Rock mechanics modeling based on soft granulation theory
📝 Abstract
This paper describes application of information granulation theory, on the design of rock engineering flowcharts. Firstly, an overall flowchart, based on information granulation theory has been highlighted. Information granulation theory, in crisp (non-fuzzy) or fuzzy format, can take into account engineering experiences (especially in fuzzy shape-incomplete information or superfluous), or engineering judgments, in each step of designing procedure, while the suitable instruments modeling are employed. In this manner and to extension of soft modeling instruments, using three combinations of Self Organizing Map (SOM), Neuro-Fuzzy Inference System (NFIS), and Rough Set Theory (RST) crisp and fuzzy granules, from monitored data sets are obtained. The main underlined core of our algorithms are balancing of crisp(rough or non-fuzzy) granules and sub fuzzy granules, within non fuzzy information (initial granulation) upon the open-close iterations. Using different criteria on balancing best granules (information pockets), are obtained. Validations of our proposed methods, on the data set of in-situ permeability in rock masses in Shivashan dam, Iran have been highlighted.
💡 Analysis
This paper describes application of information granulation theory, on the design of rock engineering flowcharts. Firstly, an overall flowchart, based on information granulation theory has been highlighted. Information granulation theory, in crisp (non-fuzzy) or fuzzy format, can take into account engineering experiences (especially in fuzzy shape-incomplete information or superfluous), or engineering judgments, in each step of designing procedure, while the suitable instruments modeling are employed. In this manner and to extension of soft modeling instruments, using three combinations of Self Organizing Map (SOM), Neuro-Fuzzy Inference System (NFIS), and Rough Set Theory (RST) crisp and fuzzy granules, from monitored data sets are obtained. The main underlined core of our algorithms are balancing of crisp(rough or non-fuzzy) granules and sub fuzzy granules, within non fuzzy information (initial granulation) upon the open-close iterations. Using different criteria on balancing best granules (information pockets), are obtained. Validations of our proposed methods, on the data set of in-situ permeability in rock masses in Shivashan dam, Iran have been highlighted.
📄 Content
- INTROUDCTION In the recent years, developing new indirect analysis methods has opened new horizons in rock engineering solutions. Tendency to the micro-view of the natural events in the rock based systems, upon the high speed PC technology; behind large- scale investigations, has allocated new challenges in this track. Transition from a general (overall) view in to the detailed descriptions, can be interpreted as relations (inter-extra relations) of the commuted “information packages”, got from accumulations of data, experience, novelty and other effective agents. Such construction of the “whole” from “part” (granules) is the current behavior of human cognition. Among the basic concepts which underlie human cognition there are three remarkable sides, which are: granulation, organization, and causation. Granulation involves decomposition of whole into parts; organization involves integration of parts in to whole; and causation relates to association of causes with effects [1]. Under this view from the discritization (meshing, blocking, latticing…) of the interior or boundary of a field to the solving steps (thinking) of the problem are the perspectives of granulation. We called first level of granulation as “hard granulation”, and second level as “soft granulation”. To better understand of the meaning of hard and soft granulation, we reproduce the general rock engineering design flowchart in figure1 [2]. Level1 can be supposed as a hard granulation where level2 is related with soft granulation. Clearly, in soft granulation; we are approaching to the real human cognition, whereas in hard packing the machine computations are distinguished. Let us, consider the last class in level2 (in category D): internet based system. Interestingly, this category shows how the discriminated projects, under the virtual world, employ the distributed information granules. Plainly, the contributions of any projects and the sub-sets of granules in construction of this
Rock mechanics modeling based on soft granulation theory
H. Owladeghaffari Dept .Mining &Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran ABSTRACT: This paper describes application of information granulation theory, on the design of rock engineering flowcharts. Firstly, an overall flowchart, based on information granulation theory has been highlighted. Information granulation theory, in crisp (non-fuzzy) or fuzzy format, can take into account engineering experiences (especially in fuzzy shape-incomplete information or superfluous), or engineering judgments, in each step of designing procedure, while the suitable instruments modeling are employed. In this manner and to extension of soft modeling instruments, using three combinations of Self Organizing Map (SOM), Neuro-Fuzzy Inference System (NFIS), and Rough Set Theory (RST) crisp and fuzzy granules, from monitored data sets are obtained. The main underlined core of our algorithms are balancing of crisp(rough or non-fuzzy) granules and sub fuzzy granules, within non fuzzy information (initial granulation) upon the “open-close iterations”. Using different criteria on balancing best granules (information pockets), are obtained. Validations of our proposed methods, on the data set of in-situ permeability in rock masses in Shivashan dam, Iran have been highlighted. general network are affected from the several parameters, concluded in “granulation level” factor. In this paper, we interest to tack in to account soft granulation in rock system. Upon this, by focusing in two categories C-1 and C-2, in figure1, we develop different soft granulation methods based on intelligent systems and approximate reasoning methods. Added to this, the bridging between hard and soft granulation is abstracted. The most main distinguished facets of the soft granules are: set theory, interval analysis, fuzzy set, rough set. Each of these theories considers part of uncertainty of information (data, words, pictures…). Due to association of uncertainty and vagueness with the monitored data set, particularly, resulted from the in-situ tests (such lugeon test), accounting relevant approaches such probability, Fuzzy Set Theory (FST) and Rough Set Theory (RST) to knowledge acquisition, extraction of rules and prediction of unknown cases, more than the past have been distinguished. Zadeh has emphasized the role of FST in geosciences will be increased during future years [3]. The RST introduced by Pawlak has often proved to be an excellent mathematical tool for the analysis of a vague description of object [4], [5]. The adjective vague, referring to the quality of information, means inconsistency, or ambiguity which follows from information granulation. The rough set philosophy is based on the assumption that with every object of the universe, is associated a certain amount of informa
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