AASHTO Mechanistic-Empirical Pavement Design Guide Research Articles

PITTRIGID ME: Simplified Mechanistic-Empirical Design Tool for Pennsylvania Rigid Pavements Design and Analysis

AbstractTo accelerate the implementation of the AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) in Pennsylvania and take advantage of locally calibrated mechanistic-empirical models for ...

Mechanical and microstructural properties of dredged sediments treated with cement and fly ash for use as road materials

Dredged sediments excavated or removed from reservoirs are classified as waste materials. The reuse of dredged sediments as road materials is an interesting solution to environmental problems. However, only the compaction technique without treatment by admixtures is insufficient to improve the strength and stiffness of the dredged sediments for use as standard road materials. This research presents the mechanical and microstructural properties of dredged sediments treated with ordinary Portland cement (OPC) and fly ash (FA). A large amount of FA was utilised for the purpose of cost-effectiveness. The dredged sediments were mixed with 1.5%–5% OPC and 5%–20% using exclusively OPC, exclusively FA and a combination of OPC and FA. The experiments in this study consisted of unconfined compressive strength, splitting tensile strength, California bearing ratio, resilient modulus, and ultrasonic pulse velocity tests. The results showed that the mechanical properties were improved by the addition of FA and that the maximum values of these properties were observed with a combination of 5% OPC and 10% FA. In addition to evaluating the mechanical properties, microscale analyses were also conducted using scanning electron microscopy and energy-dispersive X-ray spectroscopy to understand the behaviour of the dredged sediment with respect to the OPC and FA contents. Finally, stabilised sediments were assessed for possible reuse as road materials based on the Department of Highways of Thailand standard as well as the recommendations of the AASHTO Mechanistic-Empirical Pavement Design Guide and Austroads (2017)

Preliminary Local Calibration of Performance Prediction Models in AASHTOWare Pavement ME Design for Flexible Pavement Rehabilitation in Oregon

AbstractThe Oregon Department of Transportation (ODOT) is in the process of implementing the AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) for new pavement sections. However, the vast ...

Neural networks for fatigue cracking prediction using outputs from pavement mechanistic-empirical design

ABSTRACT Fatigue cracking (FC) on asphalt pavements is one of the most critical performance indicators in the AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG). However, many recent studies have shown that the accuracy of the FC transfer function is unsatisfactory even after local calibration. This study approached this issue using the highly flexible neural networks through the deep learning framework called the Tensorflow. The data involved were extracted from the reports for the project NCHRP 01-37A, on which the calibration of the MEPDG transfer functions was based. The similarity between the FC transfer function and the neural network was discussed. A total of twelve neural network models divided into two groups were constructed. One group had only two nodes in the input layer and was used to represent the transfer function. The other contained an input layer of fourteen nodes that were used to introduce more relevant information for potentially higher predictive performance. The results indicated that, when properly designed, a neural network model could significantly outperform the FC transfer function. Comparing the FC transfer function with the network composed of an input layer of fourteen nodes and four hidden layers, the coefficient was increased from 0.008 to 0.613, while the mean absolute error dropped by 30%. Compared with the best model with an input layer of two nodes, the model with four hidden layers and fourteen nodes in the input layer increased the by 56% while decreased the mean absolute error (MAE) by 29%.

Elastoplastic framework of relationships between CBR and Young’s modulus for granular material

CBR testing, developed before the Second World War, is still the most commonly used experimental procedure for pavement design. Today, CBR tests are correlated with the resilient modulus, which is a key parameter in current mechanistic pavement design methods. However, results of these correlations have been scattered, indicating that other variables are ignored. The purpose of this study is to show how the value of the CBR test is a function of other variables such as particle size and shape, crushing, and the elastic behaviour of the material. These variables were evaluated through FEM (Finite Element Method) simulations while varying several geotechnical parameters known in practical geotechnics. These FEMs include a linear elastic model and a failure criterion (elastoplastic model) and were prepared for granular soils under drained conditions. The evaluations show that the CBR not only depends on Young’s modulus (the parameter commonly used to correlate with the CBR), but also appears to depend on compressibility due to particle crushing. This appears to be a predominant parameter that can affect the CBR in the elastic domain as much as Young’s modulus does. Finally, this paper provides insights that improve the understanding of variables leading to high or low CBR values for granular materials. In addition, FEM simulations have been compared to the AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) and other methodologies used in road geotechnics.

Local calibration of the fatigue cracking models in the Mechanistic-Empirical Pavement Design Guide for Tennessee

This study summarises the local calibration of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) for flexible pavements in Tennessee. Data retrieved from the Highway Pavement Management Application of Tennessee were utilised. The alligator cracking, wheel-path longitudinal cracking, and roughness transfer functions were calibrated. The curve fitting procedure in MATLAB was used to determine the coefficients for those transfer functions. The Jackknife method was used to validate the calibrated models. Generally, MEPDG underestimated the alligator cracking for Tennessee, but overestimated the longitudinal cracking. After the calibration, both the bias and the standard error of estimate were reduced, and the predictability of those models increased appreciably. With all distress models like alligator cracking, longitudinal cracking, and rutting models being calibrated, the accuracy of the prediction from the international roughness index model increased noticeably. Jackknife is not only an unbiased way to validate the calibrated model, but also an efficient tool to detect outliers. Due to the limits in availability of data for calibrating the alligator cracking and longitudinal cracking, it is recommended that more data from different functional classes of highway, more sections with higher level of distresses and detailed traffic information be included in the future for calibration and implementation activities.

Environmental Considerations in the AASHTO Mechanistic-Empirical Pavement Design Guide: Impacts on Performance

AbstractThis paper is a continuation of an earlier overview on the subject and presents the results of a sensitivity analysis that assessed the ability of the new design guide to portray the impact...

Calibration of Pavement Rutting Prediction in Colorado Using Layer-Specific Rutting Model Coefficients for Hot-Mix Asphalt in AASHTOWare Pavement ME Design

The Colorado Department of Transportation (DOT) recently concluded a study to adopt the AASHTO Mechanistic–Empirical Pavement Design Guide (MEPDG) and the accompanying software into its routine pavement design practice. Implementation of the MEPDG in Colorado required local calibration of the MEPDG prediction models for distress and smoothness. This paper presents a discussion of the calibration of the “global” rutting model to account for Colorado’s unique climate, traffic, and soil conditions as well as the various asphalt concrete (AC) mix types used in the state. A key challenge during the local calibration effort was to produce a rutting coefficient for each AC mix type: Marshall, Superpave®, and polymer-modified asphalt (PMA). Local calibration of the MEPDG global models by using Colorado DOT input data was performed with a nonlinear model optimization tool available in the SAS statistical software. Global model coefficients were adjusted through local calibration for AC rutting, unbound aggregate base rutting, and subgrade rutting. Four of the 10 model coefficients were adjusted. The local calibration coefficient βr1turned out to be different for each of the three primary Colorado DOT ACs. As expected, of the three asphalt mixes, the PMA had the coefficient with the lowest value for βr1, and as a result this mix had the lowest AC rutting. The goodness of fit and bias test results indicated an adequate goodness of fit with minimal bias. This local calibration effort improved the overall prediction accuracy of the rutting model and its standard error.

Calibration of National Rigid Pavement Performance Models for the Pavement Mechanistic–Empirical Design Guide

AASHTOWare Pavement ME Design, software developed from the AASHTO Mechanistic–Empirical Pavement Design Guide (MEPDG), uses performance data extracted primarily from the long-term pavement performance database to calibrate the performance prediction models for rigid pavements. In this study, factorial designs were generated as a subset of the national rigid pavement database for calibration of the MEPDG rigid pavement performance models. Three separate factorial designs were included for each rigid pavement performance model for (a) jointed plain concrete pavement (JPCP) transverse cracking, (b) JPCP faulting (doweled and undoweled), and (c) continuously reinforced concrete pavement punchouts. Experimental design variables for each model were selected to provide the broadest possible representation of key design, construction, and environmental features. The three performance models were then calibrated with the developed factorial matrices. The results were presented along with the calibration procedure. A validation of the calibrated models was then conducted with sites not included in the factorial matrices. An evaluation compared design slab thicknesses required to meet specified performance criteria determined with these and other previously established calibration coefficients. Finally, conclusions and recommendations based on the experiences gained conducting this study are provided.

Alternative Source of Climate Data for Mechanistic–Empirical Pavement Performance Prediction

The AASHTO Mechanistic–Empirical Pavement Design Guide (MEPDG) and accompanying Pavement ME Design software emphasize the influence of climate on pavement performance. Several hundred weather history files from ground-based operating weather stations (OWSs) are provided with the MEPDG software. An alternate climate data source, the Modern Era Retrospective–Analysis for Research and Application (MERRA) product from the National Aeronautics and Space Administration, is evaluated here. MERRA is a global climate reanalysis procedure that combines computed model fields with ocean-, airborne-, and satellite-based observations. MERRA has the advantage of uniform worldwide spatial coverage at an approximately 0.5° by 0.67° (latitude and longitude) horizontal resolution, continuous hourly time series from 1979 to the present, and a high level of data quality control. Comprehensive statistical comparisons between MERRA climate data and those from various conventional ground-based sources are presented. Comparisons of MEPDG performance predictions with MEPDG OWS and MERRA climate data for 20 locations distributed across the contiguous United States are also summarized. The statistical and performance prediction comparisons support the conclusion that MERRA is an acceptable and advantageous source for climate data that can be used in place of conventional ground-based OWSs.

State Design Procedure for Rigid Pavements Based on the AASHTO Mechanistic–Empirical Pavement Design Guide

This paper describes research efforts sponsored by the Minnesota Department of Transportation (DOT) to update its rigid design procedure to take advantage of locally calibrated mechanistic–empirical models for performance and consequently design. The new procedure, MnPCC-ME, is based on the AASHTO Mechanistic–Empirical Pavement Design Guide (MEPDG) Version 1.1, but restricts the user to predetermined design input parameters. MnPCC-ME matches the MEPDG predicted performance at a fraction of the computation cost. The user is able to modify key design inputs for state and local pavement projects; the parameters include district climate files and traffic load spectra files (based on historical Minnesota DOT weigh-in-motion data). In addition, MnPCC-ME provides alternative reliability analysis, which is based on a Monte Carlo simulation of pavement performance predictions based on typical variability in slab thickness and flexural strength for Minnesota pavements. The MnPCC-ME program requires virtually no run time and outputs a project report with the recommended design thickness for given performance criteria and reliability level for the specified reliability analysis. Although this tool was developed for Minnesota local conditions, the approach developed in the study can be adopted by other transportation agencies for their region and conditions.

Dynamic Modulus of Western Australia Asphalt Wearing Course

In the new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG), the dynamic modulus |E*| test has been selected to assess the performance of asphalt concretes. The type of test, which relates asphalt mixtures modulus to temperature and time rate of loading, is never used in Western Australia. This paper presents a study on the dynamic modulus of typical Western Australia asphalt mixtures. Five mixtures with 10mm nominal sizes and two types of bitumen classes, i.e. C170 (Pen 60/80) and C320 (Pen 40/60) comply with Main Road Western Australia (MRWA) Specification were used in the research. Mixing and compacting process were carried out according to Austroads methods. The specimens were compacted using a gyratory compactor to achieve 5±0.5% target air void. Testing was performed at four temperatures (4, 20, 40 and 55OC) and six frequencies (25, 10, 5, 1, 0.5, 0.1 and 0.05 Hz). Dynamic modulus and phase angle master curves were generated from the results. The master curves were compared to the curves from Witczak’s predictive equation. From this preliminary study, it was found that the measured values correlated well with the predictive equation except at high temperatures or low frequencies.

Evaluation of AASHTO Mechanistic-Empirical Pavement Design Guide for Designing Rigid Pavements in Louisiana

This paper presents a recent study on using AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) design software (Pavement ME^(TM)) to evaluate the performance of typical Louisiana rigid pavement structures as compared to the existing pavement performance data available in the pavement management system (PMS). In total, 19 projects with two pavement structure types, Portland cement concrete (PCC) over unbound base and PCC over asphalt mixture blanket, were analyzed. Results show that the national model over-predicts transverse cracking and under-predicts joint faulting. Therefore, a preliminary calibration was conducted to adjust Pavement ME for Louisiana's condition. In addition to comparing the measured and predicted performance, the recommended thickness from the current and the new design methods was also compared. It was found that the two design methods are comparable with an average difference of 2 cm or 7 percent (Pavement ME requires a thinner pavement). At the end of this paper, problems, challenges and possible solutions for fully implementing the new design method are discussed.

Sensitivity analysis frameworks for mechanistic-empirical pavement design of continuously reinforced concrete pavements

The new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) performance predictions for the anticipated climatic and traffic conditions depend on the values of the numerous input parameters that characterize the pavement materials, layers, design features, and condition. This paper proposes comprehensive local sensitivity analyses (LSA) and global sensitivity analyses (GSA) methodologies to evaluate continuously reinforced concrete pavement (CRCP) performance predictions with MEPDG inputs under various climatic and traffic conditions. A design limit normalized sensitivity index (NSI) was implemented in both LSA and GSA to capture quantitative as well as qualitative sensitivity information. The GSA varied all inputs simultaneously across the entire problem domain while the LSA varied each input independently in turn. Correlations among MEPDG inputs were considered where appropriate in GSA. Two response surface modeling (RSM) approaches, multivariate linear regressions (MVLR) and artificial neural networks (ANN or NN), were developed to model the GSA results for evaluation of MEPDG CRCP input sensitivities across the entire problem domain. The ANN-based RSMs not only provide robust and accurate representations of the complex relationships between MEPDG inputs and distress outputs but also capture the variation of sensitivities across the problem domain. The NSI proposed in LSA and GSA provides practical interpretation of sensitivity relating a given percentage change in a MEPDG input to the corresponding percentage change in predicted distress relative to its design limit value. The “mean plus/minus two standard deviations (μ+2σ)” GSA-NSI metric (GSA-NSIμ±2σ) derived from ANN RSM statistics is the best and most robust design input ranking measure since it incorporates both the mean sensitivity and the variability of sensitivity across the problem domain.

Global Sensitivity Analysis of Jointed Plain Concrete Pavement Mechanistic–Empirical Performance Predictions

The new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) provides pavement analysis and performance predictions for various what-if scenarios. MEPDG performance predictions for anticipated climatic and traffic conditions will depend on the values of the input parameters that characterize pavement materials, layers, design features, and condition. A comprehensive global sensitivity analysis methodology is proposed for evaluating performance predictions for jointed plain concrete pavement to MEPDG inputs for five climatic conditions and three traffic levels. MEPDG inputs evaluated in the analysis include traffic volume, layer thicknesses, material properties, groundwater depth, and geometric parameters. Correlations between MEPDG 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 x three traffic levels). Two response surface modeling approaches, multivariate linear regressions and artificial neural networks, were developed for evaluation of MEPDG input sensitivities across the problem domain. The response surface modeling approaches based on artificial neural networks not only provided robust and accurate representations of the complex relationships between MEPDG inputs and distress outputs but also captured the variation in sensitivity across the problem domain. The normalized sensitivity index for the design limit proposed in the study provides practical interpretation of sensitivity by relating a given percentage change in an MEPDG design input to the corresponding percentage change in predicted distress relative to its design limit value.

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.

Sensitivity analysis of a new dynamic modulus (| E*|) model for asphalt mixtures

Asphalt pavement designers have been using the dynamic modulus (| E*|) as a fundamental parameter to reflect the hot-mix asphalt (HMA) performance, and since | E*| is considered as an essential input in the new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG), many models were developed for evaluating and/or predicting this property for HMA. This article focuses on the verification of a newly developed model to predict | E*| by utilising robust sensitivity analyses through tornado plots and extreme tail analyses, which are used to identify the relative importance of the input variables on the predicted | E*|. Results showed that the binder dynamic shear modulus (| G*|) has the most significant effect, followed by the air void content, on the predicted | E*|, and both properties contributed largely to the extreme predicted values.

Impact of Environmental Site Location and Groundwater Table Depth on Thickness of Flexible Airfield Pavements

This comprehensive sensitivity study of a newly developed computer code at Arizona State University incorporates the influence of environmental site factors and the groundwater table depth on flexible airfield pavement design and performance. The newly developed code, called ZAPRAM, is a mechanistically based pavement model based on limiting strain criteria. The completed study used the newly revised U.S. Army Corps of Engineers failure criteria for subgrade shear deformation. The experimental factorial conducted comprised three aircraft types, three levels of as-compacted (optimum) subgrade condition, two levels of design aircraft passes, and an environmental combination of six groundwater table depths and five environmentally different city locations. The methodology used in this study applied the same analytical method to achieve real-time environmental effects on unbound layer modulus as the new AASHTO Mechanistic–Empirical Pavement Design Guide. The results of this effort showed for the first time the quantitative impact of the significant effects of climatic conditions at the design site, coupled with the importance of the depth of the groundwater table, on the predicted design thicknesses. Significant cost savings appear to be quite reasonable by using the principles of unsaturated soil mechanics in the new airfield pavement design procedure in ZAPRAM.

Development and evaluation of hot mix asphalt stability index

This paper addresses the development and evaluation of a mix stability index referred to as the gyratory stability (GS). GS is calculated from the compaction data by summing the cumulative energies dissipated in the compaction of a gyratory sample. A wide range of commonly used mixes in the State of Idaho were selected for evaluation. Mixes were tested for dynamic modulus (E*) and flow number (F N); rutting was measured with the asphalt pavement analyser (APA). Furthermore, E* test results were used in the AASHTO Mechanistic–Empirical Pavement Design Guide (MEPDG) as level 1 inputs, to predict rutting for these mixes; GS ranked mixes similarly to the APA and F N tests and MEPDG results. Furthermore, results indicated that the GS has the potential to be used as a screening tool for asphalt mix design, especially to decide upon the acceptance of the mix aggregate structure, and as a quality control tool.

Variations of Resilient Modulus of Subgrade Soils with Postcompaction Moisture Contents

Environmental conditions such as temperature and moisture can significantly influence the performance of a pavement structure. These conditions vary within the pavement layers, degrading or enhancing the properties of pavement materials. The AASHTO 1993 design guide accounts for this influence by incorporating a single resilient modulus ( MR) value that represents the effective roadbed resilient modulus; the revised AASHTO Mechanistic–Empirical Pavement Design Guide (MEPDG) uses models that predict changes in modulus due to moisture, temperature, or both. The effect of changes in postcompaction moisture content (i.e., wetting or drying) on the resilient moduli of subgrade soils is evaluated. The effects of wetting and drying were examined by conducting resilient modulus tests on specimens compacted at various initial moisture contents: optimum moisture content (OMC), 4% drier than OMC (OMC – 4%), and 4% wetter than OMC (OMC + 4%). Results showed that MR–moisture content ( MRMC) relationships caused by drying exhibited higher values than the corresponding MRMC curves for specimens subject to wetting. The most significant finding was that changes in MR values depended on the initial compaction moisture contents; the MR values compacted at OMC exhibited a different drying and wetting trend than the ones compacted at OMC ± 4%. Finally, the MEPDG MR–moisture model was modified in this study to better predict the variations of resilient modulus with moisture changes.