ArticleScientific reports2025
Predicting axial load capacity in elliptical fiber reinforced polymer concrete steel double skin columns using machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Prediction of the axial capacity of various polygon-type section concrete-filled steel (CFST) columns utilising deep neural network and machine learning regressor.Scientific reports · 2026Article
- Evaluation of advanced AI models for accurate prediction and uncertainty analysis of aeration inception in hydrofoil-crested stepped spillways.Scientific reports · 2026Article
- An optimized machine learning approach for reliable design of hybrid FRP-steel-concrete tubular columns.Scientific reports · 2026Article
- Estimating seepage in heterogeneous earthfill dams on permeable foundations using explainable machine learning.Scientific reports · 2026Article
- Assessing the impact of groundwater abstraction and concrete dam fractures on saltwater intrusion using numerical modeling and interpretable machine learning.Scientific reports · 2026Article
- Numerical investigation on performance of concrete-steel composite beams incorporating multi-transverse holes.Scientific reports · 2026Article
- Effect of jacket configuration and anchorage in hybrid strain hardening cementitious composites with steel mesh strengthening of reinforced concrete beams.Scientific reports · 2025Article
- Machine learning glucose forecasting models for septic patients.Scientific reports · 2025Article
- Axial strength prediction of FRP reinforced concrete columns under concentric and eccentric loading using machine learning models.Scientific reports · 2025Article
- Explainable ML modeling of saltwater intrusion control with underground barriers in coastal sloping aquifers.Scientific reports · 2025Article
- Hydraulic Performance Modeling of Inclined Double Cutoff Walls Beneath Hydraulic Structures Using Optimized Ensemble Machine Learning.Scientific reports · 2025Article
- Effect of column and pile configuration and dimension on shear performance of reinforced concrete pile caps.Scientific reports · 2025Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The current study investigates the application of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in predicting the ultimate load-carrying capacity and ultimate strain ofboth hollow and solid hybrid elliptical fiber-reinforced polymer (FRP)-concrete-steel double-skin tubular columns (DSTCs) under axial loading. Implemented AI techniques include five ML models - Gene Expression Programming (GEP), Artificial Neural Network (ANN), Random Forest (RF), Adaptive Boosting (ADB), and eXtreme Gradient Boosting (XGBoost) - and one DL model - Deep Neural Network (DNN).Due to the scarcity of experimental data on hybrid elliptical DSTCs, an accurate finite element (FE) model was developed to provide additional numerical insights. The reliability of the proposed nonlinear FE model was validated against existing experimental results. The validated model was then employed in a parametric study to generate 112 data points.The parametric study examined the impact of concrete strength, the cross-sectional size of the inner steel tube, and FRP thickness on the ultimate load-carrying capacity and ultimate strain of both hollow and solid hybrid elliptical DSTCs.The effectiveness of the AI application was assessed by comparing the models' predictions with FE results.Among the models, XGBoost and RF achieved the best performance in both training and testing with respect to the determination coefficient (R
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