## 1 Introduction
[20] established a shear capacity calculation formula for RCSWs with the same structure as \(\mathrm{ACI318}\)-\(\mathrm{19}\)[21] using a mix of regression analysis and ML based on a test database, including 340 wall specimens, and the suggested formula was upgraded on the basis of \(\mathrm{ACI318}\)-\(\mathrm{19}\) by considering the effects of vertical loads, as well as longitudinal reinforcement on the shear capacity of the wall. In the study of [PERSON] et al. [22], a model combining a genetic algorithm and boosting algorithm was used to estimate the shear strength of 558 low RCSW samples with great precision, which showed the importance of optimizing the configuration of the hyperparameters and improved the predictive performance of the model effectively (R\({}^{2}\) > 0.986). [PERSON] et al. [23] and [PERSON] et al. [24] developed databases and predictive models, respectively, and the former provided information on 182 data on RCSWs with flanges, and the latter demonstrated the excellent performance of the combined SVR-HHO (SupportVector Regression and Harris Hawk Optimization) model in estimating short RCSWs' shear strength (R\({}^{2}\) = 0.94). In recent years, [PERSON] et al. (2018) evaluated the predictive performance of different ML models for the prediction of the shear bearing of 149 composite RCSWs. The findings indicated that the XGBoost model outperformed the traditional empirical formulations regarding precision and forecasting skills. For the shear strength of corroded RCSWs (R\({}^{2}\) = 0.99), [PERSON] et al. (2018) proposed a new prediction method by applying the Random Forest algorithm to identify and classify different damage patterns, showing the unique value of ML in long-term performance assessment. The studies of [PERSON] et al. (2018), using a hybrid ML model of an artificial neural network (ANN) model with a particle swarm optimization (PSO) to predict the shear strength of 168 RCSWs, failed in shear and achieved a prediction performance of R\({}^{2}\) = 0.999. [PERSON] et al. (2018), on the other hand, combined deep learning with image processing techniques to propose an innovative method for assessing the deterioration of stiffness and strength of RCSWs after an earthquake. In addition to increasing the precision of assessing building damage, this study establishes the groundwork for the creation of associated monitoring methods. [PERSON] et al. (2018) evaluated the degradation in strength and stiffness of 14 RCSW samples after earthquakes and suggested a technique that combined image processing and deep learning. By employing Damage-Net for damage detection and localization and developing the DegradeEval-Net_v2 network, the findings demonstrate that, in terms of assessment accuracy, the new approach performs noticeably better than the current standard guidelines, particularly when it comes to a notable decrease in the root mean square error (35% smaller) of stiffness and strength degradation compared to the codes in JBDPA and FEMA guidelines. [PERSON] et al. (2018) concentrated on using machine learning to forecast the lateral strength of brief RCSWs. By analyzing 639 samples, the study found that ensemble learning models (e.g., XGBoost, R\({}^{2}\) = 0.963) performed well in shear strength prediction and the use of Bayesian optimization can further improve the model performance. The aforementioned study demonstrates that the machine learning approach can more precisely forecast the shear-bearing capacity of RCSWs; however, the present investigation could be enhanced in the following ways:  
1. Current machine learning models have primarily been researched to increase machine learning fitting accuracy, with little attention paid to the predictability of their results;
2. Currently, the majority of research on RCSW shear capacity concentrates on individual or ensemble machine learning models with matching comparisons, without delving further into deep learning models' ability to forecast RCSW shear capacity;
3.