CHARACTERIZING ROCK JOINT GEOMETRY WITH JOINT SYSTEM MODELS
CHARACTERIZING ROCK JOINT GEOMETRY WITH JOINT SYSTEM MODELS
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DOI:
10.1007/bf01019674
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发表时间:
1988-01-01
影响因子:
6.2
通讯作者:
EINSTEIN, HH
中科院分区:
文献类型:
--
作者:
DERSHOWITZ, WS;EINSTEIN, HH
Rock joint system models represent a relatively recent development in the characterization of rock mass geometry. It is the intent of this paper to describe currently available models on the basis of the models' geological, geometric, and rock mechanics implications. In this manner the reader will not only see innovative developments but will also be provided with a review of many currently available geometric rock characterization techniques.Geometric and mechanical characterization of rock joints is the basis for most of the work of engineering geologists, civil and mining engineers when dealing with rock masses. Also, such a characterization plays an important role in investigations of joint genesis. However, the complete description of joints is difficult because of their three-dimensional nature and their limited exposure in outcrops, borings or tunnels. An ideal characterization of jointing would involve the specific description of each joint in the rock mass, exactly defining its geometric and mechanical properties. This is not possible for a number of reasons: 1) the visible parts of joints are limited, for instance to joint traces only, and thus prevent complete observation; 2) joints at a distance from the exposed rock surfaces cannot be directly observed; 3) direct (visual or contact measurements) and indirect (geophysical) observations have limited accuracies. For these reasons joints in a rock mass are usually described as an assemblage rather than individually. The assemblage has stochastic character in that joint characteristics vary in space. Such variations may be minute as in the case of the orientation of a set of approximately parallel joints or they may be large if a particular property has substantial variability. It is important to note that spatial variability can but does not have to imply random underlying mechanisms; spatial variability may just as