence decomposition and forecasting accuracy, thus identifying the optimal combination of hyperparameters. Finally, the analysis on simulated signals indicates that the PSO–VMD method secures the best parameters, showing a clear advantage in denoising. Denoising real TSD data validates that the approach proposed herein achieves commendable outcomes in TSD deflection noise reduction, offering a feasible strategy for TSD signal denoising. Full article

(This article belongs to the Special Issue Toward Green and Intelligent Transportation Infrastructure: Road Non-destructive Testing and Structural Health Monitoring Technologies)

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21 pages, 12610 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1194

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, 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

(This article belongs to the Special Issue Toward Green and Intelligent Transportation Infrastructure: Road Non-destructive Testing and Structural Health Monitoring Technologies)

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17 pages, 5086 KiB

Open AccessArticle

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

Cited by 3 | Viewed by 3139

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 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

(This article belongs to the Special Issue Toward Green and Intelligent Transportation Infrastructure: Road Non-destructive Testing and Structural Health Monitoring Technologies)

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17 pages, 5660 KiB

Open AccessArticle

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 1094

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 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

(This article belongs to the Special Issue Toward Green and Intelligent Transportation Infrastructure: Road Non-destructive Testing and Structural Health Monitoring Technologies)

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