A generative adversarial network optimization method for damage detection and digital twinning by deep AI fault learning: Z24 Bridge structural health monitoring benchmark validation

Abstract

The optimization-based damage detection and damage state digital twinning capabilities are examined here of a novel conditional-labeled generative adversarial network methodology. The framework outperforms current approaches for fault anomaly detection as no prior information is required for the health state of the system: a topic of high significance for real-world applications. Specifically, current artificial intelligence-based digital twinning approaches suffer from the uncertainty related to obtaining poor predictions when a low number of measurements is available, physics knowledge is missing, or when the damage state is unknown. To this end, an unsupervised framework is examined and validated rigorously on the benchmark structural health monitoring measurements of Z24 Bridge: a post-tensioned concrete highway bridge in Switzerland. In implementing the approach, firstly, different same damage-level measurements are used as inputs, while the model is forced to converge conditionally to two different damage states. Secondly, the process is repeated for a different group of measurements. Finally, the convergence scores are compared to identify which one belongs to a different damage state. The process for both healthy-to-healthy and damage-to-healthy input data creates, simultaneously, measurements for digital twinning purposes at different damage states, capable of pattern recognition and machine learning data generation. Further to this process, a support vector machine classifier and a principal component analysis procedure is developed to assess the generated and real measurements of each damage category, serving as a secondary new dynamics learning indicator in damage scenarios. Importantly, the approach is shown to capture accurately damage over healthy measurements, providing a powerful tool for vibration-based system-level monitoring and scalable infrastructure resilience.

21 pages, 23 figures, published in Structural and Multidisciplinary Optimization

References in corpus (14)

• Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons
• Deep learning for determining a near-optimal topological design without any iteration
• Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
• Bayesian System Identification based on Hierarchical Sparse Bayesian Learning and Gibbs Sampling with Application to Structural Damage Assessment
• CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection
• Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detection
• An unscented Kalman filter method for real time input-parameter-state estimation
• Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning
• A local basis approximation approach for nonlinear parametric model order reduction
• On generative models as the basis for digital twins
• On the application of generative adversarial networks for nonlinear modal analysis
• Deep recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification
• A Kullback-Leibler divergence method for input-system-state identification
• A convolutional neural network deep learning method for model class selection

Cited by in corpus (1)

• Transformer self-attention encoder-decoder with multimodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring