## 5 Experimental Results  
The case study showed that the method we proposed is applicable to modeling and simulating weapon systems. However, it is necessary to verify that the results reflect the benefits of the method designed in this study. This section identifies whether the method proposed in this study can satisfy the expected advantages, and the modeling and simulation results in relation to the expected advantages of the proposed method are outlined in detail.  
The first expected advantage is the provision of generalized structures and modules for weapon system modeling. Figure 6 shows the design of the platform and configuration modules for modeling the weapon system through the elicitation of the structure and the composition and mathematical definition through a hierarchical analysis of the actual weapon system. Through the proposed method, the characteristics of each MBT of K-series MBTs can be expressed through the common structure and changeable configuration modules, and the results can be identified in Table 14 in the Appendix A. This demonstrates that the devised method satisfies the first aspect.  
The second aspect is concerning whether the method could generate modeling that reflects the design requirements by using an intelligent method. The requirement analysis that can be observed in Figure 7 supports the formalization of the expected environmental conditions and hostile forces situation into required properties and constraints to retrieve platforms and modules for an MBT. The deduced properties, constraints, and modules are used as inputs for the modeling process using CBR, which is one of the artificial intelligence paradigms. Figure 8 shows that the required MBT model that satisfies all the required conditions can be designed through the CBR process intelligently. It means that the second aspect can be satisfied through the proposed method.  
The third aspect is regarding whether the proposed method can provide simulations to test the designed models in the expected circumstances. The environment where the designed MBT model will be deployed assumes an engagement situation with enemy guerillas and MBTs operating anti-tank weapons. To this end, the simulation experiment assumes multiple combat situations by varying the number and combination of combatant entities to interact with the designed MBT model, and it is based on the interaction process described in Section 3.3.1. To confirm the suitability of the generated MBT model, the survivability of a similar model that is not modified is compared with that of the revised model. The result of the comparison of the designed model and K1 tank in the expected situation of Figure 10 is shown below.  
Figure 10(a) illustrates the survivability between the newly designed tank and existing K1 tank according to the only increase in the number of counterforce tanks. Similarly, Figure 10(b) depicts their survivability ratio considering only the number of anti-tank guns. These results demonstrate that the designed model is more responsive to the specified enemy threats than the K1 tank with respect to the survivability rate.  
Figure 11: Plots showing the survivability rates. (**a**) Variation of survivability of each MBT model with an increasing number of enemy MBTs; (**b**) variation of survivability of MBT models with an increasing number of anti-tank guns.  
In the same context, the survival rates of both the new and K1 tanks are compared in a situation where both guerrillas operating anti-tank guns and enemy MBTs that are deployed in the main route are considered with increasing numbers in Figure 12.  
Comprehensively, these results indicate that the new tank model can respond to the given situations and has a higher survivability rate than the model of the existing tank. The experimental data are presented in Table 11 of the Appendix A. This means that the proposed method can provide simulations to identify the reaction capability of the design model in the expected circumstance.