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Structural damage identification based on autoencoder neural networks and deep learning

机译：基于自动编码器神经网络和深度学习的结构损伤识别

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摘要

Artificial neural networks are computational approaches based on machine learning to learn and make predictions based on data, and have been applied successfully in diverse applications including structural health monitoring in civil engineering. It is difficult to optimize the weights in the neural networks that have multiple hidden layers due to the vanishing gradient issue. This paper proposes an autoencoder based framework for structural damage identification, which can support deep neural networks and be utilized to obtain optimal solutions for pattern recognition problems of highly non-linear nature, such as learning a mapping between the vibration characteristics and structural damage. Two main components are defined in the proposed framework, namely, dimensionality reduction and relationship learning. The first component is to reduce the dimensionality of the original input vector while preserving the required necessary information, and the second component is to perform the relationship learning between the features with the reduced dimensionality and the stiffness reduction parameters of the structure. Vibration characteristics, such as natural frequencies and mode shapes, are used as the input and the structural damage are considered as the output vector. A pre-training scheme is performed to train the hidden layers in the autoencoders layer by layer, and fine tuning is conducted to optimize the whole network. Numerical and experimental investigations on steel frame structures are conducted to demonstrate the accuracy and efficiency of the proposed framework, comparing with the traditional ANN methods.
机译：人工神经网络是基于机器学习的计算方法，用于基于数据学习和进行预测，并且已成功应用于包括土木工程中的结构健康监控在内的各种应用中。由于梯度消失，难以在具有多个隐藏层的神经网络中优化权重。本文提出了一种基于自动编码器的结构损伤识别框架，该框架可支持深度神经网络，并可用于获得高度非线性性质的模式识别问题的最佳解决方案，例如学习振动特性与结构损伤之间的映射。提议的框架中定义了两个主要组件，即降维和关系学习。第一个组件是在保留所需必要信息的同时减小原始输入向量的维数，第二个组件是在具有减小的维数的特征与结构的刚度减小参数之间执行关系学习。振动特性（例如固有频率和振型）被用作输入，结构破坏被视为输出矢量。执行预训练方案以逐层训练自动编码器中的隐藏层，并进行微调以优化整个网络。与传统的人工神经网络方法相比，对钢框架结构进行了数值和实验研究，以证明所提出框架的准确性和效率。

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  • 来源
    《Engineering Structures》 |2018年第1期|13-28|共16页
  • 作者

    Chathurdara Sri Nadith Pathirage; Jun Li; Ling Li; Hong Hao; Wanquan Liu; Pinghe Ni;

  • 作者单位

    School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University;

    Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University;

    School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University;

    Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University,School of Civil Engineering, Guangzhou University;

    School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University;

    Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University,Department of Civil and Environmental Engineering, Hong Kong Polytechnic University;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Autoencoders; Deep learning; Deep neural networks; Structural damage identification; Pre-training;

    机译：自动编码器;深度学习;深度神经网络;结构损伤识别;预训练;
  • 入库时间 2022-08-18 00:10:12

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