基于冲击回波信号实验-仿真混合数据集迁移学习的无监督域自适应混凝土内部缺陷可视化

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Gao Shang, Jun Chen
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引用次数: 0

摘要

通过深度学习分析冲击回波信号检测混凝土内部缺陷面临两个挑战:(1)传统的信号处理方法如小波变换(WT)由于不确定性原理不能反映数据敏感的损伤特征;(2)从真实结构中获取的有限标记数据阻碍了网络训练。针对第一个问题,本文提出了基于wt的同步压缩变换(WT-SST),将时间序列数据转换为时频谱图,可以同时在时间和频域为网络提供有效的特征。为了克服第二个挑战,补充了数值模拟数据以增加标记数据。为了最大限度地减少实验和仿真数据差异的影响,本文采用无监督域自适应(DA)网络对标记的仿真数据(原始域)和未标记的实验数据(目标域)进行迁移训练。DA网络通过最大化域识别误差和最小化概率分布距离来提取域不变特征。利用训练好的模型计算损伤概率,生成混凝土试件的二维缺陷轮廓图像,并根据轮廓图像的缺陷面积估计缺陷深度,实现内部缺陷的三维可视化。最后,混合数据集训练的无监督DA网络模型的识别精度、召回率、f1得分和准确率分别达到89.4%、88.4%、88.9%和94.7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visualization of concrete internal defects based on unsupervised domain adaptation algorithm for transfer learning of experiment-simulation hybrid dataset of impact echo signals
Detecting concrete internal defects through deep learning analysis of impact echo signals faces two challenges: (1) the traditional signal processing method such as wavelet transform (WT) fails to reflect data-sensitive damage characteristics due to the uncertainty principle and (2) the limited labeled data acquired from real structures impedes network training. To address the first challenge, this paper proposes the WT-based synchrosqueezing transform (WT-SST) for the conversion of time-series data to the time-frequency spectrogram, which can provide effective features for the network in time and frequency domains simultaneously. To overcome the second challenge, numerical simulation data are supplemented for the augment of labeled data. To minimize the effect of data variance between experiments and simulations, this paper uses an unsupervised domain adaptation (DA) network for the transfer training of labeled simulation data (original domain) and unlabeled experimental data (target domain). The DA network extracts domain-invariant features by maximizing the domain recognition error and minimizing the probability distribution distance. The damage probability is calculated by the trained model to produce a 2D defect contour image of concrete specimens, and the three-dimensional visualization of internal defects by estimating the defect depth based on the defect area of contour image. Finally, the recognition precision, recall, F1-score, and accuracy of the model of unsupervised DA network trained by a hybrid dataset reaches 89.4%, 88.4%, 88.9%, and 94.7%, respectively.
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
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