ArticleScientific reports2025
Experimental and analytical study on axial behaviour of square corrugated concrete filled single and double skin tube stub columns.
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 4 papers.
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4 citing papers in PubMed.
- A convolutional neural network-based deep learning approach for predicting surface chloride concentration of concrete in marine tidal zones.Scientific reports · 2025Article
- Numerical simulation on residual axial compression bearing capacity of square in square CFDST columns after lateral impact.Scientific reports · 2025Article
- AI driven prediction of early age compressive strength in ultra high performance fiber reinforced concrete.Scientific reports · 2025Article
- Interpretable machine learning approaches to assess the compressive strength of metakaolin blended sustainable cement mortar.Scientific reports · 2025Article
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5 authors.
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Abstract
Concrete-filled double-skin steel tubular (CFDST) columns have become widely utilized in building construction and bridges, thanks to their exceptional structural capabilities. Therefore, this study investigates the axial compressive behavior of square CFDST columns. The study aims to explore the influence of external and internal plate shapes (flat or corrugated plates) and different widths of internal steel tubes on the axial compressive behavior. The effects of varying internal widths of the internal steel tube (60 mm, 116 mm, and 160 mm) on the performance of CFDST columns were examined. Additionally, the study compared the performance of concrete-filled steel tubular (CFST) and CFDST columns with external flat or corrugated plates. The findings indicated that incorporating internal corrugated plates notably improved both the load-carrying capacity and ductility of the specimens. Notably, CFDST columns featuring corrugated internal plates (116 mm width) exhibited strength enhancements of 25.3% and 7.4% compared to internal widths of 160 mm and 60 mm, respectively. Furthermore, the study proposed two machine-learning models, namely Artificial Neural Network (ANN) and Gaussian Process Regression (GPR), to estimate the ultimate compressive strength of square CFDST columns. The findings indicated that the GPR model outperformed the ANN model in predicting the bearing capacity of square CFDST columns. Additionally, the Shapley Additive Explanation technique was employed for feature analysis. The outcomes of this analysis revealed that parameters such as section width and concrete strength positively influence the compressive strength index.
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