Parametric Analysis Model for Concrete-Filled Steel Tube Tension Performance

A 2026 study by Hongtao Zhang presents a finite element model and parametric analysis of square concrete-filled steel tubular members under axial tension. The dataset includes results from six experimental specimens and ten parametric models, analyzing the effects of confinement coefficient and slenderness ratio on tensile load capacity. It also contains results from code comparisons and machine learning predictions using GAN-augmented data.

Use Cases

• Predicting ultimate tensile load of concrete-filled steel tubes based on confinement coefficient and slenderness ratio.
• Comparing the accuracy of finite element simulations against experimental results and building code predictions.
• Evaluating the effectiveness of Generative Adversarial Networks for augmenting limited structural engineering datasets.
• Training machine learning models, such as Random Forest, for rapid assessment of structural member performance.

Strengths

• Model validation shows simulation-to-test result ratios all below 0.95, indicating good agreement.
• Parametric analysis quantifies load changes: maximum load rises from 182 kN to 895 kN as confinement coefficient increases from 0 to 0.99.
• Machine learning models trained on augmented data achieved high predictive accuracy, with R² scores of 0.997 for ultimate load.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Data generated from finite element simulations, parametric studies, and machine learning experiments based on six physical specimens.

Freshness

Last updated 2026-06-01 17:33:27; freshness should be verified.