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 1087

Special Issue Editors


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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

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

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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

Published Papers (1 paper)

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Research

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 697
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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