## 5 Numerical Applications
### Software Application for the Pre-Earthquake Assessment of RC Buildings with Full Data Availability
Considering the structural data of Table 10 and under the assumptions of Section 4.4, the unknown values of the structural parameters n\({}_{\text{v1}}\), n\({}_{\text{v2}}\), and e\({}_{\text{0}}\) of the three case study buildings fluctuate into the ranges presented in Table 11.  
The eight input vectors of Equation (3) were formed for each one of the case study buildings and the 65 seismic excitations, using the values of Table 11 as input. Thus, for each one of the three case study buildings, 520 (=65 earthquakes:8 input vector types of Equation (3)) input vectors were created, and 1560 (=520 \(\times\) 3 versions of buildings) input vectors were introduced to the MFPNNs, embedded in the current version of the developed software application. Consequently, the software was automatically applied 4680 (=1560 \(\times\) 3 case study buildings) times using an appropriate script. In order to check the reliability of the results, the software was also applied, considering the real values of the structural parameters n\({}_{\text{v1}}\), n\({}_{\text{v2}}\), and e\({}_{\text{0}}\) (Table 9). The overall accuracy (OA) index values, i.e., the percentages of the correct classifications of the case study buildings subjected to the 65 selected seismic excitations into the three pre-defined SDC (DS), as calculated by the MFPNNs, are presented in Figure 16. These OA index values correspond to each one of the eight input vectors **Xi** (i = 1-8) of Equation (3) [OA(**Xi**)] as well as to the input vectors **XR**, which include the actual values of the structural parameters n\({}_{\text{v1}}\), n\({}_{\text{v2}}\), and e\({}_{\text{0}}\) ([OA(**XR**)] - black bars). The main conclusion that arises from the study of Figure 16 is that the parametric procedure, based on the consideration of eight input vectors **Xi** (Equation (3)), can effectively approach the percentages of the correct classifications extracted using the input vectors **XR**. More specifically, in the case of BB, the differences between the OA(**Xi**) index values and the corresponding OA(**XR**) index values (i.e., the \(\Delta\)(OA) values in Figure 16) fluctuate between \(-1.8\%\) and \(-18.3\%\), while the corresponding lower and upper values of differences in the cases of FIB and PB are \(-0.6\%/-9.4\%\) and \(-1.9\%/-19.9\%\), respectively. Bearing in mind that the feasible level of accuracy of the RVI methods is generally low, the abovementioned deviations between the OA(**Xi**) and the OA(**XR**) index values (of the order of 10-15%) can be considered acceptable (OA > 70% in any case). However, it must be outlined that the considered value ranges of the unknown structural parameters n\({}_{\text{v1}}\), n\({}_{\text{v2}}\), and e\({}_{\text{0}}\) strongly affect the deviation between the OA(**Xi**) and the OA(**XR**) index values. In the present paper, the ranges summarized in Table 11 were used. The software proposed enables the selection of different ranges for the values on the unknown structural parameters based on the user's expertise. Further investigation of these ranges is possible but out of the scope of the present paper.