| 原状黄土固结蠕变特性试验研究 |
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| 引用本文： | 王松鹤,骆亚生,杨静敬.原状黄土固结蠕变特性试验研究[J].水文地质工程地质,2009,36(6):72-75. |
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| 作者姓名： | 王松鹤 骆亚生 杨静敬 |
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| 作者单位： | 西北农林科技大学水利与建筑工程学院； |
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| 基金项目： | 国家自然科学基金资助项目，教育部新世纪优秀人才支持计划 |
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| 摘 要： | 以陕西杨凌地区黄土为研究对象,对不同初始含水率的原状黄土进行了单向固结试验,获取了各级法向应力下的应变-时间曲线,研究了初始含水率、法向应力水平对黄土固结蠕变耦合特性的影响。结果表明:当初始含水率一定时,法向应力水平对黄土的蠕变固结特性有较大的影响,表现为法向应力愈大,黄土的初始应变愈大;当法向应力水平一定时,含水率对黄土蠕变固结特性影响明显,表现为含水率愈大,初始应变愈大。根据应变-时间曲线特征,推导出杨凌地区黄土的应力-应变-时间关系,拟合得到其中的参数,经验证能较好地模拟杨凌地区黄土的固结蠕变耦合特性。另外,采用BP神经网络对应力-应变-时间关系进行模拟与预测,结果表明该方法具有较高的精度和较好的适用性。
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| 关 键 词： | 黄土 固结蠕变 应力-应变-时间关系 BP神经网络 |
Experimental study on creep consolidation behavior of intact loess |
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| WANG Song-he,LUO Ya-sheng,YANG Jing-jing.Experimental study on creep consolidation behavior of intact loess[J].Hydrogeology and Engineering Geology,2009,36(6):72-75. |
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| Authors: | WANG Song-he LUO Ya-sheng YANG Jing-jing |
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| Affiliation: | College of Water Resources and Architectural Engineering,Northwest A & F University,Yangling 712100,China |
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| Abstract: | By taking the loess of Yangling District in Shaanxi Province as the research object, a uniaxial compression test is conducted to obtain the characteristic creep curve of intact loess under different water content and analyze the influence of water content and normal stress on creep consolidation characteristics. The result indicates: when the water content is invariable, the normal stress influences the creep consolidation characteristics obviously; the performance is bigger for the normal stress, the strain of loess is bigger; when the normal stress is invariable, water content obviously influences the creep consolidation characteristics; the higher water content, the bigger strain. According to the typical behavior of strain-time curves, a stress-strain-time relation is obtained by fitting test data; the relation better simulates the creep consolidation characteristics of intact loess in Yangling District by model test. Beside, the strain is simulated and forecast by BP artificial neural network. |
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| Keywords: | loess creep consolidation stress-strain-time relation BP artificial neural network |
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