DeepAI AI Chat
Log In Sign Up

Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers

06/23/2022
by   Aidan J. Hughes, et al.
The University of Sheffield
0

Gaining the ability to make informed decisions on operation and maintenance of structures provides motivation for the implementation of structural health monitoring (SHM) systems. However, descriptive labels for measured data corresponding to health-states of the monitored system are often unavailable. This issue limits the applicability of fully-supervised machine learning paradigms for the development of statistical classifiers to be used in decision-support in SHM systems. One approach to dealing with this problem is risk-based active learning. In such an approach, data-label querying is guided according to the expected value of perfect information for incipient data points. For risk-based active learning in SHM, the value of information is evaluated with respect to a maintenance decision process, and the data-label querying corresponds to the inspection of a structure to determine its health state. In the context of SHM, risk-based active learning has only been considered for generative classifiers. The current paper demonstrates several advantages of using an alternative type of classifier – discriminative models. Using the Z24 Bridge dataset as a case study, it is shown that discriminative classifiers have benefits, in the context of SHM decision-support, including improved robustness to sampling bias, and reduced expenditure on structural inspections.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/12/2021

On risk-based active learning for structural health monitoring

A primary motivation for the development and implementation of structura...
01/07/2022

On robust risk-based active-learning algorithms for enhanced decision support

Classification models are a fundamental component of physical-asset mana...
06/25/2022

Mitigating sampling bias in risk-based active learning via an EM algorithm

Risk-based active learning is an approach to developing statistical clas...
01/05/2021

A probabilistic risk-based decision framework for structural health monitoring

Obtaining the ability to make informed decisions regarding the operation...
03/13/2023

Towards risk-informed PBSHM: Populations as hierarchical systems

The prospect of informed and optimal decision-making regarding the opera...
03/02/2021

Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data

In data-driven SHM, the signals recorded from systems in operation can b...
03/23/2021

A unified model of inspection and monitoring quality

Non-destructive evaluation (NDE) through inspection and monitoring is an...