A Neural Model to Estimate Carrying Capacity of Rectangular Steel Tubular Columns Filled with Concrete - Nasser, Kadhim Zuboon; Chkheiwer, Aqeel H.; F. Ojaimi, Mohammed (Anbar Journal of Engineering Sciences, 2020)
The goal of the current investigation is to construct an artificial neural network (ANN) to estimate the ultimate capacity of the composite columns consisting of a rectangular steel tube filled with concrete (RSTFC) under concentric loads. The experimental results of (222) samples collected from previous researches were used in constructing the proposed network. Totally (45) specimens were randomly chosen for network testing while the remaining (177) speci-mens were used to train the network. The information used to create the ANN model is ar-ranged into (6) variables represents the different dimensions and properties of the RSTFC col-umns. Based on the input information, a formulated network was used to estimate the columns' ultimate capacity. Results obtained from the formulated network, available laboratory tests, and Eurocode 4 and AISC equations were compared. The network values were closer to the laboratory values than the calculated values according to the specifications of the mentioned codes. It has been shown that the formulated ANN model has a high ability to estimate the RCFST ultimate capacity under concentric loads