Although Fiber Reinforced Polymer (FRP) enhances the structural performance of concrete columns, existing predictive models for square and rectangular sections remain inadequate due to corner geometry variations and experimental uncertainties. This study aims to improve the reliability and efficiency of strength prediction for FRP-confined columns, particularly in engineering applications demanding both safety and cost-effectiveness. To address these challenges, the Group Method of Data Handling (GMDH) neural network was employed to develop a predictive model that minimizes reliance on expensive, time-consuming experiments. Several modeling approaches were examined, and the final GMDH-based neural network effectively captured the nonlinear relationships between input and output parameters, providing a robust predictive framework. Model evaluation using standard error metrics indicated strong performance, with coefficients of determination (R2) of 0.88 and 0.85 for training and testing datasets, respectively. Low error values, including Root Mean Square Error (RMSE) of 0.169 and 0.201 and Mean Absolute Error (MAE) of 0.128 and 0.157 for training and testing, confirmed the model's predictive reliability. The results demonstrate a close agreement between experimental and predicted strengths, validating the GMDH-NN as a practical, efficient alternative to extensive laboratory testing for estimating the compressive strength of FRP-confined square and rectangular columns. A significant contribution of this work is the integration of advanced neural network techniques with a comprehensive dataset of 171 specimens, offering improved insights into factors influencing strength and providing engineers with a data-driven, reliable tool for optimizing FRP-confined concrete column design.
Pouyan Fakharian — 1) Institute of Research and Development, Duy Tan University, Da Nang, Vietnam; 2) School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam
Bahar Mehdizadeh, Danial Jahed Armaghani — School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Danial Rezazadeh Eidgahee — Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Biswajeet Pradhan — Centre for Advanced Modelling and Geospatial Information Systems, School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
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