大型结构健康监测中信息获取及处理的智能化研究
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摘要
结构健康监测的目标是对结构的整体行为实现实时在线监测与预测预报,要达到这一目标监测系统必须具有快速大容量的信息采集、传输和处理能力,并能够实现数据的网络共享。其中高速数据获取与传输,海量数据的存储与管理,监测数据的预处理、解读、分析和利用等是建立长期健康监测系统的关键技术问题。本论文针对上述关键技术问题开展结构健康监测中信息获取与处理的智能化研究,主要研究结构健康监测系统中的网络化数据采集与传输技术、智能信息处理技术、动态数据管理与查询技术。
(1)首先提炼出建立结构健康监测系统的指导思想,构建具有普遍意义的大型结构长期健康监测系统的体系结构；自主开发了光纤传感测试系统、基于网络数据采集的电致传感测试系统和数字温度传感测试系统,构成“小集中”分布式数据采集与传输系统,以此为基础构建了大型结构健康监测系统通用化硬件平台；开发了基于虚拟仪器技术的以数据库系统为核心的软件平台,完成对硬件系统的集成。
(2)研究了小波分析在实际结构监测信息处理中的应用。提出了一种改进的小波阈值降噪算法,该阈值函数表达式简单,能够更好地改进滤波效果,提高降噪质量；对比分析了分别以小波包系数节点能量值和傅里叶变换后的固有频率值作为损伤特征值的敏感度,分析结果表明以小波包系数节点能量值作为特征指标时敏感度更高,证明了利用小波包进行结构健康监测中的特征值提取是可行的。
(3)研究了在实际结构监测信息处理中利用BP神经网络进行损伤识别的技术。首先建立BP网络模型,对模型进行性能优化,利用性能优化后的神经网络模型对混凝土斜拉桥模型的损伤状况进行了诊断,损伤识别结果表明通过结构振动模态频率的改变很容易地识别出结构的损伤情况。
(4)自主开发了数据库管理与查询系统,采用标准关系型数据库技术对监测数据进行规范化统一管理,解决海量数据的存储、分析处理、查询、数据备份、数据安全等技术问题。
(5)上述研究成果已成功地应用于芜湖长江大桥和郑州黄河大桥两座具有标志性意义的重要桥梁的监测系统中。两座大桥监测系统运行情况表明：自主开发的基于光纤应变测试系统、电致传感测试系统和数字温度传感测试系统的“小集中”分散数据采集系统实现了信息获取和传输的智能化；所采用的小波分析技术成功的完成对监测数据的降噪处理和特征值提取,所构建的BP网模型有效地对结构进行了损伤识别,自主开发的数据管理与查询系统能实时存储监测数据、实时显示查询结果、查询方便快捷,以上技术实现了对监测数据的实时分析、处理、存储及查询,提高了信息处理的智能化程度。
研究成果将进一步丰富结构健康监测系统的工程应用理论和试验基础,具有较大的理论意义和工程实用价值,将产生巨大的社会经济效益。
The goal of structural health monitoring is to achieve real-time monitoring and prediction for the overall behavior of the structure. To achieve this goal, the monitoring system should have a fast large-capacity information collection, transmission and processing capabilities, and data sharing capabilities through network. High-speed data acquisition and transmission, mass data storage and management, monitoring data pre-processing, interpretation, analysis and utilization are the key technical issues to establish the long-term health monitoring system. For the above key technical problems, research on intelligent information acquiring and processing for structural health monitoring are carried out in this paper. The research focuses on the network-based data acquisition and transmission technology, intelligent information processing technology, and dynamic data management and query technology.
(1) The guiding ideology of building a structural health monitoring system is refined firstly. Based on the guiding ideology, a general-purpose platform of long-term health monitoring system for large-scale structure is built. The strain test system based on the optic sensor, the electro-sensor test system based on network-based data acquisition and digital temperature sensor testing system are developed independently. The above three test systems, which build up the "small concentration" distributed data acquisition system, are the core of the hardware platform and integrated by the software platform based on the virtual instrument technology and the database system.
(2) The usage of wavelet analysis in monitoring information processing for the actual structure is studied. An improved wavelet thresholding algorithm is proposed. The new algorithm, which has a simple expression, performs wave filtering well and improves the quality of noise reduction. The sensitivity of the injury characteristic value expressed by the wavelet packet node energy coefficient and the inherent frequency value after Fourier transform is compared and analyzed. The results show that the injury characteristic value expressed by the wavelet packet node energy coefficient is more sensitive, which proves the usage of wavelet packet in extraction of the injury characteristic value is feasible.
(3) The usage of BP neural network in the damage identification for the actual structure monitoring information processing is discussed. A BP network model is established first, and then it is optimized, at last it is used to diagnose the damage of concrete cable-stayed bridge model. The structural damage identification results show that the BP neural network model is easy to identify the damage by the changing of vibration modal frequency.
(4) A dynamic data management and query system based on standard relational database is developed independently to implement the unified and standardized management of mass monitoring data. The system provides a good solution to the problems such as mass data storage, data analysis and processing, data query, data backup, data security etc.
(5) The above study results have been successfully applied to the Wuhu Yangtze River Bridge and the Zhengzhou Yellow River Bridge, which are two important bridges of symbolic significance. The operation of the two bridge monitoring system shows that the strain test system based on the optic sensor, the electro-sensor test system based on network-based data collection instrument and digital temperature sensor testing system can obtain the corresponding monitoring data well provide intelligent information acquiring and transmitting. The operation of the two bridge monitoring system also shows that wavelet analysis technique selected can complete noise reduction and feature extraction of monitoring data successfully and the BP network model constructed can identify the damage of the structure effectively and the data management and query system developed is able to store monitoring data timely, display query results in real-time and query conveniently. The above technologies achieve analysis, processing, storage and query of monitoring data in real-time and improve the intelligence degree of information processing.
The results will further enrich engineering application theories and experimental basis of the structure health monitoring system. And it is of great theoretical significance and practical engineering value. In addition, it will generate significant social and economic benefits.
(1)首先提炼出建立结构健康监测系统的指导思想,构建具有普遍意义的大型结构长期健康监测系统的体系结构；自主开发了光纤传感测试系统、基于网络数据采集的电致传感测试系统和数字温度传感测试系统,构成“小集中”分布式数据采集与传输系统,以此为基础构建了大型结构健康监测系统通用化硬件平台；开发了基于虚拟仪器技术的以数据库系统为核心的软件平台,完成对硬件系统的集成。
(2)研究了小波分析在实际结构监测信息处理中的应用。提出了一种改进的小波阈值降噪算法,该阈值函数表达式简单,能够更好地改进滤波效果,提高降噪质量；对比分析了分别以小波包系数节点能量值和傅里叶变换后的固有频率值作为损伤特征值的敏感度,分析结果表明以小波包系数节点能量值作为特征指标时敏感度更高,证明了利用小波包进行结构健康监测中的特征值提取是可行的。
(3)研究了在实际结构监测信息处理中利用BP神经网络进行损伤识别的技术。首先建立BP网络模型,对模型进行性能优化,利用性能优化后的神经网络模型对混凝土斜拉桥模型的损伤状况进行了诊断,损伤识别结果表明通过结构振动模态频率的改变很容易地识别出结构的损伤情况。
(4)自主开发了数据库管理与查询系统,采用标准关系型数据库技术对监测数据进行规范化统一管理,解决海量数据的存储、分析处理、查询、数据备份、数据安全等技术问题。
(5)上述研究成果已成功地应用于芜湖长江大桥和郑州黄河大桥两座具有标志性意义的重要桥梁的监测系统中。两座大桥监测系统运行情况表明：自主开发的基于光纤应变测试系统、电致传感测试系统和数字温度传感测试系统的“小集中”分散数据采集系统实现了信息获取和传输的智能化；所采用的小波分析技术成功的完成对监测数据的降噪处理和特征值提取,所构建的BP网模型有效地对结构进行了损伤识别,自主开发的数据管理与查询系统能实时存储监测数据、实时显示查询结果、查询方便快捷,以上技术实现了对监测数据的实时分析、处理、存储及查询,提高了信息处理的智能化程度。
研究成果将进一步丰富结构健康监测系统的工程应用理论和试验基础,具有较大的理论意义和工程实用价值,将产生巨大的社会经济效益。
The goal of structural health monitoring is to achieve real-time monitoring and prediction for the overall behavior of the structure. To achieve this goal, the monitoring system should have a fast large-capacity information collection, transmission and processing capabilities, and data sharing capabilities through network. High-speed data acquisition and transmission, mass data storage and management, monitoring data pre-processing, interpretation, analysis and utilization are the key technical issues to establish the long-term health monitoring system. For the above key technical problems, research on intelligent information acquiring and processing for structural health monitoring are carried out in this paper. The research focuses on the network-based data acquisition and transmission technology, intelligent information processing technology, and dynamic data management and query technology.
(1) The guiding ideology of building a structural health monitoring system is refined firstly. Based on the guiding ideology, a general-purpose platform of long-term health monitoring system for large-scale structure is built. The strain test system based on the optic sensor, the electro-sensor test system based on network-based data acquisition and digital temperature sensor testing system are developed independently. The above three test systems, which build up the "small concentration" distributed data acquisition system, are the core of the hardware platform and integrated by the software platform based on the virtual instrument technology and the database system.
(2) The usage of wavelet analysis in monitoring information processing for the actual structure is studied. An improved wavelet thresholding algorithm is proposed. The new algorithm, which has a simple expression, performs wave filtering well and improves the quality of noise reduction. The sensitivity of the injury characteristic value expressed by the wavelet packet node energy coefficient and the inherent frequency value after Fourier transform is compared and analyzed. The results show that the injury characteristic value expressed by the wavelet packet node energy coefficient is more sensitive, which proves the usage of wavelet packet in extraction of the injury characteristic value is feasible.
(3) The usage of BP neural network in the damage identification for the actual structure monitoring information processing is discussed. A BP network model is established first, and then it is optimized, at last it is used to diagnose the damage of concrete cable-stayed bridge model. The structural damage identification results show that the BP neural network model is easy to identify the damage by the changing of vibration modal frequency.
(4) A dynamic data management and query system based on standard relational database is developed independently to implement the unified and standardized management of mass monitoring data. The system provides a good solution to the problems such as mass data storage, data analysis and processing, data query, data backup, data security etc.
(5) The above study results have been successfully applied to the Wuhu Yangtze River Bridge and the Zhengzhou Yellow River Bridge, which are two important bridges of symbolic significance. The operation of the two bridge monitoring system shows that the strain test system based on the optic sensor, the electro-sensor test system based on network-based data collection instrument and digital temperature sensor testing system can obtain the corresponding monitoring data well provide intelligent information acquiring and transmitting. The operation of the two bridge monitoring system also shows that wavelet analysis technique selected can complete noise reduction and feature extraction of monitoring data successfully and the BP network model constructed can identify the damage of the structure effectively and the data management and query system developed is able to store monitoring data timely, display query results in real-time and query conveniently. The above technologies achieve analysis, processing, storage and query of monitoring data in real-time and improve the intelligence degree of information processing.
The results will further enrich engineering application theories and experimental basis of the structure health monitoring system. And it is of great theoretical significance and practical engineering value. In addition, it will generate significant social and economic benefits.
引文
[1]张启伟.大型桥梁健康监测概念与监测系统设计[J].同济大学学报：自然科学版,2001,29(1)：65-69.
[2]张启伟.大型桥梁结构安全监测的研究现状与发展[J].同济大学学报：自然科学版,1997,25(增刊)：76-81.
[3]Muria V D, Gomez R, King C. Dynamic structural properties of cable stayed Tampico Bridge[J]. Journal of Structural Engineering, ASCE,1991,117(11):3396-3416.
[4]李兆霞,李爱群,陈鸿天,等.大跨桥梁结构以健康监测和状态评估为目标的有限元模拟[J].东南大学学报：自然科学版,2003,33(5)：562-572.
[5]Aktan A E, Ciloglu S K, Grimmelsman K A. Infrastructure health monitoring: State-of-the-art, challenges and opportunities[C]. International SAMPE Technical Conference, Long Beach, CA,2004:1-7.
[6]Pines D, Akmn A E. Status of structural health monitoring of long-span bridges in the United States [J]. Progress of Structure Engineering and Materials,2002 (4): 372-380.
[7]Fujino Y, Abe M. Structural health monitoring-current status and future[C]. Proceedings of the 2nd European workshop on structural health monitoring, Lancaster(PA):DEStech,2004:3-10.
[8]Sumitro S., Okamoto T., Matsui Y., et al. Long span bridge health monitoring system in Japan[C]. Proc. SPIE.,2001,4337(67):517-524.
[9]邬晓光,徐祖恩.大型桥梁健康监测动态及发展趋势[J].长安大学学报：自然科学版,2003,23(1)：39-42.
[10]刘西拉.重大土木与水利工程安全性及耐久性的基础研究[J].土木工程学报,2001,34(6)：1-7.
[11]史家钧,邵志常.上海徐浦大桥结构状态监测系统[C].第十三届全国桥梁学术会议论文集,上海,1998(11)：16-19.
[12]黄方林,王学敏,陈政清,等.大型桥梁健康监测研究进展[J].中国铁道科学,2005,26(2)：1-7.
[13]Habel W., Kohlhoff H., Knapp J., et al. Monitoring System for Long-term evaluation of prestressed railway bridges in the new Lehrter Bahnhof in Berlin[C]. Third World Conference on Strucutral Control, In:Como, Italy.2002:1-6.
[14]Whelan M.. Remote structural monitoring of the Cathedral of Como using an optical fibre bragg sensor sy stem [J]. Smart structures and materials,2002,4694:242-252.
[15]Nataraja R.. Structural integrity monitoring in real seas[C]. Proceedings of the 15th Annual Offshore Technology Conference,1983:221-228.
[16]柳春图.海洋石油平台结构的安全性监测与评估技术的若干进展[C].中国力学学会现代力学进步学术大会,北京,1997：397-400.
[17]欧进萍,肖仪请,黄虎杰等.海洋平台结构实时安全监测系统[J].海洋工程,2001,19(2)：1-6.
[18]李宏男,李东升.土木工程结构安全性评估、健康监测及诊断述评[J].地震工程与工程振动,2002,22(3)：82-90.
[19]Housner G W, Bergman L A, Caughey T K, et al. Structural control:past, present, and.future[J]. Journal of Engineering Mechanics, ASCE,1997,123(9):897-971.
[20]肖健,吕爱琴,陈吉忠,等.无线传感器网络技术中的关键性问题[J].传感器世界,2004(7)：14-18.
[21]Ahmed Helmy. Mobility assisted resolution of queries in large-scale mobile sensor networks (MARQ)[J]. Computer Networks (Special Issue on Wireless Sensor Networks), 2003 (43):437-458.
[22]Mendez A., Morse T. F., Mendez F.. Applications of Embedded Optical Fiber Sensors in Reinforced Concrete Buildings and Structures[C]. Proc. SPIE,1989,1170:60-69.
[23]Mendez A.. Overview of fiber optic sensors for NDT applications[C]. IV NDT Panamerican Conference,2007:1-11.
[24]李宏男,李东升,赵柏东.光纤健康监测方法在土木工程中的研究与应用进展[J].地震工程与工程振动,2002,22(6)：76-83.
[25]Choquet P., Juneau F., Dadoun F. New Generation of Fiber-Optic Sensors for Dam Monitoring[C]. Proceedings of the 1999 International Conference on Dam Safety and Monitoring, In:Hubei, China,1999:713-721.
[26]Aftab A. Mufti. Structural Health Monitoring of Innovative Canadian Civil Engineering Structures[J]. Structural Health Monitoring,2002,1(1):89-103.
[27]Friebele J. E.. Fiber Bragg grating strain sensor:present and future applications in smart structures[J]. Optics and Photonics News,1998,9:33-37.
[28]Daniele Inaudi. Overview of fibre optic sensing to structural health monitoring applications[C]. International Symposium on Innovation&Sustainability of Structures in Civil Engineering, In:Nanjing, China,2005:1-16.
[29]王丹生,朱宏平.光纤光栅传感技术在桥梁结构健康监测中的应用[J].中外公路,2002,22(6)：31-33,43.
[30]Ferram Pietro, Natale De Giuseppe. On the possible use of optical fiber bagg gratings as strain sensors for geodynamical monitoring [J]. Optics and Lasers in Engineering,2002(37):115-30.
[31]Lau, C. K., Mak, W. P. N., Wong, K. Y., et al. Structural health monitoring of three cable-supported bridges in Hong Kong[J]. Structural Health Monitoring, 1999:450-460.
[32]郑小平,查开德,廖延彪.工程结构光纤应变传感器[J].光电工程,1997,24(5)：15-21.
[33]杨建良,查开德,郭照华.光纤网络用于复合材料结构状态检测的研究[J].应用光学,1999,20(4)：32-36,40.
[34]张林,蔡德所.光纤传感检测技术在水工结构模型试验研究中的应用[J].水利发电,2000(12)：51-53.
[35]蔡德所,.戴会超,蔡顺德,等.分布式光纤传感监测三峡大坝混凝土温度场试验研究[J].水利学报,2003(5)：88-91.
[36]姜德生,张圣配.光纤灵巧复合材料研究综述[J].武汉工业大学学报,1993,15(1)：51-57.
[37]梁磊,姜德生,罗裴,等.光纤Bragg光栅在结构健康监测中的实验研究[J].山东理工大学学报,2003,17(3)：4-7.
[38]梁磊,姜德生,周雪芳,等.光纤Bragg光栅传感器在桥梁工程中的应用[J].光学与光电技术,2003,1(2)：36-39.
[39]姜德生,李盛,刘胜春.光纤光栅传感系统在桥梁重载车识别中的应用[J].中外公路,2007,27(3)：153-155.
[40]欧进萍,周智,武湛君,等.黑龙江呼兰河大桥的光纤光栅智能监测技术[J].土木工程学报,2004,37(1)：45-49,64.
[41]田石柱,赵雪峰,欧进萍,等.结构健康监测用光纤Bragg光栅温度补偿研究[J].传感器技术,2002,21(12)：8-10.
[42]万里冰,张博明,王殿富,等.结构健康监测用光纤布拉格光栅应变传感器研究[J].激光杂志,2002,23(4)：47-48.
[43]万里冰,武湛君,张博明.埋光纤光栅传感器智能土木结构应变监测[J].力学与实践,2003,25(4)：35-38.
[44]张丹,施斌,吴智深,等.BOTDR分布式光纤传感器及其在结构健康监测中的应用[J].土木工程学报,2003,36(11)：83-87.
[45]Lynch, J. P., Law K. H., Kirereidjian A. S., et al. The design of a wireless sensing unit for structural health monitoring[C].3rd International Workshop on Structural Health Monitoring, Stanford, CA,2001:1041-1050.
[46]Maser K., Egri R., Lichtenstein, et al. Development of a wireless global bridge evaluation and monitoring system (WGBEMS) [C]. Proceedings of the Specialty Conference on Infrastructure Condition Assessment:Art, Science, Practice,1997: 91-100.
[47]Mitchell K., Dang N., Liu P., et al. Web-controlled wireless network sensors for structural health monitoring[C]. Proceedings-SPIE the International Society for Optical Engineering,2001, Vol.4334:234-243.
[48]Lynch J. P., Sundararajan A., Law K. H., et al. Embedding damage detection algorithms in a wireless sensing unit for attainment of operational power efficiency. Smart Mater. Struct.,2004,13(4):800-810.
[49]李宏伟,欧进萍.无线传感器网络在土木工程应用中的试验研究[J].计算机工程与应用,2005,15：207-210,214.
[50]Ou J. P., Li H. W., Yu Y. Development and performance of wireless sensor network for structural health monitoring [C]. Proc. SPIE, Smart Structures and Materials 2004:Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems,2004,539:1765-773.
[51]Yu yan, Ou Jinping. Design and validation of wireless acceleration sensor network for structural health monitor ing [J]. High Technology Letters,2006,12(4):358-362.
[52]叶湘滨,陈利虎,胡罡.无线传感器网络在环境监测中的应用[J].计算机测量与控制,2004,12(11)：1033-1035.
[53]叶伟松,袁慎芳.无线传感网络在结构健康监测中的应用[J].传感技术学报,2006,19(3)：890-894.
[54]殷悦,袁慎芳,吴键,等.基于无线传感器网络的远距离结构健康监测[J].传感器与微
系统,2007,26(6)：33-35,38.
[55]徐春红,吉林,沈庆宏等.基于无线传感器网络的桥梁结构健康监测系统[J].电子测量技术,2008,31(11)：95-98.
[56]张毅刚,乔立岩.虚拟仪器软件开发环境Lab Windows/CVI6.0编程指南[M].机械工业出版社,2002：129-146.
[57]林正盛.浅谈虚拟仪器技术的演化与发展[J].微计算机信息,1997,13(3)：66-67.
[58]林正盛.浅谈虚拟仪器技术的演化与发展[J].微计算机信息,1997,13(4)：65-66.
[59]林正盛.浅谈虚拟仪器技术的演化与发展[J].微计算机信息,1997,13(5)：66-67.
[60]Hites M, Sekerak M, Sanders L. Implementing and evaluating Web-based "Hands-On" laboratories for undergraduate education[C]. ASEE IL/IN Sectional Conference, 1999:201-209.
[61]Ko C. C., Chen B. M.. A large scale Web-based virtual oscilloscope laboratory experiment[J]. Eng. Sci. Educ. J.,2000,9(2):69-76.
[62]Ko C. C., Chen B. M.. Development of a Web-based laboratory for control experiments on a coupled tank apparatus[J]. IEEE Transactions on Education,2001,44(1): 76-86.
[63]Marco Casini. The automatic control telelab:a user-friendly interface for distance learning[J]. IEEE Transactions on Education,2003,46(2):252-257.
[64]程虎.从智能仪器到虚拟仪器—现代仪器的重大进展[J].现代科学仪器,1994(3)：6-9.
[65]Jamal., R,吴晓峰.在Internet时代用虚拟仪器实现测量,控制和调节[J].最新电子技术应用,1997,2(3)：41-44.
[66]梅杓春,韩剑锋.组建VXI测控网络[J].计算机自动测量与控制,1999,7(1)：44-46,50.
[67]李惠,周文松,欧进萍,等.大型桥梁结构智能健康监测系统集成技术研究[J].土木工程学报,2006,39(2)：46-52.
[68]肖纯,瞿伟廉,谭冬梅.虚拟仪器技术在结构远程健康监测中的应用[J].通讯和计算机,2005,2(2)：44-47.
[69]燕延,刘玉红,高占凤.基于LabVIEW的远程桥梁健康监测系统[J].石家庄铁道学院学报,2005,18(3)：37-39,51.
[70]燕延,马增强,石彦丛.基于LabVIEW的桥梁运行状态长期监测系统的[J].仪表技术,2005(1)：15-17.
[71]高占凤,杜彦良,苏木标,等.基于虚拟仪器的桥梁远程状态数据采集系统[J].仪器仪表学报,2006,27(10)：1361-1364.
[72]高占凤,杜彦良,苏木标.桥梁振动状态远程监测系统研究[J].北京交通大学学报：自然科学版,2007,31(4)：45-48,60.
[73]高占凤,杜彦良,刘玉红,等.基于LabVIEW的远程数据采集与传输系统[J].微电子学与计算机,2007,24(34)：102-104.
[74]Venkatasubramanian V., Chan K.. A neural network methodology for process fault diagnosis[J]. Journal of AICHE,1989,35(12):1993-2002.
[75]Wu X. Ghaboussi Garrett JH. Use of neural networks in detection of structural damage [J]. Computers and Structures,1992,42(4):649-659.
[76]Donald A.. Structural health monitoring using neural network based vibrational system identif ication[C]. Proceedings of the Australia and New Zealand Conference on Intelligent Information Systems,1994:1-4.
[77]Szewezyk, P,. Hajela P.. Damage detection in structures based on feature-sensitivity neural networks[J]. Journal of Computing in Civil Engineering,1994,8(2):163-179.
[78]Elkordy M. F., Chang K. C., Lee G. C.. Application of neural networks in vibrational signature analysis[J]. Journal of Engineering Mechanics,1994,120(2):251-264.
[79]Pandey P. C., Barai S. V.. Multilayer perceptron in damage prediction of bridge structures[J]. Computers and Structures,1995,54(4):597-608.
[80]Anatha R.S., Johnson V. T.. Damage assessment of composite structures-a neural network approach[J]. Computer&Structures,1995,57(3):491-502.
[81]Rhim J., Lee S. W.. A neural network approach for damage detection and identification of structures[J]. Computational Mechanics,1995,16(6):437-443.
[82]Ka-Veng Yuen, Heung-Fai Lam. On the complexity of artificial neural networks for smart structures monitoring[J]. Engineering Structures,2006(28):977-984.
[83]韩小云,刘瑞言.基于神经网络和模糊综合评判的梁故障诊断研究[J].国防科技大学学报1996,18(1)：17-22.
[84]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].振动工程学报,2001,14(1)：60-64.
[85]王柏生,倪一清,高赞明.框架结构连接损伤识别神经网络输入参数的确定[J].振动工程学报,2000,13(1)：137-142.
[86]王柏生,丁皓江,倪一清.模型参数误差对用神经网络进行结构损伤识别的影响[J].土木工程学报,2000,33(1)：50-55.
[87]王柏生,倪一清.青马大桥桥板结构损伤位置识别的数值模拟[J].土木工程学报,2001,34(3)：67-73.
[88]姜绍飞,倪一清,高赞明.基于概率神经网络的青马悬索桥损伤定位的仿真研究[J].工程力学,2001(s)：965-969.
[89]瞿伟廉,陈伟.多层及高层框架结构地震损伤诊断的神经网络方法[J].地震工程与工程振动,2002,22(1)：43-48.
[90]瞿伟廉,陈伟,李秋胜.基于神经网络技术的复杂框架结构节点损伤的两步诊断法[J].2003,36(5)：37-45.
[91]瞿伟廉,谭冬梅,汪菁,等.基于神经网络的大型空间网架结构的有限元模型修正[J].地震工程与工程振动,2003,23(4)：83-89.
[92]袁慎芳,陶宝棋,王昕.双BP网格在损伤评估智能结构中的应用[J].实验力学,1998,13(2)：174-178.
[93]王磊,袁慎芳.Kohonen神经网络在复合材料损伤主动监测技术中的应用[J].材料科学与工程,2002,20(4)：513-516.
[94]Shenfang Yuan, Lei Wang, Ge Peng. Neural network method based on a new damage signature for structural health monitoring[J]. Thin-Walled Structures,2005 (43):553-563.
[95]Fekih A., Xu H., Chowdhury F. N.. Neural networks based system identification techniques for model based fault detection of nonlinear systems [J]. International Journal of Innovative Computing, Information and Control,2007,3(5):1073-1085.
[96]姜绍飞,付春,陈仲堂,等.基于WPNN与数据融合的损伤检测方法[J].沈阳建筑大学学报：自然科学版,2005,21(2)：86-90.
[97]郭琦,贺拴海,白云.基于神经网络的简支梁桥预应力衰减评估模型[J].长安大学学报：自然科学版,2007,27(6)：53-57.
[98]李兆,唐雪松,陈星烨.基于曲率模态和神经网络的分步损伤识别法及其在桥梁结构中的应用[J].长沙理工大学学报：自然科学版,2008,5(2)：32-37.
[99]吴子燕,杨海峰,覃小文,等.基于自适应概率神经网络的损伤模式识别研究[J].振动与冲击,2008,27(7)：8-12.
[100]Sone A. Health monitoring system of structures based on orthogonal wavelet transform. Seismic Engineering[J]. Transactions of ASME,1995,312:161-167.
[101]Owen J. S., Van n A.M., Daviesand A.. The prototype testing of Kessock Bridge: Response to vortex shedding[J]. Journal of Wind Engineering and Industrial Aerodynamics,1996(60):91-106.
[102]Liew K. M., Wang Q.. Application of wavelet theory for crack identification in structures[J]. Journal of Engineering Mechanics,1998,124(2):152-157.
[103]Wang Q., Deng X.. Damage detection with spatial wavelets[J]. International Journal of Solids and Structures,1999,36(23):3443-3468.
[104]Hou Z., Noori M., Amand, R.. Wavelet-based approach for structural damage detection[J]. ASCE Journal of Engineering Mechanics,2000,126(7):667-683.
[105]Hou Z., Noori M.. Wavelet-based approach for ASCE structural health monitoring benchmark studies[C]. Proceedings of the 3rd International Workshop on Structural Health Monitoring, Stanford University, Stanford, CA,2001:12-14.
[106]Segawa R. System identification of MDOF structures by wavelet transform[C]. Proceedings of the U. S.-Japan Joint Workshop and Third Grantees Meeting, Monbu-Kagaku-sho and NSF,2001:15-16.
[107]Lu C. J., Hsu Y. T.. Vibration analysis of an inhomogeneous string for damage detection by wavelet transform [J]. International Journal of Mechanical Sciences, 2002,44(4):745-754.
[108]Sun Z., Chang C. C.. Structural damage assessment based on wavelet packet transform[J]. Journal of Structure Engineering,2002,128(10):1354-1361.
[109]Chuang C. C., Chen L. W.. Vibration damage detection of a Timoshenko.beam by spatial wavelet based approach[J]. Applied Acoustics,2003,64(12):1217-1240.
[110]Chang C. C., Sun Z.. Structural damage localization using spatial wavelet packet signature[J]. Smart Structures and Systems,2005,1(1):29-46.
[111]Douka E., Loutridis S., Trochidis A.. Crack identification in beams using wavelet analysis[J]. International Journal of Solids and Structures,2003, 40(13-14):3557-3569.
[112]Douka E., Loutridis S., Trochidis A.. Crack identification in plates using wavelet analysis[J], Journal of Sound and Vibration,2004,270(1-2):279-295.
[113]Ovanesova A. V., Suarez L.E.. Applications of wavelet transform to damage detection in frame structures[J]. Engineering Structures,2004,26(1):39-49.
[114]Hera A., Hou Z. Application of wavelet approach for ASCE structural health
monitoring benchmark studies[J]. ASCE Journal of Engineering Mechanics,2003, 130(1):96-104.
[115]Bajaba N. S., Alnefaie K. A.. Multiple damage detection in structures using wavelet transforms[J]. Emirates Journal for Engineering Research,2005, 10(1):35-40.
[116]Castro E., Garcia-Hernandez M. T., Gallego A.. Damage detection in rods by means of the wavelet analysis of vibrations:Influence of the mode order[J]. Journal of Sound and Vibration,2006, (296):1028-1038.
[117]Ghasemi M. R., Ghorbani A.. Application of wavelet neural networks in optimization of skeletal buildings under frequency constraints [J]. International Journal of Intelligent Technologies,2007,2(4):223-231.
[118]吴耀军,陶宝祺,史习智.二进小波变换在复合材料损伤检测中的应用[J].仪器仪表学报,1997,18(6)：636-639.
[119]吴耀军,陶宝祺.基于小波神经网络的复合材料损伤检测[J].航空学报,1997,18(2)：252-256.
[120]赵霞,袁慎芳,王帮峰.用于结构健康监测的实时DSP数据采集及小波分析模块[J].测控技术,2002,22(4)：16-20.
[121]孙增寿,韩建刚,任伟新.基于小波分析的结构损伤检测研究进展[J].地震工程与工程振动,2005,25(2)：93-99.
[122]杜永峰,陈文元.小波分析与神经网络在结构损伤监测中的应用[J].兰州理工大学学报,2005,31(5)：121-124.
[123]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.
[124]刘涛,李爱群,丁幼亮.小波分析在结构损伤识别中的应用[J].地震工程与工程振动,2008,26(2)：29-35.
[125]孙亚杰,袁慎芳,王帮峰.基于HHT技术的二维结构损伤定位研究[J].压电与声光,2007,29(6)：736-739.
[126]Norden Huang, Kang Huang.基于希尔伯特-黄变换的铁路桥梁结构健康监测[J].中国铁道科学,2006,26(1)：1-7.
[127]程磊,瞿伟廉.基于Hilbert-Huang变换理论的结构损伤检测[J].防灾减灾工程学报,2007,27(3)：318-322.
[128]Sub M W, Shim M B, Kim M Y.. Crack identification using hybrid Nero-Genetic technique[J]. Journal of sound and Vibration,2000,238(4):617-635.
[129]Hong Hao, M. ASCE, Yong Xia. Vibration-based Damage Detection of Structures by Genetic Algorithm[J]. Journal of Computing in Civil Engineering,2002,. 16(3):222-229.
[130]朱劲松,肖汝诚.基于定期检测与遗传算法的大跨度斜拉桥损伤识别[J].土木工程学报,2006,39(5)：85-89.
[131]Meyyappan, Jose L., Dagli M., et al. Fuzzy-neuro system for bridge health monitoring[C]. NAFIPS2003, International Conference of the North American, 2003:8-13.
[132]Reda Taha M. M., Lucero J.. Damage identification for structural health monitoring using fuzzy pattern recognition[J]. Engineering Structures,2005,27, (12):1774-1783.
[133]马亚丽,王东炜,张爱林.在役桥梁结构健康等级的多级模糊综合评判[J].北京工业大学学报,2005,31(1)：36-40.
[134]魏华,李清富,肖理中.基于模糊数学理论的混凝土结构耐久性评估[J].水利学,2007(10)：151-154.
[135]杨宝华.基于Matlab的BP神经网络应用[J].人工智能及识别技术,2008,19(30)：124-127.
[136]宋振宇,王秋彦,丁小峰.BP神经网络训练中的实际问题研究[J].海军航空工程学院学报,2009,24(6)：704-706,720.
[137]张杨,李国强.通用频率指纹库在固接梁损伤定位中的应用[J].建筑科学与工程学报,2005,22(4)：40-44.
[138]Hwanga H. Y., Kim C.. Damage detection in structures using a few frequency response measurements[J]. Journal of Sound and Vibration,2004,270(1-2):1-14.
[2]张启伟.大型桥梁结构安全监测的研究现状与发展[J].同济大学学报：自然科学版,1997,25(增刊)：76-81.
[3]Muria V D, Gomez R, King C. Dynamic structural properties of cable stayed Tampico Bridge[J]. Journal of Structural Engineering, ASCE,1991,117(11):3396-3416.
[4]李兆霞,李爱群,陈鸿天,等.大跨桥梁结构以健康监测和状态评估为目标的有限元模拟[J].东南大学学报：自然科学版,2003,33(5)：562-572.
[5]Aktan A E, Ciloglu S K, Grimmelsman K A. Infrastructure health monitoring: State-of-the-art, challenges and opportunities[C]. International SAMPE Technical Conference, Long Beach, CA,2004:1-7.
[6]Pines D, Akmn A E. Status of structural health monitoring of long-span bridges in the United States [J]. Progress of Structure Engineering and Materials,2002 (4): 372-380.
[7]Fujino Y, Abe M. Structural health monitoring-current status and future[C]. Proceedings of the 2nd European workshop on structural health monitoring, Lancaster(PA):DEStech,2004:3-10.
[8]Sumitro S., Okamoto T., Matsui Y., et al. Long span bridge health monitoring system in Japan[C]. Proc. SPIE.,2001,4337(67):517-524.
[9]邬晓光,徐祖恩.大型桥梁健康监测动态及发展趋势[J].长安大学学报：自然科学版,2003,23(1)：39-42.
[10]刘西拉.重大土木与水利工程安全性及耐久性的基础研究[J].土木工程学报,2001,34(6)：1-7.
[11]史家钧,邵志常.上海徐浦大桥结构状态监测系统[C].第十三届全国桥梁学术会议论文集,上海,1998(11)：16-19.
[12]黄方林,王学敏,陈政清,等.大型桥梁健康监测研究进展[J].中国铁道科学,2005,26(2)：1-7.
[13]Habel W., Kohlhoff H., Knapp J., et al. Monitoring System for Long-term evaluation of prestressed railway bridges in the new Lehrter Bahnhof in Berlin[C]. Third World Conference on Strucutral Control, In:Como, Italy.2002:1-6.
[14]Whelan M.. Remote structural monitoring of the Cathedral of Como using an optical fibre bragg sensor sy stem [J]. Smart structures and materials,2002,4694:242-252.
[15]Nataraja R.. Structural integrity monitoring in real seas[C]. Proceedings of the 15th Annual Offshore Technology Conference,1983:221-228.
[16]柳春图.海洋石油平台结构的安全性监测与评估技术的若干进展[C].中国力学学会现代力学进步学术大会,北京,1997：397-400.
[17]欧进萍,肖仪请,黄虎杰等.海洋平台结构实时安全监测系统[J].海洋工程,2001,19(2)：1-6.
[18]李宏男,李东升.土木工程结构安全性评估、健康监测及诊断述评[J].地震工程与工程振动,2002,22(3)：82-90.
[19]Housner G W, Bergman L A, Caughey T K, et al. Structural control:past, present, and.future[J]. Journal of Engineering Mechanics, ASCE,1997,123(9):897-971.
[20]肖健,吕爱琴,陈吉忠,等.无线传感器网络技术中的关键性问题[J].传感器世界,2004(7)：14-18.
[21]Ahmed Helmy. Mobility assisted resolution of queries in large-scale mobile sensor networks (MARQ)[J]. Computer Networks (Special Issue on Wireless Sensor Networks), 2003 (43):437-458.
[22]Mendez A., Morse T. F., Mendez F.. Applications of Embedded Optical Fiber Sensors in Reinforced Concrete Buildings and Structures[C]. Proc. SPIE,1989,1170:60-69.
[23]Mendez A.. Overview of fiber optic sensors for NDT applications[C]. IV NDT Panamerican Conference,2007:1-11.
[24]李宏男,李东升,赵柏东.光纤健康监测方法在土木工程中的研究与应用进展[J].地震工程与工程振动,2002,22(6)：76-83.
[25]Choquet P., Juneau F., Dadoun F. New Generation of Fiber-Optic Sensors for Dam Monitoring[C]. Proceedings of the 1999 International Conference on Dam Safety and Monitoring, In:Hubei, China,1999:713-721.
[26]Aftab A. Mufti. Structural Health Monitoring of Innovative Canadian Civil Engineering Structures[J]. Structural Health Monitoring,2002,1(1):89-103.
[27]Friebele J. E.. Fiber Bragg grating strain sensor:present and future applications in smart structures[J]. Optics and Photonics News,1998,9:33-37.
[28]Daniele Inaudi. Overview of fibre optic sensing to structural health monitoring applications[C]. International Symposium on Innovation&Sustainability of Structures in Civil Engineering, In:Nanjing, China,2005:1-16.
[29]王丹生,朱宏平.光纤光栅传感技术在桥梁结构健康监测中的应用[J].中外公路,2002,22(6)：31-33,43.
[30]Ferram Pietro, Natale De Giuseppe. On the possible use of optical fiber bagg gratings as strain sensors for geodynamical monitoring [J]. Optics and Lasers in Engineering,2002(37):115-30.
[31]Lau, C. K., Mak, W. P. N., Wong, K. Y., et al. Structural health monitoring of three cable-supported bridges in Hong Kong[J]. Structural Health Monitoring, 1999:450-460.
[32]郑小平,查开德,廖延彪.工程结构光纤应变传感器[J].光电工程,1997,24(5)：15-21.
[33]杨建良,查开德,郭照华.光纤网络用于复合材料结构状态检测的研究[J].应用光学,1999,20(4)：32-36,40.
[34]张林,蔡德所.光纤传感检测技术在水工结构模型试验研究中的应用[J].水利发电,2000(12)：51-53.
[35]蔡德所,.戴会超,蔡顺德,等.分布式光纤传感监测三峡大坝混凝土温度场试验研究[J].水利学报,2003(5)：88-91.
[36]姜德生,张圣配.光纤灵巧复合材料研究综述[J].武汉工业大学学报,1993,15(1)：51-57.
[37]梁磊,姜德生,罗裴,等.光纤Bragg光栅在结构健康监测中的实验研究[J].山东理工大学学报,2003,17(3)：4-7.
[38]梁磊,姜德生,周雪芳,等.光纤Bragg光栅传感器在桥梁工程中的应用[J].光学与光电技术,2003,1(2)：36-39.
[39]姜德生,李盛,刘胜春.光纤光栅传感系统在桥梁重载车识别中的应用[J].中外公路,2007,27(3)：153-155.
[40]欧进萍,周智,武湛君,等.黑龙江呼兰河大桥的光纤光栅智能监测技术[J].土木工程学报,2004,37(1)：45-49,64.
[41]田石柱,赵雪峰,欧进萍,等.结构健康监测用光纤Bragg光栅温度补偿研究[J].传感器技术,2002,21(12)：8-10.
[42]万里冰,张博明,王殿富,等.结构健康监测用光纤布拉格光栅应变传感器研究[J].激光杂志,2002,23(4)：47-48.
[43]万里冰,武湛君,张博明.埋光纤光栅传感器智能土木结构应变监测[J].力学与实践,2003,25(4)：35-38.
[44]张丹,施斌,吴智深,等.BOTDR分布式光纤传感器及其在结构健康监测中的应用[J].土木工程学报,2003,36(11)：83-87.
[45]Lynch, J. P., Law K. H., Kirereidjian A. S., et al. The design of a wireless sensing unit for structural health monitoring[C].3rd International Workshop on Structural Health Monitoring, Stanford, CA,2001:1041-1050.
[46]Maser K., Egri R., Lichtenstein, et al. Development of a wireless global bridge evaluation and monitoring system (WGBEMS) [C]. Proceedings of the Specialty Conference on Infrastructure Condition Assessment:Art, Science, Practice,1997: 91-100.
[47]Mitchell K., Dang N., Liu P., et al. Web-controlled wireless network sensors for structural health monitoring[C]. Proceedings-SPIE the International Society for Optical Engineering,2001, Vol.4334:234-243.
[48]Lynch J. P., Sundararajan A., Law K. H., et al. Embedding damage detection algorithms in a wireless sensing unit for attainment of operational power efficiency. Smart Mater. Struct.,2004,13(4):800-810.
[49]李宏伟,欧进萍.无线传感器网络在土木工程应用中的试验研究[J].计算机工程与应用,2005,15：207-210,214.
[50]Ou J. P., Li H. W., Yu Y. Development and performance of wireless sensor network for structural health monitoring [C]. Proc. SPIE, Smart Structures and Materials 2004:Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems,2004,539:1765-773.
[51]Yu yan, Ou Jinping. Design and validation of wireless acceleration sensor network for structural health monitor ing [J]. High Technology Letters,2006,12(4):358-362.
[52]叶湘滨,陈利虎,胡罡.无线传感器网络在环境监测中的应用[J].计算机测量与控制,2004,12(11)：1033-1035.
[53]叶伟松,袁慎芳.无线传感网络在结构健康监测中的应用[J].传感技术学报,2006,19(3)：890-894.
[54]殷悦,袁慎芳,吴键,等.基于无线传感器网络的远距离结构健康监测[J].传感器与微
系统,2007,26(6)：33-35,38.
[55]徐春红,吉林,沈庆宏等.基于无线传感器网络的桥梁结构健康监测系统[J].电子测量技术,2008,31(11)：95-98.
[56]张毅刚,乔立岩.虚拟仪器软件开发环境Lab Windows/CVI6.0编程指南[M].机械工业出版社,2002：129-146.
[57]林正盛.浅谈虚拟仪器技术的演化与发展[J].微计算机信息,1997,13(3)：66-67.
[58]林正盛.浅谈虚拟仪器技术的演化与发展[J].微计算机信息,1997,13(4)：65-66.
[59]林正盛.浅谈虚拟仪器技术的演化与发展[J].微计算机信息,1997,13(5)：66-67.
[60]Hites M, Sekerak M, Sanders L. Implementing and evaluating Web-based "Hands-On" laboratories for undergraduate education[C]. ASEE IL/IN Sectional Conference, 1999:201-209.
[61]Ko C. C., Chen B. M.. A large scale Web-based virtual oscilloscope laboratory experiment[J]. Eng. Sci. Educ. J.,2000,9(2):69-76.
[62]Ko C. C., Chen B. M.. Development of a Web-based laboratory for control experiments on a coupled tank apparatus[J]. IEEE Transactions on Education,2001,44(1): 76-86.
[63]Marco Casini. The automatic control telelab:a user-friendly interface for distance learning[J]. IEEE Transactions on Education,2003,46(2):252-257.
[64]程虎.从智能仪器到虚拟仪器—现代仪器的重大进展[J].现代科学仪器,1994(3)：6-9.
[65]Jamal., R,吴晓峰.在Internet时代用虚拟仪器实现测量,控制和调节[J].最新电子技术应用,1997,2(3)：41-44.
[66]梅杓春,韩剑锋.组建VXI测控网络[J].计算机自动测量与控制,1999,7(1)：44-46,50.
[67]李惠,周文松,欧进萍,等.大型桥梁结构智能健康监测系统集成技术研究[J].土木工程学报,2006,39(2)：46-52.
[68]肖纯,瞿伟廉,谭冬梅.虚拟仪器技术在结构远程健康监测中的应用[J].通讯和计算机,2005,2(2)：44-47.
[69]燕延,刘玉红,高占凤.基于LabVIEW的远程桥梁健康监测系统[J].石家庄铁道学院学报,2005,18(3)：37-39,51.
[70]燕延,马增强,石彦丛.基于LabVIEW的桥梁运行状态长期监测系统的[J].仪表技术,2005(1)：15-17.
[71]高占凤,杜彦良,苏木标,等.基于虚拟仪器的桥梁远程状态数据采集系统[J].仪器仪表学报,2006,27(10)：1361-1364.
[72]高占凤,杜彦良,苏木标.桥梁振动状态远程监测系统研究[J].北京交通大学学报：自然科学版,2007,31(4)：45-48,60.
[73]高占凤,杜彦良,刘玉红,等.基于LabVIEW的远程数据采集与传输系统[J].微电子学与计算机,2007,24(34)：102-104.
[74]Venkatasubramanian V., Chan K.. A neural network methodology for process fault diagnosis[J]. Journal of AICHE,1989,35(12):1993-2002.
[75]Wu X. Ghaboussi Garrett JH. Use of neural networks in detection of structural damage [J]. Computers and Structures,1992,42(4):649-659.
[76]Donald A.. Structural health monitoring using neural network based vibrational system identif ication[C]. Proceedings of the Australia and New Zealand Conference on Intelligent Information Systems,1994:1-4.
[77]Szewezyk, P,. Hajela P.. Damage detection in structures based on feature-sensitivity neural networks[J]. Journal of Computing in Civil Engineering,1994,8(2):163-179.
[78]Elkordy M. F., Chang K. C., Lee G. C.. Application of neural networks in vibrational signature analysis[J]. Journal of Engineering Mechanics,1994,120(2):251-264.
[79]Pandey P. C., Barai S. V.. Multilayer perceptron in damage prediction of bridge structures[J]. Computers and Structures,1995,54(4):597-608.
[80]Anatha R.S., Johnson V. T.. Damage assessment of composite structures-a neural network approach[J]. Computer&Structures,1995,57(3):491-502.
[81]Rhim J., Lee S. W.. A neural network approach for damage detection and identification of structures[J]. Computational Mechanics,1995,16(6):437-443.
[82]Ka-Veng Yuen, Heung-Fai Lam. On the complexity of artificial neural networks for smart structures monitoring[J]. Engineering Structures,2006(28):977-984.
[83]韩小云,刘瑞言.基于神经网络和模糊综合评判的梁故障诊断研究[J].国防科技大学学报1996,18(1)：17-22.
[84]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].振动工程学报,2001,14(1)：60-64.
[85]王柏生,倪一清,高赞明.框架结构连接损伤识别神经网络输入参数的确定[J].振动工程学报,2000,13(1)：137-142.
[86]王柏生,丁皓江,倪一清.模型参数误差对用神经网络进行结构损伤识别的影响[J].土木工程学报,2000,33(1)：50-55.
[87]王柏生,倪一清.青马大桥桥板结构损伤位置识别的数值模拟[J].土木工程学报,2001,34(3)：67-73.
[88]姜绍飞,倪一清,高赞明.基于概率神经网络的青马悬索桥损伤定位的仿真研究[J].工程力学,2001(s)：965-969.
[89]瞿伟廉,陈伟.多层及高层框架结构地震损伤诊断的神经网络方法[J].地震工程与工程振动,2002,22(1)：43-48.
[90]瞿伟廉,陈伟,李秋胜.基于神经网络技术的复杂框架结构节点损伤的两步诊断法[J].2003,36(5)：37-45.
[91]瞿伟廉,谭冬梅,汪菁,等.基于神经网络的大型空间网架结构的有限元模型修正[J].地震工程与工程振动,2003,23(4)：83-89.
[92]袁慎芳,陶宝棋,王昕.双BP网格在损伤评估智能结构中的应用[J].实验力学,1998,13(2)：174-178.
[93]王磊,袁慎芳.Kohonen神经网络在复合材料损伤主动监测技术中的应用[J].材料科学与工程,2002,20(4)：513-516.
[94]Shenfang Yuan, Lei Wang, Ge Peng. Neural network method based on a new damage signature for structural health monitoring[J]. Thin-Walled Structures,2005 (43):553-563.
[95]Fekih A., Xu H., Chowdhury F. N.. Neural networks based system identification techniques for model based fault detection of nonlinear systems [J]. International Journal of Innovative Computing, Information and Control,2007,3(5):1073-1085.
[96]姜绍飞,付春,陈仲堂,等.基于WPNN与数据融合的损伤检测方法[J].沈阳建筑大学学报：自然科学版,2005,21(2)：86-90.
[97]郭琦,贺拴海,白云.基于神经网络的简支梁桥预应力衰减评估模型[J].长安大学学报：自然科学版,2007,27(6)：53-57.
[98]李兆,唐雪松,陈星烨.基于曲率模态和神经网络的分步损伤识别法及其在桥梁结构中的应用[J].长沙理工大学学报：自然科学版,2008,5(2)：32-37.
[99]吴子燕,杨海峰,覃小文,等.基于自适应概率神经网络的损伤模式识别研究[J].振动与冲击,2008,27(7)：8-12.
[100]Sone A. Health monitoring system of structures based on orthogonal wavelet transform. Seismic Engineering[J]. Transactions of ASME,1995,312:161-167.
[101]Owen J. S., Van n A.M., Daviesand A.. The prototype testing of Kessock Bridge: Response to vortex shedding[J]. Journal of Wind Engineering and Industrial Aerodynamics,1996(60):91-106.
[102]Liew K. M., Wang Q.. Application of wavelet theory for crack identification in structures[J]. Journal of Engineering Mechanics,1998,124(2):152-157.
[103]Wang Q., Deng X.. Damage detection with spatial wavelets[J]. International Journal of Solids and Structures,1999,36(23):3443-3468.
[104]Hou Z., Noori M., Amand, R.. Wavelet-based approach for structural damage detection[J]. ASCE Journal of Engineering Mechanics,2000,126(7):667-683.
[105]Hou Z., Noori M.. Wavelet-based approach for ASCE structural health monitoring benchmark studies[C]. Proceedings of the 3rd International Workshop on Structural Health Monitoring, Stanford University, Stanford, CA,2001:12-14.
[106]Segawa R. System identification of MDOF structures by wavelet transform[C]. Proceedings of the U. S.-Japan Joint Workshop and Third Grantees Meeting, Monbu-Kagaku-sho and NSF,2001:15-16.
[107]Lu C. J., Hsu Y. T.. Vibration analysis of an inhomogeneous string for damage detection by wavelet transform [J]. International Journal of Mechanical Sciences, 2002,44(4):745-754.
[108]Sun Z., Chang C. C.. Structural damage assessment based on wavelet packet transform[J]. Journal of Structure Engineering,2002,128(10):1354-1361.
[109]Chuang C. C., Chen L. W.. Vibration damage detection of a Timoshenko.beam by spatial wavelet based approach[J]. Applied Acoustics,2003,64(12):1217-1240.
[110]Chang C. C., Sun Z.. Structural damage localization using spatial wavelet packet signature[J]. Smart Structures and Systems,2005,1(1):29-46.
[111]Douka E., Loutridis S., Trochidis A.. Crack identification in beams using wavelet analysis[J]. International Journal of Solids and Structures,2003, 40(13-14):3557-3569.
[112]Douka E., Loutridis S., Trochidis A.. Crack identification in plates using wavelet analysis[J], Journal of Sound and Vibration,2004,270(1-2):279-295.
[113]Ovanesova A. V., Suarez L.E.. Applications of wavelet transform to damage detection in frame structures[J]. Engineering Structures,2004,26(1):39-49.
[114]Hera A., Hou Z. Application of wavelet approach for ASCE structural health
monitoring benchmark studies[J]. ASCE Journal of Engineering Mechanics,2003, 130(1):96-104.
[115]Bajaba N. S., Alnefaie K. A.. Multiple damage detection in structures using wavelet transforms[J]. Emirates Journal for Engineering Research,2005, 10(1):35-40.
[116]Castro E., Garcia-Hernandez M. T., Gallego A.. Damage detection in rods by means of the wavelet analysis of vibrations:Influence of the mode order[J]. Journal of Sound and Vibration,2006, (296):1028-1038.
[117]Ghasemi M. R., Ghorbani A.. Application of wavelet neural networks in optimization of skeletal buildings under frequency constraints [J]. International Journal of Intelligent Technologies,2007,2(4):223-231.
[118]吴耀军,陶宝祺,史习智.二进小波变换在复合材料损伤检测中的应用[J].仪器仪表学报,1997,18(6)：636-639.
[119]吴耀军,陶宝祺.基于小波神经网络的复合材料损伤检测[J].航空学报,1997,18(2)：252-256.
[120]赵霞,袁慎芳,王帮峰.用于结构健康监测的实时DSP数据采集及小波分析模块[J].测控技术,2002,22(4)：16-20.
[121]孙增寿,韩建刚,任伟新.基于小波分析的结构损伤检测研究进展[J].地震工程与工程振动,2005,25(2)：93-99.
[122]杜永峰,陈文元.小波分析与神经网络在结构损伤监测中的应用[J].兰州理工大学学报,2005,31(5)：121-124.
[123]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.
[124]刘涛,李爱群,丁幼亮.小波分析在结构损伤识别中的应用[J].地震工程与工程振动,2008,26(2)：29-35.
[125]孙亚杰,袁慎芳,王帮峰.基于HHT技术的二维结构损伤定位研究[J].压电与声光,2007,29(6)：736-739.
[126]Norden Huang, Kang Huang.基于希尔伯特-黄变换的铁路桥梁结构健康监测[J].中国铁道科学,2006,26(1)：1-7.
[127]程磊,瞿伟廉.基于Hilbert-Huang变换理论的结构损伤检测[J].防灾减灾工程学报,2007,27(3)：318-322.
[128]Sub M W, Shim M B, Kim M Y.. Crack identification using hybrid Nero-Genetic technique[J]. Journal of sound and Vibration,2000,238(4):617-635.
[129]Hong Hao, M. ASCE, Yong Xia. Vibration-based Damage Detection of Structures by Genetic Algorithm[J]. Journal of Computing in Civil Engineering,2002,. 16(3):222-229.
[130]朱劲松,肖汝诚.基于定期检测与遗传算法的大跨度斜拉桥损伤识别[J].土木工程学报,2006,39(5)：85-89.
[131]Meyyappan, Jose L., Dagli M., et al. Fuzzy-neuro system for bridge health monitoring[C]. NAFIPS2003, International Conference of the North American, 2003:8-13.
[132]Reda Taha M. M., Lucero J.. Damage identification for structural health monitoring using fuzzy pattern recognition[J]. Engineering Structures,2005,27, (12):1774-1783.
[133]马亚丽,王东炜,张爱林.在役桥梁结构健康等级的多级模糊综合评判[J].北京工业大学学报,2005,31(1)：36-40.
[134]魏华,李清富,肖理中.基于模糊数学理论的混凝土结构耐久性评估[J].水利学,2007(10)：151-154.
[135]杨宝华.基于Matlab的BP神经网络应用[J].人工智能及识别技术,2008,19(30)：124-127.
[136]宋振宇,王秋彦,丁小峰.BP神经网络训练中的实际问题研究[J].海军航空工程学院学报,2009,24(6)：704-706,720.
[137]张杨,李国强.通用频率指纹库在固接梁损伤定位中的应用[J].建筑科学与工程学报,2005,22(4)：40-44.
[138]Hwanga H. Y., Kim C.. Damage detection in structures using a few frequency response measurements[J]. Journal of Sound and Vibration,2004,270(1-2):1-14.