Search Results (441)

Crumb rubber-modified asphalt concrete (CMAC) has gained increasing attention as a sustainable pavement material capable of improving mechanical performance while utilizing waste tire resources. This study investigates the rutting resistance and fatigue behavior of CMAC using a combined experimental and mechanistic–empirical modeling approach. Asphalt mixtures containing 0–25% crumb rubber by binder weight were prepared and evaluated through Marshall stability and indirect tensile fatigue tests, whereas Fourier-transform infrared spectroscopy (FTIR) was used to examine binder–rubber interactions. The results indicate that crumb rubber significantly influences both the volumetric and mechanical properties of asphalt mixtures. Mixtures containing 10–15% crumb rubber exhibited optimal performances, achieving up to 36% higher Marshall stability and improved fatigue life compared with conventional asphalt mixtures. FTIR analysis revealed that rubber particle swelling and limited chemical interactions enhanced binder elasticity and improved binder–aggregate compatibility. However, excessive rubber content (≥20%) resulted in reduced stability owing to increased binder absorption and decreased effective binder film thickness. A mechanistic–empirical model incorporating viscoelastic, viscoplastic, and fatigue damage parameters successfully reproduced the experimental trends and identified the same optimal rubber content range. The findings demonstrate that CMAC with a moderate rubber content can enhance pavement durability and structural performance while promoting environmentally sustainable road construction through the reuse of waste tires. Full article

Increasingly frequent extreme rainfall commonly leads to water accumulation on the road surface, elevating vehicle tire hydroplaning to a major threat to driving safety. Existing research mainly focused on tire model optimization or predicting critical hydroplaning speed features based on empirical formulas and numerical simulations. However, there is a lack of systematic validation of the tire–water–pavement coupling interaction under realistic pavement conditions, with particular insufficient attention paid to pavement dynamic responses. In this study, numerical simulation and field full-scale accelerated loading methods were applied to investigate dynamic response characteristics of the tire–water–pavement coupling interaction system. Parametric analyses were first performed to investigate the influences of vehicle speed, vehicle load, water-film thickness, and tire lateral position on the mechanical behaviors of the fluid–structure interaction for a moving vehicle tire. Subsequently, field-measured dynamic responses’ features were used to validate the numerical model, which was then further applied to predict critical conditions of vehicle tire hydroplaning. Finally, the mechanisms of hydroplaning and corresponding mitigation measures were discussed. The study revealed that increasing vehicle speed and water-film thickness, as well as decreasing vehicle load, would reduce the pavement supporting force. The tire–pavement contact stress and strain decreased from the vehicle tire’s center position towards its shoulders. The predicted critical hydroplaning condition suggested that increasing vehicle load mitigated hydroplaning by reducing the proportion of water-induced hydrodynamic lifting force relative to the total vehicle load. When the water depth is relatively shallow, the hydroplaning risk increases rapidly with water depth, while the water’s adverse impact on tire–pavement contact force gradually diminishes as water depth continues to increase. It implies that a vehicle with a relatively low axle load driving on the pavement with a thin thickness of retained water in light rain will still face the hydroplaning risk, as the pavement’s supporting force may be substantially reduced in this weather. The findings provide theoretical foundations and experimentally supported insights on driving safety assessment and anti-skid design of water-covered pavement. Full article

To address high thermal loads and energy costs in Geothermal Road Snow-Melting Systems (GRSSs) within cold regions, this study optimizes energy efficiency through material-level intervention. We developed a composite anti-icing modifier synergistic with low enthalpy geothermal systems, comprising slow-release agents, anti-corrosive components, reinforcing materials, and active chloride salts. By regulating the thermodynamic boundary of the pavement, the freezing point is suppressed to −21 °C. This eliminates the requirement for positive pavement temperatures, significantly reducing the design thermal power. Chloride ion release patterns were analyzed via dissolution and 20-day soaking tests to evaluate structural durability. Results show optimal performance at a 5% modifier dosage and 5.3% bitumen aggregate ratio. Ion release follows a third-order polynomial law and remains stable at 35 °C, ensuring reliability during seasonal thermal cycles. Validation in Xinjiang showed a variation of only 1.5% over 20 days. This research offers an innovative material energy synergy for cascaded geothermal utilization and infrastructure decarbonization in cold regions. Full article

Water-based drill cuttings generated during onshore natural gas development are complex solid wastes that may pose environmental risks if improperly managed. This study evaluates the feasibility of reutilizing water-based drill cuttings as pavement base materials after stabilization using a novel composite stabilizer composed of cement, stabilizer liquid agent, and water-reducing powder (CLP stabilizer). Mix proportion optimization was conducted through compaction and 7-day unconfined compressive strength tests, followed by evaluation of road performance, including strength, compressive rebound modulus, water stability, and temperature shrinkage, with stabilized powder stabilized soil as a control. Microstructural characteristics were analyzed using X-ray diffraction and scanning electron microscopy, and environmental safety was assessed through heavy metal leaching tests and background soil investigation. The results show that the optimal mixture ratio of curing agent (5% cement + 2% liquid stabilizer + 8% superplasticizer powder) satisfies the strength requirement for pre-drilling road bases, exhibiting superior performance compared to the control group. When the stabilizer dosage reaches 9%, the 7-day unconfined compressive strength achieves a maximum of 3.38 MPa, representing a 51% increase over the control group. At a stabilizer dosage of 12%, the splitting tensile strength reaches a peak value of 0.901 MPa, showing a 60.3% improvement. These results indicate enhanced deformation resistance, water stability, and reduced temperature shrinkage rates. Microstructural analysis indicates that the formation of calcium silicate hydrate (C-S-H) gel and ettringite (AFt phase) leads to a denser structure and enhanced durability. Heavy metal concentrations comply with relevant standards, demonstrating controllable environmental risks and supporting sustainable pavement base application. Full article

As urbanization intensifies, improving street thermal comfort has become a critical issue in urban renewal. While existing studies generally assume that increasing the Green View Index (GVI) linearly improves pedestrian thermal comfort, this study identifies a significant “Decoupling Effect” in high-density commercial areas through field measurements and numerical simulations of three typical street types (commercial–service, ecological–recreational, and historical–cultural) in Shenyang. Integrating DeepLab V3 semantic segmentation with ENVI-met version 5.1.1 microclimate simulation, the results demonstrate a robust monotonic negative correlation between GVI and Physiological Equivalent Temperature (PET) in ecological streets (Spearman’s ρ = −0.692, p < 0.001), confirming the consistent cooling benefit of greenery in nature-dominated environments. However, a distinct “Threshold Effect” was identified in commercial streets using Piecewise Linear Regression (PLR). A critical breakpoint was detected at GVI = 22.08%. Below this threshold, visual greenery effectively contributes to cooling (slope = −0.454); yet, once GVI exceeds 22.08%, the cooling efficacy diminishes significantly (slope = −0.109), marking the onset of a “decoupling” phase. Specifically, despite Wenhua Road achieving a GVI of ~24.5% with a complex “three-board, four-belt” structure, its PET peak reaches 46.15 °C, approximately 5.5 °C higher than ecological streets. Mechanism analysis reveals that under peak thermal stress (Traffic Heat ≈ 75 W/m²), the high-intensity anthropogenic heat and hardscape radiation exceed the evaporative cooling threshold of vegetation. This study reveals the non-linear relationship between visual greenery and the physical thermal environment, suggesting that simply pursuing visual green quantity is ineffective in commercial canyon renewal; instead, a threshold-based synergistic optimization of canopy shading and pavement thermal performance is required. These findings provide a quantitative basis for sustainable street landscape planning and urban climate adaptation strategies in high-density cities. Full article

The modification of road bitumen using micro-sized carbonaceous materials offers a promising route to enhance pavement performance; however, the influence of microdispersed coke derived from coal and petroleum sources has not been sufficiently clarified. In this study, coal and petroleum coke from Pavlodar Petrochemical Plant LLC (Pavlodar, Kazakhstan) were mechanochemically activated and used as the modifiers for BND 100/130 bitumen, produced by Asphaltbeton 1 LLC (Almaty, Kazakhstan). X-ray diffraction and scanning electron microscopy were used to determine the structure and morphology of the resulting coke powders. Standard tests and the Superpave Multiple Stress Creep and Recovery (MSCR) methodology were used to determine the physico-mechanical and rheological properties of the modified binders. Microdispersed granular coke powders produced after mechanochemical activation had a minimum average particle diameter of 8.28 µm (petroleum coke) and 16.64 µm (coal coke), and were mainly an amorphous carbon phase with traces of graphite. Addition of 1 wt.% microdispersed coke resulted in better performance of binder and an enhancement in grades of BND 100/130 to BND 70/100, in line with ST RK 1373-2013. MSCR testing showed that Jnr_3.2 is between 2.0–3.0 kPa⁻¹, which is in the S category of AASHTO M 332-20. This study showed how micro-sized coal and petroleum coke can be effectively used as a high-carbon modifier in bitumen, which reflects the possibility of their practical use in asphalt pavements that are subjected to normal traffic conditions. Full article

Surface texture plays a key role in pavement safety and performance, yet its degradation is influenced by multiple interacting factors that vary across road networks. This study developed statistical models to characterize and predict surface texture evolution on Portuguese highways using linear mixed-effects modeling. Texture measurements collected on 7204 pavement sections, each 100 m in length, over three monitoring cycles were analyzed alongside traffic, climatic, pavement structural, geometric, and spatial variables. The hierarchical structure of the data, with repeated measurements nested within pavement sections, was explicitly accounted for via random intercepts and random slopes. At the same time, temporal correlation was modeled via an autoregressive error structure. Two model specifications were evaluated: a model including only traffic and climatic variables and an extended model incorporating pavement and geometric characteristics. Results indicate that texture evolution is statistically associated with cumulative traffic loading, temperature-related indicators, precipitation, surface course type, lane position, vertical alignment, and altitude. The extended model showed a significantly better fit and superior predictive performance, as confirmed by information criteria and cross-validation metrics. The findings highlight the importance of accounting for section-level heterogeneity and roadway characteristics when modeling texture degradation. The proposed modeling framework provides a statistically scalable and robust tool for texture prediction, accounting for regional-specificities and long-term pavement management decisions. Full article

Addressing the challenge that traditional flowability criteria cannot accurately characterize the grouting filling efficacy of foam concrete (FC) for cracks and voids in the base layer of asphalt pavement, this paper established a flowability evaluation method tailored for road grouting. Firstly, FC with varying flow performances were prepared by controlling the water–cement (W/C) ratio and water-reducing agent (WRA) dosage. Secondly, the flow cone method and micro-slump meter on a smooth flow degree pan method (MSM) characterized their flow performances. The porous Marshall specimens were constructed to simulate the crack–void structure of the base layer, and grouting plumpness was calculated using sectional image processing methods. Building upon this, gray relational analysis and regression analysis were employed to establish quantitative relationships between multiple factors and grouting plumpness. The results show that increasing W/C ratio and WRA dosage could improve the flow performance of FC, but reduce the compressive strength. Specifically, when the W/C ratio increased from 0.40 to 0.45, flow time decreased by 72.2% and flow diameter increased by 25%. Increasing WRA dosage from 0.3% to 0.5% could reduce flow time by 16% and increase flow diameter by 10%. Gray relational analysis revealed the strong correlations between flow indexes and grouting plumpness. The gray relational degree was 0.87 between grouting plumpness and flow diameter. In addition, the gray correlation between grouting plumpness and flow time was 0.65. Therefore, flow diameter should be first selected to measure the flow performance of FC. Furthermore, it was found that flow diameter should be higher than 230 mm to ensure that the average grouting plumpness of FC was above 80%. The results of this study provide a reliable basis for evaluating the flow performance of FC for filling cracks in the base layer of asphalt pavement. Full article

Speed management plays a critical role in road safety; however, conventional speed limits are determined based on characteristics such as geometry and traffic volume. Limited consideration is given to the structural condition of pavements and surface distress. This study proposes an integrated mechanistic–quantitative framework that links pavement distress and road safety indicators to the selection of speed limits. A flexible pavement section on Highway No. 80 in Iraq is analyzed as a case study. Mechanistic pavement analysis using KENPAVE is employed to estimate critical strains based on field traffic data and Equivalent Single-Axle Loads (ESALs). The rate of failure is estimated by comparing ESALs and the allowable load repetitions. Safety-related constraints are then derived to quantify hydroplaning risk, braking performance through stopping sight distance, and the vertical shock criterion. The results indicate that the existing pavement structure is marginal, with a high probability of fatigue failure and sensitivity to rutting under increased traffic loads. The integrated safety analysis yields a critical wet-weather speed of approximately 67–70 km/h, while localized settlements exceeding 10 mm require speed reductions of 50–60 km/h to maintain vehicle stability. The proposed framework demonstrates that pavement conditions directly influence safe speed, providing a rational basis for safety-oriented speed management. Full article

Thermal conductivity and specific heat capacity are key parameters controlling heat transfer and temperature distribution in road pavement structures. Although transient methods are increasingly used in laboratory testing, the thermal properties of asphalt mixtures have not been sufficiently studied using these methods, and no dedicated standards exist for road materials. This creates uncertainty in test procedures, specimen geometry, surface preparation, measurement location, and data interpretation, which may lead to significant errors, especially for massive and heterogeneous mixtures. The objective of this study is to systematically quantify the edge zone effect and assess its influence on the determined thermal parameters of a selected heterogeneous asphalt mixture. The study focuses on the quantitative determination of the edge zone effect, practical identification of its width in slab-shaped specimen, and the identification of scientific and practical methodological consequences, as well as the risks and limitations of applying the Modified Transient Plane Source (MTPS) method in the absence of standards. Laboratory measurements demonstrate a clear edge zone effect, with thermal conductivity and thermal diffusivity differing by up to 17% and 18%, respectively, near the specimen edges. These findings highlight the importance of methodological guidelines for slab-shaped asphalt mixture specimens and provide both scientific insight and practical guidance for the reliable application of transient method. They may also support the development of standardized testing procedures for asphalt mixtures. Full article

As core materials in pavement structures, asphalt mixtures are characterized by intensive energy consumption and significant carbon footprints throughout their construction cycle, making their construction a typical high-carbon process in road engineering. Warm-mix technology, leveraging its key advantages of reducing mixing temperatures and cutting energy consumption and emissions, has emerged as a green alternative to hot-mix mixtures. However, existing studies have lacked systematic environmental impact assessments of combinations of asphalt from different oil sources and warm-mix technologies. This study focuses on the additive type warm-mix technology (Evotherm M1) and uses three typical oil sources of 70# road petroleum asphalt. Using headspace gas chromatography–mass spectrometry (HS–GC–MS) and Life Cycle Assessment (LCA) methods, a systematic analysis was conducted across three dimensions: multi-component pollutant emissions, full life cycle stages, and multi-type warm-mix technologies. The analysis focused on the influence of warm-mix treatment on Volatile Organic Compound (VOC) emissions, as well as energy consumption and carbon emission characteristics throughout the full life cycle of the construction phase. Results indicate that warm-mix treatment significantly inhibits VOC emissions from all three oil source asphalts. The largest reduction was observed in Asp-A (74.66%), followed by Asp-C (69.27%), and the smallest in Asp-B (46.47%). The VOC compositions shifted from being dominated by oxygenates to a coexistence of multi-components such as alkanes and aromatic hydrocarbons. In the life cycle of the construction phase, compared with hot-mix mixtures, warm-mix technology reduced total energy consumption by 5.50%–5.56% and carbon emissions by 4.47%–4.52%. Raw material production and mixture mixing stages were identified as the core links for energy consumption and carbon emissions, accounting for over 80% of the totals. Differences among oil sources mainly stemmed from refinery power structure and the temperature–viscosity properties of asphalt. The research results provide theoretical support for material selection and process optimization of green construction of asphalt pavement using additive-based warm-mix technology. Full article

Sustainable pavement solutions that lessen the dependency on virgin materials are required due to mounting environmental and economic pressures. Although recycled asphalt concrete (RAC) has structural and environmental advantages, binder heterogeneity and non-linear material interactions make it difficult to predict the ideal bitumen content in RAC mixtures. This study predicts the bitumen content of asphalt mixtures infused with RAC by combining sophisticated machine learning (ML) with traditional laboratory testing. While this study combines AI-driven predictions with experimental insights to create a state-of-the-art framework for sustainable pavement engineering, 780 data points were obtained from the preparation and testing of three mixtures (0%, 30%, and 50% RAC) for volumetric and mechanical characteristics. Controlled Autoregressive Integrated Moving Average (CARIMA), Swapped Autoregressive Integrated Moving Average (SARIMA), radial basis function artificial neural network (RBF), bagging (BAG), multilayer perceptron (MLP) artificial neural network, and boosting (BOT) ensembles were among the models created. BAG-CARIMA-LGM is a new hybrid model that combines logistic probabilistic generalization, ensemble variance reduction, and time-series forecasting. Higher predictive accuracy and resilience across different RAC levels were attained by the hybrid BAG-CARIMA-LGM model, which performed noticeably better than standalone algorithms. The findings demonstrated improved Marshall stability and controlled flow along with a progressive decrease in mean bitumen content as RAC increased. While 50% RAC with rejuvenators maintained durability and structural integrity, the 30% RAC mixture produced the most balanced performance. The model’s capacity to manage non-linear interactions, volumetric variability, and aging effects was validated by statistical analyses. The BAG-CARIMA-LGM hybrid model optimizes RAC incorporation in asphalt mixtures, supports circular economy goals, and improves technical accuracy. The results point to a revolutionary route towards intelligent, environmentally friendly road systems that support international sustainability objectives. Full article

Transportation infrastructure is vital for sustaining communities and fostering economic development. Urbanization and climate change have led to the rapid deterioration of road transport systems, posing significant challenges for future sustainability. Current transportation infrastructure maintenance planning often prioritizes immediate needs and short-term deterioration indicators, which can overlook long-term changes and future funding constraints. Long-term road maintenance planning is challenged by the large number of decision variables and the complex temporal and spatial dependencies that govern pavement deterioration. Most existing optimization models overlook spatial relationships among road segments, resulting in low computational efficiency, especially for large-scale networks. To address this gap, this study proposes a Spatiotemporal Particle Swarm Optimization for Cost Allocation (SPOCA) model that integrates spatial clustering and heuristic optimization for large-scale decision-making. An age-filtered spatial clustering process first groups roads with similar ages and proximity to preserve spatial structure and reduce problem dimensionality, while a spatial relationship term embedded in the optimization captures correlations among neighboring clusters to improve coordinated decision-making. A case study of Western Australia demonstrates that the SPOCA model reduces computational time by 38% compared with the non-spatial model, while maintaining comparable accuracy and significantly improving network-level pavement quality. The SPOCA model provides a scalable and practical tool to support policymakers in developing efficient and sustainable infrastructure maintenance strategies. Full article

Accurate detection of road surface defects and their integration into geospatial representations are key requirements for scalable UAV-based inspection and maintenance systems.This work presents a lightweight processing pipeline that converts image-based pavement defect segmentations into compact geospatial vector representations suitable for integration with GIS-driven inspection workflows. In addition, we introduce and publicly release a UAV-based road defect dataset with pixel-level annotations, specifically designed for crack-like pavement damage. A deep convolutional neural network is trained to perform semantic segmentation of pavement defects using images derived from the publicly available RDD2022 dataset. Segmentation performance is evaluated across a range of probability thresholds using standard pixel-wise metrics, and a validation-selected operating point is used to generate binary defect masks. These masks are subsequently processed to identify individual defect instances and extract vector polygons that preserve the underlying geometry of crack-like structures. For illustrative geospatial integration, predicted defects are projected into geographic coordinates and exported in standard GIS formats. By transforming dense segmentation outputs into compact georeferenced polygons, the proposed framework bridges deep learning-based perception and GIS-based infrastructure assessment, enabling instance-level geometric analysis and providing a practical representation for UAV-based road inspection scenarios. Full article

For cement pavements on vast road networks, cracking has become one of the principal distresses threatening structural integrity and traffic safety. This study introduces DCP-TransUNet, a model featuring a new hybrid encoder that enhances the continuity of crack extraction under complex conditions through a DSE-CNN module and a CLMA-Transformer block. To further strengthen learning and interpretability for challenging crack imagery, a PPA bottleneck module is designed to capture additional discriminative features. Experimental results indicate strong performance: on the public dataset, DCP-TransUNet achieves mIoU 79.12%, Recall 87.96%, F1 87.06%, and Precision 86.21%; on the private dataset, it attains mIoU 68.83%, Recall 74.42%, F1 77.57%, and Precision 81.67%. Compared with other models, these outcomes demonstrate the method’s accuracy and effectiveness for crack segmentation. Full article