## 5 Discussion  
The possibility of practical application of the proposed approach for the purposes of construction works efficiency analyses depends on the available data. Data collection remains on the contractor's side as the most interested party, which, in turn, usually means an additional effort and, inevitably, cost. However, the effort put in may be counterbalanced by benefits in terms of better insight and understanding of construction works efficiency and knowledge of the issue. The study presented herein did not have an influence on the data collection process. There was no chance to decide what kind of data were collected, in what way and how relevant information was recorded. The data were used as is. The problem of collecting and recording efficiency-related data and information, however, is interesting in itself, particularly given the development of tools and technologies capable of automatically recording information on a construction site. Although this is not the main focus of this particular research, some works and their results are worth mentioning. For example, there is the use of the Global Positioning System (GPS) for the spatiotemporal recording of construction equipment location and timestamps [57], combining several tools, such as external cloud computing servers, access points, monitoring bands, smartphones, beacons, and radio-frequency identification devices into a system for monitoring construction workers on a site [58], the application of Bluetooth Low Energy (BLE) technology for tracking and collecting data for production control in construction projects [59], and a review and comprehensive discussion of the best practices in unoccupied airborne systems (UAS) aerial imagery collection and processing for construction research, including tasks, such as preconstruction planning, material tracking, project progress tracking, safety, as-built documentation, and building/structure inspection [60]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multirow{2}{*}{**Level**} & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Workers** \\ \end{tabular} } & \begin{tabular}{c} **Gang** \\ **Experience** \\ **and Skills** \\ \end{tabular} & \begin{tabular}{c} **Duration** \\ **[Hours/Day]** \\ \end{tabular} & **Temperature I’C1** & **Falls** & \begin{tabular}{c} **Day of the** \\ **Week** \\ \end{tabular} & \
\begin{tabular}{c} **Class** \\ **Identified** \\ **by SVM** \\ \end{tabular} &
\begin{tabular}{c} **Expected Total** \\ **Quantity [m\({}^{2}\)]** \\ \end{tabular} \\ \hline COLUMN & 2 & 4 & AS & 9.0 & \(-2\) & NO & FS & A & Not less than 43.3 \\ COLUMN & 1 & 4 & MS & 2.5 & 8 & YES & TWT & B & No more than 13.6 \\ WALL & 0 & 3 & HS & 9 & 14 & NO & M & A & Not less than 47.0 \\ WALL & 1 & 2 & MS & 10 & 6 & NO & FS & B & No more than 28.0 \\ SLAB & 1 & 3 & AS & 9 & 11 & NO & M & A & Not less than 51.3 \\ SLAB & 2 & 4 & AS & 10 & \(-1\) & YES & TWT & B & No more than 76.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Application of the for new cases of formworks assembly works. When it comes to efficiency modelling, the relevant literature review revealed that in studies reported from different countries, some of the factors that play a role as efficiency drivers are common; however, some of them result from regional conditions. Moreover, the scope of data, specifically the types of considered efficiency drivers, is also influenced by the type of construction work or the type of construction project. For the purpose of the quantitative-probabilistic approach, the triangular distribution appears to be a reasonable choice. While the beta distribution was originally proposed for a similar problem, i.e., the probabilistic analysis of construction work durations in the case of the PERT method, the triangular distribution is simpler and easier to apply for the specific problem of formwork assembly works.