In the built environment, one of the main concerns during the design stage is the selection of adequate structural materials and elements. A rational and sensible design of both materials and elements results not only in economic benefits and computing time reduction, but also in minimizing the environmental impact. Nowadays, Artificial Neural Networks (ANNs) are showing their potential as design tools. In this research, ANNs are used in order to foster the implementation of efficient tools to be used during the early stages of structural design. The proposed networks are applied to a dry precast concrete connection, which has been modelled by means of the Finite Element Method (FEM). The parameters are: strength of concrete and screws, diameter of screws, plate thickness, and the posttensioning load. The ANN input data are the parameters and nodal stresses obtained from the FEM models. A multilayer perceptron combined with a backpropagation algorithm is used in the ANN architecture, and a hyperbolic tangent function is applied as an activation function. Comparing the obtained predicted stresses to those of the FEM analyses, the difference is less than 9.16%. Those results validate their use as an efficient structural design tool. The main advantage of the proposed ANNs is that they can be easily and effectively adapted to different connection parameters. In addition, their use could be applied both in precast or cast in situ concrete connection design.
Efficient Structural Design of a Prefab Concrete Connection by Using Artificial Neural Networks
