Sandstone Mechanical Properties from Four Iranian Formations

Twenty sandstone samples from four Iranian formations were tested for physical and mechanical properties. The dataset contains laboratory measurements of dry unit weight, porosity, uniaxial compressive strength, and elasticity modulus, used to develop AI predictive models. It was authored by Davood Fereidooni and last updated in May 2026.

Use Cases

• Predict uniaxial compressive strength based on dry unit weight and porosity correlations.
• Predict elasticity modulus using Schmidt hammer rebound and point load index measurements.
• Compare the performance of AI models like Artificial Neural Networks, Random Forest, and K-Nearest Neighbors for geotechnical prediction.
• Analyze the relationship between physical properties and mechanical performance across different geological formations.

Strengths

• Dataset includes specific, measured property ranges: dry unit weight (23.34–26.10 kN/m³), porosity (2.07–10.16%), UCS (55.46–104.16 MPa), and E (39.86–57.03 GPa).
• Strong statistical correlations are reported, such as Pearson correlation of 0.947 between dry unit weight and UCS.
• High-performing predictive models are documented, with ANN achieving R² values up to 0.992 for UCS prediction.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Dataset is small in scale at 13.5 KB, indicating limited sample size.

Provenance

Source

figshare

Collection Method

Comprehensive laboratory tests performed on sandstone samples.

Freshness

Last updated 2026-05-15 17:33:47; freshness should be verified.

Geography

Samples from four Iranian formations.