Data-Driven Shear Strength Prediction of FRP-Reinforced Concrete Beams without Stirrups Based on Machine Learning Methods

Abstract

Due to the intrinsic complexity, there has been no widely accepted mechanics-based estimation model of the shear performance of Fiber-Reinforced Polymer (FRP)-reinforced concrete beams. Capitalizing on a large amount of previous experimental data, data-driven machine learning (ML) models could be potentially suitable for addressing this problem. In this paper, four existing shear design provisions are reviewed and four typical ML models are analyzed. The accuracy of codified methods and ML models are compared and analyzed based on our established extensive database of FRP-reinforced concrete beams with rectangular cross sections. A series of artificially selected features considering the shear-carrying mechanisms of FRP-reinforced beams are incorporated into the proposed ML models to show their influence on the model validity. Bayesian optimization is utilized to automatically tune the hyperparameters of different ML models. Compared to the most satisfying codified predictions from CSA S806, the best ML model, XGBoost, can provide more accurate and consistent predictions for the database, with R2 enhanced by 15% and the MAE and RMSE reduced by 59% and 52%, respectively. With the selected features based on domain knowledge, the performance of ML models is further enhanced, shown by the most important features being the added ones. With outstanding performance on a large database and singular test, the ML approaches have great potential in guiding the shear design of FRP-reinforced concrete.

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

“…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].…”

“…With the use of an extensive database of rectangular and T-beams, the ensemble models RF, CatBoost, and XGBoost showed superior prediction performance in predicting the shear capacity when compared with existing design guidelines. Additionally, Yang and Liu [41] conducted a comparative study of various ML models and existing codified expressions. Several ensemble models were implemented, comprising LR, decision trees (DT), RF, and XGBoost, to estimate the shear resistance of FRP-strengthened RC beams with no internal shear reinforcement.…”