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Infrastructures
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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 July 2026 | Viewed by 11509

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 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. 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 (12 papers)

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Research

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19 pages, 20254 KB  
Article
Runway Microtexture Degradation Under Operational Wear and Rubber Contamination, and Subsequent Recovery: A Case Study
by Gadel Baimukhametov and Greg White
Infrastructures 2026, 11(5), 174; https://doi.org/10.3390/infrastructures11050174 - 15 May 2026
Viewed by 312
Abstract
Runway microtexture is a key parameter governing pavement friction. In recent years, several microtexture assessment methods have been developed; however, understanding of microtexture evolution under operational conditions, as well as the effects of maintenance techniques, remains limited. In this study, a runway at [...] Read more.
Runway microtexture is a key parameter governing pavement friction. In recent years, several microtexture assessment methods have been developed; however, understanding of microtexture evolution under operational conditions, as well as the effects of maintenance techniques, remains limited. In this study, a runway at an Australian airport was investigated using laser profilometry. Measurements were conducted across multiple transverse sections, including aircraft touchdown and mid-runway zones. Microtexture deterioration rates were evaluated based on the estimated number of tire–pavement contacts, and aggregate polishing was assessed at different locations. Measurements were also performed after rubber contamination removal and rejuvenation treatments. The results indicate that approximately 25% of total microtexture reduction can be attributed to surface polishing, with a lower contribution in touchdown zones due to the protective effect of rubber deposits. A non-linear degradation trend was observed in touchdown zones, where approximately 1100 tire contacts reduced average microtexture roughness from 18 μm to 11 μm. Rubber removal effectively restored microtexture close to its original levels across the runway width. A rejuvenation treatment with a covering of fine sand initially improved microtexture; however, rapid deterioration occurred due to loss of the sand coating. These findings improve the understanding of microtexture evolution under operational runway conditions, albeit only at a case study level, and support more effective runway maintenance planning and intervention strategies. Full article
(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)
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22 pages, 2743 KB  
Article
Optimal Monitoring Section Layout for iFEM-Based Strain Reconstruction of Subsea Pipelines via Greedy Search
by Xueyu Ren, Jiawang Chen, Shang Sun, Jianling Zhou, Zhonghui Zhou and Yuan Lin
Infrastructures 2026, 11(5), 160; https://doi.org/10.3390/infrastructures11050160 - 6 May 2026
Viewed by 357
Abstract
Subsea oil and gas pipelines are critical infrastructure in marine engineering, and strain monitoring is essential for their safe operation. However, due to the complexity of the marine environment and the constraints practical deployment, engineering applications often rely on sparse monitoring points, making [...] Read more.
Subsea oil and gas pipelines are critical infrastructure in marine engineering, and strain monitoring is essential for their safe operation. However, due to the complexity of the marine environment and the constraints practical deployment, engineering applications often rely on sparse monitoring points, making it difficult to directly obtain full-field strain information. To address this issue, this paper proposes a strain field reconstruction method for subsea suspended pipelines based on the inverse finite element method (iFEM) and a greedy search strategy, and provides the corresponding optimal layout of monitoring cross-sections. Using a constructed numerical simulation library under multiple load cases, algorithm validation and parameter calibration are performed. On this basis, a comprehensive evaluation framework incorporating both global and peak errors is established. Results show that under the greedy-optimized monitoring section scheme, the comprehensive reconstruction error of iFEM ranges from 0.030 to 0.035, the axial strain error is significantly lower than the circumferential strain error, and the peak relative error stabilizes when the number of monitoring sections reaches seven. The proposed method overcomes the difficulty of acquiring full-field strain information under sparse monitoring conditions, and can provide technical support for the structural health monitoring and safety assessment of subsea oil and gas pipelines. Full article
(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)
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22 pages, 4623 KB  
Article
Impact of Dynamic Modulus Prediction Errors on Rutting Estimates in Sustainable Flexible Pavements
by Konstantina Georgouli, Christina Plati and Andreas Loizos
Infrastructures 2026, 11(4), 127; https://doi.org/10.3390/infrastructures11040127 - 2 Apr 2026
Viewed by 554
Abstract
Permanent deformation, manifested as rutting, remains one of the most critical threats to the structural integrity and functional performance of flexible pavements. The Mechanistic–Empirical Pavement Design Guide (MEPDG) includes rutting models that are highly sensitive to the dynamic modulus (E*) of asphalt mixtures—a [...] Read more.
Permanent deformation, manifested as rutting, remains one of the most critical threats to the structural integrity and functional performance of flexible pavements. The Mechanistic–Empirical Pavement Design Guide (MEPDG) includes rutting models that are highly sensitive to the dynamic modulus (E*) of asphalt mixtures—a parameter that can be determined experimentally or predicted by analytical models. In this study, the influence of E* prediction error on rutting estimation is systematically evaluated by comparing laboratory-measured E* values with those predicted by two models: NCHRP 1-37A and a locally calibrated model. The dynamic pavement behavior and rut depth predictions were determined using the finite layer program 3D-Move under standard traffic loads. Comparative analysis revealed that the NCHRP 1-37A model tends to underestimate E*, leading to significant overestimation of vertical strains and accumulated permanent deformation. In contrast, the locally calibrated model provided predictions that closely matched the laboratory measurements, resulting in minimal deviation in rut depth estimates. The results highlight the importance of local calibration and model selection to improve the reliability of mechanistic–empirical pavement predictions, enabling smarter pavement performance evaluation and supporting more sustainable pavement management practices, especially when laboratory testing is not feasible. Full article
(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)
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24 pages, 7427 KB  
Article
A Two-Stage Feature Reduction (FIRRE) Framework for Improving Artificial Neural Network Predictions in Civil Engineering Applications
by Yaohui Guo, Ling Xu, Xianyu Chen and Zifeng Zhao
Infrastructures 2026, 11(1), 29; https://doi.org/10.3390/infrastructures11010029 - 16 Jan 2026
Viewed by 442
Abstract
Artificial neural networks (ANNs) are widely used in engineering prediction, but excessive input dimensionality can reduce both accuracy and efficiency. This study proposes a two-stage feature-reduction framework, Feature Importance Ranking and Redundancy Elimination (FIRRE), to optimize ANN inputs by removing weakly informative and [...] Read more.
Artificial neural networks (ANNs) are widely used in engineering prediction, but excessive input dimensionality can reduce both accuracy and efficiency. This study proposes a two-stage feature-reduction framework, Feature Importance Ranking and Redundancy Elimination (FIRRE), to optimize ANN inputs by removing weakly informative and redundant variables. In Stage 1, four complementary ranking methods, namely Pearson correlation, recursive feature elimination, random forest importance, and F-test scoring, are combined into an ensemble importance score. In Stage 2, highly collinear features (ρ > 0.95) are pruned while retaining the more informative variable in each pair. FIRRE is evaluated on 32 civil engineering datasets spanning materials, structural, and environmental applications, and benchmarked against Principal Component Analysis, variance-threshold filtering, random feature selection, and K-means clustering. Across the benchmark suite, FIRRE consistently achieves competitive or improved predictive performance while reducing input dimensionality by 40% on average and decreasing computation time by 10–60%. A dynamic modulus case study further demonstrates its practical value, improving R2 from 0.926 to 0.966 while reducing inputs from 25 to 7. Overall, FIRRE provides a practical, robust framework for simplifying ANN inputs and improving efficiency in civil engineering prediction tasks. Full article
(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)
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25 pages, 3834 KB  
Article
Analysis of Variance in Runway Friction Measurements and Surface Life-Cycle: A Case Study of Four Australian Airports
by Gadel Baimukhametov and Greg White
Infrastructures 2026, 11(1), 20; https://doi.org/10.3390/infrastructures11010020 - 9 Jan 2026
Cited by 2 | Viewed by 851
Abstract
Runway friction is a critical factor in aircraft safety, affecting braking performance during landing and take-off. This study evaluates friction measurement variability and runway life-cycle dynamics at four typical Australian airports, using GripTester data from calibration strips and operational runways. The results show [...] Read more.
Runway friction is a critical factor in aircraft safety, affecting braking performance during landing and take-off. This study evaluates friction measurement variability and runway life-cycle dynamics at four typical Australian airports, using GripTester data from calibration strips and operational runways. The results show that friction measurements are influenced by seasonal effects, random errors, and testing equipment tire wear, with greater variability at lower speed (65 km/h) than at higher speed (95 km/h). Analysis of runway friction decay indicates that friction reduction rates are higher in touchdown zones and decelerating rate gradually decrease as friction declines, while regular rubber removal significantly restores friction, sometimes exceeding post-construction levels. Current internationally recommended friction testing intervals may not adequately ensure safety, with a sufficient probability of friction dropping below maintenance planning levels between tests. Based on observed reduction rates, updated intervals of approximately 3000 to 4000 landings are proposed to achieve 90% confidence in maintaining safe friction levels. The findings provide practical guidance for friction management and maintenance scheduling as part of an optimized airport pavement management system. Full article
(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)
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26 pages, 2900 KB  
Article
State-Dependent Asphalt Pavement Deterioration Modeling via Noise-Filtered Reaction Signatures: A Data-Driven Framework Using Korea Highway Pavement Management System (K-HPMS) Data
by Sungjin Hong, Jeongyeon Cho, Kyungyoung Yu, Duecksu Sohn and Intai Kim
Infrastructures 2026, 11(1), 15; https://doi.org/10.3390/infrastructures11010015 - 6 Jan 2026
Viewed by 632
Abstract
Conventional PMSs often rely on static age-based assumptions, which can fail to capture nonlinear, state-dependent deterioration and improvement-like responses observed in long-term monitoring data. This study addresses these limitations by proposing a reaction-oriented analytical framework using eight years of Korea Highway PMS data [...] Read more.
Conventional PMSs often rely on static age-based assumptions, which can fail to capture nonlinear, state-dependent deterioration and improvement-like responses observed in long-term monitoring data. This study addresses these limitations by proposing a reaction-oriented analytical framework using eight years of Korea Highway PMS data (2015–2022). We construct a Δ–State Vector by combining the previous-year condition grade with noise-filtered annual changes in the International Roughness Index (IRI) and Rut Depth (RD). Measurement noise is separated from structural signals via MAD-based noise bands (ΔIRI: ±0.089 m/km; ΔRD: ±0.993 mm), with a global MAD floor (minimum-threshold constraint) to avoid degenerate zero-band cases under sparse or near-constant transitions. The resulting vectors are embedded into a low-dimensional Reaction Space using UMAP and clustered with HDBSCAN. To validate interpretability, a rule-based Trend × Mode Reaction Signature taxonomy is used to assess the semantic consistency of unsupervised clusters. Five dominant reaction regimes are identified, showing strong agreement with signature-based labels (weighted purity = 0.927; coverage for purity ≥ 0.60 = 0.911). Overall, the results indicate that deterioration dynamics are governed by lane–segment heterogeneity and prior-state dependence rather than chronological age, providing a reproducible foundation for future event-sensitive, dynamic age reset frameworks. Full article
(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)
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23 pages, 2895 KB  
Article
Impact of Pavement Surface Roughness on TSD Backcalculation Outputs and Potential Mitigation Strategies
by Nariman Kazemi, Mofreh Saleh and Chin-Long Lee
Infrastructures 2025, 10(12), 350; https://doi.org/10.3390/infrastructures10120350 - 16 Dec 2025
Viewed by 882
Abstract
Deflection slopes measured by the traffic speed deflectometer (TSD) are being used to backcalculate the moduli of pavement layers. Pavement surface roughness causes variations in tyre load magnitude due to excitation, which affects TSD measurements. In this study, three rough pavement surface profiles [...] Read more.
Deflection slopes measured by the traffic speed deflectometer (TSD) are being used to backcalculate the moduli of pavement layers. Pavement surface roughness causes variations in tyre load magnitude due to excitation, which affects TSD measurements. In this study, three rough pavement surface profiles over 150 m longitudinal distances were extracted from the Long-Term Pavement Performance (LTPP) programme database. Utilising finite element method (FEM) simulation of the TSD pass at a travel speed of 80 km/h over a three-layer flexible pavement system containing the rough surface profiles and employing the Greenwood Engineering TSD backcalculation tool, it was found that tyre load excitation can lead to backcalculation errors of up to 48%. By obtaining deflection slopes at equal distance intervals along the 150 m pavement profiles, it was found that averaging the deflection slopes across 9 measurement points reduced backcalculation errors to 10%, while increasing the number of measurement points to 28 further lowered the backcalculation errors to 5%. These findings highlight the potential to mitigate the effects of tyre load excitation on TSD backcalculation outputs without relying on strain gauges, which are mounted on modern TSDs to measure instantaneous tyre load magnitudes but are sensitive to environmental conditions and require calibration. Full article
(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)
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18 pages, 3855 KB  
Article
Effect of Bonding Characteristics on Rutting Resistance and Moisture Susceptibility of Rubberized Reclaimed Asphalt Pavement
by Ling Xu, Zifeng Zhao, Yuanwen Lai, Yan Yuan, Shuyi Wang, Junjie Lin, Laura Moretti and Giuseppe Loprencipe
Infrastructures 2025, 10(12), 336; https://doi.org/10.3390/infrastructures10120336 - 7 Dec 2025
Cited by 2 | Viewed by 594
Abstract
Asphalt pavements incorporating recycled and sustainable materials have become a widely adopted strategy in road construction, particularly with the use of reclaimed asphalt pavement (RAP) and crumb rubber (CR) derived from waste tires. However, the adhesion and cohesion characteristics of rubberized RAP mixtures [...] Read more.
Asphalt pavements incorporating recycled and sustainable materials have become a widely adopted strategy in road construction, particularly with the use of reclaimed asphalt pavement (RAP) and crumb rubber (CR) derived from waste tires. However, the adhesion and cohesion characteristics of rubberized RAP mixtures remain insufficiently understood. This study investigates how interfacial bonding affects the rutting resistance and moisture susceptibility of rubberized RAP asphalt mixtures. Two RAP sources with different aging levels and two CR particle sizes (250 μm and 380 μm) were evaluated. Binder bond strength (BBS) tests showed that pull-off strength increased with the use of smaller CR particles and more highly aged RAP, while rotational viscosity and penetration tests confirmed the corresponding increase in binder stiffness. Hamburg wheel track (HWT) tests with high-temperature viscoplastic deformation analysis demonstrated improved rutting resistance in the tested mixtures. Furthermore, boiling tests supported by image analysis revealed reductions in stripping ratios, indicating enhanced moisture resistance. ANOVA results (p < 0.05) confirmed that CR content had a significant effect on bonding characteristics, whereas RAP aging and CR particle size jointly influenced rutting performance. Overall, mixtures incorporating 10% CR and 25% RAP achieved the best balance between adhesion, cohesion, and durability. These findings provide a quantitative understanding of how interfacial bonding governs the mechanical performance and moisture resistance of rubberized RAP mixtures. Full article
(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)
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17 pages, 5934 KB  
Article
The Impact of Sealed Crack Labeling on Deep Learning Accuracy for Detecting, Segmenting and Quantifying Distresses in Airport Pavements
by Valerio Perri, Misagh Ketabdari, Stefano Cimichella, Maurizio Crispino and Emanuele Toraldo
Infrastructures 2025, 10(12), 316; https://doi.org/10.3390/infrastructures10120316 - 21 Nov 2025
Viewed by 756
Abstract
Using deep learning in automated pavement distress detection has shown huge improvements for transport infrastructure, but a noticeable challenge remains in distinguishing sealed cracks from active ones, which are more evident in high-resolution aerial imagery of airport pavements. Misclassifying sealed cracks, an indicator [...] Read more.
Using deep learning in automated pavement distress detection has shown huge improvements for transport infrastructure, but a noticeable challenge remains in distinguishing sealed cracks from active ones, which are more evident in high-resolution aerial imagery of airport pavements. Misclassifying sealed cracks, an indicator of maintenance intervention, as structural distress leads to false positives that cause overestimation in distress metrics and, ultimately, inaccurate Pavement Condition Index (PCI) scores. This study tries to address this limitation by investigating whether explicitly labeling sealed cracks as a separate class during training can improve model performance. In this regard, aerial orthophotos of taxiways from one selected airport, as a case study, were collected via Unmanned aerial vehicle (UAV) surveys, and three instance segmentation models based on YOLOv11 (version 11 from You Only Look Once family) were trained on different datasets: one excluding sealed cracks (including only longitudinal and transvers cracks), one including sealed cracks without explicit labeling, and one treating sealed cracks as a separate class. Validation against ground-truth field surveys revealed that the model trained with explicit sealed crack annotations achieved significantly lower error rates, with a 56.7% reduction for longitudinal cracks and a 75.2% reduction for transverse cracks with respect to traditional detection methods. This improvement led to fewer false positives and a more reliable quantification of both longitudinal and transverse cracking. The results demonstrate that tailored annotation strategies, which in this study means distinguishing sealed cracks, substantially improve the accuracy of deep learning models for real-world pavement condition assessment. Full article
(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)
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18 pages, 2364 KB  
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
Cited by 3 | Viewed by 2494
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 KB  
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
Cited by 1 | Viewed by 1194
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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Review

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36 pages, 5272 KB  
Review
Roller-Compacted Concrete for Pavements: A Critical Review of Its Structural Design, Construction, Monitoring, and Applications
by Julián Pulecio-Díaz and Yelena Hernández-Atencia
Infrastructures 2026, 11(4), 111; https://doi.org/10.3390/infrastructures11040111 - 24 Mar 2026
Viewed by 1047
Abstract
Roller-compacted concrete (RCC) is a promising alternative to conventional pavement systems due to its structural capacity, rapid construction, and potential for sustainable performance. Nevertheless, its global adoption remains limited by the absence of standardized design protocols, variability in construction practices, and insufficient long-term [...] Read more.
Roller-compacted concrete (RCC) is a promising alternative to conventional pavement systems due to its structural capacity, rapid construction, and potential for sustainable performance. Nevertheless, its global adoption remains limited by the absence of standardized design protocols, variability in construction practices, and insufficient long-term performance assessments. This study provides a comprehensive and critical review of 125 peer-reviewed publications published between 1967 and 2025, proposing a multi-dimensional integration framework that connects material fundamentals, structural design principles, construction practices, in-service monitoring strategies, and documented applications within a unified analytical perspective. Unlike earlier reviews that addressed these aspects separately, this study explicitly articulates their interdependencies and identifies a fragmented global implementation of RCC monitoring practices, with limited integration of structural, functional, and instrumentation-based assessments across life-cycle stages. The findings consolidate a structured reference framework that supports more consistent, data-driven, and sustainability-oriented use of RCC pavements in contemporary infrastructure projects. Full article
(This article belongs to the Special Issue Pavement Performance and Maintenance: Smart Technologies and Sustainable Practices)
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