ArticleScientific reports2024
Circular rubber aggregate CFST stub columns under axial compression: prediction and reliability analysis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- An optimized machine learning approach for reliable design of hybrid FRP-steel-concrete tubular columns.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
- 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
- Prediction of ultimate strength and strain in FRP wrapped oval shaped concrete columns using machine learning.Scientific reports · 2025Article
- Experimental and analytical study on axial behaviour of square corrugated concrete filled single and double skin tube stub columns.Scientific reports · 2025Article
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Authors and funding
3 authors.
Funding
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Abstract
Extensive studies support using steel tubes to enhance the structural integrity of rubber aggregate concrete (RBAC), namely RBAC-filled steel tubes (RCFST). However, current design codes for assessing the axial compressive behaviour of circular stub RCFST (CS-RCFST) columns are limited. Furthermore, there is a scarcity of studies focused on ensuring the structural safety of these columns. Based on an extensive experimental database comprising 145 columns, this study explores machine learning (ML) capabilities for predicting the axial strength of CS-RCFST columns, using six typical machine-learning models, i.e., symbolic regression (SR), XGBoost, CatBoost, random forest, LightGBM, and Gaussian process regression models. The hyperparameter tuning of the introduced ML models is performed using the Bayesian Optimization technique. The comparison results show that the CatBoost model is the most reliable and accurate ML model (R
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