# Study on the Mechanism, Prediction, and Control of Coal Wall Spalling in Deep Longwall Panels Utilizing Advanced Numerical Simulation Methodology

Authors: Sunny Murmu; Gnananandh Budi
Journal: Geofluids
 (2022)

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## Abstract

This research looked at the occurrence of coal wall spalling, a type of longwall face failure, during the main weighing period (MWP) for panels greater than 300 m depth. FLAC3D software was exploited to simulate the ground condition of longwall panel 1 of Adriyala Longwall Project mine. The extent and severity of coal wall spalling were quantified using evaluation criteria based on the concepts of vertical stress distribution and maximum shear strain. Subsequently, a parametric analysis of thirteen factors influencing the coal wall spalling was carried out. Empirical equations for predicting coal wall spalling were suggested. Based on the parametric analysis, a coal wall spalling classification system (CWSC) chart is presented to predict spalling characteristics of coal wall that may occur at the MWP. The empirical models depicted good predictive ability with R2 values greater than 0.9. Parametric analysis revealed that coal wall spalling is highly sensitive to seam compressive strength, mining-induced stress, extraction height, and immediate roof characteristics. The CWSC system’s applicability to the five panels was in good agreement with field observations.


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## Body

## 1. Introduction

Coal is a primary source of fossil energy for a nation’s economic growth [1, 2]. However, numerous ground control issues in mining retard the stable production of coal. Spalling of the coal walls is one such ground control issue that has become a major concern in longwall panels operating in subcritical conditions [3, 4]. Massive slabs of coal from the longwall face dislodge with little or no warning due to the variation in the in situ stress state caused by excavation [5] (Figure 1). Face rib spalling and face sloughage are other terms for it. It usually occurs near the shearer, but it can happen anywhere along the face line, putting machinery and people at risk. The spalling of coal is associated with gas outburst and dust generation, making the environment of the longwall face difficult to work in. There are several other issues that emanate because of coal wall spalling, such as roof falls at the face, overloading and blocking of armoured face conveyor (AFC), damage to face equipment, ore dilution, risk to mine personnel, production delays, and even iron bounding of shields. As a result, redundant tasks are required, such as manual handling, and the need for secondary blasting of large coal lumps, preparation of an artificial roof for proper shield setting, and additional maintenance of face equipment.Figure 1
Conceptual diagram of coal wall spalling.Suppose the influence of geological anomalies are considered. In that case, a strata control engineer might expect the extent and severity of coal wall spalling to be most significant during the main weighting period instead of the periodic weighting period or normal cycle, since the gob formation is incapable of bearing the partial load of the overlying strata until the main weighting period (MWP). As a result, maximal load transfer is achieved at the face. Similar occurrence of coal wall spalling was observed in panel 1 of the Adriyala Longwall Project (ALP) in India, which is the subject of this investigation. The first significant main roof weighting or main weighting period for panel 1 of ALP was recorded between 77 m and 83 m of face retreat from the setup room over a five-day period. Significant convergence of 36 mm/day in the main gate was observed just before the main fall of the caving roof. Approximately 115 shield legs were within the weighting zone. During the main weighting, approximately 75 legs bled. The weighting zone extended from the 15th shield to the 30th shield (200 m). Most notably, there was coal wall spalling between the 40th and 114th shields (approximately 130 m) as shown in Figure2(a). Coal wall spalling with a depth of roughly 3 m caused extensive damage to the longwall face (Figure 2(b)). The major weighting was negotiated within five days, during which time the mine had to incur a loss in production as shown in Figure 2(c).Figure 2
(a, b) Coal wall spalling occurrence during the MWP of panel 1; ALP (c) slow face retreat due to coal wall spalling in panel 1 of ALP.
(a)(b)(c)Attempts have been made in the past to understand the underlying geomechanics of coal wall spalling. Using fundamental mechanical theoretical equations, the US Bureau of Mines pointed out that mining depth and extraction height are two important factors governing coal wall spalling [6]. Yong et al. used theoretical damage mechanics to assess the rib spalling of high coal walls in fully mechanized mines [7]. Theoretical models such as the limit equilibrium method, plastic failure of prismatic shaped structure, elasto-plastic laws, and Winkler’s elastic beam foundation model have been used to assess coal wall spalling [7–13]. Physical models were also created to investigate the mechanism of fracture development at the coal front [12, 14–17]. Laboratory-based failure behaviour of coal samples through acoustic emission and due moisture content was also explored [18, 19]. The majority of numerical modelling-based studies are aimed at determining the impact of shield characteristics, operational parameters, and coal seam characteristics on coal wall spalling [4, 20–23]. Yao et al. investigated the effect of seam dip and the resulting fracture evolution on the coal wall [24]. Spalling under a massive sandstone key roof was also investigated [3, 25]. The role of fracture characteristics such as its dip angle, density, strength, and stiffness on coal wall spalling was studied [26].Previous research determined that the geo-mining condition, physico-mechanical properties of the rock beds, panel geometrical parameters, in situ stress regime, powered support characteristics, structural discontinuities, panel operational parameters, and geological anomalies all influence the extent and severity of coal wall spalling. However, only a few of the factors, namely, shield characteristics, advance rate, depth, fracture characteristics, and coal seam characteristics, have been investigated. Furthermore, the assessment of coal wall spalling during the MWP, when it is most severe, is yet to be undertaken. There is a need for a comprehensive coal wall assessment tool to help strata control engineers predict spalling at the coal front. As a result, the current study seeks to define the impact of critical parameters such as coal seam characteristics, immediate roof characteristics, main roof characteristics, in situ stress regime, and extraction height on the spalling of the coal wall during the MWP. The role of thirteen parameters was investigated using FLAC3D modelling software to accomplish the objective of this study. Maximum spalling depth (MSD) and longwall face spalling index (LFSpI) are two assessment indices proposed to help determine coal wall spalling. Thereafter, a new classification system is proposed.


## 2. Numerical Modelling Scheme

### 2.1. Geological Setting of ALP

As previously stated, the current study focuses on the longwall face of panel 1 developed in seam I belonging to ALP and is located in the Ramagundem belt of the Godavari valley coalfields in Telangana, India. It is 250 m wide and 2350 m long with a depth of cover ranging from 250 m to 580 m (Figure3). Seam I thickness ranges from 5.5 m to 7 m. Because the upper portion of the seam consists of two clay bands, only the lower section (3.5 m) is being extracted. Two distinct bands of clay with thicknesses of 0.45 m and 0.23 m have been observed to overly seam I. These bands are responsible for the possibility of a major ground control problem in the gate roads. Furthermore, a massive sandstone layer overlies the clay bands with a thickness ranging from 15 m to 26 m. It is the dominant unit and largely contributes to the weighting events and load transfer to the face owing to its close juxtaposition with the seam. The stratigraphic sequence of the layers overlying seam I is depicted in Figure 4. Panel 1 consists of 5.2 m wide single-entry gate road. Chain pillars range in width from 45 m to 55 m. In panel 1, 146 numbers of 2×11300kN capacity shield supports have been deployed. The average daily rate of advancement was reported to be 4.25 m to 5.1 m.Figure 3
Key plan of ALP.Figure 4
Stratigraphic sequence of ALP.


### 2.2. Model Configuration

Panel 1 of the Adriyala Longwall Project in India was simulated as a calibration model using FLAC3D. To avoid the computational difficulties caused by geometric complexity, prolonged solution time, and large memory allocation, the central portion of the panel, consisting of one shield along the longitudinal direction, was simulated (Figure 5). The model’s length was 360 m in the Y direction, with the centermost 120 m designated as a mining zone, and enough space was left on both sides to avoid the influence of boundary effects in the results. The model’s width was 1.75 m in the X direction, which was kept same as the width of the shield. The total height included the floor (42 m), seam (3.5 m), immediate roof (3.5 m), main roof (20.5 m), upper main roof (28 m), and overburden (74 m) in the Z direction. In the model, X, Y, and Z directions represent the width of the face, face advance or panel length, and height of the model, respectively (Figure 6).Figure 5
Midsection of panel 1 simulated in FLAC3D (not to scale).Figure 6
Model geometry.The total cover depth for the seam is taken as 406.5 m. To compensate for the portions of the roof layers that were not modelled, a uniform vertical stress magnitude of 7.02 MPa was applied at the top of the model. On both sides of the model in the longitudinal direction, a gradient mean horizontal stress was applied. The empirical equation provided by Sheorey was used to estimate the mean in situ horizontal stress value [27]. The bottom of the model was fixed, and a roller-supported boundary condition was applied to both sides of the model in the transverse direction. The powered support’s canopy and base were simulated using conventional eight-noded hexahedron brick-shaped elements. The canopy and base measure 5m×1.75m and 3m×1.75m (length×width), respectively. The shield’s legs were modelled using a beam element. To model the behaviour of the powered support under roof loading, a linear elastic constitutive scheme was used. For the shield, the typical mild steel elastic modulus and Poisson’s ratio values of 210 GPa and 0.3 were chosen. The shield’s yield capacity was specified as 5.2 MN for each leg. The set capacity was 60% of the yield capacity and was applied structurally applied force by the legs at the grid points connecting the canopy and base.


### 2.3. Estimation of Rock Mass Properties

The laboratory-based intact rock properties of the rock units were obtained via geotechnical testing of the freshly obtained core specimen in accordance with the ISRM guidelines. Furthermore, scaling the geotechnical properties of the intact rock-to-rock mass is required to account for the reduction in strength caused by the presence of discontinuities. Therefore, Zhang and Einstein’s methodology for estimating the deformation modulus and compressive strength of rock mass by scaling the intact rock elastic modulus and compressive strength using rock quality designation (RQD) was implemented [28, 29].
(1)ErmEi=1.8∗100.0186RQD−1.91,where Erm and Ei are the deformation moduli of rock mass and intact rock.
(2)σcmσi=100.013RQD−1.34,where σcm and σi are the compressive strength of rock mass and intact rock. Table 1 shows the rock mass parameters of the rock beds that were obtained and used as input for modeling.Table 1
Rock mass properties taken as input for numerical simulation.
BedsThickness (m)Density (kg/m3)Bulk modulus (GPa)Shear modulus (GPa)Tensile strength (MPa)Compressive strength (MPa)Overburden7420461.710.39.7Upper main roof2820481.91.20.311.8Main roof20.520412.61.60.510.5Immediate roof3.517781.10.60.34.5Seam3.515430.90.60.34.5Floor4220812.11.20.517.6


### 2.4. Constitutive Models for Simulating the Behaviour Rock Mass

According to Peng [30], the caving zone of the panel is six to seven times the extraction height. Hence, for the height of caving and the coal seam, strain-softening constitutive model was used with an incremental plastic strain as coal wall spalling occurs in the postpeak softening stage of rock mass failure. However, the Mohr-Coulomb perfectly plastic constitutive model was adopted for the floor and overburden. To calibrate the strain-softening input parameters, a coal pillar submodel with roof and floor up to 24 m on either side was developed (Figure 7). The roof and floor acted as loading platens in the uniaxial compressive strength (UCS) test for the coal pillar. Numerous UCS tests have been carried out by varying the width-to-height ratio while applying servo-controlled velocity (10-5 m/s) over a flat square pillar with quarter symmetry. Several combinations of cohesion and friction variation with plastic strain was carried out, and the pillar’s strength obtained was calibrated with Salamon and Munro’s pillar strength formula [31]. Figures 8(a)–8(c) show the rate of cohesion and friction drop, as well as the comparison of pillar strength obtained from simulation and Salamon and Munro’s equation. It can be observed that the model findings and empirical strength displayed reasonable agreement. As a result, the numerical modelling input parameters are calibrated to be used to simulate the field conditions. It should also be noted that the zone size for the coal pillar was 0.5m×0.5m×0.5m. Zone sizes in the underlying and overlying strata are similar to the coal pillar in the X and Y directions but gradually increased in the Z direction. In Z direction, the ratios for the overlying and underlying strata are 1.44 and 0.845, respectively.Figure 7
(a) Pillar submodel for strain softening calibration. (b) Cohesion and friction drop against plastic shear strain. (c) Strength value comparison using Salamon and Munro’s empirical formula and numerical modelling.
(a)(b)(c)Figure 8
Algorithm for progressive coal extraction, deployment of powered support, and strata caving.


### 2.5. Algorithm for Progressive Face Advance, Powered Support, and Roof Caving

Initially, the model geometry was elastically solved to initialize the in situ vertical and horizontal stresses. Following which, an 8 m cut was made to represent a setup room where the powered support was deployed with support resistance controlled by roof-to-floor convergence and leg stiffness. The longwall panel was then extracted in stages with 1 m face advance until the main weighting period ceased. A FISH script was used to control the gradual extraction of the seam and the cyclic installation of powered support. A separate FISH routine was written after each stage of excavation to check if the roof had caved. Caving necessitates the removal of rock blocks from the overlying roof. FLAC3D version 6 lacks an in-built constitutive model for simulating caving. To delineate the caved zones, the critical values of plastic shear strain, εs (0.25), and plastic tension strain, εt (0.05), were chosen. Furthermore, a vertical displacement (dv) of 0.4 m was chosen as a critical value for the roof to cave. After many simulation trials, these values were chosen intuitively. Several researchers have adopted a similar approach for roof caving in longwall panels [3, 25, 32–37]. When a zone qualifies to be caved, it nulled (Figure 8).


### 2.6. Coal Wall Spalling Estimation and Model Calibration

The facility for determining coal wall spalling is not explicitly given in the FLAC3D code. Consequently, two indicators were utilized to delineate the region of spalling in the coal wall at the longwall face: (1) vertical stress distribution and (2) maximum shear strain. The spalling section of the coal wall is no longer capable of bearing vertical stress due to the formation of macroscale fractures. In other words, the probable spalling region’s rock mass strength properties have deteriorated significantly or irreversibly. It is distinguished by the gradual rise or near horizontal slope of the vertical stress distribution in the coal wall. As shown in Figure 9, the vertical stress distribution in the coal wall has a nearly flat slope that continues up to 3 m. Furthermore, maximum shear strain, another suitable coal wall spalling estimation criterion, identifies the location of irreversible high plastic strain where significant plastic flow occurs. It is one of the most reliable indicators for detecting shear fractures in the coal wall. As a result, FLAC3D elements with maximum shear strain greater than the critical shear strain (0.09 in this study) are considered spalled. Maximum shear strain is a suitable indication of shear strain localization and has been widely utilised to predict spalling by many researchers [22, 38, 39]. Additionally, coal wall spalling has been assessed using two terms: (a) maximum spalling depth (MSD) and (b) longwall spalling index (LFSpI). MSD measures the maximum extent of spalling potential that can occur ahead of the longwall face. The primary function of MSD is to designate the possible extent of the area in front of the face, which is highly unstable and prone to spalling. The LFSpI is a function of the spalled volume of the coal wall and hence explains the various modes of coal wall spalling.
(3)LFSpI=no.ofelementsinHDZhavingmaximumshearstrain>0.09totalno.ofelementswithinMSD∗100%.Figure 9
Spalling estimation of coal wall for ALP panel 1 face.The model was calibrated against the main roof weighting interval (MRWI), maximum spalling depth (MSD), longwall spalling index (LFSpI), and shield load (Table2). The main weighting period (MWP) in the field commenced at 77 m and ceased at 83 m of face advance. During the MWP, MSD was measured to be around 3 m. In Figure 10, the average value of the shield load was plotted against the numerically estimated load. According to the numerical modelling results, the MWP began at 78 m (peak stress period) and ceased with the occurrence of main fall at 80 m of shield advance. The MSD and LFSpI values found for panel 1 of ALP were 3 m and 90.5%, respectively (Figure 9). The numerically estimated MSD and LFSpI matched the field experience during the MWP closely (Table 2). A large portion of the coal wall spalled in the top region, extending all way to the floor. Furthermore, the nature of the spalling was shear because of the high abutment stress regime that evolved at the face. The overall magnitude and trend of the load development on the shield demonstrated good agreement for both computed and field values. Therefore, the modelling scheme can be regarded as calibrated.Table 2
Calibration parameters of numerical simulation.
ParametersField valuesComputed valuesReferencesMRWI77 m–83 m78 m–80 m[40]MSD3 m3 m[25]LFSpI72.2%90.5%[25]Figure 10
Load development on the shield for 90 m of face extraction.


## 2.1. Geological Setting of ALP

As previously stated, the current study focuses on the longwall face of panel 1 developed in seam I belonging to ALP and is located in the Ramagundem belt of the Godavari valley coalfields in Telangana, India. It is 250 m wide and 2350 m long with a depth of cover ranging from 250 m to 580 m (Figure3). Seam I thickness ranges from 5.5 m to 7 m. Because the upper portion of the seam consists of two clay bands, only the lower section (3.5 m) is being extracted. Two distinct bands of clay with thicknesses of 0.45 m and 0.23 m have been observed to overly seam I. These bands are responsible for the possibility of a major ground control problem in the gate roads. Furthermore, a massive sandstone layer overlies the clay bands with a thickness ranging from 15 m to 26 m. It is the dominant unit and largely contributes to the weighting events and load transfer to the face owing to its close juxtaposition with the seam. The stratigraphic sequence of the layers overlying seam I is depicted in Figure 4. Panel 1 consists of 5.2 m wide single-entry gate road. Chain pillars range in width from 45 m to 55 m. In panel 1, 146 numbers of 2×11300kN capacity shield supports have been deployed. The average daily rate of advancement was reported to be 4.25 m to 5.1 m.Figure 3
Key plan of ALP.Figure 4
Stratigraphic sequence of ALP.


## 2.2. Model Configuration

Panel 1 of the Adriyala Longwall Project in India was simulated as a calibration model using FLAC3D. To avoid the computational difficulties caused by geometric complexity, prolonged solution time, and large memory allocation, the central portion of the panel, consisting of one shield along the longitudinal direction, was simulated (Figure 5). The model’s length was 360 m in the Y direction, with the centermost 120 m designated as a mining zone, and enough space was left on both sides to avoid the influence of boundary effects in the results. The model’s width was 1.75 m in the X direction, which was kept same as the width of the shield. The total height included the floor (42 m), seam (3.5 m), immediate roof (3.5 m), main roof (20.5 m), upper main roof (28 m), and overburden (74 m) in the Z direction. In the model, X, Y, and Z directions represent the width of the face, face advance or panel length, and height of the model, respectively (Figure 6).Figure 5
Midsection of panel 1 simulated in FLAC3D (not to scale).Figure 6
Model geometry.The total cover depth for the seam is taken as 406.5 m. To compensate for the portions of the roof layers that were not modelled, a uniform vertical stress magnitude of 7.02 MPa was applied at the top of the model. On both sides of the model in the longitudinal direction, a gradient mean horizontal stress was applied. The empirical equation provided by Sheorey was used to estimate the mean in situ horizontal stress value [27]. The bottom of the model was fixed, and a roller-supported boundary condition was applied to both sides of the model in the transverse direction. The powered support’s canopy and base were simulated using conventional eight-noded hexahedron brick-shaped elements. The canopy and base measure 5m×1.75m and 3m×1.75m (length×width), respectively. The shield’s legs were modelled using a beam element. To model the behaviour of the powered support under roof loading, a linear elastic constitutive scheme was used. For the shield, the typical mild steel elastic modulus and Poisson’s ratio values of 210 GPa and 0.3 were chosen. The shield’s yield capacity was specified as 5.2 MN for each leg. The set capacity was 60% of the yield capacity and was applied structurally applied force by the legs at the grid points connecting the canopy and base.


## 2.3. Estimation of Rock Mass Properties

The laboratory-based intact rock properties of the rock units were obtained via geotechnical testing of the freshly obtained core specimen in accordance with the ISRM guidelines. Furthermore, scaling the geotechnical properties of the intact rock-to-rock mass is required to account for the reduction in strength caused by the presence of discontinuities. Therefore, Zhang and Einstein’s methodology for estimating the deformation modulus and compressive strength of rock mass by scaling the intact rock elastic modulus and compressive strength using rock quality designation (RQD) was implemented [28, 29].
(1)ErmEi=1.8∗100.0186RQD−1.91,where Erm and Ei are the deformation moduli of rock mass and intact rock.
(2)σcmσi=100.013RQD−1.34,where σcm and σi are the compressive strength of rock mass and intact rock. Table 1 shows the rock mass parameters of the rock beds that were obtained and used as input for modeling.Table 1
Rock mass properties taken as input for numerical simulation.
BedsThickness (m)Density (kg/m3)Bulk modulus (GPa)Shear modulus (GPa)Tensile strength (MPa)Compressive strength (MPa)Overburden7420461.710.39.7Upper main roof2820481.91.20.311.8Main roof20.520412.61.60.510.5Immediate roof3.517781.10.60.34.5Seam3.515430.90.60.34.5Floor4220812.11.20.517.6


## 2.4. Constitutive Models for Simulating the Behaviour Rock Mass

According to Peng [30], the caving zone of the panel is six to seven times the extraction height. Hence, for the height of caving and the coal seam, strain-softening constitutive model was used with an incremental plastic strain as coal wall spalling occurs in the postpeak softening stage of rock mass failure. However, the Mohr-Coulomb perfectly plastic constitutive model was adopted for the floor and overburden. To calibrate the strain-softening input parameters, a coal pillar submodel with roof and floor up to 24 m on either side was developed (Figure 7). The roof and floor acted as loading platens in the uniaxial compressive strength (UCS) test for the coal pillar. Numerous UCS tests have been carried out by varying the width-to-height ratio while applying servo-controlled velocity (10-5 m/s) over a flat square pillar with quarter symmetry. Several combinations of cohesion and friction variation with plastic strain was carried out, and the pillar’s strength obtained was calibrated with Salamon and Munro’s pillar strength formula [31]. Figures 8(a)–8(c) show the rate of cohesion and friction drop, as well as the comparison of pillar strength obtained from simulation and Salamon and Munro’s equation. It can be observed that the model findings and empirical strength displayed reasonable agreement. As a result, the numerical modelling input parameters are calibrated to be used to simulate the field conditions. It should also be noted that the zone size for the coal pillar was 0.5m×0.5m×0.5m. Zone sizes in the underlying and overlying strata are similar to the coal pillar in the X and Y directions but gradually increased in the Z direction. In Z direction, the ratios for the overlying and underlying strata are 1.44 and 0.845, respectively.Figure 7
(a) Pillar submodel for strain softening calibration. (b) Cohesion and friction drop against plastic shear strain. (c) Strength value comparison using Salamon and Munro’s empirical formula and numerical modelling.
(a)(b)(c)Figure 8
Algorithm for progressive coal extraction, deployment of powered support, and strata caving.


## 2.5. Algorithm for Progressive Face Advance, Powered Support, and Roof Caving

Initially, the model geometry was elastically solved to initialize the in situ vertical and horizontal stresses. Following which, an 8 m cut was made to represent a setup room where the powered support was deployed with support resistance controlled by roof-to-floor convergence and leg stiffness. The longwall panel was then extracted in stages with 1 m face advance until the main weighting period ceased. A FISH script was used to control the gradual extraction of the seam and the cyclic installation of powered support. A separate FISH routine was written after each stage of excavation to check if the roof had caved. Caving necessitates the removal of rock blocks from the overlying roof. FLAC3D version 6 lacks an in-built constitutive model for simulating caving. To delineate the caved zones, the critical values of plastic shear strain, εs (0.25), and plastic tension strain, εt (0.05), were chosen. Furthermore, a vertical displacement (dv) of 0.4 m was chosen as a critical value for the roof to cave. After many simulation trials, these values were chosen intuitively. Several researchers have adopted a similar approach for roof caving in longwall panels [3, 25, 32–37]. When a zone qualifies to be caved, it nulled (Figure 8).


## 2.6. Coal Wall Spalling Estimation and Model Calibration

The facility for determining coal wall spalling is not explicitly given in the FLAC3D code. Consequently, two indicators were utilized to delineate the region of spalling in the coal wall at the longwall face: (1) vertical stress distribution and (2) maximum shear strain. The spalling section of the coal wall is no longer capable of bearing vertical stress due to the formation of macroscale fractures. In other words, the probable spalling region’s rock mass strength properties have deteriorated significantly or irreversibly. It is distinguished by the gradual rise or near horizontal slope of the vertical stress distribution in the coal wall. As shown in Figure 9, the vertical stress distribution in the coal wall has a nearly flat slope that continues up to 3 m. Furthermore, maximum shear strain, another suitable coal wall spalling estimation criterion, identifies the location of irreversible high plastic strain where significant plastic flow occurs. It is one of the most reliable indicators for detecting shear fractures in the coal wall. As a result, FLAC3D elements with maximum shear strain greater than the critical shear strain (0.09 in this study) are considered spalled. Maximum shear strain is a suitable indication of shear strain localization and has been widely utilised to predict spalling by many researchers [22, 38, 39]. Additionally, coal wall spalling has been assessed using two terms: (a) maximum spalling depth (MSD) and (b) longwall spalling index (LFSpI). MSD measures the maximum extent of spalling potential that can occur ahead of the longwall face. The primary function of MSD is to designate the possible extent of the area in front of the face, which is highly unstable and prone to spalling. The LFSpI is a function of the spalled volume of the coal wall and hence explains the various modes of coal wall spalling.
(3)LFSpI=no.ofelementsinHDZhavingmaximumshearstrain>0.09totalno.ofelementswithinMSD∗100%.Figure 9
Spalling estimation of coal wall for ALP panel 1 face.The model was calibrated against the main roof weighting interval (MRWI), maximum spalling depth (MSD), longwall spalling index (LFSpI), and shield load (Table2). The main weighting period (MWP) in the field commenced at 77 m and ceased at 83 m of face advance. During the MWP, MSD was measured to be around 3 m. In Figure 10, the average value of the shield load was plotted against the numerically estimated load. According to the numerical modelling results, the MWP began at 78 m (peak stress period) and ceased with the occurrence of main fall at 80 m of shield advance. The MSD and LFSpI values found for panel 1 of ALP were 3 m and 90.5%, respectively (Figure 9). The numerically estimated MSD and LFSpI matched the field experience during the MWP closely (Table 2). A large portion of the coal wall spalled in the top region, extending all way to the floor. Furthermore, the nature of the spalling was shear because of the high abutment stress regime that evolved at the face. The overall magnitude and trend of the load development on the shield demonstrated good agreement for both computed and field values. Therefore, the modelling scheme can be regarded as calibrated.Table 2
Calibration parameters of numerical simulation.
ParametersField valuesComputed valuesReferencesMRWI77 m–83 m78 m–80 m[40]MSD3 m3 m[25]LFSpI72.2%90.5%[25]Figure 10
Load development on the shield for 90 m of face extraction.


## 3. Coal Wall Spalling Assessment Criterion Development

### 3.1. Parameter Selection and Experimental Design

The formulation of a comprehensive assessment criterion requires proper identification and quantification of parameters that have a substantial influence on coal wall spalling. A critical review of the literature suggested that the key parameters might be categorized into seven major groupings (Table3). Due to data acquisition and time restrictions, it is difficult to study all parameters. In this research, thirteen parameters were chosen that might be conveniently acquired at the feasibility stage of mine design. In the strata characteristics, the thickness of immediate (Ti) and main roof (Tm), the role of deformation modulus of immediate roof (Ei) and main roof (Em), and the field compressive and tensile strength of immediate roof (Scir and Stir) and main roof (Scmr and Stmr) were considered. The panel operational characteristics included the height of extraction (Hoe). The coal seam characteristics involved the deformation modulus of the seam (Ec) and field compressive and tensile strength of the coal seam (Scc and Stc). The variation in the in situ vertical stress (σv) was also considered.Table 3
Key parameters that govern coal wall spalling.
Coal wall spallingCoal seam characteristics(i) Strength(ii) Inclination(iii) StiffnessStrata characteristics(i) Thickness(ii) Strength(iii) StiffnessDiscontinuity characteristics(i) Joint stiffness(ii) Joint spacing(iii) Joint aperture(iv) Joint persistence(v) No. of joint set(vi) Joint filling(vii) Presence of geological anomalies (faults and dykes)Panel geometrical characteristics(i) Face length(ii) Panel lengthIn situ stress condition(i) Vertical and horizontal stress regimePanel operational characteristics(i) Height of extraction(ii) Rate of face advance(iii) Canopy tip to face distancePowered support characteristics(i) Capacity of shield(ii) Setting and yielding pressure of powered support(iii) No. of yielding events in a cycle(iv) Face sprag pressureThe parametric study methodology was used for the current article’s experimental design. It entails evaluating one parameter at a time while the others stay unaltered [41]. The calibrated modelling approach outlined in Section 2 was carried out with systematic modification of the parameters under consideration (Figure 11).Figure 11
Experimental design of the parametric analysis.


### 3.2. Parametric Study Results

The MSD and LFSpI are considered the target variables. Furthermore, the thirteen parameters under investigation are viewed as independent variables whose function or impact on coal wall spalling must be determined. Table4 summarises the experimental design as well as the modelling results in terms of MSD and LFSpI. Based on the findings of the parametric analysis completed through numerical simulation, an attempt was made to determine the correlation between the target and independent variables using a basic regression scatter plot approach. The best-fit regression function was chosen based on statistical significance as shown by the greatest value of the coefficient of regression (R2). Figures 12(a)–12(m) depicts the scatter plot between the target variables and each independent variable.Table 4
Experimental design and summary of parametric studies.
Model no.σv (MPa)Hoe (m)Scir (MPa)Stir (MPa)Scmr (MPa)Stmr (MPa)Ei (GPa)Em (GPa)Ti (m)Tm (m)Scc (MPa)Stc (MPa)Ec (GPa)MSD (m)LFSpI (%)17.533.50.34.30.5235154.30.314.495.821033.50.34.30.5235154.30.316.497.2312.533.50.34.30.5235154.30.31974.141533.50.34.30.5235154.30.3116.272.951043.50.34.30.5235154.30.31472.561053.50.34.30.5235154.30.313.862.571063.50.34.30.5235154.30.312.918.181035.80.34.30.5235154.30.3112.798.791038.60.34.30.5235154.30.3114.31001010312.90.34.30.5235154.30.3114.5100111033.514.30.5235154.30.31795.2121033.524.30.5235154.30.31795.2131033.50.37.70.5235154.30.316.890141033.50.312.90.5235154.30.31776.2151033.50.315.40.5235154.30.3112.865.4161033.50.34.31235154.30.318.690.7171033.50.34.32235154.30.318.690.7181033.50.34.30.5435154.30.313.294.4191033.50.34.30.5635154.30.312.883.3201033.50.34.30.5255154.30.317100211033.50.34.30.5275154.30.318.994.4221033.50.34.30.52310154.30.316.661.1231033.50.34.30.52315154.30.3110.939.4241033.50.34.30.5235204.30.317.589.6251033.50.34.30.5235254.30.318.291.7261033.50.34.30.5235155.80.310.356271033.50.34.30.5235156.40.310.333.3281033.50.34.30.5235158.60.310.116.7291033.50.34.30.5235154.30.516.497.2301033.50.34.30.5235154.3116.497.2311033.50.34.30.5235154.30.322.391.7321033.50.34.30.5235154.30.330.9100331033.50.34.30.5235154.30.340.8100Figure 12
Scatter plot showing the change in MSD and LFSpI with variation in (a)σv, (b) Hoe, (c) Scc, (d) Ec, (e) Stc, (f) Scir, (g) Ei, (h) Stir, (i) Scmr, (j) Em, (k) Stmr, (l) Ti, and (m) Tm.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)(m)Figure12(a) shows that σv has a considerable effect on MSD and LFSpI. MSD increased from 4.4 m to 16.2 m, whereas LFSpI decreased from 95.8% to 72.9%. Increased in situ vertical stress leads to an increase in front abutment pressure (FAP) at the coal wall, which leads to increased failure. According to Figure 12(b), both MSD and LFSpI dropped significantly with increasing Hoe from 3 m to 6 m, indicating that the overall spalling of the coal wall decreases with increasing Hoe. MSD and LFSpI decreased significantly as Scc was increased from 4.3 MPa to 8.6 MPa (Figure 12(c)). It was also discovered that the failure of the coal wall was shear in nature for all models at depths ranging from 300 m to 600 m. It suggests that as Scc increases, so does the shear strength, strengthening the coal wall’s capacity to withstand failure. Ec denotes the coal wall’s capacity to resist deformation. As a result, increasing Ec significantly lowered MSD from 6.4 m to 0.8 m. However, LFSpI values remained more than 90% for all Ec variations (Figure 12(d)). Stc seemed to have little effect on MSD or LFSpI, confirming that shear is the dominant mode of coal wall failure at deeper depths (Figure 12(e)). Increased Scir had a significant impact, as MSD increased by 126.6%. However, with increasing Scir, there was only a little change in LFSpI (Figure 12(f)). The onset of MWP was delayed as Scir was increased, resulting in higher FAP near the coal wall. The incremental change in Ei seems to lower MSD by 50%. LFSpI, on the other hand, remained greater than 80% for all Ei variations (Figure 12(g)). It implies that increasing Ei reduces the vertical displacement of the immediate roof, hence reducing the horizontal deformation of the coal wall. Only a 10% change in MSD was seen as Stir was increased. Furthermore, LFSpI remained relatively unchanged (Figure 12(h)). Scmr was simulated in four distinct variations (4.3 MPa, 7.7 MPa, 12.9 MPa, and 15.4 MPa). MSD increased by just 10% for the first three Scmr variations. However, a significant increase from 7 m to 12.8 m was observed when Scmr was increased from 12.9 MPa to 15.4 MPa. The value of LFSpI decreased linearly from 97.2%, 90%, 76.2% to 65.4% with increase in Scmr (Figure 12(i)). It advocated that while the extent of spalling of coal wall may increase, its volume may progressively decrease as Scmr increases. Changing Em appeared to have no effect on LFSpI, but it increased MSD by 39.1% (Figure 12(j)). Because the main roof’s failure mode was shear, there was only a little difference in MSD and LFSpI when Stm was increased from 0.5 MPa to 2 MPa (Figure 12(k)). Ti values were increased from 5 m to 10 m and then to 15 m. When Ti was increased from 5 m to 10 m, MSD increased marginally by 3.1%, while LFSpI decreased from 97.2% to 61%. When a Ti of 15 m was simulated, MSD increased sharply to 10.9 m, whereas LFSpI reduced to 39.4% (Figure 12(l)). Tm was gradually increased from 15 m to 20 m to 15 m. MSD seems to have increased from 6.4 m to 7.5 m to 8.2 m; however, LFSpI values stayed relatively static within 10% (Figure 12(m)). It is obvious from Figures 12(a)–12(m)that the majority of the independent variables indicate a nonlinear relationship with the target variables. Although the R2 for the majority of the independent variables appears to be greater than 60%, it does not imply that they are all critical parameters. Therefore, proper judgement is crucial to select only the critical independent factors to develop a criterion for assessing coal wall spalling.


### 3.3. New Coal Wall Spalling Assessment Criterion

A coal wall spalling evaluation criterion was developed using a statistical multiple regression approach. According to Figures12(a)–12(m), all independent factors shared a nonlinear relationship with the target variables. However, it was felt that the most critical independent variables that may explain coal wall spalling should be chosen after qualifying statistical tests.


#### 3.3.1. Critical Independent Variable Selection

Two essential criteria were used to determine the most appropriate independent variables: stepwise backward regression and analysis of variance (ANOVA). The stepwise backward regression provides the change in adjustedR2 as each independent parameter is removed one at a time. The model with the greatest adjusted R2 is the statistically optimal multiple regression model with a proper number of independent variables. If removing an independent variable improves the adjusted R2, the variable is eliminated from the regression model, and vice versa. ANOVA, on the other hand, renders significant indicators such as the residual sum of squares (RSS), F statistics, and P value, which aid in statistically selecting the independent variable that is to be included in the regression model. The RSS is the proportion of variation in a data set that cannot be explained by a regression model. Instead, the variation is attributed to the residuals or the error term. As a result, if the inclusion of an independent variable increases RSS, the variable is not selected, and vice versa. The F statistics is observed in conjunction with the P value. The addition of any independent variable should not reduce the value of F statistics. Furthermore, the P value associated with each variable in a regression model should be less than 0.05. The final list of independent variable selected for the formulation of regression models to predict MSD and LFSpI is shown in Table 5.Table 5
Selection of independent variables for regression model.
Model no.Variables removedIndependent variablesAdjustedR2RSSF statisticsP valueMSD1Noneσv, Stc, Stmr, Stir, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.78101.949.58<0.052Stcσv, Stmr, Stir, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.7992.1810.90<0.053Stc, Stirσv, Stmr, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.8087.7112.36<0.054Stc, Stir, Tmσv, Stmr, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.8083.5213.51<0.055Stc, Stir, Tm, Emσv, Stmr, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.8079.8314.83<0.056Stc, Stir, Tm, Em, Stmrσv, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.8178.1316.42<0.05LFSpI1Noneσv, Stc, Stmr, Stir, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911168.9424.59<0.052Stcσv, Stmr, Stir, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911112.1928.02<0.053Stc, Tmσv, Stmr, Stir, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911081.2632.07<0.054Stmr, Stc, Tmσv, Stir, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911051.7236.18<0.055Stir, Stmr, Stc, Tmσv, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911030.8740.81<0.056Em, Stir, Stmr, Stc, Tmσv, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911029.8346.48<0.057Ec, Em, Stir, Stmr, Stc, Tmσv, Ti, Ei, Scir, Scc, Scmr, Hoe0.921025.1152.48<0.05


#### 3.3.2. Multicollinearity Diagnostic

Due to the presence of multicollinearity among the independent variables, the estimation of the regression weights or coefficients becomes erroneous. This leads to the formation of models that are unable to correctly predict the state variable for a new set of data. As a result, the diagnosis of multicollinearity among independent variables is determined by calculating their variance inflation factor (VIF) and tolerance. Dormann et al. proposed a threshold value for VIF and tolerance [42]. Table 6 shows that no predictor variables exceed the threshold values of VIF larger than 10 and tolerance less than 1. As a result, it may be concluded that the variables lack multicollinearity and can be used to create regression models.Table 6
Values of the multicollinearity diagnostic indicators for the independent variables.
VariablesToleranceVIFMSDσv0.981.02Ti0.951.05Ei0.951.05Scir0.941.06Scc0.941.07Scmr0.941.07Hoe0.941.07Ec0.941.07LFSpIσv0.981.01Ti0.961.04Ei0.961.04Scir0.961.04Scc0.951.05Scmr0.951.04Hoe0.951.05


#### 3.3.3. Multiple Regression

The multivariate regression statistical method has been widely used for the assessment of mining engineering problems [32, 40, 41, 43–47]. Therefore, the multiple linear regression approach was used to develop empirical models for MSD and LFSpI prediction. The numerical modelling findings, as presented in Table 4, were utilized to develop the regression models. As stated previously in Section 3.2, the majority of the relationships between the target and the independent variables were nonlinear. Therefore, the natural logarithm was taken for MSD, LFSpI, and all independent variables that exhibited a power function regarding the target variables. The remaining independent variables had exponential relationships and were thus selected as it is for the regression. The proposed regression models are listed below.
(4)lnMSD=9.41+1.83lnσv+0.72lnScir−0.87lnEi−6.98lnScc−1.24lnHoe+0.04Scmr+0.04Ti−0.82Ec,(5)lnLFSpI=8.76−0.05σv+0.04lnScir−0.03Scmr−0.03Ei−0.09Ti−0.42Scc−0.43Hoe.The sign conventions of the regression coefficients linked to each independent variable indicate their influence (either positive or negative) on the target variable. MSD and LFSpI can be calculated as follows:(6)MSD=explnMSD,(7)LFSpI=explnLFSpI.TheR2 and root mean squared error (RMSE) were calculated to assess the reliability and predictive capabilities of the proposed regression models. R2 was found greater than 0.9 (0.95 for MSD model and 0.93 for LFSpI model), indicating a high level of prediction ability. Furthermore, RMSE values (0.28 for MSD model and 0.12 for LFSpI model) near to zero enhanced the regression model’s high prediction performance.


### 3.4. Coal Wall Spalling Classification System

It is important to emphasize that MSD predicts the extent of spalling. It defines the length of the portion ahead of the coal wall that is potentially susceptible to spalling. Furthermore, LFSpI determines the volume or region of the coal wall within the extent of MSD that is prone to spalling. A low MSD value indicates a generally stable coal wall, whereas a high MSD value indicates a disturbed and unstable coal wall with the potential for extensive spalling. However, it must be seen in tandem with LFSpI to reliably conclude that the area is highly unstable. High LFSpI values do not always imply increased spalling. However, a high MSD in combination with a high LFSpI indicates a highly distressed zone. Therefore, it is advised that both criteria be used in combination for a comprehensive assessment of coal wall spalling. Based on these criteria, a new classification method named as the “coal wall spalling classification system” (CWSC) is proposed. After a thorough examination of the numerical modelling findings, as well as previous field observed coal wall spalling examples accessible in the literature, a classification chart is presented, as shown in Figure13. In addition, Table 7 describes the features of each class in the CWSC system. It is important to note that the chart does not include all MSD values that can be achieved from Equations (5) and (6). This chart, however, may be extended for any MSD value greater than 10 m. The key is the LFSpI value, which dictates whether the coal face should be classified as class 2 or class 3. In other words, the chart region beyond 10 m for any value of LFSpI has the same characteristics as the region between MSD 8 m and 10 m. It is also worth mentioning that there is no such classification system in the literature. Since this is the first attempt, there was no possibility to compare it with any other classification system based on coal wall spalling.Figure 13
Chart of CWSC system.Table 7
Coal wall spalling classification based on CWSC system.
ClassSpalling characteristicsDescription1Favorable(i) Skin or chunk type of spalling(ii) Spalled coal easily conveyable by AFC(iii) Secondary handling (secondary blasting or manual maneuvering) of spalled coal not required(iv) Artificial reinforcement for coal wall not required(v) Roof falls at the unsupported zone may occur but is insignificant(vi) Low risk towards men and equipment(vii) Face retreat remain uninterrupted2Operationally manageable(i) Debris flow or blocky type of spalling from the upper or lower region of the coal wall(ii) Potential for AFC blockage(iii) Secondary handling of spalled coal may be required(iv) Artificial reinforcement for coal wall may be required(v) Roof falls at the unsupported zone of the face may be significant(vi) Moderate risk towards men and equipment(vii) Face retreat may be interrupted3Operationally difficult to manage (critical)(i) Integral or total failure type of spalling may be expected(ii) Blockage of AFC expected(iii) Secondary handling of spalled coal required(iv) Artificial reinforcement for coal wall required(v) Roof falls at the unsupported zone of the face significant(vi) High risk towards men and equipment(vii) Face retreat will be interrupted


### 3.5. Application of the CWSC System

The applicability of the proposed classification system was carried out by taking into consideration the coal wall spalling cases of five longwall faces including ALP. Relevant data from the five panels, namely, Moonidih (A4), Wolonghu (8102), GDK 10A (3A), GDK 10A (11), and ALP (P1), were collected. The letters in the bracket signify the panel names. Except Wolonghu (8102) which is in China, the rest of all panels belong to India. The spalling of the coal wall during the MWP for all five panels was predicted using the regression equations described above and was classified according to the proposed CWSC chart.MSD and LFSpI were estimated using the input data from the panels mentioned above, and the results are reported in Table8. To identify the class, the MSD and LFSpI values for the panels were plotted in the CWSC system chart, as shown in Figure 14. Moonidih (A4)’s coal wall spalling potential is rated as class 1 as can be observed. The remaining panels, Wolonghu (8102), GDK 10A (3A), GDK 10A (11), and ALP, were classified as class 2. For the above-mentioned panels, the extent and magnitude of coal wall spalling matched the field observation quite well.Table 8
Applications of the CWSC system.
ParametersMoonidih (A4)GDK 10A (3A)GDK 10A (11)Wolonghu (8102)ALP (P1)σv (MPa)1096.5139.7Ti (m)4.65.22.843.5Ei (GPa)8.163.25.91.6Scir (MPa)7.54.97.15.94.5Scc (MPa)4.83.94.234.5Scmr (MPa)711.815.115.410.5Hoe (m)2.63.4343.5Ec (GPa)3.222.25.62MSD (m)0.42.82.21.73LFSpI (%)76.771.610066.477.2CWSC class1-favourable2-operationally manageable2-operationally manageable2-operationally manageable2-operationally manageableFigure 14
Classification of longwall panels based on CWSC system.The CWSC method does have certain limitations and assumptions to consider. To begin with, the rock mass strength and stiffness of the rock beds were calculated using the scaling laws related with RQD provided by Zhang and Einstein [28, 29]. As a result, the intact rock properties should be scaled using the aforementioned scaling rule when employing the CWSC system. Secondly, using the CWSC system outside of its initial database limit may result in incorrect results. As a result, caution should be exercised while identifying longwall panels whose geo-mining conditions does not come within the limits of the database restrictions.


## 3.1. Parameter Selection and Experimental Design

The formulation of a comprehensive assessment criterion requires proper identification and quantification of parameters that have a substantial influence on coal wall spalling. A critical review of the literature suggested that the key parameters might be categorized into seven major groupings (Table3). Due to data acquisition and time restrictions, it is difficult to study all parameters. In this research, thirteen parameters were chosen that might be conveniently acquired at the feasibility stage of mine design. In the strata characteristics, the thickness of immediate (Ti) and main roof (Tm), the role of deformation modulus of immediate roof (Ei) and main roof (Em), and the field compressive and tensile strength of immediate roof (Scir and Stir) and main roof (Scmr and Stmr) were considered. The panel operational characteristics included the height of extraction (Hoe). The coal seam characteristics involved the deformation modulus of the seam (Ec) and field compressive and tensile strength of the coal seam (Scc and Stc). The variation in the in situ vertical stress (σv) was also considered.Table 3
Key parameters that govern coal wall spalling.
Coal wall spallingCoal seam characteristics(i) Strength(ii) Inclination(iii) StiffnessStrata characteristics(i) Thickness(ii) Strength(iii) StiffnessDiscontinuity characteristics(i) Joint stiffness(ii) Joint spacing(iii) Joint aperture(iv) Joint persistence(v) No. of joint set(vi) Joint filling(vii) Presence of geological anomalies (faults and dykes)Panel geometrical characteristics(i) Face length(ii) Panel lengthIn situ stress condition(i) Vertical and horizontal stress regimePanel operational characteristics(i) Height of extraction(ii) Rate of face advance(iii) Canopy tip to face distancePowered support characteristics(i) Capacity of shield(ii) Setting and yielding pressure of powered support(iii) No. of yielding events in a cycle(iv) Face sprag pressureThe parametric study methodology was used for the current article’s experimental design. It entails evaluating one parameter at a time while the others stay unaltered [41]. The calibrated modelling approach outlined in Section 2 was carried out with systematic modification of the parameters under consideration (Figure 11).Figure 11
Experimental design of the parametric analysis.


## 3.2. Parametric Study Results

The MSD and LFSpI are considered the target variables. Furthermore, the thirteen parameters under investigation are viewed as independent variables whose function or impact on coal wall spalling must be determined. Table4 summarises the experimental design as well as the modelling results in terms of MSD and LFSpI. Based on the findings of the parametric analysis completed through numerical simulation, an attempt was made to determine the correlation between the target and independent variables using a basic regression scatter plot approach. The best-fit regression function was chosen based on statistical significance as shown by the greatest value of the coefficient of regression (R2). Figures 12(a)–12(m) depicts the scatter plot between the target variables and each independent variable.Table 4
Experimental design and summary of parametric studies.
Model no.σv (MPa)Hoe (m)Scir (MPa)Stir (MPa)Scmr (MPa)Stmr (MPa)Ei (GPa)Em (GPa)Ti (m)Tm (m)Scc (MPa)Stc (MPa)Ec (GPa)MSD (m)LFSpI (%)17.533.50.34.30.5235154.30.314.495.821033.50.34.30.5235154.30.316.497.2312.533.50.34.30.5235154.30.31974.141533.50.34.30.5235154.30.3116.272.951043.50.34.30.5235154.30.31472.561053.50.34.30.5235154.30.313.862.571063.50.34.30.5235154.30.312.918.181035.80.34.30.5235154.30.3112.798.791038.60.34.30.5235154.30.3114.31001010312.90.34.30.5235154.30.3114.5100111033.514.30.5235154.30.31795.2121033.524.30.5235154.30.31795.2131033.50.37.70.5235154.30.316.890141033.50.312.90.5235154.30.31776.2151033.50.315.40.5235154.30.3112.865.4161033.50.34.31235154.30.318.690.7171033.50.34.32235154.30.318.690.7181033.50.34.30.5435154.30.313.294.4191033.50.34.30.5635154.30.312.883.3201033.50.34.30.5255154.30.317100211033.50.34.30.5275154.30.318.994.4221033.50.34.30.52310154.30.316.661.1231033.50.34.30.52315154.30.3110.939.4241033.50.34.30.5235204.30.317.589.6251033.50.34.30.5235254.30.318.291.7261033.50.34.30.5235155.80.310.356271033.50.34.30.5235156.40.310.333.3281033.50.34.30.5235158.60.310.116.7291033.50.34.30.5235154.30.516.497.2301033.50.34.30.5235154.3116.497.2311033.50.34.30.5235154.30.322.391.7321033.50.34.30.5235154.30.330.9100331033.50.34.30.5235154.30.340.8100Figure 12
Scatter plot showing the change in MSD and LFSpI with variation in (a)σv, (b) Hoe, (c) Scc, (d) Ec, (e) Stc, (f) Scir, (g) Ei, (h) Stir, (i) Scmr, (j) Em, (k) Stmr, (l) Ti, and (m) Tm.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)(m)Figure12(a) shows that σv has a considerable effect on MSD and LFSpI. MSD increased from 4.4 m to 16.2 m, whereas LFSpI decreased from 95.8% to 72.9%. Increased in situ vertical stress leads to an increase in front abutment pressure (FAP) at the coal wall, which leads to increased failure. According to Figure 12(b), both MSD and LFSpI dropped significantly with increasing Hoe from 3 m to 6 m, indicating that the overall spalling of the coal wall decreases with increasing Hoe. MSD and LFSpI decreased significantly as Scc was increased from 4.3 MPa to 8.6 MPa (Figure 12(c)). It was also discovered that the failure of the coal wall was shear in nature for all models at depths ranging from 300 m to 600 m. It suggests that as Scc increases, so does the shear strength, strengthening the coal wall’s capacity to withstand failure. Ec denotes the coal wall’s capacity to resist deformation. As a result, increasing Ec significantly lowered MSD from 6.4 m to 0.8 m. However, LFSpI values remained more than 90% for all Ec variations (Figure 12(d)). Stc seemed to have little effect on MSD or LFSpI, confirming that shear is the dominant mode of coal wall failure at deeper depths (Figure 12(e)). Increased Scir had a significant impact, as MSD increased by 126.6%. However, with increasing Scir, there was only a little change in LFSpI (Figure 12(f)). The onset of MWP was delayed as Scir was increased, resulting in higher FAP near the coal wall. The incremental change in Ei seems to lower MSD by 50%. LFSpI, on the other hand, remained greater than 80% for all Ei variations (Figure 12(g)). It implies that increasing Ei reduces the vertical displacement of the immediate roof, hence reducing the horizontal deformation of the coal wall. Only a 10% change in MSD was seen as Stir was increased. Furthermore, LFSpI remained relatively unchanged (Figure 12(h)). Scmr was simulated in four distinct variations (4.3 MPa, 7.7 MPa, 12.9 MPa, and 15.4 MPa). MSD increased by just 10% for the first three Scmr variations. However, a significant increase from 7 m to 12.8 m was observed when Scmr was increased from 12.9 MPa to 15.4 MPa. The value of LFSpI decreased linearly from 97.2%, 90%, 76.2% to 65.4% with increase in Scmr (Figure 12(i)). It advocated that while the extent of spalling of coal wall may increase, its volume may progressively decrease as Scmr increases. Changing Em appeared to have no effect on LFSpI, but it increased MSD by 39.1% (Figure 12(j)). Because the main roof’s failure mode was shear, there was only a little difference in MSD and LFSpI when Stm was increased from 0.5 MPa to 2 MPa (Figure 12(k)). Ti values were increased from 5 m to 10 m and then to 15 m. When Ti was increased from 5 m to 10 m, MSD increased marginally by 3.1%, while LFSpI decreased from 97.2% to 61%. When a Ti of 15 m was simulated, MSD increased sharply to 10.9 m, whereas LFSpI reduced to 39.4% (Figure 12(l)). Tm was gradually increased from 15 m to 20 m to 15 m. MSD seems to have increased from 6.4 m to 7.5 m to 8.2 m; however, LFSpI values stayed relatively static within 10% (Figure 12(m)). It is obvious from Figures 12(a)–12(m)that the majority of the independent variables indicate a nonlinear relationship with the target variables. Although the R2 for the majority of the independent variables appears to be greater than 60%, it does not imply that they are all critical parameters. Therefore, proper judgement is crucial to select only the critical independent factors to develop a criterion for assessing coal wall spalling.


## 3.3. New Coal Wall Spalling Assessment Criterion

A coal wall spalling evaluation criterion was developed using a statistical multiple regression approach. According to Figures12(a)–12(m), all independent factors shared a nonlinear relationship with the target variables. However, it was felt that the most critical independent variables that may explain coal wall spalling should be chosen after qualifying statistical tests.


### 3.3.1. Critical Independent Variable Selection

Two essential criteria were used to determine the most appropriate independent variables: stepwise backward regression and analysis of variance (ANOVA). The stepwise backward regression provides the change in adjustedR2 as each independent parameter is removed one at a time. The model with the greatest adjusted R2 is the statistically optimal multiple regression model with a proper number of independent variables. If removing an independent variable improves the adjusted R2, the variable is eliminated from the regression model, and vice versa. ANOVA, on the other hand, renders significant indicators such as the residual sum of squares (RSS), F statistics, and P value, which aid in statistically selecting the independent variable that is to be included in the regression model. The RSS is the proportion of variation in a data set that cannot be explained by a regression model. Instead, the variation is attributed to the residuals or the error term. As a result, if the inclusion of an independent variable increases RSS, the variable is not selected, and vice versa. The F statistics is observed in conjunction with the P value. The addition of any independent variable should not reduce the value of F statistics. Furthermore, the P value associated with each variable in a regression model should be less than 0.05. The final list of independent variable selected for the formulation of regression models to predict MSD and LFSpI is shown in Table 5.Table 5
Selection of independent variables for regression model.
Model no.Variables removedIndependent variablesAdjustedR2RSSF statisticsP valueMSD1Noneσv, Stc, Stmr, Stir, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.78101.949.58<0.052Stcσv, Stmr, Stir, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.7992.1810.90<0.053Stc, Stirσv, Stmr, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.8087.7112.36<0.054Stc, Stir, Tmσv, Stmr, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.8083.5213.51<0.055Stc, Stir, Tm, Emσv, Stmr, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.8079.8314.83<0.056Stc, Stir, Tm, Em, Stmrσv, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.8178.1316.42<0.05LFSpI1Noneσv, Stc, Stmr, Stir, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911168.9424.59<0.052Stcσv, Stmr, Stir, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911112.1928.02<0.053Stc, Tmσv, Stmr, Stir, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911081.2632.07<0.054Stmr, Stc, Tmσv, Stir, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911051.7236.18<0.055Stir, Stmr, Stc, Tmσv, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911030.8740.81<0.056Em, Stir, Stmr, Stc, Tmσv, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911029.8346.48<0.057Ec, Em, Stir, Stmr, Stc, Tmσv, Ti, Ei, Scir, Scc, Scmr, Hoe0.921025.1152.48<0.05


### 3.3.2. Multicollinearity Diagnostic

Due to the presence of multicollinearity among the independent variables, the estimation of the regression weights or coefficients becomes erroneous. This leads to the formation of models that are unable to correctly predict the state variable for a new set of data. As a result, the diagnosis of multicollinearity among independent variables is determined by calculating their variance inflation factor (VIF) and tolerance. Dormann et al. proposed a threshold value for VIF and tolerance [42]. Table 6 shows that no predictor variables exceed the threshold values of VIF larger than 10 and tolerance less than 1. As a result, it may be concluded that the variables lack multicollinearity and can be used to create regression models.Table 6
Values of the multicollinearity diagnostic indicators for the independent variables.
VariablesToleranceVIFMSDσv0.981.02Ti0.951.05Ei0.951.05Scir0.941.06Scc0.941.07Scmr0.941.07Hoe0.941.07Ec0.941.07LFSpIσv0.981.01Ti0.961.04Ei0.961.04Scir0.961.04Scc0.951.05Scmr0.951.04Hoe0.951.05


### 3.3.3. Multiple Regression

The multivariate regression statistical method has been widely used for the assessment of mining engineering problems [32, 40, 41, 43–47]. Therefore, the multiple linear regression approach was used to develop empirical models for MSD and LFSpI prediction. The numerical modelling findings, as presented in Table 4, were utilized to develop the regression models. As stated previously in Section 3.2, the majority of the relationships between the target and the independent variables were nonlinear. Therefore, the natural logarithm was taken for MSD, LFSpI, and all independent variables that exhibited a power function regarding the target variables. The remaining independent variables had exponential relationships and were thus selected as it is for the regression. The proposed regression models are listed below.
(4)lnMSD=9.41+1.83lnσv+0.72lnScir−0.87lnEi−6.98lnScc−1.24lnHoe+0.04Scmr+0.04Ti−0.82Ec,(5)lnLFSpI=8.76−0.05σv+0.04lnScir−0.03Scmr−0.03Ei−0.09Ti−0.42Scc−0.43Hoe.The sign conventions of the regression coefficients linked to each independent variable indicate their influence (either positive or negative) on the target variable. MSD and LFSpI can be calculated as follows:(6)MSD=explnMSD,(7)LFSpI=explnLFSpI.TheR2 and root mean squared error (RMSE) were calculated to assess the reliability and predictive capabilities of the proposed regression models. R2 was found greater than 0.9 (0.95 for MSD model and 0.93 for LFSpI model), indicating a high level of prediction ability. Furthermore, RMSE values (0.28 for MSD model and 0.12 for LFSpI model) near to zero enhanced the regression model’s high prediction performance.


## 3.3.1. Critical Independent Variable Selection

Two essential criteria were used to determine the most appropriate independent variables: stepwise backward regression and analysis of variance (ANOVA). The stepwise backward regression provides the change in adjustedR2 as each independent parameter is removed one at a time. The model with the greatest adjusted R2 is the statistically optimal multiple regression model with a proper number of independent variables. If removing an independent variable improves the adjusted R2, the variable is eliminated from the regression model, and vice versa. ANOVA, on the other hand, renders significant indicators such as the residual sum of squares (RSS), F statistics, and P value, which aid in statistically selecting the independent variable that is to be included in the regression model. The RSS is the proportion of variation in a data set that cannot be explained by a regression model. Instead, the variation is attributed to the residuals or the error term. As a result, if the inclusion of an independent variable increases RSS, the variable is not selected, and vice versa. The F statistics is observed in conjunction with the P value. The addition of any independent variable should not reduce the value of F statistics. Furthermore, the P value associated with each variable in a regression model should be less than 0.05. The final list of independent variable selected for the formulation of regression models to predict MSD and LFSpI is shown in Table 5.Table 5
Selection of independent variables for regression model.
Model no.Variables removedIndependent variablesAdjustedR2RSSF statisticsP valueMSD1Noneσv, Stc, Stmr, Stir, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.78101.949.58<0.052Stcσv, Stmr, Stir, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.7992.1810.90<0.053Stc, Stirσv, Stmr, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.8087.7112.36<0.054Stc, Stir, Tmσv, Stmr, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.8083.5213.51<0.055Stc, Stir, Tm, Emσv, Stmr, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.8079.8314.83<0.056Stc, Stir, Tm, Em, Stmrσv, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.8178.1316.42<0.05LFSpI1Noneσv, Stc, Stmr, Stir, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911168.9424.59<0.052Stcσv, Stmr, Stir, Tm, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911112.1928.02<0.053Stc, Tmσv, Stmr, Stir, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911081.2632.07<0.054Stmr, Stc, Tmσv, Stir, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911051.7236.18<0.055Stir, Stmr, Stc, Tmσv, Em, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911030.8740.81<0.056Em, Stir, Stmr, Stc, Tmσv, Ti, Ei, Scir, Scc, Scmr, Hoe, Ec0.911029.8346.48<0.057Ec, Em, Stir, Stmr, Stc, Tmσv, Ti, Ei, Scir, Scc, Scmr, Hoe0.921025.1152.48<0.05


## 3.3.2. Multicollinearity Diagnostic

Due to the presence of multicollinearity among the independent variables, the estimation of the regression weights or coefficients becomes erroneous. This leads to the formation of models that are unable to correctly predict the state variable for a new set of data. As a result, the diagnosis of multicollinearity among independent variables is determined by calculating their variance inflation factor (VIF) and tolerance. Dormann et al. proposed a threshold value for VIF and tolerance [42]. Table 6 shows that no predictor variables exceed the threshold values of VIF larger than 10 and tolerance less than 1. As a result, it may be concluded that the variables lack multicollinearity and can be used to create regression models.Table 6
Values of the multicollinearity diagnostic indicators for the independent variables.
VariablesToleranceVIFMSDσv0.981.02Ti0.951.05Ei0.951.05Scir0.941.06Scc0.941.07Scmr0.941.07Hoe0.941.07Ec0.941.07LFSpIσv0.981.01Ti0.961.04Ei0.961.04Scir0.961.04Scc0.951.05Scmr0.951.04Hoe0.951.05


## 3.3.3. Multiple Regression

The multivariate regression statistical method has been widely used for the assessment of mining engineering problems [32, 40, 41, 43–47]. Therefore, the multiple linear regression approach was used to develop empirical models for MSD and LFSpI prediction. The numerical modelling findings, as presented in Table 4, were utilized to develop the regression models. As stated previously in Section 3.2, the majority of the relationships between the target and the independent variables were nonlinear. Therefore, the natural logarithm was taken for MSD, LFSpI, and all independent variables that exhibited a power function regarding the target variables. The remaining independent variables had exponential relationships and were thus selected as it is for the regression. The proposed regression models are listed below.
(4)lnMSD=9.41+1.83lnσv+0.72lnScir−0.87lnEi−6.98lnScc−1.24lnHoe+0.04Scmr+0.04Ti−0.82Ec,(5)lnLFSpI=8.76−0.05σv+0.04lnScir−0.03Scmr−0.03Ei−0.09Ti−0.42Scc−0.43Hoe.The sign conventions of the regression coefficients linked to each independent variable indicate their influence (either positive or negative) on the target variable. MSD and LFSpI can be calculated as follows:(6)MSD=explnMSD,(7)LFSpI=explnLFSpI.TheR2 and root mean squared error (RMSE) were calculated to assess the reliability and predictive capabilities of the proposed regression models. R2 was found greater than 0.9 (0.95 for MSD model and 0.93 for LFSpI model), indicating a high level of prediction ability. Furthermore, RMSE values (0.28 for MSD model and 0.12 for LFSpI model) near to zero enhanced the regression model’s high prediction performance.


## 3.4. Coal Wall Spalling Classification System

It is important to emphasize that MSD predicts the extent of spalling. It defines the length of the portion ahead of the coal wall that is potentially susceptible to spalling. Furthermore, LFSpI determines the volume or region of the coal wall within the extent of MSD that is prone to spalling. A low MSD value indicates a generally stable coal wall, whereas a high MSD value indicates a disturbed and unstable coal wall with the potential for extensive spalling. However, it must be seen in tandem with LFSpI to reliably conclude that the area is highly unstable. High LFSpI values do not always imply increased spalling. However, a high MSD in combination with a high LFSpI indicates a highly distressed zone. Therefore, it is advised that both criteria be used in combination for a comprehensive assessment of coal wall spalling. Based on these criteria, a new classification method named as the “coal wall spalling classification system” (CWSC) is proposed. After a thorough examination of the numerical modelling findings, as well as previous field observed coal wall spalling examples accessible in the literature, a classification chart is presented, as shown in Figure13. In addition, Table 7 describes the features of each class in the CWSC system. It is important to note that the chart does not include all MSD values that can be achieved from Equations (5) and (6). This chart, however, may be extended for any MSD value greater than 10 m. The key is the LFSpI value, which dictates whether the coal face should be classified as class 2 or class 3. In other words, the chart region beyond 10 m for any value of LFSpI has the same characteristics as the region between MSD 8 m and 10 m. It is also worth mentioning that there is no such classification system in the literature. Since this is the first attempt, there was no possibility to compare it with any other classification system based on coal wall spalling.Figure 13
Chart of CWSC system.Table 7
Coal wall spalling classification based on CWSC system.
ClassSpalling characteristicsDescription1Favorable(i) Skin or chunk type of spalling(ii) Spalled coal easily conveyable by AFC(iii) Secondary handling (secondary blasting or manual maneuvering) of spalled coal not required(iv) Artificial reinforcement for coal wall not required(v) Roof falls at the unsupported zone may occur but is insignificant(vi) Low risk towards men and equipment(vii) Face retreat remain uninterrupted2Operationally manageable(i) Debris flow or blocky type of spalling from the upper or lower region of the coal wall(ii) Potential for AFC blockage(iii) Secondary handling of spalled coal may be required(iv) Artificial reinforcement for coal wall may be required(v) Roof falls at the unsupported zone of the face may be significant(vi) Moderate risk towards men and equipment(vii) Face retreat may be interrupted3Operationally difficult to manage (critical)(i) Integral or total failure type of spalling may be expected(ii) Blockage of AFC expected(iii) Secondary handling of spalled coal required(iv) Artificial reinforcement for coal wall required(v) Roof falls at the unsupported zone of the face significant(vi) High risk towards men and equipment(vii) Face retreat will be interrupted


## 3.5. Application of the CWSC System

The applicability of the proposed classification system was carried out by taking into consideration the coal wall spalling cases of five longwall faces including ALP. Relevant data from the five panels, namely, Moonidih (A4), Wolonghu (8102), GDK 10A (3A), GDK 10A (11), and ALP (P1), were collected. The letters in the bracket signify the panel names. Except Wolonghu (8102) which is in China, the rest of all panels belong to India. The spalling of the coal wall during the MWP for all five panels was predicted using the regression equations described above and was classified according to the proposed CWSC chart.MSD and LFSpI were estimated using the input data from the panels mentioned above, and the results are reported in Table8. To identify the class, the MSD and LFSpI values for the panels were plotted in the CWSC system chart, as shown in Figure 14. Moonidih (A4)’s coal wall spalling potential is rated as class 1 as can be observed. The remaining panels, Wolonghu (8102), GDK 10A (3A), GDK 10A (11), and ALP, were classified as class 2. For the above-mentioned panels, the extent and magnitude of coal wall spalling matched the field observation quite well.Table 8
Applications of the CWSC system.
ParametersMoonidih (A4)GDK 10A (3A)GDK 10A (11)Wolonghu (8102)ALP (P1)σv (MPa)1096.5139.7Ti (m)4.65.22.843.5Ei (GPa)8.163.25.91.6Scir (MPa)7.54.97.15.94.5Scc (MPa)4.83.94.234.5Scmr (MPa)711.815.115.410.5Hoe (m)2.63.4343.5Ec (GPa)3.222.25.62MSD (m)0.42.82.21.73LFSpI (%)76.771.610066.477.2CWSC class1-favourable2-operationally manageable2-operationally manageable2-operationally manageable2-operationally manageableFigure 14
Classification of longwall panels based on CWSC system.The CWSC method does have certain limitations and assumptions to consider. To begin with, the rock mass strength and stiffness of the rock beds were calculated using the scaling laws related with RQD provided by Zhang and Einstein [28, 29]. As a result, the intact rock properties should be scaled using the aforementioned scaling rule when employing the CWSC system. Secondly, using the CWSC system outside of its initial database limit may result in incorrect results. As a result, caution should be exercised while identifying longwall panels whose geo-mining conditions does not come within the limits of the database restrictions.


## 4. Conclusion

(1)
The FLAC3D code was used to determine the spalling of the coal wall in panel 1 of the ALP mine’s longwall face. For the MWP of panel 1, the MSD and LFSpI coal wall spalling estimation parameters were determined to be 3 m and 90.5%, respectively(2)
Thirteen important factors that influence coal wall spalling were considered for the parametric study. MSD was determined to be particularly sensitive to variations in theScc and Ec, Scir, and σv. Similarly, LFSpI was found to be highly sensitive to the changes in Scc, Eir, Hoe, and Scmr(3)
Stepwise regression and ANOVA were employed as statistical tools to choose the most essential variables for inclusion in developing regression models for coal wall spalling prediction. As a result,σv, Scir, Ei, Scc, Hoe, Scmr, Ti, and Ec were selected for MSD regression model. For the LFSpI regression model, σv, Scir, Scmr, Ei, Ti, Scc, and Hoe were selected(4)
The empirical models for MSD and LFSpI were shown to have high predictive ability, withR2 and RMSE values of 0.95, 0.93 and 0.28, 0.12, respectively(5)
Using MSD and LFSpI, a new coal wall spalling assessment (CWSC) chart was suggested, which could easily categorise any longwall panel into three classes. The CWSC technique can be a valuable tool for anticipating coal wall spalling during a panel’s MWP. As a result, it may be used as a coal wall spalling evaluation approach at the design stage of a panel to understand the spalling characteristics, face instability condition, support requirements, and secondary work involvement. The CWSC system’s applicability was successfully demonstrated by evaluating five different longwall panels with varying geo-mining conditions



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*Source: 5622228-2022-07-09.xml*