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Ghobadi, Mohammad Hossein, Mousavi, Sajeddin, Heidari, Mojtaba, Rafie, Behrouz. (1394). The Prediction of the Tensile Strength of Sandstones from their petrographical properties using regression analysis and artificial neural network. , 5(2), 177-187. doi: 10.22059/jgeope.2015.56094
Mohammad Hossein Ghobadi; Sajeddin Mousavi; Mojtaba Heidari; Behrouz Rafie. "The Prediction of the Tensile Strength of Sandstones from their petrographical properties using regression analysis and artificial neural network". , 5, 2, 1394, 177-187. doi: 10.22059/jgeope.2015.56094
Ghobadi, Mohammad Hossein, Mousavi, Sajeddin, Heidari, Mojtaba, Rafie, Behrouz. (1394). 'The Prediction of the Tensile Strength of Sandstones from their petrographical properties using regression analysis and artificial neural network', , 5(2), pp. 177-187. doi: 10.22059/jgeope.2015.56094
Ghobadi, Mohammad Hossein, Mousavi, Sajeddin, Heidari, Mojtaba, Rafie, Behrouz. The Prediction of the Tensile Strength of Sandstones from their petrographical properties using regression analysis and artificial neural network. , 1394; 5(2): 177-187. doi: 10.22059/jgeope.2015.56094

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

Geopersia
مقاله 8، دوره 5، شماره 2، دی 2015، صفحه 177-187    اصل مقاله (280.73 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jgeope.2015.56094
نویسندگان
Mohammad Hossein Ghobadi* 1؛ Sajeddin Mousavi2؛ Mojtaba Heidari1؛ Behrouz Rafie1
1Geology Department, Bu-Ali Sina University, Hamedan, Iran
2Geology Department, Shahid Chamran University, Ahvaz, Iran
چکیده
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.
کلیدواژه‌ها
Artificial Neural Network؛ Petrographical Features؛ regression analysis؛ Sandstone؛ Tensile Strength
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