Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review
There are two crucial resources (i.e., labor and equipment) of productivity in the construction industry. Productivity modeling of these resources would aid stakeholders in project management and improve construction scheduling and monitoring. Hence, this research aims to review machine learning (ML...
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MDPI AG
2024-11-01
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| author | Ying Terk Lim Wen Yi Huiwen Wang |
| author_facet | Ying Terk Lim Wen Yi Huiwen Wang |
| author_sort | Ying Terk Lim |
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| description | There are two crucial resources (i.e., labor and equipment) of productivity in the construction industry. Productivity modeling of these resources would aid stakeholders in project management and improve construction scheduling and monitoring. Hence, this research aims to review machine learning (ML) applications in the process of construction productivity modeling (CPM) for construction labor productivity (CLP) and construction equipment productivity (CEP) from dataset acquisition to data analysis and evaluation, which includes their trends and applicability. An extensive analysis of 131 journals focused on the application of machine learning in construction productivity (ML-CP) from 1990 to 2024 via a mixed review methodology (bibliometric analysis and systematic review) was conducted. It can be concluded that despite the rise in automated dataset collection, the traditional method has its advantages. The review further found that the selection of ML models relies on each particular application, available data, and computational resources. Noticeably, artificial neural networks, convolutional neural networks, support vector machines, and even deep learning demonstrating have been adopted due to their effectiveness in different functionalities and processes in CPM. This study will supplement the insights gained in the review with a comprehensive understanding of how ML applications operate at each stage of CPM, enabling researchers to make future improvements. |
| format | Article |
| id | doaj-art-e562fe071a7f4343915faff341aaa0f3 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-e562fe071a7f4343915faff341aaa0f32025-08-20T02:26:45ZengMDPI AGApplied Sciences2076-34172024-11-0114221060510.3390/app142210605Application of Machine Learning in Construction Productivity at Activity Level: A Critical ReviewYing Terk Lim0Wen Yi1Huiwen Wang2Department of Building and Real Estate, Faculty of Construction and Environment, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, ChinaDepartment of Building and Real Estate, Faculty of Construction and Environment, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, ChinaDepartment of Building and Real Estate, Faculty of Construction and Environment, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, ChinaThere are two crucial resources (i.e., labor and equipment) of productivity in the construction industry. Productivity modeling of these resources would aid stakeholders in project management and improve construction scheduling and monitoring. Hence, this research aims to review machine learning (ML) applications in the process of construction productivity modeling (CPM) for construction labor productivity (CLP) and construction equipment productivity (CEP) from dataset acquisition to data analysis and evaluation, which includes their trends and applicability. An extensive analysis of 131 journals focused on the application of machine learning in construction productivity (ML-CP) from 1990 to 2024 via a mixed review methodology (bibliometric analysis and systematic review) was conducted. It can be concluded that despite the rise in automated dataset collection, the traditional method has its advantages. The review further found that the selection of ML models relies on each particular application, available data, and computational resources. Noticeably, artificial neural networks, convolutional neural networks, support vector machines, and even deep learning demonstrating have been adopted due to their effectiveness in different functionalities and processes in CPM. This study will supplement the insights gained in the review with a comprehensive understanding of how ML applications operate at each stage of CPM, enabling researchers to make future improvements.https://www.mdpi.com/2076-3417/14/22/10605construction productivityactivity levelmachine learningsystematic review |
| spellingShingle | Ying Terk Lim Wen Yi Huiwen Wang Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review Applied Sciences construction productivity activity level machine learning systematic review |
| title | Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review |
| title_full | Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review |
| title_fullStr | Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review |
| title_full_unstemmed | Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review |
| title_short | Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review |
| title_sort | application of machine learning in construction productivity at activity level a critical review |
| topic | construction productivity activity level machine learning systematic review |
| url | https://www.mdpi.com/2076-3417/14/22/10605 |
| work_keys_str_mv | AT yingterklim applicationofmachinelearninginconstructionproductivityatactivitylevelacriticalreview AT wenyi applicationofmachinelearninginconstructionproductivityatactivitylevelacriticalreview AT huiwenwang applicationofmachinelearninginconstructionproductivityatactivitylevelacriticalreview |