Buckling in prestressed stayed beam–columns and intelligent evaluation

Abstract

For prestressed stayed beam–columns, the buckling behaviour is studied analytically, and the intelligent models for evaluating the nonlinear behaviour are developed using machine learning. An implicit solution, which allows the direct evaluation for the buckling behaviour of both doubly-symmetric and mono-symmetric stayed members, is determined analytically for the first time, and an explicit simplified solution of the buckling load is obtained using regression. Both the implicit and explicit solutions can simplify the traditional numerical method for evaluating the buckling load of stayed members, and they are verified using numerical modelling and excellent comparisons are obtained. Based on the explicit solution of the buckling load, machine learning models are adopted to develop the intelligent methods for evaluating the nonlinear buckling behaviour of prestressed stayed beam–columns, which can overcome the difficulties in distinguishing between pure and interactive buckling modes in the traditional method for load-carrying capacity evaluation. The results in the test set show that Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) can predict the nonlinear failure mode and ultimate load of prestressed stayed beam–columns accurately and reliably.