Rapid Prediction of Moments in High-rise Composite Frames Considering Cracking and Time-effects

Abstract

There can be a significant amount of moment redistribution in steel concrete composite frames due to cracking, creep and shrinkage in concrete. In the present study, neural network models have been developed for rapid prediction of the inelastic moments (typically for 20 years considering cracking, creep and shrinkage in concrete) in high rise composite frames. The possibility of sagging moment being developed at ends of beams due to the substantial differential shortening of adjacent columns has also been taken into account. Closed form expressions, based on the weights and biases of the trained neural networks, are proposed to predict the inelastic moments from the elastic moments (neglecting cracking and time effects). The expressions are verified for example frames of different number of spans and storeys and errors are found to be small. The expressions require computational effort that is a fraction of that required for the available methods.