Artificial Intelligence Technology Based on Deep Learning in Building Construction Management System Modeling

Abstract

In order to explore the application of AI technology in construction management system modeling, the author proposed the application of a deep learning-based AI technology in construction management system modeling. The 3 D reconstruction deep learning model is first introduced, and then the model idea of the construction progress reliability control system is designed based on BIM (building information model). Second, the construction process of the 4dbim model is described, and the construction method is introduced. The construction of the model provides data information for the construction schedule reliability control system. Finally, the three functional modules of progress monitoring, progress reliability early warning, and progress prediction are realized by combining the S-curve comparison method, and the work of the system is described through case simulation. The early warning result is from June 7 to June 11, the progress deviation is between (−2%, 2%), and the progress is basically controlled. On June 13, the planned workload was 81.099%, and the actual cumulative workload was 7.099%, which was 4% less than the planned workload. The project progress was out of date, so it needs to be closely tracked. On June 15, the planned workload was 85.511%, and the actual cumulative workload was 80.899%, 4.5% less than the planned workload. The forecast result is the line forecast of the actual cumulative completion percentage on June 17. After calculation, the forecast result on June 17 is 84.311%. The progress deviation on June 17 was 5.21%. If no timely delay is taken on June 15, the delay will get worse. In addition, the system can predict the completion period of the project. When the actual percentage of cumulative completion is greater than or equal to 100%, it indicates that the project has been completed. Therefore, we can calculate the completion period of the three-storey project, and the construction period is about June 29 or 30. When the simulation can be carried out, the simulation number is set as 1000 times, the completion probability of the project is only 40%, and the completion probability is not too high. Artificial intelligence technology can realize progress monitoring, progress reliability early warning, and progress prediction. This system model prepares for software development and is conducive to improving the progress reliability control level of construction enterprises. Starting from the schedule planning subsystem and the schedule control subsystem, this paper studies the application of the artificial intelligence technology based on deep learning in the modeling of a building construction management system. The results show that this technology can effectively improve the efficiency of the building construction schedule management. Compared with the existing management methods, it shows great advantages in terms of operating costs and ease of use. It also promotes the application of artificial intelligence technology in the construction phase.