ABSTRACT
ABSTRACT The objective of this paper is to explore the added value of full-probabilistic nonlinear finite element analysis (NLFEA), which is illustrated for two reinforced concrete (RC) structural members: a RC deep beam and a RC continuous girder. This exploration is presented gradually: step by step advancing the approximation level of mechanical and the probabilistic models. The most advanced combination uses NLFEA coupled with full-probabilistic analysis (reliability analysis) where uncertain model parameters are represented by random variables. To discuss the added value of various methods, a comparison is made between the semi-probabilistic Eurocode method, the semi-probabilistic NLFEA methods from Eurocode and fib Model Code 2010, and the full-probabilistic NLFEA. A new quantitative measure of added value is introduced and used for this comparison. It expresses the largest value of relative deficit (non-compliance) that can be compensated by a more advanced method.
For the RC deep beam example, the measure of added value of the full-probabilistic NLFEA over the semi-probabilistic Eurocode method is found to be 0.56. This means that even if the semi-probabilistic Eurocode method shows that the design action is 56% higher than the design resistance, the compliance can be demonstrated (reserves uncovered) by full-probabilistic NLFEA. For the RC continuous girder, this measure of added value is 0.48. While in case of the deep beam the gain is largely attributed to the higher approximation level of NLFEA, for the continuous girder almost solely the probabilistic models contribute to the gain.
Though the added values are case dependent, the results indicate that a more detailed physical representation of the problem and an explicit treatment of the uncertainties can compensate a substantial deficit of simplified methods. Hence, these advanced methods may uncover reserves and offer a promising alternative in the assessment of existing structures, enabling to avoid expensive measures that might be needed based on simplified methods.
