| 三轴压缩围压与峰值应力、应变关系的改进粒子群神经网络研究 |
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| 引用本文： | 易达,刘洁荣,葛修润.三轴压缩围压与峰值应力、应变关系的改进粒子群神经网络研究[J].岩土力学,2007,28(12):2639-2642. |
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| 作者姓名： | 易达 刘洁荣 葛修润 |
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| 作者单位： | 1.上海岩土工程勘察设计研究院有限公司 上海 200031；2.上海电力学院 上海 200090； 3.上海交通大学 上海 200030；4. 中国科学院武汉岩土力学研究所 武汉 430071 |
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| 摘 要： | 岩石三轴压缩试验中，峰值应力和应变是应力-应变曲线重要的控制数据，通常情况下，随着围压的增大，峰值应力和应变也会相应增大。试验结果表明，围压与峰值应力、应变之间并非简单的线性关系。使用模拟退火技术对粒子群神经网络进行了改进，提出采用改进粒子群神经网络建立围压与峰值应力、应变非线性关系的方法。通过实例，说明所提方法是可行的。
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| 关 键 词： | 三轴压缩 应力-应变曲线 围压 峰值应力 人工神经网络 模拟退火 粒子群优化算法 |
| 文章编号： | 1000-7598-（2007）12-2639-05 |
| 收稿时间： | 2005-11-22 |
| 修稿时间： | 2006-01-17 |
Research on relationship between confining pressure, peak strength and strain in triaxial compression with improved PSO-based ANN |
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| YI Da,LIU Jie-rong,GE Xiu-run.Research on relationship between confining pressure, peak strength and strain in triaxial compression with improved PSO-based ANN[J].Rock and Soil Mechanics,2007,28(12):2639-2642. |
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| Authors: | YI Da LIU Jie-rong GE Xiu-run |
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| Institution: | 1. Shanghai Geotechnical Investigation and Design Institute, Shanghai 200031, China; 2. Shanghai University of Electric Power, Shanghai 200090, China; 3. Shanghai Jiaotong University, Shanghai 200030, China; 4. Institute of Rock and Soil Mechanics , Chinese Academy of Sciences, Wuhan 430071, China |
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| Abstract: | The peak strengths and corresponding strains under different confining pressures are important controlling data of stress-strain curve in rock triaxial compression;and the values of peak strength and corresponding strain will usually increase if the confining pressure is improved.The results of tests show that the relationship between the peak strength,corresponding strain and the confining pressure is nonlinear.The particle swarm optimization(PSO) based ANN,which is improved with simulated annealing technique,is applied to establishing the relationship between the peak strength,corresponding strain and the confining pressure.An example is studied;the results show that the proposed method is feasible. |
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| Keywords: | triaxial compression stress-strain curve confining pressure peak strength artificial neural network simulated annealing particle swarm optimization |
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