Comparison of Bayesian & other approaches to the estimation of fatigue crack growth rate from 2D textural features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F17%3A00316541" target="_blank" >RIV/68407700:21340/17:00316541 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.15632/jtam-pl.55.4.1269" target="_blank" >http://dx.doi.org/10.15632/jtam-pl.55.4.1269</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15632/jtam-pl.55.4.1269" target="_blank" >10.15632/jtam-pl.55.4.1269</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Bayesian & other approaches to the estimation of fatigue crack growth rate from 2D textural features

Popis výsledku v původním jazyce

The fatigue crack growth rate can be explained using features of the surface of a structure. Among other methods, linear regression can be used to explain crack growth velocity. Non- linear transformations of fracture surface texture features may be useful as explanatory variables. Nonetheless, the number of derived explanatory variables increases very quickly, and it is very important to select only few of the best performing ones and prevent overfitting at the same time. To perform selection of the explanatory variables, it is necessary to assess quality of the given sub-model. We use fractographic data to study performance of different information criteria and statistical tests as means of the sub-model quality measurement. Furthermore, to address overfitting, we provide recommendations based on a cross-validation analysis. Among other conclusions, we suggest the Bayesian Information Criterion, which favours sub-models fitting the data considerably well and does not lose the capability to generalize at the same time.

Název v anglickém jazyce

Comparison of Bayesian & other approaches to the estimation of fatigue crack growth rate from 2D textural features

Popis výsledku anglicky

The fatigue crack growth rate can be explained using features of the surface of a structure. Among other methods, linear regression can be used to explain crack growth velocity. Non- linear transformations of fracture surface texture features may be useful as explanatory variables. Nonetheless, the number of derived explanatory variables increases very quickly, and it is very important to select only few of the best performing ones and prevent overfitting at the same time. To perform selection of the explanatory variables, it is necessary to assess quality of the given sub-model. We use fractographic data to study performance of different information criteria and statistical tests as means of the sub-model quality measurement. Furthermore, to address overfitting, we provide recommendations based on a cross-validation analysis. Among other conclusions, we suggest the Bayesian Information Criterion, which favours sub-models fitting the data considerably well and does not lose the capability to generalize at the same time.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Journal of Theoretical and Applied Mechanics

ISSN

1429-2955

e-ISSN

—

Svazek periodika

55

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

10

Strana od-do

1269-1278

Kód UT WoS článku

000413603400013

EID výsledku v databázi Scopus

2-s2.0-85032174303