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Interpretation of soil behavior from laboratory specimens subjected to non-uniform loading conditions.

机译:从不均匀加载条件下的实验室标本解释土壤行为。

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摘要

Current laboratory testing requires uniform stress and strain distribution in a specimen for convenient data reduction and interpretation of soil behavior. Available laboratory devices provide information on soil behavior over a very limited range of shearing modes while understanding of soil behavior is needed under general shearing modes to improve constitutive modeling and the solution of engineering boundary value problems. This study implements an innovative inverse analysis framework, Self-learning Simulation (SelfSim), to interpret soil behavior from laboratory specimens subjected to non-uniform loading conditions.;A general incremental strain probe is introduced to examine rate-independent constitutive model response under all possible strain loading conditions. The probing procedure reveals that the symmetry of a Neural Network (NN) based constitutive model is determined by the symmetry of training datasets. This result led to use of a 3-D finite element model for SelfSim learning in this study.;SelfSim learning is demonstrated using two simulated laboratory tests, a triaxial compression shear test with frictional loading platens, and a triaxial torsional shear test with frictional ends. SelfSim successfully extracts the diverse stress-strain behavior from within the specimens. A NN based constitutive model is developed using extracted soil behavior from both laboratory tests and used successfully in the forward prediction of the load-settlement behavior of a simulated strip footing. The results demonstrate that SelfSim establishes a direct link between laboratory testing and constitutive modeling. SelfSim is employed to interpret Racci sand drained shear behavior. The "short" sand specimens cover three different relative densities, and were tested under three different confining pressures in a triaxial cell with fully frictional loading platens. SelfSim analysis extracts stress strain behavior from within each specimen using load and displacement measurements, and reveals that both principal stress rotation and variation of intermediate stress ratio are important features of the extracted "element level" stress paths. Mobilized friction angles are interpreted for the 3-D loading conditions.;Preliminary designs of next-generation laboratory testing devices are introduced and numerically demonstrated to multitudes of generating general shearing modes from a single laboratory test through integration with SelfSim.*.;*This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Microsoft Office.
机译:当前的实验室测试要求样本中应力和应变的分布均匀,以便于减少数据和解释土壤行为。可用的实验室设备可在非常有限的剪切模式范围内提供有关土壤行为的信息,同时需要了解一般剪切模式下的土壤行为,以改善本构模型和工程边界值问题的解决方案。这项研究实施了一种创新的逆分析框架,即自学习模拟(SelfSim),以解释在非均匀载荷条件下的实验室标本中的土壤行为。可能的应变加载条件。该探测过程表明,基于神经网络(NN)的本构模型的对称性是由训练数据集的对称性决定的。该结果导致本研究中使用3-D有限元模型进行SelfSim学习。; SelfSim学习通过两个模拟实验室测试,带有摩擦载荷压盘的三轴压缩剪切测试和带有摩擦端的三轴扭转剪切测试进行了演示。 SelfSim成功地从样本中提取了各种应力应变行为。使用从两个实验室测试中提取的土壤行为来开发基于NN的本构模型,并将其成功地用于模拟带状基础的荷载沉降行为的前向预测。结果表明,SelfSim在实验室测试和本构模型之间建立了直接联系。 SelfSim用于解释Racci砂排水剪切行为。 “短”砂试样覆盖了三种不同的相对密度,并在具有完全摩擦加载压板的三轴单元中在三种不同的限制压力下进行了测试。 SelfSim分析使用载荷和位移测量从每个样本中提取应力应变行为,并揭示出主应力旋转和中间应力比的变化都是提取的“元素水平”应力路径的重要特征。解释了在3-D载荷条件下的动摩擦角。引入了下一代实验室测试设备的初步设计,并通过与SelfSim的集成,从一次实验室测试中大量生成了通用的剪切模式,并进行了数值模拟。学位论文是复合文件(作为论文的一部分,包含纸质副本和CD)。该CD需要满足以下系统要求:Microsoft Office。

著录项

  • 作者

    Fu, Qingwei.;

  • 作者单位

    University of Illinois at Urbana-Champaign.;

  • 授予单位 University of Illinois at Urbana-Champaign.;
  • 学科 Civil engineering.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 273 p.
  • 总页数 273
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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