Search Results (118)

Pavement performance models are considered a key element in pavement management systems since they can predict the future condition of pavements using historical data. Several indicators are used to evaluate the condition of pavements (such as the pavement condition index, rutting depth, and cracking severity), and the international roughness index (IRI), which is the most widely employed worldwide. This study aimed to develop an accurate IRI prediction model. Ten prediction methods were trained on a dataset of 35 independent variables. The performance of the methods was compared, and the light gradient boosting machine was identified as the best-performing method for IRI prediction. Then, the SHAP was synchronized with the best-performing method to prioritize variables based on their relative influence on IRI. The results suggested that initial IRI, mean annual temperature, and the duration between data collections had the strongest relative influence on IRI prediction. Another objective of this study was to determine the optimal uncertainty model for IRI prediction. In this regard, 12 uncertainty models were developed based on different conformal prediction methods. Gray relational analysis was performed to identify the optimal uncertainty model. The results showed that Minmax/80 was the optimal uncertainty model for IRI prediction, with an effective coverage of 93.4% and an average interval width of 0.256 m/km. Finally, a further analysis was performed on the outcomes of the optimal uncertainty model, and initial IRI, duration, annual precipitation, and a few distress parameters were identified as uncertain. The results of the framework indicate in which situations the predicted IRI may be unreliable. Full article

Unlike conventional asphalt pavements, steel bridge deck pavement (SBDP) is directly constructed on orthotropic steel deck plates characterized by relatively low flexural stiffness, rendering it more susceptible to rutting deformation under elevated temperatures and repeated loading. To investigate the mesoscopic mechanism underlying rutting formation in SBDP, a three-dimensional (3D) discrete–continuous coupled model of a steel–asphalt composite structural specimen (SACSS) was developed and employed to conduct virtual rutting simulations, which were subsequently validated against laboratory test results. The impact of surface cracking on rutting progression was then explored. In addition, the spatial motion and contact interactions of particles during the rutting process were monitored and analyzed. The influence of steel plate stiffness on the rutting resistance of SBDP was also evaluated. The numerical analyses yielded the following key findings: (1) Under three steel–asphalt interface bonding (SAIB) failure conditions (0%, 17%, and 100%), the virtual simulation results exhibited strong agreement with experimental trends in rutting depth over time, thereby confirming the validity and reliability of the coupled modeling approach. (2) At 30 °C, the presence of surface cracks is found to increase the rutting depth by 35.77%, whereas this effect is mitigated at 45 °C. (3) The meso-mechanical mechanisms governing rutting deformation in SBDP are further elucidated under different temperature conditions. (4) Moreover, at elevated temperatures, the use of a steel plate with an elastic modulus of 206 MPa effectively inhibit rutting development. This study offers mesoscopic-level insights into the effects of temperature, SAIB conditions, steel plate stiffness, and surface cracking on the macroscopic rutting behavior of SBDP, thereby providing a theoretical foundation for the design and optimization of long-lasting SBDPs. Full article

To investigate the performance degradation of emulsified asphalt cold recycled mixtures (CRM) during service, this study selected a 10 km section of the cold recycled layer (CRL) from the Changjiu Expressway reconstruction project as the research subject. The deterioration patterns of key pavement performance indicators—including the Pavement Condition Index (PCI), Riding Quality Index (RQI), Rutting Depth Index (RDI), and Pavement Structure Strength Index (PSSI)—were analyzed in relation to cumulative equivalent axle loads over a 7-year service period. Concurrently, comparative evaluations were conducted on the mechanical properties, water stability, high-temperature performance, low-temperature crack resistance, and fatigue characteristics between in-service and laboratory-prepared emulsified asphalt CRM. The results demonstrate that after seven years of service, the emulsified asphalt cold recycled pavement maintained excellent performance levels, with PCI, RQI, RDI, and PSSI values of 92.6 (excellent), 90.1 (excellent), 88.5 (good), and 93.4 (excellent), respectively. Notably, while the indirect tensile strength and unconfined compressive strength of the CRL increased with prolonged service duration, other performance metrics—including the tensile strength ratio, shear strength, fracture work, and fracture energy—exhibited an initial improvement followed by gradual deterioration. Additionally, increased traffic loading during service led to a reduction in the residual fatigue life of the CRM. Interestingly, the study observed a temporary improvement in the fatigue performance of CRM during the service period. This phenomenon can be attributed to three key mechanisms: (1) continued cement hydration, (2) secondary hot compaction effects, and (3) diffusion and rejuvenation between fresh and aged asphalt binders. These processes collectively contributed to the partial recovery of aged asphalt strength, thereby improving both the mechanical properties and overall road performance of the CRM. The findings confirm that cold recycled pavements exhibit remarkable durability and maintain a high service level over extended periods. Full article

To investigate the influence of pavement texture on tire hydroplaning, this study utilized laser scanning to capture the surface characteristics of three asphalt mixtures—AC-13, SMA-13, and OGFC-13—across fifteen rutting plate specimens. Three-dimensional (3D) pavement models were reconstructed to incorporate realistic texture data. Finite element simulations, employing fluid-structure interaction and explicit dynamics in Abaqus, were conducted to model tire-water-pavement interactions. The results indicate that the anti-skid performance ranks as OGFC > SMA > AC. However, despite OGFC and SMA exhibiting comparable anti-skid metrics (e.g., pendulum friction value and mean texture depth), OGFC’s superior texture uniformity results in significantly better hydroplaning resistance. Additionally, tire tread depth critically influences hydroplaning speed. A novel Anti-Slip Comprehensive Texture Index (ACTI) was proposed to evaluate pavement texture uniformity, providing a more comprehensive assessment of anti-skid performance. These findings underscore the importance of texture uniformity in enhancing pavement safety under wet conditions. Full article

The durability of permeable pavement needs to be further studied by accelerated pavement testing (APT). Full-scale APT facilities are commonly associated with a very high initial investment and operational costs. A piece of small-scale accelerated testing equipment, the model mobile load simulator (MMLS), was used to investigate and evaluate the mechanical properties of three types of permeable asphalt pavements, including a 4 cm porous asphalt layer with cement-treated permeable base (4PA-CTPB), 7 cm porous asphalt layer with cement-treated permeable base (7PA-CTPB), and 7 cm porous asphalt layer with cement-treated base (7PA-CTB). Under different conditions of subgrade soil, transverse and longitudinal strains at the bottom of the porous asphalt layer and average rut depth and temperature data were collected. The results indicated that 4PA-CTPB produced the maximum average rut depth but minimum resilient tensile strain. The transverse resilient tensile strain of 7PA-CTPB was significantly higher than the other two structures under both wet and dry conditions. The transverse resilient tensile strain significantly increased with increasing loading cycles with a decreasing rate, which could be affected by both load and temperature. MMLS could be used to explore and evaluate the mechanical properties of permeable asphalt pavement. From the data under dry and wet conditions, it may be better to increase the strength of the subgrade, where a suitable hydraulic conductivity coefficient should be considered. Full article

Traditional methods for predicting pavement performance rely on complex finite element modelling and empirical equations, which are computationally expensive and time-consuming. However, machine learning models offer a time-efficient solution for predicting pavement performance. This study utilizes a range of machine learning algorithms, including linear regression, decision tree, random forest, gradient boosting, K-nearest neighbour, Support Vector Regression, LightGBM and CatBoost, to analyse their effectiveness in predicting pavement performance. The input variables include axle load, truck load, traffic speed, lateral wander modes, asphalt layer thickness, traffic lane width and tire types, while the output variables consist of number of passes to fatigue damage, number of passes to rutting damage, fatigue life reduction in number of years and rut depth at 1.3 million passes. A k-fold cross-validation technique was employed to optimize hyperparameters. Results indicate that LightGBM and CatBoost outperform other models, achieving the lowest mean squared error and highest R² values. In contrast, linear regression and KNN demonstrated the lowest performance, with MSE values up to 188% higher than CatBoost. This study concludes that integrating machine learning with finite element analysis provides further improvements in pavement performance predictions. Full article

The combination of reclaimed asphalt pavement (RAP) and foamed bitumen content (FBC) in bitumen mixtures presents a viable and economically advantageous approach to asphalt pavement construction. This investigation delves into the optimal combinations of RAP and FBC to attain a perfect performance, particularly concerning rutting resistance and tensile strength, as assessed through the Hamburg Wheel Tracking Test (HWTT) and the Indirect Tensile Strength (ITS) test. Advanced artificial intelligence (AI) methodologies, such as Random Forest, Support Vector Regression (SVR), and Linear Regression, were utilized to check performance data and attain optimal mix designs. The findings indicate that RAP content ranging from 60% to 80%, in conjunction with FBC levels between 1.5% and 1.8%, yield the most adequate performance under both wet and dry conditions, confirming enhanced rutting resistance and tensile strength. Full article

The determination of the optimal asphalt content in aggregate mix design is currently conducted independently of the pavement structure. This approach fails to consider the characteristics of the pavement structure, such as layer positioning and thickness. As a result, there is a significant disconnect between the structural design of asphalt pavements and the material design of the mixtures. This limitation hampers the full and effective utilization of the deformation and crack resistance capabilities of each layer of asphalt mixture. To address this issue, this study focuses on the commonly used AC-20 asphalt mixture in the intermediate layer of asphalt pavements. The Hamburg wheel tracking test (HWTT) and overlay test (OT) were employed to evaluate the high-temperature rutting resistance and low-temperature crack resistance of the mixture, respectively. The effective range of asphalt content was first established through these tests. Additionally, the VESYS rutting prediction model was utilized to obtain the permanent deformation parameters of the asphalt mixture through experimental data. The rutting prediction was calculated based on the deflection values at the top and bottom of the asphalt layer. The optimal range of asphalt content for the intermediate layer was then determined by combining the rutting contribution rate derived from a finite element model with the allowable rut depth value for the intermediate layer. By considering the characteristics of the asphalt layer position and achieving a relative balance between the crack resistance and deformation resistance capabilities of the asphalt mixture, this study proposes a new design method for determining the optimal asphalt content. This method is of great significance for subsequent engineering applications. The feasibility of this design method was demonstrated using the intermediate layer of asphalt pavement on high-grade highways as an example. The research results show that the asphalt content designed by the balanced design method (BDM), based on the rutting resistance performance of the intermediate layer for this pavement structure and material type, is 4.3%–4.6%. In actual engineering practice, it is recommended to use 4.4% as the optimal asphalt content for AC-20. Full article

To address the issue of air pollution caused by automobile exhaust in China, a titanium dioxide/graphite carbon nitride (TiO₂/g-C₃N₄) composite photocatalyst capable of degrading automobile exhaust was prepared in this study. It was used as an additive to modify styrene–-butadiene latex (SBR) emulsified asphalt. The basic properties of modified emulsified asphalt before and after aging were analyzed, and the dosage range of TiO₂/g-C₃N₄ (TCN) was determined. The environmentally friendly micro-surfacing of degradable automobile exhaust was prepared. Based on 1 h and 6 d wet wheel wear test, rutting deformation test, surface structure depth test, and pendulum friction coefficient test, the road performance of TCN environmentally friendly micro-surfacing mixture with different contents was analyzed and evaluated, and the effect of environmentally friendly degradation of automobile exhaust was studied by a self-made degradation device. The results show that when the mass ratio of TiO₂ and melamine was 1:4, the TCN composite photocatalyst had strong photocatalytic activity. The crystal structure of TiO₂ and g-C₃N₄ was not damaged during the synthesis process. The g-C₃N₄ inhibited the agglomeration of TiO₂. The introduction of N-Ti bond changed the electronic structure of TiO₂, narrowed the band gap and broadened the visible light response range. When the TCN content was in the range of 1~7%, the softening point of SBR- modified emulsified asphalt increased with the increase in TCN content, the penetration decreased, the ductility decreased gradually, and the storage stability increased gradually. The penetration ratio and ductility ratio of the composite-modified emulsified asphalt after aging increased with the increase in TCN content, and the increment of the softening point decreased. This shows that the TCN content is beneficial to the high-temperature performance and anti-aging performance of SBR-modified emulsified asphalt, and has an adverse effect on low temperature performance and storage stability. The addition of TCN can improve the wear resistance and rutting resistance of the micro-surfacing mixture, and has no effect on the water damage resistance and skid resistance. The environment-friendly micro-surfacing asphalt mixture had a significant degradation effect on NO, CO, and HC. With the increase in TCN content, the degradation efficiency of the three gases was on the rise. When the content was 5%, the degradation rates of NO, CO, and HC were 37.16%, 25.72%, and 20.44%, respectively, which are 2.34 times, 2.47, times and 2.30 times that of the 1% content, and the degradation effect was significantly improved. Full article

Anthropization has significantly altered the natural water cycle by increasing impermeable surfaces, reducing evapotranspiration, and limiting groundwater recharge. Permeable Interlocking Concrete Pavements (PICPs) have emerged as a permeable pavement, effectively reducing runoff and improving water quality. This study investigates the base depth for PICPs regarding the strength and permeability. This study examines the hydraulic and structural performance of Permeable Interlocking Concrete Pavements (PICPs) for urban and industrial applications by evaluating the effects of subgrade conditions, traffic loads, and material properties. Using DesignPave and PermPave software, the optimal base layer thickness is determined to prevent rutting while ensuring effective stormwater infiltration beneath 110 mm-thick concrete pavers placed on a 30 mm-thick bedding course. The required base thickness for urban pavements ranges from 100 mm to 395 mm, whereas for industrial pavements, it varies between 580 mm and 1760 mm, depending on subgrade permeability, traffic volume, and loading conditions. The findings demonstrate that PICPs serve as a viable and environmentally sustainable alternative to conventional impermeable pavements, offering significant hydrological and ecological benefits. Full article

The increasing demand for sustainable road construction materials necessitates innovative solutions to overcome the challenges of Recycled Asphalt Pavement (RAP), including aged binder brittleness, reduced flexibility, and durability concerns. Waste Plastic Aggregates (WPA) offer a promising alternative; however, their thermal aging behavior and interactions with RAP remain insufficiently understood. This study evaluates the performance of RAP-based asphalt mixtures, incorporating three types of WPA—Low-Density Polyethylene (LDPE), High-Density Polyethylene (HDPE), and Polypropylene (PP)—under three thermal aging conditions: mild (60 °C for 7 days), moderate (80 °C for 14 days), and severe (100 °C for 30 days). The mixtures were designed with 30% RAP content, 10% and 20% WPA by aggregate weight, and SBS-modified binder rejuvenated with 2% and 4% sewage sludge bio-oil by binder weight. It is considered that thermal aging may impact the performance of WPA in RAP mixtures; therefore, this study evaluates the durability and mechanical properties of RAP mixtures incorporating LDPE, HDPE, and PP under varying thermal aging conditions to address these challenges. The results showed that incorporating WPA and bio-oil significantly enhanced the mechanical performance, durability, and sustainability of asphalt mixtures. Marshall Stability increased by 12–23%, with values ranging from 12.6 to 13.2 kN for WPA-enhanced mixtures compared to 12.7 kN for the control. ITS improved by 15–20% in dry conditions (1.34–1.44 MPa) and 12–18% in wet conditions (1.15–1.19 MPa), with TSR values reaching up to 82.64%. Fatigue life was extended by 28–43%, with load cycles increasing from 295,600 for the control to 352,310 for PP mixtures. High-temperature performance showed a 12–18% improvement in softening point (57.3 °C to 61.2 °C) and a 23% increase in rutting resistance, with rut depths decreasing from 7.1 mm for the control to 5.45 mm for PP mixtures after 20,000 passes. These results demonstrate that combining RAP, WPA, and bio-oil produces sustainable asphalt mixtures with superior performance under aging and environmental stressors, offering robust solutions for high-demand applications in modern infrastructure. Full article

The presence of water-filled ruts during rainy conditions poses dual risks to driving safety: extended braking distances and sudden lateral vehicle deflection. When using maximum depth alone to describe the severity of the rut, it is impossible to obtain the three-dimensional rut morphology, let alone describe its effect on the water distribution on the road. To address this limitation, this study integrates the behavioral characteristics of the road surface with the three-dimensional rut morphology to calculate the water film thickness distribution of the road surface and develop a predictive model for the adhesion coefficient of the entire road surface. Based on the CarSim 2019 dynamics software and the road surface adhesion coefficient model, a water-filled rut driving safety simulation model was established. Key evaluation indicators are identified to quantify the effects of the three-dimensional rut morphology on braking and side deflection behavior, leading to the establishment of a comprehensive safety assessment model. This framework enables the correlation between three-dimensional rut morphology detection and driving risk evaluation, providing valuable insights for traffic safety management on rainy days, particularly in sections with existing ruts. Full article

To analyse the differences in the high-temperature performance of twin-screw desulphurised rubber powder/undesulphurised rubber powder composite SBS-modified asphalt and its mixes. This paper analyses the performance differences between desulphurised rubber powder composite SBS-modified asphalt (ACR/SBS), rubber powder composite SBS-modified asphalt (CR/SBS) and SBS-modified asphalt and their mixtures by multi-stress repeated creep recovery (MSCR) and submerged Hamburg rutting tests. In addition, fluorescence microscopy was used to reveal the micro-mechanisms underlying the differences in the high-temperature performance of the three asphalts. The results show that the twin-screw desulphurisation of rubber powder can significantly improve the high-temperature performance and water damage resistance of its composite-modified asphalt and mixture. The rutting depth of ACR/SBS-MA mixes was one-third and one-thirteenth of CR/SBS-MA mixes and SBS-MA mixes, respectively, under the hydrothermal coupling condition at 80 °C. The cross-linking bonds were opened during the twin-screw desulphurisation process to form a stable cross-linking network structure with SBS. The research of this thesis can lay theoretical and technical support for the promotion and application of desulphurised rubber-modified asphalt. Full article

This paper presents datasets and analyses of 3D LiDAR scans capturing the rutting behavior of a rover wheel in a single-wheel terramechanics testbed. The data were acquired using a LiDAR sensor to record the terrain deformation caused by the wheel’s passage through a Toyoura sandbed, which mimics lunar regolith. Vertical loads of 25 N, 40 N, and 65 N were applied to study how rutting patterns change, focusing on rut amplitude, height, and inclination. This study emphasizes the extraction and processing of terrain profiles from noisy point cloud data, using methods like curve fitting and moving averages to capture the ruts’ geometric characteristics. A sine wave model, adjusted for translation, scaling, and inclination, was fitted to describe the wheel-induced wave-like patterns. It was found that the mean height of the terrain increases after the grouser wheel passes over it, forming ruts that slope downward, likely due to the transition from static to dynamic sinkage. Both the rut depth at the end of the wheel’s path and the incline increased with larger loads. These findings contribute to understanding wheel–terrain interactions and provide a reference for validating and calibrating models and simulations. The dataset from this study is made available to the scientific community. Full article

Machine traffic during timber harvesting operations induces soil compaction, which is particularly evident in the formation of ruts. Visual inspection of rut formation is labor-intensive and limits the volume of data that can be collected. This study aims to contribute to the limited knowledge base regarding the extent of soil physical disturbance caused by machine traffic on steep slopes and to evaluate the utility of LiDAR and UAV photogrammetry techniques. The selected traffic trails included single-pass uphill, single-pass downhill, three-pass round trip, and five-pass round trip trails, with an average slope of 70.7%. Traditional methods were employed to measure rut depth using a pin board and to assess soil bulk density (BD) and soil porosity (SP) from soil samples. The results revealed that the average rut depth was 19.3 cm, while the deepest ruts were observed after a single pass (uphill: 20.0 cm; downhill: 22.7 cm), where BD and SP showed the most significant changes. This study provides a rare quantitative evaluation of the applicability of remote sensing methods in forestry by comparing surface height data collected via a pin board with that derived from a Mobile LiDAR System (MLS) and UAV photogrammetry using structure-from-motion (SfM). When compared to pin board measurements, the MLS data showed an R² value of 0.74 and an RMSE of 4.25 cm, whereas the SfM data had an R² value of 0.62 and an RMSE of 5.27 cm. For rut depth estimation, SfM (16.0 cm) significantly underestimated values compared to the pin board (19.3 cm) and MLS (19.9 cm). These findings not only highlight the potential and limitations of remote sensing methods for assessing soil disturbance in steep forest environments but also contribute to addressing the knowledge gaps surrounding the effects of soil compaction in steep terrain. Full article