Probabilistic analysis of tunnel loads using variance reduction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F15%3A00236032" target="_blank" >RIV/68407700:21110/15:00236032 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.icevirtuallibrary.com/doi/full/10.1680/geng.14.00062" target="_blank" >http://www.icevirtuallibrary.com/doi/full/10.1680/geng.14.00062</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1680/geng.14.00062" target="_blank" >10.1680/geng.14.00062</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Probabilistic analysis of tunnel loads using variance reduction

Popis výsledku v původním jazyce

Selection of input parameters for numerical modelling of geotechnical structures is problematic due to their variability and uncertainty. Generally parametric studies are required to evaluate the impact of input parameters on the modelling results. Utilisation of various statistical methods can bring significant benefits, such as probabilistic distributions of the modelling outputs, the probability of failure, etc. This paper presents a variance reduction method known as Latin Hypercube Sampling (LHS).Special attention is paid to the LHS mean method representing a more efficient sampling scheme within the LHS framework. Performance of the LHS mean method is evaluated by comparison with the standard LHS median method and the necessary number of simulations is recommended. To take the statistical dependency of input variables into consideration the LHS mean is combined with the Simulated Annealing method. The effectiveness of LHS methods and their practical application is demonstrated b

Název v anglickém jazyce

Probabilistic analysis of tunnel loads using variance reduction

Popis výsledku anglicky

Selection of input parameters for numerical modelling of geotechnical structures is problematic due to their variability and uncertainty. Generally parametric studies are required to evaluate the impact of input parameters on the modelling results. Utilisation of various statistical methods can bring significant benefits, such as probabilistic distributions of the modelling outputs, the probability of failure, etc. This paper presents a variance reduction method known as Latin Hypercube Sampling (LHS).Special attention is paid to the LHS mean method representing a more efficient sampling scheme within the LHS framework. Performance of the LHS mean method is evaluated by comparison with the standard LHS median method and the necessary number of simulations is recommended. To take the statistical dependency of input variables into consideration the LHS mean is combined with the Simulated Annealing method. The effectiveness of LHS methods and their practical application is demonstrated b

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JN - Stavebnictví

OECD FORD obor

—

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Proceedings of the Institution of Civil Engineers - Geotechnical Engineering

ISSN

1353-2618

e-ISSN

—

Svazek periodika

168

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

348-357

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-84937143451