Application of Fuzzy Set Theory to Rock Engineering Classification Systems: An Illustration of the Rock Mass Excavability Index (original) (raw)
Related papers
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.
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.
2011
The Rock Mass Rating (RMR) system is a classification based on the six parameters which was defined by Bieniawski. This system may possess some fuzziness in its practical applications. For example, experts mostly relate discontinuities and ground water conditions in linguistic terms with approximation. Descriptive terms vary from one expert to the other, while in the RMR system; values which are related to these terms are probably the same. The other hand, sharp transitions between two classes create uncertainties. So it is proposed to determine weighting intervals for discontinuities and water condition. Two fuzzy models based on the Mamdani algorithm were introduced to evaluate proposed weights, so that the first fuzzy model includes 55 scores using fuzzy model and the remained scores which are related to discontinuities and ground water conditions are obtainable by the RMR system. But the second fuzzy model obtains all scores of the RMR system using fuzzy model. Results of fuzzy models are adapted with actual RMR, but second fuzzy model predicts more acceptable results, because it has the ability to use qualitative terms in fuzzy state. But first fuzzy model uses descriptive terms in classic state. So it seems, proposed weighting intervals can manage fuzzification of discontinuities and water conditions.
An Application of Fuzzy Sets to the Blastability Index (BI) Used in Rock Engineering
Periodica Polytechnica Civil Engineering, 2018
Rock masses have inherently different resistance to fragmentation by blasting. This property is hereafter referred to as the blastability of a rock mass. Empirical models for the estimation of blastability have been developed. In this study, the Mamdani fuzzy algorithm was used to express the blastability index by fuzzy sets. We use Lilly and Ghose blastability models which are important models of blastability. Parameters of these models were represented by fuzzy sets as the input variables of the fuzzy model. The output of the fuzzy model is a final blastability index rating. Experimental data is obtained from seven mine and one dam sites in Iran. BI values are obtained from both BI fuzzy inference system and conventional BI; Fuzzy sets have more adjustment than conventional model.
Fuzzy analytical hierarchy process approach for ranking the sawability of carbonate rock
International Journal of Rock Mechanics and Mining Sciences, 2012
A new classification system is presented to evaluate and ranking the sawability of carbonate rock. The sawability of carbonate rock is classified into five categories: excellent, good, fair, poor and very poor. The sawability is assumed to depend on the uniaxial compressive strength, Young's modulus, Mohs hardness, and a new abrasivity index. The FAHP approach is used to determine the weights of the above-mentioned parameters by decision makers. Moreover, in this paper, a new classification system was developed to modify Schimazek's F-abrasiveness factor. In this new abrasivity classification, each parameter has a different importance coefficient. The new abrasivity index of carbonate rocks can be obtained from this new abrasivity classification system. The calculated sawability index of developed classification is applied for Iranian carbonate rocks to evaluation the energy consumption in rock sawing process. A variety of two groups of carbonate rocks (seven types) were saw using a fully instrumented laboratory sawing rig at different feed rates, peripheral speeds, and depth of cut. Then, a new statistical model was obtained using multiple regression method based on operating parameters and rock sawability index.
Modification of rock mass rating system using soft computing techniques
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
This paper describes how to apply fuzzy inference system (FIS) to estimate the strength of field scale rock masses by judgment and experience of practicing engineers. Three important parameters believed to influence the rock mass behavior namely intact rock strength, block size, and joint surface condition are defined as fuzzy variables. The strength of jointed rock masses is then estimated by incorporating different combinations of three fuzzy inputs into Mamdani rule-based FIS model. To validate the accuracy of the model results, a comprehensive rock mass data is collected from the literature and the strength of rock masses estimated using different empirical equations is compared to the strength of rock masses estimated from the FIS model. It is concluded that the newly developed model compares well with the estimated results and it can be recommended as an alternative method for the rock mass strength predictions instead of empirical approaches in practice.
Fuzzy representation and reasoning in geotechnical site characterization
Computers and Geotechnics, 1997
Geotechnical site characterization is an important@ step in all geotechnical engineering problems. It is important to have a model of the subsurface conditions when doing any geotechnical analysis. To create this profile, samples are taken in boreholes which are then used to infer a subsurface profile. This paper discusses a method that uses fuzzy sets to represent the data collected from samples in a borehole. Then, a fuzzy reasoning system is developed to infer the subsurface projle. The resulting fuzzy system creates a new approach Science Ltd.
2009
The information on the soundness of rock is very important for enduring safety and stability of miming, marine and civil structures. The present paper mainly deals with a fuzzy approach to determine the effect of acidic watery environment on the rock strength of the sandstone rock sample. Fuzzy inference system developed here intelligently predicts the competency of the rock, based on experimental data used for the model development. The approach used would be able to predict the relative rock strength at any range of acidic to basic watery environment for any geotechnical investigation or application to achieve long-term durability.