Abstract
Obtaining the ability to make informed decisions regarding the operation and maintenance of structures, provides a major incentive for the implementation of structural health monitoring (SHM) systems. Probabilistic risk assessment (PRA) is an established methodology that allows engineers to make risk-informed decisions regarding the design and operation of safety-critical and high-value assets in industries such as nuclear and aerospace. The current paper aims to formulate a risk-based decision framework for structural health monitoring that combines elements of PRA with the existing SHM paradigm. As an apt tool for reasoning and decision-making under uncertainty, probabilistic graphical models serve as the foundation of the framework. The framework involves modelling failure modes of structures as Bayesian network representations of fault trees and then assigning costs or utilities to the failure events. The fault trees allow for information to pass from probabilistic classifiers to influence diagram representations of decision processes whilst also providing nodes within the graphical model that may be queried to obtain marginal probability distributions over local damage states within a structure. Optimal courses of action for structures are selected by determining the strategies that maximise expected utility. The risk-based framework is demonstrated on a realistic truss-like structure and supported by experimental data. Finally, a discussion of the risk-based approach is made and further challenges pertaining to decision-making processes in the context of SHM are identified.
Highlights
The field of engineering known as Structural Health Monitoring (SHM) concerns the development and implementation of data acquisition and processing systems for the purpose of damage detection in aerospace, civil or mechanical infrastructure [1]
The current paper aims to address the lack of a generalised framework for conducting risk-based monitoring of structures at the full-system scale by augmenting the current SHM paradigm with practices employed in probabilistic risk assessment and thereby facilitating the decision-making processes that motivate the implementation of SHM systems
The framework described and demonstrated in the current paper provides an approach to risk-based decision-making in the context of SHM
Summary
The field of engineering known as Structural Health Monitoring (SHM) concerns the development and implementation of data acquisition and processing systems for the purpose of damage detection in aerospace, civil or mechanical infrastructure [1]. A prime motivation for the use of SHM systems is to acquire the ability to make informed decisions regarding the operation and management of structures so as to improve safety and/or reduce costs. In the context of SHM, an agent tasked with making decisions is required to specify action policies that are robust to uncertainties that arise as a result of having imperfect information regarding the damage state of a structure. The task of decision-making for SHM is complex and highly involved and demands a thorough and systematic approach.
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