基于高边坡位移突变的安全监控模型
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摘要
高边坡受爆破、地震等强外界作用时,位移监测值会出现明显跳跃。有效辨识测值突变位置,消除或削弱位移突变对测值序列整体数值特征的影响,是提高高边坡位移监控模型拟合和预测精度的关键问题之一。基于高边坡系统演化过程中的非线性动力学特性,应用相空间重构、云模型等数值分析手段,研究了高边坡位移突变辨识等的实现方法;在对高边坡位移与影响因素相关分析的基础上,探讨了考虑动力学结构突变影响的位移预测模型构建原理与算法。该模型重点依据最近一次位移突变后的监测资料,考虑了突变后形成的相对稳定的高边坡动力系统特性,因而可以有效提高监控模型的拟合和预测精度。
The displacement monitoring values of the high slopes exhibit notable mutations when the slope is affected by blasting,earthquakes,or other strong external effects.Identification of the mutation positions of the measured values and elimination of the effects of the displacement mutation on the numerical characteristics of the monitoring sequences are one of the key problems on the improvement of the fitting and prediction precision of the displacement monitoring model of high slopes.Due to the nonlinear dynamic behavior in the evolution process of the high slope system,the phase space reconstruction and the cloud model were used to study the implementation method for the identification of the displacement mutation of high slopes..Furthermore,the building principle and algorithm of displacement prediction model considering the effects of dynamic structure mutation were discussed based on the correlation analysis between the displacement of high slopes and the affecting factors.The proposed model was dependent on the up-to-date monitoring data after the last displacement mutation,and considered the relatively stable dynamic system characteristics of the high slopes formed after the mutations,which can improve the fitting and prediction precision of the displacement monitoring model of high slopes effectively.
The displacement monitoring values of the high slopes exhibit notable mutations when the slope is affected by blasting,earthquakes,or other strong external effects.Identification of the mutation positions of the measured values and elimination of the effects of the displacement mutation on the numerical characteristics of the monitoring sequences are one of the key problems on the improvement of the fitting and prediction precision of the displacement monitoring model of high slopes.Due to the nonlinear dynamic behavior in the evolution process of the high slope system,the phase space reconstruction and the cloud model were used to study the implementation method for the identification of the displacement mutation of high slopes..Furthermore,the building principle and algorithm of displacement prediction model considering the effects of dynamic structure mutation were discussed based on the correlation analysis between the displacement of high slopes and the affecting factors.The proposed model was dependent on the up-to-date monitoring data after the last displacement mutation,and considered the relatively stable dynamic system characteristics of the high slopes formed after the mutations,which can improve the fitting and prediction precision of the displacement monitoring model of high slopes effectively.
引文
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[19]Zêzere,J.L,Trigo,R.M.and Trigo,I F.Shallow and DeepLandslides Induced by Rainfall in the Lisbon Region(Portu-gal):Assessment of Relationships with the North Atlantic Os-cillation[J].Natural Hazards and Earth System Sciences,2005,(5):331-344.
[2]付义祥,刘志强.边坡位移的混沌时间序列分析方法及应用研究[J].武汉理工大学学报(交通科学与工程版),2003,27(4):473-476.(FU Yi-xiang,LIU Zhi-qiang.Analytic Method andApplication about Chaotic Slope Deformation DestructionTime-series[J].Journal of Wuhan University of Technology(Transportation Science and Engineering),2003,27(4):473-476.(in Chinese))
[3]吴中如,潘卫平.应用Lyapunov指数研究岩土边坡的稳定判据[J].岩石力学与工程学报,1997,16(3):217-223.(WU Zhong-ru,PAN Wei-ping.Research on Stability Criterion of Rock andSoil Slopes with Lyapunov Exponent[J].Chinese Journal ofRock Mechanics and Engineering,1997,16(3):217-223.(inChinese))
[4]Moody J,Darken C.Fast Learning in Networks of Locally-tunedProcessing Units[J].Neural Computation,1989,1(2):281-294.
[5]谈小龙,徐卫亚,梁桂兰,等.高边坡变形非线性时变统计模型研究[J].岩土力学,2010,31(5):1633-1650.(TAN Xiao-long,XU Wei-ya,LIANG Gui-lan,et al.Research on Nonlinear TimeSeries Evolution Statistic Model of High Slope Displacements[J].Rock and Soil Mechanics,2010,31(5):1633-1650.(in Chi-nese))
[6]郑东健,顾冲时,吴中如.边坡变形的多因素时变预测模型[J].岩石力学与工程学报,2005,24(17):3180-3184.(ZHENGDong-jian,GU Chong-shi,WU Zhong-ru.Time Series EvolutionForecasting Model of Slope Deformation Based on MultipleFactors[J].Chinese Journal of Rock Mechanics and Engineer-ing,2005,24(17):3180-3184.(in Chinese))
[7]周创兵,陈益峰.基于相空间重构的边坡位移预测[J].岩土力学,2000,21(3):205-208.(ZHOU Chuang-bing,CHEN Yi-feng.Application of Phase Space Reconstruction in Slope Dis-placement Forecasting[J].Rock and Soil Mechanics,2000,21(3):205-208.(in Chinese))
[8]李春贵,裴留庆.一种识别混沌时间序列动力学异同性的方法[J].物理学报,2003,52(9):2114-2120.(LI Chun-gui,PEI Liu-qing.A Method for Distinguishing Dynamical Species in ChaoticTime Series[J].Acta Physica Sinica,2003,52(9):2114-2120.(in Chinese))
[9]Grassberger P,Procaccia I.Measuring the Strangeness ofStrange Attractors[J].Phys D:Nonlinear Phenomena,1983,9(1-2):189-208.
[10]Kantz H.Quantifying the Closeness of Fractal Measures[J].Phys Rev E,1994,49(6):5091-5097.
[11]Albano A M,Muench J,Schwartz C.Singular-value Decompo-sition and the Grassberger-Procaccia Algorithm[J].Phys RevA,1998,(38):3017-3026.
[12]Fraser A M.Information and Entropy in Strange Attractors[J].IEEE Trans Inform Theory,1989,35(2):245-262.
[13]Wales D J.Calculating the Rate of Information from Chaotic TimeSeries by Forcasting[J].Nature,1991,350(6318):485-488.
[14]李德毅,杜鹢.不确定性人工智能[M].北京:国防工业出版社,2005.(LI De-yi,DU Yi.Artificial Intelligence with Uncer-tainty[M].Beijing:National Defense Industry Press,2005.(inChinese))
[15]吕辉军,王晔,李德毅,等.逆向云在定性评价中的应用[J].计算机学报,2003,26(8):1009-1014.(LV Hui-jun,WANG Ye,LI De-yi.The Application of Backward Cloud in Qualitative E-valuation[J].Chinese Journal of Computers,2003,26,(8):1009-1014.(in Chinese))
[16]贾乃文.黏塑性力学及工程应用[M].北京:地震出版社,2000.(JIA Nai-wen.Visco-plastic Mechanics and EngineeringApplication[M].Beijing:Earthquake Press,2000.(in Chinese))
[17]VOIGHT B.A Relation to Describe Rate-dependent MaterialFailure[J].Science,1989,243:200-203.
[18]SAITO M.Forecasting the Time of Occurrence of Slope Failure[A].Proceedings of 6th International Congress of Soil Mechanicsand Foundation Engineering[C].Montreal:1965:537-541.
[19]Zêzere,J.L,Trigo,R.M.and Trigo,I F.Shallow and DeepLandslides Induced by Rainfall in the Lisbon Region(Portu-gal):Assessment of Relationships with the North Atlantic Os-cillation[J].Natural Hazards and Earth System Sciences,2005,(5):331-344.
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