Drivers of Machine Learning Applications in the Construction Industry of Developing Economies

Matthew Ikuabe, Clinton Aigbavboa, Ayodeji Oke, Wellington Thwala, Joseph Balogun

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

Abstract

Stakeholders in the construction industry have over the years made frantic efforts in seeking solutions to the problems facing the industry. The advent of technological innovations such as machine learning applications seek to abate some of these challenges by modernizing construction processes and activities, and ultimately improving on construction projects delivery. This study seeks to examine the propelling factors for the adoption of machine learning applications in the construction industry. Data gathered was subjected to appropriate data analysis techniques. Findings from the study revealed that the most significant drivers for the adoption of machine learning applications are the fast changing, field based and project nature of the construction industry, and the need for accurate results. Also, it was revealed that there is no difference among the different professionals’ view of the drivers of machine learning applications in the construction industry. The study made recommendations that would aid the integration of machine learning applications in construction activities for better and more efficient processes in construction project delivery.

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
Pages343-350
Number of pages8
ISBN (Print)9783031353987
DOIs
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
Volume357
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

Conference19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022
Country/TerritorySouth Africa
CityCape Town
Period26/10/2228/10/22

Keywords

  • Construction Industry
  • Developing Economies
  • Drivers
  • Machine Learning
  • Project Delivery

ASJC Scopus subject areas

  • Civil and Structural Engineering

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