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Go to Editorial ManagerThis paper investigates the possibility of recycled aggregate use in concrete slabs with hollow cores. The main variables considered in the experimental study for the slabs were the recycled aggregate percentage and the hollow core number. Six slabs with dimensions of (1000 × 500 × 120) mm was fabricated and tested. The results showed that the addition of recycled aggregate in the concrete slabs affected the ultimate strength, ductility, and energy absorption of the concrete members. An increase of the recycled aggregate percentage to 25 % decreased the ultimate strength capacity by 3.54 %, but the increase of recycled aggregate to 50 % led to a decrease in the ultimate strength of about 6.64%. The existence of a hollow core reduced the cracking and ultimate load capacity of the RCA slabs, and this reduction was according to the core number which the fabrication of more cores caused more decrement. The ductility and energy absorption were decreased when the replacement ratio of the recycled aggregate increased. Also, the core number affected the ductility and energy absorption. The energy absorption was the most property affected by the core number increase which caused an average reduction of 71.5 % when the core number increased from two to three hollow cores.
A spandrel beam is a structural member lies at the edge of a frame and is connected by a joint to the floor beam extending into the slab. The spandrel beams are primarily responsible for transferring forces from a slab to the supporting edge columns. This work investigates the possibility of using the artificial neural networks to model the complicated nonlinear relationship between the various input parameters associated with reinforced concrete spandrel beams and the actual ultimate strength of them. The descent gradient backpropagation algorithm was employed for predicting the ultimate strength of the reinforced concrete spandrel beams. The optimum topology (which gives least mean square error for both training and testing with fewer number of epochs) is presented. Effects of parameters such as, number of hidden layer(s), number of nodes in the input layer, output layer and hidden layer(s), initialization weight factors and selection of the learning rate and momentum coefficient on the behaviour of the neural network have been investigated. Because of the slow convergence of results when using descent gradient backpropagation, another algorithm which is faster called "resilient backpropagation algorithm" has been used. The neural network trained with the resilient backpropagation RPROP algorithm gives better results than that trained with the steepest descent algorithm with momentum GDM algorithm.
This paper explores the potential of using artificial neural networks to predict the ultimate moment capacity of steel-concrete composite beams with metal deck slabs. Basic information on artificial neural networks and parameters suitable for the analysis of experimental results are given. A multilayer backpropagation neural network is used for training and testing the experimental data. A comparison study between the experimental values and two models (neural network and AJSC models) is also carried out. It was found that 1he neural network model provides better results. The proposed neural network is also used to explore the effect of the various parameters on the behavior of beams.
Co-Cr alloys are widely used in dental and medical equipment since the development of the first cast Co-Cr-Mo alloy. This is due to its high mechanical properties and high resistance to wear and corrosion. This research aims to study the effect of the fabrication method (Investment Casting and Selective Laser Melting SLM by 3D printing) and heat treatments on the mechanical and tribological properties of Co-Cr-Mo alloy. It was found that the Selective Laser Melting method in general increases the ultimate tensile strength, strain and hardness compared to the Investment Casting method. Also, solution treatment and aging reduce the strength and strain values of the SLM samples and have no obvious effect on the casting samples. The wear test shows that wear rate of casting samples is lower than that of SLM samples.
The present study deals with the analysis of short reinforced concrete columns subjected to axial load. One of the efficient techniques is applied, known as artificial neural networks. The descent gradient backpropagation algorithm is employed for analysis. The optimum topology (which gives the least mean square error for both training and testing with a fewer number of epochs) is presented. The effects of the number of nodes in input and hidden layer(s), and selecting of leaming rate and momentum coefficient, on the behavior of the neural network, have been investigated. Due to the slow convergence of results when using descent gradient backpropagation, the faster algorithm called "resilient backpropagation algorithm" has been used to improve the performance of the neural network and the results have been compared with those obtained using the descent gradient backpropagation algorithm.
Mathematical programming techniques have been used to minimize the cost of reinforced concrete T-beam floor. The floor system consists of one way continuous slab and simply supported T-beams. The study presents a formulation based on elastic analysis followed by the ultimate strength method of design with the consideration of serviceability constraints as per ACI Code. The formulation of optimization problem has been made by utilizing the interior penalty function method as an optimization method with the purpose of minimizing the objective function representing the cost of one-meter length of the floor system. The cost includes cost of concrete, reinforcement, and formwork. The design variables considered in this study are the dimensions and the amounts of reinforcement for the slab and beams, in addition to the spacing of the beams. Many examples are solved to show the effect of these design variables on the optimum solution of the floor system. The effect on the optimum design of the compressive strength of concrete, yield strength of steel, concrete cost ratios, and formwork cost ratios has also been studied.
This study investigates the effect of the shear span-to-effective depth ratio (a/d) on the behavior of high-strength steel fiber–reinforced concrete deep beams without stirrups containing circular web openings. A circular opening of 12.6 cm diameter was positioned at the center of the shear span, and beam performance was evaluated in terms of crack patterns, load–deflection response, and stress–strain behavior. Four specimens were tested experimentally. The control specimen consisted of a solid deep beam without openings and without steel fibers, while the remaining three specimens were reinforced with 1% steel fibers and included circular openings. All specimens were reinforced with 2Ø12 mm top bars, 3Ø16 mm bottom bars, and two stirrups at the supports to prevent local failure. The beams had different shear span ratios (a/d = 0.75, 1.0, and 1.5) and corresponding total lengths of 1025 mm, 1200 mm, and 1550 mm, respectively. All specimens were simply supported and subjected to two-point loading. The experimental results revealed that the optimal shear span ratio for maximum performance was a/d = 0.75 when combined with 1% steel fiber reinforcement. In addition, the ultimate strength of beams with circular openings decreased as a/d increased, with a strength increase of approximately 5.48% at a/d = 0.75 compared with a/d = 1.0.
Mathematical programming techniques have been used to minimize the cost of reinforced concrete counterfort retaining wall.The study presents a formulation based on elastic analysis and the ultimate strength method of design as per ACI-M318code. A computer program is generated to handle the considered problem. The formulation of optimization problem has been made by utilizing the interior penalty function method as an optimization method with the purpose of minimizing the objective function representing the cost of one-meter length of the counterfort retaining wall. This includes cost of concrete, reinforcement, and formwork. The design variables considered in this study are the dimensions and the amounts of reinforcement. It is found that the optimal spacing of counterforts equals about (0.214 to 0.366) of total height of wall. The optimum width of the base is found in the range (0.50 to 0.78) of the total height of the wall. Also the thickness of the stem is in the range(0.0284 to 0.0377) of the total height and it is less than half thickness of the base.
Artificial Neural Networks (ANN) have been applied to structural engineering in recent years. Most of the researches are based on backpropagation neural networks due to its well-studied theory. A backpropagation neural network has been used to predict the ultimate torsional strength of reinforced concrete rectangular beams. The effects of the parameters, such as the number of nodes in the input, output and hidden layers and the pre-process of the training patterns, on the behaviour of the neural network have been investigated. The algorithm called 'resilient propagation algorithm' has been used to the performance of the neural network. After training, the generalization of the neural network was tested by the patterns not included in the training patterns. Once the neural network has been trained, the ultimate torsional strength of reinforced concrete is obtained very easily and efficiently. Based on the ANN results, a parametric analysis was carried out to study the influence of parameters affecting the ultimate torsional strength of reinforced concrete beams and these results are compared with the equations of ACI-code.