Bayesian Regularization Backpropagation Neural Network for Glass Beams in Lateral–Torsional Buckling

Abstract

The lateral–torsional buckling (LTB) performance assessment of laminated glass (LG) beams is a remarkably critical issue because—among others—it can involve major consequences in terms of structural safety. Knowledge of LTB load-bearing capacity (in terms of critical buckling load Fcr and corresponding lateral displacement dLT), in this regard, is, thus, a primary step for more elaborated design considerations. The present study examines how machine learning (ML) techniques can be used to predict the response of laterally unrestrained LG beams in LTB. The potential and accuracy of artificial neural networks (ANN), based on ML methods, are addressed based on validation toward literature data. In particular, to detect the best-performing data-driven ML model, the load-bearing capacity of LG beams (i.e., Fcr and dLT) is set as output response, while geometric properties (length, width, thickness) and material features (for glass and interlayers) are used as input variables. A major advantage is taken from a literature database of 540 experiments and simulations carried out on two-ply LG beams in LTB setup. To determine the best-performing ANN model, different strategies are considered and compared. Additionally, the Bayesian regularization backpropagation (trainbr) algorithm is used to optimize the input–output relationship accuracy. The suitability of present modeling strategy for LG beams in LTB is quantitatively discussed based on error and performance trends.