【作者】 岳桂华；

【导师】 李范春；

【作者基本信息】 哈尔滨工程大学 ， 结构工程， 2004， 硕士

【摘要】 对工程结构的损伤识别、定位及估计是近年来十分流行的研究课题，结构损伤检测技术已被广泛应用于航天、土木、机械和核工业中，是一门建立在损伤机理、传感器技术、信号分析技术、计算机技术及人工智能技术之上的多学科综合性技术。相对于传统的结构损伤检测方法，本论文主要对基于神经网络的结构损伤检测技术理论与应用进行研究。 本文通过理论分析了，适合结构损伤位置和损伤程度识别的组合参数法，(此组合参数是由固有频率的变化信息和少数选定点的模态分量合成的向量。)在此理论的基础上，分别对一个框架结构和一个悬臂梁结构进行了损伤数值模拟，同时采取合适的方法构造改进型BP神经网络的输入参数，应用训练后的神经网络对结构进行损伤检测。 本论文的主要工作有下面几个内容： 首先，通过对神经网络的工作原理进行分析，得出在理论上它能够对结构的损伤进行识别。 其次，结构的振动特性(固有频率、模态参数等)是结构物理参数(质量、刚度)的函数，即结构振动特性的变化是由结构物理参数的变化引起的。因此，通过有限元软件ANSYS对具有各种损伤程度的结构进行模态分析，得到固有频率和模态分量的数据，经过归一化处理后作为神经网络的输入向量。然后，将网络训练的结果和理论上的结果进行比较，以便在数值计算上确定神经网络是否能够准确地对结构的损伤进行识别。 最后，通过一个三层框架和一个悬臂梁的算例证明：组合参数法能够非常精确地识别出结构的损伤位置和损伤程度。只是对于小的损伤识别精度相对差一些，可以通过增大网络隐含层节点数或网络层数来加以解决。也可以通过增大输入样本的维数来提高识别精度。 经过实践证明，该法在工程损伤检测中具有一定的应用价值。更多还原

【Abstract】 The detection, location, and estimation problem of structural damage has been the subject of much current research in recent years. The damage identification methods have been widely used in aeronautical, civil, mechanical and nuclear structures. It is polytechnic method constructed on the damage theory, sensor technology, signal analysis, computer science and artificial networks. Relative to the traditional technology of structural damage identification, this paper mainly studies the method based on the combination of the static modal analysis and artificial networks.In this paper, it verified theoretically the combined-parameters that adapts to locate structural damage location and confirm damage degree. (The parameters consist of natural frequencies and mode shape data at a few selected points) based the theory, numerically simulated a three-story frame and a cantilever beam respectively. And by appropriate means form the input parameters of the improved BP algorithm neural network . Detect the structural damage by the trained networks.The paper includes the following contents:Firstly, through analysis of principle with neural network.the paper get that the combined-parameter can identify structural damage location and damage degree.Secondly, structural physics parameter (natural frequencies > modal parameters etc) is function of structural parameters(mass, stiffness ) . That is, the change of physical parameters induce the change of structural vibration. Therefore, through structural modal analysis with different damage degree using ANSYS, get natural frequencies and mode shape data as neural network input vector after unitary. By comparing the numerical results with theoretic result, to affirm if neural network can numerically identify structural damage.Lastly, through a three-layer frame and a cantilever beam testify that the method can very accurately identify structural damage location and degree. Yet with little damage the result is relatively low. To resolve the problem, augmenting node of hidden layer or number of hidden layer. At the same time, adding the dimension of input parameters.The result shows the effectiveness of this means.更多还原