## 7 Concluding Remarks  
Modeling the plastic response of different components in beam-to-column bolted connections and their interactions is a challenging issue in the structural engineering community. In this paper, an informational artificial neural network (ANN) model combined with the metaheuristic artificial bee colony (ABC) algorithm was developed to model the initial stiffness (\(S_{j,ini}\)) and maximum moment capacity (\(M_{n}\)) of top-seat angle connections  
Figure 12: Sensitivity indices of variables \(S_{j,ini}\) and (\(M_{n}/M_{p,beam}\)). with double web angles (TSACWs). Two different formulations of component-based mechanical models of the literature (as proposed by Eurocode 3 (\"EC3\") or by [PERSON] and [PERSON] in 2017 (\"KK\")) were also investigated in detail. The discussed comparisons and results confirmed that the efficiency of the component-based mechanical model depends on the number and accuracy of the relationships of the constitutive components. By defining a sufficient number of components and subsequently idealizing the physical behavior in analytical equations, the reliable application of the mechanical model to different connection configurations is possible. Nevertheless, idealization typically resulted in equations that excluded several important physical behavioral features of TSCWs, such as slippage. On the other hand, unlike the conventional mechanical modeling process that involves idealization from the observed behavior to the mathematical equations, in the informational base method, the information about essential behavior is extracted from available experimental test data and processed using an ANN. Nevertheless, the ANN model was limited to providing the global response only for bolted connections that include the contributions of all the constitutive components. Using this method, as shown, it is impossible to represent individual components and their actual contribution. Therefore, the model does not offer an insight into the underlying mechanics. Overall, the following conclusions can thus be derived:  
* both the EC3 and KK component-based models failed to capture the underlying mechanism for estimating \(S_{j,ini}\) and \(M_{n}\) parameters. As a result, these were either underestimated or overestimated for the reference specimens. On the other hand, the herein developed ABC-ANN model proved to offer a reliable prediction of required parameters, as also emphasized by the ratio of observational to computational values (\(R^{2}\)), and thus suggesting the high potential and accuracy of the proposal. * The ANN model combined with the ABC algorithm established an excellent agreement with the available experimental database. The results highlighted that the ANN model may be a reliable alternative to a component-based mechanical model to estimate the mechanical behavior of bolted beam-to-column connections. Using the values of weights and biases between the different ANN layers, the two output parameters (\(S_{j,ini}\) and \(M_{n}\)) can be accurately predicted. * The sensitivity analysis confirmed that (apart from the yield strength \(f_{y}\) that necessarily depends on material properties) the thickness of the top flange (\(th_{tc}\)) has a significant influence, while the moment inertia ratio of column to beam (\(I_{col}/I_{b}\)) has the least effect on both the predicted output parameters, \(S_{j,ini}\) and \(M_{n}\). This research paper results from a joint collaboration of all the involved authors. All authors contributed to the paper drafting and review, I.F., M.N., R.P. and C.B. All authors have read and agreed to the published version of the manuscript. This work was supported by funding from the Federal State Autonomous Educational Institution of Higher Education South Ural State University (National Research University). Further, MDPI is also acknowledged for providing waived fees (Guest Editor C.B.) in support of standard publication APCs. The study did not require ethical approval. Data Availability Statement: Data sharing not applicable.