Dynamic Modulus Of Hot Mix Asphalt Research Articles

Effects of PET and RFCC on the dynamic modulus, phase angle, and complex viscosity of asphalt concrete

With economic and social development, the total amount of domestic and industrial solid waste discharged is considerable. In 2020, Vietnam generated about 2.2 million tons of plastic waste and 2 million tons of industrial solid waste. And most of them are landfilled and take a long time to decompose. This has dramatically affected the environment, costly land resources, and considerable transportation, burial, and management costs of those landfills. Therefore, many scientists have studied to recycle these wastes. There are two approaches to using recycled waste in hot mix asphalt (HMA). Waste plastic changes the properties of the asphalt binder, and solid waste replaces coarse particles in an asphalt mixture. During the research on using recycled waste for asphalt concrete, PET (Poly Ethylene Terephthalate) and RFCC (Residue Fluid Catalytic Cracking) were chosen for the study. This paper presents a study on the influence of PET (with 0.2% content in HMA) and RFCC (with 5% content in HMA) on the viscoelastic property of asphalt concrete based on dynamic modulus, phase angle, and complex viscosity values. At high temperatures, HMA using PET and RFCC exhibit complete elastic behavior (φ ≈ 50) in contrast to the viscous elastic behavior of the control sample (φ ≈ 150). Compared control sample, RFCC significantly increased the stiffness of HMA at low and high temperatures; with the dynamic modulus of HMA using RFCC increased 1.1 times at low and 1.6 times at high temperatures; the viscosity of HMA using RFCC and PET decreased 10 times and 7 times at low temperatures.

Pre-trained deep learning for hot-mix asphalt dynamic modulus prediction with laboratory effort reduction

Evaluating the hot mix asphalt (HMA) expected performance is one of the significant aspects of highways research. Dynamic modulus (E*) presents itself as a fundamental mechanistic property that is one of the primary inputs for mechanistic-empirical models for pavements design. Unfortunately, E* testing is an expensive and complicated task that requires advanced testing equipment. Moreover, a significant source of difficulty in E* modeling is that many of the factors of variation in the HMA mixture components and testing conditions significantly influence the predicted values. For each laboratory practice, a vast number of mixes are required to estimate the E* accurately. This study aims to extend the knowledge/practice of other laboratories to a target one in order to reduce the laboratory effort required for E* determination while attaining accurate E* prediction. Therefore, the transfer learning solution using deep learning (DL) technology is adopted for the problem. By transfer learning, instead of starting the learning process from scratch, previous learnings that have been gained when solving a similar problem is used. A deep convolution neural networks (DCNNs) technique, which incorporates a stack of six convolution blocks, is newly adapted for that purpose. Pre-trained DCNNs are constructed using a large data set that comes from different sources to constitute cumulative learning. The constructed pre-trained DCNNs aim to dramatically reduce the effort elsewhere (target lab) when it comes to the E* prediction problem. Then, a laboratory effort reduction justification is investigated through fine toning the constructed pre-trained DCNNs using a limited amount of the target lab data. The performance of the proposed DCNNs is evaluated using representative statistical performance indicators and compared with well-known predictive models (e.g., η-based Witczak 1-37A, G,δ-based Witczak 1-40D and G-based Hirsch models). The proposed methodology proves itself as an excellent tool for the E* prediction compared with the other models. Moreover, it could preserve its accurate performance with less data input using the transferred learning from the previous phase of the solution.

Effects of moisture damage and anti-stripping agents on hot mix asphalt dynamic modulus

This paper presents findings from a comprehensive model of dynamic modulus (|E*|) of mix asphalt estimated with the statistical approaches of joint estimation and mixed-effects. The model consists of a typical sigmoidal master curve using a different parametrization of sigmoidal function, including the effects of binder, voids, gradation, fibres, confinement level, freeze–thaw cycles, and antistripping agents. This paper concentrates on the engineering implications related to the effects of freeze–thaw cycles and antistripping agents. The estimation results indicate that the number of cycles affects three terms of the sigmoid. The contribution of each term changes with reduced frequency but the net effect is always a reduction of log (|E*|) with a given number of cycles for any reduced frequency, with proportionally larger effects at lower reduced frequencies. The effects of freeze–thaw cycles can be completely counteracted by an antistripping agent but it depends on the number of cycles and the frequency.

Dynamic Modulus Model of Hot Mix Asphalt: Statistical Analysis Using Joint Estimation and Mixed Effects

AbstractThis paper presents the specification and estimation of a model for hot-mix asphalt dynamic modulus |E*| using 6,821 observations of 265 specimens from three different data sets containing ...

Analysis of Loading Frequency in Flexible Pavement Using Fast Fourier Transform

Accurate calculation of traffic loading frequency plays a major role in mechanistic design of flexible pavements. Previous studies have highlighted the need for a paradigm shift in determining frequency through frequency domain analysis rather than conventional time domain-based methods. However, limited studies have experimentally investigated the impact of contributing factors including response type, depth and vehicle speed on calculated frequencies in frequency domain approach. This paper presents the results of controlled vehicle testing that focused on frequency analysis of in-situ measured pulses using Fast Fourier Transform (FFT). The tests were conducted in the Integrated Road Research Facility (IRRF) in Edmonton, Alberta, Canada. Longitudinal, transverse and vertical strains at the bottom of the Hot Mix Asphalt (HMA), as well as the vertical stresses recorded at different depths within unbound layers, were taken into account for this purpose. This study showed that dominant frequencies associated with longitudinal strain were the highest amongst those of other considered responses. Besides, the impact of speed and depths on obtained frequencies were clearly reflected. Finally, comparisons were made between the accuracy of HMA dynamic modulus prediction when using FFT-calculated frequency versus using of four well-established frequency calculation methods in time domain.

Improved Hirsch model for estimating the modulus of hot-mix asphalt

This paper presents an improved version of the Hirsch model for estimating the modulus of hot-mix asphalt (HMA) from asphalt binder modulus and mixture composition. The original Hirsch model was developed in 2002 and has been shown by several independent researchers to be reasonably accurate. However, the authors believed that the model could be improved by addressing several issues: (1) simplifying the Hirsch equation mathematically; (2) including aggregate specific gravity as a predictor, which indirectly accounts for variations in aggregate modulus; (3) including strain level as a factor; (4) recalibrating the model using a data set gathered using the asphalt mixture performance tester following current standard protocols; and (5) addressing steric hardening during testing. These goals were achieved, although it appears that under normal testing conditions strain sensitivity and steric hardening are not significant factors affecting HMA dynamic modulus. Verification of the model using a limited amount of independently collected data suggests that the improved model eliminates or reduces a tendency of the original model to underestimate HMA dynamic modulus values.

Global Sensitivity Analysis of Mechanistic–Empirical Performance Predictions for Flexible Pavements

The new AASHTO Mechanistic–Empirical Pavement Design Guide (MEPDG) provides pavement analysis and performance predictions for various feasible design scenarios. The MEPDG performance predictions for the anticipated climatic and traffic conditions depends on the values of the input parameters that characterize the pavement materials layers, design features, and condition. This paper focuses on comprehensive global sensitivity analyses of flexible pavement performance predictions to MEPDG design inputs under five climatic conditions and three traffic levels. Design inputs evaluated in the analyses include traffic volume, layer thicknesses, material properties, and groundwater depth. Correlations between design inputs were considered as appropriate. The global sensitivity analysis varied all inputs simultaneously across the problem domain for each of the 15 base cases (five climates and three traffic levels). Two response surface modeling approaches—multivariate linear regressions and artificial neural networks—were developed for modeling the analysis results for evaluation of MEPDG input sensitivities across the problem domain. The response surface modeling approaches based on artificial neural networks were particularly effective in providing robust and accurate representations of the complex relationships between MEPDG inputs and distress outputs. The design limit normalized sensitivity index adopted in the study provided a practical interpretation of sensitivity by relating a given percentage change in an MEPDG input to the corresponding percentage change in predicted distress relative to its design limit value. The design inputs most consistently in the highest sensitivity categories across all distresses were the hot-mix asphalt dynamic modulus master curve, hot-mix asphalt thickness, surface shortwave absorptivity, and Poisson's ratio of hot-mix asphalt. Longitudinal and alligator fatigue cracking were also very sensitive to granular base thickness and resilient modulus and subgrade resilient modulus.

Parametric Evaluation of Design Input Parameters on the Mechanistic-Empirical Pavement Design Guide Predicted Performance

The objective of this study was to conduct a sensitivity analysis to identify the input parameters with the greatest effects on the predicted pavement performance from the Mechanistic-Empirical Pavement Design Guide (MEPDG). Three levels of analysis (low, medium, and high) with five input parameters (traffic level, hot-mix asphalt (HMA) thickness, E*, base course thickness, and subgrade type) were evaluated through a full factorial design. The main influence of each individual input parameter and the combinational interaction effects of the input parameters on the predicted distress response were quantified. It was determined that the traffic level input parameter was the main effect on all predicted pavement distresses in the MEPDG. The second main effect for international roughness index (IRI), fatigue cracking, and total pavement rutting was HMA thickness. For asphalt concrete (AC) rutting, the mixture dynamic modulus ranked second for the main effect followed by HMA thickness. For top-down cracking, it was observed that the base course thickness ranked second for the main effect followed by the HMA dynamic modulus. The influence of base thickness on top-down cracking was not expected but it may be due that the MEPDG adopted a traditional fatigue cracking model to describe this failure mechanism.

Evaluation of Predictive Models for Estimating Dynamic Modulus of Hot-Mix Asphalt in Oklahoma

Long-term performance of an asphalt pavement depends not only on the material properties but also on the stiffness achieved during compaction. Because the determination of stiffness during construction is not straightforward, a common approach uses predictive models to estimate the dynamic modulus of hot-mix asphalt (HMA) specimens. Four predictive models—the Witczak 1999, Witczak 2006, Hirsch, and Al-Khateeb models—were evaluated for their use in estimating the dynamic modulus of selected HMA mixtures that are commonly used in Oklahoma. Five mixes representing various aggregate sources, aggregate sizes, binder grades, and air void levels were tested in the laboratory, and the measured dynamic modulus of each mix was compared with the value predicted by each of the models. The performance of each predictive model was evaluated by three approaches: goodness-of-fit statistics, comparison of the measured and predicted values, and local bias statistics (slope, intercept, and average error). Analyses of the results showed that the predictive power of each model varied with the temperature and air void levels of a compacted specimen. Calibration factors were developed for each model to obtain an accurate estimate of dynamic modulus. The calibration factors are helpful for Level 2 and Level 3 designs of the Mechanistic–Empirical Pavement Design Guide.

Data Collection to Support Implementation of the Mechanistic–Empirical Pavement Design Guide for County Roads

Evaluation and calibration of the Mechanistic–Empirical Pavement Design Guide (MEPDG) has been attempted by various agencies throughout the United States. Agencies interested in adopting the MEPDG procedure must prepare a practical implementation plan that fits local conditions. The first step in the implementation plan is collection of design input data and establishment of a database for inputs. A 3-year study was conducted at Arizona State University to establish a database to support MEPDG implementation for the Maricopa County, Arizona, Department of Transportation. The implementation program included testing of asphalt binders, hot-mix asphalt dynamic modulus, and unbound-materials resilient modulus; development of climatic weather stations and training material; and collection of traffic data. The collected information can be used to calibrate the MEPDG distress models to county conditions and verify such models. The input parameters can serve as a framework for similar highway agencies and help ensure the successful implementation of the MEPDG.

Use of Mechanistic–Empirical Pavement Design Guide for Mix Performance Evaluation Using the Dynamic Modulus

The Virginia Department of Transportation has worked toward end-result specifications (ERS) in hot-mix asphalt (HMA) since the mid-1960s. With the recent advances in HMA material characterization and the completion of the new Mechanistic–Empirical Pavement Design Guide (MEPDG) under NCHRP Project 1-37, the ultimate goal of true ERS that are performance related seems more feasible. The use of the HMA dynamic modulus as an HMA quality measure is investigated. An effective reduced frequency (temperature–frequency combination) at which the HMA dynamic modulus best correlates with the MEPDG-calculated HMA rutting is determined for eight climatic conditions. This effective reduced frequency did not coincide with the modified Witczak effective temperature–frequency equation. The effect of HMA layer thickness (51 to 254 mm) on effective reduced frequency was investigated for Roanoke, Virginia. It was found that layer thickness had a minor effect on effective reduced frequency, so that the latter can be assumed independent of layer thickness for all practical purposes. Finally, the HMA rutting calculated by using the effective reduced frequency was found comparable with the MEPDG-calculated HMA rutting with average and maximum differences of 6.5% and 23%, respectively. This difference is acceptable considering the variability in actual field rutting measurements (for the same conditions) as well as accuracy of the MEPDG in determining the average HMA field rutting. Use of the effective reduced frequency can result in considerable time and money savings, especially for quality assurance applications for which testing is regularly performed.

Investigation of Hot-Mix Asphalt Dynamic Modulus by Means of Field-Measured Pavement Response

The current Mechanistic–Empirical Pavement Design Guide (MEPDG) proposes the use of the laboratory dynamic modulus test to determine time–temperature-dependent properties of hot-mix asphalt (HMA) materials. To date, limited measurements have been performed to compare the HMA behavior of laboratory dynamic modulus test with the field measured pavement response. The objectives of this study were to compare and validate laboratory-determined HMA dynamic modulus with field-measured asphalt longitudinal strains. Under constant loading frequency, the laboratory-determined dynamic modulus for Hot Laid 3 (HL3) was found to decrease exponentially when the temperature of the asphalt mix increased. Controlled wheel load experiments, performed at a constant truck speed, found that HL3 asphalt longitudinal strain increased exponentially with an increase in asphalt middepth temperature. The comparison of both exponential relationships showed that the laboratory-determined dynamic modulus was inversely proportional to the field-measured asphalt longitudinal strain.

Probabilistic Performance-Related Specifications Methodology Based on Mechanistic–Empirical Pavement Design Guide

In current construction practice, as-built asphalt concrete acceptance is typically based on several key quality assurance–quality control parameters. These parameters have pay factors that are empirically established by judgment of the pavement community. The pay factor has been used to reward or penalize the contractor for construction quality. These properties are known to influence pavement performance, but the specific effect on performance and project life has been impossible to estimate. Because of this, there is an urgent need to tie deviations of the field production from the specified job mix to performance and pavement life. This research focused on the integration of AASHTO's Mechanistic–Empirical Pavement Design Guide (NCHRP 1-37A and NCHRP 1-40D) with the Simple Performance Test (NCHRP 9-19) to develop a probabilistic quality specification for quality assurance of hot-mix asphalt (HMA) construction. This has been the major objective of NCHRP 9-22. The methodology is based on relating the HMA dynamic modulus, through a closed-form solution, to major pavement distress. A Monte Carlo simulation is run by using the project mix design to predict the as-designed distress and its associated variability. Then the remaining service life of the pavement is predicted from the as-designed distress. Similarly, Monte Carlo simulation is run for the as-built material to estimate the remaining service life. The difference between the as-built and the as-design distress provides the predicted difference in quality of construction from the mix design. The difference is used to calculate the pay factor for each distress. The total pay factor is the sum of the individual distress pay factors. Finally, the initial international roughness index, representing the ride quality pay factor, is added.

Field Verification of Superpave Dynamic Modulus

In the mechanistic-empirical pavement design guide, prediction of flexible pavement response and performance needs an input of dynamic modulus of hot-mix asphalt at all three levels of hierarchical inputs. This study was intended to find the best way to predict/derive this input. Nine Superpave pavement sections were selected as test sections in this study. Deflection data on all test sections was collected with a Dynatest 8000 falling weight deflectometer shortly after construction. The deflection data, normalized with respect to 40-kN load, were used to back-calculate asphalt layer moduli using three back-calculation algorithms. Laboratory dynamic modulus tests were conducted on asphalt concrete (AC) cores and laboratory-compacted samples. Dynamic modulus was also estimated with the Witczak model, new Witczak model, and Hirsch model. The results show that the AC moduli obtained from various back-calculation programs used in the study are generally comparable. Laboratory dynamic modulus is comparable at 4°C, but the variation increases as the test temperature increases. The Witczak model underestimates the dynamic modulus at low temperature and overestimates it at higher temperature. The parameter estimate when the laboratory dynamic modulus is used as a dependent variable and the moduli from other approaches as independent variables is close to 1. This is especially true for the AC moduli estimated by various prediction methods. The Hirsch model appears to be the best for estimation and is closely followed by the new Witczak model.

Looking to the future: the next-generation hot mix asphalt dynamic modulus prediction models

This paper describes an innovative approach related to the development of a new hot mix asphalt (HMA) dynamic modulus (|E *|) prediction model by employing the artificial neural networks (ANNs) methodology. Many studies have been conducted over the last 50 years related to the development of HMA |E *| prediction models based on the regression analysis of laboratory measurements. The current study is an attempt to replace the regression analysis with the ANNs that have proved useful for solving certain types of problems that are too complex, poorly understood or resource intensive to tackle using more traditional numerical and statistical methods. The ANN |E *| prediction models were developed using the latest comprehensive |E *| database that is available to the researchers (from the NCHRP Report 547) containing 7400 data points from 346 HMA mixtures. The ANN model predictions were compared with the 1999 version of the Witczak |E *| prediction model, which is included in the mechanistic–empirical pavement design guide (MEPDG) and the new revised version as well. The sensitivity of input variables to the ANN model predictions were also examined and discussed. The ANN |E *| models show significantly higher prediction accuracy compared with the existing regression models and could easily be incorporated into the MEPDG. This approach may lead to more accurate characterisation of the HMA dynamic modulus resulting in better performance prediction, thereby reducing the risk of premature pavement failure.

Accuracy of Predictive Models for Dynamic Modulus of Hot-Mix Asphalt

Various models have been developed over the past several decades to predict the dynamic modulus ∣ E* ∣ of hot-mix asphalt (HMA) based on regression analysis of laboratory measurements. The models most widely used in the asphalt community today are the Witczak 1999 and 2006 predictive models. Although the overall predictive accuracies for these existing models as reported by their developers are quite high, the models generally tend to overemphasize the influence of temperature and understate the influence of other mixture characteristics. Model accuracy also tends to fall off at the low and high temperature extremes. Recently, researchers at Iowa State Univ. have developed a novel approach for predicting HMA ∣ E* ∣ using an artificial neural network (ANN) methodology. This paper discusses the accuracy and robustness of the various predictive models (Witczak 1999 and 2006 and ANN-based models) for estimating the HMA ∣ E* ∣ inputs needed for the new mechanistic-empirical pavement design guide. The ANN-based...

Methodology to Predict Alligator Fatigue Cracking Distress Based on Asphalt Concrete Dynamic Modulus

This study focused on integrating the Mechanistic–Empirical Pavement Design Guide (MEPDG) methodology (NCHRP 1-37A and NCHRP 1-40D) with the simple performance test methodology (NCHRP 9-19) to develop a comprehensive bottom-up fatigue cracking distress prediction model based on the dynamic modulus in a methodology that can be implemented in a probabilistic performance-related specification (PRS) methodology for quality assurance (QA) of hot-mix asphalt (HMA) construction (NCHRP 9-22). The main approach for a comprehensive fatigue cracking–damage predictive methodology was to run MEPDG for a large number of simulations using combinations of inputs that were believed significant in classic load-associated bottom-up alligator fatigue cracking and to develop an accurate closed-form solution for the fatigue damage–cracking distress prediction based on the dynamic modulus of HMA. Four major studies have been completed for fatigue cracking prediction methodology: ( a) development of a fatigue damage model based on a two-layer pavement system, ( b) development of an asphalt concrete (AC) effective dynamic modulus model, ( c) development of a predictive methodology to transform a multilayer to a two-layer pavement system, and ( d) validation of the prediction process. The developed methodology approximately but accurately predicts the classic AC alligator fatigue cracking distress that is close to that from MEPDG. Thus, implementation of the methodology into the PRS-based QA system in NCHRP 9-22 is recommended.

Precision of AASHTO TP62-07 for Use in Mechanistic–Empirical Pavement Design Guide for Flexible Pavements

In the Mechanistic–Empirical Pavement Design Guide (MEPDG), one of the most important material inputs for designing flexible pavements is the dynamic modulus ( E*). Currently, AASHTO has provided a provisional test specification for measuring the E* of hot-mix asphalt mixtures, AASHTO TP62-07 [Standard Method of Test for Determining Dynamic Modulus of Hot-Mix Asphalt (HMA)]. Under AASHTO TP62-07, recommendations are provided for sample preparation–compaction, measurement system, testing conditions–procedures, and calculations. Unfortunately, information regarding the general precision of the test method does not exist. With many different test machines capable of conducting AASHTO TP62-07 and the importance of E* in flexible pavement design, an understanding of the general precision of the test method and its influence on the MEPDG is needed. This paper describes the results of a round-robin study conducted to address the need for a precision-type statement for AASHTO TP62-07. Seven laboratories, all having equipment compliant with the specifications in AASHTO TP62-07, were asked to laboratory age, compact, prepare, and test six HMA test specimens according to the provisions in AASHTO TP62-07. A statistical analysis was conducted on the test data in a precision statement environment. On the basis of the test results, an initial precision statement for AASHTO TP62-07 was generated and presented. The test results for the seven different laboratories were also used as inputs in the MEPDG to evaluate the relative differences in predicted pavement distresses. Results from the precision evaluation were used to recommend modifications to AASHTO TP62-07 to promote greater precision in different testing laboratories and the results were reevaluated in the MEPDG.

Micromechanical Modeling of Hot-Mix Asphalt Mixtures by Imaging and Discrete Element Methods

This study focused on the application of a clustered micromechanical discrete-element modeling (DEM) technique to predict the uniaxial compression dynamic modulus of hot-mix asphalt (HMA) mixtures. Six HMA mixtures were made with two types of asphalts and two gradations for limestone and gravel aggregates. The dynamic modulus of HMA was determined at three temperatures by using the indirect tensile test (IDT). Similarly, a frequency sweep test was used to determine the shear complex modulus ( G*) of the asphalt matrix (AM) at various temperatures. The master curves for both AM and HMA mixtures were generated at 25°C, and results were analyzed. A two-dimensional, synthetic, heterogeneous microstructure of HMA was reconstructed with scanned images of IDT specimens. Bulk characteristics were used to determine the microproperties of the AM and aggregate structure in order to conduct virtual compressive test simulations. The results indicated that the DEM simulation accurately predicted the dynamic modulus of AM and aggregates at 25°C through the quasi-elastic approach. However, the HMA moduli were underpredicted by the model for a range of AM moduli and loading frequencies at 25°C. Calibration factors for aggregate volume concentrations and particle point contacts were developed and have been discussed here.

Dynamic complex modulus predictions of hot-mix asphalt using a micromechanical-based finite element model

A micromechanical-based finite element (FE) model was used to predict the dynamic complex modulus ( E*) of the hot-mix asphalt (HMA). The microstructure of HMA was captured with a high resolution scanner. Two material phases (aggregates and sand mastic) of HMA were modelled with finite elements. The sand mastic herein was composed of fines and asphalt binder with some fine aggregates. The dynamic complex modulus of the sand mastic under different temperatures and loading frequencies was measured in an experimental program. The corresponding principles were applied to bridge the elastic simulation and viscoelastic behavior with the input of the viscoelastic mastic properties. The input parameters in the FE model include the dynamic complex modulus of the sand mastic, the elastic modulus of the aggregates, and the microstructure of the HMA. The E* values of the HMA were measured and used to compare the E* predicted from the FE model. It is found that the FE approach used in this paper has the ability to predict HMA dynamic modulus across a range of temperatures and loading frequencies. The FE prediction of the E* was compared with a recently developed discrete element modelling approach and found the E* prediction from these two approaches to be very similar.