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.
The molten reactor core-concrete interaction, which describes the effect of molten reactor spread on the concrete oor of the reactor cavity, is a very complex process to simulate and predict, but the knowledge of this process is of major importance for planning the emergency counteractions for severe accidents with respect to the Stress Tests requirements after the Fukushima-Daiichi accident. The key issue is to predict the rate and most probable focusation of the melt-through process which is affected by the concrete composition, especially by the aggregate type. A limited number of small-scale experiments have been conducted over the past years along with accompanying numerical models which focused mainly on the siliceous type of aggregate. It is common for the concrete structures that the limestone type or the mixture of these two types of aggregate are used as well. Then, the objective of this paper is to extend the knowledge gained from the experiments with the siliceous aggregate to the concrete structures which are made of limestone aggregate or their combination, such as limestone sand and siliceous gravel. The proposed one-dimensional model of the melt-through process is based on the fuzzy-logic interpretation of the thermodynamic trends which reflect the aggregate type. This approach allows estimating the asymptotic cases in terms of the melt-through depth in the concrete oor over time with respect to the aggregate type, which may help to decide the rather expensive further experimental efforts.
Applications of artificial intelligence in engineering disciplines have become widespread and have provided alternative solutions to engineering problems. Image processing technology (IPT) and artificial neural networks (ANNs) are types of artificial intelligence methods. However, IPT and ANN have been used together in extremely few studies. In this study, these two methods were used to deter- mine the compressive strength of concrete, a complex material whose mechanical features are difficult to predict. Sixty cube-shaped specimens were manufactured, and images of specific features of the specimens were taken before they were tested to determine their compressive strengths. An ANN model was constituted as a result of the process of digitizing the images. In this way, the two different artificial intelligence methods were used together to carry out the analysis. The compressive strength values of the concrete obtained via analytical modeling were compared with the test results. The results of the comparison (R² = 0:9837-0:9961) indicate that the combination of these two artificial intelligence methods is highly capable of predicting the compressive strengths of the specimens. The model's predictive capability was also evaluated in terms of several statistical parameters using a set of statistical methods during the digitization of the images constituting the artificial neural network.