بررسی تأثیر پارامترهای شاخص بر خواص استاتیک سنگ آهک در شرایط خشک و اشباع با استفاده از شبکه عصبی مصنوعی

نویسنده

استادیار گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه اراک، اراک

چکیده

مطالعات قبلی نشان داده است که رطوبت تاثیر ویژه­ای بر خصوصیات استاتیک سنگ (مقاومت فشاری تک­محوری و مدول­الاستیسیته) دارد. در این مقاله، مقاطع نازک و XRD، مقاومت تراکم تک­محوری و مدول­الاستیسیته، شاخص بار نقطه­ای، مقاومت کششی برزیلی و تخلخل نمونه­های سنگ­آهک در شرایط خشک و اشباع در ساختگاه­ سد خرسان دو در جنوب غربی ایران بررسی شده است. سپس، با استفاده از شبکه عصبی مصنوعی و رگرسیون ساده، اثر شاخص بار نقطه­ای در شرایط خشک، مقاومت کششی در شرایط خشک و اشباع و تخلخل بر مقاومت تراکم تک­محوری و مدول­الاستیسیته در شرایط خشک و اشباع بررسی شد. بررسی XRD و مقاطع نازک نمونه­ها نشان می­دهد که کلسیت کانی اصلی است و طبقه­بندی نمونه­ها از مادستون تا گرینستون متغییر است. نتایج شبکه عصبی و رگرسیون ساده نشان داد که اثر متغیرهای مستقل بر مقاومت تراکم تک­محوری و مدول­الاستیسیته در شرایط خشک دارای دقت بالاتری نسبت به شرایط اشباع می­باشند. واسنجی روابط ارائه شده محققین قبلی بر اساس نتایج آزمایشگاهی این تحقیق و با استفاده از معیارهای ضریب تعیین و خطای جذر میانگین مربعات نشان داد که اکثر روابط می­توانند جهت تخمین خصوصیات سنگ­آهک آسماری مورد استفاده قرار گیرند. بررسی نمودارهای همگنی واریانس باقی مانده­ها در سطوح مقادیر پیش­بینی شده، ضریب تعیین و خطای روش­ها نشان داد که شبکه عصبی از دقت بالاتری نسبت به رگرسیون ساده جهت تخمین خصوصیات استاتیک سنگ­آهک برخوردار است و روش شبکه عصبی در تخمین خصوصیات مقاومت تراکم تک­محوری و مدول­الاستیسیته محافظه کارانه عمل می­کند.

کلیدواژه‌ها


عنوان مقاله [English]

The effect of index parameters on the static properties of limestone in dry and saturated conditions using artificial neural network

نویسنده [English]

  • M. R. Motahari
Assist. Prof., Dept., of Civil Engineering, University of Arak, Arak
چکیده [English]

Previous studies have shown that moisture has a special effect on the static properties (uniaxial compressive strength (UCS) and elastic modulus (Es) of the rock. In this study, thin section, X-ray diffraction (XRD), porosity, UCS and Es, point load index, and Brazilian tensile strength of the limestone specimens were determined in Khersan 2 dam site, in south west of Iran. Then, using artificial neural network and simple regression, the effect of dry point load index, dry and saturated tensile strength, and porosity on UCS, Es were assessed. Microscopic studies of the samples showed that calcite is the main mineral and samples classified from the Mudstone to the Grainstone. The effect of water on the static properties showed that prediction models in dry conditions are more accurate. Calibration of the relationships presented by previous researchers based on the experimental results of this study and using the criteria of coefficient of determination and root mean square error (RMSE) showed that most of the relationships can be used to estimate the properties of Asmari limestone. Also, investigation of heteroscedasticity graphs of residual variance at predicted levels, determination coefficient and RMSE of the methods showed that the neural network has higher accuracy than simple regression. As compared to the regression method, the neural network is conservative in estimating these properties.

کلیدواژه‌ها [English]

  • Mechanical properties
  • Porosity
  • Dry and saturated conditions
  • Artificial neural network
  • Regression
  • Limestone
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