Optimal Design Prediction of a Singly Reinforced Concrete Beam Steel Using Artificial Neural Network

Abstract

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.

Exploring Analytical Hierarchy Process for Multicriteria Assessment of Reinforced Concrete Slabs

“…Flat slabs are elements directly supported on the columns, and may or may not undergo a widening in the slab-column transition. According to [14], these slabs can be formed by the two previous types, i.e., solid or waffle, as shown in Figure 3. Characteristics of this type of slab include its high productivity compared to the constructive processes.…”