Incidence of Insertion Sequences (ISs) in T. thermophilus

The use of past slope behavior experience constitutes an important ingredient in almost all design methods. The earlier described methods all stem from the theory of continuum mechanics, whereas in this section, slope design based solely on precedent is described. An early attempt toward a systematic grouping of empirical data was presented by Lutton (1970).

Data from the steepest and highest slope in a specific open pit mine were gathered from several mines, and the slope height was plotted against the slope angle. This was further developed by Hoek and Bray (1981) by adding more cases (Figure 4.8). The currently employed interramp angle for the footwall at Aitik has also been included in this figure.

The dotted line in Figure 4.8 represents an estimated (Lutton, 1970; Hoek and Bray, 1981) upper limit for stable slopes. The higher the slope, the lower must the slope angle be to maintain stability. However, for the higher slopes the angle becomes almost constant which might lead to the conclusion that there is a lower limit to the required slope angle. In reality, this is probably an effect of having too little data for higher slopes. Furthermore, the shape and location of the design curve appears to be chosen somewhat arbitrarily, judging from the cases in Figure 4.8, since there are several unstable slopes located below the design curve (on the safe side).

Empirical data on stable and unstable slope angles and slope heights has also been collected from natural slopes by Coates (1977, 1981). Coates also included data from excavated slopes in his study and concluded that the difference between slopes in different rocks decreased as the slope height increased, which could partly explain the shape of the curve in Figure 4.8.

These are only general guidelines, but the limited data do not permit the establishment of more detailed design rules. As pointed out by Lutton (1970), the design curve in Figure 4.8

represents a combined effect of many factors which makes it difficult to judge its applicability in a specific geomechanical environment.

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Figure 4.8 Slope height (in feet) versus slope angle relation for hard rock slopes (after Lutton, 1970; Hoek and Bray, 1981). The currently used interramp slope angle for the north portion of the footwall at Aitik is also shown (FN - footwall north).

Other ongoing work on empirical slope design includes the very extensive field work on both natural and engineered slopes in China (Chen, 1995). More than 100 slope cases have been collected, ranging from very small (less than 10 meters in height) to very large slopes (more than 1000 meters in height), in various rock types, and exhibiting various types of failures.

Rock mass classification (see below) has been conducted but no guidelines for slope design have as yet been developed from this work.

Rock mass classification is a more comprehensive and better structured form of empirical design. Different factors believed to affect stability are given different ratings which then are combined into a total rating representing the rock mass quality. An early attempt at a rock mass classification was that presented by Deere (1975). This classification scheme was used in the Aitik slope stability study by Call et al. (1976) and West et al. (1985). Several

classification systems have later been developed, primarily for use in tunneling design (Barton, Lien and Lunde, 1974; Bieniawski, 1976, 1989). The RMR-system (Rock Mass Rating), developed by Bieniawski (1976, 1989), has been modified into a classification system specifically aimed at rock slopes  the SMR (Slope Mass Rating) system (Romana, 1993).

The standard RMR-system is applied and four adjustment factors are then added or subtracted.

The adjustment factors account for joint and slope geometry, and the excavation method for the slope. The resulting SMR-rating is grouped into one of five stability classes which then determine the overall stability for the slope and the suggested support. The SMR classification scheme has been used, for example, in the assessment of slope stability along a highway in Spain (Miño, 1991).

A similar, although less comprehensive approach to slope stability classification was proposed by Haines and Terbrugge (1991) who based their classification on the MRMR-system (Mining Rock Mass Rating) by Laubscher (1977). The MRMR-rating and the slope height are used to obtain the stable slope angle, based on a number of case studies from Africa and South America. A simplified classification system was also developed by Hawley, Gilmore and Newcomen (1994) for use in the preliminary slope design of large scale pits in South America and Canada. Relatively recently, a more universal classification system for slope design has been developed (Arnold, 1991; Mazzocola, 1992). These studies have also served as input data to the "matrix"-based system known as REMIT  Rock Engineering Mechanisms Information Technology (Hudson, 1992). The REMIT-system is a nice way of graphically presenting the factors which affect the stability of a slope and how they interact. It is not, however, a true design system and thus of limited use for our purposes. The same can be said for the recent application of expert systems in this field (Sinha and Sengupta, 1989; Hao and Zhang, 1994), since the development of expert systems rely heavily on complete knowledge of the problem at hand. There have also been some attempts made at developing a single design

formula for slopes, similar to the formulas often used in pillar design. Sah et al. (1994) formulated an equation for the safety factor by applying regression analysis to a number of case studies. This was purely empirical and different failure modes were not differentiated amongst. The reliability of such a formula for general application must therefore be questioned.

To summarize, all slope classification systems exhibit the same major weakness in that they simply are not precise enough for the design of final slopes in open pit mining. They may be applicable for the design of small scale slopes, at least as first estimates when detailed data regarding potential failure mechanisms in the slope are not available. The concept of learning from past experience is, however, very important and should always constitute a major portion in slope design work.

Finally, a few words on trial slopes. Conducting a full scale test of slope stability is perhaps the most extreme form of empirical design. Although very cumbersome and costly, it can yield extremely valuable results on large scale strength parameters for the rock mass (Coates, 1977, 1981). Among the few tests actually carried out, the one at the Kimbley Pit in Nevada is probably the best documented. Despite the fact that the overall slope angle was increased from 45° to 61°, no massive failures occurred in this relatively weak rock mass, see also Chapter 6 of this report. For a full scale test to be successful, it is important that the objective of conducting such a test be stated in detail along with what can be expected and achieved before the test is conducted. Careful planning is imperative to all such activities. It is also important to recognize that even a successful trial slope test, i.e., a test in which the failure mechanisms and the governing parameters can be determined, does not imply that the obtained data are representative and can be used for the entire open pit, or even for a higher slope in the same area.