Title: Regression-Based Surrogate Models for the Probabilistic Study of Fire Exposed Composite Structures Considering Tensile Membrane Action
Author(s): Ranjit Kumar Chaudhary, Ruben Van Coile, and Thomas Gernay
Publication: Symposium Paper
Appears on pages(s): 123-131
Keywords: composite slab, machine learning, residual capacity, failure probability, fire
The probabilistic study of fire exposed structures is laborious and computationally challenging, especially when using advanced numerical models. Moreover, fragility curves developed through traditional approaches apply only to a particular design (structural detailing, fire scenario). Any alteration in design necessitates the computationally expensive re-evaluation of the fragility curves. Considering the above challenges, the use of surrogate models has been proposed for the probabilistic study of fire exposed structures. Previous contributions have confirmed the potential of surrogate models for developing fragility curves for single structural members including reinforced concrete slabs and columns. Herein, the potential of regression-based surrogate models is investigated further with consideration of structural systems. Specifically, an advanced finite element model for evaluating the fire performance of a composite slab panel acting in tensile membrane action is considered. A surrogate model is developed and used to establish fire fragility curves. The results illustrate the potential of surrogate modeling for probabilistic structural fire design of composite structures.