## 5 Conclusions
Extended parametric investigation was performed for the detection of the optimal configuration of each of the four categories of the MLP networks. In this investigation, the number of hidden layers, the number of neurons in each hidden layer, the types of activation functions of neurons, and the training algorithms were examined. Thus, this parametric investigation led to the optimally configured network of each of the four categories. The performance of the studied configurations was evaluated using the overall accuracy (OA) index. In cases in which this index failed to lead to clear conclusions, the other metrics defined in the framework of the confusion matrices (CMs)--i.e., the R-index and the P-index--were also used. The evaluation of networks was also based on the testing subsets that formed part of the datasets used for the investigation of their generalization ability. The main conclusions extracted from the above-described research procedure are the following:  
* Between the two different forms of A2 for the buildings classified to the SDC \"S\" for 0 = 0, \"A2/C2S\" was proven more efficient than \"A2/C3S\" for all versions of the studied RC buildings (i.e., BB, RIB, and IIB). This conclusion was based on comparisons of the values of the OA index, as well as on comparisons of the values of the R- and P-indices. The superiority of \"A2/C2S\" against \"A2/C3S\" means that the trained networks in the present study are more efficient for correct classifications in PR problems with two categories. This conclusion (which certainly cannot be characterized as being generally valid) must be further examined in future extended research. However, the low efficiency of the trained networks used in the present study to classify the testing samples to correct SDC eliminates the possibility of reliable predictions about the specific SDC of buildings for 0 = 0\({}_{\rm cr}\). Therefore, the reliable predictions concern the information about the change (or lack thereof) in the SDC for 0 = 0\({}_{\rm cr}\). Thus, the category \"A2/C2S\" in combination with the category \"A2/C2M\" (which corresponds to buildings that are classified to the SDC \"M\" for 0 = 0)--i.e., the analysis type \"A2/(C2S + C2M)\"--was used for the comparison of A2 with A1;
* A2, expressed in the form of analysis type \"A2/(C2S + C2M)\", was proven to be more efficient than A1. However, the percentages of the correct classifications extracted by A1 cannot be characterized as unacceptable, since the corresponding OA values were in all cases greater than 80%. On the other hand, the OA values extracted by \"A2/(C2S + C2M)\" were greater than 90%. The real difference between the two approaches can be reduced, since the application of A2 requires the knowledge of the SDC of buildings for 0 = 0. This knowledge can be obtained either by implementation of NTHA, or by the simulation of networks properly trained to predict the SDC of buildings for 0 = 0. In both cases, the possibility of the insertion of errors can lead to incorrect data for the implementation of \"A2/(C2S + C2M)\". On the other hand, A1 can be implemented without the knowledge of the SDC of buildings for 0 = 0; thus, A1 is not affected by these additional errors. For this reason, the two approaches can be generally characterized as almost equal;
* As regards the optimal configuration of networks, it was observed that the addition of a second hidden layer improved their classification ability in all studied cases. However, the increases in the OA index values achieved using two hidden layers instead of one are not always significant. On the other hand, the addition of the second hidden layer significantly increases the values of the R- and P-indices in all cases. The optimal number of neurons in the hidden layers cannot be estimatedwithout the implementation of parametric investigation using a predefined rule; this conclusion is consistent with the findings of the available relative literature.