Circular economy oriented future building information processing: PSO for CNN approach

Abstract

With the acceleration of urbanization, resource depletion is becoming increasingly serious. The innovative strategy of resource utilization is of great significance to the development of new green building. Therefore, this paper combines artificial intelligence technology and deeply excavates the basic information of the old building images collected by the survey. It determines the specific future building construction information from the direction of sustainable development. Based on the traditional convolutional neural network U-Net model, this paper improves its shortcomings, proposes an improved IU-Net model and optimizes the full convolutional neural network structure based on Particle Swarm Optimization (PSO). This paper analyzes and designs the future building information system platform oriented to circular economy, and it compares the improved IU-Net model with other models through experiments to obtain the accuracy and other indicators. The experimental results indicate that the system using the IU-Net model of full convolutional neural network based on PSO has higher prediction accuracy. It has better functionality for the analysis and decision-making of future building.