The occurrence of fire-induced spalling in concrete structures and shotcrete elements (aka sprayed concrete) is influenced by various factors including the type of heat and applied fire curves, temperature gradient, sample size, and quality and mixture of the concrete. Predicting the location of spalling can significantly assist with developing fire resistance tests to allow for the correct placement of instrumentation and monitoring equipment such as temperature and pressure sensors, high-speed cameras, and 3D/2D Digital Image Correlation imaging, among others, in optimal positions to be able to closely monitor the spalling process. This study examines the data from tests previously carried out to generate a model for predicting the location of fire-induced spalling in concrete samples, and then compares it experimentally to validate the proposed model. The results indicate good compatibility between the data from literature and the performed experiments.
There has been a surge in research related to fire-induced spalling in concrete and shotcrete structures in recent years due to the danger it poses and the uncertainties this phenomenon involves (Hua et al., 2022; Kodur & Banerji, 2021; Maluk et al., 2021; Saha et al., 2022; Serati et al., 2022). Spalling is an event where flakes and surface layers of concrete or shotcrete are violently and spontaneously ejected from the surface when exposed to high-temperature gradients, e.g., in the case of a tunnel fire. The damage due to spalling is often very severe and can readily turn into a catastrophic situation depending on the intensity of the fire. Spalling in the lining of tunnels can be stress-driven (e.g., due to the formation of unwanted tensile cracks in deep mines) or fire-induced (mainly due to excess vapour pressure generation). The key parameters in fire-induced concrete spalling are the applied heating rate, reinforcement, external loading, material parameters (cement, aggregate, moisture content, compressive strength etc.), and boundary conditions as well as geometric parameters such as size, shape, and concrete thickness (Kodur, 2014; Le et al., 2019; Malik et al., 2022; Saha et al., 2023; Serati et al., 2016; Serati et al., 2018).