###### Abstract
The results showed that the RF algorithm had the best prediction accuracy. However, the \"black box\" nature of ML makes it difficult to explain the relationship between input and output variables [12]. To address this issue, some propose the Explainable Boosting Machine (EBM) [13] and the Shapley Additive Explanations (SHAP) [14] algorithm to quantify the contribution of each input parameter to the output parameter, thus achieving the goal of explaining predictive models. Researchers have explored this problem; for example, [PERSON] et al. [15] used the SHAP method to quantitatively analyze the impact of various parameters on the failure mode of shear walls based on existing models. [PERSON] et al. [16] used the SHAP algorithm to estimate the relative importance of input features and discussed the influence of each input. Currently, the application of ML methods in civil engineering is constrained by small and imbalanced datasets, which have a significant impact on the predictive performance of models [17]. Due to the high cost and time-consuming nature of collecting experimental data, the number of samples in the civil engineering field's database is limited. Therefore, solving the problem of small sample databases is crucial for the application of ML in civil engineering. Some scholars have combined clustering algorithms with cloud models and used bootstrapping methods to expand such small sample datasets [18; 19]. In 2002, scholars creatively proposed the Synthetic Minority Over-sampling Technique (SMOTE) [20], which reduces the imbalance in the dataset by artificially synthesizing additional samples. However, this method cannot guarantee the similarity between the generated data and the original data. On the other hand, Generative Adversarial Networks (GANs), as a deep learning algorithm, generate synthetic data that are similar to real data by learning the distribution characteristics of real data [21], making the generated data more diverse and realistic. Some scholars have applied GANs to deal with small sample data. For example, [PERSON] et al. [22] proposed a sample synthesis method based on GANs and hybrid strategies to augment the prediction of hydrogen production in supercritical water. [PERSON] et al. [23] designed three types of GANs to augment the existing small sample concrete compressive strength database, significantly improving the predictive performance of the augmented model. Current research on the failure modes of structural walls is primarily focused on RC shear walls, and there is limited research on the damage patterns of STRC shear walls using data-driven ML algorithms. Traditional methods based on experiments [24] to explore the seismic performance of STRC shear walls are time-consuming and costly. However, applying ML methods to predict the failure modes of STRC shear walls is efficient and cost-effective, but the lack of sufficient training samples remains a major obstacle in the application of ML methods for recognizing the failure modes of STRC shear walls. To address these issues, this study leverages the GAN data augmentation technique to develop a Blending fusion model, aiming to accurately and efficiently identify the failure mode of STRC shear walls in small sample scenarios. This provides a novel approach for engineers in the field of civil engineering to apply ML methods when dealing with data scarcity. The main contributions and innovations of this paper are as follows:  
1. The use of GAN for database augmentation of STRC shear walls enables various ML models to fully learn the patterns in the data, addressing the issue of limited training samples of STRC shear walls. 2. The establishment of a Blending-CRKL fusion model that combines the advantages of Catboost, RF, KNN, and LASSO models significantly improves the prediction accuracy of failure modes of STRC shear walls compared to single ML models tested. The stability and robustness of this model are also validated. 3.