Abstract

A detailed geostructural characterization (i.e., joint aperture, spacing, filling materials and joint size) was performed to evaluate the weathered rock foundation of the Siazakh Dam site, western Iran. Rock qualities were assigned a rock mass rating classification. A positive correlation was detected between permeability and rock characteristics, and played an important role in assessing fracture media. The joint aperture had a stronger relationship to joint permeability than did other fracture characteristics. The results strongly support the contention that a new generation of permeability equations and analytical methods may be obtained in accordance with a site's geological features. In this regard an artificial neural network (ANN) is a biologically inspired computing method that is capable of predicting the permeability values of rock masses with high accuracy. ANNs showed high potential in interpreting raw data from the Lugeon test and predicting the final permeability of a testing section in a borehole. A new practical approach for the evaluation of field permeability with depth in fractured rock masses is also presented.

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