Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures

Abstract

The artificial bee colony (ABC) algorithm is a recently introduced swarm intelligence algorithm for optimization, which has already been successfully applied for the training of artificial neural network (ANN) models. This paper thoroughly explores the performance of the ABC algorithm for optimizing the connection weights of feed-forward (FF) neural network models, aiming to accurately determine one of the most critical parameters in reinforced concrete structures, namely the fundamental period of vibration. Specifically, this study focuses on the determination of the vibration period of reinforced concrete infilled framed structures, which is essential to earthquake design, using feed-forward ANNs. To this end, the number of storeys, the number of spans, the span length, the infill wall panel stiffness, and the percentage of openings within the infill panel are selected as input parameters, while the value of vibration period is the output parameter. The accuracy of the FF–ABC model is verified through comparison with available formulas in the literature. The results indicate that the artificial neural network, the weights of which had been optimized via the ABC algorithm, exhibits greater ability, flexibility and accuracy in comparison with statistical models.