4.2.2. Implementation

The proposed system has two functions, including evaluation and prediction. The evaluation function supports four operations, and users can choose one of these four operations.

Figure 2 shows the screenshots of the system. In the main menu, a user can adopt the enhanced FA to tune the LSSVM hyperparameters. Then, the parameters are set by the user, or the default values are used. Next, the user must select or not select normalization, the part between the training data and the validation data, as well as the stopping criteria.

**Figure 2.** Screenshots of system.

The results and predicted values obtained by using the Optimized-OAO-LSSVM model are displayed in the interface. Moreover, users can view and save the results as an Excel file, which includes inputs and outputs. The Optimized-OAO-LSSVM system showed the efficiency of operating the proposed model.

## **5. Engineering Applications**

This section elucidates the Optimized-OAO-LSSVM system to handle classification issues. Many case studies in engineering managemen<sup>t</sup> were used herein to evaluate the application of multi-classification system. Section 5.1 presents the results obtained by using the proposed model to solve binary-class geotechnical problems. Section 5.2 demonstrates the use of the system to solve multi-class civil engineering and construction managemen<sup>t</sup> problems.
