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Infrastructures
Special Issues
Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices
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Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: 31 January 2026 | Viewed by 781

Special Issue Editors

Dr. Zifeng Zhao

E-Mail Website
Guest Editor
Department of Civil, Environmental, and Geo-Engineering, University of Minnesota, Minneapolis, MN, USA
Interests: solid waste materials; viscoelasticity; accelerate pavement testing; data-driven analysis; non-destructive testing
Dr. Jin Li

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
Interests: infrastructure sustainability; material decarbonization; asset management; material informatics; transportation electrification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As pavement infrastructure around the world continues to age under increasing traffic loads and environmental stresses, the need for advanced tools and strategies to monitor and maintain pavement performance has become critical. Timely and effective pavement maintenance not only extends service life, but also ensures road safety, sustainability, and cost-efficiency in transportation systems. Recent developments in materials science, sensor technologies, and data-driven modeling have opened new opportunities for accurate assessment and proactive management of pavement conditions.

This Special Issue brings together cutting-edge research, practical innovations, and case studies in the field of pavement performance evaluation and maintenance. Contributions should focus on novel methods, technologies, and materials that improve the durability, monitoring, and lifecycle management of both flexible and rigid pavement structures. Submissions highlighting interdisciplinary approaches—such as non-destructive testing, AI-based condition prediction, and climate-resilient maintenance strategies—are especially encouraged.

Potential topics include but are not limited to:

  • Advanced pavement evaluation technologies: ground-penetrating radar (GPR), Falling Weight Deflectometer (FWD), Traffic Speed Deflectometer (TSD), infrared thermography, LiDAR, and other non-destructive testing methods.
  • Data-driven pavement analysis: applications of machine learning, deep learning, and artificial intelligence for pavement performance prediction, distress detection, and deterioration modeling.
  • Smart monitoring systems: integration of sensors, Internet of Things (IoT), and wireless networks for real-time pavement condition monitoring.
  • Material innovation and performance: use of recycled materials (e.g., RAP, rubber), warm mix asphalt, and modified binders for improved pavement durability.
  • Climate-resilient pavements: evaluation of pavement responses under extreme weather conditions, moisture damage, freeze–thaw cycles, and temperature-induced distresses.
  • Lifecycle performance and sustainability: life-cycle assessment (LCA), life-cycle cost analysis (LCCA), and environmental impact evaluation for pavement systems.
  • Maintenance and rehabilitation strategies: development and optimization of maintenance plans based on performance data, including preventive and adaptive techniques.
  • Case studies and field implementation: practical experiences and lessons learned from full-scale pavement monitoring and maintenance projects.

Authors are invited to submit original research papers, reviews, or technical notes on pavement performance and maintenance. Submissions should present novel methods, data-driven approaches, or practical applications that advance the field. Interdisciplinary work and real-world case studies are especially welcome. All papers will undergo rigorous peer review. This Special Issue seeks to foster innovation and collaboration in pavement engineering.

Dr. Zifeng Zhao
Dr. Jin Li
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. Infrastructures 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 1800 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 performance
  • pavement maintenance
  • non-destructive testing
  • asphalt and concrete pavements
  • sensor-based monitoring
  • predictive modeling
  • sustainable materials
  • climate impacts
  • life-cycle cost analysis
  • smart infrastructure management

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Published Papers (2 papers)

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Research

18 pages, 2364 KiB  
Article
Deterioration Modeling of Pavement Performance in Cold Regions Using Probabilistic Machine Learning Method
by Zhen Liu, Xingyu Gu and Wenxiu Wu
Infrastructures 2025, 10(8), 212; https://doi.org/10.3390/infrastructures10080212 - 14 Aug 2025
Viewed by 256
Abstract
Accurate and reliable modeling of pavement deterioration is critical for effective infrastructure management. This study proposes a probabilistic machine learning framework using Bayesian-optimized Natural Gradient Boosting (BO-NGBoost) to predict the International Roughness Index (IRI) of asphalt pavements in cold climates. A dataset only [...] Read more.
Accurate and reliable modeling of pavement deterioration is critical for effective infrastructure management. This study proposes a probabilistic machine learning framework using Bayesian-optimized Natural Gradient Boosting (BO-NGBoost) to predict the International Roughness Index (IRI) of asphalt pavements in cold climates. A dataset only for cold regions was constructed from the Long-Term Pavement Performance (LTPP) database, integrating multiple variables related to climate, structure, materials, traffic, and constructions. The BO-NGBoost model was evaluated against conventional deterministic models, including artificial neural networks, random forest, and XGBoost. Results show that BO-NGBoost achieved the highest predictive accuracy (R2 = 0.897, RMSE = 0.184, MAE = 0.107) while also providing uncertainty quantification for risk-based maintenance planning. BO-NGBoost effectively captures long-term deterioration trends and reflects increasing uncertainty with pavement age. SHAP analysis reveals that initial IRI, pavement age, layer thicknesses, and precipitation are key factors, with freeze–thaw cycles and moisture infiltration driving faster degradation in cold climates. This research contributes a scalable and interpretable framework that advances pavement deterioration modeling from deterministic to probabilistic paradigms and provides practical value for more uncertainty-aware infrastructure decision-making. Full article
(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)
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24 pages, 3897 KiB  
Article
Evolution Law and Prediction Model of Anti-Skid and Wear-Resistant Performance of Asphalt Pavement Based on Aggregate Types and Deepened Texture
by Shaopeng Zheng, Zilong Zhang, Peiwen Hao, Jian Ma and Liangliang Chen
Infrastructures 2025, 10(8), 208; https://doi.org/10.3390/infrastructures10080208 - 12 Aug 2025
Viewed by 327
Abstract
This study investigates the evolution laws and prediction models of anti-skid and wear-resistant performance for asphalt pavements during the operation period. Using a combination of indoor accelerated wear tests and field detection, mixed specimens are prepared with SBS modified asphalt, limestone, and basalt [...] Read more.
This study investigates the evolution laws and prediction models of anti-skid and wear-resistant performance for asphalt pavements during the operation period. Using a combination of indoor accelerated wear tests and field detection, mixed specimens are prepared with SBS modified asphalt, limestone, and basalt aggregates. Through accelerated wear tests of different durations, the structural depth and friction coefficient are measured. Combined with the field data from the G56 K2319 section of the Hangrui Expressway, the decay laws of anti-skid performance are analyzed, and prediction models are established. The results show that the anti-skid performance of basalt mixtures is superior to that of limestone. The deepened structure technology significantly enhances the performance of basalt but has a negative impact on the pendulum value of limestone. The influence degrees of wear duration, aggregate type, and deepened structure state on structural depth and pendulum value vary. The initial structural depth of basalt mixtures (0.85 mm) is 11.8% higher than that of limestone (0.76 mm). The longitudinal pendulum value of basalt (44) is 10% higher than that of limestone (40), while the transverse pendulum value of limestone (50) is 4.2% higher than that of basalt (48). After 21 h of wear, the structural depth of basalt (0.68 mm) is 4.6% higher than that of limestone (0.65 mm), with a decay rate 23.6% lower. The pendulum value of basalt remains above 50, while limestone’s longitudinal pendulum value drops to 36 (10% lower than its initial value), even below the unmodified state. The influence order for structural depth is deepened structure state > wear duration > aggregate type, and for lateral pendulum value, it is wear duration > deepened structure state > aggregate type. There is a significant linear relationship between structural depth/pendulum value and wear duration, and the prediction models are reliable. The indoor accelerated wear of 44.5 h is equivalent to the field operation wear of 3 years. The research findings provide a theoretical basis for the evaluation of anti-skid performance, maintenance decision-making, and material optimization of asphalt pavements. Full article
(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)
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