基于残差均方根值法的结构损伤识别理论与应用
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摘要
桥梁损伤识别与健康监控是近年来国际上的研究热点。桥梁在一个国家的交通运输和经济发展中占有重要位置,桥梁检测是保证桥梁安全运营的重要手段。基于振动的桥梁损伤检测技术是利用获取的桥梁振动信号对桥梁工作状态进行全面评估,从而达到确保桥梁的安全运营,防止事故发生的目的。同时,通过桥梁健康监控系统的预警机制,提早发现桥梁病害,从而大大节约维修费用,降低桥梁事故率。本文探索了以下三个方面:结构损伤识别方法原理、基于神经网络的残差均方根值法在简支梁损伤识别试验中的应用、长沙银盆岭大桥损伤识别的仿真分析。
本文提出衡量结构健康状况的新方法-基于BP神经网络的残差均方根值法。此方法利用完好结构在环境激励下随机振动的加速度响应信号得到自由衰减信号,将此信号作为BP神经网络的训练样本训练神经网络,则此网络记录了结构的初始信息。当其工作状态发生变化时,该网络相同测点的输入值与输出值之间会产生较大差别,定义这种差值的均方根为残差均方根,将其作为损伤指标。随着损伤位置与程度的变化,该指标也会发生相应改变。
在高强钢筋混凝土足尺梁的静载试验过程中,进行分阶段布点测试,该方法能够较好识别出裂缝出现的位置及损伤程度,识别结果与仿真结果相对比,具有较好的相似性。
建立长沙银盆岭大桥有限元模型,用白噪声信号模拟环境激励,对该桥进行时程分析,计算得到各节点的加速度响应序列,利用自然环境激励技术(Natural Excitation Technique, NExT)求取各测点的自由衰减响应序列,并使用该系列数据建立此桥神经网络的初始非参数模型,在不同程度、不同位置模拟损伤的情形下,进行损伤识别,具有良好的识别效果。
Bridge damage identification and health monitoring is recently a hot point in the world. The bridge is an important part in the transportation and economic development for the country, and the bridge damage detection can ensure the safety of the bridge. The technique of damage detection based on vibration is to evaluate the bridge’s condition using vibration signals. This technique is expected to find little damage and identify the position of the damage before dangerous events happen. And also, it can help people learn about the condition of the bridge, save repairing expense, reduce the probability of lost, and provide the proof of repairing. This paper includes three parts: structural damage identification theory, damage identification experiment of simple-supported bridge using root mean square difference method based on artificial neural network, the analysis of a bridge model damage identification using this method.
This paper presents a damage evaluating method-root mean square difference technique to evaluate the health condition of the structure. For a good structure, impulse response signals are obtained using natural excitation technique in environmental vibration condition. The impulse response signals are used to train neural network as the in and out data. The trained network saves the information of the structure vibration. When some damage happens or environmental condition changes, the recent structure signal is imported to the former network, then the out-data will differ from the in-data because the network is for the former structure. The root mean square of the difference between in and out data is defined damage index, root mean square difference. If the damage position and grade change, this index will change accordingly.
In the reinforced concrete simple-supported beam loading experiment, vibration experiment is made and the neural network method could identify the position and the grade of the damage clearly. The identification results are similar to the results of the numerical model.
Time history analysis is applied in the model of Yinpenling bridge in Changsha. Impulse response series are obtained from the Natural Excitation Technique. The neural network is established using the series and damage is identified in different damage degree condition.
本文提出衡量结构健康状况的新方法-基于BP神经网络的残差均方根值法。此方法利用完好结构在环境激励下随机振动的加速度响应信号得到自由衰减信号,将此信号作为BP神经网络的训练样本训练神经网络,则此网络记录了结构的初始信息。当其工作状态发生变化时,该网络相同测点的输入值与输出值之间会产生较大差别,定义这种差值的均方根为残差均方根,将其作为损伤指标。随着损伤位置与程度的变化,该指标也会发生相应改变。
在高强钢筋混凝土足尺梁的静载试验过程中,进行分阶段布点测试,该方法能够较好识别出裂缝出现的位置及损伤程度,识别结果与仿真结果相对比,具有较好的相似性。
建立长沙银盆岭大桥有限元模型,用白噪声信号模拟环境激励,对该桥进行时程分析,计算得到各节点的加速度响应序列,利用自然环境激励技术(Natural Excitation Technique, NExT)求取各测点的自由衰减响应序列,并使用该系列数据建立此桥神经网络的初始非参数模型,在不同程度、不同位置模拟损伤的情形下,进行损伤识别,具有良好的识别效果。
Bridge damage identification and health monitoring is recently a hot point in the world. The bridge is an important part in the transportation and economic development for the country, and the bridge damage detection can ensure the safety of the bridge. The technique of damage detection based on vibration is to evaluate the bridge’s condition using vibration signals. This technique is expected to find little damage and identify the position of the damage before dangerous events happen. And also, it can help people learn about the condition of the bridge, save repairing expense, reduce the probability of lost, and provide the proof of repairing. This paper includes three parts: structural damage identification theory, damage identification experiment of simple-supported bridge using root mean square difference method based on artificial neural network, the analysis of a bridge model damage identification using this method.
This paper presents a damage evaluating method-root mean square difference technique to evaluate the health condition of the structure. For a good structure, impulse response signals are obtained using natural excitation technique in environmental vibration condition. The impulse response signals are used to train neural network as the in and out data. The trained network saves the information of the structure vibration. When some damage happens or environmental condition changes, the recent structure signal is imported to the former network, then the out-data will differ from the in-data because the network is for the former structure. The root mean square of the difference between in and out data is defined damage index, root mean square difference. If the damage position and grade change, this index will change accordingly.
In the reinforced concrete simple-supported beam loading experiment, vibration experiment is made and the neural network method could identify the position and the grade of the damage clearly. The identification results are similar to the results of the numerical model.
Time history analysis is applied in the model of Yinpenling bridge in Changsha. Impulse response series are obtained from the Natural Excitation Technique. The neural network is established using the series and damage is identified in different damage degree condition.
引文
[1]秦权.桥梁结构的健康监测.中国公路学报,2000,13(2):40-45
[2] Alvandia A, Cremona C. Assessment of vibration– based damage identification te -chniques.Journal of Sound and Vibration, 2006, (292):179-202
[3] Jong Jae Lee, Chung Bang Yun. Damage diagnosis of steel girder bridges using ambient vibration data. Engineering Structures, 2006, (28):912-925
[4] Kosmatka J.B., Ricles J.M. Damage Detection in Structures by Model vibration Characterization. Journal of Structural Engineering, 1999,125(12):1384-1392
[5] Salawu O S.Detection of structural damage through changes in frequency:A review. Eng.Struct, 1997, 19(9):718-723
[6] Hearn G, Testa R B.Modal analysis for damage detection in structures. Journal of Structural Engineering, ASCE 1991, 117(10):3042-3063
[7] Cawley P, Adams R D. The location of defects in structures from measurements of natural frequencies. Journal of Strain Analysis, 1979, 4(2):49-57
[8] Mottershead J E, Friswell M I. Model updating in structural dynamics: A survey. Journal of Sound and Vibration, 1993, 167(2):347-375
[9]张德文,魏阜旋.模型修正与破损诊断.北京:科学出版社,1999:25-28
[10] Salawu O S. Detection of Structural Damage through Changes in Frequency: A review. Engineering Structures, 1997, 19(9):718-723
[11] P. Eigenfrequency changes of structures due to cracks, notches or other geometrical changes. Journal of Mech.Phys.Solids, 1982, 30(5):339-353
[12] Hearn G, Testa R B. Modal analysis for damage detection in structures. Journal of Structural Engineering, 1991, 117(11):3042-3063
[13]高芳清,金建明,高淑英.基于模态分析的结构损伤检测方法研究.西南交通大学学报,1998,33(1):108-113
[14] WEST W M. Illustration of the use of modal assurance criterion to detect structural changes in an orbiter test specimen. Proceedings of the 4th International Modal Analysis Conference, 1986, (1):1-6
[15] Yuen. M.M.F. A Numerical Study of the Eigenparameters of a Damaged Cantilever. Journal of Sound and Vibration, 1985, (103):301-310
[16] Pandy A K, Bisws M, Samman M M.Damage Detection from changes inCurvature Mode Shapes. Journal of Sound and Vibration, 1991, 145(2):321-332
[17] Peeters B, Roeck G D. One-year monitoring of the Z24-Bridge: environment effects versus damage events. Earthquake Engineering and Structural Dynamics, 2001, 30(2):149-171
[18] Jong Jae Lee, Jong Won Lee, Jin Hak Yi. Neural Networks-based Damage Detection for Bridges Considering Errors in Baseline Finite Element Models. Journal of Sound and Vibration, 2005, 280(3-5):555-578
[19] Zubaydi A, Haddara M R, Swamidas A S J. Damage Identification in a Ship’s Structure Using Neural Networks. Ocean Engineering, 2002, 29(10):1187-1200
[20] Ko J M, Ni Y Q, Wang J Y, Sun Z G and Zhou X T. Studies of vibration-based damage detection of three cable-supported bridges in Hong Kong. Proceedings of the International Conference on Engineering and Technological Science 2000-Session5: Civil Engineering in the 21st Century, Beijing, China, 2000: 105-112
[21] Povich C R, Lim T W. An artificial neural network approach to structural damage detection using frequency response functions. Proceedings of the 1994 AIAA/ASME Adaptive Structures Forum, 1994, 12(3):151-159
[22] Kaminski P C. The approximate location of damage through the analysis of natural frequencies with artificial neural networks. Journal of Process Mechanical Engineering, IMechE, 1995, 209(2):117-123
[23] J.N.Juang and R.S.Pappa. An Eigensystem Realization Algorithm (ERA)for Modal Parameter Identification and Model Reduction. Journal of Guidance, Control, and Dynamics, 1985,8(5):620-627
[24] Peeters B, De Roeck G. Reference-based stochastic subspace identification for output-only modal analysis. Mechanical Systems and Signal Processing,1999,13 (6):855-878
[25]李德葆,陆秋海.实验模态分析及其应用.北京:科学技术出版社,2001:228-233
[26] LIEVEN N A J,EWINS D J. Spatial correlation of modal shapes, the Coordinate Modal Assurance Criterion (COMAC). Proceedings of the 6th International Modal Analysis Conference, 1988, (1):690-695
[27]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.振动与冲击,2001,20(1):72-75
[28] Shi Z Y, Law S S. Structural Damage Localization from Modal Strain Energy Change. Journal of Sound and Vibration, 1998, 218(5):825-844
[29]袁曾任.人工神经网络及其应用.北京:清华大学出版社,1996:1-6
[30]苏娟,陆秋海,管迪华.神经网络法在定量损伤识别研究中的应用.清华大学学报(自然科学版),1999,39(4):68-70
[31] Ovanesova A V, Sua’rez L E. Applications of Wavelet Transforms to Damage Detection in Framestructures. Engineering Structures, 2004, 26(6):39-49
[32] Yam L H, Yan Y J, Jiang J S. Vibration-based Damage Detection for Composite Structures Using Wavelet Transform and Neural Network Identification. Composite Structures, 2003, 60(4):403-412
[33]葛哲学,沙威.小波分析理论与MATLABR2007实现.北京:电子工业出版社,2007:28-45
[34]杨福生.小波变换的工程分析与应用.北京:科学出版社,2000:58-89 Yang Fusheng. Analysis and application of wavelet transformation in engineering. Beijing: Science Press, 2000:58-89
[35] Hou Z, Noori M, R St Amand. Wavelet-based approach for structural damage detection. Journal of Engineering Mechanics, ASCE,2000,126(7):677-683
[36] Sun Z, Chang C C. Structural damage assessment based on wavelet packet transform. Journal of Structure Engineering, ASCE,2002,128(10):1354-1361
[37] Haase M, Widjajakusuma J. Damage identification based on ridges and maxima lines of the wavelet transform. International Journal of Engineering Science, 2003, 41(3):1423-1443
[38]孙增寿,韩建刚,任伟新.基于曲率模态和小波变换的结构损伤识别方法.振动、测试与诊断,2005,25(4):263-267
[39] Amaravadi V, Rao V, Koval LR,et al. Structural health monitoring using wavelet transforms. Smart structures and integrated systems. ProcSPIE. Newport beach, 2001, 4327(4):258-269
[40]李宏男,孙鸿敏.基于小波分析和神经网络的框架结构损伤诊断方法.地震工程与工程振动,2003,23(5):141-148
[41] Robertson AN, Park KC, Alvin KF. Extraction of impulse response data via wavelet transform for structural identification. Vibr Acoust, 1998, 120(2):252-260
[42] Robertson AN, Park KC, Alvin KF. Identification of structural dynamics models using wavelet-generated impulse response data. Vibr Acoust, 1998, 120(2): 261-266
[43]葛哲学,孙志强.神经网络理论与MATLABR2007实现.北京:电子工业出版社,2007:24-56
[44] Lee C G, Yun C B. Parameter identification of linear structural dynamic systems. Computers and Structures,1991,40(6):1475-1487
[45]应怀樵.波形和频谱分析与随机数据处理.北京:中国铁道出版社,1985:78-89
[46] James G H, Carne T G, Lauffer JP. The natural excitation technique (NExT) for modal parameter extraction from operating structures. International Journal of Analytical and Experimental Modal Analysis, 1995, 10(4):260-277
[47] Bin Xu, Zhishen Wu, Genda Chen, Koichi Yokoyama. Direct identification of structural parameters from dynamic responses with neural networks. Engineering Applications of Artificial Intelligence, 2004,17(1):931-943
[48] R·W·克拉夫,丁·彭津,于光远译.结构动力学.北京:科学出版社,1987:33-46
[2] Alvandia A, Cremona C. Assessment of vibration– based damage identification te -chniques.Journal of Sound and Vibration, 2006, (292):179-202
[3] Jong Jae Lee, Chung Bang Yun. Damage diagnosis of steel girder bridges using ambient vibration data. Engineering Structures, 2006, (28):912-925
[4] Kosmatka J.B., Ricles J.M. Damage Detection in Structures by Model vibration Characterization. Journal of Structural Engineering, 1999,125(12):1384-1392
[5] Salawu O S.Detection of structural damage through changes in frequency:A review. Eng.Struct, 1997, 19(9):718-723
[6] Hearn G, Testa R B.Modal analysis for damage detection in structures. Journal of Structural Engineering, ASCE 1991, 117(10):3042-3063
[7] Cawley P, Adams R D. The location of defects in structures from measurements of natural frequencies. Journal of Strain Analysis, 1979, 4(2):49-57
[8] Mottershead J E, Friswell M I. Model updating in structural dynamics: A survey. Journal of Sound and Vibration, 1993, 167(2):347-375
[9]张德文,魏阜旋.模型修正与破损诊断.北京:科学出版社,1999:25-28
[10] Salawu O S. Detection of Structural Damage through Changes in Frequency: A review. Engineering Structures, 1997, 19(9):718-723
[11] P. Eigenfrequency changes of structures due to cracks, notches or other geometrical changes. Journal of Mech.Phys.Solids, 1982, 30(5):339-353
[12] Hearn G, Testa R B. Modal analysis for damage detection in structures. Journal of Structural Engineering, 1991, 117(11):3042-3063
[13]高芳清,金建明,高淑英.基于模态分析的结构损伤检测方法研究.西南交通大学学报,1998,33(1):108-113
[14] WEST W M. Illustration of the use of modal assurance criterion to detect structural changes in an orbiter test specimen. Proceedings of the 4th International Modal Analysis Conference, 1986, (1):1-6
[15] Yuen. M.M.F. A Numerical Study of the Eigenparameters of a Damaged Cantilever. Journal of Sound and Vibration, 1985, (103):301-310
[16] Pandy A K, Bisws M, Samman M M.Damage Detection from changes inCurvature Mode Shapes. Journal of Sound and Vibration, 1991, 145(2):321-332
[17] Peeters B, Roeck G D. One-year monitoring of the Z24-Bridge: environment effects versus damage events. Earthquake Engineering and Structural Dynamics, 2001, 30(2):149-171
[18] Jong Jae Lee, Jong Won Lee, Jin Hak Yi. Neural Networks-based Damage Detection for Bridges Considering Errors in Baseline Finite Element Models. Journal of Sound and Vibration, 2005, 280(3-5):555-578
[19] Zubaydi A, Haddara M R, Swamidas A S J. Damage Identification in a Ship’s Structure Using Neural Networks. Ocean Engineering, 2002, 29(10):1187-1200
[20] Ko J M, Ni Y Q, Wang J Y, Sun Z G and Zhou X T. Studies of vibration-based damage detection of three cable-supported bridges in Hong Kong. Proceedings of the International Conference on Engineering and Technological Science 2000-Session5: Civil Engineering in the 21st Century, Beijing, China, 2000: 105-112
[21] Povich C R, Lim T W. An artificial neural network approach to structural damage detection using frequency response functions. Proceedings of the 1994 AIAA/ASME Adaptive Structures Forum, 1994, 12(3):151-159
[22] Kaminski P C. The approximate location of damage through the analysis of natural frequencies with artificial neural networks. Journal of Process Mechanical Engineering, IMechE, 1995, 209(2):117-123
[23] J.N.Juang and R.S.Pappa. An Eigensystem Realization Algorithm (ERA)for Modal Parameter Identification and Model Reduction. Journal of Guidance, Control, and Dynamics, 1985,8(5):620-627
[24] Peeters B, De Roeck G. Reference-based stochastic subspace identification for output-only modal analysis. Mechanical Systems and Signal Processing,1999,13 (6):855-878
[25]李德葆,陆秋海.实验模态分析及其应用.北京:科学技术出版社,2001:228-233
[26] LIEVEN N A J,EWINS D J. Spatial correlation of modal shapes, the Coordinate Modal Assurance Criterion (COMAC). Proceedings of the 6th International Modal Analysis Conference, 1988, (1):690-695
[27]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.振动与冲击,2001,20(1):72-75
[28] Shi Z Y, Law S S. Structural Damage Localization from Modal Strain Energy Change. Journal of Sound and Vibration, 1998, 218(5):825-844
[29]袁曾任.人工神经网络及其应用.北京:清华大学出版社,1996:1-6
[30]苏娟,陆秋海,管迪华.神经网络法在定量损伤识别研究中的应用.清华大学学报(自然科学版),1999,39(4):68-70
[31] Ovanesova A V, Sua’rez L E. Applications of Wavelet Transforms to Damage Detection in Framestructures. Engineering Structures, 2004, 26(6):39-49
[32] Yam L H, Yan Y J, Jiang J S. Vibration-based Damage Detection for Composite Structures Using Wavelet Transform and Neural Network Identification. Composite Structures, 2003, 60(4):403-412
[33]葛哲学,沙威.小波分析理论与MATLABR2007实现.北京:电子工业出版社,2007:28-45
[34]杨福生.小波变换的工程分析与应用.北京:科学出版社,2000:58-89 Yang Fusheng. Analysis and application of wavelet transformation in engineering. Beijing: Science Press, 2000:58-89
[35] Hou Z, Noori M, R St Amand. Wavelet-based approach for structural damage detection. Journal of Engineering Mechanics, ASCE,2000,126(7):677-683
[36] Sun Z, Chang C C. Structural damage assessment based on wavelet packet transform. Journal of Structure Engineering, ASCE,2002,128(10):1354-1361
[37] Haase M, Widjajakusuma J. Damage identification based on ridges and maxima lines of the wavelet transform. International Journal of Engineering Science, 2003, 41(3):1423-1443
[38]孙增寿,韩建刚,任伟新.基于曲率模态和小波变换的结构损伤识别方法.振动、测试与诊断,2005,25(4):263-267
[39] Amaravadi V, Rao V, Koval LR,et al. Structural health monitoring using wavelet transforms. Smart structures and integrated systems. ProcSPIE. Newport beach, 2001, 4327(4):258-269
[40]李宏男,孙鸿敏.基于小波分析和神经网络的框架结构损伤诊断方法.地震工程与工程振动,2003,23(5):141-148
[41] Robertson AN, Park KC, Alvin KF. Extraction of impulse response data via wavelet transform for structural identification. Vibr Acoust, 1998, 120(2):252-260
[42] Robertson AN, Park KC, Alvin KF. Identification of structural dynamics models using wavelet-generated impulse response data. Vibr Acoust, 1998, 120(2): 261-266
[43]葛哲学,孙志强.神经网络理论与MATLABR2007实现.北京:电子工业出版社,2007:24-56
[44] Lee C G, Yun C B. Parameter identification of linear structural dynamic systems. Computers and Structures,1991,40(6):1475-1487
[45]应怀樵.波形和频谱分析与随机数据处理.北京:中国铁道出版社,1985:78-89
[46] James G H, Carne T G, Lauffer JP. The natural excitation technique (NExT) for modal parameter extraction from operating structures. International Journal of Analytical and Experimental Modal Analysis, 1995, 10(4):260-277
[47] Bin Xu, Zhishen Wu, Genda Chen, Koichi Yokoyama. Direct identification of structural parameters from dynamic responses with neural networks. Engineering Applications of Artificial Intelligence, 2004,17(1):931-943
[48] R·W·克拉夫,丁·彭津,于光远译.结构动力学.北京:科学出版社,1987:33-46