Quantification of the uncertainty of shear strength models using Bayesian Inference

Quantification of the uncertainty of shear strength models using Bayesian Inference

Slobbe, A.
Allaix, D.L.
Yang, Y.

Lukovic, M. (editor)
Hordijk, D.A. (editor)

2017

Different analytical models exist to predict the shear strength of reinforced concrete members. Generally, each of these shear strength models consists of a formulation based on certain underlying theory and fitted model coefficients. The model fitting parameters are usually established from the comparison with test data. Hence, the predictive value of a shear strength model depends, to some extent, on the quality and representativeness of the used test data. This work investigates the predictive capability of several shear strength models for reinforced concrete beams without shear reinforcement. Particular attention is given to the application domain of relatively low reinforced and high depth concrete beams where limited shear test data is available. The predictive capability of the models for this area of interest is analyzed with Bayesian Inference. This probabilistic technique calculates the posterior distributions of uncertain parameters, given a set of measured test data and some prior knowledge. The predictive capability of each shear strength model is quantified by means of a calculated model uncertainty. Furthermore, the influence of the uncertainty in model parameter values on the calculated model uncertainties is evaluated. Bayesian Inference is also used to estimate the model evidences conditionally on the used data.

2015 Fluid & Solid Mechanics
SR - Structural Reliability
TS - Technical Sciences
Buildings and Infrastructures
Architecture and Building
2015 Urbanisation
Bayesian inference
Reinforced concrete beams without shear reinforcement
Shear strength models
Uncertainty quantification

http://resolver.tudelft.nl/uuid:6af6b7e2-b89e-406e-bca8-f6a4e504c349

777336

Springer

9783319594705

2017 FIB Symposium - High Tech Concrete: Where Technology and Engineering Meet, 12-14 June 2017, 749-757

conference paper