The present paper describes a semi-analytical fracture model based on the cracked hinge approach by Ulfkjær [1]. Some extensions of the original fomrulation are introduced and also implemented (as JAVA code) to enable the use of any softening function with arbitrary shape for the cracked part of the model, which is considered as a fictitious (cohesive) crack. The application of the model to the wedge-splitting test (WST) is validated, showing the consistency of the adopted formulations with reference data. Furthermore, the capability of the model to integrate various softening curves is verified using FEM simulations. and Obsahuje seznam literatury
The paper presents cellular automata as a promising modeling approach to simulate the diffusion process as a 2D task. The proposed methodology is applied to the degradation assessment of civil engineering structures describing more realistically the spatial and temporal variability of harmful substance ingress (e.g. chloride ions in concrete). Some illustrative examples are presented together with an example of application to a particular bridge. and Obsahuje seznam literatury
The reinforcement corrosion is the phenomenon that highly affects the reliability and durability of reinforced concrete structures. From that reason, a lot of researchers in Slovakia and in the world pay their attention to reinforcement corrosion. In the frame of the research work, the reinforced concrete girder bridges were diagnosed and observed. These bridges are influenced by reinforcement corrosion of main girders. The paper is concerned with detection and simulation of corrosion of steel reinforcement in the reinforced concrete. The cracking response of the reinforced concrete beams due to the corrosion effect of the steel reinforcement was analyzed. The effect of corrosion was simulated by the nonlinear numerical analysis using the program ATENA - 2D and 3D module. and Obsahuje seznam literatury
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.