A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection
A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection
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DOI：
10.1016/j.ins.2020.05.090
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发表时间：
2020-11-01
影响因子：
8.1
通讯作者：
Zeng, Zeng
中科院分区：
文献类型：
--
作者：
Yang, Jianxi;Zhang, Likai;Zeng, Zeng
结构损伤检测已成为各工程领域感兴趣的跨学科领域，而现有的损伤检测方法正在适应机器学习的概念。大多数基于机器学习的方法严重依赖于提取的“手工制作”特征，这些特征由领域专家提前手动选择，然后固定。最近，深度学习在传统的具有挑战性的任务上表现出了卓越的性能，例如图像分类，对象检测等，由于强大的特征学习能力。这一突破激发了研究人员探索用于结构损伤检测问题的深度学习技术。然而，现有方法已经考虑了空间关系（例如，使用卷积神经网络（CNN））或时间关系（例如，使用长短期记忆网络（LSTM））。在这项工作中，我们提出了一种新的分层CNN和门控递归单元（GRU）框架来建模空间和时间关系，称为HCG，用于结构损伤检测。具体而言，CNN用于对传感器之间的空间关系和短期时间依赖性进行建模，而CNN的输出特征被馈送到GRU中以共同学习长期时间依赖性。在IASC-ASCE结构健康监测基准和三跨连续刚构桥结构缩尺模型数据集上进行的大量实验表明，该方法在结构损伤检测方面明显优于现有的其他方法。(c)2020爱思唯尔公司All rights reserved.
Structural damage detection has become an interdisciplinary area of interest for various engineering fields, while the available damage detection methods are being in the process of adapting machine learning concepts. Most machine learning based methods heavily depend on extracted "hand-crafted" features that are manually selected in advance by domain experts and then, fixed. Recently, deep learning has demonstrated remarkable performance on traditional challenging tasks, such as image classification, object detection, etc., due to the powerful feature learning capabilities. This breakthrough has inspired researchers to explore deep learning techniques for structural damage detection problems. However, existing methods have considered either spatial relation (e.g., using convolutional neural network (CNN)) or temporal relation (e.g., using long short term memory network (LSTM)) only. In this work, we propose a novel Hierarchical CNN and Gated recurrent unit (GRU) framework to model both spatial and temporal relations, termed as HCG, for structural damage detection. Specifically, CNN is utilized to model the spatial relations and the short-term temporal dependencies among sensors, while the output features of CNN are fed into the GRU to learn the long-term temporal dependencies jointly. Extensive experiments on IASC-ASCE structural health monitoring benchmark and scale model of three-span continuous rigid frame bridge structure datasets have shown that our proposed HCG outperforms other existing methods for structural damage detection significantly. (c) 2020 Elsevier Inc. All rights reserved.