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Backcalculation of in-situ nonlinear viscoelastic properties of subgrade using a finite element-based machine learning approach

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成果类型:
期刊论文
作者:
Fan, Haishan;Gu, Fan;Zhang, Junhui;Peng, Junhui;Zheng, Jianlong
通讯作者:
Gu, F
作者机构:
[Peng, Junhui; Gu, Fan; Zhang, Junhui; Fan, Haishan] Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Changsha, Hunan, Peoples R China.
[Zheng, Jianlong] Changsha Univ Sci & Technol, Natl Engn Res Ctr Highway Maintenance Technol, Changsha 410004, Hunan, Peoples R China.
通讯机构:
[Gu, F ] C
Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Changsha, Hunan, Peoples R China.
语种:
英文
关键词:
Subgrade;Nonlinear stress-dependency;Viscoelasticity;Artificial neural network;Convolutional neural network;Light weight deflectometer
期刊:
Transportation Geotechnics
ISSN:
2214-3912
年:
2024
卷:
45
页码:
101205
基金类别:
CRediT authorship contribution statement Haishan Fan: Software, Formal analysis, Methodology, Validation, Writing – original draft. Fan Gu: Formal analysis, acquisition, Project administration, Writing – review & editing. Junhui Zhang: Supervision, Project administration. Junhui Peng: Data curation. Jianlong Zheng: Supervision.
机构署名:
本校为第一且通讯机构
院系归属:
交通运输工程学院
摘要:
This study aimed to develop a method to determine nonlinear viscoelastic properties of subgrade soil using the light weight deflectometer (LWD) test. Firstly, a constitutive model was developed to accurately characterize the nonlinear viscoelastic behavior of subgrade soil. A User-Defined Material Subroutine (UMAT) was coded to define this constitutive model in ABAQUS, which was verified by the virtual triaxial test analysis. Secondly, a numerical model was developed to simulate the LWD test, which considered the true LWD load pattern and the c...

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