AI-powered automatic design of fire sprinkler layout for random building floorplans

Abstract

Fire sprinkler system is a commonly designed safety provision in modern buildings, yet the current manual drawing preparation process is burdened by time-consuming tasks, heavy workloads, and human errors. This study introduces an intelligent framework aimed at automating the drawing preparation process for fire sprinkler layout. A database of 120 sprinkler design drawings was compiled to train a pix2pixHD generative adversarial network (GAN). After training, the GAN model can generate sprinkler placement with a protection coverage of 99.5% for new and random architectural floorplans. Apart from ensuring code-compliant design, the total number of sprinklers designed by GAN is 13% lower than those arranged by professional engineers. By adopting this intelligent method, the time needed for design drawing preparation can be saved by 76%, and the cost-benefit of the sprinkler design can be improved by using reasonable fewer sprinklers.