Measuring reliability under epistemic uncertainty: Review on non-probabilistic reliability metrics

Kang, Rui; Zhang, Qingyuan; Zeng, Zhiguo; Zio, Enrico; Li, Xiaoyang

doi:10.1016/j.cja.2016.04.004

Cited by 111 publications

(44 citation statements)

References 71 publications

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“…However, the purely probability-based approaches to risk assessment are challenged when dealing with highly unlikely industrial accidents (with extreme consequences). For these rare events, only very limited knowledge exists in support to the risk assessment and a number of alternative frameworks for uncertainty representation and treatment in risk assessment have been introduced [2,19,58,89].…”

Section: Risk Assessmentmentioning

confidence: 99%

The future of risk assessment

Zio

2018

Reliability Engineering & System Safety

263

116

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A B S T R A C TRisk assessment must evolve for addressing the existing and future challenges, and considering the new systems and innovations that have already arrived in our lives and that are coming ahead. In this paper, I swing on the rapid changes and innovations that the World that we live in is experiencing, and analyze them with respect to the challenges that these pose to the field of risk assessment. Digitalization brings opportunities but with it comes also the complexity of cyber-phyiscal systems. Climate change and extreme natural events are increasingly threatening our infrastructures; terrorist and malevolent threats are posing severe concerns for the security of our systems and lives. These sources of hazard are extremely uncertain and, thus, difficult to describe and model quantitatively.Some research and development directions that are emerging are presented and discussed, also considering the ever increasing computational capabilities and data availability. These include the use of simulation for accident scenario identification and exploration, the extension of risk assessment into the framework of resilience and business continuity, the reliance on data for dynamic and condition monitoring-based risk assessment, the safety and security assessment of cyber-physical systems.The paper is not a research work and not exactly a review or a state of the art work, but rather it offers a lookout on risk assessment, open to consideration and discussion, as it cannot pretend to give an absolute point of view nor to be complete in the issues addressed (and the related literature referenced to).

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Section: Risk Assessmentmentioning

confidence: 99%

The future of risk assessment

Zio

2018

Reliability Engineering & System Safety

263

116

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“…Epistemic or subjective uncertainties stem from the lack of su cient information during modeling and optimization. Several theories, including probability box models, fuzzy sets, Bayesian approaches, and evidence theory, have been developed to deal with epistemic uncertainties [8][9][10][11][12][13][14][15]. Evidence theory provides a general modeling of epistemic uncertainty and can be reduced to other theories.…”

Section: Introductionmentioning

confidence: 99%

Evidence‐Based Multidisciplinary Design Optimization with the Active Global Kriging Model

Yang

Liu

et al. 2019

Complexity

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This article presents an approach that combines the active global Kriging method and multidisciplinary strategy to investigate the problem of evidence-based multidisciplinary design optimization. The global Kriging model is constructed by introducing a so-called learning function and using actively selected samples in the entire optimization space. With the Kriging model, the plausibility, Pl, of failure is obtained with evidence theory. The multidisciplinary feasible and collaborative optimization strategies of multidisciplinary design optimization are combined with the evidence-based reliability analysis. Numerical examples are provided to illustrate the efficiency and accuracy of the proposed method. The numerical results show that the proposed algorithm is effective and valuable, which is valuable in engineering application.

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“…The probability measure with additivity used by these methods fails to satisfy the subadditivity axiom of epistemic uncertainty, so the nonadditive monotone measures are more suitable for developing reliability metrics that model epistemic uncertainty . Moreover, the possibility measure does not satisfy the duality axiom which might lead to counter‐intuitive results when applied in practical reliability‐related applications . Therefore, neither the probability theory nor the possibility theory can solve the problems of structural reliability under epistemic uncertainties, because both of the 2 theories cannot satisfy the duality and subadditivity simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…According to the mathematical essence of the reliability metrics, the reliability metrics obtained by the first 3 theories are the probability interval-based metrics (PIB metrics) and the reliability metrics obtained by the latter 2 theories are the monotone measure-based reliability metrics (MMB metrics). 4 However, since the PIB metrics utilize an interval to describe all the possible failure probabilities and the wider interval indicates the larger epistemic uncertainty, the PIB metrics have the problem of interval extension, and it has an adverse effect on the practicality of the results. The MMB metrics 20 can compensate for the conservatism problem of the reliability metrics caused by the consideration of epistemic uncertainty, and it can be divided into additive MMB metrics (such as the reliability metrics based on the probability measure) and nonadditive MMB metrics (such as the reliability metrics based on the possibility measure 21 and the reliability metrics based on the uncertainty measure 22 ).…”

Section: Introductionmentioning

confidence: 99%

“…23 Moreover, the possibility measure does not satisfy the duality axiom which might lead to counter-intuitive results when applied in practical reliabilityrelated applications. 4 Therefore, neither the probability theory nor the possibility theory can solve the problems of structural reliability under epistemic uncertainties, because both of the 2 theories cannot satisfy the duality and subadditivity simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Reliability analysis of structures based on a probability‐uncertainty hybrid model

Zhang

You

et al. 2018

Quality & Reliability Eng

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The traditional reliability analysis method based on probabilistic method requires probability distributions of all the uncertain parameters. However, in practical applications, the distributions of some parameters may not be precisely known due to the lack of sufficient sample data. The probabilistic theory cannot directly measure the reliability of structures with epistemic uncertainty, ie, subjective randomness and fuzziness. Hence, a hybrid reliability analysis (HRA) problem will be caused when the aleatory and epistemic uncertainties coexist in a structure. In this paper, by combining the probability theory and the uncertainty theory into a chance theory, a probability‐uncertainty hybrid model is established, and a new quantification method based on the uncertain random variables for the structural reliability is presented in order to simultaneously satisfy the duality of random variables and the subadditivity of uncertain variables; then, a reliability index is explored based on the chance expected value and variance. Besides, the formulas of the chance theory‐based reliability and reliability index are derived to uniformly assess the reliability of structures under the hybrid aleatory and epistemic uncertainties. The numerical experiments illustrate the validity of the proposed method, and the results of the proposed method can provide a more accurate assessment of the structural system under the mixed uncertainties than the ones obtained separately from the probability theory and the uncertainty theory.

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Measuring reliability under epistemic uncertainty: Review on non-probabilistic reliability metrics

Cited by 111 publications

References 71 publications

The future of risk assessment

The future of risk assessment

Evidence‐Based Multidisciplinary Design Optimization with the Active Global Kriging Model

Reliability analysis of structures based on a probability‐uncertainty hybrid model

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