تخمین مدول تغییرشکل‌پذیری سنگ آهک آسماری با استفاده از سیستم عصبی- ژنتیک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد گرایش مکانیک سنگ، دانشگاه صنعتی امیرکبیر

2 استادیار دانشکده مهندسی معدن و متالوژی، دانشگاه امیرکبیر

چکیده

مدول تغییر شکل‌پذیری توده­سنگ (Em) به عنوان مهم‌ترین خصوصیت برای طراحی پروژه­های مهندسی سنگ مطرح است و بهترین نماینده برای رفتار مکانیکی پیش از شکست توده­سنگ است. به دلیل هزینه بالا و زمان­بر بودن و مشکلات اجرایی در انجام دقیق آزمایش­های برجا، روش­های غیرمستقیم مانند روابط تجربی و شبکه­های پس انتشار عصبی (BPN) جایگاه بهتری پیدا می­کنند. از این میان  BPN دارای کاربردی گسترده در تخمین خصوصیات توده سنگ از جمله Em است. محققین متعددی از روش سعی و خطا برای ایجاد یک BPN کارا  بهره گرفته­اند که نیاز به صرف زمان و مهارت کاربر دارد. اما در این مطالعه، از الگوریتم ژنتیک برای بهینه کردن پارامتری مؤثر BPN به منظور تخمین Em در رشته   کوه­های زاگرس ایران استفاده شد.  برای این منظور، یک بانک اطلاعاتی از پروژه­های مختلف رشته کوه­های زاگرس جمع‌آوری و Em سنگ آهک آسماری تخمین زده و در نهایت نتایج به دست آمده از روش عصبی - ژنتیک با روش عصبی سعی و خطا  مقایسه شد. که براساس نتایج به دست آمده روش عصبی- ژنتیک  دارای دقت و سرعت بالاتر در تخمین Em است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Estimation of the deformation modulus of Asmari limestone in Zagros Mountains, Iran, using a neural network –genetic model

نویسندگان [English]

  • Zeinab Aliabadian 1
  • Mostafa Sharifzadeh 2
1 Rock Mechanics Department, Amirkabir University of Technology
2 Rock Mechanics Department, Amirkabir University of Technology
چکیده [English]

The deformation modulus of rock mass (Em) is the most representative parameter of the pre-failure mechanical behavior of the rock material and of the rock mass .Due to the high cost and measurement difficulties of in situ tests, the predictive models using regression based statistical methods, back propagation neural networks (BPN) and fuzzy systems are recently employed for the indirect estimation of the modulus .Among these methods, the BPN has been reported to be very useful in modeling the rock material behavior, such as Em, by many researchers .Despite its extensive applications, design and structural optimization of BPN are still done via a time-consuming reiterative trial-and-error approach . However, in this research, the genetic algorithm (GA) is utilized to find the optimal parameters of BPN, such as the optimal number of neurons in hidden layer, learning rates and momentum coefficients of hidden and output layers of network. Then, the result is compared with that of trial-and-error procedure . For the purpose, a data base including118 data sets was employed from six dam sites locations in Zagros Mountains of Iran. According to the results, the GA -ANN model has higher accuracy than the trial-and-error model in the estimation of Em 

کلیدواژه‌ها [English]

  • Deformation modulus of rock mass
  • Zagros mountains
  • Back propagation neural network
  • Genetic Algorithm

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نشریه انجمن زمین شناسی مهندسی ایران
دوره 5، شماره 1 و 2
شهریور 1391
صفحه 93-100

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سابقه مقاله

  • تاریخ دریافت: 04 آبان 1390
  • تاریخ بازنگری: 29 شهریور 1391
  • تاریخ پذیرش: 06 دی 1391

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