# Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model
## 6 Conclusions  
Understanding the complex behavior of the shear walls through their effective parameters can help to properly determine the lateral response of the structures. In this paper, an efficient computational method was proposed to estimate the strength, stiffness, and drift ratio of the steel plate and reinforced concrete shear walls from a rich library of experimental data. Over 100 papers and reports were synthesized and the available information were extracted. The parameters were mainly related to the \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **MSE** & **RMSE** & **NSE** & **MAE** & **R** \\ \hline \(P\); SPSW & 0.0014 & 0.0374 & 0.9796 & 0.0224 & 0.9939 \\ \(K\); SPSW & 0.0024 & 0.0489 & 0.9632 & 0.022 & 0.9824 \\ \(P\); RCSW & 0.0015 & 0.0389 & 0.9375 & 0.0255 & 0.9622 \\ \(K\); RCSW & 0.0028 & 0.0534 & 0.79 & 0.0278 & 0.8992 \\ \(D\); RCSW & 0.0067 & 0.082 & 0.7277 & 0.0585 & 0.8479 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of five statistical indicators to evaluate the accuracy of meta-models. Figure 7: Relative importance of the input parameters in shear wall meta-model. (**a**) SPSW, (**b**) RCSW. geometry of the shear walls (such as height, width and thickness of the plate and its surrendering frame), as well as the applied loads, and the material properties. On the other hand, the output was only considered the global behavior of the system (in terms of the stiffness, strength and drift). One may notice that the reinforcement detailing, collapse modes, and buckling parameters were not considered in this meta-modeling. The main reason can be attributed to unavailability of those information in majority of cases. In addition, the failure mode and crack pattern had more qualitative nature, and it was difficult (if not impossible) to present them in a quantitative format (using damage index concept). An artificial neural network was proposed to solve the problem. In this network, the back propagation algorithm was adopted. The network of a single hidden layer with 6 and 10 neurons would have the best performance for SPSW and RCSW, respectively. The major conclusions can be summarized as follows:  
* The MSE of test data to estimate the strength and stiffness of the SPSW was 0.000227 and 0.0108, respectively, which indicate the proper performance of the network. * The MSE of test data in RCSW was 0.0012, 0.0061, and 0.0056 for strength, stiffness and drift ratio, respectively. * Sensitivity analysis was performed to determine the relative importance of the input parameters on the shear wall's behavior. It was observed that the stiffness of the vertical boundary elements and the ultimate stresses of the infill plate had largest effect on the strength and stiffness of the SPSW, respectively. * On the other hand, the most important parameters affecting the strength, stiffness and drift ratio of RCSWs were wall's length, wall's height, and the horizontal column's reinforcement ratio, respectively. * The performance of the network based on different statistical indicators was also found to be very close. Finally, the detailed values of the ANN weights are provided (see Appendix B). The proposed network can be used to design new walls, retrofit the existing ones, and validate the finite element models. In design level, the proposed network can provide an estimation of the ultimate strength and stiffness values, which can be further correlated with codified values. This is beyond the conventional design philosophy, which is based on linear elastic models. On the other hand, the performance of existing shear walls can be controlled with this meta-model to make sure they have a safe/proper behavior under the target loads, and, if not, they can be retrofitted. Conceptualization, M.J.M. and M.A.H.-A. ; methodology, M.J.M. and M.A.H.-A. ; software, M.J.M. ; validation, M.J.M. ; formal analysis, M.J.M. ; investigation, M.J.M. and M.A.H.-A. ; resources, M.J.M. and M.A.H.-A. ; data curation, M.J.M. ; writing--original draft preparation, M.J.M.