Automating the retrospective generation of As-is BIM models using machine learning

Cite this paper

@article{d6a58434,
title = {Automating the retrospective generation of As-is BIM models using machine learning},
journal = {Automation in Construction},
volume = {152},
pages = {104937},
year = {2023},
issn = {0926-5805},
doi = {https://doi.org/10.1016/j.autcon.2023.104937},
url = {https://www.sciencedirect.com/science/article/pii/S0926580523001978},
author = {Phillip Schönfelder and Angelina Aziz and Benedikt Faltin and Markus König},
keywords = {Building information modeling, As-is BIM, Machine learning, Deep learning, Model generation, Semantic enrichment, 3D reconstruction, Systematic literature review, PRISMA},
abstract = {The manual creation of digital models of existing buildings for operations and maintenance is difficult and time-consuming. Machine learning and deep learning techniques have recently emerged to help automate this process. To assess the numerous publications in the field, this paper presents a systematic literature review and highlights potential research gaps and development opportunities. Following the procedure suggested by PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), 95 eligible publications are selected for the final review. The findings indicate that future research should explore alternative data sources, extract component attributes alongside geometries, and address retrospective infrastructure modeling, which remains widely unexplored. This paper sheds new insights on the latest research on using ML approaches to generate digital models of existing buildings, with the aim of providing guidance for researchers seeking ideas for future studies in this area.}
}