TY - GEN
T1 - Machine Learning Algorithm Application in the Construction Industry – A Review
AU - Adekunle, Samuel Adeniyi
AU - Onatayo Damilola, A.
AU - Madubuike, Obinna C.
AU - Aigbavboa, Clinton
AU - Ejohwomu, Obuks
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Construction digitisation
KW - Construction industry
KW - Emerging technologies
KW - Machine algorithm
UR - http://www.scopus.com/inward/record.url?scp=85174689278&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-35399-4_21
DO - 10.1007/978-3-031-35399-4_21
M3 - Conference contribution
AN - SCOPUS:85174689278
SN - 9783031353987
T3 - Lecture Notes in Civil Engineering
SP - 263
EP - 271
BT - Advances in Information Technology in Civil and Building Engineering - Proceedings of ICCCBE 2022 - Volume 1
A2 - Skatulla, Sebastian
A2 - Beushausen, Hans
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022
Y2 - 26 October 2022 through 28 October 2022
ER -