Machine Learning Algorithm Application in the Construction Industry – A Review

Samuel Adeniyi Adekunle, A. Onatayo Damilola, Obinna C. Madubuike, Clinton Aigbavboa, Obuks Ejohwomu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


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 languageEnglish
Title of host publicationAdvances in Information Technology in Civil and Building Engineering - Proceedings of ICCCBE 2022 - Volume 1
EditorsSebastian Skatulla, Hans Beushausen
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9783031353987
Publication statusPublished - 2024
Event19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022 - Cape Town, South Africa
Duration: 26 Oct 202228 Oct 2022

Publication series

NameLecture Notes in Civil Engineering
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565


Conference19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022
Country/TerritorySouth Africa
CityCape Town


  • Construction digitisation
  • Construction industry
  • Emerging technologies
  • Machine algorithm

ASJC Scopus subject areas

  • Civil and Structural Engineering


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