ABSTRACT This experimental study investigates the axial compression effect of rectangular pultruded glass fiber-reinforced polymer (P-GFRP) tubular column sections, examining the impact of width-to-thickness ratio (B/t), aspect ratio (H/B), and column height on their structural performance. A total of 27 GFRP columns were subjected to axial compression tests to evaluate their ultimate load and initial stiffness. The columns exhibited a uniform failure pattern, characterized by crushing, mid-section fractures, and longitudinal splitting at the corners. The results revealed a negative correlation between the ultimate load and the aspect ratio, as well as the width-to-thickness ratio. This study utilized advanced machine learning algorithms, namely Response Surface Methodology (RSM) and Artificial Neural Network (ANN), to develop predictive models for the ultimate load of GFRP columns. The RSM model achieved an R2 value of 0.8347, demonstrating good accuracy in predicting ultimate load. The ANN model outperformed the RSM model, with an R criterion exceeding 0.68807 across training, testing, and validation phases, showing a stronger correlation between experimental and predicted outcomes. This research establishes a framework for forecasting the mechanical properties of column sections.
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