# Towards risk-informed PBSHM: Populations as hierarchical systems

arXiv: https://arxiv.org/abs/2303.13533 · HTML: https://mlsanity.com/paper/2303.13533/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [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)
- [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)

## Abstract (arXiv)

The prospect of informed and optimal decision-making regarding the operation and maintenance (O&M) of structures provides impetus to the development of structural health monitoring (SHM) systems. A probabilistic risk-based framework for decision-making has already been proposed. However, in order to learn the statistical models necessary for decision-making, measured data from the structure of interest are required. Unfortunately, these data are seldom available across the range of environmental and operational conditions necessary to ensure good generalisation of the model. Recently, technologies have been developed that overcome this challenge, by extending SHM to populations of structures, such that valuable knowledge may be transferred between instances of structures that are sufficiently similar. This new approach is termed population-based structural heath monitoring (PBSHM). The current paper presents a formal representation of populations of structures, such that risk-based decision processes may be specified within them. The population-based representation is an extension to the hierarchical representation of a structure used within the probabilistic risk-based decision framework to define fault trees. The result is a series, consisting of systems of systems ranging from the individual component level up to an inventory of heterogeneous populations. The current paper considers an inventory of wind farms as a motivating example and highlights the inferences and decisions that can be made within the hierarchical representation.