Abstract
Industries like manufacturing use Machine Learning (ML) algorithms to conceive and produce excellent consumer goods. This achievement has persuaded other economic sectors, including the construction sector, to attempt and incorporate intelligent algorithms. The most recent developments in ML algorithms have made it possible to automate those non-trivial jobs that were thought unsolvable years back. Early involvement of Construction researchers in the ML process is necessary to ensure that they have sufficient awareness of the advantages and disadvantages. It is worthy of note that construction organisations have concerns due to the peculiarity of the sector. As such, adopting machine learning (ML) for profitability predictions or cost-saving results can be challenging. Construction industry stakeholders are eager to discover how ML may help improve operations, and the benefits of ML algorithms, among others, before adopting these algorithms for decision-making. To assist construction industry stakeholders in the adoption of ML algorithms, the study adopted a systematic literature review. The study helps in the proper identification of the uses of ML algorithms to improve the construction industry processes and product.
| Original language | English |
|---|---|
| Title of host publication | Advances in Information Technology in Civil and Building Engineering - Proceedings of ICCCBE 2022 - Volume 1 |
| Editors | Sebastian Skatulla, Hans Beushausen |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 263-271 |
| Number of pages | 9 |
| ISBN (Print) | 9783031353987 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022 - Cape Town, South Africa Duration: 26 Oct 2022 → 28 Oct 2022 |
Publication series
| Name | Lecture Notes in Civil Engineering |
|---|---|
| Volume | 357 |
| ISSN (Print) | 2366-2557 |
| ISSN (Electronic) | 2366-2565 |
Conference
| Conference | 19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022 |
|---|---|
| Country/Territory | South Africa |
| City | Cape Town |
| Period | 26/10/22 → 28/10/22 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- Construction digitisation
- Construction industry
- Emerging technologies
- Machine algorithm
ASJC Scopus subject areas
- Civil and Structural Engineering
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