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    ABSTRACT: Although the modulus of deformation of rock masses has crucial importance for geotechnical projects, such as tunnels and dams, the determination of this parameter by in situ tests requires considerable costs and involves difficult operational processes. For this reason, empirical equations for the indirect estimation of the modulus of deformation are an interesting issue for rock engineers and engineering geologists. This study includes assessment of the prediction performances of some existing empirical equations, using in situ plate loading test data and rock mass properties, producing an empirical equation depending on the new data, construction of a fuzzy inference system for the estimation of modulus of deformation, and making a comparison between results obtained from the empirical equations and fuzzy inference system. A series of calculations and statistical analyses were undertaken. It is concluded that the performance of the empirical equations and fuzzy inference system obtained in this study is satisfactory. However, the prediction models developed in this study are limited by the number of the data used and the rock types employed. For these reasons, a cross-check should be performed before using these prediction models for design purposes.
    International Journal of Rock Mechanics and Mining Sciences 06/2003; 40(4):607. · 1.42 Impact Factor
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    ABSTRACT: Subjective judgement normally constitutes an important element in mining geomechanics decision processes. In most instances, subjectivity arises from the imprecise or fuzzy information which in turn results from descriptive data or inaccurate test results. The paper proposes the application of fuzzy set theory in assisting mining engineers in the geomechanics decision processes for which subjectivity plays an important role. In particular, the Bellman-Zadeh optimization procedure is used to synthesize a hazard index for mining excavations. The same procedure is used to evaluate a rock mass classification rating from Bieniawski's system with incorporation of expert knowledge. The extension principle of fuzzy sets is applied to evaluate Barton's quality index Q when information on various contributing indices is fuzzy.Basic principles of fuzzy set theory are described and numerical examples are used to illustrate applications of fuzzy set theory in mining geomechanics.
    International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts.
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    ABSTRACT: A knowledge-based fuzzy model for performance prediction of a rock-cutting trencher has been developed. A trencher is a machine that uses a rotating cutting chain equipped with bits to excavate trenches in rock and soil. The performance of a trencher, and consequently the cost of a specific excavation project, is determined by its production rate and by the bit consumption (due to wear and breakage). Both these factors depend on the properties of the excavated rock material and on the trencher characteristics. Mathematical modeling of the trencher performance is difficult, since the interactions between the machine tool and the environment are dynamic, uncertain, and complex. The number of available measurements is too small to use statistical methods. Hence, an approach based on expert knowledge was applied to develop a rule-based fuzzy model. The use of fuzzy logic allows for smooth interfacing of the qualitative information involved in the rule base with the numerical input data. The developed model uses six input variables [rock strength, spacing of three joint (discontinuity) sets in the rock mass, joint orientation, and trench dimensions] to predict the production rate and bit consumption in terms of qualitative linguistic values. Numerical predictions are obtained by using a modified fuzzy-mean defuzzification which allows for straightforward adaptation of the consequent membership functions in order to fine-tune the model performance to the data. The expert knowledge is coded as if-then rules, hierarchically organized in four rule bases. The model was validated both qualitatively using dependency analysis and quantitatively using the available data. The results obtained so far are satisfactory.
    International Journal of Approximate Reasoning 01/1997; 16(1):43–66. · 1.73 Impact Factor

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