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    GENETIC PROGRAMMING BASED MODELING OF SHEAR CAPACITY OF COMPOSITE BEAMS WITH PROFILED STEEL SHEETING
    (HONG KONG INST STEEL CONSTRUCTION, 2011) Koroglu, M. A.; Koken, A.; Arslan, M. H.; Cevik, A.
    This study investigates the availability of Genetic Programming (GP) for modeling the ultimate shear capacity of composite beams with profiled steel sheeting for the first time in literature. Experimental data involving push-out test specimens of 46 composite beams from an existing database in the literature were used to develop GP model. The input parameters affecting the shear capacity were selected as stud position (strong and weak), sheeting type (width of rib of the profiled steel sheeting, depth of the rib), stud dimensions (height and diameter), slab dimensions (width, depth and height), reinforcement in the slab and concrete compression strength. Moreover, a short review of well-known building codes regarding ultimate shear capacity of composite beams is presented. The accuracy of the codes in predicting the ultimate shear capacity of composite beams was also compared with the proposed GP model with comparable way by using same test data. The study concludes that the proposed GP model predicts the ultimate shear capacity of composite beams by far more accurate than building codes.
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    Neural network prediction of the ultimate capacity of shear stud connectors on composite beams with profiled steel sheeting
    (ELSEVIER SCIENCE BV, 2013) Koroglu, M. A.; Koken, A.; Arslan, M. H.; Cevik, A.
    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, which 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 using the same test data. At the end of the study, the effect of all parameters is also discussed. The study concludes that all ANN models predict the ultimate shear capacity of beams better than codes. (C) 2013 Sharif University of Technology. All rights reserved.