Uncertainty Quantification Applied to a Fire-Exposed Glued-Laminated Timber Beam

As a natural material, the response of timber structures under normal conditions and to fire is subject to wide variability. Deterministic models therefore struggle to reflect the reality of the response of timber since small variations in input influence the output significantly. However it is relatively straightforward to quantify uncertainties in model inputs in order to determine the uncertainties in the model response by employing uncertainty quantification (UQ) techniques. UQ of structural response to fire traditionally employs Monte Carlo techniques (Eamon and Jensen 2013) which are computationally expensive for a large number of variables. Deterministic Sampling (DS) (Hessling 2013) is a relatively new efficient alternative method for UQ. DS assumes that a continuous probability density function can be replaced by an ensemble of discrete deterministic samples if the two representations have the same statistical moments. DS has been demonstrated applied to, e.g. CFD simulations (Anderson et al. 2016). This paper applies DS techniques to study glued-laminated (glulam) timber in fire. Results are compared with random sampling techniques to show the validity of this method in this application.