Modelling of Biodynamic Systems using Artificial Neural Networks for Evaluation of Reinforced Concrete Floors Dynamic Structural Behaviour when Subjected to Human Rhythmic Activities

Resumo

The current trends in structural design pointed out for the use of more efficient building floors solutions, characterized by a good structural response combined with lightness, slenderness, flexibility, and economy, when compared to conventional approaches. The development of structural systems with these characteristics frequently results in floors with low damping ratios and natural frequencies values close to those associated with human activities, such as walking, running, jumping, and dancing, inducing resonance, excessive vibration problems, and human discomfort. This way, in this study, biodynamic models are utilised to represent the effects of rhythmic human actions, specifically jumps performed in aerobics classes, on reinforced concrete building floors. To do this, Artificial Neural Networks (ANNs) were developed to generate biodynamic models associated with single degree of freedom (SDOF) mass-spring-damper systems. Based on these biodynamic models, it becomes possible to evaluate the dynamic structural response of building floors when subjected to rhythmic jumping. The proposed analysis methodology aims to demonstrate that the models developed from ANNs are capable of considering the dynamic contributions that individuals exert on building floors structures, standing out as a viable and more realistic alternative in comparison with the traditional mathematical models of dynamic loading (only force models).