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**Abstract**

A quick, simple and quantitative method for the estimation of surface subsidence susceptibility in mined areas with a lack of detailed geological and geometrical information in underground is presented in this paper. In the method only gangway depth from the surface and the attitude (dip and dip direction) of main geological features are used as input data based on the degree of availability and reliability. Underground gangways are represented as a series of points instead of closed polygons for easy calculation. The core assumption in this method is that the susceptibility to subsidence within a unit area increases both as the depth of the gangway from the surface decreases and as the number of gangways below the unit area increases. In spite of the simplicity of the proposed method, it gave satisfactory results when applied to a virtual excavation model and a closed coal mine where subsidence occurred actually.

**1. Introduction**

Several methods for predicting ground subsidence due to mining excavation such as the profile method and the influence function method have been proposed (Whittaker and Reddish, 1989; Sheory, 2000). The National Coal Board (1975) has presented a basic technique to determine the surface area affected by coal mining based on the height and width of mined areas and the angle of inclination of coal seams. All these methods were developed and verified for conditions involving horizontal coal seams and long wall mining, which are the common mining conditions in Europe. However, coal-associated geological structures in Korea are very complicated, and coal seams have various widths and irregular dip angles. Consequently, the slant chute block caving method has been widely used in Korea, and sinkhole type subsidence is more common than trough type. As a result, the conventional prediction methods must be adapted to the Korean geology and mining conditions, or new subsidence estimation methods must be developed.

The goal of this study is to develop a simple, general, quantitative and reliable method for identifying subsidence susceptibility of the closed or abandoned coal mines, which is proper to be employed in geologically complicated areas. The proposed method in this paper considers only gangway depths and attitude of geological features like dip and dip direction is an optional parameter, because these data are relatively easy to acquire and generally reliable.

**2. Estimation of subsidence susceptibility**

**2.1 Basic assumption**

The depth of gangways is selected as an input data of this study after surveying the availability and effectiveness of data because it is reliable and can be easily acquired. In fact, several researchers revealed that the magnitude (volume) and depth of excavation are the principal factors influencing on the subsidence (Whittaker and Reddish, 1989; Singh and Dhar, 1997; MIRECO, 2008).

The method proposed in this study is based on the fact that the excavation volume and shape (or distribution of coal seams) are closely related to the gangway distribution. Two basic assumptions considered in the method are that the susceptibility to subsidence within a unit surface area increases as the depth of a gangway from the surface decreases and the number of gangways below the unit area increases. The first assumption is based on the bulking of failed rock mass which can fill the excavation and prohibit the propagation of roof failure. The second assumption comes from the fact that the rock mass around the excavation is damaged due to blasting and induced stresses.

The susceptibility related to the depth of a gangway is quantified using a negative exponential equation based on the results of numerical analyses (Park et al., 2005) and statistical data of subsidence occurrences in Korean coal mines as shown in Fig. 1 (MIRECO, 2008). Park et al. investigated the influence of the depth and width of excavation and of the spacing and dip of discontinuity on ground subsidence using PFC2D capable of modeling the bulking effect and showed that the overburden remains undamaged as the mining depth increases. Fig. 1 shows that most of subsidence occurred within a depth of 100 m from the surface. The number of subsidence events decreases exponentially as the gangway depth increases.

Artificial Intelligence, asian rock mechanics symposium 29, calculation point, gangway, gangway point, influence function method, isrm international symposium, metals & mining, normalization coefficient, only gangway depth, Reservoir Characterization, structural geology, subsidence, subsidence susceptibility, sum window, Susceptibility, susceptibility value

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (0.89)

Jung, Yong-Bok (Korean Institute of Geoscience and Mineral Resources) | Park, EuiSeob (Korean Institute of Geoscience and Mineral Resources) | Kim, Hyunwoo (Korean Institute of Geoscience and Mineral Resources)

**Abstract**

The time to failure of rocks under load is governed by various factors such as rock structure including voids and fracture, loading rate and magnitude, boundary conditions, environmental conditions, and so on. The lifetime could be predicted by either empirical exponential laws or physical laws based on damage and fracture mechanics but the integration from the characterization to the modeling is not firmly established. The authors developed continuum numerical model with the subcritical crack growth test and acquisition of parameters from the laboratory test. The developed model was based on subcritical crack growth and damage mechanics considering practical applications in 2 and 3-dimensional numerical modeling of time-dependent behavior. The algorithm considers both mode-I and mode-II fracturing. Each zone or element contains a random damage state variable. Damage growth is controlled by the modified Charles equation. Macroscopic failure is the results of the coalescence of growing damaged elements. The proposed model was applied to the mode-I and mode-II subcritical crack growth test carried out in the laboratory. The comparison shows that the model describes the time-dependent behavior well. In addition, the accumulation of damaged element during the loading period can be an indicator for the instability of rock like an AE (Acoustic Emission) in a laboratory test.

**1. Introduction**

The time to failure of rocks under load is governed by various factors such as rock structure including voids and fracture, loading rate and magnitude, boundary conditions, environmental conditions, and so on. The lifetime could be predicted by either empirical exponential laws or physical laws based on damage and fracture mechanics. A few research works on subcritical crack growth (SCG) modelling had been published in spite of its importance (Konietzky et al., 2009; Li and Konietzky, 2015; Potyondy, 2007; Rinne et al., 2004). FRACOD can directly simulate crack initiation and propagation under a subcritical crack growth condition in 2D space (Rinne et al., 2004). Stress-corrosion model implemented using PFC can simulate crack initiation and propagation in 2D and 3D space but the subcritical crack growth is limited within mode-I (Potyondy, 2007). FLAC based subcritical crack growth model can indirectly simulate time-dependent behavior using fictitious crack within each element in 2D space but an extension to 3D space is not straightforward.

The authors developed continuum 2D and 3D model to simulate time-dependent behavior of rock like material under mode-I, mode-II and mixed configurations as well as to apply to engineering problems.

**2. Theories**

**2.1 Formulations**

Subcritical crack growth parameters are based on fracture mechanics but there is an equivalent crack concept that describes the incremental damage accumulation based on damage mechanics is equivalent to incremental crack growth based on fracture mechanics as shown in Fig. 1 (Legendre et al. 1984; Xie, 1993). Then, a formulation in 2D and 3D domain is almost same because the damage variable is a scalar.

The formulation of damage model from the subcritical crack growth model is straightforward under the equivalent crack concept.

SPE Disciplines:

Cheon, Dae-Sung (Korea Institute of Geoscience and Mineral Resources) | Jin, Kwangmin (Korea Institute of Geoscience and Mineral Resources) | Jung, Yong-Bok (Korea Institute of Geoscience and Mineral Resources)

**Abstract**

Microseismicity is an generated elastic wave when a crack is generated due to deformation or damage of a material, and it tends to increase sharply before macro-failure of the material. It can be used to monitor the safety of the rock mass structure such as mine and tunnel etc., and also used to determine the locations of cracks or macro-failures. In order to analyze the source location of cracks, it is important to consider the elastic wave propagation velocity, arrival picking, source location analysis algorithm, and sensor array. However, the location of the sensor may be restricted due to site conditions and economic problems, which may result in inability to interpret the source location or decrease reliability of MS monitoring. In this study, to improve the accuracy of source location analysis, we analyzed source locations according to various arrival picking method and source location algorithm. Among the methods, AIC and Generic algorithm for source location were found to be superior to other methods.

**1. Introduction**

Microseismicity(or Microseismic event) can be defined as a very small earthquake caused by natural(wave, wind etc.) or artificial (hydraulic fracturing, blasting etc.) causes. Generally, it is a small size (< M_w 2.0}) and high frequency (> 50Hz) compared to earthquakes. Earthquakes are primarily caused by nature, but microseismic event is often caused by induced earthquakes. Figure 1 is a brief summary of the frequency domain and the audible domain for earthquakes, microseismic event, and acoustic emissions. Microseismicity is an generated elastic wave when a crack is generated due to deformation or damage of a material, and it tends to increase sharply before macro-failure of the material. Microseismic monitoring can be traced back to 1938 when the U.S. Bureau of Mines attempted to relate seismic wave velocity with pillar load. It is used for geotechnical safety monitoring based on the characteristics of the increase in the number of events before major failures. Thesedays in situ microseismic monitoring of the rock mass fracturing process has been widely used in rock mechanics tests and rock engineering projects throughout the world

algorithm, arrival picking, arrival time, Artificial Intelligence, asian rock mechanics symposium 29, experiment, genetic algorithm, international symposium, machine learning, monitoring, reliability, Reservoir Characterization, rock mechanics symposium 29, rockburst, sensor, source location, source location algorithm, source simulation experiment, symposium 29, Upstream Oil & Gas

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

**ABSTRACT**

Microcracks progressively occur, propagate, coalescence and finally lead to failure when it is subjected to stress. When subjected to loads over a long period, failure may occur below the material strength. It can be considered as a time-dependent behavior of materials. During this behavior, acoustic emissions (AE) are normally generated and using these signals we can evaluate the stability of materials. In this study, we conducted both on static and dynamic long-term strength tests. In the static test, we adopted a subcritical crack growth test for Mode I and Mode II. In case of the dynamic test, cyclic four point bending test and cyclic shear test were used. From the static test, we estimated the characteristics of delayed failure and long-term strength of granite and from the dynamic test, we estimated the fatigue life of concrete and got an S-N curve. We also evaluated the static and dynamic long-term stability using cumulative acoustic emissions curve.

**1. INTRODUCTION**

Acoustic emission (AE) is one of elastic waves which is generated when new cracks or cracks propagate in material (Ishida et al., 2017). It occurs when the applied stress exceeds a certain threshold value, it can be related to the long-term strength or fatigue limit. Particularly brittle materials have small displacement or strain before failure, but even in this case, AE tends to occur continuously.

The long-term stability is important in terms of long-term utilization of rock structures such as radioactive waste disposal, CO_{2} storage, and underground storage of compressed air, etc. The long-term stability can be classified into delayed failure due to static creep and fatigue failure due to dynamic cyclic loading. The related limited researches have been conducted by some researchers due to the time limitation and experimental difficulties (Wilkins, 1980, Backers, 2006, Kim and Kemeny, 2008, Rinnie, 2008, Ko, 2008, Nara et al., 2010, Park and Jeon, 2006).

brittle material, creep curve, cumulative ae, dynamic long-term strength test, Finland Johansson, Helsinki, hydraulic fracturing, long-term stability, long-term strength test, nordic rock mechanics symposium october, Raasakka, Reservoir Characterization, specimen, static long-term strength test, strength test, subcritical crack growth test, Upstream Oil & Gas, waste management

SPE Disciplines:

**ABSTRACT:** This study introduces a method for determining Mode II dynamic fracture toughness using short-core-in-compression(SCC) rock specimens. The SCC specimens which have two notches are loaded dynamically by split Hopkinson pressure bar apparatus. A pulse shaping technique with copper disc pulse shaper was used to achieve the stress equilibrium state through the specimen before failure initiating. A High speed digital camera was used to observe the occurrence of shear cracks between the notch tips. The dynamic loading tests of SCC specimens were performed in the range of 202GPa/s ~ 343GPa/s and obtained Mode II dynamic fracture toughness was between 6.26MPa m^{1/2} ~ 7.51MPa m^{1/2}. The Mode II dynamic fracture toughness showed 2.7~ 3.1 times of Mode II static fracture toughness in this study.

**1. INTRODUCTION**

Theory of rock fracture mechanics has been applied to solve many rock engineering problems such as rock drilling, rock excavation, rock blasting and hydraulic fracturing. It is well known that rock materials contains many pre-existing microcracks which may cause complex fracturing processes depending on environmental conditions. When a pre-existing crack is subjected to an externally applied loading, stresses concentrate around the crack tip and accelerates the crack growth., to understand the stresses level around crack tip, the evaluation of stress intensity factor is of significant importance. Fracture toughness is a critical value of stress intensity factor which indicates the level of stress required for the pre-existing crack to propagate under a given crack arrangement. Three crack tip deformation modes are generally possible in fracture process, i.e. Mode I (crack opening), Mode II (crack sliding) and Mode III (crack tearing). In rock engineering problems, Mode I and Mode II or mixed mode I-II is very important and thus various researches have been conducted in the past [1-5] to evaluate the fracture toughness for these fracturing modes. However, according to the ISRM suggested methods, there is only one experimental method to determine Mode II fracture toughness while four methods have been already suggested for Mode I [6]. In addition, experimental studies about Mode II fracture toughness of rocks are mainly for the case of quasi-static loading condition, while many rock engineering applications including rock drilling and rock blasting are carried out under dynamic loading condition. Because rocks show strong loading rate/strain rate dependency [e.g., 4,7], understanding the Mode II dynamic fracture toughness is also of significant importance.

determination, fracture toughness, granite specimen, hydraulic fracturing, Loading Rate, mode II, mode ii dynamic fracture toughness, mode II fracture toughness, notch, Reservoir Characterization, reservoir geomechanics, rock specimen, scc granite specimen, SCC specimen, specimen, stress equilibrium state, stress wave, toughness, Upstream Oil & Gas

Country:

- Asia (0.69)
- North America > United States (0.47)

SPE Disciplines:

Park, Chulwhan (Korea Institute of Geoscience and Mineral Resoureces) | Synn, Joong-Ho (Korea Institute of Geoscience and Mineral Resoureces) | Park, Chan (Korea Institute of Geoscience and Mineral Resoureces) | Jung, Yong-Bok (Korea Institute of Geoscience and Mineral Resoureces)

ABSTRACT

Anisotropy is one of the mechanical properties to be considered as a factor in the design of underground structures. The object of this paper is to determine five independent elastic constants of a transversely isotropic rock experimentally. Tests are successfully accomplished and data are acceptable over all, for total 35 specimens of 7 different angles from a large block of rhyolite from Haenam area. Saint-Venant approximation is used in data analysis of the every individual angle. Near-true values of E_{1}, E_{2}, _{V1} and _{V2} can be directly measured from the two special specimens and G_{2} can be obtained from the five specimens of inclined angles. Two values of tangential modulus on the anisotropic plane, averaged G_{2} and G_{2-sv}, are almost same each other within 6 % of difference. And test data of the apparent Young's modulus can be referred as a monotonous increasing. From these results, it is proved that SV approximation may be very applicable for rhyolite. It is proposed that displacement by sliding on account of the excess tangential stress on a transversely isotropic plane may be assumed in this paper to explain a wide difference in the longitudinal strain. Sliding model can be one of the future studies to answer why Saint-Venant approximation may not become well applicable in the analysis of transversely isotropic rocks.

angle specimen, anisotropy angle, degree specimen, displacement, equation, GPa, international journal, Mining Science, plane, Reservoir Characterization, reservoir geomechanics, Rock mechanics, RSD, specimen, strain measurement, SV approximation, transversely isotropic rock, Upstream Oil & Gas, variation

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (0.95)

Jung, Yong-Bok (Korea Institute of geoscience & Mineral Resources) | Sunwoo, Choon (Korea Institute of geoscience & Mineral Resources)

The method of joint set identification using genetic algorithm was introduced. For handling of orientation data, the basic genetic algorithm was modified. We used real encoding scheme for the representation of candidate solutions and the orientation matrix for calculating mean direction of joint sets. The selection, crossover and mutation operations using real encoded chromosome were also implemented. Davies-Bouldin index and variance were used for cluster validity criteria. Finally, we developed GAC (Genetic Algorithm based Clustering), a FORTRAN program based on above algorithms and applied it to 3 different joint data sets. It is found that the results of joint set identification using GAC were acceptable for engineering design. From the application of GAC, we found that cluster validity index based on variance is more efficient in finding the number of clusters than Davis-Bouldin index. In addition, the genetic algorithm based clustering was proved to be a fast and efficient method for the joint set identification task.

The stability of structures in rock-mass, such as tunnel or rock slope is critically dependent on various characteristics of discontinuities. Therefore, it is important to survey and analyze discontinuities correctly for the design and construction of structures in rock-mass. One standard procedure of discontinuity survey and analysis is a joint set identification from a large unprocessed orientation data. Joint set identification is a tedious procedure, however is fundamental to rock engineering design such as rock mass classification, key block analysis, rock slope stability analysis and discrete fracture network modeling. Conventionally, manual method using contour plot had been widely used for this task, but this method has some short-comings such as subjective identification results, manual operations, and so on.

For these reasons, researchers introduced automatic joint set clustering techniques. Mahtab & Yegulalp (1982) developed clustering algorithm based on Poisson probability but the clustering shape is dependent on the initial value. Hammah & Curran (1998) and Jung & Jeon (2003) developed joint set clustering method using fuzzy k-mean algorithm which doesn't guarantee global or optimal solution.

In this article, joint set identification using genetic algorithm (GA) was introduced because GA provides higher probability to yield optimal solution. Basic genetic algorithm was modified for handling of orientation data and Davies-Bouldin index (DBI) and variance (VI) were used for cluster validity criteria. Finally we developed GAC, a FORTRAN program based on above algorithms and applied it to 3 different joint data sets.

GENETIC ALGORITHM

GA belongs to a class of search techniques that simulates the principles of natural selection to develop solutions of large optimization problems. GA operates by maintaining and manipulating a population of potential solutions called chromosomes. Each chromosome has an associated fitness value which is a qualitative measure of the goodness of the solution encoded in it. This fitness value is used to guide the stochastic selection of chromosomes which are then used to generate new candidate solutions through crossover and mutation. Crossover generates new chromosomes by combining sections of two or more selected parents.

Artificial Intelligence, automatic discontinuity, chromosome, cluster number, cluster scatter, control parameter, Crossover, dbi, discontinuity, evolutionary algorithm, fitness, fitness function, GAC, genetic algorithm, identification, joint data, machine learning, mean direction, orientation data, orientation matrix, selection, Upstream Oil & Gas, vector

SPE Disciplines: Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)

Technology:

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