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Applied Sciences
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New Technology for Road Surface Detection, 2nd Edition
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New Technology for Road Surface Detection, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 October 2026 | Viewed by 2888

Editors

Dr. Difei Wu

E-Mail Website
Guest Editor
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Interests: digital transportation infrastructure; road detection and evaluation; intelligent construction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hongduo Zhao

E-Mail Website
Guest Editor
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Interests: pavement monitoring; intelligent pavement construction
Special Issues, Collections and Topics in MDPI journals
Dr. Yishun Li

E-Mail Website
Guest Editor
College of Transportation, Tongji University, Shanghai 201801, China
Interests: pavement maintenance; intelligent transportation infrastructure; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Roads are essential transportation infrastructures within and between cities, and their timely and efficient maintenance and operation are crucial for ensuring their structural and functional performance. The use of advanced technologies to quickly and accurately detect and perceive road surface performance is a key link to achieving these objectives. Under the long-term influence of loads and environmental impacts, road infrastructure inevitably develop surface damages, such as cracks and potholes, as well as invisible defects and other types of damage. The use of advanced detection or sensor technologies to identify and assess these defects or early-stage performance deterioration has always been a research focus in the field of road maintenance and management.

In this Special Issue, we aim to explore, discuss, and highlight the emerging technologies revolutionizing the field of road infrastructure detection. This Special Issue offers an interdisciplinary platform for researchers, engineers, technologists, and policymakers to share the latest advancements, methodologies, and applications in road infrastructure detection technology.

Our focus revolves around innovative techniques that improve the efficiency, accuracy, and comprehensiveness of road infrastructure analysis. This includes, but is not limited to, ground-penetrating radar technology, video imaging technology, satellite remote sensing technology, lidar technology, fiber optic sensing technology, drone-based detection technology, and applications of artificial intelligence, such as deep learning.

We also encourage the discussion of the practical implications of these technologies, including the challenges and opportunities associated with their implementation, their impact on road maintenance and safety, and the economic and environmental implications of their use.

Dr. Difei Wu
Prof. Dr. Hongduo Zhao
Dr. Yishun Li
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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-destructive testing
  • smart sensing in road surface monitoring
  • AI in road surface detection
  • ground-penetrating radar (GPR)
  • satellite remote sensing in road surface detection
  • road maintenance
  • road surface performance evaluation
  • drone-based detection

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Related Special Issue

Published Papers (3 papers)

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Research

23 pages, 49249 KB  
Article
Pavement Crack Identification in UAV Images Based on Joint Context Information
by Yiling Chen, Li Li, Huailei Cheng and Changxuan He
Appl. Sci. 2026, 16(7), 3371; https://doi.org/10.3390/app16073371 - 31 Mar 2026
Viewed by 537
Abstract
Low-grade highway maintenance faces the challenge of high demand yet limited resources. Accurately identifying road damage is a key task to improve maintenance efficiency, which is crucial for addressing this demand–resource contradiction. To address this issue, YOLOv5s was selected as the foundational model [...] Read more.
Low-grade highway maintenance faces the challenge of high demand yet limited resources. Accurately identifying road damage is a key task to improve maintenance efficiency, which is crucial for addressing this demand–resource contradiction. To address this issue, YOLOv5s was selected as the foundational model due to its superior balance of detection accuracy, speed, and computational efficiency compared to other YOLO variants. Comprehensive optimizations were then implemented to further enhance its performance, including the development of a Global Context Squeeze (GS) module, a modified loss function, optimized Non-Maximum Suppression (NMS), and targeted image preprocessing strategies. The GS module is designed to effectively integrate contextual information, expand the receptive field, capture long-range dependencies, and strengthen feature extraction capabilities. A suburban road section in Shanghai with typical pavement damage was selected as the experimental site, where 8515 images were collected for model training and testing. Experiments demonstrated that the optimized YOLOv5s-G model achieved a mean average precision (mAP) of 90.7% for crack detection, a relative improvement of 18.6% over the original YOLOv5s. Furthermore, it outperformed models employing conventional optimization strategies, such as those with added small object detection layers or standard attention mechanisms. The superior performance of the YOLOv5s-G model significantly enhances pavement crack detection accuracy, offering technical support to improve low-grade highway maintenance efficiency and alleviate pressures from resource limitations. Full article
(This article belongs to the Special Issue New Technology for Road Surface Detection, 2nd Edition)
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21 pages, 2068 KB  
Article
A Physics-Informed Neural Network Framework for Seismic Signal Denoising Based on Time–Frequency Adaptive Decomposition
by Qinghua Zhang, Miantao Zhang, Houle Zhang, Yongxin Wu and Yanjie Zhang
Appl. Sci. 2026, 16(5), 2389; https://doi.org/10.3390/app16052389 - 28 Feb 2026
Viewed by 575
Abstract
Seismic signal denoising stands as a vital process that enables precise seismic data analysis because noise interference blocks the detection of weak but valuable seismic signals. The current traditional denoising methods together with deep learning-based data-driven approaches encounter difficulties when they need to [...] Read more.
Seismic signal denoising stands as a vital process that enables precise seismic data analysis because noise interference blocks the detection of weak but valuable seismic signals. The current traditional denoising methods together with deep learning-based data-driven approaches encounter difficulties when they need to remove noise from seismic signals while keeping their fundamental structural elements, especially under conditions of low signal-to-noise ratios. In this study, we propose a novel denoising framework that integrates a physics-guided neural network with adaptive time–frequency decomposition, referred to as TF-PhysNet. The system breaks down broadband seismic data into separate frequency bands. Scientists can use these to study specific noise patterns that appear at various frequency points. The system uses a shared convolutional neural network-long short-term memory architecture to remove noise from each sub-band, which helps it learn both short-term waveform patterns and extended temporal relationships. The system uses physics-guided restrictions to eliminate false signal variations, which appear during the signal recovery process. The experimental findings from synthetic and real seismic data sets show that TF-PhysNet delivers better results than standard denoising techniques and deep learning-based methods for signal-to-noise ratio improvement and correlation coefficient enhancement. Full article
(This article belongs to the Special Issue New Technology for Road Surface Detection, 2nd Edition)
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28 pages, 7150 KB  
Article
Distress-Level Prediction of Pavement Deterioration with Causal Analysis and Uncertainty Quantification
by Yifan Sun, Qian Gao, Feng Li and Yuchuan Du
Appl. Sci. 2025, 15(20), 11250; https://doi.org/10.3390/app152011250 - 21 Oct 2025
Viewed by 1382
Abstract
Pavement performance prediction serves as a core basis for maintenance decision-making. Although numerous studies have been conducted, most focus on road segments and aggregate indicators such as IRI and PCI, with limited attention to the daily deterioration of individual distresses. Subject to the [...] Read more.
Pavement performance prediction serves as a core basis for maintenance decision-making. Although numerous studies have been conducted, most focus on road segments and aggregate indicators such as IRI and PCI, with limited attention to the daily deterioration of individual distresses. Subject to the combined influence of multiple factors, pavement distress deterioration exhibits pronounced nonlinear and time-lag characteristics, making distress-level predictions prone to disturbances and highly uncertain. To address this challenge, this study investigates the distress-level deterioration of three representative distresses—transverse cracks, alligator cracks, and potholes—with causal analysis and uncertainty quantification. Based on two years of high-frequency road inspection data, a continuous tracking dataset comprising 164 distress sites and 9038 records was established using a three-step matching algorithm. Convergent cross mapping was applied to quantify the causal strength and lag days of environmental factors, which were subsequently embedded into an encoder–decoder framework to construct a BayesLSTM model. Monte Carlo Dropout was employed to approximate Bayesian inference, enabling probabilistic characterization of predictive uncertainty and the construction of prediction intervals. Results indicate that integrating causal and time-lag characteristics improves the model’s capacity to identify key drivers and anticipate deterioration inflection points. The proposed BayesLSTM achieved high predictive accuracy across all three distress types, with a prediction interval coverage of 100%, thereby enhancing the reliability of prediction by providing both deterministic results and interval estimates. These findings facilitate the identification of high-risk distresses and their underlying mechanisms, offering support for rational allocation of maintenance resources. Full article
(This article belongs to the Special Issue New Technology for Road Surface Detection, 2nd Edition)
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