| 干湿循环作用下砂岩力学特性及其本构模型的神经网络模拟 |
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| 引用本文： | 李克钢,郑东普,黄维辉.干湿循环作用下砂岩力学特性及其本构模型的神经网络模拟[J].岩土力学,2013,34(Z2):168-173. |
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| 作者姓名： | 李克钢 郑东普 黄维辉 |
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| 作者单位： | 1.昆明理工大学 国土资源工程学院，昆明 650093；2.重庆大学煤矿灾害动力学与控制国家重点实验室，重庆 400044； 3. 邢台市建设工程质量检测中心，河北 刑台 054000 |
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| 基金项目： | 国家自然科学基金（No. 41202229, No. 51064012, No. 51264018） |
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| 摘 要： | 基于不同干湿循环作用下砂岩单轴压缩试验结果，分析了干湿循环效应对砂岩变形、强度、破坏特征等力学特性的影响规律，认为随着干湿循环次数的增加，弹性模量及峰值强度均有降低的趋势，降低幅度先大后小，而且会以某一具体值为临界值发展变化，就本次试验而言为干湿循环20次时的抗压强度和弹性模量值；砂岩的破坏特征亦会受到干湿循环试验次数的影响，干湿次数越少，脆性破坏越明显，反之则延性特征增强，即砂岩会呈现一种从脆性到延性转化的破坏规律。以应变和干湿试验次数为输入层，应力为输出层，构建了2-12-1的三层神经网络本构模型。通过样本的学习与检验，证明该模型能较好地描述干湿循环作用下砂岩的力学性能，验证了利用神经网络方法建立岩石本构模型的可行性与可靠性。基于预测结果，建立了完整的考虑干湿循环效应的砂岩力学特性变化规律的数学函数关系式。
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| 关 键 词： | 干湿循环 力学特性 砂岩 本构模型 神经网络 |
| 收稿时间： | 2012-12-20 |
Mechanical behavior of sandstone and its neural network simulation of constitutive model considering cyclic drying-wetting effect |
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| LI Ke-gang,ZHENG Dong-pu,HUANG Wei-hui.Mechanical behavior of sandstone and its neural network simulation of constitutive model considering cyclic drying-wetting effect[J].Rock and Soil Mechanics,2013,34(Z2):168-173. |
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| Authors: | LI Ke-gang ZHENG Dong-pu HUANG Wei-hui |
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| Institution: | 1. Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China; 2.State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China; 3. Xingtai Construct Engineering Quality Testing Center, Xingtai, Hebei 054000, China |
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| Abstract: | Based on uniaxial compression test of sandstone under the different drying-wetting cycles, the laws of drying-wetting effect on mechanical behaviors of sandstone, such as deformation behavior, intensity behavior and failure characteristic, were analyzed. The results show that the elastic modulus and the peak strength of sandstone trend to decrease with the increase of drying-wetting cycles, and its reduction range is changed from large to small; moreover, the decrease of above data can not develop limitlessly but there is a constant value as critical value; in this test, the constant values of elastic modulus and the peak strength are experiment data of 20th drying-wetting cycles. The failure characteristic of sandstone is also affected by drying-wetting cycles, the less drying-wetting cycles are, the more obvious brittle damage is; that are, with the strengthen of drying-wetting effect, the damage law of sandstone present a transformation from brittleness to ductility. On the basis of these tests, taking the strain and drying-wetting cycles as input layer and the stress as output layer, the three layers neural network constitutive model, whose structure is 2-12-1, was proposed. Through the massive samples learned and inspected, the model can well describe the mechanical properties of sandstone under the cyclic drying-wetting effect, which also confirms that it is feasible and reliable for the neural network method to establish the rock constitutive model. At last, the intact function relations between the mechanical behavior and the cyclic drying-wetting effect were built. |
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| Keywords: | drying-wetting cycle mechanical behavior sandstone constitutive model neural network |
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