On the Treatment of Uncertainties and Probabilities in Engineering Decision Analysis

2005 ◽  
Vol 127 (3) ◽  
pp. 243-248 ◽  
Author(s):  
Michael Havbro Faber

In the present paper an introduction is initially given on the interpretation of uncertainty and probability in engineering decision analysis and it is explained how, in some cases, uncertainties may change type depending on the “scale” of the applied modeling and as a function of time. Thereafter it is attempted to identify and outline the generic character of different engineering decision problems and to categorize these as prior, posterior, and preposterior decision problems, in accordance with the Bayesian decision theory. Finally, input is given to an ongoing discussion concerning the correctness and consistency of uncertainty modeling applied in the most recent reliability updating analysis for structural requalification and inspection and maintenance planning. To this end an outline is given in regard to appropriate uncertainty treatment in the probabilistic modeling for different types of decision problems.

Author(s):  
Michael Havbro Faber

The present paper initially gives a basic introduction on the interpretation of uncertainty and probability in engineering decision analysis and explains how in some cases uncertainties may change type depending on the “scale” of the applied modeling and as a function of time. Thereafter it is attempted to identify and outline the generic character of different engineering decision problems and to categorize these as prior, posterior and pre-posterior decision problems in accordance with the Bayesian decision theory. Finally an input is given to an ongoing discussion concerning the correctness and consistency of the uncertainty modeling applied in most recent reliability updating analysis for structural re-qualification and inspection and maintenance planning. To this end an outline is given in regard to the appropriate uncertainty treatment in the probabilistic modeling for different types of decision problems.


2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.


2018 ◽  
Vol 3 (3) ◽  
pp. 32 ◽  
Author(s):  
Shane Haladuick ◽  
Markus Dann

For engineering systems, decision analysis can be used to determine the optimal decision from a set of options via utility maximization. Applied to inspection and maintenance planning, decision analysis can determine the best inspection and maintenance plan to follow. Decision analysis is relatively straightforward for simple systems. However, for more complex systems with many components or defects, the set of all possible inspection and maintenance plans can be very large. This paper presents the use of a genetic algorithm to perform inspection and maintenance plan optimization for complex systems. The performance of the genetic algorithm is compared to optimization by exhaustive search. A numerical example of life cycle maintenance planning for a corroding pressure vessel is used to illustrate the method. Genetic algorithms are found to be an effective approach to reduce the computational demand of solving complex inspection and maintenance optimizations.


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