Dynamic Modulus Predictive Models Research Articles

Laboratory testing and analysis of dynamic and static resilient modulus of subgrade soil under various influencing factors

In this study, dynamic and static resilient modulus of subgrade soil were conducted for six types of subgrade soil using the stress level and loading range on top of subgrade under moving vehicle loading. The influences of loading frequency, stress level, compaction degree, and moisture content on dynamic and static modulus were investigated. The prediction models of nonlinear parameters of dynamic resilient modulus and the relationship of dynamic and static resilient modulus were developed. Results showed that dynamic resilient modulus of different soils were significantly affected by loading frequency at the loading frequency range of 0.5–3 Hz. Dynamic and static resilient modulus were both positively related with confining pressure and compaction degree, but negatively related to the moisture content of subgrade soil. The nonlinear parameters of dynamic resilient modulus prediction models had the similar form for different types of subgrade soil, while the coefficients of nonlinear parameters varied with the type of subgrade soil. The parameters of the relationships between dynamic and static resilient modulus for different types of soil were also similar in the equation format with different parameter values. The research findings provide valuable information for using realistic subgrade soil modulus in pavement design and analysis.

Effect of local calibration of dynamic modulus and creep compliance models on predicted performance of asphalt mixes containing RAP

MEPDG software can predict long term performance of the asphalt mixes based on asphalt input data. When laboratory measured data of an asphalt mix are not available, this software uses the predictive models to estimate the mix properties. AASHTO recommended calibrating these input models based on local materials and mixes. Previous studies showed that local calibration of dynamic modulus (E∗) and creep compliance predictive model improved the reliability of the predictions. The purpose of this paper is to evaluate the impact of local calibration of E∗ and creep compliance models on long term performance of mixes containing reclaimed asphalt pavement (RAP), and to assess the sensitivity of the predicted distresses to RAP content. Three Levels of asphalt input data were considered; Level 1, calibrated Level 3, and Level 3. For calibrated Level 3, the predicted E∗ and creep compliance obtained from calibrated models were used as input data. The results showed that the Level 3 input data tend to overpredict the distress predictions of the asphalt mix compared to calibrated Level 3 or Level 1 asphalt input data. It was found that the calibrated Level 3 asphalt input data can be used for the design and analysis of mixes with comparable accuracy of Level 1 input. As conducting laboratory tests for individual mixes is expensive and time consuming, utilizing reliable calibrated models to predict E∗ and creep compliance can substantially reduce operating and testing expenses. Also, it was found that the predicted distresses are not sensitive to the RAP content.

Assessment and modification of nationally-calibrated dynamic modulus predictive model for the implementation of Mechanistic-Empirical design

This study modifies the viscosity (η) based Witczak dynamic modulus (|E∗|) predictive equation which is used in the recently developed AASHTOWare pavement Mechanistic-Empirical (ME) design software. A total of 21 Superpave (SP) mixtures with different gradations and binder grades were tested for |E∗| at different temperatures and frequencies of loading. Results show that the η-based Witczak model underestimates the |E∗| for New Mexico’s SP mixes. Therefore, revised model is developed using regression analysis. A phase angle (ϕ) predictive model is also developed using the ϕ-values obtained during the |E∗| testing. It was found that the revised η-based Witczak model determines the |E∗| more accurately compared to the original η-based Witczak model.

Viscosity-Based Complex Modulus and Phase-Angle Predictive Models for the Superpave Asphalt Mixtures of New Mexico

AbstractThe recently developed mechanistic-empirical design method of pavement uses the nationally calibrated dynamic modulus predictive model for the design and analysis of asphalt pavements. This...

Relative Comparison of Complex Dynamic Modulus Predictive Models for Non-Conventional Asphalt Concrete Mixtures

Abstract Because of the widespread use of additives, a revised dynamic modulus (|E*|) predictive approach is needed for the mechanistic–empirical analysis of asphalt pavement designs. The importance of predicting |E*| relates to its use as a sensitive input in the AASHTO Pavement ME Design software. The objective of this study is to evaluate two |E*| predictive models currently used for asphalt mixtures that include non-conventional additives, and to provide recommendations for enhancing prediction capability of the models when these types of materials are used. The focus of the paper is on finding ways to efficiently and accurately generate |E*| inputs for the software when actual pavement response is not measured. Impacts of mixture type, temperature, and binder grade on |E*| predictions are explored. The current software uses three levels of inputs, two of which require key material characteristics to predict |E*| values rather than employing laboratory-measured |E*|. This study shows that for non-conventional mixtures, 53 % of |E*| values predicted by the 2006 Witczak predictive equation (WPE) are greater than values predicted by the 1999 WPE. This trend of over-prediction is observed at both low and high test temperatures. However, the opposite effect exists between the two WPEs at intermediate test temperatures. This research shows that adjusting the original binder type of non-conventional mixtures provides a significant improvement in predictive accuracy for |E*|. The findings of this study provide suggestions for alterations to binders, to adjust the WPE model’s terms for better predictive accuracy, and supplies recommendations to practitioners who use the AASHTOWare Pavement ME Design software; however, it is still evident that the laboratory measurement of |E*| values is still the preferred approach for generating inputs for the software.

Kernel machines and firefly algorithm based dynamic modulus prediction model for asphalt mixes considering aggregate morphology

Artificial Intelligence algorithm support vector regression (SVR) has proved successful in outperforming conventional Witczak and ANN models for estimation of dynamic modulus (E∗) of asphalt mixes. However, there were two issues related to the development of E∗ prediction models that the present study addresses. Firstly, since aggregates occupy almost 95% by weight of HMA, it is quite possible that the morphology of these aggregates play an important role in influencing the E∗ values. To address this issue, aggregate shape parameters, namely, angularity, sphericity, texture and form were used with aggregate gradation for stiffness estimation. Secondly, to fine tune the hyper-parameters firefly algorithm (FA) was coupled with SVR. E∗ tests of 20 HMA mixes having different sources, sizes of aggregates, and volumetric properties were conducted at 4 temperatures and 6 frequencies. Aggregate shape parameters were measured using the automated aggregate image measurement system (AIMS). SVR-FA models were developed that predicted the E∗ with an R2 of 0.98. SVR-FA models were compared with SVR and ANN models for E∗ prediction. Further, a sensitivity analysis was conducted to identify the important input parameters. Lastly, an approach for formulation of SVR-FA model equations for direct prediction of HMA stiffness is also discussed. FA proved instrumental in improving the efficiency of SVR by optimizing the hyper-parameters with lesser manual effort. Finally, it was concluded that SVR-FA algorithm is capable of successfully predicting the E∗ values using the aggregate shape parameters.

Development of dynamic modulus master curves of in-service asphalt layers using MEPDG models

The new mechanistic-empirical pavement design guide (MEPDG) uses a combination of field and laboratory tests for structural analysis of in-service pavements. In this guide, asphalt dynamic modulus (|E*|) is one of the most important parameters used in flexible pavement design and rehabilitation approaches. This paper evaluates the validity of dynamic modulus models used in MEPDG for in-service asphalt pavements. For this purpose, nine asphalt pavement sites with various structural, ages and conditions were selected in Khuzestan and Kerman provinces in southern regions in Iran. At each site, falling weight deflectometer data, resilient modulus (determined from the cored samples) and surface condition data were collected. These three sets of data are necessary to compute damages at input levels of 1, 2 and 3 in MEPDG, respectively. Core samples were extracted, and mix volumetric properties as well as their binder characteristics were determined. Viscosity-based NCHRP 1-37A (Witczak) and G*-based NCHRP 1-40D (Modified Witczak) dynamic modulus predictive models were incorporated into these three input levels and six combinations of in situ |E*| master curves were developed. Analysis of the results indicates a weak correlation between field backcalculated moduli and those measured in laboratory. Hence, application of both field and laboratory moduli is not necessarily a good approach to determine damages, and the MEPDG proposed method would better be modified. Validation of MEPDG models showed that the NCHRP 1-37A model predicts dynamic modulus master curves with lower errors rather than the NCHRP 1-40D model. In addition, using the NCHRP 1-37A model at input level 1 would be a better approach among the others to evaluate both new and rehabilitated asphalt layers. Results will also be useful for local agencies in implementation of American Association of State Highway and Transportation Officials (AASHTO)mechanistic-empirical pavement design procedure in various climatic conditions.

Alternatives for calibration of dynamic modulus prediction models of asphalt concrete containing RAP

Dynamic modulus is a measure of stiffness of an asphalt concrete (AC) mix when subjected to cyclic sinusoidal compressive stresses. In the Mechanistic-Empirical Pavement Design Guide (MEPDG) and its software Pavement ME, dynamic modulus (E∗) is an essential parameter for the prediction of asphalt pavement distresses such as rutting and fatigue cracking. Several empirical models have been developed to estimate the E∗ from AC mix properties to compliment laboratory measured E∗ values. Two models developed under NCHRP 1-37A and NCHRP 1-40D projects have been integrated into the MEPDG program. The models estimate E∗ values when Level 2 and Level 3 inputs for AC mixes are used. This paper presents the evaluation of uncalibrated E∗ values obtained from NCHRP 1-37A and NCHRP 1-40D and compares the results to two calibrated techniques; an exponential fit of uncalibrated model outputs, and updated model coefficients using nonlinear multiple regression. In total, 51 specimens from 17 types of AC mixes containing 0–50% RAP were prepared and tested in the laboratory. E∗ was determined in the laboratory and compared to estimated values based on calibrated and uncalibrated models. The results showed that uncalibrated NCHRP 1-37A produced lower error in predicting E∗ than uncalibrated NCHRP 1-40D. Both calibration techniques enhanced the accuracy of the models, however nonlinear multiple regression showed the best potential for predicting E∗. Calibrated models showed improvement in prediction of E∗ for all RAP mixes for Level 3 inputs, although high RAP mixes showed the least improvement among the other RAP mixes.

Mechanistic-Empirical Analysis of Asphalt Dynamic Modulus for Rehabilitation Projects in Iran

In the Mechanistic–Empirical Pavement Design Guide (MEPDG), dynamic modulus of asphalt mixes is used as one of the input parameters in pavement analysis and design. For in-service pavements, MEPDG method uses a combination of some field and laboratory tests for structural evaluation of asphalt layers in rehabilitation projects. In this study, ten new and rehabilitated in-service asphalt pavements with different physical characteristics were selected in provinces of Khuzestan and Kerman in the south of Iran. These provinces are known as hot climate areas and have severe climatic conditions. At each site, Falling Weight Deflectometer (FWD) testing was conducted and core samples were taken. These samples were extracted and mix volumetric properties and binder characteristics were determined. Results of these tests were used as input parameters in Witczak dynamic modulus prediction model for determination of MEPDG undamaged dynamic modulus master curves. Finally, the damaged (in-situ) dynamic modulus master curves were developed upon modifying the undamaged master curves with the damage factors determined from back calculation analysis of FWD data. It was found that with the above mechanistic-empirical procedure, it would be possible to successfully evaluate in-service asphalt layers located in severe climatic areas.

Characterization of Various Plant-Produced Asphalt Concrete Mixtures Using Dynamic Modulus Test

This research characterizes the performance of various plant-produced asphalt concrete mixtures by dynamic modulus|E∗|test using asphalt mixture performance tester (AMPT). Marshall designed specimens of seven different mixtures were prepared using the Superpave gyratory compactor and subjected to sinusoidal compressive loading at various temperatures (4.4 to 54.4°C) and loading frequencies (0.1 to 25 Hz). A catalog of default dynamic modulus values for typical asphalt concrete mixtures of Pakistan was established by developing stress-dependent master curves separately, for wearing and base course mixtures. The sensitivity of temperature and loading frequency on determination of dynamic modulus value was observed by typical isothermal and isochronal curves, respectively. Also, the effects of various variables on dynamic modulus were investigated using statistical technique of two-level factorial design of experiment. Furthermore, two dynamic modulus prediction models, namely, Witczak and Hirsch, were evaluated for their regional applicability. Results indicated that both the Witczak and Hirsch models mostly underpredict the value of dynamic modulus for the selected conditions/mixtures. The findings of this study are envisaged to facilitate the implementation of relatively new performance based mechanistic-empirical structural design and analysis approach.

Evaluation of the Dynamic Modulus for Asphalt Mixtures with Varying Volumetric Properties

The dynamic modulus |E*| of asphalt mixtures with varying volumetric properties was evaluated in this study. The dynamic modulus test, repeated load permanent deformation test, and beam fatigue test were performed in laboratory to evaluate the stiffness, rutting, and fatigue performance for three types of mixtures, respectively. The Falling Weight Deflectometer (FWD) test was conducted to evaluate field pavement conditions of four sections from two highways in Jiangsu. It is observed from the |E*| master curves that for a specific type of mixture, |E*| decreases with the increase in binder content at the lower reduced frequency, while at the higher reduced frequency a peak |E*| value exists at the approximate optimum binder content obtained from volumetric mix design criteria. It is also found from the tests results that dynamic modulus indicators |E*| and |E*|/sinφ do not show good agreements with fatigue and rutting performance of different mixtures. In addition, it is shown that the Witczak model strongly underestimates |E*| of the mixtures used in this study and should be corrected by a shift factor of 1.8621. Finally, a dynamic modulus prediction model for asphalt mixtures with varying volumetric properties was developed in laboratory and successfully verified using in-situ FWD backcalculation.

Predictive models for dynamic modulus using weighted least square nonlinear multiple regression model

The objectives of this paper are (1) to evaluate the dynamic modulus prediction models and (2) to develop an alternative prediction model using the nonlinear multiple regression method. A total of 14 field produced mixture types (in a total of 1314 measurements) with various designed traffic levels and aggregate sizes were used. Two prediction models, the Witczak prediction model developed in 2006 and the Hirsch model developed in 2003, were revised in this study. In addition, the revised Witczak prediction equation was simplified using fewer independent variables (11 variables instead of 21 variables); and values of the newly revised coefficients with improved accuracy for Hirsch model are presented in this paper. A new model using the weighted least square nonlinear multiple regression model (WLS NLR) was developed in this study. It was found that the WLS NLR has a better prediction when compared with other prediction models used in this study.

Performance of MEPDG Dynamic Modulus Predictive Models for Asphalt Concrete Mixtures: Local Calibration for Idaho

The mechanistic-empirical pavement design guide (MEPDG) is the research version of the newly released DARWin-ME software by AASHTO. MEPDG includes two models for Levels 2 and 3 hot-mix asphalt (HMA) dynamic modulus (E*) predictions. The two models are NCHRP 1-37A and NCHRP 1-40D. The primary difference between the two is the binder stiffness parameter; viscosity or shear modulus. Moreover, MEPDG includes three levels for binder stiffness characterization: viscosity for Level 1 conventional binders, shear modulus for Level 1 superpave binders, and default values for Level 3. The influence of the binder characterization input level on the performance of the MEPDG E* predictive models is evaluated in this paper. To calibrate the models for Idaho, 27 HMA mixtures commonly used in Idaho were investigated. Results showed that the performance of the investigated models varied based on the temperature and the binder characterization method. The NCHRP1-37A E* model with MEPDG Level 3 binder inputs yielded the most accurate and least biased E* estimates. The accuracy of this model was further enhanced by introducing a local calibration factor.

Development of Artificial Neural Network Predictive Models for Populating Dynamic Moduli of Long-Term Pavement Performance Sections

This paper presents a set of dynamic modulus (|E*|) predictive models to estimate the |E*| of hot-mix asphalt layers in long-term pavement performance (LTPP) test sections. These predictive models use artificial neural networks (ANNs) trained with different sets of parameters. A large national data set that covers a substantial range of potential input conditions was utilized to train and verify the ANNs. The data consist of mixture dynamic moduli measured with two test protocols: the asphalt mixture performance tester and AASHTO TP-62, under different aging conditions. The data include binder dynamic moduli values measured under different aging conditions. The ANN predictive models were trained and ranked with a common independent data set that was not used for calibrating any of the ANN models. A decision tree was developed from these rankings to prioritize the models for any available inputs. Next, the models were used to estimate the |E*| for the LTPP database materials and ultimately to characterize the master curve and shift factor function. To ensure adequate data quality, a series of quality control checks was developed and applied to grade the inputs and outputs for each prediction. Approximately 30% to 50% of all LTPP layers contained enough information to obtain reliable moduli predictions.

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.

Characterization of Louisiana Asphalt Mixtures Using Simple Performance Tests

Abstract The main objective of this study was to characterize the performance of HMA mixes based on four laboratory tests, including three simple performance tests (SPTs) dynamic modulus ∣E∗∣, flow time (Ft), flow number (FN), and a Loaded Wheel Tracking (LWT) test. In addition, two dynamic modulus prediction models, namely Witczak and Hirsch, were evaluated. Thirteen plant-produced HMA mixtures were selected in this study. Laboratory characterization tests included the dynamic modulus ∣E∗∣, flow number (FN), and Hamburg-Type LWT tests. Test results indicated that the ∣E∗∣ test was sensitive to the nominal maximum aggregate size (NMAS) in the HMA mixture. Larger aggregates combined with recycled asphalt (RAP) tended to have high ∣E∗∣ values at high temperatures. Both the Witczak and Hirsch models could predict the dynamic modulus ∣E∗∣ values with a reasonable reliability. However, the Witczak model reliability increases for higher NMAS. On the other hand, the Hirsch model reliability increases for lower NMAS. The general ranking of the SPTs and LWT test was similar. In addition, this ranking was consistent with the field use of those mixtures in terms of their design traffic volume.