## 4 Correlation Analysis of Parameters Affecting Bond Strength
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & \\ \hline number & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\ weight & 0.0492 & 0.0899 & 0.0067 & 0.0124 & 0.1067 & 0.1697 & 0.0458 & 0.0094 \\ number & 9 & 10 & 11 & 12 & 13 & 14 & 15 & 16 \\ weight & 0.0119 & 0.1248 & 0.0802 & 0.0208 & 0.0525 & 0.0108 & 0.0323 & 0.0124 \\ number & 17 & 18 & 19 & & & & & \\ weight & 0.0248 & 0.0765 & 0.0634 & & & & & \\ \hline \hline \multicolumn{8}{c}{Average results} \\ \hline number & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\ weight & 0.0563 & 0.0832 & 0.0147 & 0.0336 & 0.0605 & 0.2245 & 0.1061 & 0.0112 \\ number & 9 & 10 & 11 & 12 & 13 & 14 & 15 & 16 \\ weight & 0.0213 & 0.0797 & 0.0706 & 0.0357 & 0.0333 & 0.0090 & 0.0208 & 0.0126 \\ number & 17 & 18 & 19 & & & & & \\ weight & 0.0279 & 0.0708 & 0.0262 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._convergence difficulty increases in the test set, as reflected by the rise in training iteration steps and, consequently, increased computational expenses. Based on the importance ranking of the input factors mentioned above, an optimization of the number of input factors was conducted. The performance of the MAML algorithm on the database Meta2 exhibited a trend of stepwise descent, where the exclusion of the first four factors ('1--Chloride Concentration', '7--Temperature', '14--CFRP Tensile Strength', '8--Humidity') do not yield significant impacts. Both the computational steps required for convergence and the accuracy of the predictions remained at satisfactory levels. Specifically, the convergence steps remained around 500, with an R-squared value above 0.91 during the training process, while the R-squared value for test results stayed above 0.87. The first occurrence of stepwise descent in the performance of the MAML algorithm appeared after the fifth computation result. During the training process, the R-squared value decreased from 0.91 to around 0.85, while, in the testing process, it decreased from 0.87 to approximately 0.83-0.80. The factors excluded during this stage included '16--CFRP Elongation', '3--Cycles', '15--CFRP Elastic Modulus', and '9--Soaking Temperature'. It is noteworthy that these factors encompassed two indirect and two direct factors, yet the computed results remained acceptable. The predictive accuracy did not decrease significantly, and the convergence speed remained relatively fast. This suggests that the optimization of direct factors is not necessarily ineffective, while the optimization of indirect factors may sometimes lead to a certain degree of decline in the predicted results. The subsequent reduction in input factors directly resulted in model underfitting, indicating that the excluded input parameters are somewhat necessary. Therefore, the approximate optimal number of parameters for optimization is around eight. In summary, the importance analysis based on the databases Meta1 and Meta2 demonstrates a certain rationality, revealing the prioritization of all factors relevant to the prediction task. The higher the importance, the greater the factor's impact on the prediction results. When analyzing the influence of each parameter, a certain degree of linear decline in computational results is observed when removing input factors according to their importance ranking. Up to a maximum reduction of eight input factors, good predictive accuracy can be maintained. However, exceeding eight factors leads to the underfitting of the network, resulting in a significant decrease in prediction accuracy.