Scientific Quarterly Journal of Iranian Association of Engineering Geology

Scientific Quarterly Journal of Iranian Association of Engineering Geology

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

Document Type : Original Article

Authors
Zeinab Aliabadian
Mostafa Sharifzadeh
Rock Mechanics Department, Amirkabir University of Technology
Abstract
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 
Keywords
Deformation modulus of rock mass
Zagros mountains
Back propagation neural network
Genetic Algorithm
Subjects
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Scientific Quarterly Journal of Iranian Association of Engineering Geology
Volume 5, Number 1 & 2
September 2012
Pages 93-100

  • Receive Date 26 October 2011
  • Revise Date 19 September 2012
  • Accept Date 26 December 2012