基于小波理论的白鹤隧道围岩稳定性非线性研究
|
摘要
论文首先对白鹤隧道地质构造、地层岩性、地形地貌、气象水文、地质环境等特征进行了介绍,并对隧道围岩稳定性进行了分析。隧道围岩稳定性分析是不确定的、非线性的动态开放复杂大系统,从应用研究看,单一的、传统的研究途径和方法不再适合于复杂的隧道稳定性分析,而多种理论和方法的有机结合与综合比较将是正确分析和解决问题的有效途径。论文首次提出基于小波理论的公路隧道围岩稳定性非线性分析方法。采用小波阈值法和小波模极大值法这两种小波降噪方法对隧道围岩接触压力监控量测数据降噪,对监测数据进行预处理,并对监测数据中的异常点进行分析,并改进了小波降噪方法。建立小波神经网络围岩稳定性分析模型,对隧道围岩稳定性进行分析,并利用粗糙集理论提高了模型的性能。首次提出采用小波分析对断面轮廓进行分形维数计算,在此基础上,分析了断面轮廓分形维数与隧道围岩稳定性的关系。论文选题具有科学意义和创新性,研究成果可为隧道开挖方案和隧道稳定性分析提供科学依据,具有应用和推广的价值。
The tunnel engineering and underground engineering would be rapid development in the twenty-first century. In recent years, countries around the world continue to build the tunnel, which has promoted the largest development of the world's tunnel industry. With the rapid development of China's national economy and mining technological advances, domestic tunnel construction has been rapid development, mainly at increasing the length and the increasing depth of the tunnel. In the tunnel engineering, landslides, karst collapse, gushing water, pouring water, cave necking, mountain deformation, cracking pit, mud debris flow and rock burst is a common problem of geological disasters. Even though the conditions of these problems happened vary, the harm caused by tunnel construction is similar.
Baihe tunnel is one of the long tunnels in the Zhurong Expressway. By many times the role of structural geology, the tunnel area have a larger change in geological conditions, and during the construction process of tunnel, there were landslides, swap block, deformation, seepage and so on. For the requirement of Baihe tunnel geological prediction, thesis focused on studying stability of Baihe tunnel surrounding rock.
Tunnel surrounding rock stability has its own law of development and a complex nonlinear process. The stability analysis of tunnels is a complex systems engineering problem. Because the tunnel surrounding rock has formed in different geological environments and many times crustal movement, and coupled with the stress and groundwater and other geological role of environmental factors, the tunnel surrounding rock formation and the physical and mechanical properties shown macro, micro, discontinuity and high non-linear features. It can be said that the rock is an uncertain system. Therefore, stability analysis of tunnel surrounding rock can be seen as uncertain, nonlinear dynamics and complex system. At the application, a single study ways and method are not suitable for the complex stability analysis of the tunnel, and combination and comprehensive comparison of many kinds of theories and methods will be the correct analysis and effective way to solve the problem.
In the paper, nonlinear analysis methods of highway tunnel surrounding rock stability based on the wavelet theory were first proposed. Using wavelet thresholding and wavelet modulus maxima method, the tunnel surrounding rock stress measurement data is pre-treated and monitoring data in the outliers is analyzed. The wavelet denoising method is improved. The wavelet neural network analysis model for stability of surrounding rock of tunnel is proposed and surrounding rock stability is analyzed. The wavelet neural network analysis model is improved by using rough set theory. In the paper, the calculation of fractal dimension by using wavelet analysis is first proposed on the cross-section outline, and the relationship between the stability of tunnel surrounding rock and the fractal dimension of cross-section outline is analyzed.
Through in-depth and systematic researching, the main results and conclusions can be obtained:
(1) At the basis of a wide range of regional geological and meteorological and hydrological information, geological overview and physical geography is summed up in the tunnel area. Through different methods to investigate the detailed analysis of the tunnel engineering geology environmental conditions and a lot of reading, the status quo of tunnel surrounding rock stability studying at home and abroad has been an overall grasp.
(2) By reading the literature, application of the tunnel stability research using artificial intelligence and non-linear theory and application using wavelet theory has been analysis. Thesis proposes to combine wavelet theory artificial intelligence and non-linear theory, and applied to tunnel stability analysis. The results show that combining wavelet theory artificial intelligence and non-linear theory is appropriate.
(3) The tunnel monitoring data usually contain many random error, and monitoring data are often interfered by a variety of random and uncertainty factors. The tunnel monitoring data is treated by using wavelet analysis method, and through of data analysis and evaluation of the effect of de-noising method, the final selection of wavelet function is Db3 function, and the largest-scale is J=3 and threshold function is heursure function.
(4) The two kind’s original signal of points T100421 in the Baihe tunnel K127 +121 cross-section (observation of contact pressure and the value of stress changes) have been de-noising treated and analyzed. Through the high-frequency signal analysis of the contact pressure of tunnel surrounding rock and the construction of the tunnel, the tunnel construction and rainfall are the main reasons for emergence of noise in monitoring data. Through de-noising analysis of the changes in the value of the day stress, it is considered that the stress change value of day is swinging around in a random way, which is part of the impact of the observational error. At the same time, in fact, the tendency of stress change value has slowly decreased. Through extracting the stress of tunnel surrounding rock and stress change value, it is considered the trend is stable, and it means that the tunnel is stable after the initial support.
(5) In this paper, wavelet de-noising algorithm is improved by EMD. The curves using EMD-wavelet de-noising are smooth than only using wavelet de-nosing, and the tendency of tunnel surrounding rock is suitable of the development of reality. The de-noising effect is better than the effect of directly using Db3 function, heursure rules and 3-scale, so the favorable method is proposed for determine the stability of tunnel surrounding rock.
(6) In this paper, the surrounding rock contact pressure values of T100421 point in Baihe tunnel K127 +121 cross-sections were trained and predicted by using wavelet neural network and BP neural network, and the results of two methods were compared: in training speed, the wavelet neural network convergence speed is quicker than the BP neural network convergence speed, and the wavelet neural network training steps is fewer than the BP neural network training steps; at precision , whether it is training accuracy or prediction accuracy, wavelet neural network is better than the BP neural network. Wavelet neural network training convergence speed is fast, but in the process of approximation, the error oscillation phenomenon is emerged. In general, the wavelet neural network is better than BP neural network in the convergence and generalization performance.
(7) The Baihe tunnel rock mass quality evaluation of six indicators is proposed, and its attributes are reduced by using rough set theory.
(8) The four types of tunnel surrounding classification neural network recognition model is built and application analysis for the rough set reduction data by using wavelet neural network and BP neural network. The training sample set of neural network is the sample set of rough set reduced, and the neural network structure is effectively simplified, and the training steps and the training time is decreased, and the network learning speed and accuracy is improved. The accuracy using rough set WNN is highest, and the recognition results are near expert discriminant system. For non-reduction WNN model, the discriminnant accuracy is worse than reduced WNN except the 1st sample, and is large difference with the expert discriminant system value. The results using BP neural network is largest difference with the expert discriminant system value. In general, the prediction accuracy using the wavelet neural networks is higher than the prediction accuracy using BP neural network, and the prediction accuracy using rough set is higher.
(9)The ability of wavelet network function approximation has been strictly proved, but it encountered difficult in actual implication. Prediction decides that the effect of the prediction accuracy can not be 100% and there is no error. If there is no way at the optimal prediction instrument, only second best, wavelet network is one of sub-optimal prediction instrument.
(10) The research of the rock cavern cross-section outline is major in the theoretical model, and is less in the actual site monitoring of the cross-section outline. In this paper, it is found that Baihe tunnel cross-section curves have statistical self-similarity and better fractal characteristics after reading a substantial literature and studying many Baihe tunnel sections data.
(11) In this paper, a statistically self-similar fractal signal was to be calculated by wavelet analysis which is successfully applied to estimate and calculate the tunnel cross-section contour fractal dimension, and the fractal dimension of the cross-section outline was calculated. With reduction of the surrounding rock designed classification, the overall fractal dimension of cross-section curve was increased. Sidewall curve fractal dimension should be slightly larger than the overall contour curves, and it is not obvious inⅡ,Ⅲtype of rock and obvious inⅣtype rock. There is primary linear relationship between cross-section profile fractal dimension and separation angle, and the effect is more pronounced for different separation angle. There is not a linear relationship between sectional profile fractal dimension and joint dip angle. With increasing of dip angle, the fractal dimension was first increased and then decreased, and large at 30-60°. With increasing of the ultra-digging percentage, the fractal dimension of cross-section outline was increased, and there is the basic linear relationship between the fractal dimension and the ultra-digging percentage. But because of influence of the individual points by the impact of unusual geological conditions, the correlation coefficient is little. There was a linear relationship between fractal dimension an RMR and Q rock mass classification. With increasing of the RMR and Q values, the fractal dimension of cross-section outline will decrease. Multiple correlations among sectional profile fractal dimension, quality evaluation of tunnel surrounding rock and the super-dug percentage are researched.
The tunnel engineering and underground engineering would be rapid development in the twenty-first century. In recent years, countries around the world continue to build the tunnel, which has promoted the largest development of the world's tunnel industry. With the rapid development of China's national economy and mining technological advances, domestic tunnel construction has been rapid development, mainly at increasing the length and the increasing depth of the tunnel. In the tunnel engineering, landslides, karst collapse, gushing water, pouring water, cave necking, mountain deformation, cracking pit, mud debris flow and rock burst is a common problem of geological disasters. Even though the conditions of these problems happened vary, the harm caused by tunnel construction is similar.
Baihe tunnel is one of the long tunnels in the Zhurong Expressway. By many times the role of structural geology, the tunnel area have a larger change in geological conditions, and during the construction process of tunnel, there were landslides, swap block, deformation, seepage and so on. For the requirement of Baihe tunnel geological prediction, thesis focused on studying stability of Baihe tunnel surrounding rock.
Tunnel surrounding rock stability has its own law of development and a complex nonlinear process. The stability analysis of tunnels is a complex systems engineering problem. Because the tunnel surrounding rock has formed in different geological environments and many times crustal movement, and coupled with the stress and groundwater and other geological role of environmental factors, the tunnel surrounding rock formation and the physical and mechanical properties shown macro, micro, discontinuity and high non-linear features. It can be said that the rock is an uncertain system. Therefore, stability analysis of tunnel surrounding rock can be seen as uncertain, nonlinear dynamics and complex system. At the application, a single study ways and method are not suitable for the complex stability analysis of the tunnel, and combination and comprehensive comparison of many kinds of theories and methods will be the correct analysis and effective way to solve the problem.
In the paper, nonlinear analysis methods of highway tunnel surrounding rock stability based on the wavelet theory were first proposed. Using wavelet thresholding and wavelet modulus maxima method, the tunnel surrounding rock stress measurement data is pre-treated and monitoring data in the outliers is analyzed. The wavelet denoising method is improved. The wavelet neural network analysis model for stability of surrounding rock of tunnel is proposed and surrounding rock stability is analyzed. The wavelet neural network analysis model is improved by using rough set theory. In the paper, the calculation of fractal dimension by using wavelet analysis is first proposed on the cross-section outline, and the relationship between the stability of tunnel surrounding rock and the fractal dimension of cross-section outline is analyzed.
Through in-depth and systematic researching, the main results and conclusions can be obtained:
(1) At the basis of a wide range of regional geological and meteorological and hydrological information, geological overview and physical geography is summed up in the tunnel area. Through different methods to investigate the detailed analysis of the tunnel engineering geology environmental conditions and a lot of reading, the status quo of tunnel surrounding rock stability studying at home and abroad has been an overall grasp.
(2) By reading the literature, application of the tunnel stability research using artificial intelligence and non-linear theory and application using wavelet theory has been analysis. Thesis proposes to combine wavelet theory artificial intelligence and non-linear theory, and applied to tunnel stability analysis. The results show that combining wavelet theory artificial intelligence and non-linear theory is appropriate.
(3) The tunnel monitoring data usually contain many random error, and monitoring data are often interfered by a variety of random and uncertainty factors. The tunnel monitoring data is treated by using wavelet analysis method, and through of data analysis and evaluation of the effect of de-noising method, the final selection of wavelet function is Db3 function, and the largest-scale is J=3 and threshold function is heursure function.
(4) The two kind’s original signal of points T100421 in the Baihe tunnel K127 +121 cross-section (observation of contact pressure and the value of stress changes) have been de-noising treated and analyzed. Through the high-frequency signal analysis of the contact pressure of tunnel surrounding rock and the construction of the tunnel, the tunnel construction and rainfall are the main reasons for emergence of noise in monitoring data. Through de-noising analysis of the changes in the value of the day stress, it is considered that the stress change value of day is swinging around in a random way, which is part of the impact of the observational error. At the same time, in fact, the tendency of stress change value has slowly decreased. Through extracting the stress of tunnel surrounding rock and stress change value, it is considered the trend is stable, and it means that the tunnel is stable after the initial support.
(5) In this paper, wavelet de-noising algorithm is improved by EMD. The curves using EMD-wavelet de-noising are smooth than only using wavelet de-nosing, and the tendency of tunnel surrounding rock is suitable of the development of reality. The de-noising effect is better than the effect of directly using Db3 function, heursure rules and 3-scale, so the favorable method is proposed for determine the stability of tunnel surrounding rock.
(6) In this paper, the surrounding rock contact pressure values of T100421 point in Baihe tunnel K127 +121 cross-sections were trained and predicted by using wavelet neural network and BP neural network, and the results of two methods were compared: in training speed, the wavelet neural network convergence speed is quicker than the BP neural network convergence speed, and the wavelet neural network training steps is fewer than the BP neural network training steps; at precision , whether it is training accuracy or prediction accuracy, wavelet neural network is better than the BP neural network. Wavelet neural network training convergence speed is fast, but in the process of approximation, the error oscillation phenomenon is emerged. In general, the wavelet neural network is better than BP neural network in the convergence and generalization performance.
(7) The Baihe tunnel rock mass quality evaluation of six indicators is proposed, and its attributes are reduced by using rough set theory.
(8) The four types of tunnel surrounding classification neural network recognition model is built and application analysis for the rough set reduction data by using wavelet neural network and BP neural network. The training sample set of neural network is the sample set of rough set reduced, and the neural network structure is effectively simplified, and the training steps and the training time is decreased, and the network learning speed and accuracy is improved. The accuracy using rough set WNN is highest, and the recognition results are near expert discriminant system. For non-reduction WNN model, the discriminnant accuracy is worse than reduced WNN except the 1st sample, and is large difference with the expert discriminant system value. The results using BP neural network is largest difference with the expert discriminant system value. In general, the prediction accuracy using the wavelet neural networks is higher than the prediction accuracy using BP neural network, and the prediction accuracy using rough set is higher.
(9)The ability of wavelet network function approximation has been strictly proved, but it encountered difficult in actual implication. Prediction decides that the effect of the prediction accuracy can not be 100% and there is no error. If there is no way at the optimal prediction instrument, only second best, wavelet network is one of sub-optimal prediction instrument.
(10) The research of the rock cavern cross-section outline is major in the theoretical model, and is less in the actual site monitoring of the cross-section outline. In this paper, it is found that Baihe tunnel cross-section curves have statistical self-similarity and better fractal characteristics after reading a substantial literature and studying many Baihe tunnel sections data.
(11) In this paper, a statistically self-similar fractal signal was to be calculated by wavelet analysis which is successfully applied to estimate and calculate the tunnel cross-section contour fractal dimension, and the fractal dimension of the cross-section outline was calculated. With reduction of the surrounding rock designed classification, the overall fractal dimension of cross-section curve was increased. Sidewall curve fractal dimension should be slightly larger than the overall contour curves, and it is not obvious inⅡ,Ⅲtype of rock and obvious inⅣtype rock. There is primary linear relationship between cross-section profile fractal dimension and separation angle, and the effect is more pronounced for different separation angle. There is not a linear relationship between sectional profile fractal dimension and joint dip angle. With increasing of dip angle, the fractal dimension was first increased and then decreased, and large at 30-60°. With increasing of the ultra-digging percentage, the fractal dimension of cross-section outline was increased, and there is the basic linear relationship between the fractal dimension and the ultra-digging percentage. But because of influence of the individual points by the impact of unusual geological conditions, the correlation coefficient is little. There was a linear relationship between fractal dimension an RMR and Q rock mass classification. With increasing of the RMR and Q values, the fractal dimension of cross-section outline will decrease. Multiple correlations among sectional profile fractal dimension, quality evaluation of tunnel surrounding rock and the super-dug percentage are researched.
引文
[1]徐则明,黄润秋.深埋特长隧道及其施工地质灾害[M].成都:西南交通大学出版社,2000.
[2]郭立.深部硬岩岩爆倾向性动态预测模型及其应用[博士学位论文][D].长沙:中南大学,2004.
[3]孙广忠,等.军都山隧道快速施工超前地质预报指南[M].北京:中国铁道出版社,1990.
[4]石文慧.当代铁路隧道发展趋势及地质灾害防治[J].铁道工程学报,1996,(2):55-62.
[5]王石春,陈光宗,何发亮.中国铁路隧道工程地质的发展[J].铁道工程学报,1996,(2):49-54.
[6]孙广忠.地质工程理论与实践[M].北京:地震出版社,1996.
[7]闫天俊,吴立.现代隧道施工中的常见地质灾害问题及防治[J].探矿工程,2003,(4):62-64.
[8]刘伟.21世纪初的我国公路隧道关键技术[J].公路交通技术,2000,No.1:30-35.
[9]易敏,韩立华.公路隧道围岩稳定性分析和衬砌技术现状综述[J].现代交通技术,2005,No.3:36-41.
[10]戴文亭,白宝玉.我国隧道及地下工程发展现状和前景展望[J].东北公路,2000,No.4:90-92.
[11]李晓红.隧道新奥法及其量测技术[M].北京:科学出版社,2002.
[12]肖树芳,杨淑碧.岩体力学.北京地质出版社1987.
[13]徐光黎.岩石地下工程岩体质量评价综述.工程地质,1999(3):14-18.
[14]周创兵.围岩质量分类的判别函数及其应用.水文地质工程地质,1991(4):25-29.
[15]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,岩石力学与工程学报,2001(增):1695-1698.
[16]张鹏,陈剑平,邱道宏.基于粗糙集的隧道围岩质量可拓学评价[J].岩土力学,2009,30(1):246-250.
[17]Deere, D. U. Technical description of rock cores for engineering purposes[J]. Felsmechanik und Ingenieugeologie, 1963, 1(1): 16-22.
[18]Terzaghi, K. 1946. Rock defects and loads on tunnel supports. In Rock tunneling with steel supports, (eds R. V. Proctor and T. L. White) 1, 17-99.
[19]Youngstown, OH:Commercial Shearing and Stamping Company. Lauffer, M. 1958. Gebirgsklassifizierung fur den stollenhua. Geologie and Bauwesen, 24(1), 46-51.
[20]铁路隧道设计规范(TBJ3-85).中国铁道出版社,1987.
[21]锚杆喷射混凝土支护技术规范(GB 50086-2001).中国计划出版社,2001.
[22] Barton N., Lien R. & Lunde J. Engineering classifcation of rock masses for design of the tunnel support. Rock Mechanics, 1974, 6(4).
[23]隧道工程岩体分级探讨(铁道部科学研究院西南研究所文集).中国铁道出版社,1987.
[24]Arild Palmstrom. Charaeterizing rock masses by the RMi for use in practical rock engineering. Part 1: The development of the Rock Mass index (RMi). Tunneling and Underground Space Technoloty, 1996(2): 175-188.
[25]Arild Palmstrom. Charaeterizing rock masses by the RMi for use in practical rock engineering. Part 2:The development of the Rock Mass index (RMi). Tunneling and Underground Space Technoloty, 1996(3):287-303.
[26]Wickham. Handbook on Mechanical Properties of Rocks. Publication Trans Tech., 1978.
[27]Bienlawski Z.T. Engineering Classification of Jointed Mass, Trans.S.Africa Inst, Civ. Engrs 1973,15(12).
[28]工程岩体分级标准(GB50218-94).中国计划出版社,1995.
[29]水利水电工程地质勘察规范(GB50287-99).中国计划出版社,1999.
[30]高谦、任天贵、明士祥.采场巷道围岩分类的概率统计分析方法及其应用[J].煤炭学报,1994,19(2):131-139.
[31]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 (3):62-66.
[32]杜时贵,李军,徐良明等.岩体质量的分形表述.地质科技情报[J],1997(1):91-96.
[33]M.N.Bagde,A.K.Raina,A.K.Chakraborty,etal·Rock mass characterization by fractal dimension[J],Engineering Geology,2002(63):141-155.
[34]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(6):25-29.
[35]陈志坚,朱代洪,张雄文.围岩质量综合评判模型和大坝建基面优选模型的建立[J].河海大学学报,2002(4):88-91.
[36]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(1):18-22.
[37]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(3):220-224.
[38]孙恭尧,黄卓星,夏宏良.坝基岩体分级专家系统在龙滩工程中的应用[J].红水河,2002(3):6-11.
[39]章杨松.岩石质量指标的计算机模拟及其风险分析[J].地质灾害与环境保护,2002(1):44-47.
[40]章杨松,罗国煌,李晓昭等.岩体质量分级的风险分析方法[J].工程地质学报,2002(3):331-336.
[41]王环玲,晏鄂川,余宏明.运用结构面模拟技术分析岩体质量特征[J].地质灾害与环境保护,2002(3):64-68.
[42]马淑芝,贾洪彪,唐辉明等.利用“岩体裂隙率”评价工程岩体的质量[J].水文地质工程地质,2002(1):10-12.
[43]田敬学,张庆贺,姜福兴.煤矿巷道围岩稳定性动态工程分类技术研究与应用[J].岩土力学,2001(23)1:29-32.
[44]徐凤毅.阜新矿区回采巷道围岩稳定性分类[J].矿山压力与顶板管理,1994,No.1:29-32.
[45]华东水利学院.岩石力学[M].水力电力出版社,1981.
[46]B.H.G.Brady, E.T.Brown. Rock mechanics for under ground mining. George Allen & Unwin, 1985.
[47]孙钧,候学渊.地下结构(上册).科学出版社,1987.
[48]蔡美峰主编.岩石力学与工程.科学出版社,2002.
[49]候学渊.地下圆形结构弹塑性理论.同济大学学报,1982(4):50-62.
[50]重庆建筑工程学院,同济大学.岩石力学.中国建筑工业出版社,1981.
[51]王龙甫.弹性理论(第二版).科学出版社,1984.
[52]陆明万,罗学富.弹性理论基础.清华大学出版社,施普林格出版社,2001.
[53]尹祥础.固体力学.地震出版社,1985.
[54]陈子荫.围岩力学分析中的解析方法.煤炭工业出版社,1994.
[55]吕爱钟,蒋斌松.岩石力学反问题.煤炭工业出版社,1998.
[56]王桂芳.无衬砌隧道围岩应力的计算[J].土木工程学报,1965(2):30-42.
[57]刘金高,王润富.马蹄形孔口和梯形孔口的应力集中问题[J].岩土工程学报,1995(5):57-64.
[58]钱伯勤.单孔无限域应力函数的通式[J].江苏力学,1990(6):66-67.
[59]王润富.弹性力学的复变函数计算机解[J].河海大学学报,1991(2):84-86.
[60]王润富.一种保角映射法及其微机实现[J].河海大学学报,1991(1):86-90.
[61]范广勤,汤澄波.应用三个绝对收敛级数相乘法解非圆形洞室的外域映射函数[J].岩石力学与工程学报,1993(3):255-264.
[62]吕爱钟.应用最优化技术求解任意截面形状巷道映射函数的新方法[J].岩石力学与工程学报,1995(3):269-274.
[63]朱大勇,钱七虎,周早生等.复杂形状洞室围岩应力的弹性解析分析[J].岩石力学与工程学报,1998(4):402-404.
[64]朱大勇,钱七虎,周早生等.复杂形状洞室映射函数的新解法[J].岩石力学与工程学报,1999(3):279-282.
[65]朱大勇,钱七虎,周早生等.复杂形状洞室围岩应力弹性解析分析[J].岩石力学与工程学报,1999(4):402-404.
[66]朱维申、何满潮著,复杂条件下围岩稳定性与岩体动态施工力学,科学出版社,1996.
[67]朱维申,李术才,陈卫忠.节理岩体破坏机理和锚固效应及工程应用.科学出版社,2002
[68]杨淑清.地下洞室围岩开挖稳定性分析.武汉水利电力学院学报,1986(2):73-82.
[69]陈霞龄,韩伯鲤,梁克读.地下洞群围岩稳定的试验研究.武汉水利电力大学学报,1994(1):17-23.
[70]赵震英,叶勇.复杂地质条件下地下洞室围岩应力及变形模拟试验研究.岩石力学与工程学报,1989(4):298-305.
[71]孙钧.岩土材料流变及其工程应用[M].北京:中国建筑工业出版社,1999.
[72]Owen D R J, Hinton E. Finite Elements in Plasticity; Theory and Practice [M]. Swansea; Pine ridge Press Limited, 1980.
[73]余卫平,汪小刚等.地下洞室群围岩稳定性分析及其结果的可视化[J].岩石力学与工程学报,2005,24(20):3030-3736.
[74]王春生,周翠英.梅河高速公路隧道稳定性数值模拟[J].中山大学学报(自然科学版),2005,44(1):38-40.
[75]胡元芳.小线间距城市双线隧道围岩稳定性分析[J].岩石力学与工程学报,2002年,21(9):1335-1338.
[76] P.Kumar. Infinite Elements for Numerical Analysis of Underground Excavations [J]. Tunnelling and Underground Technology, 2000, 15(1):117-124.
[77] Hart R, Cundall P A, Lcmos J.Formulations of three-dimensional distinct element model. PARTII: Mechanical calculation of a system composed of many polyhedral blocks. International Journal of Rock Mechanics and Mining Sciences, 1988(3).
[78]〔美〕石根华著,裴觉民译.数值流形方法与非连续性形变分析方法.清华大学出版社,1997.
[79]张清,人工智能在岩石力学与岩石工程中的应用[J].岩石力学与工程学报,1986,Vol.5,No.4:389-395.
[80]冯夏庭,智能岩石力学导论[M].科学出版社,2000.
[81]冯夏庭、林韵梅,岩石力学与工程专家系统研究新进展[J].岩石力学与工程学报,1995,Vol.14,No.1:85-91.
[82]冯夏庭,马平波.基于数据挖掘的地下硐室围岩稳定性判别[J].岩石力学与工程学报,2001,Vol.20,No.3:306-309.
[83]冯夏庭,王泳嘉等.地下工程力学综合集成智能分析的理论和方法[J].岩土工程学报,1997,Vol.19,No.1:30-36.
[84]胡建华等,地下工程围岩稳定性的MBP神经网络识别[J].岩土工程界,2001, Vol.4,No.12:63-64.
[85]丁向东等,岩爆分类的人工神经网络预测方法[J].河海人学学报(自然科学版),2003,Vol.31,No.4:424-427.
[86]朱宝龙等,基于人工神经网络的岩爆预测方法[J].地质灾害与环境保护,2002,Vol.13,No.3:56-59.
[87]白明洲等,岩爆危险性预测的神经网络模型及应用研究[J].中国安全科学学报,2002,Vol.12,No.4:65-69.
[88]何正,李晓红等,BP神经网络模型在深埋隧道岩爆预测中的应用[J].地下空间与工程学报,2008,Vol.4,No.3:494-498.
[89]Wang Xinfei,Li Xiaohong.Application of BP Neural Network into Prediction of Rockburst inTunneling[C].Progress in Safety Science and Technology,Vol.IV,Shanghai,2004,Beijing New York:Science Press,2004,617–621.
[90]冯夏庭,王泳嘉等.地下工程力学综合集成智能分析的理论和方法[J].岩土工程学报,1997,Vol.19,No.1:30-36.
[91]周建春,魏琴,刘光栋.采用BP神经网络反演隧道围岩力学参数[J].岩石力学与工程学报,2004,23(6):941-945.
[92]郝哲,刘斌.基于差分法及神经网络的硐室围岩力学参数反分析[J].岩土力学,2003, Vol.24增刊:77-80.
[93]周保生,林江.人工神经网络在煤矿巷道围岩移近量预测中的应用[J].系统工程理论与实践,2000,1:136-139.
[94]周保生,朱维申.巷道围岩参数的人工神经网络预测[J].岩土力学, 1999,Vol.20 No.1:22-26.
[95]高玮,郑颖人.巷道围岩松动圈预测的进化神经网络法[J].岩石力学与工程学报,2002,21(5):658~661.
[96]张玉样.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, Vol.20,No.3:90-93.
[97]邱道宏,陈剑平,阙金声,安鹏程.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(1):86-91.
[98]祁长青,许人平.基于遗传算法的隧道围岩变形稳定可靠性分析[J].工程地质学报,2008,16(2):258-262.
[99]孙洁,刘晓悦.基于蚁群优化神经网络的巷道围岩稳定性预测[J]. 2008,Vol.25,No.5:117-119.
[100]赖永标,乔春生.支持向量机在围岩稳定性分类中的应用[J].水利学报, 2006第37卷,第9期:1092-1096.
[101]肖云华,王清等.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,Vol.36, No.6:49-53.
[102]Mandbrot B B. The fractal geometry of nature. New York: W H Freeman, 1982.
[103]张济忠.分形[M].北京:清华大学出版社,1995.
[104] Saouma V E, Barton C C, Gamaleldin N A. Fractal characterization of fracture surfaces in concrete. Eng Fract Mech, 1990, 35(13): 47-53.
[105] Lange D A, Jennings H M, Shah S P. Relationship between fracture surface roughness and fracture behavior of cement paste and mortar. Journal of the American Cerwnic Society, 1993, 76(3): 589- 597.
[106] Issa M A, Hammad A M. Assessment and evaluation of fractal dimension of concrete fracture surface digitized in ages. Cement and Concrete Research, 1994, 24(2): 325-334.
[107] Stroeven P Stereological estimation of fractal number of fracture planes in concrete. Mater Res Soc Symp Proc, 1996, 407: 343-348.
[108] Xie H, Pariseau W G Fractal estimation of joint roughness coefficient. In: Fractured andJoint Rock Masses, Califonua: 1992, 132-139.
[109] Tse R. ,Cruden D M. Estimating joint roughness coefficients. In: Sci. Geomen Abstr Int. J Rock Mech 1979, 303-307.
[110] Wakabayashi Naruki, Ikuo Fukushige. Experimental study on the relation between fractal dimension and shear strengthen. In: Fractured and Joint Rock Masses, California: 1992, 132-139.
[111]徐永福,吴正根.断层碎屑分布的分维及其地质意义.河海大学学报,1996 24 (3): 1-4.
[112] Amitava, Ghosh, et al. Fractal characteristics of rock discontinuities. Engineering. Geology, 1993, 34:1-9.
[113] Point P R L. A method to characterize fracture density and connectivity through fractal geometry. International Journal Rock Mechanics Mining Sciences & Geomechanics Abstract, 1988, 25(6): 421-429.
[114]陈乃明等.岩体节理网络分形的不均匀性研究.见:中国青年学者岩土工程力学及其应用讨论会论文集,北京:科学出版社,1994: 100-103.
[115] Poulton, et al. Scale invariant behaviour of massive and fragmented rock. International Journal Rock Mechanics Mining Sciences & Geomechanics Abstract, 1990, 27(3): 219-221.
[116]谢和平,Sanderson D J.断层分形分布之间的相关关系.煤炭学报,1994, 19 (5): 445-449.
[117] Lee Y J, et al. The fractal dimension as a measure of the roughness of rock discontinuity profiles. International Journal Rock Mechanics Mining Sciences & Geomechanics Abstract, 1990, 27(6): 453-464.
[118]秦四清等.非线性工程地质学导引.成都:西南交通大学出版社,1993.
[119] Turk, et al. Roughness angle determination and joint closure. In: Proc. Int. Symp. on Fundamentals of Rock Jointed, Sweden: 1985, 197-204.
[120] Mandelbrot B B, Passoja D E, Paullay A J. Fractal character of fracture surfaces of metals. Nature, 1984, 308(19): 721-722.
[121]谢和平,陈至达.分形几何与岩石断裂.力学学报,1988,20(3): 264-271.
[122] Borodich F M. Fracture energy in a fractal crack propagating in concrete or rock. Trans Dokl USSR Acad Sci Earth Sci Sec, 1994, 327(8): 36-40.
[123] Borodich F M. Some fractal models of fracture. Journal of the Mechanics&Physics of Solids, 1997,45(2): 239-259.
[124] Xie H. Studies on fractal models of the microfracture of marble. Chinese Science Bulletin, 1989,34(15):1292-1296.
[125]卢波,陈剑平,葛修润.王水林节理岩体结构的分形几何研究[J].岩石力学与工程学报,2005,24(3):461-467.
[126]陈剑平,王清,谷宪民,范建华.岩体节理产状极点分布的分形维[J].岩石力学与工程学报,2007,26(3):501-508.
[127]谢和平,鞠杨.分数维空间中的损伤力学研究初探.力学学报,1999. (3): 300-310.
[128]谢和平,高峰.岩石类材料损伤演化的分形特征.岩石力学与工程学报,1991. 10 (1): 74-82.
[129]苏永华,孙晓明,赵明华.隧道围岩超挖的分形特征研究[J].中国矿业大学学报. 2006,Vol.35 No.1:89-93.
[130]孙少锐,吴继敏,魏继红.隧洞超挖问题中的广义分数维研究[J].岩石力学与工程学报, 2005,Vol.24,No.24:4478-4483.
[131]朱治军,李后强.小波分析在分形科学中的应用[J].应用基础与工程科学学报,1994,Vol.2, No.4: 275-281.
[132]李定龙,周治安小波分析在地质分形科学中的应用前景[J].世界地质,1997, 16(2):49-54.
[133]冉启文,谭立英,等.小波分析与分数傅立叶变换及其应用[M].北京:国防工业出版社,2002.
[134]刘贵忠等.小波分析及应用.西安电子科技大学出版社,1995.
[135]秦前清等.实用小波分析.西安电子科技大学出版社,1994.
[136] Mallat S. Multiresolution representations and wavelets.[Ph. D. Thesis). Philadelphia: University of Pennsylvania, 1988.
[137]李建华,李万社.小波理论发展及其应用(综述)[J].2006,Vol.22 No.2:27-31.
[138]邓东皋,彭立中.小波分析[J].数学进展, 1991, (03).
[139] (美)崔锦泰著,程正兴译.小波分析导论[M],西安交大出版社,1995.
[140]Mallat S.Theory for multi-resolution signal decomposition: The wavelet representation[J]. IEEE Trnasactionson Pattern Analysi and Machine Intelligence, 1989,11(7):674~693.
[141]韩震宇,申旭娟,石章林.信号的多分辨分析及其在消噪中的应用[J].四川联合大学学报,1999,3(1):52-58.
[142] Ma11atS, H Wang W L. Singularity detection and processing with wavelets [J]. IEEE Transaction on Information Theory, 1992, 38 (2):617-643.
[143]彭玉华,汪文秉.小波用于估测散射波波达时间及去噪[J].电子学报,1996,24(4):113-116.
[144]杜芳,卢文胜,曹文清.振动台试验测试信号去噪的小波变换方法[J].振动与冲击,1999,18(2):26-29.
[145]刘曙光,殷明.子波消噪技术及其应用[J].西北纺织工学院学报,1999,13(l):16~20.
[146]Donoho DL, Johnstone IM. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika,1994,81(3):425-455.
[147]Donoho DL. De-noising by soft thresholding[J]. IEEE Transcations on Information Theory, 1995, 41(3):613-627.
[148] Donoho DL, Johnstone IM. Adaptation to unknown Smoothness via wavelet shrinkage[J].Journal of the American Statistical Association, 1995, 90(12):1200-1224.
[149] Collin, R, and Warnant, R. application of the wavelet transform for GPS cycle slip detection and comparison with Kalman filter, Manuscripta Geodaetica 20: 161-172.
[150]黄丁发,卓健成. GPS相位观测值周跳检测的小波分析法[J].测绘学报,1997,26(4): 352-357.
[151]文鸿雁.小波多分辨分析在变形分析中的应用[J].地壳形变与地震, 2000, 20 (3 ): 27-32.
[152]文鸿雁,张正禄.非线性小波变换闽值法去噪改进[J].测绘通报,2006, (03):18-21
[153]黄声享,刘经南.GPS变形监测系统中消除噪声的一种有效方法[J].测绘学报,2002, 31(2):104-107.
[154]田胜利,周拥军,葛修润,卢允德.基于小波分解的建筑物变形监测数据处理[J].岩石力学与工程学报,2004,23(15): 2639-2642.
[155]田胜利,徐东强,葛修润.大坝水平位移监测数据的小波变换去噪处理[J].水电自动化与大坝监测,2004,28(1):49-53.
[156]聂文滨,阙沛文.桶基平台贯沉深度测量中的小波去噪方法[J].测控技术,2002,21(7):51-59.
[157]徐蕾.小波去噪算法研究及在电力系统中的应用[J].电力系统通信,2007,28(5):54-57.
[158]李英,张淑贞,许康生.小波降噪方法在地震信号处理中的应用[J].西北地震学报,2006,28(2):159-162.
[159]王坚,高井祥.沉降数据序列分析方法研究[J].测绘学院学报,2004,21(2):90-92.
[160]朱习军,戴月明,郭守军.基于小波分解的GPS监测数据消噪处理[J].山东科技大学学报,2005,24(2):17-19.
[161]张鹏,李献勇,陈剑平.基于小波降噪的隧道围岩监测数据分析[J].吉林大学学报(地球科学版).2008, 38 (6): 1010-1014.
[162] Zhang Qinghua, BenvenisteA. Wavelet Networks. IEEETrans. on Neural networks. 1992, 3 (6):859-898.
[163] BAKSHI R.B, STEPHANOPOULOS G. Wavenet: A Multiresolusion,Hierarchical Neural Network with Localized Learning. AichE Journal, 1993, 39 (1): 57-61.
[164] Zhang J, Walter GG,Miao YB, et al. Wavelet Neural Networks for Function Learning. IEEE Trams on Slgnal Processing, 1995, 43 (6):1455-1497.
[165]钱峻,邵惠鹤一种小波神经网络的在线建模和校正算法.模式识别与人工智能.2001,13(l):16~20.
[166]丁宇新,沈学勤等.基于能量密度的小波神经网络.计算机学报1997,20(9)283~838.
[167]Zhang Qinghua. Using Wavelet Neuaral In non-paramation estimation .IEEE.Trams on Nearal Networks. 1997, 8 (2):227-236.
[168] Jiao LC, Pant, Fang Y W. Multiwavelet Neural Networks and Its Approximation Propertise. IEEE Trans on Neural Network. 2001, 12 (5):1060-1066.
[169] Yiwen Yang .Stock Market Trend Prediction Based on Neural Network,Multiresolution Analysis Reconstruction. In:Proceedings of the IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, New York, USA,2000, 8 (12):155-157.
[170] J R Hull, et al. A Neural Network Algorithm using wavelets and Auto Regressive Inputs for System Identification of the 1997. IEEE Imernational Conference on Networks, 1997, 2 (4):724-727.
[171]潘国荣.变形监测数据的小波神经网络预测方法[J].大地测量与地球动力学, 2007, Vol.27, No. 4:47-50.
[172]谭云亮,孙中辉,杜学东.冲击地压AE时间序列小波神经网络预测模型[J].岩石力学与工程学报, 2000,19(增): 1034~1036.
[173]孙少锐,吴继敏,魏继红.基于地质统计模型的小波神经网络在地下洞室超挖预测中的应用[J].岩石力学与工程学报, 2003,22(8):1344-1349.
[174]张玉祥.小波神经网络遗传算法及其在矿山压力预报中的应[J].中国有色金属学报,1999, Vol.9, No.2:448-452.
[175]Arneodo A, Grasseau G, Holschneider M. Wavelet transform of multifractals. Physical Review Letter, 1988,61(14):2281-2284.
[176]李炳成,分形参数估计的子波函数方法,通信学报[J],1993, 14 (4): 95-98.
[177]朱治军,李后强,非线性科学的理论、方法和应用[M],北京:科学出版社,1997.
[178]马波,裘正定,小波变换的分形特性[J].铁道学报,1998, 20 (6): 74-80.
[179]郑继明.分形和小波变换在油气预测中的应用[J].预测,1996,6:51-53.
[180]罗建书,沙基昌,黄建华.分形信号的小波分析[J].工程图学学报,1999,2:27-34.
[181]朱治军,李后强.小波分析在分形科学中的应用[J].应用基础与工程科学学报,1994,2(4):275-281.
[182]王安良,杨春信.评价机械加工表面形貌的小波变换方法[J].机械工程学报,2001, Vol.37, No.8:66-70.
[183]王安良,杨春信.机械加工表面形貌分形特征的计算方法[J].中国机械工程, 2002, 13 (8): 714-718.
[184]王安良,杨春信.小波变换方法评价曲线的分形特征[J].机械工程学报,2002,Vo1.38,No.5:80-85.
[185]杨大勇,刘莹.基于小波变换的分形曲线维数计算方法的研究[J].润滑与密封,2007,Vol.32, No.1:40-43.
[186]孙晓丽,宋国乡.基于小波变换的IC缺陷轮廓曲线分形维估计[J].电子技术,2006,1:126-128.
[187]王文圣,向红莲,赵东水文序列分形维数估计的小波方法[J].四川大学学报(工程科学版),2005,Vol.37 No.1:1-4.
[188]侯建荣,黄培清,骆建文.多分维分形曲线维数计算的小波方法[J].上海交通大学学报,2004,38(增):18-21.
[189]杜恩祥,李科杰.基于多重分形和小波变换的声目标信号特征提取[J].自动化学报,2004,30(5):742-746.
[190]王文圣,向红莲,黄伟军.基于连续小波变换的径流分维研究[J].水利学报,2005,36(5):598-601.
[191]王文圣,赵太想,丁晶.基于连续小波变换的水文序列变化特征研究[J].四川大学学报(工程科学版),2004,36(4):6-9.
[192]赵春晖,马梅真,尚政国.基于提升小波变换和分形维数的声纳图像识别[J].声学技术,2007,26(5):811-816.
[193]韩琦,康戈文.基于小波变换的带钢表面缺陷分形特征研究[J].自动化技术与应用,2006,25(3):4-6.
[194]田杰,陈杰,张宇河.基于小波变换及分形特征的目标检测与识别[J].北京理工大学学报,2003,23(1):95-99.
[195]周卫滨.苍岭隧道岩爆预测和防治研究[D].杭州:浙江大学,2005.
[196]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.
[197]蒋树屏,赵阳.复杂地质条件下公路隧道围岩监控量测与非确定性反分析研究[J].岩石力学与工程学报,2004,23(20):3460-3464.
[198] Karmen F B,Borut P. Displacement analysis of tunnel support in soft rock around a shallow highway tunnel at Golovec[J]. Engineering Geology,2004,v75:89–106.
[199]袁勇,王胜辉,杜国平,等.双连拱隧道支护体系现场监测试验研究[J].岩石力学与工程学报,2005,24(3):480-484.
[200]崔天麟.超浅埋暗挖隧道初期支护结构内力监测及稳定性分析[J].现代隧道技术,2001,38(2):29-34.
[201]刘庆仁.三车道公路隧道监测分析研究[J].北京建筑工程学院学报,2002,18(3):34-39.
[202]李建平,唐远炎.小波分析方法的应用[M].重庆:重庆大学出版社,1999.
[203]文鸿雁.基于小波理论的变形分析模型研[D].武汉大学博士学位论文, 2004.
[204]董永生,羿旭明.基于四种改进阈值的小波去噪方法[J].数学杂志,2006,26(5):473-477.
[205]付燕.改进的小波域阈值去噪方法[J]西安科技大学学报.2004.24(4):467-469.
[206] HUANG N E, SHEN A, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[ J ].Proc R Soc Lond: A, 1998, 454: 899-995.
[207] HUANG N E, LONG S R, SHEN Z. A new view of the nonlinear water waves;the Hilbert spectrum(J].Annu Rev Fluid Mech, 1999, 31:417-457.
[208] FLANDRIN G, RILLING G, GONCALVES P. Empirical mode decomposition as a filter bank[J].IEEE Signal Processing Letters, 2004, 11( 2 ):112-114.
[209]杜修力,何立志,侯伟.基于经验模态分解(EMD)的小波闭值除噪方法[J].北京工业大学学报, 2007, Vol.33,No.3: 265-272.
[210]焦李成.神经网络的应用与实现,西安:西安电子科技大学出版社,1996.
[211]飞思科技产品研发中心著.神经网络理论与MARLAB7实现[M].北京:电子工业出版社,2005.
[212]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]吉林大学博士学位论文,2006.6.
[213]冯再勇.小波神经网络与BP网络的比较研究及应用[D].成都理工大学硕士学位论文,2007.
[214]刘志刚,王晓茹,钱清泉.小波网络的研究进展与应用[J].电力系统自动化,2003,vol.27, No.6:73-80.
[215]陈哲,冯天瑾.一种基于BP算法学习的小波神经网络[J].青岛海洋大学学报, 2001,31(1):122~128.
[216]李金屏,王风涛.BP小波神经网络快速学习算法[J].研究系统工程与电子技术,2001,Vol·23,No·8 :72-75.
[217]张文修,吴伟志,梁吉业,等.粗糙集理论与方法[M].北京:科学出版社, 2001: 10-24.
[218]宋笑雪.粗糙集理论及其应用[J].咸阳师范学院学报, 2005, 20(2): 29-31.
[219]赵会兵.钻爆法施工隧道的超欠挖概率统计规律研究.铁道工程学报.1999,16(3): 106-110.
[220]佘健,钟新樵.公路隧道超欠挖统计规律研究[J].重庆交通学院学报,2000,19(2):15-20.
[221]吴继敏,陈显春,李文奇金.丽温高速公路连拱隧道超挖预测及原因分析[J].水文地质与工程地质,2005,5:56-59.
[222]孙少锐,吴继敏,魏继红.隧洞超挖问题中的广义分数维研究[J].岩石力学与工程学报, 2005,24(24):4478-4483.
[223]苏永华,赵明华,姚爱军.地下坑道轮廓超挖的随机特征[J].湖南大学学报(自然科学版), 2005,32(3):56-60.
[224]苏永华,孙晓明,赵明华.隧道围岩超挖的分形特征研究[J].中国矿业大学学报, 2006,35(1):89-93.
[225]肖云华,王清,陈剑平.隧道围岩超欠挖与节理和洞轴线之间的关系[J].吉林大学学报(地球科学版), 2008,38(3):455-459.
[226]丁泰山,刘建民.超挖对地下洞室稳定性的影响分析[J].地下空间与工程学报, 2008,4(3):11-14.
[227] Revey G F. Effects and control of overbreak in underground mining. Mining Engineering, 1998,50(8):63-67.
[228] Franklin J A, Maerz N H, Ibarra J A. Overbreak and underbreak in underground openings part 2:causes and implications. Geotechnical and Geological Engineering, 1996, 14(4): 325-340.
[229] Maerz N H, Ibarra J A, Franklin J A. Overbreak and underbreak in underground openings part 1: measurement using the light section method and digital image processing. Geotechnical and Geological Engineering, 1996, 14(4): 307-323.
[230] Abel J F. Average percentage of overbreak beyond payment line, MN-461 course notes,Colorado School of Mines, Golden, CO, USA, 1982.
[231] Martna J. Selective overbreak in the Suorva-Vietas tunnel cased by rock pressure, Proceeding of the International Symposium on Underground Openings, Lucerne, Switzerland, 1972: 141-145.
[232]谢和平.岩石力学[M],北京:科学出版社,2004.
[2]郭立.深部硬岩岩爆倾向性动态预测模型及其应用[博士学位论文][D].长沙:中南大学,2004.
[3]孙广忠,等.军都山隧道快速施工超前地质预报指南[M].北京:中国铁道出版社,1990.
[4]石文慧.当代铁路隧道发展趋势及地质灾害防治[J].铁道工程学报,1996,(2):55-62.
[5]王石春,陈光宗,何发亮.中国铁路隧道工程地质的发展[J].铁道工程学报,1996,(2):49-54.
[6]孙广忠.地质工程理论与实践[M].北京:地震出版社,1996.
[7]闫天俊,吴立.现代隧道施工中的常见地质灾害问题及防治[J].探矿工程,2003,(4):62-64.
[8]刘伟.21世纪初的我国公路隧道关键技术[J].公路交通技术,2000,No.1:30-35.
[9]易敏,韩立华.公路隧道围岩稳定性分析和衬砌技术现状综述[J].现代交通技术,2005,No.3:36-41.
[10]戴文亭,白宝玉.我国隧道及地下工程发展现状和前景展望[J].东北公路,2000,No.4:90-92.
[11]李晓红.隧道新奥法及其量测技术[M].北京:科学出版社,2002.
[12]肖树芳,杨淑碧.岩体力学.北京地质出版社1987.
[13]徐光黎.岩石地下工程岩体质量评价综述.工程地质,1999(3):14-18.
[14]周创兵.围岩质量分类的判别函数及其应用.水文地质工程地质,1991(4):25-29.
[15]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,岩石力学与工程学报,2001(增):1695-1698.
[16]张鹏,陈剑平,邱道宏.基于粗糙集的隧道围岩质量可拓学评价[J].岩土力学,2009,30(1):246-250.
[17]Deere, D. U. Technical description of rock cores for engineering purposes[J]. Felsmechanik und Ingenieugeologie, 1963, 1(1): 16-22.
[18]Terzaghi, K. 1946. Rock defects and loads on tunnel supports. In Rock tunneling with steel supports, (eds R. V. Proctor and T. L. White) 1, 17-99.
[19]Youngstown, OH:Commercial Shearing and Stamping Company. Lauffer, M. 1958. Gebirgsklassifizierung fur den stollenhua. Geologie and Bauwesen, 24(1), 46-51.
[20]铁路隧道设计规范(TBJ3-85).中国铁道出版社,1987.
[21]锚杆喷射混凝土支护技术规范(GB 50086-2001).中国计划出版社,2001.
[22] Barton N., Lien R. & Lunde J. Engineering classifcation of rock masses for design of the tunnel support. Rock Mechanics, 1974, 6(4).
[23]隧道工程岩体分级探讨(铁道部科学研究院西南研究所文集).中国铁道出版社,1987.
[24]Arild Palmstrom. Charaeterizing rock masses by the RMi for use in practical rock engineering. Part 1: The development of the Rock Mass index (RMi). Tunneling and Underground Space Technoloty, 1996(2): 175-188.
[25]Arild Palmstrom. Charaeterizing rock masses by the RMi for use in practical rock engineering. Part 2:The development of the Rock Mass index (RMi). Tunneling and Underground Space Technoloty, 1996(3):287-303.
[26]Wickham. Handbook on Mechanical Properties of Rocks. Publication Trans Tech., 1978.
[27]Bienlawski Z.T. Engineering Classification of Jointed Mass, Trans.S.Africa Inst, Civ. Engrs 1973,15(12).
[28]工程岩体分级标准(GB50218-94).中国计划出版社,1995.
[29]水利水电工程地质勘察规范(GB50287-99).中国计划出版社,1999.
[30]高谦、任天贵、明士祥.采场巷道围岩分类的概率统计分析方法及其应用[J].煤炭学报,1994,19(2):131-139.
[31]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 (3):62-66.
[32]杜时贵,李军,徐良明等.岩体质量的分形表述.地质科技情报[J],1997(1):91-96.
[33]M.N.Bagde,A.K.Raina,A.K.Chakraborty,etal·Rock mass characterization by fractal dimension[J],Engineering Geology,2002(63):141-155.
[34]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(6):25-29.
[35]陈志坚,朱代洪,张雄文.围岩质量综合评判模型和大坝建基面优选模型的建立[J].河海大学学报,2002(4):88-91.
[36]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(1):18-22.
[37]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(3):220-224.
[38]孙恭尧,黄卓星,夏宏良.坝基岩体分级专家系统在龙滩工程中的应用[J].红水河,2002(3):6-11.
[39]章杨松.岩石质量指标的计算机模拟及其风险分析[J].地质灾害与环境保护,2002(1):44-47.
[40]章杨松,罗国煌,李晓昭等.岩体质量分级的风险分析方法[J].工程地质学报,2002(3):331-336.
[41]王环玲,晏鄂川,余宏明.运用结构面模拟技术分析岩体质量特征[J].地质灾害与环境保护,2002(3):64-68.
[42]马淑芝,贾洪彪,唐辉明等.利用“岩体裂隙率”评价工程岩体的质量[J].水文地质工程地质,2002(1):10-12.
[43]田敬学,张庆贺,姜福兴.煤矿巷道围岩稳定性动态工程分类技术研究与应用[J].岩土力学,2001(23)1:29-32.
[44]徐凤毅.阜新矿区回采巷道围岩稳定性分类[J].矿山压力与顶板管理,1994,No.1:29-32.
[45]华东水利学院.岩石力学[M].水力电力出版社,1981.
[46]B.H.G.Brady, E.T.Brown. Rock mechanics for under ground mining. George Allen & Unwin, 1985.
[47]孙钧,候学渊.地下结构(上册).科学出版社,1987.
[48]蔡美峰主编.岩石力学与工程.科学出版社,2002.
[49]候学渊.地下圆形结构弹塑性理论.同济大学学报,1982(4):50-62.
[50]重庆建筑工程学院,同济大学.岩石力学.中国建筑工业出版社,1981.
[51]王龙甫.弹性理论(第二版).科学出版社,1984.
[52]陆明万,罗学富.弹性理论基础.清华大学出版社,施普林格出版社,2001.
[53]尹祥础.固体力学.地震出版社,1985.
[54]陈子荫.围岩力学分析中的解析方法.煤炭工业出版社,1994.
[55]吕爱钟,蒋斌松.岩石力学反问题.煤炭工业出版社,1998.
[56]王桂芳.无衬砌隧道围岩应力的计算[J].土木工程学报,1965(2):30-42.
[57]刘金高,王润富.马蹄形孔口和梯形孔口的应力集中问题[J].岩土工程学报,1995(5):57-64.
[58]钱伯勤.单孔无限域应力函数的通式[J].江苏力学,1990(6):66-67.
[59]王润富.弹性力学的复变函数计算机解[J].河海大学学报,1991(2):84-86.
[60]王润富.一种保角映射法及其微机实现[J].河海大学学报,1991(1):86-90.
[61]范广勤,汤澄波.应用三个绝对收敛级数相乘法解非圆形洞室的外域映射函数[J].岩石力学与工程学报,1993(3):255-264.
[62]吕爱钟.应用最优化技术求解任意截面形状巷道映射函数的新方法[J].岩石力学与工程学报,1995(3):269-274.
[63]朱大勇,钱七虎,周早生等.复杂形状洞室围岩应力的弹性解析分析[J].岩石力学与工程学报,1998(4):402-404.
[64]朱大勇,钱七虎,周早生等.复杂形状洞室映射函数的新解法[J].岩石力学与工程学报,1999(3):279-282.
[65]朱大勇,钱七虎,周早生等.复杂形状洞室围岩应力弹性解析分析[J].岩石力学与工程学报,1999(4):402-404.
[66]朱维申、何满潮著,复杂条件下围岩稳定性与岩体动态施工力学,科学出版社,1996.
[67]朱维申,李术才,陈卫忠.节理岩体破坏机理和锚固效应及工程应用.科学出版社,2002
[68]杨淑清.地下洞室围岩开挖稳定性分析.武汉水利电力学院学报,1986(2):73-82.
[69]陈霞龄,韩伯鲤,梁克读.地下洞群围岩稳定的试验研究.武汉水利电力大学学报,1994(1):17-23.
[70]赵震英,叶勇.复杂地质条件下地下洞室围岩应力及变形模拟试验研究.岩石力学与工程学报,1989(4):298-305.
[71]孙钧.岩土材料流变及其工程应用[M].北京:中国建筑工业出版社,1999.
[72]Owen D R J, Hinton E. Finite Elements in Plasticity; Theory and Practice [M]. Swansea; Pine ridge Press Limited, 1980.
[73]余卫平,汪小刚等.地下洞室群围岩稳定性分析及其结果的可视化[J].岩石力学与工程学报,2005,24(20):3030-3736.
[74]王春生,周翠英.梅河高速公路隧道稳定性数值模拟[J].中山大学学报(自然科学版),2005,44(1):38-40.
[75]胡元芳.小线间距城市双线隧道围岩稳定性分析[J].岩石力学与工程学报,2002年,21(9):1335-1338.
[76] P.Kumar. Infinite Elements for Numerical Analysis of Underground Excavations [J]. Tunnelling and Underground Technology, 2000, 15(1):117-124.
[77] Hart R, Cundall P A, Lcmos J.Formulations of three-dimensional distinct element model. PARTII: Mechanical calculation of a system composed of many polyhedral blocks. International Journal of Rock Mechanics and Mining Sciences, 1988(3).
[78]〔美〕石根华著,裴觉民译.数值流形方法与非连续性形变分析方法.清华大学出版社,1997.
[79]张清,人工智能在岩石力学与岩石工程中的应用[J].岩石力学与工程学报,1986,Vol.5,No.4:389-395.
[80]冯夏庭,智能岩石力学导论[M].科学出版社,2000.
[81]冯夏庭、林韵梅,岩石力学与工程专家系统研究新进展[J].岩石力学与工程学报,1995,Vol.14,No.1:85-91.
[82]冯夏庭,马平波.基于数据挖掘的地下硐室围岩稳定性判别[J].岩石力学与工程学报,2001,Vol.20,No.3:306-309.
[83]冯夏庭,王泳嘉等.地下工程力学综合集成智能分析的理论和方法[J].岩土工程学报,1997,Vol.19,No.1:30-36.
[84]胡建华等,地下工程围岩稳定性的MBP神经网络识别[J].岩土工程界,2001, Vol.4,No.12:63-64.
[85]丁向东等,岩爆分类的人工神经网络预测方法[J].河海人学学报(自然科学版),2003,Vol.31,No.4:424-427.
[86]朱宝龙等,基于人工神经网络的岩爆预测方法[J].地质灾害与环境保护,2002,Vol.13,No.3:56-59.
[87]白明洲等,岩爆危险性预测的神经网络模型及应用研究[J].中国安全科学学报,2002,Vol.12,No.4:65-69.
[88]何正,李晓红等,BP神经网络模型在深埋隧道岩爆预测中的应用[J].地下空间与工程学报,2008,Vol.4,No.3:494-498.
[89]Wang Xinfei,Li Xiaohong.Application of BP Neural Network into Prediction of Rockburst inTunneling[C].Progress in Safety Science and Technology,Vol.IV,Shanghai,2004,Beijing New York:Science Press,2004,617–621.
[90]冯夏庭,王泳嘉等.地下工程力学综合集成智能分析的理论和方法[J].岩土工程学报,1997,Vol.19,No.1:30-36.
[91]周建春,魏琴,刘光栋.采用BP神经网络反演隧道围岩力学参数[J].岩石力学与工程学报,2004,23(6):941-945.
[92]郝哲,刘斌.基于差分法及神经网络的硐室围岩力学参数反分析[J].岩土力学,2003, Vol.24增刊:77-80.
[93]周保生,林江.人工神经网络在煤矿巷道围岩移近量预测中的应用[J].系统工程理论与实践,2000,1:136-139.
[94]周保生,朱维申.巷道围岩参数的人工神经网络预测[J].岩土力学, 1999,Vol.20 No.1:22-26.
[95]高玮,郑颖人.巷道围岩松动圈预测的进化神经网络法[J].岩石力学与工程学报,2002,21(5):658~661.
[96]张玉样.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, Vol.20,No.3:90-93.
[97]邱道宏,陈剑平,阙金声,安鹏程.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(1):86-91.
[98]祁长青,许人平.基于遗传算法的隧道围岩变形稳定可靠性分析[J].工程地质学报,2008,16(2):258-262.
[99]孙洁,刘晓悦.基于蚁群优化神经网络的巷道围岩稳定性预测[J]. 2008,Vol.25,No.5:117-119.
[100]赖永标,乔春生.支持向量机在围岩稳定性分类中的应用[J].水利学报, 2006第37卷,第9期:1092-1096.
[101]肖云华,王清等.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,Vol.36, No.6:49-53.
[102]Mandbrot B B. The fractal geometry of nature. New York: W H Freeman, 1982.
[103]张济忠.分形[M].北京:清华大学出版社,1995.
[104] Saouma V E, Barton C C, Gamaleldin N A. Fractal characterization of fracture surfaces in concrete. Eng Fract Mech, 1990, 35(13): 47-53.
[105] Lange D A, Jennings H M, Shah S P. Relationship between fracture surface roughness and fracture behavior of cement paste and mortar. Journal of the American Cerwnic Society, 1993, 76(3): 589- 597.
[106] Issa M A, Hammad A M. Assessment and evaluation of fractal dimension of concrete fracture surface digitized in ages. Cement and Concrete Research, 1994, 24(2): 325-334.
[107] Stroeven P Stereological estimation of fractal number of fracture planes in concrete. Mater Res Soc Symp Proc, 1996, 407: 343-348.
[108] Xie H, Pariseau W G Fractal estimation of joint roughness coefficient. In: Fractured andJoint Rock Masses, Califonua: 1992, 132-139.
[109] Tse R. ,Cruden D M. Estimating joint roughness coefficients. In: Sci. Geomen Abstr Int. J Rock Mech 1979, 303-307.
[110] Wakabayashi Naruki, Ikuo Fukushige. Experimental study on the relation between fractal dimension and shear strengthen. In: Fractured and Joint Rock Masses, California: 1992, 132-139.
[111]徐永福,吴正根.断层碎屑分布的分维及其地质意义.河海大学学报,1996 24 (3): 1-4.
[112] Amitava, Ghosh, et al. Fractal characteristics of rock discontinuities. Engineering. Geology, 1993, 34:1-9.
[113] Point P R L. A method to characterize fracture density and connectivity through fractal geometry. International Journal Rock Mechanics Mining Sciences & Geomechanics Abstract, 1988, 25(6): 421-429.
[114]陈乃明等.岩体节理网络分形的不均匀性研究.见:中国青年学者岩土工程力学及其应用讨论会论文集,北京:科学出版社,1994: 100-103.
[115] Poulton, et al. Scale invariant behaviour of massive and fragmented rock. International Journal Rock Mechanics Mining Sciences & Geomechanics Abstract, 1990, 27(3): 219-221.
[116]谢和平,Sanderson D J.断层分形分布之间的相关关系.煤炭学报,1994, 19 (5): 445-449.
[117] Lee Y J, et al. The fractal dimension as a measure of the roughness of rock discontinuity profiles. International Journal Rock Mechanics Mining Sciences & Geomechanics Abstract, 1990, 27(6): 453-464.
[118]秦四清等.非线性工程地质学导引.成都:西南交通大学出版社,1993.
[119] Turk, et al. Roughness angle determination and joint closure. In: Proc. Int. Symp. on Fundamentals of Rock Jointed, Sweden: 1985, 197-204.
[120] Mandelbrot B B, Passoja D E, Paullay A J. Fractal character of fracture surfaces of metals. Nature, 1984, 308(19): 721-722.
[121]谢和平,陈至达.分形几何与岩石断裂.力学学报,1988,20(3): 264-271.
[122] Borodich F M. Fracture energy in a fractal crack propagating in concrete or rock. Trans Dokl USSR Acad Sci Earth Sci Sec, 1994, 327(8): 36-40.
[123] Borodich F M. Some fractal models of fracture. Journal of the Mechanics&Physics of Solids, 1997,45(2): 239-259.
[124] Xie H. Studies on fractal models of the microfracture of marble. Chinese Science Bulletin, 1989,34(15):1292-1296.
[125]卢波,陈剑平,葛修润.王水林节理岩体结构的分形几何研究[J].岩石力学与工程学报,2005,24(3):461-467.
[126]陈剑平,王清,谷宪民,范建华.岩体节理产状极点分布的分形维[J].岩石力学与工程学报,2007,26(3):501-508.
[127]谢和平,鞠杨.分数维空间中的损伤力学研究初探.力学学报,1999. (3): 300-310.
[128]谢和平,高峰.岩石类材料损伤演化的分形特征.岩石力学与工程学报,1991. 10 (1): 74-82.
[129]苏永华,孙晓明,赵明华.隧道围岩超挖的分形特征研究[J].中国矿业大学学报. 2006,Vol.35 No.1:89-93.
[130]孙少锐,吴继敏,魏继红.隧洞超挖问题中的广义分数维研究[J].岩石力学与工程学报, 2005,Vol.24,No.24:4478-4483.
[131]朱治军,李后强.小波分析在分形科学中的应用[J].应用基础与工程科学学报,1994,Vol.2, No.4: 275-281.
[132]李定龙,周治安小波分析在地质分形科学中的应用前景[J].世界地质,1997, 16(2):49-54.
[133]冉启文,谭立英,等.小波分析与分数傅立叶变换及其应用[M].北京:国防工业出版社,2002.
[134]刘贵忠等.小波分析及应用.西安电子科技大学出版社,1995.
[135]秦前清等.实用小波分析.西安电子科技大学出版社,1994.
[136] Mallat S. Multiresolution representations and wavelets.[Ph. D. Thesis). Philadelphia: University of Pennsylvania, 1988.
[137]李建华,李万社.小波理论发展及其应用(综述)[J].2006,Vol.22 No.2:27-31.
[138]邓东皋,彭立中.小波分析[J].数学进展, 1991, (03).
[139] (美)崔锦泰著,程正兴译.小波分析导论[M],西安交大出版社,1995.
[140]Mallat S.Theory for multi-resolution signal decomposition: The wavelet representation[J]. IEEE Trnasactionson Pattern Analysi and Machine Intelligence, 1989,11(7):674~693.
[141]韩震宇,申旭娟,石章林.信号的多分辨分析及其在消噪中的应用[J].四川联合大学学报,1999,3(1):52-58.
[142] Ma11atS, H Wang W L. Singularity detection and processing with wavelets [J]. IEEE Transaction on Information Theory, 1992, 38 (2):617-643.
[143]彭玉华,汪文秉.小波用于估测散射波波达时间及去噪[J].电子学报,1996,24(4):113-116.
[144]杜芳,卢文胜,曹文清.振动台试验测试信号去噪的小波变换方法[J].振动与冲击,1999,18(2):26-29.
[145]刘曙光,殷明.子波消噪技术及其应用[J].西北纺织工学院学报,1999,13(l):16~20.
[146]Donoho DL, Johnstone IM. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika,1994,81(3):425-455.
[147]Donoho DL. De-noising by soft thresholding[J]. IEEE Transcations on Information Theory, 1995, 41(3):613-627.
[148] Donoho DL, Johnstone IM. Adaptation to unknown Smoothness via wavelet shrinkage[J].Journal of the American Statistical Association, 1995, 90(12):1200-1224.
[149] Collin, R, and Warnant, R. application of the wavelet transform for GPS cycle slip detection and comparison with Kalman filter, Manuscripta Geodaetica 20: 161-172.
[150]黄丁发,卓健成. GPS相位观测值周跳检测的小波分析法[J].测绘学报,1997,26(4): 352-357.
[151]文鸿雁.小波多分辨分析在变形分析中的应用[J].地壳形变与地震, 2000, 20 (3 ): 27-32.
[152]文鸿雁,张正禄.非线性小波变换闽值法去噪改进[J].测绘通报,2006, (03):18-21
[153]黄声享,刘经南.GPS变形监测系统中消除噪声的一种有效方法[J].测绘学报,2002, 31(2):104-107.
[154]田胜利,周拥军,葛修润,卢允德.基于小波分解的建筑物变形监测数据处理[J].岩石力学与工程学报,2004,23(15): 2639-2642.
[155]田胜利,徐东强,葛修润.大坝水平位移监测数据的小波变换去噪处理[J].水电自动化与大坝监测,2004,28(1):49-53.
[156]聂文滨,阙沛文.桶基平台贯沉深度测量中的小波去噪方法[J].测控技术,2002,21(7):51-59.
[157]徐蕾.小波去噪算法研究及在电力系统中的应用[J].电力系统通信,2007,28(5):54-57.
[158]李英,张淑贞,许康生.小波降噪方法在地震信号处理中的应用[J].西北地震学报,2006,28(2):159-162.
[159]王坚,高井祥.沉降数据序列分析方法研究[J].测绘学院学报,2004,21(2):90-92.
[160]朱习军,戴月明,郭守军.基于小波分解的GPS监测数据消噪处理[J].山东科技大学学报,2005,24(2):17-19.
[161]张鹏,李献勇,陈剑平.基于小波降噪的隧道围岩监测数据分析[J].吉林大学学报(地球科学版).2008, 38 (6): 1010-1014.
[162] Zhang Qinghua, BenvenisteA. Wavelet Networks. IEEETrans. on Neural networks. 1992, 3 (6):859-898.
[163] BAKSHI R.B, STEPHANOPOULOS G. Wavenet: A Multiresolusion,Hierarchical Neural Network with Localized Learning. AichE Journal, 1993, 39 (1): 57-61.
[164] Zhang J, Walter GG,Miao YB, et al. Wavelet Neural Networks for Function Learning. IEEE Trams on Slgnal Processing, 1995, 43 (6):1455-1497.
[165]钱峻,邵惠鹤一种小波神经网络的在线建模和校正算法.模式识别与人工智能.2001,13(l):16~20.
[166]丁宇新,沈学勤等.基于能量密度的小波神经网络.计算机学报1997,20(9)283~838.
[167]Zhang Qinghua. Using Wavelet Neuaral In non-paramation estimation .IEEE.Trams on Nearal Networks. 1997, 8 (2):227-236.
[168] Jiao LC, Pant, Fang Y W. Multiwavelet Neural Networks and Its Approximation Propertise. IEEE Trans on Neural Network. 2001, 12 (5):1060-1066.
[169] Yiwen Yang .Stock Market Trend Prediction Based on Neural Network,Multiresolution Analysis Reconstruction. In:Proceedings of the IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, New York, USA,2000, 8 (12):155-157.
[170] J R Hull, et al. A Neural Network Algorithm using wavelets and Auto Regressive Inputs for System Identification of the 1997. IEEE Imernational Conference on Networks, 1997, 2 (4):724-727.
[171]潘国荣.变形监测数据的小波神经网络预测方法[J].大地测量与地球动力学, 2007, Vol.27, No. 4:47-50.
[172]谭云亮,孙中辉,杜学东.冲击地压AE时间序列小波神经网络预测模型[J].岩石力学与工程学报, 2000,19(增): 1034~1036.
[173]孙少锐,吴继敏,魏继红.基于地质统计模型的小波神经网络在地下洞室超挖预测中的应用[J].岩石力学与工程学报, 2003,22(8):1344-1349.
[174]张玉祥.小波神经网络遗传算法及其在矿山压力预报中的应[J].中国有色金属学报,1999, Vol.9, No.2:448-452.
[175]Arneodo A, Grasseau G, Holschneider M. Wavelet transform of multifractals. Physical Review Letter, 1988,61(14):2281-2284.
[176]李炳成,分形参数估计的子波函数方法,通信学报[J],1993, 14 (4): 95-98.
[177]朱治军,李后强,非线性科学的理论、方法和应用[M],北京:科学出版社,1997.
[178]马波,裘正定,小波变换的分形特性[J].铁道学报,1998, 20 (6): 74-80.
[179]郑继明.分形和小波变换在油气预测中的应用[J].预测,1996,6:51-53.
[180]罗建书,沙基昌,黄建华.分形信号的小波分析[J].工程图学学报,1999,2:27-34.
[181]朱治军,李后强.小波分析在分形科学中的应用[J].应用基础与工程科学学报,1994,2(4):275-281.
[182]王安良,杨春信.评价机械加工表面形貌的小波变换方法[J].机械工程学报,2001, Vol.37, No.8:66-70.
[183]王安良,杨春信.机械加工表面形貌分形特征的计算方法[J].中国机械工程, 2002, 13 (8): 714-718.
[184]王安良,杨春信.小波变换方法评价曲线的分形特征[J].机械工程学报,2002,Vo1.38,No.5:80-85.
[185]杨大勇,刘莹.基于小波变换的分形曲线维数计算方法的研究[J].润滑与密封,2007,Vol.32, No.1:40-43.
[186]孙晓丽,宋国乡.基于小波变换的IC缺陷轮廓曲线分形维估计[J].电子技术,2006,1:126-128.
[187]王文圣,向红莲,赵东水文序列分形维数估计的小波方法[J].四川大学学报(工程科学版),2005,Vol.37 No.1:1-4.
[188]侯建荣,黄培清,骆建文.多分维分形曲线维数计算的小波方法[J].上海交通大学学报,2004,38(增):18-21.
[189]杜恩祥,李科杰.基于多重分形和小波变换的声目标信号特征提取[J].自动化学报,2004,30(5):742-746.
[190]王文圣,向红莲,黄伟军.基于连续小波变换的径流分维研究[J].水利学报,2005,36(5):598-601.
[191]王文圣,赵太想,丁晶.基于连续小波变换的水文序列变化特征研究[J].四川大学学报(工程科学版),2004,36(4):6-9.
[192]赵春晖,马梅真,尚政国.基于提升小波变换和分形维数的声纳图像识别[J].声学技术,2007,26(5):811-816.
[193]韩琦,康戈文.基于小波变换的带钢表面缺陷分形特征研究[J].自动化技术与应用,2006,25(3):4-6.
[194]田杰,陈杰,张宇河.基于小波变换及分形特征的目标检测与识别[J].北京理工大学学报,2003,23(1):95-99.
[195]周卫滨.苍岭隧道岩爆预测和防治研究[D].杭州:浙江大学,2005.
[196]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.
[197]蒋树屏,赵阳.复杂地质条件下公路隧道围岩监控量测与非确定性反分析研究[J].岩石力学与工程学报,2004,23(20):3460-3464.
[198] Karmen F B,Borut P. Displacement analysis of tunnel support in soft rock around a shallow highway tunnel at Golovec[J]. Engineering Geology,2004,v75:89–106.
[199]袁勇,王胜辉,杜国平,等.双连拱隧道支护体系现场监测试验研究[J].岩石力学与工程学报,2005,24(3):480-484.
[200]崔天麟.超浅埋暗挖隧道初期支护结构内力监测及稳定性分析[J].现代隧道技术,2001,38(2):29-34.
[201]刘庆仁.三车道公路隧道监测分析研究[J].北京建筑工程学院学报,2002,18(3):34-39.
[202]李建平,唐远炎.小波分析方法的应用[M].重庆:重庆大学出版社,1999.
[203]文鸿雁.基于小波理论的变形分析模型研[D].武汉大学博士学位论文, 2004.
[204]董永生,羿旭明.基于四种改进阈值的小波去噪方法[J].数学杂志,2006,26(5):473-477.
[205]付燕.改进的小波域阈值去噪方法[J]西安科技大学学报.2004.24(4):467-469.
[206] HUANG N E, SHEN A, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[ J ].Proc R Soc Lond: A, 1998, 454: 899-995.
[207] HUANG N E, LONG S R, SHEN Z. A new view of the nonlinear water waves;the Hilbert spectrum(J].Annu Rev Fluid Mech, 1999, 31:417-457.
[208] FLANDRIN G, RILLING G, GONCALVES P. Empirical mode decomposition as a filter bank[J].IEEE Signal Processing Letters, 2004, 11( 2 ):112-114.
[209]杜修力,何立志,侯伟.基于经验模态分解(EMD)的小波闭值除噪方法[J].北京工业大学学报, 2007, Vol.33,No.3: 265-272.
[210]焦李成.神经网络的应用与实现,西安:西安电子科技大学出版社,1996.
[211]飞思科技产品研发中心著.神经网络理论与MARLAB7实现[M].北京:电子工业出版社,2005.
[212]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]吉林大学博士学位论文,2006.6.
[213]冯再勇.小波神经网络与BP网络的比较研究及应用[D].成都理工大学硕士学位论文,2007.
[214]刘志刚,王晓茹,钱清泉.小波网络的研究进展与应用[J].电力系统自动化,2003,vol.27, No.6:73-80.
[215]陈哲,冯天瑾.一种基于BP算法学习的小波神经网络[J].青岛海洋大学学报, 2001,31(1):122~128.
[216]李金屏,王风涛.BP小波神经网络快速学习算法[J].研究系统工程与电子技术,2001,Vol·23,No·8 :72-75.
[217]张文修,吴伟志,梁吉业,等.粗糙集理论与方法[M].北京:科学出版社, 2001: 10-24.
[218]宋笑雪.粗糙集理论及其应用[J].咸阳师范学院学报, 2005, 20(2): 29-31.
[219]赵会兵.钻爆法施工隧道的超欠挖概率统计规律研究.铁道工程学报.1999,16(3): 106-110.
[220]佘健,钟新樵.公路隧道超欠挖统计规律研究[J].重庆交通学院学报,2000,19(2):15-20.
[221]吴继敏,陈显春,李文奇金.丽温高速公路连拱隧道超挖预测及原因分析[J].水文地质与工程地质,2005,5:56-59.
[222]孙少锐,吴继敏,魏继红.隧洞超挖问题中的广义分数维研究[J].岩石力学与工程学报, 2005,24(24):4478-4483.
[223]苏永华,赵明华,姚爱军.地下坑道轮廓超挖的随机特征[J].湖南大学学报(自然科学版), 2005,32(3):56-60.
[224]苏永华,孙晓明,赵明华.隧道围岩超挖的分形特征研究[J].中国矿业大学学报, 2006,35(1):89-93.
[225]肖云华,王清,陈剑平.隧道围岩超欠挖与节理和洞轴线之间的关系[J].吉林大学学报(地球科学版), 2008,38(3):455-459.
[226]丁泰山,刘建民.超挖对地下洞室稳定性的影响分析[J].地下空间与工程学报, 2008,4(3):11-14.
[227] Revey G F. Effects and control of overbreak in underground mining. Mining Engineering, 1998,50(8):63-67.
[228] Franklin J A, Maerz N H, Ibarra J A. Overbreak and underbreak in underground openings part 2:causes and implications. Geotechnical and Geological Engineering, 1996, 14(4): 325-340.
[229] Maerz N H, Ibarra J A, Franklin J A. Overbreak and underbreak in underground openings part 1: measurement using the light section method and digital image processing. Geotechnical and Geological Engineering, 1996, 14(4): 307-323.
[230] Abel J F. Average percentage of overbreak beyond payment line, MN-461 course notes,Colorado School of Mines, Golden, CO, USA, 1982.
[231] Martna J. Selective overbreak in the Suorva-Vietas tunnel cased by rock pressure, Proceeding of the International Symposium on Underground Openings, Lucerne, Switzerland, 1972: 141-145.
[232]谢和平.岩石力学[M],北京:科学出版社,2004.