Automated Prefabricated Slab Splitting Design Using a Multipopulation Coevolutionary Algorithm and BIM

• Published in: Buildings (Basel) Vol. 14; no. 2; p. 433
• Main Authors: Xu, Chengran, Zheng, Xiaolei, Wu, Zhou, Zhang, Chao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2024
• Subjects: Algorithms; Analysis; Automation; Building construction; Building information modeling; Cast in place; Coevolution; Collaboration; collaborative optimization; Concrete; Construction; Decomposition; Design; Design optimization; Efficiency; Energy consumption; Genetic algorithms; Green buildings; Human error; Installation; Manufacturing; Mathematical optimization; multipopulation coevolutionary algorithm; Open source software; Optimization; Optimization models; Particle swarm optimization; Prefabricated buildings; prefabricated composite slabs; Prefabrication; Production costs; Splitting; splitting design; Variables; China
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings14020433

The prefabricated composite slab (PCS) is an essential horizontal component in a building, which is made of a precast part and a cast-in-place concrete layer. In practice, the floor should be split into many small PCSs for the convenience of manufacturing and installation. Currently, the splitting design of PCS mostly relies on sound knowledge and valuable experience of construction. While rule-based parametric design tools using building information modeling (BIM) can facilitate PCS splitting, the generated solution is suboptimal and limited. This paper presents an intelligent BIM-based framework to automatically complete the splitting design of PCSs. A collaborative optimization model is formulated to minimize the composite costs of manufacturing and installation. Individuals with similar area information are grouped into a subpopulation, and the optimization objective is to minimize the specifications and quantities of PCSs. Through the correlation information within the subpopulation and the shared information among each other, the variable correlation is eliminated to accomplish the task of collaborative optimization. The multipopulation coevolution particle swarm optimization (PSO) algorithm is implemented for the collaborative optimization model to determine the sizes and positions of all PCSs. The proposed framework is applied in the optimized splitting design of PCSs in a standard floor to demonstrate its practicability and efficiency.