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Rock Brittleness Prediction Using Geomechanical Properties of Hamekasi Limestone: Regression and Artificial Neural Networks Analysis | ||
| Geopersia | ||
| مقاله 2، دوره 6، شماره 1 - شماره پیاپی 22287825، خرداد 2016، صفحه 19-33 اصل مقاله (1.02 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jgeope.2016.57819 | ||
| نویسندگان | ||
| M.H. Ghobadi* 1؛ Fateme Naseri2 | ||
| 1Bu_ Ali Sina university, Hamedan, Iran | ||
| 2Bu-Ali Sina university | ||
| چکیده | ||
| The cold climate is a favorable parameter for the development of tension cracks and decrease of rock brittleness. Therefore, this paper attempts to investigate the Hamekasi porous limestone in order to predict the brittleness indices during freeze-thaw cycles. The freeze–thaw test was executed for one cycle including 16 h of freezing, and 8 h of thawing. The geo mechanical properties and brittleness indices (B1, B2, B3) of limestones were measured across freeze-thaw cycles from cycle 0 (fresh rock) to cycle 40. Statistical analyses, including simple and multiple regressions, were applied to identify those geomechanical parameters that are most influenced by the progression of freeze-thaw cycles and more appropriate for the brittleness prediction. Based on simple regression, all geomechanical properties including tensile strength (), uniaxial compressive strength (), P-wave velocity (Vp), porosity (n), and quick absorption index (QAI) (except dry density ()) demonstrated good correlations with brittleness index (B3). The integrated prediction of brittleness is put forward to develop some models by multiple regression (MR) and artificial neural network (ANN) with some statistic parameters (R, RMSE, VAF and ME), based on all geomechanical properties examined in this research. It is concluded that models based on n, Vp and exhibited high performance according to the obtained statistic parameters. In spite of the fact that Vp has good correlation coefficient (R) with freeze-thaw cycles, and B3 (R2= 0.74, and 0.55, respectively) in simple regression, it does not have a prominent effect on B3 in MR models. Also, parameters with low correlation coefficient in simple regression (=0.15) cannot improve the model performance in ANNmethods | ||
| کلیدواژه‌ها | ||
| ANN Models؛ Brittleness Indices؛ Freeze–Thaw Cycles؛ Multiple Regression Models؛ Porous Limestone | ||
| عنوان مقاله [English] | ||
| پیش بینی شکنندگی سنگ با استفاده از ویژگی های ژئومکانیکی سنگ آهک همه کسی: آنالیز رگرسیون و شبکه عصبی مصنوعی | ||
| نویسندگان [English] | ||
| فاطمه ناصری2؛ | ||
| 2دانشگاه بوعلی سینا | ||
| چکیده [English] | ||
| آب و هوای سرد، پارامتر مناسبی برای توسعه ترک های کششی و کاهش شکنندگی سنگ است. بنابراین در این مقاله سعی بر بررسی سنگ آهک های متخلخل همه کسی و پیش بینی شکنندگی آنها در طی چرخه های انجماد و ذوب شدن شده است. هر چرخه از آزمایش انجماد و ذوب شدن شامل 16 ساعت انجماد و 8 ساعت ذوب شدن است. ویژگی های ژئومکانیکی و شاخص های شکنندگی (B1, B2, B3) سنگ آهک ها در طی چرخه های انجماد و آب شدن از چرخه (سنگ غیر هوازده) تا چرخه 40 اندازه گیری شده اند. آنالیز آماری شامل رگرسیون ساده و چندمتغیره به منظور شناسایی پارامترهای ژئومکانیکی که نسبت به سایر پارامترها تحت تاثیر پیشرفت چرخه های انجماد و ذوب شدن قرار گرفته و برای پیش بینی شکنندگی مناسبتر هستند، بکار رفته اند. از دیدگاه آنالیز رگرسیون ساده، تمامی پارامترهای ژئومکانیکی شامل مقاومت کششی، مقاومت فشاری تک محوری، سرعت موج p، تخلخل و جذب آب (به استثنای دانسیته خشک) ارتباط خوبی با شاخص شکنندگی نشان داده اند. در این تحقیق؛ پیش بینی یکپارچه شکنندگی به منظور توسعه مدل های چند متغیره (MR) و شبکه عصبی مصنوعی (ANN) با تعدادی پارامتر آماری (R ، RMSE، VAF و ME) و بر اساس خصوصیات ژئومکانیکی بررسی شده است. بر طبق آماره های بدست آمده، مدلهایی که بر اساس n، Vp و می باشند کارایی بیشتری نشان می دهند. علیرغم این حقیقت که Vp در رگرسیون ساده ضریب انطباق خوبی با چرخه های انجماد و آب شدن و B3 (R= 0.74 و R= 0.55) دارد، اما اثر آشکاری بر B3 در مدل های MR ندارد. همچنین پارامترهایی با ضریب تعیین کم در رگرسیون ساده (=0.15) نمی توانند سبب بهبود مدل ها در رگرسیون چند متغیره شوند | ||
| کلیدواژه‌ها [English] | ||
| سنگ آهک های متخلخل چرخه های انجماد, مدل های چند متغیره (MR), شبکه عصبی مصنوعی (ANN | ||
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