基于遗传神经网络与模态应变能的斜裂缝两阶段诊断方法
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摘要
基于遗传神经网络与模态应变能,提出了一种斜裂缝两阶段诊断方法,识别梁体中斜裂缝的位置、角度和深度。根据线弹性断裂力学与虚功原理,推导了斜裂缝梁的单元刚度矩阵,得到了其频率与振型。采用遗传算法对BP神经网络的拓扑结构、权值和阈值进行优化,从而建立了遗传神经网络,用于识别梁体中斜裂缝的位置和角度;结合斜裂缝单元的模态应变能,通过对斜裂缝应力强度因子的积分,得到斜裂缝深度的解析表达式,用于识别斜裂缝的深度。数值仿真表明:能够高精度地诊断出梁体中斜裂缝的损伤状态,包括位置、角度和深度;与BP神经网络相比,遗传神经网络具有更强的泛化能力,且对测量噪声具有较好的鲁棒性。
Based on genetic neural network and modal strain energy, a two-stage method for detecting diagonal cracks is proposed to identify the location, angle and depth of diagonal cracks in beams. According to linear elastic fracture mechanics and virtual work principle, the elemental stiffness matrix of a diagonally cracked beam is derived, and the frequencies and modes of the diagonally cracked beam are obtained. The topological structure, weight and threshold of the BP neural network are optimized using the genetic algorithm, and a genetic neural network is built to identify the location and angle of the diagonal cracks in beams. By combining the modal strain energy of the diagonally cracked element and integrating the stress intensity factor of the diagonally cracked element, the analytical expression of the depth of the diagonal crack is obtained to identify the depth of the diagonal cracks. The numerical simulation shows that the proposed method may detect the damage state, including the location, angle and depth, of the diagonal cracks in beams with high precision. By comparing with the BP neural network, the genetic neural network has stronger generalization capacity and better robustness against measuring noises.
Based on genetic neural network and modal strain energy, a two-stage method for detecting diagonal cracks is proposed to identify the location, angle and depth of diagonal cracks in beams. According to linear elastic fracture mechanics and virtual work principle, the elemental stiffness matrix of a diagonally cracked beam is derived, and the frequencies and modes of the diagonally cracked beam are obtained. The topological structure, weight and threshold of the BP neural network are optimized using the genetic algorithm, and a genetic neural network is built to identify the location and angle of the diagonal cracks in beams. By combining the modal strain energy of the diagonally cracked element and integrating the stress intensity factor of the diagonally cracked element, the analytical expression of the depth of the diagonal crack is obtained to identify the depth of the diagonal cracks. The numerical simulation shows that the proposed method may detect the damage state, including the location, angle and depth, of the diagonal cracks in beams with high precision. By comparing with the BP neural network, the genetic neural network has stronger generalization capacity and better robustness against measuring noises.
引文
[1]Chang C C,Chen L W.Detection of the location and size of cracks in the multiple cracked beam by spatial wavelet based approach[J].Mechanical Systems and Signal Processing,2005,19(1):139―155.
[2]Khiem N T,Lien T V.Multi-crack detection for beam by the natural frequencies[J].Journal of Sound and Vibration,2004,273(1/2):175―184.
[3]Nandwana B P,Maiti S K.Modelling of vibration of beam in presence of inclined edge or internal crack for its possible detection based on frequency measurements[J].Engineering Fracture Mechanics,1997,58(3):193―205.
[4]Patil D P,Maiti S K.Experimental verification of a method of detection of multiple cracks in beams based on frequency measurements[J].Journal of Sound and Vibration,2005,281(1/2):439―451.
[5]Tian J Y,Li Z,Su X Y.Crack detection in beams by wavelet analysis of transient flexural waves[J].Journal of Sound and Vibration,2003,261(4):715―727.
[6]Kim J T,Stubbs N.Crack detection in beam-type structures using frequency data[J].Journal of Sound and Vibration,2003,259(1),145―160.
[7]Law S S,Lu Z R.Crack identification in beam from dynamic responses[J].Journal of Sound and Vibration,2005,285(4/5):967―987.
[8]任宜春,易伟建.基于小波分析的梁裂缝识别研究[J].计算力学学报,2005,22(4):399―404.Ren Yichun,Yi Weijian.Crack identification by means of the wavelet analysis[J].Chinese Journal of Computational Mechanics,2005,22(4):399―404.(in Chinese)
[9]Ishak S I,Liu G R,Shang H M,Lim S P.Non-destructive evaluation of horizontal crack detection in beams using transverse impact[J].Journal of Sound and Vibration,2002,252(2):343―360.
[10]Loutridis S,Douka E,Trochidis A.Crack identification in double-cracked beams using wavelet analysis[J].Journal of Sound and Vibration,2004,277(4/5):1025―1039.
[11]王术新,姜哲.裂缝悬臂梁的振动特性及其裂缝参数识别[J].振动与冲击,2003,22(3):83―85.Wang Shuxin,Jiang Zhe.Vibration characteristic of cracked cantilever beam and identification of crack parameter[J].Journal of Vibration and Shock,2003,22(3):83―85.(in Chinese)
[12]Li B,Chen X F,Ma J X,He Z J.Detection of crack location and size in structures using wavelet finite element methods[J].Journal of Sound and Vibration,2005,285(4/5):767―782.
[13]周先雁,刘希,沈蒲生.用含裂纹的梁单元识别混凝土框架结构损伤[J].振动工程学报,1999,12(1):115―119.Zhou Xianyan,Liu Xi,Shen Pusheng.Damage assessment of reinforced concrete framed structures by cracked beam element[J].Journal of Vibration Engineering,1999,12(1):115―119.(in Chinese)
[14]Nahvi H,Jabbari M.Crack detection in beams using experimental modal data and finite element model[J].International Journal of Mechanical Sciences,2005,47(10):1477―1497.
[15]铁摩辛柯.材料力学[M].北京:科学出版社,1978.Timoshenko S P.Material mechanics[M].Beijing:Science Press.1978.(in Chinese)
[16]中国航空研究院.应力强度因子手册[M].北京:科学出版社,1981.Chinese Aircraft Research Institute.Handbook of stress intensity factor[M].Beijing:Science Press,1981.(in Chinese)
[17]李忠献,杨晓明,丁阳.应用人工神经网络技术的大型斜拉桥子结构损伤识别研究[J].地震工程与工程振动,2003,23(3):92―99.Li Zhongxian,Yang Xiaoming,Ding Yang.Research on substructural damage identification of large cable-stayed bridges using artificial neural networks[J].EarthquakeEngineering and Engineering Vibration,2003,23(3):92―99.(in Chinese)
[18]马祥森,史治宇.基于GA-BP神经网络的结构损伤位置识别[J].振动工程学报,2004,17(4):453―456.Ma Xiangsen,Shi Zhiyu.Structural damage localization based on GA-BP neural network[J].Journal of Vibration Engineering,2004,17(4):453―456.(in Chinese)
[19]何翔,李守巨,刘迎曦.基于遗传神经网络的坝基岩体渗透系数识别[J].岩石力学与工程学报,2004,23(5):751―757.He Xiang,Li Shouju,Liu Yingxi.Identification of permeability coefficient of rock mass in dam foundation based on genetic neural network[J].Chinese Journal of Rock Mechanics and Engineering,2004,23(5):751―757.(in Chinese)
[20]Zweiri Y H,Seneviratne L D,Althoefer K.Stability analysis of a three-term back propagation algorithm[J].Neural Networks,2005,18(10):1341―1347.
[21]孙宗光,高赞明,倪一清.基于神经网络的损伤构件及损伤程度识别[J].工程力学,2006,23(2):18―22.Sun Zongguang,Gao Zanming,Ni Yiqing.Identification of damaged members and damage extent in bridge deck by neural network[J].Engineering Mechanics,2006,23(2):18―22.(in Chinese)
[22]雷英杰,张善文,李续武.MATLAB遗传算法工具箱及应用[M].西安:西安电子科技大学出版社,2005.Lei Yingjie,Zhang Shanwen,Li Xuwu.Genetic algorithm toolbox of MATLAB and its application[M]Xi’an:Xidian University Press,2005.(in Chinese)
[2]Khiem N T,Lien T V.Multi-crack detection for beam by the natural frequencies[J].Journal of Sound and Vibration,2004,273(1/2):175―184.
[3]Nandwana B P,Maiti S K.Modelling of vibration of beam in presence of inclined edge or internal crack for its possible detection based on frequency measurements[J].Engineering Fracture Mechanics,1997,58(3):193―205.
[4]Patil D P,Maiti S K.Experimental verification of a method of detection of multiple cracks in beams based on frequency measurements[J].Journal of Sound and Vibration,2005,281(1/2):439―451.
[5]Tian J Y,Li Z,Su X Y.Crack detection in beams by wavelet analysis of transient flexural waves[J].Journal of Sound and Vibration,2003,261(4):715―727.
[6]Kim J T,Stubbs N.Crack detection in beam-type structures using frequency data[J].Journal of Sound and Vibration,2003,259(1),145―160.
[7]Law S S,Lu Z R.Crack identification in beam from dynamic responses[J].Journal of Sound and Vibration,2005,285(4/5):967―987.
[8]任宜春,易伟建.基于小波分析的梁裂缝识别研究[J].计算力学学报,2005,22(4):399―404.Ren Yichun,Yi Weijian.Crack identification by means of the wavelet analysis[J].Chinese Journal of Computational Mechanics,2005,22(4):399―404.(in Chinese)
[9]Ishak S I,Liu G R,Shang H M,Lim S P.Non-destructive evaluation of horizontal crack detection in beams using transverse impact[J].Journal of Sound and Vibration,2002,252(2):343―360.
[10]Loutridis S,Douka E,Trochidis A.Crack identification in double-cracked beams using wavelet analysis[J].Journal of Sound and Vibration,2004,277(4/5):1025―1039.
[11]王术新,姜哲.裂缝悬臂梁的振动特性及其裂缝参数识别[J].振动与冲击,2003,22(3):83―85.Wang Shuxin,Jiang Zhe.Vibration characteristic of cracked cantilever beam and identification of crack parameter[J].Journal of Vibration and Shock,2003,22(3):83―85.(in Chinese)
[12]Li B,Chen X F,Ma J X,He Z J.Detection of crack location and size in structures using wavelet finite element methods[J].Journal of Sound and Vibration,2005,285(4/5):767―782.
[13]周先雁,刘希,沈蒲生.用含裂纹的梁单元识别混凝土框架结构损伤[J].振动工程学报,1999,12(1):115―119.Zhou Xianyan,Liu Xi,Shen Pusheng.Damage assessment of reinforced concrete framed structures by cracked beam element[J].Journal of Vibration Engineering,1999,12(1):115―119.(in Chinese)
[14]Nahvi H,Jabbari M.Crack detection in beams using experimental modal data and finite element model[J].International Journal of Mechanical Sciences,2005,47(10):1477―1497.
[15]铁摩辛柯.材料力学[M].北京:科学出版社,1978.Timoshenko S P.Material mechanics[M].Beijing:Science Press.1978.(in Chinese)
[16]中国航空研究院.应力强度因子手册[M].北京:科学出版社,1981.Chinese Aircraft Research Institute.Handbook of stress intensity factor[M].Beijing:Science Press,1981.(in Chinese)
[17]李忠献,杨晓明,丁阳.应用人工神经网络技术的大型斜拉桥子结构损伤识别研究[J].地震工程与工程振动,2003,23(3):92―99.Li Zhongxian,Yang Xiaoming,Ding Yang.Research on substructural damage identification of large cable-stayed bridges using artificial neural networks[J].EarthquakeEngineering and Engineering Vibration,2003,23(3):92―99.(in Chinese)
[18]马祥森,史治宇.基于GA-BP神经网络的结构损伤位置识别[J].振动工程学报,2004,17(4):453―456.Ma Xiangsen,Shi Zhiyu.Structural damage localization based on GA-BP neural network[J].Journal of Vibration Engineering,2004,17(4):453―456.(in Chinese)
[19]何翔,李守巨,刘迎曦.基于遗传神经网络的坝基岩体渗透系数识别[J].岩石力学与工程学报,2004,23(5):751―757.He Xiang,Li Shouju,Liu Yingxi.Identification of permeability coefficient of rock mass in dam foundation based on genetic neural network[J].Chinese Journal of Rock Mechanics and Engineering,2004,23(5):751―757.(in Chinese)
[20]Zweiri Y H,Seneviratne L D,Althoefer K.Stability analysis of a three-term back propagation algorithm[J].Neural Networks,2005,18(10):1341―1347.
[21]孙宗光,高赞明,倪一清.基于神经网络的损伤构件及损伤程度识别[J].工程力学,2006,23(2):18―22.Sun Zongguang,Gao Zanming,Ni Yiqing.Identification of damaged members and damage extent in bridge deck by neural network[J].Engineering Mechanics,2006,23(2):18―22.(in Chinese)
[22]雷英杰,张善文,李续武.MATLAB遗传算法工具箱及应用[M].西安:西安电子科技大学出版社,2005.Lei Yingjie,Zhang Shanwen,Li Xuwu.Genetic algorithm toolbox of MATLAB and its application[M]Xi’an:Xidian University Press,2005.(in Chinese)
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