A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Treatment of modelling uncertainty of NLFEA in fib Model Code 2020
When non‐linear finite element analyses are used in design of new or assessment of existing concrete structures, one should account for the modelling uncertainty before conclusions are drawn based on the results. The present article describes the basis for how this topic is treated in the draft of fib Model Code 2020. There are two components of the modelling uncertainty: (i) within‐model, and (ii) between‐model. The within‐model uncertainty was estimated from a range of series of benchmark analyses gathered from the literature. Each series was assumed analyzed in a consistent manner, termed as the solution strategy. The main result for the within‐model component, is a set of prior parameters for Bayesian updating. In an application setting, the prior parameters should be updated after validating the selected solution strategy with a suite of relevant benchmark analyses, before using the mean and coefficient of variation in the Global Factor Method or calculating the modelling uncertainty factor to be used in the Partial Factors Method. Finally, results from blind‐prediction competitions were studied. With the present data, it cannot be concluded that a‐priori knowledge of the experimental outcome reduces the uncertainty in the prediction. Hence, there is no need for compensating for this effect by a separate partial factor.
Treatment of modelling uncertainty of NLFEA in fib Model Code 2020
When non‐linear finite element analyses are used in design of new or assessment of existing concrete structures, one should account for the modelling uncertainty before conclusions are drawn based on the results. The present article describes the basis for how this topic is treated in the draft of fib Model Code 2020. There are two components of the modelling uncertainty: (i) within‐model, and (ii) between‐model. The within‐model uncertainty was estimated from a range of series of benchmark analyses gathered from the literature. Each series was assumed analyzed in a consistent manner, termed as the solution strategy. The main result for the within‐model component, is a set of prior parameters for Bayesian updating. In an application setting, the prior parameters should be updated after validating the selected solution strategy with a suite of relevant benchmark analyses, before using the mean and coefficient of variation in the Global Factor Method or calculating the modelling uncertainty factor to be used in the Partial Factors Method. Finally, results from blind‐prediction competitions were studied. With the present data, it cannot be concluded that a‐priori knowledge of the experimental outcome reduces the uncertainty in the prediction. Hence, there is no need for compensating for this effect by a separate partial factor.
Treatment of modelling uncertainty of NLFEA in fib Model Code 2020
Engen, Morten (author) / Hendriks, Max A. N. (author) / Monti, Giorgio (author) / Allaix, Diego L. (author)
Structural Concrete ; 22 ; 3202-3212
2021-12-01
11 pages
Article (Journal)
Electronic Resource
English
Automatic Reinforcement Method Using NLFEA
| Springer Verlag | 2017
RESEARCH AND DEVELOPMENT - Modelling tension stiffening in reinforced concrete with NLFEA
| Online Contents | 2007
Material Characterisation for Nonlinear Finite Element Analysis (NLFEA)
| Springer Verlag | 2020
Application of NLFEA in the Design of Large Concrete Structures
| British Library Conference Proceedings | 2014