NEURAL NETWORKS FOR DEFLECTIONS IN CONTINUOUS COMPOSITE BEAMS CONSIDERING CONCRETE CRACKING

10.22099/ijstc.2014.1864

Abstract

Maximum deflection in a beam is a design criteria and occurs generally at or close to
the mid-span. A methodology has been developed for continuous composite beams to predict the
inelastic mid-span deflections, d i (considering the cracking of concrete) from the elastic mid-span
deflections, d e (neglecting the cracking of concrete). Nine significant structural parameters have
been identified that govern the change in mid-span deflections. Six neural networks have been
presented to cover the entire practical range of the beams. The proposed neural networks have
been validated for a number of beams with different number of spans and the errors are small for
practical purposes. The methodology enables rapid estimation of inelastic deflections in
continuous composite beams and requires a computational effort that is a fraction of that required
for the conventional iterative or incremental analysis. The methodology can easily be extended for
large composite building frames where a huge savings in computational effort would result.

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