Prediction of temperature and structural properties of fibre-reinforced polymer laminates under simulated fire exposure using artificial neural networks

Load-bearing fibre reinforced polymer laminates soften and decompose when exposed to high temperature fire which may cause significant deformation and weakening, ultimately leading to failure. A combined experimental and modelling study is presented to predict the fire structural survivability of laminates using artificial neural networks based on machine learning. Multiple experimental fire-under-tension load tests are performed under identical conditions to determine the average values and scatter to the surface temperatures, deformation rates and rupture times for an E-glass/vinyl ester laminate. A data-driven modelling strategy based on artificial neural networks is presented that can predict the temperatures and fire structural properties for the laminate when subject to combined fire exposure and tension loading. It is shown that the model gives excellent agreement to the measured surface temperatures, deformations, and time-to-failure of the laminate when exposed to one-sided heating at a constant heat flux. It is envisioned that the ANN based model could be used to assess the fire structural survivability of load-bearing composite structures exposed to fire.