基于曲率模态和神经网络的斜拉桥损伤识别
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摘要
随着大跨度桥梁数量及重要性的增加,桥梁损伤识别成为学界的研究热点。文中应用小波分析提取桥面的一阶振型,推导了梁式结构由不等间距位移模态向曲率模态转化的乘子矩阵DTOC,由损伤前后曲率模态的变异特征构造了梁式结构单损伤情况的普适概率神经网络,使之可以识别无训练样本时的损伤位置。通过数值仿真计算了某斜拉桥有限元模型桥面单元刚度折减前后在模拟脉动风荷载下的桥面板关键点的振动时程,讨论了小波参数识别技术和该普适概率神经网络的联合应用在不同级别的测量噪声影响下的斜拉桥桥面板损伤识别效果,说明在振动测量信号的信噪比达到一定要求时该方法具有实际效果。
With the increase of the quantity and significance of the long-span bridges,the damage identification for bridges becomes the hot point in scientific field.In this paper,wavelet analysis technology has been used to obtain the first mode shape,and the convert matrix DTOC which transfers unequally spaced displacement mode shapes to curvature mode shapes has been deduced.Using the character of the difference between the curvatures mode shapes before and after damage occurs as damage index,a widely used probability neural network(PNN) for beam-like structures has been constructed to identify the damage mode lack of training samples.and the efficient of the networks are checked by the damage index extracted from the vibration time history with noises.At the last,in numerical simulation the simulated fluctuating wind induced vibration of the key points of bridge are extracted from a finite element model of a cable stayed bridge,and it is discussed that the effect of the collaboration of the wavelet mode parameter identification technology and PNN for detecting the damage of deck for cable stayed bridges under differenct signal-to-noise ratios.The discussion shows that the method may be used as the signal-to-noise ratio is high enough.
With the increase of the quantity and significance of the long-span bridges,the damage identification for bridges becomes the hot point in scientific field.In this paper,wavelet analysis technology has been used to obtain the first mode shape,and the convert matrix DTOC which transfers unequally spaced displacement mode shapes to curvature mode shapes has been deduced.Using the character of the difference between the curvatures mode shapes before and after damage occurs as damage index,a widely used probability neural network(PNN) for beam-like structures has been constructed to identify the damage mode lack of training samples.and the efficient of the networks are checked by the damage index extracted from the vibration time history with noises.At the last,in numerical simulation the simulated fluctuating wind induced vibration of the key points of bridge are extracted from a finite element model of a cable stayed bridge,and it is discussed that the effect of the collaboration of the wavelet mode parameter identification technology and PNN for detecting the damage of deck for cable stayed bridges under differenct signal-to-noise ratios.The discussion shows that the method may be used as the signal-to-noise ratio is high enough.
引文
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[16]董军,邓洪洲,刘学利.高层建筑脉动风荷载时程模拟的AR模型方法[J].南京建筑工程学院学报,2000(2):20-25.
[2]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):64-68.
[3]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.
[4]李建光.人工神经网络在斜拉桥检测中的应用研究[D].合肥:合肥工业大学,2003.
[5]郑婷婷,虞庐松.基于神经网络的斜拉索损伤识别研究[J].兰州交通大学学报,2006,25(4):32-34.
[6]陆秋海,李德葆,张维.利用模态试验参数识别结构损伤的神经网络法[J].工程力学,1999,16(01):35-42.
[7]杨晓楠,姜绍飞,倪一清,等.基于特征向量简化的复杂土木结构损伤检测[J].沈阳建筑工程学院学报:自然科学版,2003,19(2):81-84.
[8]滕军,朱焰煌,周峰,等.基于复Morlet小波变换的大跨空间结构模态参数识别研究[J].振动与冲击,2009,28(8):25-29.
[9]罗光坤,张令弥.基于Morlet小波变换的模态参数识别研究[J].振动与冲击,2007,26(7):135-138.
[10]Sahin M,Shenoi R A.Quantification and Localisation of Damage in Beam-like Structures by Using Artificial Neural Networkswith Experimental Validation[J].Engineering Structures,2003,25(14):1785-1802.
[11]杜永峰,张冬兵.曲率模态和神经网络在损伤识别中的应用[J].公路交通科技,2007,24(11):77-80.
[12]任伟新,韩建刚,孙增寿.小波分析在土木工程结构中的应用[M].北京:中国铁道出版社,2006.
[13]傅志方,华宏星.模态分析理论与应用[M].上海:上海交通大学出版社,2000.
[14]苏翼林,王燕群,赵志岗,等.材料力学[M].天津:天津大学出版社,2001.
[15]周林仁.大跨度斜拉桥实验室健康监测模型设计与分析[D].哈尔滨:哈尔滨工业大学,2007.
[16]董军,邓洪洲,刘学利.高层建筑脉动风荷载时程模拟的AR模型方法[J].南京建筑工程学院学报,2000(2):20-25.
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