Determinants of Design with Multilayer Perceptron Neural Networks: A Comparison with Logistic Regression

This research focuses on harnessing artificial neural networks (ANNs) to enhance the design of steel structures. The design process encompasses various stages, including defining the building’s geometry, estimating loads, selecting an appropriate structural system, sizing components, and creating detailed plans. Optimizing the weight of these structures is vital for reducing costs, improving efficiency, and minimizing environmental impact. This study specifically investigates multilayer perceptron (MLP) neural networks to optimize steel structure design. It evaluates different ANN configurations with varying numbers of hidden layers and neurons to find the most effective arrangement. Additionally, the performance of MLP networks is compared to that of logistic regression. The results demonstrate that MLP networks deliver superior accuracy in optimizing the design of steel structures compared to logistic regression. The process of designing steel structures at an early stage can reduce the consumption of energy and raw materials before the production of the structures themselves begins. This is important from an economic point of view because some costs can be reduced during the design process. When designing steel structures, it is also possible to take into account changing conditions, such as the growing share of renewable energy sources in the total energy balance in many countries.