.png){kind=icon}
.png){kind=icon}
Return
Codification Challenges for Data Science in Construction
.png){kind=icon}
PRE
AI
DOI：10.1061/(ASCE)CO.1943-7862.0001846.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
New forms of data science, including machine learning and data analytics, are enabled by machine-readable information but are not widely deployed in construction. A qualitative study of information flow in three projects using building information modeling (BIM) in the late design and construction phase is used to identify the challenges of codification that limit the application of data science. Despite substantial efforts to codify information with common data environment (CDE) platforms to structure and transfer digital information within and between teams, participants work across multiple media in both structured and unstructured ways. Challenges of codification identified in this paper relate to software usage (interoperability, information loss during conversion, multiple modelling techniques), information sharing (unstructured information sharing, drawing and file based sharing, document control bottlenecks, lack of process change), and construction process information (loss of constraints and low level of detail). This paper contributes to the current understanding of data science in construction by articulating the codification challenges and their implications for data quality dimensions, such as accuracy, completeness, accessibility, consistency, timeliness, and provenance. It concludes with practical implications for developing and using machine-readable information and directions for research to extract insight from data and support future automation.
Keywords:
Building information modeling (BIM)
Codification
Artificial intelligence (AI)
Automation
Data science
Machine readability
Construction
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
5.1
Papers:
5.1K
Citations:
1.4W