Codification Challenges for Data Science in Construction
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 7
Abstract
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.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
The authors are grateful to the research participants in the three case studies. The Ph.D. research of the first author is cofunded by Bentley Systems UK and through a Skempton Scholarship from the Department of Civil and Environmental Engineering, Imperial College, London. During the development of this paper, this author was supported by the Ph.D. enrichment scholarship from the Alan Turing Institute, the United Kingdom’s National Institute for Data Science and AI. The second author acknowledges the support of Laing O’Rourke and the Royal Academy of Engineering for cosponsoring her Professorship; and Lloyds Register Foundation/ATI Data Centric Engineering Programme.
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Journal of Construction Engineering and Management
Volume 146 • Issue 7 • July 2020
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©2020 American Society of Civil Engineers.
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Received: Apr 18, 2019
Accepted: Dec 12, 2019
Published online: Apr 27, 2020
Published in print: Jul 1, 2020
Discussion open until: Sep 27, 2020
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