Prediction of dynamic increase factor for steel fibre reinforced concrete using a hybrid artificial intelligence model

Abstract

Steel fibre reinforced concrete (SFRC) has been increasingly used in the engineering structures subjected to intense dynamic loads. In structural design and analysis, a dynamic increase factor (DIF) has been usually used to characterize strain-rate effect on the dynamic mechanical behaviour of SFRC. At present, several analytical equations that contain one or two variables have been utilised to predict the DIF values for material strengths of SFRC. However, this may lead to unsatisfactory results as the rate sensitivity of SFRC is influenced by multiple variables. In this study, a hybrid model, integrating random forest (RF) technique and firefly algorithm (FA), is proposed for predicting DIF values for SFRC. RF is utilized to discover the non-linear relationship between the influencing variables and DIF, while FA optimizes the hyper-parameters of RF. A total of 193 and 314 DIF data samples for compressive and tensile strengths of SFRC are retrieved from the reported studies to train and verify the proposed model. The input variables for the predictive model include strain rate, matrix strength, fibre dosage, and fibre properties (i.e. fibre shape, fibre aspect ratio and fibre tensile strength). The predicted results denote that the developed model is an efficient and accurate method to predict the DIF values for SFRC. Additionally, the relative importance of each input variable is investigated. It is found that the DIF values of SFRC are most sensitive to the matrix strength.