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Title: | Utilization of numerical and machine learning to predict the monotonic compressive response of square double-skin tubular columns (SDSTC) | Authors: | Ren, Yang Isleem, Haytham F. Jahami, Ali Tipu, Rupesh Kumar |
Affiliations: | Department of Civil and Environmental Engineering | Keywords: | Artificial Intelligence Double-skin tubular columns Fiber reinforced polymer Monotonic loading Numerical analysis Stress-strain behavior |
Issue Date: | 2025-02-01 | Publisher: | Elsevier | Part of: | Structures | Volume: | 72 | Abstract: | The comprehensive study embarks on an interdisciplinary approach, merging the rigorous analysis of numerical simulations with the predictive capabilities of Artificial Intelligence (AI), to investigate the behavior of double-skin tubular columns (DSTCs) under monotonic loading conditions. This research stands at the intersection of traditional structural engineering and modern computational techniques, aiming to unravel the complexities associated with the stress-strain responses of DSTC columns. By leveraging advanced AI models, the study not only enhances the accuracy of predictions in scenarios laden with complex variables but also significantly contributes to the optimization of structural systems. Compared to other ML approaches, Adam-Boosted Gradient boosting regression exhibited the best performance metrics with R2 and RMSE of 0.993 and 51.20 kN and 1 and 4.70685E-18 for load carrying capacity and ultimate strain capacity, respectively. The implications of this integration are profound, offering pathways to more resilient, efficient, and sustainable construction methodologies. The detailed understanding gained from this research provides a solid foundation for future explorations into the use of FRP materials in construction, paving the way for a new era of engineering solutions that harmonize strength, durability, and environmental stewardship. |
URI: | https://scholarhub.balamand.edu.lb/handle/uob/7782 | DOI: | 10.1016/j.istruc.2025.108206 | Ezproxy URL: | Link to full text | Type: | Journal Article |
Appears in Collections: | Department of Civil and Environmental Engineering |
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