Manara - Qatar Research Repository
1-s2.0-S2214509522007124-main.pdf (1.95 MB)

Evaluation and calibration of dynamic modulus prediction models of asphalt mixtures for hot climates: Qatar as a case study

Download (1.95 MB)
journal contribution
submitted on 2023-11-13, 06:47 and posted on 2023-11-13, 08:00 authored by Ahmad Al-Tawalbeh, Okan Sirin, Mohammed Sadeq, Haissam Sebaaly, Eyad Masad

The dynamic modulus (|E*|) of asphalt mixtures is one of the most important inputs in Mechanistic-Empirical (ME) pavement analysis and design. Several models have been developed to predict the dynamic modulus based on mixture volumetrics and material properties. This study aimed to calibrate and validate two commonly used models (i.e., Hirsch model and Alkhateeb model) for predicting the dynamic modulus of asphalt mixtures in Qatar. Based on the study outcomes, the Hirsch model was found to have a high prediction performance of asphalt mixture moduli before calibration with a coefficient of determination (R2) of 87.2 % between predicted and measured values. This R2 value improved slightly after calibration to 89.2 %, Alkhateeb model, on the other hand, had a coefficient of determination of 70.8 % before calibration, which also improved to 89.2 % after calibration. The moduli predicted by the Hirsch model before and after calibration were employed in this study to perform a mechanistic-empirical analysis of the performance of various typical pavement sections in Qatar. According to the findings, the percentage change in the predicted fatigue damage due to the use of the calibrated Hirsch model reached more than 50 % with an average value of 17.33 %, while the percent change in rutting reached 14 % with an average value of 3.65 %. These results highlight the importance of using locally calibrated models for the dynamic modulus in order to improve performance predictions.

Other Information

Published in: Case Studies in Construction Materials
See article on publisher's website:


Open Access funding provided by the Qatar National Library



  • English



Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Qatar University
  • College of Engineering - QU
  • Seero Engineering Consulting LLC
  • Texas A&M University at Qatar

Geographic coverage


Usage metrics

    Qatar University



    Ref. manager