A simple hypoplastic model for unsaturated soils considering wetting collapse

IF 5.3 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers and Geotechnics Pub Date : 2024-09-30 DOI:10.1016/j.compgeo.2024.106781
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引用次数: 0

Abstract

Partially saturated soils are ubiquitous in natural environments but still pose significant challenges for constitutive modeling. In this paper, a simple hypoplastic constitutive model incorporating a structural factor to describe the wetting-induced collapse behavior of unsaturated soils is proposed. The model features a straightforward formulation with robust prediction capacity using 10 material parameters, most of which can be calibrated through conventional laboratory tests. Comparison between numerical simulations of element tests and experimental results demonstrates that the proposed model is able to replicate the salient features of unsaturated soils, including shear dilatation, strain softening, and wetting collapse.
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考虑湿润塌陷的非饱和土壤的简单次塑性模型
部分饱和土壤在自然环境中无处不在,但仍给构造模型的建立带来巨大挑战。本文提出了一个简单的低塑性构造模型,其中包含一个结构因子,用于描述非饱和土的湿陷性塌陷行为。该模型使用 10 个材料参数,其中大部分参数可通过常规实验室测试进行校准。元素测试的数值模拟与实验结果之间的比较表明,所提出的模型能够复制非饱和土壤的显著特征,包括剪切扩张、应变软化和湿润塌陷。
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来源期刊
Computers and Geotechnics
Computers and Geotechnics 地学-地球科学综合
CiteScore
9.10
自引率
15.10%
发文量
438
审稿时长
45 days
期刊介绍: The use of computers is firmly established in geotechnical engineering and continues to grow rapidly in both engineering practice and academe. The development of advanced numerical techniques and constitutive modeling, in conjunction with rapid developments in computer hardware, enables problems to be tackled that were unthinkable even a few years ago. Computers and Geotechnics provides an up-to-date reference for engineers and researchers engaged in computer aided analysis and research in geotechnical engineering. The journal is intended for an expeditious dissemination of advanced computer applications across a broad range of geotechnical topics. Contributions on advances in numerical algorithms, computer implementation of new constitutive models and probabilistic methods are especially encouraged.
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