This paper presents a robust model for predicting the bond-slip between the concrete and steel reinforced bar at elevated temperatures. The model is established based on a partly cracked thick-wall cylinder theory and the smeared cracking approach is adopted to consider the softening behaviour of concrete in tension. The model is able to consider a number of parameters: such as different concrete properties and covers; different steel bar diameters and geometries. The proposed model has been incorporated into the Vulcan program for 3D analysis of reinforced concrete structures in fire. The model has been validated against previous test results. KEYWORDS:Bond-slip, concrete structures, fire, splitting failure. RESEARCH HIGHLIGHTS: Develop a robust model for predicting the bond-slip between the concrete and steel reinforced bar at elevated temperatures.
In this paper a robust model has been developed to predict the average bond stress-slip relationship between the strands and concrete of prestressed concrete structural members. Two bond-slip curves have been proposed to represent the bond-slip characteristics for the three-wire and seven-wire strands. This model considers the variation of concrete properties, strands' geometries and the type of strand surface, smooth or indented. The degradation of materials and bond characteristic at elevated temperatures are also included in the model. The proposed model has been validated against previous experimental results at both ambient and elevated temperatures.
This paper presents a comprehensive parametric study on the bond behaviour of reinforced concrete members under fire conditions. The study identified some most important factors affecting the bond characteristics between concrete and reinforcement within reinforced concrete beams and slabs at elevated temperatures. These factors are: steel bar yielding, concrete cover, concrete compressive strength and concrete spalling. The results indicated that concrete cover has a great influence on the bond strength, by providing the confinement to the reinforcement for both the reinforced concrete beam, and the slab. The impact of concrete spalling on the beam is very significant, for both full bond and partial bond cases. The behaviour of the reinforced concrete frame under different fire scenarios was also investigatedassuming a two hours fire resistance rate. Those results indicated that isolated members behave differently, compared to those members within a building. Indicating that continuity of the members and its surrounding cooler structures significantly affects the behaviour of the members within the fire compartment.
Accurate and reliable prediction of Perfobond Rib Shear Strength Connector (PRSC) is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network (DLNN) for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression (SVR), artificial neural network (ANN), and M5 tree model. In the second scenario, a comparable AI model hybridized with genetic algorithm (GA) as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient (R = 0.96) and lowest value root mean square error (RMSE = 0.03936 KN). However, the other comparable models such as GA-M5Tree, GA-ANN, and GA-SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model.
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