Shear Strength Prediction of Slender Concrete Beams Reinforced with FRP Rebar Using Data-Driven Machine Learning Algorithms

Abstract Estimating the shear strength of a fiber-reinforced polymer (FRP)–reinforced-concrete (RC) beam is a complex task that depends on multiple design variables. The use of FRP bars has emerged as a promising alternative to diminish the corrosion problems that are associated with steel reinforcement in adverse environments; however, an accurate and reliable method of shear strength prediction is needed to ensure the economical use of materials and robust designs. Several optimized design equations are available in the literature; however, when utilizing these equations a substantial difference is observed between the predicted outcome (Vpred) and the experimental shear strength (Vexp) result. Therefore, this paper presented a novel approach toward implementing machine learning (ML) algorithms to accurately estimate the shear strength of FRP–RC beams. A large database that consisted of 302 shear test results on FRP-reinforced slender concrete beams without stirrup was collected from the literature to formulate the most efficient prediction model. The performance of each ML algorithm model was compared with the existing design provisions and models. The model interpretation was performed through feature importance analysis to explain the model output compared with a black box. The proposed data-driven ML models demonstrated a high level of accuracy and excellent performance and were superior to the existing shear strength models. In addition, a simple graphical user interface (GUI) was developed to aid practicing engineers when estimating shear strength without the need for complicated design procedures. Practical Applications The shear strength of FRP–RC beams is calculated using various design codes and guidelines that are heuristically developed based on previous test results. In general, the developed equations are either mechanics-based or empirical. However, this paper demonstrated that data-driven ML algorithms could generate a more reliable and appropriate prediction of the shear strength of FRP–RC beams. Furthermore, as the database increases, it could be automatically updated, which would result in more accurate and reliable results. Designers and practitioners could conveniently use the developed algorithms for the reliable and quick prediction of the shear strength of FRP–RC beams. In addition, the developed GUI is innovative and user-friendly. It allows users to determine the design shear strength without referring to an existing code by emp