A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Drivers of Machine Learning Applications in the Construction Industry of Developing Economies
https://orcid.org/http://orcid.org/0000-0003-3380-2387
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.
Drivers of Machine Learning Applications in the Construction Industry of Developing Economies
https://orcid.org/http://orcid.org/0000-0003-3380-2387
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.
Drivers of Machine Learning Applications in the Construction Industry of Developing Economies
https://orcid.org/http://orcid.org/0000-0003-3380-2387
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.
Drivers of Machine Learning Applications in the Construction Industry of Developing Economies
Lecture Notes in Civil Engineering
Skatulla, Sebastian (editor) / Beushausen, Hans (editor) / Ikuabe, Matthew (author) / Aigbavboa, Clinton (author) / Oke, Ayodeji (author) / Thwala, Wellington (author) / Balogun, Joseph (author)
International Conference on Computing in Civil and Building Engineering ; 2022 ; Cape Town, South Africa
Advances in Information Technology in Civil and Building Engineering ; Chapter: 26 ; 343-350
2023-09-30
8 pages
Article/Chapter (Book)
Electronic Resource
English
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