基于密集多点变形监测的公路滑坡主滑方向动态判别
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摘要
滑坡主滑方向是研究滑坡形成机理和确定抗滑防治方案的重要依据。滑坡随着时间的演化,其主滑方向也是一个动态变化的过程。滑坡主滑方向是滑坡位移状态的综合反映,在滑坡体上布设密集多点变形监测系统,根据小样本监测数据服从t分布的规律,使用肖维勒准则剔除监测数据中的异常值,运用区间估计的方法来求取所有监测点位移方向的均值,通过主滑方向-时间过程曲线来动态判别滑坡主滑方向。将该方法运用到某公路滑坡工程实例研究中,在滑坡体上布设由53个地表位移监测点和19个深部位移监测点组成的密集多点变形监测系统,运用由全站仪和GPS组成的监测仪器系统,对该滑坡开展为期25个月的地表水平位移监测。通过地表位移数据分析对比,采用此方法估计的主滑方向和滑坡体宏观变形体现的滑动方向基本一致。经过误差分析和显著性检验,表明此方法可以较好地对滑坡主滑方向进行了动态判别。将此结果与深部测斜监测到的滑动方向进行了对比,结果基本保持一致,再次说明运用上述方法来动态判别滑坡主滑方向是可行的。在防治工程中,确定了准确的滑坡主滑方向后,指导抗滑桩长轴布设方向和锚索主要受力方向均沿此确定的主滑方向实施,经过长达8个月的持续监测表明,防治措施起到了良好的控制作用,体现了该方法的工程应用价值。
The accurate discrimination of main slip direction is an important basis to study the formation mechanism and design the control scheme of landslides. In the process of landslide evolution with time, the main slip direction also changes dynamically, while main slip direction is the comprehensive reflection of landslide displacement state. A systematic monitoring network consisting of the dense multi-point surface displacement monitoring points is set up on the sliding mass. According to the rule of t-distribution for small sample data, the outliers in monitoring data are eliminated by using Chauvenet Criterion. The method of interval estimation is used to obtain the mean value of displacement direction of all monitoring points, and the main slip direction of landslide is dynamically identified by the curve of the main slip direction and time. In the case study of a highway landslide, a monitoring system consisting of 53 surface displacement monitoring points and 19 deep displacement monitoring holes is arranged on the sliding mass, while the monitoring instrument system is composed of the total station and GPS. The surface horizontal displacement monitoring of the landslide is carried out for a period of 25 months. The main slip direction estimated by the above method reflects the macro deformation of the sliding mass is basically consistent. The results of error analysis and the significance test show that the method can effectively estimate the main slip direction. The comparison of the slip direction monitored by the deep displacement monitoring show that they are basically consistent. This proves that the above-mentioned method is feasible to dynamically distinguish the main slip direction of landslides. In the actual control project of the landslide, after the accurate main slip direction is determined, the long axis direction of stabilizing pile and the main forced direction of the anchor cable are carried out along the main slip direction. The continuous monitoring that lasted for 8 months shows that the effect of control scheme is productive, which reflects the engineering application value of the above-mentioned method.
The accurate discrimination of main slip direction is an important basis to study the formation mechanism and design the control scheme of landslides. In the process of landslide evolution with time, the main slip direction also changes dynamically, while main slip direction is the comprehensive reflection of landslide displacement state. A systematic monitoring network consisting of the dense multi-point surface displacement monitoring points is set up on the sliding mass. According to the rule of t-distribution for small sample data, the outliers in monitoring data are eliminated by using Chauvenet Criterion. The method of interval estimation is used to obtain the mean value of displacement direction of all monitoring points, and the main slip direction of landslide is dynamically identified by the curve of the main slip direction and time. In the case study of a highway landslide, a monitoring system consisting of 53 surface displacement monitoring points and 19 deep displacement monitoring holes is arranged on the sliding mass, while the monitoring instrument system is composed of the total station and GPS. The surface horizontal displacement monitoring of the landslide is carried out for a period of 25 months. The main slip direction estimated by the above method reflects the macro deformation of the sliding mass is basically consistent. The results of error analysis and the significance test show that the method can effectively estimate the main slip direction. The comparison of the slip direction monitored by the deep displacement monitoring show that they are basically consistent. This proves that the above-mentioned method is feasible to dynamically distinguish the main slip direction of landslides. In the actual control project of the landslide, after the accurate main slip direction is determined, the long axis direction of stabilizing pile and the main forced direction of the anchor cable are carried out along the main slip direction. The continuous monitoring that lasted for 8 months shows that the effect of control scheme is productive, which reflects the engineering application value of the above-mentioned method.
引文
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[14] 王仁铎.地质统计学的发展趋势[J].地质科技情报,1996,15(2):99-102.
[15] 唐启义,冯明光.实用统计分析及其DPS数据处理系统[M].北京:科学出版社,2002.
[16] 杨震,胡光道,梅红波,等.剩余异常分量因子分析法在个旧西区化探异常提取中的应用[J].地质科技情报,2013,32(6):95-99.
[17] 龙悦,徐光黎,高幼龙,等.数据预处理在滑坡位移相关分析中的应用[J].地质科技情报,2012,31(2):122-127.
[18] Russo F,Biancardo S A,Busiello M.Operating speed as a key factor in studying the driver behaviour in a rural context[J].Transport,2016,31(2):260-270.
[2] 刘勇,秦志萌,刘曼,等.基于状态划分的滑坡位移预测方法研究[J].地质科技情报,2018,37(3):192-194
[3] Angeli M G,Pasuto A,Silvano S.A critical review of landslide monitoring experiences[J].Engineering Geology,2010,55(3):133-147.
[4] Kawamura K,Ogawa Y,Oyagi N,et al.Structural and fabric analyses of basal slip zone of the Jin′nosuke-dani landslide,northern central Japan:Its application to the slip mechanism of decollement[J].Landslides,2007,4(4):371-380.
[5] 张泰丽,周爱国,孙强,等.基于深部位移监测的浙江省中林村滑坡变形特征分析[J].地质科技情报,2017,36(3):212-217.
[6] 王恭先.滑坡学与滑坡防治技术[M].北京:中国铁道出版社,2004.
[7] 杨文辉.南昆铁路柏子村1号隧道病害研究[J].铁道工程学报,2013,30(4):86-91.
[8] 黄秋香,汪家林,邓建辉.基于多点位移计监测成果的坡体变形特征分析[J].岩石力学与工程学报,2009,28(增刊1):2667-2673.
[9] Fei Y,Gao L,Zhang C.Determination of the main sliding direction of space rock landslide[C]//International conference on mechanic automation & control engineering.[S.l.]:[s.n.],2011.
[10] Benoit L,Briole P,Martin O,et al.Monitoring landslide displacements with the Geocube wireless network of low-cost GPS[J].Engineering Geology,2015,195(6):111-121.
[11] Wang F,Wang G,Sassa K,et al.Displacement monitoring and physical exploration on the shuping landslide reactivated by impoundment of the Three Gorges Reservoir,China[J].Landslides,2005,2(4):313-319.
[12] Yin Y,Zheng W,Liu Y,et al.Integration of GPS with InSAR to monitoring of the Jiaju landslide in Sichuan,China[J].Landslides,2010,7(3):359-365.
[13] 孙培勤,孙绍晖.试验数据设计处理与计算机模拟[M].北京:中国石化出版社,2018.
[14] 王仁铎.地质统计学的发展趋势[J].地质科技情报,1996,15(2):99-102.
[15] 唐启义,冯明光.实用统计分析及其DPS数据处理系统[M].北京:科学出版社,2002.
[16] 杨震,胡光道,梅红波,等.剩余异常分量因子分析法在个旧西区化探异常提取中的应用[J].地质科技情报,2013,32(6):95-99.
[17] 龙悦,徐光黎,高幼龙,等.数据预处理在滑坡位移相关分析中的应用[J].地质科技情报,2012,31(2):122-127.
[18] Russo F,Biancardo S A,Busiello M.Operating speed as a key factor in studying the driver behaviour in a rural context[J].Transport,2016,31(2):260-270.