Influence of Training-Testing Data Variation on ML-Based Deflection Prediction of GFRP-Reinforced High-Strength...

Glass Fiber Reinforced Polymer (GFRP)-reinforced concrete beams have gained significant prominence in structural engineering due to their advantageous mechanical and durability characteristics. However, the influence of training-testing data partitioning on machine learning (ML)-based deflection prediction for such members remains insufficiently explored. This study addresses this gap by evaluating the predictive performance of the K-Nearest Neighbors (KNN) regression algorithm in estimating the load-deflection behavior of GFRP-reinforced high-strength concrete beams. The experimental program comprised nine beams manufactured with concrete strength classes C45, C50, and C65, followed by ML-based deflection analyses using multiple data-splitting strategies. Findings indicate that the KNN model employing an 80:20 training-testing ratio provides the most accurate deflection predictions, achieving approximately 80% agreement with experimental results, while a higher prediction accuracy of approximately 85% was observed for beams with the highest concrete compressive strength (C65). Experimentally recorded deflections ranged from approximately 20 mm to values exceeding 50 mm, depending on the concrete strength class and loading level. Owing to its superior performance, the KNN model with an 80:20 training-testing ratio is recommended for predicting the deflection capacities of GFRP-reinforced high-strength concrete members. The study further examined the structural response associated with the use of GFRP as longitudinal tensile reinforcement. A consistent failure mechanism was observed across all beams, characterized by the formation of a single, wide vertical crack initiating at the beam's soffit, regardless of concrete strength class. These observations contribute to a deeper understanding of the flexural behavior and fracture characteristics of GFRP-reinforced high-strength concrete beams and provide a foundation for future modeling efforts.