Tensile Strength Predictive Modeling of Natural-Fiber-Reinforced Recycled Aggregate Concrete Using Explainable Gradient Boosting Models

Natural fiber composites have gained significant attention in recent years due to their environmental benefits and unique mechanical properties. These materials combine natural fibers with polymer matrices to create sustainable alternatives to traditional synthetic composites. In addition to natural fiber reinforcement, the usage of recycled aggregates in concrete has been proposed as a remedy to combat the rapidly increasing amount of construction and demolition waste in recent years. However, the accurate prediction of the structural performance metrics, such as tensile strength, remains a challenge for concrete composites reinforced with natural fibers and containing recycled aggregates. This study aims to develop predictive models of natural-fiber-reinforced recycled aggregate concrete based on experimental results collected from the literature. The models have been trained on a dataset consisting of 482 data points. Each data point consists of the amounts of cement, fine and coarse aggregate, water-to-binder ratio, percentages of recycled coarse aggregate and natural fiber, and the fiber length. The output feature of the dataset is the splitting tensile strength of the concrete. Extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and extra trees regressor models were trained to predict the tensile strength of the specimens. For optimum performance, the hyperparameters of these models were optimized using the blended search strategy (BlendSearch) and cost-related frugal optimization (CFO). The tensile strength could be predicted with a coefficient of determination greater than 0.95 by the XGBoost model. To make the predictive models accessible, an online graphical user interface was also made available on the Streamlit platform. A feature importance analysis was carried out using the Shapley additive explanations (SHAP) approach.