基于可拓学和支持向量机理论的砂土液化势综合评价研究
|
摘要
我国是世界上地震多发国家之一，全国有60％左右的地区处于高烈度区，而砂土液化是引起地震灾害的主要原因，因此砂土液化是地震工程中的重要问题。砂土液化的发生、发展是一个复杂的过程，其影响因素很多，随机性大，且各因素之间呈高度的非线性。砂土液化势评价是抗震防灾工作的重要环节，也是岩土工程勘察中的一项重要内容，所以客观、准确地评价砂土液化势，是对有可能发生液化的区域或地段进行有针对性的预防处理的前提，是关系到工程建筑的安全使用和工程建设的经济效益的重要问题。
本文在评析传统方法的基础上，将可拓学和支持向量机理论引入到砂土液化势评价工作中，为砂土液化势评价找到了新的途径。首先，阐述了基于可拓学的综合评价理论和实现方法，并在此基础上利用网络编程技术开发了基于Web平台的可拓综合评价系统，将其应用于砂土液化势的评价研究。在详细分析影响砂土液化因素的基础上，选取了震级M、地面最大加速度g_(max)、标准贯入击数N_(63.5)、比贯入阻力P_s、相对密实度D_r、平均粒径D_(50)、地下水位d_w等7个参数作为评价指标，建立了砂土液化势的可拓评价模型，并对历史数据进行仿真评价，取得了较好的应用效果；在对支持向量机的理论进行介绍后，本文建立了基于支持向量机的砂土液化势评价模型，并利用在基于支持向量机理论的Libsvm软件，对若干国内外场地的现场实测数据进行了评价，评价结果与现场表现基本上一致。研究表明，可拓评价方法和支持向量机对于砂土液化势进行评价是行之有效的。
China is among the countries where earthquakes take place most frequently in the world. 60 percent of the areas have high earthquake intensity, and sand liquefaction is the main reason that causes seismic disasters, so that the sand liquefaction is an important problem in earthquake engineering. Sand liquefaction comes through a complicate process, and it involves a great deal of influence factors, which have strong radomness and reciprocal nonlinearity. Assessment of sand liquefaction potential is the important link in the works of antiknock and disater-prevention, and is an important content in reconnaissance of geotechnical engineering, so evaluating sand liquefaction objuectively and exactly is the precondition of taking preventive measures on areas where liquefaction easily occurs. It also relates closely to the safety and economic benefits of the projects.
The paper analyses and assesses traditional methods, and brings forward the theory of
extenics and support vector machine to the assessment of sand liquefaction potential, so as to find out a new path in the field of assessment of sand liquefaction potential.
This paper has illustrated the essential theory of assessment method based on the extenics and realization technique of the method. According to that, a web assessment system based on the extenics theory has been developed with the web program technique, and this system was applied to the assessmento of sand liquefaction potential. Through detailed analyzing some influencing factiors of sand liquefaction, seven parameters were selected as assessment indexes. They are earthquake magnitude, peak ground surface acceleration, standard penetration value, specific penetration resistance, relative compaction, average particle diameter, and water table. The extension assessment model has been established that was applied to assessment of sand liquefaction potential. The model had been trained by the historical data and has been assessed in virtue of sand liquefaction from the historical data by simulating, and got a excellent result. After introduction of the support vector machine theory, this paper established the assessment model of sand liquefaction based on the support vector machine, and assessed in sand liquefaction from virtue data of some fields in home and overseas with the tool Libsvm, which was programmed based on the support vector machine theory. And got a good coherency between the assessement results and the actual liquefaction. The research expresses that the extension assessment method and the support vector machine are useful tool in the assessment and prediction of sand liquefaction potential.
本文在评析传统方法的基础上，将可拓学和支持向量机理论引入到砂土液化势评价工作中，为砂土液化势评价找到了新的途径。首先，阐述了基于可拓学的综合评价理论和实现方法，并在此基础上利用网络编程技术开发了基于Web平台的可拓综合评价系统，将其应用于砂土液化势的评价研究。在详细分析影响砂土液化因素的基础上，选取了震级M、地面最大加速度g_(max)、标准贯入击数N_(63.5)、比贯入阻力P_s、相对密实度D_r、平均粒径D_(50)、地下水位d_w等7个参数作为评价指标，建立了砂土液化势的可拓评价模型，并对历史数据进行仿真评价，取得了较好的应用效果；在对支持向量机的理论进行介绍后，本文建立了基于支持向量机的砂土液化势评价模型，并利用在基于支持向量机理论的Libsvm软件，对若干国内外场地的现场实测数据进行了评价，评价结果与现场表现基本上一致。研究表明，可拓评价方法和支持向量机对于砂土液化势进行评价是行之有效的。
China is among the countries where earthquakes take place most frequently in the world. 60 percent of the areas have high earthquake intensity, and sand liquefaction is the main reason that causes seismic disasters, so that the sand liquefaction is an important problem in earthquake engineering. Sand liquefaction comes through a complicate process, and it involves a great deal of influence factors, which have strong radomness and reciprocal nonlinearity. Assessment of sand liquefaction potential is the important link in the works of antiknock and disater-prevention, and is an important content in reconnaissance of geotechnical engineering, so evaluating sand liquefaction objuectively and exactly is the precondition of taking preventive measures on areas where liquefaction easily occurs. It also relates closely to the safety and economic benefits of the projects.
The paper analyses and assesses traditional methods, and brings forward the theory of
extenics and support vector machine to the assessment of sand liquefaction potential, so as to find out a new path in the field of assessment of sand liquefaction potential.
This paper has illustrated the essential theory of assessment method based on the extenics and realization technique of the method. According to that, a web assessment system based on the extenics theory has been developed with the web program technique, and this system was applied to the assessmento of sand liquefaction potential. Through detailed analyzing some influencing factiors of sand liquefaction, seven parameters were selected as assessment indexes. They are earthquake magnitude, peak ground surface acceleration, standard penetration value, specific penetration resistance, relative compaction, average particle diameter, and water table. The extension assessment model has been established that was applied to assessment of sand liquefaction potential. The model had been trained by the historical data and has been assessed in virtue of sand liquefaction from the historical data by simulating, and got a excellent result. After introduction of the support vector machine theory, this paper established the assessment model of sand liquefaction based on the support vector machine, and assessed in sand liquefaction from virtue data of some fields in home and overseas with the tool Libsvm, which was programmed based on the support vector machine theory. And got a good coherency between the assessement results and the actual liquefaction. The research expresses that the extension assessment method and the support vector machine are useful tool in the assessment and prediction of sand liquefaction potential.
引文
[1] 刘颖，谢君斐．砂土震动液化[M]．北京：地震岀版社，1984．
[2] Liu, Andrew H, Stewart, Jonathan P, Abrahamson, Norman A, et al. Equivalent Number of Uniform Stress Cycles for Soil Liquefaction Analysis[J]. Journal of geotechnical and geoenvironmental engineering, 2001,127(12):1017.
[3] H.B. Seed. Soil liquefaction and cyclic mobility evaluation for level ground during earthquakes[J]. Journal of Geotechnical Engineering, 1979,2(105).
[4] Seed H B, Idriss I M. Simplified procedure for evaluating soil liquefaction potential[J]. Geotech Engrg Div, 1971,97(9):1279-1273.
[5] Peacock W.H,H.B. Seed. Sand liquefaction under cyclic loading simple shear conditions[J]. Journal of Soil Mechinical, 1979,3(105).
[6] Anthony T.C. Gol. Seismic liquefaction potential assessed by neural network[J]. Journal of Geotechnical Engineering, 1994,9(120).
[7] Juang, C Hsein, Rosowsky, David V, Tang, Wilson H. Reliability-Based Method for Assessing Liquefaction Potential of Soils[J]. Journal of geotechnical and geoenvironmental engineering, 1999,125(8):684.
[8] Juang, C H, Chen, C J, Tang, W H, et al. CPT-based liquefaction analysis, Part 1: Determination of limit state function[J]. Geotechnique, 2000,50(5):583.
[9] Juang, C H, Chen, C J, Rosowsky, D V, et al. CPT-based liquefaction analysis, Part 2: Reliability for design[J]. Geotechnique, 2000,50(5):593.
[10] Tokimatsu K,Yoshimi Y. Empirical correlation of soil liquefaction based on SPT N-value and-fines content[J]. Soils Found, 1983,23(4):56-74.
[11] 汪闻韶．土工抗震研究进展[J]．岩土工程学报，1993(6)：80-82．
[12] 王余庆，栾芳，韩清宇等．预测轻亚粘土液化势的统计公式[J]．岩土工程学报，1980(3)：103-112．
[13] 顾宝和，张荣祥，石兆吉．地震液化效应的综合评价[J]．工程地质学报，1995，1995(3)：1-10．
[14] 王钟琦．地震液化宏观研究[J]．岩土工程学报，1982，4(3)：1-10．
[15] 方鸿琪．地震液化宏观机制及震害效应[J]．工程勘察，1980(1)：1-6．
[16] 汪明武，罗国煜．最优化法在砂土液化势评价中的应用[J]．岩土工程学报，1999，21(6)：704-706．
[17] 汪明武，李丽，张拥军．岩土工程可视化及砂土液化评价可视化的探讨[J]．合肥工业大学学报 (自然科学版)，2003，26(1)：63-66．
[18] 汪明武，李丽，罗国煜．基于Monte Carlo模拟的砂土液化评估研究[J]．工程地质学报，2001，9(2)：214-217．
[19] 汪明武，李丽，章杨松等．混合遗传算法在砂土液化势评价中的应用[J]．2002，25(4)：505—509．
[20] 汪明武，罗国煜．可靠性分析在砂土液化势评价中的应用[J]．岩土工程学报，2000，22(5)：542-544．
[21] 朱宝龙，夏玉成．基于matlab下bp网络在砂土液化评价中的应用[J]．华东地质学院学报，2001，24(1)：30-33．
[22] 罗战友，龚晓南．基于经验的砂土液化灰色关联系统分析与评价[J]．工业建筑，2002，32(11)：36-39．
[23] 季倩倩．砂土地震液化的模糊优化判别[J]．岩土工程技术，2001(3)：155-158．
[24] 张小敏．模糊模式识别模型在砂土液化势评价中的应用[J]．西部探矿工程，2003(9)：17-20．
[25] 蔡煜东，宫家文等．砂土液化预测的人工神经网络模型[J]．岩土工程学报，1993(6)：53—58．
[26] 刘勇健，刘义健．人工神经网络在砂土液化评价中的应用[J]．勘察科学技术，1999(1)：3-5．
[27] 陈奎，刘兴昌．BP神经网络在砂土震动液化评价中的应用[J]．西安工程学院学报，2000．22(1)：46-49．
[28] 徐洪钟，吴浩，李．基于人工神经网络的砂土液化势评价[J]．水文地质工程地质，1998(1)：16-18．
[29] 阮永芬，陈昆梅，韦敏才．粉土地震液化等级评判的人工神经网络技术应用[J]．昆明理工大学学报，2000，25(4)：63-65．
[30] 中国建筑科学研究院．建筑抗震设计规范(GB50011-2001)[M]．北京：中国建筑工业出版社，2001．
[31] 蔡文．物元模型及其应用[M]．北京：科学技术文献出版社，1994．
[32] 蔡文．物元分析[M]．广州：广东高等教育出版社，1987．
[33] 蔡文，杨春燕，林伟初．可拓工程方法[M]．北京：科学出版社，1997．
[34] 张学工．关于统计学习理论与支持向量机[J]．自动化学报，2000，26(1)：32-42．
[35] 赵洪波．非线性岩土力学行为的支持向量机研究[D]．中国科学院武汉岩土力学研究所：2003．
[36] Bhasia, Manoj, Reinherz, Ellis L, Reche, Pedro A. Recognition and Classification of Histones Using Support Vector Machine[J]. Journal of Computational Biology, 2006,13(1):102-112.
[37] Kriegl, Jan, Arnhold, Thomas, Beck, Bernd, et al. A support vector machine approach to classify human cytochrome P450 3A4 inhibitors[J]. Journal of Computer-Aided Molecular Design, 2005,19(3):189-201.
[38] Mangasarian, O. L. Support vector machine classification via parameterless robust linear programming[J]. Optimization Methods and Software, 2005, 20(1) :115-125.
[39] Soares, Carlos, Brazdil, Pavel B, Kuba, Petr. A Meta-Learning Method to Select the Kernel Width in Support Vector Regression[J]. Machine Learning, 2004, 54(3):195-209.
[40] Zhang, Shao-Wu, Pan, Quan, Zhang, Hong-Cai, et al. Classification of protein quaternary structure with support vector machine[J]. Bioinformatics, 2003, 19(18):2390-2396.
[41] Liang, Faming. An Effective Bayesian Neural Network Classifier with a Comparison Study to Support Vector Machine[J]. Neural Computation, 2003, 15(8):1959-1989.
[42] Decoste, Dennis, Schkopf, Bernhard. Training Invariant Support Vector Machines[J]. Machine Learning, 2002, 46 (1-3) : 161-190.
[43] Hua, Sujun, Sun, Zhirong. Support vector machine approach for protein subcellular localization prediction[J]. Bioinformatics, 2001, 17 (8):721.
[44] Umehara, Shuichi, Yamazaki, Teru, Sugai, Yasuo. A precipitation estimation system based on support vector machine and neural network[J]. Electronics and communications in Japan. Part, 2006, 3(89) :38.
[45] Cui, Dapeng, Curry, David. Prediction in Marketing Using the Support Vector Machine[J]. Marketing science : the marketing journal of TIMS/ORSA, 2005, 24(4):595.
[46] 蒋淳，田山，陈化然等．地震综合预测物元模型及其应用[J]。地震学报，2000，22(4)：418-423．
[47] 刘立民，刘汉龙，陈爱道等．建筑物塌陷损坏评价的物元模型及其应用[J]．防灾减灾工程学报，2003，23(2)：94-98．
[48] 王锦国，周志芳，袁永生．可拓评价方法在环境质量综合评价中的应用[J]．河海大学学报，2002，30(1)：15-17．
[49] 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．
[50] 袁金丽，吉海彦，郭志涛．可拓工程方法在储粮害虫分类识别中的应用研究[J]．农业工程学报，2004，20(5)：170-172．
[51] 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．
[52] 陶华，蔡国梁，汤凯．市政道路工程质量综合评价的可拓方法[J]．扬州大学学报(自然科学版)，2005，8(1)：69-73．
[53] 李祚泳．台风年频次分类预测的物元模型[J]．系统工程理论与实践，1998，18(1)：131-134．
[54] 姚令侃．可拓学在泥石流预报减灾决策上的应用[J]．系统工程理论与实践，1998，18(1)：139-144．
[55] 张甫仁．室内环境评价物元模型及可拓评价方法[J]．天津大学学报，2005，38(4)：307-312．
[56] 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．
[57] 杨春燕，蔡文．可拓集合的新定义[J]．广东工业大学学报，2001，18(1)：59-60．
[58] 石兆吉．砂土液化判别和评价综合方法的研究[J]．地震工程与工程振动，1997．17(1)：82-86．
[59] 陈国兴，胡庆兴，刘学珠．关于砂土液化判别的若干意见[J]．地震工程与工程振动，2002，22(1)：141-151．
[60] 刘金稻，金晓媚．饱和砂土液化判别方法中问题浅析[J]．工程地质学报，2000，8(3)：379-384．
[61] 顾晓鲁，钱鸿缙．地基与基础[M]．北京：中国建筑工业出版社，2003．
[62] 张晓晖，吴亚萍，尚伟宏等．利用模糊神经网络进行砂土液化势评判[J]．工程地质学报，2001，9(2)：209-213．
[63] 赵胜利，赵红英，刘燕．基于SOFM神经网络的砂土液化评价[J]．华中科技大学学报(城市科学版)，2005，22(2)：23-26．
[64] 董贤哲．基于补偿模糊神经网络的砂土液化势评价研究[D]．中国地质大学：2005．
[65] 刘恢先．唐山大地震震害(第一分册)[M]．北京：地震出版社，1985．
[66] Vladimir N, Vapnik. The Nature of Statistical Learning Theory[M]. New York:Springer-Verlag, 1995.
[67] Vapink V 著，张学工译．统计学习理论的本质[M]．北京：清华大学出版社，2000．
[68] 李国正，王猛，曾华军等译．支持向量机导论[M]．北京：电子工业出版社，2004．
[69] Harris Drucker, Donghui Wu, Vladimir N. Vapnik. Support Vector Machines for Spam Categorizatioin[J]. IEEE Trans. on Neturat Networks, 1999,10(5):1048-1054.
[70] Lothar Hermes, Dieter Frieauff, Jan Puzicha, et al. SVM for Land Usage Classification in Landsat TM Imagery[J]. Proc. IGARSS'99, 1999,1:348-350.
[71] Yonas B. Dibike, Slaveo Velickov, Dimitri Solomatine, et al. Model Induction with Support Vector Machines: Introduction and Applications[J]. Journal of computing in civil engineering, 2001,15(3):208.
[72] 朱辉，杨扬．SVM在小字符集手写体汉字识别中的研究[J]．微计算机信息，2004，20(4)：74-76．
[73] Osuna E, Freund R, Girosi F. Improved training algorithm for support vector machines[J]. 7th IEEE workshop on Neural Networks for Signal Processing, 1997:276-285.
[74] Bernd Heisele, Thomas Serre, Sam Prentice, et al. Hierarchical classification and feature reduction for fast detection with support vector machines[J]. Pattern Recognition, 2003,36:2007-2017.
[75] 张翔，田金文，肖小玲等．支持向量机及其在医学图像分类中的应用[J]．信号处理，2004，20(2)：208-212．
[76] 赵洪波，冯夏庭．非线性位移时间序列预测的进化支持向量机方法与应用[J]．岩土工程学报，2003，25(4)：468-471．
[77] 赵洪波，冯夏庭，李邵军等．福宁高速公路八尺门滑坡变形演化规律预测研究[J]．岩土力学，2003，24(4)：640-643．
[78] 冯夏庭，赵洪波．岩爆预测的支持向量机[J]．东北大学报(自然科学版)，2002，23(1)：57-59．
[79] 赵洪波，冯夏庭．支持向量机函数拟合的边坡稳定性估计[J]．岩石力学与工程学报，2003，24(4)：241-245．
[80] 赵洪波，冯夏庭，尹顺德．基于支持向量机的岩体工程分级[J]．岩土力学，2002，23(6)：698-701．
[81] Miguel Moreia, Eddy Mayoraz. Improved pairwise coupling classification with correcting classifiers[J]. Tenth Europen Conference Machine Learning(ECML'98) Chemnitz, 1998:21-24.
[82] 解可新，韩立兴，林友联．最优化方法[M]．天沣：天津大学出版社，1997．
[83] Chih-Wei Hsu, Chih-Chung Chang, Chih-Jen Lin. A Practical Guide to Support Vector Classification[EB/OL].
[84] piaip, piaip's Using (lib)SVM Tutorial[Eg/OL].
[85] Keerthi, S. Sathiya, Lin, Chih-Jen. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel[J]. Neural Computation, 2003,15(7):1667-1689.
[86] Lin, H.-T,C.-J. Lin. A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods[EB/OL].
[87] Goh, Anthony T.C. Seimic liquefaction potential assessed by neural networks[J]. Journal of Geotechnical Engineering, 120(9):1467-1480.
[2] Liu, Andrew H, Stewart, Jonathan P, Abrahamson, Norman A, et al. Equivalent Number of Uniform Stress Cycles for Soil Liquefaction Analysis[J]. Journal of geotechnical and geoenvironmental engineering, 2001,127(12):1017.
[3] H.B. Seed. Soil liquefaction and cyclic mobility evaluation for level ground during earthquakes[J]. Journal of Geotechnical Engineering, 1979,2(105).
[4] Seed H B, Idriss I M. Simplified procedure for evaluating soil liquefaction potential[J]. Geotech Engrg Div, 1971,97(9):1279-1273.
[5] Peacock W.H,H.B. Seed. Sand liquefaction under cyclic loading simple shear conditions[J]. Journal of Soil Mechinical, 1979,3(105).
[6] Anthony T.C. Gol. Seismic liquefaction potential assessed by neural network[J]. Journal of Geotechnical Engineering, 1994,9(120).
[7] Juang, C Hsein, Rosowsky, David V, Tang, Wilson H. Reliability-Based Method for Assessing Liquefaction Potential of Soils[J]. Journal of geotechnical and geoenvironmental engineering, 1999,125(8):684.
[8] Juang, C H, Chen, C J, Tang, W H, et al. CPT-based liquefaction analysis, Part 1: Determination of limit state function[J]. Geotechnique, 2000,50(5):583.
[9] Juang, C H, Chen, C J, Rosowsky, D V, et al. CPT-based liquefaction analysis, Part 2: Reliability for design[J]. Geotechnique, 2000,50(5):593.
[10] Tokimatsu K,Yoshimi Y. Empirical correlation of soil liquefaction based on SPT N-value and-fines content[J]. Soils Found, 1983,23(4):56-74.
[11] 汪闻韶．土工抗震研究进展[J]．岩土工程学报，1993(6)：80-82．
[12] 王余庆，栾芳，韩清宇等．预测轻亚粘土液化势的统计公式[J]．岩土工程学报，1980(3)：103-112．
[13] 顾宝和，张荣祥，石兆吉．地震液化效应的综合评价[J]．工程地质学报，1995，1995(3)：1-10．
[14] 王钟琦．地震液化宏观研究[J]．岩土工程学报，1982，4(3)：1-10．
[15] 方鸿琪．地震液化宏观机制及震害效应[J]．工程勘察，1980(1)：1-6．
[16] 汪明武，罗国煜．最优化法在砂土液化势评价中的应用[J]．岩土工程学报，1999，21(6)：704-706．
[17] 汪明武，李丽，张拥军．岩土工程可视化及砂土液化评价可视化的探讨[J]．合肥工业大学学报 (自然科学版)，2003，26(1)：63-66．
[18] 汪明武，李丽，罗国煜．基于Monte Carlo模拟的砂土液化评估研究[J]．工程地质学报，2001，9(2)：214-217．
[19] 汪明武，李丽，章杨松等．混合遗传算法在砂土液化势评价中的应用[J]．2002，25(4)：505—509．
[20] 汪明武，罗国煜．可靠性分析在砂土液化势评价中的应用[J]．岩土工程学报，2000，22(5)：542-544．
[21] 朱宝龙，夏玉成．基于matlab下bp网络在砂土液化评价中的应用[J]．华东地质学院学报，2001，24(1)：30-33．
[22] 罗战友，龚晓南．基于经验的砂土液化灰色关联系统分析与评价[J]．工业建筑，2002，32(11)：36-39．
[23] 季倩倩．砂土地震液化的模糊优化判别[J]．岩土工程技术，2001(3)：155-158．
[24] 张小敏．模糊模式识别模型在砂土液化势评价中的应用[J]．西部探矿工程，2003(9)：17-20．
[25] 蔡煜东，宫家文等．砂土液化预测的人工神经网络模型[J]．岩土工程学报，1993(6)：53—58．
[26] 刘勇健，刘义健．人工神经网络在砂土液化评价中的应用[J]．勘察科学技术，1999(1)：3-5．
[27] 陈奎，刘兴昌．BP神经网络在砂土震动液化评价中的应用[J]．西安工程学院学报，2000．22(1)：46-49．
[28] 徐洪钟，吴浩，李．基于人工神经网络的砂土液化势评价[J]．水文地质工程地质，1998(1)：16-18．
[29] 阮永芬，陈昆梅，韦敏才．粉土地震液化等级评判的人工神经网络技术应用[J]．昆明理工大学学报，2000，25(4)：63-65．
[30] 中国建筑科学研究院．建筑抗震设计规范(GB50011-2001)[M]．北京：中国建筑工业出版社，2001．
[31] 蔡文．物元模型及其应用[M]．北京：科学技术文献出版社，1994．
[32] 蔡文．物元分析[M]．广州：广东高等教育出版社，1987．
[33] 蔡文，杨春燕，林伟初．可拓工程方法[M]．北京：科学出版社，1997．
[34] 张学工．关于统计学习理论与支持向量机[J]．自动化学报，2000，26(1)：32-42．
[35] 赵洪波．非线性岩土力学行为的支持向量机研究[D]．中国科学院武汉岩土力学研究所：2003．
[36] Bhasia, Manoj, Reinherz, Ellis L, Reche, Pedro A. Recognition and Classification of Histones Using Support Vector Machine[J]. Journal of Computational Biology, 2006,13(1):102-112.
[37] Kriegl, Jan, Arnhold, Thomas, Beck, Bernd, et al. A support vector machine approach to classify human cytochrome P450 3A4 inhibitors[J]. Journal of Computer-Aided Molecular Design, 2005,19(3):189-201.
[38] Mangasarian, O. L. Support vector machine classification via parameterless robust linear programming[J]. Optimization Methods and Software, 2005, 20(1) :115-125.
[39] Soares, Carlos, Brazdil, Pavel B, Kuba, Petr. A Meta-Learning Method to Select the Kernel Width in Support Vector Regression[J]. Machine Learning, 2004, 54(3):195-209.
[40] Zhang, Shao-Wu, Pan, Quan, Zhang, Hong-Cai, et al. Classification of protein quaternary structure with support vector machine[J]. Bioinformatics, 2003, 19(18):2390-2396.
[41] Liang, Faming. An Effective Bayesian Neural Network Classifier with a Comparison Study to Support Vector Machine[J]. Neural Computation, 2003, 15(8):1959-1989.
[42] Decoste, Dennis, Schkopf, Bernhard. Training Invariant Support Vector Machines[J]. Machine Learning, 2002, 46 (1-3) : 161-190.
[43] Hua, Sujun, Sun, Zhirong. Support vector machine approach for protein subcellular localization prediction[J]. Bioinformatics, 2001, 17 (8):721.
[44] Umehara, Shuichi, Yamazaki, Teru, Sugai, Yasuo. A precipitation estimation system based on support vector machine and neural network[J]. Electronics and communications in Japan. Part, 2006, 3(89) :38.
[45] Cui, Dapeng, Curry, David. Prediction in Marketing Using the Support Vector Machine[J]. Marketing science : the marketing journal of TIMS/ORSA, 2005, 24(4):595.
[46] 蒋淳，田山，陈化然等．地震综合预测物元模型及其应用[J]。地震学报，2000，22(4)：418-423．
[47] 刘立民，刘汉龙，陈爱道等．建筑物塌陷损坏评价的物元模型及其应用[J]．防灾减灾工程学报，2003，23(2)：94-98．
[48] 王锦国，周志芳，袁永生．可拓评价方法在环境质量综合评价中的应用[J]．河海大学学报，2002，30(1)：15-17．
[49] 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．
[50] 袁金丽，吉海彦，郭志涛．可拓工程方法在储粮害虫分类识别中的应用研究[J]．农业工程学报，2004，20(5)：170-172．
[51] 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．
[52] 陶华，蔡国梁，汤凯．市政道路工程质量综合评价的可拓方法[J]．扬州大学学报(自然科学版)，2005，8(1)：69-73．
[53] 李祚泳．台风年频次分类预测的物元模型[J]．系统工程理论与实践，1998，18(1)：131-134．
[54] 姚令侃．可拓学在泥石流预报减灾决策上的应用[J]．系统工程理论与实践，1998，18(1)：139-144．
[55] 张甫仁．室内环境评价物元模型及可拓评价方法[J]．天津大学学报，2005，38(4)：307-312．
[56] 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．
[57] 杨春燕，蔡文．可拓集合的新定义[J]．广东工业大学学报，2001，18(1)：59-60．
[58] 石兆吉．砂土液化判别和评价综合方法的研究[J]．地震工程与工程振动，1997．17(1)：82-86．
[59] 陈国兴，胡庆兴，刘学珠．关于砂土液化判别的若干意见[J]．地震工程与工程振动，2002，22(1)：141-151．
[60] 刘金稻，金晓媚．饱和砂土液化判别方法中问题浅析[J]．工程地质学报，2000，8(3)：379-384．
[61] 顾晓鲁，钱鸿缙．地基与基础[M]．北京：中国建筑工业出版社，2003．
[62] 张晓晖，吴亚萍，尚伟宏等．利用模糊神经网络进行砂土液化势评判[J]．工程地质学报，2001，9(2)：209-213．
[63] 赵胜利，赵红英，刘燕．基于SOFM神经网络的砂土液化评价[J]．华中科技大学学报(城市科学版)，2005，22(2)：23-26．
[64] 董贤哲．基于补偿模糊神经网络的砂土液化势评价研究[D]．中国地质大学：2005．
[65] 刘恢先．唐山大地震震害(第一分册)[M]．北京：地震出版社，1985．
[66] Vladimir N, Vapnik. The Nature of Statistical Learning Theory[M]. New York:Springer-Verlag, 1995.
[67] Vapink V 著，张学工译．统计学习理论的本质[M]．北京：清华大学出版社，2000．
[68] 李国正，王猛，曾华军等译．支持向量机导论[M]．北京：电子工业出版社，2004．
[69] Harris Drucker, Donghui Wu, Vladimir N. Vapnik. Support Vector Machines for Spam Categorizatioin[J]. IEEE Trans. on Neturat Networks, 1999,10(5):1048-1054.
[70] Lothar Hermes, Dieter Frieauff, Jan Puzicha, et al. SVM for Land Usage Classification in Landsat TM Imagery[J]. Proc. IGARSS'99, 1999,1:348-350.
[71] Yonas B. Dibike, Slaveo Velickov, Dimitri Solomatine, et al. Model Induction with Support Vector Machines: Introduction and Applications[J]. Journal of computing in civil engineering, 2001,15(3):208.
[72] 朱辉，杨扬．SVM在小字符集手写体汉字识别中的研究[J]．微计算机信息，2004，20(4)：74-76．
[73] Osuna E, Freund R, Girosi F. Improved training algorithm for support vector machines[J]. 7th IEEE workshop on Neural Networks for Signal Processing, 1997:276-285.
[74] Bernd Heisele, Thomas Serre, Sam Prentice, et al. Hierarchical classification and feature reduction for fast detection with support vector machines[J]. Pattern Recognition, 2003,36:2007-2017.
[75] 张翔，田金文，肖小玲等．支持向量机及其在医学图像分类中的应用[J]．信号处理，2004，20(2)：208-212．
[76] 赵洪波，冯夏庭．非线性位移时间序列预测的进化支持向量机方法与应用[J]．岩土工程学报，2003，25(4)：468-471．
[77] 赵洪波，冯夏庭，李邵军等．福宁高速公路八尺门滑坡变形演化规律预测研究[J]．岩土力学，2003，24(4)：640-643．
[78] 冯夏庭，赵洪波．岩爆预测的支持向量机[J]．东北大学报(自然科学版)，2002，23(1)：57-59．
[79] 赵洪波，冯夏庭．支持向量机函数拟合的边坡稳定性估计[J]．岩石力学与工程学报，2003，24(4)：241-245．
[80] 赵洪波，冯夏庭，尹顺德．基于支持向量机的岩体工程分级[J]．岩土力学，2002，23(6)：698-701．
[81] Miguel Moreia, Eddy Mayoraz. Improved pairwise coupling classification with correcting classifiers[J]. Tenth Europen Conference Machine Learning(ECML'98) Chemnitz, 1998:21-24.
[82] 解可新，韩立兴，林友联．最优化方法[M]．天沣：天津大学出版社，1997．
[83] Chih-Wei Hsu, Chih-Chung Chang, Chih-Jen Lin. A Practical Guide to Support Vector Classification[EB/OL].
[84] piaip, piaip's Using (lib)SVM Tutorial[Eg/OL].
[85] Keerthi, S. Sathiya, Lin, Chih-Jen. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel[J]. Neural Computation, 2003,15(7):1667-1689.
[86] Lin, H.-T,C.-J. Lin. A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods[EB/OL].
[87] Goh, Anthony T.C. Seimic liquefaction potential assessed by neural networks[J]. Journal of Geotechnical Engineering, 120(9):1467-1480.