Automated steel reinforcement detailing in reinforced concrete frames using evolutionary optimization and artificial potential field

Abstract

Steel reinforcement detailing is critical in the reinforced concrete (RC) frame design of collision-free rebar layouts. This paper presents a methodological framework for automated steel reinforcement detailing. An optimization model is developed to calculate optimal rebar combinations, where material cost and construction convenience are considered. The neighborhood field optimization (NFO) algorithm is applied to obtain the solution, and an integrated artificial potential field (APF) and NFO method is proposed to automatically generate a collision-free rebar layout, where the NFO enables agents to escape from the local minimum point of the APF. The developed framework is applied to two RC frames to determine the diameter, length, and position of each rebar. The results show that the developed framework can rapidly and accurately complete steel reinforcement detailing for various RC frames.