Automatic tower crane layout planning system for high-rise building construction using generative adversarial network

Abstract

With the spring up of high-rise building projects, tower crane layout planning (TCLP) is increasingly crucial to avoid construction costs, safety issues, and productivity deficiencies. Current optimization approaches require manual data extraction and become more complex as projects scale growing. To further alleviate the planning burden, an automatic TCLP system is proposed, using a generative adversarial network (GAN) called CraneGAN. It generates tower crane layouts from drawing inputs, eliminating the need for manual information extraction. CraneGAN is trained on a high-quality dataset and evaluated based on its computational time and crane transportation time. By adjusting hyperparameters and applying data augmentation, CraneGAN achieves robust and efficient results compared to genetic algorithms (GA) and the exact analytics method. After validating through a numerical analysis for construction project, this proposed approach overcomes complexity limitations and streamlines the manual data extraction process to better facilitate layout planning decision-making.