Mechanistic-empirical Research Articles

Predicting Long-Term Coefficient of Thermal Expansion of Paving Concrete

The coefficient of thermal expansion (CTE) of concrete is an important parameter that affects the design and performance analysis of concrete pavements. Higher CTE value results in increased curling and related stresses. A 28-day CTE value is used for designing rigid pavements. Though previous studies have revealed that coarse aggregate mineralogy has substantial effects on the CTE value of paving concrete, it is not known yet how CTE value changes with the age of concrete in the long-term. In this study, seven concrete mixes with different coarse aggregate mineralogy are tested in the laboratory and data is analyzed to examine CTE. Results show that limestone has the lowest CTE values compared with other coarse aggregates. Concrete CTE increases from 6.4% to 12.6% as it ages. This increase in CTE may result in increased thermal distresses as concrete pavement ages. Therefore, a single value of 28-day CTE should not be used in the design of concrete pavements. In this study, a prediction model is developed to determine aged CTE incorporating mixture volumetrics and concrete strength properties. The same can be incorporated in Pavement Mechanistic Empirical (ME) Design software to better predict the rigid pavement performance.

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.

ALTERNATIVE OPTION FOR ROAD MAINTENANCE MANAGEMENT IN BANGLADESH UNDER CLIMATE CHANGE CONDITION

Bangladesh is one of the worst climate-vulnerable countries in the world. It is predicted that the temperature of the country will increase 10- 30 Celsius and precipitation 20% by 2050. Bangladesh being a flood-prone country, roads in the lowlying areas will likely to be flooded more frequently than before due to increased rainfall. The study has developed a methodology by Mechanistic-Empirical (M-E) method to find out the effect of moisture and temperature on the pavement. It has also provided solutions to minimize the additional damage due to traffic movement on the affected pavement. It is found that damage by a standard axle load during saturation period of 30 days is almost 3 times than that in dry condition. However, during 30 days of saturation if the axle is overloaded by 6 ton, the damage is 20 times than that of a standardaxle at the dry condition. By M-E method it is found that imposing 30 % restriction on standard axle load during pavement saturation condition will minimize the additional damage. It has suggested using improved materials to fight against temperature rise. The outcome of the study will support road engineers of Bangladesh to combat the climate change effect by adopting axle load restriction.

Functional grading of viscoelastic defined hot mix asphalt layers

In the design of layered flexible pavements with the mechanistic-empirical (M-A) method, the material characterization of layers is critical for the correct calculation of mechanical responses. The surface layers of flexible pavements produced as hot mix asphalt (HMA) behave as a visco-elastic material under real field conditions. In this study, in which the HMA surface layer was defined viscoelastic by creep-compliance method, functional grading was applied to the surface layer at the same time to increase the fatigue strength of the pavement. Functional grading application was performed in the form of 3, 5, 7, and 9 sub-layers with linear and exponential functions. The pavement life values due to the mechanical responses that occurred in the pavement were determined by the Asphalt Institute and Shell methods. The study results show that the fatigue strength of the viscoelastic surface layer increases significantly as a result of functional grading. Especially in sections with exponential grading, an improvement of more than 10% was achieved even when a small number of sub-layers used. These results show that functional grading can also be successfully applied in field studies, and even with three sub-layers, significant improvements can be provided

Local Recalibration of JPCP Performance Models and Pavement-ME Implementation Challenges in Michigan

AbstractThe AASHTOWare Pavement Mechanistic-Empirical (ME) Design version 2.0 performance models were locally calibrated in 2014. Subsequently, the software was updated to versions 2.2 (2015) and 2...

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.

Finding an equivalent Hot-Mix Asphalt for Cold-in-Place recycled asphalt using laboratory testing and numerical analysis

Cold-in-Place Recycling (CIR) asphalt material is gaining increased popularity due to its innovative properties, performance and recycling values. However, detailed material properties of this material are required to design pavement using the CIR materials. This study finds out an equivalent conventional Hot-Mix Asphalt (HMA) which behaves very similar to the CIR material. As a first step, detailed properties of the CIR materials used in Colorado has been measured in the laboratory. Then, the performances of ten pavements which are built using the CIR are predicted using the AASHTOWare Pavement Mechanistic-Empirical (ME) Design (PMED) software. Then, different HMA mixes are used to run the PMED software and are compared with the performance of the CIR material. Results show that the PMED software analysis showed the PMED-predicted IRI and rutting values are close to the values measured in the field. However, the PMED-predicted fatigue cracking values are less than values measured in the field. CIR material behaves very similar to the HMA SX (75) PG 58-28 mix and thus may be used in place of CIR while analyzing a pavement using the PMED software.

Investigating the performance of Superpave through the mechanistic–empirical (M-E) approach, field-evaluated performance, and laboratory test results: a case study on Ontario highways

In North America, highway agencies have started using Superpave as it incorporates a performance-based asphalt binder specification and a mix design analysis system. However, in a pavement management system (PMS), the performance of the pavement structure significantly influences management decisions. In this regard, accurate prediction and evaluation of performance is a very important aspect. With this in mind, this study investigates the performance of Superpave through the mechanistic–empirical (M-E) approach, field-evaluated performance, and laboratory performance tests. It considers 15 sections of highways from Ontario. The investigation found that the international roughness index (IRI) and permanent deformation are overpredicted in the M-E approach compared with field observations. Additionally, to better understand the performance, the dynamic modulus of asphalt mixtures and binder rheological testing are also performed. The master curve developed for the surface mixtures suggests a lower level of fatigue resistance that justifies the bottom-up fatigue failure in the field-observed scenarios.

Updating Traffic Inputs for Use in the Pavement Mechanistic-Empirical Design in Michigan

A study, completed in 2009, characterized traffic inputs in support of implementing the new Mechanistic-Empirical Pavement Design Guide for the state of Michigan. The inputs included monthly adjustment factors, hourly distribution factors, vehicle class distributions, axle groups per vehicle, and axle load distributions for different axle configurations. During the last 7 years, new traffic data were collected that reflect recent economic growth, and additional and downgraded weigh-in-motion (WIM) sites. Performance models for the Pavement mechanistic-empirical (ME) design were recently calibrated to local conditions in Michigan. Hence it was appropriate to incorporate these changes in the re-evaluation of traffic inputs. Weight and classification data were obtained from 41 WIM sites throughout the Michigan to develop Level 1 (site-specific) traffic inputs. Cluster analyses were conducted to develop Level 2A inputs. Classification models (decision trees, random forests) were developed to assign a new site to these clusters; however, this proved difficult, hence, an alternative, simplified method to develop Level 2B inputs by grouping sites with similar attributes was also adopted. The optimal set of attributes for developing Level 2B inputs was identified by using an algorithm developed in this study. The effects of the developed hierarchical traffic inputs on the predicted performance of rigid and flexible pavements were investigated using Pavement-ME. Based on the statistical and practical significance of the life differences, appropriate levels were established for each traffic input. The methodology for developing traffic inputs is intuitive and could easily be adopted by Michigan Department of Transportation for future updates.

Artificial Neural Networks for Predicting the Response of Unbonded Concrete Overlays in a Faulting Prediction Model

Transverse joint faulting is a common distress in unbonded concrete overlays (UBOLs). However, the current faulting model in Pavement mechanistic-empirical (ME) is not suitable for accurately predicting the response of UBOLs. Therefore, to develop a more accurate faulting prediction model for UBOLs, the first step was to develop a predictive model that would be able to predict the response (deflections) of these structures. To account for the conditions unique to UBOLs, a computational model was developed using the pavement-specific finite element program ISLAB, to predict the response of these structures. The model was validated using falling weight deflectometer (FWD) data from existing field sections at the Minnesota Road Research Facility (MnROAD) as well as sections in Michigan. A factorial design was performed using ISLAB to efficiently populate a database of fictitious UBOLs and their responses. The database was then used to develop predictive models, based on artificial neural networks (ANNs), to rapidly estimate the structural response of UBOLs to environmental and traffic loads. The structural response can be related to damage through the differential energy concept. Future work will include implementation of the ANNs developed in this study into a faulting prediction model for designing UBOLs.

Investigation of accuracy of pavement mechanistic empirical prediction performance by incorporating Level 1 inputs

Abstract The recently developed AASHTOWare pavement mechanistic empirical (M-E) design requires advanced characterization of pavement materials. In particular, M-E Level 1 analysis requires materials input parameters to be obtained from the laboratory test results. In cooperation with the New Mexico Department of Transportation (NMDOT), six pavement sites were selected to compare M-E Level 1 predicted performances from laboratory tested data with the field performances. Pavement materials such as subgrade, base, asphalt concrete, binder, aggregate, etc., were collected from these pavements sites during construction. Laboratory dynamic modulus, fatigue endurance limit (FEL), dynamic shear rheometer, and resilient modulus tests were conducted on the collected materials. Using these parameters, pavement sections, as constructed, were modeled and analyzed in M-E at Levels 1, 2 and 3. The predicted distress quantities for international roughness index (IRI) and rutting were compared with the field collected data as well as among the three design levels. The analysis results demonstrated that M-E overpredicts IRI and rutting in the pavement compared to the field performance. It was also observed that Level 1 showed the lowest rutting, whereas the maximum rutting was found at Level 2. The dynamic modulus of AC layer was underpredicted at Levels 2 and 3 resulting in higher rutting and IRI.

Determining asphalt surface temperature using weather parameters

Abstract Temperature affects the structural response of asphalt concrete (AC) pavement. To evaluate the performance, it is important to accurately predict the pavement surface temperature. This study develops three regression models to determine the surface temperature of an asphalt pavement using field measured data such as air temperature, wind speed, wind direction, relative humidity, and solar radiation. Three models namely, 24-h model, day-time model and night-time model were developed based on one-year continuous data. The weather data were measured from October 2012 to October 2013 at an instrumented pavement section on Interstate I-40 near Albuquerque, New Mexico. The developed regression models were validated using October 2014 to June 2015 weather data, which were not used in developing the regression models. Surface temperatures predicted using the developed regression models were then compared to those predicted using the pavement mechanistic-empirical (ME) software's default model. It is observed that the regression models are better predictors of pavement surface temperatures than the ME default models. A parametric study was also performed in this study to identify the effects of weather parameters on the pavement surface temperature prediction. The authors of this study expect that this study will be useful in pavement design especially in cold regions.

IMPACT OF CONCRETE MATERIAL INPUTS ON PERFORMANCE OF CONTINUOUSLY REINFORCED CONCRETE PAVEMENT

Continuously reinforced concrete pavement (CRCP) is increasingly being used in the United States based on its longer service life with minimal maintenance. CRCP provides excellent performance for heavily loaded traffic volume, if designed and constructed properly. The performance of CRCP is evaluated based on punch-outs and pavement roughness according to the Pavement ME (mechanistic-empirical) Design procedure which is the state-of-the-art pavement design tool. Design of CRCP is dependent on various factors including traffic, climate, material inputs and pavement geometry. This study focuses on evaluating the effects of concrete mechanical and thermal properties and traffic volume on CRCP performance. CRCP design simulations were conducted using Pavement ME Design software version 2.3 to evaluate the effects of material input data on CRCP performance and it was observed that there is significant difference between the performance predictions with time series data in comparison to the default data. The impact of material inputs is more significant on punch-outs distress as compared to the pavement roughness. This necessitates the importance of lab tested material inputs for accurate design of CRCP. The impact of traffic volume on CRCP performance was also evaluated and it was found that with increase in traffic volume, the pavement distresses increases and the performance decreases.

Performance Evaluation of Whitetopping with Improved Design Practices in Texas

Abstract The design method of whitetopping in Texas is a method suggested by the American Concrete Pavement Association (ACPA). However, one of the limitations on this particular design method is that an existing asphalt pavement condition is not considered for slab thickness. Recently, the Texas Department of Transportation developed an improved whitetopping design procedure considering mechanistic-empirical (ME) behavior. In the present study, performance of whitetopping projects constructed by the ACPA design method in Texas were investigated, and improved design details were suggested in order to minimize distresses, such as corner break and slab movement, in transition areas. To evaluate the structural performance of whitetopping, various tests, including the falling weight deflectometer, ground penetration radar, and investigation of concrete coring were conducted on US 90. According to the field survey and tests results, most of the distress was caused by insufficient slab support conditions related to asphalt concrete pavement materials and inadequate slab thickness design. In addition, early-age behavior of whitetopping constructed by the improved ME design method was evaluated and analyzed, and various gage installations, such as vibrating wire strain gage, electric resistance-type strain gages, porous nonstress cylinder, and impervious nonstress cylinder, are discussed.

Thermo-mechanical finite element prediction of the structural long-term response of asphalt pavements subjected to periodic traffic load: Tire-pavement interaction and rutting

Permanent, inelastic deformations of one or several pavement and subgrade layers originate from insufficient material (mix rutting) or structural design (subgrade rutting) of the asphalt pavement setting subjected to periodic traffic load (frequency- and load-dependent). Several studies revealed that the correct numerical assessment of the complex tire-pavement interaction (detailed consideration of tire and pavement) is crucial for reliable and accurate long-term predictions. In this contribution, the structural long-term response of asphalt pavements (modeled as elastoplastic solid at large strains) under periodic traffic load is thermo-mechanically investigated within the finite element method (FEM). To efficiently predict irreversible, permanent deformations of the pavement surface due to repetitive tire overrun, the time homogenization technique in combination with an Arbitrary Lagrangian Eulerian (ALE) formulation for the finite element (FE) discretized tire and pavement structures is employed. Time homogenization is used to deal with different time scales in the response of the tire-pavement system to short-term loading (tire overrun), day-night as well as seasonal temperature changes over several years. Different aspects and influence quantities for the problem considered are discussed and enable a qualitative prediction of pavement rutting via a fully FE based approach for tire and pavement instead of traditionally used mechanistic-empirical (M-E) approaches.

Development and Demonstration of Hot Mix Asphalt Design Guidance for Mix Performance-Related Specifications

As they adopt mechanistic-empirical (ME) methods for flexible pavement design, road agencies are starting to incorporate performance-related specifications (PRS) for hot mix asphalt materials to assure that the as-built materials meet the performance requirements assumed in the pavement structural design. PRS pose new challenges for materials producers and contractors who have never had to relate volumetric mix design parameters to performance requirements such as fatigue life and rutting resistance. The objective of this paper is to describe the development of guidance for materials producers and contractors to support their decision making with regard to changes to mix designs to meet PRS requirements. The guidance was first developed based on known experience. To validate the guidance and demonstrate its usage, a production mix meeting Superpave volumetric requirements for California state highways was selected as the starting point for a set of adjustments applied to the mix. The effects of each adjustment were evaluated by conducting laboratory tests to determine mechanistic performance parameters used in structural design. The mechanistic performance parameters evaluated in this study include stiffness, fatigue resistance, and rutting resistance. The initial experience-based mix design guidance was found to be generally consistent with the laboratory test results for the example mix, albeit with some minor exceptions. The mix design guidance was then revised based on findings from this study. It is recommended that the revised guidance be used as non-mandatory advisory information for projects with PRS and that it be further improved with more results.

Highway Asset Management Framework for Longevity of Infrastructure – A Case Study for China-Pakistan Economic Corridor

Pavement management system (PMS) affords objective information and useful data analysis to make consistent, cost-effective, and defensible decisions related to the pavement preservation at network and project level. Pavement functional and structural evaluations using performance indicators such as international roughness index (IRI), surface distress, rutting, deflection data etc. are the key element of PMS. A departure from exiting stand-alone evaluation practices, this research presents project-level functional and structural evaluation and its integration into a PMS framework for more accurate and realistic forecasting of the pavement needs over the analysis period. A case study of an in-service pavement is presented to demonstrate the applicability of proposed framework. Owing to the non-availability of time-series performance data at present, pavement serviceability and structural capacity data before and after of an in-service pavement rehabilitation was explored for overlay treatments (conventional and crumb rubber- modified (CRM) asphalt mixtures) effectiveness analyses. Results of functional evaluation quantified the extent to which CRM improves the functional performance of the pavement, in terms of the drop in IRI. CRM asphalt mixture exhibited relatively superior functional performance in contrast to conventional asphaltic wearing course by a margin of 8% higher drop in IRI, on average. Non-destructive testing technique for pavement structural evaluation using falling weight deflectometer (FWD) data was employed to assess structural capacity of pavements. A computer-aided program was developed for estimation of structure number effective (SNeff) to be used as an input for overlay design using AASHTO empirical method. Moreover, mechanistic-empirical (M-E) design method was employed using evaluation of layer moduli and overlay design (ELMOD) program for overlay thickness design and pavement remaining service life (RSL). Comparison of pre- and post-rehabilitation deflection data endorsed roadbed soil stiffness and structural adequacy. RSL estimated through traffic data (truck load repetitions) validated the results obtained using empirical and M-E methods. Application of KENPAVE program for evaluation of pavement overlay thickness design was also demonstrated by conducting damage analysis. Integrating structural evaluation with functional evaluation into an overall framework of PMS is envisaged to provide systematic and objective procedures for monitoring and evaluating pavement performance, selecting optimal type of treatment and its thickness design.

Statistical Distributions of Pavement Damage Associated with Overweight Vehicles: Methodology and Case Study

The trucking industry is the primary mode of transporting for goods and commodities in the United States. Currently, there is an increasing trend in the use of overweight (OW) vehicles on the highway network. State highway agencies (SHAs) are challenged to address this increase, particularly relative to associated pavement damage. In this study, a probabilistic method was developed to evaluate rutting and fatigue cracking damage caused by OW vehicles under different road and environmental conditions. The influential input parameters in this analysis included gross vehicle weight (GVW), axle configuration, axle weight, pavement temperature, and vehicle-miles traveled (VMT). Necessary information was obtained by analyzing more than 170,000 entries of a Nevada Department of Transportation (NDOT) OW permit database. The developed model was based on mechanistic-empirical (ME) approach and considered asphalt concrete (AC) viscoelastic characterization. The results of this study were distributions of AC critical responses, load equivalency factors (LEFs), and relative damage factors (RDFs). The analysis showed that load equivalent factor (LEF) distributions could be incorporated in pavement design methods to account for OW vehicles. Furthermore, the damage induced by specific OW vehicles could be assessed using the relative damage factor (RDF) concept and may be efficiently used by SHAs during the permit application process. A case study was presented illustrating the impact of an OW axle configuration on pavement damage. Finally, a method was suggested for developing RDF tables with damage ranges corresponding to different axle configurations and the GVW that could be a tool for SHAs to evaluate and understand pavement damage induced by OW vehicles.

Data needs and implementation of the Pavement ME Design

ABSTRACTHighway agencies across the United States are moving from empirical design procedures towards the mechanistic-empirical (ME) based pavement design. The Pavement ME Design presents a new paradigm shift with several important improvements, and it requires extensive data needs for full implementation. Based on extensive research conducted in the past two decades, this paper provides a comprehensive review of the data needs and processes related to the Pavement ME Design. A full spectrum of the art-of-the-state implementation activities in sensitivity analysis, climate data, traffic inputs, material characterization, and local calibration approaches and practices have been investigated. Special emphasis is on Weigh-in-Motion (WIM) data processing algorithms and software developed by the authors, and the interpretation of the damage models in the Pavement ME Design. The efforts in this paper can aid in the continuous implementation and enhancement of the Pavement ME Design.

Incorporating spatial variability of pavement foundation layers stiffness in reliability-based mechanistic-empirical pavement performance prediction

Abstract Adequate pavement quality and performance are critical for road users’ safety, ride comfort, vehicle operation and travel delay costs, and vehicle durability. An accurate and robust pavement design is essential for realistic life cycle cost analysis, as well as overall management of the infrastructure. Compared to deterministic design methods, probabilistic methods are more realistic and can capture the inherent uncertainty in pavement and foundation materials; and loading conditions. In this study, spatial variability and systematic measurement errors in foundation layers’ (including the base and subgrade layers) stiffness are incorporated in reliability-based mechanistic-empirical (ME) pavement performance models. Geospatial models are used to characterize both, the spatial variability and systematic measurement errors. To predict the long term pavement performance, the geospatial models were used to construct stochastic finite element (FE) models, which were then used to predict the performance based on the mechanistic-empirical pavement design guide equations (MEPDG). It is found that the typical covariance functions, also known as semivariograms or variograms, should be handled carefully when used in probabilistic performance modeling. Separating the inherent spatial variability from other uncertainties is necessary for performing risk and reliability analysis. Moreover, incorporating the inherent spatial variability in the stochastic FE models can alter the location of the critical response as described in the MEPDG.