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Direct Substructural Identification Methodology Using Acceleration Measurements with Neural Networks

机译：使用神经网络加速测量的直接子结构识别方法

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摘要

A substructural identification methodology by the direct use of acceleration measurements with neural networks is proposed. The rationality of the substructural identification methodology employing a substructural acceleration-based emulator neural network (SAENN) and a substructural parametric evaluation neural network (SPENN) is explained. Based on the discrete time solution of the state space equation of the substructure, the theory basis for the construction of SAENN and SPENN is described. An evaluation index called root mean square of prediction difference vector (RMSPDV) corresponding to acceleration response is presented to evaluate the condition of object structure. The performance of the SAENN for acceleration forecasting and SPENN for parametric identification is examined by numerical simulations with a substructure of a 50-DOFs shear structure involving all stiffness and damping coefficient values unknown. Based on the trained SAENN and the PENN, the inter-storey stiffness and damping coefficients of the substructure are identified. Since the strategy does not require structural modes or frequencies extraction, it is computationally efficient, thus providing a possibly viable tool for structural identification and damage detection of large-scale infrastructures.
机译：提出了一种直接利用神经网络进行加速度测量的子结构识别方法。解释了采用基于子结构加速的仿真器神经网络（SAENN）和子结构参数评估神经网络（SPENN）的子结构识别方法的合理性。基于子结构状态空间方程的离散时间解，描述了构造SAENN和SPENN的理论基础。提出了一种与加速度响应相对应的称为预测差异向量均方根（RMSPDV）的评价指标，以评价物体结构的状况。 SAENN用于加速度预测的性能和SPENN用于参数识别的性能通过数值模拟来检验，该模拟具有50自由度剪切结构的子结构，其中涉及所有未知的刚度和阻尼系数值。根据受过训练的SAENN和PENN，确定子结构的层间刚度和阻尼系数。由于该策略不需要结构模式或频率提取，因此它的计算效率很高，因此为大型基础设施的结构识别和损坏检测提供了可能可行的工具。

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