INFLUENCE OF FIBER PROPERTIES ON SHEAR FAILURE OF STEEL FIBER REINFORCED BEAMS WITHOUT WEB REINFORCEMENT: ANN MODELING

Abstract

IN THIS PAPER, AN ARTIFICIAL NEURAL NETWORK (ANN-10) MODEL WAS DEVELOPED TO PREDICT THE ULTIMATE SHEAR STRENGTH OF STEEL FIBER REIN-FORCED CONCRETE (SFRC) BEAMS WITHOUT WEB REINFORCEMENT. ANN-10 IS A FOUR-LAYERED FEED FORWARD NETWORK WITH A BACK PROPAGATION TRAINING ALGORITHM. THE EXPERIMENTAL DATA OF 70 SFRC BEAMS REPORTED IN THE TECHNICAL LITERATURE WERE UTILIZED TO TRAIN AND TEST THE VALIDITY OF ANN-10. THE INPUT LAYER RECEIVES 10 INPUT SIGNALS FOR THE FIBER PROPERTIES (TYPE, ASPECT RATIO, LENGTH AND VOLUME CONTENT), SECTION PROPERTIES (WIDTH, OVERALL DEPTH AND EFFECTIVE DEPTH) AND BEAM PROPERTIES (LONGITUDINAL REINFORCEMENT RATIO, COMPRESSIVE STRENGTH OF CONCRETE AND SHEAR SPAN TO EFFECTIVE DEPTH RATIO). ANN-10 HAS EXHIBITED EXCELLENT PREDICTIVE PERFORMANCE FOR BOTH TRAINING AND TESTING DATA SETS, WITH AN AVERAGE OF 1.002 FOR THE AVERAGE OF PREDICTED TO EXPERIMENTAL VALUES. THIS PERFORMANCE OF ANN-10 ESTABLISHED THE PROMISING POTENTIAL OF ARTIFICIAL NEURAL NETWORKS (ANNS) TO SIMULATE THE COMPLEX SHEAR BEHAVIOR OF SFRC BEAMS. ANN-10 WAS APPLIED TO INVESTIGATE THE INFLUENCE OF THE FIBER VOLUME CONTENT, TYPE, ASPECT RATIO AND LENGTH ON THE ULTIMATE SHEAR STRENGTH OF SFRC.