A GIS-based prediction method to evaluate subsidence-induced damage from coal mining beneath a reservoir, Kyushu, Japan T. Esaki, I. Djamaluddin & Y. Mitani Institute of Environmental Systems, Graduate School of Engineering, Kyushu University, Motooka 744, Nishi-Ku, Fukuoka, 819-0395 Japan (e-mail:
[email protected])
he occurrence of subsidence caused by mining may be a complex process that causes damage to the environment. In the last century there was significant development in prediction methods for calculating surface subsidence. However, because mining may take place by multi-seam extraction, the use of current prediction methods to obtain the distribution of subsidence is a difficult and time-consuming task. Furthermore, it is impracticable to evaluate damage accurately by this method. In this paper, a new prediction method has been developed to calculate 3D subsidence by combining a stochastic model of ground movements and a geographical information system (GIS). All the subsidence calculations are implemented by a computational program, where the components of the GIS are used to fulfil the spatial–temporal analysis function. This subsidence prediction technique has been applied to calculate ground movements resulting from 21 years of coal mining under a reservoir in Japan. Details of movement were sequentially predicted and simulated in terms of years. Furthermore, subsidence-induced damage owing to progressive horizontal strain was assessed. These values conformed to the acceptable strains in reservoir dams, thus ensuring safety against tensile failure of the concrete and consequent flooding.
T
Subsidence is the 3D progressive movement, involving vertical–horizontal displacements developed at the ground surface, in response to the extraction of coal that has been partially or completely removed. Differential vertical–horizontal ground displacements can cause strain on surface structures such as buildings, roads, railways, and dams. The major causes of damage from surface subsidence have been identified as five components: vertical displacement, slope, curvature, horizontal displacement, and strain (Braeuner 1973). Each of these has different effects on the types of structure subject to subsidence. For example, horizontal strain is a major component in cracking, tensile failure of a concrete dam structure, and leakage of water from a reservoir when it is undermined. However, this is not a simple problem of predicting the response of a structure to a particular numerical value of subsidence, as it is common for a structure to be subjected to compression and extension Quarterly Journal of Engineering Geology and Hydrogeology, 41, 381–392
strain in different directions. A structure also can be subjected to alternating stages of strain owing to the sequence of extraction, and a spatial–temporal effect must therefore be considered. The interaction of the 3D progressive ground movements and structures is a significant factor in controlling, mitigating or preventing damage to the surface environment from subsidence. Various methods have been developed for subsidence prediction (Whittaker & Reddish 1989). Many of these have been developed for the prediction of subsidence based on empirical methods, such as those described in the Subsidence Engineering Handbook (Anon 1975), and by Peng & Chyan (1981) and Hood et al. (1983). Profile functions have been effectively applied to predict subsidence above a longwall panel (Daemen & Hood 1981; Asadi et al. 2004). Semi-empirical methods of subsidence prediction based on influence functions also have been widely used to calculate subsidence at any surface point (Lin et al. 1992; Sheorey et al. 2000). In addition, numerical analyses have been used in subsidence modelling and in calculating the movement of rock strata (Yao et al. 1989; Alejano et al. 1999; Zhao et al. 2004). The use of current prediction methods is effective in predicting vertical and horizontal components of subsidence, and it is possible to derive all the other factors of movement, such as slope, curvature, and horizontal strain. However, in complex, multi-seam mining area with complex mine geometry, it is difficult to use the existing prediction methods to obtain the distribution of subsidence. Therefore it is not possible to assess accurately environmental damage. Computer-based analytical methods that realistically simulate spatially distributed, time-dependent subsidence processes are desirable for the reliable design of mining layout to minimize structural damage. The recent development of the geographical information system (GIS) comprises a technology designed to support integrative modelling, to conduct interactive spatial analysis and for understanding various processes. It is also possible to obtain complex land-surface properties to drive the models on the basis of innovative thematic mapping (Longley et al. 2001). In particular, comprehensive damage evaluation and simulation that integrate more than one process may be involved. In the present paper, an innovative prediction method has been developed for calculating the distribution of subsidence at arbitrary surface points. This has been 1470-9236/08 $15.00 2008 Geological Society of London
382
T. ESAKI ET AL.
validated on a wide range of longwall panel layouts by using a stochastic model of 3D ground movement and a GIS. By using a 3D, GIS-polygon, geometrical model, a more rigorous prediction system has been established to evaluate prediction of subsidence caused by multi-seam extraction sequences. Efficient algorithms for GIS-based prediction analysis have been developed to simulate ground movement resulting from the sequence of underground extractions in time and space. Furthermore, a case study is presented for the prediction of subsidenceinduced damage from 21 years of continuous coal mining beneath a reservoir in Japan, to demonstrate the effectiveness of this proposed method. In particular, mining beneath a reservoir increases the risks that result from progressive horizontal tensile strain, which might cause cracks in concrete dam structures and leakage of water. Therefore the objective of this prediction method is to evaluate the safety of a dam and reservoir, by predicting the expected subsidence as a result of underground mining.
A strategy to integrate a GIS and the prediction model A GIS generally provides an excellent platform for dealing with spatial data and graphical output. However, the general purpose of a GIS, which provides only a basic tool, cannot be employed to model specific problems. This raises the question of how the interface between a GIS and subsidence-prediction modelling might be achieved. Coupling a model with a GIS is an information-integration problem, somewhat like coupling one GIS to another for data-transfer purposes (Marble 2000). For instance, the design of analytical subsidence-calculation points that represent ground movements for application to a simulation model can be automated and more directly linked to the GIS. At the same time, undertaking a modelling study in a GIS provides a basis for simplification of the interaction between the various users involved through the establishment of a common data structure that can be visualized using the same GIS-based system. Combining the variety of data, models, and tools into a robust GIS-based system is a research topic that is approached ranging from so-called ‘loose’ integration to ‘tight’ integration (Fortheringham & Wegener 2000). In this study a close coupling strategy based on Component Object Model (COM) technology–GIS was used to overcome the problems of data model integration. A close coupling is one with integrated data management services in which automated exchange of data is possible through a standardized interface using COM. COM is a standard that enhances software interoperability by allowing different GIS components, possibly written in different programming languages, to communicate directly (Matthew & Goodchild 2002).
The prediction model is achieved within the GIS system so that an effective and efficient calculation method can be obtained. The subsidence-prediction code, which uses COM technology running in Visual Basic, assigns a mining-geometry model to the boundary GIS 3D-polygon nodes and writes the calculation results to GIS grid points.
Synthesis prediction method using a GIS It is complicated to model ground movements that result from mining, as the overlying rock mass and soil profiles behave in a complex manner. Geological features and conditions such as faults, groundwater, tectonic strains, etc. can greatly influence subsidence. Furthermore, surface topography, although not generally affecting magnitudes of subsidence, will change the position and shape of the subsidence trough (Whittaker & Reddish 1989). Several kinds of idealized media have been used in mining-subsidence prediction (Litwiniszyn 1957; Berry 1964). In these models the stochastic theory of ground movement has been widely employed for 3D subsidence prediction because the overlying strata behave in a complex manner and movement of the rock mass is governed by a number of known and unknown factors (Liu 1995; Esaki et al. 2004). In addition, prediction of 3D movements induced by a sequence of complex mining processes is more complicated than prediction of final subsidence, and thus the stochastic theory of ground movements can be applied as a universal method to obtain the five components of 3D subsidence.
Basic calculation using the stochastic theory A stochastic model is a tool for estimating probability distributions of potential outcomes by allowing for random variation in one or more inputs over time. To calculate subsidence of a surface point P using the stochastic model, an extraction panel can be divided into infinitesimal extraction areas. According to the principle of superposition, the consequence for the extraction panel would be equal to the sum of the effects caused by these infinitesimal extraction areas. Based on the stochastic theory, the occurrence of a rock-mass movement over the extraction element may be a random event that takes place with a certain probability. An extraction with an infinitesimal unit width, length and thickness ()w)l)m) is called the extraction element. The vertical displacement of any point in the subsidence trough is called the basic influence function of vertical displacement (Se). The occurrence of an event of surface movements in an infinitesimal area, dA = dxdy, at horizon z, with point P(x, y, z) at its centre, is equivalent to the simultaneous occurrence of two events composed of a movement in the horizontal strip dx and the horizontal strip dy through P (Fig. 1). Fundamentally, the
SUBSIDENCE-INDUCED DAMAGE FROM COAL MINING
383
Fig. 1. Illustration showing the calculation of probability of subsidence at a point on the surface as a result of the extraction element within a given extraction panel.
probability can be written separately for these two events as C(x2)dx and C(y2)dy, respectively, where C is the subsidence trough function. The probability of a simultaneous occurrence of these two events is P(dA) = C(x2)dx C(y2)dy = C(x2)C(y2)dA.
(1)
The stochastic influence coefficient governs the geometric rule for distribution of subsidence owing to the extraction element. Finally, the calculation point P(x, y), located on the ground surface, allows the subsidence of surface point P1 to be derived as follows:
Cy =
Cx =
1 2
S(x,y) = Smax Cy Cx
(2)
Smax = m a z cos
(3)
F S# D
erfc
S#
yl
F S# D
erfc
S#
xw
1 2
erfc
erfc
y r
x
r1
r
r2
DG
Without a GIS, the construction of a 3D stochastic calculation model would be a difficult and timeconsuming task for a large mining area with multi-panel extractions. Within the functions of the GIS, the five components of subsidence-calculation-related data can be represented as GIS vector layers. For each vector layer a 3D polygon and grid-point-based layer can be constructed using the GIS and the grid-point calculation can be established with precision. A more detailed description of the method is given below.
Prediction model based on GIS components Calculation system using a 3D GIS-polygon By integrating the effect of all extraction elements in an extraction panel as shown in Figure 2, all the subsidence components (dSp) related to a corresponding surface point
(4)
DG (5)
where Smax is the maximum subsidence; Cx is the subsidence trough in the x direction; Cy is the subsidence trough in the y direction; erfc is the error function; m is the coal-seam thickness; a is the subsidence factor; z is the time-delay factor; is the angle of dip of the seam; l is the panel length along strike; w is the panel width along dip; r = H/tan , r1 = H1/tan r, r2 = H2/tan d is the radius of the circle of influence (where r is the angle of draw to the rise, d is the angle of draw to the dip, H is the depth along strike, H1 is the depth along the boundary of the rise side, and H2 is the depth along the boundary of the dip side).
Fig. 2. Probability distribution of subsidence at a grid point as a result of a given 3D polygon extraction.
384
T. ESAKI ET AL.
Fig. 3. An example of 3D polygon panels with the related spatial data geometry and features table of attributes dataset.
(Sp), as illustrated in the figure by a 3D view of grid points (calculation points) and a 3D polygon (extraction panel), may be calculated. For the extraction panel, with reference to the vector-based polygon (Fig. 3), the spatial data of geometry, panel sequence, subsidence parameters, and extraction depth can be obtained from the 3D polygon. In this polygon panel dataset, a feature table of the 3D polygon is used to relate the subsidence parameters. In the 3D polygon attribute table, ‘PolygonZM’ is the shape of 3D polygon attributes, and ‘ID’ is the extraction sequence. ‘AngDip’ is the dip inclination of the seam panel, ‘UpwardAng’ is the upward angle of the panel from the east, and ‘Thick’ is the extraction thickness. All of these are related to the geometrical coordinates. The subsidence parameters are represented by ‘SubFac’ (subsidence factor), ‘HoMoFac’ (horizontal movement factor), ‘TiFac’ (timedelayed subsidence factor), ‘UpTan’ (tangent of draw angle in rise side), and ‘DownTan’ (tangent of draw angle in dip side). Each 3D polygon has values for depth vertices that give spatial geometry in x, y, and z. Therefore, a 3D polygon has spatial geometry that can be used to identify the main strike direction and dip inclination of each panel. A spatial model and a ‘TIN’ (triangulated irregular network) model in the GIS are used to identify a 3D polygon panel for strike direction and dip inclination. Polygon coordinate transformation for influence circles Three-dimensional polygon panels as geometrical extraction areas are used to calculate subsidence at the surface points at which each panel of the polygon is referenced to the global coordinate system in the GIS. The panel inclination of a 3D polygon has an upward angle direction (φ) in which the upward angle is referenced from the east. To obtain the distribution of surface subsidence from inclined panels, the global coordinate panel (X, Y) is transformed to local coordinates (X$, Y$) in which the upward angle direction of
Fig. 4. (a) Coordinate transformation of a 3D polygon panel. (b) Obtaining the radius of subsidence-influence circles on the transformed panel.
each panel is set to be the same as the east direction. The radius of the subsidence-influence circle of each panel of the polygon is assumed to be the downward and upward part of the panel, and the main direction of the radius of the influence circle is set to be the same as the upward angle of the panel. Figure 4a shows
SUBSIDENCE-INDUCED DAMAGE FROM COAL MINING
385
an example of a polygon panel before and after coordinate transformation. The coordinate transform polygon panel vertex (x$, y$) that gives the depth can be determined by simple equations. Let global coordinate X, Y and its transform coordinate X$, Y$ be derived as follows:
F S
X$ = X cos φ
180
DG
F S
+ Y sin φ
180
DG (6)
F S
Y$ = Y cos φ
180
DG
F S
X sin φ
180
DG (7)
where (X, Y) are global coordinates, and (X$, Y$) are local coordinates. The angle direction of the subsidence-influence circle is set basically as the main direction of the upward panel angle. The main direction of the upward angle (φ) and the main dip inclination of the panel () are determined by the value of the depth of the vertices of a panel in the polygon area. The minimum and maximum values of the x-coordinate of the panel vertices can be obtained, which refer to the coordinate transform. To calculate the radius of the subsidence-influence circle of each panel, the dip inclination of the panel should be determined first. The radii of the subsidence-influence panel circles can be determined by referencing known x-coordinate values, after transformation, that give the depths. Figure 4b is an example of a panel after coordinate transformation, and a section plan (X$, Z) that shows the values of the inclined panel’s depth vertices, which influence the radius distances at the upward (r1) and downward (r2) parts of the panel. The solution of the equations for the radius distances can be derived as follows: Rupward = [Hi + (Xmin Xi) tan]/tanr
(8)
Rdownward = [Hi + (Xmax Xi) tan]/tand
(9)
where Hi is the the depth of the panel vertex, Xmin and Xmax are the minimum and maximum values of the x-coordinate of panel vertices for local coordinates, Xi is the x-coordinate of the panel vertex, is the dip inclination of the panel, and r and d are the angles of draw for the upward and downward parts of the panel, respectively.
Computational implementation of the prediction model Algorithm of the 3D calculation system Figure 5 shows the flow chart for the algorithm of prediction analysis using GIS functions to calculate 3D surface movements. In the computational subsidence process, first, the panel
Fig. 5. Stochastic prediction algorithm for subsidence calculation utilizing the GIS environment.
on the global coordinate system is transformed into the local coordinate system. The transformation coordinate panel is used to obtain the angle direction of the zone radius of the main subsidence influences. Then, the prediction of subsidence distribution is calculated using the stochastic-prediction procedures. Finally, the calculated subsidence results are obtained according to the working panel number. All the entered data (such as extraction panels, calculation points, and subsidence parameters) for subsidence prediction are available using the functions of the GIS. The input parameters for subsidence calculations are divided into two categories: (1) the generation of surface grid points to provide calculation points, including the distance of each grid point in the x and y directions, at which the number of points along the grid lines, the number of grid lines, point intervals along the grid lines, and the grid-line directions need to be established; (2) entering the subsidence-calculation data, including the extraction sequence number (panel ID), dip inclination, upward angle of seam, extraction thickness, subsidence factor, horizontal-movement factor, time factor, and angle of draw. By entering these data into the prediction system, the 3D movements are calculated with respect to the extraction panels. Computational processes using spatial analysis The subsidence calculations can be performed within or outside the GIS. If the calculations are performed outside the GIS, the GIS system is used only as a spatial-related subsidence database for storing, displaying, and updating the entered data. The main advantage of this approach is that existing external subsidence-prediction
386
T. ESAKI ET AL.
GIS workflow-based subsidence analysis A three-step process is used to implement a subsidenceprediction model in the GIS environment. Step 1 involves establishing spatial extents of the study area, deciding on an appropriate working projection, and assembling variously used spatial data from the study area so that the spatial component can be overlapped correctly. This step combines the appropriate subsidence data together into a GIS spatial database such as a mining-panel map, a seam layer, a surface-elevation layer, and spatial information from overburden strata. Step 2 involves constructing the spatial geometry of the mining panel for subsidence-calculation parameters. Usually the mining-panel map is digitized manually, transformed from an image into GIS vector data. Because it is necessary to enter the elevation value of the 3D polygon panel into the subsidence computational model, each panel is required for the depth of panel vertices (coordinate in the z direction). Step 3 combines the integration of data, provided the value of subsidence prediction is arrived at correctly and is entered to correspond to the 3D polygon. Fig. 6. Algorithm connecting stochastic prediction analysis with the GIS spatial analysis function.
A case study
models can be used without losing time in programming the model algorithms into the GIS. A disadvantage of doing model calculations outside the GIS is the complication caused by the conversion of complex geometrical data to and from external models. Prediction models calculate the movements of a surface point for an extraction panel in three dimensions. Because a mine may consist of multiple panels and complex geometry, the use of the 3D prediction model to obtain the spatial distribution of subsidence is very time-consuming without the GIS, as each mining panel has to be calculated separately. To overcome the problem of complex geometrical data conversion, the model of subsidence calculations can be created within the GIS. Figure 6 illustrates that all modules are related to the GIS spatialanalysis function, which is implemented by a GIS component. In this process the function of the data module is used to obtain all the subsidence-related spatial geometry, surface-point calculation data, and the subsidence parameters. The stochastic prediction module is used to calculate surface subsidence as well as vertical displacement, slope, curvature, horizontal displacement, and horizontal strain. Finally, the surface subsidence grid-point calculation and its subsidence kriging interpolation can be obtained by a function of the GIS spatial analysis. Because a GIS component is implemented in the calculation model, the 3D subsidenceprediction problem can be computed effectively.
A water reservoir, in part of the study area, is located in southwestern Kyushu, Japan (Fig. 7a). The study covers an area of 30 km2, extending 5 km from east to west and 6 km from south to north. There are four concrete gravity dams along the reservoir with a maximum water capacity of 2.6 106 m3. These are Seita Dam, Nakatani Dam, Kamogatani Dam, and Omokaze Dam, with a height and length of 23.5 and 217.6 m, 18.0 and 49.4 m, 21.0 and 186.2 m, and 17.0 and 76.3 m, respectively. Longwall coal mining began in 1944 and ended in 1965. The direction of the coal seam along strike is N20–30(SW, and the dip 15–20(NE, determined from a combination of geological observations during mining and borehole data. There are four productive coal seams and their extraction depth varied from 200 to 1200 m below the ground surface. The thickness of the upper, second, third, and lower seams is 1.20, 0.83, 1.43, and 1.80 m, respectively. The area is underlain mainly by Tertiary sedimentary rocks, comprising sandstones, siltstones, and shales. Figure 7b shows the surface geological map in the vicinity of the reservoir. The subsidence and horizontal strain at each dam were measured during mining by the supervision committee of the concerned authorities to preserve the reservoir. The horizontal ground movement around the dam was obtained using triangular and distance measurements by the Invar wire technique along the survey observation lines. Furthermore, the horizontal strain at each dam was measured with steel tape along the axis of the dam.
SUBSIDENCE-INDUCED DAMAGE FROM COAL MINING
387
Fig. 7. (a) Study area location and air photograph of the reservoir taken in 1967. (b) Surface geological map in the vicinity of the reservoir.
The progressive horizontal strain data were obtained by recording the length of the steel tape over time. The vertical displacement around the dam was also measured by a precise levelling technique with a precision of 0.1 mm. The layout involves positioning 50 subsidence stations along the observation lines.
Construction of 3D coal-mining geometry There are three main processes in a GIS for constructing 3D geometrical models: image-scanned projection, raster to vector conversion, and the extraction value of the raster seam-depth layer to the vector polygon layer.
388
T. ESAKI ET AL.
Fig. 8. A 3D view of constructed polygon coal seam extractions and surface features layers.
In image-scanned projection, digital maps using the coordinate-projection system and scanned images must be geometrically rectified to obtain the same projection system as the actual coordinate position. In raster to vector conversion, an on-screen digitized method was used to obtain the coal extraction polygon from a scanned-image mining map. The extraction value of raster seam depth to coal polygon can be found using GIS tools. Point depth parameters are used to predict each depth point of polygon panel vertices. The GIS interpolation method was used to predict the unknown value (z) of the 3D polygon by reference to the depth values of the measurement points. A 3D view of the constructed 3D polygon layers of the underground coal mining, overlain by related surface features, is shown in Figure 8.
Identification of the inclination of the mining panels In this study the measured reference depth points of coal seams are employed to determine strike direction and dip of the mining panels, by using GIS spatial analysis. The flow chart of GIS data analysis for identifying the inclination of the mining panels is given in Figure 9a. In this process, 3D polygons of mining panels are transferred to depth points in the output point feature class. This point feature class is created on the basis of the point vertices of the 3D polygon. Using the created point feature class, a linear-trend interpolation method is used to generate a surface raster layer. In addition, a global polynomial interpolation is created that fits a smooth surface defined by a mathematical function to the entered sample points, such as a measurement depth point. Then, a surface analysis is used to calculate the rate of maximum change in z-value from each cell in the surface raster layer, deriving aspect and slope raster layers. The strike direction and dip of each polygon panel can be obtained by extracting main slope and
Fig. 9. (a) Flow chart of GIS data analysis to identify characteristics (dip and strike) of a mining panel. (b) Dip inclination of extraction panels identified using the GIS.
aspect raster values into the attribute 3D polygon. Figure 9b shows the panel-inclination map produced by using the GIS.
Prediction of subsidence as a result of mining sequence Sequential subsidence calculations, simulating the extraction process between 1944 and 1967, were carried out by employing parameters of subsidence factor (a) = 0.85, horizontal movement factor (b) = 0.21, and tangent of draw angle () = 1.428, obtained from
SUBSIDENCE-INDUCED DAMAGE FROM COAL MINING
389
Fig. 10. Simulation of subsidence distribution for mining up to (a) 1950, (b) 1961, and (c) 1967.
previous results. The effect of the time factor, which expresses time-delayed subsidence, shows the final value, completed in 3 years (first year (z1) = 0.83, second year (z2) = 0.90, third year (z3) = 1). After coal mining was completed the maximum subsidence recorded was 3.27 m up to 1967. Figure 10a–c shows the progressive simulation of subsidence distribution from past mining
in stages including 1950, 1961 and 1967. For these spatial simulations, all calculation points were interpolated by using the kriging interpolation method. The calculated maximum vertical displacement up to 1944 is 1364 mm, and after 6 years of mining (up to 1950) the calculated maximum vertical displacement is 1994 mm. Up to 1955, 1958, and 1961 the increment of subsidence
390
T. ESAKI ET AL.
Fig. 13. Simulation of progressive horizontal strain along the Omokaze Dam resulting from mining sequences in terms of years.
Fig. 11. Comparison of measured and calculated subsidence values at two point locations around the reservoir dams.
magnitude is 2505, 2576, and 3012 mm, respectively. At the same time, the zero subsidence contours are indicated by the dotted line. This line encloses the subsidence area and identifies the beginning of subsidence and the limit of ground movements that may cause damage to surface structures. According to the simulation results, the significant influence of progressive surface subsidence at the reservoir area grew approximately from 1956 to 1967. During the 21 year mining period, eight monitoring surveys were undertaken to obtain vertical-displacement data, each year, around the reservoir. The comparison between measured and calculated values at different point locations is given in Figure 11. The GIS-based prediction method indicates a value of subsidence that differs from the measured subsidence. This may be attributed to the fact that, in this model, the effects of geological conditions, such as the existence of two faults intercepting the surface near the dams, as well as the surface topography in the vicinity of the reservoir have not been taken into consideration.
Damage evaluation to water-reservoir dams
Fig. 12. (a) A 3D visualization of the reservoir area showing the calculation points along the Omokaze Dam. (b) A photograph of the Omokaze Dam taken in 1967.
Generally speaking, the type of the engineered structure, such as construction materials, shape, and design, significantly influences the intensity of damage. As a result, only general guidelines can be applied for the purpose of predicting the severity of damage and assigning safe subsidence strain values to help protect the dams. The
SUBSIDENCE-INDUCED DAMAGE FROM COAL MINING
391
Fig. 14. Comparison of measured and calculated horizontal strain at the reservoir dams.
aim of this assessment is to evaluate the reservoir, resulting from extensional strain, in which the limiting tensile strain of the concrete reservoir dam is 0.25 mm m1. The measured strain values from 1958 to 1967 along the survey lines on the concrete dams were obtained by the field monitoring surveys. The horizontal strain can be obtained from the calculated limited points of final subsidence and from an arbitrary point along the axis of the dams throughout the duration of mining. The horizontal strain distributions were predicted in the longitudinal direction along the dams by using 1 m grid scale calculation points. Figure 12a shows the 3D vizualization of the concrete dam, overlain with a digital elevation model, and Figure 12b shows a photograph of the Omokaze gravity dam taken in 1967. In this case, the Omokaze Dam is given as an example of progressive strain analysis and simulation. The simulation of progressive horizontal strain along this dam, resulting from mining in terms of years, is presented in Figure 13. According to this simulation result, most of the influence of progressive strain was around point A in comparison with point A#. Furthermore, irregular horizontal strains were produced at different sections of the dam. The GIS-based prediction method indicates a value of maximum strain that is small, indicating negligible strain over the structures as a result of the subsidence event. This is also supported by strain measurements at the four dams as shown in Figure 14. For example, the calculated maximum average horizontal tensile strain at
Omokaze Dam and Kamogatani Dam was about 0.20 and 0.05 mm m1, respectively, whereas the calculated maximum average horizontal tensile strain at Nakatani Dam and Seita Dam was about 0.25 and 0.05 mm m1, respectively. The larger difference between measured and calculated strain at the Omokaze Dam and Nakatani Dam may be attributed either to the accuracy of the measuring devices in measuring such small strains or to the large uncertainty associated with the various parameters set in this model. Figure 14 also suggests that it may not be reliable to evaluate the subsidence damage at a particular surface structure based only on final subsidence calculations; subsidence progression in the context of space and time should also be considered.
Discussion and conclusion This paper has demonstrated a GIS-based method to predict subsidence caused by underground coal mining and to monitor damage at the ground surface. The strategy of close coupling between a prediction model and a GIS using Component Object Model (COM) technology has been proposed in this study. Using a GIS-based prediction method, it is possible to calculate spatial and temporal ground-surface movements induced by multi-seam longwall mining. Differentialsubsidence characteristics, such as progressive vertical displacement, slope, curvature, horizontal displacement, and horizontal strain, can be computed using the GIS.
392
T. ESAKI ET AL.
Subsidence in the study area of Japan was successfully simulated. The calculated result has been used to evaluate subsidence-induced damage resulting from mining beneath a reservoir. The comparison of subsidence calculation and measurement value shows good agreement. Moreover, the calculated maximum tensile strains conformed to the allowable strains in the dam, ensuring the integrity of the reservoir against leakage. For future development, this prediction technique needs to be extended to areas of more complex geology, such as the effect of steeply dipping coal seams (more than 20(), and the influences of faults and surface topography.
References A 1975. Subsidence Engineers Handbook. National Coal Board, London. A, L.R., R-O, P. & T, J. 1999. FDM predictive methodology for subsidence due to flat and inclined seam mining. International Journal of Rock Mechanics and Mining Sciences, 36, 475–491. A, A., S, K. & G, K. 2004. Profiling function for surface subsidence prediction in mining inclined coal seams. Journal of the Mining Science, 40, 142–146. B, D.S. 1964. The ground considered as a transversely isotropic material. International Journal of Rock Mechanics and Mining Sciences, 1, 159–167. B, G. 1973. Subsidence due to underground mining. Ground movements and mining damage. U.S. Dept. of the Interior, Bureau of Mines, Washington, 8572. D, J.J.K. & H, M. 1981. Subsidence profile functions derived from mechanistic rock mass models. In: P, S.S. & H, M. (eds) Proceedings of the Workshop on Surface Subsidence due to Underground Mining, Morgantown, WV. West Virgina University, Morgantown, 124–140. E, T., D, I. & M, Y. 2004. Synthesis subsidence prediction method due to underground mining integrated with GIS. In: O, Y. & A, K. (eds) Proceedings of the ISRM International Symposium, 3rd ARMS, Kyoto, 147–152. F, S.A. & W, M. 2000. Spatial Models and GIS, New Potential and New Models. Taylor & Francis, London.
H, M., E, R.T. & R, L.R. 1983. Empirical methods of subsidence prediction, a case study from Illinois. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 20, 153–170. L, S., W, B.N. & R, D.J. 1992. Application of asymmetrical influence functions for subsidence prediction of gently inclined seam extractions. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 29, 479–490. L, J. 1957. The theories and model research of movements of ground masses. In: Proceedings of the European Congress on Ground Movement. University of Leeds, Leeds, 202–209. L, B. 1995. In: W, Z. & R, Y. (eds) Rock Mechanics in China. Science Press, Beijing, 52–70. L, P.A., G, M.F., M, D.J. & R, D.W. 2001. Geographic Information Systems and Science. Wiley, New York. M, D. 2000. Some thoughts on the integration of spatial analysis and Geographic Information Systems. Journal of the Geographical Systems, 2, 31–35. M, J.U. & G, M.F. 2002. Integrating spatial data analysis and GIS: a new implementation using the Component Object Model (COM). International Journal of Geographical Information Science, 16, 41–53. P, S.S. & C, C.T. 1981. Surface subsidences, surface structural damages and subsidence predictions and modeling in the Northern Appalachian Coalfield. In: P, S.S. & H, M. (eds) Proceedings of the Workshop on Surface Subsidence due to Underground Mining, Morgantown, WV. West Virgina University, Morgantown, 73–84. S, P.R., L, J.P., S, K. & S, S.K. 2000. Ground subsidence observations and a modified influence function method for complete subsidence prediction. International Journal of Rock Mechanics and Mining Sciences, 37, 801–818. W, B.N. & R, D.J. 1989. Subsidence Occurrence, Prediction and Control. Elsevier, Amsterdam. Y, X.L., R, D.J. & W, B.N. 1989. Nonlinear finite element analysis of surface subsidence arising from inclined seam extraction. International Journal of Rock Mechanics and Mining Sciences, 30, 431–441. Z, D.S., X, T. & T, C.A. 2004. Numerical simulation of bed separation of overburden strata induced by mining excavation. In: O, Y. & A, K. (eds) Proceedings of the ISRM International Symposium, 3rd ARMS, Kyoto. Millpress, Rotterdam, 475–478.
Received 5 September 2007; accepted 16 May 2008.
