## 1 Introduction  
As the primary component of high-rise structures and earthquake-resistant structures that resist lateral forces, RCSW members can effectively resist the loads caused by wind loads and seismic actions to ensure structural stability and safety [1]. At the same time, it lessens the lateral distortion of the structures and makes the building more rigid, thus reducing the risk of nonstructural damage. In seismic design, the design and calculation of the shear capacity of RCSWs directly affect the performance of the building under seismic actions [2]. Compared with beams and columns, RCSWs have larger section height-to-thickness ratios and are subjected to axial loads, resulting in a more complex shear mechanism [3]. Previous scholars have proposed more shear capacity calculation models by combining the force characteristics of RCSWs, such as the truss model [4; 5; 6], the strut-and-tie model [7; 8; 9], the modified compression field theory [10; 11; 12], the truss-arch model [13; 14], etc. However, the assumptions used in each theoretical model are not the same, which results in differences in the prediction performance of each model. Numerous factors influence the RCSWs' shear strength capacity, such as wall height, section length and thickness, section form, longitudinal tendons, horizontal set of vertical distribution tendons, etc., and the aforementioned theoretical models find it challenging to fully account for each factor's impact on the RCSWs' shear-bearing capacity. Machine learning, on the other hand, utilizes its advantages of strong nonlinear processing capability, high-dimensional data, autonomous feature selection, strong integrated learning capability, strong adaptability, high robustness, and interpretability, which can deeply capture the mapping relationship between each input feature and the target value, and provides a more flexible and effective prediction method for RCSW shear-bearing capacity. Currently, ML models for RCSW shear capacity have been intensively studied. [PERSON] and [PERSON] [15] built a 536 beam-column joint test database; ML techniques were used to establish the corresponding damage modes, as well as shear capacity calculation models, and the results of the study show that Lasso regression (LR) is more accurate and efficient than other techniques. In the study of the shear capacity of low RCSWs, [PERSON] et al. [16] developed a model combining the eXtreme Gradient Boosting (XGBoost) algorithm and the SHAP method and extracted information on the nonlinear relationship between the basic characteristics of RC RCSWs and their bearing capacity from 434 RCSW samples. The study's findings indicate that the developed ML model achieves 97% validation accuracy, which significantly outperforms the traditional mechanical modeling approach. [PERSON] et al. [17], on the other hand, trained an artificial neural network (ANN) with 369 experimental data samples to create a forecasting model for shear-bearing capacity for low RCSWs with flanges. The study also delved into the influence of input parameters on the shear capacity of the RCSWs and provided corresponding design recommendations. [PERSON] et al. [18] demonstrated an optimized machine learning model can be used to better accurately forecast the shear capacity of RCSWs, the potential of a data-driven approach (534 wall specimens) for improving the reliability of shear capacity design for RCSWs by combining different input variables to build the model and comparing them with traditional design codes, and the model built by XGBoost obtained the best prediction performance with R\({}^{2}\) of 0.978. [PERSON] et al. [19] explored the application of various ML algorithms in forecasting the failure modes and strengths of 198 RCSW specimens, and the results revealed the specific parameters (e.g., flexural and shear strength ratios and shear-span ratios) on the failure modes are critical. [PERSON] et al.