管道悬索跨越结构抗震能力和健康诊断研究
|
摘要
本文围绕长输油气管道悬索跨越工程的抗震能力和健康诊断技术,利用理论分析、模型实验和数值模拟手段进行了较为系统的研究。在总结震害经验和分析受力特点的基础上,概括归纳了该类结构的三种基本损伤模式。提出了基于模式识别的多级整体健康诊断方法,建立并验证了健康诊断的神经网络模型。此外,还建议了基于简单缆索受力监测的局部诊断方法,该方法可与整体诊断方法结合使用,判断结构系统的损伤模式、损伤位置和损伤程度。
主要研究内容和成果如下:
1 设计制作了管道悬索跨越工程的1:8 缩尺模型。在总结震害经验和分析受力特点的基础上,概括归纳了该类结构的三种基本损伤模式(塔架损伤模式、吊索损伤模式和斜拉索损伤模式)。就系统的完好状态和三种损伤模式分别进行了实验研究。进行了模态实验、敲击激振实验、白噪声和地震波输入的振动台实验,以及缆索拉力和振动频率的测试。实验结果为评估结构体系的抗震能力和建立系统的健康诊断方法提供了依据。
2 利用ANSYS 程序建立了原型结构和模型结构的有限元分析模型,考虑缆索的预应力和大变形、进行了完好状态和不同损伤模式下的静力分析和模态分析,也尝试进行了地震反应分析。就索力和体系自振频率而言,模型结构的实验结果和数值模拟结果基本吻合。数值分析也表明,原型结构和模型结构具有相似性,模型实验结果可作为分析实际工程的依据。
3 模型实验和数值模拟表明,管道悬索跨越工程具有较强的抗震承载能力和系统的稳定性。在三向地震动同时作用下,当输入地震动加速度达到0.5g(相当于地震烈度ⅸ度)时,系统构件仍未达到强度极限、未发生破坏;即使在人为造成的三种损伤模式下,体系仍保持稳定,可维持运行功能。
4 对实验得到的结构体系自振频率参数进行了统计特征分析、区别不同损伤模式利用符号检验法进行了实测数据的显著性检验,确定前两阶频率参数可作为健康诊断的指标。建立了基于频率参数(频率和频率比)的多级整体健康诊断方法,可逐次识别结构的三种损伤模式。
5 运用MATLAB6.5 工具箱,基于整体模式识别方法,建立了用于管道悬索跨越工程健康诊断的BP 人工神经网络。利用模型实验所得到的部分模态参数进行BP 网络训练,再用其余部分数据对网络功能进行验证。结果表明,该网络模型对三种损伤模式具有良好的识别能力。
6 讨论了对缆索应力进行局部监测的技术方法。实验和分析表明,简单拉索的索力和振动频率间存在简单的线性关系,可利用敲击激振方法实测估
The aseismic capacity and health diagnosis technology for cable-suspended structures of oil and gas pipelines are studied systematically by the theory analyses and the model experiments and numerical simulation analyses. Three kinds of basic damage modes of structures are briefly induced based on the experience from earthquake damage and the characteristics of structure’s mechanics. The multilevel method of whole health diagnosis is put forward according to mode discrimination. A neural net model is set up and tested by experiment datum. Furthermore, the local health diagnosis methods are proposed through monitoring the cable forces. The damage modes and the damage positions and the damage degrees of structure system can be judged by using the whole health diagnosis methods in combination with the local health diagnosis methods.
The main content and outcome of this paper are as following:
Firstly, the model scaled to be one eighth of the actual cable-suspended structure of pipelines is designed and made. Three kinds of basic damage modes of the structure (the damage of tower frame, the breaking of a sling and the inclined cable loses its function) are briefly induced based on the experience from earthquake damage and the characteristics of structure’s mechanics. The experiment studies on non-damage mode and three kinds of damage modes are completed respectively. The experiments include modal tests, vibration tests by knocking, shaking table tests by inputting white-noises and seismic waves, and tests of cable forces and vibration frequencies. The experiment results supply a demand for evaluating the aseismic capacity of structural system and putting forward health diagnosis methods of the structural system.
Secondly, two finite models are built by ANSYS according to the actual structure and the model structure respectively. Under non-damage mode and three kinds of damage modes the static analyses and modal analyses are made taking cable prestress and large deformation into consideration. The earthquake response analyses are tried to be done. The experiment results basically tally with the values of numerical simulation analyses in cable forces and free frequencies of structural system. Numerical analyses also show that the actual structure is in the similitude of the model structure, and the result of model experiments provides the basis for analyzing actual engineering.
Thirdly, the model experiments and numerical analyses all show that the cable-suspended structures of pipelines have a good aseismic capability and systematic stability. While inputting three dimension ground motion acceleration reaches 0.5g (is equivalent to China earthquake intensity ⅸ) the element strengths in the structure are still under the limit of strength, and no element damages. Even
though the structure is under three man-made damage modes, the structure system keeps stability and maintains normal function. Fourthly, the first and the second frequencies are determined as index of health diagnoses through analyzing statistical characters of structure systematic free-frequencies got by experiments and checking prominence of the experiment datum by symbol test method in different damage modes. A multi-level whole health diagnosis method is set up based on frequency parameters (frequencies and ratio of frequencies), and can be used to recognize three damage modes of the structure. Fifthly, a BP artificial neural net used for health diagnoses of cable-suspended structures of pipelines is established by the tool box in the MATLAB6.5. The net is trained by parts of modal parameters got by the experiments, and is verified by the rest of modal parameters. The results show that this net model has strong ability to distinguish three kinds of damage modes. Sixthly, the local monitoring technology methods of testing cable stresses are discussed. The experiments and analyses show that the cable forces are linear with the frequencies. The cable forces can be estimated by knock-excited methods. The structural health diagnoses can be made according to the cable forces, because the inclined cables have key function in keeping the system stability and the inclined cable forces vary in different damaged modes. Through combining the local monitoring methods with the whole health diagnosis methods, the damaged modes can be distinguished, and the degrees can be estimated, and the location can be determined.
主要研究内容和成果如下:
1 设计制作了管道悬索跨越工程的1:8 缩尺模型。在总结震害经验和分析受力特点的基础上,概括归纳了该类结构的三种基本损伤模式(塔架损伤模式、吊索损伤模式和斜拉索损伤模式)。就系统的完好状态和三种损伤模式分别进行了实验研究。进行了模态实验、敲击激振实验、白噪声和地震波输入的振动台实验,以及缆索拉力和振动频率的测试。实验结果为评估结构体系的抗震能力和建立系统的健康诊断方法提供了依据。
2 利用ANSYS 程序建立了原型结构和模型结构的有限元分析模型,考虑缆索的预应力和大变形、进行了完好状态和不同损伤模式下的静力分析和模态分析,也尝试进行了地震反应分析。就索力和体系自振频率而言,模型结构的实验结果和数值模拟结果基本吻合。数值分析也表明,原型结构和模型结构具有相似性,模型实验结果可作为分析实际工程的依据。
3 模型实验和数值模拟表明,管道悬索跨越工程具有较强的抗震承载能力和系统的稳定性。在三向地震动同时作用下,当输入地震动加速度达到0.5g(相当于地震烈度ⅸ度)时,系统构件仍未达到强度极限、未发生破坏;即使在人为造成的三种损伤模式下,体系仍保持稳定,可维持运行功能。
4 对实验得到的结构体系自振频率参数进行了统计特征分析、区别不同损伤模式利用符号检验法进行了实测数据的显著性检验,确定前两阶频率参数可作为健康诊断的指标。建立了基于频率参数(频率和频率比)的多级整体健康诊断方法,可逐次识别结构的三种损伤模式。
5 运用MATLAB6.5 工具箱,基于整体模式识别方法,建立了用于管道悬索跨越工程健康诊断的BP 人工神经网络。利用模型实验所得到的部分模态参数进行BP 网络训练,再用其余部分数据对网络功能进行验证。结果表明,该网络模型对三种损伤模式具有良好的识别能力。
6 讨论了对缆索应力进行局部监测的技术方法。实验和分析表明,简单拉索的索力和振动频率间存在简单的线性关系,可利用敲击激振方法实测估
The aseismic capacity and health diagnosis technology for cable-suspended structures of oil and gas pipelines are studied systematically by the theory analyses and the model experiments and numerical simulation analyses. Three kinds of basic damage modes of structures are briefly induced based on the experience from earthquake damage and the characteristics of structure’s mechanics. The multilevel method of whole health diagnosis is put forward according to mode discrimination. A neural net model is set up and tested by experiment datum. Furthermore, the local health diagnosis methods are proposed through monitoring the cable forces. The damage modes and the damage positions and the damage degrees of structure system can be judged by using the whole health diagnosis methods in combination with the local health diagnosis methods.
The main content and outcome of this paper are as following:
Firstly, the model scaled to be one eighth of the actual cable-suspended structure of pipelines is designed and made. Three kinds of basic damage modes of the structure (the damage of tower frame, the breaking of a sling and the inclined cable loses its function) are briefly induced based on the experience from earthquake damage and the characteristics of structure’s mechanics. The experiment studies on non-damage mode and three kinds of damage modes are completed respectively. The experiments include modal tests, vibration tests by knocking, shaking table tests by inputting white-noises and seismic waves, and tests of cable forces and vibration frequencies. The experiment results supply a demand for evaluating the aseismic capacity of structural system and putting forward health diagnosis methods of the structural system.
Secondly, two finite models are built by ANSYS according to the actual structure and the model structure respectively. Under non-damage mode and three kinds of damage modes the static analyses and modal analyses are made taking cable prestress and large deformation into consideration. The earthquake response analyses are tried to be done. The experiment results basically tally with the values of numerical simulation analyses in cable forces and free frequencies of structural system. Numerical analyses also show that the actual structure is in the similitude of the model structure, and the result of model experiments provides the basis for analyzing actual engineering.
Thirdly, the model experiments and numerical analyses all show that the cable-suspended structures of pipelines have a good aseismic capability and systematic stability. While inputting three dimension ground motion acceleration reaches 0.5g (is equivalent to China earthquake intensity ⅸ) the element strengths in the structure are still under the limit of strength, and no element damages. Even
though the structure is under three man-made damage modes, the structure system keeps stability and maintains normal function. Fourthly, the first and the second frequencies are determined as index of health diagnoses through analyzing statistical characters of structure systematic free-frequencies got by experiments and checking prominence of the experiment datum by symbol test method in different damage modes. A multi-level whole health diagnosis method is set up based on frequency parameters (frequencies and ratio of frequencies), and can be used to recognize three damage modes of the structure. Fifthly, a BP artificial neural net used for health diagnoses of cable-suspended structures of pipelines is established by the tool box in the MATLAB6.5. The net is trained by parts of modal parameters got by the experiments, and is verified by the rest of modal parameters. The results show that this net model has strong ability to distinguish three kinds of damage modes. Sixthly, the local monitoring technology methods of testing cable stresses are discussed. The experiments and analyses show that the cable forces are linear with the frequencies. The cable forces can be estimated by knock-excited methods. The structural health diagnoses can be made according to the cable forces, because the inclined cables have key function in keeping the system stability and the inclined cable forces vary in different damaged modes. Through combining the local monitoring methods with the whole health diagnosis methods, the damaged modes can be distinguished, and the degrees can be estimated, and the location can be determined.
引文
[1] 常怀民:陕京输气管道黄河跨越施工,油气储运,1998,17(3),35-37
[2] 戴葵等译,Matin T.Hagan 等著:神经网络设计,北京:机械工业出版社,2002
[3] 高东方,季天晶:天然气长输管道工程建设及设计技术展望,油气田地面工程,2004(9)
[4] 高俊凯,陈国彬主编:全国主要江河油气管道穿跨越工程资料汇编, 北京:石油工业出版社,1992
[5] 高赞明,孙宗光,倪一清:基于振动方法的汲水门大桥损伤检测研究地震工程与工程震动, 2001,21(4):117-123
[6] 何亚东:基于智能理论的建筑结构系统识辨、半主动开展及控制优化研究,博士学位论文,天津大学,2001
[7] 胡守仁,余少波,戴葵:神经网络导论,长沙:国防科技大学出版社,1997
[8] 李爱群等:润杨长江大桥结构健康监测系统研究,东南大学学报,2003, 09 Vol.33 No.5
[9] 梁艳春:计算智能与力学反问题中的若干问题,力学进展,2000,30(3): 321-331
[10] 李德寅:第聂伯河上跨度720 米悬索管道桥实桥试验,国外桥梁,1996
[11] 李德寅:悬索管道桥梁—兼论第聂伯河上720m管道桥,国外桥梁, 1995(4)
[12] 李戈,秦权,董聪:用遗传算法选择悬索桥监测系统中传感器的最优布点,工程力学,2000,(1):25-34
[13] 李宏男,李东升:土木工程结构安全性评估、健康监测及诊断评述
[14] 李宏男,阎时,林皋:智能结构控制发展综述,地震工程与工程震动,1999, 19(2):29-36
[15] 李宏伟:结构健康监测的无线传感器网络系统研究与应用,博士学位论文,哈尔滨工业大学,2005
[16] 林元陪:斜拉桥,北京:人民交通出版社,1994,164-166
[17] 李少慧,宁雅农等译,Culshaw B.等著:光纤传感器,武汉:华中理工大 学出版社,1997
[18] 李铁山:徒骇河大型悬索跨越施工技术,油气储运,1998,17(12),27-29
[19] 刘迎春,叶湘滨编著:现代新型传感器原理与应用,北京:国防工业出版社,1998
[20] 李向京,胡云昌:基于遗传算法-神经网络混合训练技术的结构近似可靠分析方法,土木工程学报,2000,(5):40-45
[21] 栾英泉,梁力:桥梁无损健康监测系统研究简介,东北公路,2000,Vol.23 No.1
[22] 欧进萍:土木工程智能结构体系的研究与发展,地震工程与工程震动,1999,6,Vol.19 No.2
[23] 潘家英,程庆国:大跨度悬索桥有限位移分析,土木工程学报,1994,(4):5-9
[24] 钱冬生,陈仁福:大跨悬索桥的设计与施工(修订版),西南交通大学出版社,1999
[25] 任若恩,王惠文:多元统计数据分析—理论、方法、实例,北京:国防工业出版社,1997
[26] 数学手册编写组:数学手册,北京:人民教育出版社,1979
[27] 孙剑平,朱晞:结构控制方法综述,力学进展,2000,30(4):495-505
[28] 陶宝祺:智能材料结构,北京:国防工业出版社,1997
[29] 王惠文:光纤传感技术及应用,北京:国防工业出版社,2001
[30] 王建华,与孟蕻,李众编著:智能控制基础,科学出版社1998
[31] 王吉军,扬大智:Ni-Ti 形状记忆合金振动与主动控制研究,大连理工大学学报,1997(37)6
[32] 王茂章:用作智能材料的碳纤维,新型碳材料,1996,11(4):11-17
[33] 王文涛:斜拉桥换索工程,北京:人民交通出版社,1997。111-114。
[34] 魏驱虎,李作诗:断桥时间追踪,扬子晚报,2001,11,16
[35] 吴波,李惠,孙科学:形状记忆合金在土木工程中的应用,世界地震工程,1999,15(3),1-13
[36] 吴大宏,赵人达:基于遗传算法与神经网络的桥梁结构健康监测系统研究初探,四川建筑科学研究,2002,09,Vol.28 No.3
[37] 肖仪清:现役固定式海洋平台结构体系可靠度分析与安全评定,哈尔滨: 哈尔滨工业大学出版社,2001,65-85
[38] 杨大智:智能材料与系统,天津:天津大学出版社,2000
[39] 袁慎芳:进行损伤评估的智能材料结构的研究,南京:南京航空航天大学,1996
[40] 臧彦民:我国油气长输管道建设及相关行业的发展前景,天津冶金,2000(5)
[41] 张建仁,刘扬:遗传算法在斜拉桥索塔可靠性分析中的应用,中国公路学会桥梁和结构工程学会1999年桥梁学术讨论会论文集,北京:人民交通出版社,2000
[42] 张陵,郭惠勇,孙清,张爱社,范晓军:长输管道抗震研究的进展与趋向,西安交通大学学报,Vol35,No2,2001
[43] 张敏政:关于土木工程健康监测研究的若干问题,重大工程灾变行为与健康监测学术研讨会,2001,北京
[44] 张启伟:桥梁结构模型修正与损伤识别,同济大学博士论文,1999。
[45] 张志广:涩宁兰管道八盘峡黄河悬索跨越的设计,油气储运,2002,21(6),20-23
[46] 中华人民共和国石油天然气行业标准,原油和天然气输送管道穿跨越工程设计规范(SY/T0015.2-98)
[47] 中华人民共和国标准,构筑物抗震设计规范(GB50191-93)
[48] 中华人民共和国标准,建筑抗震设计规范(GB50011-2001)
[49] 周智,欧进萍:用于土木工程的智能监测传感材料性能及比较研究,建筑技术,2002,33(4):270-272
[50] Abe M. 1988 Structural Monitoring Using Vibration Measurement Current Practice and Future, Artificial Intelligence in Structural Engineering Ian Smith, ed, Lecture Notes in Artificial Intelligence 1454, Springer, pp118
[51] Abe, M.Fujino, Y.Kajimura, T.Yanagihara, M. and Sato, M.1999. Monitoring of a Long Span Suspension Bridge by Ambient Vibration Measurement, The Second International Conference on Structural Health Monitoring, Stanford University, California, pp.400-407
[52] Allen D.W. et al. Utilize the Sequential Probability Ratio Test for Building Monitoring. NDE for Monitoring and Diagnostics, San Diego, 2002, 4704-01
[53] Amaravadi V. et al. Structural Integrity Monitoring of Composite Patch Repairs Using Wavelet Analysis and Neural Network. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4701-17
[54] Amizic B. et al. Two Dimensional Wavelet Mapping Techniques for Damage Detection. Smart Structures and Materials, San Diego, 2002, 4693-32
[55] Bathe, K. J., Finite Element Procedures, Prentice-Hall, Englewood Cliffs (1996).
[56] B.F.Spancer,Jr.,Structural Health Monitoring and Damage Detection, July,2001
[57] Bounopane S and Billington D. Theory and history of suspension bridge design from 1823 to 1940. J. Struct. Engrg., ASCE, 1993. 119(3): 954-977.
[58] Cawley P, Adams CJ.A Vibration Technique for Non-destructively Assessing the integrity of Structure, Journal of Mechanics Engineering Science, 1978,20(2),93-100
[59] C. Lanczos, Solution of Systems of Linear Equations by Minimized Iterations, J. Res. Natl. Bur. Stand. 49 (1952) 33–53.
[60] Dang, N. H. et al. Embedded Systems for the Assessment of Structural Damages. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4701-18
[61] Echevarria J. et al. Fiber Bragg Grating First and Second order Diffraction Wavelengths Based Transducer Optimized Design. Smart Structure and Materials, San Diego, 2002, 4694-20
[62] Ellen Van Campa, Nicola Mastronardia, Marc Van Barela; Two Fast Algorithms for Solving Diagonal-plus-semi separable Linear Systems, Journal of Computational and Applied Mathematics 164–165 (2004) 731–747.
[63] Elkordy et al. A Structural Damage Neural Network Monitoring System. Microcomputers in Civil Engineering, 1994, 9 83~96
[64] E.Reissiner, on Finite Deformations of Space Curved Beams, J.Appl.Math.Phys, 32(1981) 734-744.
[65] Findeis D. et al. Vibration Isolation Technique to Suitable Electronic Speckle Pattern Interferometer. NED for Health Monitoring and Diagnostics, San Diego,
2002, 4704-24
[66] Flint A.R., Mcfadyen A.N., Lau C.K., and Wong K.Y.: Wind and Structure Health Monitoring System (WASHMS) for Lantau Fixed Crossing, Part 2 Performance Requirements for the Bridge Instrumentation System. Bridge into 21st century, HK, Oct.25, 1995.
[67] Grimes, R.G., Lewis, J.G., and Simon, H.D., A Shifted Block Lanczos Algorithm for Solving Sparse Symmetric Generalized Eigenproblems, SIAM Journal Matrix Analysis Applications, Vol. 15 (1), pp. 228-272 (1994).
[68] Guemes A. et al. Embedded Fiber Bragg Grating as Local Damage Sensors for Composites. Smart Structure and Materials, San Diego, 2002, 4694-15
[69] Hagan, M. T., H. B. Demuth, and M. H. Beale, Neural Network Design, Boston, MA: PWS Publishing, 1996.12-14.
[70] Housner G.W.et al. Structural Control: Past, Present, and Future. ASCE, Journal of Engineering Mechanics, 1997, 123(9):897-971
[71] Ibrahimbegovic, Adnan, On Finite Element Implementation of Geometrically Nonlinear Reissner's Beam Theory: Three-dimensional Curved Beam Elements, Computer Methods in Applied Mechanics and Engineering, Vol. 122, pp. 11-26 (1995)
[72] Inaudi D., Development of Reusable Software Components for Monitoring Data Management, Visualization and Analysis. Smart Structures and Materials, San Diego, 2002, 4696-02
[73] Iwaki H. et al. Structural Health Monitoring System Using FBG-Based Sensors for Damage Tolerant Building. Smart Structure and Materials, San Diego, 2002, 4696-09
[74] J.M.Ko, Y.Q.Ni, X.T.Zhou and J.Y.Wang 2000. Structural Damage Alarming in Ting Kau Bridge Using Auto-Associative Neural Networks, Advance in Structural Dynamics, J.M.Ko and Y.L.Xu(eds.), Elsevier Science Ltd., Oxford, UK, Vol.1,111-122
[75] Kaito K, Abe M., Fujono Y., and Yoda H. 2000 Detection of Structural Damage by Ambient Vibration Measurement Using Laser Doppler Vibrometer,
Non-Destructive Testing in Civil Engineering 2000, Uomono(ed), Tokyo
[76] Kaminski P C., The Approximation Location of Damage through the Analysis of Natural Frequencies with Artificial Neural Networks. Journal of Process Mechanical Engineering. 1995, 209(E2):117~124
[77] Kammer D.C. Sensor Placement for On-orbit Modal Identification and Correlation of Large Space Structures, Journal of guidance, Control and Dynamics, 1991, 14(9), 251-259
[78] Keiji Shiba, Hitoshi Kumagai, Fiber Optic Distributed Sensor for Monitoring of Concrete Structures, Structural Health Monitoring, pp.459-468
[79] Kudva, J.N., C.Marantidis, and J.D., Smart Structures Concept Requirements Definition, Final Report, Northrop Corporation.
[80] K. Worden. 1997. Structural Fault Detection Using a Novelty Measure, Journal of Sound and Vibration, 201:85-101
[81] Law S, Howe D and Zhu X Q. Modal Strain Energy Change in Neural Network Damage Assessment. International Conference on Advances in Structural Dynamics. Hong Kong, 2000
[82] Lin M. et al. Advances in Utilization of Structurally Integrated Sensor Networks for Health Monitoring in Commercial Applications. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4701-63
[83] Lockyer, A.J.J.H. Bilderbeck, Smart Metallic Structures, Preliminary Final Report, Northrop Grumman. February 2000.
[84] Ma J. et al. Detecting Multiple Damages in a civil structure model. Smart Structures and Materials, San Diego, 2002, 4696-18
[85] Mark M. Derriso: Structural Health Monitoring Applications for Current and Future Aerospace Vehicles. Structural Health Monitoring,
[86] Martin W. N. et al. Artificial Nerve System for Structural Monitoring. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4702-06
[87] Masri S F et al., Neural network approach to detection in structural parameters. Journal of Engineering Mechanics, 1996, 122(4):350~360
[88] Matrat J., K.Levin1999. Effect of debonding on strain measurement with embedded Bragg grating sensors, Structural Health Monitoring 2000
[89] Monaco E. et al. Non Destructive Technique Based on Vibrations Measurements and Piezoelectric Patches Array for Monitoring Corrosion Phenomena. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4701-10
[90] Ni Y Q, Wang B S and Ko J M. Selection of Input to Neural Networks for Structural Damage Identification. Smart System for Bridges, Structures and Highways, SPIE, 1999, 3671:270~280
[91] Ono S. et al. Research about Damage Identification of the Structural Building Using the Quasi-Newton Method. Smart Structures and Materials, San Diego, 2002, 4696-16
[92] Pai P.F.et al. Detection and estimation of Defects in a Circular Plate Using Operation Deflection Shapes. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4702-46
[93] Papadopoulos M. et al., Sensor Placement Methodologies for Dynamic Testing. AIAA, 1998, 36(2), 256-263
[94] Penny JET, Friswell MI, Garvey SD, Automatic Choice of Measurement Locations for Dynamic Testing. AIAA Journal, 1994, 32(2), 407-414
[95] Qiu F.et al. Development of the Software for Strain Monitoring of Human Beidge. Smart Structures and Materials, San Diego, 2002, 4702-30
[96] Report No. WASHMS-04(a).1999. Damage Detection Simulation for the Tsing Ma Suspension Bridge, The Hong Kong Polytechnic University, Hong Kong,133p.
[97] Riedmiller, M., and H. Braun, A Direct Adaptive Method for Faster Back propagation Learning: The RPROP algorithm, Proceedings of the IEEE International Conference on Neural Networks, 1993.
[98] Schulz W. et al. Real-Time Damage Assessment of Civil Structure using Fiber Grating Sensors and Modal Analysis. Smart Structure and Materials, San Diego, 2002, 4696-28
[99] Simo, J.C. and Vu-Quoc, L., Three Dimensional Finite Strain Rod Model. Part II: Computational Aspects, Computer Methods in Applied Mechanics and Engineering, Vol. 58, pp. 79-116 (1986).
[100]Skontorp A.1999. Effect of Embedded Optical Fibers on the Structural Integrity of Composites, Proc. Twelfth International Conference on Composite materials, ICCM-12, Paris, France, pp. 761
[101] Steinman D B. A Practical Treatise on Suspension Bridges. 2nd Ed., Wiley, New York. 1929
[102] Stephanie A. et al. Computational Study on Plate Damage Identification. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4702-17
[103] Studer M. et al. Embedded Optical Fiber Bragg Grating Sensors for the Measurement of Crack Bridging Forces in Composites. Smart Structure and Materials, San Diego, 2002, 4694-13
[104] Tadeda N. et al. Application of Chirped Fiber Bragg Grating Sensors for Damage Identification in Composites. Smart Structure and Materials, San Diego, 2002, 4694-14
[105] Takeda S. et al. Detection of Delimitation in Composite Laminates Using Small-Diameter FBG Sensors. Smart Structure and Materials, San Diego, 2002, 4694-17
[106]Taylor, R. L., Beresford, P. J., and Wilson, E. L., A Non-Conforming Element for Stress Analysis, International Journal for Numerical Methods in Engineering, Vol. 10, pp. 1211-1219 (1976)
[107] T.H.T.Chan, Y.Q.Ni and J.M.Ko. 1999. Neural Network Novelty Filtering for Anomaly Detection of Tsing Ma Bridge Cable, Structural Health Monitoring 2000, F.K.Chang(ed.), Technomic Publishing Co.,Lancaster,Pennsylvania,430-439
[108] Townsend C.P. et al. Scalable, Wireless, Web-based Sensor Networks. Smart Structures and Materials, San Diego, 2002, 4696-01
[109] Wilson, E. L., Taylor, R. L., Doherty, W. P., and Ghaboussi, J., Incompatible Displacement Models, Numerical and Computer Methods in Structural Mechanics, edited by S. J. Fenves, et al., Academic Press, Inc., N. Y. and London, pp. 43-57 (1973).
[110] Worden K A et al. Neural networks for fault location.11th International Modal Analysis Conference.1993, USA: 47~54
[111] Xie b. et al. Damage Location Identification for Aluminum Plate by Wavelet Analysis. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4701-16
[112] X.T.Zhou, J.M.Ko, and Y.Q.Ni, Shaking Table Test of a Concrete Frame Model for Health Monitoring Study, Structural Health Monitoring, pp.651-659
[113] Y.Fujino and M.Abe Structural Health Monitoring in Civil Infrastructures and R&D of Bridges at the University of Tokyo, Structural Health Monitoring pp.61-78
[114] Y.Q.Ni, B.S.Wang and J.M.Ko.1999 Selection of Input Vectors to Neural Networks for Structural Damage Identification, Smart Structures and Materials, Smart System for Bridges, Structures, and Highways, S.C.Liu(ed),SPIE Vol. 3671, 270-280
[115] Y.Q.NI, J.M.KO, and X.T.Zhou, Damage Region Identification of Cable-Supported Bridges Using Neural Network-Based Novelty Detectors Structural Health Monitoring, pp.449-458
[116] Yunus, Shah M., Pawlak, Timothy P., and Cook, R. D., Solid Elements with Rotational Degrees of Freedom, Part 1 and Part 2, International Journal for Numerical Methods in Engineering, Vol. 31, pp. 573-610 (1991).
[117] Zienkiewicz, O. C., The Finite Element Method, McGraw-Hill Company, London, (1977).
[2] 戴葵等译,Matin T.Hagan 等著:神经网络设计,北京:机械工业出版社,2002
[3] 高东方,季天晶:天然气长输管道工程建设及设计技术展望,油气田地面工程,2004(9)
[4] 高俊凯,陈国彬主编:全国主要江河油气管道穿跨越工程资料汇编, 北京:石油工业出版社,1992
[5] 高赞明,孙宗光,倪一清:基于振动方法的汲水门大桥损伤检测研究地震工程与工程震动, 2001,21(4):117-123
[6] 何亚东:基于智能理论的建筑结构系统识辨、半主动开展及控制优化研究,博士学位论文,天津大学,2001
[7] 胡守仁,余少波,戴葵:神经网络导论,长沙:国防科技大学出版社,1997
[8] 李爱群等:润杨长江大桥结构健康监测系统研究,东南大学学报,2003, 09 Vol.33 No.5
[9] 梁艳春:计算智能与力学反问题中的若干问题,力学进展,2000,30(3): 321-331
[10] 李德寅:第聂伯河上跨度720 米悬索管道桥实桥试验,国外桥梁,1996
[11] 李德寅:悬索管道桥梁—兼论第聂伯河上720m管道桥,国外桥梁, 1995(4)
[12] 李戈,秦权,董聪:用遗传算法选择悬索桥监测系统中传感器的最优布点,工程力学,2000,(1):25-34
[13] 李宏男,李东升:土木工程结构安全性评估、健康监测及诊断评述
[14] 李宏男,阎时,林皋:智能结构控制发展综述,地震工程与工程震动,1999, 19(2):29-36
[15] 李宏伟:结构健康监测的无线传感器网络系统研究与应用,博士学位论文,哈尔滨工业大学,2005
[16] 林元陪:斜拉桥,北京:人民交通出版社,1994,164-166
[17] 李少慧,宁雅农等译,Culshaw B.等著:光纤传感器,武汉:华中理工大 学出版社,1997
[18] 李铁山:徒骇河大型悬索跨越施工技术,油气储运,1998,17(12),27-29
[19] 刘迎春,叶湘滨编著:现代新型传感器原理与应用,北京:国防工业出版社,1998
[20] 李向京,胡云昌:基于遗传算法-神经网络混合训练技术的结构近似可靠分析方法,土木工程学报,2000,(5):40-45
[21] 栾英泉,梁力:桥梁无损健康监测系统研究简介,东北公路,2000,Vol.23 No.1
[22] 欧进萍:土木工程智能结构体系的研究与发展,地震工程与工程震动,1999,6,Vol.19 No.2
[23] 潘家英,程庆国:大跨度悬索桥有限位移分析,土木工程学报,1994,(4):5-9
[24] 钱冬生,陈仁福:大跨悬索桥的设计与施工(修订版),西南交通大学出版社,1999
[25] 任若恩,王惠文:多元统计数据分析—理论、方法、实例,北京:国防工业出版社,1997
[26] 数学手册编写组:数学手册,北京:人民教育出版社,1979
[27] 孙剑平,朱晞:结构控制方法综述,力学进展,2000,30(4):495-505
[28] 陶宝祺:智能材料结构,北京:国防工业出版社,1997
[29] 王惠文:光纤传感技术及应用,北京:国防工业出版社,2001
[30] 王建华,与孟蕻,李众编著:智能控制基础,科学出版社1998
[31] 王吉军,扬大智:Ni-Ti 形状记忆合金振动与主动控制研究,大连理工大学学报,1997(37)6
[32] 王茂章:用作智能材料的碳纤维,新型碳材料,1996,11(4):11-17
[33] 王文涛:斜拉桥换索工程,北京:人民交通出版社,1997。111-114。
[34] 魏驱虎,李作诗:断桥时间追踪,扬子晚报,2001,11,16
[35] 吴波,李惠,孙科学:形状记忆合金在土木工程中的应用,世界地震工程,1999,15(3),1-13
[36] 吴大宏,赵人达:基于遗传算法与神经网络的桥梁结构健康监测系统研究初探,四川建筑科学研究,2002,09,Vol.28 No.3
[37] 肖仪清:现役固定式海洋平台结构体系可靠度分析与安全评定,哈尔滨: 哈尔滨工业大学出版社,2001,65-85
[38] 杨大智:智能材料与系统,天津:天津大学出版社,2000
[39] 袁慎芳:进行损伤评估的智能材料结构的研究,南京:南京航空航天大学,1996
[40] 臧彦民:我国油气长输管道建设及相关行业的发展前景,天津冶金,2000(5)
[41] 张建仁,刘扬:遗传算法在斜拉桥索塔可靠性分析中的应用,中国公路学会桥梁和结构工程学会1999年桥梁学术讨论会论文集,北京:人民交通出版社,2000
[42] 张陵,郭惠勇,孙清,张爱社,范晓军:长输管道抗震研究的进展与趋向,西安交通大学学报,Vol35,No2,2001
[43] 张敏政:关于土木工程健康监测研究的若干问题,重大工程灾变行为与健康监测学术研讨会,2001,北京
[44] 张启伟:桥梁结构模型修正与损伤识别,同济大学博士论文,1999。
[45] 张志广:涩宁兰管道八盘峡黄河悬索跨越的设计,油气储运,2002,21(6),20-23
[46] 中华人民共和国石油天然气行业标准,原油和天然气输送管道穿跨越工程设计规范(SY/T0015.2-98)
[47] 中华人民共和国标准,构筑物抗震设计规范(GB50191-93)
[48] 中华人民共和国标准,建筑抗震设计规范(GB50011-2001)
[49] 周智,欧进萍:用于土木工程的智能监测传感材料性能及比较研究,建筑技术,2002,33(4):270-272
[50] Abe M. 1988 Structural Monitoring Using Vibration Measurement Current Practice and Future, Artificial Intelligence in Structural Engineering Ian Smith, ed, Lecture Notes in Artificial Intelligence 1454, Springer, pp118
[51] Abe, M.Fujino, Y.Kajimura, T.Yanagihara, M. and Sato, M.1999. Monitoring of a Long Span Suspension Bridge by Ambient Vibration Measurement, The Second International Conference on Structural Health Monitoring, Stanford University, California, pp.400-407
[52] Allen D.W. et al. Utilize the Sequential Probability Ratio Test for Building Monitoring. NDE for Monitoring and Diagnostics, San Diego, 2002, 4704-01
[53] Amaravadi V. et al. Structural Integrity Monitoring of Composite Patch Repairs Using Wavelet Analysis and Neural Network. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4701-17
[54] Amizic B. et al. Two Dimensional Wavelet Mapping Techniques for Damage Detection. Smart Structures and Materials, San Diego, 2002, 4693-32
[55] Bathe, K. J., Finite Element Procedures, Prentice-Hall, Englewood Cliffs (1996).
[56] B.F.Spancer,Jr.,Structural Health Monitoring and Damage Detection, July,2001
[57] Bounopane S and Billington D. Theory and history of suspension bridge design from 1823 to 1940. J. Struct. Engrg., ASCE, 1993. 119(3): 954-977.
[58] Cawley P, Adams CJ.A Vibration Technique for Non-destructively Assessing the integrity of Structure, Journal of Mechanics Engineering Science, 1978,20(2),93-100
[59] C. Lanczos, Solution of Systems of Linear Equations by Minimized Iterations, J. Res. Natl. Bur. Stand. 49 (1952) 33–53.
[60] Dang, N. H. et al. Embedded Systems for the Assessment of Structural Damages. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4701-18
[61] Echevarria J. et al. Fiber Bragg Grating First and Second order Diffraction Wavelengths Based Transducer Optimized Design. Smart Structure and Materials, San Diego, 2002, 4694-20
[62] Ellen Van Campa, Nicola Mastronardia, Marc Van Barela; Two Fast Algorithms for Solving Diagonal-plus-semi separable Linear Systems, Journal of Computational and Applied Mathematics 164–165 (2004) 731–747.
[63] Elkordy et al. A Structural Damage Neural Network Monitoring System. Microcomputers in Civil Engineering, 1994, 9 83~96
[64] E.Reissiner, on Finite Deformations of Space Curved Beams, J.Appl.Math.Phys, 32(1981) 734-744.
[65] Findeis D. et al. Vibration Isolation Technique to Suitable Electronic Speckle Pattern Interferometer. NED for Health Monitoring and Diagnostics, San Diego,
2002, 4704-24
[66] Flint A.R., Mcfadyen A.N., Lau C.K., and Wong K.Y.: Wind and Structure Health Monitoring System (WASHMS) for Lantau Fixed Crossing, Part 2 Performance Requirements for the Bridge Instrumentation System. Bridge into 21st century, HK, Oct.25, 1995.
[67] Grimes, R.G., Lewis, J.G., and Simon, H.D., A Shifted Block Lanczos Algorithm for Solving Sparse Symmetric Generalized Eigenproblems, SIAM Journal Matrix Analysis Applications, Vol. 15 (1), pp. 228-272 (1994).
[68] Guemes A. et al. Embedded Fiber Bragg Grating as Local Damage Sensors for Composites. Smart Structure and Materials, San Diego, 2002, 4694-15
[69] Hagan, M. T., H. B. Demuth, and M. H. Beale, Neural Network Design, Boston, MA: PWS Publishing, 1996.12-14.
[70] Housner G.W.et al. Structural Control: Past, Present, and Future. ASCE, Journal of Engineering Mechanics, 1997, 123(9):897-971
[71] Ibrahimbegovic, Adnan, On Finite Element Implementation of Geometrically Nonlinear Reissner's Beam Theory: Three-dimensional Curved Beam Elements, Computer Methods in Applied Mechanics and Engineering, Vol. 122, pp. 11-26 (1995)
[72] Inaudi D., Development of Reusable Software Components for Monitoring Data Management, Visualization and Analysis. Smart Structures and Materials, San Diego, 2002, 4696-02
[73] Iwaki H. et al. Structural Health Monitoring System Using FBG-Based Sensors for Damage Tolerant Building. Smart Structure and Materials, San Diego, 2002, 4696-09
[74] J.M.Ko, Y.Q.Ni, X.T.Zhou and J.Y.Wang 2000. Structural Damage Alarming in Ting Kau Bridge Using Auto-Associative Neural Networks, Advance in Structural Dynamics, J.M.Ko and Y.L.Xu(eds.), Elsevier Science Ltd., Oxford, UK, Vol.1,111-122
[75] Kaito K, Abe M., Fujono Y., and Yoda H. 2000 Detection of Structural Damage by Ambient Vibration Measurement Using Laser Doppler Vibrometer,
Non-Destructive Testing in Civil Engineering 2000, Uomono(ed), Tokyo
[76] Kaminski P C., The Approximation Location of Damage through the Analysis of Natural Frequencies with Artificial Neural Networks. Journal of Process Mechanical Engineering. 1995, 209(E2):117~124
[77] Kammer D.C. Sensor Placement for On-orbit Modal Identification and Correlation of Large Space Structures, Journal of guidance, Control and Dynamics, 1991, 14(9), 251-259
[78] Keiji Shiba, Hitoshi Kumagai, Fiber Optic Distributed Sensor for Monitoring of Concrete Structures, Structural Health Monitoring, pp.459-468
[79] Kudva, J.N., C.Marantidis, and J.D., Smart Structures Concept Requirements Definition, Final Report, Northrop Corporation.
[80] K. Worden. 1997. Structural Fault Detection Using a Novelty Measure, Journal of Sound and Vibration, 201:85-101
[81] Law S, Howe D and Zhu X Q. Modal Strain Energy Change in Neural Network Damage Assessment. International Conference on Advances in Structural Dynamics. Hong Kong, 2000
[82] Lin M. et al. Advances in Utilization of Structurally Integrated Sensor Networks for Health Monitoring in Commercial Applications. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4701-63
[83] Lockyer, A.J.J.H. Bilderbeck, Smart Metallic Structures, Preliminary Final Report, Northrop Grumman. February 2000.
[84] Ma J. et al. Detecting Multiple Damages in a civil structure model. Smart Structures and Materials, San Diego, 2002, 4696-18
[85] Mark M. Derriso: Structural Health Monitoring Applications for Current and Future Aerospace Vehicles. Structural Health Monitoring,
[86] Martin W. N. et al. Artificial Nerve System for Structural Monitoring. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4702-06
[87] Masri S F et al., Neural network approach to detection in structural parameters. Journal of Engineering Mechanics, 1996, 122(4):350~360
[88] Matrat J., K.Levin1999. Effect of debonding on strain measurement with embedded Bragg grating sensors, Structural Health Monitoring 2000
[89] Monaco E. et al. Non Destructive Technique Based on Vibrations Measurements and Piezoelectric Patches Array for Monitoring Corrosion Phenomena. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4701-10
[90] Ni Y Q, Wang B S and Ko J M. Selection of Input to Neural Networks for Structural Damage Identification. Smart System for Bridges, Structures and Highways, SPIE, 1999, 3671:270~280
[91] Ono S. et al. Research about Damage Identification of the Structural Building Using the Quasi-Newton Method. Smart Structures and Materials, San Diego, 2002, 4696-16
[92] Pai P.F.et al. Detection and estimation of Defects in a Circular Plate Using Operation Deflection Shapes. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4702-46
[93] Papadopoulos M. et al., Sensor Placement Methodologies for Dynamic Testing. AIAA, 1998, 36(2), 256-263
[94] Penny JET, Friswell MI, Garvey SD, Automatic Choice of Measurement Locations for Dynamic Testing. AIAA Journal, 1994, 32(2), 407-414
[95] Qiu F.et al. Development of the Software for Strain Monitoring of Human Beidge. Smart Structures and Materials, San Diego, 2002, 4702-30
[96] Report No. WASHMS-04(a).1999. Damage Detection Simulation for the Tsing Ma Suspension Bridge, The Hong Kong Polytechnic University, Hong Kong,133p.
[97] Riedmiller, M., and H. Braun, A Direct Adaptive Method for Faster Back propagation Learning: The RPROP algorithm, Proceedings of the IEEE International Conference on Neural Networks, 1993.
[98] Schulz W. et al. Real-Time Damage Assessment of Civil Structure using Fiber Grating Sensors and Modal Analysis. Smart Structure and Materials, San Diego, 2002, 4696-28
[99] Simo, J.C. and Vu-Quoc, L., Three Dimensional Finite Strain Rod Model. Part II: Computational Aspects, Computer Methods in Applied Mechanics and Engineering, Vol. 58, pp. 79-116 (1986).
[100]Skontorp A.1999. Effect of Embedded Optical Fibers on the Structural Integrity of Composites, Proc. Twelfth International Conference on Composite materials, ICCM-12, Paris, France, pp. 761
[101] Steinman D B. A Practical Treatise on Suspension Bridges. 2nd Ed., Wiley, New York. 1929
[102] Stephanie A. et al. Computational Study on Plate Damage Identification. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4702-17
[103] Studer M. et al. Embedded Optical Fiber Bragg Grating Sensors for the Measurement of Crack Bridging Forces in Composites. Smart Structure and Materials, San Diego, 2002, 4694-13
[104] Tadeda N. et al. Application of Chirped Fiber Bragg Grating Sensors for Damage Identification in Composites. Smart Structure and Materials, San Diego, 2002, 4694-14
[105] Takeda S. et al. Detection of Delimitation in Composite Laminates Using Small-Diameter FBG Sensors. Smart Structure and Materials, San Diego, 2002, 4694-17
[106]Taylor, R. L., Beresford, P. J., and Wilson, E. L., A Non-Conforming Element for Stress Analysis, International Journal for Numerical Methods in Engineering, Vol. 10, pp. 1211-1219 (1976)
[107] T.H.T.Chan, Y.Q.Ni and J.M.Ko. 1999. Neural Network Novelty Filtering for Anomaly Detection of Tsing Ma Bridge Cable, Structural Health Monitoring 2000, F.K.Chang(ed.), Technomic Publishing Co.,Lancaster,Pennsylvania,430-439
[108] Townsend C.P. et al. Scalable, Wireless, Web-based Sensor Networks. Smart Structures and Materials, San Diego, 2002, 4696-01
[109] Wilson, E. L., Taylor, R. L., Doherty, W. P., and Ghaboussi, J., Incompatible Displacement Models, Numerical and Computer Methods in Structural Mechanics, edited by S. J. Fenves, et al., Academic Press, Inc., N. Y. and London, pp. 43-57 (1973).
[110] Worden K A et al. Neural networks for fault location.11th International Modal Analysis Conference.1993, USA: 47~54
[111] Xie b. et al. Damage Location Identification for Aluminum Plate by Wavelet Analysis. NDE for Health Monitoring and Diagnostics, San Diego, 2002, 4701-16
[112] X.T.Zhou, J.M.Ko, and Y.Q.Ni, Shaking Table Test of a Concrete Frame Model for Health Monitoring Study, Structural Health Monitoring, pp.651-659
[113] Y.Fujino and M.Abe Structural Health Monitoring in Civil Infrastructures and R&D of Bridges at the University of Tokyo, Structural Health Monitoring pp.61-78
[114] Y.Q.Ni, B.S.Wang and J.M.Ko.1999 Selection of Input Vectors to Neural Networks for Structural Damage Identification, Smart Structures and Materials, Smart System for Bridges, Structures, and Highways, S.C.Liu(ed),SPIE Vol. 3671, 270-280
[115] Y.Q.NI, J.M.KO, and X.T.Zhou, Damage Region Identification of Cable-Supported Bridges Using Neural Network-Based Novelty Detectors Structural Health Monitoring, pp.449-458
[116] Yunus, Shah M., Pawlak, Timothy P., and Cook, R. D., Solid Elements with Rotational Degrees of Freedom, Part 1 and Part 2, International Journal for Numerical Methods in Engineering, Vol. 31, pp. 573-610 (1991).
[117] Zienkiewicz, O. C., The Finite Element Method, McGraw-Hill Company, London, (1977).