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 ($$SF$$) for a factored biaxial load, steel ratio ($${\rho _s}$$), and aspect ratio of columns ($$b/h$$) 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 ($$C{I_c}$$), $$C{O_2}$$ emission, and column weight ($${W_c}$$) are identified. Three-dimensional interaction diagrams are obtained based on optimized $$C{I_c}$$, $$C{O_2}$$ emission, and $${W_c}$$ based on preassigned $$SF$$ 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.

Keywords

• artificial neural networks
• ai-based reverse designs
• src columns
• optimal p-m diagrams
• optimization trends