NEURAL NETWORK PREDICTION OF THE ULTIMATE CAPACITY OF SHEAR STUD CONNECTORS ON COMPOSITE BEAMS WITH PROFILED STEEL SHEETING

In this paper, the efficiency of different artificial neural networks (ANNs) in predicting the ultimate shear capacity of shear stud connectors is explored. Experimental data involving push-out test specimens of 118 composite beams from an existing database in the literature were used to develop the ANN model. The input parameters affecting the shear capacity were selected as sheeting, stud dimensions, slab dimensions, reinforcement in the slab and concrete compression strength. Each parameter was arranged in an input vector and a corresponding output vector that includes the ultimate shear capacity of composite beams. For the experimental test results, the ANN models were trained and tested using three layered back-propagation methods. The prediction performance of the ANN was obtained. In addition to these, the paper presents a short review of the codes in relation to the design of composite beams. The accuracy of the codes in predicting the ultimate shear capacity of composite beams was also examined in a comparable way by using the same test data. At the end of the study, the effect of the all parameters is also discussed. The study concludes that all ANN models predict the ultimate shear capacity of beams better than codes.