## 1 Introduction  
Reinforced concrete deep beams (RCDBs) are structural elements characterized by their small shear span-to-depth ratio (\(a/d\) approximately \(\leq 2.5\)) and/or effective span-to-height ratio (\(l_{e}/h<5\) in GB 50010, \(l_{e}/h<4\) in ACI 318, and \(l_{e}/h<3\) in EU2), thereby offering a greater load-carrying capacity than equivalent slender beams. For this reason, RCDBs are extensively utilized in large-scale projects such as bridges, high-rise buildings, and offshore structures. Nevertheless, due to the relatively small \(a/d\) ratio, the ultimate load-carrying capacity is predominantly governed by shear behavior, which is difficult to model accurately compared to slender beams, as the theory of plane sections is not valid due to the nonlinear strain distributions [1]. Hence, the main question in the design and application of RCDBs revolves around how to accurately model and predict their shear strength (SS) to avoid catastrophic failure. Since 1960, various approaches have been proposed to predict the SS of RCDBs. The most commonly adopted approach among those is the strut-and-tie model (STM) [2], which has been adopted by many codes of practice such as ACI 318, CSA A23.3, and EU2. STM represents a simplistic and powerful method that predominantly provides conservative strength predictions. The other approaches have also shown reasonable strength predictions for a limited set of experimental tests. However, the shear resisting mechanism of RCDBs is more complicated as it is governed by several complex behaviors, such as size effects, dowel actions, and bond-slip effects of tensile reinforcements, aggregate interlock at crack interfaces, shear transverse reinforcement effect, flexure-shear interaction, etc., as shown in Figure 1. Hence, all these behaviors are difficult to capture in a unique model using theoretical approaches, which makes the model unstable and may result in inaccurate predictions. Moreover, adopting these traditional approaches requires some assumptions and simplifications, time-consuming calibration processes, as well as being mostly complicated for practical implementation. These limitations have motivated structural engineers toward dealing with advanced soft computing techniques, which can provide a promising alternative. With rapid development in artificial intelligence, data-driven approaches such as machine learning (ML) are capable of solving and overcoming the aforementioned limitations. Accordingly, ML approaches have been successfully applied to predict the SS of RCDBs. For example, an early ML application was made by [PERSON] and [PERSON] [3] using an ANN algorithm with a database of 111 specimens and 10 input parameters. [PERSON] and [PERSON] [4] examined the potential of the SVR algorithm in predicting the SS of RCDBs, considering a database of 106 specimens and 9 input variables. [PERSON] et al. [5] employed the ANFIS algorithm with a database of 139 specimens to build their predictive model for RCDBs. [PERSON] et al. [6] proposed a hybrid predictive model using an LS-SVR algorithm coupled with SFA. A database of 214 specimens and 9 input features was used to calibrate their model. [PERSON] et al. [7] developed an improved predictive model for RCDBs by adopting a genetic algorithm. Their predictive model was calibrated utilizing a database of 381 specimens and 8 input parameters. [PERSON] et al. [8] developed a new predictive model, namely OSVM-AEW, by combining SVM and LS-SVM with SOS algorithms. The model was constructed using 214 samples and 7 input parameters. Recently, [PERSON] et al. [9] adopted four ensemble ML algorithms, namely RF, AdaBoost, GBRT, and XGBoost, to develop predictive models for SS of RCDBs. For this purpose, a database of 271 specimens and 16 input variables was used in calibration. The results showed that the XGBoost model has a superior performance over all other models. [PERSON]  
Figure 1: Failure mechanisms in RCDBs. et al.