dc.contributor.author |
Zabin, Asem |
|
dc.contributor.author |
González, Vicente A |
|
dc.contributor.author |
Zou, Yang |
|
dc.contributor.author |
Amor, Robert |
|
dc.date.accessioned |
2022-06-13T02:56:26Z |
|
dc.date.available |
2022-06-13T02:56:26Z |
|
dc.date.issued |
2022-01-01 |
|
dc.identifier.citation |
(2022). Advanced Engineering Informatics: the science of supporting knowledge-intensive activities, 51, 101474-. |
|
dc.identifier.issn |
0954-1810 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/59773 |
|
dc.description.abstract |
As Building Information Modeling (BIM) workflows are becoming very relevant for the different stages of the project's lifecycle, more data is produced and managed across it. The information and data accumulated in BIM-based projects present an opportunity for analysis and extraction of project knowledge from the inception to the operation phase. In other industries, Machine Learning (ML) has been demonstrated to be an effective approach to automate processes and extract useful insights from different types and sources of data. The rapid development of ML applications, the growing generation of BIM-related data in projects, and the different needs for use of this data present serious challenges to adopt and effectively apply ML techniques to BIM-based projects in the Architecture, Engineering, Construction and Operations (AECO) industry. While research on the use of BIM data through ML has increased in the past decade, it is still in a nascent stage. In order to asses where the industry stands today, this paper carries out a systematic literature review (SLR) identifying and summarizing common emerging areas of application and utilization of ML within the context of BIM-generated data. Moreover, the paper identifies research gaps and trends. Based on the observed limitations, prominent future research directions are suggested, focusing on information architecture and data, applications scalability, and human information interactions. |
|
dc.language |
en |
|
dc.publisher |
Elsevier BV |
|
dc.relation.ispartofseries |
Advanced Engineering Informatics |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.subject |
Science & Technology |
|
dc.subject |
Technology |
|
dc.subject |
Computer Science, Artificial Intelligence |
|
dc.subject |
Engineering, Multidisciplinary |
|
dc.subject |
Computer Science |
|
dc.subject |
Engineering |
|
dc.subject |
Building information modeling |
|
dc.subject |
Artificial intelligence |
|
dc.subject |
Machine learning |
|
dc.subject |
Data mining |
|
dc.subject |
Systematic literature review |
|
dc.subject |
DECISION-MAKING |
|
dc.subject |
SUPPORT-SYSTEM |
|
dc.subject |
LIFE-CYCLE |
|
dc.subject |
DESIGN |
|
dc.subject |
INFORMATION |
|
dc.subject |
CONSTRUCTION |
|
dc.subject |
PERFORMANCE |
|
dc.subject |
MODEL |
|
dc.subject |
NETWORK |
|
dc.subject |
FRAMEWORK |
|
dc.subject |
08 Information and Computing Sciences |
|
dc.subject |
09 Engineering |
|
dc.title |
Applications of machine learning to BIM: A systematic literature review |
|
dc.type |
Journal Article |
|
dc.identifier.doi |
10.1016/j.aei.2021.101474 |
|
pubs.begin-page |
101474 |
|
pubs.volume |
51 |
|
dc.date.updated |
2022-05-04T10:12:56Z |
|
dc.rights.holder |
Copyright: The author |
en |
pubs.author-url |
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000744249100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e41486220adb198d0efde5a3b153e7d |
|
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Review |
|
pubs.subtype |
Journal |
|
pubs.elements-id |
877432 |
|
pubs.org-id |
Engineering |
|
pubs.org-id |
Science |
|
pubs.org-id |
School of Computer Science |
|
pubs.org-id |
Civil and Environmental Eng |
|
dc.identifier.eissn |
1873-5320 |
|
pubs.number |
101474 |
|
pubs.record-created-at-source-date |
2022-05-04 |
|