Reliability analysis of discrete-state performance functions via adaptive sequential sampling with detection of failure surfaces
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F22%3APU146359" target="_blank" >RIV/00216305:26110/22:PU146359 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.cma.2022.115606" target="_blank" >https://doi.org/10.1016/j.cma.2022.115606</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.cma.2022.115606" target="_blank" >10.1016/j.cma.2022.115606</a>
Alternative languages
Result language
angličtina
Original language name
Reliability analysis of discrete-state performance functions via adaptive sequential sampling with detection of failure surfaces
Original language description
The paper presents a new efficient and robust method for rare event probability estimation for computational models of an engineering product or a process returning categorical information only, for example, either success or failure. For such models, most of the methods designed for the estimation of failure probability, which use the numerical value of the outcome to compute gradients or to estimate the proximity to the failure surface, cannot be applied. Even if the performance function provides more than just binary output, the state of the system may be a non-smooth or even a discontinuous function defined in the domain of continuous input variables. This often happens because the mathematical model features non-smooth components or discontinuities (e.g., in the constitutive laws), bifurcations, or even domains in which no reasonable model response is obtained. In these cases, the classical gradient-based methods usually fail. We propose a simple yet efficient algorithm, which performs a sequential adaptive selection of points from the input domain of random variables to extend and refine a simple distance-based surrogate model. Two different tasks can be accomplished at any stage of sequential sampling: (i) estimation of the failure probability, and (ii) selection of the best possible candidate for the subsequent model evaluation if further improvement is necessary. The proposed criterion for selecting the next point for model evaluation maximizes the expected probability classified by using the candidate. Therefore, the perfect balance between global exploration and local exploitation is maintained automatically. If there are more rare events such as failure modes, the method can be generalized to estimate the probabilities of all these event types. Moreover, when the numerical value of model evaluation can be used to build a smooth surrogate, the algorithm can accommodate this information to increase the accuracy of the estimated probabilities. Lastly, we de
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
21100 - Other engineering and technologies
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
ISSN
0045-7825
e-ISSN
1879-2138
Volume of the periodical
401
Issue of the periodical within the volume
115606
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
46
Pages from-to
„“-„“
UT code for WoS article
000872540500004
EID of the result in the Scopus database
2-s2.0-85139301092