双峰隧道围岩稳定性非线性系统研究
|
摘要
论文以“岩体结构控制论”、“广义系统科学”及数学建模的思想为基本指导思想,从围岩分类角度对隧道围岩稳定性进行研究。论文通过总结研究围岩分类的发展过程,指出现有围岩分类方法中存在几点问题,并针对存在的几点问题,以浙江诸永高速公路双峰隧道为项目依托,对围岩分类方法进行了详细的研究和探讨,从而对隧道围岩稳定性进行分析评价。论文首次详细阐述了BP神经网络和支持向量机在围岩分类中的应用思路问题和模型检验失效性问题,针对相关问题提出了自己的检验思路和具体步骤;首次详细阐述了基于类比法思想的模糊综合评判法、灰色聚类法和可拓评判法在实际应用中的一些问题,详细分析了权重的意义,建立了一个比RMR法精度更高的评价模型,同时结合可靠度理论,为隧道支护设计与施工提供了更加详细的围岩信息。研究结果应用于双峰隧道的围岩稳定性评价获得了可靠的结论。研究成果具有实用和推广价值,同时对工程地质领域的其他分类问题也同样具有重要意义。
During the construction and use of road tunnels, the tunnel’s safe is a primary problem. If safety event happen, not only the period delay, cost significantly improve, but also may lead to the technical staff is imperiled, these have bad effects to the society. If these safety events are not handled properly, the quality of the project will be bad and repair work will be more difficultly. In fact, the stability of tunnel suurounding rock is the fundamental problem in tunnel safety. Before excavating, the surrounding rock is in a certain state of equilibrium. And after excacating, the surrounding rock is caused stress redistribution. If the rock strength is high enough, the damage will not happen; but to the low rock strength, damage will happen and the surrounding rock will lose its stability. Because of limitations of human science and technology, it tends to occur in a variety of security events in the process of tunnel construction and use. In addition, the stress on the tunnel overestimate, or underestimate the strength of the rock, it will cause the tunnel design too conservatively, and improve project cost, resulting in unnecessary waste. Therefore, the analysis of tunnel stability is directly related to the evaluation of the success or failure of the tunnel project, and it is of great practical significance and the enormous economic and social benefits. At the same time, studies on stability of surrounding rock will not only rich in theory in tunnel engineering, but also to promote awareness of the rock, similar to other rock engineering significance. Therefore, study on the stability of surrounding rock is very important to academic value and practical significance.
In underground engineering, the class of rock mass quality is the scale of the surrounding rock stability. An objective surrounding rock classification can reflect to the basic characteristics of rock mass, is a correct understanding to the suruounding rock, guide the reasonable design and is the basis for project budget. It is very important practical.
Base on the scientific research idea of“Rock Mass Structure Control Theory”,“Generalized Syetem Science”and the idea of mathmematical modeling, thesis study the stability of surrounding rock of tunnel from the perspective of rock classification. By summing up the development process of the rock mass classification, thesis points the there are some questions in the existing methods to rock mass classificatio. To the questions for the existence, based on the shuangfeng tunnel in the Zhejiang Zhuyong freeway, thesis detailed study the methods of the rock mass classification and get the following conclusions:
1, The development process of the rock mass classification indicates that the study on the rock mass classification is mastering its influencing factors and seting up the model which describes the relationship between the influencing factors and the level of the rock mass.
2, By the field investigation and experiment, the author grasps a great deal of geological information and data and analyse influencing factors to the stability of the surrounding rock of Shuangfeng tunnel. The main factors are rock strength, rock mass discontinuity, groundwater and the situation between the strike-dip of discontinuities and the direction of tunnel axis. The quantitative description to the main factors are a uniaxial compressive strength of rock, intactness index of rock mass, the state of the discontinuity, groundwater flow and the angel between the hole axis and the main discontinuity.
3, Using the numerical simulation method, thesis discusses detailedly the problem about BP neural network and support vector machine used in the surrounding rock classification. There are a multi-solution characteristic on the modeling of BP neural network and support vector machine in the surrounding rock classification. It’s very necessary to chech the model set up by BP neural network and support vector machine. The existing checking method is completely ineffective.
4, For the first time, thesis point out that BP neural network and support vector machine just provide a possibility of finding the realistic model for the surrounding rock classification and that the possibility is closed related to the distribution of the learning samples.
5, Thesis points out that it is not the key to just focus on improving learning efficiency and effectiveness, when using BP neural network and support vector machine to solve the surrounding rock classification. And it’s just a misunderstanding. Thesis also points out that it is not reliable to use the learning effectiveness for modeling standard or use the learning effectiveness and testing effectiveness.
6, It also points out that the scope of application of the model is the range of the learning samples, rather than the actual problem space.
7, Thesis puts forward a basic idea of model checking: model checking should be based on numerical simulation, closely contact the basic theory and experience, using a combination of qualitative and quantitative methods, comparing the simulated results between the existing method and the model in a large number of random samples, and determine the accuracy and reliability of the model. Thesis elaborates the specific steps through an example about Shuangfeng tunnel.
8, We can get the same result from using three methods which are the fuzzy comprehensive evaluation method, gray clustering method and the extension evaluation method when each method uses different method of calculating the similarity or different weight. This is cased by the principle of the maximum membership degree. The principle can make these models, which are modeled by these methods, be step functions. So it is not extremely sensitive to the evaluation result when changing the weight or membership function within a certain range in each category. As for the points near the border it is more sensitive and prone to error evaluation.
9, With the influencing factors changing, the stability of surrounding rock is variational. It is a continuous variable. The class of the surrounding rock is a measure of the stability of surrounding rock. It loses too much useful information with discrete values evaluating the surrounding rock. Thesis uses the characteristic values of class to evaluate the surrounding rock. So the class of the surrounding rock is continuous and it is more detailed description of the practical and used conveniently.
10, Thesis points out the weight should be a variable and reflect influence degree of facts to the evaluation result in the current method of calculating similarity and the various factors that in some cases. Based on this, thesis presents a method which translates the weight problem into an optimization problem to calculate the weight. The method indicates that the weight is a mean with statistically significant in the scope of the samples.If the weight is variational, calculated weight is not the same within different scope of samples. If the scope of the samples is close to a point and samples are reliable enough, then the weight is infuence degree in the current method of calculating similarity and the various factors that in the point.
11, Based on extension evaluation method, combined with the put forward a new method of calculating the weight, thesis set up a high-precision method than the RMR of the evaluation model. By calculating, some sections of the shuangfeng tunnel is betweenⅢandⅣ, in accordance with the standard for surrounding rock classification in highway tunne. Based on the evaluation results, the stability of these sections is evaluated, being combined with field investigation.
12, Based on the random characteristics of the rock and soil, the reliability theory of geotechnical engineering is introduced into surrounding rock classification. From the classification point of view, the current reliability theory of geotechnical engineering is in fact a probability analysis theory about two classes (reliability and failure).In this paper, the reliability theory to geotechnical engineering is extended to multiclass classification problem. And it is used for surrounding rock classification.The examples shows that the evaluation result is more realistic.
13, For the classification problems in other fields, studying on methods of surrounding rock classification for engineering geological and geotechnical engineering also has great significance. And the above conclusions also are suitable for them.
During the construction and use of road tunnels, the tunnel’s safe is a primary problem. If safety event happen, not only the period delay, cost significantly improve, but also may lead to the technical staff is imperiled, these have bad effects to the society. If these safety events are not handled properly, the quality of the project will be bad and repair work will be more difficultly. In fact, the stability of tunnel suurounding rock is the fundamental problem in tunnel safety. Before excavating, the surrounding rock is in a certain state of equilibrium. And after excacating, the surrounding rock is caused stress redistribution. If the rock strength is high enough, the damage will not happen; but to the low rock strength, damage will happen and the surrounding rock will lose its stability. Because of limitations of human science and technology, it tends to occur in a variety of security events in the process of tunnel construction and use. In addition, the stress on the tunnel overestimate, or underestimate the strength of the rock, it will cause the tunnel design too conservatively, and improve project cost, resulting in unnecessary waste. Therefore, the analysis of tunnel stability is directly related to the evaluation of the success or failure of the tunnel project, and it is of great practical significance and the enormous economic and social benefits. At the same time, studies on stability of surrounding rock will not only rich in theory in tunnel engineering, but also to promote awareness of the rock, similar to other rock engineering significance. Therefore, study on the stability of surrounding rock is very important to academic value and practical significance.
In underground engineering, the class of rock mass quality is the scale of the surrounding rock stability. An objective surrounding rock classification can reflect to the basic characteristics of rock mass, is a correct understanding to the suruounding rock, guide the reasonable design and is the basis for project budget. It is very important practical.
Base on the scientific research idea of“Rock Mass Structure Control Theory”,“Generalized Syetem Science”and the idea of mathmematical modeling, thesis study the stability of surrounding rock of tunnel from the perspective of rock classification. By summing up the development process of the rock mass classification, thesis points the there are some questions in the existing methods to rock mass classificatio. To the questions for the existence, based on the shuangfeng tunnel in the Zhejiang Zhuyong freeway, thesis detailed study the methods of the rock mass classification and get the following conclusions:
1, The development process of the rock mass classification indicates that the study on the rock mass classification is mastering its influencing factors and seting up the model which describes the relationship between the influencing factors and the level of the rock mass.
2, By the field investigation and experiment, the author grasps a great deal of geological information and data and analyse influencing factors to the stability of the surrounding rock of Shuangfeng tunnel. The main factors are rock strength, rock mass discontinuity, groundwater and the situation between the strike-dip of discontinuities and the direction of tunnel axis. The quantitative description to the main factors are a uniaxial compressive strength of rock, intactness index of rock mass, the state of the discontinuity, groundwater flow and the angel between the hole axis and the main discontinuity.
3, Using the numerical simulation method, thesis discusses detailedly the problem about BP neural network and support vector machine used in the surrounding rock classification. There are a multi-solution characteristic on the modeling of BP neural network and support vector machine in the surrounding rock classification. It’s very necessary to chech the model set up by BP neural network and support vector machine. The existing checking method is completely ineffective.
4, For the first time, thesis point out that BP neural network and support vector machine just provide a possibility of finding the realistic model for the surrounding rock classification and that the possibility is closed related to the distribution of the learning samples.
5, Thesis points out that it is not the key to just focus on improving learning efficiency and effectiveness, when using BP neural network and support vector machine to solve the surrounding rock classification. And it’s just a misunderstanding. Thesis also points out that it is not reliable to use the learning effectiveness for modeling standard or use the learning effectiveness and testing effectiveness.
6, It also points out that the scope of application of the model is the range of the learning samples, rather than the actual problem space.
7, Thesis puts forward a basic idea of model checking: model checking should be based on numerical simulation, closely contact the basic theory and experience, using a combination of qualitative and quantitative methods, comparing the simulated results between the existing method and the model in a large number of random samples, and determine the accuracy and reliability of the model. Thesis elaborates the specific steps through an example about Shuangfeng tunnel.
8, We can get the same result from using three methods which are the fuzzy comprehensive evaluation method, gray clustering method and the extension evaluation method when each method uses different method of calculating the similarity or different weight. This is cased by the principle of the maximum membership degree. The principle can make these models, which are modeled by these methods, be step functions. So it is not extremely sensitive to the evaluation result when changing the weight or membership function within a certain range in each category. As for the points near the border it is more sensitive and prone to error evaluation.
9, With the influencing factors changing, the stability of surrounding rock is variational. It is a continuous variable. The class of the surrounding rock is a measure of the stability of surrounding rock. It loses too much useful information with discrete values evaluating the surrounding rock. Thesis uses the characteristic values of class to evaluate the surrounding rock. So the class of the surrounding rock is continuous and it is more detailed description of the practical and used conveniently.
10, Thesis points out the weight should be a variable and reflect influence degree of facts to the evaluation result in the current method of calculating similarity and the various factors that in some cases. Based on this, thesis presents a method which translates the weight problem into an optimization problem to calculate the weight. The method indicates that the weight is a mean with statistically significant in the scope of the samples.If the weight is variational, calculated weight is not the same within different scope of samples. If the scope of the samples is close to a point and samples are reliable enough, then the weight is infuence degree in the current method of calculating similarity and the various factors that in the point.
11, Based on extension evaluation method, combined with the put forward a new method of calculating the weight, thesis set up a high-precision method than the RMR of the evaluation model. By calculating, some sections of the shuangfeng tunnel is betweenⅢandⅣ, in accordance with the standard for surrounding rock classification in highway tunne. Based on the evaluation results, the stability of these sections is evaluated, being combined with field investigation.
12, Based on the random characteristics of the rock and soil, the reliability theory of geotechnical engineering is introduced into surrounding rock classification. From the classification point of view, the current reliability theory of geotechnical engineering is in fact a probability analysis theory about two classes (reliability and failure).In this paper, the reliability theory to geotechnical engineering is extended to multiclass classification problem. And it is used for surrounding rock classification.The examples shows that the evaluation result is more realistic.
13, For the classification problems in other fields, studying on methods of surrounding rock classification for engineering geological and geotechnical engineering also has great significance. And the above conclusions also are suitable for them.
引文
[1]关宝树,国兆林.隧道及地下工程[M].成都:西南交通大学出版社,2000,2.
[2]关宝树.隧道工程施工要点集[M].北京:人民交通出版社,2003.
[3]张倬元,王士天,王兰生.工程地质分析原理[M].北京:地质出版社,1994.
[4]冯夏庭.智能岩石力学导论[M].北京:科学出版社,2000.
[5]薛守义.岩体力学的基本问题与科学方法论[J].岩石力学与工程学报,1996,15,405-406.
[6]黄润秋.正确的思维方式是学科发展的源泉[J].岩石力学与工程学报,1992,13(3):283-287.
[7]孙广忠.工程地质与地质工程[M].北京:地震出版社,1993.
[8]孙广忠.岩体力学基础[M].北京:科学出版社,1983.
[9]Terzaghi K.Rock defects and load on tunnel support.In: Proctor R V,White,editors.Rock tunneling with steel supports.Youngstown,OH:Commercial Shearing Co.,1946.
[10]Deere D.U.,Miller R.P.Engineering classification and index properties for intact rocks.Technical Report No.AFNL-TR-65-116, Air Force Weapons Laboratory,New Mexico,1966.
[11]Deere D.U.Technical description of rock cores for engineering purposes.Rock Mech. & Engng. Geol.,1964,1:17-22.
[12]Barton N.,Lien R.,Lunde J.Engineering classification of rock masses for the design of tunnel support.J.Rock Mech.,1974,6(4):189-236.
[13]Barton N. Some new Q-value correlations to assist in site characterization and tunnel design.Int.J.Rock Mech.&Min.Sci.Geomech. Abstr.,2002,39(2):185-216.
[14]Bieniawski Z. T.Engineering classification of jointed rock masses. Trans. S. Afr. Inst. Civ.Eng.,1973,15(12):335-344.
[15]Bieniawski Z T. Classification of rock masses for engineering: the RMR system and future trends.In: Hudson J A,editor.Comprehensive rock engineering,vol.3.UK:Pergamon Press,1993, 553-573.
[16]Bieniawski Z T.Rock mass classification in rock engineering.In: Bieniawski ZT,editor. Proceedings of the Symposium on Exploration for Rock Engineering,vol.1.Rotterdam: A.A.Balkema,1976.
[17]Bieniawski Z T.Engineering rock mass classifications: a complete manual for engineers and geologists in mining,civil and petroleum engineering.New York: Wiley, 1989.
[18]Hoek E.Strength of rock and rock masses.ISRM New Journal,1994,2(2): 4-16.
[19]Hoek E,Marinos P,Benissi M. Applicability of the geological strength index(GSI) classification for very weak and sheared rock masses The case of Athens Schist Formation.Bull Eng Geol Env,1998,57: 151–160.
[20]Hoek E.,Brown E.T. Practical estimates of rock mass strength. Int. J. Rock Mech. & Min. Sci. Geomech. Abstr.,1997,34(8): 1165–1186.
[21]Palmstrom A. Characterizing rock masses by the RMi for use in practical rock engineering,Part 1: the development of the rock mass index (RMi). Tunnelling Underground Space Technol.,1996,11(2):175–188.
[22]Palmstrom A. Characterizing rock masses by the RMi for use in practical rock engineering, Part 2: some practical applications of the rock mass index (RMi). Tunnelling and Underground Space Technol.,1996,11(3):287–303.
[23]Palmstrom A. RMi—a rock mass characterization system for rock engineering purposes. PhD thesis,University of Oslo,Norway,1995.
[24]王广德.复杂条件下围岩分类研究[D].成都理工大学,2006.
[25]陈详.黄岛地下水封石油洞库岩体质量评价及围岩稳定性分析[D].武汉:中国地质大学,2007.
[26]黄卫东.道路隧道围岩的模糊分类法[J].长沙交通学院学报,1988,4(3),63-72.
[27]张清,田盛丰,莫元彬.铁路隧道围岩分类的专家系统[J].铁道学报,1989.
[28]刘康和.灰色聚类在围岩稳定性分类中的应用[J].勘察科学技术,1993,2:31-34.
[29]冯夏庭,王丽娜.利用神经网络学习的岩体分级[J].东北工学院学报,1993,14(3):226-230.
[30]贾超,肖树芳,刘宁.可拓学理论在洞室岩体质量评价中的应用[J].岩石力学与工程学报,2003,22(5):751-756.
[31]赵洪波,冯夏庭.基于支持向量机的岩体工程分级[J].岩土力学,2002,23(6):698-701.
[32]宫凤强,李夕兵.距离判别分析法在岩体质量等级分类中的应用[J].岩石力学与工程学报,2007,26(01),190-194.
[33]宫凤强,李夕兵.隧洞围岩稳定性评价的Bayes判别分析法及应用[J].地下空间与工程学报,2007,03(06):1138-1141.
[34]杜时贵,李军,徐良明等.岩体质量的分形表述[J].地质科技情报,1997,16(1):91-96.
[35]杨新安,黄宏伟.围岩变形内表比及一种新的巷道围岩分类法[J].岩土工程学报,1998,20(3):23-26.
[36]蒋晗,徐卫亚,谢守益等.三维数字模型超单元生成法在工程岩体质量评价的应用[J].工程地质学报,2000,8(1):123-126.
[37]赵玉绂.国内外主要围岩分类换算原则和方法[J].工程地质学报,1993,1(2):24-31.
[38]莫元彬,张清,围岩分类专家系统中的不确定性推理处理方法[J],土木工程学报,1989,22(2):65-77.
[39]冯夏庭,林韵梅.专家系统在围岩分类中的应用[J].吉林冶金,1991,(4):12-16.
[40]韩凤山,康立勋.煤矿巷道锚杆支护围岩分类专家系统[J].岩石力学与工程学报,2002,21(增):2238-2241.
[41]孙建国,李天斌,黄润秋等.地下工程围岩分类模糊推理系统设计[J].工程地质学报,2003,11(03):269-274.
[42]杨小永,伍法权,苏生瑞.公路隧道围岩模糊信息分类的专家系统[J].岩石力学与工程学报,2006,25(1):100～105.
[43]李长洪.岩体分类的灰色聚类理论及应用[J].冶金矿山设计与建设,1995,(02):20-25.
[44]漆太岳,邹喜正,李世龙.灰色关联分析在回采巷道围岩分类中的应用[J].矿山压力与顶板管理,1990,(02):49-71.
[45]陈昌富.岩体质量灰色关联度分析法及其在哈图金矿的应用[J].黄金,1993,14(08):11-14.
[46]冯玉国.灰色优化理论模型在地下工程围岩稳定性分类中的应用[J].岩土工程学报,1996,18(03):62-66.
[47]乔兰,丁立丰.围岩稳定性分类的灰色优化模型及其在新城金矿的应用[J].有色金属(矿山部分),2000,(02):30-33.
[48]杨朝晖,刘浩吾.对“灰色优化理论模型在地下工程围岩稳定性分类中的应用”一文的讨论[J].岩土工程学报,1997,(06):115-116.
[49]李俊宏,何淑媛,袁文秀.地下围岩稳定分级的灰色关联优化模型[J].水利水电科技进展,2007,27(06):21-23.
[50]宁云才,邢中光.煤矿矿区回采巷道围岩稳定性分类的灰色Fuzzy聚类分析及应用[J].系统工程理论与实践,1997,(08):115-124.
[51]何浏,赵金,杨芷华.模糊灰色理论在围岩类别评定中的应用[J].武汉水利电力大学(宜昌)学报,2000,22(04):295-298.
[52]陶振宇,彭祖赠.模糊数学在岩石工程分类中的应用[J].岩土工程学报,1981,03(01):36-45.
[53]陶振宇,彭祖赠.岩体工程分类的模糊数学方法[J].武汉水利电力学院学报,1982,(03):19-28.
[54]Fairhurst C,Lin D.. Fuzzy methodology in tunnel support design[A].In: the 26th US Symposium onRock Mechanics[C]. Rapid City,DS,USA: 1985,233-245.
[55]刘启千,徐光黎.工程岩体质量分级的模糊综合评判[J].地球科学——中国地质大学学报,1989,14(03):291-296.
[56]漆太岳,盖维珍,程根马.古书院矿回采巷道围岩稳定性分类研究[J].1990,35-40.
[57]黄宏伟,刘怀恒.影响围岩稳定性的不确定因素分析[J].1990,50-61.
[58] Habibagahi G., Katebi S.. Rock mass classification using theory of fuzzy sets[J]. Iranian Journal of Science and Technology, 1996, 20(03):273-284.
[59]陆兆溙，王京，吕亚平.模糊模式识别法在围岩稳定性分类上的应用[J].河海大学学报,1991,19(06):97-101.
[60]陈守煜.模糊最优归类理论模型及其在围岩稳定性分类与场地土类别评定中的应用[J].水利学报,1993,(12):26-36.
[61]朱浮胜,郑雨天,谭云亮等.锚杆支护回采巷道围岩类型的最优模糊识别[J].煤炭学报,1997,22(03):265-269.
[62]吕小平.隧道围岩分类的层次类比系统方法[J].水文地质工程地质,1993,(04):10-13.
[63]于震平.勘探阶段围岩岩体质量分级的模糊综合评判[J].煤田地质与勘探,1994,22(05):44-47.
[64]于震平,李铁汉.一种围岩稳定性分类方案的模糊处理[J].水文地质工程地质,1996,(04):5-7.
[65]白明洲,王家鼎.地下洞室中裂隙岩体围岩稳定性研究的模糊信息优化处理方法[J].成都理工学院学报,1999,26(03):291-294.
[66]才博.模糊模式识别理论在洞库围岩分类中的应用[J].河北工业大学学报,2000,29(03):99-101.
[67]向小东.一种新的巷道围岩稳定性分类模型及其应用[J].辽宁工程技术大学学报(自然科学版),2001,20(01):17-20.
[68]许传华,任青文.地下工程围岩稳定性的模糊综合评判法[J].岩石力学与工程学报,2004,23(11):1852-1855.
[69]邵中勇,冯德顺.公路隧洞围岩稳定性模糊评判方法[J].武汉理工大学学报(交通科学与),2004,28(05):771-774.
[70]李洪,蒋金泉,张开智.回采巷道围岩分类的模糊聚类分析方法[J].西安科技大学学报,2005,25(01):12-16.
[71]余晓琳,乐小刚.隧道围岩分类模糊评判改进模型及应用[J].西部探矿工程,2005,(11):132-134.
[72]曹文贵,张永杰.地下结构岩体质量分类的变权重二级模糊综合评判方法研究[J].岩石力学与工程学报,2006,25(08):1612-1618.
[73]陈守煜,韩晓军.围岩稳定性评价的模糊可变集合工程方法[J].岩石力学与工程学报,2006,25(09):1857-1861.
[74]韩桂武,刘斌,范鹤.模糊择近原则在隧洞围岩分类中的应用[J].东北大学学报(自然科学版),2006,27(07):819-822.
[75]曹文贵,张永杰,杨期君.岩体质量分类区间非线性模糊评判方法研究[J].岩石力学与工程学报,2007,26(03):620-625.
[76]王明年,关宝树.神经网络在地下工程中的应用[J].地下空间,1995,15(02):94-101.
[77]王德润.神经网络在煤巷围岩分类中的应用[J].矿业压力与顶板管理,1997,105-107.
[78]霍润科,刘汉东.神经网络法在地下洞室围岩分类中的应用[J].华北水利水电学院学报,1998,19(02):61-63.
[79]杨朝晖,刘浩吾.地下工程围岩稳定性分类的人工神经网络模型[J].四川联合大学学报(工程科学版),1999,03(04):66-72.
[80]李强.BP神经网络在工程岩体质量分级中的应用研究[J].西北地震学报,2002,24(03):220-224.
[81]王彪,陈剑平,李钟旭等.人工神经网络在岩体质量分级中的应用[J].世界地质,2004,23(01):64-68.
[82]张玉祥.巷道围岩稳定性识别模糊神经网络与模糊数学研究[J].岩土工程学报,1998,20(09):90-93.
[83]张国平,阮怀宁.ANFIS模型在围岩稳定性分类中的应用[J].黄金,2007,28(02):27-30.
[84]饶运章,侯运炳.神经网络方法在围岩稳定性分级评价中的应用[J].黄金,2001,22(10):15-17.
[85]盛建龙,方奕晖,胡建华.巷道围岩稳定性分类的MBP神经网络预测研究[J].武汉科技大学学报(自然科学版),2004,27(04):349-351.
[86]宋国新,杨子荣,孙祥.模糊神经网络法在隧洞围岩分类中的应用[J].岩土工程技术,2004,18(05):224-226.
[87]周翠英,张亮,黄显艺.基于改进BP网络算法的隧道围岩分类[J].中国地质大学学报,2005,30(4):480～486.
[88]王玲.遗传算法BP神经网络在隧洞围岩分类中的应用[J].山西建筑,2006,32(17):357-359.
[89]李天斌,王睿.ART1神经网络在隧道围岩分类中的应用[J].成都理工大学学报(自然科学版),2006,33(05):455-459.
[90]邱道宏,陈剑平,阙金声等.基于粗糙集和人工神经网络的洞室岩体质量评价[J].吉林大学学报(地球科学版),2008,38(01):86-91.
[91]胡宝清.可拓评判方法在围岩稳定性分类中的应用[J].水利学报,2000,(02):66-70.
[92]王锦国,周志芳,杨建等.溪洛渡水电站坝基岩体工程质量的可拓评价[J].勘察科学技术,2001,(06):25-29.
[93]连建发,慎乃齐张杰坤.基于可拓方法的地下工程围岩评价研究[J].岩石力学与工程学报,2004,23(9):1450-1453.
[94]原国红,陈剑平马琳.可拓评判方法在岩体质量分类中的应用[J].岩石力学与工程学报,2005,24(9):1539-1544.
[95]黄祥志,佘成学.基于可拓理论的围岩稳定分类方法的研究[J].岩土力学,2006,27(10):1800-1804,1814.
[96]冯玉国.地下工程围岩分类物元分析模型与应用[J].岩土工程师,2001,13(02):1-5.
[97]康志强,冯夏庭,周辉.基于层次分析法的可拓学理论在地下洞室岩体质量评价中的应用[J].岩石力学与工程学报,2006,25(增2):3687-3693.
[98]左昌群,陈建平.基于可拓学理论的围岩分级方法在变质软岩隧道中的应用[J].地质科技情报,2007,26(03):75-78.
[99]王彦武.地下采矿工程岩体质量可拓模糊评价方法[J].岩石力学与工程学报,2002,21(1):18-22.
[100]赵洪波.煤巷围岩分类的新方法[J].煤炭工程,2004,(11):65-67.
[101]朱一丁,马文涛.回采巷道围岩分类的支持向量机方法[J].采矿与安全工程学报,2006,23(03):362-365.
[102]赵洪波.非线性岩土力学行为的支持向量机研究[D].武汉:中国科学院岩土力学研究生博士论文,2007.
[103]赵洪波.岩体分级的进化支持向量机方法研究[J].绍兴文理学院学报,2004,24(07):82-84.
[104]赖永标,乔春生,刘开云等.支持向量机在围岩稳定性分类中的应用[J].水利学报,2006,37(09):1092-1096.
[105]肖云华,王清,陈剑平等.基于粗糙集和支持向量机的融合算法在岩体质量评价中的应用[J].煤田地质与勘探,2008,36(06):49-53.
[106]宫凤强,李夕兵,张伟.隧道围岩分级的距离判别分析模型及应用[J].铁道学报,2008,30(03):119-123.
[107]王吉亮,陈剑平,杨静.距离判别法在公路隧道围岩分类中的应用[J].吉林大学学报(地球科学版),2008,38(06):999-1004
[108]文畅平.岩体质量分级的Bayes判别分析方法[J].煤炭学报,2008,33(04):395-404.
[109]Zekai S. Fractal Dimension and Rock Quality Charts. Bulletin of the Association of Engineering Geologists, 1992,1.
[110]秦四清,张倬元,王士天等.节理岩体的分维特征及其工程地质意义[J].工程地质学报,1993,1(2):14-23
[111]连建发,慎乃齐,张杰坤.分形理论在岩体质量评价中的应用研究[J].岩石力学与工程学报,2001,20(增):1695-1698.
[112]刘树新,张飞.三维岩体质量的多重分形评价及分类[J].岩土力学,2004,25(7):1116-1121.
[113]刘艳章,盛建龙,葛修润等。基于岩体结构面分布分形维的岩体质量评价[J].岩土力学,2007,28(5):971-975.
[114]李宁,陈蕴生,陈方方等.地下洞室围岩稳定性评判方法新探讨[J].岩石力学与工程学报,2006,25(9):1941-1944.
[115]钟登华,李明超,王刚等.基于三维地层模型的岩体质量可视化分级[J].岩土力学,2005,26(01):11-16.
[116]汪斌,唐辉明,简文星等.结构面三维网络模拟在岩体质量评价中的应用[J].2006,27(04):594-596.
[117]郝哲,王来贵,王振伟等.公路隧道岩体分级的神经网络方法[J].岩石力学与工程学报,2005,24(A01):5248-5255.
[118]刘沐宇,冯夏庭.基于神经网络范例推理的边坡稳定性评价方法[J].岩土力学,2005,26(2):193-197.
[119]陈守煜,郭瑜.模糊可变集合与模型及在岩爆分级预测中的应用[J].岩石力学与工程学报,2005,24(A01):4603-4609.
[120]阙金声,三峡工程涪陵区水库塌岸非线性预测研究[D],吉林大学博士学位论文,2007.
[121]陈国兴,李方明.基于径向基函数神经网络模型的砂土液化概率判别方法[J].岩土工程学报,2006,28(3):301-305.
[122]吕海波,赵艳林,孔令伟等.自适应模糊神经网络在膨胀土胀缩等级分类中的应用[J].岩土力学,2006,27(6):908-912.
[123]姚辉,王靖涛.多重应力路径下粘土本构关系的神经网络模型[J].岩石力学与工程学报,2003,22(9):1454-1457.
[124]邓子胜.基于径向基神经网络的深基坑非线性位移反分析[J].岩土工程学报,2005,27(5):554-557.
[125]胡斌,冯夏庭,王国峰等.龙滩水电站左岸高边坡泥板岩体蠕变参数的智能反演[J].岩石力学与工程学报,2005,24(17):3064-3070.
[126]金长宇,马震岳,张运良等.神经网络在岩体力学参数和地应力场反演中的应用[J].岩土力学,2006,27(8):1263-1266,1271.
[127]吕爱钟,莫晓明.智能预测在三峡永久船闸中隔墩时效变形分析中的应用[J].岩土力学,2007,28(5):1066-1068.
[128]苏永华.岩体参数模糊隶属函数的构造方法及应用[J].岩土工程学报,2007,29(12):1772-1779.
[129]浙江省交通规划设计研究院,浙江省浙南综合工程勘察院.诸永高速公路金华段第11合同,两阶段施工图设计工程地质勘查报告[M].浙江,2004,12.
[130]邱道宏.括苍山高速公路隧道岩爆非线性预测研究[D].长春:吉林大学,2008.
[131]王坛华.基于三维网络模拟技术的裂隙网络水力研究及隧道涌水非线性预测[D].长春:吉林大学,2008.
[132]周卫滨.苍岭隧道岩爆预测和防治研究[D].杭州:浙江大学,2005.
[133]张秉鹤.括苍山特长公路隧道相对浅埋洞段岩爆机理及防治措施研究[D].长春:吉林大学,2007.
[134]冯夏庭,刁心宏.智能岩石力学(1)—导论[J].岩石力学与工程学报,1999,18(2):222-226.
[135]冯夏庭,杨成祥.智能岩石力学(2)——参数与模型的智能辨识[J].岩石力学与工程学报,1999,18(3):350-353.
[136]冯夏庭.智能岩石力学(3)—智能岩石工程[J].岩石力学与工程学报,1999,18(4):475-478.
[137]冯夏庭.智能岩石力学导论[M].北京:科学出版社,2000.
[138]葛宏伟,梁艳春,刘玮等.人工神经网络与遗传算法在岩石力学中的应用[J].岩石力学与工程学报,2004,23(9):1542-1550.
[139]高玮,李继祥.人工神经网络在岩石力学中的应用[J].武汉工业学院学报,2004,23(2):63-67.
[140]王旭,王宏,王文辉编著.人工神经元网络原理与应用[M].东北大学出版社,2000.
[141]袁曾任.人工神经元网络及其应用[M].北京:清华大学出版社,1999.10:88-130.
[142]戴葵等译.神经网络设计[M].北京:机械工业出版,2002.
[143]徐佩华.基于人工神经网络方法的锦屏一级水电站枢纽区高边坡稳定性分区研究[D].长春:吉林大学,2006.
[144]阙金声.三峡工程涪陵区水库塌岸非线性预测研究[D].长春:吉林大学,2007.
[145]闻新,周露,李翔等,Matlab神经网络仿真与应用[M],北京:科学出版社,2003.
[146]周翠英,张亮,黄显艺,基于改进BP网络算法的隧洞围岩分类[J].中国地质大学学报,2005,30(4):480-486.
[147]V.Vapink.The Nature of Statistical Learning Theory[M].Springer,N.Y.,1995.
[2]Vapnik V,LevinE,LeCunY. Measuring the VC-dimension of A Learning Machine[Jl.Nueral Computation,1994,6;851-876.
[148]褚蕾蕾,陈绥阳,周梦.计算智能的数学基础[M].北京:科学出版社.2002.
[149]邱道宏.括苍山高速公路隧道岩爆非线性预测研究[D].长春:吉林大学,2008.
[150]阎平凡,张长水.人工神经网络与模拟进化计算[M].北京:清华大学出版社,2005.
[151]赵洪波,冯夏庭.基于支持向量机的岩体工程分级[J].岩土力学,2002,23(6):698-701.
[152]杜时贵.公路隧道围岩定量分类系统研究设想和建设[J].西安交通大学学报,1996,16(4):32-37.
[153]中华人民共和国水利部.工程岩体分级标准(GB50218-94)及条文说明[M].1994.
[154]连建发,慎乃齐,张杰坤.基于可拓方法的地下工程围岩评价研究[J].岩石力学与工程学报,2004,23(9):1450-1453.
[155]杨伦标,高英仪.模糊数学原理及其应用[M].广州:华南理工大学出版社,1995.
[156]邓聚龙.灰色聚类基本方法[M].武汉:华中理工人学出版社,1987.
[157]蔡文.物元分析[M].广州:广东高等教育出版社,1987.
[158]蔡文.物元模型及应用[M].北京:科学技术文献出版社,1994.
[159]蔡文,杨春燕,林伟初.可拓工程方法[M].北京:科学技术出版社,1997.
[160]蔡文,杨春燕,何斌.可拓逻辑初步[M].北京:科学技术出版社,1997.
[161]杨春燕,蔡文.可拓工程研究[J].中国工程科学,2000, 2(12):90-96.
[162]春燕.事元及其应用[J].系统工程理论与实践, 1998,18(2):80-86.
[163]原国红,陈剑平,马琳.可拓评判方法在岩体质量分类中的应用[J].岩石力学与工程学报,2005,24(9):1539-1544.
[164]谢全敏,夏元友.岩体边坡稳定性的可拓聚类预测方法研究[J].岩石力学与工程学报,2003,22(3):438-441.
[165]汪明武,金菊良,李丽.可拓学在膨胀土胀缩等级评判中的应用[J].岩土工程学报,2003,25(6):754-757.
[166]王征亮.三峡库区长寿区库岸塌岸预测的可拓学研究[D].长春:吉林大学,2005.
[167]阙金声.三峡工程涪陵区水库塌岸非线性预测研究[D].长春:吉林大学,2007.
[168]邱道宏.括苍山高速公路隧道岩爆非线性预测研究[D].长春:吉林大学,2008.
[169]苏永华.岩土参数模糊隶属函数的构造方法及应用[J].岩土工程学报,2007,29(12):1772-1779.
[170]李世辉.隧道支护设计新论[M].北京:科学出版社.1999.
[171]浙江省交通设计院.公路设计规范(附条文说明)JTJ026-90[M].北京:人民交通出版社.1990.
[172]建筑结构设计通用符号、计量单位和基本术语(GBJ83-85)[M].北京:中国建筑工业出版社,1985.
[173]建筑结构设计统计标准(GBJ68-84)[M].北京:中国建筑工业出版社,1989.
[174]姚怡文.沉降控制复合桩基承载力与变形可靠度研究[D].上海:同济大学,2007.
[175]Cornell C. A. Structural safety specification based on second-moment reliability. Symposium on conceptes of safety and methods of design. International Association of Bridge and Structure Engineering. London,1969:235-245.
[176]Hasofer A. M. & Lind N. C. Exact and invariant second-moment code format. Journal of the Engineering Mechanics Division. ASCE 1974,100(1):111-121.
[177]Rachwitz R. Practical probabilistic approach to design. Bulletin 112. Comite European du Beton,Paris,1976.
[178]曹炽康,张惠英,刑秋顺译.土木工程实用概率和统计[M].上海:同济大学出版社,1980.
[179]Rachwitz R. & Fiessler B.. Structural reliability under combined random load sequences. Computers and Structures,1978,Vol.9: 489-494.
[180]Paloheimo E. & Hannus H. Structural Design Based on Weighted Fractiles. Journal of the Strucural Division,ASCE,1974,100(7): 1367-1378.
[181]徐钟济.蒙特卡罗方法[M].上海:上海科技出版社,1985.
[182]石丙飞.广州科学城林语山庄人工高边坡稳定性评价及设计研究[D].长春:吉林大学,2006.
[183]阙金声,陈剑平,石丙飞等.广州科学城岩质边坡稳定性可靠度分析[J].岩石力学与工程学报,2006,S2:3737-3742.
[184]于学馥.岩石力学的科学主题—第一层次科学方法论[J].岩石力学与工程,1996,15(4): 401-404.
[2]关宝树.隧道工程施工要点集[M].北京:人民交通出版社,2003.
[3]张倬元,王士天,王兰生.工程地质分析原理[M].北京:地质出版社,1994.
[4]冯夏庭.智能岩石力学导论[M].北京:科学出版社,2000.
[5]薛守义.岩体力学的基本问题与科学方法论[J].岩石力学与工程学报,1996,15,405-406.
[6]黄润秋.正确的思维方式是学科发展的源泉[J].岩石力学与工程学报,1992,13(3):283-287.
[7]孙广忠.工程地质与地质工程[M].北京:地震出版社,1993.
[8]孙广忠.岩体力学基础[M].北京:科学出版社,1983.
[9]Terzaghi K.Rock defects and load on tunnel support.In: Proctor R V,White,editors.Rock tunneling with steel supports.Youngstown,OH:Commercial Shearing Co.,1946.
[10]Deere D.U.,Miller R.P.Engineering classification and index properties for intact rocks.Technical Report No.AFNL-TR-65-116, Air Force Weapons Laboratory,New Mexico,1966.
[11]Deere D.U.Technical description of rock cores for engineering purposes.Rock Mech. & Engng. Geol.,1964,1:17-22.
[12]Barton N.,Lien R.,Lunde J.Engineering classification of rock masses for the design of tunnel support.J.Rock Mech.,1974,6(4):189-236.
[13]Barton N. Some new Q-value correlations to assist in site characterization and tunnel design.Int.J.Rock Mech.&Min.Sci.Geomech. Abstr.,2002,39(2):185-216.
[14]Bieniawski Z. T.Engineering classification of jointed rock masses. Trans. S. Afr. Inst. Civ.Eng.,1973,15(12):335-344.
[15]Bieniawski Z T. Classification of rock masses for engineering: the RMR system and future trends.In: Hudson J A,editor.Comprehensive rock engineering,vol.3.UK:Pergamon Press,1993, 553-573.
[16]Bieniawski Z T.Rock mass classification in rock engineering.In: Bieniawski ZT,editor. Proceedings of the Symposium on Exploration for Rock Engineering,vol.1.Rotterdam: A.A.Balkema,1976.
[17]Bieniawski Z T.Engineering rock mass classifications: a complete manual for engineers and geologists in mining,civil and petroleum engineering.New York: Wiley, 1989.
[18]Hoek E.Strength of rock and rock masses.ISRM New Journal,1994,2(2): 4-16.
[19]Hoek E,Marinos P,Benissi M. Applicability of the geological strength index(GSI) classification for very weak and sheared rock masses The case of Athens Schist Formation.Bull Eng Geol Env,1998,57: 151–160.
[20]Hoek E.,Brown E.T. Practical estimates of rock mass strength. Int. J. Rock Mech. & Min. Sci. Geomech. Abstr.,1997,34(8): 1165–1186.
[21]Palmstrom A. Characterizing rock masses by the RMi for use in practical rock engineering,Part 1: the development of the rock mass index (RMi). Tunnelling Underground Space Technol.,1996,11(2):175–188.
[22]Palmstrom A. Characterizing rock masses by the RMi for use in practical rock engineering, Part 2: some practical applications of the rock mass index (RMi). Tunnelling and Underground Space Technol.,1996,11(3):287–303.
[23]Palmstrom A. RMi—a rock mass characterization system for rock engineering purposes. PhD thesis,University of Oslo,Norway,1995.
[24]王广德.复杂条件下围岩分类研究[D].成都理工大学,2006.
[25]陈详.黄岛地下水封石油洞库岩体质量评价及围岩稳定性分析[D].武汉:中国地质大学,2007.
[26]黄卫东.道路隧道围岩的模糊分类法[J].长沙交通学院学报,1988,4(3),63-72.
[27]张清,田盛丰,莫元彬.铁路隧道围岩分类的专家系统[J].铁道学报,1989.
[28]刘康和.灰色聚类在围岩稳定性分类中的应用[J].勘察科学技术,1993,2:31-34.
[29]冯夏庭,王丽娜.利用神经网络学习的岩体分级[J].东北工学院学报,1993,14(3):226-230.
[30]贾超,肖树芳,刘宁.可拓学理论在洞室岩体质量评价中的应用[J].岩石力学与工程学报,2003,22(5):751-756.
[31]赵洪波,冯夏庭.基于支持向量机的岩体工程分级[J].岩土力学,2002,23(6):698-701.
[32]宫凤强,李夕兵.距离判别分析法在岩体质量等级分类中的应用[J].岩石力学与工程学报,2007,26(01),190-194.
[33]宫凤强,李夕兵.隧洞围岩稳定性评价的Bayes判别分析法及应用[J].地下空间与工程学报,2007,03(06):1138-1141.
[34]杜时贵,李军,徐良明等.岩体质量的分形表述[J].地质科技情报,1997,16(1):91-96.
[35]杨新安,黄宏伟.围岩变形内表比及一种新的巷道围岩分类法[J].岩土工程学报,1998,20(3):23-26.
[36]蒋晗,徐卫亚,谢守益等.三维数字模型超单元生成法在工程岩体质量评价的应用[J].工程地质学报,2000,8(1):123-126.
[37]赵玉绂.国内外主要围岩分类换算原则和方法[J].工程地质学报,1993,1(2):24-31.
[38]莫元彬,张清,围岩分类专家系统中的不确定性推理处理方法[J],土木工程学报,1989,22(2):65-77.
[39]冯夏庭,林韵梅.专家系统在围岩分类中的应用[J].吉林冶金,1991,(4):12-16.
[40]韩凤山,康立勋.煤矿巷道锚杆支护围岩分类专家系统[J].岩石力学与工程学报,2002,21(增):2238-2241.
[41]孙建国,李天斌,黄润秋等.地下工程围岩分类模糊推理系统设计[J].工程地质学报,2003,11(03):269-274.
[42]杨小永,伍法权,苏生瑞.公路隧道围岩模糊信息分类的专家系统[J].岩石力学与工程学报,2006,25(1):100～105.
[43]李长洪.岩体分类的灰色聚类理论及应用[J].冶金矿山设计与建设,1995,(02):20-25.
[44]漆太岳,邹喜正,李世龙.灰色关联分析在回采巷道围岩分类中的应用[J].矿山压力与顶板管理,1990,(02):49-71.
[45]陈昌富.岩体质量灰色关联度分析法及其在哈图金矿的应用[J].黄金,1993,14(08):11-14.
[46]冯玉国.灰色优化理论模型在地下工程围岩稳定性分类中的应用[J].岩土工程学报,1996,18(03):62-66.
[47]乔兰,丁立丰.围岩稳定性分类的灰色优化模型及其在新城金矿的应用[J].有色金属(矿山部分),2000,(02):30-33.
[48]杨朝晖,刘浩吾.对“灰色优化理论模型在地下工程围岩稳定性分类中的应用”一文的讨论[J].岩土工程学报,1997,(06):115-116.
[49]李俊宏,何淑媛,袁文秀.地下围岩稳定分级的灰色关联优化模型[J].水利水电科技进展,2007,27(06):21-23.
[50]宁云才,邢中光.煤矿矿区回采巷道围岩稳定性分类的灰色Fuzzy聚类分析及应用[J].系统工程理论与实践,1997,(08):115-124.
[51]何浏,赵金,杨芷华.模糊灰色理论在围岩类别评定中的应用[J].武汉水利电力大学(宜昌)学报,2000,22(04):295-298.
[52]陶振宇,彭祖赠.模糊数学在岩石工程分类中的应用[J].岩土工程学报,1981,03(01):36-45.
[53]陶振宇,彭祖赠.岩体工程分类的模糊数学方法[J].武汉水利电力学院学报,1982,(03):19-28.
[54]Fairhurst C,Lin D.. Fuzzy methodology in tunnel support design[A].In: the 26th US Symposium onRock Mechanics[C]. Rapid City,DS,USA: 1985,233-245.
[55]刘启千,徐光黎.工程岩体质量分级的模糊综合评判[J].地球科学——中国地质大学学报,1989,14(03):291-296.
[56]漆太岳,盖维珍,程根马.古书院矿回采巷道围岩稳定性分类研究[J].1990,35-40.
[57]黄宏伟,刘怀恒.影响围岩稳定性的不确定因素分析[J].1990,50-61.
[58] Habibagahi G., Katebi S.. Rock mass classification using theory of fuzzy sets[J]. Iranian Journal of Science and Technology, 1996, 20(03):273-284.
[59]陆兆溙，王京，吕亚平.模糊模式识别法在围岩稳定性分类上的应用[J].河海大学学报,1991,19(06):97-101.
[60]陈守煜.模糊最优归类理论模型及其在围岩稳定性分类与场地土类别评定中的应用[J].水利学报,1993,(12):26-36.
[61]朱浮胜,郑雨天,谭云亮等.锚杆支护回采巷道围岩类型的最优模糊识别[J].煤炭学报,1997,22(03):265-269.
[62]吕小平.隧道围岩分类的层次类比系统方法[J].水文地质工程地质,1993,(04):10-13.
[63]于震平.勘探阶段围岩岩体质量分级的模糊综合评判[J].煤田地质与勘探,1994,22(05):44-47.
[64]于震平,李铁汉.一种围岩稳定性分类方案的模糊处理[J].水文地质工程地质,1996,(04):5-7.
[65]白明洲,王家鼎.地下洞室中裂隙岩体围岩稳定性研究的模糊信息优化处理方法[J].成都理工学院学报,1999,26(03):291-294.
[66]才博.模糊模式识别理论在洞库围岩分类中的应用[J].河北工业大学学报,2000,29(03):99-101.
[67]向小东.一种新的巷道围岩稳定性分类模型及其应用[J].辽宁工程技术大学学报(自然科学版),2001,20(01):17-20.
[68]许传华,任青文.地下工程围岩稳定性的模糊综合评判法[J].岩石力学与工程学报,2004,23(11):1852-1855.
[69]邵中勇,冯德顺.公路隧洞围岩稳定性模糊评判方法[J].武汉理工大学学报(交通科学与),2004,28(05):771-774.
[70]李洪,蒋金泉,张开智.回采巷道围岩分类的模糊聚类分析方法[J].西安科技大学学报,2005,25(01):12-16.
[71]余晓琳,乐小刚.隧道围岩分类模糊评判改进模型及应用[J].西部探矿工程,2005,(11):132-134.
[72]曹文贵,张永杰.地下结构岩体质量分类的变权重二级模糊综合评判方法研究[J].岩石力学与工程学报,2006,25(08):1612-1618.
[73]陈守煜,韩晓军.围岩稳定性评价的模糊可变集合工程方法[J].岩石力学与工程学报,2006,25(09):1857-1861.
[74]韩桂武,刘斌,范鹤.模糊择近原则在隧洞围岩分类中的应用[J].东北大学学报(自然科学版),2006,27(07):819-822.
[75]曹文贵,张永杰,杨期君.岩体质量分类区间非线性模糊评判方法研究[J].岩石力学与工程学报,2007,26(03):620-625.
[76]王明年,关宝树.神经网络在地下工程中的应用[J].地下空间,1995,15(02):94-101.
[77]王德润.神经网络在煤巷围岩分类中的应用[J].矿业压力与顶板管理,1997,105-107.
[78]霍润科,刘汉东.神经网络法在地下洞室围岩分类中的应用[J].华北水利水电学院学报,1998,19(02):61-63.
[79]杨朝晖,刘浩吾.地下工程围岩稳定性分类的人工神经网络模型[J].四川联合大学学报(工程科学版),1999,03(04):66-72.
[80]李强.BP神经网络在工程岩体质量分级中的应用研究[J].西北地震学报,2002,24(03):220-224.
[81]王彪,陈剑平,李钟旭等.人工神经网络在岩体质量分级中的应用[J].世界地质,2004,23(01):64-68.
[82]张玉祥.巷道围岩稳定性识别模糊神经网络与模糊数学研究[J].岩土工程学报,1998,20(09):90-93.
[83]张国平,阮怀宁.ANFIS模型在围岩稳定性分类中的应用[J].黄金,2007,28(02):27-30.
[84]饶运章,侯运炳.神经网络方法在围岩稳定性分级评价中的应用[J].黄金,2001,22(10):15-17.
[85]盛建龙,方奕晖,胡建华.巷道围岩稳定性分类的MBP神经网络预测研究[J].武汉科技大学学报(自然科学版),2004,27(04):349-351.
[86]宋国新,杨子荣,孙祥.模糊神经网络法在隧洞围岩分类中的应用[J].岩土工程技术,2004,18(05):224-226.
[87]周翠英,张亮,黄显艺.基于改进BP网络算法的隧道围岩分类[J].中国地质大学学报,2005,30(4):480～486.
[88]王玲.遗传算法BP神经网络在隧洞围岩分类中的应用[J].山西建筑,2006,32(17):357-359.
[89]李天斌,王睿.ART1神经网络在隧道围岩分类中的应用[J].成都理工大学学报(自然科学版),2006,33(05):455-459.
[90]邱道宏,陈剑平,阙金声等.基于粗糙集和人工神经网络的洞室岩体质量评价[J].吉林大学学报(地球科学版),2008,38(01):86-91.
[91]胡宝清.可拓评判方法在围岩稳定性分类中的应用[J].水利学报,2000,(02):66-70.
[92]王锦国,周志芳,杨建等.溪洛渡水电站坝基岩体工程质量的可拓评价[J].勘察科学技术,2001,(06):25-29.
[93]连建发,慎乃齐张杰坤.基于可拓方法的地下工程围岩评价研究[J].岩石力学与工程学报,2004,23(9):1450-1453.
[94]原国红,陈剑平马琳.可拓评判方法在岩体质量分类中的应用[J].岩石力学与工程学报,2005,24(9):1539-1544.
[95]黄祥志,佘成学.基于可拓理论的围岩稳定分类方法的研究[J].岩土力学,2006,27(10):1800-1804,1814.
[96]冯玉国.地下工程围岩分类物元分析模型与应用[J].岩土工程师,2001,13(02):1-5.
[97]康志强,冯夏庭,周辉.基于层次分析法的可拓学理论在地下洞室岩体质量评价中的应用[J].岩石力学与工程学报,2006,25(增2):3687-3693.
[98]左昌群,陈建平.基于可拓学理论的围岩分级方法在变质软岩隧道中的应用[J].地质科技情报,2007,26(03):75-78.
[99]王彦武.地下采矿工程岩体质量可拓模糊评价方法[J].岩石力学与工程学报,2002,21(1):18-22.
[100]赵洪波.煤巷围岩分类的新方法[J].煤炭工程,2004,(11):65-67.
[101]朱一丁,马文涛.回采巷道围岩分类的支持向量机方法[J].采矿与安全工程学报,2006,23(03):362-365.
[102]赵洪波.非线性岩土力学行为的支持向量机研究[D].武汉:中国科学院岩土力学研究生博士论文,2007.
[103]赵洪波.岩体分级的进化支持向量机方法研究[J].绍兴文理学院学报,2004,24(07):82-84.
[104]赖永标,乔春生,刘开云等.支持向量机在围岩稳定性分类中的应用[J].水利学报,2006,37(09):1092-1096.
[105]肖云华,王清,陈剑平等.基于粗糙集和支持向量机的融合算法在岩体质量评价中的应用[J].煤田地质与勘探,2008,36(06):49-53.
[106]宫凤强,李夕兵,张伟.隧道围岩分级的距离判别分析模型及应用[J].铁道学报,2008,30(03):119-123.
[107]王吉亮,陈剑平,杨静.距离判别法在公路隧道围岩分类中的应用[J].吉林大学学报(地球科学版),2008,38(06):999-1004
[108]文畅平.岩体质量分级的Bayes判别分析方法[J].煤炭学报,2008,33(04):395-404.
[109]Zekai S. Fractal Dimension and Rock Quality Charts. Bulletin of the Association of Engineering Geologists, 1992,1.
[110]秦四清,张倬元,王士天等.节理岩体的分维特征及其工程地质意义[J].工程地质学报,1993,1(2):14-23
[111]连建发,慎乃齐,张杰坤.分形理论在岩体质量评价中的应用研究[J].岩石力学与工程学报,2001,20(增):1695-1698.
[112]刘树新,张飞.三维岩体质量的多重分形评价及分类[J].岩土力学,2004,25(7):1116-1121.
[113]刘艳章,盛建龙,葛修润等。基于岩体结构面分布分形维的岩体质量评价[J].岩土力学,2007,28(5):971-975.
[114]李宁,陈蕴生,陈方方等.地下洞室围岩稳定性评判方法新探讨[J].岩石力学与工程学报,2006,25(9):1941-1944.
[115]钟登华,李明超,王刚等.基于三维地层模型的岩体质量可视化分级[J].岩土力学,2005,26(01):11-16.
[116]汪斌,唐辉明,简文星等.结构面三维网络模拟在岩体质量评价中的应用[J].2006,27(04):594-596.
[117]郝哲,王来贵,王振伟等.公路隧道岩体分级的神经网络方法[J].岩石力学与工程学报,2005,24(A01):5248-5255.
[118]刘沐宇,冯夏庭.基于神经网络范例推理的边坡稳定性评价方法[J].岩土力学,2005,26(2):193-197.
[119]陈守煜,郭瑜.模糊可变集合与模型及在岩爆分级预测中的应用[J].岩石力学与工程学报,2005,24(A01):4603-4609.
[120]阙金声,三峡工程涪陵区水库塌岸非线性预测研究[D],吉林大学博士学位论文,2007.
[121]陈国兴,李方明.基于径向基函数神经网络模型的砂土液化概率判别方法[J].岩土工程学报,2006,28(3):301-305.
[122]吕海波,赵艳林,孔令伟等.自适应模糊神经网络在膨胀土胀缩等级分类中的应用[J].岩土力学,2006,27(6):908-912.
[123]姚辉,王靖涛.多重应力路径下粘土本构关系的神经网络模型[J].岩石力学与工程学报,2003,22(9):1454-1457.
[124]邓子胜.基于径向基神经网络的深基坑非线性位移反分析[J].岩土工程学报,2005,27(5):554-557.
[125]胡斌,冯夏庭,王国峰等.龙滩水电站左岸高边坡泥板岩体蠕变参数的智能反演[J].岩石力学与工程学报,2005,24(17):3064-3070.
[126]金长宇,马震岳,张运良等.神经网络在岩体力学参数和地应力场反演中的应用[J].岩土力学,2006,27(8):1263-1266,1271.
[127]吕爱钟,莫晓明.智能预测在三峡永久船闸中隔墩时效变形分析中的应用[J].岩土力学,2007,28(5):1066-1068.
[128]苏永华.岩体参数模糊隶属函数的构造方法及应用[J].岩土工程学报,2007,29(12):1772-1779.
[129]浙江省交通规划设计研究院,浙江省浙南综合工程勘察院.诸永高速公路金华段第11合同,两阶段施工图设计工程地质勘查报告[M].浙江,2004,12.
[130]邱道宏.括苍山高速公路隧道岩爆非线性预测研究[D].长春:吉林大学,2008.
[131]王坛华.基于三维网络模拟技术的裂隙网络水力研究及隧道涌水非线性预测[D].长春:吉林大学,2008.
[132]周卫滨.苍岭隧道岩爆预测和防治研究[D].杭州:浙江大学,2005.
[133]张秉鹤.括苍山特长公路隧道相对浅埋洞段岩爆机理及防治措施研究[D].长春:吉林大学,2007.
[134]冯夏庭,刁心宏.智能岩石力学(1)—导论[J].岩石力学与工程学报,1999,18(2):222-226.
[135]冯夏庭,杨成祥.智能岩石力学(2)——参数与模型的智能辨识[J].岩石力学与工程学报,1999,18(3):350-353.
[136]冯夏庭.智能岩石力学(3)—智能岩石工程[J].岩石力学与工程学报,1999,18(4):475-478.
[137]冯夏庭.智能岩石力学导论[M].北京:科学出版社,2000.
[138]葛宏伟,梁艳春,刘玮等.人工神经网络与遗传算法在岩石力学中的应用[J].岩石力学与工程学报,2004,23(9):1542-1550.
[139]高玮,李继祥.人工神经网络在岩石力学中的应用[J].武汉工业学院学报,2004,23(2):63-67.
[140]王旭,王宏,王文辉编著.人工神经元网络原理与应用[M].东北大学出版社,2000.
[141]袁曾任.人工神经元网络及其应用[M].北京:清华大学出版社,1999.10:88-130.
[142]戴葵等译.神经网络设计[M].北京:机械工业出版,2002.
[143]徐佩华.基于人工神经网络方法的锦屏一级水电站枢纽区高边坡稳定性分区研究[D].长春:吉林大学,2006.
[144]阙金声.三峡工程涪陵区水库塌岸非线性预测研究[D].长春:吉林大学,2007.
[145]闻新,周露,李翔等,Matlab神经网络仿真与应用[M],北京:科学出版社,2003.
[146]周翠英,张亮,黄显艺,基于改进BP网络算法的隧洞围岩分类[J].中国地质大学学报,2005,30(4):480-486.
[147]V.Vapink.The Nature of Statistical Learning Theory[M].Springer,N.Y.,1995.
[2]Vapnik V,LevinE,LeCunY. Measuring the VC-dimension of A Learning Machine[Jl.Nueral Computation,1994,6;851-876.
[148]褚蕾蕾,陈绥阳,周梦.计算智能的数学基础[M].北京:科学出版社.2002.
[149]邱道宏.括苍山高速公路隧道岩爆非线性预测研究[D].长春:吉林大学,2008.
[150]阎平凡,张长水.人工神经网络与模拟进化计算[M].北京:清华大学出版社,2005.
[151]赵洪波,冯夏庭.基于支持向量机的岩体工程分级[J].岩土力学,2002,23(6):698-701.
[152]杜时贵.公路隧道围岩定量分类系统研究设想和建设[J].西安交通大学学报,1996,16(4):32-37.
[153]中华人民共和国水利部.工程岩体分级标准(GB50218-94)及条文说明[M].1994.
[154]连建发,慎乃齐,张杰坤.基于可拓方法的地下工程围岩评价研究[J].岩石力学与工程学报,2004,23(9):1450-1453.
[155]杨伦标,高英仪.模糊数学原理及其应用[M].广州:华南理工大学出版社,1995.
[156]邓聚龙.灰色聚类基本方法[M].武汉:华中理工人学出版社,1987.
[157]蔡文.物元分析[M].广州:广东高等教育出版社,1987.
[158]蔡文.物元模型及应用[M].北京:科学技术文献出版社,1994.
[159]蔡文,杨春燕,林伟初.可拓工程方法[M].北京:科学技术出版社,1997.
[160]蔡文,杨春燕,何斌.可拓逻辑初步[M].北京:科学技术出版社,1997.
[161]杨春燕,蔡文.可拓工程研究[J].中国工程科学,2000, 2(12):90-96.
[162]春燕.事元及其应用[J].系统工程理论与实践, 1998,18(2):80-86.
[163]原国红,陈剑平,马琳.可拓评判方法在岩体质量分类中的应用[J].岩石力学与工程学报,2005,24(9):1539-1544.
[164]谢全敏,夏元友.岩体边坡稳定性的可拓聚类预测方法研究[J].岩石力学与工程学报,2003,22(3):438-441.
[165]汪明武,金菊良,李丽.可拓学在膨胀土胀缩等级评判中的应用[J].岩土工程学报,2003,25(6):754-757.
[166]王征亮.三峡库区长寿区库岸塌岸预测的可拓学研究[D].长春:吉林大学,2005.
[167]阙金声.三峡工程涪陵区水库塌岸非线性预测研究[D].长春:吉林大学,2007.
[168]邱道宏.括苍山高速公路隧道岩爆非线性预测研究[D].长春:吉林大学,2008.
[169]苏永华.岩土参数模糊隶属函数的构造方法及应用[J].岩土工程学报,2007,29(12):1772-1779.
[170]李世辉.隧道支护设计新论[M].北京:科学出版社.1999.
[171]浙江省交通设计院.公路设计规范(附条文说明)JTJ026-90[M].北京:人民交通出版社.1990.
[172]建筑结构设计通用符号、计量单位和基本术语(GBJ83-85)[M].北京:中国建筑工业出版社,1985.
[173]建筑结构设计统计标准(GBJ68-84)[M].北京:中国建筑工业出版社,1989.
[174]姚怡文.沉降控制复合桩基承载力与变形可靠度研究[D].上海:同济大学,2007.
[175]Cornell C. A. Structural safety specification based on second-moment reliability. Symposium on conceptes of safety and methods of design. International Association of Bridge and Structure Engineering. London,1969:235-245.
[176]Hasofer A. M. & Lind N. C. Exact and invariant second-moment code format. Journal of the Engineering Mechanics Division. ASCE 1974,100(1):111-121.
[177]Rachwitz R. Practical probabilistic approach to design. Bulletin 112. Comite European du Beton,Paris,1976.
[178]曹炽康,张惠英,刑秋顺译.土木工程实用概率和统计[M].上海:同济大学出版社,1980.
[179]Rachwitz R. & Fiessler B.. Structural reliability under combined random load sequences. Computers and Structures,1978,Vol.9: 489-494.
[180]Paloheimo E. & Hannus H. Structural Design Based on Weighted Fractiles. Journal of the Strucural Division,ASCE,1974,100(7): 1367-1378.
[181]徐钟济.蒙特卡罗方法[M].上海:上海科技出版社,1985.
[182]石丙飞.广州科学城林语山庄人工高边坡稳定性评价及设计研究[D].长春:吉林大学,2006.
[183]阙金声,陈剑平,石丙飞等.广州科学城岩质边坡稳定性可靠度分析[J].岩石力学与工程学报,2006,S2:3737-3742.
[184]于学馥.岩石力学的科学主题—第一层次科学方法论[J].岩石力学与工程,1996,15(4): 401-404.
© 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 |