Pavement Surface Type Research Articles

Automatic Classification of Pavement Type and Service Age Benchmarked with Standard Texture Databases Using the Machine Learning Method: A Pilot Study

Pavement intelligent management systems have attracted considerable interest from researchers. However, various service conditions of pavement surface concerning the pavement type, texture service age, and so forth, inhibit a universal algorithm that is feasible for all cases. In this regard, the automatic classification of pavement type and service age is an essential premise to unblock the bottleneck stated above. Based on the surface texture data, a pilot study of the automatic classification approach to identify pavement surface textures using convolutional neural networks (CNNs) is presented. For comparison, the efficiency of the support vector machine (SVM) is also investigated. In total, three cases, (i) pavement types, (ii) texture service ages, and (iii) a combination of (i) and (ii), are involved in the automatic classification. The results indicate that the CNN outperforms the SVM, and the CNN models show a favorable classification accuracy for the above three cases with 93.0%, 81.1%, and 83.8%, respectively. In conclusion, the CNN demonstrates a high capability in expressing the pavement texture features and achieves satisfactory identification results for pavement surface types, but is inferior for texture service age. It is promising that the presented results could serve as a foundational exploration in the automatic identification of texture service conditions benchmarked with standard texture databases to facilitate pavement management systems.

Modeling of Restriping of Waterborne Paints Using Transverse Test Deck Data in Hot and Humid Climate

The National Transportation Product Evaluation Program (NTPEP) database is a promising tool for U.S. state agencies to evaluate the performance of waterborne paint pavement markings. Out of the total 50 U.S. states, 29 states (57%) utilize the NTPEP data to assess the performance of waterborne paints to be included in the approved materials list. Yet, this dataset is based on transverse retroreflectivity (RL) measurements, which are significantly different from the longitudinal RL measurements in real-world conditions. As such, the key objective of this study was to develop a methodology that converts the transverse RL measurements of waterborne paints to the corresponding longitudinal measurements in hot and humid climate. This approach could be used by transportation agencies to predict the service life of their waterborne paints and make reliable restriping decisions. To achieve this objective, three road sections were selected in Louisiana, U.S., and four different waterborne paints were installed in the transverse and longitudinal directions. These road sections consisted of seven subsections with varying traffic levels and pavement surface types. The data collected included durability readings and longitudinal and transverse RL measurements at five time periods between October 2020 and April 2022. Based on the collected data, two non-linear regression models (for asphalt and concrete surfaces) were developed, with reasonable accuracy, to predict the longitudinal RL of waterborne paints based on the corresponding transverse RL. The predicted longitudinal RL could then be used to determine the expected service life of waterborne paints and make reliable restriping decisions.

Characteristics of thermal pollution from stormwater runoff from impermeable/permeable pavement surfaces via a lab-scale experiment

Thermal pollution from stormwater runoff has been the focus of many studies in recent years due to its potential harm to aquatic microorganisms. However, there were few studies on the thermal pollution caused by stormwater runoff from various types of urban pavement surfaces. A lab-scale experiment was conducted to compare the thermal load of stormwater runoff from impermeable and permeable pavements and the influencing factors were investigated. The experimental findings demonstrated that the rainfall return period and initial temperature of various pavement surfaces significantly impacted the thermal load. The stormwater runoff absorbed more heat as the initial temperature, and rainfall return period increased. The difference of the thermal load of stormwater runoff between permeable brick pavement (PBP) and the impermeable asphalt pavement (IAP) increased from 305.26 to 436.70 kJ/m2, when the initial surface temperature rose from 35 to 47 °C. The average runoff temperature decreased by 1.39–1.90 °C for PBP compared to the IAP, with an increase in surface temperature from 35 to 47 °C. Under the various initial surface temperatures, the mean temperature of the infiltration effluent from the PBP was 3.12–4.20 °C lower than the average temperature of stormwater runoff from the surface layer. Therefore, a PBP can effectively alleviate thermal pollution from stormwater runoff and safeguard the receiving waters' quality.

Modelling Approach to Develop a Multimodal Transportation Network

Multimodal transportation (MMT) refers to a system that utilizes various means of transportation to transfer people & goods from one place to another. Transit operations are measured as the maximum number of passengers that can be carried past a fixed route, in a given period. Investing in and using multiple modes of transportation does not only bring efficiencies but also goes a long way to reduce time & delay, as well as the overall cost of transportation. This paper presents the efficient mode of transportation system at selected routes in the Bengaluru city, the route stretch selected is 12.2 km in length and it has 8 major intersections. The type of pavement surface consists of both bituminous and Reinforced cement concrete (R.C.C). The complete study stretch is a 2-way 4-lane road. This study provides walkability-index, traffic volume studies in terms of passenger car unit (PCU), time and delay study, and Cost & time analysis. Four modes of transportation are used in this study to find the effective multimodal-transportation system namely, 2-wheeler, 4-wheeler, para-transit [15], and buses/metro. The outcome of this paper is to arrive at the shortest distance for reducing delay & time, and cost-effective travel using multi-mode transport. While focusing on the present condition of the para-transit transport facilities. The time, delay & cost during MMT and to provide a safe, cost-effective & convenient way to travel or transport. The result shows the reduction in time, cost & delay of transportation by using the MMT model. This MMT system could be adopted as an efficient mode of transportation on this route. Similar rural-urban junction routes around the world could use this approach for effective transit by making a modal study.

Impact of Emulsion Type, Application Rate, and Adverse Construction Conditions on Tack Coat Performance

The tack coat is a bonding agent applied between new and existing asphalt pavement layers. Roadway construction operations inherently introduce adverse conditions, such as dust on the pavement surface and nonuniform coverage (streaking), which can compromise tack coat bond quality. Climatic conditions such as heavy rainfall also create issues late in the construction season. Newly developed engineered tack coat emulsions, which utilize stiff asphalt binders and polymer modifiers, are purported to improve interlayer bonding characteristics and reduce the propensity of early fatigue cracking and pavement failure. This study evaluates engineered tack coats against conventionally used tack coats in a laboratory setting to identify their benefits when subjected to different pavement surface types, application rates, and commonly experienced adverse roadway construction conditions. Monotonic direct shear testing was employed to characterize interlayer bonding using two response parameters, interlayer shear strength and interlayer bond energy. This study advances the knowledge of engineered tack coat performance under real-world conditions and also employs a laboratory sample preparation methodology that is representative of pavement construction in the field.

Analysis of MATSim Modeling of Road Infrastructure in Cyclists’ Choices in the Case of a Hilly Relief

For too long, many refined transportation models have focused solely on private and public transportation, assuming that bicycles only require simple models, such as bird flight distance or trips on horizontal tracks at a constant speed. This paper aims to study the impact of the road characteristics, such as road gradient, type of road and pavement surface of the road, on cyclists’ behavior using dedicated modules of MATSim. For that, we compare two approaches: a standard approach which does not consider the road characteristics, and a second approach that uses MATSim bicycle extension of Ziemke et al. The two approaches are analyzed over a sub-regional area around a district, focusing on a suburban city with an undulating relief made of average-to-steep hills. The focus is on the bicycle transportation model because the catchment area has a particularly challenging altitude profile and a large variety of roads, whether in type—from residential to national highway—or in pavement surface due to the number of green areas, such as parks and forests. This area is defined as a rather large 7 × 12 km, including five suburban cities in the South of Paris, France. A synthetic population of 126,000 agents was generated at a regional scale, with chains of activity made of work, education, shopping, leisure, restaurant and kindergarten, with activity-time choice, location choice and modal choice. We wanted to know how accurately a standard model of bicycle travels can be made with a 2D flat Earth assumption by comparing it to an algorithm extension that explicitly considers road characteristics in cyclists’ route choices. Our finding is that the MATSim bicycle extension model impacts mainly the long trips. Otherwise, the differences are minimal between the two models in terms of travel time and travel distance.

Assessment of vehicular fuel consumption and interaction with pavement characteristics using HDM-4 on Indian urban road network: A case of Pune city

Rapid urbanization and exponential increase in the number of vehicles on city roads have triggered issues like air pollution and accidents, endangering the health of millions of people in Indian cities. Vehicular emission is a major contributor to air pollution in Indian cities. This study primarily analyzes the relative effects of riding quality and pavement surface type on vehicular fuel consumption (FC) in an urban environment. This study utilizes the Road Asset Management System (RAMS) applications to predict FC in different scenarios of pavement maintenance. Highway Development and Management (HDM-4) software was integrated into RAMS for network & economic analysis and FC prediction of the road network. The study was conducted on the Pune city road network in India, and the results revealed a 2.32 % reduction in fuel use if timely maintenance is provided. Over 0.128 million liters of fuel per 1000 vehicle-km can be saved over the study network of only 36.5 km over the next 15 years, equivalent to US$ 155,206.18 in 2021. The analysis of covariance test results showed the significant effect of pavement surface type on fuel consumption, with 4.86 % less fuel consumed on cement concrete surfaces than bituminous surfaces. About 99.431 liters per 1000 vehicle-km of fuel were utilized by the vehicle fleet on concrete pavements, while 104.513 liters per 1000 vehicle-km of fuel were consumed on bituminous pavements after adjusting for roughness characteristics of both the surface types.

Five-Year Project-Level Statewide Pavement Performance Forecasting Using a Two-Stage Machine Learning Approach Based on Long Short-Term Memory

An accurate pavement performance forecasting model is essential for transportation agencies to perform pavement maintenance, rehabilitation, and reconstruction (MR&R) in a predictive and cost-effective manner. Although some forecasting methods have been successful in forecasting short-term (e.g., 1–2 year) pavement conditions at either the project level or network level, accurately forecasting long-term (e.g., 3–5 year) pavement conditions at both project level and network level under real-world conditions is still challenging. Thus, the goal of this paper is to propose a two-stage machine learning approach based on long short-term memory (LSTM) to achieve not only the short-term, but also the long-term, forecasting accuracy at both the project level and network level. The proposed method involves LSTM in the first stage and an artificial neural network (ANN) in the second stage, resulting into a two-stage model. The LSTM first learns the pattern of pavement deterioration based on sequential data (e.g., historical pavement conditions). Then, the ANN further learns the impacts of roadway factors (e.g., traffic parameter, pavement surface type, working district) to adjust the final forecasting results. The accuracy of the proposed two-stage model has been compared with baseline machine learning methods in 2016 on a large, statewide Florida dataset at both the project level and network level to demonstrate the superior capability of the proposed method. In addition, the proposed method has been tested further to forecast future (5-year) pavement conditions (2016–2020). Results show a promising forecasting accuracy for both the short-term and long-term in comparison with the ground truth.

Effect of Cement Grouts Containing Irradiated Polyethylene Terephthalate on Properties of Semi-Flexible Mixtures

The seme-flexible pavement surface is a hybrid type of pavement surface that combines the effect of both asphalt mix skeleton and cement grout. The current study investigates the influence of cement grouts containing irradiated waste polyethylene terephthalate (PET) on the performance of semi-flexible mixtures. The ordinary Portland cement was partially replaced by regular and irradiated PET as well as with fly ash (FA) in cement grouts. The air voids analysis, degree of grout saturation, Marshal stability, indirect tensile strength (ITS), and tensile strength ratio (TSR) of semi-flexible specimens were evaluated. The semi-flexible mixtures showed superior performance in terms of strength properties. The results indicate that with the irradiation process more waste PET (almost double) can be recycled as compared to regular waste PET in cement grouts for semi-flexible pavement surfaces. This approach will lead to a sustainable solution for recycling waste PET in highway materials for the construction of a hybrid type of pavement surface.

In-door laboratory high-speed testing of tire-pavement noise

ABSTRACT The ability to measure traffic-speed tire-pavement noise in the laboratory is useful for predicting the tire-pavement noise performance of a design mix without conducting expensive and time-consuming field trials. This research developed an in-door high-speed test facility HTP2 for tire-pavement noise measurements. Housed in a semi-anechoic room, HTP2 has a circular test track of vertical wall, and a horizontal rotating arm system mounted with full-scale test tires. The test track can accommodate 12 specimens in each test run. The highest test speed achievable is currently 70 km/h, eventually 100 km/h when fully developed. The On-Board Sound Intensity Method was adopted for tire-pavement noise measurement. Tests were conducted to assess the reliability and repeatability of the test facility. Additional tests involving eight different pavement surface types were performed to illustrate that HTP2 was able to (i) measure sound intensity-frequency characteristics of tire-pavement noise matching those of actual vehicles and pavements, (ii) differentiate sound intensity of tire-pavement noise generated by different pavement types, (iii) identify increase in tire-pavement noise caused by texturing of concrete pavement, (iv) detect differences between tire-pavement noise generated by longitudinally and transversely tined concrete pavements, and (v) measure effects of surface macrotexture on tire-pavement noise.

A Direct Shear Approach to Assess the Interface Bonding Performance

The objective of this study was to investigate the effects of various factors affecting bond strength between hot mix asphalt (HMA) overlay and underlying pavement layers in the field. The effect of interface bonding on short-term pavement performance was also evaluated. A list of candidate HMA field rehabilitation projects was identified across the United States, representing different traffic and environmental conditions. These projects included the rehabilitation of new, existing, and milled HMA pavements, and PCC pavements. Each field project involved at least one slow setting and one rapid setting non-tracking tack coat material, thereby creating one or more pairs of tack coats for comparison. The HMA overlay construction used different types of tack coat materials at various residual application rates. Specimens were cored from the evaluated test sections at different service times to determine the interface shear strength (ISS) according to AASHTO TP 114, “Standard Method of Test for Determining the Interlayer Shear Strength of Asphalt Pavement Layers.” Results of this study showed that ISS was largely dependent on the type of pavement surface (i.e., HMA vs. PCC) receiving tack coat, and pavement surface texture (i.e., milled vs. non-milled). In general, milled HMA surface yielded the highest ISS, followed by new HMA, existing HMA, and PCC surface types. Non-tracking rapid setting tack coats with stiff base asphalt cement exhibited the highest ISS compared to slow setting tack coats. With respect to the effect of service time, ISS increased with service time due to tack coat curing effect and densification of overlays. Laboratory measured ISS values correlated well with short-term cracking performance of field pavements. Results presented herein were part of NCHRP Project 9-40A on the “Field Implementation of the Louisiana Interface Shear Strength Test.”

Prediction models for distress noise generated due to tire-pavement surface interaction

The major objective of this research is to assess the distress noise generated due to tire-pavement surface interaction for different modes and to evolve a noise prediction model for any manner taking into consideration the different factors impacting the distress noise generation. Seven flexible and four rigid pavement roads were chosen for the calculation of the noise in Baghdad city. Tire-pavement interaction noise was calculated using Onboard Sound Intensity (OBSI) Method by restricting the noise generated from the vehicle exhaust and the engine systems. Prediction models were developed for assessing tire pavement noise from vehicle speed, pavement age, wheel load, mean texture depth (MTD) and pavement distresses. Four statistical models were obtained from this research to assess the distress noise generated due to tire-pavement surface interaction using linear regression stepwise method. These models showed the effect of different types of distresses in addition to the factors that have been taken into consideration on tire pavement noise. Vehicle speed was the greatest considerable variable influencing the noise generated due to tire-pavement surface interaction. Some distresses and factors have been excluded from the models due to their poor relationship with tire-pavement noise. These models have been categorized based on the type of pavement surface (flexible or rigid) and the presence or absence of pavement distresses. The predicted statistical models were verified with calculation, by comparing the data estimated by the models and data form field tests. Therefore, it can be used as a method of detecting distresses rather than visual inspection.

Laboratory evaluation on comprehensive performance of polyurethane rubber particle mixture

Poroelastic road surface is a type of pavement surface which makes full use of waste rubber and at the same time effectively reduces traffic noise. However, the comprehensive performance of polyurethane rubber particle mixture (PRPM) prepared with different kinds of rubber particles and polyurethane is rarely studied. This study aims to evaluate the comprehensive performance of PRPMs, such as the high-temperature performance, anti-aging performance, skid resistance performance, etc. Rubber particles and four different kinds of polyurethane were adopted to prepare polyurethane rubber particle mixtures. A dog bone shape mold for forming specimens was self-designed, and a slab specimen forming device was created to form slab specimens. Different test methods were conducted to evaluate the comprehensive performance of the mixtures. The results showed that four types of polyurethane rubber particle mixtures exhibited high rutting resistance and high temperature performance. Mixture F5 had the best anti-aging performance and noise reduction performance. The skid-resistance of the polyurethane rubber particle mixture was slightly lower than that of conventional asphalt mixture, but it still met the road specification requirements. The polyurethane rubber particle mixture had more interconnected pores, which was found to have greater drainage function and was expected to reduce noise. Polyurethane rubber particle mixture F5 had superior comprehensive performance, except for slightly lower skid resistance, and it was determined to be the most suitable polyurethane rubber particle mixture type for future application.

Experimental Study on Wet Skid Resistance of Asphalt Pavements in Icy Conditions.

In this research, the durability of skid resistance during the ice melting process with temperature increasing from −5 °C to 10 °C was characterized by means of a British Pendulum Skid Tester. Four types of pavement surfaces were prepared and tested. The difference between two antiskid layers prepared with bitumen emulsion was the aggregate. The detailed angularity and form 2D index of fine aggregates used for antiskid surfaces, characterized by means of the Aggregate Image Measure System (AIMS) with micro image analysis methods, were then correlated with British Pendulum Number (BPN) values. Results indicate that skid resistance has the lowest value during the ice-melting process. The investigated antiskid layers can increase the surface friction during icy seasons. In icy conditions, the skid resistance behavior first worsens until reaches the lowest value, and then increases gradually with increasing temperature. Results from ice-melting conditions on four investigated pavement surfaces give the same temperature range where there will be lowest skid resistance. That temperature range is from 3 °C to 5 °C. A thicker ice layer will result in a lower skid resistance property and smaller “lowest BPN”.

High-Definition Field Texture Measurements for Predicting Pavement Friction

Monitoring and managing skid resistance properties are crucial activities to reduce the number of highway accidents and fatalities. However, current methodologies to measure pavement surface friction present several disadvantages that make them impractical. Thus, it is necessary to evaluate alternative methods to estimate friction. The principal objective of this study was to develop friction models based on pavement texture. We implemented a Line Laser Scanner (LLS) to obtain an improved characterization of the pavement texture which includes macrotexture and incorporates microtexture description using eight different parameters. Field measurements of friction and texture were collected around Texas using the British Pendulum Test (BPT), the Dynamic Friction Test (DFT), the micro-GripTester, and the LLS. The experimental results showed that there is not a unique relationship between texture and friction; though strong and statistically significant, the relationship is different for each type of pavement surface. Thus, regression analysis pooling all data cannot be utilized to quantify this relationship. For this reason, we applied a panel data analysis approach that allows the incorporation of the type of surface and provides a more robust analysis. The results indicate that the prediction of friction is significantly improved when incorporating information from both macrotexture and microtexture into the prediction model. Therefore, a measure of microtexture should be included into friction models based on texture. In addition, the study of different texture parameters suggests that the mean profile depth (MPD) is the most significant parameter for macrotexture and for microtexture to explain the distinct friction measures.

Investigation of Particle-Related Clogging of Sustainable Concrete Pavements

Permeable pavement has been considered an effective low impact development (LID) strategy in attempts to mitigate the environmental impacts of natural surface depletion brought about by urbanization. A concern associated with the pavement’s hydraulic performance is its sensitivity to clogging. This study aims to investigate the permeability reduction due to particle-related clogging of pervious concrete (PC), a type of sustainable pavement surface. Permeability tests revealed that the flow within PC samples shows turbulence, and a nonlinear relationship between discharge velocity and hydraulic gradient is necessary to measure the permeability coefficient. Permeability loss due to particle-clogging is influenced by the size of both PC aggregates and clogging particles. Clogging with graded sand particles causes more severe reduction compared to single-sized sands.

Development and Validation of a Model to Predict Interface Bonding Between Pavement Layers

During the planning of overlay construction activities, a contractor and/or state agency may be interested in selecting a tack coat material and application rate that would provide adequate interface bonding at the site. The objective of this study was to develop an interface shear strength (ISS) predictive model to estimate interface bonding between the hot-mix asphalt (HMA) overlay and the underlying pavement layer. To achieve this objective, 10 field rehabilitation projects were selected in six states that included 33 in-service test sections representing different traffic and climatic conditions. The overlay construction used different types of tack coat at various residual application rates. Field cores were obtained to measure ISS using a direct shear test device, referred to as the Louisiana Interlayer Shear Strength Tester (LISST). Influencing factors to be included in the model were pavement surface type and mean texture depth, residual application rate, and characteristics of tack coat residues. The experimental data were complemented with test results from a recently completed study for model development and validation. Two types of statistical regression models (linear and nonlinear) were evaluated. Results of the analysis showed that the predicted ISS obtained from the proposed models were in good agreement with the measurements. Further, the developed nonlinear model showed better prediction accuracy than the linear model. The nonlinear model can be used effectively for optimum selections of tack coats given the project conditions. The results presented herein were part of NCHRP Project 9-40A, Field Implementation of the Louisiana Interface Shear Strength Test.

FEM-BEM analysis of tyre-pavement noise on porous asphalt surfaces with different textures

Pavement surface characteristics such as surface texture and acoustic absorption play important roles in tyre-pavement interaction noise. The present study aims to investigate the effects of pavement surface texture and porous surface on tyre-pavement noise using a coupled modelling approach with finite element method–boundary element (FEM-BEM) analysis. A radial tyre model was first built using FEM, and tyre surface accelerations under the excitation from pavement texture were obtained from modal analysis. The radiation sound fields caused by tyre vibration were then solved using BEM for noise prediction. Texture profiles and sound absorption coefficients of different pavement surface types were considered in the analysis. The prediction results were calibrated and validated with the measurements reported in the literature. The results show that, with proper calibration, the proposed numerical model can predict sound pressure levels and overall noise with specific inputs of surface texture spectra and acoustic absorption spectra. In general, the surface type with the higher texture levels usually generates greater noise for both porous and non-porous surfaces. The overall noise decreases with the increase of porosity under the same surface texture condition. However, the noise variation due to different sound adsorption coefficients of porous pavement is not as significant as the effect of surface texture on noise.

Suitability of PoroElastic Road Surface (PERS) for urban roads in cold regions: Mechanical and functional performance assessment

Reuse of scrap tires has become a challenge with the ever-growing traffic volume and usage of vehicles in recent years. Poroelastic Road Surface (PERS) is a novel type of pavement surface, which recycles tire rubber into low-noise pavements. This study aims to explore the suitability of PERS for urban roads in cold regions. Both laboratory tests and numerical simulations were conducted to characterize the mechanical and functional performances of PERS using a conventional porous asphalt (PA) as reference. The results indicated that the tensile strength reserve, which is the difference between the average tensile strength and the cooling-related tensile stress at a certain temperature, of PERS is higher than that of the PA at low temperature, while the ultimate tensile strain of the PERS is much larger than PA. The sound absorption coefficients of PERS have higher and wider peak compared with PA, indicating better noise absorption performance. The pavement with PA surface is more prone to surface cracks due to large tensile stresses at some specific offset locations from the loads, which do not exist in PERS. The deformation of the ice layer on top the PERS layer is larger than that on top of the PA layer at the same temperature, and the maximum horizontal strain of the PERS layer is larger than that of the PA. These findings prove the suitability of PERS for urban roads in cold regions, which can lead to significant economic and social benefits.

Wavelength sensitivity of roughness measurements using connected vehicles

ABSTRACTResearchers previously demonstrated that a roughness index called the road impact factor (RIF) is directly proportional to the international roughness index (IRI) when measured under identical conditions. A RIF-transform converts inertial signals from connected vehicle accelerometers and speed sensors to produce RIF-indices in real time. This research examines the relative sensitivities of the RIF and the IRI to variations in dominant profile wavelengths. The findings are that both indices characterise roughness from spatial wavelengths up to 2 m with equal sensitivity. However, the RIF-transform maintains its sensitivity when characterising roughness from wavelengths beyond that. The case studies used a certified inertial profiler to collect both RIF and IRI data simultaneously from five different pavement surface types. The RIF/IRI proportionality factors distributed normally among the profiles tested. This result affirms that the RIF and IRI generally agrees. However, differences in the dominant profile wavelength among pavements will produce some spread in the degree of roughness that the indices express.