Predicting the Axial Load Carrying Capacity of Columns Reinforced with GFRP Rebars Using ANN Modelling

Abstract

In recent years most of the concrete structures are getting exposed to environments that are resulting in the corrosion of steel. To eliminate this, studies have been carried out to replace steel in RCC by Glass Fiber Reinforced Polymer (GFRP) rebars. In this paper, several experimental results were considered and the impacts of substituting steel by GFRP rebars were studied. Parameters affecting the load-carrying capacity of columns reinforced with GFRP rebars were identified from various literature and a database has been created. Twelve such parameters describing the material property and geometric configuration are chosen as inputs and the axial load carrying capacity as an output. An ANN model is developed with optimized architecture for predicting the compressive strength of columns reinforced with GFRP rebars. The model is then trained, tested, and validated on this database. The accuracy of the ANN model is evaluated by various regression evaluation metrics such as MSE, RMSE and R2. Comparison with the existing empirical equations and code provisions showed that the ANN model outperformed all these models. For the purpose of determining the efficiency of ANN model, a subset of the experimental data collected from work done on GFRP reinforced columns is used. Sensitivity analysis is carried out and the results showed that the most important parameters for the estimation of the strength of GFRP reinforced columns are the geometrical dimensions of the column. The results obtained showed that the ANN model is in good agreement with the experimental results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.