Estimation of Uniaxial Compressive Strength, Los Angeles Abrasion Loss and Frost Loss in Basalts with Flexible Calculation Methods

Özer İ., Kolay E., Koçak I., Bilen Atay H.

Journal of Geological Society of India, cilt.102, sa.3, ss.401-411, 2026 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 102 Sayı: 3
  • Basım Tarihi: 2026
  • Doi Numarası: 10.17491/jgsi/2026/17436
  • Dergi Adı: Journal of Geological Society of India
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.401-411
  • Yozgat Bozok Üniversitesi Adresli: Evet

Özet

This study examines the estimation of Uniaxial Compressive

Strength (UCS), Los Angeles abrasion loss (LA), and Na2

SO4

Freeze Loss (FL) in basalts using flexible computational

methods. The analysis was based on 67 experimental sets from

Yozgat (Turkey) basalts, where properties such as dried unit

weight, porosity, water absorption, and point load strength

index were measured. Advanced regression models, including

Random Forest, Multi-Layer Perceptron (MLP), Support

Vector Regression (SVR), Linear Regression, and ExtraTree,

were employed to predict key mechanical properties. Among

these models, the ExtraTree algorithm achieved the highest

performance, with R2 values of 1.000 for UCS, 0.999 for

LA, and 1.000 for FL, and Variance Accounted For (VAF)

scores of 99.980, 99.882, and 99.965%, respectively. These

results highlight the model’s ability to accurately capture

the complex, non-linear relationships between the input

parameters and the mechanical properties of basalts. The

study demonstrates that low-cost rapid tests, such as unit

weight, porosity, water absorption, and point load strength

index, can effectively predict critical basalt properties.

This approach offers significant labour savings and

improves efficiency in engineering applications. The strong

performance of the ExtraTree and Random Forest models

suggests their practical utility in a range of construction

and geological engineering projects. Future research should

focus on expanding the dataset to encompass a broader range

of conditions and exploring additional features or ensemble

methods to further enhance prediction accuracy. This study

offers valuable insights into the use of machine learning

techniques in geotechnical engineering, supporting more

efficient, reliable, and cost-effective approaches to assessing

the mechanical properties of basalts.