Deep Learning-based Methods for Structural Health Monitoring Data Improvement and Augmentation
Access Status
Fulltext not available
Embargo Lift Date
2027-03-10
Date
2024Supervisor
Jun Li
Hong Hao
Type
Thesis
Award
PhD
Metadata
Show full item recordFaculty
Science and Engineering
School
School of Civil and Mechanical Engineering
Collection
Abstract
This thesis addresses data quality challenges in Structural Health Monitoring (SHM) using deep learning techniques. A Transformer-based generative adversarial network is developed to reconstruct missing signal. An unsupervised domain adaptation-based methodology is proposed to impute missing data. A segmentation method for detecting anomalous data is developed by employing denoising diffusion probabilistic models (DDPMs). Additionally, a generative model using DDPMs is proposed to synthesize realistic monitoring data, enhancing reconstruction accuracy and data augmentation in SHM applications.
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