NEURAL NETWORKS FOR DEFLECTIONS IN CONTINUOUS COMPOSITE BEAMS CONSIDERING CONCRETE CRACKING

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.