## 4 Results
The characteristics of the two models are presented in Table 10.  
General performance of the obtained SVM-based models in terms of proper classification--either class A or class B is given below:  
* model SVMct1: 80.19% for the training subset and 84.78% for a testing subset,
* model SVMct2: 91.51% for the training subset and 84.78% for the testing subset. Results are also presented in Table 11 in a more detailed way. The table depicts the confusion matrix. Underlined values stand for properly classified cases. Further classification performance measures were computed based on the confusion matrix and are presented in Table 12. The measures include accuracy of classification for classes A and B, sensitivity for class A, and specificity for class B. The measures are presented separately for the training and testing processes. The results of classification are satisfactory. Especially the accuracy of classification in the case of testing, which verifies the performance and ability of knowledge generalisation by the models, was above 80%. This suggests that the models have the ability to generalise and make satisfactory predictions for new cases. (Nonetheless, it is important to note that \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Support Vectors**} & **Bounded** & **Cross Validation** \\  & & & **Support Vectors** & **Relevance** \\ \hline SVMct1 & C = 5.0, \(b\) = 0.9057 & 69 (A: 34, B: 35) & 52 & 73.58\% \\ SVMct2 & \(v\) = 0.3, \(b\) = 0.2319 & 57 (A: 26, B: 31) & 16 & 72.61\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Characteristics of SVM-based classification models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} \\ \cline{2-5}  & **Class** & **A** & **B** & **A** & **B** \\ \hline SVMct1 & accuracy & 79.25\% & 81.13\% & 88.00\% & 80.95\% \\  & sensitivity/specificity & 80.77\% & 79.63\% & 84.62\% & 85.00\% \\ \hline SVMct2 & accuracy & 89.58\% & 93.10\% & 86.67\% & 81.25\% \\  & sensitivity/specificity & 91.49\% & 91.53\% & 89.66\% & 76.47\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance of classification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**SVM Based Type**} & \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Lower Boundary**} & \multirow{2}{*}{**Step**} & \multirow{2}{*}{**Upper Boundary**} \\  & & & & & \\ type 1 & C & 1 & 1 & 10 \\ type 2 & \(v\) & 0.1 & 0.1 & 0.5 \\ \hline \hline \end{tabular} \({}^{\dagger}\)With regard to Equations (7)–(8) and (9)–(10).  
\end{table} 
Table 9: Parameters range considered for SVM-based classification problem. the models were trained on a specific dataset and may not perform as well on datasets with different characteristics). The developed SVM models were used to assess work efficiencies for six new cases, which were neither used in the training nor testing processes. The results are presented in Table 13.  
Table 13 presents the classification for the new cases. It must be pointed out that some values were planned (specifically: Element, Level, Number of workers, Gang experience and skills, Duration, and Day of the week), and some had to be forecasted as there is no influence on their actual values (namely: Temperature and Falls). For the new cases as presented in the table, either class A or B was recognised by the developed models. (It is noteworthy that classification results were consistent between the two models, that is, SVMct1 and SVMct2, for the new case.) Classes identified by SVM models for each new case are presented in the relevant column. The classification was followed by a qualitative assessment of the daily output of formworks assembly. The results of this step of analysis are given in the column \"Expected total quantity\" in m2 of assembled formworks.