Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21579
Title: Deep learning in the construction industry: A review of present status and future innovations
Authors: Akinosho, TD
Oyedele, L
Bilal, M
Ajayi, A
Delgado, M
Akinade, O
Ahmed, A
Keywords: deep learning;construction industry;convolutional neural networks;autoencoders;generative adversarial networks
Issue Date: 19-Sep-2020
Publisher: Elsevier
Citation: Akinosho, T.D. et al. (2020) 'Deep learning in the construction industry: A review of present status and future innovations', Journal of Building Engineering, 32, 101827, pp. 1 - 14. doi: 10.1016/j.jobe.2020.101827.
Abstract: Copyright © 2020 The Author(s). 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.
URI: https://bura.brunel.ac.uk/handle/2438/21579
DOI: https://doi.org/10.1016/j.jobe.2020.101827
ISSN: 2352-7102
Other Identifiers: ORCID iD: Ashraf A. Ahmed https://orcid.org/0000-0003-4287-8295
101827
Appears in Collections:Dept of Civil and Environmental Engineering Research Papers

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