Developing coal pillar stability chart using logistic regression

Wattimena, Ridho K.; Kramadibrata, S.; Sidi, Indra Djati; Azizi, Masagus Ahmad

doi:10.1016/j.ijrmms.2012.09.004

Cited by 64 publications

(31 citation statements)

References 7 publications

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“…As the properties of coal can vary significantly at different geologies, and the critical safety factor for a stable pillar is different in different coal seams, reliability-based models have been used in underground mine stability assessments; for example, Sun [91] developed a reliability-based PESM (point estimate and safety margin) method for the stability assessment of mine entry; Wattimena et al [92] proposed a probability-based stability prediction model with logistic regression.…”

Section: Other Methodsmentioning

confidence: 99%

Subsidence Mechanism and Stability Assessment Methods for Partial Extraction Mines for Sustainable Development of Mining Cities—A Review

Chen

Deng

et al. 2018

Sustainability

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Abstract:Partial extraction methods such as underground strip pillar mining or room-and-pillar mining are widely adopted techniques to control ground subsidence. However, pillar failure in partial extraction mines may introduce violent secondary ground collapses. The stability of partial extraction mines dictates the safety of ground surface structures and the environmental health state of the surrounding mining areas. To reuse mining subsidence lands, it is necessary to evaluate the stability of the land through mine subsidence assessments. This paper summarizes current pillar stability assessment methods and their limitations, and the rock mechanics associated with the stability of abandoned mines. The effects of multiple factors that affect mine stability are discussed in detail; special attention has been extended to discuss the weathering effects associated with infused water and spontaneous combustion, as these are some key reasons for pillar strength degradation in abandoned mines. The mechanism of mine collapse and the corresponding post-mining disasters are also summarized. Finally, suggestions and strategies to improve current mine stability assessment methods are proposed based on the perspective of subsidence control.

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Section: Other Methodsmentioning

confidence: 99%

Subsidence Mechanism and Stability Assessment Methods for Partial Extraction Mines for Sustainable Development of Mining Cities—A Review

Chen

Deng

et al. 2018

Sustainability

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“…Wattimena et al (2013) applied their model for predicting coal pillar stability for given geometry (pillar width to height ratio) and stress conditions (pillar strength to stress ratio). Ghasemi et al (2010) investigated the effect of variability in parameters such as uniaxial compressive strength of coal specimen, pillar width, pillar height, entry width, and depth of cover on pillar safety factors using a Monte Carlo simulation.…”

Section: Selection Of Input and Output Variablesmentioning

confidence: 99%

“…As noted above, various researchers have determined strength of the hard rock pillars based on empirical formulas, which is based on parameter pillar width, pillar height, and UCS (Hedley and Grant 1972;Lunder 1994;Martin and Maybee 2000;Esterhuizen et al 2011). On the other hand, the pillar stability graph typically involves two parameters: (1) pillar stress to UCS ratio and pillar width to height ratio (K) (Lunder and Pakalnis 1994;Martina and Maybee 2000;Wattimena et al 2014) or (2) pillar strength to stress ratio (SF) and K (Wattimena et al 2013). Based on these conditions, six relevant indicators are adopted in this study.…”

Section: Selection Of Input and Output Variablesmentioning

confidence: 99%

“…Zhou et al (2011) presented two models for predicting pillar stability applying support vector machine and Fisher discriminant analysis techniques. Wattimena et al (2013) developed the logistic regression model for predicting the probability of stability of a coal pillar. On the other hand, different types of artificial neural networks are based on combining different learning techniques, such as hybrid or ensemble techniques.…”

Section: Introductionmentioning

confidence: 99%

“…The use of SL algorithms for the development of predictive and descriptive data mining models has become widely accepted in mining and geotechnical applications, promising powerful new tools for practicing engineers (Garzón et al 2006;Berrueta et al 2007;Sakiyama et al 2008;Pino-Mejías et al 2008, 2010Pozdnoukhov et al 2009;Tesfamariam and Liu 2010;Zhou et al 2011Zhou et al , 2012Zhou et al , 2013González-Rufino et al 2013;Liu et al 2013Liu et al , 2014. Numerous approaches for PS prediction have been developed based on different SL techniques during recent decades (Tawadrous and Katsabanis 2007;Zhou et al 2011;Wattimena et al 2013;Ghasemi et al 2014a, b). However, there is no comparison of SL techniques over the PS estimation.…”

Section: Introductionmentioning

confidence: 99%

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Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction

2015

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The prediction of pillar stability (PS) in hard rock mines is a crucial task for which many techniques and methods have been proposed in the literature including machine learning classification. In order to make the best use of the large variety of statistical and machine learning classification methods available, it is necessary to assess their performance before selecting a classifier and suggesting improvement. The objective of this paper is to compare different classification techniques for PS detection in hard rock mines. The data of this study consist of six features, namely pillar width, pillar height, the ratio of pillar width to its height, uniaxial compressive strength of the rock, pillar strength, and pillar stress. A total of 251 pillar cases between 1972 and 2011 are analyzed. Six supervised learning algorithms, including linear discriminant analysis, multinomial logistic regression, multilayer perceptron neural networks, support vector machine (SVM), random forest (RF), and gradient boosting machine, are evaluated for their ability to learn for PS based on different input parameter combinations. In this study, the available data set is randomly split into two parts: training set (70 %) and test set (30 %). A repeated tenfold cross-validation procedure (ten repeats) is applied to determine the optimal parameter values during modeling, and an external testing set is employed to validate the prediction performance of models. Two performance measures, namely classification accuracy rate and Cohen's kappa, are employed. The analysis of the accuracy together with kappa for the PS data set demonstrates that SVM and RF achieve comparable median classification accuracy rate and Cohen's kappa values. All models are fitted by ''R'' programs with the libraries and functions described in this study.

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Evolution law and application of permeability in gob without coal pillar and self‐formed roadway

Liu

Feng

2022

Energy Science & Engineering

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Roof-cutting and pressure-releasing technology are used to cut out the pressure transmission line of the critical blocks in the roof, causing the roof to totally collapse in the gob, while the NPR large deformation anchor cable support keeps the highway in its self-formed state. The gangue compactness has increased greatly when fully collapsed and filled to the gob's edge. At the end of the working face, the void ratio between rock blocks and the loose area is decreased, resulting in an observable shortening of the airflow disturbance distance at the working face and a change in the disaster gas accumulation law in the deep gob. It is critical to obtain the permeability characteristics of gob to avoid hazardous gas migration. The evolutionary model of the permeability of the mined-out region is created based on the analysis of the movement law of the overburden strata and the collapsed law of the gangue in the gob, and the "Z" ventilation model is built to research the airflow law in the gob. The following are the key conclusions reached: The 0-10 m airflow exchange zone is severe, the 10-45 m airflow return face is weak, the 45-190 m section airflow exchange is weak, and the 190-280 m section airflow confluence gob enhances the top corner's weak air area. The 0-50 m part of the self-formed Roadway has a wide air leakage channel, and good sealing methods have a significant impact on air leakage in the working face. The two-dimensional model constructed by comprehensive quantification of the main controlling factors, such as broken-expand, pressure, and filling degree, can represent the law of gas migration in the panel to a significant extent, and the reliability of the permeability evolution law is confirmed through data comparison and analysis.

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Developing coal pillar stability chart using logistic regression

Cited by 64 publications

References 7 publications

Subsidence Mechanism and Stability Assessment Methods for Partial Extraction Mines for Sustainable Development of Mining Cities—A Review

Subsidence Mechanism and Stability Assessment Methods for Partial Extraction Mines for Sustainable Development of Mining Cities—A Review

Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction

Evolution law and application of permeability in gob without coal pillar and self‐formed roadway

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