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Sigmoidal crack growth rate curve: statistical modelling and applications
Author(s) -
PAOLINO D. S.,
CAVATORTA M. P.
Publication year - 2013
Publication title -
fatigue and fracture of engineering materials and structures
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.887
H-Index - 84
eISSN - 1460-2695
pISSN - 8756-758X
DOI - 10.1111/ffe.12001
Subject(s) - sigmoid function , pooling , monte carlo method , statistical model , experimental data , fracture mechanics , mathematics , paris' law , statistics , structural engineering , computer science , engineering , crack closure , artificial intelligence , machine learning , artificial neural network
The present paper proposes a statistical model for describing sigmoidal crack growth rate curves. Major novelties are: a) exploitation of the maximum likelihood principle for obtaining material estimates by pooling together experimental data belonging to the different crack propagation regions; b) a general formulation which allows to adopt different sigmoidal models and any kind of statistical distribution for the model variables; c) fatigue life predictions through numerical integration of analytical functions with no need of Monte Carlo simulations. Experimental data taken from NASGRO database are used to check the validity of the statistical model in estimating material parameters included in the crack growth NASGRO algorithm. Illustrative plots of number of cycles to failure and crack length after a given number of cycles are presented, showing good agreement between the proposed statistical model and NASGRO results.