# On risk-based active learning for structural health monitoring

arXiv: https://arxiv.org/abs/2105.05622 · HTML: https://mlsanity.com/paper/2105.05622/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 37 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 1 of 1)

- [Domain Adaptation for One-Class Classification: Monitoring the Health of Critical Systems Under Limited Information](https://mlsanity.com/paper/1907.09204/) (2019)

## Built on by (most cited first, 15 of 15)

- [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)
- [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)
- [A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic](https://mlsanity.com/paper/2204.06362/) (2022)
- [Quantifying the value of information transfer in population-based SHM](https://mlsanity.com/paper/2311.03083/) (2023)
- [Canonical-Correlation-Based Fast Feature Selection for Structural Health Monitoring](https://mlsanity.com/paper/2106.08247/) (2021)
- [A decision framework for selecting information-transfer strategies in population-based SHM](https://mlsanity.com/paper/2307.06978/) (2023)
- [Active learning for regression in engineering populations: A risk-informed approach](https://mlsanity.com/paper/2409.04328/) (2024)
- [Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers](https://mlsanity.com/paper/2206.11616/) (2022)
- [Mitigating sampling bias in risk-based active learning via an EM algorithm](https://mlsanity.com/paper/2206.12598/) (2022)
- [Deep learning for structural health monitoring: An application to heritage structures](https://mlsanity.com/paper/2211.10351/) (2022)
- [Towards risk-informed PBSHM: Populations as hierarchical systems](https://mlsanity.com/paper/2303.13533/) (2023)
- [Cost-informed dimensionality reduction for structural digital twin technologies](https://mlsanity.com/paper/2409.11236/) (2024)
- [Towards an active-learning approach to resource allocation for population-based damage prognosis](https://mlsanity.com/paper/2409.18572/) (2024)
- [Active transfer learning for structural health monitoring](https://mlsanity.com/paper/2510.27525/) (2025)

## Abstract (arXiv)

A primary motivation for the development and implementation of structural health monitoring systems, is the prospect of gaining the ability to make informed decisions regarding the operation and maintenance of structures and infrastructure. Unfortunately, descriptive labels for measured data corresponding to health-state information for the structure of interest are seldom available prior to the implementation of a monitoring system. This issue limits the applicability of the traditional supervised and unsupervised approaches to machine learning in the development of statistical classifiers for decision-supporting SHM systems. The current paper presents a risk-based formulation of active learning, in which the querying of class-label information is guided by the expected value of said information for each incipient data point. When applied to structural health monitoring, the querying of class labels can be mapped onto the inspection of a structure of interest in order to determine its health state. In the current paper, the risk-based active learning process is explained and visualised via a representative numerical example and subsequently applied to the Z24 Bridge benchmark. The results of the case studies indicate that a decision-maker's performance can be improved via the risk-based active learning of a statistical classifier, such that the decision process itself is taken into account.