# Zero-Shot Transfer Learning for Structural Health Monitoring using Generative Adversarial Networks and Spectral Mapping

arXiv: https://arxiv.org/abs/2212.04002 · HTML: https://mlsanity.com/paper/2212.04002/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 59 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Deep Learning using Rectified Linear Units (ReLU)](https://mlsanity.com/paper/1803.08375/) (2018)
- [Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture](https://mlsanity.com/paper/2011.13511/) (2020)
- [DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text](https://mlsanity.com/paper/1802.01345/) (2018)
- [Foundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spaces](https://mlsanity.com/paper/2103.03655/) (2021)
- [Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis](https://mlsanity.com/paper/2008.03071/) (2020)
- [Applying physics-based loss functions to neural networks for improved generalizability in mechanics problems](https://mlsanity.com/paper/2105.00075/) (2021)
- [Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detection](https://mlsanity.com/paper/2112.03478/) (2021)
- [System-reliability based multi-ensemble of GAN and one-class joint Gaussian distributions for unsupervised real-time structural health monitoring](https://mlsanity.com/paper/2102.01158/) (2021)

## Built on by (most cited first, 6 of 6)

- [Quantifying the value of information transfer in population-based SHM](https://mlsanity.com/paper/2311.03083/) (2023)
- [A decision framework for selecting information-transfer strategies in population-based SHM](https://mlsanity.com/paper/2307.06978/) (2023)
- [A generative adversarial network optimization method for damage detection and digital twinning by deep AI fault learning: Z24 Bridge structural health monitoring benchmark validation](https://mlsanity.com/paper/2511.00099/) (2025)
- [Beyond development: Challenges in deploying machine learning models for structural engineering applications](https://mlsanity.com/paper/2404.12544/) (2024)
- [Quantifying the value of positive transfer: An experimental case study](https://mlsanity.com/paper/2407.14342/) (2024)
- [Seeing the Unseen: Towards Training-Free Inspection for Wind Turbine Blades Using Knowledge-Augmented Vision Language Models](https://mlsanity.com/paper/2510.22868/) (2025)

## Abstract (arXiv)

Gathering properly labelled, adequately rich, and case-specific data for successfully training a data-driven or hybrid model for structural health monitoring (SHM) applications is a challenging task. We posit that a Transfer Learning (TL) method that utilizes available data in any relevant source domain and directly applies to the target domain through domain adaptation can provide substantial remedies to address this issue. Accordingly, we present a novel TL method that differentiates between the source's no-damage and damage cases and utilizes a domain adaptation (DA) technique. The DA module transfers the accumulated knowledge in contrasting no-damage and damage cases in the source domain to the target domain, given only the target's no-damage case. High-dimensional features allow employing signal processing domain knowledge to devise a generalizable DA approach. The Generative Adversarial Network (GAN) architecture is adopted for learning since its optimization process accommodates high-dimensional inputs in a zero-shot setting. At the same time, its training objective conforms seamlessly with the case of no-damage and damage data in SHM since its discriminator network differentiates between real (no damage) and fake (possibly unseen damage) data. An extensive set of experimental results demonstrates the method's success in transferring knowledge on differences between no-damage and damage cases across three strongly heterogeneous independent target structures. The area under the Receiver Operating Characteristics curves (Area Under the Curve - AUC) is used to evaluate the differentiation between no-damage and damage cases in the target domain, reaching values as high as 0.95. With no-damage and damage cases discerned from each other, zero-shot structural damage detection is carried out. The mean F1 scores for all damages in the three independent datasets are 0.978, 0.992, and 0.975.