Fire pipe layout method of high-rise office building based on graph convolutional neural network

Abstract

The floor and space layout of high-rise office buildings is usually complex and variable, and the layout of fire pipes must take into account the structural limitations of the building. The plumbing system must be coordinated with the building structure without affecting the use of the floor and meeting the fire safety requirements. This paper proposes a Graph Convolutional Network (GCN) based fire pipe layout method for high-rise office buildings. Use GCN to complete the classification of fire pipes. Based on this, the directed graph of the fire pipe network is analyzed, the connection matrix of the fire pipe network is established, the shortest path of the fire pipe network is calculated, and the layout of the fire pipe network is completed. The experimental results show that: Under the application of the research method, the ability of fire pipeline to transport water resources is higher, and the investment cost of pipeline network layout is lower.