Advancing Shear Capacity Estimation in Rectangular RC Beams: A Cutting-Edge Artificial Intelligence Approach for Assessing the Contribution of FRP

Abstract

Shear strength prediction in FRP-bonded reinforced concrete beams is crucial for ensuring structural integrity and safety. In this extensive investigation, advanced machine learning algorithms are harnessed to achieve precise shear strength predictions for rectangular RC beams reinforced with FRP sheets. The aim of this research is to enhance the accuracy and reliability of shear strength estimation, providing valuable insights for the design and assessment of FRP-strengthened structures. The primary contributions of this study lie in the meticulous comparison of various machine learning algorithms, including Xgboost, Gradient Boosting, Random Forest, AdaBoost, K-nearest neighbors, and ElasticNet. Through comprehensive evaluation based on predictive performance, the most suitable model for accurately estimating the shear strength of FRP-reinforced rectangular RC beams is identified. Notably, Xgboost emerges as the superior performer, boasting an impressive R2 value of 0.901. It outperforms other algorithms and demonstrates the lowest RMSE, MAE, and MAPE values, establishing itself as the most accurate and reliable predictor. Furthermore, a sensitivity analysis is conducted using artificial neural networks to assess the influence of input variables. This additional research facet sheds light on the critical factors shaping shear strength outcomes. The study, as a whole, represents a substantial contribution to advancing the development of accurate and dependable prediction models. The practical implications of this work are far-reaching, particularly for engineering applications in the realm of structures reinforced with FRP. The findings have the potential to transform the approach to the design and assessment of such structures, elevating safety, efficiency, and performance to new heights.