A new hybrid model of information entropy and unascertained measurement with different membership functions for evaluating destressability in burst-prone underground mines

Zhou, Jian; Chen, Chao; Du, Kun; Armaghani, Danial Jahed; Li, Chuanqi

doi:10.1007/s00366-020-01151-3

Cited by 20 publications

(8 citation statements)

References 57 publications

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“…The single index measurement judgment matrix is calculated by the membership function. In this paper, we will use four types membership functions (Zhou et al, 2020b;Zhou et al, 2021a): linear, parabolic, exponential and sine functions, as shown in Eqs 5-8 respectively.…”

Section: Single-index Measurement Matrixmentioning

confidence: 99%

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Developing an evaluation model based on unascertained measurement for evaluation of tunnel squeezing

et al. 2022

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Tunnel squeezing brought great difficulties to the construction and severely threatened the safety of on-site operators. The researches regarding large deformation evaluation have been widely developed, but actual conditions of tunnels are considerably complex, producing a large variety of uncertainty information existing in the evaluation process. Therefore, we constructed an unascertained measurement model incorporating four membership functions for evaluation of tunnel squeezing based on the collected datasets. Simultaneously, information entropy was introduced to objectively calculate the index importance for each index. For the first group data (GPI), the accuracy associated with four membership functions are 100%, 83.33%, 50%, and 83.33%, respectively, while the accuracy of GPII are 70%、77.5%、67.5%, and 70%, respectively. Linear function and parabolic function show better performance on uncertainty information interpretation according to the classification results. The results revealed that the uncertainty model constructed in this study can enrich the available uncertainty evaluation system.

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Section: Single-index Measurement Matrixmentioning

confidence: 99%

“…Where, L ik is the grade of sample i th , and L ik ∈ Ω, (Ω {L 1 , L 2 , L 3 , ..., L k , ..., L K }. λ(λ ≥ 0.5), normally, λ 0.6 or λ 0.7 (Zhou et al, 2020a;Zhou et al, 2020b;Zhou et al, 2021b;Chen et al, 2021).…”

Section: Credible Identification Principlementioning

confidence: 99%

Developing an evaluation model based on unascertained measurement for evaluation of tunnel squeezing

et al. 2022

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“…Unascertained measurement is a method that can effectively deal with fuzzy and uncertain information [55]. However, the traditional unascertained measurement approach uses a confidence criterion for calculations [56][57][58][59][60][61], which introduces some subjectivity. To ensure the objectivity of discrimination results, the idea of distance discrimination can be introduced to optimize an attribute recognition model.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Predicting Rockburst Intensity Based on an Improved Unascertained Measurement and an Improved Game Theory

et al. 2023

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A rockburst is a dynamic disaster that may result in considerable damage to mines and pose a threat to personnel safety. Accurately predicting rockburst intensity is critical for ensuring mine safety and reducing economic losses. First, based on the primary parameters that impact rockburst occurrence, the uniaxial compressive strength (σc), shear–compression ratio (σθ/σc), compression–tension ratio (σc/σt), elastic deformation coefficient (Wet), and integrity coefficient of the rock (KV) were selected as the evaluation indicators. Second, an improved game theory weighting method was introduced to address the problem that the combination coefficients calculated using the traditional game theory weighting method may result in negative values. The combination of indicator weights obtained using the analytic hierarchy process, the entropy method, and the coefficient of variation method were also optimized using improved game theory. Third, to address the problem of subjectivity in the traditional unascertained measurement using the confidence identification criterion, the distance discrimination idea of the Minkowski distance was used to optimize the identification criteria of the attributes in an unascertained measurement and was applied to rockburst prediction, and the obtained results were compared with the original confidence identification criterion and the original distance discrimination. The results show that the improved game theory weighting method used in this model makes the weight distribution more reasonable and reliable, which can provide a feasible reference for the weight determination method of rockburst prediction. When the Minkowski distance formula was introduced into the unascertained measurement for distance discrimination, the same rockburst predictions were obtained when the distance parameter (p) was equal to 1, 2, 3, and 4. The improved model was used to predict and analyze 40 groups of rockburst data with an accuracy of 92.5% and could determine the rockburst intensity class intuitively, providing a new way to analyze the rockburst intensity class rationally and quickly.

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“…(Ozcelik et al 2013;Ulusay et al 1994;Irfan 1996; Du et al 2020cDu et al , 2021Wang et al 2020;Tuğrul and Zarif, 1999;Dunn et al 1973;Ozcelik et al 2013;KH 1974;Wong et al 1996). The uniaxial compressive strength (σ c ), tensile strength (σ t ), and elasticity (E) as three commonly used parameters to reflect the differences among different rock types (Torabi-Kaveh et al 2015;Du et al 2019Du et al , 2022Zhou et al 2022aZhou et al , 2022bTao et al 2021). A number of scholars have studied the effects of rock mineral composition (Tuğrul and Zarif 1999;Richard et al 1970;Hecht et al 2005;Gunsallus et al 1984), grain size, and shape (Tuğrul and Zarif 1999;Wong et al 1996;Brace 1961;Onodera and Asoka Kumara 1980;Přikryl 2001;Olsson 1974;Handin et al 1957), weathering (Irfan 1996;Irfan and Dearman 1978;Gupta and Rao 2000), porosity (Dunn et al 1973;Ozcelik et al 2013;Li and Dearman 2003;Baud et al2014).…”

Section: Introductionmentioning

confidence: 99%

Mineral Composition and Grain Size Effects on the Fracture and Acoustic Emission (AE) Characteristics of Rocks Under Compressive and Tensile Stress

Sun

Zhou

et al. 2022

Rock Mech Rock Eng

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The influence of rock mineral composition and mineral grain size on basic rock strength performance and AE characteristics have been studied, 13 different rocks microstructures are analyzed in an optical microscope thin section using petrographic image analysis, making it possible to determine the mineral composition and mineral texture characteristics of rocks. Then, the basic strength parameters of rock and AE signals generated during fracture propagation were obtained by UCT (uniaxial compression test) and BIT (Brazilian intension test). Finally, the relationship between basic strength parameters and AE characteristics of rock with mineral composition and grain size was analyzed. The results showed that different mineral constituents have significant effects on rock strength. The positive influence of plagioclase content on igneous strength was obtained. Sedimentary rocks strength increases initially and then decreases with the increase of plagioclase content. Besides, with the increase in quartz and K-feldspar content, the strength of the rock was weakened obviously. It is also found that the greater the dimensional deviation of mineral grain, the greater the strength of the rock. The strength of igneous rocks was inversely proportional to the mineral grain size, but there is no correlation between the sedimentary rocks strength and the mineral grain size. Furthermore, the tension–shear crack propagation of rock can effectively distinguish by judging that the data set of the AF–RA density graph was nearby the AF axis or RA axis and the peak frequency data sets of below 100 kHz or more than. Alterations in the rock nature are the main key reasons for the differences between AE hit rate, AE count rate, AE energy, and cumulative energy. The plagioclase content and grain size play a decisive role in AE signal characteristics and failure mode.

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A new hybrid model of information entropy and unascertained measurement with different membership functions for evaluating destressability in burst-prone underground mines

Cited by 20 publications

References 57 publications

Developing an evaluation model based on unascertained measurement for evaluation of tunnel squeezing

Developing an evaluation model based on unascertained measurement for evaluation of tunnel squeezing

A Novel Method for Predicting Rockburst Intensity Based on an Improved Unascertained Measurement and an Improved Game Theory

Mineral Composition and Grain Size Effects on the Fracture and Acoustic Emission (AE) Characteristics of Rocks Under Compressive and Tensile Stress

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