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Coatings
Special Issues
Pavement Surface Status Evaluation and Smart Perception
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Pavement Surface Status Evaluation and Smart Perception

A special issue of Coatings (ISSN 2079-6412). This special issue belongs to the section "Environmental Aspects in Colloid and Interface Science".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1248

Special Issue Editors

Dr. Shihao Dong

E-Mail Website
Guest Editor
College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China
Interests: pavement surface functionality; pavement surface texture reconstruction and evaluation; resource utilization of solid waste in road engineering
Special Issues, Collections and Topics in MDPI journals
Dr. You Zhan

E-Mail Website
Guest Editor
School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
Interests: pavement skid resistance and road traffic safety; intelligent testing and evaluation of pavement service performance; intelligent operation and maintenance of road infrastructure; functional pavement
Special Issues, Collections and Topics in MDPI journals
Dr. Haoyuan Luo

E-Mail Website
Guest Editor
Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Interests: tire–pavement contact mechanics; intelligent detection of pavement skid-resistance; high wear-resistant material for pavement
Dr. Bin Yang

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: tire/road interaction noise; pavement surface functional performance

Special Issue Information

Dear Colleagues,

This Special Issue, entitled “Pavement Surface Status Evaluation and Smart Perception”, aims to collate papers that showcase recent advances in the measurement, analysis, and intelligent interpretation of pavement surface conditions. With the rapid development of sensing technologies, computer vision, machine learning, and digital twins, the evaluation of pavement texture, surface distresses, skid resistance, and structural performance has entered a new stage characterized by automation, precision, and intelligence.

However, challenges remain in robust perception under complex road environments, multi-source data fusion, quantitative interpretation of three-dimensional surface features, and the integration of smart sensing technologies into pavement maintenance decision-making. This Special Issue welcomes the submission of high-quality contributions related to pavement surface characterization, 3D texture measurement, depth estimation, image-based and sensor-based perception, smart data acquisition, AI-driven pavement evaluation, and intelligent monitoring systems.

Submissions including original research articles, technical notes, case studies, reviews, and methodological innovation papers are encouraged. Studies integrating computer vision and deep learning with pavement engineering practice are particularly welcome.

Dr. Shihao Dong
Dr. You Zhan
Dr. Haoyuan Luo
Dr. Bin Yang
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Coatings is an international peer-reviewed open access monthly 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 2600 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

  • pavement surface evaluation
  • pavement surface reconstruction
  • intelligent sensing
  • deep learning for pavement engineering

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

22 pages, 1830 KB  
Article
Comparative Life-Cycle Assessment of Innovative Pavement Surface Coatings for Sustainable Road Maintenance
by Ana María Rodríguez-Alloza and Daniel Garraín
Coatings 2026, 16(5), 512; https://doi.org/10.3390/coatings16050512 - 23 Apr 2026
Viewed by 229
Abstract
Road pavement rehabilitation increasingly incorporates innovative surface technologies aimed at improving pavement performance while reducing environmental impacts. In addition to conventional recycled asphalt pavement (RAP) maintenance strategies, advanced pavement surface systems such as reflective coatings, rejuvenator-based self-healing mixtures, and thin low-noise asphalt layers [...] Read more.
Road pavement rehabilitation increasingly incorporates innovative surface technologies aimed at improving pavement performance while reducing environmental impacts. In addition to conventional recycled asphalt pavement (RAP) maintenance strategies, advanced pavement surface systems such as reflective coatings, rejuvenator-based self-healing mixtures, and thin low-noise asphalt layers have been developed to enhance durability and functional performance. This study presents a comparative Life Cycle Assessment (LCA) of four pavement surface technologies using primary inventory data obtained from full-scale road sections. The systems evaluated include a conventional maintenance mixture and three alternative surface solutions: reflective pavement coatings, RAP mixtures incorporating rejuvenator-based self-healing systems, and thin low-noise asphalt layers. The assessment follows ISO 14040 and ISO 14044 standards and applies the ILCD 2011 midpoint+ (EF 2.0) method. To enable comparability between technologies with different durability, the functional unit was defined as 1 m2 of rehabilitated pavement per year of service life. The results indicate that thin low-noise asphalt layers provide the highest environmental benefits across most impact categories due to significant material savings associated with reduced layer thickness. Reflective pavement coatings decrease several impacts, particularly fossil resource depletion and atmospheric emissions, although higher burdens are observed in some categories due to synthetic binder production. RAP mixtures incorporating rejuvenator-based self-healing systems improve resource efficiency and extend pavement durability but may increase impacts associated with binder manufacturing. Overall, the findings highlight relevant environmental trade-offs between different pavement surface technologies and demonstrate that parameters such as layer thickness, binder composition, recycled material content, and service life strongly influence environmental performance. The study illustrates how comparative Life Cycle Assessment supports the development and selection of more sustainable pavement surface systems. Full article
(This article belongs to the Special Issue Pavement Surface Status Evaluation and Smart Perception)
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20 pages, 4366 KB  
Article
Intelligent Detection of Asphalt Pavement Cracks Based on Improved YOLOv8s
by Jinfei Su, Jicong Xu, Chuqiao Shi, Yuhan Wang, Shihao Dong and Xue Zhang
Coatings 2026, 16(3), 359; https://doi.org/10.3390/coatings16030359 - 12 Mar 2026
Viewed by 473
Abstract
The intelligent detection of asphalt pavement cracks has become increasingly important for ensuring service performance of road infrastructure. Traditional manual detection has significant safety hazards and insufficient accuracy. Furthermore, existing deep learning models still face challenges, including missed detection, false alarms, and poor [...] Read more.
The intelligent detection of asphalt pavement cracks has become increasingly important for ensuring service performance of road infrastructure. Traditional manual detection has significant safety hazards and insufficient accuracy. Furthermore, existing deep learning models still face challenges, including missed detection, false alarms, and poor performance in small target detection under complex conditions. This investigation adopts unmanned aerial vehicles (UAVs) to acquire pavement distress information and develops an intelligent detection approach for asphalt pavement crack based on improved YOLOv8s. First, the Spatial Pyramid Pooling Fast (SPPF) module is replaced with the Spatial Pyramid Pooling Fast with Cross Stage Partial Connections (SPPFCSPC) module in the backbone network to enhance the multi-scale feature fusion capability. Secondly, the Convolutional Block Attention Module (CBAM) module is introduced to the neck network to optimize the feature weights in both channel and spatial attention. Meanwhile, the Efficient Intersection over Union (EIoU) loss is adopted to improve accuracy. Finally, the Crack_Dataset is established, and the ablation experiments are conducted to verify the reliability of the detection model. The research indicates that the improved model achieves Precision, Recall, and mAP@0.5 of 83.9%, 79.6%, and 83.9%, respectively, representing increases of 1.5%, 1.3%, and 1.4%, compared with the baseline model. In comparison with mainstream object detection algorithms such as YOLOv5s and YOLOv8s, the proposed method attains an F1-score, mAP@0.5, and mAP@[0.5–0.95] of 0.82, 83.9%, and 46.6%, respectively, demonstrating a performance improvement. Based on the improved detection model, a pavement crack detection system was designed and implemented using PyQt5. This system supports image, video, and real-time camera input and detection. Full article
(This article belongs to the Special Issue Pavement Surface Status Evaluation and Smart Perception)
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