A Data-Intensive Numerical Modeling Method for Large-Scale Rock Strata and Its Application in Mining Subsidence Prediction

Gong, Yaqiang; Guo, Guangli; Wang, Liping; Li, Huaizhan; Zhang, Guangxue; Fang, Zhen

doi:10.1007/s00603-021-02745-z

Cited by 12 publications

(12 citation statements)

References 46 publications

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“…In this study, 121 rockburst cases were included (Feng and Wang 1994, Gong and Li 2007, Gong et al 2010, Jin-Lin et al 2010, Lee, Tsui, Tham, Wang and Li 1998, Liu et al 2008, Ming-Zhou et al 2002, Ran et al 2019, Tang et al 2003, Wang et al 2009, Wang et al 2010, Zhang et al 2011, Zheng et al 2008, Zhou et al 2012, involving industries such as mining, hydropower, and transportation.…”

Section: Rockburst Casesmentioning

confidence: 99%

Probability prediction method for rockburst intensity based on rough set and multidimensional cloud model uncertainty reasoning

Long,

Wang,

et al. 2024

Environ Earth Sci

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Rockburst is a serious disaster caused by the sudden release of rock energy during underground construction in high-stress environments, resulting in severe damage to underground structures. Accurately predicting rockburst intensity is challenging, and establishing a reliable and precise prediction model is of great importance. In this study, we proposed a novel hybrid model for predicting rockburst intensity by integrating rough set theory and multidimensional cloud model uncertainty reasoning. The key steps of the proposed method are as follows: (1) Rockburst cases are collected, and the maximum shear stress 𝜎 𝜃 , uniaxial compressive strength 𝜎 𝑐 , uniaxial tensile strength 𝜎 𝑡 , and elastic energy index 𝑊 𝑒𝑡 are used as predictors for rockburst strength. (2) The Shannon entropy method is used to determine the weights of the four indicators, and a rockburst potential expression is constructed. (3) Rough set theory is used to reduce the number of indicators to construct a rockburst strength prediction rule library. (4) Qualitative data are transformed into quantitative data by using the rules library and multidimensional cloud model to establish an uncertainty inference framework for predicting rockburst strength. Finally, we compared the performance of the hybrid model with existing models, and the results demonstrate that the proposed approach achieves similar or even higher prediction accuracy. The use of cloud droplets in the model offers a significant advantage in the prediction of mixed rockburst intensities, enabling intuitive, rapid, and effective determination of the occurrence intensity of rockbursts.

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Section: Rockburst Casesmentioning

confidence: 99%

Probability prediction method for rockburst intensity based on rough set and multidimensional cloud model uncertainty reasoning

Long,

Wang,

et al. 2024

Environ Earth Sci

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“…According to Equation (26), when p = 1, sample 1 could be calculated using the distance discriminant method to predict rockburst class III, which belongs to the medium rockburst classification.…”

Section: Rockburst Intensity Class Predictionmentioning

confidence: 99%

“…The multi-factor rockburst prediction models are mainly divided into uncertainty evaluation models and intelligent optimization evaluation models. In the field of rockburst prediction uncertainty evaluation, there are several models available, including the distance discrimination method [26,27], the cloud model [28,29], extenics [30,31], the rough set theory [32], the efficacy coefficient method [33,34], set pair analysis [18,35], the attribute measure [36], the ideal point method [24,37], TOPSIS [38,39], fuzzy set theory [40,41], etc. All of these models consider a combination of rockburst indicators and their non-linear relationships with rockburst risk.…”

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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“…In terms of rockburst warning, scholars have proposed various empirical criteria and assessment methods for evaluating rockburst risk based on the factors affecting rockburst occurrences (Zhou et al, 2018;Li et al, 2019a;Gong et al, 2020;He et al, 2023). These include criteria such as Russense criteria (Russenes, 1974), Barton criteria (Barton et al, 1974), Hoek criteria (Hoek and Brown, 1980), strain energy storage index (Kidybinski, 1981), potential energy of elastic strain (Wang and Park, 2001), residual elastic energy index (Gong et al, 2021), rock brittleness index (Xu and Wang, 1999), brittle deformation coefficient (Zhang et al, 2017), distance discrimination method (Gong and Li, 2007), artificial neural network method (Wang et al, 2024), etc. These criteria and assessment methods have greatly guided the prevention and control of rockburst risks and have achieved good economic and social benefits.…”

Section: Introductionmentioning

confidence: 99%

Rockburst hazard assessment and prevention and control strategies in the Ruihai gold mine

Li,

Wang,

Chen

et al. 2024

Front. Earth Sci.

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Ruihai gold mine is a super large underwater gold mine with an initial mining depth of more than 1,400 m. Deep mining may face rockburst disaster. In order to scientifically control the potential rockburst risk, ensure the safety of underground personnel and equipment, and realize the safe and efficient mining of the mine, it is necessary to evaluate the rockburst risk of the mine and formulate appropriate rockburst risk warning and mitigation strategies. XRD mineral composition analysis and conventional rock mechanical tests were carried out on the parent rock granite samples. The results show that there are differences in the lithologic composition and rock mechanics parameters of the three colors of granite, which are preliminarily determined as three kinds of granite; The rockburst tendency of three kinds of granites is evaluated by using three classical rockburst criteria, and the discrimination results of the three rockburst criteria are not completely consistent or even contradictory. The application limitations rockburst risk assessment based on index criteria are discussed and analyzed in detail. It is proposed to apply microseismic monitoring technology to warn and manage the rockburst risk of Ruihai gold mine in development and mining stages, and put forward microseismic monitoring layout schemes and targeted rockburst prevention and control measures in different stages. The research results can provide reference for similar projects.

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A Data-Intensive Numerical Modeling Method for Large-Scale Rock Strata and Its Application in Mining Subsidence Prediction

Cited by 12 publications

References 46 publications

Probability prediction method for rockburst intensity based on rough set and multidimensional cloud model uncertainty reasoning

Probability prediction method for rockburst intensity based on rough set and multidimensional cloud model uncertainty reasoning

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

Rockburst hazard assessment and prevention and control strategies in the Ruihai gold mine

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