Permanent Deformation of Asphalt Pavement Under Horizontal Moving Load

doi:10.3969/j.issn.1000-565X.2011.03.024

Journal of South China University of Technology (Natural Science Edition) ›› 2011, Vol. 39 ›› Issue (3): 125-129.doi: 10.3969/j.issn.1000-565X.2011.03.024

• Electronics, Communication & Automation Technology • Previous Articles Next Articles

Permanent Deformation of Asphalt Pavement Under Horizontal Moving Load

Li Ling-lin Huang Xiao-ming

School of Transportation,Southeast University,Naming 210096,Jiangsu,China

Received:2010-06-24 Revised:2010-10-13 Online:2011-03-25 Published:2011-02-01
Contact: 李凌林(1983-)，男，博士生，主要从事路基路面研究 E-mail:lilinglin_19830808@163.com
About author:李凌林(1983-)，男，博士生，主要从事路基路面研究
Supported by:
国家“863”计划项目(2006AA1IZ110)

Abstract

Abstract:

In order to find the cause of the severe asphalt permanent deformation in such sections as large longitudinal slope,traffic intersections and bus stations,the response of asphalt pavement to horizontal moving load is calculated with ABAQUS and its subroutine UTRACLOAD and DSLOAD.The results show that the vertical permanent deformations introduced by both the horizontal shear force and the vertical pressure are serious and need strictly control,that both the vertical permanent deformation amplitude and the cumulative permanent deformation of the pavement under the same horizontal load decrease with the acceleration of load moving,and that the sensitivity of horizontal moving load to vertical permanent deformation well matches that of the corresponding action time.

Key words: horizontal moving load, asphalt pavement, FEA, ABAQUS, permanent deformation

Li Ling-lin Huang Xiao-ming. Permanent Deformation of Asphalt Pavement Under Horizontal Moving Load[J]. Journal of South China University of Technology (Natural Science Edition), 2011, 39(3): 125-129.

[1]	LI Haiyan, YIN Haolin, LI Peng, et al.. Image Inpainting Algorithm Based on Dense Feature Reasoning and Mix Loss Function [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(9): 99-109.
[2]	WANG Shiyong, QIAN Guokang, LI Di, et al.. Real-Time Template Matching Method for Edge Features [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(9): 1-10.
[3]	LI Jiachun, LI Bowen, LIN Weiwei. AdfNet: An Adaptive Deep Forgery Detection Network Based on Diverse Features [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(9): 82-89.
[4]	DENG Kailing, WANG Duanyi, FANG Qiuping. Fracture Energy Test Method and Influencing Factors of Asphalt-Aggregate Interface [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(8): 12-20.
[5]	GUO Enqiang, FU Xinsha. Dropped Object Detection Method Based on Feature Similarity Learning [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(6): 30-41.
[6]	LI Xiaohua, YIN Shanshan, LI Jiewen, et al. Commutation Failure Detection Based on Temporal Feature of AC Maximum Current on the Valve-Side [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(7): 108-117.
[7]	LIU Yiqi, HUANG Zhipeng, YU Guangping, et al. Full Life-cycle Intelligent Detection, Diagnosis and Analysis for Sludge Bulking [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(6): 91-99,110.
[8]	CHEN Kejia, ZHENG Jingjing. Product Feature Extraction Method Based on Seed Constraint-LDA [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(6): 37-48,70.
[9]	YANG Chunling, LING Xi, LÜ Zeyu. Feature-domain Multi-Hypothesis Prediction Reconstruction Neural Network for Compressed Video Sensing [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(6): 80-90.
[10]	KONG Xiangyu, CHEN Yalin, LUO Jiayu, et al. Research on Multi-characteristic Complex Process Monitoring Method based on Partial Least Squares [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(6): 100-110.
[11]	SUN Xiaohe, SHI Chenghua, LIU Linghui, et al. Concrete Crack Image Recognition System Based on Improved Seed Filling Algorithm [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(5): 127-136,146.
[12]	YANG Chunling YANG Yajing. A Deep Neural Network Based on Layer-by-Layer Fusion of Multi-Scale Features for No-Reference Image Quality Assessment [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(4): 81-89,141.
[13]	LIU Xiaolei, DUAN Zhengyu, YU Qing, et al. Passenger Flow Forecast of Urban Rail Transit Based on Graph Convolution and Recurrent Neural Network [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(3): 21-27.
[14]	ZENG Jingxiang, ZHANG Jinxi, CAO Dandan, et al. Intelligent Detection of Asphalt Pavement Roughness with kNN Method [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(3): 50-56.
[15]	FU Xinsha, ZENG Yanjie, MA Li, et al. Weather Recognition of Highway Surveillance Scenes Based on Light-Weight Deep Neural Network [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(3): 1-8.

Permanent Deformation of Asphalt Pavement Under Horizontal Moving Load

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments