## 2 Literature Review
In the pursuit of predicting punching shear resistance in reinforced concrete slabs, machine learning models such as artificial neural networks, decision trees, random forests, and extreme gradient boosting made significant strides [17]. Their predictive accuracy surpassed traditional design code models, marking a remarkable advancement. Complementing these developments, a hybrid Extreme Learning Machine for Regression (EPR) technique emerged, melding AI, robust multivariate techniques, and an Akaike weight-based method [18]. This innovative approach provided an accurate, unbiased model for shear strength in RC beams. Simultaneously, the Symbolic Regression--Modified Compression Field Theory (SR-MCFT), a hybrid grey-box model, took a step further [19]. By integrating the modified compression field theory and machine learning, it excelled in predicting punching shear resistance in Fiber-Reinforced-Polymer (FRP)-reinforced slabs. The Evolutionary Polynomial Regression (EPR) added another dimension to this field, successfully enhancing the prediction of shear strength in reinforced concrete circular columns and refining existing models and empirical rules [20]. Lastly, a unique hybrid model that combined Particle Swarm Optimization and Support Vector Regression (PSO-SVR) showcased its predictive powers, notably improving the prediction of punching shear strength in two-way reinforced slabs and emphasizing the importance of slab depth and thickness [21]. Despite the success of previously developed black-box ML models in predicting the punching shear capacity of RC flat slabs, black-box models cannot generate explicit equations or mathematical representations that elucidate the relationships between input parameters and output predictions. It is crucial to consider models that offer a balance between interpretability and complexity. This balance ensures that engineers and designers can gain a comprehensive understanding of the relationships among various influencing factors, while still benefiting from the enhanced predictive accuracy provided by advanced ML techniques. MSP, a model-tree-based algorithm, presents a promising alternative as it combines the simplicity of decision trees with the power of linear regression, allowing for a greater interpretability without sacrificing predictive performance [22]. This unique combination enables practitioners to make informed decisions while leveraging the advantages of ML in structural design and analyses. The primary objective of this study was to develop and assess the performance of the M5P model, a model-tree-based algorithm, in accurately predicting the punching shear strength of RC flat slabs without shear reinforcement. To achieve this objective, this study first gathered a comprehensive dataset of experimental results and relevant parameters influencing the punching shear strength of RC flat slabs. This dataset was then used to train and test the M5P model, ensuring its ability to capture the intricate relationships among various influencing parameters, which are the (1) effective depth of the slab (\(d\)), (2) longitudinal reinforcement ratio (\(\rho\)), (3) compressive strength of concrete (\(f_{c}\)), (4) span-depth ratio (\(\lambda\)) (the distance between the face of the support column and the edge of the slab divided by the effective depth of the slab), (5) yield strength of reinforcement (\(f_{y}\)), and (6) equivalent column width (\(b\)) defined latter. In addition, this research compared the performance of the proposed M5P model with existing empirical formulas and analytical models. This comparative analysis helps to demonstrate the effectiveness and advantages of the M5P model in terms of predictive accuracy and interpretability. Finally, this research investigated the sensitivity of the developed model to the input parameters. The sensitivity analysis identified the most influential parameters on the punching shear strength of a RC flat slab and their effect on the accuracy of the model.