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Applied Sciences
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Advance in Road and Pavement Engineering
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Advance in Road and Pavement Engineering

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 October 2026 | Viewed by 754

Editors

Dr. Yishun Li

E-Mail Website
Guest Editor
College of Transportation, Tongji University, Shanghai 201801, China
Interests: pavement maintenance; intelligent transportation infrastructure; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuchuan Du

E-Mail Website
Guest Editor
College of Transportation, Tongji University, Shanghai 201801, China
Interests: connected and automated vehicles; smart infrastructures
Special Issues, Collections and Topics in MDPI journals
Dr. Zihang Weng

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: pavement skid resistance; tire–pavement interaction; laser scanning sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Road and pavement engineering is rapidly evolving toward a data-driven and intelligence-enabled paradigm, fueled by breakthroughs in advanced sensing, large-scale data analytics, and artificial intelligence. Emerging technologies—including LiDAR, 3D vision, hyperspectral imaging, ground-penetrating radar, unmanned aerial platforms, and connected vehicle data—are enabling high-resolution, non-contact, and network-level monitoring of pavement condition and infrastructure performance. At the same time, advanced computational approaches such as machine learning, computer vision, multimodal data fusion, and digital twins are reshaping how infrastructure systems are modeled, analyzed, and managed.

Beyond traditional inspection and evaluation, there is a growing shift toward predictive, adaptive, and autonomous decision-making in pavement maintenance and operation. Optimization-based scheduling, lifecycle management, and intelligent maintenance strategies are increasingly supported by data-centric frameworks. Notably, the emergence of large AI models and intelligent agents provides new opportunities to integrate perception, reasoning, and decision-making into unified, end-to-end solutions for road infrastructure systems.

This Special Issue aims to attract high-quality contributions that address cutting-edge developments in sensing technologies, data analytics, intelligent modeling, and decision-making for road infrastructure. We particularly encourage submissions that explore the integration of foundation models, AI agents, and multimodal data with traditional engineering approaches. Interdisciplinary studies, real-world applications, and scalable solutions are especially welcome.

Topics of interest include, but are not limited to, the following:

  • Advanced sensing and non-contact pavement inspection
  • Multimodal data fusion and infrastructure perception
  • AI-driven pavement condition assessment and prediction
  • Digital twins and smart infrastructure systems
  • Intelligent maintenance, scheduling, and lifecycle optimization
  • Large models and agent-based approaches for infrastructure management

Dr. Yishun Li
Prof. Dr. Yuchuan Du
Dr. Zihang Weng
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-anonymized 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

  • road and pavement engineering
  • infrastructure sensing
  • pavement condition assessment
  • multimodal data fusion
  • deep learning
  • digital twins
  • predictive maintenance
  • intelligent decision-making
  • large AI models and agents

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Published Papers (2 papers)

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Research

17 pages, 12478 KB  
Article
Real-Time Road Distress Detection Deployment on Jetson TX2 Using Layer-Adaptive Magnitude Pruning and Channel-Wise Knowledge Distillation
by Hua Xu, Ziyi Yang and Hui Wang
Appl. Sci. 2026, 16(12), 5766; https://doi.org/10.3390/app16125766 - 8 Jun 2026
Viewed by 121
Abstract
To enable the deployment of road distress detection models on resource-constrained embedded platforms, this paper presents a compression case study based on the LRDD-YOLOv8n detector designed for real-time 1080p video input. Layer-adaptive magnitude-based pruning (LAMP) was integrated with channel-wise knowledge distillation. First, LAMP [...] Read more.
To enable the deployment of road distress detection models on resource-constrained embedded platforms, this paper presents a compression case study based on the LRDD-YOLOv8n detector designed for real-time 1080p video input. Layer-adaptive magnitude-based pruning (LAMP) was integrated with channel-wise knowledge distillation. First, LAMP performs structured pruning adaptive global sparsity allocation to reduce parameters and FLOPs. Then, a larger teacher model (LRDD-YOLOv8s) with high structural similarity guides the pruned student to recover feature representations. Compared to the baseline LRDD-YOLOv8n (64.4% mAP@0.5, 2.02 M parameters, 5.9G FLOPs, and 55.5 ms GPU inference time on Jetson TX2), our compressed model under a 1/1.4 target compression ratio achieves a mAP@0.5 of 65.1% (an slight accuracy increment of 0.7%), while reducing parameters by 36.1% (to 1.29 M) and FLOPs by 30.5% (to 4.1 G). Deployed on the BOXER-8120AI edge platform (Jetson TX2), the optimized model achieves an average inference time of 48.3 ms per frame (a 13.0% latency reduction compared to the baseline model). In addition, a 20 FPS detection rate was sustained under the 30 FPS maximum hardware acquisition limit of the industrial camera stream. Kinematic and geometric analysis validates that this processing rate utilizes 66.7% of all physically available frames and establishes a 95.4% consecutive frame-to-frame spatial overlap at typical inspection vehicle speeds (40–60 km/h). Full article
(This article belongs to the Special Issue Advance in Road and Pavement Engineering)
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11 pages, 1770 KB  
Article
Predicting Pavement Three-Dimensional Texture from Reflectance Intensity Images Using a Conditional Generative Adversarial Network
by Peiyan Chen, Hongxu Yang, Haochun Yang, Qingli Shi and Zihang Weng
Appl. Sci. 2026, 16(11), 5244; https://doi.org/10.3390/app16115244 - 23 May 2026
Viewed by 179
Abstract
The three-dimensional (3D) texture of pavement surfaces critically influences skid resistance, noise, and rolling resistance, but high-resolution 3D acquisition is time-consuming and requires specialized equipment. This study investigates the use of a conditional generative adversarial network (cGAN) to predict 3D pavement texture from [...] Read more.
The three-dimensional (3D) texture of pavement surfaces critically influences skid resistance, noise, and rolling resistance, but high-resolution 3D acquisition is time-consuming and requires specialized equipment. This study investigates the use of a conditional generative adversarial network (cGAN) to predict 3D pavement texture from more efficiently acquired 2D reflectance intensity images. Co-registered 3D height maps and intensity data were captured using a high-precision line laser scanner. The intensity images were preprocessed into three representations: raw intensity, histogram-equalized, and watershed-segmented images. Four input configurations, each stacking three channels of these representations, were evaluated to determine the optimal input. Additionally, the proposed cGAN was compared with mainstream image-to-image translation models using the best-performing input. Model performance was assessed using root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The results show that the configuration using only histogram-equalized images achieved the best overall performance (SSIM = 0.4065). In the model comparison, the proposed cGAN attained the highest SSIM. These findings indicate that the proposed approach can produce 3D texture maps that capture the main structural features of pavement surfaces, suggesting its potential for efficient surface characterization. Full article
(This article belongs to the Special Issue Advance in Road and Pavement Engineering)
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