- Email: [email protected]
Investigation of factors influencing roof stability at a Western U.S. longwall coal mine
International Journal of Mining Science and Technology xxx (xxxx) xxx
Contents lists available at ScienceDirect
International Journal of Mining Science and Technology journal homepage: www.elsevier.com/locate/ijmst
Investigation of factors influencing roof stability at a Western U.S. longwall coal mine Meriel Young a,⇑, Gabriel Walton a, Elizabeth Holley b a b
Department. Geology & Geol. Engineering, Colorado School of Mines, Golden, CO 80401, USA Department Mining Engineering, Colorado School of Mines, Golden, CO 80401, USA
a r t i c l e
i n f o
Article history: Received 14 June 2018 Received in revised form 29 July 2018 Accepted 15 August 2018 Available online xxxx Keywords: Coal mine roof stability CMRR Western U.S. coal Case study
a b s t r a c t The coal mine roof rating (CMRR) was developed to bridge the gap between geological variation in underground coal mines and engineering design. The CMRR accounts for the compressive strength of the immediate roof, the shear strength and intensity of any discontinuities present, and the moisture sensitivity of the immediate roof. The CMRR has been widely used and validated in Eastern US coal mines, but it has seen limited application in the Western US. This study focuses on roof behavior at a Western coal mine (Mine A). Mine A shows significant lateral geological variation, along with localized faulting and a laterally extensive sandstone channel network. The CMRR is not used to predict roof instability at the mine. It is, therefore, hypothesized that there are other factors that are correlated with roof instability in underground coal mines that could potentially also be considered in conjunction with the CMRR. This hypothesis was tested by collecting 30 CMRR measurements at Mine A. At each measurement location, a binary record of the roof condition (stable or unstable) was made, and other parameters such as depth of cover, presence of faulting, and sandstone channels were also recorded. ANOVA tests showed that the CMRR values and the roof conditions were not strongly correlated, indicating that the CMRR input criteria are not fully predictive of roof stability at this mine. The CMRR values showed statistically significant correlations (p less than 0.05) with faulting as well as with location at an intersection. For areas that had previously experienced roof fall but were currently stable, faulting was correlated with roof condition (p less than 0.05) only when the condition was classified as unstable. Ó 2018 Published by Elsevier B.V. on behalf of China University of Mining & Technology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction and background Roof fall is one of the greatest hazards facing underground coal miners [1]. In 2017, 91 lost-time injuries occurred due to roof fall in US underground coal mines. An additional 48 roof falls occurred with no lost days in 2017 [2]. The geology, geometry, timing and frequency of roof falls can usually give some indication of the cause of failure. Documentation of these variables on a mine-wide basis can make it possible to characterize the combination of factors which contribute to a high number of roof falls. Most ground control failures are related to geology, and it is therefore critical to understand as much as possible about variation in the mine geology [3]. The coal mine roof rating (CMRR) classification was developed by U.S. National Institute of Occupational Safety and Health (NIOSH) researchers Molinda and Mark to quantify the geological description of mine roof into a single value that could indicate ⇑ Corresponding author. E-mail address: [email protected] (M. Young).
mine roof stability and be used in engineering design [4]. It is widely used in the Eastern US as an input for the analysis of longwall pillar stability (ALPS) in underground coal mines. It provides an excellent start with respect to roof stability assessment; however, it is arguably not fully comprehensive, nor is it widely used in the Western US. Currently the CMRR includes the following parameters as inputs: uniaxial compressive strength (UCS), spacing and persistence of discontinuities, cohesion and roughness of discontinuities, moisture sensitivity, presence of a strong bed on the bolted interval, number of layers in the roof (bolted interval), groundwater presence, and surcharge of overlying beds. The CMRR is focused on the character of the units in the immediate roof. If the roof geology is uniform throughout the mine, the CMRR values throughout the mine should be the similar, thus implying the roof stability is approximately the same everywhere in the mine. The CMRR currently focuses only on geological features. This means that any parameters relating to stress (such as depth of cover) are not included. It is likely that the current CMRR is best used in conjunction with other analyses to fully evaluate the potential for roof instability [5,6].
https://doi.org/10.1016/j.ijmst.2018.11.019 2095-2686/Ó 2018 Published by Elsevier B.V. on behalf of China University of Mining & Technology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: M. Young, G. Walton and E. Holley, Investigation of factors influencing roof stability at a Western U.S. longwall coal mine, International Journal of Mining Science and Technology, https://doi.org/10.1016/j.ijmst.2018.11.019
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M. Young et al. / International Journal of Mining Science and Technology xxx (xxxx) xxx
During a visit to a Western U.S. longwall coal mine (Mine A), significant lateral geological variation was observed in the roof. There was also moisture sensitive mudstone at various locations, as well as locally present sandstone and slickensides. The mine geologists have mapped the sandstone as a network of channels spanning the entire mine, and it has been informally observed that where the sandstone channel is present in the immediate roof, the roof is less stable. The CMRR is not used to predict roof stability at Mine A. The observations outlined above suggest that there may be other factors in addition to those included in the CMRR that could also predict roof instability. Where the input parameters currently considered by the CMRR are constant, there may be other factors that are correlated with roof instability in underground coal mines that could be used in conjunction with the CMRR for roof stability assessment. By investigating a single case study, this paper provides an initial discussion of the CMRR’s comprehensiveness and how it may be improved. A much larger data set would be required in order to make any definitive conclusions.
2. Methods To test the hypothesis that the parameters currently considered by the CMRR do not fully explain roof instability, CMRR values were collected at 30 sites at Mine A. At each site, the roof condition was recorded as stable or unstable. A suite of other parameters were recorded in addition to the CMRR inputs, including slope grade of surface topography, presence of faulting, localization of the faulting, presence of sandstone channels, location at an intersection, rib and floor condition, and evidence of horizontal stressinduced damage. At Mine A, there were eight locations for which drill core and core logs were still accessible. Since the CMRR may be calculated from both drill core and underground exposures, the CMRR at these locations was calculated using both methods. The other sites at Mine A were selected randomly to represent the spectrum of geological conditions at the mine, including the presence or absence of sandstone channels in the immediate roof. The CMRR component values were recorded using the methods outlined by Molinda and Mark [4]. The roof condition was also recorded as stable or unstable at each site. There is undoubtedly a spectrum between stable and unstable; however, in order to examine correlation, binary measurements were deemed simplest. It is important to note that this assessment is subjective and creates a significant source of uncertainty because the difference between stable and unstable conditions could be subtle in some cases. Where there was evidence of significant skin failure or high levels of support installed, the roof was recorded as unstable. Where minimal support was required and there was little evidence of skin failure or instability, the roof was recorded as stable. The depth of cover and slope grade of surface topography were determined from pre-existing mine maps. The other parameters described above (if applicable) were recorded individually at each location. The data from Mine A were analyzed for correlation between the CMRR and roof condition. A strong correlation would suggest that the CMRR includes the most influential parameters that are predictive of roof instability at Mine A. If there is little correlation between CMRR and roof condition, it is likely that there are other parameters that influence roof instability at Mine A and merit further investigation. An analysis of variance (ANOVA) was performed in Matlab to test the null hypothesis that the roof condition and CMRR are not related. For a p value less than 0.05, the null hypothesis may be rejected at 95% confidence, indicating that any observed correlation between stability and CMRR values is not likely due to random chance. Scatter plots were generated to illus-
trate the relationships between CMRR and depth of cover, as well CMRR and surface topography. Additionally, ANOVA was used to evaluate the relationships between the CMRR and each of the parameters collected (e.g., CMRR vs. depth of cover, CMRR vs. location at an intersection, etc.). Next, the correlation between roof condition and each parameter was evaluated. ANOVA was used to analyze the correlation with depth of cover and surface topography as these parameters were recorded numerically. However, ANOVA cannot evaluate the correlation between two categorical variables. In order to examine the correlation between roof condition (recorded as stable or unstable) and other parameters which were also recorded as binary (e.g., sandstone channel presence as yes or no), the fisher exact test was used [7]. This test evaluates the null hypothesis that there is no association between the independent and dependent variables. If the p value generated is less than 0.05, the null hypothesis may be rejected at 95% confidence. Two sets of analyses were performed for the sites where a roof fall had occurred but was now considered to be stable: first, the statistics were run with these locations recorded as stable, and second, the statistics were run with these locations recorded as unstable.
3. Results and discussion Fig. 1 shows the results from the ANOVA analysis evaluating correlation between the CMRR and roof condition. The p value for the analysis was calculated to be 0.5, indicating there is little correlation between roof condition and CMRR at Mine A. Thus it is likely that there are other factors in addition to those included in the CMRR that are indicative of roof instability at Mine A. Figs. 2–6 show the results from the ANOVA analyses in Matlab evaluating correlation between CMRR and the other parameters recorded at Mine A. The p values for each of these analyses are summarized in Table 1. The data presented above show that the CMRR value is correlated with the presence of faulting and the location of the data collection site at an intersection. The CMRR values are not correlated with the depth of cover, gradient of surface topography, and presence of the sandstone channel in the immediate roof. The CMRR does not account for the depth of cover surface topography or large-scale geological features, such as sandstone channels. The CMRR does not explicitly account for roof being weaker at an intersection. However, the roof at intersections may be recorded as having a lower strength, cohesion, and friction angle than the roof in the surrounding area, in which case the CMRR would implicitly capture this parameter. Although the CMRR does not explicitly record fault displacement or slip, these features appear to be captured in the CMRR parameter which records the presence of slickensides.
Fig. 1. Comparative boxplot result from the ANOVA analysis of CMRR and roof condition, p = 0.5.
Please cite this article as: M. Young, G. Walton and E. Holley, Investigation of factors influencing roof stability at a Western U.S. longwall coal mine, International Journal of Mining Science and Technology, https://doi.org/10.1016/j.ijmst.2018.11.019
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M. Young et al. / International Journal of Mining Science and Technology xxx (xxxx) xxx
Fig. 6. Scatter plot showing the relationship between CMRR and gradient of surface topography, p = 0.9 (linear regression). Fig. 2. Comparative boxplot result from the ANOVA analysis of CMRR and faulting, p = 0.007.
Fig. 3. Comparative boxplot result from the ANOVA analysis of CMRR and the presence of the sandstone channel in the immediate roof, p = 0.23.
Table 1 Summary of p values for ANOVA analysis of CMRR and the parameters collected at each site. Parameter evaluated with CMRR
p value
Roof condition Faulting Sandstone channel Intersections Depth of cover Surface topography
0.5 0.007 0.23 0.005 0.54 0.91
Table 2 Summary of p values for the analysis of roof condition and the parameters collected at each site. Parameter evaluated with roof condition
p value
Faulting Sandstone channel Intersections Depth of cover Surface topography
0.34 0.69 0.69 0.44 0.71
Fig. 4. Comparative boxplot result from the ANOVA analysis of CMRR and the measurement location, p = 0.005.
Fig. 7. Comparative boxplot from the ANOVA analysis of roof condition and the depth of cover, p = 0.44.
Fig. 5. Scatter plot showing the relationship between CMRR and depth of cover, p = 0.54 (linear regression).
Perhaps the more important question is whether individual parameters are correlated with actual roof condition. In order to address this, the correlations between the roof condition and the non-CMRR parameters were evaluated using the ANOVA analysis and the Fisher exact test. The p values from each analysis are presented in Table 2, and the boxplot results from the ANOVA analyses are shown in Figs. 7 and 8. The statistical results do not show a correlation between roof condition and location at an intersection, or between roof
Fig. 8. Comparative boxplot from the ANOVA analysis of roof condition and the surface topography, p = 0.71.
condition and the presence of faulting, despite the fact that these parameters are well correlated with the CMRR. The depth of cover and surface topography also show little correlation with the roof
Please cite this article as: M. Young, G. Walton and E. Holley, Investigation of factors influencing roof stability at a Western U.S. longwall coal mine, International Journal of Mining Science and Technology, https://doi.org/10.1016/j.ijmst.2018.11.019
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condition; this could be because the geology is the primary control on roof condition at Mine A, overshadowing any effect of stressrelated parameters. Lastly, the correlations between roof condition and the CMRR (Fig. 1) and between roof condition and other parameters (Table 2) were reanalyzed using the ‘‘unstable” classification for the six sites where the roof was currently considered stable but had seen significant roof instability in the past (Fig. 9 and Table 3). Figs. 10 and 11 show the boxplot results from these ANOVA analyses of the amended roof condition with depth of cover and slope angle respectively.
Fig. 9. Comparative boxplot result from the ANOVA analysis of CMRR and roof condition, p = 0.2.
For the data points collected from stable sites that had previously experienced roof fall, Table 3 shows that rerunning the analysis with the roof classified as unstable caused the correlation between faulting and roof condition to become statistically significant (for a significance threshold of a = 0.05). The p values for the correlation with sandstone channels and surface topography also decreased, although these results are still not statistically significant. The pvalues increased slightly for the correlations between roof stability and intersections, as well as between roof stability and depth of cover. The results in Table 3 also illustrate how much uncertainty is introduced through the binary recording of the roof condition. Faulting was the only parameter to show a statistically significant correlation with roof condition at Mine A, and only for the version of the dataset wherein currently stable areas with previous roof fall were classified as unstable. It is possible that there is insufficient data or a bias in the data collection locations, which resulted in data that is not fully representative of Mine A. It is also possible that the other parameters show more significant relationships with roof condition at other mines. This potential variability between mines makes it difficult to have a ‘‘one size fits all” approach. A much larger data set spanning multiple mines is needed to make any definitive conclusions about which additional parameters outside the CMRR could be used to predict roof failure, and how stress-related parameters should (or should not) be combined with the CMRR for roof stability assessment. 4. Conclusions
Table 3 Summary of p values for the analysis of roof condition and the parameters collected at each site with stable areas that had previously experienced roof fall classified as ‘‘unstable” Parameter evaluated with roof condition
p value
Faulting Sandstone channel Intersections Depth of cover Surface topography
0.03 0.48 0.72 0.53 0.21
The data from one Western U.S. longwall coal mine (Mine A) show no statistically significant correlation between the CMRR and the recorded roof condition using a binary stability classification. This implies that there are other parameters in addition to those included in the CMRR that are indicative of roof instability at Mine A. The CMRR values did correlate with the presence of faulting and location at an intersection. Roof locations that are currently stable but had previously been unstable showed a statistically significant correlation with faulting when the unstable condition was used in the statistical analysis. The analyses of areas previously affected by roof fall illustrate the uncertainty introduced as a result of recording a time-sensitive characteristic such as roof condition as binary data (stable or unstable). The data also suggest that faulting is the main control on roof stability at Mine A; and the other parameters did not show any statistically significant correlation with roof condition. 5. Future work
Fig. 10. Comparative boxplot from the ANOVA analysis of roof condition and the depth of cover, p = 0.40.
The same data collection and analysis procedure should be carried out at other mines to broaden the data pool and allow comparison of results with those obtained at Mine A. In order to determine the sensitivity of the CMRR classification system to each of its input parameters, the CMRR values should be recalculated at each location for all but one of the inputs (e.g. calculate the CMRR without the moisture sensitivity parameter or the discontinuity intensity parameter). The correlation should be evaluated between the according CMRR output and roof condition, as well as with the other parameters. This will give an indication of how each constituent in the CMRR influences the CMRR output values. Lastly, the dataset should be reexamined with gradational rather than binary classification of roof stability. Acknowledgments
Fig. 11. Comparative boxplot from the ANOVA analysis of roof condition and the slope angle, p = 0.21.
This work was supported by a NIOSH Capacity Building grant (No. 200-2016-90154) to Drs. G. Walton and E. Holley and
Please cite this article as: M. Young, G. Walton and E. Holley, Investigation of factors influencing roof stability at a Western U.S. longwall coal mine, International Journal of Mining Science and Technology, https://doi.org/10.1016/j.ijmst.2018.11.019
M. Young et al. / International Journal of Mining Science and Technology xxx (xxxx) xxx
collaborators at the Colorado School of Mines. The data were collected as part of an MS project by Meriel Young, who acknowledges personnel at Mine A for their support during the data collection process. The authors also thank Mark Larson and Bo Kim at NIOSH for their continued support and input on ground control research efforts at the Colorado School of Mines. The authors would also like to thank Chris Mark of MSHA for accommodating Meriel Young’s visit to the Pittsburgh facility in Spring 2018 and taking the time to discuss the CMRR with her in detail. References [1] Barczak TM, Dolinar DR, Mark C, Signer SP, Tuchman RJ, Wopat PF. Proceedings: New Technology for Coal Mine Roof Support. Information Circular 9453. U.S. Department of Health and Human Services: National Institute for Occupational Safety and Health; 2000. p. 274.
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[2] Mining MSHA. Industry Accident, Injuries, Employment and Production Statistics and Reports. U.S. Department of Labor Mine Safety and Health Administration; 2018. [3] Peng SS. Topical areas of research needs in ground control–a state of the art review on coal mine ground control. Int J Min Sci Technol 2015;25(1):1–6. [4] Molinda GM, Mark C. Coal Mine Roof rating (CMRR): A Practical Rock Mass Classification for Coal Mines. Vol. 9387 of Information Circular. U.S. Department of Interior, Bureau of Mines; 1994. p. 83. [5] Calleja J. Rapid Rating using coal mine roof rating to provide rapid mine roof characterization from exploration drilling. Proceedings of the Coal Operators’ Conference. Wollongong, Australia. University of Wollongong; 2006. [6] Hill D. Practical experiences with application of the coal mine roof rating (CMRR) in Australian coal mines. In: International Workshop on Rock Mass Classification in Underground Mining. U.S. Department of Health and Human Services: National Institute for Occupational Safety and Health; 2007. p. 65–72. [7] Freeman JV, Campbell MJ. The Analysis of Categorical Data: Fisher’s Exact Test. Scope; 2007. p. 16.
Please cite this article as: M. Young, G. Walton and E. Holley, Investigation of factors influencing roof stability at a Western U.S. longwall coal mine, International Journal of Mining Science and Technology, https://doi.org/10.1016/j.ijmst.2018.11.019
Contents lists available at ScienceDirect
International Journal of Mining Science and Technology journal homepage: www.elsevier.com/locate/ijmst
Investigation of factors influencing roof stability at a Western U.S. longwall coal mine Meriel Young a,⇑, Gabriel Walton a, Elizabeth Holley b a b
Department. Geology & Geol. Engineering, Colorado School of Mines, Golden, CO 80401, USA Department Mining Engineering, Colorado School of Mines, Golden, CO 80401, USA
a r t i c l e
i n f o
Article history: Received 14 June 2018 Received in revised form 29 July 2018 Accepted 15 August 2018 Available online xxxx Keywords: Coal mine roof stability CMRR Western U.S. coal Case study
a b s t r a c t The coal mine roof rating (CMRR) was developed to bridge the gap between geological variation in underground coal mines and engineering design. The CMRR accounts for the compressive strength of the immediate roof, the shear strength and intensity of any discontinuities present, and the moisture sensitivity of the immediate roof. The CMRR has been widely used and validated in Eastern US coal mines, but it has seen limited application in the Western US. This study focuses on roof behavior at a Western coal mine (Mine A). Mine A shows significant lateral geological variation, along with localized faulting and a laterally extensive sandstone channel network. The CMRR is not used to predict roof instability at the mine. It is, therefore, hypothesized that there are other factors that are correlated with roof instability in underground coal mines that could potentially also be considered in conjunction with the CMRR. This hypothesis was tested by collecting 30 CMRR measurements at Mine A. At each measurement location, a binary record of the roof condition (stable or unstable) was made, and other parameters such as depth of cover, presence of faulting, and sandstone channels were also recorded. ANOVA tests showed that the CMRR values and the roof conditions were not strongly correlated, indicating that the CMRR input criteria are not fully predictive of roof stability at this mine. The CMRR values showed statistically significant correlations (p less than 0.05) with faulting as well as with location at an intersection. For areas that had previously experienced roof fall but were currently stable, faulting was correlated with roof condition (p less than 0.05) only when the condition was classified as unstable. Ó 2018 Published by Elsevier B.V. on behalf of China University of Mining & Technology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction and background Roof fall is one of the greatest hazards facing underground coal miners [1]. In 2017, 91 lost-time injuries occurred due to roof fall in US underground coal mines. An additional 48 roof falls occurred with no lost days in 2017 [2]. The geology, geometry, timing and frequency of roof falls can usually give some indication of the cause of failure. Documentation of these variables on a mine-wide basis can make it possible to characterize the combination of factors which contribute to a high number of roof falls. Most ground control failures are related to geology, and it is therefore critical to understand as much as possible about variation in the mine geology [3]. The coal mine roof rating (CMRR) classification was developed by U.S. National Institute of Occupational Safety and Health (NIOSH) researchers Molinda and Mark to quantify the geological description of mine roof into a single value that could indicate ⇑ Corresponding author. E-mail address: [email protected] (M. Young).
mine roof stability and be used in engineering design [4]. It is widely used in the Eastern US as an input for the analysis of longwall pillar stability (ALPS) in underground coal mines. It provides an excellent start with respect to roof stability assessment; however, it is arguably not fully comprehensive, nor is it widely used in the Western US. Currently the CMRR includes the following parameters as inputs: uniaxial compressive strength (UCS), spacing and persistence of discontinuities, cohesion and roughness of discontinuities, moisture sensitivity, presence of a strong bed on the bolted interval, number of layers in the roof (bolted interval), groundwater presence, and surcharge of overlying beds. The CMRR is focused on the character of the units in the immediate roof. If the roof geology is uniform throughout the mine, the CMRR values throughout the mine should be the similar, thus implying the roof stability is approximately the same everywhere in the mine. The CMRR currently focuses only on geological features. This means that any parameters relating to stress (such as depth of cover) are not included. It is likely that the current CMRR is best used in conjunction with other analyses to fully evaluate the potential for roof instability [5,6].
https://doi.org/10.1016/j.ijmst.2018.11.019 2095-2686/Ó 2018 Published by Elsevier B.V. on behalf of China University of Mining & Technology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: M. Young, G. Walton and E. Holley, Investigation of factors influencing roof stability at a Western U.S. longwall coal mine, International Journal of Mining Science and Technology, https://doi.org/10.1016/j.ijmst.2018.11.019
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M. Young et al. / International Journal of Mining Science and Technology xxx (xxxx) xxx
During a visit to a Western U.S. longwall coal mine (Mine A), significant lateral geological variation was observed in the roof. There was also moisture sensitive mudstone at various locations, as well as locally present sandstone and slickensides. The mine geologists have mapped the sandstone as a network of channels spanning the entire mine, and it has been informally observed that where the sandstone channel is present in the immediate roof, the roof is less stable. The CMRR is not used to predict roof stability at Mine A. The observations outlined above suggest that there may be other factors in addition to those included in the CMRR that could also predict roof instability. Where the input parameters currently considered by the CMRR are constant, there may be other factors that are correlated with roof instability in underground coal mines that could be used in conjunction with the CMRR for roof stability assessment. By investigating a single case study, this paper provides an initial discussion of the CMRR’s comprehensiveness and how it may be improved. A much larger data set would be required in order to make any definitive conclusions.
2. Methods To test the hypothesis that the parameters currently considered by the CMRR do not fully explain roof instability, CMRR values were collected at 30 sites at Mine A. At each site, the roof condition was recorded as stable or unstable. A suite of other parameters were recorded in addition to the CMRR inputs, including slope grade of surface topography, presence of faulting, localization of the faulting, presence of sandstone channels, location at an intersection, rib and floor condition, and evidence of horizontal stressinduced damage. At Mine A, there were eight locations for which drill core and core logs were still accessible. Since the CMRR may be calculated from both drill core and underground exposures, the CMRR at these locations was calculated using both methods. The other sites at Mine A were selected randomly to represent the spectrum of geological conditions at the mine, including the presence or absence of sandstone channels in the immediate roof. The CMRR component values were recorded using the methods outlined by Molinda and Mark [4]. The roof condition was also recorded as stable or unstable at each site. There is undoubtedly a spectrum between stable and unstable; however, in order to examine correlation, binary measurements were deemed simplest. It is important to note that this assessment is subjective and creates a significant source of uncertainty because the difference between stable and unstable conditions could be subtle in some cases. Where there was evidence of significant skin failure or high levels of support installed, the roof was recorded as unstable. Where minimal support was required and there was little evidence of skin failure or instability, the roof was recorded as stable. The depth of cover and slope grade of surface topography were determined from pre-existing mine maps. The other parameters described above (if applicable) were recorded individually at each location. The data from Mine A were analyzed for correlation between the CMRR and roof condition. A strong correlation would suggest that the CMRR includes the most influential parameters that are predictive of roof instability at Mine A. If there is little correlation between CMRR and roof condition, it is likely that there are other parameters that influence roof instability at Mine A and merit further investigation. An analysis of variance (ANOVA) was performed in Matlab to test the null hypothesis that the roof condition and CMRR are not related. For a p value less than 0.05, the null hypothesis may be rejected at 95% confidence, indicating that any observed correlation between stability and CMRR values is not likely due to random chance. Scatter plots were generated to illus-
trate the relationships between CMRR and depth of cover, as well CMRR and surface topography. Additionally, ANOVA was used to evaluate the relationships between the CMRR and each of the parameters collected (e.g., CMRR vs. depth of cover, CMRR vs. location at an intersection, etc.). Next, the correlation between roof condition and each parameter was evaluated. ANOVA was used to analyze the correlation with depth of cover and surface topography as these parameters were recorded numerically. However, ANOVA cannot evaluate the correlation between two categorical variables. In order to examine the correlation between roof condition (recorded as stable or unstable) and other parameters which were also recorded as binary (e.g., sandstone channel presence as yes or no), the fisher exact test was used [7]. This test evaluates the null hypothesis that there is no association between the independent and dependent variables. If the p value generated is less than 0.05, the null hypothesis may be rejected at 95% confidence. Two sets of analyses were performed for the sites where a roof fall had occurred but was now considered to be stable: first, the statistics were run with these locations recorded as stable, and second, the statistics were run with these locations recorded as unstable.
3. Results and discussion Fig. 1 shows the results from the ANOVA analysis evaluating correlation between the CMRR and roof condition. The p value for the analysis was calculated to be 0.5, indicating there is little correlation between roof condition and CMRR at Mine A. Thus it is likely that there are other factors in addition to those included in the CMRR that are indicative of roof instability at Mine A. Figs. 2–6 show the results from the ANOVA analyses in Matlab evaluating correlation between CMRR and the other parameters recorded at Mine A. The p values for each of these analyses are summarized in Table 1. The data presented above show that the CMRR value is correlated with the presence of faulting and the location of the data collection site at an intersection. The CMRR values are not correlated with the depth of cover, gradient of surface topography, and presence of the sandstone channel in the immediate roof. The CMRR does not account for the depth of cover surface topography or large-scale geological features, such as sandstone channels. The CMRR does not explicitly account for roof being weaker at an intersection. However, the roof at intersections may be recorded as having a lower strength, cohesion, and friction angle than the roof in the surrounding area, in which case the CMRR would implicitly capture this parameter. Although the CMRR does not explicitly record fault displacement or slip, these features appear to be captured in the CMRR parameter which records the presence of slickensides.
Fig. 1. Comparative boxplot result from the ANOVA analysis of CMRR and roof condition, p = 0.5.
Please cite this article as: M. Young, G. Walton and E. Holley, Investigation of factors influencing roof stability at a Western U.S. longwall coal mine, International Journal of Mining Science and Technology, https://doi.org/10.1016/j.ijmst.2018.11.019
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M. Young et al. / International Journal of Mining Science and Technology xxx (xxxx) xxx
Fig. 6. Scatter plot showing the relationship between CMRR and gradient of surface topography, p = 0.9 (linear regression). Fig. 2. Comparative boxplot result from the ANOVA analysis of CMRR and faulting, p = 0.007.
Fig. 3. Comparative boxplot result from the ANOVA analysis of CMRR and the presence of the sandstone channel in the immediate roof, p = 0.23.
Table 1 Summary of p values for ANOVA analysis of CMRR and the parameters collected at each site. Parameter evaluated with CMRR
p value
Roof condition Faulting Sandstone channel Intersections Depth of cover Surface topography
0.5 0.007 0.23 0.005 0.54 0.91
Table 2 Summary of p values for the analysis of roof condition and the parameters collected at each site. Parameter evaluated with roof condition
p value
Faulting Sandstone channel Intersections Depth of cover Surface topography
0.34 0.69 0.69 0.44 0.71
Fig. 4. Comparative boxplot result from the ANOVA analysis of CMRR and the measurement location, p = 0.005.
Fig. 7. Comparative boxplot from the ANOVA analysis of roof condition and the depth of cover, p = 0.44.
Fig. 5. Scatter plot showing the relationship between CMRR and depth of cover, p = 0.54 (linear regression).
Perhaps the more important question is whether individual parameters are correlated with actual roof condition. In order to address this, the correlations between the roof condition and the non-CMRR parameters were evaluated using the ANOVA analysis and the Fisher exact test. The p values from each analysis are presented in Table 2, and the boxplot results from the ANOVA analyses are shown in Figs. 7 and 8. The statistical results do not show a correlation between roof condition and location at an intersection, or between roof
Fig. 8. Comparative boxplot from the ANOVA analysis of roof condition and the surface topography, p = 0.71.
condition and the presence of faulting, despite the fact that these parameters are well correlated with the CMRR. The depth of cover and surface topography also show little correlation with the roof
Please cite this article as: M. Young, G. Walton and E. Holley, Investigation of factors influencing roof stability at a Western U.S. longwall coal mine, International Journal of Mining Science and Technology, https://doi.org/10.1016/j.ijmst.2018.11.019
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condition; this could be because the geology is the primary control on roof condition at Mine A, overshadowing any effect of stressrelated parameters. Lastly, the correlations between roof condition and the CMRR (Fig. 1) and between roof condition and other parameters (Table 2) were reanalyzed using the ‘‘unstable” classification for the six sites where the roof was currently considered stable but had seen significant roof instability in the past (Fig. 9 and Table 3). Figs. 10 and 11 show the boxplot results from these ANOVA analyses of the amended roof condition with depth of cover and slope angle respectively.
Fig. 9. Comparative boxplot result from the ANOVA analysis of CMRR and roof condition, p = 0.2.
For the data points collected from stable sites that had previously experienced roof fall, Table 3 shows that rerunning the analysis with the roof classified as unstable caused the correlation between faulting and roof condition to become statistically significant (for a significance threshold of a = 0.05). The p values for the correlation with sandstone channels and surface topography also decreased, although these results are still not statistically significant. The pvalues increased slightly for the correlations between roof stability and intersections, as well as between roof stability and depth of cover. The results in Table 3 also illustrate how much uncertainty is introduced through the binary recording of the roof condition. Faulting was the only parameter to show a statistically significant correlation with roof condition at Mine A, and only for the version of the dataset wherein currently stable areas with previous roof fall were classified as unstable. It is possible that there is insufficient data or a bias in the data collection locations, which resulted in data that is not fully representative of Mine A. It is also possible that the other parameters show more significant relationships with roof condition at other mines. This potential variability between mines makes it difficult to have a ‘‘one size fits all” approach. A much larger data set spanning multiple mines is needed to make any definitive conclusions about which additional parameters outside the CMRR could be used to predict roof failure, and how stress-related parameters should (or should not) be combined with the CMRR for roof stability assessment. 4. Conclusions
Table 3 Summary of p values for the analysis of roof condition and the parameters collected at each site with stable areas that had previously experienced roof fall classified as ‘‘unstable” Parameter evaluated with roof condition
p value
Faulting Sandstone channel Intersections Depth of cover Surface topography
0.03 0.48 0.72 0.53 0.21
The data from one Western U.S. longwall coal mine (Mine A) show no statistically significant correlation between the CMRR and the recorded roof condition using a binary stability classification. This implies that there are other parameters in addition to those included in the CMRR that are indicative of roof instability at Mine A. The CMRR values did correlate with the presence of faulting and location at an intersection. Roof locations that are currently stable but had previously been unstable showed a statistically significant correlation with faulting when the unstable condition was used in the statistical analysis. The analyses of areas previously affected by roof fall illustrate the uncertainty introduced as a result of recording a time-sensitive characteristic such as roof condition as binary data (stable or unstable). The data also suggest that faulting is the main control on roof stability at Mine A; and the other parameters did not show any statistically significant correlation with roof condition. 5. Future work
Fig. 10. Comparative boxplot from the ANOVA analysis of roof condition and the depth of cover, p = 0.40.
The same data collection and analysis procedure should be carried out at other mines to broaden the data pool and allow comparison of results with those obtained at Mine A. In order to determine the sensitivity of the CMRR classification system to each of its input parameters, the CMRR values should be recalculated at each location for all but one of the inputs (e.g. calculate the CMRR without the moisture sensitivity parameter or the discontinuity intensity parameter). The correlation should be evaluated between the according CMRR output and roof condition, as well as with the other parameters. This will give an indication of how each constituent in the CMRR influences the CMRR output values. Lastly, the dataset should be reexamined with gradational rather than binary classification of roof stability. Acknowledgments
Fig. 11. Comparative boxplot from the ANOVA analysis of roof condition and the slope angle, p = 0.21.
This work was supported by a NIOSH Capacity Building grant (No. 200-2016-90154) to Drs. G. Walton and E. Holley and
Please cite this article as: M. Young, G. Walton and E. Holley, Investigation of factors influencing roof stability at a Western U.S. longwall coal mine, International Journal of Mining Science and Technology, https://doi.org/10.1016/j.ijmst.2018.11.019
M. Young et al. / International Journal of Mining Science and Technology xxx (xxxx) xxx
collaborators at the Colorado School of Mines. The data were collected as part of an MS project by Meriel Young, who acknowledges personnel at Mine A for their support during the data collection process. The authors also thank Mark Larson and Bo Kim at NIOSH for their continued support and input on ground control research efforts at the Colorado School of Mines. The authors would also like to thank Chris Mark of MSHA for accommodating Meriel Young’s visit to the Pittsburgh facility in Spring 2018 and taking the time to discuss the CMRR with her in detail. References [1] Barczak TM, Dolinar DR, Mark C, Signer SP, Tuchman RJ, Wopat PF. Proceedings: New Technology for Coal Mine Roof Support. Information Circular 9453. U.S. Department of Health and Human Services: National Institute for Occupational Safety and Health; 2000. p. 274.
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[2] Mining MSHA. Industry Accident, Injuries, Employment and Production Statistics and Reports. U.S. Department of Labor Mine Safety and Health Administration; 2018. [3] Peng SS. Topical areas of research needs in ground control–a state of the art review on coal mine ground control. Int J Min Sci Technol 2015;25(1):1–6. [4] Molinda GM, Mark C. Coal Mine Roof rating (CMRR): A Practical Rock Mass Classification for Coal Mines. Vol. 9387 of Information Circular. U.S. Department of Interior, Bureau of Mines; 1994. p. 83. [5] Calleja J. Rapid Rating using coal mine roof rating to provide rapid mine roof characterization from exploration drilling. Proceedings of the Coal Operators’ Conference. Wollongong, Australia. University of Wollongong; 2006. [6] Hill D. Practical experiences with application of the coal mine roof rating (CMRR) in Australian coal mines. In: International Workshop on Rock Mass Classification in Underground Mining. U.S. Department of Health and Human Services: National Institute for Occupational Safety and Health; 2007. p. 65–72. [7] Freeman JV, Campbell MJ. The Analysis of Categorical Data: Fisher’s Exact Test. Scope; 2007. p. 16.
Please cite this article as: M. Young, G. Walton and E. Holley, Investigation of factors influencing roof stability at a Western U.S. longwall coal mine, International Journal of Mining Science and Technology, https://doi.org/10.1016/j.ijmst.2018.11.019