Big Data in the construction industry: A review of present status, opportunities, and future trends

Bilal, M. and Oyedele, L.O. and Qadir, J. and Munir, K. and Ajayi, S.O. and Akinade, O.O. and Owolabi, H.A. and Alaka, Hafiz and Pasha, M. (2016) Big Data in the construction industry: A review of present status, opportunities, and future trends. Advanced Engineering Informatics, 30 (3). pp. 500-521. ISSN 1474-0346

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Abstract

The ability to process large amounts of data and to extract useful insights from data has revolutionised society. This phenomenon—dubbed as Big Data—has applications for a wide assortment of industries, including the construction industry. The construction industry already deals with large volumes of heterogeneous data; which is expected to increase exponentially as technologies such as sensor networks and the Internet of Things are commoditised. In this paper, we present a detailed survey of the literature,
investigating the application of Big Data techniques in the construction industry. We reviewed related works published in the databases of American Association of Civil Engineers (ASCE), Institute of Electrical and Electronics Engineers (IEEE), Association of Computing Machinery (ACM), and Elsevier
Science Direct Digital Library. While the application of data analytics in the construction industry is not new, the adoption of Big Data technologies in this industry remains at a nascent stage and lags the broad uptake of these technologies in other fields. To the best of our knowledge, there is currently no comprehensive survey of Big Data techniques in the context of the construction industry. This paper fills the void and presents a wide-ranging interdisciplinary review of literature of fields such as statistics, data
mining and warehousing, machine learning, and Big Data Analytics in the context of the construction industry. We discuss the current state of adoption of Big Data in the construction industry and discuss the future potential of such technologies across the multiple domain-specific sub-areas of the construction industry. We also propose open issues and directions for future work along with potential pitfalls associated with Big Data adoption in the industry.

Item Type: Article
Uncontrolled Keywords: Big Data Engineering Big Data Analytics Construction industry Machine learning
Subjects: K200 Building
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Engineering and the Built Environment > Integrated Design Construction
UoA Collections > UoA16: Architecture, Built Environment and Planning
Depositing User: Oana-Andreea Dumitrascu
Date Deposited: 18 May 2017 06:34
Last Modified: 09 Aug 2017 07:22
URI: http://www.open-access.bcu.ac.uk/id/eprint/4453

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