Artificial neural network prediction of the initial stiffness of semi-rigid beam-to-column connections

Abstract

[Abstract]: Joints are significant components in the design and construction of steel structures. The characteristic parameters of the connections must be reproduced in a reliable way to represent the actual behaviour of a structure. Accordingly, the study of semi-rigid joints is essential to better understand this issue. Among the different types of semi-rigid joints, angle connections stand out as a suitable solution in many cases. This paper presents a methodology using artificial neural networks for predicting the initial rotational stiffness of major axis symmetrical angle connections according to the Eurocode description. A consistent stiffness database was developed from the existing data in the Steel Connection Data Bank. Then, the database was cleansed to provide with a robust training set. Different network architectures were analysed until a topology that showed a good performance and generalisation features was obtained. The network was successfully checked with some saved tests from the database and with off-database tests; the network could be reliably used within the range of the training input parameters.