Experimental study on rheological characteristics and performance of high modulus asphalt binder with different modifiers

2017 ◽  
Vol 155 ◽  
pp. 26-36 ◽  
Author(s):  
Chao Wang ◽  
Hao Wang ◽  
Lidong Zhao ◽  
Dongwei Cao
2021 ◽  
Vol 294 ◽  
pp. 123629
Author(s):  
Chaohui Wang ◽  
Xiaolei Zhou ◽  
Huazhi Yuan ◽  
Haoyu Chen ◽  
Liwei Zhou ◽  
...  

Heliyon ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. e05982
Author(s):  
Daryadokht Masror Roudsari ◽  
Shahoo Feizi ◽  
Mahtab Maghsudlu

2017 ◽  
Vol 10 (3) ◽  
pp. 262-273 ◽  
Author(s):  
Yanping Sheng ◽  
Haibin Li ◽  
Jiuguang Geng ◽  
Yu Tian ◽  
Zuzhong Li ◽  
...  

2010 ◽  
Vol 168-170 ◽  
pp. 916-919
Author(s):  
Ke Fei Liu

Epoxy asphalt has fundamentally changed the thermoplastic of asphalt and endowed the asphalt with excellent physical and mechanical properties. This paper analyses the developing technical requirement of thermosetting epoxy asphalt and points out its main problems during preparation and application process. Aiming at the steel deck paving characteristics, the author has independently developed epoxy asphalt binder and tested its performances, the results have showed that this binder can meet the basic requirement of various pavings, and its further research are in process.


2019 ◽  
Vol 148 ◽  
pp. 200-211 ◽  
Author(s):  
Xiaosong Cheng ◽  
Donggen Peng ◽  
Yonggao Yin ◽  
Shaohua Xu ◽  
Danting Luo

2021 ◽  
Author(s):  
Yahia Zakaria ◽  
Mayada Hadhoud ◽  
Magda Fayek

Deep learning for procedural level generation has been explored in many recent works, however, experimental comparisons with previous works are rare and usually limited to the work they extend upon. This paper's goal is to conduct an experimental study on four recent deep learning procedural level generators for Sokoban to explore their strengths and weaknesses. The methods will be bootstrapping conditional generative models, controllable & uncontrollable procedural content generation via reinforcement learning (PCGRL) and generative playing networks. We will propose some modifications to either adapt the methods to the task or improve their efficiency and performance. For the bootstrapping method, we propose using diversity sampling to improve the solution diversity, auxiliary targets to enhance the models' quality and Gaussian mixture models to improve the sample quality. The results show that diversity sampling at least doubles the unique plan count in the generated levels. On average, auxiliary targets increases the quality by 24% and sampling conditions from Gaussian mixture models increases the sample quality by 13%. Overall, PCGRL shows superior quality and diversity while generative adversarial networks exhibit the least control confusion when trained with diversity sampling and auxiliary targets.


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