Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: An approach

Palei, Sanjay Kumar; Das, Sourav

doi:10.1016/j.ssci.2008.01.002

Cited by 90 publications

(27 citation statements)

References 4 publications

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“…The occupation variable (OCCUPATION) has five sub-categories: maintenance personnel, driver, worker, mining machine operator and technical supervisorÀothers. The common responsibilities for the occupational job groups include driving trucks for the drivers and mining machines (such Non-fatal accidents are important for costs such as payment of workers, down-times, medical treatments, machinery breakdown and reduced production rates (Palei & Das, 2009). The SPSS version 17.00 package program was used for binary logistic regression analyses, which were used to predict the probability of non-fatal accidents that resulted in greater or less than 3 lost workdays.…”

Section: Mine Description Risk Estimation Studies and Analysis Of Acmentioning

confidence: 99%

Analyses of non-fatal accidents in an opencast mine by logistic regression model – a case study

Önder

Mutlu

2016

International Journal of Injury Control and Safety Promotion

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Accidents cause major damage for both workers and enterprises in the mining industry. To reduce the number of occupational accidents, these incidents should be properly registered and carefully analysed. This study efficiently examines the Aegean Lignite Enterprise (ELI) of Turkish Coal Enterprises (TKI) in Soma between 2006 and 2011, and opencast coal mine occupational accident records were used for statistical analyses. A total of 231 occupational accidents were analysed for this study. The accident records were categorized into seven groups: area, reason, occupation, part of body, age, shift hour and lost days. The SPSS package program was used in this study for logistic regression analyses, which predicted the probability of accidents resulting in greater or less than 3 lost workdays for non-fatal injuries. Social facilities-area of surface installations, workshops and opencast mining areas are the areas with the highest probability for accidents with greater than 3 lost workdays for non-fatal injuries, while the reasons with the highest probability for these types of accidents are transporting and manual handling. Additionally, the model was tested for such reported accidents that occurred in 2012 for the ELI in Soma and estimated the probability of exposure to accidents with lost workdays correctly by 70%.

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Section: Mine Description Risk Estimation Studies and Analysis Of Acmentioning

confidence: 99%

Analyses of non-fatal accidents in an opencast mine by logistic regression model – a case study

Önder

Mutlu

2016

International Journal of Injury Control and Safety Promotion

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“…Furthermore, extensive researches have been conducted to control and assess roof fall risk in coal mines but not during retreat mining. Some of these researches have been carried out by Molinda et al (2000), van der Merve et al (2001), Deb (2003), Molinda (2003), Duzgun and Einstein (2004), Duzgun (2005), Palei and Das (2008), Shahriar and Bakhtavar (2009), Maiti and Khanzode (2009), Palei and Das (2009), Ghasemi et al (2013), Razani et al (2013) and Farid et al (2013).…”

Section: Introductionmentioning

confidence: 99%

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

Ghasemi

Ataei

Shahriar

2017

Archives of Mining Sciences

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Retreat mining is always accompanied by a great amount of accidents and most of them are due to roof fall. Therefore, development of methodologies to evaluate the roof fall susceptibility (RFS) seems essential. Ghasemi et al. (2012) proposed a systematic methodology to assess the roof fall risk during retreat mining based on risk assessment classic approach. The main defect of this method is ignorance of subjective uncertainties due to linguistic input value of some factors, low resolution, fixed weighting, sharp class boundaries, etc. To remove this defection and improve the mentioned method, in this paper, a novel methodology is presented to assess the RFS using fuzzy approach. The application of fuzzy approach provides an effective tool to handle the subjective uncertainties. Furthermore, fuzzy analytical hierarchy process (AHP) is used to structure and prioritize various risk factors and sub-factors during development of this method. This methodology is applied to identify the susceptibility of roof fall occurrence in main panel of Tabas Central Mine (TCM), Iran. The results indicate that this methodology is effective and efficient in assessing RFS.Keywords: Coal mining; Room and pillar; Retreat mining; Roof fall susceptibility (RFS); Analytical hierarchy process (AHP); Risk assessment fuzzy approach Wybieraniu w kierunku od pola towarzyszy zazwyczaj większa ilość wypadków, większość z nich spowodowana jest zawałem stropu. Dlatego też opracowanie skutecznej metody oceny skłonności stropu do zawału jest kwestią kluczową. Ghasemi et al. (2012) zaproponował metodologię określania ryzyka zawału stropu w trakcie prowadzenia prac górniczych w kierunku od pola w oparciu o klasyczne metody oceny ryzyka. Główną wadą tej metody jest to, iż nie uwzględnia ona subiektywnych niepewności na poziomie językowym związanych z określaniem wartości wejściowych charakteryzujących czynniki ryzyka, inne niedociągnięcia to niska rozdzielczość metody, stałe przyporządkowania wag, przyjęcie ostrych granic pomiędzy kolejnymi klasami. Aby usunąć te niedociągnięcia i w ten sposób udoskonalić metodę, zaproponowano nowe podejście do określania stabilności stropu wykorzystujące elementy logiki rozmytej. Zastosowanie logiki rozmytej jest efektywnym narzędziem w przypadku niepewności na poziomie językowym. Ponadto podejście bazujące na określeniu hierarchii procesów i wykorzystujące elementy logiki rozmytej zastosować można do określania wagi poszczególnych czynników ryzyka oraz czynników cząstkowych. Opracowaną metodę zastosowano do oceny skłonności stropu do zawału w polu głównym wybierania w kopalni Tabas Central Mine, w Iranie. Uzyskane wyniki potwierdzają skuteczność metody prognozowania stabilności stropu.Słowa kluczowe: górnictwo węgla, wybieranie filarowo-komorowe, wybieranie w kierunku od pola, podatność stropu na zawał, analityczne badanie hierarchii procesów, ocena ryzyka z wykorzystaniem elementów logiki rozmytej

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“…The mining damage of underground surrounding rock was caused by mining, changing the balance of underground rock [1,2]; it was a common disaster that the impact of mining led to the occurrence of rock burst in underground works. Because evolution and formation of rock burst were a nonlinear process, the nonlinearity was the root cause of rock burst [3][4][5][6]. As shown in literature [3], the severities of roof fall accidents had been attempted to predict based on some major contributing parameters using the binary logistic regression model.…”

Section: Introductionmentioning

confidence: 99%

Classification Identification of Acoustic Emission Signals from Underground Metal Mine Rock by ICIMF Classifier

Zuo

Luo

2014

Mathematical Problems in Engineering

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To overcome the drawback that fuzzy classifier was sensitive to noises and outliers, Mamdani fuzzy classifier based on improved chaos immune algorithm was developed, in which bilateral Gaussian membership function parameters were set as constraint conditions and the indexes of fuzzy classification effectiveness and number of correct samples of fuzzy classification as the subgoal of fitness function. Moreover, Iris database was used for simulation experiment, classification, and recognition of acoustic emission signals and interference signals from stope wall rock of underground metal mines. The results showed that Mamdani fuzzy classifier based on improved chaos immune algorithm could effectively improve the prediction accuracy of classification of data sets with noises and outliers and the classification accuracy of acoustic emission signal and interference signal from stope wall rock of underground metal mines was 90.00%. It was obvious that the improved chaos immune Mamdani fuzzy (ICIMF) classifier was useful for accurate diagnosis of acoustic emission signal and interference signal from stope wall rock of underground metal mines.

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Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: An approach

Cited by 90 publications

References 4 publications

Analyses of non-fatal accidents in an opencast mine by logistic regression model – a case study

Analyses of non-fatal accidents in an opencast mine by logistic regression model – a case study

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

Classification Identification of Acoustic Emission Signals from Underground Metal Mine Rock by ICIMF Classifier

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