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

arXiv: https://arxiv.org/abs/2511.00099 · HTML: https://mlsanity.com/paper/2511.00099/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 122 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next](https://mlsanity.com/paper/2201.05624/) (2022)
- [Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons](https://mlsanity.com/paper/2201.07766/) (2022)
- [Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems](https://mlsanity.com/paper/1912.08177/) (2019)
- [Stabilizing Generative Adversarial Networks: A Survey](https://mlsanity.com/paper/1910.00927/) (2019)
- [Deep learning for determining a near-optimal topological design without any iteration](https://mlsanity.com/paper/1801.05463/) (2018)
- [Managing engineering systems with large state and action spaces through deep reinforcement learning](https://mlsanity.com/paper/1811.02052/) (2018)
- [A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies](https://mlsanity.com/paper/2208.14197/) (2022)
- [Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification](https://mlsanity.com/paper/2402.15115/) (2024)
- [A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows](https://mlsanity.com/paper/2310.04690/) (2023)
- [Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning](https://mlsanity.com/paper/2209.01092/) (2022)
- [Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems](https://mlsanity.com/paper/2212.07933/) (2022)
- [A local basis approximation approach for nonlinear parametric model order reduction](https://mlsanity.com/paper/2003.07716/) (2020)
- [Inverse airfoil design method for generating varieties of smooth airfoils using conditional WGAN-gp](https://mlsanity.com/paper/2110.00212/) (2021)
- [CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection](https://mlsanity.com/paper/2202.07831/) (2022)
- [Zero-Shot Transfer Learning for Structural Health Monitoring using Generative Adversarial Networks and Spectral Mapping](https://mlsanity.com/paper/2212.04002/) (2022)
- [Bayesian System Identification based on Hierarchical Sparse Bayesian Learning and Gibbs Sampling with Application to Structural Damage Assessment](https://mlsanity.com/paper/1701.03550/) (2017)
- [Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detection](https://mlsanity.com/paper/2112.03478/) (2021)
- [Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakes](https://mlsanity.com/paper/2202.13309/) (2022)
- [Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials](https://mlsanity.com/paper/2310.04286/) (2023)
- [On generative models as the basis for digital twins](https://mlsanity.com/paper/2203.04384/) (2022)
- [On the application of generative adversarial networks for nonlinear modal analysis](https://mlsanity.com/paper/2203.01229/) (2022)
- [Automatic selection of the best neural architecture for time series forecasting](https://mlsanity.com/paper/2501.12215/) (2025)

## Built on by (most cited first, 1 of 1)

- [Transformer self-attention encoder-decoder with multimodal deep learning for response time series forecasting and digital twin support in wind structural health monitoring](https://mlsanity.com/paper/2604.01712/) (2026)

## Abstract (arXiv)

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.