A comparative assessment of ANN and PNN model for low-frequency stochastic free vibration analysis of composite plates

Susmita Naskar, Tanmoy Mukhopadhyay, Srinivas Sriramula

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter quantifies the effect of uncertainty in natural frequencies of laminated composite plates based on neural network–based approach coupled with finite element analysis. An exhaustive comparative investigation on the performance of artificial neural network and polynomial neural network is carried out from the viewpoint of accuracy and computational efficiency. The stochastic system parameters are modeled following a layer-wise random variable–based approach, where the random system properties are considered to be different at different layers of the laminate for a particular realization of Monte Carlo simulation (MCS). Both individual and combined variations of stochastic input parameters are considered to address the aspect of low and high dimensional input parameter spaces, respectively. The convergence of the proposed neural network–based algorithm is verified and validated with original finite element method and direct MCS.
Original languageEnglish
Title of host publicationHandbook of Probabilistic Models
EditorsPijush Samui, Dieu Tien Bui, Subrata Chakraborty, Ravinesh C. Deo
PublisherButterworth-Heinemann
Chapter22
Pages527-547
Number of pages20
ISBN (Electronic)978-0-12-816514-0
ISBN (Print)9780128165461
DOIs
Publication statusPublished - 2020

Keywords

  • Artificial neural network
  • Comparative stochastic natural frequency analysis
  • Low-frequency vibration
  • Polynomial neural network
  • Surrogate-based monte carlo simulation

ASJC Scopus subject areas

  • Computer Science(all)

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    Naskar, S., Mukhopadhyay, T., & Sriramula, S. (2020). A comparative assessment of ANN and PNN model for low-frequency stochastic free vibration analysis of composite plates. In P. Samui, D. T. Bui, S. Chakraborty, & R. C. Deo (Eds.), Handbook of Probabilistic Models (pp. 527-547). Butterworth-Heinemann . https://doi.org/10.1016/B978-0-12-816514-0.00022-9