Typical Pavement Research Articles

Evaluating Bicycle Path Roughness: A Comparative Study Using Smartphone and Smart Bicycle Light Sensors

The quality of bicycle path surfaces significantly influences the comfort of cyclists. This study evaluates the effectiveness of smartphone sensor data and smart bicycle lights data in assessing the roughness of bicycle paths. The research was conducted in Hasselt, Belgium, where various bicycle path pavement types, such as asphalt, cobblestone, concrete, and paving tiles, were analyzed across selected streets. A smartphone application (Physics Toolbox Sensor Suite) and SEE.SENSE smart bicycle lights were used to collect GPS and vertical acceleration data on the bicycle paths. The Dynamic Comfort Index (DCI) and Root Mean Square (RMS) values from the data collected through the Physics Toolbox Sensor Suite were calculated to quantify the vibrational comfort experienced by cyclists. In addition, the data collected from the SEE.SENSE smart bicycle light, DCI, and RMS computed results were categorized for a statistical comparison. The findings of the statistical tests revealed no significant difference in the comfort assessment among DCI, RMS, and SEE.SENSE. The study highlights the potential of integrating smartphone sensors and smart bicycle lights for efficient, large-scale assessments of bicycle infrastructure, contributing to more informed urban planning and improved cycling conditions. It also provides a low-cost solution for the city authorities to continuously assess and monitor the quality of their cycling paths.

Accelerometer-Based Pavement Classification for Vehicle Dynamics Analysis Using Neural Networks

This research examines the influence of various pavement types on vehicle dynamics, specifically concentrating on vertical acceleration and its implications for unsprung mass, including the wheels and suspension system. The objective of this project was to categorize pavement types with accelerometer data, enabling a deeper comprehension of the impact of road surface conditions on vehicle stability, comfort, and mechanical stress. Two categorization methods were utilized: a neural network and a multinomial logistic regression model. Accelerometer data were gathered while a car navigated diverse terrain types, such as grates, potholes, and cobblestones. The neural network model exhibited exceptional performance, with 100% accuracy in categorizing all surface types, while the multinomial logistic regression model reached 97.14% accuracy. The neural network demonstrated exceptional efficacy in differentiating intricate surface types such as potholes and grates, surpassing the logistic regression model which had difficulties with these surfaces. These results underscore the neural network’s effectiveness in the real-time categorization of road surfaces, enhancing the comprehension of vehicle dynamics influenced by pavement conditions. Future studies must tackle the difficulty of identifying analogous surfaces by enhancing methodologies or integrating more data attributes for greater precision.

Research on high-precision recognition model for multi-scene asphalt pavement distresses based on deep learning

Accurate detection of asphalt pavement distress is crucial for road maintenance and traffic safety. However, traditional convolutional neural networks usually struggle with this task due to the varied distress patterns and complex background in the images. To enhance the accuracy of asphalt pavement distress identification across various scenarios, this paper introduces an improved model named SMG-YOLOv8, based on the YOLOv8s framework. This model integrates the space-to-depth module and the multi-scale convolutional attention mechanism, while optimizing the backbone’s C2f structure with a more efficient G-GhostC2f structure. Experimental results demonstrate that SMG-YOLOv8 outperforms the YOLOv8s baseline model, achieving Pmacro and mAP50 scores of 81.1% and 79.4% respectively, marking an increase of 8.2% and 12.5% over the baseline. Furthermore, SMG-YOLOv8 exhibits clear advantages in identifying various types of pavement distresses, including longitudinal cracks, transverse cracks, mesh cracks, and potholes, when compared to YOLOv5n, YOLOv5s, YOLOv6s, YOLOv8n, and SSD models. This enhancement optimizes the network structure, reducing the number of parameters while maintaining excellent detection performance. In real-world scenarios, the SMG-YOLOv8 model, when applied to image data collected from projects, achieves a Pmacro of 80.5% and an Rmacro of 86.2%. This result demonstrates its excellent generalization capability and practicality. The model provides significant technical support for the intelligent detection of pavement distress.

Design of Two-Layer Roller Compacted Concrete with Joints by KENSLAB

Roller compacted concrete (RCC) is a type of rigid pavement that is designed in a similar way to conventional concrete pavements reflecting its mode of failure. The thickness design of RCC pavements is based on keeping the flexural stresses and fatigue damage in the pavement caused by wheel loads within allowable limits. As in all jointed concrete pavements, there is a greater effect from loads placed along edges and less at interior locations in the pavement. Joints in RCC pavement are clearly critical areas and form weak points. RCC is typically constructed with saw-cut joints to prevent random cracking, improve the appearance of the pavement surface, and maintain the highest possible load transfer across the joints. This paper presents an approach to designing the thickness of a two-layer RCC with induced cracks (i.e., joints) based on load transfer stiffness and fatigue damage, using the KENSLAB program to obtain pavement life, predict joint deterioration, and pavement damage.

How sustainable are low-noise pavements? Answers from a life-cycle-assessment including the use-phase and noise mitigation of typical Swiss pavements

Despite continuous efforts to reduce road noise emissions, road noise is still the dominant source of noise in Switzerland. Low-noise pavements are an effective measure for road noise mitigation, but besides their higher cost, their major drawback is a reduced service lifetime as compared to conventional pavements. How do these arguments compare in a comprehensive view? To address this question, we performed a cradle-to-grave life-cycle-analysis of four different pavement types: a conventional pavement as reference, two types of semi-dense low-noise pavements and an acoustically optimized type of asphalt concrete. Besides the needed raw materials, the construction, maintenance and demolition, this life-cycle-analysis also includes the use-phase of the road. Thus, the noise mitigation (based on latest acoustic ageing models) and changes in fuel consumption (based on rolling resistance) are included in the assessment. We evaluate the results in the dimensions of ecological scarcity, greenhouse gas emissions, primary energy consumption and life cycle costs. The impact assessment method "Swiss ecological scarcity" allows to weight noise emissions and fuel consumption directly against resource demands of the different pavement types. Our results show that in densely populated areas, the noise reduction and energy savings clearly outweigh the increased resource demand of low-noise pavements.

Low noise pavement characterization through TCN analysis

The use of low-noise pavements is a valid solution to reduce road traffic noise at source. Further studies are needed to optimize their design and ensure their durability. In this work, low-noise pavements were characterised using innovative and traditional methods on four different pavements: two of them contained crumb rubber added by dry and wet processes, one had an optimized texture and the last one was a traditional dense asphalt concrete. Using a sensor inside the tyre, the tyre cavity noise (TCN) was recorded and analysed, putting it in relation with the Close Proximity Method (CPX) data and the pavement texture measured by a laser profilometer. This paper aims to improve the knowledge of the relationship between the vibrational components of the tyre-pavement interaction using new and old descriptors, extending the types of pavements studied in past researches.

A Life Cycle Cost Comparison of Flexible Versus Rigidpavements in Nigeria: Case Study of Papalanto - Sagamu Road

The condition of roads in Nigeria, most of which are made from asphalt, is generally considered unsatisfactory. This has led to calls for a blanket switch from asphalt pavements to concrete pavements for new road construction. This study examines the characteristics and relative advantages of the two pavement types; and carried out the design and life cycle cost comparison of a case study road with heavy traffic and poor subgrade conditions. The results indicate that, the initial cost of construction is actually less for the concrete pavement by 12% when compared with the asphalt alternative, while the life cycle cost analysis further yielded an advantage of 36% for the concrete pavement. The paper therefore recommends that major roads, particularly those on the national and international transit corridor, being of heavy traffic, should be reconstructed as the need arises, as concrete pavements. The paper however further recommends that, axle load control should be enforced through the introduction of weigh bridges and that routine maintenance should mandatorily be carried out, as even the best constructed concrete pavements have failed due to lack of routine maintenance like clearing the road verges of weed and road sand and desilting of drainage structures

Experimental Analysis of Noise Characteristics on Different Types of Pavements inside and outside Highway Tunnels

Aiming to reduce noise pollution and optimize the acoustic quality in highway tunnels, the noise characteristics on different types of pavements were analyzed and compared in this research, based on the on-site noise measurement in two tunnels with the free fields as a control group. Specifically, the noise characteristics include two aspects: various noise and noise time attenuation performance. Various noise includes on-board sound intensity (OBSI) noise and cabin noise. The noise time attenuation performance uses the indicator of reverberation time. Three types of pavements were measured, including dense-graded asphalt concrete (DAC) and single-layered and double-layered porous asphalt (PA) pavement. The results showed that, for the same type of pavement, compared with the free fields, the difference in OBSI noise in tunnels was within a range of less than 1 dBA; the cabin noise increased by 3.4 dBA~6.6 dBA. The noise level in tunnels was greater than that outside tunnels, and the longer tunnel exhibited higher traffic noise and worse noise time attenuation performances. For the same tunnel, PA pavement could reduce the cabin noise by 3.8 dBA~6.7 dBA. PA pavement also exhibited shorter reverberation time. The application of PA pavement could effectively improve the acoustic quality in the tunnel. This research contributes to noise pollution abatement and the improvement of the comfort and safety of drivers in tunnels.

Resilient roads in challenging terrain: a case study of Siddhartha highway in Nepal

Nepal is a country known for its diverse and challenging topography, and it relies heavily on a robust road infrastructure network to connect its remote regions and urban centers. This study addresses the critical need for enhanced road safety and infrastructure resilience on the Siddhababa road section of the Siddhartha Highway, Nepal, notorious for its high accident rates and susceptibility to landslides. Given the road's strategic importance in connecting remote regions and its challenging topographical conditions, our research aimed to identify the most suitable pavement type to mitigate these issues. Through a detailed examination incorporating eight different soil tests, alongside evaluations of traffic loads, weather conditions, and existing pavement performance, we adopted a comparative analysis methodology to assess the viability of flexible versus rigid pavements within this unique context. Results revealed that the soil composition and environmental conditions of the Siddhababa section significantly influence pavement performance, with specific gravity, moisture content, and California Bearing Ratio (CBR) tests indicating a nuanced suitability for both pavement types under varying circumstances. Our analysis concluded that, despite the economic and staged reinforcement benefits of flexible pavements, the durability, safety, and maintenance considerations favor the adoption of rigid pavement for the Siddhababa road section. However, acknowledging the economic constraints, a hybrid approach is recommended, emphasizing rigid pavements for the most vulnerable sections and flexible pavements elsewhere. This study contributes to the pavement engineering field by providing a model for pavement type selection in mountainous regions, aiming to enhance road safety and durability amidst challenging environmental conditions.

Analysis of rapid reconstruction airport pavements

The article discusses principles and guidelines for rapid reconstruction of military airfield pavements. The possible repair materials to be used as well as the basic types of lightweight collapsible pavements were defined . Various technologies and principles for organizing construction work related to the repair of damaged pavements in accordance with the STANAG 2929 standard are presented. The article presents selected scientific principles of work organization that affect the maintenance of a high pace of construction project implementation. Additionally, the article highlights the need to protect aircraft and other sensitive elements of airport infrastructure, suggesting the use of double-bent sheet metal structures. These structures are relatively inexpensive and can be erected in a very short time.

Experimental Study to Investigate the Performance-Related Properties of Modified Asphalt Concrete Using Nanomaterials Al2O3, SiO2, and TiO2.

The dual nature of asphalt binder necessitates improvements to mitigate rutting and fatigue since it performs as an elastic material under the regime of rapid loading or cold temperatures and as a viscous fluid at elevated temperatures. The present investigation assesses the effectiveness of Nano Alumina (NA), Nano Silica (NS), and Nano Titanium Dioxide (NT) at weight percentages of 0, 2, 4, 6, and 8% in asphalt cement to enhance both asphalt binder and mixture performance. Binder evaluations include tests for consistency, thermal susceptibility, aging, and workability, while mixture assessments focus on Marshall properties, moisture susceptibility, resilient modulus, permanent deformation, and fatigue characteristics. NS notably improves binder viscosity by about 138% and reduces penetration by approximately 40.8% at 8% nanomaterial (NM) content, significantly boosting hardness and consistency. NS also enhances Marshall stability and decreases air voids, increasing the mix's durability. For moisture resistance, NS at 8% NM content elevates the Tensile Strength Ratio (TSR) to 91.0%, substantially surpassing the 80% standard. Similarly, NA and NT also show improved TSR values at 8% NM content, with 88.0% and 84.1%, respectively. Additionally, NS, NA, and NT reduce permanent deformation by 82%, 69%, and 64% at 10,000 cycles at 8% NM content, illustrating their effectiveness in mitigating pavement distress. Notably, while higher NM content generally results in better performance across most tests, the optimal NM content for fatigue resistance is 4% for NS and 6% for both NA and NT, reflecting their peak performance against various types of pavement distresses. These results highlight the significant advantages of nanoparticles in improving asphalt's mechanical properties, workability, stability, and durability. The study recommends further field validation to confirm these laboratory findings and ensure that enhancements translate into tangible improvements in real-world pavement performance and longevity.

Reducing Energy Demand in Concrete Pavements by the Use of Blended Cements

This research explores strategies to minimise energy consumption and enhance environmental sustainability in road construction. Focusing on concrete pavement structures, the study evaluates the impact of substituting Portland cement with environmentally friendly alternatives such as fly ash and blast furnace slag. A comprehensive model is employed to analyse the energy demands of different pavement types, considering various cement replacements over their lifetime, from the initial extraction of materials to the conclusion of construction. Results indicate an energy saving potential of 8.63% by substituting 10% of Portland cement with fly ash, while an impressive reduction of 58.63% in cement production energy is achieved by replacing Portland cement with 80% blast furnace slag. The study underscores the significant role of cement variations in mitigating energy consumption, emphasizes the potential of blast furnace slag as a sustainable alternative as well as highlights the significance of alternative cement types in reducing energy consumption in concrete pavement construction, aligning with environmental sustainability goals and offering insights for more eco-friendly infrastructure development.

The Production of Porous Asphalt Mixtures with Damping Noise Reduction and Self-Healing Properties through the Addition of Rubber Granules and Steel Wool Fibers.

Conventional asphalt roads are noisy. Currently, there are two main types of mainstream noise-reducing pavements: pore acoustic absorption and damping noise reduction. However, a single noise reduction method has limited noise reduction capability, and porous noise-reducing pavements have a shorter service life. Therefore, this paper aimed to improve the noise-damping performance of porous asphalt mixture by adding rubber granules and extending its service life using electromagnetic induction heating self-healing technology. Porosity and permeability coefficient test, Cantabro test, immersion Marshall stability test, freeze-thaw splitting test, a low-temperature three-point bending experiment, and Hamburg wheel-tracking test were conducted to investigate the pavement performance and water permeability coefficients of the mixtures. A tire drop test and the standing-wave tube method were conducted to explore their noise reduction performance. Induction heating installation was carried out to study the heating rate and healing performance. The results indicated that the road performance of the porous asphalt mixture tends to reduce with an increasing dosage of rubber granules. The road performance is not up to the required standard when the dosage of rubber granules reaches 3%. The mixture's performance of damping and noise tends to increase with the increase of rubber granule dosage. Asphalt mixtures with different rubber granule dosages have different noise absorption properties, and the mixture with 2% rubber granules has the best overall performance (a vibration attenuation coefficient of 7.752 and an average absorption factor of 0.457). The optimum healing temperature of the porous asphalt mixture containing rubber granules and steel wool fibers is 120 °C and the healing rate is 74.8% at a 2% rubber granule dosage. This paper provides valuable insights for improving the noise reduction performance and service life of porous asphalt pavements while meeting road performance standards.

Spectral noise reduction of double-layer porous asphalt: From laboratory to field

The double-layer porous asphalt (DLPA) pavement is a practical way to mitigate traffic noise from the source. However, there hasn't been much research done on the spectral noise characteristics of the car/truck on this type of pavement with different surface conditions yet. The objective of this paper was to reveal these noise characteristics and the behaviors of the DLPA pavement by laboratory tests and field measurements. First, measurements were made of the road performance of different porous asphalt (PA) mixtures and DLPA structures. Then, a vector impedance measurement system was utilized to evaluate the effects of porosity, aggregate size, and thickness on the sound absorption coefficient (SAC) of the DLPA slabs. Third, a sound meter was used to record the noise levels outside/inside the car/truck on the roads with three surface conditions for the DLPA (PA-10+PA-20) and stone matrix asphalt (SMA) test section, which were paved on an elevated urban road. Finally, an analysis was done on the overall and spectral noise characteristics. The results demonstrated good road performance of these PA mixtures and DLPA structures. Relatively fine and coarse aggregates in the upper and lower layers, respectively, enhanced the sound absorption. But an increase in porosity beyond 20 % and a slight variation in thickness did not considerably improve the sound absorption. Compared to SMA pavement, the overall noises levels of the DLPA pavement were 4–8 dBA lower on the “dry” surface and 6 dBA lower on the “wet” surface. The DLPA pavement exhibited a noise reduction at the full frequency band, which attributed to the pore structure and the relatively fine aggregate used. The interior sound energy always concentrated on the low-frequency band. However, the exterior sound energy on the “dry” and “wet” road surfaces tended to the low-frequency and high-frequency bands, respectively. These findings would shed light on the spectral noise characteristics of the DLPA pavement and provide a new knowledge about the behaviors of the lower layer and upper layer in relation to noise reduction and road performance.

Comparative Study of Airport Layer Thickness Planning Between US Army Corp Graphical Method and Federal Aviation Administration (FAA) PCN-ACN and PCR-ACR Method

Airport pavement is designed following the US Corporation of Engineers method or better known as the California Bearing Ratio (CBR) method and the FAA (Federal Aviation Administration) method which issues regulations for calculating airport pavement structures, namely AC (Advisory Circular) 150_5320_6D which same as the method CBR. In 2021, the FAA issued a standard for calculating airport pavement structures, namely AC (Advisory Circular) 150_5320_6G which uses the FAARFIELD assistance program. The difference in pavement thickness is at most 7.6 cm, on the flexible pavement type base course. In the FAARFIELD Auxiliary Program, all aircraft loads are taken into account as a contributor to pavement damage indicated by the CDF value that can accommodate aircraft loads, in contrast to the graphical method where aircraft are converted to design aircraft. The thickness of the base course using the graphical method is greater than that of the FAARFIELD Assistance Program, this is because when performing calculations, the initial base course value is the minimum value based on the minimum base course table for the use of top foundation layer material (AC No.150_5320_6G). The thickness of the surface course pavement is the same according to FAA provisions for the critical thickness of the surface course which is 4 in or 102 mm.

A Review on Experimental Study of Design of Rigid Pavement by using Recycled Coarse Aggregate with M30 Mix Design

Concrete, a fundamental construction material, boasts attributes like durability, versatility, and cost- effectiveness. However, the depletion of natural resources and the escalating concrete waste production necessitate innovative solutions. In this rapidly industrializing world, recycling construction materials plays a pivotal role in preserving our finite resources. Concrete pavements, widely practiced in developed countries, are relatively new in India. Selecting the right pavement type is crucial, but equally important is determining the optimal pavement thickness based on traffic levels, subgrade conditions, and environmental factors. Unfortunately, many existing methodologies overlook critical factors (such as vehicle loads, support loss, thermal gradients, and environmental stress), leading to inaccurate thickness calculations. In this context, rigid pavement construction using recycled coarse aggregate (RCA) emerges as an eco-friendly and cost- effective solution. In a recent research endeavour, Recycled Coarse Aggregates (RCAs) sourced from demolished material took center stage. These demolished materials are meticulously crushed to a suitable size and repurposed as recycled coarse aggregate. Meanwhile, natural sand continues to serve as the fine aggregate. Notably, the concrete industry consumes a staggering 12.6 billion tons of raw materials annually, making it the world’s largest natural resource consumer. The environmental impact of concrete production—especially the extraction of raw ingredients such as cement, coarse aggregates, and fine aggregates—is considerable. To address this, the study employed a trial-and-error approach for mix design, adhering to relevant standards (such as IS code and IRC: 44-2008). For M30 grade cement concrete, varying percentages (0%, 25%, 50%, 75%, and 100%) of recycled coarse aggregates replaced conventional coarse aggregates. Casting used cube and beam models to obtain laboratory test results for rigid pavement to determine how much optimal percentage of RCA can be substituted in place of NCA. By integrating RCAs, we not only mitigate environmental impact but also contribute to sustainable rigid pavement structures. Key Words: Rigid Pavement Design, Recycled Coarse Aggregate, RCA Concrete, Natural Coarse Aggregate, NCA Concrete, Mix Proportion, Pavement Thickness, Life Cycle Cost, And Environmental Benefits etc.

The Addition of Pore Holes in Paving Blocks in an Effort to Increase Infiltration

The problem of waterlogging and surface runoff on impermeable pavement surfaces requires finding new ways to manage water flow, especially from rainwater. The type of permeable pavement that is often used is paving block. The use of paving blocks for pavement materials is expected to reduce puddles that occur after rain. One way is to modify the paving block to be greater in seeping water into the soil. This study aims to determine the extent to which the effect of adding pore holes in paving block can increase water infiltration in surrounding soil. This research was conducted through testing in the Laboratory. The type of research is quantitative with experimental method using a rain simulator tool with variations of paving block. From the research results obtained that the addition of pores in the paving block affects the infiltration that occurs. This is evidenced by the increae in the value of infiltration, the infiltration rate value of normal paving block is 182.92-10.43 mm/hour, 2% ratio is 183.75-10.75 mm/hour, 5% ratio is 190.00-11.30 mm/hour, 7% ratio is 231.33-11.46 mm/hour, 10% ratio is 236.88-17.61 mm/hour. From the test results between pore holes with infiltration rate has a significant relationship of 0.037 (<0,05).

Prediction of runoff characteristics in permeable pavements using experimental data and intelligent models.

Considering the growing use of permeable pavements, the prediction of runoff passing through this pavement model is of considerable importance. The prediction of rainfall-runoff relationships can be a challenge because of several factors including data uncertainty, non-linear relationships, and high temporal and spatial variability. To deal with these challenges, intelligent algorithms are often used to predict such complex phenomena. In this research, runoff control parameters were investigated in two types of permeable pavements (permeable interlocking concrete pavement (PICP) and high strength clogging resistant permeable pavement (CRP)) using support vector machine (SVM), support vector machine-bat (SVM-BA) and support vector machine-grasshopper (SVM-GOA). Variables used in the models included percentage of coverage by permeable pavement (A), rainfall intensity (I), slope (S), and pavement type coefficient (K) as input data, and runoff coefficient (C), time to runoff (Tr), and peak discharge (Qp) as output data. In this research, from the total of 108 data extracted from the experimental results, 86 data were used in the training period, and 22 data were used in the test period. The results of the test period show that the SVM-BA model has the best performance with values of MAE = 0.010 in predicting C, MAE = 1.330 min in predicting Tr, and MAE = 0.029 lit/min in predicting Qp. The SVM-GOA model is ranked second with values of MAE = 0.051 in predicting C, MAE = 3.285 min in predicting Tr, and MAE = 0.097 lit/min in predicting Qp. Also, the SVM model is ranked third with values of MAE = 0.063 in predicting C, MAE = 4.470 min in predicting Tr, and MAE = 0.121 lit/min in predicting Qp. In summary, the SVM-BA algorithm showed the best performance and the SVM algorithm showed the weakest performance in predicting runoff characteristics in permeable pavements.

Road-pavement classification by artificial neural network model based on tire-pavement noise and road-surface image

This study focuses on an artificial neural network (ANN) model for classifying pavement types using acoustic and image data. While conventional studies often use road-surface images for pavement classification, they face challenges with image quality degradation owing to external factors, such as sunlight angle, shadows, and lighting. Therefore, in this study, tire-pavement noise, which has different noise characteristics depending on the material and surface treatment, is used independently and in conjunction with image data for ANN training. To construct the training dataset, tire-pavement noise, and road-surface images are collected from 11 highway sampling sites in South Korea. Two simultaneous measurements are used: the tire-pavement noise is collected using the On-board sound intensity (OBSI) method, and the camera captures the road-surface images. 1/3 octave SIL, spectrum, MFCC, GLCM, and HOG are extracted from the raw data, and the ANN models are trained by these features. Using the spectrum as an input feature for the ANN yields a classification accuracy of 95.18%. However, the total number of parameters in the ANN is double that of the other models. To reduce the ANN size, 1/3 octave band SIL is used for training, and the model size is halved. However, the accuracy decreases by 13.47 percentage points. To overcome this significant decrease, the 1/3 octave bands SIL and image features were used to train ANN, simultaneously. This approach increases the accuracy by 93.85%. By training the ANN using MFCC, which is commonly used as an acoustic feature in other machine learning studies, the highest classification accuracy of 96.84% is achieved. Additionally, MFCC models are affected by the number of coefficients and the signal length. To include the dominant frequency of tire-pavement noise, more than 13 coefficients are used, a number generally known to be suitable for speech recognition. Increasing the number of coefficients from 13 to 40 improves accuracy by 1.17 percentage points. The interval for slicing raw WAV files is reduced to increase the training data and classify the pavement using shorter signals without statistically significant accuracy loss. Although accuracy does not decrease until the signal lengths reach 0.5 seconds, it rapidly decreases when the signal lengths become shorter than 0.4 seconds.

Study on the influence of tire polishing on surface texture durability and skid resistance deterioration of asphalt pavement

As an inherent attribute of asphalt pavement, texture durability is the essential reason for the change in skid resistance. However, different scale texture deterioration behavior remains unclear by tire wearing at present. To explore the influence of the durability of multi-scaled textures on skid resistance, nine pavement specimens were tested using the Harbin polishing and dynamic friction integrated experimental machine (HPFM). The pavement anti-skid texture tester (PATT-II) was used for high-precision texture scanning and reconstruction. The results show that the texture richness and skid resistance of the three types of pavements in 300 min polishing are ranked as Open-graded Friction Coarse (OGFC) > Stone Mastic Asphalt (SMA) > Asphalt Concrete (AC), while SMA presents the best durability, determined by the material skeleton structure of asphalt mixture. The macro-texture deterioration occurs slightly at first and then tends to stabilize in 300 min polishing. The micro-texture deterioration behavior is the same as the μHPFM, increasing rapidly at the beginning, then slowly decreasing, and finally remaining constant. This deterioration is caused by the asphalt damage and continuously wearing aggregate surface. The 2D-PSD deterioration behavior is more significant with a smaller texture scale in 300min polishing, and the most considerable contribution wavelength in the microscopic scale range is 0.15 mm–0.3 mm, which is determined by the adhesion characteristics and molecular mechanism between a mineral mixture and asphalt material. The influence of pavement texture on the dynamic friction coefficient is significantly different under different water film thicknesses, the micro-texture and macro-texture play the main roles in boundary and mixed lubrication, respectively.