Towards an active-learning approach to resource allocation for population-based damage prognosis

TL;DR

This paper proposes an active-learning method for resource allocation in population-based damage prognosis, aiming to improve predictive accuracy by optimally assigning high-fidelity monitoring to structures with evolving damage.

Contribution

It introduces an active-learning framework for resource allocation in population-based structural health monitoring, focusing on selecting structures for high-fidelity monitoring to enhance damage prognosis accuracy.

Findings

Active-learning improves damage prognosis accuracy.

Optimal resource allocation enhances monitoring efficiency.

Method effectively identifies structures needing high-fidelity data.

Abstract

Damage prognosis is, arguably, one of the most difficult tasks of structural health monitoring (SHM). To address common problems of damage prognosis, a population-based SHM (PBSHM) approach is adopted in the current work. In this approach the prognosis problem is considered as an information-sharing problem where data from past structures are exploited to make more accurate inferences regarding currently-degrading structures. For a given population, there may exist restrictions on the resources available to conduct monitoring; thus, the current work studies the problem of allocating such resources within a population of degrading structures with a view to maximising the damage-prognosis accuracy. The challenges of the current framework are mainly associated with the inference of outliers on the level of damage evolution, given partial data from the damage-evolution phenomenon. The current approach considers an initial population of structures for which damage evolution is extensively observed. Subsequently, a second population of structures with evolving damage is considered for which two monitoring systems are available, a low-availability and high-fidelity (low-uncertainty) one, and a widely-available and low-fidelity (high-uncertainty) one. The task of the current work is to follow an active-learning approach to identify the structures to which the high-fidelity system should be assigned in order to enhance the predictive capabilities of the machine-learning model throughout the population.