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This study aims to propose artificial neural networks (ANNs) to solve structural engineering problems and to provide optimal designs for reinforced concrete columns with H-shaped steel sections. A reverse scenario based on preassigned safety factor () for a factored biaxial load, steel ratio (), and aspect ratio of columns () is presented, meeting code requirements. A back-substitution (BS) method using a chained training scheme with a revised sequence (CRS) is implemented to optimize training. Effects of rebar and steel ratios on objective functions, cost index (),emission, and column weight () are identified. Three-dimensional interaction diagrams are obtained based on optimized,emission, andbased on preassignedequivalent to 1.0. Predictions from ANNs are ascertained using structural mechanics, demonstrating a significance of an accuracy of the optimized design obtained by the proposed ANNs. This study provides a useful and practical design for SRC columns by lessening engineer’s effort while increasing design accuracies.

Reverse design-based optimizations for reinforced concrete columns encasing H-shaped steel section using ANNs