Sherif M. El-Badawy

Performance of MEPDG Dynamic Modulus Predictive Models for Asphalt Concrete Mixtures: Local Calibration for Idaho

AbstractThe mechanistic-empirical pavement design guide (MEPDG) is the research version of the newly released DARWin-ME software by AASHTO. MEPDG includes two models for Levels 2 and 3 hot-mix asphalt (HMA) dynamic modulus (E*) predictions. The two models are NCHRP 1-37A and NCHRP 1-40D. The primary difference between the two is the binder stiffness parameter; viscosity or shear modulus. Moreover, MEPDG includes three levels for binder stiffness characterization: viscosity for Level 1 conventional binders, shear modulus for Level 1 superpave binders, and default values for Level 3. The influence of the binder characterization input level on the performance of the MEPDG E* predictive models is evaluated in this paper. To calibrate the models for Idaho, 27 HMA mixtures commonly used in Idaho were investigated. Results showed that the performance of the investigated models varied based on the temperature and the binder characterization method. The NCHRP1-37A E* model with MEPDG Level 3 binder inputs yielded ...

Evaluation of the MEPDG Dynamic Modulus Prediction Models for Asphalt Concrete Mixtures

HMA dynamic modulus (E*) is one of key inputs to the Mechanistic-Empirical Pavement Design Guide (MEPDG). There are two different E* models in the MEPDG; the NCHRP 1-37A viscosity-based model, and the NCHRP 1-40D, which is based on the binder shear modulus. This paper focuses on evaluating the influence of the binder characterization input level on the predicted E* in MEPDG. Laboratory E* tests were conducted on samples from 15 different HMA plant-produced mixtures. The shear modulus (G*) and phase angle (δ) for each binder were also determined in the laboratory. Results showed that MEPDG levels 1and 3 binder characterization inputs with both E* predictive models yielded E* values that are in excellent to fair agreement with laboratory measured E* . However, the 1-37A model showed better results than the 1-40D model. On the other hand, high bias in E* values was observed when level 1 binder characterization data was used.

Methodology to Predict Alligator Fatigue Cracking Distress Based on Asphalt Concrete Dynamic Modulus

This study focused on integrating the Mechanistic–Empirical Pavement Design Guide (MEPDG) methodology (NCHRP 1-37A and NCHRP 1-40D) with the simple performance test methodology (NCHRP 9-19) to develop a comprehensive bottom-up fatigue cracking distress prediction model based on the dynamic modulus in a methodology that can be implemented in a probabilistic performance-related specification (PRS) methodology for quality assurance (QA) of hot-mix asphalt (HMA) construction (NCHRP 9-22). The main approach for a comprehensive fatigue cracking–damage predictive methodology was to run MEPDG for a large number of simulations using combinations of inputs that were believed significant in classic load-associated bottom-up alligator fatigue cracking and to develop an accurate closed-form solution for the fatigue damage–cracking distress prediction based on the dynamic modulus of HMA. Four major studies have been completed for fatigue cracking prediction methodology: (a) development of a fatigue damage model based on a two-layer pavement system, (b) development of an asphalt concrete (AC) effective dynamic modulus model, (c) development of a predictive methodology to transform a multilayer to a two-layer pavement system, and (d) validation of the prediction process. The developed methodology approximately but accurately predicts the classic AC alligator fatigue cracking distress that is close to that from MEPDG. Thus, implementation of the methodology into the PRS-based QA system in NCHRP 9-22 is recommended.

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

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

Performance Evaluation of Construction and Demolition Waste Materials for Pavement Construction in Egypt

AbstractThis paper focuses on the feasibility of using construction and demolition (C&D) waste materials, particularly blends of recycled concrete aggregate (RCA) with recycled clay masonry (RCM), ...

Comparison of AASHTO 1993 and MEPDG considering the Egyptian climatic conditions

The current flexible pavement design system in Egypt was adopted primarily from the AASHTO 1993 Design Guide. It is an empirical design method based on the results of the late 1950s AASHO Road test with many limitations. Thus, the Mechanistic-Empirical Pavement Design Guide (MEPDG) which is now called Pavement ME Design was developed to overcome those limitations. Unlike the AASHTO 1993 method, MEPDG considers the variation in moisture and temperature on the mechanical properties of the pavement layers. The main objective of this paper is to compare the AASHTO 1993 and MEPDG methods based on a recently developed Egyptian climatic data. The AASHTO 1993 method was used to design the thickness of a typical flexible pavement structure, using two different levels of traffic (low and high) and two different types of subgrade strength (weak and strong). Then, the AASHTO 1993 designed structures were simulated with MEPDG to predict performance over 20 years of service life. The comparative analysis of AASHTO 1993 design guide and MEPDG revealed that although all pavement sections in this study were designed with the AASHTO 1993 method for the same serviceability loss, they exhibited different performance as predicted by MEPDG. The variation of the MEPDG-predicted performance of the AASHTO 1993 designed pavement structures increased with the increase in traffic level and decrease in the subgrade strength. This variation was different for different climatic conditions. For the Egyptian conditions, the predominant distress was rutting. Finally, the climatic conditions showed a significant effect on distress occurrence and time to failure, especially the AC rutting.

Prediction of the Subgrade Resilient Modulus for the Implementation of the MEPDG in Idaho

This paper focuses on developing two models for subgrade soil characterization for use in the Mechanistic-Empirical Pavement Design Guide (MEPDG). First, a multiple regression model can be used to predict R-value as a function of the soil plasticity index and percent passing No 200 sieve. Second, a Mr predictive model is based on the estimated R-value of the soil. Hence, the models can be used to estimate the Mr value as a Level 2 data input in the MEPDG. For the R-value model development, 8233 records were used from a historical database of Idaho Transportation Department (ITD) laboratory testing on subgrade soils. For the Mr model development, laboratory measured Mr values for different subgrade soils were gathered from literature nationwide. The R-values for these soils were then predicted using the developed R-value model. Hence, a relationship between the Mr and R-value was established. A limited sensitivity analysis was also conducted to address the influence of the material strength upon the predicted distresses by MEPDG.

Comparison of Idaho Pavement Design Procedure with AASHTO 1993 and MEPDG Methods

Several in service pavements located in different regions of Idaho that have been designed according to the ITD design method were redesigned using the AASHTO 1993 as well as the Mechanistic-Empirical Pavement Design Guide (MEPDG) procedures. All designs were conducted at a 50% reliability level. The nationally calibrated MEPDG (version 1.1) was used to predict the performance of the three design methods. Level 2 subgrade material characterization inputs were used in the MEPDG analysis. All other MEPDG inputs were level 3. Performance indicators predicted using MEPDG related to the three design methods were compared to each other. Results showed that, relative to AASHTO 1993 and MEPDG procedures, ITD design method significantly overestimates the thickness of the pavement structure, and particularly the thickness(s) of the unbound layer(s). On the other hand, the AASHTO 1993 and MEPDG guides show reasonable agreement on the resulting pavement structure.

Influence of Unbound Material Type and Input Level on Pavement Performance Using Mechanistic–Empirical Pavement Design Guide

For material characterization, the Mechanistic–Empirical Pavement Design Guide (MEPDG) has three input levels. Level 1 input values must be obtained through direct laboratory testing. Level 2 inputs are determined through correlations with other material properties. Level 3 inputs are simply typical default values. The level chosen for each design input parameter, however, may have a significant effect on project design, cost, performance, and reliability. The influence of the unbound granular base material type and material characterization input level on pavement performance, as predicted by MEPDG, was investigated for a typical flexible pavement section in climatic conditions in Egypt. Researchers analyzed seven types of virgin and recycled base materials at three weather stations representing climatic regions in Egypt: Alexandria, Cairo, and Aswan. For the typical pavement system, Level 1 data for the resilient modulus from measured laboratory values (k1, k2, and k3 elastic response coefficients) were used for the first set of MEPDG simulation runs. The second set of computer simulation runs was conducted with Level 2 data—values of the California bearing ratio—for the investigated materials. The final set of runs used default resilient modulus values for the investigated materials on the basis of the AASHTO class (Level 3). MEPDG-predicted pavement distresses and roughness for the three input levels and seven base materials were compared, and the results showed significant variation in predicted performance as a result of the change in the input level and material type.

Soil Reinforcement Using Recycled Plastic Waste for Sustainable Pavements

Resilient modulus (Mr) is a representative property for characterizing unbound granular materials and subgrade soils. It exhibits the elastic behavior as well as the load-bearing ability of pavement materials under cyclic traffic loads. This paper investigates the influence of using recycled plastic Polyethylene Terephthalate (PET) as a soil reinforcement material on the Mr of a clayey soil; ordinary soil in the delta region in Egypt. A comprehensive laboratory testing was conducted at Mansoura University Highway and Airport Engineering Laboratory (H&AE-LAB). The conducted testing includes standard engineering tests and repeated-loading triaxial tests (RLTT). Laboratory specimens were prepared at four different percentages of the recycled PET (0%, 0.2%, 0.6%, and 1.0%). RLTT results shows that the Mr of 0.6% PET-reinforced specimens increases by 58% compared to the Mr of the control specimen (0% PET). However, the Mr of the reinforced soil is found to decrease with the increase of PET percentage. Moreover, the universal Mr model exhibits excellent Mr predictions for the control and the PET-reinforced clay soil. Economically, the initial cost for constructing a 10-km road segment decreases by 8% using the 0.6% PET-reinforced Subgrade compared to the control Subgrade. Finally, damage analysis using the KENLAYER software is used to manifest the enhancement of pavement performance by reinforcing the Subgrade with PET.