Pavement Performance Study Research Articles

Pavement performance and model studies with modified bitumen binder

Flexible pavements experience premature deterioration due to several factors, such as high volumes of traffic, repeated wheel loads, overloading, adverse weather conditions, inferior quality of materials, and other related issues. One of the most commonly found types of distress is rutting, which severely affects pavement performance; hence, the prediction of rutting behaviour is crucial. Research has been done by adding various industrial wastes, chemicals, additives, and modifiers such as rubber, natural rubber, polymers, and waste plastic into the bitumen. These materials are expected to have a substantial impact on enhancing the strength, durability, and performance of pavement while simultaneously reducing the cost of construction. The present research deals with a laboratory investigation of bituminous mixes using both Marshall stability and rutting tests. The performance tests were carried out by Roller Compactor cum Rut Analyzer (RCRA) equipment and Finite Element (FE) analysis in ABAQUS. The experimental findings show that the modified bitumen exhibits 48% greater strength and 20% more resistance to rutting than the conventional bituminous mixes. The model analysis was successfully validated by experimental results. The findings of the research resulted in the development of a mathematical equation that helps to calculate in-service pavement rut depth.

Study of Pavement Performance and Temperature Regulation Capacity of Asphalt Binders Modified with Dual-Phase-Change Materials

Due to the temperature changes caused by seasonal changes and extreme weather, asphalt pavement suffers from rutting, cracking, and other damage. With commonly used pavement additives, the high-temperature performance and the low-temperature performance of asphalt pavement show opposite trends, with related research endeavoring to find a balance between the two. In this study, a dual-phase-change material (DPCM) with both high- and low-temperature effects was prepared. The chemical stability and rheological properties of modified asphalt binders were characterized using Fourier transform infrared spectroscopy (FTIR) and a dynamic shear rheometer (DSR). Temperature control tests of the DPCM-modified asphalt binders were carried out with an indoor simulation device. The results show that the DPCMs could improve the rutting resistance of the asphalt binders at a high temperature, but the fatigue performance of the modified asphalt binder with different DPCM contents was reduced. The FTIR results showed that no chemical reaction occurred in the mixing of the asphalt binder and the DPCM. In the indoor simulation temperature control test, the 40% DPCM-content-modified asphalt binder reduced the high-temperature extreme value by 4.2 °C and increased the low-temperature extreme value by 2.5 °C, showing a good temperature control effect and practical application value.

Decision Tree Analysis for Developing Weigh-in-Motion Spectra in the Californian Pavement Management System (PaveM)

Weigh-in-motion (WIM) devices record vehicle axle loads on highways. WIM data include axle loads, inter-axle spacings, vehicle classification, gross vehicle weights, vehicle length, and travel speed. Pavement design, management, and performance studies all require WIM data, directly as a spectrum or indirectly as standard axles. In 2016, 132 WIM devices were operating on California’s highway network, one of the densest and best-maintained clusters in the United States. The University of California Pavement Research Center (UCPRC) has been working with these data since 2002, and it completed the analysis of data collected from 1998 to 2003 in 2008. This research investigated similarities in axle load distributions at the WIM sites and grouped them to generate traffic inputs for pavement design. This paper is an extension of that work, including processing the 2004–2015 data from 80 WIM sites, identifying the axle load distribution for each site, updating the original groupings, and developing a new procedure to assign WIM spectra to other locations. For the past 11 years, axle loads and gross vehicle weight showed similar patterns, but average truck speeds decreased by 11%. A combination of clustering and cut-tree analysis was used to create a decision tree to classify the stations’ data into five WIM axle load spectra. The methodology has been used to compute traffic for the entire network used in the Caltrans pavement management system, PaveM. An application tool was also developed for generating truck traffic input files for the CalME and PavementME design tools.

Analytical methods to determine effectiveness of slurry seals in wet/freeze climate using pavement management information systems

With increasing economic pressures worldwide, the amount of money spent on pavement preservation needs to become more effectively utilised. Historically, the Long-Term Pavement Performance (LTPP) study has led to data-driven performance expectations for treatments. Pavement management systems (PMS) allowed many to form analytical methods to evaluate pavement performance, from trend fitting to benefit analysis. Taking these methods and applying them to the Iowa Department of Transportation’s (DOT) pavement management information system (PMIS) database can determine preservation utility as well as provide expectations of pavement treatments. Thirteen slurry seal projects across the wet/freeze climate of Iowa, U.S.A., were analysed to determine the service life extensions and yearly benefit for their pavement condition, rutting, riding, and cracking indices. This study aims to provide a framework for future analysis of more preservation treatments and other PMS databases. Understanding local performance of various preservation methods leads to better pavement management and economically sound decisions.

Corroboration of Modelling Input data for AC Pavements

The earliest study of asphaltic concrete pavement performance indicated that the deterioration process is a non-linear function of distresses that affect the AC fabric.Since AC pavements are non-homogeneous composites which are designed and often constructed to a number of different standards and requirements, the modelling of their progressive deterioration and future performance is a rather complex issue.There are a great number of pavement performance models used by road managing authorities, but do these models meet their expectations? To answer this question, a study was recently conducted into the maintenance practices adopted by local road authorities.In this study, prime consideration was given to the types of models that were adopted by these authorities and the quality of the output data generated by these models. Consequently, it was important to find out the quality of input data adopted for the older pavements which had some maintenance treatments applied and whether the model outcomes were satisfactory?The outcome of this study instigates the fact that the pavement modelling system does not perform satisfactorily on all levels of modelling and particularly on pavements that have very limited data in terms of structural capacities. Evaluation of pavement condition is carried out at certain intervals, usually between three to five years, and often the predicted model output does not match the actual road pavement conditions. In order to synchronise such a match, management uses some artificial ranking as they have no other means of measuring pavement structural strength and capacity. The available non-destructive testing methods are time consuming and often financially unaffordable for the whole road network. Destructive testing is not recommended and as it is also time consuming and costly. Therefore, they greatly rely on the old methods of pavement surface evaluations based on a visual assessment of the surface conditions. However, this type of evaluation is very restrictive in terms of accuracy and defects classification as well as establishing meaningful performance values that could be confidently used as modelling input data. In addition, this method does not provide any specific comparison between different pavements and this ultimately makes modelling limited.The aim of this publication is to establish a feasible evaluation method of a pavement deterioration process that would provide realistic numerical values relating to pavement strength and capacity. Consequently, this could be used as modelling input data.The ultimate solution to the existing problem would be the creation of a simple, easily accessible and inexpensive engineering instrument that would enable the measurement of pavement strength in-situ conditions.

Using support vector machines to predict the probability of pavement failure

This paper presents a method to predict the probability of structural failure of road pavements using information contained in road data sets. Expert knowledge was used to develop failure charts to identify the potential factors that may contribute towards pavement failure. A computational technique (a support vector machine) was built to use this information to determine, from the data sets, the probability of failure of individual road sections. With this prediction comes an indication of the predominant failure types, the causes of structural failure and the risk profile of a road network. The usefulness of the approach was demonstrated on a data set taken from the New Zealand long-term pavement performance study of state highways. Analysis of the data set showed that the network was in good condition, but a small number of pavement sections with a high likelihood of failure were identified. Furthermore, the application of the failure paths examined the three predominant failure types occurring on the network and identified their possible causes. Rutting appears to be significantly influenced by the road pavement strength, fatigue cracking seems to be affected notably by the environment (i.e. water ingress) and shear failure is caused primarily by the combination of traffic, pavement composition and strength. In addition, it was confirmed that measured functional pavement condition alone is not a good identifier of failure and that the inclusion of a parameter related to strength, such as pavement deflection, is essential.

Pavement Performance Comparison of Bituminous Mixtures at Surface Course

The pavement performance study of bituminous mixture at surface course is very important. This paper focused on comparing the pavement performance of AC-l6C, AC-l6F, Super-12.5 and SMA-16 bituminous mixtures. First the gradations and material we used were illustrated. Then the dynamic stability, water stability and texture depth of these bituminous mixtures was tested. After comparing the test data, we found that the SMA-16 bituminous mixture has the best comprehensive pavement performance and it is most suitable for the surface layer bituminous mixture of freeways in Chinese Hubei Province. Super-12.5 and AC-l6C is applicable too, but AC-l6F is improper in this region.

Re-Evaluation of the AASHTO-Flexible Pavement Design Equation with Neural Network Modeling

Here we establish that equivalent single-axle loads values can be estimated using artificial neural networks without the complex design equality of American Association of State Highway and Transportation Officials (AASHTO). More importantly, we find that the neural network model gives the coefficients to be able to obtain the actual load values using the AASHTO design values. Thus, those design traffic values that might result in deterioration can be better calculated using the neural networks model than with the AASHTO design equation. The artificial neural network method is used for this purpose. The existing AASHTO flexible pavement design equation does not currently predict the pavement performance of the strategic highway research program (Long Term Pavement Performance studies) test sections very accurately, and typically over-estimates the number of equivalent single axle loads needed to cause a measured loss of the present serviceability index. Here we aimed to demonstrate that the proposed neural network model can more accurately represent the loads values data, compared against the performance of the AASHTO formula. It is concluded that the neural network may be an appropriate tool for the development of databased-nonparametric models of pavement performance.

Friction Study of Long-Term Pavement Performance Special Pavement Study 9A Sections in Connecticut

Pavement surface friction is one of the most important contributing factors in highway safety. It is a function of a pavement's micro- and macro texture. Therefore, tracking changes in pavement surface friction and texture depth during a pavement's service life is critical for maintaining driving safety and comfort. As part of FHWA's Long-Term Pavement Performance (LTPP) study, many state highway agencies have been collecting friction data to track these changes on designated test sections. In Connecticut, six LTPP sections were constructed in 1997 to serve as Special Pavement Study 9A sites for the characterization of Superpave® asphalt mixtures. This paper presents an analysis of pavement friction and texture data collected by the Connecticut Department of Transportation on these sections. Friction numbers were obtained with an ASTM E274 locked-wheel pavement friction tester by using smooth (ASTM E524) and ribbed (ASTM E501) tires and a GripTester fixed-slip pavement friction tester. Estimated texture depths were obtained in accordance with ASTM E1845 at each site with a Dynatest laser texture device mounted to the friction tester. In addition, mean profile depths were measured with a Circular Track Meter in accordance with ASTM E2157. The analysis of the results targeted three main objectives. First, the variability of the skid number values, measured by the locked-wheel and the braked-wheel, fixed-slip methods, was evaluated. Next, some empirical relationships between friction number and texture depth developed in previous studies were verified. Last, the friction and texture data were analyzed to correlate the measurements with surface material properties.

Viscoelastic Genetic Algorithm for Inverse Analysis of Asphalt Layer Properties from Falling Weight Deflections

The falling weight deflectometer (FWD) is a nondestructive test whose results are typically used for backcalculating in situ layer properties of pavements. Most backcalculation methods assume the pavement to be a layered elastic half-space. However, asphalt pavements behave more like multilayered viscoelastic systems, especially in response to small or short-duration load applications. Hence, although elastic analysis is computationally efficient and well accepted in the engineering community, the theory cannot produce the viscoelastic properties of the asphalt concrete (AC) layer. In this study, a new inverse analysis method is proposed to backcalculate both linear elastic and viscoelastic properties of pavement layers as well as the AC time–temperature shift factor. In this method, the FWD load–response history of a single FWD drop and variation in temperature along the depth of the AC layer during the drop are used for performing the computations. The underlying (viscoelastic) forward solver is approximate and disregards dynamic effects; these factors, in return, make it computationally efficient. A genetic algorithm–based optimization scheme is offered to search for the pavement properties. As an example, two sections from the long-term pavement performance study were selected for investigation. The back-calculation results were positive, which indicates that unless a stiff layer exists close to the surface, it should be possible to infer linear viscoelastic properties from a single FWD drop.

Long-Term Pavement Performance Effectiveness of Preventive Maintenance Treatments Using Markov Chain Algorithm

In the Long-term Pavement Performance (LTPP) study, the SPS-3 experiment was designed to assess the performance of different flexible pavement maintenance treatments, relative to the performance of untreated control sections. The experiment consists of a control section and four maintenance treatments: thin overlay, slurry seal, chip seal, and crack seal. Several studies in the past have evaluated the effectiveness of the maintenance treatments; however, there is a need to re-evaluate the results as more performance data become available. This paper uses Markov chain algorithm (MCA) to evaluate the effectiveness of maintenance treatments at the network level. For each treatment, the transition matrices were determined from the observed time series performance data for ride quality, fatigue cracking, and rutting. The advantages of using MCA includes the ability to dynamically model pavement deterioration and improvement at the same time, evaluate the impact of initial pavement conditions on the short- and long-term performance, and relative comparison of pavement performance among different maintenance treatments. The results show that different maintenance treatments have varying effectiveness depending on the distress type. For example, thin overlay is more effective in the long-term for improving IRI and rutting while chip seal seems to be a better choice in case of alligator cracking. Generally, different seals considered in the SPS-3 experiment are more effective when applied to a network in good condition while overlay is more effective for a network in poor condition.

A Study on the Test of Rubber Crumb Modified Asphalt Mixture Road Performance

The article is based on the rubber crumb modified asphalt’s fundamental characteristics, paving a test road. Through test and compacted study of indoor pavement performance and technical index on-site, summarizing relevant experience, providing reference for similar engineering application

Pavement Performance Study of Construction Waste and River Sludge Used as Subgrade Filling

In this paper, the feasibility of river sludge subgrade with construction waste is studied through CBR test and limit moisture content test. The variation that the carrying capacity of river silt with the increase of mixed proportion of construction waste is analysed, and the suitable mixing ratio is obtained that to filling roadbed with river sludge and construction waste.

Experimental estimation of the relative deterioration of flexible pavements under increased axle loads

The deterioration of Australia's unbound granular pavements with a wearing surface of stone aggregate embedded in a thin bituminous binder seal is likely to increase with increases in axle loads due to the predicted doubling of the freight task carried by heavy vehicles travelling interstate over the next 15 years. Experimental pavement deterioration data gained from accelerated load testing (ALT) were used to derive relative performance factors for rutting and roughness deterioration for increases in axle loads. The ALT involved applying increased axle loads to test pavements in an enclosure that maintained a constant environment. The relative performance factors can be applied to the observed deterioration of pavements under known levels of axle loading to develop network-level road deterioration (RD) models that predict the influence of increased axle loads on deterioration. Several long-term pavement performance studies have collected observational data for this purpose of network RD model development.

Toward Monte Carlo Simulation-Based Mechanistic-Empirical Prediction of Asphalt Pavement Performance

The mechanistic-empirical study of pavement performance requires that immediate pavement responses due to tire loading be mechanistically computed for pavement structures, and the long-term pavement performance be related to the computed pavement responses. The problem becomes very complicated when variability is considered for loading, pavement and environmental conditions. A Monte Carlo simulation-based mechanistic-empirical pavement design/analysis procedure was verified in this study. The complex tire-pavement interaction was more realistically handled and computed using finite element models and measured tire-pavement contact stress data. The computation time problem involved in pavement response computations was resolved by using a computationally efficient method that relates critical pavement responses to tire loading and pavement structural conditions. In the Monte Carlo simulation, different truck classes were drawn from the truck population empirically based on actual traffic volume data. Axle load spectra were characterized by actual axle load data collected at a weigh-in-motion site. Results from a survey of truck configurations were used to describe the distribution of truck tire pressure. Pavement structural parameters and relationships between material moduli and environmental conditions were obtained from the Long-Term Pavement Performance data. The distress models developed in National Cooperative Highway Research Program Project 1–37A were employed to predict pavement performance. The simulation study estimates the effects of increased tire pressure and steering axle load on a typical pavement structure for 2 million truck passes. The Monte Carlo simulation method and models used in this study can provide useful experience for the development of future flexible pavement design and analysis procedures.

Calibration of HDM4 for Use in Pavement Management Systems (PMS)

One of major problems of road management in developing countries is the lack of decision making toolsfor preparing road maintenance programmes. This results in poor resource allocation and poor utilisationof existing assets. Pavement management systems (PMS) have been developed around the world in order to assist with the pavement management process in a co-ordinated and systematic manner. Some developed countries have derived pavement performance models that are suited to local conditions by setting up long term pavement performance studies. Conversely, most developing countries do not have the resources required to set up similar large-scale field experiments. However, validation and calibration of models developed from comprehensive studies elsewhere offers a viable lternative for such countries. This paper evaluates the feasibility of using HDM-4 as a support software and determines level 1 calibration factors for PMS in Tanzania. The paper demonstrates the use of a calibrated HDM-4 to determine the required road management information in developing countries.

Experimental Estimation of the Relative Deterioration of Surface Maintenance Treatments

The performance of Australia's unbound granular pavements with a wearing surface of stone aggregate embedded in a thin bituminous binder seal is highly dependent on maintaining a serviceable surface for the road users. Experimental pavement deterioration data gathered by means of accelerated load testing (ALT) was used to estimate relative performance factors for cumulative rutting and roughness deterioration for various surface maintenance treatments. The ALT experiments were on various surface treatments over separate test pavements undertaken in an enclosure to control the environment, which was either dry or continuously wet. The experimental deterioration data was also used to establish the limit of gradual pavement deterioration prior to the onset of rapid deterioration. The relative performance factors can be applied to the observed deterioration of given surface treatments to develop network level road deterioration (RD) models that predict the influence of various surface treatments on deterioration. Several long term pavement performance studies, including a study directed specifically at maintenance treatments, have collected observational data for the purpose of network level RD model development.

Applicability of the International Roughness Index as a Predictor of Asphalt Pavement Condition

This note establishes the relationship between the surface distress of an asphalt pavement and its roughness, as conveyed respectively by the pavement condition index (PCI) and the international roughness index (IRI). The DataPave software provides the roughness of varied roadway pavement sections from the North Atlantic region that were investigated under the long term pavement performance (LTPP) study. The MicroPAVER1 software system computes the condition of the same sections using cross-referenced distress data from DataPave. A transformed linear regression model predicts pavement condition given roughness. It confirms the acceptability of the IRI as a, albeit not the sole, predictor variable of the PCI whereby the former accounts for the majority, close to 59%, of the variations in the latter. Further, an analysis of variance confirms the existence of a strong relationship between both variables.

Characterization of Bituminous Mixtures for Numerical Modeling of Permanent Deformations

ABSTRACT Because of recent increase in traffic intensity and axle loads, the relevance of rutting due to permanent deformation of the bituminous layers has increased on road pavements. In fact, recent pavement performance studies indicate that rutting in the bituminous layers is a major deterioration mechanism in flexible pavements. Most of the models used for pavement design do not take this mechanism explicitly into account. One of the reasons for that is the difficulty to determine the parameters that describe the materials behaviour for use in advanced models. A simple laboratory test and interpretation methodology is necessary to evaluate its permanent deformation behaviour. Ideally, it should be able to simulate “in situ” conditions and the test specimen should be representative of the bituminous materials applied on the field. However, more “realistic” test methods involve more complex and expensive equipment together with high levels of technical expertise. This paper deals with the use of uniaxial cyclic compression tests to evaluate the permanent deformation of bituminous mixtures. The results derived from these tests can be used for the prediction of permanent deformation in bituminous pavements assuming that the bituminous mixtures have viscoelastic behaviour.

Characterization of Bituminous Mixtures for Numerical Modeling of Permanent Deformations

ABSTRACT Because of recent increase in traffic intensity and axle loads, the relevance of rutting due to permanent deformation of the bituminous layers has increased on road pavements. In fact, recent pavement performance studies indicate that rutting in the bituminous layers is a major deterioration mechanism in flexible pavements. Most of the models used for pavement design do not take this mechanism explicitly into account. One of the reasons for that is the difficulty to determine the parameters that describe the materials behaviour for use in advanced models. A simple laboratory test and interpretation methodology is necessary to evaluate its permanent deformation behaviour. Ideally, it should be able to simulate “in situ” conditions and the test specimen should be representative of the bituminous materials applied on the field. However, more “realistic” test methods involve more complex and expensive equipment together with high levels of technical expertise. This paper deals with the use of uniaxial cyclic compression tests to evaluate the permanent deformation of bituminous mixtures. The results derived from these tests can be used for the prediction of permanent deformation in bituminous pavements assuming that the bituminous mixtures have viscoelastic behaviour.