The layout planning (CSLP) of the construction site is of essential importance for the optimization of the placement of temporary facilities (TFS), but integrates insufficient features of the tower crane and cause inefficient material transport and security risk. The current decision -making is based on labor -intensive data extraction, complex mathematical models and fragmented workflows that are incompatible with special software. In this article, an automated, data-controlled lift-centered CSLP decision-making approach with the modeling of building information (BIM) and AI is suggested to improve TF placement efficiency. The approach comprises three phases: Automated data extraction from the BIM model with the advertising of the user, the development of data-controlled lifting-election multi-lens CSLP decision engines and the evaluation of the generated TFS placement by BIM-based simulations. The validation shows that over 92 % of the AI-generated CSLP results surpass conventional methods (genetic algorithm (GA)). Experiments on a real project show that this approach lowers the processing time to 7.93 % of the GA and the functional costs by 11.60 %. This method supports designers in accelerating the CSLP decision-making process with BIM models.
Data-controlled lift-centered building-layout planning decision with BIM
