Rock multi-field coupling test system and damage evaluation and prediction method thereof

Abstract

The invention discloses a rock multi-field coupling test system and a damage evaluation and prediction method thereof, belongs to the technical field of geotechnical engineering and geomechanics experiments, and solves the problem that the rock state under the multi-field coupling condition cannot be monitored in real time in the prior art. The system comprises a multi-physics field coupling loading system, a multi-field data monitoring system and a multi-field data acquisition system. The system can simulate and apply axial pressure, confining pressure, pore pressure and temperature of an in-situ environment to a rock sample, integrates an acoustic monitoring system, a deformation monitoring system and a CT scanning system to realize multi-dimensional real-time monitoring of rock sample damage, overcomes the limitation of a single monitoring method, realizes multi-system electrical connection by taking an upper computer as a data acquisition center, and realizes multi-dimensional monitoring of rock sample damage. And a multi-parameter model is constructed by fusing sound waves, deformation and CT data, so that the whole-process quantitative evaluation from microcrack initiation to macroscopic fracture is realized, and the damage evaluation is comprehensively performed.

Description

Technical Field

[0001] This invention relates to the fields of geotechnical engineering and geomechanical experimental technology, specifically to a multi-field coupling testing system for rocks and its damage assessment and prediction method. Background Technology

[0002] In the development of deep solid resources, the combined physical effects of geostress, geothermal temperature, and seepage fields significantly influence the stability of surrounding rocks. However, current rock mechanics testing techniques face challenges in accurately simulating this multi-field coupled environment and effectively monitoring rock damage processes. For example, in high-pressure confined cavities, traditional sensors based on electrical measurement principles are not only susceptible to electromagnetic crosstalk but also face a high risk of encapsulation failure, hindering the accurate capture of the dynamic strain field across the entire rock sample. Meanwhile, damage assessment methods relying on acoustic emission (AE) counting or single-point displacement measurement are essentially macroscopic or localized indirect representations, unable to quantitatively characterize the complete spatiotemporal evolution path of microcracks from initiation and propagation to penetration. Although step-by-step X-ray computed tomography (CT) technology offers the possibility of damage visualization, its discontinuous "load-unload-scan" working mode leads to relaxation and redistribution of the stress field within the rock sample, making it impossible to capture the true structural evolution information under in-situ stress conditions. Summary of the Invention

[0003] To address the aforementioned problems in the prior art, this invention provides a rock multi-field coupling testing system and its damage assessment and prediction method, solving the problem that the prior art cannot monitor the rock state under multi-field coupling conditions in real time.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: On one hand, a multi-field coupling testing system for rocks is provided, including a multi-physics field coupling loading system, a multi-field data monitoring system, and a multi-field data acquisition system. The multi-physics field coupling loading system includes a mechanical loading system, a seepage loading system, and a temperature loading system. The mechanical loading system includes a loading mechanism with a cavity containing a rock sample wrapped in a heat-shrinkable film. The loading mechanism is used to apply axial pressure and confining pressure to the rock sample. The seepage loading system is used to apply osmotic pressure to the rock sample. The temperature loading system is used to apply a temperature field to the rock sample. The multi-field data monitoring system includes a mechanical monitoring system, an acoustic monitoring system, a deformation monitoring system, and a CT scanning system. The mechanical monitoring system includes a controller in the loading mechanism. The acoustic monitoring system includes a longitudinal wave detector and a transverse wave detector mounted on the rock sample. The deformation monitoring system includes a DAS system, which includes distributed fiber optic acoustic sensors spirally wound at equal intervals on the rock sample. The distributed fiber optic acoustic sensors are electrically connected to a distributed fiber optic demodulator. The CT scanning system is used to perform CT scans on the rock sample. The multi-field data acquisition system includes a host computer electrically connected to the multi-field data monitoring system, the seepage loading system, and the temperature loading system.

[0005] The beneficial effects of this scheme are that it can simulate the application of in-situ environmental axial pressure, confining pressure, pore pressure and temperature to rock samples, and integrates acoustic monitoring system, deformation monitoring system and CT scanning system to achieve multi-dimensional real-time monitoring of rock sample damage. It overcomes the limitations of single monitoring methods, and uses a host computer as a data acquisition center to realize the electrical connection of multiple systems, realize synchronous data acquisition and processing, provide a comprehensive basis for damage assessment, and solve the problem that existing technologies cannot monitor the state of rocks under multi-field coupling conditions in real time.

[0006] Furthermore, the seepage loading system includes an inlet pipe and an outlet pipe that are respectively connected to the top and bottom of the cavity. The inlet and outlet pipes simulate in-situ seepage conditions.

[0007] Furthermore, the acoustic monitoring system also includes a data transmitter electrically connected to a host computer. The data transmitter is electrically connected to a signal transmitter, which is electrically connected to the transmitters of both the longitudinal wave detector and the transverse wave detector. The receivers of the longitudinal wave detector and the transverse wave detector are electrically connected to the longitudinal wave signal receiver and the transverse wave signal receiver, respectively. Both the longitudinal wave signal receiver and the transverse wave signal receiver are electrically connected to the host computer. Through the electrical connection structure of the data transmitter, signal transmitter, and receiver, efficient acquisition and real-time transmission of acoustic wave data are achieved, reducing electromagnetic crosstalk and signal loss.

[0008] Furthermore, the host computer is electrically connected to the data acquisition unit, which in turn is electrically connected to the longitudinal wave signal receiver, the transverse wave signal receiver, and the distributed fiber optic demodulator. Through these electrical connections between the host computer, the data acquisition unit, and the receivers, centralized acquisition and synchronous processing of acoustic wave and deformation data are achieved. This avoids noise interference caused by data dispersion, improves system stability, ensures the consistency of multi-source data, and provides a reliable foundation for fusion analysis.

[0009] Furthermore, the CT scanning system includes a CT scanning transmitter and a CT imaging screen connected to a host computer. The connection between the CT scanning transmitter and the imaging screen to the host computer allows for controlled CT scanning during loading.

[0010] On the other hand, a damage assessment and prediction method for a multi-field coupled rock testing system is provided, including the following steps: S100. Install the rock sample into the cavity and obtain the initial state characterization of the rock sample through the acoustic monitoring system and CT scanning system. S200: Apply the set confining pressure, pore pressure and temperature to the rock sample through the mechanical loading system, seepage loading system and temperature loading system respectively, and let it stand for a period of time; S300, Start the loading mechanism to apply axial load to the rock sample at a constant axial strain rate; During the axial load loading process, obtain the damage evolution threshold node of the rock sample according to the real-time stress and strain curve of the rock sample, and the loading mechanism pauses the axial load loading at the damage evolution threshold node until the CT scanning system completes the CT scan of the current node. S400. After the rock sample has completely destabilized or reached the preset residual strength, stop and remove the axial load, and perform the final CT scan. S500 collects experimental data of rock samples throughout the experiment using a multi-field data acquisition system. The experimental data includes acoustic data, deformation data, and CT data obtained from the acoustic monitoring system, deformation monitoring system, and CT scanning system, respectively. A multi-parameter rock damage assessment and prediction model is constructed by fusing the acoustic data, deformation data, and CT data.

[0011] In this scheme, based on the real-time stress and strain curves of rock samples, threshold nodes are dynamically identified during axial load loading to ensure the timing of CT scans and capture key damage stages. By fusing acoustic, deformation, and CT data to construct a multi-parameter model, quantitative assessment of the entire process from microcrack initiation to macroscopic rupture is achieved, avoiding localized defects.

[0012] Furthermore, the diffuse volumetric damage index of the rock sample was obtained using acoustic data. , The expression is: in, Let be the dynamic shear modulus of the rock sample at time t; The initial density of the rock sample; Let be the transverse wave velocity of the rock sample at time t; Let be the dynamic bulk modulus of the rock sample at time t; Let be the longitudinal wave velocity of the rock sample at time t; and These are the initial dynamic shear modulus and dynamic bulk modulus of the rock sample, respectively.

[0013] Furthermore, the dynamic localized damage index of the rock sample was obtained through deformation data. , The expression is: in, Let be the energy rate of the vibration signal of the rock sample at time t; This represents the maximum energy rate of the vibration signal of the rock sample throughout the entire experiment. The spatial clustering index for acoustic emission events. For adjustment Sensitivity setting; Furthermore, the structural geometric damage index of the rock sample was obtained using CT data. , The expression is: in, This is a set of threshold nodes for rock sample damage evolution. , , and These are the times for the 1st, 2nd, and kth scan nodes, respectively. The feature length of the maximum connected crack extracted by the CT scanning system through CT image analysis at time t; The characteristic height dimension of the rock, This is the magnification factor; Let be the fractal dimension of the crack network of the rock at time t.

[0014] Furthermore, the expression in the multi-parameter rock damage assessment and prediction model is as follows: in, The damage assessment index for the rock at time t; , and They are respectively , and Normalized weighting coefficients; , and They are respectively , and The weighting function; Let be the strain of the rock sample at time t. , The axial strain rate; The strain at the boundary node between the elastic stage and the plastic deformation stage of the rock sample; The damage weight attenuation coefficient is set; This represents the peak strain of the rock sample. The distribution width coefficient is set; The set amplitude; This is the critical strain at which the rock sample begins to dominate the rock mechanical behavior at the macroscopic main fracture surface; The set fracture weight growth factor. (Through...) , and and , and They are respectively , and Dynamically integrated , and This approach addresses the limitations of a single exponent model by adaptively adjusting the weighting function based on strain (e.g., emphasizing overall damage during the elastic stage and local damage during the peak strain stage). This allows the model to better reflect the actual deformation stages of rock samples, improving the accuracy and predictive power of damage assessment. Furthermore, the model is applicable to different rock types and loading conditions, enhancing its versatility. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the structure of a multi-field coupling testing system for rocks; The components include: 1. CT imaging screen; 2. cavity; 3. signal transmitter; 4. data transmitter; 5. water inlet pipe; 6. water inlet; 7. shear wave detector; 8. longitudinal wave detector; 9. rock sample; 10. digital fluorescence oscilloscope; 11. host computer; 12. CT scan emission source; 13. seepage loading system; 14. heat shrink film; 15. distributed fiber optic acoustic sensor; 16. oil pump system; 17. loading mechanism; 18. reserved mounting hole; 19. data acquisition instrument; 20. oil gauge; 21. longitudinal wave signal receiver; 22. hydraulic oil; 23. hydraulic oil port; 24. water outlet pipe; 25. shear wave signal receiver; 26. distributed fiber optic demodulator. Detailed Implementation

[0016] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0017] Example 1 refer to Figure 1 This embodiment provides a rock multi-field coupling test system to solve the problem that the existing technology cannot monitor the rock state under multi-field coupling conditions in real time. It includes a multi-physics field coupling loading system, a multi-field data monitoring system and a multi-field data acquisition system.

[0018] Specifically, the multiphysics coupled loading system includes a mechanical loading system, a seepage loading system 13, and a temperature loading system.

[0019] The mechanical loading system includes a loading mechanism 17, which contains a cavity 2. The cavity 2 contains a sealed rock sample 9 wrapped with a heat-shrink film 14. The loading mechanism 17 applies axial pressure and confining pressure to the rock sample 9. The cavity 2 is connected to an oil pump system 16 via a hydraulic port 23. An oil gauge 20 is installed on the pipeline of the oil pump system 16 to facilitate observation of the hydraulic pressure of the hydraulic oil 22 in the cavity 2.

[0020] The seepage loading system 13 is used to apply seepage pressure to the rock sample 9, and includes an inlet pipe 5 and an outlet pipe 24 connected to the top and bottom of the cavity 2, respectively. The seepage water circulating through the inlet 6 of the inlet pipe 5 and the outlet of the outlet pipe 24 simulates the in-situ seepage conditions.

[0021] The temperature loading system indirectly applies a temperature field to the rock sample 9 by heating the hydraulic oil 22 inside the cavity 2. Since controlling the temperature of the hydraulic oil 22 is an existing technology, its specific working principle and connection relationship will not be described in detail in this embodiment.

[0022] Specifically, the multi-field data monitoring system includes a mechanical monitoring system, an acoustic monitoring system, a deformation monitoring system, and a CT scanning system.

[0023] The mechanical monitoring system includes a controller in the loading mechanism 17, which monitors the axial pressure and confining pressure of rock sample 9 in real time during the mechanical loading process, making it convenient to construct the real-time stress and strain curve of rock sample 9.

[0024] The acoustic monitoring system includes a longitudinal wave detector 8 and a transverse wave detector 7 mounted on the rock sample 9. The system also includes a data transmitter 4, which is electrically connected to a digital fluorescence oscilloscope 10 and a host computer 11. The data transmitter 4 is also electrically connected to a signal transmitter 3, which is electrically connected to the transmitting ends of both the longitudinal wave detector 8 and the transverse wave detector 7. The receiving ends of the longitudinal wave detector 8 and the transverse wave detector 7 are electrically connected to a longitudinal wave signal receiver 21 and a transverse wave signal receiver 25, respectively. Both the longitudinal wave signal receiver 21 and the transverse wave signal receiver 25 are electrically connected to the host computer 11. Through the electrical connection structure of the data transmitter 4, the signal transmitter 3, and the receivers, efficient acquisition and real-time transmission of acoustic wave data are achieved, reducing electromagnetic crosstalk and signal loss.

[0025] The deformation monitoring system includes a DAS system, which includes distributed fiber optic acoustic sensors 15 that are spirally wound at equal intervals on the rock sample 9. The distributed fiber optic acoustic sensors 15 are electrically connected to a distributed fiber optic demodulator 26.

[0026] The CT scanning system is used to perform CT scans on rock sample 9. The CT scanning system includes a CT scanning transmitter 12 and a CT imaging screen 1, which are electrically connected to a host computer 11. The CT scanning transmitter 12 and the imaging screen are electrically connected to the host computer 11, allowing controlled CT scans to be performed during loading.

[0027] Specifically, the multi-field data acquisition system includes a host computer 11 electrically connected to the multi-field data monitoring system, the seepage loading system 13, and the temperature loading system. The host computer 11 is electrically connected to a data acquisition instrument 19, which in turn is electrically connected to a longitudinal wave signal receiver 21, a transverse wave signal receiver 25, and a distributed fiber optic demodulator 26. Through the electrical connections of the host computer 11, the data acquisition instrument 19, and the receivers, centralized acquisition and synchronous processing of acoustic wave data and deformation data are achieved. This avoids noise interference caused by data dispersion, improves system stability, ensures the consistency of multi-source data, and provides a reliable foundation for fusion analysis.

[0028] In this embodiment, when installing the distributed fiber optic acoustic sensor 15 on the rock sample 9, the sidewalls of the rock sample 9 need to be cleaned. Using high-strength, low-modulus epoxy resin or a special adhesive, the distributed fiber optic acoustic sensor 15 is tightly and evenly wound in a spiral shape and fixed to the cylindrical surface of the rock sample 9. The starting point and number of turns of the optical fiber are recorded for subsequent spatial positioning. The signal end of the distributed fiber optic acoustic sensor 15 is connected to the distributed acoustic sensor demodulator through the reserved mounting hole 18 in the loading mechanism 17. Then, the rock sample 9 with the distributed fiber optic acoustic sensor 15 wound on is sealed with a high-temperature heat-shrinkable film 14 to ensure isolation from the external medium under confined pressure and seepage conditions. Good acoustic contact is also ensured between the longitudinal wave detector 8 and the transverse wave detector 7 and the end face of the rock sample 9.

[0029] In summary, the beneficial effects of this plan are as follows: This solution can simulate the in-situ environment of axial pressure, confining pressure, pore pressure and temperature on rock sample 9, and integrates an acoustic monitoring system, a deformation monitoring system and a CT scanning system to achieve multi-dimensional real-time monitoring of damage to rock sample 9. It overcomes the limitations of single monitoring methods, and uses the host computer 11 as a data acquisition center to realize the electrical connection of multiple systems, realize the synchronous acquisition and processing of data, provide a comprehensive foundation for damage assessment, and solve the problem that existing technologies cannot monitor the state of rocks under multi-field coupling conditions in real time.

[0030] Example 2 This embodiment is a further improvement based on Embodiment 1. The specific improvement is to provide a damage assessment and prediction method for a rock multi-field coupling test system. Other technologies not mentioned refer to Embodiment 1 or existing technologies.

[0031] A damage assessment and prediction method for a multi-field coupled rock testing system includes the following steps: S100. Rock sample 9 is installed into the cavity, and its initial state characterization is obtained through an acoustic monitoring system and a CT scanning system. The CT scanning system performs an initial scan of rock sample 9 to obtain its initial three-dimensional structure before loading and identify any possible primary micro-fractures within it. The acoustic monitoring system transmits and receives transverse and longitudinal wave signals, measures and records the initial wave velocity, calculates the initial longitudinal wave velocity and transverse wave velocity, and obtains the initial dynamic shear modulus and dynamic bulk modulus of rock sample 9.

[0032] In this embodiment, rock sample 9 is a homogeneous in-situ rock without obvious macroscopic cracks obtained through core sampling technology. It is prepared into a standard-sized cylindrical specimen according to the standards of the International Society for Rock Mechanics (ISRM). The end faces of rock sample 9 are finely polished to ensure its flatness and perpendicularity, which is crucial for ensuring uniform axial loading and good coupling of ultrasonic signals. Preferably, rock sample 9 has a diameter of 50 mm and a height of 100 mm.

[0033] S200: The rock sample 9 is subjected to the set confining pressure, pore pressure, and temperature through the mechanical loading system, seepage loading system 13, and temperature loading system, respectively, and then left to stand for a period of time. This standing period allows the internal stress and seepage pressure of the rock sample 9 to reach equilibrium, stabilizing the signal in the multi-field data monitoring system.

[0034] S300, the loading mechanism 17 is started to apply an axial load to the rock sample 9 at a constant axial strain rate; during the axial load loading process, the damage evolution threshold node of the rock sample 9 is obtained according to the real-time stress and strain curve of the rock sample 9, and the loading mechanism 17 pauses the axial load loading at the damage evolution threshold node until the CT scanning system completes the CT scan of the current node.

[0035] Specifically, the damage evolution threshold nodes of rock sample 9 can include the initial state of rock sample 9, approximately 50% peak strength, 80% peak strength, and when it just enters the plastic stage. When rock sample 9 is at the aforementioned preset key nodes, the loading mechanism 17 pauses axial loading, maintains the current axial stress constant while performing a CT scan to obtain the distribution of internal cracks in the rock sample under this stress state. After the scan is completed, axial loading continues at the original rate.

[0036] S400. After rock sample 9 has completely become unstable or reached the preset residual strength, stop and remove the axial load, and perform the final CT scan.

[0037] S500: The experimental data of rock sample 9 were collected throughout the experiment through a multi-field data acquisition system. The experimental data included acoustic data, deformation data and CT data obtained by the acoustic monitoring system, deformation monitoring system and CT scanning system respectively. A multi-parameter rock damage assessment and prediction model was constructed by fusing the acoustic data, deformation data and CT data.

[0038] In this embodiment, based on the real-time stress and strain curves of rock sample 9, threshold nodes are dynamically identified during axial load loading to ensure the timing of CT scans and capture key damage stages. By fusing acoustic, deformation, and CT data to construct a multi-parameter model, quantitative assessment of the entire process from microcrack initiation to macroscopic rupture is achieved, avoiding localized defects.

[0039] Specifically, the acoustic data was used to obtain the dispersion volume damage index of rock sample 9. , The expression is: in, Let be the dynamic shear modulus of rock sample 9 at time t; The initial density of rock sample 9; Let be the transverse wave velocity of rock sample 9 at time t; Let be the dynamic bulk modulus of rock sample 9 at time t; Let be the longitudinal wave velocity of rock sample 9 at time t; and These represent the initial dynamic shear modulus and dynamic bulk modulus of rock sample 9, respectively. Dispersible volume damage index. It can reflect the deterioration of material stiffness in rock sample 9 during multiple loading processes.

[0040] Specifically, the deformation data was used to obtain the dynamic localization damage index of rock sample 9. , The expression is: in, The energy rate of the vibration signal of rock sample 9 at time t is obtained from the acoustic data amplitude obtained by the DAS system, and is calculated by integrating the square of the amplitude. It mainly reflects the severity of the current fracture. The maximum energy rate of the vibration signal of rock sample 9 during the entire experiment was used for normalization. The acoustic emission event spatial clustering index is used to characterize the degree of spatial clustering of microfracture events inside rock sample 9. For adjustment The sensitivity setting is preferably set to 0~1, which is used to adjust the sensitivity of the clustering index.

[0041] In this embodiment, The initial value is approximately 1, when A value greater than 1 indicates that a fracture zone is forming inside rock sample 9. Cluster analysis of acoustic emission event locations reveals spatial clustering patterns, transforming discrete events into quantitative indices. To facilitate understanding of the working principle of acoustic emission events, the basic formula for acoustic emission event localization is provided: The meaning of this formula is: Internal points of rock sample 9 exist A micro-fracture event occurred at a certain moment, and the longitudinal wave velocity of the micro-fracture event was... Dissemination, in The time has reached the i-th measurement point on the distributed fiber optic acoustic sensor. .in, and The coordinates are respectively and The location of the acoustic emission event can be accurately calculated using the winding method of the distributed fiber optic acoustic sensor. Based on the ultra-high resolution characteristics of the distributed fiber optic acoustic sensor, the source location of the acoustic emission event is determined using the least squares method. And the timing of acoustic emission events Ultimately, the spatial distribution characteristics of microfracture events within the rock were obtained, and cluster analysis was performed on the locations of acoustic emission events to calculate... .

[0042] Dynamic localized damage index The effect is that when the acoustic emission energy release rate is high and these events are highly concentrated in space, the dynamic localization damage index will increase exponentially, indicating that macroscopic fracturing of rock sample 9 is about to occur.

[0043] Specifically, CT data was used to obtain the structural geometric damage index of rock sample 9. Image processing software was used to perform 3D reconstruction, denoising, and segmentation of CT images at different loading stages. Geometric parameters of the crack network, such as crack volume, density, length, aperture, and orientation (dip and strike), were extracted and quantified. Percolation theory and fractal geometry concepts were introduced to define damage, resulting in a structural geometric damage index. , The expression is: in, This is the set of damage evolution threshold nodes for rock sample 9. , , and These are the times for the 1st, 2nd, and kth scan nodes, respectively. and These represent the initial CT scan time and the last CT data scan time, respectively. The feature length of the maximum connected crack extracted by the CT scanning system through CT image analysis at time t; For the characteristic height dimension of the rock, when and When they are close together, it means that rock sample 9 is destroyed; This is the magnification factor; used to amplify the main crack and length effect, with a value of 0~2. denoted as the fractal dimension of the crack network. The more complex the crack network, the closer it is to crack penetration. The maximum value is 3, reflecting the complexity of the crack network.

[0044] As a further solution in this embodiment, through the above... , and Dynamic weighting functions are proposed for acoustic wave data, distributed acoustic sensor data, and CT scan data, respectively. , and It is not fixed, but changes with the loading stage, reflecting the evolution of rock damage patterns at different loading stages. The specific weighting coefficients are described as follows: 1. Weighting function for diffuse volumetric damage : In the early stages of loading (elastic and compaction stages) of rock sample 9, it was absolutely dominant, but its importance gradually and smoothly decreased as the crack began to propagate steadily. The expression is: in, Let be the strain of rock sample 9 at time t. , The axial strain rate; The strain of rock sample 9 at the boundary node between the elastic stage and the plastic deformation stage; The damage weight attenuation coefficient is set to control the rate of weight decrease. Its value is related to the lithology of the rock. When the rock is hard rock, the value is larger (2~5), and when the rock is soft rock, the value is smaller (0.1~2).

[0045] 2. For dynamic localized damage weighting function : In the initial stage of loading, rock sample 9 is in the elastic stage. At this time, there is no crack propagation inside rock sample 9, and the dynamic localization damage weight has almost no effect. When cracks begin to accumulate and damage tends to be localized, its importance rises rapidly and reaches its peak when it is about to reach the peak strength. After that, as the main fracture surface is completed, its role may decrease or remain. The expression is: in, This represents the peak strain of rock sample 9; The distribution width coefficient is set to 5% for hard rock and 1% for soft rock. For amplitude values, take 2 for hard rock and 1~1.5 for soft rock.

[0046] 3. Structural geometric damage weighting morphology function : It has almost no effect in the early and middle stages of loading. Only when the damage on rock sample 9 becomes highly localized and the main fracture surface begins to form and penetrate does its importance emerge and quickly become absolutely dominant. The expression is as follows: in, The critical strain corresponding to the start of the dominant rock mechanical behavior at the macroscopic main fracture surface of rock sample 9 is related to the peak intensity point and the initial stage after the peak of rock sample 9. The fracture weight growth coefficient is set to control the speed at which the weight grows from 0 to dominance. It also reflects the brittle characteristics of the rock. When the rock is hard rock, the value is larger (1~2), and when the rock is soft rock, the value is smaller (0~1).

[0047] Based on the above, a multi-parameter rock damage assessment model is finally obtained, which is derived from acoustic wave data, acoustic signals, CT scan data, and mechanical and strain data. The expression in the multi-parameter rock damage assessment and prediction model is as follows: in, The damage assessment index for the rock at time t; , and They are respectively , and The normalized weighting coefficients.

[0048] As a further aspect of this embodiment, the discontinuity of time t in the multi-parameter rock damage assessment and prediction model is due to... Due to the limitations of scanning time, in order to ensure time continuity in the multi-parameter rock damage assessment and prediction model, and considering that the loading mechanism 17 operates at a constant axial strain rate, Since it is loaded, interpolation can be used to make it... As a continuous function, the multi-parameter rock damage assessment and prediction model is made time-continuous, preserving the structural accuracy of CT data while meeting the requirements of continuous assessment throughout the entire process, providing complete timeline support for early warning.

[0049] The multi-parameter rock damage assessment and prediction model nonlinearly integrates three core data streams: acoustic data, deformation data (DAS system data), and CT data, forming a damage assessment method for rock sample 9 at various stages during multi-field loading. The multi-parameter rock damage assessment and prediction model yields a graph with time or strain as the horizontal axis and... The curve, with values ​​ranging from 0 to 1 on the vertical axis, visually depicts the entire process of rock sample 9 from intact to completely destroyed, and can be used for subsequent damage assessment prediction of rock sample 9 in a multi-field coupled testing system.

[0050] The specific stage information is as follows: a, The smooth phase: Rock sample 9 is in the linear elastic or initial compaction stage, with very little internal damage or diffuse microcrack closure and initiation. Risk state corresponding to rock sample 9: safe and stable stage.

[0051] b、 The stage of slow and steady growth: Rock sample 9 is in the stage of stable fracture propagation. The damage began to increase steadily, and acoustic emission events captured by the DAS system started to appear, but their distribution was relatively random. Rock sample 9 corresponds to the risk state: the damage accumulation stage, where risk begins to emerge.

[0052] c. The inflection point and the beginning of "accelerated" growth: the key turning point in the localization of damage in rock sample 9. This accelerated growth was mainly due to... The driving force—acoustic emission events not only increase dramatically in energy and frequency, but more importantly, they begin to cluster highly in space. Rock sample 9 corresponds to the risk state: the instability precursor stage, where the risk level rapidly escalates, representing the most critical early warning interval.

[0053] d、 Approaching a steep stage (likely a 1): Rock sample 9 is in an unstable fracture propagation stage. CT images will show one or more macroscopic cracks that have formed and are nearing completion. A sharp increase. In multi-parameter rock damage assessment models and This is the maximum risk level. Rock sample 9 corresponds to the risk status: impending or already occurred, at the highest risk level.

[0054] In summary, the beneficial effects of the present invention are as follows: This invention overcomes the shortcomings of existing technologies, such as limited monitoring methods, isolated data, and damage assessment that remains qualitative or at a single scale. Through integrated hardware and software design, it achieves quantitative, visualized, and multi-scale collaborative assessment of the entire process of rock damage, from microscopic damage initiation to localized mesoscopic damage and macroscopic fracture penetration. This provides a novel and reliable technical means for accurately identifying precursors of instability and achieving early warning of rock failure.

[0055] Although specific embodiments of the invention have been described in detail with reference to the accompanying drawings, this should not be construed as limiting the scope of protection of this patent. Various modifications and variations that can be made by a person skilled in the art without inventive effort within the scope described in the claims still fall within the scope of protection of this patent.

Claims

1. A multi-field coupling testing system for rocks, characterized in that, include: Multiphysics coupled loading systems include: A mechanical loading system includes a loading mechanism (17), in which a cavity (2) is provided, and a rock sample (9) wrapped with a heat-shrinkable film (14) is sealed and contained in the cavity (2). The loading mechanism (17) is used to apply axial pressure and confining pressure to the rock sample (9). A seepage loading system (13) is used to apply seepage pressure to the rock sample (9); A temperature loading system is used to apply a temperature field to the rock sample (9); Multi-field data monitoring system, including: The mechanical monitoring system includes a controller in the loading mechanism (17); The acoustic monitoring system includes a longitudinal wave detector (8) and a transverse wave detector (7) mounted on the rock sample (9). The deformation monitoring system includes a DAS system, which includes a distributed fiber optic acoustic sensor (15) that is spirally wound at equal intervals on a rock sample (9), and the distributed fiber optic acoustic sensor (15) is electrically connected to a distributed fiber optic demodulator (26). A CT scanning system is used to perform CT scans on the rock sample (9); The multi-field data acquisition system includes a host computer (11) electrically connected to the multi-field data monitoring system, the seepage loading system (13), and the temperature loading system.

2. The rock multi-field coupling testing system according to claim 1, characterized in that, The seepage loading system (13) includes an inlet pipe (5) and an outlet pipe (24) that are respectively connected to the top and bottom of the cavity (2).

3. The rock multi-field coupling testing system according to claim 1, characterized in that, The acoustic monitoring system also includes a data transmitter (4) electrically connected to the host computer (11), the data transmitter (4) being electrically connected to a signal transmitter (3), the signal transmitter (3) being electrically connected to the transmitters of the longitudinal wave detector (8) and the transverse wave detector (7), the receivers of the longitudinal wave detector (8) and the transverse wave detector (7) being electrically connected to the longitudinal wave signal receiver (21) and the transverse wave signal receiver (25), respectively, and both the longitudinal wave signal receiver (21) and the transverse wave signal receiver (25) being electrically connected to the host computer (11).

4. The rock multi-field coupling testing system according to claim 3, characterized in that, The host computer (11) is electrically connected to the data acquisition instrument (19), and the data acquisition instrument (19) is electrically connected to the longitudinal wave signal receiver (21), the transverse wave signal receiver (25), and the distributed optical fiber demodulator (26), respectively.

5. The rock multi-field coupling testing system according to claim 1, characterized in that, The CT scanning system includes a CT scanning transmitter (12) and a CT imaging screen (1) that are electrically connected to a host computer (11).

6. The damage assessment and prediction method for a multi-field coupled rock testing system according to claim 1, characterized in that, Including the following steps: S100. The rock sample (9) is installed into the cavity (2), and the initial state characterization of the rock sample (9) is obtained through the acoustic monitoring system and the CT scanning system. S200. Apply the set confining pressure, pore pressure and temperature to the rock sample (9) through the mechanical loading system, the seepage loading system (13) and the temperature loading system respectively, and let it stand for a period of time. S300, Start the loading mechanism (17) to load the rock sample (9) with a constant axial strain rate; During the axial load loading process, the damage evolution threshold node of the rock sample (9) is obtained according to the real-time stress and strain curve of the rock sample (9), and the loading mechanism (17) pauses the loading of the axial load at the damage evolution threshold node until the CT scanning system completes the CT scan of the current node; S400. After the rock sample (9) is completely unstable or reaches the preset residual strength, stop and remove the axial load, and perform the last CT scan. S500. The experimental data of the rock sample (9) during the entire experimental process are collected by the multi-field data acquisition system. The experimental data includes acoustic data, deformation data and CT data obtained by the acoustic monitoring system, the deformation monitoring system and the CT scanning system respectively. A multi-parameter rock damage assessment and prediction model is constructed by fusing the acoustic data, the deformation data and the CT data.

7. The damage assessment and prediction method according to claim 6, characterized in that, The diffuse volume damage index of rock sample (9) was obtained from the acoustic data. , The expression is: in, Let be the dynamic shear modulus of rock sample (9) at time t; The initial density of rock sample (9); Let be the transverse wave velocity of rock sample (9) at time t; Let be the dynamic bulk modulus of rock sample (9) at time t; Let be the longitudinal wave velocity of rock sample (9) at time t; and These are the initial dynamic shear modulus and dynamic bulk modulus of rock sample (9), respectively.

8. The damage assessment and prediction method according to claim 7, characterized in that, The dynamic localization damage index of rock sample (9) was obtained from the deformation data. , The expression is: in, Let be the energy rate of the vibration signal of rock sample (9) at time t; The maximum energy rate of the vibration signal of rock sample (9) during the entire experiment; The spatial clustering index for acoustic emission events. For adjustment Sensitivity setting.

9. The damage assessment and prediction method according to claim 8, characterized in that, The structural geometric damage index of rock sample (9) was obtained from the CT data. , The expression is: in, This is the set of damage evolution threshold nodes for rock sample (9). , , and These are the times for the 1st, 2nd, and kth scan nodes, respectively. The feature length of the maximum connected crack extracted by the CT scanning system through CT image analysis at time t; The characteristic height dimension of the rock, This is the magnification factor; Let be the fractal dimension of the crack network of the rock at time t.

10. The damage assessment and prediction method according to claim 9, characterized in that, The expression in the multi-parameter rock damage assessment and prediction model is as follows: in, The damage assessment index for the rock at time t; , and They are respectively , and Normalized weighting coefficients; , and They are respectively , and The weighting function; The strain of rock sample (9) at time t, , The axial strain rate; The strain of rock sample (9) at the boundary node between the elastic stage and the plastic deformation stage; The damage weight attenuation coefficient is set; The peak strain of rock sample (9) is given. The distribution width coefficient is set; The set amplitude; The critical strain corresponding to the start of dominant rock mechanical behavior at the macroscopic main fracture surface of rock sample (9); The set fracture weight growth coefficient.

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