Wheel Path Research Articles

Impact of Nanocarbon-Coated Calcium Carbonate on Asphalt Rutting: Experimental and Numerical Analyses

Rutting is a significant form of pavement distress that arises from irreversible strains accumulating along wheel paths, directly impacting pavement safety. This research investigates the effectiveness of nanocarbon-coated micronized calcium carbonate powder as a modified filler to mitigate rutting, utilizing numerical methods via finite element software. The study specifically examines the addition of 5% by weight of this modified filler to the asphalt mix. To validate the numerical results, laboratory wheel-tracking tests were conducted on samples incorporating both conventional and modified fillers. The findings reveal that the modified calcium carbonate filler enhances the asphalt’s resistance to rutting, with the 5% inclusion demonstrating a marked improvement in durability and performance. The study also underscores the necessity of characterizing the elastic and visco-plastic properties of materials through rigorous testing methods, such as elastic modulus and dynamic creep tests, to better understand their behavior under load. Numerical analysis based on linear elastic conditions was prioritized over viscous conditions to effectively compare the results of these specialized materials. The strong correlation between the numerical simulations and laboratory results reinforces the effectiveness of finite element methods in predicting pavement behavior and optimizing asphalt mixtures.

Wheel path planning for flute grinding of non-cylindrical solid end-mills based on circular arc projection

Flute grinding is a crucial process in solid end-mill manufacturing, because the generated flute parameters, i.e. helix angle, rake angle, core radius, and flute width, have significant effects on the cutting performance of end-mills. However, the flute grinding of non-cylindrical solid end-mills is still challenging due to the variation of the flute parameters along the tool axis. This paper proposes an accurate and efficient method of wheel path planning for flute grinding of non-cylindrical solid end-mills based on circular arc projection. The wheel path determined by the new method can accurately generate the designed helix angle, rake angle, core radius, and flute width, even if all of these parameters vary along the tool axis. The helix angle and rake angle are ensured by the conjugate condition between the wheel surface and the cutting-edge curve. Based on circular arc projection, equivalent errors of the core radius and flute width are introduced to quantify the machining errors of the two parameters, without the need to predict the flute profile. Simulations and experiments are conducted to validate the proposed method. Three types of non-cylindrical end-mills i.e. tapered end-mill, tapered barrel end-mill, and barrel end-mill, are considered in the validation. The measured results indicate that the generated flute parameters coincide well with the designed ones at different axial positions of the end-mills.

Field Study of Asphalt Pavement Texture and Skid Resistance under Traffic Polishing Using 0.01 mm 3D Images

Pavement texture and skid resistance are pivotal surface features of roadway to traffic safety, especially under wet weather. Engineering interventions should be scheduled periodically to restore these features as they deteriorate over time under traffic polishing. While many studies have investigated the effects of traffic polishing on pavement texture and skid resistance through laboratory experiments, the absence of real-world traffic and environmental factors in these studies may limit the generalization of their findings. This study addresses this research gap by conducting a comprehensive field study of pavement texture and skid resistance under traffic polishing in the real world. A total of thirty pairs of pavement texture and friction data were systematically collected from three distinct locations with different levels of traffic polishing (middle, right wheel path, and edge) along an asphalt pavement in Oklahoma, USA. Data acquisition utilized a laser imaging device to reconstruct 0.01 mm 3D images to characterize pavement texture and a Dynamic Friction Tester to evaluate pavement friction at different speeds. Twenty 3D areal parameters were calculated on whole images, macrotexture images, and microtexture images to investigate the effects of traffic polishing on pavement texture from different perspectives. Then, texture parameters and testing speeds were combined to develop friction prediction models via linear and nonlinear methodologies. The results indicate that Random Forest models with identified inputs achieved excellent performance for non-contact friction evaluation. Last, the friction decrease rate was discussed to estimate the timing of future maintenance to restore skid resistance. This study provides more insights into how engineers should plan maintenance to restore pavement texture and friction considering real-world traffic polishing.

A probabilistic investigation of long-term field performance of warm mix asphalt pavements

Warm-mix asphalt (WMA) has been gaining popularity in sustainable road engineering due to its reduced environmental impacts. However, uncertainties remain regarding its long-term pavement performance compared to hot mix asphalt (HMA). Especially, the critical factors influencing the field performance of WMA are yet to be determined. Hence, this study analyzes the long-term field performance of pavement overlaid with WMA, using data from the Specific Pavement Studies 10 (SPS-10) of the long-term pavement performance (LTPP) program. The evaluation includes 58 field sections from nine projects across the United States, encompassing two WMA types (foaming processing and chemical additive) and their corresponding HMA control sections. Performance indicators such as alligator cracking, longitudinal cracking in the wheel path, transverse cracking, rutting, and roughness (measured by the international roughness index, IRI) were considered. Per-state and per-climate region Bayesian multilevel models were built to account for site- and region-level heterogeneity. The study results demonstrated that pavement with WMA performed equally well to the HMA counterparts under similar conditions, indicating that their potential for widespread application in meeting both performance standards and environmental conservation requirements. Also, WMA technologies did not differ in pavement performance, and thus the field performance of WMA was not a dominant factor in deciding WMA technology types. Furthermore, Bayesian multilevel models proved highly effective in performance data varying across construction sites and regions.

Assessment of Relationship between Asphalt Concrete Pavement Performance Indicators: Pavement Condition Index and RoadRoid based International Roughness Index

Pavement condition assessment is one of the important tasks to be carried out to prioritize maintenance needs of the road. The Pavement Condition Index and International Roughness Index are some of the most widely used and comprehensive indices for the assessment of pavement condition. The maintenance planning is generally carried out in terms of SDI in Nepal which is only a subjective measure so PCI can be used as a more objective indicator than SDI. The PCI is more representative of the overall condition of the pavement surface by considering all forms of distresses. Whereas, IRI, which is a measure of perceived road roughness is mainly concerned with the ride quality and is based on the distresses occurring on the wheel path. The research aimed to develop the correlation between the two indices to look for usability of either of the indices whenever required. For the determination of PCI, visual inspection survey was carried out based on which the distress type, density and severity were quantified. The IRI data was collected with the help of RoadRoid application after calibration and validation using Laser Profiler. The data matching in respective sections was carried out and relationships between the two indices are determined in terms of linear, logarithmic and polynomial models of different degree. The coefficient of determination for polynomial relationship model was found to be the highest with coefficient of determination of 0.7858. All the models presented with negative correlation between the indices indicating the increase in IRI would cause the decrease in PCI value and vice versa.

Ensemble Learning Approach for Developing Performance Models of Flexible Pavement

This research utilizes the Long-Term Pavement Performance database, focusing on devel-oping a predictive model for flexible pavement performance in the Southern United States. Analyzing 367 pavement sections, this study investigates crucial factors influencing asphaltic concrete (AC) pavement deterioration, such as structural and material components, air voids, compaction density, temperature at laydown, traffic load, precipitation, and freeze–thaw cycles. The objective of this study is to develop a predictive machine learning model for AC pavement wheel path cracking (WpCrAr) and the age at which cracking initiates (WpCrAr) as performance indicators. This study thoroughly investigated three ensemble machine learning models, including random forest, extremely randomized trees (ETR), and extreme gradient boosting (XGBoost). It was observed that XGBoost, optimized using Bayesian methods, emerged as the most effective among the evaluated models, demonstrating good predictive accuracy, with an R2 of 0.79 for WpCrAr and 0.92 for AgeCrack and mean absolute errors of 1.07 and 0.74, respectively. The most important features influencing crack initiation and progression were identified, including equivalent single axle load (ESAL), pavement age, number of layers, precipitation, and freeze–thaw cycles. This paper also showed the impact of pavement material combinations for base and subgrade layers on the delay of crack initiation.

Ride Comfort Prediction on Urban Road Using Discrete Pavement Roughness Index

The prediction of ride comfort holds significant potential for enhancing the driving experience of both human drivers and autonomous vehicles, as it is closely correlated with pavement roughness. However, in urban road scenarios, the presence of shorter road segments and local irregularities introduces added complexity to ride comfort prediction. To better capture and characterize the irregularities and short road sections’ unevenness, we adopt the discrete roughness index (DRI) instead of the commonly used international roughness index (IRI) for assessing road profile unevenness, which is more suitable for urban roads. Ride comfort prediction is developed through numerical simulations using an eight-degree-of-freedom full-car model. The maximum transient vibration value (MTVV) is adopted to assess ride comfort. Through comparing the correlations between the MTVV and pavement roughness indices, it is indicated that the fitting degree of MTVV-DRI outperforms that of MTVV-IRI on short sections. Then, a set of speed-related DRI thresholds to estimate ride comfort distribution on a given road section is proposed, with considerations of vehicle speed, time period, and wheel paths. A hyperbolic-tangent-based speed control strategy is also proposed to avoid abrupt speed and acceleration changes during deceleration. This prediction method can assist drivers or autonomous vehicles in generating driving control strategies and maintaining a high level of ride comfort.

Driving Comfort Analysis Method of Highway Bridge Based on Human-Vehicle-Bridge Interaction

Research on evaluating highway bridge performance through vehicle-bridge interaction (VBI) analysis has made significant advancements. However, when assessing driving comfort, using vehicle acceleration instead of human acceleration poses challenges in accurately representing comfort. First, the paper proposes a finite element analysis method for human-vehicle-bridge spatial interactions (HVBSIs). Then, the importance of wheel path roughness difference is explored when assessing driving comfort. Furthermore, a new method for evaluating driving comfort that includes human and vehicle vibration responses has been proposed, and a simulation example of the steel-concrete composite beam bridge (SCCBB) is used to verify the effectiveness of the proposed method. The results demonstrate that the HVBSI analysis method effectively simulates the interconnected vibrations of the human body, the spatial vehicle model, and the three-dimensional (3D) bridge model. Differences in wheel path roughness significantly impact the roll vehicle vibration responses, which are crucial in driving comfort analysis. The driver’s body vibration response is essential for evaluating driving comfort, and its inclusion leads to increased comfort indices values. In comparison to traditional methods, the overall vibration total value (OVTV) increases by a maximum of 109.04%, and the level of weighted vibration (Leq) increases by a maximum of 6.74%. This leads to an upgrade from grade IV to grade V in terms of comfort level, indicating a reduced comfort.

Analysis of Pore Water Pressure Effect in Asphalt Patch Failure: An Integrated Modelling and Experiment Study

Moisture has been widely accepted as the main cause of asphalt patch failure, while the investigation of pore water pressure (PWP) in asphalt patch is limited. This research investigated the PWP generated in asphalt patch and quantified its impact on mechanical performance considering the inadequate compaction of asphalt patch. A hydro-mechanical model was first developed to analyze the PWP under field conditions, considering the effects of patch geometry, vehicle wandering, traffic loading, and temperature. The moisture-induced stress tester (MIST) was then used to condition patching materials under different PWP levels in a laboratory. After that, indirect tensile (IDT) and interface shear strengths of patching materials were evaluated to quantify the impact of PWP on material degradation. The numerical modeling results show that the maximum PWP can be within or at the interface of the asphalt patch, depending on the wheel path. The magnitude of PWP is related to the patch geometry, traffic load, and temperature. The maximum PWP, whether inside or at the interface of the asphalt patch, can be from 100 kPa to over 300 kPa under common field conditions. The laboratory experimental results suggest that the first 500 cycles of MIST conditioning led to over 30% decrease in both IDT and interface shear strengths of asphalt patch, and the strength ratio was much lower than the recommended level. These findings indicate that asphalt patches with poor compaction quality are very susceptible to moisture-induced damage, and the mechanical performance of patching material should be evaluated after conditioning with PWP effect.

Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking

Roads degrade over time due to various factors such as traffic loads, environmental conditions, and the quality of materials used. Significant investments have been poured into road construction globally, necessitating regular evaluations and the implementation of maintenance and rehabilitation (M&R) strategies to keep the infrastructure performing at a satisfactory level. The development and refinement of performance prediction models are essential for forecasting the condition of pavements, especially to address longitudinal cracking distress, a major issue in thick asphalt pavements. This research leverages multiple machine learning methods to create models predicting non-wheel path (NWP) and wheel path (WP) longitudinal cracking using data from the Long-Term Pavement Performance (LTPP) program. This study highlights the marked differences in distress conditions between WP and NWP, underscoring the importance of precise models that cater to their unique features. Aging trends for both types of cracking were identified through correlation analysis, showing an increase in WP cracking with age and a higher initial International Roughness Index (IRI) linked to NWP cracking. Factors such as material characteristics, kinematic viscosity, pavement thickness, air voids, particle size distribution, temperature, KESAL, and asphalt properties were found to significantly influence both WP and NWP cracking. The Exponential Gaussian Process Regression (GPR) emerged as the best model for NWP cracking, showcasing exceptional accuracy with the lowest RMSE of 89.11, MSE of 7940.72, and an impressive R-Squared of 0.63. For WP cracking, the Squared Exponential GPR model was most effective, with the lowest RMSE of 12.00, MSE of 143.93, and a high R-Squared of 0.62. The GPR models, with specific kernels for each cracking type, proved their adaptability and efficiency in various pavement scenarios. A comparative analysis highlighted the superiority of our new machine learning model, which achieved an R2 of 0.767, outperforming previous empirical models, demonstrating the strength and precision of our machine learning approach in predicting longitudinal cracking.

Laboratory cracking and interlayer bond strength performance characterization of full-scale accelerated geosynthetic reinforced asphalt concrete

This study was conducted to evaluate the performance of geosynthetic reinforced asphalt pavements through full-scale and laboratory testing. The study also investigated the field cracking performance of geosynthetic test sections under accelerated loading and validated with the laboratory cracking performance results. Three sections were constructed including: control, geogrid and geosynthetic reinforced AC. After applying the accelerated loading, cores and slabs were extracted from the wheel path (W) and outside of the wheel path (OW) to evaluate the impact of accelerated loading on cracking performance of AC. The laboratory cracking performance and shear bond strength were assessed using Indirect Tension Asphalt Cracking Test (IDEAL-CT), Interlayer Shear bond Strength (ISS), Bending beam Fatigue (BBF), and Texas overlay tests. Additionally, strain response on top of pre-existing notch was also analyzed from the accelerated test sections to monitor field cracking potential of reinforced AC and to validate it with laboratory cracking performance. According to the BFF, OT and ISS results, the W AC showed degraded performance compared to OW AC, however, IDEAL-CT test results mentioned that W AC showed better cracking performance compared to OW AC. Overall, geosynthetic reinforced AC showed better laboratory cracking performance and lower shear bond strength. At full-scale section, higher tensile strains were registered indicating that geosynthetic reinforcement can undergo potentially larger displacement without failing in the pre-existing crack area. Both laboratory and field test results compliment the findings of each other.

Calculation of wheel path for 3+2-axis grinding of brazed carbide profile mill cutters for wood and plastic

Calculation of wheel path for 3+2-axis grinding of brazed carbide profile mill cutters for wood and plastic

Hydro-mechanical analysis of water-induced pothole patch failure in asphalt pavement

Pothole repair is one of most common roadway maintenance to improve driving comfort and reduce safety risk. This study aimed to conduct hydro-mechanical analysis of water-induced pothole patch failure in asphalt pavement. Three-dimensional finite element models are used to analyze the coupled hydraulic and mechanical responses of surface patching under moving traffic loading considering different wheel path locations and asphalt patch sizes. The model results under dry and saturated conditions were validated against field measurements and simulation results from the literature, respectively. Laboratory tests were conducted to measure dynamic modulus and permeability of asphalt patch material. Analysis results show that regardless of patch size and wheel path, the maximum pore water pressure occurs either at the patch body or the interface between the patch and surrounding pavement when traffic loading approaches the pothole. The hydraulic scouring effect was observed with positive and negative pore pressure under moving loads. The maximum interface shear stress at the interface between the patch and surrounding pavement under traffic loading was found close to the bonding strength, especially for big-shallow patch. The comparison of asphalt patch responses at dry and saturated conditions indicates that water intrusion mainly accelerates edge disintegration of pothole patch.

Pavement maintenance strategy for Provincial roads in Tarakan City, North Kalimantan

Tarakan City has several road segments on provincial roads that have experienced significant damage in the form of sinkholes and substantial potholes on the road surface. These deteriorations are primarily attributed to the impact of traffic, excessive traffic loads, subgrade soil conditions that fall short of requirements, and the use of pavement materials that do not meet the specified standards. To devise effective road repair plans, the collection of field data is crucial. Two distinct methods are employed for data collection to assess pavement conditions: the International Roughness Index (IRI) and the Surface Distress Index (SDI). The IRI method measures road roughness using the Rough meter III instrument, while the SDI method focuses on parameters like crack area, crack width, pothole count, and wheel path rut depth. The surveyed road sections encompass Amal Lama, Gunung Selatan, Aji Iskandar, Bhayangkara, Aki Pingka, and the Ring Road of Tarakan City. Research findings reveal the surface damage level of roads in Tarakan City using the SDI and IRI methods, with the following breakdown: roads in good condition account for 42.86%, those in moderate condition make up 51.90%, slightly damaged roads constitute 4.8%, and heavily damaged roads represent 0.44%. Additionally, the study has determined the percentage of each type of maintenance required: Routine Maintenance at 94.77%, Periodic Maintenance at 4.8%, and Reconstruction at 0.44%.

Modelling the effects of flexible pavement distresses in the long-term pavement performance database on performance

Evaluating flexible pavement performance is mandatory for managing transport infrastructure. This study focuses on modeling the relationships between international roughness index (IRI) and a total of 10 types of pavement distress, including alligator, block, wheel path length, wheel path longitudinal, non-wheel path longitudinal, transverse crackings, patches, bleeding, raveling areas, and pumping. The data recorded under the Long-Term Pavement Performance was used to develop the models. Data sets covering General Pavement Studies from seven states of the United States were used in modeling. The study used modeling approaches, including nonlinear regression analysis, multivariate adaptive regression splines, and artificial neural networks (ANN), in which IRI was the dependent variable and pavement distress was the independent variable. In the developed models, 0.516, 0.623, and 0.684 regression coefficients values were obtained for nonlinear regression analysis, multivariate adaptive regression splines, and artificial neural networks approaches, respectively. The analysis results have determined that the artificial neural networks technique performs more successfully than the other techniques. The statistical error analyses of the root mean square error, Nash-Sutcliffe coefficient of efficiency, mean absolute error, and normalized root mean square error also showed that the same modeling approach performs more successfully. With these data generated from a universally used database, it has been determined once again that ANN is the most efficient mathematical approach in modeling the relationships between surface distresses and IRI.

Estimating vehicle braking distance over wet and rutted pavement surface through back-propagation neural network

Due to repeated load cycles and climate impacts, road pavement deteriorates. One of the primary causes of pavement degradation is the formation of rutting under the wheel path on the road surface. Rutting's impact on vehicle performance, especially on rainy days (since the rain would fill the rutting depression), leads to longer braking distances compared to dry conditions. This study developed a MATLAB-based model for calculating vehicle braking distance on wet asphalt pavement affected by rutting, using dynamic skid resistances generated through Back-Propagation Neural Network (BPNN) analysis. This study addresses the worst-case scenario in which rutting is filled with water, then calculates the required vehicle braking distance under various Water Film Thickness (WFT) conditions. The developed model can perform these evaluations for different operational conditions across various input ranges, such as precipitation intensity, number of lanes, lane width, cross slope, average texture depth, rutting depths, and accumulated WFT. As an outcome, the vehicle braking distance can be estimated as a function of Rutting Depth (RD) within a known vehicle speed interval. After validating the proposed model against existing approaches from the literature, several sensitivity analyses are conducted to assess the impact of influencing parameters on the results. Moreover, the study examines the relationship between AASHTO braking distance requirements and the RD threshold levels adopted by several highway agencies. Furthermore, this model is also applicable to real-world case studies, enabling the calculation of vehicle braking distances with varying RDs in the presence of various WFTs on the pavement surface.

Installation and Use of a Pavement Monitoring System Based on Fibre Bragg Grating Optical Sensors

The evolution of technological tools, namely affordable sensors for data collection, and the growing concerns about maintaining roads in adequate conditions have promoted the development of continuous pavement monitoring systems. This paper presents the installation and use of an innovative pavement monitoring system, which was developed to measure the effects of vehicle loads and temperature on the performance of a pavement structure. The sensors used are based on fibre Bragg grating optical technology, collecting data about the strains imposed in the pavement and the temperature at which those measurements are made. The site selection for the system’s installation and the essential installation details to ensure successful data collection are addressed. A calibration procedure was implemented by performing falling weight deflectometer tests and passing preweighed heavy vehicles over the sensors. In addition to validating the system installation, the results obtained in the calibration confirmed the importance of adequately choosing the distance between sensors. Differences of 50 mm in the position of the load may cause differences of about 20% to 25% in the resulting strains. These results confirmed the importance of increasing the sensor concentration in wheel paths. Furthermore, for loads between 25 kN and 65 kN, raising the temperature by 8 °C caused an increase of about 20% in the horizontal tensile strains measured in the pavement. In summary, it was possible to conclude that this innovative system is capable of capturing the effects of temperature and vehicle speed on the response of the pavement, which may be considered an advantage of this type of monitoring system when compared to those that are only used to determine the loads applied to the pavement or to characterise the type of vehicle.

An internet of things prototype to quantify the lateral wheel path distributions of local roads in New Zealand

ABSTRACT The actual lateral wheel path distribution (LWD) on roads differs from the design assumptions. This leads to premature deterioration of chip-sealed pavements in the form of flushing and ravelling resulting in subsequent maintenance costs. The location of LWD is key to calibrating the variable-rate bitumen spray bar used to minimise flushing and ravelling during chip seal construction. Current methods used to estimate the location of LWD rely on visual inspection. This is subjective and reliant on individual expertise. This study aims to automate obtaining LWD by developing an Internet of Things (IoT) prototype to locate the LWDs. This prototype was tested by collecting LWDs from 22 locations in Christchurch, New Zealand. Results show that narrow roads exhibit a concentrated wheel path distribution. Specifically, increasing single lane carriageway width leads to a significant rise in the standard deviation (spread) and position of the LWDs among 22 locations investigated. Cycle lanes or parking bays significantly influence the location and distribution of LWD. There is no clear link between road camber and LWD. The findings can be used to improve the calibration of the variable-rate bitumen spray bar and inform pavement and traffic engineers to enhance construction and maintenance practices. .

Effect of Wheel Path in Raster Grinding on Surface Accuracy of an Off-Axis Parabolic Mirror

Off-axis parabolic mirrors have extensive applications in X-ray optics, with the precision of their curvature directly impacting grazing-incidence focusing performance. Notably, the off-axis parabolic surface has non-rotating and non-symmetrical characteristics. Ultra-precision raster grinding utilizing a diamond wheel is a common method. Crucially, establishing an optimal wheel path stands as the key to ensuring surface accuracy during off-axis paraboloid grinding. In this study, according to the double curvature property of an off-axis parabolic surface, two different wheel paths were compared: one tracing the meridian direction (parabolic generatrix) and the other following the arc vector direction (arc). The results showed that the wheel path in raster grinding stepping along the arc vector direction can obtain a smaller scallop height and higher surface accuracy. The surface accuracy of one step along the arc vector direction is 9.6 μm, and that of the other step along the meridian direction is 32.6 μm. A model of the scallop height was established based on the relative relationship between adjacent wheel paths, and the error is within 5%. According to the correlation between scallop height and shape error, we conducted an analysis of the spatial distribution of shape errors under varying wheel paths. The wheel path that steps along the arc vector is more suitable for raster grinding of the off-axis paraboloid. The above study can provide theoretical guidance for the wheel path planning of off-axis parabolic mirrors with high surface accuracy.

Flood impact on structural response of asphalt pavement: A finite element modeling approach

The frequency and intensity of floods have increased due to climate change. These natural events negatively affect asphalt pavements, submerging their layers and reducing structural resistance. This work introduces a computational finite element model suitable for investigating the impact of floods on asphalt pavement response. Unlike previous works, which consider a multi-layer analysis, in this research, the resilient modulus varies locally as a function of moisture, which could be variable in the transverse direction of the cross-section. The moisture distribution in the pavement structure is found by solving the Richards’ equation through non-linear finite element analysis. This moisture distribution is coupled to the three-dimensional elasticity problem to find the structural response to traffic loads. The structural problem was solved using the semi-analytical finite element method. Results show that the reduction of the resilient modulus in the unbound and subgrade layers is heterogeneous, with the areas near the edges of the pavement being the most affected. It was also found that the increase in elastic surface deflection depends on both the load position and time, which suggests the damage caused by vehicles could be different for each wheel path, depending on the time in which the vehicles circulate. This research presents a valuable tool for evaluating the impact of flooding on asphalt pavement structures.