@inproceedings{37ee17df513d4946a42219c50593f737,
title = "Drivers of Machine Learning Applications in the Construction Industry of Developing Economies",
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{\textquoteright} 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.",
keywords = "Construction Industry, Developing Economies, Drivers, Machine Learning, Project Delivery",
author = "Matthew Ikuabe and Clinton Aigbavboa and Ayodeji Oke and Wellington Thwala and Joseph Balogun",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022 ; Conference date: 26-10-2022 Through 28-10-2022",
year = "2024",
doi = "10.1007/978-3-031-35399-4_26",
language = "English",
isbn = "9783031353987",
series = "Lecture Notes in Civil Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "343--350",
editor = "Sebastian Skatulla and Hans Beushausen",
booktitle = "Advances in Information Technology in Civil and Building Engineering - Proceedings of ICCCBE 2022 - Volume 1",
address = "Germany",
}