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

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

doi:10.1007/978-3-031-35399-4_26

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

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

Construction Management and Quantity Surveying

University of Johannesburg
University of South Africa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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 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	343-350
Number of pages	8
ISBN (Print)	9783031353987
DOIs	https://doi.org/10.1007/978-3-031-35399-4_26
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)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Construction Industry
Developing Economies
Drivers
Machine Learning
Project Delivery

ASJC Scopus subject areas

Civil and Structural Engineering

Access to Document

10.1007/978-3-031-35399-4_26

Cite this

Ikuabe, M., Aigbavboa, C., Oke, A., Thwala, W., & Balogun, J. (2024). Drivers of Machine Learning Applications in the Construction Industry of Developing Economies. In S. Skatulla, & H. Beushausen (Eds.), Advances in Information Technology in Civil and Building Engineering - Proceedings of ICCCBE 2022 - Volume 1 (pp. 343-350). (Lecture Notes in Civil Engineering; Vol. 357). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-35399-4_26

Ikuabe, Matthew ; Aigbavboa, Clinton ; Oke, Ayodeji et al. / Drivers of Machine Learning Applications in the Construction Industry of Developing Economies. Advances in Information Technology in Civil and Building Engineering - Proceedings of ICCCBE 2022 - Volume 1. editor / Sebastian Skatulla ; Hans Beushausen. Springer Science and Business Media Deutschland GmbH, 2024. pp. 343-350 (Lecture Notes in Civil Engineering).

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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.",

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author = "Matthew Ikuabe and Clinton Aigbavboa and Ayodeji Oke and Wellington Thwala and Joseph Balogun",

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Ikuabe, M, Aigbavboa, C, Oke, A, Thwala, W & Balogun, J 2024, Drivers of Machine Learning Applications in the Construction Industry of Developing Economies. in S Skatulla & H Beushausen (eds), Advances in Information Technology in Civil and Building Engineering - Proceedings of ICCCBE 2022 - Volume 1. Lecture Notes in Civil Engineering, vol. 357, Springer Science and Business Media Deutschland GmbH, pp. 343-350, 19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022, Cape Town, South Africa, 26/10/22. https://doi.org/10.1007/978-3-031-35399-4_26

Drivers of Machine Learning Applications in the Construction Industry of Developing Economies. / Ikuabe, Matthew; Aigbavboa, Clinton; Oke, Ayodeji et al.
Advances in Information Technology in Civil and Building Engineering - Proceedings of ICCCBE 2022 - Volume 1. ed. / Sebastian Skatulla; Hans Beushausen. Springer Science and Business Media Deutschland GmbH, 2024. p. 343-350 (Lecture Notes in Civil Engineering; Vol. 357).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Drivers of Machine Learning Applications in the Construction Industry of Developing Economies

AU - Ikuabe, Matthew

AU - Aigbavboa, Clinton

AU - Oke, Ayodeji

AU - Thwala, Wellington

AU - Balogun, Joseph

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

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ER -

Ikuabe M, Aigbavboa C, Oke A, Thwala W, Balogun J. Drivers of Machine Learning Applications in the Construction Industry of Developing Economies. In Skatulla S, Beushausen H, editors, Advances in Information Technology in Civil and Building Engineering - Proceedings of ICCCBE 2022 - Volume 1. Springer Science and Business Media Deutschland GmbH. 2024. p. 343-350. (Lecture Notes in Civil Engineering). doi: 10.1007/978-3-031-35399-4_26

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

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this