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

Abstract

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 ((Formula presented.)) for a factored biaxial load, steel ratio ((Formula presented.)), and aspect ratio of columns ((Formula presented.)) 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 ((Formula presented.)), (Formula presented.) emission, and column weight ((Formula presented.)) are identified. Three-dimensional interaction diagrams are obtained based on optimized (Formula presented.), (Formula presented.) emission, and (Formula presented.) based on preassigned (Formula presented.) equivalent 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.