Dynamic Modulus of Asphalt Mixtures for Development of Korean Pavement Design Guide

2007 ◽  
Vol 35 (2) ◽  
pp. 100045 ◽  
Author(s):  
K Lee ◽  
H Kim ◽  
N Kim ◽  
Y Kim
Keyword(s):  
Dynamic Modulus ◽  
Asphalt Mixtures ◽  
Pavement Design ◽  
Design Guide

scholarly journals Effect of Different Rheological Models on the Distress Prediction of Composite Pavement

Materials ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 229
Author(s):  
Ki Hoon Moon ◽  
Augusto Cannone Falchetto ◽  
Hae Won Park ◽  
Di Wang
Keyword(s):  
Dynamic Modulus ◽  
Asphalt Mixtures ◽  
Pavement Design ◽  
Reflective Cracking ◽  
Rheological Models ◽  
Pavement Distress ◽  
Upper Limits ◽  
Distress Prediction ◽  
Design Guide ◽  
Cam Model

In this paper, three different rheological models including a newly developed formulation based on the current Christensen Anderson and Marateanu (CAM) model, named sigmoidal CAM model (SCM), are used to estimate the evolution of roughness, rutting, and reflective cracking in a typical composite pavement structure currently widely adopted in South Korea. Three different asphalt mixtures were prepared and dynamic modulus tests were performed. Then, the mechanistic-empirical pavement design guide (MEPDG) was used for predicting the progression of the pavement distress and to estimate the effect of the three different models on such phenomena. It is found that the three different mathematical models provide lower and upper limits for roughness, rutting, and reflective cracking. While the CAM model may not be entirely reliable due to its inability in fitting the data in the high-temperature domain, SCM might result in moderately more conservative pavement design.

Evaluating Dynamic Modulus for Indiana Mechanistic-Empirical Pavement Design Guide Practice

2019 ◽  
Vol 2673 (2) ◽  
pp. 346-357 ◽  
Author(s):  
Dario Batioja-Alvarez ◽  
Jusang Lee ◽  
Tommy Nantung
Keyword(s):  
Dynamic Modulus ◽  
Functional Performance ◽  
Aggregate Size ◽  
Asphalt Mixtures ◽  
Pavement Design ◽  
Maximum Aggregate Size ◽  
Pavement Distress ◽  
Design Guide ◽  
Pavement Structures ◽  
Level I

After the implementation of the Mechanistic-Empirical Pavement Design Guide (MEPDG) in Indiana, an overall evaluation of the stiffness characteristics of local AC mixtures and the ability of level III MEPDG predictive equations to estimate dynamic modulus (E*) with local mixtures was required. Therefore, the primary objectives of this study were to identify significant differences among Indiana asphalt mixtures, to evaluate the performance of commonly used E* predictive models, and to assess the influence of level III E* input on the pavement design life of typical pavement structures. It was found that Indiana mixtures do not show extensive variability among mixtures having the same nominal maximum aggregate size. When conducting a statistical analysis to group asphalt mixtures having similar characteristics, few mixtures were left out of the groups. In general, it was observed that mixtures having Ndes equal to 75, showed the lowest E* values along the entire frequency range. The Witczak 1-37A showed the most accurate and less biased E* predictions for Indiana mixtures. It showed the highest R2, and the least deviation from the measured E* values. However, predicted E* input values produced higher levels of pavement distress compared with measured E* values, indicating general overprediction. Besides, using level III (predictive) rather than level I (measured) E* input values can influence the pavement thickness design due to the functional performance (i.e., the International Roughness Index (IRI)). When a structural performance (i.e., bottom-up cracking) was taken into consideration, no influence of the E* input type on the design AC layer thickness was observed.

Dynamic Modulus of Western Australia Asphalt Wearing Course

2014 ◽  
Vol 71 (3) ◽  
Author(s):  
Gunawan Wibisono ◽  
Hamid Nikraz
Keyword(s):  
Western Australia ◽  
Dynamic Modulus ◽  
Asphalt Mixtures ◽  
Predictive Equation ◽  
Master Curves ◽  
Time Rate ◽  
Low Frequencies ◽  
Design Guide ◽  
Preliminary Study ◽  
Air Void

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

Evaluation of the Response Analysis Approach Used in the Mechanistic-Empirical Pavement Design Guide

2020 ◽  
pp. 036119812097401
Author(s):  
Guozhi Fu ◽  
Yanqing Zhao ◽  
Wanqiu Liu ◽  
Changjun Zhou
Keyword(s):  
Phase Angle ◽  
Dynamic Modulus ◽  
Master Curve ◽  
Compressive Strain ◽  
Pavement Design ◽  
Base Layer ◽  
Response Analysis ◽  
Rate Dependency ◽  
Rate Dependent ◽  
Design Guide

Asphalt concrete (AC) is a typical viscoelastic material exhibiting rate-dependent behavior. The rate-dependency of AC should be properly taken into consideration in pavement response analysis to accurately evaluate pavement performance and life. In the Mechanistic-Empirical Pavement Design Guide (MEPDG), the dynamic modulus master curve is used to account for the rate-dependency of the dynamic modulus of AC. However, the rate-dependent phase angle is ignored and a constant phase angle of 0 is assumed. The partial characterization of rate-dependent properties of AC in the MEPDG may lead to inaccurate results. This study compares the pavement responses computed using the MEPDG approach and the layered viscoelastic theory (LVET) which utilizes the complex modulus master curve to fully characterize the rate-dependent properties of AC. Typical three-layer pavement structures were analyzed at three temperatures (−10°C, 20°C and 50°C) and four speeds (10, 40, 80 and 120 km/h). The results show that the horizontal tensile stresses at the bottom of cement-treated base layer obtained from the two approaches are almost the same, and for other responses analyzed, the results obtained from the MEPDG approach are larger than those from the LVET approach, especially for the responses in the AC layer. The normalized difference of the vertical compressive strain at the mid-depth of the AC layer between the two approaches can be up to 100% and that for the horizontal tensile strain at the bottom of the AC layer can be more than 50%.

A Modified Rheological Model for the Dynamic Modulus of Asphalt Mixtures

2020 ◽  
Author(s):  
Augusto Cannone Falchetto ◽  
Ki Hoon Moon ◽  
Di Wang ◽  
Hae-Won Park
Keyword(s):  
Dynamic Modulus ◽  
Rheological Model ◽  
Asphalt Mixtures ◽  
Pavement Design ◽  
Experimental Results ◽  
Alternative Formulation ◽  
Sigmoidal Function ◽  
Rheological Models ◽  
Design Software ◽  
Cam Model

In this paper, five rheological models, including a newly developed formulation based on the combination of the Christensen-Anderson-Marasteanu (CAM) model and the sigmoidal function, are used to evaluate the dynamic modulus of three different asphalt mixtures types. The effectiveness of the models in representing the experimental results is graphically and statistically compared. Clear differences in dynamic modulus computation are observed when using sigmoidal function-based models and CAM formulations. The newly introduced CAM model modified by the sigmodal function appears to provide reasonable fitting compared to the previously developed models and may represent an alternative formulation to be evaluated in the current pavement design software.

Evaluation of the Effect of Lime Modification on the Dynamic Modulus Stiffness of Hot-Mix Asphalt

2005 ◽  
Vol 1929 (1) ◽  
pp. 10-19 ◽  
Author(s):  
Javed Bari ◽  
Matthew W. Witczak
Keyword(s):  
Dynamic Modulus ◽  
Hot Mix Asphalt ◽  
High Volume ◽  
Hydrated Lime ◽  
Standard Test ◽  
Pavement Design ◽  
State University ◽  
Arizona State University ◽  
Design Guide ◽  
Pavement Structures

Hydrated lime is often used as a mineral filler or antistripping additive in hot-mix asphalt (HMA). Many agencies across North America require the use of lime in all HMA mixtures being placed on high-volume roadways. Despite this wide use of lime, its effects on the HMA mixture dynamic modulus (E*) stiffness have rarely been evaluated. The new mechanistic–empirical (M-E) pavement design guide, Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures, developed under NCHRP Project 1–37A uses E* as the primary material property of asphalt mixtures for the HMA characterization. A comprehensive study was completed at Arizona State University to assess the effect of lime addition on the E* stiffness of HMA mixtures. The study demonstrated that the standard test and design methodologies of the new M-E pavement design guide could be used effectively for lime-modified HMA mixes. With these methodologies, hydrated lime was found to increase the E* of HMA mixtures by 17% to 65% across the range of mixtures, lime contents, and temperature, with an overall average of 25% increase found from 17 mixture–lime percentage combinations across six different HMA mixes. This paper also outlines a provisional protocol for evaluating the E* master curve for lime-modified HMA mixtures using any of the three hierarchical levels found in the new NCHRP Project 1–37A pavement design guide.

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

2021 ◽  
pp. 036119812199670
Author(s):  
Zhe “Alan” Zeng ◽  
Kangjin “Caleb” Lee ◽  
Youngsoo Richard Kim
Keyword(s):  
Dynamic Modulus ◽  
Master Curve ◽  
Pavement Design ◽  
Asphalt Pavements ◽  
Levels Of Analysis ◽  
Damage Factor ◽  
Master Curves ◽  
Pavement Rehabilitation ◽  
Design Guide ◽  
Level 1

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.

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