This study focuses on using various machine learning (ML) models to evaluate the shear behaviors of ultra-high-performance concrete (UHPC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The main objective of the study is to predict the shear strength of UHPC beams reinforced with GFRP bars using ML models. We use four different ML models: support vector machine (SVM), artificial neural network (ANN), random forest (R.F.), and extreme gradient boosting (XGBoost). The experimental database used in the study is acquired from various literature sources and comprises 54 test observations with 11 input features. These input features are likely parameters related to the composition, geometry, and properties of the UHPC beams and GFRP bars. To ensure the ML models' generalizability and scalability, random search methods are utilized to tune the hyperparameters of the algorithms. This tuning process helps improve the performance of the models when predicting the shear strength. The study uses the ACI318M-14 and Eurocode 2 standard building codes to predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams. The ML models' predictions are compared to the results obtained from these building code standards. According to the findings, the XGBoost model demonstrates the highest predictive test performance among the investigated ML models. The study employs the SHAP (SHapley Additive exPlanations) analysis to assess the significance of each input parameter in the ML models' predictive capabilities. A Taylor diagram is used to statistically compare the accuracy of the ML models. This study concludes that ML models, particularly XGBoost, can effectively predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams.

This study integrates previous experimental data and employs machine learning (ML) methods, including Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and eXtreme Gradient Boosting (XGBoost), to predict the compressive strength (CS) and tensile strength (TS) of engineered cementitious composites (ECC). XGBoost emerged as the superior model among the four ML models, providing an interpretable and highly accurate predictive framework. To optimize the model performance, hyperparameter tuning using a fivefold cross-validation approach with the data divided into 80% training and 20% testing subsets. The Shapley Additive Explanations (SHAP) algorithm was also employed to reveal the impact of important features, such as the water/binder ratio, fly ash content, and water reducer dosage, on the model’s predictions and their interrelationships. The XGBoost demonstrates the most exemplary performance, as reflected in the R² values of 0.92 and 0.97 for CS and TS testing, respectively. The SHAP analysis provided insights into the impact of individual features on CS and TS, shedding light on how specific characteristics influence the predictive accuracy of these properties. This highly accurate prediction model uncovers insights into correlated features, aids in creating new mix designs of ECC, and supports global efforts toward a low-carbon future in the construction industry by reducing carbon emissions.