A soft post-earthquake damage identification methodology using vibration time series
A soft post-earthquake damage identification methodology using vibration time series
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DOI：
10.1088/0964-1726/14/3/014
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发表时间：
2005-06
影响因子：
4.1
通讯作者：
Bin Xu;Zhishen Wu;K. Yokoyama;T. Harada;Ge-wei Chen
中科院分区：
文献类型：
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作者：
Bin Xu;Zhishen Wu;K. Yokoyama;T. Harada;Ge-wei Chen
提出了一种基于神经网络的基于振动测量的智能结构震后损伤识别方法。为了便于震后震害识别，构建了两个神经网络。阐述了该方法的合理性，并根据结构状态空间方程的离散时间解，阐述了仿真器神经网络（ENN）和参数评价神经网络（PENN）的理论基础。提出了一种评价指标——预测差向量的均方根（RMSPDV）来评价不同关联结构的状态。基于训练好的健康状态下物体结构的非参数化模型ENN和描述结构参数与相应RMSPDVs组成之间关系的PENN，识别出受损物体结构的层间刚度。以多层剪力建筑结构为例，通过数值模拟验证了该策略在不同地基激励下的准确性、敏感性和有效性。由于该方法不需要从测量中提取结构动态特性，如频率和模态振型，因此它有可能成为智能工程结构健康监测的实用工具。
A neural-network-based post-earthquake damage identification methodology for smart structures with the direct use of vibration measurements is developed. Two neural networks are constructed to facilitate the process of post-earthquake damage identification. The rationality of the proposed methodology is explained and the theory basis for the construction of an emulator neural network (ENN) and a parametric evaluation neural network (PENN) are described according to the discrete time solution of the structural state space equation. An evaluation index called the root mean square of the prediction difference vector (RMSPDV) is presented to evaluate the condition of different associated structures. Based on the trained ENN, which is a non-parametric model of the object structure in a healthy state, and the PENN that describes the relation between structural parameters and the components of the corresponding RMSPDVs, the inter-storey stiffness of the damaged object structure is identified. The accuracy, sensibility and efficacy of the proposed strategy for different ground excitations are also examined using a multi-storey shear building structure by numerical simulations. Since the methodology does not require the extraction of structural dynamic characteristics such as frequencies and mode shapes from measurements, it has the potential of being a practical tool for health monitoring of smart engineering structures.