Reliable machine learning for the shear strength of beams strengthened using externally bonded FRP jackets

Abstract

All over the world, shear strengthening of reinforced concrete elements using external fiber-reinforced polymer jackets could be used to improve building sustainability. However, reports issued by the American Concrete Institute called for heavy scrutiny before actual field implementation. The very limited number of proposed shear equations lacks reliability and accuracy. Thus, further investigation in this area is needed. In addition, machine-learning techniques are being implemented successfully to develop strength models for complex problems including shear, flexure, and torsion. This study aims to provide a reliable machine-learning model for reinforced concrete beams strengthened in shear using externally reinforced fiber polymer sheets. The proposed model was developed and validated against the experimental database and the very limited models in existing literature. The model showed better agreement with the experimentally measured strength compared to the previous models, which accounted for the effect of various parameters including but not limited to: the element geometry, strengthening details, and configurations. The model could guide the further developments of design codes and mechanical models.

The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering

“…Based on an extensive dataset of 304 experiments, the results demonstrated that the TGAN technique yielded superior accuracy in comparison with existing design codes, with an R 2 of 0.96. Most recently, an ANN model was developed for the shear strength prediction of RC beams strengthened with EB-FRP jackets [37,38]. Based on extensive datasets collected covering a wide range of parameters, the ANN model had high prediction accuracy, confirming the potential of employing the ML technique in the development of current design codes.…”

“…The shear capacity of RC beams was predicted mathematically using a variety of ML approaches [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. The use of the well-known artificial neural network (ANN) technique was adopted to investigate the impact of various crucial variables on the shear strength of FRP-RC beams [25].…”

Shear Design Equations for EB FRP-RC Beams Based on Data Fitting Approach

“…Based on the equilibrium of forces in a cross-section of a beam at failure, Pellegrino and Moderna [14] have obtained a theoretical equation for effective FRP strain. Nehdi et al [15], [16], and Kara [17] have taken a genetic algorithm approach whereas Hosseini and others [18], [19], [20], [21] have adopted machine learning and neural networks. Anvari et al [22] have also used an evolutionary machine learning approach, named genetic expression programming.…”