The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.
Deep learning in the construction industry: A review of present status and future innovations
Open accessAbstract
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.
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Cites
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Cited by
Opportunities and Adoption Challenges of AI in the Construction Industry: A PRISMA Review
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GPT models in construction industry: Opportunities, limitations, and a use case validation
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Jia, J., & Li, Y. (2023) · Sensors · doi:10.3390/s23218824 · Cited by 182
“This is followed by three papers on cracking [99–101], one on data science [102], and three on the construction industry [35,103,104].”
BackgroundSaka, A. B., Oyedele, L. O., Akanbi, L. A., Ganiyu, S. A., Chan, D. W. M., & Bello, S. A. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101869 · Cited by 161
Self-citation
Metadata
- Authors
- Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A.
- Venue
- Journal of Building Engineering
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 32
- Pages
- 101827
- ISSN
- 2352-7102 (ISSN-L)
- OpenAlex
- W3087508233
- Cited by
- 537
- References
- 110
- FWCI
- 27.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- 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. https://doi.org/10.1016/j.jobe.2020.101827
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