基于stockwell变换和深度卷积神经网络的建筑结构退化与损伤识别新方法

V. Gharehbaghi, H. Kalbkhani, E. N. Farsangi, Tony T. Y. Yang, A. Nguyen, S. Mirjalili, C. Málaga‐Chuquitaype
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引用次数: 6

摘要

本文提出了一种新的劣化与损伤识别方法,并将其应用于建筑模型中。在这类结构上的应用所面临的挑战与响应的强相关性有关,当处理具有高噪音水平的真实环境振动时,这个问题变得更加复杂。因此,设计了一种DIP,利用低成本的环境振动来分析加速度响应,使用斯托克韦尔变换(ST)生成频谱图。随后,ST输出成为两组卷积神经网络(cnn)的输入,用于识别建筑模型的劣化和损坏。据我们所知,这是第一次通过ST和CNN的结合在建筑模型上以高精度评估损伤和退化。
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A novel approach for deterioration and damage identification in building structures based on Stockwell-Transform and deep convolutional neural network
ABSTRACT In this paper, a novel deterioration and damage identification procedure (DIP) is presented and applied to building models. The challenge associated with applications on these types of structures is related to the strong correlation of responses, an issue that gets further complicated when coping with real ambient vibrations with high levels of noise. Thus, a DIP is designed utilizing low-cost ambient vibrations to analyze the acceleration responses using the Stockwell transform (ST) to generate spectrograms. Subsequently, the ST outputs become the input of two series of Convolutional Neural Networks (CNNs) established for identifying deterioration and damage on the building models. To the best of our knowledge, this is the first time that both damage and deterioration are evaluated on building models through a combination of ST and CNN with high accuracy.
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来源期刊
CiteScore
3.90
自引率
9.50%
发文量
24
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