Mechanistic-Empirical Pavement Design Guide Model Research Articles

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.

Application of Monte Carlo filtering method in regional sensitivity analysis of AASHTOWare Pavement ME design

Abstract Since AASHTO released the Mechanistic-Empirical Pavement Design Guide (MEPDG) for public review in 2004, many highway research agencies have performed sensitivity analyses using the prototype MEPDG design software. The information provided by the sensitivity analysis is essential for design engineers to better understand the MEPDG design models and to identify important input parameters for pavement design. In literature, different studies have been carried out based on either local or global sensitivity analysis methods, and sensitivity indices have been proposed for ranking the importance of the input parameters. In this paper, a regional sensitivity analysis method, Monte Carlo filtering (MCF), is presented. The MCF method maintains many advantages of the global sensitivity analysis, while focusing on the regional sensitivity of the MEPDG model near the design criteria rather than the entire problem domain. It is shown that the information obtained from the MCF method is more helpful and accurate in guiding design engineers in pavement design practices. To demonstrate the proposed regional sensitivity method, a typical three-layer flexible pavement structure was analyzed at input level 3. A detailed procedure to generate Monte Carlo runs using the AASHTOWare Pavement ME Design software was provided. The results in the example show that the sensitivity ranking of the input parameters in this study reasonably matches with that in a previous study under a global sensitivity analysis. Based on the analysis results, the strengths, practical issues, and applications of the MCF method were further discussed.

Development of regression equations for local calibration of rutting and IRI as predicted by the MEPDG models for flexible pavements using Ontario's long-term PMS data

Local calibration is an important step before a transportation agency adopts the American Association of State Highway and Transportation Officials' (AASHTO) mechanistic-empirical pavement design guide (MEPDG). This paper presents the challenges of and findings from the local calibration of flexible pavements in provincial highways under the jurisdiction of the Ministry of Transportation of Ontario (MTO). A calibration database was developed that involved a hierarchical framework of the input parameters required for AASHTOWare Pavement ME (the MEPDG software) and the historical field performance data based on the MTO's second-generation pavement management system. A regression analysis is carried out for preliminary calibration of rutting and international roughness index (IRI) models by comparing the predicted distress to observed distress. The analysis suggested that whereas the MEPDG provided fairly unbiased prediction of the IRI value, it often over-predicted the total rutting. Calibrated predicted IRI and rut depth are found for Ontario's local conditions from MEPDG distress prediction models. A further clustering analysis based on Functional Class and geographical zone for the rutting and IRI, respectively, improved the precision of the locally calibrated models.

Soil Resilient Modulus Regressed from Physical Properties and Influence of Seasonal Variation on Asphalt Pavement Performance

Subgrade soil, as the critical underlying support for other pavement layers and traffic loads, should be stiff enough to maintain the integrity of pavement structures and the smoothness of pavement surface. The resilient modulus, as an indicator of subgrade stiffness, is an essential input in the American Association of State Highway and Transportation Officials (AASHTO) Mechanistic-Empirical Pavement Design Guide (MEPDG). At input level 1 of MEPDG, the MEPDG generalized model is required to describe resilient modulus of subgrade soil, and the coefficients of this model are used for pavement design. The change of the resilient modulus model has raised the interest of many state highway agencies and made it necessary to convert old resilient modulus test data into new ones required for the MEPDG model. In this study, the coefficients of the generalized and the universal models for soil resilient modulus were obtained through regression of the results of 13 soils in Tennessee. The coefficients of the two models were also compared. There is a potential risk that the coefficients from the universal model may be mistakenly used in the MEPDG instead of those coefficients from the generalized model. The consequence of this improper use was demonstrated in the comparison between the miscalculated and the real resilient moduli. The coefficients of the generalized model were correlated to soil physical properties, which provided an alternate time-saving and economical method to obtain soil resilient modulus as level 2 inputs. The coefficients were obtained at different post-compaction water contents, to allow the estimation of pavement response under seasonal moisture variation of subgrade. Rutting and roughness of two typical pavement sections were analyzed to investigate the influence of the seasonal variation of soil resilient modulus on pavement performance. The results showed that moisture variation had a significant effect on subgrade resilient modulus and, subsequently, on pavement performance. It is recommended that seasonal change in soil resilient modulus be considered in the analysis on pavement performance.

Parametric Analysis of Resilient Modulus Modeling for Recycled Asphalt Pavement in Base Layer

The resilient modulus (MR) has been found to be the most important parameter in the Mechanistic–Empirical Pavement Design Guide (MEPDG) for the base layer. A literature review showed that the use of recycled asphalt pavement (RAP) in the base layer had many economic and structural benefits. The prediction of MR with the specific MEPDG model for the base layer mixed with RAP was found to be reliable for several field conditions. However, further studies of the MEPDG model are needed for an understanding of the physical meaning of each parameter in the model. The most important factor affecting MR for the base layer is the state of stresses, especially the confining pressure. This type of state of stresses was found in previous research to be more effective than deviator stresses. The present study focuses on the parametric analysis of each constant in the model versus various confining pressure states under various field conditions and at various RAP concentrations. This parametric analysis is compared with the traditional granular coarse aggregates used for the base layer before RAP is used so that any difference in the physical meaning when RAP is mixed in the base layer blend can be determined. The prediction of MR by the MEPDG model appears to be sound for RAP, but only with confining pressure levels below 10 psi and without significant variation in water content and maximum dry density. Freeze–thaw cycles do not negatively influence the prediction of MR for both RAP and granular coarse aggregates.

International Roughness Index Prediction for Rigid Pavements: An Artificial Neural Network Application

nternational Roughness Index (IRI) is an important parameter that indicates the ride quality and pavement condition. In this study, an Artificial Neural Network (ANN) model was developed to predict the IRI for Jointed Plain Concrete Pavement (JPCP) sections. The inputs for this model are: initial IRI value, pavement age, transverse cracking, percent joints spalled, flexible and rigid patching areas, total joint faulting, freezing index, and percent subgrade passing No. 200 U.S. sieve. This data was obtained from the Long Term Pavement Performance (LTPP) Program. It is the same data and inputs used for the development of the Mechanistic-Empirical pavement Design Guide (MEPDG) IRI model for JPCP. The data includes a total of 184 IRI measurements. The results of this study shows that using the same input variables, the ANN model yielded a higher prediction accuracy (coeficint of determination: R2= 0.828, and ratio of standard error of estimate (predicted) to standard deviation of the measured IRI values: Se/Sy=0.414) compared to the MEPDG model (R2= 0.584, Se/Sy=0.643). In addition, the bias in the predicted IRI values using the ANN model was significantly lower compared to the MEPDG regression model.

Validating MEPDG with Tennessee Pavement Performance Data

To implement the Mechanistic-Empirical Pavement Design Guide (MEPDG) developed by AASHTO for pavement construction and rehabilitation, it is necessary to evaluate its performance prediction models using actual pavement performance data, material properties, traffic information, and environmental factors. This paper validated the MEPDG models using the performance of typical pavements in the state of Tennessee from pavement management system (PMS). With traffic and pavement structural information collected from PMS, the performance of selected highway pavement sections was analyzed with MEPDG Version 1.100 software. The predicted present serviceability index (PSI) and rutting were compared with the actual measurement values. An initial value for the international roughness index (IRI) was suggested for Tennessee highways through investigation into the actual roughness data. The dynamic moduli of asphalt mixtures for input level 1 were obtained from laboratory testing. Those for input level 3 were estimated with the Witczak model. The results show that rutting of asphalt concrete (AC) pavements was more accurately predicted at input level 1, whereas it was overpredicted at input level 3. Traffic level was found to be an important factor affecting predicted pavement roughness. It was also found that MEPDG software was relatively conservative for highway pavements with low traffic level. However, MEPDG with nationally averaged default parameters was not sensitive enough to differentiate various climates, traffic, and materials in Tennessee for the prediction of PSI.

Investigation and Modification of Available Mechanistic–Empirical Procedures for Reflective Cracking in Asphalt Overlays of Concrete Pavements

In recent decades, state, local, and federal use of asphalt concrete overlays to rehabilitate portland cement concrete pavements has increased significantly. This growth has increased the attention given to mechanistic– empirical modeling of reflective cracking. This paper describes recent research that assessed and modified the state of the art in mechanistic– empirical modeling of reflective cracking. The research reviewed the original Mechanistic–Empirical Pavement Design Guide model and the CalME reflective cracking model. The paper also details research in which the CalME model was modified to facilitate its integration into the design guide for pavement projects of composite asphalt concrete over portland cement concrete.

Evaluation of zero-stress temperature prediction model for Portland cement concrete pavements

The current mechanistic-empirical pavement design guide (MEPDG) postulates that zero-stress temperature (ZST) has substantial effects on the behavior and performance of Portland cement concrete (PCC) pavements. To date, the only available ZST prediction model for PCC pavements is the one proposed by the MEPDG, which was quite simplified with a function of only two input variables: cementitious content and mean monthly temperature for the month of construction. In this study, the feasibility of the current ZST prediction model in the MEPDG was assessed by comparing the ZSTs evaluated from a series of field tests with the ones estimated from the MEPDG model. In order to find ZSTs in actual PCC pavements, accurate evaluations of early-age concrete stress developments were essential. To achieve this aim, non-stress cylinders (NCs), which directly measure stress-independent strain in the field concrete structures, were employed. Once the stress-dependent strain history was estimated based on the measured total and stress-independent strain histories, it was converted to the stress history using a step-by-step numerical method considering creep and modulus developments of concrete. The results of this study showed that about 4.5–7.7°C of differences existed between the measured and predicted ZST values. This finding strongly suggests a need for development of a more advanced ZST prediction model with reasonable assumptions and diverse parameters.

Simulation of unbound material resilient modulus effects on mechanistic-empirical pavement designs

The variability of resilient modulus (M R) of unbound materials and subgrade due to laboratory test conditions affect pavement performance and designs. The performance-based mechanistic-empirical pavement design guide (MEPDG) is gaining more popularity in recent years for pavement design use. However, limited research efforts have quantitatively studied M R effects based on ME models. This research targets to evaluate the influences of M R variability on pavement performance and designs based on the MEPDG performance models. With a normal-distribution of M R seed values, pavement responses were computed with a layer-elastic analysis model, pavement performance was then predicted using MEPDG models, and design variability was studied via Monte Carlo simulation. Results indicate that the relationship between layer design thickness and M R varies from almost linear to nonlinear, which is highly dependent on the pavement structure and material properties. For the evaluated specific pavement structure and range of M R values, the least susceptible is the HMA design thickness as a function of M R under fatigue with a design Coefficient of Variance (CV) of 7.51 %, while the most susceptible is the base design thickness as a function of M R also under fatigue with a CV of 54.32 %. The combined effect of both rut depth and fatigue life considering the variability of both base and subgrade results in a design CV of 22.58 % for asphalt layer and 26.08 % for base layer. When using a weaker base layer or a thinner HMA layer, the modeled thickness design CV has changed −4.19 to 1.14 %.

Mechanistic–empirical damage analysis and impacts of reduced tire pressure on thaw weakened flexible pavements using the AI and MEPDG models

A damage analysis was conducted on a spring weight restricted flexible pavement to quantify the effects of reduced tire pressure on pavement life and to compare the damage predictions from the Asphalt Institute (AI) and the Mechanistic Empirical Pavement Design Guide (MEPDG) models. The models were used to predict the number of repetitions to fatigue and rutting failure at three maximum loads and at high and low tire pressures. Based on the results, the AI and MEPDG predictions were statistically different for both fatigue cracking and rutting damage, based on the t-test at 95% confidence limits. The AI model predicted 31% lower fatigue damage than the MEPDG, but 56% higher rutting damage. However, both models produced similar trends in predicting the relative effects of reduced tire pressure and load levels on pavement life. The methodology and results of the analysis are presented in this paper.

Evaluation of the Mechanistic–Empirical Pavement Design Guide model for permanent deformations in asphalt concrete

The model for permanent deformation in asphalt concrete (AC) layers used in the Mechanistic–Empirical Pavement Design Guide (MEPDG) was evaluated. Two instrumented flexible pavements were subjected to heavy vehicle simulator testing at three constant temperatures. Permanent deformation modelling was carried out using the MEPDG software v1.003 at the highest accuracy level. The nationally calibrated model resulted in underprediction by 35–45%. Model results were successfully calibrated to fit observation using field calibration factors that increased temperature susceptibility by 45–50% and decreased the dependency of the number of loadings by 30–40%. The field calibration factors should be confirmed by studying existing pavements before being employed. The model did not assess the permanent deformation contribution of each AC layer correctly. Therefore, the model should be refined with further trench studies using in-service pavements.

Development of Traffic Inputs for Mechanistic–Empirical Pavement Design Guide in Michigan

Characterizing traffic and developing accurate and desirable traffic inputs for the new Mechanistic–Empirical Pavement Design Guide (MEPDG) are critical but challenging activities. The purpose of this study was to develop a process for characterizing traffic inputs in support of the new MEPDG for the state of Michigan. These traffic characteristics include monthly distribution factors, hourly distribution factors, truck traffic classifications, axle groups per vehicle, and axle load distributions for different axle configurations. Axle weight and vehicle classification data were obtained from 44 weigh-in-motion and classification stations located throughout the state of Michigan to develop Level 1 (site-specific) traffic inputs. Cluster analyses were conducted to group sites with similar characteristics for development of Level 2 (regional) inputs. Finally, data from all sites were averaged to establish the statewide Level 3 inputs. The effects of the developed hierarchical traffic inputs on the predicted performance of rigid pavements were investigated with the MEPDG models. An algorithm based on discriminant analysis was developed to acquire the appropriate Level 2 traffic characteristic inputs for pavement design. For pavement analysis and design, it is recognized that site-specific data should be used wherever available. For projects in which site-specific data are not available, it is necessary to know whether Level 2 or Level 3 data are acceptable at a minimum for design. The MEPDG was used to investigate the impact of traffic input levels on predicted pavement performance for rigid pavements. The results of the analysis showed that for pavement design in Michigan, statewide averages should be used instead of MEPDG Level 3 data.

Effects of Asphalt Mixture Properties on Permanent Deformation Response

The hot-mix asphalt (HMA) rutting prediction model in the Mechanistic-Empirical Pavement Design Guide (MEPDG) uses a relationship that includes the effects of mix characteristics only through the resilient strain, which in turn is a function of the dynamic modulus (|E*|) of the mix. However, increasing evidence suggests that the use of |E*| alone may be insufficient to characterize completely the permanent deformation behavior of HMA. In addition to effects already considered by the MEPDG model with |E*|, the effects of mix characteristics on permanent deformation are analyzed with the use of the results of repetitive axial permanent deformation tests from laboratory-compacted HMA specimens. Results of multiple linear regression analysis indicate that binder type, effective binder content, and air void content have significant effects on model parameters for permanent deformation. The potential effects of mix characteristics on these parameters are analyzed with the use of the MEPDG model and an HMA pavement section with four levels of compaction. Scenarios in which the mixture characteristics are incorporated solely by means of |E*| are compared with scenarios in which the effects of air void content and asphalt content are incorporated into the rutting prediction model by adjusting its parameters according to relationships established in the laboratory. Empirical laboratory evidence supports the hypotheses that, regardless of mixture properties, universal values for permanent deformation model parameters do not fully account for mixture-specific contributions to rutting and that other mix characteristics (e.g., air void content) may be needed to supplement |E*| for the appropriate characterization of the permanent deformation of asphalt mixtures.

Calibration of Distress Models from the Mechanistic–Empirical Pavement Design Guide for Rigid Pavement Design in Argentina

This paper presents the main results of a calibration of distress models from the Mechanistic–Empirical Pavement Design Guide (MEPDG) to local conditions in Argentina to be used in rigid pavement structural design. Test sections with rigid pavements were selected to cover a wide range of climatic conditions in the central region of the country. Local road agencies provided information about pavement structure, traffic volume, and load spectra. Field distress data and international roughness index (IRI) values were collected and processed with MEPDG software; calibration factors were determined for the different distress models of rigid pavements. The accuracy of distress prediction was significantly improved if calibration factors that considered additional influence of climatic conditions, soil and base type, slab length, and pavement age were incorporated into the transverse joint-faulting and transverse-cracking models. Through the use of calibrated factors instead of default values provided in MEPDG models, the errors in distress predictions were reduced by more than one-half in all cases. Construction procedures seemed to influence the IRI roughness prediction. Better calibration results were obtained if different after-construction IRI values were considered for pavements constructed before and after the 1990s to reflect the difference between older and newer constructive techniques.

A Fracture Energy Approach to Model the Thermal Cracking Performance of Asphalt Rubber Mixtures

ABSTRACT The existing Thermal Cracking Model (TCMODEL) that is currently an integral part of the Mechanistic Empirical Pavement Design Guide (MEPDG) has been shown to adequately predict low temperature cracking of asphalt concrete mixtures utilizing conventional binders. However, test results from several asphalt rubber mixtures in this research study showed that the TCMODEL in the MEPDG falls short in predicting the observed thermal cracking resistance of these mixtures in the field. This paper presents development and findings of a new method for thermal cracking potential evaluation in asphalt mixtures, with a focus on asphalt rubber mixtures. Refinements of the existing IDT test protocol are presented, and a new Crack Depth Fracture Model is proposed to include a fracture energy parameter. The new fracture energy model is evaluated with laboratory test results and rationality corresponding with field observations. The new model proved to be satisfactory in predicting the thermal cracking performance for both conventional and asphalt rubber mixtures. Its use in future MEPDG models is recommended.

NCDOT Quality Control Methods for Weigh-in-Motion Data

At hundreds of weigh-in-motion (WIM) stations, state departments of transportation (DOTs) collect WIM data 24/7 to meet federal traffic reporting requirements. The North Carolina Department of Transportation (NCDOT) collects WIM data using procedures consistent with recommended industry practices to estimate static vehicle axle weights based on dynamic traffic measurements. Data errors and poor quality data are captured regardless of the technology used, and a quality control (QC) process is an important part of all WIM data systems. WIM data must undergo a series of sequential and well-defined QC procedures to ensure that the data meet the federal requirements and new standards for the Mechanistic Empirical Pavement Design Guide (MEPDG) process. This article documents the NCDOT WIM QC procedures. The results of the QC analysis provide reliable data sets for use in developing Levels 1, 2, and 3 traffic data inputs for the North Carolina MEPDG models.

Enhanced Model for Resilient Response of Soils Resulting from Seasonal Changes as Implemented in Mechanistic–Empirical Pavement Design Guide

The present study deals with the revision of the current model in the Mechanistic–Empirical Pavement Design Guide (MEPDG) used to predict the environmental factor for unfrozen unbound materials (FU), which is used to adjust the resilient response of soils resulting from seasonal changes. A large database with data from the existing literature and studies at Arizona State University was developed to evaluate the model. The results suggest that the environmental factor is underestimated for fine-grained materials with high plasticity under dry (arid) conditions. However, insufficient data were available to enhance the FU models for wetter conditions. Three fundamental factors that may have impacts on the FU values were evaluated in this study: stress state, compaction energy (soil density), and soil type. The stress state was found to have little to no impact on the predictions of FU. But density changes and soil type were found to be important. The potential for soil index properties to be predictive variables was assessed. Models dependent on enhanced moisture content accounting for the effect of soil type are proposed for nonplastic and plastic materials. The range of predicted FU values is in close agreement with the actual measured FU values found from laboratory studies. It is recommended that the new models be adopted in the revision of the MEPDG model for the drier conditions described in this report and that research be conducted to enhance the FU approach in the current MEPDG for wetter conditions brought on for a variety of reasons (e.g., groundwater table change, increased rainfall, and frost effects).

Local Calibration of Mechanistic–Empirical Pavement Design Guide Rutting Model

The AASHTO interim Mechanistic–Empirical Pavement Design Guide (MEPDG) was recently introduced in the United States. Many state agencies have conducted validation and local calibration of the MEPDG performance prediction models. In this study, time history rutting performance data for pavement sections at the Minnesota Department of Transportation full-scale pavement research facility [Minnesota Road Research Project (MnROAD)] have been used for an evaluation and local calibration of the MEPDG rutting model. A detailed comparison of the predicted total rutting, asphalt layer rutting, and measured rutting is presented. The reason that a conventional MEPDG model calibration was not feasible is discussed. Also, a modification of the rutting model is recommended. It was found that the locally calibrated model greatly improved the MEPDG rutting prediction for various pavement designs in MnROAD conditions.

Pavement Smoothness Prediction Based on Fuzzy and Gray Theories

Abstract: Pavement smoothness has been recognized as one of the measures of pavement performance. In the Mechanistic-Empirical Pavement Design Guide (MEPDG), pavement smoothness indicated by the International Roughness Index (IRI) was predicted based on various distresses using traditional regression analysis approaches. Recognizing the limitations of linear regression method, a Gray Theory-based technique was previously proposed by the authors for the development of pavement smoothness prediction models. In this article, instead of using the conventional least squares method to determine the coefficients for gray prediction models, fuzzy regression method is proposed to solve this gray problem. With pavement IRI and distresses data exported from the Long Term Pavement Performance (LTPP) database, Fuzzy and Gray Model (FGM)-based smoothness predictions are established using influencing factors similar to those in MEPDG. Based on the comparisons among results originated from MEPDG models, conventional GM models, FGM models, and actual LTPP data, it is shown that the Gray Theory-based prediction methods with fuzzy regression for estimating model coefficients provide promising results and are useful for modeling pavement performance.