The paper presents quasi-static numerical simulations of the behaviour of short reinforced concrete beams without shear reinforcement under mixed shear-tension failure using the FEM and four various constitutive continuum models for concrete. First, an isotropic elasto-plastic model with a Drucker-Prager criterion defined in compression and with a Rankine criterion defined in tension was used. Next, an anisotropic smeared crack and isotropic damage model were applied. Finally, an elasto-plastic-damage model was used. To ensure mesh-independent FE results, to describe strain localization in concrete and to capture a deterministic size effect, all models were enhanced in a softening regime by a characteristic length of micro-structure by means of a non-local theory. Bond-slip between concrete and reinforcement was considered. The numerical results were directly compared with the corresponding laboratory tests performed by Walraven and Lehwalter (1994). The advantages and disadvantages of enhanced models to model the reinforced concrete behaviour were outlined.

In this study, Artificial Neural Networks (ANN) analysis is used to predict the compression strength of polypropylene fibre mixed concrete. Polypropylene fibre admixture increases the compression strength of concrete to a certain extent according to mix proportion. This proportion and homogenous distribution are important parameters on compression strength. Determination of compression strength of fibre mixed concrete is significant due to the veridicality of capacity calculations. Plenty of experiments shall be completed to state the compression strength of concrete which have different fibre admixture. In each case, it is known that performing the laboratory experiments is costly and time-consuming. Therefore, ANN analysis is used to predict the 7 and 28 days of compression strength values. For this purpose, 156 test specimens are produced that have 26 different types of fibre admixture. While the results of 120 specimens are used for training process, 36 of them are separated for test process in ANN analysis to determine the validity of experimental results. Finally, it is seen that ANN analysis predicts the compression strength of concrete successfully.

This paper deals with application of a non-linear continuum model for reinforced concrete affected by alkali-aggregate reaction (AAR) to analysis of some nuclear structures. The macroscopic behaviour of the material affected by AAR is described by incorporating a homogenization/averaging procedure. The formulation addresses the main stages of the deformation process, i.e., a homogeneous deformation mode as well as that involving localized deformation, associated with formation of macrocracks. The formulation is applied to examine the mechanical behaviour of some reinforced concrete structures in nuclear power facilities located in Quebec (Canada). First, a containment structure is analyzed subjected to 45 years of continuing AAR. Later, an inelastic analysis is carried out for the spent fuel pool taking into account the interaction with the adjacent jointed rock mass foundation. In the latter case, the structure is said to be subjected to continuing AAR that is followed by a seismic event.

Concrete structures need repairing due to various reasons such as deteriorative effects, overloading, poor quality of workmanship and design failures. Cement based repair mortars are the most widely used solutions for concrete repair applications. Various factors may affect the bond strength between concrete substrate and repair mortars. In this paper, the effects of polymer additives, strength of the concrete substrate, surface roughness, surface wetness and aging on the bond between concrete substrate and repair mortar has been investigated. Full factorial experimental design is employed to investigate the main and interaction effects of these factors on the bond strength. Analysis of variance (ANOVA) under design of experiments (DOE) in Minitab 14 Statistical Software is used for the analysis. Results showed that the interaction bond strength is higher when the application surface is wet and strength of the concrete substrate is comparatively high. According to the results obtained from the analysis, the most effective repair mortar additive in terms of bonding efficiency was styrene butadiene rubber (SBR) within the investigated polymers and test conditions. This bonding ability improvement can be attributed to the self-flowing ability, high flexural strength and comparatively low air content of SBR modified repair mortars. On the other hand, styrene acrylate rubber (SAR) modified mortars was found incompatible with the concrete substrate.

This paper unveils a new computer based stabilization methodology for automated modeling analysis and its experimental verification for corrosion in reinforced concrete structures under the effect of varying oxygen concentration. Various corrosion cells with different concrete compositions under four different environmental conditions (air dry, submerged, 95% R.H and alternate wetting-drying) have been investigated under controlled laboratory conditions. The results of these laboratory tests were utilized with an automated computer-aided simulation model. This model based on mass and energy stabilization through the porous media for the corrosion process was coupled with modified stabilization methodology. By this coupling, it was possible to predict, maintain and transfer the influence of oxygen concentration on the corrosion rate of the reinforcement in concrete under various defined conditions satisfactorily. The variation in oxygen concentration available for corrosion reaction has been taken into account simulating the actual field conditions such as by varying concrete cover depth, relative humidity, water-cement ratio etc. The modeling task has been incorporated by the use of a computer based durability model as a finite element computational approach for stabilizing the effect of oxygen on corrosion of reinforced concrete structures.

In 1999 Marmara and 2011 Van earthquakes in Turkey, majority of the existing buildings either sustained severe damage or collapsed. These buildings include masonry infill walls in both the interior and exterior R/C frames. The material of the masonry infill is the main variant, ranging from natural stones to bricks and blocks. It is demanding to design these buildings for satisfactory structural behavior. In general, masonry infill walls are considered by its weights not by interaction between walls and frames. In this study, R/C buildings with infill walls are considered in terms of structural behavior. Therefore, 5 and 8-story R/C buildings are regarded as the representative models in the analyses. The R/C representative buildings, both with and without infill walls were analyzed to determine the effects of structural behavior change. The differences in earthquake behavior of these representative buildings were investigated to determine the effects of infill walls leading structural capacity. First, pushover curves of the representative buildings were sketched. Aftermath, time history analyses were carried out to define the displacement demands. Finally, fragility analyses were performed. Throughout the fragility analyses, probabilistic seismic assessment for R/C building structures both with and without infill walls were provided. In this study, besides the deterministic assessment methodology, a probabilistic approach was followed to define structural effect of infill walls under seismic loads.

Numerous studies have been conducted to understand the shear behavior of reinforced concrete (RC) beams since it is a complex phenomenon. The diagonal cracking strength of a RC beam is critical since it is essential for determining the minimum amount of stirrups and the contribution of concrete to the shear strength of the beam. Most of the existing equations predicting the diagonal cracking strength of RC beams are based on experimental data. A powerful computational tool for analyzing experimental data is an artificial neural network (ANN). Its advantage over conventional methods for empirical modeling is that it does not require any functional form and it can be easily updated whenever additional data is available. An ANN model was developed for predicting the diagonal cracking strength of RC slender beams without stirrups. It is shown that the performance of the ANN model over the experimental data considered in this study is better than the performances of six design code equations and twelve equations proposed by various researchers. In addition, a parametric study was conducted to study the effects of various parameters on the diagonal cracking strength of RC slender beams without stirrups upon verifying the model.

In this study, experiment and numerical analysis were conducted to identify the heat transfer characteristics and behavior of high-strength concrete upon a fire. The numerical analysis was employed to forecast the characteristics and properties of the high-strength concrete upon a fire, which can not be accomplished through a fire test due to the specific conditions and restrictions associated with the test. The result of the numerical analysis was compared with that of the test to verify the reliability of the analysis. In the numerical analysis of the heat transfer characteristics and behavior of 80 and 100 MPa high-strength concrete upon a fire, the commercial software of ABAQUS(V.6.8) was used. It was observed from the experiment that the contraction of the concrete with fiber-cocktail was mitigated by 25~55 % compared with that without fiber-cocktail because the fiber controlled the heat transfer of the concrete and thus improved the fire-resistance performance of the column.