###### Abstract  
The steel tube-reinforced concrete (STRC) shear wall plays an important role in the seismic design of high-rise building structures. Due to the synergistic collaboration between steel tubes and concrete, they effectively enhance the ductility and energy dissipation capacity of conventional shear walls. To identify vulnerable areas prone to brittle failure and optimize the design, it is essential to develop a rapid method for identifying the failure mode of STRC shear walls. In this study, a fast identification method of STCR shear wall failure modes based on a Blending fusion model with Generative Adversarial Network (GAN) augmented data is proposed. The GAN is employed to address the issue of inadequate experimental data by generating new samples. This method combines classification boosting (Cathosost), Random Forest (RF), K-Nearest Neighbors (KNN), and Least Absolute Shrinkage and Selection Operator (LASSO) to establish the Blending-CRKL fusion model to improve the prediction accuracy of the failure mode of STRC shear walls. The results reveal a significant improvement in the prediction performance of KNN, Backpropagation Neural Network (BPNN), RF, Light Gradient Boosting Machine (LightGBM), Catboost, and Blending-CRKL models after augmenting the training set with GAN. On average, the accuracy increased by 13%, precision increased by 81%, recall increased by 48%, and F1 score increased by 67%. The proposed Blending-CRKL fusion model outperforms the tested KNN, BPNN, RF, LightGBM, and Catboost models, achieving an accuracy rate of 97% in predicting the failure mode of STRC shear walls. Additionally, the stability and robustness of the Blending-CRKL model were validated, while the important features and value ranges of different failure modes were analyzed. This study provides a reference for the rapid identification of the failure mode of STRC shear walls. steel tube-reinforced concrete shear walls; machine learning; failure modes; fusion mode 2023131243312433124331243  
only related to the shear-span ratio but also depends on factors such as the loading mechanism, material parameters, and geometric dimensions. Relying solely on the shear-span ratio cannot accurately determine the failure mode. In the past, identification methods mainly relied on experiments and numerical simulations, which ensured accuracy but were time-consuming and costly. Therefore, establishing how to quickly and accurately predict the failure modes of STRC shear walls remains a challenging problem that needs to be addressed. Due to the availability of data [6], data-driven artificial intelligence analysis methods have made some progress in predicting the failure modes of RC walls in recent years. For instance, [PERSON] et al. [7] established an experimental database consisting of 393 RC shear walls and used eight machine learning (ML) algorithms to predict the failure modes of RC shear walls. The results showed that the Random Forest (RF) algorithm had the highest accuracy in predicting the failure modes, with an accuracy of 86%. [PERSON] et al. [8] established a database of 429 RC shear walls and used nine ML algorithms to predict the failure modes of RC shear walls. The results showed that the extreme gradient boosting (XGBoost) and gradient boosting (GB) algorithms had the best accuracy in predicting the failure modes of RC walls, with an accuracy of 97%. [PERSON] et al. [9] established a database of 351 RC shear walls and used an automated ML algorithm to predict the failure modes of RC shear walls. The results showed that the automated ML algorithm outperformed five manual ML algorithms in predicting the failure modes of RC shear walls. [PERSON] et al. [10] used unsupervised ML algorithms to predict the failure modes and slip of reinforced masonry shear walls, and the predicted results were consistent with the experimental results. [PERSON] et al. [11] established a database of 107 STRC shear walls and compared the predictive performance of six ML algorithms for the failure modes of these walls.