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

• Autorzy: Mojzeš M. , Kukal J. , Lauschmann H.

Identyfikatory

DOI

10.15632/jtam-pl.55.4.1269

• Języki publikacji: EN

Abstrakty

EN

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. Nonlinear 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.

Słowa kluczowe

EN

quantitative fractography; optimization; heuristic; linear regression; sub-model selection

• Wydawca: Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
• Czasopismo: Journal of Theoretical and Applied Mechanics
• Rocznik: 2017
• Tom: Vol. 55 nr 4
• Strony: 1269--1278
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Mojzeš M.

• Czech Technical University in Prague, Faculty of Nuclear Sciences and Physical Engineering, Department of Software Engineering, Praque, Czech Republic

autor

Kukal J.

• Czech Technical University in Prague, Faculty of Nuclear Sciences and Physical Engineering, Department of Software Engineering, Praque, Czech Republic

autor

Lauschmann H.

• Czech Technical University in Prague, Faculty of Nuclear Sciences and Physical Engineering, Department of Materials, Praque, Czech Republic

Bibliografia

• 1. Akaike H., 1974, A new look at the statistical model identification, IEEE Transactions on Automatic Control, AC-19, 716-723
• 2. Andel J., 1978, Mathematical Statistics (in Czech), SNTL/Alfa, Praha
• 3. Kunz J., Kovarik O., Lauschmann H., Siegl J., Augustin P., 2010, Fractographic reconstitution of fatigue crack growth in integrally stiffened panels, FATIGUE 2010, 2, 1711-1720
• 4. Kvasnicka V., Pospichal J., Tino P. , 2000, Evolutionary Algorithms (in Slovak), STU Bratislava
• 5. Lauschmann H., Goldsmith N., 2009, Textural fractography of fatigue fractures, [In:] Fatigue Crack Growth: Mechanics, Behavior and Prediction, A.F. Lignelli (Edit.), Nova Science Publishers, Inc., 125-166
• 6. Lauschmann H., Kunz J., Kovarik O., 2011, Analyzing crack growth rate in steel P92 (in Czech), Research report V-KMAT-839/11, FNSPE CTU Prague
• 7. Lauschmann H., Siegl J., Sumbera J., Siska F., Nedbal I., 2006, An unifying concept for fatigue: The reference crack growth rate, Materials Characterization, 56, 257-265
• 8. Lauschmann H., Siska F., 2012, The reference texture: A proposal of a physical explanation, International Journal of Fatigue, 43, 120-127
• 9. Mojzes M., Klimt M., Kukal J., 2016, Feature selection via competitive levy flights, 2016 International Joint Conference on Neural Networks (IJCNN)
• 10. Mojzes M., Kukal J., Lauschmann H., 2012, Sub-model testing in fractographic analysis, Proceedings of Mendel 2012 Soft Computing Conference, Brno University Technology Press, 350-355
• 11. Nedbal I., Lauschmann H., Siegl J., Kunz J., 2008, Fractographic reconstitution of fatigue crack history – Part II, Fatigue and Fracture of Engineering Materials and Structures, 31, 177-183
• 12. Ralston A., Rabinowitz P., 2001, A First Course in Numerical Analysis, Courier Dover Publications
• 13. Sekeresova Z., Lauschmann H., 2008, Multi-fractal features of fatigue crack surfaces in relation to crack growth rate, Materials Structure and Micromechanics of Fracture V, 567-568, 129-132
• 14. Schwarz G., 1978, Estimating the dimension of a model, Annals of Statistics, 6, 461-464
• 15. Tvrdik J., Krivy I. , 2011, Hybrid adaptive differential evolution in partitional clustering, Proceedings of 17th International Conference on Soft Computing, Mendel 2011, VUT Brno, Brno, 1-8
• 16. Wilks S.S., 1962, Mathematical Statistics, John Wiley & Sons, Inc., New York
• 17. Wooldridge J.M., 2002, Econometric Analysis of Cross Section and Panel Data, Cambridge, MA: MIT Press