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Undamaged-to-Damaged Structural Response Mapping for Structural Health Monitoring Using Generative A dversarial Networks (GANs)

Rahmani, Mobina Sadat | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58042 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Rahimzadeh Rofooei, Fayyaze
  7. Abstract:
  8. In this study, a Cycle-Generative Adversarial Network (Cycle-GAN)-based approach is proposed to transform the dynamic response of structures from a healthy to a damaged state and vice versa, aiming to improve damage identification and reduce the need for paired data. This method enhances signal conversion accuracy, facilitating the prediction of damage presence or absence while minimizing dependency on extensive experimental data. Traditional Structural Health Monitoring (SHM) methods, such as mechanical sensors and numerical analyses, face limitations such as high costs, operator dependency, and the requirement for fully paired data. Deep learning algorithms, particularly those based on recurrent networks and generative adversarial networks, provide effective solutions to overcome these challenges. However, the primary challenge in SHM is the lack of high-quality data for training deep models and the need to generate valid and diverse synthetic data as traditional SHM methods strongly rely on large high-quality data that is not always accessible. To evaluate the proposed models, datasets were collected from extensive numerical simulations based on the ASCE benchmark structure. The simulated model used in this study is a 12-degree-of-freedom shear building analyzed under three cases: symmetric loading with excitation via a shake table, symmetric loading with rooftop excitation (simulating wind loads), and asymmetric loading with excitation via a shake table. The structural acceleration response in all three cases was recorded under 20 artificial random earthquakes using 16 sensors placed on four floors in all four directions. These datasets include vibrational signals of healthy and damaged structures under various conditions, and the predicted and actual responses were compared using both time-domain and frequency-domain analyses. To improve the quality of the reconstructed data, evaluation metrics such as FID Score and MMSC Score were examined. In this research, two models, BiLSTM and Cycle-GAN, were assessed for analyzing and predicting the dynamic response of structures. BiLSTM processes time sequences bidirectionally, extracting time-dependent features; however, it requires large and fully paired datasets. In contrast, Cycle-GAN, without the need for paired data, reconstructs nonlinear damage patterns and provides high accuracy in converting damaged data into healthy data, albeit at higher epochs. The results demonstrated that Cycle-GAN successfully reduced the need for training data by up to 30% and performed optimally in data-scarce conditions. In reconstructing frequency-domain features and structural response signals, Cycle-GAN showed a significant advantage over BiLSTM. The FID and MMSC metrics, which indicate data reconstruction quality, confirmed the substantial improvement achieved by the proposed method. In complex scenarios such as asymmetric mass interaction, BiLSTM also performed well; however, given the reduced training data, Cycle-GAN demonstrated high accuracy and efficiency in SHM. Furthermore, this study highlights that generative adversarial networks can serve as an innovative approach for reconstructing structural dynamic data, simulating structural failures, and reducing dependence on experimental data
  9. Keywords:
  10. Structural Health Monitoring ; Deep Learning ; Generative Adversarial Networks ; Damage Identification ; Frequency Analysis ; CNN-BiLSTM Deep Neural Networks ; Recurrent Neural Networks ; Data Reconstruction ; Artificial Intelligence in Civil Engineering

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