Yüksel, S. BahadırArslan, M. Hakan2020-03-262020-03-262010Yüksel, S. B., Arslan, M. H., (2010). Design Force Estimation Using Artificial Neural Network for Groups of Four Cylindrical Silos. Advances in Structural Engineering, 13(4), 681-693. Doi: 10.1260/1369-4332.13.4.6811369-43322048-4011https://dx.doi.org/10.1260/1369-4332.13.4.681https://hdl.handle.net/20.500.12395/24760The computation of design forces for the reinforced concrete groups of four cylindrical silos (GFCS) is fairly difficult because of the continuity of the walls between the adjacent silos. In this study, the efficiency of the artificial neural network (ANN) in predicting the design forces and the design moments of the GFCS due to interstice and internal loadings was investigated. Previously obtained finite element (FE) analyses results in the literature were used to train and test the ANN models. Each parameter (silo wall thickness, intersection wall thickness and the central angle spanning the intersection walls of the GFCS) affecting design forces and moments was set to be an input vector. The outputs of the ANN models would be the bending moments, hoop forces and shear forces at the supports and crowns of the interstice walls due to interstice loadings; the maximum axial forces and maximum bending moments at the external walls due to internal loadings. All the outputs of the ANN models were trained and tested by three-layered 11 back-propagation methods widely used in the literature. The obtained results presented that these 11 different methods were capable of predicting the design forces and the design moments at the interstice and external walls of the GFCS used in the training and testing phases of the study.en10.1260/1369-4332.13.4.681info:eu-repo/semantics/openAccessgrouped silosinterstice loadinginternal loadinginterstice wallsintersection wallsartificial neural networksback-propagation methodsArticle134681693Q1WOS:000280465600011Q3