Prediction of fire spalling behaviour of fibre-reinforced concrete

Abstract

Fire spalling prediction of fibre-reinforced concrete containing polypropylene (PP) or steel fibre at elevated temperatures is challenging. It is difficult to use conventional methods, such as finite-element modelling and discrete-element modelling, to solve the problem, because of the complicate coupling mechanism of PP and steel fibres in concrete. To this end, two artificial neural network (ANN) models, one (ANN1) based on a concrete mix study and the other (ANN2) based on a compressive strength study, were introduced to assess the resistance of concrete to explosive spalling. Test data (321 and 318 items, respectively) gathered from the literature were utilised to train the proposed models. Twenty-four concrete mixes (96 groups), that is, seven plain concrete mixes, four high performance concrete mixes reinforced with PP fibre, three ultra-high-performance concrete (UHPC) mixes with reinforced PP fibre and ten UHPC mixes reinforced with PP and steel hybrid fibres were designed and tested to validate the accuracy of the models. The study demonstrates that ANN1 and ANN2 can achieve a predictive accuracy of 89.6% and 84.4% for the explosive spalling, respectively, indicating the feasibility of the proposed models for predicting explosive spalling threat of the hybrid fibre-reinforced concrete.