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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 February 2025 | Viewed by 6330

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

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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

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Published Papers (6 papers)

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Research

26 pages, 4366 KiB  
Article
Accelerometer-Based Pavement Classification for Vehicle Dynamics Analysis Using Neural Networks
by Vytenis Surblys, Edward Kozłowski, Jonas Matijošius, Paweł Gołda, Agnieszka Laskowska and Artūras Kilikevičius
Appl. Sci. 2024, 14(21), 10027; https://doi.org/10.3390/app142110027 - 3 Nov 2024
Viewed by 727
Abstract
This research examines the influence of various pavement types on vehicle dynamics, specifically concentrating on vertical acceleration and its implications for unsprung mass, including the wheels and suspension system. The objective of this project was to categorize pavement types with accelerometer data, enabling [...] Read more.
This research examines the influence of various pavement types on vehicle dynamics, specifically concentrating on vertical acceleration and its implications for unsprung mass, including the wheels and suspension system. The objective of this project was to categorize pavement types with accelerometer data, enabling a deeper comprehension of the impact of road surface conditions on vehicle stability, comfort, and mechanical stress. Two categorization methods were utilized: a neural network and a multinomial logistic regression model. Accelerometer data were gathered while a car navigated diverse terrain types, such as grates, potholes, and cobblestones. The neural network model exhibited exceptional performance, with 100% accuracy in categorizing all surface types, while the multinomial logistic regression model reached 97.14% accuracy. The neural network demonstrated exceptional efficacy in differentiating intricate surface types such as potholes and grates, surpassing the logistic regression model which had difficulties with these surfaces. These results underscore the neural network’s effectiveness in the real-time categorization of road surfaces, enhancing the comprehension of vehicle dynamics influenced by pavement conditions. Future studies must tackle the difficulty of identifying analogous surfaces by enhancing methodologies or integrating more data attributes for greater precision. Full article
(This article belongs to the Special Issue New Technology for Road Surface Detection)
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13 pages, 10365 KiB  
Article
Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
by Yani Niu, Songhua Fan, Xin Cheng, Xinpeng Yao, Zijian Wang and Jingmei Zhou
Appl. Sci. 2024, 14(18), 8100; https://doi.org/10.3390/app14188100 - 10 Sep 2024
Viewed by 820
Abstract
As one of the early manifestations of road pavement structure degradation, road cracks will accelerate the deterioration of the road if not detected and repaired in time. Aiming at the problems of low recall and incomplete crack detection in current road detection, based [...] Read more.
As one of the early manifestations of road pavement structure degradation, road cracks will accelerate the deterioration of the road if not detected and repaired in time. Aiming at the problems of low recall and incomplete crack detection in current road detection, based on the U-Net network, this paper proposed an Attention-Dynamic Snake Convolution U-Net (ADSC-U-Net) network. Firstly, the dynamic snake-shaped convolution was added to the normal downsampling process to make the network adaptively focus on the slender and curved local features, which can solve the problem of low accuracy of small crack detection. Secondly, the attention mechanism was used to pay better attention to the significant features of positive samples under the condition of a large proportion gap between positive and negative samples, which solved the problem of the poor crack integrity detection effect. Finally, the dataset was expanded by random vertical and horizontal flip operations, which solved the problem of network training overfitting caused by the small-scale datasets. The experimental results showed that, when the input image had a resolution of 480 × 320, evaluation indices P, R, and F1 of ADSC-U-Net on the self-built dataset were 74.44%, 68.77%, and 69.42%, respectively. Compared to SegNet, DeepLab, and DeepCrack, the P was improved by 1.90%, 2.49%, and 11.64%, respectively; the R was improved by 8.01%, 4.70%, and 59.58%, respectively; and the comprehensive evaluation index F1 was improved by 5.73%, 4.02%, and 55.87%, respectively, which proves the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue New Technology for Road Surface Detection)
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27 pages, 3326 KiB  
Article
MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization
by Wu Bo, Zhendong Qian, Bo Yu, Haisheng Ren, Can Yang, Kunming Zhao and Jiazhe Zhang
Appl. Sci. 2024, 14(17), 7670; https://doi.org/10.3390/app14177670 - 30 Aug 2024
Viewed by 630
Abstract
Affected by climatic factors (e.g., low temperature and intense ultraviolet radiation), high-altitude regions experience numerous pavement diseases, which compromise driving safety and negatively impact user travel experience. Timely planning and execution of pavement maintenance are particularly critical. In this paper, considering the characteristics [...] Read more.
Affected by climatic factors (e.g., low temperature and intense ultraviolet radiation), high-altitude regions experience numerous pavement diseases, which compromise driving safety and negatively impact user travel experience. Timely planning and execution of pavement maintenance are particularly critical. In this paper, considering the characteristics of pavement maintenance in high-altitude regions (e.g., volatility of traffic volume, seasonality of maintenance timing, and fragility of the ecological environment), we aim to derive optimal monthly maintenance plans. We develop a multi-objective nonlinear optimization model that comprehensively accounts for minimizing maintenance costs, affected traffic volume and carbon emissions, and maximizing pavement maintenance effectiveness. Utilizing linearization methods, the model is reconstructed into a typical mixed-integer linear programming (MILP) model, enabling it to be solved directly using conventional solvers. We consider five types of decision strategies to reflect the preferences of different decision-makers. Given the uncertainty of maintenance costs, we also utilize the robust optimization method based on the acceptable objective variation range (AOVR) to construct a robust optimization model and discuss the characteristics of optimistic, robust, and pessimistic solutions. The results suggest that different decision strategies show differences in the indicators of maintenance costs, affected traffic volume, carbon emissions, and pavement performance. When multiple decision objectives are comprehensively considered, the indicators are between the maximum and minimum values, which can effectively balance the decision needs of maintenance effectiveness, maintenance timing, and environmental protection. The number of maintenance workers, the requirement of the minimum pavement condition index (PCI), and the annual budget influence the maintenance planning. The obtained robust solution can somewhat overcome the conservative nature of the pessimistic solution. The method proposed in this paper helps address the complexities of pavement maintenance decisions in high-altitude regions and provides guidance for pavement maintenance decisions in such areas. Full article
(This article belongs to the Special Issue New Technology for Road Surface Detection)
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18 pages, 5817 KiB  
Article
Application of Automated Pavement Inspection Technology in Provincial Highway Pavement Maintenance Decision-Making
by Li-Ling Huang, Jyh-Dong Lin, Wei-Hsing Huang, Chun-Hung Kuo and Mao-Yuan Huang
Appl. Sci. 2024, 14(15), 6549; https://doi.org/10.3390/app14156549 - 26 Jul 2024
Viewed by 624
Abstract
Taiwan’s provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human [...] Read more.
Taiwan’s provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human resources and time without providing quantitative results. This study addresses current maintenance practices by introducing automated pavement damage detection technology to replace manual surveys. This technology significantly improves inspection efficiency and reduces costs. For example, traditional methods inspect 1 km per day, while automated survey vehicles cover 4 km per day, increasing efficiency fourfold. Additionally, automated surveys reduce inspection costs per kilometer by about 1.7 times, lowering long-term operational costs. Inspection results include the crack rate, rut depth, and roughness (IRI). Using K-means clustering analysis, maintenance thresholds for these indicators are established for decision-making. This method is applied to real cases and validated against actual maintenance decisions, showing that the introduced detection technology efficiently and objectively guides maintenance decisions and meets the needs of maintenance units. Finally, the inspection results are integrated into a pavement management platform, allowing direct maintenance decision-making and significantly enhancing management efficiency. Full article
(This article belongs to the Special Issue New Technology for Road Surface Detection)
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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
Cited by 1 | Viewed by 1165
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 1341
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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