基于神经网络的滑坡预测及其控制研究
|
摘要
随着社会经济的飞速发展，人类城市化进程不断加快，植被绿化面积逐年减少，尤其是由于工业化的需要在滑坡灾害高发区所进行的切坡、地下采矿等活动，在很大程度上提高了稳定状态或准稳定状态的斜坡岩土体向不稳定状态转变的可能，使得斜坡岩土体沿着贯通的剪切破坏面发生滑移，从而产生滑坡这种地质灾害。滑坡具有自然、社会和经济等多种属性，威胁人类生命财产安全的同时，阻碍国家和地区经济的健康发展。为了积极做好滑坡灾害应急等方面的工作，尽可能减少滑坡造成的损失，对滑坡灾害的实时准确预测及控制是必要的。本文分析了滑坡研究的发展现状和技术趋势，在对滑坡稳定性做出评价的基础上，进行滑坡累积位移预测以及滑坡的控制。
滑坡稳定性分析是滑坡预测与控制研究的首要问题之一。针对传统稳定性评价方法中参数选择过度简化所导致的稳定系数计算过程过于主观、结果可信度不高的局限性，考虑到实际项目中的小样本、模型非线性及高维的特征，本文提出了一种基于支持向量机（SVM）的滑坡稳定性评价方法。以影响滑坡稳定的六个主要因素作为SVM的判别因子，选择径向基函数作为核函数，采用交叉验证的方法寻找最优的惩罚系数和方差。与传统方法相比，该方法提高了滑坡稳定性判别的精度和速度。
不稳定状态的斜坡岩土体需要预测其即将发生的形变。考虑到滑坡的力学行为和变形趋势呈现出确定性与随机性共存的非线性特征，为了避免建立复杂的力学系统模型，本文针对滑坡预测预报这一复杂性问题提出一种基于前向反馈传播（BP）神经网络的预测模型，描述了滑坡系统输入输出之间的非线性关系。为克服BP网络后期收敛速度慢、易陷入局部极小点等缺点，以遗传算法（GA）和模拟退火算法（SA）相结合的方式即遗传-模拟退火（GSA）算法对网络权值进行优化。用该方法预测滑坡位移获得了较好的效果。
进一步考虑降雨量这一外界因素对滑坡位移产生的影响。据“中国地质灾害数据库”中的数据显示，大部分滑坡的发生与降雨有密切的关系。目前关于降雨量与滑坡位移两者间关系的研究多数基于静态神经网络或其他静态模型，难以反映这种关系的动态特性。对滑坡位移的预测可以看作是对一个非线性动态系统的辨识过程。基于此，本文采用Elman递归神经网络建立系统预测模型，并用GA优化网络的初始权值。该方法具有较高的收敛速度和准确度，并且具有较强的适应性和灵活性。
论文最后引入控制论的观点，探讨滑坡的控制策略。由于滑坡系统的复杂性，很难获得精确的数学模型，采用预测控制方法对滑坡系统进行控制则避开了这一难点。本文根据滑坡动力学原理选择滑坡推力作为系统控制量，利用GA-Elman神经网络对滑坡系统进行辨识，经过滚动优化和反馈校正，得到控制滑坡发生的最优控制量时间序列。
本文系统地探讨了滑坡系统中的三个主要问题：稳定性分析、位移预测及位移控制，对工程实践具有一定的理论指导意义。
With rapid economic development and urbanization process, green area has been reducedyear after year. Especially some human activities such as slope cut and underground mining inhigh incidence area of slope geological disasters make slope rock mass less stable to a largeextent, so the possibility of slippage of slope rock mass along connecting shear failure surfacewill be larger. Finally the failure of slope will happen. Besides natural attribute, Slope also hassocial and economic attributes. It directly threatens people's safety of life and wealth, andhurts development of national and regional economy. In order to implement disasteremergency management and reduce the loss caused by landslides, prediction and controlshould be in real time and accurate. Based on summarizing overseas and domestic researchstatus and development tendency of landslide prevention and control, evaluation of slopestability is discussed, then prediction and control methods of slope cumulative displacementare proposed in this dissertation.
The first step is evaluation of slope stability. As over-simplification of parametersselection in traditional stability evaluation methods leads to subjective computational processof stability coefficient and reduces credibility of results, the evaluation method based onsupport vector machine (SVM for short) is proposed, considering the characteristics of smallsample, nonlinearity model and high-dimensional data in actual projects.6major factorsinfluencing slope stability are considered as discriminant factors of SVM. Radial basisfunction is selected as kernel function. Then cross validation method is used to determineoptimal penalty coefficient and variance. Compared with conventional methods, SVMapproach increases accuracy and speed of evaluation.
It’s necessary to predict imminent deformation of unstable slope rock masses.Considering nonlinearity characteristic (determinacy and stochasticity coexist) presented inmechanical behavior and deformation tendency of slope, the prediction model based on BPneural network is proposed to describe non-linear relationship between input and output ofslope system. Modeling complicated mechanical system can be avoided by use of BP neuralnetwork. Genetic algorithm (GA) and simulated annealing (SA) algorithmare combined,which is genetic-simulated annealing (GSA) algorithm, to optimize network weights, so thatthe shortcomings of BP neural network such as slow rate of convergence and local minimumtrouble in later stage can be overcome.
As an external factor, rainfall capacity has an effect on slope displacement. According todata in Chinese geological disaster database, the occurrences of most landslides are caused byrainfall. Currently the research on relationship between rainfall capacity and slopedisplacement is mainly based on static neural network or other static models. These staticmodels have trouble in reflecting dynamic characteristics of this relationship. The predictionof slope displacement can be treated as identification process of a nonlinear dynamic system.Elman recurrent neural network is used to build systematic prediction model, and initialnetwork weight values are optimized by genetic algorithm. This method is adaptive andflexible, it has high rate of convergence and accurate result.
Finally the opinions of cybernetics are introduced to discuss control strategy of slope.Due to the complexity of slope system, it’s hard to obtain accurate mathematical model. Theusage of predictive control methods for slope system can avoid this trouble. Landslide thrustforce is selected as systems control variable according to principle of landslide dynamics, thenGA-Elman neural network is used to identify slope system. By rolling optimization andfeedback compensation, we can find time series of optimal control variables.
This dissertation systematically discusses3main problems of slope system which arestability analysis, displacement prediction and displacement control. It provides theoreticaldirection for further slope system research.
滑坡稳定性分析是滑坡预测与控制研究的首要问题之一。针对传统稳定性评价方法中参数选择过度简化所导致的稳定系数计算过程过于主观、结果可信度不高的局限性，考虑到实际项目中的小样本、模型非线性及高维的特征，本文提出了一种基于支持向量机（SVM）的滑坡稳定性评价方法。以影响滑坡稳定的六个主要因素作为SVM的判别因子，选择径向基函数作为核函数，采用交叉验证的方法寻找最优的惩罚系数和方差。与传统方法相比，该方法提高了滑坡稳定性判别的精度和速度。
不稳定状态的斜坡岩土体需要预测其即将发生的形变。考虑到滑坡的力学行为和变形趋势呈现出确定性与随机性共存的非线性特征，为了避免建立复杂的力学系统模型，本文针对滑坡预测预报这一复杂性问题提出一种基于前向反馈传播（BP）神经网络的预测模型，描述了滑坡系统输入输出之间的非线性关系。为克服BP网络后期收敛速度慢、易陷入局部极小点等缺点，以遗传算法（GA）和模拟退火算法（SA）相结合的方式即遗传-模拟退火（GSA）算法对网络权值进行优化。用该方法预测滑坡位移获得了较好的效果。
进一步考虑降雨量这一外界因素对滑坡位移产生的影响。据“中国地质灾害数据库”中的数据显示，大部分滑坡的发生与降雨有密切的关系。目前关于降雨量与滑坡位移两者间关系的研究多数基于静态神经网络或其他静态模型，难以反映这种关系的动态特性。对滑坡位移的预测可以看作是对一个非线性动态系统的辨识过程。基于此，本文采用Elman递归神经网络建立系统预测模型，并用GA优化网络的初始权值。该方法具有较高的收敛速度和准确度，并且具有较强的适应性和灵活性。
论文最后引入控制论的观点，探讨滑坡的控制策略。由于滑坡系统的复杂性，很难获得精确的数学模型，采用预测控制方法对滑坡系统进行控制则避开了这一难点。本文根据滑坡动力学原理选择滑坡推力作为系统控制量，利用GA-Elman神经网络对滑坡系统进行辨识，经过滚动优化和反馈校正，得到控制滑坡发生的最优控制量时间序列。
本文系统地探讨了滑坡系统中的三个主要问题：稳定性分析、位移预测及位移控制，对工程实践具有一定的理论指导意义。
With rapid economic development and urbanization process, green area has been reducedyear after year. Especially some human activities such as slope cut and underground mining inhigh incidence area of slope geological disasters make slope rock mass less stable to a largeextent, so the possibility of slippage of slope rock mass along connecting shear failure surfacewill be larger. Finally the failure of slope will happen. Besides natural attribute, Slope also hassocial and economic attributes. It directly threatens people's safety of life and wealth, andhurts development of national and regional economy. In order to implement disasteremergency management and reduce the loss caused by landslides, prediction and controlshould be in real time and accurate. Based on summarizing overseas and domestic researchstatus and development tendency of landslide prevention and control, evaluation of slopestability is discussed, then prediction and control methods of slope cumulative displacementare proposed in this dissertation.
The first step is evaluation of slope stability. As over-simplification of parametersselection in traditional stability evaluation methods leads to subjective computational processof stability coefficient and reduces credibility of results, the evaluation method based onsupport vector machine (SVM for short) is proposed, considering the characteristics of smallsample, nonlinearity model and high-dimensional data in actual projects.6major factorsinfluencing slope stability are considered as discriminant factors of SVM. Radial basisfunction is selected as kernel function. Then cross validation method is used to determineoptimal penalty coefficient and variance. Compared with conventional methods, SVMapproach increases accuracy and speed of evaluation.
It’s necessary to predict imminent deformation of unstable slope rock masses.Considering nonlinearity characteristic (determinacy and stochasticity coexist) presented inmechanical behavior and deformation tendency of slope, the prediction model based on BPneural network is proposed to describe non-linear relationship between input and output ofslope system. Modeling complicated mechanical system can be avoided by use of BP neuralnetwork. Genetic algorithm (GA) and simulated annealing (SA) algorithmare combined,which is genetic-simulated annealing (GSA) algorithm, to optimize network weights, so thatthe shortcomings of BP neural network such as slow rate of convergence and local minimumtrouble in later stage can be overcome.
As an external factor, rainfall capacity has an effect on slope displacement. According todata in Chinese geological disaster database, the occurrences of most landslides are caused byrainfall. Currently the research on relationship between rainfall capacity and slopedisplacement is mainly based on static neural network or other static models. These staticmodels have trouble in reflecting dynamic characteristics of this relationship. The predictionof slope displacement can be treated as identification process of a nonlinear dynamic system.Elman recurrent neural network is used to build systematic prediction model, and initialnetwork weight values are optimized by genetic algorithm. This method is adaptive andflexible, it has high rate of convergence and accurate result.
Finally the opinions of cybernetics are introduced to discuss control strategy of slope.Due to the complexity of slope system, it’s hard to obtain accurate mathematical model. Theusage of predictive control methods for slope system can avoid this trouble. Landslide thrustforce is selected as systems control variable according to principle of landslide dynamics, thenGA-Elman neural network is used to identify slope system. By rolling optimization andfeedback compensation, we can find time series of optimal control variables.
This dissertation systematically discusses3main problems of slope system which arestability analysis, displacement prediction and displacement control. It provides theoreticaldirection for further slope system research.
引文
[1]全国地质灾害通报. http:www.cigem.gov.cn.
[2] Kilburn CRJ, Petley DN. Forecasting giant, catastrophic slope collapse: lessons fromVajont, Northern Italy. Geomorphology,2003,54:21-32.
[3]贺可强，周敦云，王思敬.降雨型堆积层滑坡的加卸载响应比特征及其预测作用与意义.岩石力学与工程学报,2004,23(16):2665-2670.
[4]汪洋，殷坤龙.水库库案滑坡初始涌浪叠加的摄动方法.岩石力学与工程学报,2004,23(5):717-720.
[5]黄昌乾,丁恩保.边坡工程常用稳定性分析方法.水电站设计.1999,15(1):53-58.
[6]杨志法,尚彦军,刘英.关于岩土工程类比法的研究.1997,5(4):299-305.
[7] Phillips, F.C. The use of stereographic projection in structural geology[M].1971,London: Edward Arnold.
[8]陈炳贵,方宏伟.论赤平极射投影在山口岩水利工程中的应用.地球物理学进展.2008,23(2):631-635.
[9]夏元友,朱瑞赓.边坡稳定性分析专家系统研制.灾害学,1997,12(4):10-14.
[10]Hudson J.A. Atlas of rock engineering mechanisms. International Journal of RockMechanics and Mining&Geomechanics Abstracts.1992,29(2):157-159.
[11]李聪,姜清辉,周创兵,周钟,宋胜武,龚满福.基于实例推理系统的滑坡预警判据研究.岩土力学,2011,32(4):1069-1708.
[12]Fyson M, Toll DG. Case-based system for slope design. Computers and Geotechnics.2008,35(3):468-478.
[13]Liu MY, Zhu RG. Case-based reasoning for slope stability evaluation and itsapplication. Journal of Wuhan University of Technology-Materials Science Edition.2001,16(4):62-65.
[14]Shi B, Liu Y, Mu Z. Reliability analysis of slope stability at seismic action based onartificial fish-swarm algorithm. Applied Mechanics and Mechanical Engneering,2010,29-32:1512-1516.
[15]Wei Z, Yin G, Wang J, Wan L, Jin L. Stability analysis and supporting system designof a high-steep cut soil slope on an ancient landslide during highway construction ofTehran-Chalus. Environmental Earth Sciences,2012,67(6):1651-1662.
[16]Wang W, Li C, Wang S. Slope instability risk analysis in earth dams consideringuncertain factors. Materials and Computational Mechanics,2012,117-119:1475-1478.
[17]Zhang G, Wang, L. Stability analysis of strain-softening slope reinforced withstabilizing piles. Journal of Geotechnical and Geoenvironmental Engineering,2010,136(11):1578-1582.
[18]Babu GLS, Bijoy AC. Appraisal of Bishop's method of slope stability analysis. SlopeStability Engineering,1999,1-2:249-252.
[19]Houam M A, Nuyens J. Application of the simplified Bishop method to embankmentsreinforced with geotextiles. Canadian Geotechnical Journal,1993,30(5):812-820.
[20]Li RJ, Xu Q, Zheng W, Lin, HC. The stability analyses of unsaturated slope based onthe Sarma method. Biotechnology, Chemical and Materials Enginering,2012,393-395:1569-1573.
[21]Zhang B. Study on rock slope stability based on unified strength theory. in:Proceedings of2010International Symposium on Multi-field Coupling Theory ofRock and Soil Media and its Applications. Marrickville: Orient ACAD Forum,2010.660-664.
[22]Corkum AG, Martin CD. Analysis of a rock slide stabilized with a toe-berm: a casestudy in British Columbia, Canada. International Journal of Rock Mechanics andMining Science,2004,41(7):1109-1121.
[23]Bafghi ARY, Verdel T. Sarma-based key-group method for rock slope reliabilityanalyses. International Journal for Numerical and Analytical Methods inGeomechanics,2005,29(10):1019-1043.
[24]Jie M, Chen CY, Zhang JJ. General stability of two-dimensional slopes based onSarma's method. International Journal for Numerical and Analytical Methods inGeomechanics,1999,23(5):413-426.
[25]Sarma HK, Bentsen RG. A Method for reducing model error when estimating relativepermeabilities from displacement data. Journal of Petroleum Science and Engineering,1989,2(4):331-347.
[26]Xiao SG, Feng WK. Numerical analysis method for dynamic stability of a high andsteep rock slope in the Sichuan Wenchuan earthquake. Advances in BuildingMaterials,2011,261-263:1182-1190.
[27]Cho SE. Effects of spatial variability of soil properties on slope stability. EngineeringGeology,2007,92(3-4):97-109.
[28]Sharma S, Moudud A. Interactive slope analysis using Spencer Method. Stability andPerformance of Slopes and Embankments-II,1992,1-2(31):506-520.
[29]Jiang JC, Yamagami T. Three-dimensional slope stability analysis using an extendedspencer method. Soils and Foundations,2004,44(4):127-135.
[30]邓东平,李亮.两种滑动面型式下边坡稳定性计算方法的研究.岩土力学,2013,34(2):372-380.
[31]Bi RN, Ehret D, Xiang W, Rohn J, Schleier M, Jiang JW. Landslide reliabilityanalysis based on transfer coefficient method: a case study from Three GorgesReservoir. Journal of Earth Science,2012,23(2):187-198.
[32]Liu CH, Jaksa MB, Meyers AG. A transfer coefficient method for rock slope toppling.Canadian Geotechnical Journal,2009,46(1):1-9.
[33]赵广春,徐光黎,苏爱军,殷坤龙,马霄汉.传递系数法在滑坡稳定性评价中的几个问题探讨.地球与环境,2005,33:7-11.
[34]Ishii K, Suzuki M. Stochastic finite element method for slope stability analysis.Structural Safety,1986,4(2):111-129.
[35]Chen JX, Ke PZ, Zhang G. Slope stability analysis by strength reduction elasto-plasticFEM. Key Engineering Materials,2007,345-346:625-628.
[36]Yi F, Liang B, Jin JX. Analysis of highway slope stability by strength reduction FEMunder solid-fluid coupling. Flow in Porous Media-from Phennmena to Eegineeringand Beyond,2009:305-308.
[37]van den Ham G, Rohn J, Meier T, Czurda K. Finite Element simulation of a slowmoving natural slope in the Upper-Austrian Alps using a visco-hypoplasticconstitutive model. Geomorphology,2009,103(1):136-142.
[38]Farah K, Ltifi M, Hassis H. Reliability analysis of slope stability using stochasticfinite element method. Procedia Engineering,2011,10:1402-1407.
[39]Mandolini A, Minutolo V, Ruocco E. Coupling of underground pipelines and slowlymoving landslides by BEM analysis. CMES-Computer Modeling in Engineering&Sciences,2001,2(1):39-47.
[40]邓琴,郭明伟,李春光,葛修润.基于边界元法的边坡矢量和稳定分析.岩土力学,2010,31(6):1971-1976.
[41]Jiang YS, Zimmermann Th. Indirect boundary element algorithm for slope stabilityanalysis. Engineering Analysis with Boundary Elements,1992,9(3):209-217.
[42]Chang CS. Discrete element method for slope stability analysis. Journal ofGeotechnical engineering,1992,118(12):1889-1905.
[43]Kim JS, Kim JY, Lee SR. Analysis of soil nailed earth slope by discrete elementmethod. Computers and Geotechnics,1997,20(1):1-14.
[44]Lu W, Zhou Z, Liu T, Liu YH. Discrete element simulation analysis of rock slopestability based on UDEC. Advanced Materials Research,2012,461:384-388.
[45]Chugh AK, Stark TD. Permanent seismic deformation analysis of a landslide.Landslides,2006,3(1):2-12.
[46]Singh TN, Gulati A, Dontha L, Bhardwaj V. Evaluating cut slope failure by numericalanalysis—a case study. Natural Hazards,2008,47(2):263-279.
[47]MacLaughlin M, Sitar N, Doolin D, Abbot T. Investigation of slope-stabilitykinematics using discontinuous deformation analysis. International Journal of RockMechanics and Mining Sciences,2001,38(5):753-762.
[48]Wu JH. Seismic landslide simulations in discontinuous deformation analysis.Computers and Geotechnics,2010,37(5):594-601.
[49]Naserizadeh R, Bingham HB, Noorzad A. A coupled boundary element-finitedifference solution of the elliptic modified mild slope equation. Engineering Analysiswith Boundary Elements,2011,35(1):25-33.
[50]Ning YJ, An XM, Ma GW. Footwall slope stability analysis with the numericalmanifold method. International Journal of Rock Mechanics and Mining Sciences,2011,48(6):964-975.
[51]Ma GW, An XM, He L. The numerical manifold method: a review. InternationalJournal of Computational Methods,2010,7(1):1-32.
[52]Zhou G, Esaki T, Mitani Y, Xie M, Mori J. Spatial probabilistic modeling of slopefailure using an integrated GIS Monte Carlo simulation approach. EngineeringGeology,2003,68(3-4):373-386.
[53]Giasi CI, Masi P, Cherubini C. Probabilistic and fuzzy reliability analysis of a sampleslope near Aliano. Engineering Geology,2003,67(3-4):391-402.
[54]高克昌,崔鹏,赵纯勇,韦方强.基于地理信息系统和信息量模型的滑坡危险性评价——以重庆万州为例.岩石力学与工程学报,2006,25(5):991-996.
[55]楚敬龙,王金生,滕彦国,王小娜.基于GIS的滑坡灾害危险性区划研究——以重庆市万州区为例.地球科学与环境学报,2010,32(4):409-415.
[56]Kaunda RB, Chase RB, Kehew AE, Kaugars K, Selegean JP. Neural networkmodeling applications in active slope stability problems. Environmental EarthSciences,2010,60(7):1545-1558.
[57]Da SK, Biswal RK, Sivakugan N, Das B. Classification of slopes and prediction offactor of safety using differential evolution neural networks. Environmental EarthSciences,2011,64(1):201-210.
[58]Rampone S, Valente A. Neural network aided evaluation of landslide susceptibility insouthern Italy. International Journal of Modern Physics C,2012,23(1).
[59]Li JC, Li PL. Analysis of landslide stability based on neural network. SustainableDevelopment of Urban Environment and Building Material,2012,374-377:2346-2351.
[60]Erzin Y, Cetin T. The use of neural networks for the prediction of the critical factor ofsafety of an artificial slope subjected to earthquake forces. Scientia Iranica,2012,19(2):188-194.
[61]Erzin Y, Cetin T. The prediction of the critical factor of safety of homogeneous finiteslopes using neural networks and multiple regressions. Computers&Geosciences,2013,51:305-313.
[62]陈新民,罗国煜.基于经验的边坡稳定性灰色系统分析与评价.岩土工程学报,1999,21(5):638-641.
[63]Lu P, Rosenbaum M. Artificial neural networks and grey systems for the prediction ofslope stability. Natural Hazards,2003,30(3):383-398.
[64]Park HJ, Um JG, Woo IK, Kim JW. Application of fuzzy set theory to evaluate theprobability of failure in rock slopes. Engineering Geology,2012,125(27):92-101.
[65]Saito M. Forecasting the time of occurrence of a slope failure. in: Proceedings of the6th International Conference on Soil Mechanics and Foundation Engineering.1965,2:537-541.
[66]Saito M. Forecasting time of slope failure by tertiary creep. in: Proceedings of the6thInternational Conference on Soil Mechanics and Foundation Engineering.1969,2:677-683.
[67]Noriaki S. Normalization of displacement rate for roughly predicting the time tofailure in rock slope. Oyo Technical RePort,2001,21:1-8.
[68]Hayashi S, Komamura F, Park B, et al. On the forecast of time to failure of slope-approximate forecast in the early period of the tertiary creep. Journal of JapanLandslide Society,1988,25(3):11-16.
[69]Voight B. A method for Prediction of volcanic eruption. Nature,1988,332:125-130.
[70]Voight B. A relation to describe rate-dependent material failure. Scienee,1989,243:200-203.
[71]Federico A, Popescu M, Fidelibus C, et al. On the prediction of the time of occurrenceof a slope failure: a review. in: Proceedings of the9th International Symposium onLandslides.2004,2:979-983.
[72]晏同珍.滑坡统计预测方法.滑坡文集.北京:中国铁道出版社,1988.
[73]晏同珍.水文工程地质与环境保护.武汉:中国地质大学出版社,1994.
[74]殷坤龙,晏同珍.滑坡预测及相关模型.岩石力学与工程学报,1996,15(l):1-8.
[75]殷坤龙.滑坡灾害预测预报.武汉:中国地质大学出版社,2003.
[76]陈明东,王兰生.新滩滑坡的灰色预报分析.新滩滑坡讨论会文集,1986.
[77]夏元友.滑坡灰色系统预测模型及其应用.自然灾害学报,1995,4(l):74-78.
[78]李朝甫,徐迎,谭跃虎,张涛.灰色系统理论在滑坡位移信息分析中的应用.系统工程理论与实践,2001,2:129-132.
[79]王朝阳,许强,范宣梅,曾金华.灰色新陈代谢GM(1,1)模型在滑坡变形预测中的应用.水文地质工程地质,2009,31(2):108-112.
[80]李晓红,靳晓光,亢会明等. GM(1,1)优化模型在滑坡预测中的应用.山地学报,2001,19(3):265-26.
[81]何习平,华锡生,何秀凤.加权多点灰色模型在高边坡变形预测中的应用.岩土力学,2007,28(6):1187-1191.
[82]姚颖康,张春艳,张坤.改进的GM(1,1)模型在滑坡变形预测中的应用.水文地质工程地质,2009,36(5):102-106.
[83]张贵钢,杨志强,朱健.基于残差改正的动态GM(1,1)模型在公路边坡变形监测中的应用.测绘科学,2010,35(4):148-150.
[84]孙景恒,李振明,苏万益. Pearl模型在边坡失稳时间预报中的应用.中国地质灾害与防治学报,1993,4(2):36-41.
[85]徐峻龄,马辉.滑坡临滑预报方法之讨论.中国地质灾害与防治学报,1998,9:364-369.
[86]王建锋.滑坡发生的时间预测分析.中国地质灾害与防治学报,2003,14(2):1-7.
[87]阳吉宝.堆积层滑坡临滑预报的新判据.工程地质学报,1995,3(2):70-73.
[88]阳吉宝,钟正雄.滑坡时间预报的双参数判据.中国地质灾害与防治学报,1996,7(增刊):61-66.
[89]阳吉宝,贺可强.堆积层滑坡时间预报问题的讨论.河北地质学院学报,1995,18(1):46-50.
[90]贺可强,阳吉宝,王思敬.堆积层边坡表层位移矢量角及其在稳定性预测中的作用与意义.岩石力学与工程学报,2003,22(12):1976-1983.
[91]He KQ, Wang SJ. Constituents and features of displacement vector angle of colluviallandslide. Environmental Geology,2003,45(2):279-285.
[92]Hu W, Yang XG, Xu FG, Hao MH. Exponential smoothing method based onwavelet transform for slope displacement prediction. in: Proceedings of the2ndInternational Conference on Uncertainty Reasoning and Knowledge Engineering(URKE).2012,264-267.
[93]刘晓，唐辉明，刘瑜.基于集对分析和模糊马尔可夫链的滑坡变形预测新方法研究.岩土力学.2009,30(11):3399-3407.
[94]张悼元,黄润秋等.岩体破坏时间预测的黄金分割数法.第三次全国工程地质大会文集.成都:成都科技大学出版社,1988,1236-1237.
[95]Xu J, Ma FH, Yang F, Ji HF. Application of nonlinear time series analysis in slopedeformation analysis and forecast. Mine Hazards Prevention and Control Technology,2007,226-230.
[96]徐峰,汪洋,杜娟,叶疆.基于时间序列分析的滑坡位移预测模型研究.岩石力学与工程学报,2011,30(4):746-751.
[97]Caudill M. Neural Networks Primer. San Francisco, CA: Miller Freeman Publicat-ions,1989.
[98]Binaghi E, Boschetti M, Brivio PA, Gallo I, Pergalani F, Rampini A. Prediction ofdisplacements in unstable areas using a neural model. Nature Hazards,2004,32(1):135-154.
[99]吴益平,滕伟福,李亚伟.灰色–神经网络模型在滑坡变形预测中的应用.岩石力学与工程学报,2007,26(3):632-636.
[100] Meng FZ, Li SJ, Zhang ZH. Determination of mechanical parameters of reservoirlandslide based on back analysis using evolutionary artificial network. Progress inCivil Engineering,2012,170-173:729-734.
[101] Chen YY, Wang SW, Chen N, Long XQ, Tang XR. Forecasting cohesionless soilhighway slope displacement using modular neural network. Discrete Dynamics inNature and Society,2012, DOI:10.1155/2012/504574.
[102]秦四清.非线性工程地质学导引.成都:西南交通大学出版社,1993,83-90.
[103]秦四清,王思敬.斜坡滑动失稳演化的非线性机制与过程研究进展.地球与环境,2005,33(3):75-81.
[104]刘汉东.边坡失稳定时预报理论与方法.郑州:黄河水利出版社,1996,10-36.
[105]龙辉,秦四清,朱世平,万志清.滑坡演化的非线性动力学与突变分析.工程地质学报,2001,9(3):331-335.
[106]文宝萍.滑坡预测预报研究现状及发展趋势.地学前缘,1996,3(1-2):86-91.
[107]罗红明,唐辉明,胡斌等.基于突变理论的反倾层状岩石边坡稳定性研究.地质科技情报,2007,6:101-104.
[108]姜永东,鲜学福,郭臣业.层状岩质边坡失稳的燕尾突变模型.重庆大学学报,2008,5:553-557.
[109]孙强,刘天霸,秦四清等.斜坡失稳的燕尾突变模型.工程地质学报,2006,06:852-855.
[110]郑明新,王恭先,王兰生.分形理论在滑坡预报中的应用研究.地质灾害与环境保护,1998,9(2):18-24.
[111]许强,黄润秋.用加卸载响应比理论探讨边坡失稳前兆.中国地质灾害与防治学报,1995,6(2):25-30.
[112] Zhang WJ, ChenYM, Zhan LT. Loading/Unloading response ratio theory applied inpredicting deep-seated landslides triggering. Engineering Geology,2006,82:234-240.
[113]贺可强,王荣鲁,李新志等.堆积层滑坡的地下水加卸载动力作用规律及其位移动力学预测——以三峡库区八字门滑坡分析为例.岩石力学与工程学报,2008,27(8):1644-1650.
[114]许建聪,尚岳全,王建林.松散土质滑坡位移与降雨量的相关性研究.岩石力学与工程学报,2006,25(51):2854-2860.
[115]姜彤,马瑾,许兵.基于加卸载响应比理论的边坡动力稳定分析方法.岩石力学与工程学报,2007,26(3):626-631.
[116]黄润秋,许强.斜坡失稳时间的协同预测模型.山地研究,1997,15(l):7-12.
[117]孙强,张小宝,秦四清等.关于边坡非线性研究的几点思考.铁道勘察,2007,4:67-70.
[118]周翠英,陈恒,朱凤贤.基于渐进演化的高边坡非线性动力学预警研究.岩石力学与工程学报,2008,4:818-824.
[119]许强,黄润秋,李秀珍.滑坡时间预测预报研究进展.地球科学进展,2004,19(3):478-483.
[120]贺可强,孙林娜,王思敬.滑坡位移分形参数Hurst指数及其在堆积层滑坡预报中的应用.岩石力学与工程学报,2009,28(6):1107-1115.
[121]廖小平.滑坡破坏时间新理论探讨.地质灾害与环境保护,1994,5(3):25-29.
[122]伍法权,王年生.一种滑坡位移动力学预报方法探讨.中国地质灾害与防治学报,1996,7(增刊):38-41.
[123]贺可强,白建业,王思敬.降雨诱发堆积层滑坡的位移动力学特征分析.岩土力学,2005,26(5):705-709.
[124] Vulliet L, Hutter K. Viscous-type sliding laws for landslides. Canadian GeotechnicalJoumal,1988,25(3):467-477.
[125] Desai CS, Samtani NC, Vulliet L. Constitutive modeling and analysis of creepingslopes. Journal of Geotechnical Engineering,1995,121(1):43-56.
[126] Desai CS, Somasundaram S, Frantziskonis G. A hierarchical approach forconstitutive modeling of geologic materials. International Journal for Numerical andAnalytical Methods in Geomechanics,1996,10:225-257.
[127] Angeli MG, Caspatetto P, Menotti RM, etal. A viso-plastic model for slope analysisapplied to a mudslide in Cortinad Ampezzo, Italy. Quarterly Joumal of EngineeringGeology,1996,29:233-240.
[128] Faure RM, Gress JC, Rojat F. An easy to use model for taking in account rainfall insoil displacement. European Slope Stability Symposium,2002.
[129] Ferlisi S. A simple mechanical model for the interpretation of translational activelandslides involving detrital covers. in: Proceedings of the Ⅸ InternationalSymposium on Landslides, Rio de Janeiro,2004,2:1227-1232.
[130] Leroueil S, Locat J, Vaunat J, etal. Geotechnical characterization of landslides. in:Proceedings of the7th International Symposium on Landslides.1996,53-74.
[131] Corominas J, Moya J, Ledesma A, etal. Prediction of ground displacement andvelocities from ground water level changes at the Vallcebre landslide (EasternPyrenees). Landslides,2005,2:83-96.
[132] Maugeri M, Motta E, Raciti E. Mathematical modeling of the landslide occurred atCagliano Castelferrato (Italy). Natural Hazards and Earth System Sciences,2006,6:133-143.
[133] Van AschTWJ, Van Beek LPH, Bogaard TA. Promblems in predicting the mobilityof slow-moving landslides, Engineering Geology,2007,91:46-55.
[134] Duncan JM. State of the art: limit equilibrium and finite-element analysis of slope.Journal of Geotechnical Engineering,1996,3:577-596.
[135] Chen H, Lee CF. A dynamic model for rainfall-induced landslides on natural slopes.Geomorphology,2003,51(4):269-288.
[136] Maffei A, Martino S, Prestininzi A. From the geological to the numerical model inthe analysis of gravity-induced slope deformations: an example from the CentralApennines (Italy). Engineering Geology,2005,78:215-236.
[137] Hurlimann M, Ledesma A, Corominas J, et al. The deep-seated slope deformation atEncampadana, Andorra: representation of morphologic features by numericalmodelling. Engineering Geology,2006,83:343-357.
[138] Stead D, Eberhardt E, Coggan JS. Developments in the characterization of complexrock slope deformation and failure using numerical modelling techniques.Engineering Geology,2006,83:217-235.
[139]钟荫乾.黄蜡石滑坡综合信息预报方法研究.中国地质灾害与防治学报,1995,6(4):68-75.
[140]胡高社,门玉明,刘玉海等.新滩滑坡预报判据研究.中国地质灾害与防治学报,1996,7(增刊):67-72.
[141]李东山,黄润秋,许强等.三峡库区滑坡综合预报系统的设计与实现.中国地质灾害与防治学报,2003,14(2):24-27.
[142]万全,范书龙,林炎.滑坡的多模型综合预测预报研究.水土保持研究,2005,12(5):181-185.
[143] Calvello M, Cascini L, Sorbino G. A numerical procedure for predicting rainfall-induced movements of active landslides along pre-existing slip surfaces.International Journal for Numerical and Analytical Methods in Geomechanics,2008,32:327-351.
[144]贺可强,雷建和.边坡稳定性的神经网络预测研究.地质与勘探,2001,37(6):72-75.
[145]黄志全.滑坡工程非线性分析理论及应用[M].郑州:黄河水利出版社,2005.
[146] Gluege S, Hamid OH, Wendemuth A. A simple recurrent network for implicitlearning of temporal sequences. Cognitive Computation,2010,2(4):265-271.
[147] Subirats JL, Jerez JM, Gomez I. Multiclass pattern recognition extension for the newC-Mantec constructive neural network algorithm. Cognitive Computation,2010,2(4):285-290.
[148] Rao S, Aleksander I. A position identification and path labeling mechanism for aneural model of visual awareness. Cognitive Computation,2010,2(4):360-372.
[149] Indiveri G, Chicca E, Douglas RJ. Artificial cognitive systems: from VLSI networksof spiking neurons to neuromorphic cognition. Cognitive Computation,2009,1(2):119-127.
[150] Garagnani M, Wennekers T, Pulvermueller F. Recruitment and consolidation of cellassemblies for words by way of Hebbian learning and competition in a multi-layerneural network. Cognitive Computation,2009,1(2):160-176.
[151] Haikonen POA. The role of associative processing in cognitive computing.Cognitive Computation,2009,1(1):42-49.
[152] Gros C. Cognitive computation with autonomously active neural networks: anemerging field. Cognitive Computation,2009,1(1):77-90.
[153] Jaroudi E, Makhoul J. A new error criterion for posterior probability estimation withneural nets. in: Proceedings of International Joint Conference on Neural Networks,1990,185-192.
[154] Ducker H, Cuny YL. Improving generalization using double back propagation. IEEETransactions on Neural Networks,1992,3(6):991-997.
[155] Mayoraz F, Vulliet L. Neural networks for slope movement prediction. TheInternational Journal Geomechanics,2002,2(2):153-173.
[156] Das SK, Basudhar PK. Prediction of coefficient of lateral earth pressure usingartificial neural networks. The Electronic Journal of Geotechnical Engineering,2005,10:1-5.
[157]史峰,王辉,胡斐,郁磊. MATLAB30智能算法30个案例分析.北京:北京航空航天大学出版社.2011.
[158]李德营.三峡库区具台阶状位移特征的滑坡预测预报研究.中国地质大学博士学位论文,2010.
[159] Ran Y, Xiong G, Li S, et al. Study on deformation prediction of landslide based ongenetic algorithm and improved BP neural network. Kybernetes,2010,39(8):1245-1254.
[160]孙广忠.中国典型滑坡[M].北京:科学出版社,1998.
[161] Li XZ, Kong JM, Wang ZY. Landslide displacement prediction based on combiningmethod with optimal weight. Natural Hazards,2012,61:635-646.
[162] Fukuzono T. A new method for predicting the failure time of a slope. in: Proceedingsof the4th International Conference and Field Workshop in Landslides, Tokyo.1985:145-150.
[163]徐峻龄,廖小平,李荷生.黄茨大型滑坡的预报及其理论和方法.中国地质灾害与防治学报,1996,7(3):18-25.
[164] Helmstetter A, Sornette D, Grasso JR, Andersen JV, Gluzman S, Pisarenko V. Slider-block friction model for landslides: application to Vaiont and La Clapiere Landslides.Journal of Geophysical Research: Solid Earth (1978-2012),2004,109:1-15.
[165] Sornette D, Helmstetter A, Andersen JV, Gluzman S, Grasso JR, Pisarenko V.Towards landslide predictions: two case studies. Physica A: Statistical Mechanicsand its Applications,2004,338(3-4):605-632.
[166] Jibson RW. Regression models for estimating coseismic landslide displacement.Engineering Geology,2007,91(2):209-218.
[167] Long H, Qin SQ, Zhu SP, et al. Nonlinear dynamic model and catastrophe analysisof slope evolution. Journal of Engineering Geology,2001,9:331-335.
[168] Neaupane KM, Achet SH. Use of backpropagation neural network for landslidemonitoring: a case study in the higher Himalaya. Engineering Geology,2004,74(3-4):213-226.
[169] Feng XT, Zhao H, Li S. Modeling non-linear displacement time series of geo-materials using evolutionary support vector machines. International Journal of RockMechanics and Mining Sciences,2004,41(7):1087-1107.
[170] Cagdas HA, Erol E, Ufuk Y. Forecast combination by using artificial neuralnetworks. Neural Processing Letters,2010,32:269-276.
[171] Lee H, Park Y. Nonlinear system identification using recurrent networks. in:Proceedings of the1991IEEE International Joint Conference on Neural Networks,1991,2410-2415.
[172] Karaboga D, Kalinli A. Training recurrent neural networks for dynamic systemidentification using parallel Tabu Search Algorithm. in: Proceedings of the12thIEEE International Symposium on Intelligent Control,1997,113-117.
[173] Yu W. Nonlinear system identification using discrete-time recurrent neural networkswith stable learning algorithms. Information Sciences,2004,158:131-147.
[174] Grefenstette JJ. Optimization of control parameters for genetic algorithms. IEEETransactions on Systems, Man and Cybernetics,1986,16(1):122-128.
[175] S rensen PH, N rgaard M, Ravn O, Poulsen NK. Implementation of neural networkbased non-linear predictive control. Neurocomputing,1999,28(1-3):37-51.
[176] Huang JQ, Lewis FL. Neural-network predictive control for nonlinear dynamicsystems with time-delay. IEEE Transactions on Neural Networks,2003,14(2):377-389.
[177] Song Y, Chen Z, Yuan Z. New chaotic PSO-based neural network predictive controlfor nonlinear process. IEEE Transactions on Neural Networks,2007,18(2):595-601.
[178] Na J, Ren X, Shang C, Guo Y. Adaptive neural network predictive control fornonlinear pure feedback systems with input delay. Journal of Process Control,2012,22(1):194-206.
[2] Kilburn CRJ, Petley DN. Forecasting giant, catastrophic slope collapse: lessons fromVajont, Northern Italy. Geomorphology,2003,54:21-32.
[3]贺可强，周敦云，王思敬.降雨型堆积层滑坡的加卸载响应比特征及其预测作用与意义.岩石力学与工程学报,2004,23(16):2665-2670.
[4]汪洋，殷坤龙.水库库案滑坡初始涌浪叠加的摄动方法.岩石力学与工程学报,2004,23(5):717-720.
[5]黄昌乾,丁恩保.边坡工程常用稳定性分析方法.水电站设计.1999,15(1):53-58.
[6]杨志法,尚彦军,刘英.关于岩土工程类比法的研究.1997,5(4):299-305.
[7] Phillips, F.C. The use of stereographic projection in structural geology[M].1971,London: Edward Arnold.
[8]陈炳贵,方宏伟.论赤平极射投影在山口岩水利工程中的应用.地球物理学进展.2008,23(2):631-635.
[9]夏元友,朱瑞赓.边坡稳定性分析专家系统研制.灾害学,1997,12(4):10-14.
[10]Hudson J.A. Atlas of rock engineering mechanisms. International Journal of RockMechanics and Mining&Geomechanics Abstracts.1992,29(2):157-159.
[11]李聪,姜清辉,周创兵,周钟,宋胜武,龚满福.基于实例推理系统的滑坡预警判据研究.岩土力学,2011,32(4):1069-1708.
[12]Fyson M, Toll DG. Case-based system for slope design. Computers and Geotechnics.2008,35(3):468-478.
[13]Liu MY, Zhu RG. Case-based reasoning for slope stability evaluation and itsapplication. Journal of Wuhan University of Technology-Materials Science Edition.2001,16(4):62-65.
[14]Shi B, Liu Y, Mu Z. Reliability analysis of slope stability at seismic action based onartificial fish-swarm algorithm. Applied Mechanics and Mechanical Engneering,2010,29-32:1512-1516.
[15]Wei Z, Yin G, Wang J, Wan L, Jin L. Stability analysis and supporting system designof a high-steep cut soil slope on an ancient landslide during highway construction ofTehran-Chalus. Environmental Earth Sciences,2012,67(6):1651-1662.
[16]Wang W, Li C, Wang S. Slope instability risk analysis in earth dams consideringuncertain factors. Materials and Computational Mechanics,2012,117-119:1475-1478.
[17]Zhang G, Wang, L. Stability analysis of strain-softening slope reinforced withstabilizing piles. Journal of Geotechnical and Geoenvironmental Engineering,2010,136(11):1578-1582.
[18]Babu GLS, Bijoy AC. Appraisal of Bishop's method of slope stability analysis. SlopeStability Engineering,1999,1-2:249-252.
[19]Houam M A, Nuyens J. Application of the simplified Bishop method to embankmentsreinforced with geotextiles. Canadian Geotechnical Journal,1993,30(5):812-820.
[20]Li RJ, Xu Q, Zheng W, Lin, HC. The stability analyses of unsaturated slope based onthe Sarma method. Biotechnology, Chemical and Materials Enginering,2012,393-395:1569-1573.
[21]Zhang B. Study on rock slope stability based on unified strength theory. in:Proceedings of2010International Symposium on Multi-field Coupling Theory ofRock and Soil Media and its Applications. Marrickville: Orient ACAD Forum,2010.660-664.
[22]Corkum AG, Martin CD. Analysis of a rock slide stabilized with a toe-berm: a casestudy in British Columbia, Canada. International Journal of Rock Mechanics andMining Science,2004,41(7):1109-1121.
[23]Bafghi ARY, Verdel T. Sarma-based key-group method for rock slope reliabilityanalyses. International Journal for Numerical and Analytical Methods inGeomechanics,2005,29(10):1019-1043.
[24]Jie M, Chen CY, Zhang JJ. General stability of two-dimensional slopes based onSarma's method. International Journal for Numerical and Analytical Methods inGeomechanics,1999,23(5):413-426.
[25]Sarma HK, Bentsen RG. A Method for reducing model error when estimating relativepermeabilities from displacement data. Journal of Petroleum Science and Engineering,1989,2(4):331-347.
[26]Xiao SG, Feng WK. Numerical analysis method for dynamic stability of a high andsteep rock slope in the Sichuan Wenchuan earthquake. Advances in BuildingMaterials,2011,261-263:1182-1190.
[27]Cho SE. Effects of spatial variability of soil properties on slope stability. EngineeringGeology,2007,92(3-4):97-109.
[28]Sharma S, Moudud A. Interactive slope analysis using Spencer Method. Stability andPerformance of Slopes and Embankments-II,1992,1-2(31):506-520.
[29]Jiang JC, Yamagami T. Three-dimensional slope stability analysis using an extendedspencer method. Soils and Foundations,2004,44(4):127-135.
[30]邓东平,李亮.两种滑动面型式下边坡稳定性计算方法的研究.岩土力学,2013,34(2):372-380.
[31]Bi RN, Ehret D, Xiang W, Rohn J, Schleier M, Jiang JW. Landslide reliabilityanalysis based on transfer coefficient method: a case study from Three GorgesReservoir. Journal of Earth Science,2012,23(2):187-198.
[32]Liu CH, Jaksa MB, Meyers AG. A transfer coefficient method for rock slope toppling.Canadian Geotechnical Journal,2009,46(1):1-9.
[33]赵广春,徐光黎,苏爱军,殷坤龙,马霄汉.传递系数法在滑坡稳定性评价中的几个问题探讨.地球与环境,2005,33:7-11.
[34]Ishii K, Suzuki M. Stochastic finite element method for slope stability analysis.Structural Safety,1986,4(2):111-129.
[35]Chen JX, Ke PZ, Zhang G. Slope stability analysis by strength reduction elasto-plasticFEM. Key Engineering Materials,2007,345-346:625-628.
[36]Yi F, Liang B, Jin JX. Analysis of highway slope stability by strength reduction FEMunder solid-fluid coupling. Flow in Porous Media-from Phennmena to Eegineeringand Beyond,2009:305-308.
[37]van den Ham G, Rohn J, Meier T, Czurda K. Finite Element simulation of a slowmoving natural slope in the Upper-Austrian Alps using a visco-hypoplasticconstitutive model. Geomorphology,2009,103(1):136-142.
[38]Farah K, Ltifi M, Hassis H. Reliability analysis of slope stability using stochasticfinite element method. Procedia Engineering,2011,10:1402-1407.
[39]Mandolini A, Minutolo V, Ruocco E. Coupling of underground pipelines and slowlymoving landslides by BEM analysis. CMES-Computer Modeling in Engineering&Sciences,2001,2(1):39-47.
[40]邓琴,郭明伟,李春光,葛修润.基于边界元法的边坡矢量和稳定分析.岩土力学,2010,31(6):1971-1976.
[41]Jiang YS, Zimmermann Th. Indirect boundary element algorithm for slope stabilityanalysis. Engineering Analysis with Boundary Elements,1992,9(3):209-217.
[42]Chang CS. Discrete element method for slope stability analysis. Journal ofGeotechnical engineering,1992,118(12):1889-1905.
[43]Kim JS, Kim JY, Lee SR. Analysis of soil nailed earth slope by discrete elementmethod. Computers and Geotechnics,1997,20(1):1-14.
[44]Lu W, Zhou Z, Liu T, Liu YH. Discrete element simulation analysis of rock slopestability based on UDEC. Advanced Materials Research,2012,461:384-388.
[45]Chugh AK, Stark TD. Permanent seismic deformation analysis of a landslide.Landslides,2006,3(1):2-12.
[46]Singh TN, Gulati A, Dontha L, Bhardwaj V. Evaluating cut slope failure by numericalanalysis—a case study. Natural Hazards,2008,47(2):263-279.
[47]MacLaughlin M, Sitar N, Doolin D, Abbot T. Investigation of slope-stabilitykinematics using discontinuous deformation analysis. International Journal of RockMechanics and Mining Sciences,2001,38(5):753-762.
[48]Wu JH. Seismic landslide simulations in discontinuous deformation analysis.Computers and Geotechnics,2010,37(5):594-601.
[49]Naserizadeh R, Bingham HB, Noorzad A. A coupled boundary element-finitedifference solution of the elliptic modified mild slope equation. Engineering Analysiswith Boundary Elements,2011,35(1):25-33.
[50]Ning YJ, An XM, Ma GW. Footwall slope stability analysis with the numericalmanifold method. International Journal of Rock Mechanics and Mining Sciences,2011,48(6):964-975.
[51]Ma GW, An XM, He L. The numerical manifold method: a review. InternationalJournal of Computational Methods,2010,7(1):1-32.
[52]Zhou G, Esaki T, Mitani Y, Xie M, Mori J. Spatial probabilistic modeling of slopefailure using an integrated GIS Monte Carlo simulation approach. EngineeringGeology,2003,68(3-4):373-386.
[53]Giasi CI, Masi P, Cherubini C. Probabilistic and fuzzy reliability analysis of a sampleslope near Aliano. Engineering Geology,2003,67(3-4):391-402.
[54]高克昌,崔鹏,赵纯勇,韦方强.基于地理信息系统和信息量模型的滑坡危险性评价——以重庆万州为例.岩石力学与工程学报,2006,25(5):991-996.
[55]楚敬龙,王金生,滕彦国,王小娜.基于GIS的滑坡灾害危险性区划研究——以重庆市万州区为例.地球科学与环境学报,2010,32(4):409-415.
[56]Kaunda RB, Chase RB, Kehew AE, Kaugars K, Selegean JP. Neural networkmodeling applications in active slope stability problems. Environmental EarthSciences,2010,60(7):1545-1558.
[57]Da SK, Biswal RK, Sivakugan N, Das B. Classification of slopes and prediction offactor of safety using differential evolution neural networks. Environmental EarthSciences,2011,64(1):201-210.
[58]Rampone S, Valente A. Neural network aided evaluation of landslide susceptibility insouthern Italy. International Journal of Modern Physics C,2012,23(1).
[59]Li JC, Li PL. Analysis of landslide stability based on neural network. SustainableDevelopment of Urban Environment and Building Material,2012,374-377:2346-2351.
[60]Erzin Y, Cetin T. The use of neural networks for the prediction of the critical factor ofsafety of an artificial slope subjected to earthquake forces. Scientia Iranica,2012,19(2):188-194.
[61]Erzin Y, Cetin T. The prediction of the critical factor of safety of homogeneous finiteslopes using neural networks and multiple regressions. Computers&Geosciences,2013,51:305-313.
[62]陈新民,罗国煜.基于经验的边坡稳定性灰色系统分析与评价.岩土工程学报,1999,21(5):638-641.
[63]Lu P, Rosenbaum M. Artificial neural networks and grey systems for the prediction ofslope stability. Natural Hazards,2003,30(3):383-398.
[64]Park HJ, Um JG, Woo IK, Kim JW. Application of fuzzy set theory to evaluate theprobability of failure in rock slopes. Engineering Geology,2012,125(27):92-101.
[65]Saito M. Forecasting the time of occurrence of a slope failure. in: Proceedings of the6th International Conference on Soil Mechanics and Foundation Engineering.1965,2:537-541.
[66]Saito M. Forecasting time of slope failure by tertiary creep. in: Proceedings of the6thInternational Conference on Soil Mechanics and Foundation Engineering.1969,2:677-683.
[67]Noriaki S. Normalization of displacement rate for roughly predicting the time tofailure in rock slope. Oyo Technical RePort,2001,21:1-8.
[68]Hayashi S, Komamura F, Park B, et al. On the forecast of time to failure of slope-approximate forecast in the early period of the tertiary creep. Journal of JapanLandslide Society,1988,25(3):11-16.
[69]Voight B. A method for Prediction of volcanic eruption. Nature,1988,332:125-130.
[70]Voight B. A relation to describe rate-dependent material failure. Scienee,1989,243:200-203.
[71]Federico A, Popescu M, Fidelibus C, et al. On the prediction of the time of occurrenceof a slope failure: a review. in: Proceedings of the9th International Symposium onLandslides.2004,2:979-983.
[72]晏同珍.滑坡统计预测方法.滑坡文集.北京:中国铁道出版社,1988.
[73]晏同珍.水文工程地质与环境保护.武汉:中国地质大学出版社,1994.
[74]殷坤龙,晏同珍.滑坡预测及相关模型.岩石力学与工程学报,1996,15(l):1-8.
[75]殷坤龙.滑坡灾害预测预报.武汉:中国地质大学出版社,2003.
[76]陈明东,王兰生.新滩滑坡的灰色预报分析.新滩滑坡讨论会文集,1986.
[77]夏元友.滑坡灰色系统预测模型及其应用.自然灾害学报,1995,4(l):74-78.
[78]李朝甫,徐迎,谭跃虎,张涛.灰色系统理论在滑坡位移信息分析中的应用.系统工程理论与实践,2001,2:129-132.
[79]王朝阳,许强,范宣梅,曾金华.灰色新陈代谢GM(1,1)模型在滑坡变形预测中的应用.水文地质工程地质,2009,31(2):108-112.
[80]李晓红,靳晓光,亢会明等. GM(1,1)优化模型在滑坡预测中的应用.山地学报,2001,19(3):265-26.
[81]何习平,华锡生,何秀凤.加权多点灰色模型在高边坡变形预测中的应用.岩土力学,2007,28(6):1187-1191.
[82]姚颖康,张春艳,张坤.改进的GM(1,1)模型在滑坡变形预测中的应用.水文地质工程地质,2009,36(5):102-106.
[83]张贵钢,杨志强,朱健.基于残差改正的动态GM(1,1)模型在公路边坡变形监测中的应用.测绘科学,2010,35(4):148-150.
[84]孙景恒,李振明,苏万益. Pearl模型在边坡失稳时间预报中的应用.中国地质灾害与防治学报,1993,4(2):36-41.
[85]徐峻龄,马辉.滑坡临滑预报方法之讨论.中国地质灾害与防治学报,1998,9:364-369.
[86]王建锋.滑坡发生的时间预测分析.中国地质灾害与防治学报,2003,14(2):1-7.
[87]阳吉宝.堆积层滑坡临滑预报的新判据.工程地质学报,1995,3(2):70-73.
[88]阳吉宝,钟正雄.滑坡时间预报的双参数判据.中国地质灾害与防治学报,1996,7(增刊):61-66.
[89]阳吉宝,贺可强.堆积层滑坡时间预报问题的讨论.河北地质学院学报,1995,18(1):46-50.
[90]贺可强,阳吉宝,王思敬.堆积层边坡表层位移矢量角及其在稳定性预测中的作用与意义.岩石力学与工程学报,2003,22(12):1976-1983.
[91]He KQ, Wang SJ. Constituents and features of displacement vector angle of colluviallandslide. Environmental Geology,2003,45(2):279-285.
[92]Hu W, Yang XG, Xu FG, Hao MH. Exponential smoothing method based onwavelet transform for slope displacement prediction. in: Proceedings of the2ndInternational Conference on Uncertainty Reasoning and Knowledge Engineering(URKE).2012,264-267.
[93]刘晓，唐辉明，刘瑜.基于集对分析和模糊马尔可夫链的滑坡变形预测新方法研究.岩土力学.2009,30(11):3399-3407.
[94]张悼元,黄润秋等.岩体破坏时间预测的黄金分割数法.第三次全国工程地质大会文集.成都:成都科技大学出版社,1988,1236-1237.
[95]Xu J, Ma FH, Yang F, Ji HF. Application of nonlinear time series analysis in slopedeformation analysis and forecast. Mine Hazards Prevention and Control Technology,2007,226-230.
[96]徐峰,汪洋,杜娟,叶疆.基于时间序列分析的滑坡位移预测模型研究.岩石力学与工程学报,2011,30(4):746-751.
[97]Caudill M. Neural Networks Primer. San Francisco, CA: Miller Freeman Publicat-ions,1989.
[98]Binaghi E, Boschetti M, Brivio PA, Gallo I, Pergalani F, Rampini A. Prediction ofdisplacements in unstable areas using a neural model. Nature Hazards,2004,32(1):135-154.
[99]吴益平,滕伟福,李亚伟.灰色–神经网络模型在滑坡变形预测中的应用.岩石力学与工程学报,2007,26(3):632-636.
[100] Meng FZ, Li SJ, Zhang ZH. Determination of mechanical parameters of reservoirlandslide based on back analysis using evolutionary artificial network. Progress inCivil Engineering,2012,170-173:729-734.
[101] Chen YY, Wang SW, Chen N, Long XQ, Tang XR. Forecasting cohesionless soilhighway slope displacement using modular neural network. Discrete Dynamics inNature and Society,2012, DOI:10.1155/2012/504574.
[102]秦四清.非线性工程地质学导引.成都:西南交通大学出版社,1993,83-90.
[103]秦四清,王思敬.斜坡滑动失稳演化的非线性机制与过程研究进展.地球与环境,2005,33(3):75-81.
[104]刘汉东.边坡失稳定时预报理论与方法.郑州:黄河水利出版社,1996,10-36.
[105]龙辉,秦四清,朱世平,万志清.滑坡演化的非线性动力学与突变分析.工程地质学报,2001,9(3):331-335.
[106]文宝萍.滑坡预测预报研究现状及发展趋势.地学前缘,1996,3(1-2):86-91.
[107]罗红明,唐辉明,胡斌等.基于突变理论的反倾层状岩石边坡稳定性研究.地质科技情报,2007,6:101-104.
[108]姜永东,鲜学福,郭臣业.层状岩质边坡失稳的燕尾突变模型.重庆大学学报,2008,5:553-557.
[109]孙强,刘天霸,秦四清等.斜坡失稳的燕尾突变模型.工程地质学报,2006,06:852-855.
[110]郑明新,王恭先,王兰生.分形理论在滑坡预报中的应用研究.地质灾害与环境保护,1998,9(2):18-24.
[111]许强,黄润秋.用加卸载响应比理论探讨边坡失稳前兆.中国地质灾害与防治学报,1995,6(2):25-30.
[112] Zhang WJ, ChenYM, Zhan LT. Loading/Unloading response ratio theory applied inpredicting deep-seated landslides triggering. Engineering Geology,2006,82:234-240.
[113]贺可强,王荣鲁,李新志等.堆积层滑坡的地下水加卸载动力作用规律及其位移动力学预测——以三峡库区八字门滑坡分析为例.岩石力学与工程学报,2008,27(8):1644-1650.
[114]许建聪,尚岳全,王建林.松散土质滑坡位移与降雨量的相关性研究.岩石力学与工程学报,2006,25(51):2854-2860.
[115]姜彤,马瑾,许兵.基于加卸载响应比理论的边坡动力稳定分析方法.岩石力学与工程学报,2007,26(3):626-631.
[116]黄润秋,许强.斜坡失稳时间的协同预测模型.山地研究,1997,15(l):7-12.
[117]孙强,张小宝,秦四清等.关于边坡非线性研究的几点思考.铁道勘察,2007,4:67-70.
[118]周翠英,陈恒,朱凤贤.基于渐进演化的高边坡非线性动力学预警研究.岩石力学与工程学报,2008,4:818-824.
[119]许强,黄润秋,李秀珍.滑坡时间预测预报研究进展.地球科学进展,2004,19(3):478-483.
[120]贺可强,孙林娜,王思敬.滑坡位移分形参数Hurst指数及其在堆积层滑坡预报中的应用.岩石力学与工程学报,2009,28(6):1107-1115.
[121]廖小平.滑坡破坏时间新理论探讨.地质灾害与环境保护,1994,5(3):25-29.
[122]伍法权,王年生.一种滑坡位移动力学预报方法探讨.中国地质灾害与防治学报,1996,7(增刊):38-41.
[123]贺可强,白建业,王思敬.降雨诱发堆积层滑坡的位移动力学特征分析.岩土力学,2005,26(5):705-709.
[124] Vulliet L, Hutter K. Viscous-type sliding laws for landslides. Canadian GeotechnicalJoumal,1988,25(3):467-477.
[125] Desai CS, Samtani NC, Vulliet L. Constitutive modeling and analysis of creepingslopes. Journal of Geotechnical Engineering,1995,121(1):43-56.
[126] Desai CS, Somasundaram S, Frantziskonis G. A hierarchical approach forconstitutive modeling of geologic materials. International Journal for Numerical andAnalytical Methods in Geomechanics,1996,10:225-257.
[127] Angeli MG, Caspatetto P, Menotti RM, etal. A viso-plastic model for slope analysisapplied to a mudslide in Cortinad Ampezzo, Italy. Quarterly Joumal of EngineeringGeology,1996,29:233-240.
[128] Faure RM, Gress JC, Rojat F. An easy to use model for taking in account rainfall insoil displacement. European Slope Stability Symposium,2002.
[129] Ferlisi S. A simple mechanical model for the interpretation of translational activelandslides involving detrital covers. in: Proceedings of the Ⅸ InternationalSymposium on Landslides, Rio de Janeiro,2004,2:1227-1232.
[130] Leroueil S, Locat J, Vaunat J, etal. Geotechnical characterization of landslides. in:Proceedings of the7th International Symposium on Landslides.1996,53-74.
[131] Corominas J, Moya J, Ledesma A, etal. Prediction of ground displacement andvelocities from ground water level changes at the Vallcebre landslide (EasternPyrenees). Landslides,2005,2:83-96.
[132] Maugeri M, Motta E, Raciti E. Mathematical modeling of the landslide occurred atCagliano Castelferrato (Italy). Natural Hazards and Earth System Sciences,2006,6:133-143.
[133] Van AschTWJ, Van Beek LPH, Bogaard TA. Promblems in predicting the mobilityof slow-moving landslides, Engineering Geology,2007,91:46-55.
[134] Duncan JM. State of the art: limit equilibrium and finite-element analysis of slope.Journal of Geotechnical Engineering,1996,3:577-596.
[135] Chen H, Lee CF. A dynamic model for rainfall-induced landslides on natural slopes.Geomorphology,2003,51(4):269-288.
[136] Maffei A, Martino S, Prestininzi A. From the geological to the numerical model inthe analysis of gravity-induced slope deformations: an example from the CentralApennines (Italy). Engineering Geology,2005,78:215-236.
[137] Hurlimann M, Ledesma A, Corominas J, et al. The deep-seated slope deformation atEncampadana, Andorra: representation of morphologic features by numericalmodelling. Engineering Geology,2006,83:343-357.
[138] Stead D, Eberhardt E, Coggan JS. Developments in the characterization of complexrock slope deformation and failure using numerical modelling techniques.Engineering Geology,2006,83:217-235.
[139]钟荫乾.黄蜡石滑坡综合信息预报方法研究.中国地质灾害与防治学报,1995,6(4):68-75.
[140]胡高社,门玉明,刘玉海等.新滩滑坡预报判据研究.中国地质灾害与防治学报,1996,7(增刊):67-72.
[141]李东山,黄润秋,许强等.三峡库区滑坡综合预报系统的设计与实现.中国地质灾害与防治学报,2003,14(2):24-27.
[142]万全,范书龙,林炎.滑坡的多模型综合预测预报研究.水土保持研究,2005,12(5):181-185.
[143] Calvello M, Cascini L, Sorbino G. A numerical procedure for predicting rainfall-induced movements of active landslides along pre-existing slip surfaces.International Journal for Numerical and Analytical Methods in Geomechanics,2008,32:327-351.
[144]贺可强,雷建和.边坡稳定性的神经网络预测研究.地质与勘探,2001,37(6):72-75.
[145]黄志全.滑坡工程非线性分析理论及应用[M].郑州:黄河水利出版社,2005.
[146] Gluege S, Hamid OH, Wendemuth A. A simple recurrent network for implicitlearning of temporal sequences. Cognitive Computation,2010,2(4):265-271.
[147] Subirats JL, Jerez JM, Gomez I. Multiclass pattern recognition extension for the newC-Mantec constructive neural network algorithm. Cognitive Computation,2010,2(4):285-290.
[148] Rao S, Aleksander I. A position identification and path labeling mechanism for aneural model of visual awareness. Cognitive Computation,2010,2(4):360-372.
[149] Indiveri G, Chicca E, Douglas RJ. Artificial cognitive systems: from VLSI networksof spiking neurons to neuromorphic cognition. Cognitive Computation,2009,1(2):119-127.
[150] Garagnani M, Wennekers T, Pulvermueller F. Recruitment and consolidation of cellassemblies for words by way of Hebbian learning and competition in a multi-layerneural network. Cognitive Computation,2009,1(2):160-176.
[151] Haikonen POA. The role of associative processing in cognitive computing.Cognitive Computation,2009,1(1):42-49.
[152] Gros C. Cognitive computation with autonomously active neural networks: anemerging field. Cognitive Computation,2009,1(1):77-90.
[153] Jaroudi E, Makhoul J. A new error criterion for posterior probability estimation withneural nets. in: Proceedings of International Joint Conference on Neural Networks,1990,185-192.
[154] Ducker H, Cuny YL. Improving generalization using double back propagation. IEEETransactions on Neural Networks,1992,3(6):991-997.
[155] Mayoraz F, Vulliet L. Neural networks for slope movement prediction. TheInternational Journal Geomechanics,2002,2(2):153-173.
[156] Das SK, Basudhar PK. Prediction of coefficient of lateral earth pressure usingartificial neural networks. The Electronic Journal of Geotechnical Engineering,2005,10:1-5.
[157]史峰,王辉,胡斐,郁磊. MATLAB30智能算法30个案例分析.北京:北京航空航天大学出版社.2011.
[158]李德营.三峡库区具台阶状位移特征的滑坡预测预报研究.中国地质大学博士学位论文,2010.
[159] Ran Y, Xiong G, Li S, et al. Study on deformation prediction of landslide based ongenetic algorithm and improved BP neural network. Kybernetes,2010,39(8):1245-1254.
[160]孙广忠.中国典型滑坡[M].北京:科学出版社,1998.
[161] Li XZ, Kong JM, Wang ZY. Landslide displacement prediction based on combiningmethod with optimal weight. Natural Hazards,2012,61:635-646.
[162] Fukuzono T. A new method for predicting the failure time of a slope. in: Proceedingsof the4th International Conference and Field Workshop in Landslides, Tokyo.1985:145-150.
[163]徐峻龄,廖小平,李荷生.黄茨大型滑坡的预报及其理论和方法.中国地质灾害与防治学报,1996,7(3):18-25.
[164] Helmstetter A, Sornette D, Grasso JR, Andersen JV, Gluzman S, Pisarenko V. Slider-block friction model for landslides: application to Vaiont and La Clapiere Landslides.Journal of Geophysical Research: Solid Earth (1978-2012),2004,109:1-15.
[165] Sornette D, Helmstetter A, Andersen JV, Gluzman S, Grasso JR, Pisarenko V.Towards landslide predictions: two case studies. Physica A: Statistical Mechanicsand its Applications,2004,338(3-4):605-632.
[166] Jibson RW. Regression models for estimating coseismic landslide displacement.Engineering Geology,2007,91(2):209-218.
[167] Long H, Qin SQ, Zhu SP, et al. Nonlinear dynamic model and catastrophe analysisof slope evolution. Journal of Engineering Geology,2001,9:331-335.
[168] Neaupane KM, Achet SH. Use of backpropagation neural network for landslidemonitoring: a case study in the higher Himalaya. Engineering Geology,2004,74(3-4):213-226.
[169] Feng XT, Zhao H, Li S. Modeling non-linear displacement time series of geo-materials using evolutionary support vector machines. International Journal of RockMechanics and Mining Sciences,2004,41(7):1087-1107.
[170] Cagdas HA, Erol E, Ufuk Y. Forecast combination by using artificial neuralnetworks. Neural Processing Letters,2010,32:269-276.
[171] Lee H, Park Y. Nonlinear system identification using recurrent networks. in:Proceedings of the1991IEEE International Joint Conference on Neural Networks,1991,2410-2415.
[172] Karaboga D, Kalinli A. Training recurrent neural networks for dynamic systemidentification using parallel Tabu Search Algorithm. in: Proceedings of the12thIEEE International Symposium on Intelligent Control,1997,113-117.
[173] Yu W. Nonlinear system identification using discrete-time recurrent neural networkswith stable learning algorithms. Information Sciences,2004,158:131-147.
[174] Grefenstette JJ. Optimization of control parameters for genetic algorithms. IEEETransactions on Systems, Man and Cybernetics,1986,16(1):122-128.
[175] S rensen PH, N rgaard M, Ravn O, Poulsen NK. Implementation of neural networkbased non-linear predictive control. Neurocomputing,1999,28(1-3):37-51.
[176] Huang JQ, Lewis FL. Neural-network predictive control for nonlinear dynamicsystems with time-delay. IEEE Transactions on Neural Networks,2003,14(2):377-389.
[177] Song Y, Chen Z, Yuan Z. New chaotic PSO-based neural network predictive controlfor nonlinear process. IEEE Transactions on Neural Networks,2007,18(2):595-601.
[178] Na J, Ren X, Shang C, Guo Y. Adaptive neural network predictive control fornonlinear pure feedback systems with input delay. Journal of Process Control,2012,22(1):194-206.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |