LU502697B1 - Intelligent prediction method and system for ground pressure disasters of wall rock affected by mining - Google Patents
Intelligent prediction method and system for ground pressure disasters of wall rock affected by mining Download PDFInfo
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
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Abstract
Provided are an intelligent prediction method and system for ground pressure disasters of wall rock (1) affected by mining, relating to the technical field of the prediction and prevention of ground pressure disasters due to deep mining. The method comprises the following steps: S1: providing a sensor in wall rock (1) affected by mining in order to acquire wall rock data; S2: constructing a stress-strain curve by using the acquired wall rock data, and extracting data before a wall rock stress reaches a stress limit to serve as pre-peak data, and data after the wall rock stress reaches the stress limit to serve as post-peak data; S3: substituting consecutive pre-peak data or post-peak data, within a period of time, into a machine learning model to perform impending disaster warning, so as to obtain a preliminary prediction result; S4: evaluating the preliminary prediction result on the basis of a wall rock instability theory and external environment information to obtain an evaluation result; and S5: completing final disaster decision-making according to the evaluation result. In this way, the preliminary prediction for ground pressure disasters can be completed by means of machine learning model, and the prediction result is verified according to a certain standard and model, such that both excellent accuracy and practicability are provided.
Description
Description Intelligent Prediction Method and System for Ground Pressure Disasters of Wall Rock Affected by Mining
TECHNICAL FIELD The present invention relates to the technical field of disaster prediction, in particular to an intelligent prediction method and system for ground pressure disasters of wall rock affected by mining.
BACKGROUND In a process of roadway (tunnel) construction, ground pressure disasters generated in deep mining are extremely harmful, and geological disasters such as water gushing, mud outburst and landslide are important factors that affect the safety and cost of roadway (tunnel) construction. If the occurrence of geological disasters can be predicted in advance and corresponding measures are taken, the construction risk and the construction cost can be greatly lowered. In China, the success rate of predicting ground pressure disasters of wall rock affected by mining has been significantly improved. However, most prediction methods are realized with the help of human experience at present, and the principles, working ways, interpretation and application of these methods are not mature and accurate enough to meet the needs of practical projects. In actual construction, there are still many safety accidents such as water gushing, sand gushing, debris flow and landslide, which have caused heavy casualties and economic losses. Therefore, it is necessary to continuously strengthen the prediction of ground pressure disasters of wall rock affected by mining as well as formulate reasonable corresponding measures to 1 minimize the occurrence of disasters.
SUMMARY In view of this, the present invention aims to provide an intelligent prediction method and system for ground pressure disasters of wall rock affected by mining, which can realize preliminary prediction of ground pressure disasters through a machine learning mode and verify the prediction results by a certain standard and theory model, thus realizing excellent accuracy and practicability. According to a first aspect, the present invention provides an intelligent prediction method for ground pressure disasters of wall rock affected by mining, which includes the following steps of: S1, data acquisition: arranging a plurality of sensor modules in wall rock affected by mining, wherein the sensor modules include a stress-strain sensing module and an environment sensing module, and the sensor modules are used for acquiring wall rock data and external environment information; S2, data preprocessing: constructing a stress-strain curve by using the acquired wall rock data acquired by the stress-strain sensing module, and extracting data before a wall rock stress reaches a stress limit to serve as pre-peak data and data after the wall rock stress reaches the stress limit to serve as post-peak data; S3, preliminary prediction of disasters: substituting consecutive pre-peak data and/or post-peak data, within a period of time, into a machine learning model to perform impending disaster warning, so as to obtain a preliminary prediction result; S4, result evaluation: evaluating the preliminary prediction result on the basis of a wall rock instability theory and external environment information acquired by the environment sensing module to obtain an evaluation result; and SS, final decision-making: completing final disaster decision-making according to 2 the evaluation result. Further, the acquired data are divided into pre-peak data and post-peak data according to a yield point in a full stress-strain curve model of the wall rock; the pre-peak data are used to predict the yield point and determine whether a stress of the wall rock will reach the yield point in a certain time or not, and the post-peak data or both the pre-peak data and the post-peak data are used to predict instability of the wall rock and determine whether the wall rock will be fractured and become instable in a certain time. Further, the stress-strain sensing module includes a stress sensor and a strain sensor; and the environment sensing module includes a temperature sensor, an acoustic emission sensor and a microquake sensor. Further, boreholes are formed in a surface of the wall rock, and the sensors are arranged inside the boreholes. Further, the result evaluation in the S4 specifically includes: S41: determining a deformation stage of the wall rock according to a full stress-strain curve model of the wall rock, and analyzing and predicting the acquired wall rock data based on an instability mode of the wall rock and instability mechanisms of different parts of the wall rock at the deformation stage to obtain an intermediate prediction result; S42: adjusting the intermediate prediction result in combination with the acquired external environment information to obtain a re-prediction result; and S43: comparing the re-prediction result with the preliminary prediction result to obtain an evaluation result.
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Further, the external environment information includes a temperature, noise and vibration.
Further, it is considered that the wall rock is prone to being instable if there is a higher temperature, therefore, an allowable stress should be appropriately lowered, it is considered that the wall rock is prone to being instable if there is louder noise, therefore, an allowable stress should be appropriately lowered; and it is considered that the wall rock is prone to being instable if there is stronger vibration, therefore, an allowable stress should be appropriately lowered.
Further, the preliminary prediction of disasters in the S3 specifically includes: S31, preliminarily constructing a machine learning model according to the characteristics of a pre-peak long-term index of a disaster and a post-peak impending index of the disaster; S32, taking the pre-peak long-term data and the post-peak impending data in existing disaster data as training sets to complete preliminary training of the machine learning model; S33, preliminarily analyzing and predicting the consecutive pre-peak data and/or post-peak data in the S2 within a period of time by using the machine learning model; and S34, when the number of the pre-peak data and post-peak data in the S2 meets the training requirement, taking the pre-peak data and post-peak data in the S2 as new training sets to complete iterative optimization of the machine learning model.
Further, the evaluation result in the step S43 specifically includes: directly taking the preliminary prediction result as a final disaster prediction result if the re-prediction result is the same as the preliminary prediction result;
4 if the re-prediction result is different from the preliminary prediction result, prolonging the period of time in the S3, repeating the steps S3-S4 to re-output an evaluation result; and if the newly obtained re-prediction result is still different from the preliminary prediction result, taking the re-prediction result as the final disaster prediction result, and training and optimizing the machine learning model in the S3 by using the re-prediction result and the pre-peak data and/or post-peak data in the prolonged period of time.
According to a second aspect, the present invention provides an intelligent prediction system for ground pressure disasters of wall rock affected by mining. The system 1s used for executing the method according to the first aspect of the present invention, which includes an acquisition unit, a pretreatment unit, a prediction unit, an evaluation unit and a decision-making unit, the acquisition unit is used for acquiring wall rock data and includes a stress-strain sensing module and an environment sensing module; the stress-strain sensing module is used for acquiring a stress and a strain of wall rock affected by mining in a borehole in real time; and the environment sensing module is used for acquiring a temperature, noise and vibration of the wall rock affected by mining in the borehole in real time; the pretreatment unit is used for receiving the wall rock data and pretreating the wall rock data; the prediction unit is used for preliminarily predicting the occurrence of disasters; the evaluation unit is used for evaluating the preliminary prediction result based on a wall rock instability theory and external environment information acquired by the environment sensing module; and the decision-making unit is used for completing final disaster decision-making. Further, the pretreatment unit includes a transmission base station and a ground receiving terminal platform, the transmission base station can be used for encrypting and transmitting the wall rock data in batches to the ground receiving terminal platform.
Further, the stress-strain sensing module and the environment sensing module are arranged at the same position at a tail end of a borehole far away from a roadway. Further, the borehole extends to the wall rock affected by mining in a direction perpendicular to a sidewall of the roadway and includes an extension part and a monitoring part, one end of the extension part is connected with the roadway, and the other end is connected with the wall rock affected by mining through the monitoring part. Further, the monitoring part includes a first accommodating space, a second accommodating space and a third accommodating space which are sequentially connected perpendicular to an extension direction of the extension part. Further, the temperature sensor and the acoustic emission sensor are sequentially arranged in a direction perpendicular to the extension direction of the extension part, and both are arranged in the second accommodating space. Further, the stress sensor and the strain sensor are both arranged in the first accommodating space; one end of the stress sensor and one end of the strain sensor are connected with the second accommodating space; and the other ends are connected with the wall rock affected by mining. Further, the microquake sensor is arranged in the third accommodating space; one end 6 of the microquake sensor is connected with the second accommodating space; and the 0060 other end of the microquake sensor is connected with the wall rock affected by mining. Compared with the prior art, the intelligent prediction method and system for ground pressure disasters of wall rock affected by mining achieves simple operation and use, high precision and accurate determination, and has the following outstanding characteristics:
1. The acquired data are divided into pre-peak data and post-peak data according to the yield point in the full stress-strain curve model of the wall rock; the pre-peak data is used to predict the yield point and determine whether the stress of the wall rock will reach the yield point in a certain time or not; the post-peak data or both the pre-peak data and the post-peak data are used to predict instability of the wall rock and determine whether the wall rock will be fractured and become instable in a certain time or not. By setting the use of different data, the method of the invention can achieve the effect of rapid and accurate real-time prediction.
2. By virtue of the machine learning model, the preliminary prediction of disasters 1s completed in real time, and the preliminary prediction results are evaluated and determined based on the wall rock instability theory and the external environmental information, so as to ensure that disasters will not be omitted and misreported, thus avoiding irreparable losses.
BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which form a part of the present invention, are intended to provide a further understanding of the present invention, and the illustrative embodiments of the present invention and the description thereof are intended to explain the present invention rather than to unduly limit the present invention. In the 7 accompanying drawings: FIG 1 is a flow chart of the intelligent prediction method for ground pressure disasters of wall rock affected by mining according to an embodiment of the present invention; FIG 2 is a schematic diagram of the full stress-strain curve according to an embodiment of the present invention; FIG 3 is an overall schematic diagram of the intelligent prediction system for ground pressure disasters of wall rock affected by mining according to an embodiment of the present invention; FIG 4 is a schematic diagram of an acquisition unit of the intelligent prediction system for ground pressure disasters of wall rock affected by mining according to an embodiment of the present invention; FIG 5 is a side fracture model of a roadway with two weak sides according to Embodiment 1 of the present invention; and FIG 6 is a unidirectional stressed model of a shallow rock mass of a side part according to Embodiment 1 of the present invention. In the figures: 1-wall rock affected by mining, 2-borehole, 3-second accommodating space, 4-first accommodating space, and 5-third accommodating space.
DETAILED DESCRIPTION Exemplary embodiments, shown in the drawings, will be explained in detail here. When the following description relates to the drawings, the same numerals in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments are not representative of all embodiments consistent with the present invention. Rather they are only examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.
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The terms "first", "second" and the like in the specification and claims of the present invention are used to distinguish similar objects and need not be used to describe a particular order or priority.
It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein.
In addition, the terms "including" and "comprising" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units need not be limited to those clearly listed, but may include other steps or units that are not clearly listed or inherent to such processes, methods, products, or devices. "A plurality of" includes two or more.
It should be understood that the term "and/or" used in the present invention is merely an association relationship that describes associated objects, indicating that there may be three relationships.
For example, A and/or B can mean that only A exists; or both A and B exist; or only B exists.
An intelligent prediction method for ground pressure disasters of wall rock affected by mining as shown in FIG. 1 includes the following steps: S1, data acquisition: a plurality of sensor modules are arranged in wall rock affected by mining 1, the sensor modules include a stress-strain sensing module and an environment sensing module; and the sensor modules are used for acquiring wall rock data and external environment information; S2, data preprocessing: a stress-strain curve is constructed by using the acquired wall rock data acquired by the stress-strain sensing module, and data before a wall rock
9 stress reaches a stress limit are extracted to serve as pre-peak data and data after the 0060 wall rock stress reaches the stress limit are extracted to serve as post-peak data; S3, preliminary prediction of disasters: consecutive pre-peak data and/or post-peak data, within a period of time, are substituted into a machine learning model to perform impending disaster warning so as to obtain a preliminary prediction result; S4, result evaluation: the preliminary prediction result is evaluated on the basis of a wall rock instability theory and external environment information acquired by the environment sensing module to obtain an evaluation result; and SS, final decision-making: final disaster decision-making is completed according to the evaluation result.
The acquired data are divided into pre-peak data and post-peak data according to the yield point in the full stress-strain curve model of the wall rock; the pre-peak data is used to predict the yield point and determine whether the stress of the wall rock will reach the yield point in a certain time or not; the post-peak data or both the pre-peak data and the post-peak data are used to predict instability of the wall rock and determine whether the wall rock will be fractured and become instable in a certain time or not.
In the FIG 2, the O-A section represents an original fracture compaction stage.
At an initial loaded stage of the wall rock, a stress-strain curve of the wall rock shows an upward curving trend and the slope of the curve gradually increases due to the cumulative increase of load.
The deformation of the wall rock mainly compacts small cracks and small cavities in the wall rock.
The deformation of the wall rock is mainly plastically deformation form.
In relatively compact wall rock, there is little deformation due to a short duration time (if upper and lower loaded ends of a wall rock test piece are not completely horizontal due to errors of lab processing,
deformation at an O-A stage still occurs at an initial loading stage). The A-B section represents a pre-peak linear elastic deformation stage.
During the deformation of the wall rock, a deformation curve of the wall rock will increase linearly due to the cumulative increase of load.
Meanwhile, the slope of the curve will be kept at a fixed value, which is mainly determined by the elastic constant of the material of the wall rock.
At this stage, the deformation of the wall rock is mainly elastically deformation form.
With the increase of load, extremely tiny fracture will appear in the wall rock from a mesoscopic perspective.
From a macroscopic perspective, a linear deformation stage is shown, and meanwhile a stress value at a point B in the curve is called an elastic limit value of the material of the wall rock.
The B-C section represents a stable elastic-plastic transition stage of a fracture.
At this deformation stage of the wall rock, with the cumulative increase of load, the stress-strain curve of the wall rock begins to deviate from the point B and increase linearly.
Although the curve increases continuously, but is still bent downwards, and the slope of the curve gradually decreases.
At this stage, the tiny cracks in the wall rock expand in a small range, and the deformation of the wall rock mainly include expansion of new cracks, which finally shows a plastic deformation characteristic.
At this deformation stage, with the increasing load, more tiny cracks continuously occur in the wall rock.
Under the expansion of the wall rock, the volume of the wall rock changes from the initial compression deformation to expansion deformation, and the value of a point C is called a yield limit.
The C-D section represents an accelerated fracture plastic deformation stage.
At this deformation stage of the wall rock, with cumulative increase of load, the stress-strain curve of the wall rock continues to rise upward to the right from a point C, an increase
11 trend is still kept, but the slope of the curve gradually decreases. With the increasing load, the tiny cracks in the wall rock are further expanded and generated. When it is close to a peak D, the fracture speed of the wall rock is accelerated, and the volume is expanded rapidly under the expansion effect.
The D-E section represents a post-peak strength macroscopic failure stage. At this stage, after the stress of the wall rock reaches a peak strength, the mechanical characteristics of rock failure can be divided into two situations, such as the two types of curves shown in the figure: the first type of curve shows a "gradual and stable" decrease; and the second kind of curve shows a "sharp and sudden" decrease.
The E-F section represents a post-peak residual strength stage. In a post-peak residual strength process, the accelerated failure of the wall rock changes from the initial macroscopic fracture to macroscopic breakage, the failure of the wall rock is in the form of unstably sliding along a breakage section of the wall rock; and at the moment, the internal cohesion of the wall rock is zero. However, the wall rock still has a certain residual strength due to friction and occlusion between rock blocks.
The stress-strain sensing module includes a stress sensor and a strain sensor; and the environment sensing module includes a temperature sensor, an acoustic emission sensor and a microquake sensor.
Boreholes 2 are formed in the surface of the wall rock affected by mining 1; and sensors are arranged in the boreholes 2.
The result evaluation in the S4 specifically includes: S41, a deformation stage of the wall rock is determined according to a full 12 stress-strain curve model of the wall rock shown in FIG 2, and the acquired wall rock 0060 data are analyzed and predicted based on an instability mode and instability mechanisms of different parts of the wall rock at the deformation stage to obtain an intermediate prediction result; S42, the intermediate prediction result is adjusted with combination with the acquired external environment information to obtain a re-prediction result; and S43, the re-prediction result is compared with the preliminary prediction result to obtain an evaluation result.
The external environment information includes a temperature, noise and vibration.
It is considered that the wall rock is prone to being instable if there is a higher temperature, therefore, an allowable stress should be appropriately lowered, it is considered that the wall rock is prone to being instable if there is louder noise, therefore, an allowable stress should be appropriately lowered; and it is considered that the wall rock is prone to being instable if there is stronger vibration, therefore, an allowable stress should be appropriately lowered.
The preliminary prediction of disasters in the S3 specifically includes: S31, a machine learning model is preliminarily constructed according to the characteristics of a pre-peak long-term index of a disaster and a post-peak impending index of the disaster; S32, the pre-peak long-term data and the post-peak impending data in existing disaster data are taken as training sets to complete preliminary training of the machine learning model, S33, the consecutive pre-peak data and/or post-peak data in the S2 within a period of time is preliminarily analyzed and predicted by using the machine learning model; 13 and S34, when the number of the pre-peak data and post-peak data in the S2 meets the training requirement, the pre-peak data and post-peak data in the S2 are taken as new training sets to complete iterative optimization of the machine learning model.
The evaluation result in the S43 specifically includes: the preliminary prediction result is directly takend as a final disaster prediction result if the re-prediction result is the same as the preliminary prediction result; if the re-prediction result is different from the preliminary prediction result, the period of time in the S3 is prolonged, the steps S3-S4 are repeated to re-output an evaluation result; if the newly obtained re-prediction result is still different from the preliminary prediction result, the re-prediction result is taken as the final disaster prediction result, and the machine learning model in the S3 is trained and optimized by using the re-prediction result and the pre-peak data and/or post-peak data in the prolonged period of time.
An intelligent prediction system for ground pressure disasters of wall rock affected by mining shown in FIG 3 is used for executing a method according to the first aspect of the present invention and includes an acquisition unit, a pretreatment unit, a prediction unit, an evaluation unit and a decision-making unit; the acquisition unit is used for acquiring wall rock data and includes a stress-strain sensing module and an environment sensing module, the stress-strain sensing module is used for acquiring a stress and a strain of the wall rock in a borehole 2 in real time; and the environment sensing module is used for acquiring a temperature, noise and vibration of the wall rock in the borehole 2 in real time; the pretreatment unit is used for receiving the wall rock data and pretreating the wall rock data;
14 the prediction unit is used for preliminarily predicting the occurrence of disasters; the evaluation unit is used for evaluating the preliminary prediction result based on a wall rock instability theory and the external environment information acquired by the environment sensing module; and the decision-making unit is used for completing final disaster decision-making.
The pretreatment unit includes a transmission base station and a ground receiving terminal platform; and the transmission base station can encrypt and transmit the wall rock data in batches to the ground receiving terminal platform.
The stress-strain sensing module and the environment sensing module are arranged at the same position at a tail end of the borehole 2 far away from a roadway.
As shown in FIG 4, the borehole 2 extends to the wall rock affected by mining 1 in a direction perpendicular to a sidewall of the roadway and includes an extension part and a monitoring part, one end of the extension part is connected with the roadway, and the other end is connected with the wall rock affected by mining 1 through the monitoring part.
The monitoring part includes a first accommodating space 4, a second accommodating space 3 and a third accommodating space 5 which are sequentially connected perpendicular to an extension direction of the extension part.
The temperature sensor and the acoustic emission sensor are sequentially arranged in a direction perpendicular to the extension direction of the extension part, and both are arranged in the second accommodating space 3.
The stress sensor and the strain sensor are both arranged in the first accommodating SR space 4; one end of the stress sensor and one end of the strain sensor are arranged in the second accommodating space 3; and the other ends are connected with the wall rock affected by mining 1. The microquake sensor is arranged in the third accommodating space 5; one end of the microquake sensor is connected with the second accommodating space 3; and the other end of the microquake sensor is connected with the wall rock affected by mining
1. Embodiment 1 In this embodiment, the instability of two sides of the wall rock is taken as an example to explain how to obtain the intermediate prediction result in the step S41. The two sides of the wall rock are generally weak rock strata with low strength. Therefore, after the excavation of the wall rock, without the action of a supporting force, in a compressive stress field with a vertical stress as the maximum principal stress, the sides of the wall rock will be subjected to compression and shear failure, and the stress deformation state is approximately to a one-way compression state. However, there is a free surface on only one side of the sides of the wall rock, so a V-shaped fracture area approximating to a transverse arch appears in the side of the wall rock. With the expansion and loosening of the fracture area, the two sides will generate wall caving under the action of transverse dilatancy force and gravity, which will eventually lead to the instability of the two sides. FIG 5 shows the fracture model of two sides of the wall rock with two weak sides, and FIG. 6 shows a unidirectional compression model of the wall rock.
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Under unidirectional compression, the rock mass 1s mainly subjected to compression and shear fracture, whereas the instability condition of brittle dilatancy fracture of the rock mass can be considered as the instability of the rock mass under a vertical pressure when the applied load exceeds its ultimate strength. The instability of the compression and shear fracture of the rock mass is determined according to the form of shear strength. The instability is shown in the form of shear strength: CF 8 ; T, = AG — Ty (4-1) ° Fe , À ; ; APE 1 = {m Na +48) (4-2) nr a Jel 4 A #=—urctan—{—-T""} (4-3) 3 AR 5, a ~~, bo es y Too ass 4 4 7 2 {bid fo frame DITS A [tea ge TY US {4-4} 2 AB 5, AB 5, In the formulas: A represents an empirical constant; B represents a material compression constant; 7/7 represents rock mass shear strength;
0. represents uniaxial compressive strength of the rock mass; 0 represents a positive stress of a shear-fracture surface; Brepresents a shear slide angle; Trepresents a shear stress of a shear bevel; and m and s represent nondimensional testing constants; s represents the integrity of the rock mass; for complete rock, s is equal to 1; m is related with factors such as rock properties which can be determined through a test or determined through engineering classification of the rock mass. 17
Rock mass failure instability: if | 7| is larger than or equal to | 7/|, the rock mass will be instable due to slide, which can be used to determine failure instability of the rock mass; and a slide angle can be calculated through the formula 4-3. Embodiment 2 The temperature, noise and vibration can each affect the allowable strength of the wall rock.
In this embodiment, the influence of the temperature on granite will be taken as an example to illustrate the influence of an external environment on the allowable strength.
A damage mechanism of the granite under a high temperature: (1) Thermal fracture effect.
Granite is composed of various mineral particles.
Due to different particle sizes, thermal expansion coefficients and thermoelastic properties of various mineral particles in granite, under the action of temperature, the boundaries of the particles have inconsistent thermal expansion; tensile and compressive stresses, 1.e. structural thermal stresses, are produced between or within the mineral particles, which leads to tiny cracks in granite.
Subsequently, primary and secondary cracks are expanded and communicate with each other, which shows the deterioration of physical and mechanical properties of granite macroscopically.
In addition, under the action of a high temperature, some minerals will have homogeneous polymorphic variations, accompanied by volume changes, which further aggravates the thermal fracture and physical and mechanical property changes of granite.
For example, quartz is the most important mineral component of granite.
With the change of a temperature, quartz particles will change as follows, and the
18 volume will be changed as well: 117C=# a -tridymite= B -tridymite + 0.2% 163 C7 Bi -tridymite= a -tridymite + 0.2% 180°C~270°C+= B - cristobalite == à cristobalite + 2.0% 573°C B - quartz + a -cristobalite + 0.82% 870 C+ a - quartz + a -tridymite + 16% 1000°C+= a - quartz a -cristobalite + 15% (2) Thermal activation effect.
Due to a thermal motion or a stress effect of a mass point of the rock crystal, the rock crystal will have defects (such as mislocation, namely local crystal lattices make certain lattice slide along an atomic surface.
Crystal defects spread inside the crystal lattices and the mass points are disordered at the boundary between the slided and non-slided parts of the lattices), which causes easy breakage of the material.
When the rock experiences a high temperature of about 400 °C and after the -OH atomic group in a test piece is thermally activated, an original silicon-oxygen bond is substituted by a hydroxyl bond, which promotes the dislocation increase in the rock crystal and weakens the test piece.
In the present invention, after the granite undergoes a temperature of 400 °C and is quickly cooled in water, an axial peak strain decreases suddenly, and a radial peak strain also decreases quickly with temperature increase after the temperature is higher than 400 °C.
It can be supposed that after undergoing a temperature of 400 °C, the granite is remarkably thermally activated during cooling in water; more silicon-oxygen bonds are substituted by hydroxyl bonds; the sudden strong thermal activation effect causes quick decrease of the axial strain; and the radial peak strain also decreases quickly after the temperature is higher than 400 C. (3) Thermochemical effect.
The mineral composition of rock determines the physical
19 and mechanical properties of rock to a great extent. Under a high temperature environment, the chemical reaction of granite minerals changes the mineral composition. When at about 300 °C, SiO, and CaO react to generate CaSiO3; when at 400 °C to 550 °C, CaO and FezO react to generate calcium ferrite; meanwhile, CaO and CO; also react to generate CaCO3; and after the temperature is above 800 °C, the CaCO; is discomposed into CaO and CO,. When at 600 °C, MgO and Fe:O; have solid-phase reaction to generate magnesium-iron mixed crystal. At about 700 °C, FezO; and CaO react to generate fayalite. The chemical composition of Biotite is K(Mg, Fe);AlSi:010(OH),. Due to the presence of (OH), the biotite has a trend to being thermally decomposed from 200 °C, starts to be thermally decomposed when the temperature is higher than 450 °C and is thickened; after the temperature is 600 ‘Cor above, the biotite is thermally decomposed acutely and is completely decomposed when the temperature is as high as 900 °C; when at different temperatures, the biotite is decomposed to generate obviously different mineral components, which mainly include magnetite, potash feldspar, etc.
Therefore, under a high temperature environment, the mineral composition in rock will change to varying degrees, which will affect the physical and mechanical properties of granite and lower the allowable strength of rock.
It should be noted that, the terms "including", "comprising" or any other variation thereof used herein are intended to cover non-exclusive inclusions, so that a process, method, article or device that includes a series of elements includes not only those elements but also other elements that are not explicitly listed, or also elements inherent to such a process, method, article or device. In the absence of more restrictions, elements defined by the statement "including a..." can also include other identical elements existing in the process, method, article or device including these elements. The above serial numbers of the embodiments of the present invention are for description only and do not represent the priority of the embodiments. From the above description of the embodiments, those skilled in the art can clearly know that the above implementation modes can be implemented by means of software plus necessary common hardware platforms, and of course also by means of hardware, but in many cases the former is a preferred implementation mold. Based on this, essentially the technical solution of the present invention or a part that contributes to the prior art can be embodied in the form of software products; the computer software products are stored in a storage medium (such as an ROM/RAM, a magnetic disk, an optical disk) and include a plurality of instructions to realize that one terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) can be used to execute the methods described in various embodiments of the present invention. The embodiments of the present invention are described above in conjunction with the accompanying drawings. However, the present invention 1s not limited to the specific embodiments described above. The embodiments described above are merely illustrative and do not limit the present invention. Those of ordinary skill in the art may take many forms with reference to the present invention without departing from the scope of the object and claims of the present invention, all of which fall within the protection of the present invention.
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Claims (15)
1. An intelligent prediction method for ground pressure disasters of wall rock affected by mining, comprising the following steps: S1, data acquisition: arranging a plurality of sensor modules in wall rock affected by mining, wherein the sensor modules include a stress-strain sensing module and an environment sensing module, and the sensor modules are used for acquiring wall rock data and external environment information; S2, data preprocessing: constructing a stress-strain curve by using the acquired wall rock data acquired by the stress-strain sensing module, and extracting data before a wall rock stress reaches a stress limit to serve as pre-peak data and data after the wall rock stress reaches the stress limit to serve as post-peak data; S3, preliminary prediction of disasters: substituting consecutive pre-peak data and/or post-peak data, within a period of time, into a machine learning model to perform impending disaster warning, so as to obtain a preliminary prediction result; S4, result evaluation: evaluating the preliminary prediction result on the basis of a wall rock instability theory and external environment information acquired by the environment sensing module to obtain an evaluation result; and SS, final decision-making: completing final disaster decision-making according to the evaluation result.
2. The intelligent prediction method for ground pressure disasters of wall rock affected by mining according to claim 1, characterized in that the stress-strain sensing module comprises a stress sensor and a strain sensor; and the environment sensing module comprises a temperature sensor, an acoustic emission sensor and a microquake sensor.
1
3. The intelligent prediction method for ground pressure disasters of wall rock 0060 affected by mining according to claim 2, characterized in that boreholes are formed in a surface of the wall rock affected by mining, and the sensors are arranged inside the boreholes.
4. The intelligent prediction method for ground pressure disasters of wall rock affected by mining according to claim 1, characterized in that the result evaluation in the S4 specifically comprises: S41: determining a deformation stage of the wall rock according to a full stress-strain curve model of the wall rock, and analyzing and predicting the acquired wall rock data based on an instability mode of the wall rock and instability mechanisms of different parts of the wall rock at the deformation stage to obtain an intermediate prediction result; S42: adjusting the intermediate prediction result in combination with the acquired external environment information to obtain a re-prediction result; and S43: comparing the re-prediction result with the preliminary prediction result to obtain an evaluation result.
5. The intelligent prediction method for ground pressure disasters of wall rock affected by mining according to claim 4, characterized in that the external environment information comprises a temperature, noise and vibration.
6. The intelligent prediction method for ground pressure disasters of wall rock affected by mining according to claim 1, characterized in that the preliminary prediction of disasters in the S3 specifically comprises: S31, preliminarily constructing a machine learning model according to the characteristics of a pre-peak long-term index of a disaster and a post-peak impending 2 index of the disaster; S32, taking the pre-peak long-term data and the post-peak impending data in existing disaster data as training sets to complete preliminary training of the machine learning model; S33, preliminarily analyzing and predicting the consecutive pre-peak data and/or post-peak data in the S2 within a period of time by using the machine learning model; and S34, when the number of the pre-peak data and post-peak data in the S2 meets the training requirement, taking the pre-peak data and post-peak data in the S2 as new training sets to complete iterative optimization of the machine learning model.
7. The intelligent prediction method for ground pressure disasters of wall rock affected by mining according to claim 4, characterized in that the evaluation result in the S43 specifically comprises: directly taking the preliminary prediction result as a final disaster prediction result if the re-prediction result is the same as the preliminary prediction result; if the re-prediction result is different from the preliminary prediction result, prolonging the period of time in the S3, repeating the steps S3-S4 to re-output an evaluation result; and if the newly obtained re-prediction result is still different from the preliminary prediction result, taking the re-prediction result as the final disaster prediction result, and training and optimizing the machine learning model in the S3 by using the re-prediction result and the pre-peak data and/or post-peak data in the prolonged period of time.
8. An intelligent prediction system for ground pressure disasters of wall rock affected by mining, used for executing the method according to any one of claims 1 to 7, characterized in that comprising an acquisition unit, a pretreatment unit, a prediction 3 unit, an evaluation unit and a decision-making unit, the acquisition unit is used for acquiring wall rock data and comprises a stress-strain sensing module and an environment sensing module; the stress-strain sensing module is used for acquiring a stress and a strain of wall rock affected by mining in a borehole in real time; and the environment sensing module is used for acquiring a temperature, noise and vibration of the wall rock affected by mining in the borehole in real time: the pretreatment unit is used for receiving the wall rock data and pretreating the wall rock data; the prediction unit is used for preliminarily predicting the occurrence of disasters; the evaluation unit is used for evaluating the preliminary prediction result based on the wall rock instability theory and the external environment information acquired by the environment sensing module; and the decision-making unit is used for completing final disaster decision-making.
9. The intelligent prediction system for ground pressure disasters of wall rock affected by mining according to claim 8, characterized in that the pretreatment unit comprises a transmission base station and a ground receiving terminal platform; and the transmission base station can encrypt and transmit the wall rock data in batches to the ground receiving terminal platform.
10. The intelligent prediction system for ground pressure disasters of wall rock affected by mining according to claim 8, characterized in that the stress-strain sensing module and the environment sensing module are arranged at the same position at a tail end of a borehole far away from a roadway.
4
11. The intelligent prediction system for ground pressure disasters of wall rock affected by mining according to claim 10, characterized in that the borehole extends to the wall rock affected by mining in a direction perpendicular to a sidewall of the roadway and comprises an extension part and a monitoring part, wherein one end of the extension part is connected with the roadway, and the other end is connected with the wall rock affected by mining through the monitoring part.
12. The intelligent prediction system for ground pressure disasters of wall rock affected by mining according to claim 11, characterized in that the monitoring part comprises a first accommodating space, a second accommodating space and a third accommodating space which are sequentially connected perpendicular to an extension direction of the extension part.
13. The intelligent prediction system for ground pressure disasters of wall rock affected by mining according to claim 12, characterized in that the temperature sensor and the acoustic emission sensor are sequentially arranged in a direction perpendicular to the extension direction of the extension part, and both are arranged in the second accommodating space.
14. The intelligent prediction system for ground pressure disasters of wall rock affected by mining according to claim 12, characterized in that the stress sensor and the strain sensor are both arranged in the first accommodating space; one end of the stress sensor and one end of the strain sensor are connected with the second accommodating space; and the other ends are connected with the wall rock affected by mining.
15. The intelligent prediction system for ground pressure disasters of wall rock affected by mining according to claim 12, characterized in that the microquake sensor is arranged in the third accommodating space; one end of the microquake sensor is connected with the second accommodating space; and the other end of the microquake sensor is connected with the wall rock affected by mining. 6
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| CN112215438B (en) * | 2020-11-09 | 2021-05-14 | 广东新禾道信息科技有限公司 | Emergency disaster early warning analysis data processing method and system |
| CN113756869A (en) * | 2021-08-16 | 2021-12-07 | 中煤科工开采研究院有限公司 | Rock burst local monitoring and early warning system and method |
| CN113868897B (en) * | 2021-11-19 | 2024-11-01 | 大连海事大学 | Layered rock statistical damage calculation method under thermal-force coupling condition |
| CN114383717B (en) * | 2021-12-10 | 2023-11-17 | 中煤科工开采研究院有限公司 | Rock burst roadway safety monitoring method and device |
| CN114324610A (en) * | 2021-12-24 | 2022-04-12 | 广西大学 | Multi-band acoustic signal monitoring method, device and equipment for multi-scale fracture of hard rock and storage medium |
| CN114396312A (en) * | 2022-01-15 | 2022-04-26 | 安徽铜冠（庐江）矿业有限公司 | Multi-source monitoring and coupling early warning method suitable for large goaf ground pressure activity |
| CN114611835B (en) * | 2022-03-31 | 2024-05-31 | 安徽理工大学 | Method for predicting and analyzing disaster risk of rock burst of mine with hard roof |
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| CN115508548B (en) * | 2022-11-22 | 2023-04-07 | 山东科技大学 | Method and system for determining supporting structure combining stress environment and surrounding rock structure |
| CN116792155B (en) * | 2023-06-26 | 2024-06-07 | 华南理工大学 | Tunnel health state monitoring and early warning method based on distributed optical fiber sensing |
| CN117074171B (en) * | 2023-07-21 | 2024-03-08 | 中国矿业大学(北京) | Deep coal rock stress field coupling induced coal rock instability early warning method |
| CN116777085B (en) * | 2023-08-23 | 2023-11-10 | 北京联创高科信息技术有限公司 | Coal mine water damage prediction system based on data analysis and machine learning technology |
| CN117967405B (en) * | 2024-03-28 | 2024-06-07 | 山东金恒力建工有限公司 | Intelligent monitoring and control system based on coal mine filling |
| CN118257630B (en) * | 2024-05-29 | 2024-10-15 | 陕西延长石油矿业有限责任公司 | Intelligent monitoring method for surrounding rock of coal mine shaft |
Family Cites Families (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SU1171676A1 (en) * | 1984-05-14 | 1985-08-07 | Новосибирский государственный университет им.Ленинского комсомола | Device for measuring rock pressure in well |
| US8498853B2 (en) * | 2009-07-20 | 2013-07-30 | Exxonmobil Upstream Research Company | Petrophysical method for predicting plastic mechanical properties in rock formations |
| CN102155231B (en) * | 2011-03-18 | 2013-05-08 | 大连海事大学 | Quick feedback analyzing system in tunnel constructing process |
| CN102505965B (en) * | 2011-11-11 | 2013-10-23 | 中国矿业大学(北京) | Method for identifying rock mass failure instability early warning |
| CN103883352B (en) * | 2014-04-08 | 2016-07-06 | 中煤科工集团重庆研究院有限公司 | Acoustic emission early warning method for dynamic disaster of underground coal instability |
| CN104198679B (en) * | 2014-09-17 | 2016-06-01 | 辽宁工程技术大学 | A kind of oil from coal process Full wave shape simultaneous synthesis Monitoring systems and method |
| CN105260575B (en) * | 2015-11-17 | 2018-12-04 | 中国矿业大学 | A kind of deformation of the surrounding rock in tunnel prediction technique neural network based |
| CN105759010B (en) * | 2016-02-04 | 2017-11-07 | 山东大学 | A kind of dynamic monitoring of mining influence tunnel and Stability Assessment method |
| CN105866835B (en) * | 2016-03-28 | 2018-11-02 | 中国石油大学(华东) | A kind of tomography three dimensional closure quantitative evaluation method based on crustal stress distribution |
| CN106959748A (en) * | 2017-02-20 | 2017-07-18 | 西安科技大学 | A kind of roadway surrounding rock calamity emergency prediction scheme system and method based on virtual reality |
| CN107563092B (en) * | 2017-09-19 | 2020-08-04 | 山东蓝光软件有限公司 | Holographic early warning method for mine dynamic disasters |
| CN107590357B (en) * | 2017-10-31 | 2020-07-17 | 石家庄铁道大学 | Method for judging stability of tunnel in different construction stages |
| CN109918696B (en) * | 2018-11-13 | 2023-05-26 | 山西潞安环保能源开发股份有限公司常村煤矿 | Classification method and device for rock burst strength grade |
| CN109632016A (en) * | 2019-02-20 | 2019-04-16 | 湖北理工学院 | Rock And Soil adit digging and surrouding rock stress, strain monitoring experimental rig and its method |
| CN110159347B (en) * | 2019-05-05 | 2020-05-08 | 北京科技大学 | Dynamic disaster monitoring and early warning method for deep high-stress hard roof stope |
| CN110390800B (en) * | 2019-06-06 | 2021-04-20 | 北京市地质研究所 | Network type disaster monitoring and early warning system |
| CN210071003U (en) * | 2019-07-22 | 2020-02-14 | 北京市地质研究所 | Ground body surface strain stress acquisition equipment and net type disaster monitoring and early warning system |
| CN110928181B (en) * | 2019-12-13 | 2021-04-13 | 东北大学 | Intelligent control method for hard rock post-peak damage process under true triaxial surface disturbance |
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