# On statistic alignment for domain adaptation in structural health monitoring

arXiv: https://arxiv.org/abs/2205.12052 · HTML: https://mlsanity.com/paper/2205.12052/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 76 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 16 of 16)

- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Domain-Specific Batch Normalization for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1906.03950/) (2019)
- [Learning to Optimize Domain Specific Normalization for Domain Generalization](https://mlsanity.com/paper/1907.04275/) (2019)
- [Partial Transfer Learning with Selective Adversarial Networks](https://mlsanity.com/paper/1707.07901/) (2017)
- [Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach](https://mlsanity.com/paper/1808.05464/) (2018)
- [Balanced Distribution Adaptation for Transfer Learning](https://mlsanity.com/paper/1807.00516/) (2018)
- [Facial Expression Analysis under Partial Occlusion: A Survey](https://mlsanity.com/paper/1802.08784/) (2018)
- [Beyond H-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence](https://mlsanity.com/paper/2007.15567/) (2020)
- [On risk-based active learning for structural health monitoring](https://mlsanity.com/paper/2105.05622/) (2021)
- [Domain Adaptation for One-Class Classification: Monitoring the Health of Critical Systems Under Limited Information](https://mlsanity.com/paper/1907.09204/) (2019)
- [Knowledge Transfer between Buildings for Seismic Damage Diagnosis through Adversarial Learning](https://mlsanity.com/paper/2002.09513/) (2020)

## Built on by (most cited first, 8 of 8)

- [On the hierarchical Bayesian modelling of frequency response functions](https://mlsanity.com/paper/2307.06263/) (2023)
- [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)
- [When does a bridge become an aeroplane?](https://mlsanity.com/paper/2411.18406/) (2024)
- [Structural State Translation: Condition Transfer between Civil Structures Using Domain-Generalization for Structural Health Monitoring](https://mlsanity.com/paper/2212.14048/) (2022)
- [Physics-informed transfer learning for SHM via feature selection](https://mlsanity.com/paper/2507.19519/) (2025)
- [Active transfer learning for structural health monitoring](https://mlsanity.com/paper/2510.27525/) (2025)
- [Transfer learning via interpolating structures](https://mlsanity.com/paper/2603.22621/) (2026)

## Abstract (arXiv)

The practical application of structural health monitoring (SHM) is often limited by the availability of labelled data. Transfer learning - specifically in the form of domain adaptation (DA) - gives rise to the possibility of leveraging information from a population of physical or numerical structures, by inferring a mapping that aligns the feature spaces. Typical DA methods rely on nonparametric distance metrics, which require sufficient data to perform density estimation. In addition, these methods can be prone to performance degradation under class imbalance. To address these issues, statistic alignment (SA) is discussed, with a demonstration of how these methods can be made robust to class imbalance, including a special case of class imbalance called a partial DA scenario. SA is demonstrated to facilitate damage localisation with no target labels in a numerical case study, outperforming other state-of-the-art DA methods. It is then shown to be capable of aligning the feature spaces of a real heterogeneous population, the Z24 and KW51 bridges, with only 220 samples used from the KW51 bridge. Finally, in scenarios where more complex mappings are required for knowledge transfer, SA is shown to be a vital pre-processing tool, increasing the performance of established DA methods.