Login
Registrati
Reimposta password
Pubblica & Distribuisci
Soluzioni Editoriali
Soluzioni di Distribuzione
Temi
Architettura e design
Arti
Business e Economia
Chimica
Chimica industriale
Farmacia
Filosofia
Fisica
Geoscienze
Ingegneria
Interesse generale
Legge
Letteratura
Linguistica e semiotica
Matematica
Medicina
Musica
Scienze bibliotecarie e dell'informazione, studi library
Scienze dei materiali
Scienze della vita
Scienze informatiche
Scienze sociali
Sport e tempo libero
Storia
Studi classici e del Vicino Oriente antico
Studi culturali
Studi ebraici
Teologia e religione
Pubblicazioni
Riviste
Libri
Atti
Editori
Blog
Contatti
Cerca
EUR
USD
GBP
Italiano
English
Deutsch
Polski
Español
Français
Italiano
Carrello
Home
Riviste
Studia Geotechnica et Mechanica
Volume 44 (2022): Numero 1 (March 2022)
Accesso libero
An iterative algorithm for random upper bound kinematical analysis
Marcin Chwała
Marcin Chwała
| 10 nov 2021
Studia Geotechnica et Mechanica
Volume 44 (2022): Numero 1 (March 2022)
INFORMAZIONI SU QUESTO ARTICOLO
Articolo precedente
Articolo Successivo
Sommario
Articolo
Immagini e tabelle
Bibliografia
Autori
Articoli in questo Numero
Anteprima
PDF
Cita
CONDIVIDI
Article Category:
Original Study
Pubblicato online:
10 nov 2021
Pagine:
13 - 25
Ricevuto:
29 mag 2021
Accettato:
27 set 2021
DOI:
https://doi.org/10.2478/sgem-2021-0027
Parole chiave
random bearing capacity
,
shallow foundation
,
scale of fluctuation
,
iterative algorithm
,
upper bound
,
spatial variability
© 2022 Marcin Chwała, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1
The three algorithms discussed in the study. Path ‘A’ is the base iterative algorithm, which is described in detail in the text, the path ‘B’ differs only from ‘A’ in Step 3 (see the description in the text), and the path ‘C’ is dedicated to a constant covariance matrix. Both ‘A’, ‘B’, and ‘C’ are repeated N times in the Monte Carlo framework.
Figure 2
Failure geometry for two-layered soil for the probabilistic case. The indicated points and lengths are used to determine the failure geometry.
Figure 3
Eight randomly selected results among 200 Monte Carlo realizations for the case of θh=2 m, θv=1 m, and a=2 m.
Figure 4
Bearing capacity mean values as a function of covariance matrix iteration number (a). Bearing capacity standard deviations as a function of covariance matrix iteration number (b). Case k=0 is for the constant covariance matrix. Results for two-layered soil, vertical scale of fluctuation θv=0.5 m,, foundation width b=1 m and averaging lengths a=2 m and a=8 m.
Figure 5
Bearing capacity mean values as a function of covariance matrix iteration number (a). Bearing capacity standard deviations as a function of covariance matrix iteration number (b). Case k=0 is for the constant covariance matrix. Results for two-layered soil, vertical scale of fluctuation θv=1 m,, foundation width b=1 m and averaging lengths a=2 m and a=8 m.
Figure 6
Three-dimensional failure geometry of the rough foundation base for the probabilistic case. The indicated angles and lengths are used to determine the failure geometry.
Figure 7
Bearing capacity mean values as a function of covariance matrix iteration number (a). Bearing capacity standard deviations as a function of covariance matrix iteration number (b). Case k=0 is for the constant covariance matrix. Results for a square foundation of size 1 m × 1 m.
Figure 8
Bearing capacity mean values as a function of covariance matrix iteration number (a). Bearing capacity standard deviation as a function of covariance matrix iteration number (b). Case k=0 is for the constant covariance matrix. Results for a rectangular foundation of size 1 m × 4 m.
Figure 9
Bearing capacity mean values (a), standard deviations (b), and coefficient of variations (c) as a function of coefficient of variation of undrained shear strength. Two-layered soil considered with homogenous top layer (all parameters not mentioned here are the same as for earlier analyses).
Figure 10
Stabilisation of bearing capacity mean values (a) and standard deviations (b) for the iterative approache for k=6.
Anteprima