## 2 Literature Review  
In recent years, several academics have proposed empirical formulas and analytical models for predicting the punching shear resistance. [PERSON] and [PERSON] [2] presented an easy-to-understand analytical model, which was applied to 60 experimental slab-column connections, and the results were compared with previously developed models. [PERSON] et al. [3] created two condensed empirical formulations using 244 experimental data points, and the examination of the prediction accuracies revealed that the two suggested models were capable of generalizing the gathered experimental data. Moreover, a formula for predicting punching shear resistance was introduced by [PERSON] et al. [4], based on the experimental results of 61 high-strength concrete slabs. The empirical formula could accurately predict the punching shear resistance of concrete slabs, making it an essential tool for designers and engineers to ensure the safety and stability of RC structures. Also, various formulae have been presented in design codes, such as BS 8110-97 [5], ACI 318-19 [6], and Eurocode 2 [7], for predicting the punching shear resistance of RC interior slabs. Moreover, a significant amount of experimental research has been undertaken to determine the crucial factors affecting the punching shear resistance of these slabs. In order to investigate the impact of input factors like the reinforcement ratio, column size, and concrete strength on the punching shear resistance, [PERSON] and [PERSON] [8] conducted 39 experiments and found a considerable effect of concrete strength on the punching shear resistance. Furthermore, the influence of slab thickness on the punching shear resistance was explored by [PERSON] and [PERSON] [9], providing additional insight into the structural behavior of RC interior slabs. In recent years, the application of black-box machine learning (ML) and artificial intelligence techniques has shown great promise in predicting complex structural behaviors [1; 10; 11; 12; 13; 14; 15; 16; 17]. These data-driven methods can have the potential to improve the accuracy and reliability of punching shear capacity predictions by capturing intricate relationships among the influencing factors. XGBoost was applied to predict the punching shear resistance in RC interior flat slabs, achieving the best prediction compared to two other ML models and various design codes [14]. The most significant input variable was shown to be the effective depth of the slab (distance from the bottom face of the slab to the average plane of the upper tensile flexural distributed reinforcements in the punching shear  
Figure 1: Punching shear failure in RC flat slabs without shear reinforcement. zone). A graphical user interface was created for preliminary estimation. Other research assessed the effectiveness of ML techniques in estimating the punching shear strength of flat slabs without shear reinforcement, utilizing 380 experimental data points [1]. The extreme gradient boosting model surpasses other models and current code provisions, achieving a coefficient of variation of 0.09 and a coefficient of determination of 0.98. The SHapley Additive exPlanation (SHAP) approach was used to elucidate the factors influencing the predictions [1]. In another study [16], an effective prediction model was developed, chosen from eight ML-based models, to determine the failure mode of flat slabs based on 610 experimental data points. XGBoost surpasses other models, achieving a 99.02% accuracy rate. The SHAP analysis offers valuable understanding of the relationship between the failure mode and contributing factors. A study [12] examined design provisions for RC flat slabs, identifying influential parameters through a sensitivity analysis. In this study [12], a Bat-ANN hybrid model was developed for estimating the punching shear strength, achieving a superior accuracy. Key parameters were assessed, highlighting the importance of the flexural reinforcement.