A Bayesian framework for the calibration of cohesive zone models. Issue 4 (21st March 2018)

• Record Type: Journal Article
• Authors: Albuquerque, Emerson B.; Guzman, Cynthia; Borges, Lavinia A.; Castello, Daniel A.
• Is Part Of: Journal of adhesion. Volume 94:Issue 4(2018)
• Page Start: 255
• Page End: 277
• Publication Date: 2018-03-21
• Subjects: Damage mechanics -- finite eere mechanics -- Bayesian analysis -- interface model -- uncertainty quantification; Adhesion -- Periodicals; 541.33
• DOI: 10.1080/00218464.2016.1268055
• Languages: English
• ISSNs: 0021-8464

Abstract

ABSTRACT: Adhesively bonded joints are used in several industrial sectors. Cohezive Zone Modes can be used to predict the adhesive mechanical behaviour. This work presents an approach to calibrate Cohesive Zone Models (CZM) by means of Statistical Inverse Analysis. The Bayesian framework for Inverse Problems is used to infer about the CZM model parameters. The solution corresponds to the exploration of the posterior probability density function of the model parameters. The exploration of the posterior density is performed by means of Markov Chain Monte Carlo (MCMC) methods mixing Population-Based MCMC with Adaptive Metropolis (AD) strategies. The assessment of the approach is performed using measured data from a single-lap shear experimental set-up. Measured data from 5 test-specimens is used for calibration and measured data from five other test-specimens is used for model validation. It is proposed a stochastic effective model for the CZM parameters. The predictions of maximum force and maximum displacement that are provided by the effective model are in accordance with measured data that is used for validation.