Research on cutting stock optimization of rebar engineering based on building information modeling and an improved particle swarm optimization algorithm
Ke Ren, Jia Lu, Jiantao Huang, Min Wu
Abstract
Rebar is one of the most important construction materials in engineering projects. For reinforced concrete structures, rebar consumption accounts for about 16% of the total project cost. Total project cost could be significantly reduced by precise computation on the cutting length of rebars. In this paper, a novel two-stage framework of rebar cutting stock is proposed to produce minimum residual rate of rebar and the minimum number of cutting patterns. In the first stage, a fast and accurate rebar lofting technique based on building information modeling (BIM) is designed to automatically generate rebar-cutting data. In the second stage, a greedy strategy-based adaptive particle swarm optimization algorithm (GAPSO) is developed to optimize the rebar-cutting scheme. Finally, the proposed framework is used in the reinforcement laying-off of a comprehensive indemnificatory housing project, and the result verified the practicability and efficiency of the framework.