基于NMR技术及分形理论预测SWRC

陶高梁; 陈银; 袁波; 甘世朝; 吴小康; 朱学良

doi:10.11779/CJGE201808012

基于NMR技术及分形理论预测SWRC English Version

湖北工业大学土木建筑与环境学院,湖北武汉 430068

基金项目: 国家自然科学基金项目（51209084）; 湖北省教育厅科研计划项目（D20161405）及优秀中青年科技创新团队项目（T201605）

详细信息

作者简介:
陶高梁(1979- ),男,副教授,主要从事土体孔隙结构及非饱和土等研究工作。E-mail：tgl1979@126.com。

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出版历程
- 收稿日期: 2017-07-24
- 发布日期: 2018-08-24

Predicting soil-water retention curve based on NMR technology and fractal theory

School of Civil, Architectural ＆ Environmental Engineering, Hubei University of Technology, Wuhan 430068, China

摘要

摘要: 土-水特征曲线（SWRC）是非饱和土力学中的基础本构关系,在研究非饱和土强度、体变及渗透系数等方面具有重要作用。通过试验直接测量SWRC,耗时较长,且通常得到的只是离散数据点,缺乏连续性,不能完全满足非饱和土研究的需要,因此,通过间接方法快速预测SWRC的完整数学表达式具有实际意义。以无损伤的核磁共振（nuclear magnetic resonance）技术为基础,结合Young-Laplace理论,建立基质吸力ψ与T₂值的关系式;利用分形理论,推导出质量含水率w与T₂值的关系式,最终建立了预测SWRC的数学模型,研究结果表明其预测结果与实测值吻合较好。
- 核磁共振 /
- 分形理论 /
- SWCC /
- 预测
Abstract: It has been recognized that the soil-water retention curve (SWRC) is a fundamental constitutive relationship of unsaturated soil mechanics, which plays a decisive role in the researches on the strength, volume change and permeability coefficient of unsaturated soils. The direct experimental methods for measuring the SWRC are time-consuming, and they usually provide some discrete data points which are short of sufficient continuity, thus they cannot completely meet the needs of researches on unsaturated soils. So, it is necessary to fast obtain the complete mathematical expression for SWRCs by using the indirect method. Based on the non-invasive nuclear magnetic resonance (NMR) technology, the relationship between matric suction ψ and relaxation time T₂ is established by using the Young-Laplace theory. Then, by using the fractal theory, an expression for the relationship between mass water content w and relaxation time T₂ is derived. On this basis, a specific NMR-fractal method is promoted to predict SWRCs. The experimental results show good agreement with the predicted values by the proposed fractal method.
- nuclear magnetic resonance /
- fractal theory /
- soil-water retention curve /
- prediction

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参考文献(19)

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