Soman, R K and Whyte, J K (2020) Codification challenges for data science in construction. Journal of Construction Engineering and Management, 146(7): 04020072, ISSN 0733-9364
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence; automation; building information modeling; codification; data science; machine readability |
| Index terms: | common data environment, construction process, machine learning, accuracy, information flow, drawing, science, information sharing, media, design and construction, interoperability, artificial intelligence, modelling, automation, dimension, low level, platform, conversion, accessibility, building information modelling, process change, qualitative study |
| Subjects: | systems and processes, information systems, sociology, measurement and scaling, building construction, digital design, professional development, automation and robotics, inclusive design, data exchange, management, manufacturing engineering, health monitoring assessment and metrics, analytical methods, contractual arrangements, artificial intelligence, technical documentation, computing systems, specialized education, research design and methodology |
| Topics: | Digital Applications, Design Practice, Education, Site Management, Engineering Principles, Information Management, Research Practice, Health and Safety, Business Strategy, Procurement |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here