| 基于复杂地质体的地应力场智能识别方法研究 |
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| 引用本文： | 李茂辉,杨志强,高谦,翟淑花,王有团.基于复杂地质体的地应力场智能识别方法研究[J].岩土力学,2014,35(Z2):638-644. |
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| 作者姓名： | 李茂辉 杨志强 高谦 翟淑花 王有团 |
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| 作者单位： | 1.北京科技大学 土木与环境工程学院，北京 100083；2.北京科技大学 金属矿山高效开采与安全教育部重点实验室，北京 100083； 3.金川集团股份有限公司，甘肃 金昌 737100；4.北京市地质研究所，北京 100120 |
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| 基金项目： | 国家973计划项目（No. 2010CB731501）。 |
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| 摘 要： | 初始地应力是地下开采及施工所需要考虑的重要影响因素，地应力值的准确获得通常采用现场测量与反演计算结合实现。针对某铜镍矿复杂地质体，在已有监测数据的基础上建立三维正交数值模型，获得监测点的地应力计算值。利用遗传规划程序，回归地应力监测点的地应力与各自变量的函数关系。建立地应力实测值和计算值之差的平方和最小的目标优化函数，使用遗传算法智能算法对目标函数进行优化，最终获得了矿区的原岩应力场。检测样本验证结果显示，计算值与实测值的误差控制在10%以内，一定程度上证明了该地应力场智能识别方法的可靠性，可为以后复杂地质体矿区地应力场的计算提供较为可行的研究方法。
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| 关 键 词： | 复杂地质体 地应力 智能算法 反演识别 |
| 收稿时间： | 2014-03-12 |
Research on intelligent identification methods for in-situ stress field base on complex geological body |
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| LI Mao-hui,YANG Zhi-qiang,GAO Qian,ZHAI Shu-hua,WANG You-tuan.Research on intelligent identification methods for in-situ stress field base on complex geological body[J].Rock and Soil Mechanics,2014,35(Z2):638-644. |
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| Authors: | LI Mao-hui YANG Zhi-qiang GAO Qian ZHAI Shu-hua WANG You-tuan |
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| Institution: | 1. School of Civil and Environment Engineering, University of Science and Technology Beijing, Beijing 100083, China; 2. Key Laboratory of Ministry of Education for High-Efficient Mining and Safety of Metal Mines, University of Science and Technology Beijing, Beijing 100083, China; 3. Jinchuan Group Co., Ltd., Jinchang, Gansu 737100, China; 4. Beijing Institute of Geology, Beijing 100120, China |
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| Abstract: | In-situ stress is an important factor for underground mining and construction. We usually use back calculation combined with field measurements to obtain the accurate in-situ stress values. Based on the monitoring data of a certain complex copper-nickel mine, we establish a 3D orthogonal numerical model and use this model to obtain the difference between the measured and calculated values, the sum of the squares of the minimum objective function optimization of in-situ stress. Finally, the genetic algorithm of intelligent algorithm is used to optimize the objective function so as to acquire the original rock stress field in this copper-nickel mine. Test sample validation results show that the relative error between measured and calculated values within 10%; it is proved that the intelligent recognition method of in-situ stress is reliable in some extent and will provide a more viable method of in-situ stress field for complex geological body. |
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| Keywords: | complex geological body in-situ stress intelligent algorithm inverse recognition |
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