Model uncertainty and Bayesian updating in reliability-based inspection

Zhang, Ruoxue; Mahadevan, Sankaran

doi:10.1016/s0167-4730(00)00005-9

Cited by 197 publications

(71 citation statements)

References 13 publications

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“…The mathematical expressions can be of variable degree of complexity and can depend on a limited number of variables, and the model can be selected from a range of different models describing the same physical phenomenon (Zhang & Mahadevan 2000, Beck & Yuen 2004. The degree of complexity and the number of variables is usually limited either by lack of knowledge or for practical reasons.…”

Section: Modelling Uncertaintiesmentioning

confidence: 99%

A quantification of the modelling uncertainty of non-linear finite element analyses of large concrete structures

et al. 2017

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Section: Modelling Uncertaintiesmentioning

confidence: 99%

A quantification of the modelling uncertainty of non-linear finite element analyses of large concrete structures

et al. 2017

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“…Most of the applications in these fields use classical methods for calculation, require that data be abundantly available, and study is limited to simple models, e.g., random variables or vectors. Applications include Bayesian model updating for structural dynamics and structural reliability [7,8,62,76,90,97], Bayesian analysis of model form uncertainty [3], Appendix D, [58], and structural fatigue [101]. Very few applications use the decision-theoretic approach; applications include structural reliability [38,46,51], and engineering materials [92].…”

Section: Bayesian Methodsmentioning

confidence: 99%

Methods for model selection in applied science and engineering.

Field¹

2004

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Mathematical models are developed and used to study the properties of complex systems and/or modify these systems to satisfy some performance requirements in just about every area of applied science and engineering. A particular reason for developing a model, e.g., performance assessment or design, is referred to as the model use. Our objective is the development of a methodology for selecting a model that is sufficiently accurate for an intended use.Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. Methods are developed for selecting the optimal member from a class of candidate models for the system. The optimal model depends on the available information, the selected class of candidate models, and the model use.Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, as well as a method employing a decisiontheoretic approach, are formulated to select the optimal model for numerous applications. There is no requirement that the candidate models be random. Classical methods for model selection ignore model use and require data to be available. Examples are used to show that these methods can be unreliable when data is limited. The decision-theoretic approach to model selection does not have these limitations, and model use is included through an appropriate utility function. This is especially important when modeling high risk systems, where the consequences of using an inappropriate model for the system can be disastrous. 3The decision-theoretic method for model selection is developed and applied for a series of complex and diverse applications. These include the selection of the: (1) optimal order of the polynomial chaos approximation for non-Gaussian random variables and stationary stochastic processes, (2) optimal pressure load model to be applied to a spacecraft during atmospheric re-entry, and (3) optimal design of a distributed sensor network for the purpose of vehicle tracking and identification.4

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“…For example, in the case of the NHPP, probabilities would be assigned to different values of β and λ which are continuously revised based on newly obtained condition data. This approach to representing the probability of failure is considered particularly valuable in cases where failures are rare events, and thus little historical data to assess the suitability of different probabilistic models exists [37].…”

Section: Failure Probabilitymentioning

confidence: 99%

Case Study for the Return on Investment of Internet of Things Using Agent-Based Modelling and Data Science

Houston

Gooberman-Hill

Mathie

et al. 2017

Systems

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Abstract:As technology advances towards new paradigms such as the Internet of Things, there is a desire among business leaders for a reliable method to determine the value of supporting these ventures. Traditional simulation and analysis techniques cannot model the complex systems inherent in fields such as infrastructure asset management, or suffer from a lack of data on which to build a prediction. Agent-based modelling, through an integration with data science, presents an attractive simulation method to capture these underlying complexities and provide a solution. The aim of this work is to investigate this integration as a refined process for answering practical business questions. A specific case study is addressed to assess the return on investment of installing condition monitoring sensors on lift assets in a London Underground station. An agent-based model is developed for this purpose, supported by analysis from historical data. The simulation results demonstrate how returns can be achieved and highlight features induced as a result of stochasticity in the model. Suggestions of future research paths are additionally outlined.

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Model uncertainty and Bayesian updating in reliability-based inspection

Cited by 197 publications

References 13 publications

A quantification of the modelling uncertainty of non-linear finite element analyses of large concrete structures

A quantification of the modelling uncertainty of non-linear finite element analyses of large concrete structures

Methods for model selection in applied science and engineering.

Case Study for the Return on Investment of Internet of Things Using Agent-Based Modelling and Data Science

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