Transition probabilities matrix of Markov Chain in the fatigue crack growth model

Markov model is one of the reliable method to describe the growth of the crack from the initial until fracture phase. One of the important subjects in the crack growth models is to obtain the transition probability matrix of the fatigue. Determining probability transition matrix is important in Mark...

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Bibliographic Details
Published inAIP conference proceedings Vol. 1782; no. 1
Main Authors Nopiah, Zulkifli Mohd, Januri, Siti Sarah, Ariffin, Ahmad Kamal, Masseran, Nurulkamal, Abdullah, Shahrum
Format Journal Article Conference Proceeding
LanguageEnglish
Published Melville American Institute of Physics 25.10.2016
Subjects
Crack propagation
Fatigue failure
Fatigue life
Fracture mechanics
Growth models
Markov analysis
Markov chains
Probability
Transition probabilities
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ISSN0094-243X
1551-7616
DOI10.1063/1.4966055

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Summary:Markov model is one of the reliable method to describe the growth of the crack from the initial until fracture phase. One of the important subjects in the crack growth models is to obtain the transition probability matrix of the fatigue. Determining probability transition matrix is important in Markov Chain model for describing probability behaviour of fatigue life in the structure. In this paper, we obtain transition probabilities of a Markov chain based on the Paris law equation to describe the physical meaning of fatigue crack growth problem. The results show that the transition probabilities are capable to calculate the probability of damage in the future with the possibilities of comparing each stage between time.
Bibliography:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISSN:0094-243X
1551-7616
DOI:10.1063/1.4966055