# Quantifying the value of information transfer in population-based SHM

arXiv: https://arxiv.org/abs/2311.03083 · HTML: https://mlsanity.com/paper/2311.03083/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 6 Nov 2023.

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

- [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018)
- [Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning](https://mlsanity.com/paper/1710.08904/) (2017)
- [Foundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spaces](https://mlsanity.com/paper/2103.03655/) (2021)
- [On risk-based active learning for structural health monitoring](https://mlsanity.com/paper/2105.05622/) (2021)
- [Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask Learning](https://mlsanity.com/paper/2204.12404/) (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)
- [Value of information from vibration-based structural health monitoring extracted via Bayesian model updating](https://mlsanity.com/paper/2103.07382/) (2021)
- [On robust risk-based active-learning algorithms for enhanced decision support](https://mlsanity.com/paper/2201.02555/) (2022)
- [On statistic alignment for domain adaptation in structural health monitoring](https://mlsanity.com/paper/2205.12052/) (2022)
- [Zero-Shot Transfer Learning for Structural Health Monitoring using Generative Adversarial Networks and Spectral Mapping](https://mlsanity.com/paper/2212.04002/) (2022)
- [On the hierarchical Bayesian modelling of frequency response functions](https://mlsanity.com/paper/2307.06263/) (2023)
- [A decision framework for selecting information-transfer strategies in population-based SHM](https://mlsanity.com/paper/2307.06978/) (2023)

## Built on by (most cited first, 3 of 3)

- [Quantifying the value of positive transfer: An experimental case study](https://mlsanity.com/paper/2407.14342/) (2024)
- [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)

## Abstract (arXiv)

Population-based structural health monitoring (PBSHM), seeks to address some of the limitations associated with data scarcity that arise in traditional SHM. A tenet of the population-based approach to SHM is that information can be shared between sufficiently-similar structures in order to improve predictive models. Transfer learning techniques, such as domain adaptation, have been shown to be a highly-useful technology for sharing information between structures when developing statistical classifiers for PBSHM. Nonetheless, transfer-learning techniques are not without their pitfalls. In some circumstances, for example if the data distributions associated with the structures within a population are dissimilar, applying transfer-learning methods can be detrimental to classification performance -- this phenomenon is known as negative transfer. Given the potentially-severe consequences of negative transfer, it is prudent for engineers to ask the question `when, what, and how should one transfer between structures?'. The current paper aims to demonstrate a transfer-strategy decision process for a classification task for a population of simulated structures in the context of a representative SHM maintenance problem, supported by domain adaptation. The transfer decision framework is based upon the concept of expected value of information transfer. In order to compute the expected value of information transfer, predictions must be made regarding the classification (and decision performance) in the target domain following information transfer. In order to forecast the outcome of transfers, a probabilistic regression is used here to predict classification performance from a proxy for structural similarity based on the modal assurance criterion.