Prediction of Rock Mass Rating using Fuzzy Logic with Special Attention to Discontinuities and Ground Water Conditions (original) (raw)
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Prediction of rock mass rating using fuzzy logic and multi-variable RMR regression model
International Journal of Mining Science and Technology, 2014
Rock mass rating system (RMR) is based on the six parameters which was defined by Bieniawski (1989) [1]. Experts frequently relate joint and discontinuities and ground water conditions in linguistic terms with rough calculation. As a result, there is a sharp transition between two modules which create doubts. So, in this paper the proposed weights technique was applied for linguistic criteria. Then by using the fuzzy inference system and the multi-variable regression analysis, the accurate RMR is predicted. Before the performing of regression analysis, sensitivity analysis was applied for each of Bieniawski parameters. In this process, the best function was selected among linear, logarithmic, exponential and inverse functions and finally it was applied in the regression analysis for construction of a predictive equation. From the constructed regression equation the relative importance of the input parameters can also be observed. It should be noted that joint condition was identified as the most important effective parameter upon RMR. Finally, fuzzy and regression models were validated with the test datasets and it was found that the fuzzy model predicts more accurately RMR than regression models.
The First step in analysis of slope stability in open pit mines is punctual definition of rock mass characteristics. Several rock mass classification systems have been presented so far in the area of geomechanics. One of the most widely used rock mass classification systems are the geomechanics classification (RMR) by Bieniawiski. The RMR classification is based on the definition of classic membership functions. So characterization of rock masses and determination of their strength may involve some uncertainties due to their complex nature. The fuzzy set theory is one of the tools to handle such uncertainties. This paper describes the application of fuzzy set theory to the RMR system by incorporating fuzzy sets, and mamadani fuzzy algorithm was constructed using "if-then" rules for evaluating RMR parameters and their rating considered in the RMR system. Firstly, RMR classification is redefined by using the fuzzy logic. In the second step, the tables for a case study in Iran are calculated based on field and laboratory measurements.
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Engineering with Computers, 2018
Classification systems such as rock mass rating (RMR) are used to evaluate rock mass quality. This paper intended to evaluate RMR based on a fuzzy clustering algorithm to improve linguistic and empirical criteria for the RMR classification system. In the proposed algorithm, membership functions were first extracted for each RMR parameter based on the questionnaires filled out by experts. RMR clustering algorithm was determined by considering the percent importance of each parameter in the RMR classification system. In all implementation stages of the proposed algorithm, no empirical judgment was made in determining the classification classes in the RMR system. According to the obtained results, the proposed algorithm is a powerful tool to modify the rock mass rating system and can be generalized for future research.
Estimating strength of rock masses using fuzzy inference system
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Computers and Geotechnics, 2011
The rock engineering classification system is based on six parameters defined by Bieniawski [5] , who employed parallel sets of linguistic and numerical criteria that were acknowledged to influence the behaviour of rock masses and the stability of rock structures. Consequently, experts frequently relate rock joints and discontinuities as well as ground water conditions in linguistic terms, with rough calculations. Recently, intelligence system approaches such as artificial neural network (ANN) and neuro-fuzzy methods have been used successfully for time series modelling. Using neuro-fuzzy approaches, which enable the information that is stored in trained networks to be expressed in the form of a fuzzy rule base, would help to overcome this issue. This paper presents the results of a study of the application of neuro-fuzzy methods to predict rock mass rating. We note that the proposed weights technique was applied in this process. We show that neuro-fuzzy methods give better predicti...
Rock Mechanics and Rock Engineering, 2010
The characterization of rock masses is one of the integral aspects of rock engineering. Over the years, many classification systems have been developed for characterization and design purposes in mining and civil engineering practices. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This results in subjective uncertainties, leading to the misuse of such classifications in practical applications. Fuzzy set theory is an effective tool to overcome such uncertainties by using membership functions and an inference system. This study illustrates the potential application of fuzzy set theory in assisting engineers in the rock engineering decision processes for which subjectivity plays an important role. So, the basic principles of fuzzy set theory are described and then it was applied to rock mass excavability (RME) classification to verify the applicability of fuzzy rock engineering classifications. It was concluded that fuzzy set theory has an acceptable reliability to be employed for all rock engineering classification systems.
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One of the useful methods for rock slope stability analysis is the Romana SMR classification. This method is a developed version of the Bieniawski's 'rock mass rating' (RMR) system. This classification is based on classic set theory. Characterization of rock's mass is very complex and may result in some ambiguous. The classic Sets Theory Classification is not able to yield to unambiguous results. Using fuzzy set theory is an effective approach to quantify these ambiguities. This paper describes the application of fuzzy set theory to SMR classification by incorporating fuzzy sets. In the proposed approach the Mamdani fuzzy algorithm was constructed using 825 "if-then" rules for evaluating rock slope stability. In addition, slope instabilities in an open pit mine were tested and results were evaluated to confirm the accuracy of implementation this proposed approach.
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Rock tensile strength (TS) is an essential parameter for designing structures in rock-based projects such as tunnels, dams, and foundations. During the preliminary phase of geotechnical projects, rock TS can be determined through laboratory works, i.e., Brazilian tensile strength (BTS) test. However, this approach is often restricted by laborious and costly procedures. Hence, this study attempts to estimate the BTS values of rock by employing three non-destructive rock index tests. BTS predictive models were developed using 127 granitic rock samples. Since the simple regression analysis did not yield a meaningful result, the development of models that integrate multiple input parameters were considered to improve the prediction accuracy. The effects of non-destructive rock index tests were examined through the use of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) approaches. Different strategies and scenarios were implemented during modelling of M...
Prediction of the blastability designation of rock masses using fuzzy sets
International Journal of Rock Mechanics and Mining Sciences, 2010
The main objective of rock blasting design is to achieve a balance among optimum powder factor, proper fragmentation, throws, ground vibration, etc. The in-situ rock mass properties are among the most important contributory factors in fragmentation. The term blastability is used to indicate the susceptibility of the rock mass to blasting and its characterization has become a pressing task for blasting operations. Several approaches have been used for estimating blastability. Despite their widespread use in practice, they have some common deficiencies leading to uncertainties in their practical applications through sharp transitions between two adjacent rating classes and the subjective uncertainties on data, which are close to the range boundaries of rock classes. In this study, the fuzzy set theory was applied to blastability designation (BD) classification systems. Furthermore, a new methodology in terms of ''Effective Rules'' is developed in construction of rule base part of the Mamdani fuzzy inference system structure, to efficiently solve fuzzy inference systems with a large number of fuzzy rules (e.g. nearly 400,000 rules). In comparison with the conventional methods, it was seen that the fuzzy model operated more consistently. Moreover, it was shown that the fuzzy set theory could effectively overcome the uncertainties encountered in the practical applications of conventional classification systems.