The DOI matches its Crossref record; 98 of the 98 references it cites were found in a registry or an index.
Construction 4.0: A Literature Review
Open accessAbstract
The construction industry is experiencing changes in its processes and work methods, and the advancement of new technologies in recent decades has led to a new concept known as Construction 4.0, coined in 2016 in Germany. Since its definition is still diffuse, it was deemed necessary to conduct a review on the publications in this field to grasp how this concept is being understood. For that purpose, a bibliometric analysis was conducted among 260 research articles using seven keywords. The results reveal that the number of publications is growing exponentially, with the USA, the UK, and China being leaders in this field; besides, four technologies are essential to understand Construction 4.0 at present time: 3D printing, big data, virtual reality, and Internet of Things. The results of this review suggest that further reviews should be conducted every 3 years to grasp the rapid evolution of Construction 4.0.
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Cites
Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2444095 · Cited by 8,630
Internet of Things for Smart Cities
Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2306328 · Cited by 6,292
Internet of Things in industries: A survey
Xu, L. D., He, W., & Li, S. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2300753 · Cited by 5,172 · cited 1×
“One of the main concerns is related to the privacy of information coming from the so-called “smart homes”, which integrate IoT in a variety of home devices, especially those related to disaster information, healthcare [43], or energy management [58], with the challenge of handling large amounts of data transmitted ove…”
BackgroundLasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014) · Business & Information Systems Engineering · doi:10.1007/s12599-014-0334-4 · Cited by 4,276
Beyond the hype: Big data concepts, methods, and analytics
Gandomi, A., & Haider, M. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.10.007 · Cited by 4,226
Artificial intelligence in service
Huang, M.-H., & Rust, R. T. (2018) · Journal of Service Research · doi:10.1177/1094670517752459 · Cited by 3,978
Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913 · cited 1×
“It is defined as a great volume of data that is difficult to store, process, and analyze in real-time with traditional technologies [44].”
BackgroundThe Internet of Things (IoT): Applications, investments, and challenges for enterprises
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Integration of Cloud computing and Internet of Things: A survey
Botta, A., de Donato, W., Persico, V., & Pescapé, A. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2015.09.021 · Cited by 2,388
Security, privacy and trust in Internet of Things: The road ahead
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Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
Robotics in the Construction Sector: Trends, Advances, and Challenges
Liu, Y., A.H., A., Haron, N. A., N.A., B., & Wang, H. (2024) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-024-02104-4 · Cited by 60
Perceptions of long-term monitoring for civil and structural engineering
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Metadata
- Authors
- Forcael, E., Ferrari, I., Opazo-Vega, A., & Pulido-Arcas, J. A.
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2020-11-23
- Volume
- 12
- Issue
- 22
- Pages
- 9755
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W3109259686
- Cited by
- 319
- References
- 98
- FWCI
- 20.57
- Type
- journal article
- Language
- en
- Open access
- gold
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Data sources
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.
