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REP02732
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REP02732
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Last modified
8/24/2016 11:33:36 PM
Creation date
11/26/2007 10:18:55 PM
Metadata
Fields
Template:
DRMS Permit Index
Permit No
M1974004
IBM Index Class Name
Report
Doc Date
2/14/2007
Doc Name
Annual Report
From
Lafarge West, Inc.
To
DRMS
Permit Index Doc Type
Annual Reclamation Report
Media Type
D
Archive
No
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' 6.0 Analysis of Structural Discontinuities <br />The stability of the rock mass that forms the highwalls of the quarry is mainly <br />controlled by the discontinuities that exist within the rock mass. Discontinuities <br />create the surfaces that sliding can occur upon and the intersections of multiple <br />discontinuities define the boundaries of wedges or blocks that have the potential <br />' to slide. As observed throughout the quarry, the discontinuity sets all have a <br />range of dip and dip direction, and therefore it is possible that discontinuity <br />' orientations in a specific location of the quarry deviate from the general <br />discontinuity orientations assumed for the analyses presented in this report. <br />6.1 Evaluation of Discontinuity Measurements <br />t As the database for discontinuity measurements has increased, various methods <br />' have been utilized to efficiently analyze the new geologic data relative to the <br />existing data. The analysis of the discontinuity data was conducted using Dips <br />' S.0 (Rocscience, 2002), which is an interactive program for the analysis of <br />discontinuity orientations based on geological data inputs. For this study, the <br />' data were evaluated with stereographic plots using a combination of contour <br />analyses and cluster analyses to determine representative dip directions and dip <br />~ angles for the predominant discontinuity set. <br />The contour analysis of the entire dataset (i.e., 2,978 points) resulted in a plot of <br />density contours, which facilitated our determination of the general discontinuity <br />orientations (Figure 4). The cluster analysis has helped to further refine the data <br />by utilizing an iterative approach to group the data measurements into <br />representative clusters. The nucleus of each cluster represents the <br />concentration center for the data set (Figure 6). The differences between each <br />of the individual year's analyses can best be seen on a stereographic <br />presentation showing each of the concentration poles for the clusters for multiple <br />years analyses (Figure 7). A combination of the clusters from Figure 6 and the <br />results of the contour and cluster analysis of the entire dataset (Figure 7) were <br />15- <br />
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