Modelling the High Strain Rate Tensile Behavior of Steel Fiber Reinforced Concrete Using Artificial Neural Network Approach

Abstract

Conventional concrete material shows relatively low ductility and energy dissipation capacity under high strain rate tensile loads. The use of steel fibers into concrete can significantly improve the tensile behavior of concrete subjected to high strain rate loads by fibers bridging the concrete crack surfaces, resulting in a high impact resistance and energy dissipation capacity. Experimental research evidenced that the parameters of volume fraction, aspect ratio and tensile strength of steel fibers affect the characteristics of steel fiber reinforced concrete (SFRC) composite materials under high strain rate tensile loads. However, the existing design codes, i.e. CEB-fib model code 1990 and fib model code 2010, recommend design formulations for the prediction of the behavior of normal concrete under different strain rate loads, which are only function of strain rate of the loads. Accordingly, development of the design models to predict the behavior of SFRC materials when subjected to high strain rate loads is still lacking in the literature. Hence, the current paper aims to improve the design models recommended in the existing design codes (e.g. fib model code 2010). An artificial neural network approach is adopted to predict more accurately the tensile behavior of SFRC materials. Besides the strain rate load effect, this approach considers the effects of the volume fraction, aspect ratio and tensile strength of steel fibers. Finally, the predictive performance of the proposed model was evaluated by simulating relevant experimental tests.