No abstract

No abstract

No abstract

In the present work, the structural responses of 12 UHPC beams to four-point loading conditions were experimentally and analytically studied. The inclusion of a fibrous system in the UHPC material increased its compressive and flexural strengths by 31.5% and 237.8%, respectively. Improved safety could be obtained by optimizing the tensile reinforcement ratio (ρ) for a UHPC beam. The slope of the moment–curvature before and after steel yielding was almost typical for all beams due to the inclusion of a hybrid fibrous system in the UHPC. Moreover, we concluded that as ρ increases, the deflection ductility exponentially increases. The cracking response of the UHPC beams demonstrated that increasing ρ notably decreases the crack opening width of the UHPC beams at the same service loading. The cracking pattern the beams showed that increasing the bar reinforcement percentages notably enhanced their initial stiffness and deformability. Moreover, the flexural cracks were the main cause of failure for all beams; however, flexure shear cracks were observed in moderately reinforced beams. The prediction efficiency of the proposed analytical model was established by performing a comparative study on the experimental and analytical ultimate moment capacity of the UHPC beams. For all beams, the percentage of the mean calculated moment capacity to the experimentally observed capacity approached 100%.

In this paper, an artificial neural network (ANN-10) model was developed to predict the ultimate shear strength of steel fiber reinforced 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.