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首页> 外文期刊>Structural health monitoring >Development and application of a deep learning-based sparse autoencoder framework for structural damage identification
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Development and application of a deep learning-based sparse autoencoder framework for structural damage identification

机译：基于深度学习的稀疏自动编码器框架在结构损伤识别中的开发与应用

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摘要

This article proposes a deep sparse autoencoder framework for structural damage identification. This framework can be employed to obtain the optimal solutions for some pattern recognition problems with highly nonlinear nature, such as learning a mapping between the vibration characteristics and structural damage. Three main components are defined in the proposed framework, namely, the pre-processing component with a data whitening process, the sparse dimensionality reduction component where the dimensionality of the original input vector is reduced while preserving the required necessary information, and the relationship learning component where the mapping between the compressed dimensional feature and the stiffness reduction parameters of the structure is built. The proposed framework utilizes the sparse autoencoders based deep neural network structure to enhance the capability and performance of the dimensionality reduction and relationship learning components with a pre-training scheme. In the final stage of training, both components are jointly optimized to fine-tune the network towards achieving a better accuracy in structural damage identification. Since structural damages usually occur only at a small number of elements that exhibit stiffness reduction out of the large total number of elements in the entire structure, sparse regularization is adopted in this framework. Numerical studies on a steel frame structure are conducted to investigate the accuracy and robustness of the proposed framework in structural damage identification, taking into consideration the effects of noise in the measurement data and uncertainties in the finite element modelling. Experimental studies on a prestressed concrete bridge in the laboratory are conducted to further validate the performance of using the proposed framework for structural damage identification.
机译：本文提出了一种用于结构损伤识别的深度稀疏自动编码器框架。该框架可用于获得具有高度非线性性质的某些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。提出的框架中定义了三个主要组件，即具有数据白化过程的预处理组件，稀疏的维数缩减组件，其中在保留所需必要信息的同时降低了原始输入向量的维数，以及关系学习组件在其中建立压缩尺寸特征和结构刚度减小参数之间的映射。提出的框架利用基于稀疏自动编码器的深度神经网络结构，通过预训练方案来增强降维和关系学习组件的功能和性能。在培训的最后阶段，将两个组件共同优化以对网络进行微调，以在结构损伤识别中获得更高的准确性。由于结构损坏通常只发生在整个结构中大量的刚度中，刚度降低的极少数元素，因此在此框架中采用稀疏规则化。考虑到测量数据中的噪声和有限元建模的不确定性，对钢框架结构进行了数值研究，以研究所提出框架在结构损伤识别中的准确性和鲁棒性。在实验室中对预应力混凝土桥梁进行了实验研究，以进一步验证使用建议的框架进行结构损伤识别的性能。

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  • 来源
    《Structural health monitoring》 |2019年第1期|103-122|共20页
  • 作者

    Pathirage Chathurdara Sri Nadith; Li Jun; Li Ling; Hao Hong; Liu Wanquan; Wang Ruhua;

  • 作者单位

    Curtin Univ, Sch Elect Engn Comp & Math Sci, Bentley, WA, Australia;

    Curtin Univ, Sch Civil & Mech Engn, Ctr Infrastruct Monitoring & Protect, Kent St, Bentley, WA 6102, Australia|Guangzhou Univ, Sch Civil Engn, Guangzhou, Guangdong, Peoples R China;

    Curtin Univ, Sch Elect Engn Comp & Math Sci, Bentley, WA, Australia;

    Curtin Univ, Sch Civil & Mech Engn, Ctr Infrastruct Monitoring & Protect, Kent St, Bentley, WA 6102, Australia|Guangzhou Univ, Sch Civil Engn, Guangzhou, Guangdong, Peoples R China;

    Curtin Univ, Sch Elect Engn Comp & Math Sci, Bentley, WA, Australia;

    Curtin Univ, Sch Elect Engn Comp & Math Sci, Bentley, WA, Australia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Deep learning; neural networks; sparse autoencoders; structural damage identification; pre-training;

    机译：深度学习;神经网络;稀疏自动编码器;结构损伤识别;预训练;
  • 入库时间 2022-08-18 04:13:32

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