Details
| Original language | English |
|---|---|
| Article number | 103287 |
| Journal | Probabilistic Engineering Mechanics |
| Volume | 69 |
| Early online date | 29 Apr 2022 |
| Publication status | Published - Jul 2022 |
Abstract
Typically, non-deterministic models of spatial or time dependent uncertainty are modelled using the well-established random field framework. However, while tailored for exactly these types of time and spatial variations, stochastic processes and random fields currently have only limited success in industrial engineering practice. This is mainly caused by its computational burden, which renders the analysis of industrially sized problems very challenging, even when resorting to highly efficient random field analysis methods such as EOLE. Apart from that, also the methodological complexity, high information demand and rather indirect control of the spatial (or time) variation has limited its cost–benefit potential for potential end-users. This data requirement was recently relaxed by some of the authors with the introduction of imprecise random fields, but so far the method is only applicable to parametric p-box valued stochastic processes and random fields. This paper extends these concepts by expanding the framework towards distribution-free p-boxes. The main challenges addressed in this contribution are related to both the non-Gaussianity of realisations of the imprecise random field in between the p-box bounds, as well as maintaining the imposed auto-correlation structure while sampling from the p-box. Two case studies involving a dynamical model of a car suspension and the settlement of an embankment are included to illustrate the presented concepts.
Keywords
- Imprecise probability, Probability box, Random field, Scarce data, Stochastic process
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Statistical and Nonlinear Physics
- Engineering(all)
- Civil and Structural Engineering
- Energy(all)
- Nuclear Energy and Engineering
- Engineering(all)
- Aerospace Engineering
- Physics and Astronomy(all)
- Condensed Matter Physics
- Engineering(all)
- Ocean Engineering
- Engineering(all)
- Mechanical Engineering
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In: Probabilistic Engineering Mechanics, Vol. 69, 103287, 07.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Distribution-free P-box processes based on translation theory
T2 - Definition and simulation
AU - Faes, Matthias G.R.
AU - Broggi, Matteo
AU - Chen, Guan
AU - Phoon, Kok Kwang
AU - Beer, Michael
N1 - Funding Information: Matthias Faes acknowledges the partial financial support of the Research Foundation Flanders (FWO), Belgium under grant number 12P3519N as well as the Alexander von Humboldt foundation, Germany under the Humboldt Research Fellowship framework
PY - 2022/7
Y1 - 2022/7
N2 - Typically, non-deterministic models of spatial or time dependent uncertainty are modelled using the well-established random field framework. However, while tailored for exactly these types of time and spatial variations, stochastic processes and random fields currently have only limited success in industrial engineering practice. This is mainly caused by its computational burden, which renders the analysis of industrially sized problems very challenging, even when resorting to highly efficient random field analysis methods such as EOLE. Apart from that, also the methodological complexity, high information demand and rather indirect control of the spatial (or time) variation has limited its cost–benefit potential for potential end-users. This data requirement was recently relaxed by some of the authors with the introduction of imprecise random fields, but so far the method is only applicable to parametric p-box valued stochastic processes and random fields. This paper extends these concepts by expanding the framework towards distribution-free p-boxes. The main challenges addressed in this contribution are related to both the non-Gaussianity of realisations of the imprecise random field in between the p-box bounds, as well as maintaining the imposed auto-correlation structure while sampling from the p-box. Two case studies involving a dynamical model of a car suspension and the settlement of an embankment are included to illustrate the presented concepts.
AB - Typically, non-deterministic models of spatial or time dependent uncertainty are modelled using the well-established random field framework. However, while tailored for exactly these types of time and spatial variations, stochastic processes and random fields currently have only limited success in industrial engineering practice. This is mainly caused by its computational burden, which renders the analysis of industrially sized problems very challenging, even when resorting to highly efficient random field analysis methods such as EOLE. Apart from that, also the methodological complexity, high information demand and rather indirect control of the spatial (or time) variation has limited its cost–benefit potential for potential end-users. This data requirement was recently relaxed by some of the authors with the introduction of imprecise random fields, but so far the method is only applicable to parametric p-box valued stochastic processes and random fields. This paper extends these concepts by expanding the framework towards distribution-free p-boxes. The main challenges addressed in this contribution are related to both the non-Gaussianity of realisations of the imprecise random field in between the p-box bounds, as well as maintaining the imposed auto-correlation structure while sampling from the p-box. Two case studies involving a dynamical model of a car suspension and the settlement of an embankment are included to illustrate the presented concepts.
KW - Imprecise probability
KW - Probability box
KW - Random field
KW - Scarce data
KW - Stochastic process
UR - http://www.scopus.com/inward/record.url?scp=85130091780&partnerID=8YFLogxK
U2 - 10.1016/j.probengmech.2022.103287
DO - 10.1016/j.probengmech.2022.103287
M3 - Article
AN - SCOPUS:85130091780
VL - 69
JO - Probabilistic Engineering Mechanics
JF - Probabilistic Engineering Mechanics
SN - 0266-8920
M1 - 103287
ER -