基于熵权-功效系数法的岩爆烈度分级预测研究
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摘要
岩爆是高地应力区开挖过程中常遇到的工程地质灾害,发生突然且破坏性极大,因此岩爆的预测一直是岩爆课题的研究热点。在综合分析岩爆影响因素的基础上,选取切向应力与单轴抗压强度比值σθ/σc、岩石脆性系数σc/σt、围岩弹性储能Wet及岩石完整性系数Kv作为评价指标,将熵权法与功效系数法进行结合,采用熵权法确定各指标权重,并利用功效系数法进行综合评价,从而建立出岩爆预测的熵权-功效系数法模型。利用国内外17组典型工程实例对模型进行验证,将所得结果与功效系数法、熵权-正态云模型、RS-TOPSIS模型预测结果及实际情况进行对比。为进一步考查模型的适用性,利用该模型对猴子岩水电站及锦屏二级水电站进行岩爆烈度等级预测。研究结果表明,熵权-功效系数法模型能够综合考虑影响岩爆发生的主要因素,预测准确性较高且原理简单易懂,具有一定的工程运用前景。
Rockburst is one of the main geological hazards encountered during excavation in high ground stress area. It happens suddenly and sometimes may bring devastating consequence. Therefore, rockburst prediction is always a hot research topic of rockburst subject. On the basis of comprehensive analysis of influencing factors of rockburst, the stress coefficient of rock σθ/σc, the brittleness coefficient of rock σc/σt, the elastic energy index of rock Wetand the integrality coefficient of rock Kvare selected as evaluation indices. By adopting entropy method to determine the weighting coefficient of each evaluation index, and using efficacy coefficient method to evaluate four indices comprehensively, the entropy-efficacy coefficient method model of rockburst intensity prediction is established. Using 17 groups of typical engineering examples at home and abroad to verify this model, and the results are compared with the prediction results of the efficacy coefficient method, the entropy and normal cloud model, the RS-TOPSIS model and practical situation. To further verify the practicability, this model has applied to rockburst intensity prediction of Houzaiyan hydropower station and Jinping II hydropower station. The research result shows that the entropy-efficacy coefficient method model which is easy to understand can evaluate main factors influencing rockburst comprehensively and possesses high prediction accuracy. Therefore, this model possesses certain prospect of engineering application.
Rockburst is one of the main geological hazards encountered during excavation in high ground stress area. It happens suddenly and sometimes may bring devastating consequence. Therefore, rockburst prediction is always a hot research topic of rockburst subject. On the basis of comprehensive analysis of influencing factors of rockburst, the stress coefficient of rock σθ/σc, the brittleness coefficient of rock σc/σt, the elastic energy index of rock Wetand the integrality coefficient of rock Kvare selected as evaluation indices. By adopting entropy method to determine the weighting coefficient of each evaluation index, and using efficacy coefficient method to evaluate four indices comprehensively, the entropy-efficacy coefficient method model of rockburst intensity prediction is established. Using 17 groups of typical engineering examples at home and abroad to verify this model, and the results are compared with the prediction results of the efficacy coefficient method, the entropy and normal cloud model, the RS-TOPSIS model and practical situation. To further verify the practicability, this model has applied to rockburst intensity prediction of Houzaiyan hydropower station and Jinping II hydropower station. The research result shows that the entropy-efficacy coefficient method model which is easy to understand can evaluate main factors influencing rockburst comprehensively and possesses high prediction accuracy. Therefore, this model possesses certain prospect of engineering application.
引文
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[20]谷明成,何发亮,陈成宗.秦岭隧道岩爆的研究[J].岩石力学与工程学报, 2002, 21(9):1324-1329.
[21]贾哲强,张茹,张艳飞,等.猴子岩水电站地下厂房岩爆综合预测研究[J].岩土工程学报, 2016, 38(s2):110-116.
[2]张镜剑,傅冰骏.岩爆及其判据和防治[J].岩石力学与工程学报, 2008, 27(10):2034-2042.
[3] KIDYBINSKI A. Bursting liability indices of coal[J]. Rock Mech Mining Sciences, 1981,18(6):295-304.
[4]王元汉,李卧东,李启光,等.岩爆预测的模糊数学评判方法[J].岩石力学与工程学报,1998, 17(5):493-501.
[5]周科平,雷涛,胡建华.深部金属矿山RS-TOPSIS岩爆预测模型及其运用[J].岩石力学与工程学报,2013,32(s2):3705-3711.
[6]龚剑,胡乃联,崔翔,等.基于AHP-TOPSIS评判模型的岩爆倾向性预测[J].岩石力学与工程学报, 2014, 33(7):1442-1447.
[7]王迎超,尚岳全,孙红月,等.基于功效系数法的岩爆烈度分级预测研究[J].岩土力学, 2010, 31(2):529-534.
[8]王迎超,靖洪文,吉咸伟,等.深埋地下工程岩爆烈度分级预测的RS-功效系数模型[J].中南大学学报(自然科学版), 2014,45(6):1992-1997.
[9]宫凤强,李夕兵.岩爆发生和烈度分级预测的距离判别方法及应用[J].岩石力学与工程学报, 2007, 26(5):1012-1018.
[10]王吉亮,陈剑平,杨静,等.岩爆等级判定的距离判别分析方法及应用[J].岩土力学, 2009, 30(7):2203-2208.
[11]贾义鹏,吕庆,尚岳全,等.基于粗糙集-理想点法的岩爆预测[J].浙江大学学报(工学版), 2014, 48(3):498-503.
[12]贾义鹏,吕庆,尚岳全.基于粒子群算法和广义回归神经网络的岩爆预测[J].岩石力学与工程学报, 2013, 32(2):343-348.
[13]张光存,高谦,杜聚强,等.基于人工神经网络及非线性回归的岩爆判据[J].中南大学学报(自然科学版), 2013, 44(7):2977-2981.
[14]葛启发,冯夏庭.基于AdaBoost组合学习方法的岩爆分类预测研究[J].岩土力学, 2008, 29(4):943-948.
[15]史秀志,周健,董蕾,等.未确知测度模型在岩爆烈度分级预测中的运用[J].岩石力学与工程学报,2010,29(s1):2720-2726.
[16]周科平,林允,胡建华,等.基于熵权-正态云模型的岩爆烈度分级预测研究[J].岩土力学, 2016, 37(s1):596-602.
[17]李天斌,孟陆波,王兰生.高地应力隧道稳定性及岩爆、大变形灾害防治[M].北京:科学出版社, 2016.
[18]沈明荣,陈建峰.岩体力学[M].上海:同济大学出版社, 2006.
[19]邹成杰.地下工程中岩爆灾害发生规律与岩爆预测问题研究[J].中国地质灾害与防治学报, 1992, 3(4):48-53.
[20]谷明成,何发亮,陈成宗.秦岭隧道岩爆的研究[J].岩石力学与工程学报, 2002, 21(9):1324-1329.
[21]贾哲强,张茹,张艳飞,等.猴子岩水电站地下厂房岩爆综合预测研究[J].岩土工程学报, 2016, 38(s2):110-116.