In recent years, there have been several instances of building collapse incidents resulting from shear failure. The complicated stress state and extremely nonlinear behavior seen in RC structures under shear loading are responsible for this phenomenon. Scholars and engineers worldwide have yet to propose a suitable model to address the issue correctly. Most computational approaches currently in use rely on semi‐empirical and semi‐theoretical formulations. The primary modes of shear transmission in cracked reinforced concrete include shear stress inside the uncracked concrete region, aggregate interlock, dowel action, stirrup action, and tensile residual stress. The shear force calculation is contingent upon the choice of the shear transfer model. This study comprehensively overviews the current shear calculation techniques and their respective applications. The pros and cons of these techniques were thoroughly examined. The present study investigated the suitability of these shear calculation formulas for RC structures subjected to intricate natural conditions, mechanical stress, and high‐strength and high‐performance concrete structures.

There are many factors that affect the shear capacity of FRP (fiber-reinforced polymer)-strengthened reinforced concrete (RC) beams, and traditional capacity models based on empirical or semi-empirical formulas often suffer from insufficient accuracy. To enhance the predictive accuracy and generalization ability of the shear capacity of FRP-strengthened RC beams, this study proposes an interpretable machine learning model based on the Jaya-CNN-LSTM model. A comprehensive database consisting of 315 test data on shear capacity of FRP-strengthened RC beams, encompassing various FRP reinforcement modes, has been established. Key feature parameters for predicting the shear capacity of FRP-strengthened RC beams are selected through Pearson correlation coefficient analysis. Based on the Jaya algorithm, the hyperparameters of the ensemble CNN-LSTM prediction model are adaptively optimized. A comparative analysis is conducted between the proposed method, other machine learning models, and existing empirical formulas to evaluate the proposed model’s efficacy. The results demonstrate that the proposed model outperforms other machine learning models and empirical formulas in terms of prediction accuracy and stability. Furthermore, the machine learning-based predictions align more closely with experimental values than those derived from empirical formulas. Additionally, the SHAP method is utilized to quantify the critical parameters’ impact on predicting the shear capacity of FRP-strengthened RC beams. The results reveal that there is an explicit mapping relationship between key features such as shear-span ratio, concrete strength, and yield strength of stirrups and the shear capacity of FRP-strengthened RC beams, providing technical support for practical applications.