The Development and Optimization of Machine Learning Models for Predicting the Shear Capacity of Corroded Reinforced Concrete Beams

Abstract

The deterioration of steel reinforcement through corrosion triggers cracking and loss of concrete cover, ultimately weakening the structure’s strength and ductility. In practical design and assessment, it is vital to precisely quantify the shear capacity of corroded reinforced concrete beams (CRCBs). In this paper, machine learning (ML) models are developed to predict the shear capacity of CRCBs, including kernel ridge regression (KRR), K-nearest neighbors (KNN), decision trees (DT), random forest (RF), gradient-boosted regression trees (GBRT), and extreme gradient boosting (XGBoost). A total of 408 data entries on the shear strength of CRCBs under different corrosion conditions were collected to establish an extensive database. The reliability of the proposed ML models is examined by contrasting their outputs with the experimental data. The XGBoost model demonstrated superior predictive capability, achieving an R² value of 0.994 and outperforming all other tested models, including RF, GBRT, and DT. The Shapley Additive Explanations (SHAP) algorithm is adopted to reveal the contribution of each input feature to the predicted shear capacity of CRCBs. The interpretive SHAP results show that the ultimate shear capacity of CRCBs is most influenced by beam depth (h), with the shear span-to-depth ratio (λ) and concrete compressive strength ($f_{cl,150}$) being the subsequent key contributors. A comparative assessment between the XGBoost model and traditional analytical models was carried out to estimate the shear strength of CRCBs. Results demonstrate that the XGBoost model delivers enhanced predictive accuracy and improved performance. A parametric investigation examined its robustness under variations in geometry and material properties, while a user-friendly interface was created to support its practical use.

1. Introduction

Steel reinforcement corrosion is common in reinforced concrete (RC) structures (Figure 1), leading to a decrease in reinforcement areas and a weakening of the bond between the steel and concrete [1,2,3,4]. Corrosion in steel reinforcement diminishes its strength and ductility, reducing the load-carrying capacity and compromising the structural integrity of RC members [5,6,7]. The weakened bond between steel and concrete decreases the resistance to cracking and deformation. These phenomena result in reduced stiffness, and flexural and shear strengths of the structural components [8,9]. It is widely recognized that RC beams are intended to fail in flexure rather than shear, as shear failure is a brittle mode that occurs without any warning signs [10,11,12]. Therefore, RC beams are generally proportioned to exhibit higher shear strength relative to their flexural capacity. However, over time, exposure to aggressive environments, such as marine atmospheres or regions subjected to deicing salts, can lead to the corrosion of embedded steel reinforcement, as chloride ions penetrate the concrete cover or carbonation progressively advances through it.

Corrosion of steel reinforcement in concrete is a key durability issue, particularly in marine environments with high chloride concentrations [14,15,16]. Chloride ions penetrate the concrete and break down the protective passive film, initiating corrosion [17]. In saline soils, sulfate ions react with hydration products to form expansive compounds such as gypsum and ettringite, leading to cracking, reduced concrete integrity, and accelerated chloride-induced corrosion [18,19,20,21]. Additionally, under the combined effects of freeze–thaw cycles and seawater exposure, An et al. [22] investigated the flexural behavior of RC beams in Arctic-like environments. High-strength concrete improved cracking and ultimate loads by 56% and 7%, respectively, while seawater exposure reduced cracking but decreased ductility. Freeze–thaw cycles further intensified damage and increased crack width, highlighting strong interaction effects on flexural performance and durability. For concrete structures submerged in seawater, RC members are often located in partially saturated zones and subjected to repeated wetting and drying cycles. This exposure condition makes concrete more vulnerable to corrosion compared to fully saturated environments. The findings indicate that the duration of wetting and drying significantly influences the formation of compounds such as chloroaluminate, quartz, brucite, and dolomite, which contribute to the deterioration of concrete [23]. Therefore, corrosion mechanisms in reinforced concrete are strongly governed by environmental exposure conditions, where coupled actions of chlorides, sulfates, temperature variations [24], and wetting–drying cycles [25] significantly accelerate material degradation and alter the long-term durability of structural members.

Prior studies have analyzed how corrosion intensity influences the shear characteristics of RC beams. According to Rodriguez et al. [26], corrosion-induced degradation of material properties can shift the failure mode of RC beams from flexure to shear, even when the beams are properly designed. Thus, reliable prediction of the shear strength of corroded beams throughout their lifecycle is vital for structural integrity. Higgins and Farrow [27] observed that lightly corroded beams typically fail by shear-compression, whereas heavily corroded ones fail due to stirrup rupture. Juarez et al. [28] concluded that as deterioration progresses from moderate to severe, the ultimate shear strength is substantially compromised. Furthermore, Xu and Niu [29] found that higher shear span-to-depth ratios (a/d) lead to greater reductions in shear capacity, suggesting that size effects warrant attention in future research. Imam and Azad [30] conducted an experimental study on thirteen corroded and four uncorroded beams subjected to accelerated corrosion. The results indicate that shear strength loss is primarily attributed to reduced stirrup area and corrosion-induced cracking. Val et al. [31] examined the influence of stirrup corrosion on the reliability of RC beams. Results indicated that low and medium corrosion degrees (including uniform and pitting corrosion) minimally affect beam reliability. However, under high corrosion degrees, beam shear performance significantly decreases, with pitting corrosion posing a greater risk than uniform corrosion. In their work, Sayed et al. [32] analyzed slender beams ($a_v/d$ = 3.0) and concluded that 20% corrosion caused an 18% mass loss alongside a 19% decline in shear resistance. Xia et al. [33] similarly found that deep beams exposed to corrosion exhibited a shift in failure mechanism, from compression in uncorroded specimens to shear at 30–50% corrosion levels. Additionally, Suffern et al. [34] identified that 20% corrosion could reduce the shear strength of deep beams by nearly 53%.

Additionally, previous studies proposed empirical and theoretical methods [33,35,36,37,38,39,40,41,42,43,44] and developed finite element (FE) models [45,46] to estimate the residual shear capacity of CRCBs. Existing analytical models for predicting the shear capacity of CRCBs typically rely on theories and design codes developed for uncorroded RC beams, including the strut-and-tie model (STM) [42,43], the limit equilibrium theory [38], the equivalent truss theory [47], the modified compression field theory [42], ACI 318 [37,42,43], GB50010-2002 [39,48], JSCE 2007 [40], SIA 262 [44], BS 8110 [49], BIS 2000 [41], and Eurocode 2 [35,43]. Analytical models have been proposed for predicting the shear capacity of CRCBs [50]. Despite decades of research, traditional analytical and empirical methods for predicting the shear capacity of CRCBs face inherent limitations. These methods rely on simplified mechanical idealizations, such as planar truss or strut-and-tie representations, which cannot fully capture the complex, interacting effects of non-uniform corrosion, varying pitting morphologies, cracking patterns, and material degradation. Moreover, existing design-oriented equations are typically calibrated on small datasets with limited parameter ranges, leading to poor generalization when applied to new corrosion scenarios or different beam configurations. The nonlinear, multi-dimensional nature of the corrosion-shear interaction—involving geometric, material, and corrosion-related parameters—makes it increasingly evident that conventional approaches are no longer sufficient. These limitations have motivated the exploration of data-driven alternatives capable of capturing such complex behaviors more effectively.

Machine learning (ML) represents a unique approach by learning directly from data, bypassing the need for simplifying assumptions typical in analytical models [51,52,53,54]. This is advantageous for predicting corrosion-induced shear capacity, where complex interactions such as non-uniform corrosion and crack propagation occur. With training on large, diverse datasets, ML models excel in generalization, resulting in more accurate predictions across various design scenarios. The application of ML has shown strong potential in various areas of structural engineering [55,56], including structural health monitoring [57,58], damage detection [59,60,61,62,63], performance evaluation [64,65,66,67,68], and structural parameter identification [69,70]. In addition, ML-based models have achieved considerable accuracy in predicting the shear performance of RC beams. A notable early study by Goh [71] utilized artificial neural networks (ANNs) to evaluate the ultimate shear strength of deep beams. In a comparable effort, Zhang et al. [72] applied a random forest (RF) model coupled with an optimization algorithm to estimate the shear capacity of RC beams while incorporating the effect of stirrups. Meanwhile, Cladera and Marí [73] constructed an Artificial Neural Network (ANN) framework to predict the shear strength of beams governed by diagonal tension failure. It was observed that the shear strength does not increase linearly with the amount of shear reinforcement. Accordingly, new analytical expressions were formulated for beams made of normal and high-strength concrete, achieving a closer match with experimental outcomes than the EC2 and ACI code formulations. Wakjira et al. [74] employed several advanced machine learning algorithms, including Adaptive Boosting, Classification and Regression Tree, gradient-boosted regression trees, extreme gradient boosting, and Super-Learner, to predict the flexural strength of fiber-reinforced polymer (FRP)-reinforced concrete beams. Of all the algorithms tested, the Super-Learner achieved the best overall performance. Uddin et al. [75] further showed that GBRT provided more accurate shear strength predictions for RC beams than ANN, random forest (RF), or gene expression programming (GEP). Yaseen [76] analyzed 112 FRP-RC beam cases with transverse reinforcement using M5-Tree, extreme learning machine (ELM), and random forest (RF), concluding that the M5-Tree offered superior precision ($R^2$ = 0.9313; RMSE = 35.51 kN) based on nine influential parameters.

According to Kumar et al. [77], the predictive performance of artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), decision trees (DT), and extreme gradient boosting (XGBoost) models was evaluated for estimating the shear capacity of CRCBs. Based on a dataset containing 140 samples covering multiple corrosion stages, XGBoost exhibited the most accurate predictions, achieving an $R^2$ of 0.99 and a lower mean absolute percentage error (MAPE) compared to the remaining approaches. In their study, Asteris and Nguyen [78] developed an ANN model to estimate the shear strength of CRC beams at various service stages. Using a database of 158 shear tests and twelve input features related to beam geometry, materials, reinforcement, and corrosion, the model achieved a high coefficient of determination ($R^2$ = 0.989). Similarly, Fu and Feng [79] utilized a GBRT model incorporating six dimensionless variables tied to geometric and material attributes, reinforcement details, and corrosion level, attaining an average $R^2$ greater than 0.9.

From the literature, it is evident that analytical models for predicting the shear capacity of corroded reinforced concrete beams (CRCBs) are hindered by simplifying assumptions and inadequate consideration of influencing parameters, resulting in low predictive accuracy. Despite the growing application of machine learning (ML) techniques, including XGBoost, for predicting the shear strength of CRCBs, existing studies are generally limited by relatively small datasets and constrained input feature spaces. For example, previous works have utilized datasets of 140 [77] and 158 [78,79] samples, respectively, which may restrict the generalization capability of the developed models. This study addresses this gap by employing six machine learning algorithms, namely KRR, KNN, DT, RF, GBRT, and XGBoost, trained on 408 experimental data points. This expanded dataset enhances the robustness and reliability of the predictive models. Furthermore, a comprehensive set of ten input variables is employed to better capture the complex interactions influencing shear capacity. A rigorous preprocessing framework is adopted, including normalization of all input variables within the range of [−1, 1] and systematic screening to eliminate duplicate entries. These steps improve numerical stability and reduce potential bias in the learning process. Model performance is further optimized through hyperparameter tuning. In addition, SHAP analysis is employed to interpret feature importance and quantify the contribution of each parameter, while a parametric study is conducted to evaluate model sensitivity to variations in geometric and material properties. The predictive performance of the developed models is benchmarked against existing analytical formulations. Finally, the most accurate ML model is implemented within a Python-based graphical user interface (GUI), providing a practical and efficient tool for engineering design applications.

2. Experimental Data of CRCBs

To acquire experimental data concerning the shear capacity of CRCBs, an extensive review of the literature was conducted. The collected studies were carefully selected from peer-reviewed and reliable experimental sources to ensure data consistency, accuracy, and relevance to the investigated problem. A rigorous screening process was also applied to remove incomplete, duplicate, or inconsistent records, thereby improving the overall quality and reliability of the dataset. Through this process, 408 high-quality data samples were gathered from different references [26,27,28,30,32,33,38,47,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105], and the compiled dataset is detailed in

Table A1. Geometrical configuration and material parameters of the dataset.

References	No. of Tests	${\mathit{f}}_{\mathit{c}\mathit{l},150}$ (MPa)	h (mm)	${\mathbf{b}}_{\mathbf{w}}$ (mm)	${\mathit{\rho }}_{\mathit{l}}$ (%)	${\mathit{\rho }}_{\mathit{v}}$ (%)	${\mathit{f}}_{\mathit{y}}$ (MPa)	${\mathit{f}}_{\mathit{y}\mathit{v}}$ (MPa)	λ	${\mathit{\eta }}_{\mathit{l},\mathit{s}\mathit{n}}$ (%)	${\mathit{\eta }}_{\mathit{w},\mathit{s}\mathit{n}}$ (%)	${\mathit{V}}_{\mathit{e}\mathit{x}\mathit{p}}$ (kN)
Rodriguez, Ortega, and Casal [26]	10	35–37	200	150	1.77	0.22–0.45	585	626	4.7	10.7–16.9	66–97.2	26.6–38.6
Xu and Niu [38]	21	28.4–30.6	200	120	1.92	0.32	416	275	1–2	0	0–40.3	47.7–146.8
Higgins and Farrow III [27]	8	29.3–33.40	610	254	1.9	0.39–0.49	441	496	2.04	0	0–33.8	405–594
Xuetian and Huiguang [81]	10	27	200	150	2.3	0.25	210	275	1.5–2.2	0–2.9	0–2	75.9–131
Zhao and Jin [47]	28	22.5	180	150	2.26–2.79	0.19–0.45	369	332	2.2–3.1	0–19	0–8.3	60–104
Xue, Seki, and Chen [84]	8	33.3–39.3	240	120	2.17	0.39	706	300	1.5–3.2	5–16.6	0	70.5–124.3
Xue, Seki, and Song [86]	10	33.1–35.1	240	120	2.17	0.39–0.52	706	300	2.6	0–1.8	0–34.2	69.5–87.9
Xia, Jin, and Li [33]	18	22.5	200	120	2.62	0.48–0.56	300–435	322–464	1.5	0	0–54.15	85.2–138.2
El-Sayed, Hussain, and Shuraim [32]	6	34.6–44.4	350	200	3.27	0.25–0.5	480	495	3	0	9.8–24.5	136–204
Imam and Azad [30]	13	33.1	220–240	140–150	1.22–1.48	0.84–0.9	580	560	1.57–1.76	0	22.8–52.7	80.9–119.1
Lu, Li, Li, Zhao, Tang, and Sun [91]	20	40.8–50	300	200	2.2	0.1–0.2	390	339–524	2–3.5	0–16.5	0–55.6	93.8–181.5
Liu [83]	24	32–40	240–350	200	2.15	0.14–0.25	390	339–524	2–3.5	0–12.1	0–60.1	93.8–181.5
Taqi, Mashrei, and Oleiwi [100]	19	34.5–45	150	100	1.25	0–0.84	560	0–534	2.8	0–20	0	39.5–78
Juarez, Guevara, Fajardo, and Castro-Borges [28]	16	21	350	200	1.68	0.25–0.33	420	420	2	0	0–21.25	68–121
Fu, Huang, Dong, Song, and Zhang [103]	12	24	150	100	2.46	0.81	400	325	1.17–2.34	0–9.64	0–19.48	47.3–109.9
Li, Huang, Lu, Zhou, Mansour, Kai, Qin, and Wang [104]	9	27.4	370	180	4.56	0.31	410	325	1–3	0	0–12	163.9–313.6
Tan and Kien [99]	8	20.1	200	150	1.67	0	328.5	0	2.5	0–4.99	0	30.2–37.6
Alaskar, Alqarni, Alfalah, El-Sayed, Mohammadhosseini, and Alyousef [98]	9	38–44	350	200	4	0.14–0.57	480	380–400	3	0	0–15.6	126.5–238
Azam and Soudki [82]	8	47.3	350	150	2.17	0–0.18	400	0–384	1.6	0–4.64	0	191.63–497.13
El-Sayed, Hussain, and Shuraim [89]	14	29.4–38	350	200	3	0.25–0.5	480	495	1–2	0	0–24	512–1105
Guo, Wang, Xie, Shi, and Yu [94]	14	20.1	200	120	1.47	0.56	335	235	2.4–3	0–14.77	0	60.11–92.32
Sahmaran, Anil, Lachemi, Yildirim, Ashour, and Acar [87]	10	45.5–46.3	220	150	2.62	0.37	400	235	2.5	0–20.41	0–20.41	62.3–227
Huang, Ye, Jin, Jin and Xu [96]	12	44.8	150	100	1.8	0.38	450	380	1.69	0–19.83	0–31.67	54.3–131.4
Biswas, Iwanami, Chijiwa, and Uno [93]	7	31	350	200	0.62	0.2	463	352	2.75	0–38	0	90–272
Tan, Kien, and Giang [102]	8	25.1	200	150	1.67	0	328	0	2.5	0–4.99	0	30.2–37.6
Sathe and Patil [105]	4	30	150	150	1.37	0.45	510	510	5	0	0–16.1	75–110
Suffern [80]	15	35.7–45.4	350	125	2.62	0–0.83	414	0–414	1–2	0	0–18.7	150–473
Shehab, Mahmoud, and Mansoor [97]	8	36.64–42	200	150	1.65	0.25–0.34	603	523–543	2.5–3	0	0–15.3	104.41–142.43
Han, Lee, Yi and Kim [95]	8	39.2	250	170	2.24	0	400	0	2.96	0–7.91	0	58.5–153.1
Zheng, Li, Zhang, and Yan [101]	4	37.24	300	150	2	0.188	442	370	2.31	0	0–13.9	183.77–213.27
Wang, Zhang, Zhang, Ma, and Liu [88]	14	25.96–38.32	500	250	1.64	0.31	365.7	350	1.77	0	0–51	225–370
Ye, Zhang, and Gu [90]	13	26.85–32.19	260	130	1.74–2.6	0.515	450.5–459	369.6	2.22	0–14	0–27	50.3–91.9
Lachemi, Al-Bayati, Sahmaran, and Anil [85]	20	42.9–45.5	220	150	2.2	0.38	450	369	2.5	0–20.41	0	45.1–200.6
Maximum		50.0	610.0	254.0	4.6	0.9	706.0	626.0	5.0	38.0	97.2	1105.0
Minimum		20.1	150.0	100.0	0.6	0.0	210.0	0.0	1.0	0.0	0.0	26.6
Mean		33.2	262.7	156.1	2.2	0.3	437.8	338.1	2.3	4.2	13.9	155.6
SD		8.6	97.3	39.9	0.7	0.2	95.7	153.3	0.8	6.4	19.0	150.6

. Additionally, the steps for establishing machine learning models to predict the shear capacity of CRCBs were depicted in Figure 2. It should be noted that while Table A1 reports the global minimum and maximum values for each parameter across all references, the histograms in Figure 3 reflect the actual frequency distribution of the 408 compiled samples, which may exhibit clustering within narrower sub-ranges.

Among the major factors influencing CRCB shear strength are the concrete compressive strength ($f_{cl,150}$), beam depth (h), beam width ($b_w$), stirrups ratio (${\rho }_v$), longitudinal reinforcement ratio (${\rho }_l$), yield strength of longitudinal reinforcement ($f_y$), yield strength of stirrups ($f_{yv}$), shear span-to-depth ratio (λ), and reinforcement area loss ratio of longitudinal bars (${\eta }_{l,sn}$) and stirrups (${\eta }_{w,sn}$). The output parameter is the experimental ultimate shear capacity ($V_{exp}$) of CRCBs. Figure 3 illustrates the distribution of input parameters in the collected dataset. It should be noted that the corrosion levels reported in the collected studies were measured using different approaches, such as mass loss and cross-sectional area reduction. To address this heterogeneity and ensure consistency within the dataset, all corrosion-related measurements were standardized and expressed in terms of reinforcement area loss ratios (${\eta }_{l,sn}$ and ${\eta }_{w,sn}$), providing a unified and physically meaningful representation of steel degradation directly linked to structural capacity. For clarity, the shear span (a) for all simply supported beams was measured from the load to the support centers. According to the guidelines established by Hastie et al. [106], the dataset in question meets the requirement of having over ten times the number of independent variables necessary to adequately construct a predictive model, thus making it suitable for model development. Additionally, it should be noted that while Table A1 reports the global minimum and maximum values for each parameter across all references, the histograms in Figure 3 reflect the actual frequency distribution of the 408 compiled samples, which may exhibit clustering within narrower sub-ranges.

3. ML-Based Predictive Models

3.1. Fundamentals of ML Techniques

Machine learning is a computational approach that facilitates the training of a computer system to generate precise predictions on novel data. The process entails providing the system with starting data and a learning algorithm, which is then repeatedly refined until the system’s performance aligns with the intended criteria. The accuracy of a machine learning model is contingent upon both the original dataset and the efficacy of the learning algorithm.

In this study, six different ML algorithms—KRR, KNN, DT, RF, GBRT, and XGBoost—have been utilized to predict the shear capacity of CRCBs. The best model is determined by comparing the accuracy of the predictions by all models. These six ML algorithms are chosen for their unique strengths and versatility in handling various data and problems. Kernel Ridge Regression (KRR) combines ridge regression’s simplicity with kernel methods’ flexibility, effectively managing nonlinear relationships and preventing overfitting through regularization [107]. K-nearest neighbors (KNN) is easy to understand and implement, classifying based on the majority vote of nearest neighbors. It makes no assumptions about data distribution, advantageous for complex data [108]. Decision tree (DT) models are interpretable and visual, handling both numerical and categorical data with minimal preprocessing. They can model nonlinear relationships effectively [109]. Random forest (RF) enhances predictive accuracy by combining multiple decision trees, reducing overfitting. It handles large, high-dimensional datasets well and provides feature-importance insights [110]. Gradient-boosted regression trees (GBRT) iteratively improve model performance by correcting previous errors known for high accuracy in various tasks with tuneable hyperparameters [111]. XGBoost excels in performance and accuracy, handling missing values and providing efficient memory use, making it ideal for competitive and real-world applications [112]. In XGBoost, base learners (trees) are created sequentially by minimizing the objective function defined in Equation (1), which combines the loss function and regularization term.

$${\displaystyle {\sum }_{i=1}^n}L(y_i, \widehat{y_i})+{\displaystyle {\sum }_{i=1}^T}\mathsf{\Omega }\left(f_t\right)$$

(1)

$$\mathsf{\Omega }\left(f\right)=\gamma T+{\displaystyle \frac{1}{2}}\lambda {\displaystyle {\sum }_{j=1}^T}{w}_j^2$$

(2)

where $\gamma$ represents the complexity of each leaf, $w_j$ is the weight of leaf j, and λ is the penalty parameter.

3.2. Construction of ML-Based Predictive Models

The machine learning (ML) models developed in this study incorporate ten input variables, whose descriptive statistics have been presented previously. These variables were carefully selected to optimize the prediction of shear capacity by emphasizing the most influential features, thereby reducing data variability and enhancing computational efficiency. To ensure consistency and reduce bias in the learning process, all input variables were normalized within the range of [−1, 1] prior to model development. This preprocessing step minimizes the influence of differences in variable scales, improves numerical stability, and enhances the generalization capability of the ML models, including XGBoost. The normalization procedure was applied uniformly across the dataset without incorporating any target-related information, thereby preventing unintended data leakage from output variables into the input space. In addition, the dataset was carefully screened to remove duplicate entries, ensuring that identical data points were not repeated. Overall, this preprocessing strategy stabilizes the training process and prevents the dominance of specific features that could otherwise bias the model performance. Consequently, the high predictive accuracy achieved is attributed to the robustness of the developed models and the quality of the compiled dataset. Model accuracy was quantified using MAE, MAPE, RMSE, and R², as expressed mathematically in

Table 1. Formulations of model performance indicators.

Name of the Measure	Notation	Expression
Coefficient of determination	$R^2$	${\mathrm{R}}^2={\displaystyle \frac{{\left[{\displaystyle {\sum }_{\mathrm{i}=1}^{\mathrm{n}}}(\mathrm{y}-\overline{\mathrm{y}})(\dot{\mathrm{y}}-\overline{\dot{\mathrm{y}}})\right]}^2}{{\displaystyle {\sum }_{\mathrm{i}=1}^{\mathrm{n}}}{(\mathrm{y}-\overline{\mathrm{y}})}^2{\displaystyle {\sum }_{\mathrm{i}=1}^{\mathrm{n}}}{(\dot{\mathrm{y}}-\overline{\dot{\mathrm{y}}})}^2}}$
Root mean squared error	RMSE	RMSE = $\sqrt{{\displaystyle \frac{1}{\mathrm{n}}}{\displaystyle {\sum }_{\mathrm{i}=1}^{\mathrm{n}}}{(\mathrm{y}-\dot{\mathrm{y}})}^2}$
Mean absolute error	MAE	MAE = ${\displaystyle \frac{1}{\mathrm{n}}}{\displaystyle {\sum }_{\mathrm{i}=1}^{\mathrm{n}}}\left|\mathrm{y}-\dot{\mathrm{y}}\right|$
Mean absolute percentage error	MAPE	$MAPE={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^N}\left|{\displaystyle \frac{y_i-{\dot{y}}_i}{y_i}}\right|$

[113,114,115,116].

Hyperparameter values play a crucial role in defining both the predictive power and generalization capacity of a model. These parameters are fine-tuned through optimization techniques, with grid search being a common method [117,118,119]. In this study, the selected hyperparameter ranges were carefully defined based on three main considerations: (i) commonly recommended ranges reported in previous studies, (ii) standard practices in machine learning applications for structural engineering problems, and (iii) preliminary trial runs conducted to ensure adequate coverage of the search space while avoiding overfitting and high computational cost. To further enhance model reliability, K-fold cross-validation is incorporated into the tuning process to reduce the risk of overfitting. The collected experimental dataset was randomly separated into two main subsets: a training set (80%) for building the ML models and tuning their parameters, and a testing set (20%) used to validate the model outputs and assess performance [120]. The steps involved in K-fold cross-validation are

▪

Split the training dataset into K non-overlapping folds of equal size, ensuring each observation participates exactly once in training and validation.

▪

Train the model on K − 1 folds and validate using the remaining fold.

▪

Perform this cycle K times to compute K performance measures.

The overall performance of the model is obtained by averaging the K-fold results. A 10-fold cross-validation procedure (K = 10) along with grid search is used in this study to adjust hyperparameters. In each fold, 10% of the training data functions as the validation set, while 90% is applied for training [121,122]. Specifically, XGBoost performance depends on the initial hyperparameter settings, including the number of trees (n_estimators) and the learning rate [121]. The results of XGBoost hyperparameter tuning with 10-fold cross-validation are presented in four separate figures, corresponding to n_estimators values of 100, 200, 300, and 400. Each figure includes four curves for maximum depths 3, 4, 5, and 6. The learning rate appears on the X-axis and the R² score on the Y-axis. As shown in Figure 4, $R^2$ generally improves with increasing learning rate ($L_r$) up to 0.2, achieving peak performance when the learning rate is 0.2, n_estimators is 400, and max depth is 6. The optimal hyperparameter values for KRR, KNN, DT, RF, GBRT, and XGBoost are listed in

Table 2. Best performing hyperparameter settings for each ML model.

Model	Parameters
KRR	alpha = 0.01, degree = 1, kernel = “rbf”, gamma = 0.41.
KNN	n_neighbors = 2, p = 1, weights = ‘distance’, algorithm = ‘auto’, leaf_size = 40.
DT	max_depth = 10, min_samples_leaf = 1, min_samples_split = 2, max_features = ‘sqrt’.
RF	max_depth = 20, max_features = ‘auto’, min_samples_leaf = 1, min_samples_split = 2, n_estimators = 200.
GBRT	learning_rate = 0.1, max_depth = 20, max_features =‘sqrt’, min_samples_leaf = 3, min_samples_split = 10, n_estimators = 100.
XGBoost	colsample_bytree = 0.8, learning_rate = 0.2, max_depth = 6, n_estimators = 400, subsample = 1.

.

4. Results and Discussion

4.1. Evaluation of Model Predictions

The predictive capabilities of the six developed ML models are examined in this section. After selecting the optimal hyperparameters, each model was trained on the dataset. Figure 5 presents scatter plots comparing predicted and experimental CRCB shear capacities. High correlation is observed for all models ($R^2$ ≥ 0.97). KRR shows lower predictive performance relative to the others, while XGBoost achieves superior accuracy. Figure 6 depicts the residuals of predicted CRCB shear capacities ($V_{exp}-V_{pred}$) for both training and test datasets. The XGBoost model demonstrates residuals closely centered on zero, indicating excellent predictive capability and strong correlation with measured values. It achieves the highest performance, with $R^2$ = 0.994, RMSE = 11.61, MAE = 3.388, and MAPE = 0.0237. By contrast, KRR shows the lowest $R^2$ (0.9784) and larger RMSE, MAE, and MAPE values of 22.10, 12.76, and 0.1040, respectively. The performance metrics for all models are summarized in

Table 3. Evaluation metrics of all ML models.

Model	Dataset	Performance Measures
		RMSE (kN)	MAE (kN)	MAPE (%)	${\mathit{R}}^2$ (%)
KRR	Training dataset	15.96	10.15	8.35	99.00
	Test dataset	37.65	23.16	18.55	92.00
	All	22.10	12.76	10.40	97.84
KNN	Training dataset	2.10	0.42	0.373	1.00
	Test dataset	36.88	19.84	10.87	96.23
	All	16.64	4.32	2.48	98.77
DT	Training dataset	10.42	4.38	4.56	99.40
	Test dataset	40.5	22.64	15.97	95.50
	All	20.41	8.048	6.86	98.16
RF	Training dataset	13.60	7.75	5.45	99.00
	Test dataset	29.50	16.66	11.83	97.60
	All	17.95	9.54	6.74	98.57
GBRT	Training dataset	3.940	1.69	1.195	1.00
	Test dataset	39.40	21.28	14.70	91.3
	All	18.00	5.630	3.91	98.57
XGBoost	Training dataset	2.00	0.418	0.371	1.00
	Test dataset	25.60	15.20	10.31	98.20
	All	11.61	3.388	2.37	99.40

.

It is also observed that some models, particularly KRR and DT, exhibit an increased spread of residuals at higher predicted shear capacities, indicating the presence of heteroscedasticity. This behavior can be attributed to the increased variability and complexity associated with high-capacity beams, where nonlinear interactions between parameters become more pronounced. Models such as KRR and DT have limited capability in capturing such complex relationships, leading to larger prediction errors in these regions. In contrast, tree-based ensemble models—especially XGBoost—demonstrate a more stable residual distribution due to their ability to model nonlinearities and interactions more effectively, thereby reducing heteroscedastic effects.

The superior performance of tree-based ensemble models (RF, GBRT, and XGBoost) compared to KRR and KNN can be attributed to their ability to effectively capture complex nonlinear relationships and high-order interactions among input variables without requiring prior assumptions about data distribution. In particular, XGBoost benefits from a gradient boosting framework that iteratively minimizes prediction errors, incorporates regularization to control overfitting, and efficiently handles multicollinearity and heterogeneous feature scales. In contrast, KRR relies on kernel functions that may struggle to generalize well in high-dimensional spaces with complex feature interactions, while KNN is sensitive to data distribution, noise, and feature scaling, which can reduce its predictive robustness.

Given its higher predictive accuracy, XGBoost outperforms all other algorithms in estimating CRCB shear capacity. Hence, the ensuing analysis centers on the XGBoost model, employing Shapley Additive Explanations (SHAP) to interpret the results.

4.2. Comparison with Existing Analytical Models

This section compares the XGBoost model predictions of CRCB shear capacities with outputs from established analytical models proposed by Cladera et al. [123] and Lu et al. [50], as outlined in

Table 4. Comparison of analytical and ML prediction models for CRCB shear strength.

Literature	Equations
Cladera et al. [123]	For slender beams (a/d ≥ 2.5) $V_R=V_{cu}+V_{su}\le V_{max}$ $V_{cu}=0.3\xi {\displaystyle \frac{x}{d}}{f_{cm}^{2/3}{b}_{w}d\nless V}_{cu,min}$ $=0.25\left(\xi K_c+{\displaystyle \frac{20}{d_o}}\right)f_{cm}^{2/3}{b}_{w}d$ $V_{su}=1.4{\displaystyle \frac{A_{sw}}{s}}{f}_{yw}\left(d-x\right)cot\theta ; V_{max}$ $=a_{cw}{b}_{w}z{v}_1{f}_{cm}{\displaystyle \frac{cot\theta }{1+{cot}^2\theta }}\approx 0.225 f_{cm}{b}_{w}d$ in which $\xi ={\displaystyle \frac{2}{\sqrt{1+\frac{d_o}{200}}}}{\left({\displaystyle \frac{d}{a}}\right)}^{0.2}\nless 0.45d$ $and d_o in mm;$ ${\displaystyle \frac{x}{d}}=n{\rho }_l\left(-1+\sqrt{1+{\displaystyle \frac{2}{n{\rho }_l}}}\right)\approx 0.75{\left(n{\rho }_l\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$3$}\right.}$ $cot\theta ={\displaystyle \frac{0.85d}{d-x}}\le 2.5$ For non-slender beams (a/d < 2.5) $V_R=V_{cu}+V_{su}\le V_{max}$ $V_{cu}=0.3\xi {\displaystyle \frac{x}{d}}{K_{ad}f}_{cm}^{2/3}{b}_{w}d$ $V_{su}=V_{swy}+V_{swx};$ $V_{swy}={\displaystyle \frac{A_{swy}}{s_x}}\left(d-x_1\right)cot\theta {\sigma }_{swy}$ $V_{swx}={\displaystyle \frac{A_{swx}}{s_y}}\left(d-x_1\right)cot\theta {\sigma }_{swx}$ in which $K_{ad}=1+{\left(2.5-{\displaystyle \frac{a}{d}}\right)}^2;$ ${\displaystyle \frac{x_1}{d}}={\displaystyle \frac{x}{d}}+\left(1-{\displaystyle \frac{x}{d}}\right){\left(1-0.4{\displaystyle \frac{a}{d}}\right)}^2\le 1.00$ $cot\theta ={\displaystyle \frac{a}{d}} \ge 0.5;$ ${\sigma }_{swy}={\displaystyle \frac{f_{ctm}{K}_{ad}}{{\rho }_l}}{\displaystyle \frac{x_1}{d}}{cot}^3\theta \le f_{yw};$ ${\sigma }_{swx}={\displaystyle \frac{f_{ctm}{K}_{ad}}{{\rho }_l}}{\displaystyle \frac{x_1}{d}}{cot}^3\theta \le f_{yw}$
Lu et al. [50]	$V_u=\psi V_c+V_s=\psi Max\left(V_{c1} ,V_{c2} \right)+A_{vc}{f}_{yv}{jh}_o/s$ in which $\psi =\left\{\begin{array}{c}{0.008e}^{-{\displaystyle \frac{0.122a}{h_0}}}-0.003{\eta }_{sn,w}+1.01,{\displaystyle \frac{a}{h_0}}<2.5\\ {0.1e}^{-{\displaystyle \frac{0.122a}{h_0}}}-0.003{\eta }_{sn,w}+1.38, {\displaystyle \frac{a}{h_0}}\ge 2.5\end{array}\right.$ $V_{c1}=0.2\sqrt[3]{100f_{cyl,150}{\rho }_l} \sqrt[4]{{10}^3/h_0}\left(0.75+1.4h_0/a \right)b{h}_0 ;$ $$\begin{gathered} V_{c2}=0.24 \sqrt[3]{f_{cyl,150}^2} (1+ \\ \sqrt{100{\rho }_{lc}})(1+3.33r/h_0)b{h}_0/[1+{(a/h_0)}^2]; \end{gathered}$$ where ${\rho }_{lc}={\rho }_l\left(1-{\eta }_{l,sn}\right)$

. The compression chord capacity model (CCCM) by Cladera et al. [123] is applied to estimate the shear strength of RC beams affected by corrosion. CCCM predictions were tested against experimental data from 146 slender and non-slender beams exhibiting shear failure, with corrosion impacting stirrups and/or longitudinal reinforcement. The comparison between the experimental results of 146 beams and the shear capacities predicted by Cladera et al.’s model [123] and XGBoost is shown in Figure 7a,b. For the $V_{Exp}/V_{Pred}$ ratio, Cladera et al.’s model yields a mean of 1.19 and SD of 0.237, while XGBoost produces a mean of 1.00476 and SD of 0.0785. This demonstrates a marked improvement in prediction accuracy using XGBoost. Furthermore, an analytical model proposed by Lu et al. [50] predicts the shear strength of CRCBs using a reduction coefficient that considers stirrup corrosion and the shear span-to-depth ratio. Predictions were compared to experimental data from 158 shear-failing beams. Figure 8a,b show the comparison between experimental results and predictions from Lu et al.’s model [51] and XGBoost, respectively. The $V_{Exp}/V_{Pred}$ ratios exhibit means (μ) and standard deviations (SD) of 1.002 and 0.234 for Lu et al.’s model, and 1.00358 and 0.071 for XGBoost, demonstrating the improved performance of XGBoost.

4.3. XGBoost Model Explainability Using SHAP Approach

To investigate the contribution of each input variable to the XGBoost model, two SHAP-based visualizations were employed: (a) the SHAP feature importance plot and (b) the SHAP summary plot. The feature importance plot, shown in Figure 9, orders features by their average absolute Shapley values. Beam depth (h) is identified as the most critical factor affecting CRCB shear capacity, followed by shear span-to-depth ratio, concrete compressive strength ($f_{cl,150}$), and longitudinal reinforcement ratio (${\rho }_l$), while the influence of other variables is relatively minor. To analyze the influence of input features on prediction outcomes, a SHAP summary plot was constructed (Figure 10). Each dot represents a prediction instance, with SHAP values indicating whether the feature positively or negatively affects the predicted shear capacity. The color-coding shows feature magnitude, from low (blue) to high (red). It can be observed that, except for λ, ${\eta }_{l,sn}$, and ${\eta }_{w,sn}$ the features generally contribute positively, implying that higher values enhance CRCB shear capacity, whereas lower values diminish it.

4.4. Discussion and Comparative Analysis with Previously Developed XGBoost-Based Structural Models

The results obtained in this study demonstrate that the developed XGBoost model provides highly accurate predictions of the shear capacity of CRCBs, achieving superior performance compared to all other tested machine learning approaches. The model achieved exceptional predictive accuracy, with an R² value of 0.994, indicating excellent agreement between predicted and experimental shear capacities. This level of accuracy is notably higher than that reported in several related studies on structural performance prediction. For example, Kumar et al. [77] developed an XGBoost model for CRCBs using a smaller dataset (140 samples), reporting an R² of 0.99. Although their results are strong, the limited dataset size constrained the model’s generalization capability. In contrast, the present study significantly expands the dataset to 408 samples, which enhances model robustness and improves its ability to capture diverse corrosion scenarios and structural behaviors. Similarly, Fu and Feng [79] applied gradient-boosted regression trees (GBRT) to predict shear capacity using only six input variables, achieving R² values slightly above 0.90. While their study confirmed the effectiveness of boosting-based algorithms, the reduced feature space limited the representation of key physical mechanisms influencing shear strength degradation. In comparison, the current study incorporates ten influential parameters, allowing a more comprehensive representation of geometric, mechanical, and corrosion-related effects.

In broader structural engineering applications, XGBoost has also been successfully applied for predicting flexural strength, material behavior, and structural damage assessment. However, most existing studies typically report R² values ranging between 0.90 and 0.98, depending on dataset complexity and feature selection. The current model exceeds this range, which can be attributed to three main factors: (i) the use of a larger and more diverse dataset, (ii) systematic data preprocessing including normalization and duplicate removal, and (iii) rigorous hyperparameter optimization using 10-fold cross-validation.

Another important aspect is the interpretability of the model. Unlike many previous XGBoost applications that focus solely on predictive accuracy, this study integrates SHAP analysis to quantify feature contributions. The results confirm that beam depth, shear span-to-depth ratio, and concrete compressive strength are the most influential variables, which is consistent with classical structural mechanics principles. This agreement between data-driven insights and physical understanding enhances the reliability and engineering interpretability of the proposed model.

4.5. Graphical User Interface

A graphical user interface (GUI) platform utilizing the XGBoost model has been developed. The primary objective of this platform is to enhance the accessibility of the shear capacity of CRCBs for practicing engineers and researchers in this field. The platform exhibits a high degree of user-friendliness, obviating the need for a profound understanding of machine learning principles. The Python library was used to develop an interactive user interface (UI) for the model [124], enabling users to enter feature values and promptly get the appropriate shear capacity result. The GUI platform can be accessed through the reference [125]. It is essential to emphasize that this GUI is exclusively applicable to CRCBs with geometric and material properties detailed in Table A1, as the XGBoost algorithm was trained based on these specific ranges. Users can input numerical values for the parameters and then click the “submit” button to retrieve the shear capacity of the CRCB.

5. Parametric Study

A comprehensive parametric study was conducted to further evaluate the reliability and robustness of machine learning (ML) models in predicting the performance of corroded reinforced concrete beams (CRCBs). This study aimed to explore how different geometric and material properties, such as beam dimensions, reinforcement ratios, and material strengths, influence the overall behavior and performance of CRCBs, particularly their shear capacity. The analysis utilized the XGBoost model.

To gain a thorough understanding of these effects, eight distinct groups of CRCBs, each with varying combinations of geometric and material properties, were analyzed using the developed ML model. The specific properties considered for each group, such as the beam size, corrosion level, concrete strength, and reinforcement details, are systematically presented in

Table A2. Overview of CRCB geometric and material properties considered in the parametric investigation.

Group	${\mathit{f}}_{\mathit{c}\mathit{l},150}$	h	${\mathbf{b}}_{\mathbf{w}}$	${\mathit{\rho }}_{\mathit{l}}$	${\mathit{\rho }}_{\mathit{v}}$	${\mathit{f}}_{\mathit{y}}$	${\mathit{f}}_{\mathit{y}\mathit{v}}$	λ	${\mathit{\eta }}_{\mathit{l},\mathit{s}\mathit{n}}$	${\mathit{\eta }}_{\mathit{w},\mathit{s}\mathit{n}}$
	MPa	mm	mm	%	%	MPa	MPa	--	%	%
G1	35	200	150	1.77	0.22	585	626	4.7	0	10
	35	200	150	1.77	0.22	585	626	4.7	1.25	10
	35	200	150	1.77	0.22	585	626	4.7	2.5	10
	35	200	150	1.77	0.22	585	626	4.7	3.5	10
	35	200	150	1.77	0.22	585	626	4.7	5	10
	35	200	150	1.77	0.22	585	626	4.7	7.5	10
	35	200	150	1.77	0.22	585	626	4.7	10	10
	35	200	150	1.77	0.22	585	626	4.7	12.5	10
	35	200	150	1.77	0.22	585	626	4.7	15	10
G2	35	200	150	1.77	0.22	585	626	4.7	2.5	0
	35	200	150	1.77	0.22	585	626	4.7	2.5	5
	35	200	150	1.77	0.22	585	626	4.7	2.5	10
	35	200	150	1.77	0.22	585	626	4.7	2.5	15
	35	200	150	1.77	0.22	585	626	4.7	2.5	20
	35	200	150	1.77	0.22	585	626	4.7	2.5	25
	35	200	150	1.77	0.22	585	626	4.7	2.5	30
	35	200	150	1.77	0.22	585	626	4.7	2.5	40
	35	200	150	1.77	0.22	585	626	4.7	2.5	50
G3	22.5	180	150	2.79	0.25	369	332	3.1	0	30
	22.5	200	150	2.79	0.25	369	332	3.1	0	30
	22.5	250	150	2.79	0.25	369	332	3.1	0	30
	22.5	300	150	2.79	0.25	369	332	3.1	0	30
	22.5	350	150	2.79	0.25	369	332	3.1	0	30
G4	28.4	200	120	1.92	0.32	416	275	1	0	20
	28.4	200	140	1.92	0.32	416	275	1	0	20
	28.4	200	160	1.92	0.32	416	275	1	0	20
	28.4	200	180	1.92	0.32	416	275	1	0	20
	28.4	200	200	1.92	0.32	416	275	1	0	20
G5	21	300	200	2.2	0.16	390	373	3.5	15.6	44
	22.5	300	200	2.2	0.16	390	373	3.5	15.6	44
	25	300	200	2.2	0.16	390	373	3.5	15.6	44
	27.5	300	200	2.2	0.16	390	373	3.5	15.6	44
	30	300	200	2.2	0.16	390	373	3.5	15.6	44
	32.5	300	200	2.2	0.16	390	373	3.5	15.6	44
	35	300	200	2.2	0.16	390	373	3.5	15.6	44
	37.5	300	200	2.2	0.16	390	373	3.5	15.6	44
	40	300	200	2.2	0.16	390	373	3.5	15.6	44
	42.5	300	200	2.2	0.16	390	373	3.5	15.6	44
G6	29.7	200	120	1.92	0.32	416	275	1	0	40.3
	29.7	200	120	1.92	0.32	416	275	1.5	0	40.3
	29.7	200	120	1.92	0.32	416	275	2	0	40.3
	29.7	200	120	1.92	0.32	416	275	2.5	0	40.3
	29.7	200	120	1.92	0.32	416	275	3	0	40.3
	29.7	200	120	1.92	0.32	416	275	3.5	0	40.3
	29.7	200	120	1.92	0.32	416	275	4	0	40.3
	29.7	200	120	1.92	0.32	416	275	4.5	0	40.3
G7	27	200	150	0.99	0.25	210	275	2.2	2.5	2
	27	200	150	1.9	0.25	210	275	2.2	2.5	2
	27	200	150	1.92	0.25	210	275	2.2	2.5	2
	27	200	150	2.17	0.25	210	275	2.2	2.5	2
	27	200	150	2.26	0.25	210	275	2.2	2.5	2
	27	200	150	2.3	0.25	210	275	2.2	2.5	2
	27	200	150	2.62	0.25	210	275	2.2	2.5	2
	27	200	150	2.79	0.25	210	275	2.2	2.5	2
G8	32	350	200	2.15	0.14	390	476	2.5	2.2	17.6
	32	350	200	2.15	0.19	390	476	2.5	2.2	17.6
	32	350	200	2.15	0.2	390	476	2.5	2.2	17.6
	32	350	200	2.15	0.25	390	476	2.5	2.2	17.6
	32	350	200	2.15	0.39	390	476	2.5	2.2	17.6
	32	350	200	2.15	0.48	390	476	2.5	2.2	17.6
	32	350	200	2.15	0.52	390	476	2.5	2.2	17.6

. This approach allowed for a detailed investigation into which factors most significantly affect the structural integrity and shear capacity of CRCBs, providing valuable insights for designing and assessing these beams under different conditions.

5.1. Longitudinal Reinforcement and Stirrups’ Corrosion Ratio

CRCBs in Groups 1 and 2 were analyzed to investigate the effect of the longitudinal reinforcement corrosion ratio and stirrup corrosion ratio on their ultimate shear capacity. The variation in the ultimate shear capacity is presented in Figure 11a,b. Additionally, trend lines obtained from regression analysis are displayed in both figures. From Figure 11a, the XGBoost model predicts a gradually decreasing trend in shear capacity with increasing longitudinal reinforcement corrosion ratio (0–15%). However, this trend appears approximately linear, whereas experimental evidence suggests that the influence of longitudinal corrosion on shear capacity—governed by dowel action and aggregate interlock degradation—may be nonlinear, with minor effects at low corrosion levels and more pronounced deterioration at advanced stages [38,49]. This discrepancy is attributed to the limited availability of high-corrosion data and the presence of coupled corrosion effects in the dataset. Additionally, from Figure 11b, it is noticeable that the shear capacity typically decreases with increasing corrosion ratio of stirrups. The effect of stirrup corrosion on shear capacity appears to be greater than that of longitudinal corrosion. This is due to the fact that stirrups offer significantly greater shear resistance compared to longitudinal reinforcement [126]. Moreover, stirrups are smaller in diameter and more vulnerable to corrosion compared to longitudinal reinforcement. For example, considering the first CRC beam in Group 1, with ${\eta }_{l,sn}$ equal to 0% and ${\eta }_{w,sn}$ equal to 10%, the XGBoost model predicts a shear capacity of 44.50 kN. However, if ${\eta }_{l,sn}$ is 10% and ${\eta }_{w,sn}$ is 0%, the shear capacity increases to 47.70 kN. This example highlights that the effect of stirrup corrosion on shear capacity outweighs that of longitudinal corrosion.

5.2. Effective Beam Depth and Beam Width

The relationship between ultimate shear capacity and beam dimensions is presented in Figure 12a,b, with regression trend lines included. Figure 12a indicates that an increase in beam depth (Group 3) reduces the nominal shear stress; additionally, Figure 12b demonstrates that wider beams (Group 4) experience a marked decrease in nominal shear stress.

5.3. Concrete Strength and Shear Span-to-Depth Ratio

The influence of concrete compressive strength ($f_{cl,150}$) on shear behavior was assessed by plotting the ultimate shear capacity against ten different cylinder strengths (Group 5) in Figure 13a. The results demonstrate that higher concrete strength leads to a notable increase in shear capacity. Figure 13b shows the variation in ultimate shear capacity with the shear span-to-depth ratio (λ). The trend indicates that shear capacity declines significantly as λ increases up to 2.5 (Group 6) but remains relatively constant for λ values above 2.5.

5.4. Longitudinal Reinforcements and Stirrups’ Normalized Strength

The influence of normalized reinforcement strength on ultimate shear capacity is presented in Figure 14. In Figure 14a, the shear capacity rises progressively with the normalized longitudinal reinforcement strength (${{\rho }_l{f}_y/f}_{cl,150}$) for Group 7. Figure 14b illustrates the variation in shear capacity as a function of the normalized shear reinforcement strength (${{\rho }_v{f}_{yv}/f}_{cl,150}$). As illustrated in Figure 12b, the ultimate shear capacity of CRCBs rises as the normalized shear reinforcement strength (${{\rho }_v{f}_{yv}/f}_{cl,150}$) increases (Group 8). The predicted results from the parameter analysis are consistent with prior studies [38,50,81], supporting the structural trends of CRCBs. These findings highlight the robust predictive performance of the XGBoost model across a range of geometric and material configurations.

6. Conclusions

This paper has presented six ML models for predicting the shear capacity of corroded reinforced concrete beams, including kernel ridge regression (KRR), K-nearest neighbors (KNN), decision trees (DT), random forest (RF), gradient-boosted regression trees (GBRT), and extreme gradient boosting (XGBoost). ML models were trained on large datasets of experimental data to learn complex relationships between input parameters and the corresponding shear capacity. The experimental database contains 408 data points on the shear capacity of CRCBs with different degrees of corrosion. The accuracy of the existing formulas proposed by other researchers for calculating the shear capacity of CRCBs was also evaluated. A parametric study was performed to further assess the robustness of the ML models, and a user-friendly GUI tool has been developed that can be used by practicing engineers to calculate the shear capacity of CRCBs. The following outcomes are obtained from this study:

• Corrosion significantly reduces the performance of RC beams by decreasing reinforcement area, weakening bond strength, and causing brittle shear failure. The study confirms that beam depth, shear span-to-depth ratio, and concrete compressive strength are the key parameters controlling shear capacity degradation and corrosion-induced deterioration in reinforced concrete systems.
• The XGBoost model demonstrates superior performance in predicting the shear capacity of CRCBs, achieving an $R^2$ of 0.994 along with the lowest RMSE (11.61), MAE (3.388), and MAPE (2.37%). By comparison, the KRR model exhibits the lowest predictive accuracy, with an $R^2$ of 0.9784 and higher error values: RMSE of 22.10, MAE of 12.76, and MAPE of 10.40%.
• The use of an expanded dataset (408 experimental samples) and systematic hyperparameter tuning significantly improved model stability and generalization compared to previously reported studies, making the proposed model more reliable for diverse structural conditions.
• Compared to the formulas by Cladera et al. [123] and Lu et al. [50], the XGBoost model significantly improves the prediction of the shear capacities of CRCBs, with a mean value (μ) and the standard deviation (SD) value of the $V_{Exp}/V_{Pred}$ ratio are 1.004 and 0.07, respectively.
• The relative importance of input parameters ranked by SHAP on the shear capacity of CRCBs is in the following order: beam depth (h), shear span-to-depth ratio (λ), concrete compressive strength ($f_{cl,150}$), and longitudinal reinforcement ratio (${\rho }_l$).
• The parametric study highlights that the XGBoost model demonstrates superior performance and reliability under varying geometric and material properties of CRCBs.
• The developed model provides a fast and practical tool for estimating the shear capacity of corroded RC beams and can support structural assessment and maintenance planning. Nevertheless, its application should be limited to conditions similar to those represented in the dataset, and further validation using additional experimental or field data is recommended for broader generalization.