The Prediction of the Tensile Strength of Sandstones from their petrographical properties using regression analysis and artificial neural network

Document Type : Research Paper

Authors

1 Geology Department, Bu-Ali Sina University, Hamedan, Iran

2 Geology Department, Shahid Chamran University, Ahvaz, Iran

Abstract

This study investigates the correlations among the tensile strength, mineral composition, and textural features of twenty-nine
sandstones from Kouzestan province. The regression analyses as well as artificial neural network (ANN) are also applied to evaluate
the correlations. The results of simple regression analyses show no correlation between mineralogical features and tensile strength.
However, the tensile strength of the sandstone was decreased by cement content reduction. Among the textural features, the packing
proximity, packing density, and floating contact as well as sutured contact are the most effective indices. Meanwhile, the stepwise
regression analyses reveal that the tensile strength of the sandstones strongly depends on packing density, sutured contact, and cement
content. However, in artificial neural network, the key petrographical parameters influencing the tensile strength of the sandstones are
packing proximity, packing density, sutured contact and floating contact, concave-convex contact, grain contact percentage, and
cement content. Also, the R-square obtained ANN is higher than that observed for the stepwise regression analyses. Based on the
results, ANN were more precise than the conventional statistical approaches for predicting the tensile strength of these sandstones from
their petrographical characteristics.

Keywords

Article Title [Persian]

پیش بینی مقاومت کششی ماسه سنگها از خصوصیات سنگ شناسی با استفاده از آنالیز رگرسیون و شبکه عصبی مصنوعی

Authors [Persian]

  • محمدحسین قبادی 1
  • ساجدین موسوی 2
  • مجتبی حیدری 1
  • بهروز رفیعی 1
1
2

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Geopersia
Volume 5, Issue 2 - Serial Number 2
Summer & Autumn
October 2015
Pages 177-187

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History

  • Receive Date: 22 November 2014
  • Revise Date: 17 November 2015
  • Accept Date: 25 November 2015

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