# Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review
## 3 Overview of the Application of Machine Learning in Construction Productivity Publications
A total of 112 out of 133 publications, or 84.2%, were published by a primary contributor from the leading ten countries/regions. This huge amount highlights the inputs of these authors to the research of ML-CP is substantially greater compared to those authors from other countries/regions, thus indicating how much emphasis is attributed to the improvement of construction productivity by the leading countries/regions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name of Journal** & **Number of** \\
**Publications** & **\\ \hline \multicolumn{2}{c}{\"Automation in Construction\"} & 45 \\ \multicolumn{2}{c}{\"Journal of Construction Engineering and Management\"} & 30 \\ \multicolumn{2}{c}{\"Journal of Computing in Civil Engineering\"} & 15 \\ \multicolumn{2}{c}{\"Advanced Engineering Informatics\"} & 14 \\ \multicolumn{2}{c}{\"Engineering, Construction, and Architectural Management\"} & 6 \\ \multicolumn{2}{c}{\"Construction Innovation\"} & 5 \\ \multicolumn{2}{c}{\"Canadian Journal of Civil Engineering\"} & 4 \\ \multicolumn{2}{c}{\"Computer-Aided Civil and Infrastructure Engineering\"} & 3 \\ \multicolumn{2}{c}{\"Construction Management and Economics\"} & 1 \\ \multicolumn{2}{c}{\"Computing in Civil Engineering\"} & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top ten ML-CP journals ranked by number of publications. Figure 4: Top ten countries/regions’ number of publications related to ML-CP. Figure 5 highlights the distribution of keywords across various research, as well as the occurrence rates of the keywords with their relationships to each theme in the research area. For the purpose of visualizing their frequency, the recurrent keywords are displayed in circles of larger size. Among these keywords, \"construction\", \"productivity\", \"machine learning\", and \"neural network\" exhibit the highest associations. The details of the top 20 keywords are shown in Table 3, where the occurrences indicating the frequency count for each keyword were acquired from the available publications. For instance, other than the main keywords \"productivity\", \"construction\", \"machine learning\", and \"neural network\", \"construction equipment\" and \"construction labor\" appear frequently, indicating the research on CLP and CEP are widely explored separately and in conjunction with \"machine learning\", as well as \"neural network\", as shown in Figures 6 and 7, respectively. Moreover, the total strength linked to each item is determined by the value of the total link strength. For example, higher values indicate stronger inter-relatedness between the keywords. Figure 6: Visualization of relationships for the isolated “construction equipment” node. Figure 7: Visualization of relationships for the isolated “construction labor” node. Critical Review of the Application of Machine Learning in Construction Productivity Modeling (ML-CPM)  
This review study examines the application of ML-CPM published since the 1990s [30; 31; 39]. To delve deeper into ML-CPM-related studies, a systematic review was performed right after the bibliometric assessment of the identified publications. It was found that ML-CPM research mainly focused on construction productivity at the activity level in terms of CLP and CEP [6; 27; 40]. Figure 8 illustrates the framework for the systematic review of ML-CPM. The applications of ML on both CLP and CEP were elaborated on as follows.