Mechanistic-Empirical Pavement Research Articles

Heavy rainfall and moisture susceptibility of pavement foundation: A case study coupling finite element method and MnROAD moisture monitoring data

The Midwest region, including Minnesota, has been experiencing increased heavy precipitation events due to climate change, and the Minnesota Department of Transportation (MnDOT) is currently investigating the effect of climate change on pavement foundation and other transportation assets. As part of this effort, a study was conducted to investigate the impact of heavy rainfall on pavement foundation performance by focusing on moisture dynamics and resilient modulus changes in the pavement base layer. This study is aimed at understanding the adverse effects of heavy rainfall on moisture fluxes within pavement foundation and corresponding stiffness of the base aggregate layer. A two-step approach was adopted for predicting changes in saturation when estimating corresponding resilient modulus values using the resilient modulus prediction equation employed in AASHTOWare Pavement Mechanistic-Empirical (ME) Design. PLAXIS 3D, a finite-element analysis tool, was utilized to simulate the movement of moisture within the pavement layers under varying heavy rainfall scenarios. By incorporating predicted saturation from PLAXIS 3D simulations into the Pavement ME equation, corresponding resilient modulus values were estimated for the base layer. To ensure its accuracy and reliability, the model was validated using field sensor data from the MnROAD facility. Multiple linear regression models were developed to provide a means for estimating resilient modulus changes due to heavy rainfall. This study highlights the importance of considering moisture effects in pavement design and maintenance in regions prone to heavy rainfall events, and findings can be used by transportation agencies as part of their transportation/geotechnical asset management programs.

An artificial-intelligence based approach for predicting structural damages of paved-road systems under superloads

When studies on mechanistic-based pavement analysis and design are actively conducted, highly-accurate and meaningful data related to structural damages caused by traffic loads have accumulated. Although widely-used pavement design programs such as the American Association of State Highway and Transportation Officials (AASHTO)Ware Pavement Mechanistic-Empirical (M-E) Design software supports M-E pavement design for comprehensive pavement structures and traffic loadings, it is still unable to predict extrapolated mechanistic responses when pavements are subjected to superloads having non-standardized loading configurations not yet included in the software. In this study, artificial neural-network (ANN)-based surrogate models were developed and optimized to provide high accuracy in predicting critical pavement responses related to representative structural damages of jointed plain-concrete pavements (JPCPs) and flexible pavements when subjected to a single pass of various superload types, thereby extensively broadening the scope of constrained mappings in terms of loading variables. Sensitivity analysis on pavement structural and loading variables was performed using the ANN models developed in this study to identify the significance level of each explanatory variable in generating target-pavement responses.

Critical fatigue strain calculations of flexible pavements in the mechanistic-empirical pavement design method, shortcomings, and improved methodologies

Fatigue Performance is considered one of the most effective measures for designing and maintaining flexible pavements. AASHTO proposed a Pavement Mechanistic-Empirical (PME) design framework in which the fatigue strains are calculated by the mechanistic part and then turned to fatigue cracking by the empirical part. However, it fails to compute actual fatigue strains since it utilizes multilayered linear elastic analysis and predefined calculation points. This paper aims to discuss the PME approach's deficiency in calculating fatigue strains and to develop simplified methodologies for addressing this issue. This study presents three solutions by comparing the fatigue life estimated from the multilayered linear elastic and viscoelastic analysis. The first solution modifies Asphalt Institute's model by suggesting adjusting factors for each axle load configuration. The second one proposes new response locations instead of predefined ones. The last solution involves developing an Artificial Neural Network (ANN) model to predict pavement fatigue life more accurately.

Fatigue of Cold Recycled Cement-Treated Pavement Layers: Experimental and Modeling Study

Fatigue is the main design criterion for cold recycled cement-treated mixtures (CRCTMs). However, the literature shows that the fatigue behavior of such mixtures is still not well known. For example, the effect of increasing reclaimed asphalt pavement (RAP) contents is yet a topic of discussion. This experimental and modeling study helps fill knowledge gaps on CRCTM fatigue behavior using long-term curing fatigue tests and three design methods currently being used in different countries. The objectives of this study were: (1) to characterize the mechanical and fatigue behavior of mixtures of RAP, aggregates and cement; (2) to evaluate the fatigue life of pavements with base and subbase layers of such mixtures using the novel Brazilian design method (MeDiNa); and (3) to compare the results with those obtained using the South African Pavement Engineering Manual (SAPEM) transfer functions and the American Association of State Highway and Transportation Officials AASHTOWare Pavement Mechanistic-Empirical Design (PMED) software. The mixtures were tested in the laboratory using flexural static and cyclic tests, and the required parameters to use the methods were obtained. Experimental results and modeling demonstrated a superior fatigue behavior of recycled layers with higher RAP contents. On the other side, layers with lower RAP contents abruptly lost stiffness in short periods, making thicker structures necessary. Therefore, using high RAP contents is not only a sustainable practice, but also a technical benefit. The equivalent single axle loads obtained using the SAPEM were higher than those obtained using MeDiNa, while the PMED ones were higher than both previous methods. Despite the inherent differences, this suggests that MeDiNa is more conservative. It also highlights the importance of calibration based on long-term pavement performance data.

MatFEA: Efficient Finite Element Framework for Analyzing Pavement Structures With Nonlinear Unbound Materials

This paper reports the details of a computationally efficient finite element analysis code (called MatFEA), designed specifically for flexible pavements, where stress-dependent characteristics of unbound layers are considered. The stress-dependent nonlinearity of the unbound materials has been modeled in MatFEA through an iterative approach. MatFEA was mainly developed to improve the Mechanistic-Empirical Asphalt Pavement Analysis (MEAPA) web application with capability of analysis/design of nonlinear pavement structures. A specific-purpose mesh generation algorithm was developed for the MatFEA in which the mesh density was dynamically adjustable based on the expected stress magnitude. To verify the results of the MatFEA, 12,000 nonlinear pavement structures with different structural and loading properties were generated and analyzed with MatFEA. The same pavement structures were modeled in a general-purpose FE-based ABAQUS™ program. The results of this study showed that the critical pavement structural responses from the MatFEA were almost identical with those modeled by ABAQUS™ program. The average runtime of MatFEA for analyzing each pavement structure was about 1.13 s. Finally, it was concluded that application of the dynamically adjustable mesh generation algorithm in the MatFEA helped achieve a reasonably high accuracy as well as efficient runtime, which brings the advantage of implementing the MatFEA as a pavement structural response model in the mechanistic-empirical pavement analysis/design procedures.

Collective influence of autonomous trucks and climate change on asphalt concrete pavement performance

ABSTRACT Autonomous vehicles (AVs) movement in a narrower traffic lane and anticipated climate change are crucial for asphalt concrete (AC) pavement distress. This study assesses the combined effect of AVs and climate change on the performance of AC pavement for a road section in Ontario, Canada. The performance of AC pavement due to AVs and climate change has been evaluated using the AASHTOWare Mechanistic-Empirical (ME) pavement design. AVs were incorporated in ME pavement design using traffic factors such as adjusting traffic volume with the load equivalency and lane distribution factors. This analysis was carried out to determine the individual and combined influence of AVs and climate change on pavement performance. This study determines the combined impacts of AVs and climate change by comparing pavement performances for human-driven vehicles with historical climate and AVs with projected climate. The comparative performance analyses of human-driven vehicles and AVs with projected climate demonstrated the effect of climate change. AVs and climate change combinedly and AVs alone accelerate the accumulation of AC rutting and bottom-up fatigue cracking. The regulation of AVs explicitly to ensure uniform loading distribution and the placement of AVs with non-AVs in a controlled manner were the best alternatives to minimise pavement distress.

Regional implementation of the mechanistic empirical pavement design and analysis approach: Egyptian case study

The Mechanistic-Empirical (ME) pavement design approaches have many advantages including; considering a wide range of traffic loadings along with a diversity of climatic conditions. This is in addition to the ability to comprehensively and thoroughly characterizing the pavement materials, which consequently led to the enhanced capability of predicting pavement performance. However, the implementation of the ME procedures has many challenges including the huge amount of traffic and climatic data needed, the needed advanced characterization of the pavement materials, and the need for the calibration and validation to the local conditions. Accordingly, an increased required time to develop and evaluate the design is warranted. Furthermore, the lack of the experience with the ME pavement design approaches is considered an additional challenge. In order to encourage the ME design procedures implementation in a scarcity of data and lack of experience regions, this research proposes a ME regional implementation model. This model was developed then implemented in Egypt as a case study representing the developing countries of scarcity of both data and experience of the ME approaches. First, libraries of the local material/traffic inputs were created. Second, the performance of typical designed pavement sections was predicted using the ME approach in terms of the most regional prevailing performance indicators; rutting, fatigue cracking, and thermal cracking. The Machine Learning (ML) technique was then used to develop correlation models. Third, the developed models were presented in the form of ME pavement design/analysis tool. Three performance ML based-models were developed with good R2 (coefficient of determination) of 0.789, 0.755, and 0.959 for fatigue cracking, thermal cracking, and rutting, respectively. Finally, the developed performance models were implemented in the form of a user-friendly design/analysis tool ready for use as a mechanistic empirical tool developed based on the regional conditions. The Egyptian case study proved a good validation and verification of the developed model.

Development of an Artificial Neural Network-Based k-Value Prediction Model to Improve the Sensitivity of Base Layer on Rigid Pavement Performance

The performance of rigid pavement is greatly affected by the properties of the base/subbase and subgrade layer. However, the performance predicted by the AASHTOWare Pavement Mechanistic-Empirical (ME) Design shows low sensitivity to the properties of base and subgrade layers. In this study, a new set of modified models, that is, resilient modulus ( MR) and modulus of subgrade reaction ( k-value) are adopted to better reflect these layers’ influence on the overall performance. An artificial neural network (ANN) model is developed to predict the modified k-value based on finite element (FE) analysis. The training and validation data sets in the ANN model consist of 27,000 simulation cases with different combinations of pavement layer thickness, layer modulus, and slab-base interface bond ratio. Eight pavement section data are collected from the long-term pavement performance (LTPP) database and modeled using the FE software ISLAB2000 to evaluate the sensitivity of the modified MR model and k-values to pavement performance. The computational results indicate that the modified MR values have a higher sensitivity to water content in the base layer on fatigue cracking (bottom-up and top-down cracking) and the faulting performance of rigid pavements compared with the results using the Pavement ME design model. It is also observed that the k-values using the ANN model have a greater sensitivity to cracking and faulting performance as a result of changes in any bonded conditions (fully bonded, partially bonded, zero bonded), whereas the Pavement ME design model can only calculate at two extreme bonding conditions (i.e., fully bonded and zero bonded).

Influence of material factors on the determination of dynamic moduli and associated prediction models for different types of asphalt mixtures

In a Mechanistic-Empirical (ME) pavement design system, the dynamic modulus is used to characterise asphalt mixtures, which are affected by many factors including environmental conditions and material properties. This study examined twenty types of asphalt mixtures containing nine types of bituminous binders that are commonly used for construction of road pavements in Norway. The objective of the study was to investigate the relationship between material factors and dynamic modulus. The dynamic moduli of asphalt mixtures were experimentally determined employing cyclic indirect tensile tests and were modelled with master curves implementing the sigmoidal function. The rheological properties of bitumen were characterised using a dynamic shear rheometer. The viscosity was simulated using a temperature-based log-linear relationship and the complex modulus was obtained based on the modified Huet-Sayegh model. The grey relational analysis revealed the correlation existing between the material factors and the dynamic modulus. A prediction model was established using a Multiple Linear Regression (MLR). The results indicate that the dynamic modulus of asphalt mixtures is greatly affected by the following parameters ranked in order of decreasing significance: viscosity and complex modulus of the binder, void filled with bitumen, air void content, bitumen penetration and softening point. Based on the MLR model, the correlation between the dynamic modulus and the bitumen viscosity and complex modulus fit well with Coefficient of Determination (R2) ≥ 0.901. The determination of the sigmoidal function parameters to predict the dynamic modulus had good reliability with R2 ≥ 0.973. This study contributes to the development of an accurate and convenient method to predict the dynamic moduli of asphalt mixtures based on the material factors for a ME pavement design system.

Exploring lessons learned from partnerships to establish a regional accelerated pavement testing facility in Wyoming

ABSTRACT A foremost accelerated pavement testing (APT) test road facility is proposed along interstate 80 for the dry-freeze climate by the Wyoming Department of Transportation (WYDOT). A survey was undertaken among states and industries in the region as part of the feasibility study to identify research needs and interests in the test road project. This paper summarises the responses of the survey and lessons learned from the experiences of 3 APT test road facilities in the U.S. Respondents perceive the APT to be relevant and critical to the study and state-of-practice of pavement engineering. Respondents’ research needs include local calibration for the mechanistic-empirical (ME) pavement design, pavement preservations alternatives, and recycled materials for pavements. The presented information and the lessons learned will guide future APT programmes at the planning stage on nurturing partnerships and avoiding the mistakes of earlier APTs to minimise the wastage of scarce resources.

Calibration of pavement ME rutting model for geogrid stabilized roadways

Many transportation agencies started implementing Mechanistic-Empirical (ME) approach for designing and analyzing new and rehabilitated pavements. Although the widely popular empirical design approach based on 1993 AASHTO Design Guide is still in practice, the need for performance-based design and accurate prediction of pavement distresses throughout the service life led to the adoption of the ME approach. The two major steps associated with the ME approach are the computation of pavement stress–strain responses using mechanistic analysis and estimation of pavement distresses using empirical analysis. Layered Elastic Analysis (LEA) or Finite Element Analysis (FEA) is normally utilized for mechanistic analysis whereas regression analysis is typically used for empirical analysis. Although the empirical models (i.e., transfer functions) were calibrated using the Long Term Pavement Performance (LTPP) dataset, these models do not consider the benefits of geogrid stabilization. Research and field studies had shown improvement in pavement performance when the pavement is stabilized with geogrids. This paper aims at incorporating the effect of geogrid in one of the distress models – more specifically, the rutting model – of AASHTOWare Pavement ME. Model calibration factors were developed based on the recent full-scale accelerated pavement test conducted at the research facility of the US Army Corps of Engineers. Two types of punched and drawn triangular aperture geogrids were utilized in the full-scale test. Pavement surface deformations measured during the trafficking period were used for calibrating the rutting model. The calibrated rutting model was validated with the LTPP and Louisiana Transportation Research Center (LTRC) dataset.

Machine learning of Truck Traffic Classification groups from Weigh-in-Motion data

The pavement Mechanistic-Empirical (ME) design requires high-dimensional traffic feature inputs by categories, including Vehicle Class Distributions (VCD), Monthly Distribution Factors (MDF), Hourly Distribution Factors (HDF), and Normalized Axles Load Spectra (NALS). In simplifying the Pavement ME design practice, Truck Traffic Classification (TTC) groups are commonly used for characterizing traffic inputs. Thus, properly defining TTC groups is critical for state-specific pavement ME design practice. In this study, the truck traffic data from existing Weight-in-Motion (WIM) stations were mined to develop specific TTC groups to assist with pavement ME design practice in Georgia. An effective data analytics procedure was developed by leveraging unsupervised machine learning techniques to reduce the high-dimensional traffic features by stratified Principal Component Analysis (PCA), followed by K-means clustering to establish appropriate TTC groups. For a case study, the performance of two typical designs was evaluated using the AASHTOWare pavement mechanistic-empirical (ME) design software with respect to two scenarios of traffic inputs: (1) the derived cluster-based groups, and (2) the national default TTC groups. The results indicated that direct application of the national default TTC groups resulted in over-design of pavement structure in Georgia. Therefore, it is highly recommended that customized TTC groups should be developed using state-specific WIM data.

Pavement Fatigue Damage Simulations Using Second-Generation Mechanistic-Empirical Approaches

This article aims to demonstrate the advanced features of two second-generation mechanistic-empirical (ME) pavement analysis engines by focusing on their ability to conduct fatigue performance analysis. First, a comprehensive review is presented of both mechanistic and empirical damage models, underlining the additional features of CalME and FlexPAVE™ over AASHTOWare Pavement ME Design. Then, the capabilities of these methodologies are demonstrated by simulating the fatigue damage performance of an example study section. For these simulations the mechanical properties of four asphalt concrete mixtures, assembled in the laboratory with similar mix design attributes but diverse fatigue characteristics, were utilized. The empirical transfer functions were initially calibrated against field cracking for the unmodified mixture cracking predictions. After that, fatigue damage simulations for the other three mixtures were performed. The results showed a similar ranking in fatigue cracking performance for both software simulations. Polymer-modified mixtures exhibited higher fatigue cracking resistance, whereas the unmodified mixture showed the worst cracking resistance. However, significant differences in cracking initiation and progression rates were observed for all mixture simulations before and after calibration. This discrepancy was related to the different approaches to considering traffic loads in each software system, single axle in FlexPAVE™ and axle spectrum in CalME. Finally, current, and future enhancements for both analysis engines are briefly discussed.

Input Parameters for the Mechanistic-Empirical Design of Full-Depth Reclamation Projects

Cold recycling technologies such as full-depth reclamation (FDR) are sustainable and cost-effective techniques for pavement rehabilitation that reduce environmental impacts and construction costs and time. The limited information available on the material properties of FDR mixtures and their characterization in mechanistic-empirical (M-E) pavement design hinders the full deployment of FDR. Previous research has found current M-E default values to be non-representative and overly conservative, leading to an underestimation of the true performance capabilities of FDR materials. To address this gap, this paper analyzes the performance of 11 FDR sites constructed throughout Colorado, U.S., and compares their long-term performance with M-E predictions. The objective of this paper is to recommend input values for the M-E design of FDR base materials that result in reliable predictions of FDR long-term performance. The analysis includes both non-stabilized and emulsion-stabilized FDR projects. Both initial International Roughness Index (IRI) and resilient modulus were found to have a significant impact on M-E predictions and were calibrated in a two-step process. The proposed input parameters lead to a conservative design of FDR projects and result in improved IRI predictions compared with the ones derived from current design criteria. With the current design parameters, IRI predictions were, on average, overestimated by 51 in./mi, whereas the proposed input parameters make it possible to reduce this difference to 17 in./mi. Future research is needed to improve current models in M-E pavement design software to adequately model cold in-place recycled layers such as FDR.

Determination of Dynamic Modulus Master Curve of Damaged Asphalt Pavements for Mechanistic–Empirical Pavement Rehabilitation Design

For pavement rehabilitation design, the current mechanistic–empirical (ME) pavement design guide provides three levels of analysis methodology to determine dynamic modulus master curves for existing asphalt pavements. First, the ME pavement design guide recommends that Witczak’s predictive equation is employed to obtain the “undamaged” modulus master curve. Depending on the chosen level of analysis, either a falling weight deflectometer test (Level 1) or a condition survey (Levels 2 and 3) is conducted to determine the damage factor(s). The damage factor is used to shift the undamaged master curve downward to match the field conditions and obtain the “damaged” master curve. In this study, two pavement structures in North Carolina Highway 96 were selected to evaluate the accuracy of the ME pavement design guide using its three levels of analysis. Because this roadway is a multilayer full-depth pavement, the extracted field cores were divided into a top layer, bottom layer, and total core for investigative and comparative purposes. Accordingly, both laboratory measurements and pavement ME predictions of the dynamic modulus values were conducted separately. Results show that the predicted undamaged master curves are always higher than the measured master curves and Levels 1, 2, and 3 can each lead to significantly different damaged master curves. Considering both efficiency and accuracy for transportation agency practice, the Level 1 method is recommended, and if the existing pavement is a multilayered asphalt pavement, a total core extracted from all the layers is recommended to generate the input properties for Witczak’s predictive equation.

Effect of climate on asphalt pavement performance using two mechanistic-empirical methods

ABSTRACT The objective of this study is to assess the impact of climate on the performance of bituminous pavements with two different methods, and then to examine their impact on actual design scenarios. Three climate cases were selected for the comparisons involving the effect of temperature: one in France (Bordeaux) and two in the United States (Seattle and Phoenix). The case of an experimental site in Québec was also used for comparisons concerning consideration for seasonal variations in ground bearing capacity. The analyses were based on French (Alizé-LCPC) and US (AASHTOWare Pavement ME Design) mechanistic-empirical pavement design approaches. Interesting features of the two design methods are outlined and their fatigue cracking models are linked together. The results of the study highlight the importance, for design purposes, of the empirical fatigue equations as a function of temperature. For the site studied in France, the design results in terms of AC base layer thicknesses indicate that both methods show a similar trend with an increase in temperature. For the site in Québec, which is submitted to severe freeze/thaw cycles, the results show that taking temperature and moisture into account for unbound materials leads to a prediction of more severe permanent deformation. Highlights Presentation of two mechanistic-empirical (ME) methods for pavement design Description of the fatigue equations governing asphalt mixes for design purposes Identification of the parameters used in the design of pavement structures Study of the influence of the climate in France and Québec on pavement performance Comparison of the French and US methods for pavement design and correspondence between them

Predictive Framework for Modeling Changes in Asphalt Mixture Moduli with Oxidative Aging

This paper presents a predictive framework for asphalt mixture moduli as a function of aging time with two levels of sophistication. This work is built on the method currently implemented in Pavement mechanistic-empirical (ME) that uses an effective time/frequency concept based on time-aging superposition to model the effect of aging on a mixture’s modulus. Time-aging superposition implies that an asphalt mixture’s modulus mastercurves, corresponding to different aging levels, coincide when they are shifted horizontally on the log-frequency axis. This study improves the accuracy of the existing model by decoupling the time-temperature and time-aging shifts. The new framework also uses the binder dynamic shear modulus | G*| as an aging index instead of the viscosity, which is used in Pavement ME. The | G*| aging index is used to calculate an effective frequency at short-term aging (STA), which is then used in the asphalt mixture sigmoidal model to calculate the corresponding asphalt mixture modulus with aging. The pavement aging model introduced by NCHRP 09-54 predicts log | G*| at 64°C and 10 rad/s for a specific field-aged condition and pavement depth. The proposed framework can use the predicted log | G*| to predict the mixture’s corresponding dynamic modulus (| E*|) at that aging level and pavement depth. Level 1 of this framework requires characterizing the | G*| at STA and calibrating the NCHRP 09-54 pavement aging model as well as measuring the mixture | E*| at STA. Level 2 does not require any binder testing, providing relatively less accurate predictions but relieving some testing requirements.

Effects of Concrete Age on Coefficient of Thermal Expansion of Paving Mixes and Its Significance in Unbonded Overlay Design

Abstract With the advancement in rigid pavement design and advent of AASHTOWare Pavement Mechanistic-Empirical (ME) Design (American Association of State Highway and Transportation Officials, Washington, DC) as the latest design tool, increasing emphasis is being laid on the coefficient of thermal expansion (CTE) of concrete. The CTE affects the performance of rigid pavements in a significant manner, with a higher CTE corresponding to greater curling and resulting in greater pavement distresses. Prior research has conflicting views regarding CTE variation with age, and there has been very little to no research on the effects of CTE on the performance of unbonded concrete overlays (UBCOs). This study is performed to investigate the variation of CTE with concrete age through laboratory testing and further quantify the effects of CTE variation on UBCO design. To facilitate, four concrete paving mixes with different types of coarse aggregates and different mix properties from different districts of New Mexico were collected. The cast specimens from each of the mixes were tested for CTE as per the AASHTO T-336, Coefficient of Thermal Expansion of Hydraulic Cement Concrete, protocol, at the ages of 7, 14, 28, 60, 90, 120, and 180 days, respectively. The test results were analyzed, and it was observed that there is an increase in CTE over the range of 3.49 % to 9.77 %, between 28 days and 180 days. Further analysis was conducted with simulations in Pavement ME Design Version 2.3® that indicated a significantly deteriorated performance of the UBCO with increased CTE. The impact on transverse cracking and joint faulting is the most significant, with up to 13.3 % increase in cracking and up to 19.7 % increase in faulting distress. An unbonded overlay designed with 28 days CTE value may not perform for the design life because of the increase in CTE with age progression.

Mechanistic-Empirical Compatible Traffic Data Generation: Portable Weigh-in-Motion versus Cluster Analysis

Abstract Axle load distribution factors (ALDFs) are used as one of the primary traffic data inputs for mechanistic-empirical (ME) pavement design methods for predicting the impact of varying traffic loads on pavement performance with a higher degree of accuracy than empirical methods that are solely based on equivalent single axle load (ESAL) concept. Ideally, to ensure optimal pavement structural design, site-specific traffic load spectra data—generated from weigh-in-motion (WIM) systems—should be used during the pavement design process. However, because of the limited number of available permanent WIM stations (in Texas, for example), it is not feasible to generate a statewide ALDFs database for each highway or project from permanent WIM data. In this study, two possible alternative methods, namely, the direct measurement using a portable WIM system and the cluster analysis technique, were explored for generating site-specific ME-compatible traffic data for a highway test section, namely, state highway (SH) 7 in Bryan District (Texas). The traffic data were then used for estimating pavement performance using a ME pavement design software, namely, the Texas Mechanistic-Empirical Thickness Design System (TxME). The TxME-predicted pavement performance (e.g., rutting) using the portable WIM-generated traffic input parameters closely matched with the actual field performance. Overall, the study findings indicated that the portable WIM (with proper installation and calibration) constitutes an effective means for rapidly collecting reliable site-specific ME-compatible traffic data.

Moisture effects on moduli of pavement bases

ABSTRACT Mechanistic-Empirical (ME) pavement design approaches consider seasonal moisture variations in the subgrade and unbound aggregates base (UAB) layers as an important design parameter. Understanding such moisture variation is essential for modelling the pavement section and predicting its performance. The Enhanced Integrated Climate Models (EICM) are often used to predict the moisture contents in the UAB and subgrade layers for different seasons so that the resilient moduli of the layers can be adjusted. This paper provides an in-depth analysis of the moisture effects on the moduli of a UAB material with four different gradations. Commonly used laboratory and field-testing methods were conducted to measure the moduli of the specimens prepared at different moisture contents and fine contents from the same UAB source. The UAB moduli decreased with the increase in the moisture content. However, the patterns in the reduction in modulus depended on the laboratory or field test method used. Although the EICM module of the AASHTOWare Pavement ME Design software can predict the laboratory resilient moduli at moisture contents greater than the optimum moisture content, it was not possible to measure the moduli at those elevated moisture contents for the selected UAB.