Effect of Long Term Oven Aging on Dynamic Modulus of Hot Mix Asphalt

2011 ◽  
Author(s):  
Dharamveer Singh ◽  
Musharraf Zaman ◽  
Sesh Commuri
Keyword(s):  
Dynamic Modulus ◽  
Hot Mix Asphalt

Laboratory Investigation of Pavement Backfilling Materials for Micro-Trenching in Cold Regions

2018 ◽  
Vol 2672 (12) ◽  
pp. 62-71
Author(s):  
Leila Hashemian ◽  
Vinicius Afonso Velasco Rios ◽  
Alireza Bayat
Keyword(s):  
Laboratory Investigation ◽  
Dynamic Modulus ◽  
Laboratory Tests ◽  
Hot Mix Asphalt ◽  
Cement Grout ◽  
Host Material ◽  
High Quality ◽  
Cold Temperatures ◽  
Asphalt Mixes ◽  
Freeze Thaw

This study investigated the performance of different materials in a micro-trench composite backfilling design. Laboratory tests were conducted to evaluate the effect of cold temperatures and freeze/thaw cycles on a cement grout and seven preparatory cold asphalt mixes. To compare the performance of cold mix asphalt and epoxy grout with hot mix asphalt as the host material, rutting tests and dynamic modulus tests at different loading frequencies and temperatures were conducted. Finally, laboratory scale micro-trench samples were prepared using different backfilling materials and were loaded using a wheel tracker after freeze/thaw conditioning. The results showed that cement grout could effectively be used to secure the conduit inside the trench. It was also concluded that using high-quality cold mix asphalt, a compatible material with hot mix asphalt, could improve micro-trench durability compared with epoxy grout.

Long term performance of warm mix asphalt versus hot mix asphalt

2013 ◽  
Vol 20 (1) ◽  
pp. 256-266 ◽  
Author(s):  
Ziari Hasan ◽  
Behbahani Hamid ◽  
Izadi Amir ◽  
Nasr Danial
Keyword(s):  
Hot Mix Asphalt ◽  
Warm Mix Asphalt ◽  
Long Term Performance ◽  
Term Performance

Accuracy of MEPDG Dynamic Modulus Predictions for Short-Term and Long-Term Aged Asphalt Mixtures

2019 ◽  
Vol 145 (3) ◽  
pp. 04019025
Author(s):  
Mir S. Arefin ◽  
Tanvir Quasem ◽  
Munir Nazzal ◽  
Samer Dessouky ◽  
Ala R. Abbas
Keyword(s):  
Dynamic Modulus ◽  
Asphalt Mixtures ◽  
Short Term ◽  
Aged Asphalt

Research of Rigid Pavement on Structure Combination of HMA Overlays

2013 ◽  
Vol 477-478 ◽  
pp. 765-769
Author(s):  
Tao Liu ◽  
Guang Wei Hu ◽  
Ying Chun Gu
Keyword(s):  
Sandwich Structure ◽  
Hot Mix Asphalt ◽  
Reflective Cracking ◽  
Rigid Pavement ◽  
Asphalt Overlays ◽  
Long Term Performance ◽  
Term Performance

To study the influence of structure combination on performance of rigid pavement hot-mix asphalt (HMA) overlays, four Hot-mix asphalt overlays are prepared for the research at the base of long-term performance of experimental roads. The results indicate that the SMA +AC+SMA sandwich structure can effectively restrain reflective cracking and rut. In addition, the structure can reduce the thickness and cost of pavement.

scholarly journals Advanced approaches to hot-mix asphalt dynamic modulus prediction

2008 ◽  
Vol 35 (7) ◽  
pp. 699-707 ◽  
Author(s):  
Halil Ceylan ◽  
Kasthurirangan Gopalakrishnan ◽  
Sunghwan Kim
Keyword(s):  
Prediction Model ◽  
Dynamic Modulus ◽  
Prediction Models ◽  
Hot Mix Asphalt ◽  
Logical Structure ◽  
Ann Model ◽  
Model Predictions ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
National Cooperative

The dynamic modulus (|E*|) is one of the primary hot-mix asphalt (HMA) material property inputs at all three hierarchical levels in the new Mechanistic–empirical pavement design guide (MEPDG). The existing |E*| prediction models were developed mainly from regression analysis of an |E*| database obtained from laboratory testing over many years and, in general, lack the necessary accuracy for making reliable predictions. This paper describes the development of a simplified HMA |E*| prediction model employing artificial neural network (ANN) methodology. The intelligent |E*| prediction models were developed using the latest comprehensive |E*| database that is available to researchers (from National Cooperative Highway Research Program Report 547) containing 7400 data points from 346 HMA mixtures. The ANN model predictions were compared with the Hirsch |E*| prediction model, which has a logical structure and a relatively simple prediction model in terms of the number of input parameters needed with respect to the existing |E*| models. The ANN-based |E*| predictions showed significantly higher accuracy compared with the Hirsch model predictions. The sensitivity of input variables to the ANN model predictions were also examined and discussed.

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