This paper presents the Fuzzy logics approach for a singly reinforced concrete beam. The rules are generated for the FIS for the variables. The width, and the moment constitutes the crisp data for the inputs and the steel ratio represents the crisp output. The MATLAB fuzzy tool kit is used to execute the simulation. The code prescription of the design of a singly reinforced concrete beam is a straight forward calculation. However, the overestimation of reinforcement can be enormous. This can be very impacting on the cost of the project. This work attempts to use fuzzy logic predictive power to design a singly reinforced concrete beam. The result reasonable agreement between the code provisions and the fuzzy logic predictions. It was established that fuzzy logic can be adopted as a significant technique for the optimization of the design of a singly reinforced concrete beam.

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.

This paper presents the prediction of a singly reinforced concrete beam tension reinforcement design requirements using Artificial Neural Networks (ANN). The method was adopted for cost optimization of the tension reinforcement in 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.