# 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
### Data Analysis and Evaluation
#### 4.2.1 Model Selection
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref** & **Year** & \begin{tabular}{c} **Type of** \\ **Productivity** \\ \end{tabular} & **Description** & \
\begin{tabular}{c} **Data Collection** \\ **Method** \\ \end{tabular} & **Productivity Input** &
\begin{tabular}{c} **Number of** \\ **Indencing** \\ **Factors** \\ \end{tabular} & **ML Model** & **Accuracy** \\ \hline
[1] & 2012 & Labor & \begin{tabular}{c} Prediction of labor productivity \\ of marble finishing works. \\ Labor productivity prediction of \\ reinforced concrete foundation \\ works. \\ \end{tabular} & \
\begin{tabular}{c} Traditional (Direct \\ observation) \\ \end{tabular} &
\begin{tabular}{c} Work sampling \\ **(1)** \\ \end{tabular} & 9 & ANN & 90.90\% \\
[3] & 2017 & Labor & \begin{tabular}{c} Prediction of labor productivity prediction of \\ reinforced concrete foundation \\ works. \\ \end{tabular} & \
\begin{tabular}{c} Traditional \\ **(1)** \\ \end{tabular} &
\begin{tabular}{c} Total quantities and m\({}^{\text{a}}\) \\ completed per total works \\ days spent \\ \end{tabular} & 29 & ANN & 94.43\% \\
[4] & 2019 & Labor & \begin{tabular}{c} Comparing various ANN labor \\ productivity models for \\ network installation. \\ Labor productivity model for \\ network installation. \\ Labor productivity model for \\ network installation using \\ S0S-LSSVM-PS. \\ \end{tabular} & \
\begin{tabular}{c} Traditional (Direct \\ observation) \\ \end{tabular} &
\begin{tabular}{c} Square meters of formwork \\ per labor-hour \\ \end{tabular} & 9 & (GRNN, BNN, RBFNN, and ANFIS) & 94.9\% (BNN) \\
[5] & 2021 & Labor & \
\begin{tabular}{c} Individual (Direct \\ observation) \\ \end{tabular} &
\begin{tabular}{c} Square meters of formwork \\ per labor-hour \\ \end{tabular} & 9 & SOS-LSSVM-PS & MAPE 3.67\% \\
[6] & 2008 & Labor & \
\begin{tabular}{c} Steel drafting productivity. \\ (Question) \\ \end{tabular} &
\begin{tabular}{c} Traditional \\ piece-by-piece basis \\ \end{tabular} & 19 & ANN & 90\% \\
[15] & 2015 & Labor & \begin{tabular}{c} Concrete foundation labor \\ productivity. \\ \end{tabular} & \begin{tabular}{c} Traditional \\ **(1)** \\ \end{tabular} & \begin{tabular}{c} Earned work hours per \\ expended work hours \\ \end{tabular} & 15 & ANN + BR and & \
\begin{tabular}{c} -95\% \\ ANN + ES \\ \end{tabular} \\ \cline{3-6}  & &
\begin{tabular}{c} Determine productivity of \\ selected sets of machines \\ (exavators, trucks, and platform) \\ for earthworks. \\ \end{tabular} & Automated (Vision) & 8 & BFNN-CGB & Set 3 \\
[43] & 2005 & Equipment & \begin{tabular}{c} Pile construction productivity \\ prediction. \\ \end{tabular} & \begin{tabular}{c} Traditional \\ **(Question)** \\ \end{tabular} & Cycle time & 7 & ANN & AVP \textgreater{} -90\% \\  & & \begin{tabular}{c} Formwork installation labor \\ productivity prediction. \\ \end{tabular} & \begin{tabular}{c} Traditional \\ **(2)** \\ \end{tabular} & \begin{tabular}{c} Amount of work done per \\ day (m\({}^{2}\) per number of labor \\ \end{tabular} & 9 & \
\begin{tabular}{c} Voting-ensemble and \\ Stacking-ensemble \\ (Stacking) \\ \end{tabular} &
\begin{tabular}{c} R= 0.7967 \\ (Stacking) \\ \end{tabular} \\
[86] & 2020 & Labor & \begin{tabular}{c} Labor productivity model for \\ formwork installation. \\ \end{tabular} & \begin{tabular}{c} Traditional \\ **(1)** \\ \end{tabular} & \
\begin{tabular}{c} Manual of work done per \\ day (m\({}^{2}\) per number of labor \\ per day's work hour \\ \end{tabular} & 9 & ANN-based PIs &
\begin{tabular}{c} At 95\% CL = 92.5\% \\ PICP \\ \end{tabular} \\
[87] & 2016 & Labor & \begin{tabular}{c} Labor productivity model for \\ formwork installation. \\ Improving productivity \\ estimation for construction \\ operations. \\ \end{tabular} & \begin{tabular}{c} Traditional \\ **(1)** \\ \end{tabular} & \
\begin{tabular}{c} Volume of poured concrete in \\ cubic meters per man-hour \\ \end{tabular} & 9 & ANN-based PIs &
\begin{tabular}{c} PICP \\ \end{tabular} \\
[88] & 2006 & Equipment & \begin{tabular}{c} Dover productivity estimation. \\ **(2)** \\ \end{tabular} & \begin{tabular}{c} Traditional (Field \\ data) \\ \end{tabular} & \
\begin{tabular}{c} Loose cubic meter per hour \\ \end{tabular} & 7 & MR and ANN &
\begin{tabular}{c} improvement in \\ MSE \\ \end{tabular} \\
[89] & 2004 & Equipment & \begin{tabular}{c} Estimating concrete cyclic \\ operations productivity. \\ \end{tabular} & \begin{tabular}{c} Traditional \\ **(2)** \\ \end{tabular} & \begin{tabular}{c} Treatment (Field \\ data) \\ \end{tabular} & \
\begin{tabular}{c} Loose cubic meter per hour \\ \end{tabular} & 7 & MR and ANN &
\begin{tabular}{c} 0.00027 (ANN \\ MSB) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}