Intelligent Optimization Method for Rebar Cutting in Pump Stations Based on Genetic Algorithm and BIM

• Published in: Buildings (Basel) Vol. 15; no. 11; p. 1790
• Main Authors: Fu, Xiang, Ji, Kecheng, Zhang, Yali, Xie, Qiang, Huang, Jiayu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2025
• Subjects: Accuracy; Algorithms; Analysis; BIM; Building; Building information modeling; Case studies; Construction industry; Cost control; cutting stock problem; Design specifications; Digital mapping; Digital technology; Efficiency; Genetic algorithms; Heuristic; improved genetic algorithm; Information management; intelligent construction assistance; Linear programming; Logistics; Management; Mathematical models; Methods; Onsite; Optimization; Optimization models; Planning; Production costs; Project management; Pumping stations; Raw materials; Rebar; Software
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings15111790

As the construction industry shifts from an extensive development model to one characterized by intelligent structural systems, the imperative to enhance productivity and management efficiency has emerged as a critical challenge. Conventional rebar construction processes heavily rely on manual operations—such as on-site rebar cutting, manual transcription of material lists, and decentralized processing—which are susceptible to subjective errors and often result in significant material waste. This issue is particularly pronounced in large-scale projects, where disorganized management of rebar quantities and placements exacerbates inefficiencies. To address these challenges, this study proposes an integrated approach that synergistically combines a genetic algorithm-based rebar-cutting optimization model with BIM technology, thereby optimizing rebar management throughout the construction process. The research is structured into two primary components. Firstly, a one-dimensional mathematical model for rebar-cutting optimization is developed, incorporating an innovative real-number encoding strategy within the genetic algorithm framework to maximize material utilization. A case study conducted on a pump station project reveals that the utilization rates for 32 mm and 16 mm rebar reach 86.76% and 93.90%, respectively, significantly exceeding the industry standard of 80%. Secondly, an automated batch modeling tool is developed using C# and the Revit API, which enables the efficient generation of rebar components; a unique coding system is employed to establish a bidirectional mapping between the digital model and the physical rebar, ensuring precise positioning and effective information management. Overall, this integrated method—encompassing rebar-cutting optimization, digital modeling, and on-site intelligent management—not only mitigates material waste and reduces production costs but also markedly enhances construction efficiency and accuracy in complex projects, thereby providing robust technical support for the seamless integration of intelligent construction and industrialized building practices.