NCHRP 1-37A Research Articles

Revolutionising Egyptian pavement design: a comprehensive E* database and advanced modeling approach for contextually informed performance predictions

ABSTRACT This research assesses the dynamic modulus (E*) as a pivotal rheological attribute for characterizing viscoelastic behaviour in various indigenous asphalt mixtures across diverse scenarios. An exhaustive investigation involving 32 laboratory-designed asphalt mixes and two field mixes explores the influence of traffic loading patterns, air voids, binder sources, and climatic conditions on E*. Utilizing Superpave gyratory compaction, 68 E*-specimens are compacted and subjected to E*-testing, leading to master curve development. Evaluation of NCHRP 1-37A and NCHRP 1-40D Witczak models demonstrates their fair to excellent accuracy in predicting E* for Egyptian mixes. The NCHRP 1-40D model stands out with an R² of 0.94, showcasing superior accuracy and minimal bias. This study integrates AASHTOW are Pavement ME Design (PMED) to analyse pavement sections, revealing the impact of design factors, characterized by measured E*, on predicted pavement performance, encompassing asphalt concrete rutting, alligator cracking, longitudinal cracking, and terminal international roughness index. The investigation highlights the marginal impact of substituting predicted E* for measured E* on AASHTOWare pavement performance indicators for Egyptian mixes under default binder characteristic values. This research contributes valuable insights into the interplay of E* and pavement performance, facilitating wellinformed decisions in pavement design and analysis across diverse conditions in the Egyptian context.

Boosting Hot Mix Asphalt Dynamic Modulus Prediction Using Statistical and Machine Learning Regression Modeling Techniques

The prediction of asphalt mixture dynamic modulus (E*) was investigated based on 1128 E* measurements, using three regression and thirteen machine learning models. Asphalt binder properties and mixture volumetrics were characterized using the same feeding features in the NCHRP 1-37A Witczak model. However, three aggregate gradation characterization approaches were involved in both modelling techniques: the NCHRP 1-37A gradation parameters, Weibull distribution factors, and Bailey method parameters. This study evaluated the performance of these models based on various performance indicators, using both statistical and machine learning regression modeling techniques. K-fold cross-validation and learning curve analysis were conducted to assess the models’ generalization capabilities. The conclusions of this study demonstrate the superiority of the ML models, particularly the Catboost ensemble learning regression (CbR). Hyperparameter optimization and residual analysis were performed to fine-tune and confirm the heteroscedasticity of the CbR model. The Bailey-based CbR model showed the highest coefficient of determination (R2) of 0.998 and the lowest root mean square error (RMSE) of 220 MPa. Moreover, SHAP values interpreted the CbR model and showed the relative importance of its feeding features. Based on the findings of this study, the CbR model is suggested to accurately predict E* for a variety of asphalt mixtures. This information can be used to improve pavement design and construction, leading to more durable and long-lasting pavements.

Dynamic Modulus Prediction Validation for the AASHTOWare Pavement ME Design Implementation in Egypt

Dynamic Modulus, E* is a crucial property of the hot mix asphalt (HMA). For the AASHTOWare Pavement ME design, E* is an essential material input. E* can be measured in the laboratory or predicted using different models based on some fundamental properties of the HMA. The NCHRP 1-37A and NCHRP 1-40D prediction models are the two main models adopted by the AASHTOWare to predict the E* based on the HMA mixture volumetrics, gradation, and binder properties. The main objective of this research was to validate these two prediction models using local HMA mixes for the purpose of the regional application of the AASHTOWare Pavement ME design in Egypt. For this purpose, the E* values of ten locally plant-produced HMA mixes were measured in the laboratory. The two E* prediction models were then used to estimate the E* values for the same materials. Consequently, the performance of both models was studied by comparing the measured values to the estimated values. The results showed that the NCHRP 1-40D prediction model can satisfactorily predict the E* of the Egyptian HMA mixes with minimal bias and high accuracy. The model yielded an adjusted coefficient of determination (R2) of 0.86 based on 480 E* measurements. On the other hand, the NCHRP 1-37A prediction accuracy was not satisfactory, with very poor accuracy (Adjusted R2 = 0.18) and high bias. Afterwards, the effect of the predicted E* from the NCHRP 1-40D model on the AASHTOWare Pavement ME predicted pavement performance in terms of rutting, cracking, and roughness was further studied. Accordingly, twenty-four simulation runs for typical Egyptian design cases were conducted using, first, the laboratory measured E* values and, then, the NCHRP 1-40D predicted E* values. The results showed that the NCHRP 1-40D predictions had no significant effect on the pavement performance predicted by the AASHTOWare Pavement ME with R2 of the different pavement distresses ranged from 0.980, for the AC rutting, to 0.9996 for the International Roughness Index (IRI). Hence, the NCHRP 1-40D model can be used satisfactorily to predict E* for the Egyptian HMA mixes without compromising the structural pavement design.

An Appraisal of Mechanistic-Empirical Models (MEMs) in Pavement Deterioration

This research paper presents an appraisal of Mechanistic-Empirical Models (MEMs) in pavement deterioration assessment. The study examines various MEMs used in the analysis of flexible pavements, including Westergaard's Equation, Boussinesq's Equation, NCHRP 1-37A, AASHTO MEPDG, EICM, and FPO Software. The equations, parameters, limitations, and advantages of each model are thoroughly evaluated. The significance of MEMs in pavement deterioration assessment is highlighted, emphasizing their ability to provide a mechanistic understanding of pavement behavior and enable accurate predictions of performance. The paper offers recommendations for practitioners and researchers, including the adoption and implementation of MEMs, collaboration and data sharing, enhanced model inputs, standardized calibration protocols, continuous model improvement, and training and education. The conclusion emphasizes the value of MEMs in pavement engineering while acknowledging the need for further improvement and research. The findings of this study contribute to advancing the appraisal and utilization of MEMs in pavement deterioration assessment, ultimately leading to improved pavement designs, optimized maintenance strategies, and informed decision-making in the field of pavement engineering.

Improving asphalt mix design by predicting alligator cracking and longitudinal cracking based on machine learning and dimensionality reduction techniques

• Reasonable asphalt mixture design can mitigate the cracking problems. • Alligator cracking (AC) and longitudinal (LC) cracking are predicted from asphalt mixture properties. • The effect of three different dimension reduction methods on the performance of ML models is analyzed. • ML algorithms includes Support vector regression, Artificial Neural Networks, Kernel ridge regression, Gradient boosting, Extra-trees, eXtreme Gradient Boosting. • The paper improves the current asphalt mix design by adding the criteria of AC and LC and ML models. The asphalt mix design based on traditional laboratory fatigue cracking tests of asphalt mixture is not reasonable due to the difficulty of simulating the circumstance where asphalt mixture experiences in the pavement structure and under realistic climate and traffic conditions. The study aims to mitigate the cracking problems during the pavement design life, by improving the asphalt mix design process by introducing machine learning (ML) models, which are used to predict alligator cracking (AC) and longitudinal cracking (LC), and their criteria (required ranges). The data containing AC and LC were extracted from the NCHRP 1-37A report and the long-term pavement performance (LTPP) program. A total of 33 input features about climate condition, traffic, pavement structure, and materials properties of each pavement layer were selected. Support Vector Regression (SVR), Artificial Neural Networks (ANN), Kernel ridge regression (KRR), Gradient boosting (GB), Extra-trees, and eXtreme Gradient Boosting (XGBoost) were used. Meanwhile, three dimensionality reduction approaches, including Auto-Encoder (AE), Principal Component Analysis (PCA), and Recursive feature elimination with Random Forest, were combined with the six ML algorithms (18 hybrid models produced) in order to decrease the computation complexity and search for the optimum model. The 24 models (6 basic ML models plus 18 hybrid models) were trained, and their hyperparameters were tuned using Bayesian optimization. Different evaluation criteria R 2 , RMSE, MAE, SMAPE, and SI are calculated to evaluate the performance of these models. The results of the study showed that except for ANN and SVR, the basic ML models can get better for predicting AC and LC when they are combined with PCA or AE. Based on the performance indices, the optimum among these developed models proved to be PCA-ANN for predicting AC (R 2 = 0.84) and LC (R 2 = 0.83). Compared to other pavement layers, the asphalt surface course is the highest contributor to AC and LC. The improved mix design process was implemented to determine the mix proportion for the asphalt surface course of a construction project and Mix-2 with 5.1 % asphalt content was recommended.

Performance characteristics of asphalt mixtures with industrial waste/by-product materials as mineral fillers under static and cyclic loading

The mineral filler is an integral constituent of asphalt mixture, which affects its performance. This research investigates the effect of using four local industrial waste/by-product materials (marble, granite, steel slag and hydrated lime powder) as mineral fillers on asphalt mixtures characteristics. The detailed performance of these materials as well as asphalt binder and asphalt mixture characteristics are presented. In Egypt, limestone powder is a typical mineral filler in asphalt mixture composition. A control asphalt mixture with limestone mineral filler is designed using Marshall method. Other four asphalt mixtures are prepared using similar constituents and volumetrics except replacing the limestone filler with other types of industrial waste/by-product materials, while maintaining the same percentage of mineral filler as in the control mix. Marshall stability and moisture-induced damage of the investigated asphalt mixtures are determined and compared to the control asphalt mixture results. In addition, the performance characteristics of the investigated mixtures are evaluated by conducting cyclic performance tests including the dynamic modulus (E*) and flow number (FN). Testing results show that the asphalt mixture containing marble as a filler yields the highest stability; while, the hydrated lime filler noticeably enhances the performance of the asphalt mixture in terms of moisture damage resistance, E*, and FN. Unexpectedly, steel slag filler does not improve the asphalt mixture performance. Finally, the stiffness of the investigated mixtures is evaluated using three well-known E*-predictive models (NCHRP 1-37A, Hirsh, and NCHRP 1-40D); while, the resistance of the investigated mixtures to permanent deformation is evaluated using a calibrated version of Apeagyei-literary model based on the investigated mixtures to predict FN at 54.4°C and 1 Hz frequency.

Modeling and performance evaluation of asphalt mixtures and aggregate bases containing steel slag

The demand on using industrial by-products and waste materials in the construction industry has increased in recent years. This study focused on the feasibility of using Electric Arc Furnace Steel slag (EAFS) as a replacement to the natural aggregates (Limestone, LS) in both Hot Mix Asphalt (HMA) and granular base layers. Four HMAs were investigated, two mixes were prepared using proportions of the EAFS/LS of 0/100 and 100/0% of the total aggregate weight, while for the other two mixes, the coarse limestone aggregate fractions were replaced by 60 and 80% coarse EAFS aggregates. The granular base EAFS/LS blends were (0/100, 20/80, 40/60, 60/40, 80/20, and 100/0%) of the total aggregate weight. EAFS, LS aggregates and asphalt binder were first characterized in the laboratory, and then the performance of the investigated asphalt mixtures in terms of Marshall stability, loss of stability, indirect tensile strength and tensile strength ratio was evaluated. On the other hand, the granular base blends were characterized through routine tests and static and Repeated Load Triaxial (RLT) tests to evaluate the behaviour of these blends under traffic loading. RLTT results showed that as the EAFS increases, the stiffness generally increases. A new revised universal model is proposed to incorporate the EAFS effect on the resilient modulus (Mr) prediction having an excellent accuracy. The (E*) values for the investigated HMA were predicted using Witczak NCHRP 1-37A model. While, the Mr values for the investigated granular base blends were predicted by the universal model at the anticipated field stress, which were obtained by “ANSYS” Finite Element Analysis software. Moreover, the field pavement performance in terms of Asphalt Concrete (AC) rutting and fatigue cracking was predicted for different typical sections using the Quality-Related Specifications Software (QRSS). Results showed better performance for HMA mixes and unbound aggregate bases containing EAFS compared with the control section containing only natural aggregates.

Comparativo de Projeto de Pavimentos Flexíveis através Métodos de Dimensionamento do Brasil e da AASHTO

Este trabalho tem como objetivos fazer a análise de pavimentos dimensionado por meio do método oficial do DNIT confrontando com do uso do software SisPavBR desenvolvido na UFRJ e também o software AASHTOWare Pavement, que carrega consigo o método de dimensionamento proposto pelo AASHTO ME, NCHRP 1-37A. Para isto, foi então feito uso da contagem de veículos realizados para BR116/RS; no entanto, foi necessário criar bandas de volume de tráfego (N), devido o método do DNIT não considerar o arranjo dos eixos como o SisPavBR e AASHTOWare fazem em suas avaliações. Foi realizado também o levantamento do custo executivo do km de pavimento dimensionado por cada método. Desta maneira, verificou-se que enquanto um pavimento dimensionado pelo DNIT necessita de uma espessura de revestimento de 12,5 cm, no SisPavBR a camada necessária é de 19,5 cm e a conforme a AASHTO 40 cm. Da mesma forma, o custo do pavimento foi mais elevado para o dimensionamento da AASHTO e do SisPavBR, em relação ao método oficial. Adicionalmente foi realizada a análise da vida de projeto do pavimento dimensionado pelo DNIT, o qual ficou bem abaixo do esperado em ambas as análises, chegando em um caso a durar apenas 2 meses. Fica patente a necessidade de discutir a evolução do método oficial para uma abordagem mais moderna e em sintonia com a Mecânica de Pavimentos.

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.

Investigation on the pavement performance of asphalt mixture based on predicted dynamic modulus

The objective of this study is to evaluate the pavement performance through model analysis using predicted dynamic modulus (E∗). E∗ is predicted based upon the two global prediction models (NCHRP 1-37A and NCHRP 1-40D models). Christensen–Anderson–Marasteanu (CAM) model is utilized to fit master curve to the predicted E∗. Results indicate that the models are applicable to predict E∗ of asphalt mixture. Mastercurve of predicted E∗ can characterize the pavement performance by nonlinear fitting based on CAM model. Comparison of predicted results of pavement performance (rutting, cracking and fatigue) with measured results is made to verify the applicability of this approach. It is believed that the Witczak models are useful in estimating pavement performance associated with the CAM model, when actual E∗ tests or other performance tests are not possible or practicable in practice.

Mechanistic-empirical approach to characterizing permanent deformation of reinforced soft soil subgrade

This study focuses on characterizing the permanent deformation of geogrid-reinforced soft soil subgrade by using the mechanistic-empirical (ME) approach based on both experimental measurements and results of numerical modeling. Two sets of small-scale pavement sections were built over two types of soft soil subgrade and subjected to cyclic moving loads by means of a reduced-scale accelerated pavement testing (APT) device. Each set of pavement sections included one control section and three sections reinforced by different geogrids placed at the base–subgrade interface. The pavement sections were instrumented to measure vertical deformation and vertical stresses at the top of the subgrade. Strains developed in the geogrids were also measured by strain gauges throughout the construction and accelerated testing. Simplified finite element (FE) models were created to simulate both the control and geogrid-reinforced pavement sections and to compute the pavement mechanistic responses. With the experimental measurements from the first set of tests and results of FE analysis, the permanent deformation model in NCHRP 1-37A for unbound layers was adapted and calibrated to model the permanent deformation of the geogrid-modified subgrade. The effects of geogrids on the subgrade permanent deformation were integrated into the ME performance model through both the mechanistic response modeling and the empirical calibration. Predictions from the permanent deformation model were then compared with the second set of measurements and found to underestimate the permanent deformation of the geogrid-modified subgrade. However, the model was able to distinguish the difference in performance among the sections, i.e. the predicted rank of the performance was consistent with the measurements.

Implications of zero-stress temperature for the long-term behavior and performance of continuously reinforced concrete pavement

The excellent performance of continuously reinforced concrete pavement (CRCP) has been well known. Over the years, various efforts are underway to further improve the long-term performance of CRCP in terms of material, design, construction, and quality control. According to the national mechanistic-empirical pavement design guide (MEPDG) developed as part of NCHRP 1-37A, one of the material factors with a substantial effect on the long-term performance of CRCP is zero-stress temperature (ZST) of early-age concrete. However, there is no documented evidence that the long-term performance of CRCP depends on ZST. In this paper, implications of ZST for the long-term behavior and performance of CRCP are investigated. To accomplish this objective, a series of field evaluations was made in seven experimental CRCP sections with known material properties, design details, and early-age temperature histories, all of which are as old as 7–22years after construction. Performance indicators such as transverse crack spacing, crack width, and typical CRCP distresses, namely punchout and spalling, were evaluated and then compared with the ZST to find a possible correlation between ZST and resulting CRCP performance. Results revealed that there is no strong correlation between them, which suggests needed improvements to the current MEPDG considering more realistic CRCP behavior.

Modified dynamic modulus test and customised prediction model of asphalt-treated drainage layer materials for M-E pavement design

The past two decades have seen great developments and application of asphalt treated open-graded mixtures as a drainage layer in pavement in the United States. Nevertheless, the previous research has also indicated weak mechanical properties of the drainage layer compared with dense-graded asphalt concrete. This study sought to quantify the stiffness of the drainage layer materials for incorporating them into a mechanistic-empirical pavement design framework. It was found in this study that both the current dynamic modulus test method and the empirical prediction model, which were developed for dense-graded asphalt concrete, may not be applicable to drainage layer mixtures with high porosity. Under this circumstance, a modified dynamic modulus test and a calibrated model to predict the dynamic modulus for drainage layer materials have been developed based on the AASHTO TP 342-11 test method and the NCHRP 1-37A model in this study.

Using polymers to improve the rutting resistance of asphalt concrete

This study assesses rutting on two types of modified asphalt mixtures containing: (i) amorphous polyolefin polymer and (ii) a particular polymer obtained by combining LDPE (low density polyethylene) and EVA (ethyl-vinyl-acetate). Rutting tests were performed by a wheel tracking device. Stiffness and fatigue tests were carried out to confirm the performance of the asphalt mixtures. The testing showed that polymer modification in this study improved rut resistance without compromising the stiffness and fatigue behavior. The rutting results were fit in the NCHRP 1-37A model and the in situ rutting performance of asphalt mixtures can be predicted.

Evaluation of Witczak E* predictive models for the implementation of AASHTOWare-Pavement ME Design in the Kingdom of Saudi Arabia

AASHTOWare-Pavement ME Design is the production version of the Mechanistic-Empirical Pavement Design Guide (MEPDG) which includes two different models for hot mix asphalt (HMA) dynamic modulus (E*) prediction. The first model is the NCHRP 1-37A while the second one is the NCHRP 1-40D model. This paper focuses on the implementation of the ME Design in the Kingdom of Saudi Arabia by developing a database containing mechanical properties of a wide variety of HMA mixtures. Then checking the predictive power of both models using the developed database. Laboratory E* tests were conducted on 25 different HMA mixtures. Dynamic Shear Rheometer and Brookfield tests were also conducted on the binders. Results showed that the performance of the investigated models varied by temperature and the binder characterization method. The results also showed that the NCHRP 1-37A E* model along with Level 3 binder inputs yielded the most accurate and least biased E* estimates.

Predicted E* dynamic moduli of the Arizona mixes using asphalt binders placed over a 25-year period

The objective of this study was to predict the E* dynamic moduli of the actual field mixtures placed in Arizona using more than 25years of data at two different levels of aging: original and RTFO aged conditions. E* predictions were based upon the three global E* predictive equations: Hirsch, NCHRP 1-37A, and NCHRP 1-40D Models. A total of 2834 test sections were used for E* predictions at ten temperatures amounting to a total of 28,340 data points for each aging condition. In general, E* decreased with increasing temperature for all the three models at two aging conditions with the predicted E* for RTFO mixtures being higher than the original by at least 40%. At both aging conditions, NCHRP Models had excellent correlations (R2>0.90) with respect to the Hirsch Model with low bias and high precision. Sensitivity of the model parameters was evaluated using aggregate size, air voids, and asphalt content at three temperatures for both aging conditions. Prediction performance was assessed by comparing the predicted and measured E* on similar mixtures; best predictions were obtained by NCHRP 1-40D Model, followed by Hirsch and NCHRP 1-37A Models. It is envisioned that these E* prediction assessments are deemed to be helpful in examining the field asphalt mixtures’ aging and pavement performance, based on the MEPDG where actual E* tests are not possible/practicable to be conducted.

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.

Importance of strain level in evaluating resilient modulus of granular materials

This paper examines the importance of strain level, in addition to stresses, in evaluating the resilient modulus of unbound granular base/subbase materials used for pavement construction. A series of cyclic triaxial resilient modulus testing on selected granular materials were carried out following different loading schemes with both the applied stresses and resilient strain levels being adjusted. The experimental results clearly showed that the resilient modulus may not be uniquely determined from the bulk stress and the total deviator stress, particularly when the actual stress states were different from those in standard resilient modulus testing. Variation of resilient modulus with strain level can be described by a hyperbolic function, whereas the stress level dependence of resilient modulus can still be reasonably estimated by either Uzan's universal model or that recommended in NCHRP 1-37A.

Evaluation of fatigue and rut damage prediction methods for asphalt concrete pavements subjected to multiple axle loads

The new Mechanistic–Empirical Pavement Design Guide (MEPDG) developed under NCHRP 1-37A does away with the American Association of State Highway and Transportation Officials-derived equivalent single axle load concept and calculates damage caused by various axle configurations directly. However, the calculation of pavement damage caused by multiple axles (tandem, tridem, etc.) is significantly affected by the summation method used to describe the response of the pavement due to the passage of a given axle type or truck configuration. In this paper, the results from different methods of accounting for the passage of a given axle group are compared using laboratory fatigue and rut data from repeated cyclic load tests. The evaluation criterion for these different summation methods was the degree of agreement with the measured laboratory performance. The results show that for fatigue damage, dissipated energy and strain area methods have an excellent agreement with the laboratory values, whereas peak and peak mid-way methods have poor agreement. This implies that it is important to consider the entire strain pulse when calculating fatigue damage under multiple axles. For rutting damage, the peak strain method has the best agreement with the laboratory values, whereas dissipated energy and peak mid-way methods underestimate the rutting damage. Finally, the MEPDG procedure for calculating the strain under multiple axles considerably underestimates the damage for both fatigue and rutting. However, model calibration in the MEPDG does improve the prediction of the damage due to multiple axles for fatigue and to a lesser extent for rutting.