A METHOD OF CONSTRUCTING CONFIDENCE INTERVALS FOR PERCENTILES OF COMPOSITE STRENGTH RANDOM VARIABLE USING BOOTSTRAP SIMULATION
https://doi.org/10.26896/1028-6861-2017-83-11-73-77
Abstract
A method is proposed for constructing basic sets (confidence intervals for percentiles) using bootstrap simulation as an alternative to currently used approaches. Bootstrap simulation is a method for numerical modeling of distributions based on multiple data reproduction the without using any information regarding the distribution laws. Since the strength characteristics are random variables, statistical estimation with construction of the interval characteristics is required. This is the goal of the study. An illustrative example of constructing the confidence intervals for mean strength value using bootstrap-modeling is considered. To construct the confidence intervals for percentiles of the distributions of the strength characteristics we recommend to assign the distribution to one of the currently known (normal, lognormal distribution or Weibull) laws, unlike the existing non-parametric approach that generally gives a conservative (too low) and thus undesirable results, which is the reason for developing a new approach. A comparison of B-bases, determined by the newly proposed and traditional method is carried out on real samplings of the strength characteristics of composite materials. The specific examples of strength parameters for shear and tensile strength of the specimens made of prepreg HexPly (composite materials, semiproducts) using an autoclave molding method are presented.
About the Authors
I. V. Gadolina
Институт машиноведения им. А. А. Благонравова РАН, Москва
Russian Federation
Russian Federation
N. G. Lisachenko
АО «ОНПП «Технология» им. А. Г. Ромашина» Госкорпорации «Ростех», Москва
Russian Federation
Russian Federation
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Review
For citations:
Gadolina I.V., Lisachenko N.G. A METHOD OF CONSTRUCTING CONFIDENCE INTERVALS FOR PERCENTILES OF COMPOSITE STRENGTH RANDOM VARIABLE USING BOOTSTRAP SIMULATION. Industrial laboratory. Diagnostics of materials. 2017;83(11):73-77. (In Russ.) https://doi.org/10.26896/1028-6861-2017-83-11-73-77
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