Multi-Stage Classification of Construction Site Modeling Objects Using Artificial Intelligence Based on BIM Technology

Abstract

Background: AI, IoT, and BIM technologies are revolutionizing the building business. This study applies these methods to the multi-stage categorization of construction site modeling items to create an "evolutionary" digital twin. Objective: Develop and test a BIM-AI method integrating CNN and FFNN architectures. The goals are BIM project identification, classification, and assessment throughout their life cycle. Methods: The approach uses moving cameras for picture modeling and IoT integration. Augmented reality and big data technologies explore the dynamic transformation of actual building structures into BIM representations. An AI system that analyzes construction site modeling items using CNN and FFNN is a major part of the study. Results: The article highlights the usefulness of site conformity detection during BIM model building, displaying consistency and quantifying ongoing operations. The study shows that scaling point cloud and mesh models and optimizing the "evolutionary" BIM project of the building site's digital twin are promising. Conclusion: The findings of this study provide important insights and improve BIM modeling techniques, notably in creating a multi-stage "evolutionary" digital twin of the building site. This pioneering construction site modeling using BIM and AI opens the door to future developments and improvements.

Keywords

• bim technology; artificial intelligence; digital twin; construction site; multi-stage classification; modeling objects.