The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
Opportunities and Adoption Challenges of AI in the Construction Industry: A PRISMA Review
Open accessAbstract
Artificial intelligence (AI) is a powerful technology with a range of capabilities, which are beginning to become apparent in all industries nowadays. The increased popularity of AI in the construction industry, however, is rather limited in comparison to other industry sectors. Moreover, despite AI being a hot topic in built environment research, there are limited review studies that investigate the reasons for the low-level AI adoption in the construction industry. This study aims to reduce this gap by identifying the adoption challenges of AI, along with the opportunities offered, for the construction industry. To achieve the aim, the study adopts a systematic literature review approach using the PRISMA protocol. In addition, the systematic review of the literature focuses on the planning, design, and construction stages of the construction project lifecycle. The results of the review reveal that (a) AI is particularly beneficial in the planning stage as the success of construction projects depends on accurate events, risks, and cost forecasting; (b) the major opportunity in adopting AI is to reduce the time spent on repetitive tasks by using big data analytics and improving the work processes; and (c) the biggest challenge to incorporate AI on a construction site is the fragmented nature of the industry, which has resulted in issues of data acquisition and retention. The findings of the study inform a range of parties that operate in the construction industry concerning the opportunities and challenges of AI adaptability and help increase the market acceptance of AI practices.
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Cites
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682
Digital twin-driven product design, manufacturing and service with big data
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920
Oesterreich, T. D., & Teuteberg, F. (2016) · Computers in Industry · doi:10.1016/j.compind.2016.09.006 · Cited by 1,526
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
Towards a semantic Construction Digital Twin: Directions for future research
Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103179 · Cited by 1,324
Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Davila Delgado, J. M., Bilal, M., Akinade, O. O., & Ahmed, A. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.103299 · Cited by 1,015
Davila Delgado, J. M., Oyedele, L., Ajayi, A., Akanbi, L., Akinade, O., Bilal, M., & Owolabi, H. (2019) · Journal of Building Engineering · doi:10.1016/j.jobe.2019.100868 · Cited by 607
Darko, A., Chan, A. P. C., Adabre, M. A., Edwards, D. J., Hosseini, M. R., & Ameyaw, E. E. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103081 · Cited by 603
3D Printing of Buildings and Building Components as the Future of Sustainable Construction?
Hager, I., Golonka, A., & Putanowicz, R. (2016) · Procedia Engineering · doi:10.1016/j.proeng.2016.07.357 · Cited by 593
Lee, M., Yun, J. J., Pyka, A., Won, D., Kodama, F., Schiuma, G., Park, H., Jeon, J., Park, K., Jung, K., et al. (2018) · Journal of Open Innovation: Technology, Market, and Complexity · doi:10.3390/joitmc4030021 · Cited by 584
Cited by
Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023) · Entrepreneurial Business and Economics Review · doi:10.15678/eber.2023.110201 · Cited by 584
“The adoption of AI-generated content can also diminish human autonomy and creativity (Regona et al. , 2022).”
BackgroundSon, T. H., Weedon, Z., Yigitcanlar, T., Sanchez, T., Corchado, J. M., & Mehmood, R. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104562 · Cited by 396
Self-citationYigitcanlar, T., Li, R. Y. M., Beeramoole, P. B., & Paz, A. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2023.101833 · Cited by 142
Self-citationPatnaik, P., & Bakkar, M. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102750 · Cited by 100
Network Slicing-Based Learning Techniques for IoV in 5G and Beyond Networks
Hamdi, W., Ksouri, C., Bulut, H., & Mosbah, M. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3372083 · Cited by 63
Metadata
- Authors
- Regona, M., Yigitcanlar, T., Xia, B., & Li, R. Y. M.
- Venue
- Journal of Open Innovation: Technology, Market, and Complexity
- Publisher
- Elsevier BV
- Published
- 2022-03
- Volume
- 8
- Issue
- 1
- Pages
- 45
- ISSN
- 2199-8531 (ISSN-L)
- OpenAlex
- W4214622289
- Cited by
- 543
- References
- 94
- FWCI
- 43.49
- Type
- journal article
- Language
- en
- Open access
- gold
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- Regona, M., Yigitcanlar, T., Xia, B., & Li, R. Y. M. (2022). Opportunities and Adoption Challenges of AI in the Construction Industry: A PRISMA Review. Journal of Open Innovation: Technology, Market, and Complexity. https://doi.org/10.3390/joitmc8010045
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
