An Application of Graph Neural Network Model Design for Residential Building Layout Design

Abstract

In the current process of residential building layout design, there are problems such as low design efficiency, excessive manual intervention, and difficulty in meeting personalized needs. To address these issues, a residential building layout design method based on graph neural network model is proposed to improve the intelligence level of residential building layout design. Firstly, the residential building floor plan layout design data are transformed into graph data suitable for graph neural network model processing. Then, deep learning techniques are used to analyse and identify the spatial distribution characteristics of the main functional areas in the space. Finally, the trained graph neural network model is applied to the actual residential building floor plan layout design and compared with the traditional method. The experimental results show that compared with the traditional computer-aided design method, the residential building floor plan layout design and optimisation method improves the completeness of the design scheme by about 2.3%, the rationality by about 3.6%, the readability by about 1.9%, and the effectiveness by about 10.3%. The method improves the efficiency and accuracy of residential building floor plan layout design, helps to shorten the design cycle and reduce the design cost, and helps to promote technological progress and sustainable development in the field of architectural design.