基于最大Lyapunov指数的高边坡安全监控优化模型
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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,组合应用相空间重构、最大Lyapunov指数、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.该模型重点依据的是最近一次位移突变后的监测资料,考虑的是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.
Displacement monitoring values exhibit notable mutations if a high slope is affected by blasting,earthquakes,or other strong outside effects.Among the key problems in improving the fitting and prediction precision of the high slope displacement monitoring model include effectively identifying the mutation positions of measured values and eliminating or weakening the effect of mutations on the numerical characteristics of the overall series of measurements.This paper studies the realization method of high slope displacement mutations identification based on the nonlinear dynamic behavior in the high slope system evolution process,which combines phase space reconstruction,the largest Lyapunov exponent,the cloud model,and other numerical analysis methods.Furthermore,based on the correlation analysis between high slope displacement and the affecting factors,the current work discusses the building principle and algorithm of displacement considering dynamic structure mutation effects.The proposed model depends on up-to-date monitoring data after the last displacement mutations,and considers the relatively stable high slope dynamic system characteristics formed after these mutations.Therefore,the fitting and prediction precision of the high slope displacement monitoring model can be effectively improved.
Displacement monitoring values exhibit notable mutations if a high slope is affected by blasting,earthquakes,or other strong outside effects.Among the key problems in improving the fitting and prediction precision of the high slope displacement monitoring model include effectively identifying the mutation positions of measured values and eliminating or weakening the effect of mutations on the numerical characteristics of the overall series of measurements.This paper studies the realization method of high slope displacement mutations identification based on the nonlinear dynamic behavior in the high slope system evolution process,which combines phase space reconstruction,the largest Lyapunov exponent,the cloud model,and other numerical analysis methods.Furthermore,based on the correlation analysis between high slope displacement and the affecting factors,the current work discusses the building principle and algorithm of displacement considering dynamic structure mutation effects.The proposed model depends on up-to-date monitoring data after the last displacement mutations,and considers the relatively stable high slope dynamic system characteristics formed after these mutations.Therefore,the fitting and prediction precision of the high slope displacement monitoring model can be effectively improved.
引文
[1]黄润秋.中国西南岩石高边坡的主要特征及其演化[J].地球科学进展,2005(3):292-297.
[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.
[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.
[4]Moody J,Darken C.Fast learning in Networks of Lo-cally-tuned Processing Units[J].Neural Computation,1989,1(2):281-294.
[5]谈小龙,徐卫亚.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.
[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.
[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.
[8]Albano A M,Muench J,Schwartz C.Singular-valueDecomposition and the Grassberger-Procaccia Algorithm[J].Phys Rev A,1998,(38):3017-3026.
[9]Fraser A M.Information and Entropy in Strange At-tractors[J].IEEE Trans Inform Theory,1989,35(2):245-262.
[10]Wolf A,Swift J B,Swinney H L,et al.Determining Lya-punov Exponents from a Time Series[J].Physica D,1985,16(3):285-317.
[11]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.
[12]顾冲时.大坝与坝基安全监控理论和方法及其应用[M].南京:河海大学出版社,2006:177-181.
[13]李德毅,杜鹅.不确定性人工智能[M].北京:国防工业出版社,2005.
[14]吕辉军,王晔.逆向云在定性评价中的应用[J].计算机学报,2003,26(8):1009-1014.
[15]贾乃文.粘塑性力学及工程应用[M].北京:地震出版社,2000.
[16]VOIGHT B.A Relation to Describe Rate-dependentMaterial failure[J].Science,1989,243:200-203.
[17]SAITO M.Forecasting the Time of Occurrence of SlopeFailure[C].Proceedings of 6th International Congressof Soil Mechanics and Foundation Engineering.Montre-al:[s.n.],1965:537-541.
[18]Zêzere J L,Trigo R M,Trigo I F.Shallow and DeepLandslides Induced by Rainfall in the Lisbon Region(Portugal):Assessment of Relationships with the NorthAtlantic Oscillation[J].Natural Hazards and Earth Sys-tem 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.
[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.
[4]Moody J,Darken C.Fast learning in Networks of Lo-cally-tuned Processing Units[J].Neural Computation,1989,1(2):281-294.
[5]谈小龙,徐卫亚.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.
[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.
[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.
[8]Albano A M,Muench J,Schwartz C.Singular-valueDecomposition and the Grassberger-Procaccia Algorithm[J].Phys Rev A,1998,(38):3017-3026.
[9]Fraser A M.Information and Entropy in Strange At-tractors[J].IEEE Trans Inform Theory,1989,35(2):245-262.
[10]Wolf A,Swift J B,Swinney H L,et al.Determining Lya-punov Exponents from a Time Series[J].Physica D,1985,16(3):285-317.
[11]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.
[12]顾冲时.大坝与坝基安全监控理论和方法及其应用[M].南京:河海大学出版社,2006:177-181.
[13]李德毅,杜鹅.不确定性人工智能[M].北京:国防工业出版社,2005.
[14]吕辉军,王晔.逆向云在定性评价中的应用[J].计算机学报,2003,26(8):1009-1014.
[15]贾乃文.粘塑性力学及工程应用[M].北京:地震出版社,2000.
[16]VOIGHT B.A Relation to Describe Rate-dependentMaterial failure[J].Science,1989,243:200-203.
[17]SAITO M.Forecasting the Time of Occurrence of SlopeFailure[C].Proceedings of 6th International Congressof Soil Mechanics and Foundation Engineering.Montre-al:[s.n.],1965:537-541.
[18]Zêzere J L,Trigo R M,Trigo I F.Shallow and DeepLandslides Induced by Rainfall in the Lisbon Region(Portugal):Assessment of Relationships with the NorthAtlantic Oscillation[J].Natural Hazards and Earth Sys-tem Sciences,2005,5:331-344.
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