| 吸力内摩擦角的确定方法研究 |
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| 引用本文： | 许尚杰,党发宁,高闯洲,党维维.吸力内摩擦角的确定方法研究[J].岩土力学,2009,30(Z2):22-27. |
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| 作者姓名： | 许尚杰 党发宁 高闯洲 党维维 |
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| 作者单位： | 1.西安理工大学岩土工程研究所，西安 710048；2.山东省水利科学研究院，济南 250013 |
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| 基金项目： | 国家自然科学基金重点项目，国家自然科学基金资助项目 |
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| 摘 要： | 吸力内摩擦角在非饱和土抗剪强度理论中是一个关键参数，因受设备、测试技术以及试验方法等的限制，对于其值的确定难度较大。详细地分析了土的基本物理力学性质、测试方法对吸力内摩擦角数值的影响，并通过定性分析和定量计算结合，认为干密度、含水率、内摩擦角、黏聚力与吸力内摩擦角之间存在着一定的非线性关系，因而借用人工智能--BP神经网络的方法构建了4:9:1的网络结构模型，实现了对吸力内摩擦角 的预测和计算。通过预测结果的分析可知，该模型模拟和预测的精度均较高，因此，该种方法可以应用到非饱和土吸力内摩擦角值的预测之中去。
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| 关 键 词： | 吸力内摩擦角 BP神经网络 TRAINGDX训练函数 抗剪强度 |
| 收稿时间： | 2009-07-28 |
Research on the method of determining suction internal friction angle |
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| XU Shang-jie,DANG Fa-ning,GAO Chuang-zhou,DANG Wei-wei.Research on the method of determining suction internal friction angle[J].Rock and Soil Mechanics,2009,30(Z2):22-27. |
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| Authors: | XU Shang-jie DANG Fa-ning GAO Chuang-zhou DANG Wei-wei |
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| Institution: | 1.Institute of Geotechnical Engineering, Xi?an University of Technology, Xi?an 710048, China; 2.Shandong Provincial Water Resources Research Institute, Ji’nan 250013, China |
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| Abstract: | In shearing strength theory for unsaturated soil, the suction friction angle is a key parameter, it is difficult to determine its value, which is limited by the test of equipment, technology and method. The influence factors for such as the soils′ basic physico-mechanical properties and the test method are analyzed detailedly. At the same time, by using qualitative analysis and quantitative calculation methods, it is considered that those factors have a certain degree of nonlinear relationship, which includes dry density, water content and internal friction angle. Based on artifical intelligence--the BP neural network, the 4:9:1 network structure is built to calculate and predict the suction internal friction angle for unsaturated soils. The validity of the model shows that the precisions of simulation and prediction are high. This model can be applied to forecast the index of the suction internal friction angleφ~b. |
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| Keywords: | suction internal friction angle BP neural network TRAINGDX training function shearing strength |
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