Information system of multi-stage analysis of the building of object models on a construction site

Abstract

This study focuses on the development of a multi-stage analysis of building object models (BOM) on a construction site for modeling an “evolutionary” digital twin, by integrating building information modeling (BIM) technology and an artificial intelligence system. The concepts of photo modeling of the construction site using a group of moving cameras were outlined, as well as the possibility of integrating IoT technologies. The dynamic transition of real building structures into intermediate BIM representations of digital twins was investigated, with the prospect of enabling augmented reality technology. An artificial intelligence system combining Convolutional Neural Network (CNN) and Feed Forward Neural Network (FFNN) architectures has been developed as a comprehensive mechanism for the detection, categorization, and evaluation of BIM projects at all stages of their life cycle. The paper addresses the scaling prospects for the development of point cloud and mesh models, as well as the use of big data technology while optimizing the representation of the “evolutionary” BIM project of the digital twin of the construction site. The effectiveness of site conformance detection during the step-by-step construction of a BIM model, which shows consistency and provides a quantitative assessment of the processes occurring on the site, has been determined. The results of this research can be used to improve BIM modeling methods and concepts, in particular towards a multi-stage “evolutionary” representation of the digital twin of the construction site.