A quantification of the modelling uncertainty of non-linear finite element analyses of large concrete structures. (January 2017)

• Record Type: Journal Article
• Authors: Engen, Morten; Hendriks, Max A.N.; Köhler, Jochen; Øverli, Jan Arve; Åldstedt, Erik
• Is Part Of: Structural safety. Volume 64(2017)
• Page Start: 1
• Page End: 8
• Publication Date: 2017-01
• Subjects: Non-linear finite element analyses -- Bayesian inference -- Large concrete shell structures -- Modelling uncertainty -- Global resistance -- Characterization of failure mode; Structural stability -- Periodicals; Safety factor in engineering -- Periodicals; Reliability (Engineering) -- Periodicals; Constructions -- Stabilité -- Périodiques; Coefficient de sécurité en ingénierie -- Périodiques; Fiabilité -- Périodiques; 620.86
• DOI: 10.1016/j.strusafe.2016.08.003
• Languages: English
• ISSNs: 0167-4730

Abstract

Highlights: The modelling uncertainty of NLFEA of concrete structures is quantified. A new method for characterizing the failure mode is proposed. The physical uncertainties are found to influence the modelling uncertainty. Other uncertainties should be treated with care in a reliability assessment. Abstract: In order to make non-linear finite element analyses applicable during assessment of the global resistance of large concrete structures, there is need for a solution strategy with a low modelling uncertainty. A solution strategy comprises choices regarding force equilibrium, kinematic compatibility and constitutive relations. Relatively large solid finite elements and a fully triaxial material model for concrete were used in the present work. Bayesian inference was applied to results from 38 benchmark analyses. The results indicated that the modelling uncertainty could be represented as a log-normally distributed random variable with mean 1.10 and standard deviation of 0.12. A new method for characterizing the failure mode was developed. The results indicated that the physical uncertainties influenced the estimated parameters of the modelling uncertainty, and that this should be considered when other uncertainties are included in a reliability assessment.