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Unsupervised deep learning approach using a deep auto-encoder with an one-class support vector machine to detect structural damage
Structural Health Monitoring ( IF 6.6 ) Pub Date : 2020-07-08 , DOI: 10.1177/1475921720934051
Zilong Wang 1 , Young-Jin Cha 1
Affiliation  

This article proposes an unsupervised deep learning–based approach to detect structural damage. Supervised deep learning methods have been proposed in recent years, but they require data from an intact structure and various damage scenarios of monitored structures for their training processes. However, the labeling work on the training data is typically time-consuming and costly, and sometimes collecting sufficient training data from various damage scenarios of infrastructures in service is impractical. In this article, the proposed unsupervised deep learning method based on a deep auto-encoder with an one-class support vector machine only uses the measured acceleration response data acquired from intact or baseline structures as training data, which enables future structural damage to be detected. The major contributions and novelties of the proposed method are as follows. First, an appropriate deep auto-encoder is carefully designed through comparative studies on the depth of neural networks. Second, the designed deep auto-encoder is taken as an extractor to obtain damage-sensitive features from the measured acceleration response data, and an one-class support vector machine is used as a damage detector. Third, experimental and numerical studies validate the high accuracy of the proposed method for damage detection: a 97.4% mean average for a 12-story numerical building model and a 91.0% accuracy for a laboratory-scaled steel bridge. Fourth, the proposed method also detects light damage (i.e. a 10% reduction in stiffness) with 96.9% to 99.0% accuracy, which shows its superior performance compared with the current state of the art. Fifth, it provides stable and more robust damage detection performance with reduced tuning parameters.

中文翻译:

使用带有一类支持向量机的深度自动编码器来检测结构损坏的无监督深度学习方法

本文提出了一种基于无监督深度学习的方法来检测结构损坏。近年来提出了有监督的深度学习方法,但它们需要来自完整结构和受监控结构的各种损坏场景的数据来进行训练。然而,对训练数据的标记工作通常既耗时又昂贵,有时从服务中的基础设施的各种损坏场景中收集足够的训练数据是不切实际的。在本文中,所提出的基于具有一类支持向量机的深度自动编码器的无监督深度学习方法仅使用从完整或基线结构中获取的测量加速度响应数据作为训练数据,从而能够检测到未来的结构损坏. 所提出方法的主要贡献和新颖性如下。首先,通过对神经网络深度的比较研究,精心设计了合适的深度自动编码器。其次,将设计的深度自动编码器作为提取器,从测量的加速度响应数据中获取损伤敏感特征,并使用一类支持向量机作为损伤检测器。第三,实验和数值研究验证了所提出的损伤检测方法的高精度:12 层数值建筑模型的平均平均值为 97.4%,实验室规模的钢桥的准确度为 91.0%。第四,所提出的方法还以 96.9% 到 99.0% 的准确度检测到轻损伤(即刚度降低 10%),这表明与当前最先进的技术相比其性能优越。第五,
更新日期:2020-07-08
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