Big data in construction: current applications and future opportunities

Munawar, Hafiz Suliman and Ullah, Fahim ORCID: https://orcid.org/0000-0002-6221-1175 and Qayyum, Siddra and Shahzad, Danish (2022) Big data in construction: current applications and future opportunities. Big Data and Cognitive Computing, 6 (1):18.

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Abstract

Big data have become an integral part of various research fields due to the rapid advancements in the digital technologies available for dealing with data. The construction industry is no exception and has seen a spike in the data being generated due to the introduction of various digital disruptive technologies. However, despite the availability of data and the introduction of such technologies, the construction industry is lagging in harnessing big data. This paper critically explores literature published since 2010 to identify the data trends and how the construction industry can benefit from big data. The presence of tools such as computer-aided drawing (CAD) and building information modelling (BIM) provide a great opportunity for researchers in the construction industry to further improve how infrastructure can be developed, monitored, or improved in the future. The gaps in the existing research data have been explored and a detailed analysis was carried out to identify the different ways in which big data analysis and storage work in relevance to the construction industry. Big data engineering (BDE) and statistics are among the most crucial steps for integrating big data technology in construction. The results of this study suggest that while the existing research studies have set the stage for improving big data research, the integration of the associated digital technologies into the construction industry is not very clear. Among the future opportunities, big data research into construction safety, site management, heritage conservation, and project waste minimization and quality improvements are key areas.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Surveying and Built Environment (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Surveying and Built Environment (1 Jan 2022 -)
Date Deposited: 28 Feb 2022 23:55
Last Modified: 01 Mar 2022 00:02
Uncontrolled Keywords: big data; big data engineering; construction big data; digital technologies; construction industry
Fields of Research (2008): 10 Technology > 1005 Communications Technologies > 100504 Data Communications
12 Built Environment and Design > 1299 Other Built Environment and Design > 129999 Built Environment and Design not elsewhere classified
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
10 Technology > 1099 Other Technology > 109999 Technology not elsewhere classified
12 Built Environment and Design > 1202 Building > 120201 Building Construction Management and Project Planning
Fields of Research (2020): 33 BUILT ENVIRONMENT AND DESIGN > 3302 Building > 330202 Building construction management and project planning
33 BUILT ENVIRONMENT AND DESIGN > 3399 Other built environment and design > 339999 Other built environment and design not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460503 Data models, storage and indexing
46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460501 Data engineering and data science
40 ENGINEERING > 4006 Communications engineering > 400602 Data communications
40 ENGINEERING > 4005 Civil engineering > 400504 Construction engineering
33 BUILT ENVIRONMENT AND DESIGN > 3302 Building > 330201 Automation and technology in building and construction
46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery
Identification Number or DOI: https://doi.org/10.3390/bdcc6010018
URI: http://eprints.usq.edu.au/id/eprint/47032

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