| 山区谷底沉陷预测模型及其参数反演 |
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| 引用本文： | 郭庆彪,郭广礼,吕 鑫,陈 涛,王金涛.山区谷底沉陷预测模型及其参数反演[J].岩土力学,2016,37(5):1351-1356. |
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| 作者姓名： | 郭庆彪 郭广礼 吕 鑫 陈 涛 王金涛 |
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| 作者单位： | 1.中国矿业大学 环境与测绘学院，江苏 徐州 221008；2.滁州学院 地理信息与旅游学院，安徽 滁州 239000 |
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| 基金项目： | 国家十二五科技支撑计划（No. 2012BAB13B03）；江苏省高校优势学科建设工程资助项目（No. SZBF2011-6-B35）。 |
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| 摘 要： | 我国西部地区地势复杂，沟壑纵横，地下开采极易导致边坡失稳，引发采动滑坡。在地下采动沉降与滑坡体挤压上升的叠加影响下，谷底区域地表沉降值明显小于类似地质采矿条件下的平原地区。为准确预测山区谷底区域地表沉降值，基于简支梁的弹性变形理论，并借助概率密度函数建立了山区谷底区域地表沉陷预计修正模型，明确模型参数物理意义及其取值方法。依据修正模型，以实测值和预测值之差平方和最小为原则构建适应值函数，基于模拟退火粒子群算法提出新的模型参数反演方法，借助MATLAB语言编制了相应的参数反演程序。最后将研究成果应用于山西某矿，得到谷底区域预测结果中误差为73 mm，与实测值基本一致，取得了较好的工程实践效果。
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| 关 键 词： | 采动滑坡 修正模型 模拟退火粒子群算法 参数反演 |
| 收稿时间： | 2015-09-27 |
Prediction model for surface subsidence and parameters inversion in valley bottom area |
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| GUO Qing-biao,GUO Guang-li,Lü Xin,CHEN Tao,WANG Jin-tao.Prediction model for surface subsidence and parameters inversion in valley bottom area[J].Rock and Soil Mechanics,2016,37(5):1351-1356. |
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| Authors: | GUO Qing-biao GUO Guang-li Lü Xin CHEN Tao WANG Jin-tao |
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| Institution: | 1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, Jiangsu 221008, China; 2. Geographic Information and Tourism College, Chuzhou University, Chuzhou, Anhui 239000, China |
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| Abstract: | On account of the complex terrain in the west region of China, the underground mining could break the stability of slope and lead to the slope failure easily. Under the superposed influences of underground mining and landslides pressure, the surface subsidence in valley bottom areas is obviously smaller than that in the flat areas with the similar geological and mining conditions. To accurately predict the surface subsidence in valley bottom areas, a simply supported beam and probability density function (PDF) are employed to update the prediction model. The physical meanings and computing methods of parameters of the updated prediction model are confirmed. The fitness function is built based on the principle that the sum of squares of the difference between the measured value and the predicted value should be the minimum. A new method of parameter inversion is proposed based on the simulated annealing particle swarm optimization (SAPSO), and the corresponding parameters inversion program is developed with MATLAB language. Finally, the research results are applied to a mine in Shanxi province, and the error of mean squares of prediction values is 73 mm which is basically in accordance with the measured values in valley bottom area. |
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| Keywords: | mining landslide updated model simulated annealing particle swarm optimization algorithm parameter inversion |
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