Estimating Axial Load Capacity of Concrete-Filled Double-Skin Steel Tubular Columns of Multiple Shapes Using Genetic Algorithm-Optimized Artificial Neural Networks

Abstract

AbstractConcrete-filled double-skin tubular (CFDST) columns are optimized for a high strength-to-weight ratio by having their concrete core confined between inner and outer steel tubes. The confined concrete behavior in these composite columns is affected by the shape of the inner and outer steel tubes. A new hybrid approach using genetic algorithm (GA)-optimized artificial neural networks (ANNs) is proposed in this study to estimate the axial load capacity of CFDST columns for multiple combinations of square and circular steel tubes, i.e., circle-circle (CC), circle-square (CS), square-square (SS) and square-circle (SC) cross-sections. The present study used a total dataset of 171-CFDST columns (i.e., 51 of CC, 43 of CS, 38 of SC and 39 of SS shapes) for demonstration. The axial load capacity of CFDST columns was obtained using calibrated nonlinear finite element analyses. For the training of ANNs, a design set of 100 CFDST columns was used from among hypothetical set to map geometric and material properties to their ultimate axial load capacity. The remaining 71 columns (combined hypothetical and actual tested specimens) were used to test and check the generalization ability of the ANN-based prediction model. The network parameters of ANNs were reoptimized with GA to reduce the maximum absolute error on the testing set columns from 26 to 14%. Thus, the hybrid GA-optimized ANNs can more accurately predict the ultimate axial load capacity of CFDST columns of multiple shapes (about 14 and 7%, respectively, the maximum and mean absolute errors in this study) than traditional ANNs.KeywordsConcrete-filled tubular columnsMultiple cross-sectional shapesUltimate axial load capacityArtificial neural networksGenetic algorithmFinite element modeling