Prediction of rock burst classification using cloud model with entropy weight

Zhou, Keping; Lin, Yun; Deng, Hongwei; Li, Jielin; Liu, Chuanju

doi:10.1016/s1003-6326(16)64313-3

Cited by 108 publications

(43 citation statements)

References 10 publications

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“…In this CBR system, two methods of calculating the feature weights are adopted: the even weight method and the entropy weight method. 26) For even weight, the weight of each feature is equal. For entropy weight, according to the degree of variation of each feature, the entropy of each feature based on information entropy is calculated.…”

Section: Weights Setmentioning

confidence: 99%

Wear Debris Classification of Steel Production Equipment using Feature Fusion and Case-based Reasoning

et al. 2018

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Wear debris classification is of great significance for identifying machine wear states. In this paper, a method of wear debris classification using feature fusion and CBR is proposed. The method integrates local feature LBP, global feature FD and Tamura coarseness, and then the fused features are applied in CBR system with different weights and different similarity, which is adaptable, extendable, modular and fast. The results show that the subdivision of wear debris images into size 32*32 when calculating LBP is helpful for improving the classification, the combination of local features and global features can get better results. The comparative experimental results of different classification methods show that the CBR system has the shortest time-consuming while maintaining high classification accuracy.

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Section: Weights Setmentioning

confidence: 99%

Wear Debris Classification of Steel Production Equipment using Feature Fusion and Case-based Reasoning

et al. 2018

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“…It implements the transformation between a qualitative concept and its quantitative instantiations, and also embodies the fuzziness and randomness of the research object. The Cloud model has been widely used in numerous different engineering fields, such as tunnel gas outburst (Zhang et al 2019), rock slope (Liu et al 2014), rock burst (Liu et al 2013;Zhou et al 2016) etc. In this paper, synthesizing the standardization process and the analytic hierarchy process (AHP), a novel Cloud model is established.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of water inrush in tunnel through water-rich fault based on AHP-Cloud model

Peng

Zuo

et al. 2020

Geomatics, Natural Hazards and Risk

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2020)Risk assessment of water inrush in tunnel through water-rich fault based on AHP-Cloud model, ABSTRACTWater inrush is a serious geological disaster in tunnel. Due to the complexity of geological conditions, large-scale water inrush is prone to occur in tunnel through water-rich fault during construction. Based on the comprehensive analysis of influencing factors, 8 evaluation indexes and the corresponding grading criteria are put forward to assess the risk of water inrush, and the risk of water inrush is divided into 4 levels. Synthesizing the standardization process and the analytic hierarchy process (AHP), a novel Cloud model is established to assess the risk of tunnel water inrush. The model is applied to the Longjinxi Tunnel to assess the risk level of water inrush. It is proved that the assessment results of the tunnel through water-rich fault is consistent with the actual situation, which verifies the reliability of evaluation model. ARTICLE HISTORY

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“…The cloud model discussed herein is a conceptual model for addressing fuzzy problems proposed by D. Li et al [35,36], who addressed the ambiguity and randomness of data from a membership perspective. This cloud model has been applied to various engineering problems, such as the prediction of rock burst [37,38], the stability analysis of surrounding rock [39], the quality classification of rock masses, and the evaluation of the spontaneous combustion tendency of ore. This model has also been widely used in military fields and has achieved good results for power grid performance, agricultural production, water quality assessment, and other areas [40][41][42][43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Application of an Improved Cloud Model and Distance Discrimination to Evaluate Slope Stability

Guo

Wang

Liu

2019

Mathematical Problems in Engineering

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With the rapid development of China's economy, the construction of infrastructure has continuously improved. In the past few years, the construction of water conservancy projects has been constantly developing, and related geological disasters have become increasingly prominent. The stability of water conservancy slopes is related to whether water conservancy projects can be safely constructed and built to function safely and effectively, which has become a topic of increasing concern for geologists and related researchers. This paper selects the Jinping 1 Hydropower Station in Sichuan, China, for analysis. Four categories of evaluation factors (geological, engineering, environmental, and monitoring) and 24 subfactors (17 quantitative indicators and 7 qualitative indicators) are selected to ascertain the risk of the slope more accurately. By investigating the deficiencies of the traditional cloud model, the related concepts and computational models of a finite-area cloud model are proposed. By obtaining the characteristic parameters, the degrees of membership of the measurement samples belonging to different risk levels are further obtained. The weights of the indicators determined by the cloud processor and the weighted distance discriminant method are used to determine the final weights and achieve a final classification of the slope stability level. The research results demonstrate that the weighted distance discriminant algorithm combined with the improved finite-interval cloud model can consider the comprehensive information of each evaluation index and the degrees of mutual influence between the indicators, making the evaluation results more objective. Moreover, the proposed approach can quickly and accurately classify slope stability and deliver a prediction of the safety, thereby providing new ideas for evaluating the stability of slopes.

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Prediction of rock burst classification using cloud model with entropy weight

Cited by 108 publications

References 10 publications

Wear Debris Classification of Steel Production Equipment using Feature Fusion and Case-based Reasoning

Wear Debris Classification of Steel Production Equipment using Feature Fusion and Case-based Reasoning

Risk assessment of water inrush in tunnel through water-rich fault based on AHP-Cloud model

Application of an Improved Cloud Model and Distance Discrimination to Evaluate Slope Stability

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