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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: 30 January 2026 | Viewed by 454

Special Issue Editors


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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

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

E-Mail Website
Guest Editor
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Interests: smart infrastructure performance monitoring; preventive maintenance decision-making and evaluation

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

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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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Published Papers (1 paper)

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Research

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 345
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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