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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Bibliographic Details
Published in:Applied sciences 2024-11, Vol.14 (22), p.10605
Main Authors: Lim, Ying Terk, Yi, Wen, Wang, Huiwen
Format: Article
Language:English
Subjects:
activity level
Algorithms
Artificial intelligence
Automation
Bibliometrics
Building
Construction equipment
Construction industry
construction productivity
Construction workers
Data analysis
Data collection
Datasets
Keywords
Machine learning
Neural networks
Productivity measurement
Research methodology
Science
Systematic review
Trends
Citations: Items that this one cites
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Description
Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app142210605