Stability prediction for asphalt mixture based on evolutional characterization of aggregate skeleton

被引:47
作者
Jin, Can [1 ]
Wan, Xiaodong [1 ]
Liu, Pengfei [2 ]
Yang, Xu [3 ]
Oeser, Markus [2 ]
机构
[1] Hefei Univ Technol, Sch Automot & Transportat Engn, Hefei, Anhui, Peoples R China
[2] Rhein Westfal TH Aachen, Inst Highway Engn, Mies van der Rohe Str 1, D-52074 Aachen, Germany
[3] Changan Univ, Sch Highway, Xian, Peoples R China
基金
中国国家自然科学基金;
关键词
FORCE CHAINS; PERFORMANCE;
D O I
10.1111/mice.12742
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The aggregate skeleton significantly influences the deformation resistance of asphalt mixtures. A stable skeleton has good interlocking effects and load transfer ability to resist deformation of mixtures during loading. Therefore, the methodology to accurately evaluate the morphology of aggregate skeleton based on the mechanism of load transfer inside mixtures is of great value to predict the mixture stability. The objective of this study is to predict and validate mixture stability from the perspective of evolutional morphologies of the skeleton. The methodology has three main steps, which are as follows: (1) the initial morphology of the aggregate skeleton is characterized and evaluated; (2) the topological and non-topological evolutions of the skeleton morphology during external loading are characterized; and (3) the strain energy of the mixture and the stress distribution on contact regions in the skeleton are used to analyze and validate the prediction of the mixture's stability. Simulations of uniaxial displacement-controlled tests of three cylindrical specimens drilled in a field-compacted test track were conducted, and 7.5, 15, 22.5, and 30 s were selected as time points in the simulation to analyze mixture stability. Results indicate that a mixture has a better load-bearing capacity when its initial skeleton contains more chains with higher evaluation indices, which proves the reliability of the stability prediction using the proposed method.
引用
收藏
页码:1453 / 1466
页数:14
相关论文
共 39 条
[21]   Effects of asphalt mixture structure types on force chains characteristics based on computational granular mechanics [J].
Liu, Guoqiang ;
Han, Dongdong ;
Zhao, Yongli ;
Zhang, Jiupeng .
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 2022, 23 (04) :1008-1024
[22]   Modelling and evaluation of aggregate morphology on asphalt compression behavior [J].
Liu, Pengfei ;
Hu, Jing ;
Wang, Dawei ;
Oeser, Markus ;
Alber, Stefan ;
Ressel, Wolfram ;
Falla, Gustavo Canon .
CONSTRUCTION AND BUILDING MATERIALS, 2017, 133 :196-208
[23]   Micromechanical response of aggregate skeleton within asphalt mixture based on virtual simulation of wheel tracking test [J].
Ma, Tao ;
Zhang, Deyu ;
Zhang, Yao ;
Hong, Jinxiang .
CONSTRUCTION AND BUILDING MATERIALS, 2016, 111 :153-163
[24]   Generative adversarial network for road damage detection [J].
Maeda, Hiroya ;
Kashiyama, Takehiro ;
Sekimoto, Yoshihide ;
Seto, Toshikazu ;
Omata, Hiroshi .
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2021, 36 (01) :47-60
[25]   A new DEM-based method to predict packing density of coarse aggregates considering their grading and shapes [J].
Mostofinejad, Davood ;
Reisi, Mohammad .
CONSTRUCTION AND BUILDING MATERIALS, 2012, 35 :414-420
[26]   Characterization of force chains in granular material [J].
Peters, JF ;
Muthuswamy, M ;
Wibowo, J ;
Tordesillas, A .
PHYSICAL REVIEW E, 2005, 72 (04)
[27]   A new framework for understanding aggregate structure in asphalt mixtures [J].
Pouranian, M. Reza ;
Haddock, John E. .
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 2021, 22 (09) :1090-1106
[28]  
Ran Xu, 2019, Key Engineering Materials, V803, P253, DOI 10.4028/www.scientific.net/KEM.803.253
[29]   Investigating mechanical characteristics of aggregate structure for road materials [J].
Ren, Jiaolong ;
Yin, Chao .
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 2022, 23 (02) :372-386
[30]   Measurement of coarse aggregates movement characteristics within asphalt mixture using digital image processing methods [J].
Shi, Liwan ;
Wang, Duanyi ;
Jin, Changning ;
Li, Ben ;
Liang, Hehao .
MEASUREMENT, 2020, 163