基于神经网络的EMI结构健康监测及损伤检测研究
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摘要
在复杂的服役环境中工程结构常会遭受各种损伤,当这些损伤积累到一定程度将会导致结构破坏,甚至导致灾难性事故,因此,对结构的健康监测和损伤检测技术进行的研究具有十分重要的理论、现实意义及社会价值和经济价值。论文在国家自然科学基金(No.50778179)的资助下进行了集压电驱动／传感一体化技术的结构在线健康监测的研究。
本文首先概述了基于振动测试的结构损伤检测的研究现状、介绍了基于压电阻抗的结构损伤检测的新方法,总结了基于计算智能的结构损伤检测的国内外研究进展。介绍了EMI (Electro-mechanical Impedance,简称EMI)方法用于结构损伤检测的基本理论,包括智能材料与结构的概念、特点和PZT在智能结构中的应用：压电振动相关的基本理论,PZT与结构耦合电阻抗的推导。然后运用两端固定支撑条件下钢梁的损伤检测试验验证了EMI方法应用于结构损伤检测的有效性,运用损伤健康指数定性地评价不同损伤工况下钢梁的损伤破坏程度。
介绍了神经网络的基本理论、BP神经网络模型和设计、介绍了神经网络在结构损伤识别的应用因素、一般过程及基本原理。通过对一个悬臂仿梁建立仿真模型,运用BP神经网络对其进行损伤位置识别,结果表明,BP神经网络对单一损伤位置识别效果很好。
采用BP神经网络和EMI技术综合方法进行了结构损伤检测研究。通过实验得到PZT电导纳频谱曲线作为神经网络的输入参数,然后根据输入参数的训练进行结构损伤的识别研究。结果表明,设计的BP神经网络可以成功地识别结构的损伤。该综合技术充分利用了高频压电阻抗和神经网络的特性,是一个非常有潜力的结构损伤检测的方法。
最后,总结了本课题的研究成果,并对后续的研究工作做了展望。
Engineering structures serving in complicated environment often suffer different kinds of damages, and the failure will occur in the structure, evenly result in catastrophes because of the accumulation of such damages, thereby, health diagnosis and safety appraisal are most crucial for engineering structures. Therefore, it has important theoretical and realistic meaning and social and economic value to carry out research in the health monitoring and damage diagnosis technology. Supported by the National Natural Science Foundation of China (No.50778179), the thesis has studied structure online health monitoring based on the integration technology of piezoelectric driving and sensing.
Firstly, structural damage detection based on vibration test are discussed in this thesis. The new method using the electro-mechanical impedance (EMI) method to detect the structural damage is also introduced. And research progress of structural damage detection based on computational intelligence at home and abroad are summarized. The basic theory of the application of the PZT impedance method in structural damage identification is analyzed which includes the notion and feature of the intelligent material, the application of PZT in the intelligent structure, the basic theory of piezo-electricity vibration and the deduction the relation between PZT and the structure coupling electrical impedance. Then, the effectiveness of the proposed EMI method used in the structural damage detection has been verified by the damage detection experiments of steel beams with fixed support conditions. the damage magnitudes of the steel beam in different damage states are quantitatively identified by using a damage condition factor.
The basic theory of neural network, the BP neural network model and designing, the factors、the process and the basic principle of neural networks used in structural damage identification are introduced. A cantilever beam model is established by simulation. And using BP neural network identify the damage location. The results reveal that the BP neural network for single damage identification.
Study on damage detection is also presented using electro-mechanical impedance (EMI) signatures and artificial neural networks (ANNs). Piezo-electricity admittance spectrum curves obtained by experiment were used as input parameters of neural networks. Then, structural damage identification are researched by the training of input parameters. The experimental results reveal that the designed BP neural network can identify the damage successfully. The hybrid technique, fully making use of the high-frequency EMI signatures and neural network features, is a potential method of detecting damages in structures.
Finally, some results are summarized in this project with prospects for the further research.
本文首先概述了基于振动测试的结构损伤检测的研究现状、介绍了基于压电阻抗的结构损伤检测的新方法,总结了基于计算智能的结构损伤检测的国内外研究进展。介绍了EMI (Electro-mechanical Impedance,简称EMI)方法用于结构损伤检测的基本理论,包括智能材料与结构的概念、特点和PZT在智能结构中的应用：压电振动相关的基本理论,PZT与结构耦合电阻抗的推导。然后运用两端固定支撑条件下钢梁的损伤检测试验验证了EMI方法应用于结构损伤检测的有效性,运用损伤健康指数定性地评价不同损伤工况下钢梁的损伤破坏程度。
介绍了神经网络的基本理论、BP神经网络模型和设计、介绍了神经网络在结构损伤识别的应用因素、一般过程及基本原理。通过对一个悬臂仿梁建立仿真模型,运用BP神经网络对其进行损伤位置识别,结果表明,BP神经网络对单一损伤位置识别效果很好。
采用BP神经网络和EMI技术综合方法进行了结构损伤检测研究。通过实验得到PZT电导纳频谱曲线作为神经网络的输入参数,然后根据输入参数的训练进行结构损伤的识别研究。结果表明,设计的BP神经网络可以成功地识别结构的损伤。该综合技术充分利用了高频压电阻抗和神经网络的特性,是一个非常有潜力的结构损伤检测的方法。
最后,总结了本课题的研究成果,并对后续的研究工作做了展望。
Engineering structures serving in complicated environment often suffer different kinds of damages, and the failure will occur in the structure, evenly result in catastrophes because of the accumulation of such damages, thereby, health diagnosis and safety appraisal are most crucial for engineering structures. Therefore, it has important theoretical and realistic meaning and social and economic value to carry out research in the health monitoring and damage diagnosis technology. Supported by the National Natural Science Foundation of China (No.50778179), the thesis has studied structure online health monitoring based on the integration technology of piezoelectric driving and sensing.
Firstly, structural damage detection based on vibration test are discussed in this thesis. The new method using the electro-mechanical impedance (EMI) method to detect the structural damage is also introduced. And research progress of structural damage detection based on computational intelligence at home and abroad are summarized. The basic theory of the application of the PZT impedance method in structural damage identification is analyzed which includes the notion and feature of the intelligent material, the application of PZT in the intelligent structure, the basic theory of piezo-electricity vibration and the deduction the relation between PZT and the structure coupling electrical impedance. Then, the effectiveness of the proposed EMI method used in the structural damage detection has been verified by the damage detection experiments of steel beams with fixed support conditions. the damage magnitudes of the steel beam in different damage states are quantitatively identified by using a damage condition factor.
The basic theory of neural network, the BP neural network model and designing, the factors、the process and the basic principle of neural networks used in structural damage identification are introduced. A cantilever beam model is established by simulation. And using BP neural network identify the damage location. The results reveal that the BP neural network for single damage identification.
Study on damage detection is also presented using electro-mechanical impedance (EMI) signatures and artificial neural networks (ANNs). Piezo-electricity admittance spectrum curves obtained by experiment were used as input parameters of neural networks. Then, structural damage identification are researched by the training of input parameters. The experimental results reveal that the designed BP neural network can identify the damage successfully. The hybrid technique, fully making use of the high-frequency EMI signatures and neural network features, is a potential method of detecting damages in structures.
Finally, some results are summarized in this project with prospects for the further research.
引文
[1]孙鸿敏,李宏男.土木工程结构健康监测的研究进展[J].防灾减灾工程学报,2003,23(3)：92-97.
[2]杨智春,于哲峰.结构健康监测中的损伤检测技术研究进展[J].力学进展,2004.34(2)：215-222.
[3]杨大智.智能材料与系统[M].天津：天津大学出版社,2000.
[4]Houser G.W. et al. Structural control:past,present,and future. ASCE,Journal of Engineering Mechanics,1997,123(9):897-971.
[5]李国强,李杰.工程结构动力检测理论及应用.北京：科学出版社,2002.
[6]Friswell M I,Penny J E T,Wilson D A L.Using vibration data and statistical measures to locate damage in structures.The International Journal of Analytical and Experimental Model Analysis,1994,9(4):239-254.
[7]Salawu,0 S.Detection of structural damage through changes in frequency:a review.Engineering Structures,1997,19(9):718-723.
[8]张东利,李霆,孙锡龙.利用固有频率特征量诊断混凝土结构损伤位置[J].测试技术学报.2003,17(3)：265-269.
[9]李学平,余志武.一种基于频率的损伤识别新方法[J].工业建筑.2006,36,213-214.
[10]郑栋梁,李中付,华宏星.结构早期识别技术的现状和发展趋势[J].振动与冲击,2002,21(2)：1-10.
[11]Salawu,0 S,Williams C.Damage location using vibration mode shapes. Proceedings of the 12th International Modal Analy sis Conference,1994:933-939.
[12]袁向荣.梁的破损对频率振型及振型曲率的影响[J].振动、测试与诊断,1994,2：40-44
[13]郭国会,易伟建.基于模态参数进行连续梁损伤诊断的数值研究[J].振动与冲击,2001,20(1)：72—75.
[14]Banan M R,Hjelmstad KD.Parameter estimation of structures from static response.Journal of Structural Engineering,1994,120(11):3243-3283.
[15]崔飞,袁万城,史家钧.基于静态应变及位移测量的结构损伤识别法[J].同济大学学报,2001,28(1)：5-8.
[16]Sanayei M,Saletnik M J.Parameter estimation of structure from strain measurements. Journal of Structural Engineering,1996,122(5):555-572.
[17]顾培英,陈厚群,李同春等.用应变模态技术诊断梁结构的损伤[J].地震工 程与工程振动,2005,25(4)：50-53.
[18]Pandy A K, Bismas M. Damage detection in structures using changes in flexibility [J]. Journal of Sound and Vibration,1994,169(1):3-17.
[19]Park K C, Alvin K F. A partitioned system identification procedure for substructural flexibility determination [J]. AIAA Paper,1996,1297:270-278.
[20]唐天国,朱以文,蔡德所,刘浩吾.结构裂缝损伤识别的虚拟柔度矩阵法[J].四川大学学报.2006,38(2)：39-42.
[21]宗周红,D-Z-Huang,T-L-Wang.钢-混凝土组合桥损伤诊断[J].土木工程学报.2004,37(5)：59-64.
[22]杨艳娟.钢桁架桥梁裂纹识别的模型试验研究[J].低温建筑技术.2009,8：37-39.
[23]Sun F P,Chaudhry C,Liang C.Truss structure integrity identification using PZT sensor-actuator.Journal of Intelligent Material Systems and Structures,1995,6:134-139.
[24]EstebanJ.Wave localization due to material damping. Computer Methods in Applied Mech. and Engrg.,1999,177(1-2):93-107.
[25]Giurgiutiu V,Zagrai AN. Damage Detection in Simulated Aging Aircraft Panels Using the Electro-Mechanical Impedance Technique[A].Adaptive structue and material systems symposium, ASME Winter annual meeting[C]. Orlando, Florida, 2000.5-10.
[26]Tseng K. Non-parametric damage detection and characterization using smart piezoceramic material[J]. SmartMatls. and Struct.2002,11(3):317-329.
[27]Suresh Bhalla,Chee Kiong Soh.Structural impedance based damage diagnosis by piezo-transducers[J].Earthquake Engineering and Structure Dynamics. 2003,32:1897-1916.
[28]S.Bhalla and C.K.Soh.Structural health monitoring by piezo-impedance transducers Ⅰ:Modeling Journal of Aerospace Engineering,2004,17(4):154-165.
[29]S.Bhalla and C.K.Soh.Structural health monitoring by piezo-impedance transducersII:Applications Journal of Aerospace Engineering,2004,17(4):166-175.
[30]Ihn J.B,Chang F.K.Detection and monitoring of hidden fatigue crack growth using a built-in piezoelectric sensor/actuator network:Ⅰ.Diagnostics.Smart Mater. Struct.2004,13(3):609—620.
[31]Ihn J.B.,Chang.F.K,Detection and monitoring of hidden fatigue crack Growthusing a built-in piezoelectric sensor/actuator network:Ⅱ.Validation using rivetedjoints and repair patches. Smart Mater.Struct.2004,13(3):621-630.
[32]高峰,李以农,王德俊,沈亚鹏,江钟伟.用于结构健康诊断的压电阻抗技术[J].振动工程学报,2000,13(1),94-99.
[33]李以农,陈兵奎,郑玲.压电材料在结构缺陷识别中的应用机理研究[J].机械工程材料,2003,27(6),25-28.
[34]熊先锋,杨光瑜,杨拥民,宋立军.压电阻抗技术用于结构健康诊断的一种方法[J].传感器技术,2003,22(10),62-64.
[35]鲍凯,万小朋.动态系统分析的阻抗方法及其在状态监控智能结构中的应用[J].无损检测,2003,25(8),399-402.
[36]张兢,徐霞,王玉菡,尹敏.基于压电陶瓷动态信息的结构损伤检测技术[J].重庆工学院学报,2007,21(5),11-14.
[37]王燕.应用时间序列分析[M].北京：中国人民大学出版,2005.
[38]Krishnan Nair K, Anne S Kiremidjian, Kincho H Lawb. Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure[J]. Journal of Sound and Vibration,2006,291(1):349-368.
[39]曾浩,李书进.基于时间序列分析的结构损伤诊断[D],武汉理工大学,2006.
[40]马高,屈文忠,陈明祥.基于时间序列的结构损伤在线诊断.武汉大学学报.2008,41(1),81-85.
[41]王真,程远胜.基于时间序列模型自回归系数灵敏度分析的结构损伤识别方法.工程力学2008,25(10),38-49.
[42]三田彰著.薛松涛,陈铭译.结构健康监测动力学,西安：西安交通大学出版社,2004.
[43]樊可清,倪一清,高赞明.基于频域系统辨识和支持向量机的桥梁状态监测方法.工程力学,2004,21(5),25-30.
[44]晋侃,薛松涛.结构损伤检测的最小二乘支持向量机回归方法研究[D],同济大学,2005.
[45]薛松涛,张茂雨等.加权特征向量LS-SVM在线结构损伤识别.四川建筑科学研究,2007,3,62-66.
[46]刘西拉,薛庆.评估钢筋混凝土结构破损状态的专家系统[J].土木工程学报,1987,(03)：1-8.
[47]唐海燕,虞和济.结构损伤诊断的专家系统[J].东北大学学报,1996,17(2),142-146.
[48]邓天鹏,韩艳.基于产生式规则专家系统在结构损伤诊断中的应用[J].河 南理工大学学报,2007,26(2),202-206.
[49]邓聚龙.灰色系统理论教程.武汉：华中理工大学出版社,1990.
[50]薛松涛,钱宇音,陈镕,王远功.应用灰色关联度分析检测结构损伤的位置和程度.振动与冲击,2005,24(1),66-69.
[51]易伟建,刘霞.基于遗传算法的结构损伤诊断研究.工程力学,2001,18(2),64-71.
[52]M.Ananda Rao,J.Srinivas and B.S.N.Murthy.Damage detection invibrating bodies using genetie algorithms[J].Computesr&Sturetures,2004,82:963-968
[53]王广伟,李强,徐海峰.遗传算法在结构损伤模式识别中的应用.浙江海洋学院学报,2008,27(3),335-339.
[54]Hou Z K, Noori M, Amand R ST. Wavelet-based approach for structural damage detection [J]. Journal of EM, ASCE,2000,126(7):677-683.
[55]张伟伟,王志华,马宏伟.基于小波分析的悬臂梁裂纹参数识别方法研究.机械强度,2007,29(2)：201-205.
[56]王保同,李雪梅.小波分析在结构损伤监测中的应用[J].建筑技术开发,2005,12(12),44-45.
[57]Venkatasubramanian V and Chan K.A neural network methodology for process fault diagnosis[J]. Journal Saddle of AICHE,1989,35(12):1993-1996.
[58]王柏生,倪一清,高赞明.用概率神经网络进行结构损伤位置识别[J].2001,14(1),60-64.
[59]郭杏林,陈建林.基于神经网络技术的结构损伤探测[J].大连理工大学学报,2002,Vol.42 No.3,269-273.
[60]饶文碧,吴代华.RBF神经网络及其在结构损伤识别中的应用研究.固体力学学报,2002,23(4),477-482.
[61]周宏.灰色神经网络及在硷结构使用寿命评估中的应用[D].武汉理工大学,2004,
[62]张松鹤,韩利思,罗跃纲.建筑结构损伤的灰色网络识别研究[J].大连民族学院学报,2005,7(5),76-78.
[63]郑猛,陈恩利,史玉红.基于曲率模态和神经网络的结构损伤诊断[J].石家庄铁道学院学报,2005,18(1),29-32.
[64]张敬芬,孟光,赵德有.基于模糊神经网络的薄板不同指标裂纹诊断[J].机械工程学报,2006,42(3),145～149.
[65]姜绍飞,张帅.基于模糊神经网络的数据融合结构损伤识别方法[J].工程力学,2008,Vol.25 No.2,95～100.
[66]刘书贤,赵艳霞,李奎明.遗传算法-神经网络方法的结构损伤识别[J].辽宁工程技术大学学报,2004,23(5),636-637.
[67]胡勇,崔灵珍,秦真,谈怀江.基于GA-BP和GA-RBF网络的结构损伤识别[J].武汉理工大学学报,2006,28(4),48-50.
[68]蒋洪涛,李海军.结合遗传算法的BP神经网络训练方法研究[J].北方交通,2006(8),70-71.
[69]邱颖,任青文.小波变换与神经网络的结构损伤检测[J].河海大学学报.2004,32(3)：295-299
[70]鞠彦忠,阎贵平,陈建斌,陈景彦,陈建华.用小波神经网络检测结构损伤[J].工程力学,2003,20(6),176-181.
[71]陈文元,李雪梅,迟晓梅.小波分析与神经网络在结构多处损伤监测中的应用[J].四川建筑科学研究,2006,32(3),67-71.
[72]姜德生,]Richard O Claus(美)著.智能材料器件结构与应用.武汉：武汉工业大学出版社,2003.
[73]高培德.智能材料和结构.功能材料与器件学报,2002,8(2)：93-98.
[74]Egusa, S. Piezoelectric paints as one approach to smart structural materials with health monitoring capabilities. Smart Materials and Structures,1998.7(41): 438-445.
[75]孙四海,刘军.智能材料与智能结构及其应用[J].淮南职业技术学院学报,2003,2(3)：74-76.
[76]孙慷,张福学.压电学[D].北京：国防工业出版社,1984.
[77]徐茂华.结构损伤检测PZT阻抗法的理论与试验研究：[硕士学位论文].华中科技大学,2005.
[78]袁曾任.人工神经元网络及其应用[M].北京：清华大学出版社,1999.
[79]Lopez V, Park G, Cudney HH, Inman DJ. Impedance-based structural health monitoring with artificial neural networks. Journal of Intelligent Material Systems and Structures,2000,11:206-214.
[80]严蔚.EMI结构健康监测技术与定量化分析：[博士学位论文].浙江大学,2007.
[81]Zagrai A,Giurgiutiu V.Electro-Mechanical Impedance Method for Crack Detection in Thin Plates.Journal of Intelligent Material Systems and Structures,12(2001),709-718.
[2]杨智春,于哲峰.结构健康监测中的损伤检测技术研究进展[J].力学进展,2004.34(2)：215-222.
[3]杨大智.智能材料与系统[M].天津：天津大学出版社,2000.
[4]Houser G.W. et al. Structural control:past,present,and future. ASCE,Journal of Engineering Mechanics,1997,123(9):897-971.
[5]李国强,李杰.工程结构动力检测理论及应用.北京：科学出版社,2002.
[6]Friswell M I,Penny J E T,Wilson D A L.Using vibration data and statistical measures to locate damage in structures.The International Journal of Analytical and Experimental Model Analysis,1994,9(4):239-254.
[7]Salawu,0 S.Detection of structural damage through changes in frequency:a review.Engineering Structures,1997,19(9):718-723.
[8]张东利,李霆,孙锡龙.利用固有频率特征量诊断混凝土结构损伤位置[J].测试技术学报.2003,17(3)：265-269.
[9]李学平,余志武.一种基于频率的损伤识别新方法[J].工业建筑.2006,36,213-214.
[10]郑栋梁,李中付,华宏星.结构早期识别技术的现状和发展趋势[J].振动与冲击,2002,21(2)：1-10.
[11]Salawu,0 S,Williams C.Damage location using vibration mode shapes. Proceedings of the 12th International Modal Analy sis Conference,1994:933-939.
[12]袁向荣.梁的破损对频率振型及振型曲率的影响[J].振动、测试与诊断,1994,2：40-44
[13]郭国会,易伟建.基于模态参数进行连续梁损伤诊断的数值研究[J].振动与冲击,2001,20(1)：72—75.
[14]Banan M R,Hjelmstad KD.Parameter estimation of structures from static response.Journal of Structural Engineering,1994,120(11):3243-3283.
[15]崔飞,袁万城,史家钧.基于静态应变及位移测量的结构损伤识别法[J].同济大学学报,2001,28(1)：5-8.
[16]Sanayei M,Saletnik M J.Parameter estimation of structure from strain measurements. Journal of Structural Engineering,1996,122(5):555-572.
[17]顾培英,陈厚群,李同春等.用应变模态技术诊断梁结构的损伤[J].地震工 程与工程振动,2005,25(4)：50-53.
[18]Pandy A K, Bismas M. Damage detection in structures using changes in flexibility [J]. Journal of Sound and Vibration,1994,169(1):3-17.
[19]Park K C, Alvin K F. A partitioned system identification procedure for substructural flexibility determination [J]. AIAA Paper,1996,1297:270-278.
[20]唐天国,朱以文,蔡德所,刘浩吾.结构裂缝损伤识别的虚拟柔度矩阵法[J].四川大学学报.2006,38(2)：39-42.
[21]宗周红,D-Z-Huang,T-L-Wang.钢-混凝土组合桥损伤诊断[J].土木工程学报.2004,37(5)：59-64.
[22]杨艳娟.钢桁架桥梁裂纹识别的模型试验研究[J].低温建筑技术.2009,8：37-39.
[23]Sun F P,Chaudhry C,Liang C.Truss structure integrity identification using PZT sensor-actuator.Journal of Intelligent Material Systems and Structures,1995,6:134-139.
[24]EstebanJ.Wave localization due to material damping. Computer Methods in Applied Mech. and Engrg.,1999,177(1-2):93-107.
[25]Giurgiutiu V,Zagrai AN. Damage Detection in Simulated Aging Aircraft Panels Using the Electro-Mechanical Impedance Technique[A].Adaptive structue and material systems symposium, ASME Winter annual meeting[C]. Orlando, Florida, 2000.5-10.
[26]Tseng K. Non-parametric damage detection and characterization using smart piezoceramic material[J]. SmartMatls. and Struct.2002,11(3):317-329.
[27]Suresh Bhalla,Chee Kiong Soh.Structural impedance based damage diagnosis by piezo-transducers[J].Earthquake Engineering and Structure Dynamics. 2003,32:1897-1916.
[28]S.Bhalla and C.K.Soh.Structural health monitoring by piezo-impedance transducers Ⅰ:Modeling Journal of Aerospace Engineering,2004,17(4):154-165.
[29]S.Bhalla and C.K.Soh.Structural health monitoring by piezo-impedance transducersII:Applications Journal of Aerospace Engineering,2004,17(4):166-175.
[30]Ihn J.B,Chang F.K.Detection and monitoring of hidden fatigue crack growth using a built-in piezoelectric sensor/actuator network:Ⅰ.Diagnostics.Smart Mater. Struct.2004,13(3):609—620.
[31]Ihn J.B.,Chang.F.K,Detection and monitoring of hidden fatigue crack Growthusing a built-in piezoelectric sensor/actuator network:Ⅱ.Validation using rivetedjoints and repair patches. Smart Mater.Struct.2004,13(3):621-630.
[32]高峰,李以农,王德俊,沈亚鹏,江钟伟.用于结构健康诊断的压电阻抗技术[J].振动工程学报,2000,13(1),94-99.
[33]李以农,陈兵奎,郑玲.压电材料在结构缺陷识别中的应用机理研究[J].机械工程材料,2003,27(6),25-28.
[34]熊先锋,杨光瑜,杨拥民,宋立军.压电阻抗技术用于结构健康诊断的一种方法[J].传感器技术,2003,22(10),62-64.
[35]鲍凯,万小朋.动态系统分析的阻抗方法及其在状态监控智能结构中的应用[J].无损检测,2003,25(8),399-402.
[36]张兢,徐霞,王玉菡,尹敏.基于压电陶瓷动态信息的结构损伤检测技术[J].重庆工学院学报,2007,21(5),11-14.
[37]王燕.应用时间序列分析[M].北京：中国人民大学出版,2005.
[38]Krishnan Nair K, Anne S Kiremidjian, Kincho H Lawb. Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure[J]. Journal of Sound and Vibration,2006,291(1):349-368.
[39]曾浩,李书进.基于时间序列分析的结构损伤诊断[D],武汉理工大学,2006.
[40]马高,屈文忠,陈明祥.基于时间序列的结构损伤在线诊断.武汉大学学报.2008,41(1),81-85.
[41]王真,程远胜.基于时间序列模型自回归系数灵敏度分析的结构损伤识别方法.工程力学2008,25(10),38-49.
[42]三田彰著.薛松涛,陈铭译.结构健康监测动力学,西安：西安交通大学出版社,2004.
[43]樊可清,倪一清,高赞明.基于频域系统辨识和支持向量机的桥梁状态监测方法.工程力学,2004,21(5),25-30.
[44]晋侃,薛松涛.结构损伤检测的最小二乘支持向量机回归方法研究[D],同济大学,2005.
[45]薛松涛,张茂雨等.加权特征向量LS-SVM在线结构损伤识别.四川建筑科学研究,2007,3,62-66.
[46]刘西拉,薛庆.评估钢筋混凝土结构破损状态的专家系统[J].土木工程学报,1987,(03)：1-8.
[47]唐海燕,虞和济.结构损伤诊断的专家系统[J].东北大学学报,1996,17(2),142-146.
[48]邓天鹏,韩艳.基于产生式规则专家系统在结构损伤诊断中的应用[J].河 南理工大学学报,2007,26(2),202-206.
[49]邓聚龙.灰色系统理论教程.武汉：华中理工大学出版社,1990.
[50]薛松涛,钱宇音,陈镕,王远功.应用灰色关联度分析检测结构损伤的位置和程度.振动与冲击,2005,24(1),66-69.
[51]易伟建,刘霞.基于遗传算法的结构损伤诊断研究.工程力学,2001,18(2),64-71.
[52]M.Ananda Rao,J.Srinivas and B.S.N.Murthy.Damage detection invibrating bodies using genetie algorithms[J].Computesr&Sturetures,2004,82:963-968
[53]王广伟,李强,徐海峰.遗传算法在结构损伤模式识别中的应用.浙江海洋学院学报,2008,27(3),335-339.
[54]Hou Z K, Noori M, Amand R ST. Wavelet-based approach for structural damage detection [J]. Journal of EM, ASCE,2000,126(7):677-683.
[55]张伟伟,王志华,马宏伟.基于小波分析的悬臂梁裂纹参数识别方法研究.机械强度,2007,29(2)：201-205.
[56]王保同,李雪梅.小波分析在结构损伤监测中的应用[J].建筑技术开发,2005,12(12),44-45.
[57]Venkatasubramanian V and Chan K.A neural network methodology for process fault diagnosis[J]. Journal Saddle of AICHE,1989,35(12):1993-1996.
[58]王柏生,倪一清,高赞明.用概率神经网络进行结构损伤位置识别[J].2001,14(1),60-64.
[59]郭杏林,陈建林.基于神经网络技术的结构损伤探测[J].大连理工大学学报,2002,Vol.42 No.3,269-273.
[60]饶文碧,吴代华.RBF神经网络及其在结构损伤识别中的应用研究.固体力学学报,2002,23(4),477-482.
[61]周宏.灰色神经网络及在硷结构使用寿命评估中的应用[D].武汉理工大学,2004,
[62]张松鹤,韩利思,罗跃纲.建筑结构损伤的灰色网络识别研究[J].大连民族学院学报,2005,7(5),76-78.
[63]郑猛,陈恩利,史玉红.基于曲率模态和神经网络的结构损伤诊断[J].石家庄铁道学院学报,2005,18(1),29-32.
[64]张敬芬,孟光,赵德有.基于模糊神经网络的薄板不同指标裂纹诊断[J].机械工程学报,2006,42(3),145～149.
[65]姜绍飞,张帅.基于模糊神经网络的数据融合结构损伤识别方法[J].工程力学,2008,Vol.25 No.2,95～100.
[66]刘书贤,赵艳霞,李奎明.遗传算法-神经网络方法的结构损伤识别[J].辽宁工程技术大学学报,2004,23(5),636-637.
[67]胡勇,崔灵珍,秦真,谈怀江.基于GA-BP和GA-RBF网络的结构损伤识别[J].武汉理工大学学报,2006,28(4),48-50.
[68]蒋洪涛,李海军.结合遗传算法的BP神经网络训练方法研究[J].北方交通,2006(8),70-71.
[69]邱颖,任青文.小波变换与神经网络的结构损伤检测[J].河海大学学报.2004,32(3)：295-299
[70]鞠彦忠,阎贵平,陈建斌,陈景彦,陈建华.用小波神经网络检测结构损伤[J].工程力学,2003,20(6),176-181.
[71]陈文元,李雪梅,迟晓梅.小波分析与神经网络在结构多处损伤监测中的应用[J].四川建筑科学研究,2006,32(3),67-71.
[72]姜德生,]Richard O Claus(美)著.智能材料器件结构与应用.武汉：武汉工业大学出版社,2003.
[73]高培德.智能材料和结构.功能材料与器件学报,2002,8(2)：93-98.
[74]Egusa, S. Piezoelectric paints as one approach to smart structural materials with health monitoring capabilities. Smart Materials and Structures,1998.7(41): 438-445.
[75]孙四海,刘军.智能材料与智能结构及其应用[J].淮南职业技术学院学报,2003,2(3)：74-76.
[76]孙慷,张福学.压电学[D].北京：国防工业出版社,1984.
[77]徐茂华.结构损伤检测PZT阻抗法的理论与试验研究：[硕士学位论文].华中科技大学,2005.
[78]袁曾任.人工神经元网络及其应用[M].北京：清华大学出版社,1999.
[79]Lopez V, Park G, Cudney HH, Inman DJ. Impedance-based structural health monitoring with artificial neural networks. Journal of Intelligent Material Systems and Structures,2000,11:206-214.
[80]严蔚.EMI结构健康监测技术与定量化分析：[博士学位论文].浙江大学,2007.
[81]Zagrai A,Giurgiutiu V.Electro-Mechanical Impedance Method for Crack Detection in Thin Plates.Journal of Intelligent Material Systems and Structures,12(2001),709-718.