## 4 Correlation Analysis of Parameters Affecting Bond Strength  
Through MAML, the association of uncorrelated factors affecting the same problem has been achieved. In this section, the built-in weight matrices of MAML are utilized to provide a ranking of the importance of the input factors on the outcome. Furthermore, after distinguishing between primary and secondary factors, the necessity of secondary factors and their impact on the prediction results are calculated, aiming to optimize and simplify the input structure of the model. Table 6 displays the weight coefficients of the 19 input factors in the MAML model, with a total of four repeated calculations. The final importance coefficient for each factor is obtained by averaging the results of the three calculations  
As shown in Table 6, the final ranking of the importance weights of the influencing factors is as follows: Heating Time > Temperature > Sulfate Concentration > Drying Temperature > CFRP Dimensions > Heating Temperature > Drying Time > Chloride Concentration > Paste Length > Soaking Time > Paste Layers > CFRP Smface Density > Concrete Strength > Soaking Temperature > CFRP Elastic Modulus > Cycles > CFRP Elongation > Humidity > CFRP Tensile Strength. It is worth noting that the lower significance of 'Chloride Concentration' compared to 'Sulfate Concentration' is attributed to the absence of the consideration of chloride ions in the validation dataset. The same situation also applies to missing factors in the test set. Due to these characteristics being missed or unchanged in the test set, a similar importance analysis should correspond to specific problems rather than being universally applicable. For the problem of degradation in the bond performance of CFRP-to-concrete interfaces under the influence of hydrothermal and salt attacks, it is considered that factors related to hydrothermal and salt are direct factors, while the material properties of the CFRP and concrete are indirect factors. According to traditional neural network prediction algorithms, direct factors are often indispensable, while indirect factors can be pruned or ignored. However, based on the computed weight coefficients in Table 6, many direct factors do not have high weight coefficients, whereas indirect factors like 'CFRP dimensions' rank fifth in terms of their importance coefficients. Therefore, if there is a need to optimize the number of input parameters, it is not as straightforward as ignoring indirect factors in the MAML algorithm; rather, a certain pruning optimization must be carried out based on the ranking of the factors' importance. Due to the missingness of the database Meta2, the first factors to be eliminated are two factors not included in the test set, which are 'Chloride Concentration' and 'Temperature'. Table 7 displays the comparative results of the algorithm's predictive accuracy after sequentially eliminating non-significant influential factors sorted by importance. \[RMSE\ =\ \sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{6}\]  
where \(y_{i}\) is the ith test value and \(\hat{y}_{i}\) is the ith predicting value. A higher R-squared value, approaching 1, indicates a closer fit between the predicted and actual values, implying greater accuracy in predictions. In Table 7, it is evident that, as the number of input factors decreases, there is a consistent decline in the R-squared values, from the highest at 0.914 in the training set to 0.878 in the test set. Furthermore, the