A Hybrid Optimization Method for Rebar Cutting in Structural Reinforcement

With the growing application of digital technologies in construction, reinforcement detailing and cutting are becoming increasingly refined. However, existing cutting methods struggle to meet the dual requirements of low waste and high computational efficiency when facing diverse rebar types, multiple splice points, and complex constraints. This paper proposes a hybrid optimization algorithm for large-scale rebar cutting that achieves efficient joint optimization of splice positions and cutting schemes. Numerical simulations verify the performance of the proposed algorithm under normal and uniform length distributions, with comparisons against traditional methods. Results show that the proposed method maintains the waste ratio below 1% for large-scale numerical datasets while achieving much higher computational efficiency than heuristic algorithms with good stability and scalability. Two engineering examples further validate this approach. In column longitudinal reinforcement, the waste ratio in each story was kept below 1%, and in precast bridge segmental beams, the method flexibly incorporated customized raw rebar lengths, reducing the waste ratio to as low as 0.4%. The proposed method effectively balances material utilization and cutting efficiency, offering a practical solution for intelligent rebar cutting across a wide range of components and construction scenarios.