A method for identifying multi-source damage of overburden strata disturbed by mining

Abstract

The present application relates to the technical field of mine rock mass mechanics, and particularly relates to a mining disturbance overburden rock multi-source damage identification method, which extracts macroscopic performance damage parameters and microstructure damage parameters of overburden rock mass by combining geological statistics, test testing and numerical simulation; the self-adaptive nonlinear coupling of multi-source damage variables is realized by introducing a synergistic gain coefficient and a path sensitivity coefficient, so that the macroscopic damage and the microstructure damage dynamically represent the overall damage state according to the evolution stage and the synergistic relationship; the overall multi-source damage variable is updated in the subsections of the step loading cycle section, the step unloading cycle section and the stop mining constant load section, and the overburden damage partition and the key stage identification result are output. The present application overcomes the shortcomings of the traditional single-scale representation method, and can provide a unified quantifiable damage parameter for overburden long-term stability evaluation and multi-physical field coupling analysis.

Description

Technical Field

[0001] This invention relates to the field of mining rock mechanics technology, and in particular to a method for identifying multi-source damage to overburden caused by mining disturbance. Background Technology

[0002] During coal mining, the overburden does not bear a single static load, but rather undergoes significant cascaded cyclic loading and unloading disturbances under the advancing action of the working face, and these disturbances exhibit marked spatial variability. On the coal pillar side, the overburden typically exhibits cascaded loading stress cycles, with the bearing pressure increasing progressively; while on the goaf side, the overburden exhibits cascaded unloading stress cycles, with the constraint conditions gradually relaxing. This difference in disturbance patterns due to different spatial locations determines that overburden damage has significant spatial variability and path dependence characteristics.

[0003] Existing methods for identifying overburden damage primarily focus on single mechanical indicators, such as the elastic modulus degradation method, peak strength reduction method, or simple fracture number statistics method. While these methods can reflect the degree of damage to some extent, they are fundamentally insufficient in characterizing the complex evolution of overburden damage under coal mining disturbance conditions. Existing coupling methods often employ simple linear superposition, lacking descriptions of the synergistic enhancement mechanisms of damage at different scales, making it difficult to accurately reflect the development law of overburden damage from gradual evolution to abrupt instability under coal mining disturbance. Especially in the context of inclined coal seams and abandoned mines, the impact of early mining disturbances on deformation and failure in the subsequent constant load stage is significant, and using only one type of damage variable is insufficient to accurately reveal the true damage state. Summary of the Invention

[0004] The purpose of this invention is to provide a method for identifying multi-source damage to overburden caused by mining disturbance, in order to solve the technical problems of insufficient single-scale characterization, inadequate consideration of path effects, and unclear coupling mechanism of multi-source damage in existing methods for identifying overburden damage in coal mines.

[0005] This invention provides a method for identifying multi-source damage to overburden caused by mining disturbance, comprising: Obtain the physical and mechanical parameters of the overlying rock and the parameters of the mining disturbance path; Macroscopic performance damage parameters of overburden rock mass were extracted and macroscopic damage variables were constructed by indoor triaxial stepped cyclic-constant load combined test; A discrete element numerical simulation model was established to extract damage parameters of the overburden microstructure and construct microstructure damage variables. An adaptive coupling method for multi-source damage variables is constructed, the synergistic gain coefficient is calculated and the overall multi-source damage variables are constructed, and the variables are updated in segments according to the cascade loading cycle segment, the cascade unloading cycle segment and the stop mining constant load segment to obtain the time-series evolution results of the multi-source damage variables. Based on the temporal evolution results of the multi-source damage variables, their growth rate with respect to time is calculated, and the overburden damage process is divided into a compaction adjustment stage, a stable accumulation stage, and an accelerated instability stage according to the growth rate; based on the numerical threshold range of the overall multi-source damage variables, the overburden is identified as a high-damage zone, an unstable zone, and a damage buffer zone. The output includes the overburden damage zoning and key stage identification results for the damage stage and damage area type.

[0006] In some embodiments, constructing macroscopic damage variables includes: The parameters of elastic modulus degradation, plastic strain accumulation, and dissipated energy evolution were obtained through indoor triaxial cyclic-constant load combined tests. Based on the aforementioned parameters, a macroscopic damage variable is constructed, the expression of which is:

[0007] in, 0 represents the initial elastic modulus. Let i be the equivalent elastic modulus of the i-th stage. This refers to the elastic modulus near the instability state. , For the accumulated plastic strain in stage i, , To accumulate plastic strain before instability, , For the accumulated dissipated energy in stage i, , To accumulate and dissipate energy before instability , , Let be the weight coefficient, and satisfy... + + =1.

[0008] In this invention, the near-instability state is determined as follows: During cyclic loading or constant loading, when the axial strain rate increases by more than 5 times the initial strain rate for three consecutive measurement cycles, or when the acoustic emission event rate suddenly increases, the sample is determined to be near instability, and the parameters under this near-instability state are recorded. The above determination criteria apply to all scenarios in which this invention confirms the parameters of the final instability stage.

[0009] In some embodiments, the weighting coefficients are determined using the coefficient of variation method. Statistical analysis is performed on the dispersion of the three indicators—elastic modulus degradation rate, plastic strain accumulation rate, and dissipated energy accumulation rate—under multi-level loading or unloading paths, and the indicators with greater dispersion are assigned higher weighting coefficients.

[0010] In some embodiments, constructing the mesoscopic damage variable includes: A microscopic numerical model was established using the discrete element method for particle flow, and the particle contact stiffness, friction coefficient, and bonding strength were calibrated using macroscopic stress-strain curves. Statistical analysis was conducted on the number of fractures, the total length of the fracture skeleton, and the percentage of the area of ​​the largest connected cluster. Based on the aforementioned parameters, a mesoscopic damage variable is constructed, the expression of which is:

[0011] in, Let i be the number of cracks in stage i. This represents the total number of fractures during the final instability stage. Let be the total length of the fracture skeleton in stage i. This represents the final total length of the fractured skeleton. The percentage of the area of ​​the largest connected cluster in the i-th stage. This represents the final maximum connected cluster area percentage. 1. 2. 3 is the weighting coefficient, and satisfies 1+ 2+ 3 = 1.

[0012] In some embodiments, the adaptive coupling method for constructing multi-source damage variables includes: Calculate the rate of change of macroscopic and microscopic damage variables between adjacent stages; The cooperative gain coefficient is constructed based on the rate of change, and its expression is as follows:

[0013] in, is the path sensitivity coefficient, used to reflect the difference in the degree of macroscopic and microscopic damage coordination under loading and unloading disturbances.

[0014] In some embodiments, the path sensitivity coefficient is differentiated according to the stress path type, taking a first value under the step loading cycle path and a second value under the step unloading cycle path, and the first value is greater than the second value.

[0015] In some embodiments, The overall multi-source damage variable is constructed based on the synergistic gain coefficient, and its expression is as follows:

[0016] When macroscopic performance damage and microstructural damage increase rapidly in tandem... As the damage increases, the overall damage accelerates and amplifies; when the two evolve asynchronously... Reduce and avoid overestimating the damage caused by simple coupling.

[0017] The temporal evolution described in this invention refers to arranging the calculated overall multi-source damage variables corresponding to each stage of the stress path of the overburden (step loading cycle, step unloading cycle, and shutdown constant load stage) in chronological order, forming a data sequence. This sequence quantitatively describes the dynamic accumulation process of overburden damage degree with the progress of mining and shutdown. Based on the temporal evolution results, the growth rate of the overall multi-source damage variables with respect to time is further calculated, and its expression is:

[0018] in, This represents the increment of the overall damage variable between two adjacent monitoring time points. This corresponds to the time interval. This growth rate. It is a key criterion for classifying the stages of overburden damage, and its value and trend directly reflect the degree of risk of overburden instability.

[0019] In some embodiments, the output overburden damage zoning and key stage identification results include: Based on the growth rate of the overall damage variables, the overburden damage process is divided into a compaction adjustment stage, a stable accumulation stage, and an accelerated instability stage. when ,and When the value gradually decreases and approaches 0, it is determined to be in the compaction adjustment stage. This stage corresponds to the compaction and closure of micropores inside the overburden, and the accumulation of damage is slow. when ,and When the damage remains within a relatively stable low range, it is considered to be in the stable accumulation stage. During this stage, damage exhibits quasi-linear growth, and the crack propagates stably. when ,and When the damage shows a continuous increasing trend and exceeds the preset acceleration threshold, it is determined to be in the accelerated instability stage. In this stage, macroscopic and microscopic damage are enhanced synergistically, and the overburden tends to become unstable and fail.

[0020] High-damage areas, unstable areas, and damage buffer zones are identified based on the threshold range of the overall damage variable; when This area was identified as a high-damage zone. The bearing capacity of the overlying strata in this area has been severely weakened. when Furthermore, if the area is in an accelerated instability phase or the late stage of a stable accumulation phase, it is identified as a region prone to instability. This region is in a critical stable state and requires close monitoring. when This area was identified as a damage buffer zone. The structural integrity of this area is relatively good, and its load-bearing capacity has not significantly decreased.

[0021] In some embodiments, the overall multi-source damage variable is updated in segments according to the disturbance stage, and is calculated separately in the cascade loading cycle segment, the cascade unloading cycle segment, and the stop-mining constant load segment, so as to realize the dynamic identification of the damage state of the overburden throughout the entire mining process.

[0022] Compared with the prior art, the present invention has the following beneficial effects: 1. By synergistically constructing macroscopic and microscopic damage variables, a unified dual-scale characterization of overburden damage is achieved. Macroscopic damage variables characterize the macroscopic deterioration of overburden bearing capacity from three dimensions: elastic modulus degradation, plastic strain accumulation, and dissipated energy dissipation. Microscopic damage variables characterize the evolution of the integrity of the internal structure of the rock mass from four dimensions: number of fractures, fracture skeleton length, proportion of connected clusters, and directional order. The two complement each other to form a complete description of overburden damage. 2. By introducing a synergistic gain coefficient and a path sensitivity coefficient, adaptive nonlinear coupling of multi-source damage variables is achieved. When macroscopic damage and mesoscopic damage evolve synchronously, the coupling coefficient amplifies the damage effect. When the two evolve asynchronously, the coupling coefficient suppresses damage overestimation, thus avoiding the irrationality of the traditional linear superposition method. 3. Through a segmented update mechanism, the overall multi-source damage variables are calculated in the cascade loading cycle, cascade unloading cycle, and constant load shutdown cycle, respectively, so as to realize the dynamic tracking and quantitative identification of the damage state of the overburden throughout the entire mining process. This provides unified and quantifiable damage parameters for the long-term stability evaluation of the overburden, the creep constitutive correction after disturbance, the time-varying reconstruction of permeability, and the simulation of coalbed methane migration. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1This is a flowchart of the multi-source damage identification method for coal mine overburden disturbance considering the present invention. Detailed Implementation

[0025] The following will be based on embodiments of the present invention. Figure 1 The technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0026] The multi-source damage identification method considering overburden disturbance in coal mining provided by this invention achieves dual-scale unified characterization and full-process dynamic tracking of overburden damage state under coal mining disturbance conditions through a technical route combining indoor triaxial stepped cyclic-constant load joint test and particle flow discrete element numerical simulation.

[0027] In practical applications, taking a certain inclined coal seam mine as an example, during the advancement of the working face, the overburden is subjected to drastically different stress disturbance paths at different spatial locations. The overburden on the coal pillar side experiences cyclic stress with step loading, the overburden on the goaf side experiences cyclic stress with step unloading, while the area near the stop line transitions from the cyclic disturbance stage to the constant load stage.

[0028] The complex stress environment described above determines that overburden damage exhibits significant spatial variability and path dependence, making it difficult for traditional single-scale or single-index damage identification methods to accurately reveal its true damage state. This invention addresses this technical problem by establishing a synergistic coupling relationship between macroscopic performance degradation and microscopic structural evolution, thereby achieving comprehensive and accurate identification of overburden damage state under complex mining conditions.

[0029] Example In the process of obtaining the physical and mechanical parameters of the overlying strata and the parameters of the mining disturbance path, the physical and mechanical parameters of the overlying strata of the coal mine working face and the parameters of the mining disturbance path are obtained by combining geological structural statistics, field data collection and laboratory experiments. This provides complete basic data support for the subsequent design of indoor test schemes and the establishment of discrete element numerical models.

[0030] Specifically, firstly, the mineable coal seam and its overlying strata are determined from the mine's geological structure map. Stratigraphic information, including stratigraphic position, thickness distribution, dip angle variation, lithological characteristics, and burial depth, is then compiled for each stratum. Taking a specific example, the No. 3 coal seam in this mine has a dip angle of 25 degrees and a burial depth ranging from 450 to 650 meters. The overlying strata include various lithologies such as mudstone, sandy mudstone, fine sandstone, and medium sandstone, with a total thickness of approximately 180 meters. The spatial distribution patterns and stratigraphic relationships of each stratum can be obtained through borehole columnar sections and seismic exploration data.

[0031] Subsequently, the basic mechanical parameters of the overburden rock mass were obtained through field testing and conventional triaxial tests in the laboratory. In the field testing, equipment such as borehole deformable gauges and borehole stress gauges were used to measure the in-situ elastic modulus and initial in-situ stress state of the overburden rock mass. In the laboratory, representative samples were selected from the drilled core specimens, and conventional triaxial compression tests were conducted under different confining pressures according to the testing methods recommended by the International Society for Rock Mechanics. During the tests, parameters such as axial stress, axial strain, and volumetric strain were recorded, and basic mechanical parameters such as elastic modulus, compressive strength, Poisson's ratio, and density were calculated. In this specific embodiment, the mudstone sample has an average elastic modulus of 8.5 GPa, a peak compressive strength of 42.3 MPa, a Poisson's ratio of 0.28, and a density of 2450 kg / m³; the sandy mudstone sample has an average elastic modulus of 12.1 GPa, a peak compressive strength of 58.7 MPa, a Poisson's ratio of 0.24, and a density of 2580 kg / m³; and the fine sandstone sample has an average elastic modulus of 18.6 GPa, a peak compressive strength of 76.4 MPa, a Poisson's ratio of 0.21, and a density of 2680 kg / m³.

[0032] When determining the stress path type, based on the working face advancement method and the stress state after mining shutdown, the stress paths experienced by the overburden are clearly distinguished into three types. The stepped loading cyclic stress path is suitable for describing the stress evolution characteristics of the overburden on the coal pillar side during working face advancement. Its characteristic is that the axial stress increases stepwise with working face advancement, and after each loading stage, it maintains a brief period of stable pressure before entering the next loading stage. The stepped unloading cyclic stress path is suitable for describing the stress adjustment process of the overburden on the goaf side under the influence of mining. Its characteristic is that as the goaf expands, the lateral constraints are gradually released, and the overburden undergoes a stepwise unloading process. The shutdown constant load stress path describes the transition process of the stress state experienced by the overburden from cyclic disturbance to continuous constant stress after the working face stops mining.

[0033] In the macroscopic performance damage parameter extraction stage, indoor triaxial stepped cyclic-constant load combined tests were conducted to extract the macroscopic performance damage parameters of the overburden rock mass. The macroscopic damage variable DM is composed of three sub-terms: elastic modulus degradation term, plastic strain accumulation term, and dissipated energy damage term. The relationship between each sub-term and the macroscopic damage variable DM constitutes a complete macroscopic damage characterization system.

[0034] Specifically, firstly, conventional triaxial compression tests were conducted on overburden samples under different confining pressures to obtain peak strength, initial elastic modulus, and reference strain parameters. In this specific embodiment, three confining pressure conditions of 5 MPa, 10 MPa, and 15 MPa were selected for comparative tests. The sample size adopted was a standard cylindrical sample with a diameter of 50 mm and a height of 100 mm, as specified by international standards. During sample preparation, the flatness of the end faces and coaxiality errors were strictly controlled to eliminate the influence of end effects and eccentric loading on the test results. During the test, hydrostatic pressure was first applied to the sample to the target confining pressure. Subsequently, axial deviation stress was applied at a rate of 0.5 MPa / s until the sample reached its peak strength, at which point loading was stopped. The initial elastic modulus E0 and peak strength of each sample were obtained through stress-strain curve analysis. and peak strain Equal reference parameters.

[0035] When designing the cascade cyclic stress path, two types of cascade loading cyclic path and cascade unloading cyclic path are set according to the actual mining disturbance characteristics. The design scheme of the cascade loading cyclic path is as follows: first, apply the initial deviation stress to 60% of a certain target value, and then unload it to 30% of the initial confining pressure state to complete the first cycle; then, increase the target value by 10% and repeat the above loading and unloading process to form a cascade increasing cyclic stress sequence.

[0036] The design scheme of the stepped unloading cyclic path is the opposite, setting the initial target value to 80% of the peak intensity, and then gradually reducing the target value by 10% at each level, forming a stepped decreasing cyclic stress sequence. The shape characteristics of the stress-strain loop, the area of ​​the hysteresis loop, the irreversible strain increment, and the evolution data of dissipated energy are recorded for each cycle stage.

[0037] During the constant load stage, the axial stress and confining pressure were kept constant, and the deformation characteristics and energy dissipation state of the specimen were continuously monitored. During this stage, the axial strain and volumetric strain data were recorded every 5 minutes, and the modulus degradation, plastic strain increment and cumulative dissipation energy of the specimen were calculated. The constant load duration was set to 48 hours to fully capture the creep damage evolution characteristics of the overburden under long-term constant load.

[0038] When constructing macroscopic damage variables, the following mathematical expression is used to describe them. Calculation method:

[0039] in, 0 represents the initial elastic modulus. Let i be the equivalent elastic modulus of the i-th stage. This refers to the elastic modulus near the instability state. , For the accumulated plastic strain in stage i, , To accumulate plastic strain before instability, , For the accumulated dissipated energy in stage i, , To accumulate and dissipate energy before instability , , Let be the weight coefficient, and satisfy... + + =1.

[0040] The elastic modulus degradation term reflects the degree of stiffness attenuation of the overburden under cyclic disturbance and constant load. Under a stepped loading cyclic path, the elastic modulus degradation exhibits a gradual cumulative characteristic; the equivalent elastic modulus measured after each loading stage is lower than before loading, indicating that the continuous initiation and propagation of microcracks within the rock sample leads to a decrease in bearing stiffness. Under a stepped unloading cyclic path, the elastic modulus degradation shows a different response characteristic than that under the loading path. The elastic modulus may briefly rebound in the initial stage of unloading, and then gradually decrease in subsequent cycles. This difference can effectively distinguish the different degradation modes of the overburden bearing capacity on the coal pillar side and the goaf side.

[0041] The cumulative plastic strain term reflects the degree of irreversible deformation accumulation in the overburden during cyclic loading and unloading. Plastic strain exhibits path memory characteristics; even after multiple cyclic loading cycles, the plastic strain will still accumulate, even if the stress returns to its initial state. This degree of irreversible deformation accumulation is closely related to the evolution of internal damage in the rock sample and can quantitatively characterize the contribution of the disturbance history to the current damage state. In this specific embodiment, after 10 stepped loading cycles, the cumulative plastic strain of the mudstone sample reached 12.5% ​​of the peak strain; the cumulative plastic strain of the sandy mudstone sample under the same conditions was 8.3% of the peak strain; and the cumulative plastic strain of the fine sandstone sample was only 5.7% of the peak strain. These differences reflect the varying resistance to plastic deformation in overburden of different lithologies.

[0042] The dissipated energy damage term reflects the level of energy dissipation within the overburden. Dissipated energy is closely related to the initiation, propagation, and penetration of microcracks. When new crack surfaces form within the rock sample, a certain amount of energy is required to overcome the fracture toughness. During cyclic loading, the hysteresis loop area represents the dissipated energy in each cycle. The accumulation of dissipated energy reflects the irreversible accumulation characteristic of damage.

[0043] The weighting coefficients were determined using the coefficient of variation method. Specifically, the dispersion of three indicators—elastic modulus degradation rate, plastic strain accumulation rate, and dissipated energy accumulation rate—under multi-stage loading or unloading paths was statistically analyzed. In this specific embodiment, statistical analysis of 30 sets of test data under different working conditions yielded coefficients of variation of 0.35, 0.28, and 0.37 for the three indicators. After normalizing the coefficients of variation for each indicator, the weighting coefficients were obtained as α=0.33, β=0.26, and γ=0.41. Indicators with larger coefficients of variation were assigned higher weighting coefficients. This approach enhances the responsiveness of macroscopic damage variables to path effects and stage differences.

[0044] In the microstructure damage parameter extraction stage, a particle flow discrete element numerical simulation model is established to extract the microstructure damage parameters and microstructure damage variables of the overburden. Ds It is composed of three weighted sub-items: the ratio of the number of fractures, the ratio of the length of the fracture skeleton, and the proportion of the area of ​​the largest connected cluster. Each sub-item is related to the mesoscopic damage variable. Ds These together form a complete microscopic damage characterization system.

[0045] In establishing the discrete element numerical model, a microscopic numerical model matching the size and mechanical properties of the laboratory test specimens was constructed using the particle flow method. The geometric dimensions of the model were consistent with those of the laboratory test specimens, namely a cylindrical region with a diameter of 50 mm and a height of 100 mm. Spherical particles with a certain size distribution were randomly generated within this region, with particle radii ranging from 0.5 mm to 1.2 mm, and the number of particles was approximately 15,000. Adjacent particles were connected by a parallel bonding model, which can simultaneously transmit forces and torques, and can simulate the gradual failure process of rock materials from continuous to discontinuous.

[0046] The calibration process for micro-parameters involved adjusting micro-parameters such as particle contact stiffness, friction coefficient, and bond strength to ensure that the macro-stress-strain curves obtained from numerical simulations matched the results of laboratory tests. In practice, after repeated adjustments, the final determined values ​​for the micro-parameters were: particle contact modulus of 15 GPa, parallel bond modulus of 12 GPa, ratio of particle normal stiffness to tangential stiffness of 1.5, friction coefficient of 0.4, parallel bond normal strength of 60 MPa, and parallel bond tangential strength of 45 MPa.

[0047] By comparing the stress-strain curves of numerical simulation and laboratory tests, the errors in peak strength, elastic modulus and failure mode were all controlled within 8%, indicating that the numerical model can better reflect the mechanical behavior characteristics of the overburden rock sample.

[0048] Numerical simulations were conducted under the same loading path as in indoor experiments, outputting data on the number of broken granular bonds, the total length of the fracture skeleton, the proportion of the largest connected cluster area, and the fracture orientation distribution at the end of each simulation stage. When parallel bonds fracture, new fracture surfaces are generated. In the implementation, Python scripts were used to automatically extract and statistically analyze fracture information, generating a three-dimensional image and statistical data of the fracture distribution by tracking the spatial location and orientation of all broken bonds.

[0049] At the mesoscopic level, the number of cracks, total crack length, crack connectivity, and crack direction dispersion are statistically analyzed to form the basic parameter set for mesoscopic damage identification. The crack number is counted by counting the number of cracks corresponding to all fracture bonds, and each group of adjacent fracture bonds is aggregated into one crack to avoid double counting. The crack skeleton length is counted by calculating the equivalent length of each crack and summing the equivalent lengths of all cracks to obtain the total length of the crack skeleton. The crack connectivity is counted by using connected component analysis to identify the largest connected cluster in the crack network and calculating the ratio of its area to the total crack area. The crack direction dispersion is counted by using the information entropy formula to calculate the probability distribution entropy value of the crack direction. The higher the entropy value, the more uniform the crack direction distribution; the lower the entropy value, the more concentrated the crack direction distribution.

[0050] When constructing the mesoscopic damage variable, the following mathematical expression is used to describe the mesoscopic damage variable. Ds Calculation method:

[0051] in, Let i be the number of cracks in stage i. This represents the total number of fractures during the final instability stage. Let be the total length of the fracture skeleton in stage i. This represents the final total length of the fractured skeleton. The percentage of the area of ​​the largest connected cluster in the i-th stage. This represents the final maximum connected cluster area percentage. 1. 2. 3 is the weighting coefficient, and satisfies 1+ 2+ 3 = 1.

[0052] The number of fractures reflects the scale of microscopic damage. Fracture initiation is the initial manifestation of damage. In the early stage of loading, the number of fractures increases rapidly, indicating that microcracks continue to initiate inside the rock sample. As loading progresses, the growth rate of the number of fractures gradually decreases, indicating that the damage evolution has transitioned from a stage dominated by fracture initiation to a stage dominated by fracture propagation.

[0053] The ratio of fracture skeleton length reflects the extent of fracture expansion. The increase in skeleton length characterizes the spatial extension of damage. Under the stepped loading cycle path, the increase in fracture skeleton length exhibits a step-like characteristic. At the end of each loading stage, the fracture skeleton length increases sharply and then remains relatively stable during the unloading stage.

[0054] The proportion of the largest connected cluster area reflects the evolution of fracture connectivity. When the proportion of the largest connected cluster area increases rapidly, it indicates that the damage has evolved from isolated fractures to a connected network, which is a key precursor to the instability and failure of the overburden. In this specific embodiment, when the proportion of the largest connected cluster area exceeds 0.65, a significant stress drop phenomenon is observed in the numerical model, indicating that the rock sample has already undergone instability and failure.

[0055] The method for determining the weighting coefficients is consistent with the application of the coefficient of variation method in macroscopic damage variables. The weighting coefficients are finally determined by statistically analyzing the coefficients of variation of the three indicators.

[0056] In the adaptive coupling of multi-source damage variables, adaptive nonlinear coupling of multi-source damage variables is achieved through a synergistic gain coefficient, and macroscopic damage variables... and micro-damage variables Ds The calculation method of the rate of change between adjacent stages, and the role of the synergistic gain coefficient λ and the path sensitivity coefficient μ in the overall multi-source damage variable. D total It plays a core role in the construction process.

[0057] First, the rate of change of macroscopic damage variables and microscopic damage variables between adjacent stages is calculated to characterize their synchronous evolution characteristics in the damage evolution process. A synergistic gain coefficient is constructed based on the rate of change to quantitatively characterize the synergistic enhancement effect between macroscopic performance damage and mesoscopic structural damage. The expression for the synergistic gain coefficient is:

[0058] in, m is the path sensitivity coefficient, used to reflect the difference in the degree of macroscopic and microscopic damage coordination under loading and unloading disturbances.

[0059] Path sensitivity coefficient mDifferentiated values ​​were selected based on the stress path type. Under the stepped loading cyclic path, the axial stress on the overlying rock of the coal pillar increases progressively, and the degradation of macroscopic mechanical properties and the development of microscopic fractures show strong synchronicity, resulting in a significant synergistic enhancement effect; therefore, higher values ​​were selected. Under the stepped unloading cyclic path, the stress state of the overlying rock in the goaf is dominated by unloading, and the synchronicity between macroscopic response and microscopic structural evolution is relatively weak, leading to a reduced synergistic enhancement effect; therefore, lower values ​​were selected. In specific implementation, the loading path was determined through experimental data fitting analysis. m The value is 0.8, located in the uninstallation path. m The value is 0.5.

[0060] When macroscopic performance damage and mesoscopic structural damage grow rapidly and synchronously, the synergistic gain coefficient is greater than 1 and its value increases, indicating an accelerated amplification of the overall multi-source damage variable. This reflects the physical nature of the rapid deterioration of the overburden caused by the synergistic enhancement of multi-scale damage. When macroscopic and mesoscopic damage evolve asynchronously, the synergistic gain coefficient approaches 1, avoiding damage overestimation caused by simple coupling.

[0061] The overall multi-source damage variable is constructed based on the synergistic gain coefficient, and its expression is as follows:

[0062] When macroscopic performance damage and microstructural damage increase rapidly in tandem... As the damage increases, the overall damage accelerates and amplifies; when the two evolve asynchronously... Reduce and avoid overestimating the damage caused by simple coupling.

[0063] The overall multi-source damage variables are updated in segments according to the disturbance stage, and are calculated separately in the cascade loading cycle segment, cascade unloading cycle segment, and stop-mining constant load segment to achieve full-process dynamic identification of the damage state of the overburden throughout the entire mining process. In the cascade loading cycle segment, as the working face advances, the stress of the overburden gradually increases, and the damage shows a cumulative growth characteristic. In the cascade unloading cycle segment, as the goaf expands, the stress of the overburden gradually releases, and the damage evolution shows the response characteristics unique to unloading. In the stop-mining constant load segment, the overburden is subjected to continuous and constant stress, and the damage continues to evolve in the time dimension.

[0064] In the output of the overburden damage zoning and key stage identification results, the overburden damage zoning and key stage identification results are output based on the temporal evolution results of the overall multi-source damage variables. The overburden damage process is divided into three key stages: compaction adjustment stage, stable accumulation stage, and accelerated instability stage, according to the growth rate of the overall damage variables. Based on the threshold range of the overall damage variables, three types of overburden damage areas are identified: high damage area, unstable area, and damage buffer zone.

[0065] In terms of key stage division, the compaction adjustment stage corresponds to the early evolution stage with a low overall damage variable growth rate. The main characteristic of this stage is that the original micropores and microfractures within the rock mass gradually close under stress, resulting in a slight initial enhancement followed by gradual degradation of macroscopic mechanical properties. Microstructurally, this is manifested by a slow increase in the number of fractures, and the fracture skeleton has not yet formed a connected network. In specific implementation, the compaction adjustment stage corresponds to the overall damage variable... D total Within the range of 0 to 0.15, the growth rate is approximately 0.002 / h.

[0066] The stable accumulation stage corresponds to the intermediate evolution stage where the overall damage variable shows linear or quasi-linear growth. The main characteristics of this stage are the simultaneous and stable advancement of macroscopic mechanical property degradation and microscopic fracture structure evolution. During this stage, the elastic modulus shows a continuous decreasing trend, plastic strain accumulates steadily, and dissipated energy increases uniformly; the number of fractures at the microscopic level increases rapidly, the fracture skeleton gradually extends, and connectivity gradually improves. In this specific embodiment, the stable accumulation stage corresponds to the overall damage variable... D total Within the range of 0.15 to 0.60, the growth rate is approximately 0.008 / h.

[0067] The accelerated instability stage corresponds to the later evolution stage where the overall damage variable growth rate increases sharply. The main characteristics of this stage are the rapid deterioration of macroscopic properties and the rapid connection of microscopic fractures, leading to the instability and failure of the overburden. During this stage, macroscopic mechanical parameters exhibit a sudden drop, and the microscopic fracture network rapidly connects to form a continuous zone, resulting in a sharp loss of the rock sample's bearing capacity. In this specific embodiment, the accelerated instability stage corresponds to the overall damage variable... D total In the range exceeding 0.60, the growth rate increases sharply to over 0.025 / h.

[0068] Regarding damage area type identification, high-damage areas correspond to regions where the overall damage variable exceeds a first preset threshold, indicating areas where damage is highly developed. In this specific embodiment, the first preset threshold is set to 0.70. The bearing capacity of the overlying rock in high-damage areas has been severely weakened, and local instability and failure may have already occurred, requiring reinforcement or monitoring measures.

[0069] The unstable zone corresponds to an area where the overall damage variable is between the second and first preset thresholds and the growth rate continues to increase, indicating an area about to enter an accelerated instability phase. In specific implementation, the second preset threshold is set to 0.50. The overlying rock in the unstable zone is in a critically stable state and may rapidly evolve into an unstable state under external disturbances, requiring strengthened monitoring and early warning.

[0070] The damage buffer zone corresponds to the area where the overall damage variable is below the second preset threshold, and the area where the damage is still in the early stage of evolution. The integrity of the overlying rock structure within the damage buffer zone is well maintained, and the bearing capacity has not decreased significantly. It can be used as a reference for subsequent mining design.

[0071] By using the overall multi-source damage variables and their temporal evolution data as input parameters for subsequent damage constitutive models, permeability time-varying models, and coalbed methane migration models, the unified quantification and transfer of damage parameters required for multi-physics coupling analysis is achieved. In specific implementation, the overall damage variables... D total Establish a quantitative relationship with penetration rate: k= ·exp(b· D total ) in, denoted as initial permeability, and b as the damage-permeability coupling coefficient.

[0072] This relationship allows for the prediction of spatiotemporal evolution of overburden permeability based on damage variables, providing parameter input for coalbed methane migration simulation.

[0073] To better understand this invention, taking an inclined coal seam mine as an example, the method of this invention is used to identify overburden damage during the working face advance process.

[0074] In this mine, the No. 3 coal seam has a dip angle of 25 degrees and a working face length of 180 meters, employing inclined longwall mining. During the working face advance, the overlying rock on the coal pillar side experiences cyclic stress disturbance through stepped loading, while the overlying rock on the goaf side experiences cyclic stress disturbance through stepped unloading. Field monitoring tests conducted in this mine verified the effectiveness of the method of this invention.

[0075] Monitoring boreholes were installed on the side of the coal pillar, penetrating the coal seam to reach the immediate roof stratum. Multiple displacement gauges and borehole stress gauges were installed within the boreholes to monitor overburden deformation and stress changes. Surface settlement monitoring points and deep displacement monitoring points were also installed on the side of the goaf to monitor overburden movement characteristics within the mining-affected area. Monitoring results show that the overburden damage evolution pattern identified using the method of this invention agrees well with the field monitoring results, verifying the reliability of the method.

[0076] In engineering applications, the overall multi-source damage variables identified by the method of this invention are used as quantitative indicators for evaluating overburden stability. Combined with numerical simulation analysis, the overburden on the goaf side within 40 to 60 meters behind the working face is identified as a vulnerable instability zone, requiring targeted support measures. According to the damage evolution curve prediction, within 180 days after mining ceases, the overburden in this area will transition from a stable accumulation stage to an accelerated instability stage. It is recommended to complete relevant safety assessments and remediation work before this point.

[0077] This invention achieves the following technical effects through the above-mentioned technical solution: By synergistically constructing macroscopic and mesoscopic damage variables, a unified dual-scale characterization of overburden damage is realized. Macroscopic damage variables characterize the macroscopic deterioration of overburden bearing capacity from three dimensions: elastic modulus degradation, plastic strain accumulation, and dissipated energy dissipation. Mesoscopic damage variables characterize the evolution of the internal structural integrity of the rock mass from four dimensions: the number of fractures, fracture skeleton length, proportion of connected clusters, and directional order. The two complement each other to constitute a complete description of overburden damage. Furthermore, by introducing synergistic gain coefficients and path sensitivity coefficients, multi-source damage variables are realized. The adaptive nonlinear coupling amplifies the damage effect when macroscopic and mesoscopic damage evolve synchronously, and suppresses damage overestimation when they evolve asynchronously, thus avoiding the irrationality of the traditional linear superposition method. Through a segmented update mechanism, the overall multi-source damage variables are calculated separately in the cascade loading cycle, cascade unloading cycle, and shutdown constant load cycle, realizing the dynamic tracking and quantitative identification of the damage state of the overburden throughout the entire mining process. This provides unified and quantifiable damage parameters for the long-term stability evaluation of the overburden, the creep constitutive correction after disturbance, the time-varying reconstruction of permeability, and the simulation of coalbed methane migration.

[0078] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0079] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for identifying multi-source damage to overlying rock caused by mining disturbance, characterized in that, include: Obtain the physical and mechanical parameters of the overlying rock and the parameters of the mining disturbance path; Macroscopic performance damage parameters of overburden rock mass were extracted and macroscopic damage variables were constructed by indoor triaxial stepped cyclic-constant load combined test; A discrete element numerical simulation model was established to extract damage parameters of the overburden microstructure and construct microstructure damage variables. An adaptive coupling method for multi-source damage variables is constructed, the synergistic gain coefficient is calculated and the overall multi-source damage variables are constructed, and the variables are updated in segments according to the cascade loading cycle segment, the cascade unloading cycle segment and the stop mining constant load segment to obtain the time-series evolution results of the multi-source damage variables. Based on the temporal evolution results of the multi-source damage variables, their growth rate with respect to time is calculated, and the overburden damage process is divided into a compaction adjustment stage, a stable accumulation stage, and an accelerated instability stage according to the growth rate; at the same time, based on the numerical threshold range of the overall multi-source damage variables, the overburden is identified as a high-damage zone, an unstable zone, and a damage buffer zone. The output includes the overburden damage zoning and key stage identification results for the damage stage and damage area type.

2. The method according to claim 1, characterized in that, The constructed macroscopic damage variables include: The parameters of elastic modulus degradation, plastic strain accumulation, and dissipated energy evolution were obtained through indoor triaxial cyclic-constant load combined tests. Based on the aforementioned parameters, a macroscopic damage variable is constructed, the expression of which is: in, 0 represents the initial elastic modulus. Let i be the equivalent elastic modulus of the i-th stage. This refers to the elastic modulus near the instability state. , For the accumulated plastic strain in stage i, , To accumulate plastic strain before instability, , For the accumulated dissipated energy in stage i, , To accumulate and dissipate energy before instability , , Let be the weight coefficient, and satisfy... + + =1.

3. The method according to claim 2, characterized in that, The weighting coefficients are determined using the coefficient of variation method. Statistical analysis is performed on the dispersion of the three indicators—elastic modulus degradation rate, plastic strain accumulation rate, and dissipated energy accumulation rate—under multi-stage loading or unloading paths. Indicators with greater dispersion are assigned higher weighting coefficients.

4. The method according to claim 1, characterized in that, The constructed mesoscopic damage variables include: A microscopic numerical model was established using the discrete element method for particle flow, and the particle contact stiffness, friction coefficient, and bonding strength were calibrated using macroscopic stress-strain curves. Statistical analysis was conducted on the number of fractures, the total length of the fracture skeleton, and the percentage of the area of ​​the largest connected cluster. Based on the aforementioned parameters, a mesoscopic damage variable is constructed, the expression of which is: in, Let i be the number of cracks in stage i. This represents the total number of fractures during the final instability stage. Let be the total length of the fracture skeleton in stage i. This represents the final total length of the fractured skeleton. The percentage of the area of ​​the largest connected cluster in the i-th stage. This represents the final maximum connected cluster area percentage.

1.

2. 3 is the weighting coefficient, and satisfies 1+ 2+ 3 = 1.

5. The method according to claim 1, characterized in that, The adaptive coupling method for constructing multi-source damage variables includes: Calculate the rate of change of macroscopic and microscopic damage variables between adjacent stages; The cooperative gain coefficient is constructed based on the rate of change, and its expression is as follows: in, is the path sensitivity coefficient, used to reflect the difference in the degree of macroscopic and microscopic damage coordination under loading and unloading disturbances.

6. The method according to claim 5, characterized in that, The path sensitivity coefficient is assigned a different value based on the stress path type. The first value is taken under the step loading cycle path, and the second value is taken under the step unloading cycle path, with the first value being greater than the second value.

7. The method according to claim 1, characterized in that, The overall multi-source damage variable is constructed based on the synergistic gain coefficient, and its expression is as follows: When macroscopic performance damage and microstructural damage increase rapidly in tandem... As the damage increases, the overall damage accelerates and amplifies; when the two evolve asynchronously... Reduce and avoid overestimating the damage caused by simple coupling.

8. The method according to claim 7, characterized in that, The output includes the overburden damage zoning and key stage identification results for the damage stage and damage region type, including: Based on the growth rate of the overall damage variables, the overburden damage process is divided into a compaction adjustment stage, a stable accumulation stage, and an accelerated instability stage. High-damage areas, unstable areas, and damage buffer zones are identified based on the threshold range of the overall damage variable.

9. The method according to claim 1, characterized in that, The overall multi-source damage variables are updated in segments according to the disturbance stage, and are calculated separately in the cascade loading cycle segment, the cascade unloading cycle segment, and the stop-mining constant load segment, so as to realize the dynamic identification of the damage state of the overburden throughout the entire mining process.

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