Texture Depth Research Articles

Multi-Field Coupling Numerical Analysis and Experimental Validation of Surface-Textured Metal Seals in Roller Cone Bits

Metal seals play a crucial role in ensuring the dependable functioning of the bearing system in roller cone bits. Due to the intricate nature of downhole conditions, the microstructure design of sealing end faces, specifically the surface texture, is rarely used in metal seals. This study focuses on examining the applicability of surface texture technology in metal seals for cone bits to enhance the lubrication characteristics and friction and wear properties of liquid film between end faces. A multi-field coupling model of surface-textured metal seals was established and experimentally verified. The liquid film carrying the capacity and sealing performance of five different shapes of surface-textured end faces (circle, ellipse I (horizontal), ellipse II (vertical), triangle, and chevron) were investigated under varying rotational speeds and environmental pressures. The influences of texture parameters, including depth and area ratio, on the lubrication characteristics and friction reduction effects of surface-textured metal seals were investigated, and the microscopic wear morphology characteristics of the sealing end faces were analyzed. The results show that surface textures can effectively improve the liquid film state and comprehensive performance of metal seals. Specifically, triangle textures significantly enhance the hydrodynamic pressure effect and weaken the abrasive wear and adhesive wear of the end faces. However, when the environmental pressure is p ≥ 30 MPa, the surface textures gradually lose the advantages of dynamic lubrication but can still reduce friction. The proper texture depth and area ratio can achieve zero leakage (obtained theoretically) and minimal friction in metal seals.

Evaluation of Asphalt Pavement Texture Using Multiview Stereo Reconstruction Based on Deep Learning

To quickly obtain texture information and accurately evaluate the skid resistance of asphalt pavement, a multiview stereo reconstruction method based on deep learning is proposed for evaluating the texture depth of asphalt pavement. First, a depth camera and a digital camera are used to collect RGB-D (i.e., Red, Green, Blue, Depth) and RGB (i.e., Red, Green, Blue) multiview image datasets on different types of pavements for model training and validation respectively. Then, the model precision is validated by calculating the overlap (intersection over union, IoU) between the ground truth point cloud and the reconstructed point cloud. The model performances under different training strategies are compared to obtain the best model, and the effects of image resolution, number of views, and types of pavement material on the model precision are analyzed. Finally, image processing methods and texture depth characterization indexes are proposed to obtain pavement texture information from the reconstructed depth map, thus validating the effectiveness of the model. The results show that the trained model using the transfer learning strategy achieves a reconstruction precision of 0.77 when the image resolution is 3024×3024 and the number of views is 7. Furthermore, the reconstruction performance remains stable across different pavement materials, suggesting the model is suitable for accurately reconstructing depth maps for asphalt pavements. The errors between the predicted texture depth value calculated based on the volume-based index (MTDp) and the profile-based index (MPDp) using the depth maps after image processing and the measured texture depth value (MTDe) using the sand patch method are 11.72% and 18.85%, respectively. It is believed that the pavement texture depth can be effectively evaluated from the reconstructed depth map using the volume-based metric (MTDp). In contrast to traditional testing methods, this approach requires using only a digital camera and a personal computer, making it a lightweight and intelligent analysis method for obtaining pavement texture depth information.

Assessment of habitat and hydrological parameters of the lotic and lentic wetlands of the Sonitpur district, Assam, India

This paper represents the riparian zone and water parameters in twelve selected sampling sites of both lotic and lentic wetlands of Sonitpur district. The study has been carried out from January 2021 to February 2023. Data were collected representing all the four seasons of the year. Physical Characteristics viz; soil colour, soil moisture, soil texture, vegetation coverage, canopy coverage and litter depth were assessed during the survey period. Vegetation and canopy coverage of the riparian zone adjacent to the lotic wetlands were found to be the highest. Litter depth (3.1cm) was found to be recorded at a single sampling site S4 . Water quality assessment recorded that Water temperature was found to be one of the major factors having the most intense direct and indirect effect on the riverine system ranged from 4.85±0.02 to 27.60±0.10 in the lotic wetlands and between 11.15±0.02 to 27.44±0.05 in the lentic wetlands. The overall pH value ranged from 6.09±0.05 to 7.24±0.32 in the lotic wetlands and between 6.44±0.02 to 7.52±0.08 in the lentic wetlands which is a convenient zone. DO values ranged from 6.01±0.32 to 7.37±0.22 in the lotic wetlands and between 6.39±0.30 to 7.63±0.30 in the lentic wetlands. Maximum DO value was recorded in the monsoon period. Water velocity was observed to vary from 0.20±0.03 to 1.30±0.28 in the lotic wetlands. Highest water velocity was recorded during the monsoon period and lowest in the winter period.

RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion.

Raw depth images captured in indoor scenarios frequently exhibit extensive missing values due to the inherent limitations of the sensors and environments. For example, transparent materials frequently elude detection by depth sensors; surfaces may introduce measurement inaccuracies due to their polished textures, extended distances, and oblique incidence angles from the sensor. The presence of incomplete depth maps imposes significant challenges for subsequent vision applications, prompting the development of numerous depth completion techniques to mitigate this problem. Numerous methods excel at reconstructing dense depth maps from sparse samples, but they often falter when faced with extensive contiguous regions of missing depth values, a prevalent and critical challenge in indoor environments. To overcome these challenges, we design a novel two-branch end-to-end fusion network named RDFC-GAN, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map. The first branch employs an encoder-decoder structure, by adhering to the Manhattan world assumption and utilizing normal maps from RGB-D information as guidance, to regress the local dense depth values from the raw depth map. The other branch applies an RGB-depth fusion CycleGAN, adept at translating RGB imagery into detailed, textured depth maps while ensuring high fidelity through cycle consistency. We fuse the two branches via adaptive fusion modules named W-AdaIN and train the model with the help of pseudo depth maps. Comprehensive evaluations on NYU-Depth V2 and SUN RGB-D datasets show that our method significantly enhances depth completion performance particularly in realistic indoor settings.

Experimental investigation on wear behavior of asphalt pavement based on three-dimensional surface texture scan technology

Functional-based failure assessment and design for the service life of pavements have gained significant attention, particularly concerning wear behavior. The long-term wear characteristics of asphalt pavements are influenced by various factors and can be simulated as surface texture wear. This study employs two different indoor accelerated loading abrasion devices to investigate the effects of temperature, load, mixture type, and number of abrasion cycles on asphalt pavement wear. Surface textures of asphalt pavements were captured using a structured laser light camera, and gray & depth data were collected at the same time. Texture indicators including estimated mean texture depth (EMTD), fractal dimension (FD), and optimized structure similarity index measure (SSIM) are utilized to assess changes in surface texture. Key observations based on the small abrasion simulation equipment include the need for specific acceleration conditions in low-temperature abrasion tests, the elimination of deformation effects for high-temperature wear analysis, and a three-stage wear trend, which is more pronounced under the heavy load condition (100 kg) with no unusual fluctuations. EMTD can be used to effectively distinguish textural differences between mixtures, while SSIM shows a good trend of wear for specific mix types. Long-term lead test with MLS-11 and higher data collection frequency has verified the regularity of EMTD and SSIM, and the three stages of the asphalt mixture wear. The BPN test results are used to cross-check the failure of the skid resistance function corresponding to the wear behavior.

Measuring the Macrotexture of Pavement Surface Using an Image Processing Technique

The objective of this study was to propose an image processing-based index for measuring pavement macrotexture at the network level. This index enables macrotexture to be measured easily and inexpensively using images collected at traffic speed. The study involved collecting pavement surface images at a constant traffic speed on a test section specifically designed and constructed for this purpose, with three surface course mixes that are commonly used in India, namely, bituminous concrete, stone matrix asphalt, and gap-graded rubberized bituminous mix. Additionally, macrotexture data with regard to mean texture depth (MTD) from the sand patch test and mean profile depth (MPD) from laser sensor-based measurements were obtained at the locations where the images were captured. The surface macrotexture index (SMI), which was derived from wavelet transform-based image texture analysis, was compared with the MTD and MPD data. The results showed that the SMI is an accurate indicator of pavement surface macrotexture. In addition, the study showcased the application of an unsupervised machine learning algorithm to identify and replace outliers in the SMI data that resulted from isolated spots with dirt, pavement markings, and wet surfaces. The research also established relationships between the proposed SMI and MTD/MPD. These relationships are reliable and can be used to predict the commonly used pavement surface construction quality measure MTD and the network-level skid resistance indicator MPD.

Testing and evaluation for skid resistance of steel slag asphalt wearing course based on surface texture characteristics

Steel slag can be utilized in asphalt wearing course as a substitute for aggregates. The surface texture of pavement is the critical factor affecting the friction behaviors and skid resistance. However, further research is needed to analyze the relationship between the surface texture and the skid-resistance performance of steel slag wearing course. In this study, four kinds of asphalt wearing course with different steel slag dosage (0%, 25%, 50% and 75% by volume) and two kinds of gradation (SMA-10, OGFC-10) were designed. Skid resistance of different specimen under different abrasion time was tested by sand patch test and British pendulum test. Laser scanning technology was used to capture and analyze the surface texture characteristics. The results showed that replacing basalt with steel slag could increase the skid resistance and anti-abrasion performance significantly and asphalt wearing course with 50% steel slag has the best skid resistance. The correlation coefficients between skid resistance and surface texture indexes of the specimens were higher than 0.9 before and after abrasion. Ra is more suitable than texture depth (TD) to represent the surface texture characteristics of the specimen and predict anti-skid properties of pavements.

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.

Effect of soil management practice on soil phosphorus dynamics: A meta‐analysis

AbstractSoil phosphorus (P) is pivotal for crop health and growth. We collated a dataset spanning 65 studies from 19 countries to quantify the effects of both natural and anthropogenic factors on P dynamics (dataset provided). Our meta‐analysis encompassed five variables: the type of fertilizer (chemical or organic fertilizer), tillage practices (tillage or no‐till), land use (cultivated or forest), soil texture (either loam soil or sandy loam soil) and soil depth (surface or subsurface). The interplay between soil management strategies, especially those encompassing fertilizer application and tillage, and soil phosphorus dynamics is well established. Our quantitative data suggests the use of organic fertilizers relates to a significant 82% increase in soil Olsen‐P content. Additionally, while the no‐till method has resulted in only a slight 1.6% increase in Total‐P, it has brought about a remarkable 60% increase in organic phosphorus, especially NaHCO3‐Po, in contrast to conventionally tilled soils. Further, we found that the concentrations of Total‐P and Olsen‐P in surface loam soils is elevated, being 24.8% and 1023.6% higher, respectively, compared with subsurface loam soils. In sandy loam soils, these values are 35.3% and 872.2% higher, respectively. These meta‐analysis data highlight how soil management practices can cause variation in P species in soil. They not only underscore potential strategies for refined phosphorus use in diverse farming contexts but also pave the way for further research focusing on agricultural P management.

Defining the Multiscalar Index Timescale—Soil Water Depth Continuum for the Southwestern United States

AbstractThe lack of long‐term reliable in‐situ soil moisture data sets creates unique drought monitoring challenges for the semi‐arid Southwestern United States (“Southwest”). Land managers use multiscalar meteorological drought indices, like the Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI), as proxies for soil water availability; however, objectively identifying the index and timescale that best represents soil water availability in semi‐arid environments remains a significant gap for applying available climate information to land management action. Here, we couple site‐specific soil modeling with high resolution, spatially continuous meteorological data sets to define the relationship between index timescale length and soil water availability of different depths. By creating new matric potential indices (MPI) from 0 to 200 cm, we correlate MPI time series with the SPI and SPEI at different timescale lengths to identify the best index‐timescale combination for each depth. Results indicate the general relationship between highest correlating index‐timescale and MPI‐depth operates roughly on a 1‐month:5 cm step progression at shallow depths (<80 cm). Analysis by soil texture class shows that soils with higher clay content produce shallower sloped relationships (>1‐month:5 cm) than sandy soils (<1‐month:5 cm). Overall, the SPI produced higher correlations and less error with the MPI compared to the SPEI across all texture classes and depths. Thus, the SPI is a good indicator of soil water availability at shallow depths (<80 cm) and should be considered for soil drought monitoring on Southwestern drylands. However, land managers should consult local soils information, if available, given the impacts of texture class on timescale‐depth relationships.

Microstructure, mechanical properties and wear behaviors of TiN/Ti composite coating on laser surface textured Ti6Al4V alloy fabricated by MHz picosecond laser surface alloying

Ceramic-reinforced metal matrix composites (MMCs) coating fabricated by laser surface melting effectively improves the surface mechanical and wear properties of titanium alloys. However, power of several hundred watts is generally used in traditional laser processing, which introduces excessive heat input and leads to thermal deformation and thermal effects, resulting in cracks and reducing mechanical properties and service life of coatings. In order to improve the microhardness and anti-wear properties of Ti6Al4V alloy and extend the service life, a picosecond laser (ps-laser) with a power of 23 W and repetition rate of 19 MHz was utilized to fabricate the uniform and crack-free MMCs coating (TiN/Ti) by melting TiN powders on the textured Ti6Al4V surface. Three different textures with different texture densities and depths were used to evaluate their effects on coatings. Results showed that the reinforced TiN phase was in the form of fine dendrites with an average secondary dendrite arm spacing of 0.84 µm. The microhardness and the anti-wear properties were both inversely proportional to the texture densities because of controlling the volume fraction of TiN in the coating. The microhardness of the sample with greatest microhardness increase was significantly improved from 355 HV0.5 (base material) to 1000 HV0.5 due to the addition of the TiN phase. The sample with a texture density of 5% showed the best anti-wear properties (about 22 times that of the substrate) due to the effective suppression of three-body abrasive wear.

Analysis of the correlation between the pavement structure combinations and pavement performance using experimental sections

The new generation of pavement technology with the goal of longevity is an important supporting technology that can promote the achievement of sustainable development of high-speed roadways. To further investigate the evolution trend of long-life pavement performance, this paper paved experimental sections to explore the correlations between pavement structure combinations and pavement performances. This paper presented four experimental sections with different pavement structures, asphalt concrete layer thicknesses, and pavement materials. Then, this paper analyzed the effects of the seasonal factors, pavement structure, and lanes on the deflection value and rut depth from three dimensions by the Pearson correlation coefficient (PCC). Finally, this paper used the analysis of variance (ANOVA) to analyze the relationships between the layer thickness of various materials in the pavement structure and the pavement performances, including the deflection value, international roughness index (IRI), texture depth (TD), British Pendulum Number (BPN), sideway force coefficient (SFC), rut depth, and disease area. The results showed that the seasonal factors significantly affected the deflection values of pavement structures with PCCs of 0.61, 0.72, 0.53, and 0.78. The high temperatures increased the average deflection values by 22.85%, 72.88%, 77.61%, and 88.13%, respectively. Under the influence of high temperature in summer and traffic loads, the increased ranges of average rut depth were −0.2%, 4.89%, 9.56%, and 7.31%, respectively. The results of ANOVA showed that the pavement structure type and thickness of each structural layer significantly affected the deflection value, and there also was a strong correlation between the pavement structure type, thickness, BPN, and SFC with p-values less than 0.05. Increasing the thickness of the asphalt surface was beneficial for reducing the area of defects, while laying the semi-rigid base layer was beneficial for maintaining the deflection value and rut depth at a lower level.

Accident Severity Analysis On the North-South Expressway Using Binomial Logistic Regression

Accidents on Malaysian expressways must be monitored on a regular basis since severe accidents occur on expressways due to higher posted speeds than on other roadways. This study aims to analyse accident severities based on pavement conditions using binomial logistic regression. Data on accident severities and pavement conditions were gathered from the Malaysian Highway Authority (MHA) and the The Royal Malaysian Police (PDRM) respectively. Three binomial logistic regression models were constructed based on four accident severity categories of death, serious injury, minor injury and damage. The accident severity was grouped into different classifications in which Model 1, Model 2 and Model 3 were developed. To assess the model's predictive capabilities, predicted accident severity levels were distinguished with actual accident severity levels. Based on the results, Model 1 and Model 2 except Model 3 have significant pavement conditions and are viable for accident severity predictions whereby both models exhibited high prediction accuracy, has good fit and good in differentiating between two classifications. The model's classifier is better at classifying accident severity classes with more samples than categorizing accident severity classes with fewer data. The odds ratio of both Model 1 and Model 2 revealed that International Roughness Index (IRI) has greater influence in predicting accident severity compared to rut depth (RD) and mean texture depth (MTD) particularly on predicting death. This study suggested that a comparable study be conducted on other road classifications.

Texture Depth Measurement using a Micro-volume Measurement Technique for Gyratory Compactor Specimens

Introduction: Road safety plays a pivotal role in transportation infrastructure, as the quality of road surfaces is a crucial factor in ensuring safe driving conditions. One of the main indicators of road quality is the Mean texture depth (MTD), which greatly affects a vehicle's traction and skid resistance. Multiple methods are available for analyzing fabric depth, with the volumetric method, known as the sand patch test and standardized under ASTM E 965 – 96, being the traditional approach. Aim: This research aims to adapt a Sand Patch test procedure to evaluate surface texture depth on laboratory specimens with a 100 mm diameter and determine the required amount of sand, both in terms of weight and volume, to conduct this test effectively. Methods: To achieve this, the relationship between the amount of sand required and the sample size was determined. Two different types of asphalt mixtures were studied Stone Mastic Asphalt (SMA) and Asphalt Concrete (AC), with five gradation limits for each. Results: In the case of the AC10 mixture, the MTD ranged from 0.60 to 1.139 mm, while in SMA 20, the MTD ranged from 1.91 to 2.574 mm. These results are consistent with previous studies, confirming the reliability of the testing methodology, even when working with limited sample areas. Conclusion: In conclusion, the study highlights the potential simplicity and cost-effectiveness of using the Sand Patch micro-volume measurement method. When employed at the initial design stage, this method can provide valuable insights into surface texture, enhancing the design process and potentially further improving road safety.

Impact of the Undersaturation Level on the Longevity of Superhydrophobic Surfaces in Stationary Liquids.

Although the longevity of superhydrophobic surface (SHS) induced by diffusive gas transfer has been extensively studied, the scaling relation between SHS longevity and undersaturation level of the liquid is still an open question. In this study, we address this question by performing experiments where the plastron decay is visualized by a nonintrusive optical technique based on light reflection, the gas diffusion is introduced by using liquid with low dissolved gas concentrations, and the SHS longevity is measured based on the status of gas on the entire surface. We find that the SHS longevity (tf) follows a scaling relation: tf ∼ (1 - s)-2, where s is the ratio of the gas concentration in liquid to that in the plastron. This scaling relation implies that as the gas is dissolving into the liquid, mass flux J reduces with time as J ∼ t-0.5. Furthermore, we find that the diffusion length LD reduces as the undersaturation level increases, following the scaling relation of LD ∼ (1 - s)-1. Lastly, we show that an SHS with a greater texture depth has a longer longevity and a larger LD. Our results provide a better understanding of SHS longevity in undersaturated liquid.

Investigation of impact of soil texture, depth and gamma ray energy on the mass attenuation coefficient and determination of soil bulk density using Gamma-Ray Spectrometry

The determination of the gamma-ray attenuation coefficient is a critical parameter in the characterization of the extent and diffusion of gamma radiation within intricate materials, such as soil. Soils exhibit a complex and diverse composition, characterized by the presence of several phases, including minerals, gasses, organic matter, water, and metals. The study demonstrates the utilization of gamma-ray spectrometry to determine the mass attenuation coefficient and soil bulk density. The impact of incident gamma-ray energy, soil texture, and soil depth on the mass attenuation coefficient is also investigated. The experimental measurements were conducted utilizing a gamma spectrometer equipped with a 2″ × 2″ NaI(Tl) detector at gamma energies of 0.356, 0.662, and 1.332 MeV using 133Ba, 137Cs, and 60Co sources, respectively. The soil samples corresponding to two textures and three depths were collected from the research farms of the Department of Soil Science, Punjab Agricultural University, Ludhiana. The study revealed that the attenuation coefficient depends on the energy of incident radiation and the nature as well as the composition of the soil. The mass attenuation coefficient was observed to decrease rapidly with the increase in gamma radiation energy. Mass attenuation coefficient was found to be higher for sandy loam compared to sandy clay loam textured soil. Moreover, the attenuation coefficient was higher for the lower depths than the upper depth. The comparison between the experimental and predicted values of the mass attenuation coefficient, utilizing the XCOM-NIST framework, demonstrated a satisfactory level of concordance within a 10% margin for gamma energies measuring 0.356 and 0.662 MeV. However, the agreement was slightly less favorable, with discrepancies of less than 12% for sandy clay loam samples and less than 15% for sandy loam textured soil samples, when examining a higher gamma energy of 1.332 MeV. The measured value of mass attenuation coefficient was used to calculate soil bulk density, which agreed reasonably well with the values obtained with the conventional method. Specifically, the sandy clay loam samples displayed a range of 7–14% deviation, while the sandy loam textured soil samples exhibited a range of 14–23% deviation. The investigation can be expanded to ascertain significant soil properties, such as porosity and moisture content.

Optimization design of irregular grooved texture on the surface of sliding pair based on adaptive genetic algorithm

PurposeThis study aims to investigate the effects of irregular groove textures on the friction and wear performance of sliding contact surfaces. These textures possess multiple depths and asymmetrical features. To optimize the irregular groove texture structure of the sliding contact surface, an adaptive genetic algorithm was used for research and optimization purposes.Design/methodology/approachUsing adaptive genetic algorithm as an optimization tool, numerical simulations were conducted on surface textures by establishing a dimensionless form of the Reynolds equation and setting appropriate boundary conditions. An adaptive genetic algorithm program in MATLAB was established. Genetic iterative methods were used to calculate the optimal texture structure. Genetic individuals were selected through fitness comparison. The depth of the groove texture is gradually adjusted through genetic crossover, mutation, and mutation operations. The optimal groove structure was ultimately obtained by comparing the bearing capacity and pressure of different generations of micro-convex bodies.FindingsAfter about 100 generations of iteration, the distribution of grooved textures became relatively stable, and after about 320 generations, the depth and distribution of groove textures reached their optimal structure. At this stage, irregular texture structures can support more loads by forming oil films. Compared with regular textures, the friction coefficient of irregular textures decreased by nearly 47.01%, while the carrying capacity of lubricating oil films increased by 54.57%. The research results show that irregular texture structures have better lubrication characteristics and can effectively improve the friction performance of component surfaces.Originality/valueSurface textures can enhance the friction and lubrication performance of metal surfaces, improving the mechanical performance and lifespan of components. However, surface texture processing is challenging, as it often requires multiple experimental comparisons to determine the optimal texture structure, resulting in high trial-and-error costs. By using an adaptive genetic algorithm as an optimization tool, the optimal surface groove structure can be obtained through simulation and modeling, effectively saving costs in the process.

Co-optimization for hydrodynamic lubrication and leakage of V-shape textured bearings via linear weighting summation

In this study, the linear weighting summation method is adopted for the co-optimization of V-shape textured bearings to maximize the load-carrying capacity and minimize the leakage and friction. Optimal parameters including texture angle and depth and ratio of the texture width are identified based on the established numerical model. Moreover, to improve the comprehensive performance, especially leakage reduction, two profile modifications are introduced: micro-slope and micro-step. The results show that the two proposed modifications are effective for improving the comprehensive performance of V-shape textured journal bearings, and optimal values exist for both the modifications. Furthermore, the overall performance of a bearing with micro-step modification is superior to that of a bearing with micro-slope modification, but the bearing with micro-slope modification is more sensitive to leakage. In summary, this study aims to provide theoretical foundation for the multi-objective optimization design of V-shape textured bearings.

Finite Element Analysis of Tyre Contact Interaction Considering Simplified Pavement with Different Aggregate Sizes

This study considered the effect of pavement aggregate grain size on tyre–pavement contact interaction during the late stages of pavement skid resistance. First, hemispherical shells 7, 9, and 13 mm in diameter were used to simulate coarse pavement aggregates. Subsequently, a three-dimensional finite element tyre–pavement contact model developed using ABAQUS was employed to analyse the contact interaction between each simplified pavement type and the tyre under steady–state rolling and braking conditions. Finally, the concept of occlusal depth was proposed and applied to characterise pavement skid resistance. The results showed that under steady–state rolling conditions, the peak contact stress of the simplified pavement increased with the pavement mean texture depth, whereas the contact area decreased. Under steady–state braking conditions, the effect of the contact interaction between the tyre and simplified pavement aggregates was ranked in order of superiority as aggregate grain sizes of 9, 7, and 13 mm, indicating that aggregate grain size did not exhibit any correlation with tyre–pavement contact interaction. Additionally, the squares of linear correlation coefficients between the pavement cumulative occlusal depth and horizontal braking force reached 0.921, 0.941, 0.889, and 0.894 for vehicle speeds of 30, 60, 90, and 120 km/h, respectively, indicating that they could be used to assess pavement skid resistance.

Microsurfacing: a predictive macrotexture model from mix design parameters

The main functional characteristics of pavement (friction, noise, drainability, and rolling resistance) are strictly related to the surface texture. Considering its importance, it would be appropriate to consider surface texture as a key parameter for road engineering already during the design stages. The objective of this study is confined to the setting up of a macrotexture prediction model (in terms of mean texture depth, MTD) for Microsurfacing, a preventive solution for pavement maintenance that provides a regular and even surface texture with remarkable improvement in skid resistance. Four models were developed – by using multiple linear regression (MLR) – considering a volumetric approach based on mix design-related factors (aggregate gradation, bitumen content, and mineral filler rates). In an attempt to confirm the good generalization of the models and their basic assumptions, a K-fold cross-validation method was used. Several statistical metrics aim at evaluating the goodness of fit and the errors in prediction. The results obtained reveal a strong correlation and low errors between the observed and estimated values, confirming the adequacy of the models (R2 of 0.76–0.90). A more comprehensive dataset and experimental data can further improve the performance of the model. Results can benefit both researchers and practitioners.