Improving the Method of Roof Fall Susceptibility Assessment based on Fuzzy Approach

Ghasemi, Ebrahim; Ataei, Mohammad; Shahriar, K.

doi:10.1515/amsc-2017-0002

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…The loss of longwall stability, which reveals itself in the form of minor or major roof falls (Fig. 1), generally exists in all underground mines and has been confirmed in the form of statistical data from hard coal mines located in Poland, as well as in the USA, India, Iran, Turkey and China (Biliński, 1968;Ghasemi et al, 2017;Gu et al, 2018;Iannacchione et al, 2007;Mark et al, 2011;Martyka et al, 2013;Palei et al, 2008).…”

Section: Introductionmentioning

confidence: 82%

“…The proposed classification of roof fall hazard during retreat mining in the form of the level of roof fall risk (R rf ), when R rf < 28 the roof fall risk category is low and at an acceptable level, but when R rf > 70 the roof fall risk is unacceptable due to all parameters being in their most risky condition. Another methodology is presented in (Ghasemi et al, 2017) to assess the roof fall risk (RFS) using the fuzzy approach in order to identify the susceptibility of roof fall occurrence. In Table 2 was shown the making decision model components formed by (Ghasemi et al, 2017).…”

Section: Value Of Index 'G'mentioning

confidence: 99%

“…Another methodology is presented in (Ghasemi et al, 2017) to assess the roof fall risk (RFS) using the fuzzy approach in order to identify the susceptibility of roof fall occurrence. In Table 2 was shown the making decision model components formed by (Ghasemi et al, 2017). The factors and sub-factors are the input data in RFS method.…”

Section: Value Of Index 'G'mentioning

confidence: 99%

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Archives of Mining Sciences

Janoszek

2020

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The use of computer techniques at the design stage of industrial facilities is essential in modern times. The ability to shorten the time required to develop a project and assess the safety of the use of assumptions, often enables the reduction of the costs incurred in the future. The possibility to skip expensive prototype tests by using 3D prototyping is why it is currently the prevailing model in the design of industrial facilities, including in the mining industry. In the case of a longwall working, its stability requires the maintenance of the geometric continuity of floor rocks in cooperation with a powered roof support.The paper investigates the problem of longwall working stability under the influence of roof properties, coal properties, shield loading and the roof-floor interaction. The longwall working stability is represented by an index, factor of safety (FOS), and is correlated with a previously proposed roof capacity index 'g'. The topic of the paper does address an issue of potential interest.The assessment of the stability of the roof in longwalls was based on the numerical analysis of the factor of safety (FOS), using the Mohr-Coulomb stress criterion. The Mohr-Coulomb stress criterion enables the prediction of the occurrence of failures when the connection of the maximum tensile principal stress σ 1 and the minimum compressive principal stress σ 3 exceed relevant stress limits. The criterion is used for materials which indicates distinct tensile and compressive characteristics. The numerical method presented in the paper can be utilized in evaluating the mining natural hazards through predicting the parameters, which determine the roof maintenance in the longwall working.One of the purposes of the numerical analysis was to draw attention to the possibilities that are currently created by specialized software as an important element accompanying the modern design process, which forms part of intelligent underground mining 4.0.

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Section: Introductionmentioning

confidence: 82%

Section: Value Of Index 'G'mentioning

confidence: 99%

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Archives of Mining Sciences

Janoszek

2020

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“…Roof fall accidents are predicted using binary logistic regression model [4]. Roof fall risk is identified using risk assessment classical methodology [5] with fuzzy based approach. It projects that the roof fall is due to various features like low resolution, fixed weighting, sharp class boundaries of the building.…”

Section: A Literature Reviewmentioning

confidence: 99%

Exploration of Multiple Linear Regression with Ensembling Schemes for Roof Fall Assessment using Machine Learning

Devi*¹,

Basheer²,

Mathew³

2019

IJITEE

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Roof fall of the building is the major threat to the society as it results in severe damages to the life of the people. Recently, engineers are focusing on the prediction of roof fall of the building in order to avoid the damage to the environment and people. Early prediction of Roof fall is the social responsibility of the engineers towards existence of health and wealth of the nation. This paper attempts to identify the essential attributes of the Roof fall dataset that are taken from the UCI Machine learning repository for predicting the existence of roof fall. In this paper, the important features are extorted from the various ensembling methods like Gradient Boosting Regressor, Random Forest Regressor, AdaBoost Regressor and Extra Trees Regressor. The extracted feature importance of each of the ensembling methods is then fitted with multiple linear regression to analyze the performance. The same extracted feature importance of each of the ensembling methods are subjected to feature scaling and then fitted with multiple linear regression to analyze the performance. The Performance analysis is done with the performance parameters such as Mean Squared Log Error (MSLE), Mean Absolute error (MAE), R2 Score, Mean Squared error (MSE) and Explained Variance Score (EVS). The execution is carried out using python code in Spyder Anaconda Navigator IP Console. Experimental results shows that before feature scaling, Extra Tree Regressor is found to be effective with the MSE of 0.06, MAE of 0.07, R2 Score of 87%, EVS of 0.89 and MSLE of 0.02 as compared to other ensembling methods. In the same way, after applying feature scaling, the feature importance extracted from the Extra Tree Regressor is found to be effective with the MSE of 0.04, MAE of 0.03, R2 Score of 96%, EVS of 0.9 and MSLE of 0.01 as compared to other ensembling methods.

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“…By using AHP, Yang et al suggested a fuzzy evaluation model for mine water and sand inrush caused by underground mining [37]. By using fuzzy AHP to prioritize risk factors, Ghasemi et al presented a methodology to evaluate the roof fall susceptibility during retreat mining [38]. Wang et al suggested a fuzzy fault tree analysis method to evaluate the risk of coal dust explosions [39].…”

Section: Introductionmentioning

confidence: 99%

Safety Risk Assessment of a Pb-Zn Mine Based on Fuzzy-Grey Correlation Analysis

Dong

Wei

et al. 2020

Electronics

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Improving safety management and risk evaluation methods is important for the global mining industry, which is the backbone of the industrial development of our society. To prevent any accidental loss or harm to human life and property, a safety risk assessment method is needed to perform the continuous risk assessment of mines. Based on the requirements of mine safety evaluation, this paper proposes the Pb-Zn mine safety risk evaluation model based on the fuzzy-grey correlation analysis method. The model is compared with the risk assessment model based on the fuzzy TOPSIS method. Through the experiments, our results demonstrate that the proposed fuzzy-grey correlation model is more sensitive to risk and has less effect on the evaluation results under different scoring attitudes (cautious, rational, and relaxed).

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Improving the Method of Roof Fall Susceptibility Assessment based on Fuzzy Approach

Cited by 5 publications

References 33 publications

Archives of Mining Sciences

Archives of Mining Sciences

Exploration of Multiple Linear Regression with Ensembling Schemes for Roof Fall Assessment using Machine Learning

Safety Risk Assessment of a Pb-Zn Mine Based on Fuzzy-Grey Correlation Analysis

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