赵家沟尾矿坝稳定安全系数预测研究
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摘要
针对某尾矿库在实际运行管理中需要快速判断库体稳定的实际需要,在仔细分析尾矿坝失稳原因的基础上结合赵家沟的工程实际情况确定了浸润线埋深、退坡距离、干滩长度、粘聚力、摩擦力等5个主要的影响因素,通过正交试验设计方法设计了不同的试验组合,利用GEO-Slope边坡稳定计算分析软件计算得到各因素组合下安全系数作为样本数据。根据改进的神经网络原理建立了影响因素与安全系数间的非线性映射网络模型,通过对样本数据的训练得到预测模型,并将该模型用于安全系数的预测,通过实际值与预测值的对比表明所建立的预测模型具有较高的精确度,可以用于尾矿坝坝坡稳定预测。利用本文所建立的尾矿坝坝坡稳定预测模型,通过给定影响因素值,可快速得到坝坡稳定安全系数,计算方便,可用于指导尾矿坝填筑单位在实际生产运行过程中对堆积坝填筑的控制,保障尾矿坝安全。
In view of the actual needs of a tailings dam,it was necessary to judge the stability of the tailings dam quickly during the actual operation and management.Based on the thorough analysis of the instability cause of the tailings dam,and combined the actual situation of Zhaojiagou,five main influencing factors were determined,including the depth of infiltration line,the distance of slope,the length of dry beach,cohesion and friction.On this basis,different test combinations were designed by orthogonal experimental design method,and the safety factor of each factor combination was calculated as sample data by GEOslope Slope.According to the principle of the improved BP neural network,a nonlinear mapping network model between influencing factors and safety factors was established.The prediction model was obtained through the training of sample data and applied to the prediction of safety factor.By comparing the actual values and the predicted values,it showed that the model had high accuracy and could be used to predict the stability of the tailings dam.Using the established model in this paper,the safety factor of the dam slope could be quickly obtained by given the influencing factor value,which was convenient and could be used to guide the filling process of the tailings dam during the actual production operation and ensure the safety of tailings dam.
In view of the actual needs of a tailings dam,it was necessary to judge the stability of the tailings dam quickly during the actual operation and management.Based on the thorough analysis of the instability cause of the tailings dam,and combined the actual situation of Zhaojiagou,five main influencing factors were determined,including the depth of infiltration line,the distance of slope,the length of dry beach,cohesion and friction.On this basis,different test combinations were designed by orthogonal experimental design method,and the safety factor of each factor combination was calculated as sample data by GEOslope Slope.According to the principle of the improved BP neural network,a nonlinear mapping network model between influencing factors and safety factors was established.The prediction model was obtained through the training of sample data and applied to the prediction of safety factor.By comparing the actual values and the predicted values,it showed that the model had high accuracy and could be used to predict the stability of the tailings dam.Using the established model in this paper,the safety factor of the dam slope could be quickly obtained by given the influencing factor value,which was convenient and could be used to guide the filling process of the tailings dam during the actual production operation and ensure the safety of tailings dam.
引文
[1]王昆,杨鹏,Karen Hudson-Edwards,等.尾矿库溃坝灾害防控现状及发展[J].工程科学报,2018,40(5):526-539.
[2]LV SHURAN,LV SHUJIN.The discussion about the safety management of the mine tailings pond near the mine stope[J].Procedia Engineering,2011,26:1901-1906.
[3]朱远乐,鲁龙飞,杨荣.尾矿库排渗工程优化设计及治理效果研究[J].矿业研究与开发,2018,38(8):129-134.
[4]张力霆.尾矿库溃坝研究综述[J].水利学报,2013,44(5):594-600.
[5]李权,党发宁,郭振世,等.尾矿坝干滩长度确定方法及影响因素分析[J].水利与建筑工程学报,2014,12(4):14-17,37.
[6]戴妙林,屈佳乐,刘晓青,等.基于GA-BP算法的岩质边坡稳定性和加固效应预测模型及其应用研究[J].水利水电技术,2018,49(5):165-171.
[7]Li A J,KHOO S,LAMIN A V,et al.Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm[J].Automation in Construction,2016,65:42-50.
[8]徐黎明,王清,陈剑平,等.基于BP神经网络的泥石流平均流速预测[J].吉林大学学报(地球科学版),2013,43(1):186-191.
[9]李长洪,卜磊,陈龙根.尾矿坝致灾机理研究现状及发展态势[J].工程科学学报,2016,38(8):1039-1049.
[10]吴贵生.试验设计与数据处理[M].北京:冶金工业出版社,1997.
[11]焦李成,杨淑媛,刘芳,等.神经网络七十年:回顾与展望[J].计算机学报,2016,39(8):1697-1716.
[12]Hao Pan.An Improved Back-Propagation Neural Network Algorithm[J].Applied Mechanics and Materials,2014,3207(556).
[13]李小娟,马红彬.基于神经网络的铁路路基沉降因素敏感度分析[J].铁道标准设计,2018,62(11):1-6.
[2]LV SHURAN,LV SHUJIN.The discussion about the safety management of the mine tailings pond near the mine stope[J].Procedia Engineering,2011,26:1901-1906.
[3]朱远乐,鲁龙飞,杨荣.尾矿库排渗工程优化设计及治理效果研究[J].矿业研究与开发,2018,38(8):129-134.
[4]张力霆.尾矿库溃坝研究综述[J].水利学报,2013,44(5):594-600.
[5]李权,党发宁,郭振世,等.尾矿坝干滩长度确定方法及影响因素分析[J].水利与建筑工程学报,2014,12(4):14-17,37.
[6]戴妙林,屈佳乐,刘晓青,等.基于GA-BP算法的岩质边坡稳定性和加固效应预测模型及其应用研究[J].水利水电技术,2018,49(5):165-171.
[7]Li A J,KHOO S,LAMIN A V,et al.Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm[J].Automation in Construction,2016,65:42-50.
[8]徐黎明,王清,陈剑平,等.基于BP神经网络的泥石流平均流速预测[J].吉林大学学报(地球科学版),2013,43(1):186-191.
[9]李长洪,卜磊,陈龙根.尾矿坝致灾机理研究现状及发展态势[J].工程科学学报,2016,38(8):1039-1049.
[10]吴贵生.试验设计与数据处理[M].北京:冶金工业出版社,1997.
[11]焦李成,杨淑媛,刘芳,等.神经网络七十年:回顾与展望[J].计算机学报,2016,39(8):1697-1716.
[12]Hao Pan.An Improved Back-Propagation Neural Network Algorithm[J].Applied Mechanics and Materials,2014,3207(556).
[13]李小娟,马红彬.基于神经网络的铁路路基沉降因素敏感度分析[J].铁道标准设计,2018,62(11):1-6.