.png){kind=icon}
返回
Multi-channel response reconstruction using transformer based generative adversarial network
.png){kind=icon}
OA
AI
DOI：10.1002/eqe.3960.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurate measurement data are a basic prerequisite for effective structural health monitoring (SHM). However, data loss are inevitable in the long-term monitoring of large-scale structures. To solve this problem, this research proposes a transformer-based generative adversarial network (GAN) to reconstruct lost measurements from observed measurements. The generator of GAN is an encoder-decoder structure using transformer as the backbone combined with discrete wavelet transform. Skip connections are used between the encoder part and decoder part to promote multi-scale information flow. A novel discriminator is designed to assess the reality of wavelet spectra of reconstructed samples. To deceive the discriminator, the generator must generate samples that are accurate over the full frequency band. The developed model is used to reconstruct linear responses of a footbridge under pedestrian excitations and nonlinear responses of a suspension bridge under typhoon events. Experimental results demonstrate that lost responses can be reconstructed accurately, even when a large proportion of data are lost. The effectiveness of the proposed method is further verified by comparing the reconstruction accuracy of the proposed model with those of other three state-of-the-art models. The results demonstrate that an improved performance of applying the proposed approach for dynamic structural response reconstruction is achieved and validated with in-field testing data under ambient and extreme excitation conditions.
Keyword:
dynamic response reconstruction
generative adversarial network
structural health monitoring
transformer
vibration
wavelet transform
期刊
E
IF:
5
论文数:
3.7K
被引数:
1.8W
机构
引用论文
Effects of electrically transferred molecular information on the germination of garden cress电学转移分子信息对庭院萝卜种子萌发的影响OAAI
OAAI分享收藏Model-free data reconstruction of structural response and excitation via sequential broad learning
PREAI
分享收藏Dynamic response reconstruction for structural health monitoring using densely connected convolutional networks基于密集连接卷积网络的结构健康监测动态响应重构
PREAI
分享收藏Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach用于结构健康监测的压缩感知数据重建: 一种机器学习方法
OAAI
OAAI分享收藏Convolutional neural network-based data recovery method for structural health monitoring基于卷积神经网络的结构健康监测数据恢复方法
PREAI
分享收藏Multi-type sensor placement and response reconstruction for structural health monitoring of long-span suspension bridges大跨度悬索桥结构健康监测的多类型传感器布置及响应重构SCIENCE BULLETIN
SCIENCE BULLETINIF21.1
PREAI
分享收藏
查看更多内容