ME Design Software Research Articles

Approaches for local calibration of mechanistic-empirical pavement design guide joint faulting model: a case study of Ontario

ABSTRACT The Mechanistic-Empirical Pavement Design Guide (MEPDG) has been employed by agencies as an innovative method for pavement design since the National Cooperative Highway Research Program (NCHRP) Project 1-37A was implemented in 2004. Over the years, the MEPDG has evolved into the AASHTOWare Pavement ME Design software (AASHTOWare®). Local calibration of the performance models in the AASHTOWare® is a crucial and challenging task to improve the effectiveness of its application. The accuracy of the calibration depends on efficient methods and validation processes. This paper aims at developing local calibration methods for joint faulting prediction model of Jointed Plain Concrete Pavement (JPCP). This study not only focuses on improving the prediction accuracy of the joint faulting model but also demonstrating the various optimisation procedures in detail. A total of 27 representative JPCP sections were used in the processes of calibration. Three optimisation approaches were used: (1) One-At-a-Time (OAT) through the trial-and-error procedure, (2) generalised reduced gradient (GRG) using MS Excel® Solver, and (3) Levenberg-Marquardt Algorithm (LMA) fitting the functions. The prediction accuracy of local models was improved as compared with the global ones. Average Bias (AB) reduced from 0.3083 to 0.0578, and Standard Error of the Estimate (SEE) reduced from 0.3345 to 0.1912. Among the three local calibration approaches, approach 2 and approach 3 had more significant improvement on results than approach 1. Finally, the integral procedures were provided for local calibration in Ontario.

Quantifying the impact of coefficient of thermal expansion of overlay concrete on unbonded concrete overlay performance

With the advancement in pavement design and performance analysis procedures, the coefficient of thermal expansion (CTE) of concrete has emerged as a significant design input with a direct impact on concrete pavement performance parameters including transverse cracking, joint faulting, and pavement roughness. CTE is the measure of change in concrete volume with temperature change and the resulting curling of concrete pavement slab is directly proportional to CTE. Un-Bonded Concrete Overlay (UBCO) is a cost-effective and sustainable rehabilitation technique on Jointed Plain Concrete Pavements (JPCP) to improve the performance of deteriorated concrete pavements. This study examines the effects of variability of CTE on the performance of unbonded JPCP overlays for two different climatic regions. Simulations were conducted using AASHTO pavement ME design software with varying CTE values in the range of 6.8–10.8 micro-strain/°C and keeping all other design variables as constant. The performance predictions were evaluated for different values of CTE and the results indicated that with an increase in CTE value, the performance of UBCO is adversely affected by the increase in pavement distresses. Amongst all the performance parameters, transverse cracking is the most significantly affected parameter with the change in CTE. The impact of geometric properties of overlay pavement including transverse joint spacing and slab thickness on the pavement performance was also analyzed which indicated that these have a direct impact on the performance parameters. The overlay performance can be improved by increased overlay slab thickness or reduced joint spacing and with these modifications, the adverse effects of higher CTE can be compensated. Field performance data of UBCO extracted from the LTPP database showed that the pavement ME design software can accurately predict the performance of UBCO pavement systems.

Effects of existing concrete pavement condition on performance of unbonded jointed plain concrete overlay

Abstract Unbonded concrete overlay (UBCO) is the most used pavement rehabilitation technique across United States with an overall usage of 47% amongst all the rehabilitation methods. It is aimed to improve the performance of any deteriorated/cracked jointed plain concrete pavement (JPCP). Unbonded JPCP overlay involves placement of a separation layer of hot mix asphalt (HMA), which acts as a stress relief layer between the existing concrete pavement and the overlay. There are numerous factors that affect the design and performance of UBCOs and out of these, existing pavement condition or the severity of damage of existing pavement is a prime factor. The severity of damage is described by the distressed elastic modulus thus, accurate determination of the distressed elastic modulus of the existing concrete pavement is essential for predicting the accurate performance of the unbonded overlay. This study focuses on analyzing the impact of distressed modulus by conducting simulations in the AASHTOWare pavement ME design software version 2.3 and evaluating the predicted performance of JPCP overlay for two different climatic regions. The results indicated that the distressed modulus of existing concrete pavement affects the performance of the overlay with regards to transverse cracking, joint faulting and pavement roughness. Transverse cracking is the most affected performance parameter with a change of 0.27%–2.31% with a unit change in distressed modulus. The impact of climatic conditions on the performance of unbonded overlay was also observed. The adverse effects of distressed modulus can be minimized by reducing the joint spacing or increasing the overlay slab thickness.

Spatial variability of compacted aggregate bases

Abstract This paper evaluates the construction quality of aggregate base course layers in terms of the variability and uniformity in density and modulus based measurements. Field and laboratory tests were conducted on 10 aggregate base course layers during HMA pavement construction in Wisconsin. Field tests consisted of in-place unit weight, LWD, DCP, and Stiffness Gauge measurement while laboratory tests included particle size analysis, compaction test, and repeated load triaxial test. Moreover, sensitivity analysis was conducted to assess the influence of the base course layer input parameters on the long-term performance of HMA pavements using the AASHTOWare Pavement ME Design software. Field and laboratory test results demonstrated the existence of high spatial variability in both density (relative compaction ranged from 82.6 to 109.1%, with 17% of test points having values

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.

Characterization and prediction of permanent deformation properties of unbound granular materials for Pavement ME Design

The objective of this study is to characterize and predict the permanent deformation properties of unbound granular materials (UGMs) for Pavement ME Design. First, laboratory repeated load triaxial (RLT) tests are conducted on the UGMs from 11 quarries in Texas to measure the permanent strain curves. The shakedown theory is applied to evaluate the permanent deformation behavior of the selected UGMs. It is found that using Werkmeister’s criteria to define the shakedown range boundaries is not suitable for the selected UGMs. Under this circumstance, new criteria are proposed to redefine the shakedown range boundaries for the flexible base materials in Texas. The new criteria are consistent with the current Texas flexible base specification in terms of aggregate classification. Second, the mechanistic-empirical design guide (MEPDG) model is used to determine the permanent deformation properties of the selected UGMs on the basis of the measured permanent strain curves. The determined permanent deformation properties are assigned as target values for the development of permanent deformation prediction models. Third, a series of performance-related base course properties are used to comprehensively characterize the UGMs, which include the dry density, moisture content, aggregate gradation, morphological properties, percent fines content, and methylene blue value. These performance-related base course properties are assigned as the inputs of the permanent deformation prediction models. Fourth, a multiple regression analysis is conducted to develop the prediction models for permanent deformation properties using these performance-related properties. The developed models are capable of accurately predicting the permanent deformation properties of UGMs. Compared to other prediction models (e.g., simple indicators-based models and Pavement ME Design models), the developed models have the highest prediction accuracy. It is also found that the Pavement ME model-predicted permanent strains are much lower than those measured from the RLT tests. This demonstrates that the current Pavement ME Design software substantially underestimates the rutting that occurs in base course. Finally, the developed prediction models are validated by comparing the predicted and measured permanent strains of other four base materials. The obtained R-squared value of 0.81 indicates that the developed models have a desirable accuracy in the prediction of permanent deformation properties of UGMs.

Calibration and sensitivity analysis of rut prediction model for semi-rigid pavement using AASHTOWare ME design

Rutting is one of the most serious distresses of semi-rigid asphalt pavements in Jiangsu. In this research, AASHTOWare pavement ME Design software was used for rutting prediction of semi-rigid pavements. A semi-rigid pavement is composed of a flexible layer and a rigid layer. Local calibration was done first by taking into account local materials, traffic information, and environmental conditions. A total of eight sites representing typical mixture types of Jiangsu province were selected to complete the calibration and another four highways were chosen to make the validation. Then the calibrated software was used to identify sensitive factors for rutting prediction. Critical inputs needed for the calibration and sensitive analysis were tested or collected from local project-level Pavement Management System (PMS) database. The modulus of semi-rigid base was back-calculated from deflection basin data. Calibration coefficients for each typical structure and the optimal coefficients for this region were identified, respectively. Results also demonstrate that traffic inputs and thickness of Hot Mixed Asphalt (HMA) layers are more sensitive to the predicted rutting depth than void contents of HMA mixtures.

Prediction of geogrid-reinforced flexible pavement performance using artificial neural network approach

This study aimed to develop a methodology to incorporate geogrid material into the Pavement ME Design software for predicting the geogrid-reinforced flexible pavement performance. A large database of pavement responses and corresponding material and structure properties were generated based on numerous runs of the developed geogrid-reinforced and unreinforced pavement models. The artificial neural network (ANN) models were developed from the generated database to predict the geogrid-reinforced pavement responses. The developed ANN models were sensitive to the change of base and subgrade moduli, and the variation of geogrid sheet stiffness and geogrid location. The ANN model-predicted geogrid-reinforced pavement responses were then used to determine the modified material properties due to geogrid reinforcement. The modified material properties were finally input into the Pavement ME Design software to predict geogrid-reinforced pavement performance. The ANN approach was rapid and efficient to predict geogrid-reinforced pavement performance, which was compatible with the Pavement ME Design software.

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.

Fiber-Reinforced Hot-Mix Asphalt: Idaho Case Study

This paper presents the findings of a research study to determine the benefits of fiber-reinforced asphalt mixtures used to mitigate distresses observed in the field. Control asphalt mixture test sections were constructed on US-30 in Idaho, along with fiber-reinforced asphalt mixture test sections. Three types of fibers were evaluated: Fiber 1 (i.e., aramid and polyolefin fiber), Fiber 2 (i.e., wax-treated aramid fiber), and Fiber 3 (i.e., glass fiber). Several laboratory tests were conducted on laboratory-prepared test samples and extracted field cores. The laboratory tests included dynamic modulus, flow number, Hamburg wheel-track test, indirect tensile test, creep compliance, and semicircular bending, in addition to performance evaluation with AASHTOWare Pavement ME Design software. The laboratory results showed no significant improvement to the properties of asphalt mixtures at the fiber content recommended by the manufacturer. However, the performance of asphalt mixtures did improve at a fiber dosage higher than the one recommended by the manufacturer. This study demonstrated the need to evaluate different fiber contents when asphalt mixtures are designed, because the dosage recommended by the manufacturer may not be optimal for various asphalt mixtures.

Implementation of the Thin Bonded Concrete Overlay of Existing Asphalt Pavement Design Procedure in the AASHTOWare Pavement ME Design Software

This paper describes the implementation of the bonded concrete overlay of asphalt pavement mechanistic–empirical (BCOA-ME) design procedure, developed by the University of Pittsburgh, into the AASHTOWare Pavement ME Design software. The BCOA-ME procedure was generally compatible with the mechanistic–empirical framework of Pavement ME and thus adaptable into the design framework. A thin bonded concrete overlay of existing asphalt pavements includes short to medium joint spacings (typically 6 × 6 ft) and a strong bond or high-contact friction between the portland cement concrete slab and the existing asphalt concrete surface. As much of the theory, concepts, assumptions, and inputs in the BCOA-ME design procedure as possible were implemented into the Pavement ME software. Differences included those required to match the computational procedures of Pavement ME (e.g., axle load spectra versus equivalent single-axle loads, monthly asphalt concrete damaged dynamic modulus, monthly portland cement concrete strength and modulus, and monthly unbound material resilient modulus). Longitudinal joint spacing ranging from 5 to 8 ft (not <5 ft) was included. Longitudinal fatigue cracking was directly considered as in the BCOA-ME, which initiates at the bottom of the slab. The calibration of the longitudinal cracking transfer function produced excellent goodness-of-fit statistics with no significant bias. The new procedure was incorporated into Pavement ME Version 2.3, released in July 2016.

Adaptation of NCHRP Project 1-41 Reflection Cracking Models for Semirigid Pavement Design in AASHTOWare Pavement ME Design

Semirigid pavements are composite pavements that comprise a surface asphalt concrete (AC) layer overlying a cement stabilized base, granular subbase, and subgrade foundation. When subjected to traffic and climate loading, semirigid pavements provide good performance through resistance to rutting or deformation of base and subgrade, moisture damage, and AC fatigue. However, semirigid pavements can develop fatigue and transverse shrinkage cracks in the underlying cement stabilized base layer, which ultimately propagates through the surface AC thickness with repeated applications of axle loads and temperature cycles (i.e., reflection cracking). Although new semirigid pavements have been designed and constructed in the United States since the 1960s, a new semirigid pavement design methodology was not included in Version 1.0 of AASHTOWare Pavement ME Design software. This methodology was omitted because developers and researchers at that time concluded that there were no suitable existing mechanistic-based fatigue or transverse reflection cracking models available to be adapted and included in the design procedure. Under NCHRP Project 1-41—Models for Predicting Reflection Cracking of Hot-Mix Asphalt Overlays, mechanistic-based AC fatigue and transverse reflection cracking models were developed. The models were developed to be compatible and thus easily adaptable into the AASHTOWare Pavement ME Design framework. Work done to adapt the NCHRP Project 1-41 reflection cracking models for the design of new semirigid pavement in AASHTOWare Pavement ME Design is presented.

Sensitivity of flexible pavement design to Michigan’s climatic inputs using pavement ME design

Climate condition is an important factor that affects the performance of pavements and distress predictions using mechanistic-empirical analyses. This study aims to analyse the sensitivity of flexible pavement distress predictions to climatic inputs using the AASHTOWare Pavement ME Design software in the state of Michigan. Typical traffic parameters, pavement structures and material properties for the state of Michigan were used as inputs for the analysis of flexible pavement performance. Six representative sites geographically distributed throughout Michigan and two typical traffic levels (high and medium) were incorporated in a comprehensive analysis of the effects of climate on flexible pavement performance predictions. A normalised sensitivity index was adopted to quantitatively evaluate the sensitivity of distress predictions to the five individual climatic variables: temperature, wind speed, precipitation, percent sunshine and relative humidity. The results of this study showed that the prediction of flexible pavement performance in Michigan is most sensitive to changes in temperature with other climatic factors such as wind speed, percent sunshine, precipitation and relative humidity impacting predictions to a lesser extent. Higher temperature and percent sunshine at a given location increased rutting and International Roughness Index (IRI) predictions, but reduced the likelihood of fatigue cracking. An increase in wind speed or precipitation reduced rutting and IRI predictions, but increased fatigue cracking predictions. Ambient relative humidity had a negligible effect on all flexible pavement distress predictions. These findings provide insights into the sensitivity of flexible pavement designs under different climate conditions. The sensitivity results are also beneficial for the Michigan Department of Transportation as they seek to improve the existing climatic files in PMED through evaluation of new climatic data sources.

Development of Pavement Preservation Strategies with Pavement ME Design Software for the State of Rhode Island

The objective of this study was to propose an approach to integrate AASHTOWare Pavement ME Design software into pavement preservation strategies of the Rhode Island Department of Transportation. The use of Pavement ME Design software could assist in the prioritization of mitigation alternatives; this procedure would optimize resources in Rhode Island. Four representative pavement sections on state highways in Rhode Island were selected and then analyzed with the Pavement ME Design software. Weigh-in-motion traffic data also were collected from nine stations in Rhode Island to establish representative state traffic patterns. This information was used as input to Pavement ME Design, and the predicted performance measures (i.e., international roughness index, rutting, longitudinal cracking) were then used to determine when the typical Rhode Island pavement mitigation alternatives [e.g., crack seals; level and overlay; mill and overlay with and without friction course; paver placed elastomeric surface treatment; rubberized chip seals; reconstruction and reclamation; and stress-absorbing membrane interlayer (SAMI)] were triggered. In addition, a cost-based approach and an incremental benefit–cost alternative prioritization method were analyzed to determine the most cost-effective alternatives and the most beneficial alternatives, respectively. On the basis of the analysis, the Pavement ME Design software was found to have potential for use in the enhancement of pavement preservation strategies. Crack seals, rubberized chip seals, and SAMI were found to be the most cost-effective and the most beneficial of all alternatives assessed.

Investigation of Different Methods for Obtaining Asphalt Mixture Creep Compliance for Use in Pavement Mechanistic–Empirical Design Software

Thermal cracking is a major distress type observed in flexible pavements, especially in regions with cold climates (e.g., northern United States and Canada). The Pavement ME Design Guide software developed under NCHRP Project 1-37A is the pavement analysis and design tool most widely used by state highway agencies for pavement designs. For thermal cracking predictions in flexible pavements, the most important material properties are the creep compliance and indirect tensile strength of an asphalt mixture in Level 1 and Level 2 analyses. Level 3 analyses use material properties and mixture volumetrics to predict creep compliance and indirect tensile strength. This study was part of a comprehensive research effort to characterize asphalt mixtures commonly used in the state of Michigan for implementation of the Pavement ME Design software. The main objective of the study presented in this paper was the investigation of methods of obtaining asphalt mixture creep compliance [D(t)] for use in flexible pavement analysis and design using the Pavement ME Design software. In this study, numerical interconversion between dynamic modulus (|E*|) and the creep compliance of asphalt mixtures with the Prony series method was investigated and validated. Then, the creep compliance of numerous asphalt mixtures was computed from the measured |E*| data. With these data, the creep compliance predictive equation used in Pavement ME Design software was evaluated and locally calibrated. In addition, an analytical model as well as an artificial neural network–based model was developed for improved prediction of D(t) from the mixture volumetrics.

Interlaboratory Study and Precision Statement for AASHTO T 336 Test Method

With the recent release of AASHTOWare Pavement ME Design software, there will be a greater emphasis on measuring the coefficient of thermal expansion (CTE) of concrete because of its significance on predicted distress and design life. The most widely used test method to measure the CTE of concrete is the AASHTO T 336 test method. Data are presented from an interlaboratory (round robin) study conducted by FHWA; this study included 20 CTE units and laboratories representing FHWA, state highway agencies, universities, commercial testing laboratories, and the concrete paving industry. As part of the study, each laboratory tested nine concrete specimens (three concrete mixtures 3 three specimens per mixture) that spanned the typical range of CTE values for concrete. On the basis of this interlaboratory study for the AASHTO T 336 test method, the within-laboratory single operator standard deviation was found to be 0.12 mstrain/°C and the between-laboratory standard deviation was found to be 0.28 mstrain/°C. Data from the study also showed that there was no statistically significant difference between custom-built CTE devices versus commercially available testing devices. This demonstrates the ruggedness of the current test method.

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.

Calibration of Pavement ME Design and Mechanistic-Empirical Pavement Design Guide Performance Prediction Models for Iowa Pavement Systems

AbstractThe AASHTO mechanistic-empirical pavement design guide (MEPDG) pavement performance models and the associated AASHTOWare pavement ME design software are nationally calibrated using design inputs and distress data largely from the national long-term pavement performance (LTPP). Further calibration and validation studies are necessary for local highway agencies’ implementation by taking into account local materials, traffic information, and environmental conditions. This study aims to improve the accuracy of MEPDG/pavement ME design pavement performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 70 sites from Iowa representing both jointed plain concrete pavements (JPCPs) and hot mix asphalt (HMA) pavements were selected. The accuracy of the nationally calibrated MEPDG prediction models for Iowa conditions was evaluated. The local calibration factors of MEPDG performance prediction models were identified using both linear and nonlinear opti...

Sensitivity Analysis of Field-to-Laboratory Subgrade Conversion Factors with AASHTOWare Pavement ME Design

The new AASHTOWare Pavement ME design software incorporates conversion factors into its procedure to correlate the field subgrade soil resilient modulus, measured with the falling-weight deflectometer, with the laboratory equivalent modulus for use in performance prediction. The national conversion factors recommended for use with Pavement ME were determined on the basis of data obtained from the Long-Term Pavement Performance database and were not necessarily accurate for all regions and soil types. Sensitivity analysis results are presented for the effect of field-to-laboratory subgrade modulus conversion factors on the performance of three types of pavement structures in Ontario, Canada: conventional flexible, deep-strength flexible, and composite. This work included simulation of real-world case studies extracted from the Ontario provincial pavement management system (Ontario PMS2) database. Three projects were selected for this study–-Highway 6, Highway 401, and Queen Elizabeth Way–-all located in southwestern Ontario. Required inputs including traffic, material characteristics, and layer thickness information were extracted from PMS2 and used to set up the projects in AASHTOWare Pavement ME. The AASHTOWare Pavement ME was executed with ranges of field-to-laboratory conversion factors for different pavement types. Statistical analyses were carried out to evaluate the effect of changing conversion factors on predicted performance. Study results suggest that the predicted performance of conventional flexible pavement is significantly affected by the subgrade modulus conversion factor. However, the impact of subgrade modulus conversion factors on predicted performance is insignificant for composite and deep-strength flexible pavement sections.

Surface Texture Measurements of Crack Surface to Establish Joint Shear Stiffness

Pavement design has evolved from a purely empirical approach to a more mechanistic-based approach. One of the primary distresses considered when designing jointed plain concrete pavements is faulting. The development of faulting is largely a function of the load transfer efficiency at the joints. For undoweled joints, the load is primarily transferred through the aggregate interlock provided by the texture resulting from the meander in the transverse crack propagating off from the sawed joint. This paper shows how volumetric surface texture, a measurement of the surface texture at the crack face, can be used for this purpose. A method is presented for estimating the joint stiffness parameter, Jagg, required for the faulting prediction model incorporated into the AASHTOWare Pavement ME Design software based on the measured surface texture. This method allows the effects of aggregate size, type, and quantity to be captured and joint stiffness to be quantified when faulting is being predicted.