Statistical distribution for prediction of stress intensity factor using bootstrap s-version finite element model

Stress intensity factor (SIF) is one of the most fundamental and useful parameters in all of fracture mechanics. The SIF describes the stress state at a crack tip, is related to the rate of crack growth, and used to establish failure criteria due to fracture. The SIF is determined to define whether...

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Main Authors: M. N., M. Husnain, M. R. M., Akramin, Z. L., Chuan, K., Rozieana
Format: Conference or Workshop Item
Language:English
English
Published: Universiti Malaysia Pahang 2019
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/26468/
http://umpir.ump.edu.my/id/eprint/26468/1/57.%20Statistical%20distribution%20for%20prediction%20of%20stress%20intensity.pdf
http://umpir.ump.edu.my/id/eprint/26468/2/57.1%20Statistical%20distribution%20for%20prediction%20of%20stress%20intensity.pdf
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author M. N., M. Husnain
M. R. M., Akramin
Z. L., Chuan
K., Rozieana
author_facet M. N., M. Husnain
M. R. M., Akramin
Z. L., Chuan
K., Rozieana
author_sort M. N., M. Husnain
building UMP Institutional Repository
collection Online Access
description Stress intensity factor (SIF) is one of the most fundamental and useful parameters in all of fracture mechanics. The SIF describes the stress state at a crack tip, is related to the rate of crack growth, and used to establish failure criteria due to fracture. The SIF is determined to define whether the crack will grow or not. The aims of this paper is to examine the best sampling statistical distributions in SIF analysis along the crack front of a structure. Box-Muller transformation is used to generate the statistical distributions which is in normal and lognormal distributions. This method transformed from the random number of the variables within range zero and one. The SIFs are computed using the virtual crack-closure method (VCCM) in bootstrap S-version finite element model (BootstrapS-FEM). The normal and lognormal distributions are represented in 95% of confidence bounds from the one hundred of random samples. The prediction of SIFs are verified with Newman-Raju solution and deterministic S-FEM in 95% of confidence bounds. The prediction of SIFs by BootstrapS-FEM in different statistical distribution are accepted because of the Newman-Raju solution is located in between the 95% confidence bounds. Thus, the lognormal distribution for SIFs prediction is more acceptable between normal distributions.
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format Conference or Workshop Item
id ump-26468
institution Universiti Malaysia Pahang
institution_category Local University
language English
English
last_indexed 2025-11-15T02:42:55Z
publishDate 2019
publisher Universiti Malaysia Pahang
recordtype eprints
repository_type Digital Repository
spelling ump-264682022-01-18T02:16:13Z http://umpir.ump.edu.my/id/eprint/26468/ Statistical distribution for prediction of stress intensity factor using bootstrap s-version finite element model M. N., M. Husnain M. R. M., Akramin Z. L., Chuan K., Rozieana TS Manufactures Stress intensity factor (SIF) is one of the most fundamental and useful parameters in all of fracture mechanics. The SIF describes the stress state at a crack tip, is related to the rate of crack growth, and used to establish failure criteria due to fracture. The SIF is determined to define whether the crack will grow or not. The aims of this paper is to examine the best sampling statistical distributions in SIF analysis along the crack front of a structure. Box-Muller transformation is used to generate the statistical distributions which is in normal and lognormal distributions. This method transformed from the random number of the variables within range zero and one. The SIFs are computed using the virtual crack-closure method (VCCM) in bootstrap S-version finite element model (BootstrapS-FEM). The normal and lognormal distributions are represented in 95% of confidence bounds from the one hundred of random samples. The prediction of SIFs are verified with Newman-Raju solution and deterministic S-FEM in 95% of confidence bounds. The prediction of SIFs by BootstrapS-FEM in different statistical distribution are accepted because of the Newman-Raju solution is located in between the 95% confidence bounds. Thus, the lognormal distribution for SIFs prediction is more acceptable between normal distributions. Universiti Malaysia Pahang 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26468/1/57.%20Statistical%20distribution%20for%20prediction%20of%20stress%20intensity.pdf pdf en http://umpir.ump.edu.my/id/eprint/26468/2/57.1%20Statistical%20distribution%20for%20prediction%20of%20stress%20intensity.pdf M. N., M. Husnain and M. R. M., Akramin and Z. L., Chuan and K., Rozieana (2019) Statistical distribution for prediction of stress intensity factor using bootstrap s-version finite element model. In: International Conference on Mechanical Engineering Research , 30-31 July 2019 , Kuantan, Pahang. pp. 1-7.. (Unpublished) (Unpublished)
spellingShingle TS Manufactures
M. N., M. Husnain
M. R. M., Akramin
Z. L., Chuan
K., Rozieana
Statistical distribution for prediction of stress intensity factor using bootstrap s-version finite element model
title Statistical distribution for prediction of stress intensity factor using bootstrap s-version finite element model
title_full Statistical distribution for prediction of stress intensity factor using bootstrap s-version finite element model
title_fullStr Statistical distribution for prediction of stress intensity factor using bootstrap s-version finite element model
title_full_unstemmed Statistical distribution for prediction of stress intensity factor using bootstrap s-version finite element model
title_short Statistical distribution for prediction of stress intensity factor using bootstrap s-version finite element model
title_sort statistical distribution for prediction of stress intensity factor using bootstrap s-version finite element model
topic TS Manufactures
url http://umpir.ump.edu.my/id/eprint/26468/
http://umpir.ump.edu.my/id/eprint/26468/1/57.%20Statistical%20distribution%20for%20prediction%20of%20stress%20intensity.pdf
http://umpir.ump.edu.my/id/eprint/26468/2/57.1%20Statistical%20distribution%20for%20prediction%20of%20stress%20intensity.pdf