# Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review
###### Abstract  
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. construction productivity; activity level; machine learning; systematic review +
Footnote †: journal: Applied sciences  
use of ML for analyzing vast volumes of data, effective productivity management, and progress monitoring [10; 11]. ML holds a promising approach not only in CPM but also during dataset acquisition for the measurement of productivity, as well as quantifying influential factors [9]. By leveraging ML, automated data collection methods become possible. In addition, exploiting the data-driven features and potent computing potential of ML aims to improve CPM by promoting increased automation in the measurement of productivity, accurate estimation, and optimization in construction scheduling and monitoring [5; 12]. The development and application of ML-based models for various facets of productivity are common. This is so to increase the accuracy in estimation of construction productivity [5]. Apart from productivity estimation, automated measurement of productivity [13; 14] and quantification of productivity influential factors [3; 15; 16] have also been actively explored due to their quick reliable output values. As construction productivity possesses great volume in the construction industry, several reviews, as tabulated in Table 1, have been conducted related to either construction productivity, ML, or both. Some reviews focused solely on the types of productivity monitoring methods [17], while others outlined specifically on vision-based progress monitoring and tracking [18; 19]. In addition, active exploration of ML applications in construction productivity led to several studies on deep learning (DL) as well [20]. However, research topics concerning construction productivity are commonly diversified into labor and equipment, and there are limited systematic analyses of ML applications on them. Furthermore, these previous reviews primarily concentrated on productivity monitoring techniques or ML applications separately. In addition, they have fallen short of offering an extensive and methodical understanding of the broader research terrain of ML applications from dataset acquisition to data analysis and evaluation. It is essential to explore dataset acquisition for CLP and CEP separately, as they do have their differences (e.g., data collection techniques and influential factors). Despite these significant efforts by past researchers, previous reviews have not covered a holistic approach to CPM without considering the dataset acquisition, which is the input for CPM.