This paper investigated the optimization of a singly reinforced concrete beam using the Simulated Annealing. The code provisions usually lead to the overestimation of reinforcement and thus an expensive cost of construction. The optimized design reduces the cost significantly. There are many methods of structural optimization but Simulated Annealing has been found to very efficient for constrained optimization of reinforced concrete beam design. The variables considered are the width, depth, compression steel, tension steel, and cost while the constraints are steel ratios, ultimate moment of resistance, maximum and minimum areas of reinforcements Materials costs are considered as the objective function. It is demonstrated that using the concrete compressive strength of 25MPa, Simulated Annealing can be used to optimize the design of concrete beams effectively. The results also indicate that the complications connected to the actual and genuine evaluation of costs of structures and the connectivity with the compulsory restraints can be adequately resolved using this method.

The optimization of the doubly reinforced concrete beam was investigated in this paper using the simulated annealing. Materials costs are considered as the objective function. The variables are the width, depth, compression steel, tension steel and cost. The constraints are the ultimate moment of resistance, compression/tension-steel ratio, minimum and maximum area of reinforcements. At the concrete compressive strength of 25 MPa, it is demonstrated that simulated annealing method can be used to optimize the design of concrete beams.

This paper presents the prediction of a singly reinforced concrete beam using Artificial Neural Networks (ANN). The method was adopted for cost optimization of the structural element and compared with the requirements of Eurocode 2 design. The code provisions for the design of a singly reinforced beam can vary from place to place. The use of a system immune from the code variation is an excellent means of predicting the reinforcement’s need of a rectangular concrete beam. In this work, an artificial neural network (ANN) is employed to forecast the reinforcement of such a beam. Artificial neural network has the potential to simulate the data that are hard to produce in arithmetical analysis. The scheme was established using the MATLAB tool kit. The design variables were the depth of the beam, the width of the beam, and the moments. A forward pass supervised backward propagation training. The regression analysis of the results is one to one match. The predicted and target values are completely in accord.

This research compares the Reliability-Based Design (RBD) method with the Eurocode 2 (EC2) for a singly reinforced concrete beam. Advanced First Order Second Moment (AFOSM) was used and the equation for the ultimate limit state design consideration for Moment of resistance and Applied moment was applied. The parameters which were all functions of the Ultimate load, G were replaced respectively with variables and their derivatives G(x) obtained with being the characteristic strengths of concrete and the yield stress of steel respectively. Given the original vector space and the transformed reduced vector space, the Taylor series was used to expand the reduced vector space and thus reliability index ßx expressed. The procedure was then coded using JAVA to produce the Reliability Index and Probability of failure for respective steel ratio. The required probability index of 3.9 corresponds to 0.048% of the steel ratio. Typical analysis of a singly reinforced beam according to EC2 for b=300mm, h=500mm, d=447mm, cover of 40 mm, gave a steel ratio of 1.31 %. Comparing this to the reliability-based design of 0.048%, the code provision exceeded the RBD output by 2000 times and 63 times the minimum reinforcement requirement by the code.

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