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Applied Sciences
Special Issues
New Technology for Road Surface Detection
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New Technology for Road Surface Detection

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 July 2024 | Viewed by 1686

Special Issue Editors

Dr. Difei Wu

E-Mail Website
Guest Editor
Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Tongji University, Shanghai, China
Interests: intelligent sensing; pavement maintenance; pavement detection
Prof. Dr. Hongduo Zhao

E-Mail Website
Guest Editor
Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Tongji University, Shanghai, China
Interests: pavement monitoring; intelligent pavement construction

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 the road surface performance is a key link to achieving these objectives. Under the long-term influence of loads and environmental impacts, road surfaces inevitably develop surface damages, such as cracks and potholes, as well as invisible defects such as voids and debonding. 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 the "New Technologies for Road Surface Detection" Special Issue, we aim to explore, discuss, and highlight the emerging technologies revolutionizing the field of road surface detection. This Special Issue offers an interdisciplinary platform for researchers, engineers, technologists, and policymakers to share the latest advancements, methodologies, and applications in road surface detection technology.

Our focus revolves around innovative techniques that improve the efficiency, accuracy, and comprehensiveness of road surface 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, and applications of artificial intelligence, such as deep learning in this context.

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
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 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-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

Published Papers (2 papers)

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Research

18 pages, 2540 KiB  
Article
Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements
by Jianming Ling, Zengyi Wang, Shifu Liu and Yu Tian
Appl. Sci. 2024, 14(9), 3850; https://doi.org/10.3390/app14093850 - 30 Apr 2024
Viewed by 382
Abstract
Long-term preventive maintenance planning using finite annual budgets is vital for maintaining the service performance of airport runway composite pavements. Using the pavement condition index (PCI) to quantify composite pavement performance, this study investigated the PCI deterioration tendencies of middle runways, [...] Read more.
Long-term preventive maintenance planning using finite annual budgets is vital for maintaining the service performance of airport runway composite pavements. Using the pavement condition index (PCI) to quantify composite pavement performance, this study investigated the PCI deterioration tendencies of middle runways, terminal runways, and taxiways and developed prediction models related to structural thickness and air traffic. Performance jump (PJ) and deterioration rate reduction (DRR) were used to measure maintenance benefits. Based on 112 composite pavement sections in the Long-term Pavement Performance Program, this study analyzed the influences of five typical preventive maintenance technologies on PJ, DRR, and PCI deterioration rates. The logarithmic regression relationship between PJ and PCI was obtained. For sections treated with crack sealing and crack filling, the DRR was nearly 0. For sections treated with fog seal, thin HMA overlay, and hot-mix recycled AC, the DRR was 0.2, 0.7, and 0.8, respectively. To solve the multi-objective maintenance problem, this study proposed a decision-making optimization method based on dynamic programming, and the solution algorithm was optimized, which was applied in a five-year maintenance plan. Considering different PCI deterioration tendencies of airport regions, as well as PJ, DRR, and costs of maintenance technologies, the preventive maintenance decision-making optimization method meets performance and financial requirements sufficiently. Full article
(This article belongs to the Special Issue New Technology for Road Surface Detection)
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17 pages, 11167 KiB  
Article
Temporal Convolutional Network-Based Axle Load Estimation from Pavement Vibration Data
by Zeying Bian, Mengyuan Zeng, Hongduo Zhao, Mu Guo and Juewei Cai
Appl. Sci. 2023, 13(24), 13264; https://doi.org/10.3390/app132413264 - 14 Dec 2023
Viewed by 853
Abstract
Measuring the axle loads of vehicles with more accuracy is a crucial step in weight enforcement and pavement condition assessment. This paper proposed a vibration-based method, which has an extended sensing range, high temporal sampling rate, and dense spatial sampling rate, to estimate [...] Read more.
Measuring the axle loads of vehicles with more accuracy is a crucial step in weight enforcement and pavement condition assessment. This paper proposed a vibration-based method, which has an extended sensing range, high temporal sampling rate, and dense spatial sampling rate, to estimate axle loads in concrete pavement using distributed optical vibration sensing (DOVS) technology. Temporal convolutional networks (TCN), which consist of non-causal convolutional layers and a concatenate layer, were proposed and trained by over 6000 samples of vibration data and ground truth of axle loads. Moreover, the TCN could learn the complex inverse mapping between pavement structure inputs and outputs. The performance of the proposed method was calibrated in two field tests with various conditions. The results demonstrate that the proposed method obtained estimated axle loads within 11.5% error, under diverse circumstances that consisted of different pavement types and loads moving at speeds ranging from 0~35 m/s. The proposed method demonstrates significant promise in the field of axle load reconstruction and estimation. Its error, closely approaching the 10% threshold specified by LTPP, underscores its efficacy. Additionally, the method aligns with the standards set by Cost-323, with an error level-up to category C. This indicates its capability to provide valuable support in the assessment and decision-making processes related to pavement structure conditions. Full article
(This article belongs to the Special Issue New Technology for Road Surface Detection)
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