On risk-based active learning for structural health monitoring

TL;DR

This paper introduces a risk-based active learning approach for structural health monitoring that guides data labeling by expected information value, improving decision-making accuracy with limited labeled data.

Contribution

It presents a novel risk-based active learning framework tailored for SHM, integrating decision-making considerations into the data labeling process.

Findings

Improved decision-maker performance using risk-based active learning.

Effective application demonstrated on Z24 Bridge benchmark.

Enhanced classifier training with limited labeled data.

Abstract

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.