This paper evaluates the feasibility of using an Artificial Neural Network (ANN) model for estimating the nominal shear capacity of Reinforced Concrete (RC) beams against diagonal shear failure subjected to shear and flexure. A feedforward back-propagation ANN model was developed utilizing 622 experimental data points of RC beams, which include 111 deep beams data and 20 beams tested for low longitudinal steel ratios. The ANN model was trained on 70% of the data and then it was validated using the remaining 30% data (new data were not used for training). The trained ANN model was compared with three existing approaches, including the American Concrete Institute (ACI) code. The ANN model predictions when compared to the experimental data were very favorable, regarding also the other approaches. The prediction of ANN model was also checked for size effect and deep beams separately. The ANN model was found to be very robust in all situations. The safe form of ANN model was also derived and compared with the design equations of the three methods.
We attempt the identifícation, study and modeling of possible sources of size effects in concrete structures acting both separately and together. We are particularly motivated by the interplay of several identified scaling lengths stemming from the material, boundary conditions and geometry. We model the well published results of direct tensile tests of dog-bone specimens with rotating boundary conditions using methods of stochastic nonlinear fracture mechanics. Firstly, we model the specimens using microplane material law to show that a large portion of the dependence of
nominal strength on structural size can be explained deterministically. However, it is clear that more sources of size effect play a part, and we consider two of them. Namely, we model local material strength using an autocorrelated random field attempting
to capture a statistical part of the complex size effect, scatter inclusive. Next to it, the strength drop noticeable with small specimens, which was obtained in the experiments is explained by the presence of a weak surface layer of constant thickness (caused e.g. by drying, surface damage, aggregate size limitation at the
boundary, or other irregularities). All three named sources (deterministic-energetic, statistical size effects, and the weak layer effect) are believed to be the sources most contributing to the observed strength size effect; the model combining all of them is
capable of reproducing the measured data. The computational approach represents a marriage of advanced computational nonlinear fracture mechanics with simulation techniques for random fields representing spatially varying material properties. Using a numerical example, we document how different sources of size effects detrimental to strength can interact and result in relatively complex quasibrittle failure processes. The presented study documents the well known fact that the experimental determination of material parameters (needed for the rational and safe design of structures) is very difficult for quasibrittle materials such as concrete. and Obsahuje seznam literatury