01552767

Component

https://doi.org/10.3141/2507-10

Since the introduction of the dynamic modulus E* concept in the recent Mechanistic–Empirical Pavement Design Guide, there has been considerable interest in establishing reliable prediction models for E*. An investigation of the effectiveness of commonly used predictive models shows that E* predictions exhibit significant scatter around the measured values, with percentage of errors reaching about ±200%. A need exists for characterizing the uncertainties that are inherent in E* to serve as input to any future robust reliability analysis that aims at properly determining the probability of unsatisfactory performance of asphalt pavement systems. The primary objective of this study was to present a probabilistic model that would allow the user to determine a priori probability distribution for E* given knowledge about temperature and frequency. The seven-parameter model was based on the sigmoidal function and the shift factor that related reduced frequency to real frequency and temperature. The model was calibrated on the basis of a well-known published database that included 7,400 laboratory measurements of E* for 346 asphalt mixes. Monte Carlo simulations were used to propagate the uncertainties in the seven model parameters and determine realistic estimates of the mean, coefficient of variation, and probability distribution of E* at different frequencies and temperatures. Results showed that E* could be modeled by using a lognormal distribution with a mean that was estimated from the mean values of the parameters and a coefficient of variation that varied from a minimum of 0.55 for high values of reduced frequency to a maximum of 1.55 for lower values of reduced frequency.

01593337

15-4392

English

pp 90–99

2015

Transportation Research Record: Journal of the Transportation Research Board

9780309369336

Print

Figures (6) ; References (34)

Distributions (Statistics); Dynamic modulus of elasticity; Frequency (Electromagnetism); Hot mix asphalt; Monte Carlo method; Simulation; Temperature

Mechanistic-Empirical Pavement Design Guide

Master curves; Probabilistic models

Highways; Materials; Pavements

TRIS, TRB, ATRI

Dec 30 2014 1:26PM

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• Dynamic Modulus of Asphalt Mixtures: Evaluation of Effects on Pavement Performance Prediction
• Dynamic Modulus Prediction of Asphalt Concrete Mixtures Through Computational Micromechanics
• Flow Number Test and Assessment of AASHTO TP 79-13 Rutting Criteria: Comparison of Rutting Performance of Hot-Mix and Warm-Mix Asphalt Mixtures
• Investigating Reflective Cracking Mechanisms in Grid-Reinforced Asphalt Specimens: Use of Four-Point Bending Notched Beam Fatigue Tests and Digital Image Correlation
• Laboratory-Measured Dynamic Modulus and Predicted Performance of Asphalt Mixtures: Effects of Specimen Orientation
• Measurement of G* in Fine Asphalt Mixes: Dynamic Mechanical Analyzer Shear Test Implementation
• Prediction of Fatigue Failure at Asphalt Concrete Layer Interface from Monotonic Testing
• Prediction of Field Aging Gradient in Asphalt Pavements
• Testing and Modeling of Fine Aggregate Matrix and Its Relationship to Asphalt Concrete Mix
• Top-Down Fatigue Cracking in High-Temperature Environments
• Understanding Sources of Variability of Overlay Test Procedure
• Use of Mechanistic Models to Investigate Fatigue Performance of Asphalt Mixtures: Effects of Asphalt Mix Design Targets and Compaction