Thus, one can employ such method as a new mythology in designing of rock engineering flowcharts. 

The main benefits are considering of the roles of the expert's experiences and educations, the missing or vagueness information and utilizing of advantages of soft and hard computing methods. A new advantages of the mentioned method, is to considering of the outward changes in "*words format*" and *"calculation with words and perception" (CWP).* 
Under mentioned methodology and to develop modeling instruments, authors have (are) proposed (proposing) new algorithms with neural networks, fuzzy logic (possibility theory), rough set theory, and meta heuristic optimization methods which are accompanied by close-open world idea. Random selection of initial precise granules can be set as "close world" assumption (CWA). But in many applications, the assumption of complete information is not feasible (CWA) nor realistic, and only cannot be used. In such cases, an open world assumption (OWA), 
where information not known by an agent is assumed to be unknown, is often accepted. The aim of open-world is to achieve complete knowledge of the universe by set of classified rules or by modifying rules. The close-open iteration accomplishes the balancing of crisp and sub rough granules by some random selection of initial granules and increment (or decreasing) of supporting rules, gradually. Figure 3, shows one of our proposed algorithms, apart from contributing of FL and free-derivative optimization methods. One may employ other shape of open world assumption's implementation. For instance to raise the quality of approximation by much more categories, but the optimum numbers of such scaling could be approached by an algorithm, so that an adaptive disceritization besides balancing of the initial granules with open world is accomplished First step is to collect data sets by using 1-1 modeling (here, hard computing methods as one of the main part of the direct modeling methods are selected). Applying of SOM as a preprocessing step and discretization tool is second process. For continuous valued attributes, the feature space needs to be discretized for defining indiscernibilty relations and equivalence classes. We discretize each feature in to three levels by SOM: *"low, medium, and high"*; finer discretization may lead to better accuracy at the cost of a higher computational load. Because of the generated rules by a rough set are coarse and therefore need to be fine-tuned, here, we have used the preprocessing step on data set to crisp granulation by SOM, so that extraction of best initial granules and then rough granules is rendered in close-open iteration. In open world iteration phase, we use a simple idea associated with human's cognition of the surround world*:"simplicity of rules"* (dominant to the problem) whether in numbers, applied operators or/and the length of rules. Here, we take in to account three main parameters in rules generation: 
length of rules, strength of object and number of rules. By setting the adaptive threshold error level and the number of close-open iteration, stability of the algorithm is guaranteed. After obtain best rules, the monitored data set as real decision parts are compared with the extracted rules and best conditional parts are picked up. These parts, associated with the reduced data sets by SOM, are the approximation of the wanted parameters as well as back analysis results. 

## 4 An Illustrative Example: Failure On The Southeast Wall- Jeffrey Mine

This section describes how one can acquired approximated values of the effective parameters under a reverse analysis. To this aim and by using finite difference method, as well as FLAC (), the failure on the southeast wall in Jeffrey mine (Quebec) has been evaluated. 

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