# Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management
## 5 Engineering Applications
### Binary-Class Problems  
Two binary-class datasets associated with seismic hazards in coal mines and the early warning of liquefaction disasters are taken from the literature [76,77]. Table 4 presents the variables and their descriptive statistics of the datasets.  
In monitoring seismic hazards in coal mines, an early warning model can be applied to forecast the occurrence of hazard events and withdraw workers from threatened areas, reducing the risk of mechanical seismic impact to save the lives of mine workers. The dataset has 170 samples, representing a hazardous state (Class 1) and 2414 samples, representing a non-hazardous state (Class 2).  
Soil liquefaction is a major effect of an earthquake and may seriously damage buildings and infrastructure and cause loss of life. The deformation of soil by a high pore-water pressure causes the liquefaction. A soil deposit under a dynamic load generates pore water, which reduces its strength and causes liquefaction. The proposed model is used to predict the liquefaction or non-liquefaction of soil. This database embraces 226 examples comprising 133 instances of liquefaction (Class 1) and 93 instances of non-liquefaction (Class 2).  
[PERSON] et al., (2016) combined the smart firefly algorithm with the LSSVM (SFA-LSSVM) to solve seismic bump and soil liquefaction problems [78]. They compared the performance of the SFA-LSSVM model with the experimental performance of other models and concluded that the SFA-LSSM is the best model for solving such problems.  
Therefore, to demonstrate the effectiveness and efficiency of the proposed model in solving binary-class problems, the results obtained using the proposed model were compared with those obtained using the SFA-LSSVM model. Table 5 presents the results of using the Optimized-OAO-LSSVM and SFA-LSSVM models for predicting seismic bumps and soil liquefaction in original-value and feature-scaling cases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Unit** & **Max. Value** & **Min. Value** & **Mean** & **Standard** \\ \hline \multicolumn{6}{c}{Dataset 1—Seismic bumps, 2584 samples, Poland [76]} \\ \hline \hline \begin{tabular}{c} Genergy \\ Gpuls \\ Gdenergy \\ Gdpuls \\ Energy \\ \end{tabular} & \begin{tabular}{c} N/A \\ N/A \\ N/A \\ N/A \\ \end{tabular} & \begin{tabular}{c} 2,595,650.00 \\ 2.00 \\ 245.00 \\ 838.00 \\ 402,000.00 \\ 400,000.00 \\ 278.85 \\ 1 \\ \end{tabular} & \begin{tabular}{c} 100.00 \\ 538.58 \\ 80.32 \\ 63.17 \\ 20,450.83 \\ 19,357.45 \\ \end{tabular} \\ \hline \multicolumn{6}{c}{Dataset 2—Soil Liquefaction, 226 samples, U.S.A., China and Taiwan [77]} \\ \hline \hline \
\begin{tabular}{c} Cone tip resistance (\(q_{c}\)) \\ Sleeve friction ratio (\(R_{g}\)) \\ Effective stress (\(\sigma^{v}\)) \\ Total stress (\(\sigma_{v}\)) \\ Horizontal ground surface acceleration (\(a_{max}\)) \\ Earthquake movement magnitude (\(M_{w}\)) \\ Soil liquefaction (1 = exists, 2 = not) \\ \end{tabular} &
\begin{tabular}{c} MPa \\ \% \\ \  
The computational time of the Optimized-OAO-LSSVM model was substantially shorter than that of the SFA-LSSVM model, although its predictive accuracy was not significantly higher. With seismic bumps dataset, the Optimized-OAO-LSSVM model had an accuracy of 93.42% in 1136.60 s whereas the SFA-LSSVM model had an accuracy of 93.46% in 355,913.59 s.  
Similarly, the Optimized-OAO-LSSVM model had a shorter computing time than the SFA-LSSVM model with soil liquefaction data (57.22 s and 19,884.82 s with original value case, respectively). Therefore, the Optimized-OAO-LSSVM is an effective and efficient model for solving binary-class classification problems.