A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Automating the retrospective generation of As-is BIM models using machine learning
https://orcid.org/0000-0002-8685-436X
https://orcid.org/0000-0003-1354-7817
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.
Graphical abstract Display Omitted
Highlights A systematic literature review of as-is building information model generation approaches aided by machine learning. Current research focusses on point cloud-processing methods, high-rise buildings and classifying structural components. Future research should address alternative data sources, especially building documentation. Future investigations should explore the extraction of components’ attributes and building topology.
Automating the retrospective generation of As-is BIM models using machine learning
https://orcid.org/0000-0002-8685-436X
https://orcid.org/0000-0003-1354-7817
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.
Graphical abstract Display Omitted
Highlights A systematic literature review of as-is building information model generation approaches aided by machine learning. Current research focusses on point cloud-processing methods, high-rise buildings and classifying structural components. Future research should address alternative data sources, especially building documentation. Future investigations should explore the extraction of components’ attributes and building topology.
Automating the retrospective generation of As-is BIM models using machine learning
https://orcid.org/0000-0002-8685-436X
https://orcid.org/0000-0003-1354-7817
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.
Graphical abstract Display Omitted
Highlights A systematic literature review of as-is building information model generation approaches aided by machine learning. Current research focusses on point cloud-processing methods, high-rise buildings and classifying structural components. Future research should address alternative data sources, especially building documentation. Future investigations should explore the extraction of components’ attributes and building topology.
Automating the retrospective generation of As-is BIM models using machine learning
Schönfelder, Phillip (author) / Aziz, Angelina (author) / Faltin, Benedikt (author) / König, Markus (author)
2023-05-11
Article (Journal)
Electronic Resource
English
Automating realization of integrated project models
| British Library Online Contents | 1999
Automating realization of integrated project models
| Online Contents | 1999