On the Value of Monitoring Information for the Structural Integrity and Risk Management

Abstract

This article introduces an approach and framework for the quantification of the value of structural health monitoring (SHM) in the context of the structural risk and integrity management for systems. The quantification of the value of SHM builds upon the Bayesian decision and utility theory, which facilitates the assessment of the value of information associated with SHM. The principal approach for the quantification of the value of SHM is formulated by modeling the fundamental decision of performing SHM or not in conjunction with their expected utilities. The expected utilities are calculated accounting for the probabilistic performance of a system in conjunction with the associated structural integrity and risk management actions throughout the life cycle, the associated benefits, structural risks, and costs and when performing SHM, the SHM information, their probabilistic outcomes, and costs. The calculation of the expected utilities necessitates a comprehensive and rigorous modeling, which is introduced close to the original formulations and for which analysis characteristics and simplifications are described and derived. The framework provides the basis for the optimization of the structural risk and integrity management based on utility gains including or excluding SHM and inspection information. Studies of fatigue deteriorating structural systems and their characteristics (1) provide decision support for the performance of SHM, (2) explicate the influence of the structural component and system characteristics on the value of SHM, and (3) demonstrate how an integral optimization of SHM and inspection strategies for an efficient structural risk and integrity management can be performed.

Integrative modeling of performance deterioration and maintenance effectiveness for infrastructure assets with missing condition data

“…Related studies on prioritizations of inspection, and MRR activities of municipal assets can be found in, for example, Bagloee, Sarvi, Patriksson, and Asadi (), Bailey et al. (), Breysse, Vasconcelos, and Schoefs (), Fenner (), Kerwin and Adey (), Memarzadeh and Pozzi (), Tagherouit, Bennis, and Bengassem (), New England Interstate Water Pollution Control Commission (), and Thöns ().…”

Plausible Petri nets as self‐adaptive expert systems: A tool for infrastructure asset monitoring

“…Future research steps in the context of this specific application include the consideration of other subsystems within the expert system model, such as signaling, electrification, switchings and crossings, etc. Building on this work, one desirable future research direction to enhance PPNs as self‐adaptive expert systems for complex civil infrastructures is to explore PPN architectures that allow the modelng of foreseen intervention scenarios. Also, an additional challenge would be how to incorporate massive and heterogenous data (Thaduri, Galar, & Kumar, ; Thöns, ) from infrastructure monitoring within the PPN methodology.…”

“…• Building on this work, one desirable future research direction to enhance PPNs as self-adaptive expert systems for complex civil infrastructures is to explore PPN architectures that allow the modelng of foreseen intervention scenarios. Also, an additional challenge would be how to incorporate massive and heterogenous data (Thaduri, Galar, & Kumar, 2015;Thöns, 2018) from infrastructure monitoring within the PPN methodology.…”

Value of pre‐construction proof loading information for structural design

“…The theory of value of information (VoI) is part of the Bayesian decision theory developed by Raiffa and Schlaifer An example of a comprehensive study of the quantification of the value of structural health monitoring (SHM) information for the structural risk and integrity management is given by Thöns In order to obtain the VoI in the context of civil engineered structures with preposterior information, the expected value of sample information (eg, from SHM or proof load testing) is quantified as the difference between life cycle benefit as determined by preposterior decision analysis and life cycle benefit according to a scenario without SHM utilization or proof load testing based on prior models. a pr represents the action alternatives that are possible with prior information; thus, a pr are all action options allowed in the value of information analysis.…”