Details
| Original language | English |
|---|---|
| Title of host publication | Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023 |
| Editors | Fabio Biondini, Dan M. Frangopol |
| Pages | 670-677 |
| Number of pages | 8 |
| ISBN (electronic) | 9781003323020 |
| Publication status | Published - 2023 |
| Event | 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023 - Milan, Italy Duration: 2 Jul 2023 → 6 Jul 2023 |
Publication series
| Name | Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023 |
|---|
Abstract
In stochastic model updating, hybrid uncertainties are typically characterized by the distributional p-box. It assigns a certain probability distribution to model parameters and assumes its hyper-parameters as interval values. Thus, regardless of the updating method employed, the distribution family needs to be known a priori to parameterize the distribution. Meanwhile, a novel class of the random variable, called staircase random variable, can discretely approximate a wide range of distributions by solving moment-matching optimization problem. The first author and his co-workers have recently developed a distribution-free stochastic updating framework, in which model parameters are considered as staircase random variables and their hyper-parameters are inferred in a Bayesian fashion. This framework can explore an optimal distribution from a broad range of potential distributions according to the available data. This study aims to further demonstrate the capability of this framework through a simple numerical example with a parameter following various types of distributions.
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Safety, Risk, Reliability and Quality
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023. ed. / Fabio Biondini; Dan M. Frangopol. 2023. p. 670-677 (Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Distribution-free stochastic model updating with staircase density functions
AU - Kitahara, M.
AU - Kitahara, T.
AU - Bi, S.
AU - Broggi, M.
AU - Beer, M.
PY - 2023
Y1 - 2023
N2 - In stochastic model updating, hybrid uncertainties are typically characterized by the distributional p-box. It assigns a certain probability distribution to model parameters and assumes its hyper-parameters as interval values. Thus, regardless of the updating method employed, the distribution family needs to be known a priori to parameterize the distribution. Meanwhile, a novel class of the random variable, called staircase random variable, can discretely approximate a wide range of distributions by solving moment-matching optimization problem. The first author and his co-workers have recently developed a distribution-free stochastic updating framework, in which model parameters are considered as staircase random variables and their hyper-parameters are inferred in a Bayesian fashion. This framework can explore an optimal distribution from a broad range of potential distributions according to the available data. This study aims to further demonstrate the capability of this framework through a simple numerical example with a parameter following various types of distributions.
AB - In stochastic model updating, hybrid uncertainties are typically characterized by the distributional p-box. It assigns a certain probability distribution to model parameters and assumes its hyper-parameters as interval values. Thus, regardless of the updating method employed, the distribution family needs to be known a priori to parameterize the distribution. Meanwhile, a novel class of the random variable, called staircase random variable, can discretely approximate a wide range of distributions by solving moment-matching optimization problem. The first author and his co-workers have recently developed a distribution-free stochastic updating framework, in which model parameters are considered as staircase random variables and their hyper-parameters are inferred in a Bayesian fashion. This framework can explore an optimal distribution from a broad range of potential distributions according to the available data. This study aims to further demonstrate the capability of this framework through a simple numerical example with a parameter following various types of distributions.
UR - http://www.scopus.com/inward/record.url?scp=85186657522&partnerID=8YFLogxK
U2 - 10.1201/9781003323020-81
DO - 10.1201/9781003323020-81
M3 - Conference contribution
AN - SCOPUS:85186657522
T3 - Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023
SP - 670
EP - 677
BT - Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023
A2 - Biondini, Fabio
A2 - Frangopol, Dan M.
T2 - 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023
Y2 - 2 July 2023 through 6 July 2023
ER -