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Deep learning in the construction industry: A review of present status and future innovations

Akinosho, Taofeek D.; Oyedele, Lukumon O.; Bilal, Muhammad; Ajayi, Anuoluwapo O.; Delgado, Manuel Davila; Akinade, Olugbenga O.; Ahmed, Ashraf A.

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Authors

Taofeek Akinosho Taofeek.Akinosho@uwe.ac.uk
Research Associate - Big Data Application Development

Muhammad Bilal Muhammad.Bilal@uwe.ac.uk
Associate Professor - Big Data Application

Manuel Davila Delgado Manuel.Daviladelgado@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence

Olugbenga Akinade Olugbenga.Akinade@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence

Ashraf A. Ahmed



Abstract

The construction industry is known to be overwhelmed with resource planning, risk management and logistic challenges which often result in design defects, project delivery delays, cost overruns and contractual disputes. These challenges have instigated research in the application of advanced machine learning algorithms such as deep learning to help with diagnostic and prescriptive analysis of causes and preventive measures. However, the publicity created by tech firms like Google, Facebook and Amazon about Artificial Intelligence and applications to unstructured data is not the end of the field. There abound many applications of deep learning, particularly within the construction sector in areas such as site planning and management, health and safety and construction cost prediction, which are yet to be explored. The overall aim of this article was to review existing studies that have applied deep learning to prevalent construction challenges like structural health monitoring, construction site safety, building occupancy modelling and energy demand prediction. To the best of our knowledge, there is currently no extensive survey of the applications of deep learning techniques within the construction industry. This review would inspire future research into how best to apply image processing, computer vision, natural language processing techniques of deep learning to numerous challenges in the industry. Limitations of deep learning such as the black box challenge, ethics and GDPR, cybersecurity and cost, that can be expected by construction researchers and practitioners when adopting some of these techniques were also discussed.

Citation

Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, https://doi.org/10.1016/j.jobe.2020.101827

Journal Article Type Article
Acceptance Date Sep 14, 2020
Online Publication Date Sep 19, 2020
Publication Date Nov 1, 2020
Deposit Date Nov 25, 2020
Publicly Available Date Dec 3, 2020
Journal Journal of Building Engineering
Electronic ISSN 2352-7102
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 32
Article Number 101827
DOI https://doi.org/10.1016/j.jobe.2020.101827
Keywords Mechanics of Materials; Civil and Structural Engineering; Safety, Risk, Reliability and Quality; Architecture ; Building and Construction
Public URL https://uwe-repository.worktribe.com/output/6886575
Additional Information This article is maintained by: Elsevier; Article Title: Deep learning in the construction industry: A review of present status and future innovations; Journal Title: Journal of Building Engineering; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jobe.2020.101827; Content Type: article; Copyright: © 2020 The Author(s). Published by Elsevier Ltd.

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