# Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model
## 6 Conclusions
[139] & 0 & 150 & 240 & 2.200 & 2.000 & 414.0 & 1800 & 1800 & 150 & 0.600 & 1.000 & 414.0 & 61.00 & 588.0 & 200 & 2.20 \\ [PERSON] and [PERSON] [140] & 0 & 150 & 240 & 1.050 & 0.840 & 1254.0 & 1800 & 1800 & 150 & 0.560 & 1.000 & 1254.0 & 40.00 & 550.0 & 207 & 2.20 \\ [PERSON] [141] & 0 & 101 & 76.2 & 5.000 & 0.530 & 512.0 & 1016 & 101 & 0.710 & 0.710 & 600.0 & 46.19 & 362.0 & 45.44 & 2.50 \\ [PERSON] [141] & 0 & 101 & 94.00 & 1.000 & 453.0 & 1016 & 101 & 0.810 & 0.830 & 0.440 & 44.69 & 476.0 & 92.64 & 2.30 \\ [PERSON] [141] & 0 & 101 & 76.2 & 9.400 & 0.980 & 480.0 & 1320 & 1016 & 101 & 0.710 & 0.710 & 660.0 & 46.88 & 392.7 & 454.4 & 2.20 \\ [PERSON] [141] & 0 & 101 & 101 & 13.000 & 1.400 & 482.0 & 1320 & 1016 & 101 & 0.670 & 0.670 & 463.0 & 42.47 & 529.0 & 125.8 & 2.20 \\ [PERSON] [141] & 0 & 101 & 101 & 11.000 & 1.200 & 482.0 & 1320 & 1016 & 101 & 0.500 & 0.500 & 463.0 & 37.92 & 476.0 & 88.9 & 2.40 \\ [PERSON] [142] & 600 & 152 & 140 & 11.000 & 0.240 & 450.0 & 3900 & 1000 & 152.00 & 0.490 & 450.0 & 38.70 & 334.0 & 8.97 & 2.90 \\ [PERSON] et al. [143  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & **w11** & **w21** & **w31** & **w41** & **w51** & **w61** & **w71** & **w81** & **w91** & **w101** \\ \hline _A.L_ & 0.27836 & 0.34179 & 0.55152 & 0.79469 & 1.25982 & 0.90687 & \(-0.02805\) & 0.79850 & 0.09437 & \(-0.06686\) \\ \(W_{\rm 1b}\) & \(-0.35504\) & \(-0.29662\) & 0.63076 & 1.04187 & 0.28381 & \(-1.12608\) & 0.60906 & 0.77589 & 0.17191 & \(-0.05209\) \\ \(W_{\rm 2b}\) & \(0.38848\) & 0.60607 & \(-0.07384\) & \(-0.10202\) & 0.11835 & 0.68601 & \(-0.51639\) & 0.69812 & \(-0.33039\) & \(-0.81421\) \\ \(\rho_{\rm cr}\) & \(0.00685\) & \(-0.42597\) & \(-0.25653\) & \(-0.27964\) & 0.77599 & 0.48441 & \(-0.13054\) & 0.268263 & \(-0.50540\) & 0.18407 \\ \(\rho_{\rm ch}\) & \(-0.18496\) & \(-0.93420\) & \(-0.21477\) & \(-1.30458\) & \(-0.25130\) & \(-0.16112\) & 1.17348 & \(-0.46040\) & \(-0.10113\) & \(-0.93252\) \\ \(F_{\rm Fe}\) & \(-0.38246\) & \(0.19449\) & \(-0.00322\) & \(-0.23876\) & 0.07009 & \(-0.24757\) & 0.12753 & \(-0.93046\) & \(-0.73564\) & \(-1.19427\) \\ \(h_{\rm low}\) & \(-2.48643\) & \(-0.53283\) & \(-0.52311\) & \(-1.16902\) & \(-1.33119\) & \(-0.02211\) & 0.01571 & \(-0.43720\) & \(-1.38721\) & \(1.51850\) \\ \(I_{\rm uv}\) & \(0.33724\) & \(0.90620\) & 0.57674 & 0.18346 & \(-0.22445\) & 0.17460 & \(-0.49526\) & \(-0.55005\) & 0.45015 & 0.01144 \\ \(t_{\rm uv}\) & \(-0.05230\) & \(0.71080\) & \(-0.49103\) & \(-0.72176\) & 0.15461 & \(-0.04615\) & 0.24655 & \(-0.73673\) & 0.44326 & \(-0.53894\) \\ \(\rho_{\rm uv}\) & \(1.1946\) & \(1.35835\) & \(-0.56388\) & 0.44006 & \(-0.21862\) & \(-0.43304\) & \(-0.10659\) & 1.16227 & \(-0.28698\) & 0.63142 \\ \(\rho_{\rm low}\) & \(0.56298\) & \(0.40776\) & 0.68201 & \(-0.37318\) & \(-0.10378\) & 0.10838 & 0.18270 & \(-0.76181\) & \(-0.20352\) & 0.46643 & 0.20279 \\ \(F_{\rm Fe}\) & \(0.12877\) & \(-0.02556\) & 0.16279 & 0.55430 & \(-0.10325\) & \(-0.35100\) & 1.00406 & \(-0.47198\) & 0.09076 & \(-0.07678\) \\ \(f_{\rm c}^{\prime}\) & \(-0.25932\) & \(-0.90033\) & 0.23902 & 0.28850 & 0.11517 & 0.17102 & \(-0.33770\) & \(-0.05328\) & 0.31861 & \(-0.72958\) \\ \hline \multicolumn{12}{c