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Natural and Synthetic Layered Fillers as Advanced Matter for Improvement of Composites Performance Read chapter Read first chapter

2021 | OriginalPaper | Chapter

13. Surrogate Model Validation and Verification for Random Failure Analyses of Composites

Authors : Subrata Kushari, Arunasis Chakraborty, Tanmoy Mukhyopadhyay, Ravi Ranjan Kumar, Saiket Ranjan Maity, Sudip Dey

Published in: Recent Advances in Layered Materials and Structures

Publisher: Springer Singapore

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Abstract

In the present chapter, multivariate adaptive regression splines (MARS) is explored as a surrogate model in conjunction to Monte Carlo simulation (MCS) to analyse the random first-ply failure loads of graphite–epoxy laminated composite plates. The five failure criteria, namely maximum strain theory, maximum stress theory, Tsai–Hill theory, Tsai–Wu theory, and Hoffman theory, are considered. The numerical validation of deterministic failure load is presented first. Thereafter, a concise investigation is carried out to examine the capability of MARS model for efficiently predicting the first-ply failure loads. Comparative results are presented using scatter plots and probability density function plots to access the prediction capability with respect to direct MCS. The current results portray the successful application of MARS as the surrogate model to achieve computational efficiency and analyse the first-ply failure loads.

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previous chapter Statistical Energy Analysis Parameters Investigation of Composite Specimens Employing Theoretical and Experimental Approach
next chapter Iosipescu Shear Test of Glass Fibre/epoxy Composite with Different Delamination Geometries: A Shear Behaviour Study
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Metadata
Title
Surrogate Model Validation and Verification for Random Failure Analyses of Composites
Authors
Subrata Kushari
Arunasis Chakraborty
Tanmoy Mukhyopadhyay
Ravi Ranjan Kumar
Saiket Ranjan Maity
Sudip Dey
Copyright Year
2021
Publisher
Springer Singapore
Book
Recent Advances in Layered Materials and Structures

Print ISBN: 978-981-334-549-2

Electronic ISBN: 978-981-334-550-8

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-33-4550-8_13

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