Abstract
An innovative approach was developed in this research to generate aggregates based on real shape through DEM. The shape indexes of aggregates were captured by Aggregate Image Measuring System (AIMS). The output was then processed byMATLAB to obtain the edge points of particles. The edge points were used to generate aggregate in DEM. Based on the former procedures, the aggregates morphological database that record shape indexes were established. The generation of DE models was optimized in the study to approach more realistic model geometries. Clump-based models with real morphologies were compared with laboratory tests and conventional ball-based models in the repose angle test. The clump-based model fitted better with the laboratory tests compared with ball-based models. Finally, a pavement compaction model was established. The influence of aggregate gradation and the movement of asphalt mixture during the compaction were investigated.
| Original language | English |
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| Title of host publication | Advances in Materials and Pavement Performance Prediction II - Contributions to the 2nd International Conference on Advances in Materials and Pavement Performance Prediction, AM3P 2020 |
| Editors | K. Anupam, A.T. Papagiannakis, A. Bhasin, D. Little |
| Publisher | CRC Press/Balkema |
| Pages | 109-112 |
| Number of pages | 4 |
| ISBN (Electronic) | 9780367461690 |
| Publication status | Published - 9 Dec 2020 |
| Event | 2nd International Conference on Advances in Materials and Pavement Performance Prediction, AM3P 2020 - San Antonio, United States Duration: 27 May 2020 → 29 May 2020 |
Publication series
| Name | Advances in Materials and Pavement Performance Prediction II - Contributions to the 2nd International Conference on Advances in Materials and Pavement Performance Prediction, AM3P 2020 |
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Conference
| Conference | 2nd International Conference on Advances in Materials and Pavement Performance Prediction, AM3P 2020 |
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| Country/Territory | United States |
| City | San Antonio |
| Period | 27/05/20 → 29/05/20 |
Funding
This research is supported by the research project of the National Natural Science Foundation of China (NSFC) (No. 51878063, 51578075), and is based on a part of the research project carried out at the request of the German Research Foundation (DFG), under research project No. OE 514/1-2 (FOR2089). The authors also appreciate the support from China Scholarship Council (Grant No. 201706560034, Grant No. 201706560033). The authors are solely responsible for the content.