## 5 Conclusions  
In this study, a meta-learning neural network technique for a coupling factors-affected problem is proposed and based on the MAML algorithm. This neural network possesses \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Factors**} & **R-Square of** & **R-Square of** & **RMSE of** & **RMSE of** & **Convergence** \\  & **Training** & **Testing** & **Training** & **Testing** & **Steps** \\ \hline F & 0.914 & 0.878 & 0.096 & 0.098 & 489 \\ F1-1-7 & 0.912 & 0.878 & 0.096 & 0.098 & 502 \\ F2-14 & 0.909 & 0.870 & 0.097 & 0.099 & 500 \\ F3-8 & 0.910 & 0.871 & 0.097 & 0.099 & 488 \\ F4-16 & 0.857 & 0.832 & 0.106 & 0.113 & 553 \\ F5-3 & 0.855 & 0.833 & 0.106 & 0.112 & 570 \\ F6-15 & 0.848 & 0.833 & 0.108 & 0.112 & 704 \\ F7-9 & 0.825 & 0.791 & 0.116 & 0.120 & 1080 \\ F8-19 & 0.626 & 0.554 & 0.171 & 0.177 & 2223 \\ F9-17 & 0.582 & 0.522 & 0.173 & 0.181 & 2200 \\ F10-13 & 0.575 & 0.501 & 0.173 & 0.182 & 2466 \\ F11-4 & 0.393 & 0.336 & 0.212 & 0.235 & 3110 \\ \hline \hline \end{tabular}  
* \(\overline{F}\) indicates the inclusion of all 19 factors, and 'F1' represents the parameters included in the first row of the table, and so forth. ‘-’ signifies the excluded factors. R-square’ is represented by Equation (5), and ‘RMSE’ is represented by Equation (6). The convergence steps represent the number of iterations required during the testing process for the loss function to become less than 0.002.  
\end{table} 
Table 7: Results of predictive accuracy. the capability of coupling multiple non-associated factors that influence the same problem, enabling efficient regression predictions, with fewer samples, under the influence of compound factors. This capability is particularly applicable to the complexities faced in the field of civil engineering today, where problems are intricate, influencing factors are numerous and relatively independent, and the scarcity of samples often renders traditional neural networks inadequate for prediction tasks. The following conclusions are drawn:  
The MAML algorithm has successfully achieved the prediction of the degradation of the performance of the interfacial bond between CFRP and concrete under the influence of multiple factors, based on prior knowledge of single-factor influences, using 65 samples. Its R-squared value in the test set reached 0.88, demonstrating its high accuracy and rapid convergence speed. In contrast to the conventional neural network method BPNN used for regression problems in situations where multiple unrelated factors coexist, leading to dataset degradation, the MAML algorithm remains effective in its learning and prediction. In such scenarios, the BPNN algorithm only yields underfitting results due to its low network learning capability. In the degradation of the interfacial bond strength between CFRP and concrete under hydrothermal and salt environments, a total of 19 influencing factors were identified. Among these, 11 are direct environmental factors, while 8 are indirect factors related to the materials or specimens themselves. Utilizing the weight matrix embedded within the MAML algorithm, the importance of each factor in predicting the outcome was ranked. The rank top five factors are as follows: Heating Time > Temperature > Sulfate Concentration > Drying Temperature > CFRP Dimensions, accounting for a combined total of 56.43% of the overall importance. When pruning the input factors based on their importance ranking, it was observed that the predictive accuracy of the algorithm exhibited a stepwise decline. Initially, removing four factors resulted in minimal change in predictive accuracy. However, when the number of removed factors increased to 5-8, there was a noticeable decrease in computational accuracy, although it remained within an acceptable range. Beyond this threshold, as the number of removed factors continued to increase, the learning capability of the neural network model significantly declined, leading to underfitting results of predictive accuracy.