A rock mass quality advance prediction method, device, equipment and storage medium

Abstract

The present invention relates to the field of underground engineering technology, and discloses a method, device, equipment and storage medium for advanced prediction of rock mass quality. The method comprises: conducting a geophysical detection comparative test on a known target body to determine the noise critical value and optimize the inversion algorithm; obtaining multi-point geophysical detection results on the tunnel face facing the intended excavation direction, performing three-dimensional modeling and slicing processing on the multi-point geophysical detection results, and generating a two-dimensional map of the geophysical detection results; obtaining engineering geological survey parameters of multiple field survey points in the two-dimensional map of the geophysical detection results, and calculating the rock mass quality pre-evaluation value of each field survey point; obtaining the geophysical detection value of each field survey point, establishing a large model for advanced prediction of rock mass quality, drawing a rock mass quality fitting curve, and determining the advanced prediction value of the rock mass quality of the point to be predicted based on the geophysical detection results of the point to be predicted. The present application realizes the advanced quantitative evaluation of the rock mass quality in the area where the shaft and tunnel are to be excavated, effectively ensuring the safety of shaft and tunnel engineering construction in advance, and improving construction efficiency.

Description

Technical Field

[0001] The present invention relates to the field of underground engineering technology, and in particular to a method, device, equipment and storage medium for advanced prediction of rock mass quality. Background Art

[0002] With the growing demand and scale of underground engineering projects in my country, especially mining projects, the engineering geological conditions for their construction are becoming increasingly complex. These complex geological conditions lead to a complex distribution of engineering rock mass quality, creating unpredictable challenges for tunneling projects. Many tunneling projects have experienced uncontrollable collapses and large deformations in the short term after tunneling. These problems are primarily due to a lack of understanding of the engineering geological conditions and rock mass quality prior to tunneling. Therefore, proactively predicting rock mass quality prior to tunneling is crucial for effectively accelerating construction progress and preventing disasters such as tunneling collapses.

[0003] In recent years, the prediction of rock quality in the area to be excavated for shaft and tunnel engineering is generally carried out after the surrounding rock of the shaft and tunnel engineering is exposed. There are also advanced engineering geological predictions through geophysical methods, but they only use geophysical parameters such as resistivity or wave velocity to characterize engineering geological conditions. These methods cannot be converted into quantitative parameters for evaluating rock quality, nor can they directly guide the formulation of engineering construction countermeasures. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to overcome the deficiencies in the prior art and to provide a method, device, equipment and storage medium for advanced prediction of rock mass quality.

[0005] The present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a method for advanced prediction of rock mass quality, the method comprising:

[0007] Conduct geophysical exploration comparative tests on known targets to determine the noise threshold of the test environment and optimize the inversion algorithm;

[0008] Performing geophysical detection on a plurality of detection points arranged on the tunnel face in the intended excavation direction to obtain multi-point geophysical detection results, performing data processing on the multi-point geophysical detection results according to the noise threshold and the optimized inversion algorithm to obtain a three-dimensional modeling diagram of the geophysical detection results, and slicing the three-dimensional modeling diagram of the geophysical detection results to generate a two-dimensional diagram of the geophysical detection results;

[0009] Obtaining engineering geological survey parameters of a plurality of field survey points correspondingly arranged in the two-dimensional map of the geophysical exploration results, and calculating a rock mass quality preliminary evaluation value of each field survey point based on the engineering geological survey parameters of each field survey point;

[0010] Obtain the geophysical detection value of each of the on-site investigation points in the two-dimensional diagram of the geophysical detection results, establish a large model for advanced prediction of rock quality based on the rock quality pre-evaluation value and the geophysical detection value of each of the on-site investigation points, and draw a rock quality fitting curve, and determine the advanced prediction value of the rock quality of the point to be predicted in front of the tunnel face based on the geophysical detection result of the point to be predicted according to the rock quality fitting curve and the large model for advanced prediction of rock quality.

[0011] In an optional embodiment, the data processing of the multi-point geophysical detection results according to the noise critical value and the optimized inversion algorithm to obtain a three-dimensional modeling diagram of the geophysical detection results, and slicing the three-dimensional modeling diagram of the geophysical detection results to generate a two-dimensional diagram of the geophysical detection results, including:

[0012] removing harmonic noise greater than the noise threshold in the multi-point geophysical detection results to obtain a denoised multi-point geophysical detection result;

[0013] Using the optimized inversion algorithm to perform data processing on the denoised multi-point geophysical detection results, and establishing a two-dimensional plane model of the denoised multi-point geophysical detection results;

[0014] Using three-dimensional software to interpolate the two-dimensional plane model to establish a three-dimensional modeling diagram of the geophysical exploration results;

[0015] Slices are made on both sides of the tunnel along the planned excavation direction on the three-dimensional modeling diagram of the geophysical exploration results to generate a two-dimensional diagram of the geophysical exploration results.

[0016] In an optional embodiment, obtaining engineering geological survey parameters corresponding to a plurality of on-site survey points arranged in the two-dimensional map of the geophysical exploration results includes:

[0017] Arranging a plurality of the on-site survey points at predetermined intervals in the two-dimensional map of the geophysical exploration results;

[0018] The engineering geological survey parameters of each of the field survey points are obtained, including the number of volume joints, the number of dominant joint groups, the roughness of the joint surface, the degree of joint alteration and the state of joint water.

[0019] In an optional embodiment, the calculating of the rock mass quality preliminary evaluation value of each field survey point according to the engineering geological survey parameters of each field survey point includes:

[0020] Calculating a first ratio of the number of volume joints to the number of dominant joint groups at each of the on-site investigation points, and calculating a second ratio of the joint surface roughness to the degree of joint alteration at each of the on-site investigation points;

[0021] The product of the first ratio, the second ratio and the joint water state of each of the on-site investigation points is calculated to obtain a preliminary evaluation value of the rock mass quality of each of the on-site investigation points.

[0022] In an optional embodiment, the step of establishing a large model for advanced prediction of rock mass quality based on the rock mass quality pre-evaluation value and the geophysical detection value of each field survey point, and drawing a rock mass quality fitting curve, comprises:

[0023] According to the rock mass type of each on-site investigation point, a rock mass quality pre-evaluation value database and a geophysical exploration value database are respectively constructed for each rock mass type;

[0024] Determine whether the number of samples at the field survey points of each rock type in the rock quality pre-evaluation value database and the geophysical detection value database is greater than a preset threshold. If so, establish a large model for advanced rock quality prediction consisting of each rock quality pre-evaluation value and each geophysical detection value, and draw a rock quality fitting curve consisting of the rock quality pre-evaluation value and the geophysical detection value.

[0025] In an optional embodiment, the step of determining the rock mass quality advance prediction value of the point to be predicted based on the geophysical exploration result of the point to be predicted ahead of the tunnel face according to the rock mass quality fitting curve and the large model for advanced rock mass quality prediction includes:

[0026] Performing geophysical exploration on the point to be predicted in front of the tunnel face to obtain a geophysical exploration result of the point to be predicted;

[0027] Determine the geophysical detection value of the point to be predicted based on the geophysical detection result of the point to be predicted, and obtain the rock mass quality pre-evaluation value of the point to be predicted corresponding to the geophysical detection value of the point to be predicted based on the large model for advanced prediction of rock mass quality and the rock mass quality fitting curve;

[0028] Acquiring the stress state of the point to be predicted, and determining the stress reduction coefficient of the point to be predicted according to the stress state;

[0029] The ratio of the preliminary evaluation value of the rock mass quality of the point to be predicted to the stress reduction coefficient is calculated to obtain the advanced prediction value of the rock mass quality of the point to be predicted.

[0030] In an optional embodiment, the known target body includes at least one of a goaf, a water-bearing body and a fault fracture zone, and the detection points are evenly staggered on the tunnel face facing the intended excavation direction.

[0031] In a second aspect, the present invention provides a device for advanced prediction of rock mass quality, comprising:

[0032] The test module is used to conduct geophysical exploration and comparison tests on known targets to determine the noise threshold of the test environment and optimize the inversion algorithm;

[0033] a processing module for performing geophysical detection on a plurality of detection points arranged on the tunnel face in the intended excavation direction to obtain multi-point geophysical detection results, performing data processing on the multi-point geophysical detection results according to the noise threshold and the optimized inversion algorithm to obtain a three-dimensional modeling diagram of the geophysical detection results, and slicing the three-dimensional modeling diagram of the geophysical detection results to generate a two-dimensional diagram of the geophysical detection results;

[0034] A calculation module is used to obtain engineering geological survey parameters of a plurality of field survey points arranged correspondingly in the two-dimensional map of the geophysical exploration results, and calculate a rock mass quality preliminary evaluation value of each field survey point based on the engineering geological survey parameters of each field survey point;

[0035] A determination module is used to obtain the geophysical detection value of each of the on-site investigation points in the two-dimensional diagram of the geophysical detection results, establish a large model for advanced prediction of rock quality based on the rock quality pre-evaluation value and the geophysical detection value of each of the on-site investigation points, and draw a rock quality fitting curve. According to the rock quality fitting curve and the large model for advanced prediction of rock quality, the advanced prediction value of the rock quality of the point to be predicted is determined by the geophysical detection result of the point to be predicted in front of the tunnel face.

[0036] In a third aspect, a computer device is provided in an embodiment of the present disclosure, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the method for advanced prediction of rock quality described in the first aspect when executing the computer program.

[0037] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for advanced prediction of rock mass quality described in the first aspect are implemented.

[0038] Beneficial effects of this application:

[0039] The method for advanced prediction of rock mass quality provided by the embodiment of the present application includes: conducting a geophysical detection comparative test on a known target body to determine the noise critical value and the optimized inversion algorithm of the test environment; conducting geophysical detection on a plurality of detection points arranged on the tunnel face in the direction of the proposed excavation to obtain multi-point geophysical detection results, and performing data processing on the multi-point geophysical detection results according to the noise critical value and the optimized inversion algorithm to obtain a three-dimensional modeling diagram of the geophysical detection results, and slicing the three-dimensional modeling diagram of the geophysical detection results to generate a two-dimensional diagram of the geophysical detection results; obtaining the corresponding arrangement in the two-dimensional diagram of the geophysical detection results The engineering geological survey parameters of multiple field investigation points are obtained, and the rock mass quality pre-evaluation value of each field investigation point is calculated according to the engineering geological survey parameters of each field investigation point; the geophysical detection value of each field investigation point in the two-dimensional diagram of the geophysical detection results is obtained, and a large model for advanced prediction of rock mass quality is established according to the rock mass quality pre-evaluation value and the geophysical detection value of each field investigation point, and a rock mass quality fitting curve is drawn, and according to the rock mass quality fitting curve and the large model for advanced prediction of rock mass quality, the advanced prediction value of the rock mass quality of the point to be predicted in front of the tunnel face is determined according to the geophysical detection result of the point to be predicted. This application realizes the continuous updating and optimization of the large model and the quantitative evaluation of the rock quality in the planned excavation area through geophysical exploration and rock quality evaluation, based on the constructed large model for advanced prediction of rock quality. It plays an extremely important guiding role in making construction decisions in advance and formulating reasonable surrounding rock control measures for shaft and tunnel projects, reducing disasters such as instantaneous collapse of shafts and tunnels caused by unclear rock quality, ensuring the construction safety of shaft and tunnel projects and improving construction efficiency. It has the characteristics of simple method, strong operability and good advanced prediction effect.

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. Similar components are numbered similarly in the various drawings.

[0042] Figure 1 A flow chart of a method for advanced prediction of rock mass quality provided by an embodiment of the present application is shown;

[0043] Figure 2 A schematic diagram of a proposed detection range provided in an embodiment of the present application is shown;

[0044] Figure 3 A schematic diagram of a multi-point interleaved geophysical exploration of a tunnel face array provided by an embodiment of the present application is shown;

[0045] Figure 4 A schematic diagram of the arrangement of survey points on one side of a tunnel to be excavated is shown in an embodiment of the present application;

[0046] Figure 5 A schematic diagram of geophysical detection value contour lines near a single-side wall survey point of a tunnel to be excavated provided in an embodiment of the present application is shown;

[0047] Figure 6 A schematic diagram of a rock mass quality fitting curve provided in an embodiment of the present application is shown;

[0048] Figure 7 A schematic structural diagram of a rock mass quality advance prediction device provided in an embodiment of the present application is shown;

[0049] Figure 8 A structural diagram of a computer device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0050] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0051] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used in the template description herein are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0053] Example 1

[0054] like Figure 1 FIG. 1 is a flow chart of a method for advanced prediction of rock mass quality in an embodiment of the present application. The method for advanced prediction of rock mass quality provided in an embodiment of the present application includes the following steps:

[0055] Step S110 , performing a geophysical detection comparison test on a known target object to determine the noise threshold of the test environment and optimize the inversion algorithm.

[0056] In this embodiment, in order to improve the accuracy of geophysical detection and eliminate existing interference sources in the mine, known target bodies such as known goafs, water-bearing bodies and fault fracture zones in the test environment are detected respectively, and field geophysical detection comparison experiments are carried out. These target bodies usually have obvious abnormal responses in geophysical detection. By comparing the detection results under different detection parameters and conditions, the noise critical value of the test environment is determined, which is used to remove noise in subsequent data processing and determine a suitable optimized inversion algorithm that can highlight the abnormality of the target body.

[0057] For example, Figure 2 As shown in the figure (labeled 1 is the tunnel face, and label 2 is the side wall of the planned tunnel), a certain mine has two types of lithology: dolomite and limestone. All tunnel sections are 3000mm wide by 3500mm high, with a side wall height of 2000mm. Due to the complex geological conditions of the mine engineering, the mining process encountered drastic changes in the strata and severe fault interspersed. To this end, before excavating the planned tunnel, a comparative test of geophysical exploration using the reverse flux transient electromagnetic method was conducted within a range of 40m ahead. Through the experiment, the noise threshold of the mine was determined to be 50Hz. At the same time, it was found that the firefly swarm intelligent inversion algorithm can highlight target anomalies and improve the accuracy of geophysical exploration data processing.

[0058] Through comparative testing, the above method can accurately identify and eliminate interference sources in mines, thereby improving the accuracy of geophysical exploration. Determining the noise threshold helps effectively remove noise in subsequent data processing, while optimizing the inversion algorithm can more accurately highlight abnormal characteristics of the target body, providing reliable data support for subsequent rock mass quality assessment.

[0059] Step S120, performing geophysical detection on multiple detection points arranged on the tunnel face facing the intended excavation direction to obtain multi-point geophysical detection results, and performing data processing on the multi-point geophysical detection results according to the noise critical value and the optimized inversion algorithm to obtain a three-dimensional modeling diagram of the geophysical detection results, and slicing the three-dimensional modeling diagram of the geophysical detection results to generate a two-dimensional diagram of the geophysical detection results.

[0060] Understandably, multi-point geophysical exploration is conducted over a certain length of the tunnel face facing the intended excavation direction, ensuring that the detection range covers the entire intended excavation area (a staggered array arrangement can achieve a detection coverage rate of 95%, which is superior to a traditional grid arrangement). Multi-point geophysical exploration results are obtained. The staggered array multi-point geophysical exploration is preferably arranged in a uniform staggered pattern along the tunnel face, and there are no interfering objects such as rigid support and high-power power supply devices near the multi-point geophysical exploration.

[0061] For example, Figure 3 As shown in the diagram (number 1 is the tunnel face, number 3 is the detection point), a staggered array multi-point geophysical survey was conducted over a 40-meter length on the tunnel face facing the planned excavation direction. Four detection points were placed on the tunnel face during each survey at this mine, with the detection points spaced 1500mm horizontally and 1000mm vertically, staggered vertically.

[0062] Furthermore, a filtering algorithm such as a comb filter algorithm is used to remove harmonic noise greater than the noise threshold in the multi-point geophysical detection results, and the denoised multi-point geophysical detection results are obtained. Then, an optimized inversion algorithm (such as the firefly swarm intelligence inversion algorithm) is used to process the denoised multi-point geophysical detection results and establish a two-dimensional plane model of the denoised multi-point geophysical detection results. The two-dimensional plane model is imported into the three-dimensional software, and the three-dimensional software is used to interpolate the two-dimensional plane model to establish a three-dimensional modeling diagram of the geophysical detection results, and combined with the Figure 2 On the three-dimensional modeling diagram of the geophysical exploration results, two-dimensional slices are made on each side of the tunnel along the two sides of the planned excavation direction to generate a two-dimensional diagram of the geophysical exploration results of the two single-side sides of the tunnel, such as Figure 4 (No. 2 is the side of the tunnel to be excavated, No. 4 is the investigation point, and No. 5 is the joint).

[0063] It should be noted that, in an optional implementation, in addition to removing noise during data processing, filtering, smoothing and other methods may also be considered to further improve the accuracy and readability of the data, which will not be described in detail in the embodiments of the present application.

[0064] The staggered array multi-point geophysical survey method described above fully covers the planned excavation area, ensuring data integrity and accuracy. By removing noise and optimizing inversion, data reliability can be further improved, providing a high-quality foundation for subsequent geological surveys and rock mass quality assessments. Furthermore, the construction of two-dimensional maps allows for data visualization, facilitating subsequent analysis and prediction.

[0065] Step S130 , obtaining engineering geological survey parameters of a plurality of field survey points arranged correspondingly in the two-dimensional map of the geophysical exploration results, and calculating a rock mass quality preliminary evaluation value of each field survey point according to the engineering geological survey parameters of each field survey point.

[0066] As the tunnel in the proposed excavation direction continues to advance, a representative on-site survey point is arranged at a preset interval in the two-dimensional map of the geophysical exploration results. The representative on-site survey point should be located within the range of two vertical points of the array multi-point geophysical exploration in terms of height. The spacing between the on-site survey points along the excavation direction is generally not less than 5m, and the on-site survey points include points where the geophysical exploration results change significantly and evenly. For example, Figure 4 As shown in the figure, within the 40m tunnel length, a field investigation point is arranged every 5000mm, and the field investigation point is located at the 1 / 2 height of the side.

[0067] Furthermore, by conducting engineering geological surveys on both sides of the roadway at each on-site survey point, the corresponding engineering geological survey parameters were obtained, including the volume joint number ROD (Rock Quality Designation), the number of dominant joint groups J n , joint surface roughness J r , joint alteration degreeJ a and joint water state J w .

[0068] Among the above engineering geological survey parameters, the volume joint number ROD is used to evaluate the integrity of the rock. The higher the ROD value, the more complete the rock and the better the quality. The number of dominant joint groups J is used to evaluate the integrity of the rock. n That is, the number of joints per unit volume or unit area, which is an important parameter for evaluating the degree of joint development in rocks; the roughness of the joint surface J r It is one of the key factors in evaluating the mechanical properties of rocks. The roughness of the joints affects their friction and shear strength. The rougher the joint surface, the higher the friction coefficient. a It refers to the changes in the joint surface caused by physical and chemical effects such as weathering, hydration, and mineralization; the joint water state J w Used to quantify the state of water in joints, such as water content, water flow state, etc. Water has a significant impact on the mechanical properties of rocks, especially in rocks with developed joints, where the presence of water can reduce the strength and stability of rocks.

[0069] Based on the engineering geological survey parameters obtained from the survey of each field survey point, the rock mass quality preliminary evaluation value of each field survey point is calculated. The specific calculation method is: first, the first ratio of the number of volume joints to the number of dominant joint groups is calculated at each field survey point. Then, the second ratio of the joint surface roughness to the joint alteration degree is calculated at each field survey point. Finally, the product of the first ratio, the second ratio and the joint water state is calculated at each field survey point to obtain the rock mass quality preliminary evaluation value of each field survey point. It can be expressed by the following formula:

[0070]

[0071] Where, is the preliminary evaluation value of rock mass quality at the i-th field investigation point, ROD i is the volume joint number of the ith on-site investigation point, is the number of dominant joint groups at the i-th field investigation point, is the joint surface roughness of the i-th field investigation point, is the joint alteration degree of the ith field investigation point, is the joint water status of the i-th field investigation point.

[0072] Through engineering geological surveys, the above-mentioned methods can obtain key information about rock integrity, joint development, joint surface roughness, joint alteration severity, and joint water status. This information is crucial for assessing rock mass quality. By calculating preliminary rock mass quality assessment values, a quantitative assessment of rock mass quality at the survey site can be performed, providing a scientific basis for subsequent advanced predictions.

[0073] Step S140, obtaining the geophysical detection value of each of the field investigation points in the two-dimensional diagram of the geophysical detection results, establishing a large model for advanced prediction of rock quality based on the rock quality pre-evaluation value and the geophysical detection value of each of the field investigation points, and drawing a rock quality fitting curve, and determining the advanced prediction value of the rock quality of the point to be predicted in front of the tunnel face according to the geophysical detection result of the point to be predicted based on the rock quality fitting curve and the large model for advanced prediction of rock quality.

[0074] Understandably, if Figure 5 (No. 2 is the side of the tunnel to be excavated, No. 4 is the survey point, and No. 6 is the contour line of geophysical detection value). On the generated two-dimensional map of geophysical detection results, the geophysical detection values ​​of each on-site survey point are extracted.

[0075] Next, according to the rock mass type (e.g., dolomite and limestone) to which each field survey point belongs, a rock mass quality pre-evaluation value database and a geophysical detection value database are constructed for each rock mass type. When the number of samples of field survey points accumulated for each rock mass type in the rock mass quality pre-evaluation value database and the geophysical detection value database is greater than a preset threshold (e.g., 200), a large rock mass quality advance prediction model consisting of each rock mass quality pre-evaluation value and each geophysical detection value is established, and a rock mass quality fitting curve is drawn with the rock mass quality pre-evaluation value and the geophysical detection value as the horizontal and vertical coordinates, respectively, as shown in FIG. Figure 6 shown.

[0076] It should be noted that the large model for advanced rock mass quality prediction and the rock mass quality fitting curve must accumulate a sufficient number of survey point samples and be verified for accuracy through multiple rock mass quality pre-evaluation values ​​and geophysical exploration values ​​before they can be used for advanced rock mass quality prediction. In addition, in this embodiment, the lower limit of the rock mass quality pre-evaluation value in the rock mass quality fitting curve is set to infinitely approach 0, and the upper limit is set to infinitely approach 1000. The specific values ​​can be determined based on actual conditions and are not limited in this embodiment of the application.

[0077] Furthermore, before the tunnel is driven at the predicted point in front of the face, a geophysical exploration is performed on the predicted point to obtain the geophysical exploration result of the predicted point, and then the geophysical exploration value of the predicted point is obtained based on the geophysical exploration result of the predicted point. Then the geophysical detection value of the point to be predicted is Input into the rock mass quality advance prediction model, since the model has revealed the relationship between the rock mass quality pre-evaluation value and the geophysical detection value, it can Value, the corresponding calculation and reasoning are performed within the model. At the same time, Figure 6 As shown in the figure, the rock mass fitting curve is a visual representation of this relationship, which also assists the model in the given Determine the corresponding rock mass quality pre-evaluation value

[0078] Understandably, the stress state of the point to be predicted can be obtained by on-site measurement or by constructing a three-dimensional model based on the measured results of limited points in other areas, and then the corresponding stress reduction factor (SRF) can be determined according to the stress state. The SRF is used to consider the influence of the stress state on the rock mass quality. In rock mass engineering, the mechanical properties of rock are often affected by the stress state of the surrounding rock mass. The SRF is used to quantify this influence and adjust the pre-evaluation value of the rock mass quality accordingly to obtain a more accurate advance prediction result. The specific adjustment method is: calculate the ratio of the pre-evaluation value of the rock mass quality of the point to be predicted to the stress reduction factor, and obtain the advance prediction value of the rock mass quality of the point to be predicted, which is expressed by the following formula:

[0079]

[0080] Where Q j is the advanced prediction value of the rock mass quality at the jth point to be predicted, is the rock mass quality pre-evaluation value of the jth predicted point, SRF j is the stress reduction coefficient of the jth point to be predicted.

[0081] It should be noted that, in this embodiment, the stress reduction coefficient can be obtained through direct ground stress measurement, numerical simulation, stress field analysis, etc., which will not be described in detail in this embodiment.

[0082] By constructing a large-scale model for advanced rock quality prediction and plotting a rock quality fitting curve, the above method reveals the relationship between pre-evaluated rock quality values ​​and geophysical survey values. This relationship provides a reliable prediction method for rock quality assessment at the predicted point. By inputting the geophysical survey value of the predicted point, the predicted rock quality value can be quickly and accurately obtained. This bridges the gap between the commonly used quantitative methods of geophysical survey and rock quality assessment, making advanced quantitative prediction of rock quality possible and providing decision support for tunneling and rock engineering. Furthermore, the conventional geophysical methods, engineering geological survey methods, and rock quality assessment methods commonly used in mines are based on daily production geological work and can directly complete related on-site and indoor work. The method is simple, routine, and easy to operate.

[0083] The method for advanced prediction of rock quality provided in the embodiment of the present application realizes continuous updating and optimization of the large model and advanced prediction and quantitative evaluation of rock quality in the planned excavation area through geophysical exploration and rock quality evaluation, based on the constructed large model for advanced prediction of rock quality. It plays an extremely important guiding role in making construction decisions in advance and formulating reasonable surrounding rock control measures for shaft and tunnel projects, and reducing disaster events such as instantaneous collapse of shafts and tunnels caused by unclear rock quality, ensuring the construction safety of shaft and tunnel projects and improving construction efficiency. It has the characteristics of simple method, strong operability and good advanced prediction effect.

[0084] Example 2

[0085] like Figure 7 FIG. 1 is a schematic diagram of a rock mass quality advance prediction device 700 according to an embodiment of the present application, wherein the device comprises:

[0086] The test module 710 is used to perform geophysical exploration comparative tests on known targets to determine the noise threshold of the test environment and optimize the inversion algorithm;

[0087] Processing module 720 is configured to perform geophysical exploration on a plurality of detection points arranged on the tunnel face in the intended direction of excavation to obtain multi-point geophysical exploration results, perform data processing on the multi-point geophysical exploration results according to the noise threshold and the optimized inversion algorithm to obtain a three-dimensional modeling diagram of the geophysical exploration results, and slice the three-dimensional modeling diagram of the geophysical exploration results to generate a two-dimensional diagram of the geophysical exploration results;

[0088] A calculation module 730 is configured to obtain engineering geological survey parameters corresponding to a plurality of field survey points arranged in the two-dimensional map of the geophysical exploration results, and calculate a rock mass quality preliminary evaluation value for each field survey point based on the engineering geological survey parameters of each field survey point;

[0089] Determination module 740 is used to obtain the geophysical detection value of each of the field investigation points in the two-dimensional diagram of the geophysical detection results, establish a large model for advanced prediction of rock quality based on the rock quality pre-evaluation value and the geophysical detection value of each of the field investigation points, and draw a rock quality fitting curve, and determine the advanced prediction value of the rock quality of the point to be predicted in front of the tunnel face according to the rock quality fitting curve and the large model for advanced prediction of rock quality based on the geophysical detection result of the point to be predicted.

[0090] The rock mass quality advance prediction device provided in the embodiment of the present application can implement each process of the rock mass quality advance prediction method corresponding to Example 1, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0091] The rock quality advance prediction device provided in the embodiment of the present application realizes continuous updating and optimization of the large model and advance prediction and quantitative evaluation of the rock quality of the planned excavation area through geophysical exploration and rock quality evaluation, based on the constructed large model of rock quality advance prediction. It plays an extremely important guiding role in making construction decisions in advance and formulating reasonable surrounding rock control measures for shaft and tunnel projects, and reducing disaster events such as instantaneous collapse of shafts and tunnels caused by unclear rock quality. It ensures the construction safety of shaft and tunnel projects and improves construction efficiency. It has the characteristics of simple method, strong operability and good advance prediction effect.

[0092] Example 3

[0093] The present application also provides a computer device. Figure 8 , Figure 8 This is a basic structural block diagram of the computer device in this embodiment.

[0094] The computer device 8 includes a memory 81, a processor 82, and a network interface 83 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 8 with a memory 81, a processor 82, and a network interface 83, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0095] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0096] The memory 81 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or D slot compatibility test memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 81 can be an internal storage unit of the computer device 8, such as the hard disk or memory of the computer device 8. In other embodiments, the memory 81 can also be an external storage device of the computer device 8, such as a plug-in hard disk equipped on the computer device 8, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Of course, the memory 81 can also include both the internal storage unit of the computer device 8 and its external storage device. In this embodiment, the memory 81 is generally used to store the operating system and various application software installed on the computer device 8, such as computer-readable instructions for the slot compatibility test method. In addition, the memory 81 can also be used to temporarily store various types of data that have been output or are to be output.

[0097] In some embodiments, the processor 82 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other rock mass quality advanced prediction chip. The processor 82 is generally used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is used to execute computer-readable instructions or process data stored in the memory 81, such as computer-readable instructions for executing the slot compatibility testing method.

[0098] The network interface 83 may include a wireless network interface or a wired network interface. The network interface 83 is generally used to establish a communication connection between the computer device 8 and other electronic devices.

[0099] The computer device provided in this embodiment can execute the above-mentioned rock mass quality advance prediction method, which can be the rock mass quality advance prediction method of each of the above-mentioned embodiments.

[0100] Example 4

[0101] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for advanced prediction of rock mass quality in the embodiment are implemented.

[0102] In this embodiment, the computer-readable storage medium includes flash memory, hard disks, multimedia cards, card-type memories (e.g., SD or DX memories), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, magnetic disks, optical disks, etc. In some embodiments, the computer-readable storage medium may be an internal storage unit of a computer device, such as the hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk equipped with the computer device, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Of course, the computer-readable storage medium may also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the computer-readable storage medium is generally used to store the operating system and various application software installed on the computer device. In addition, the computer-readable storage medium may also be used to temporarily store various types of data that have been output or are about to be output.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or flowchart, and the combination of boxes in the structure diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0104] In addition, the functional modules or units in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0105] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium can be a non-volatile storage medium or a volatile storage medium. For example, the storage medium can be: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and other media that can store program codes.

[0106] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A method for advanced prediction of rock mass quality, characterized in that: The method comprises: Conduct geophysical exploration comparative tests on known targets to determine the noise threshold of the test environment and optimize the inversion algorithm; Performing geophysical detection on a plurality of detection points arranged on the tunnel face in the intended excavation direction to obtain multi-point geophysical detection results, performing data processing on the multi-point geophysical detection results according to the noise threshold and the optimized inversion algorithm to obtain a three-dimensional modeling diagram of the geophysical detection results, and slicing the three-dimensional modeling diagram of the geophysical detection results to generate a two-dimensional diagram of the geophysical detection results; Obtaining engineering geological survey parameters of a plurality of field survey points correspondingly arranged in the two-dimensional map of the geophysical exploration results, and calculating a rock mass quality preliminary evaluation value of each field survey point based on the engineering geological survey parameters of each field survey point; Obtain the geophysical detection value of each of the on-site investigation points in the two-dimensional diagram of the geophysical detection results, establish a large model for advanced prediction of rock quality based on the rock quality pre-evaluation value and the geophysical detection value of each of the on-site investigation points, and draw a rock quality fitting curve, and determine the advanced prediction value of the rock quality of the point to be predicted in front of the tunnel face based on the geophysical detection result of the point to be predicted according to the rock quality fitting curve and the large model for advanced prediction of rock quality.

2. The method for advanced prediction of rock mass quality according to claim 1, characterized in that: The method includes performing data processing on the multi-point geophysical detection results according to the noise critical value and the optimized inversion algorithm to obtain a three-dimensional modeling diagram of the geophysical detection results, and slicing the three-dimensional modeling diagram of the geophysical detection results to generate a two-dimensional diagram of the geophysical detection results, including: removing harmonic noise greater than the noise threshold in the multi-point geophysical detection results to obtain a denoised multi-point geophysical detection result; Using the optimized inversion algorithm to perform data processing on the denoised multi-point geophysical detection results, and establishing a two-dimensional plane model of the denoised multi-point geophysical detection results; Using three-dimensional software to interpolate the two-dimensional plane model to establish a three-dimensional modeling diagram of the geophysical exploration results; Slices are made on both sides of the tunnel along the planned excavation direction on the three-dimensional modeling diagram of the geophysical exploration results to generate a two-dimensional diagram of the geophysical exploration results.

3. The method for advanced prediction of rock mass quality according to claim 1, characterized in that: The obtaining of engineering geological survey parameters of a plurality of field survey points corresponding to the two-dimensional map of the geophysical exploration results includes: Arranging a plurality of the on-site survey points at predetermined intervals in the two-dimensional map of the geophysical exploration results; The engineering geological survey parameters of each of the field survey points are obtained, including the number of volume joints, the number of dominant joint groups, the roughness of the joint surface, the degree of joint alteration and the state of joint water.

4. The method for advanced prediction of rock mass quality according to claim 3, characterized in that: Calculating the rock mass quality preliminary evaluation value of each field survey point based on the engineering geological survey parameters of each field survey point includes: Calculating a first ratio of the number of volume joints to the number of dominant joint groups at each of the on-site investigation points, and calculating a second ratio of the joint surface roughness to the degree of joint alteration at each of the on-site investigation points; The product of the first ratio, the second ratio and the joint water state of each of the on-site investigation points is calculated to obtain a preliminary evaluation value of the rock mass quality of each of the on-site investigation points.

5. The method for advanced prediction of rock mass quality according to claim 1, characterized in that: The method of establishing a large model for advanced prediction of rock mass quality based on the rock mass quality pre-evaluation value and the geophysical detection value of each field investigation point and drawing a rock mass quality fitting curve includes: According to the rock mass type of each on-site investigation point, a rock mass quality pre-evaluation value database and a geophysical exploration value database are respectively constructed for each rock mass type; Determine whether the number of samples at the field survey points of each rock type in the rock quality pre-evaluation value database and the geophysical detection value database is greater than a preset threshold. If so, establish a large model for advanced rock quality prediction consisting of each rock quality pre-evaluation value and each geophysical detection value, and draw a rock quality fitting curve consisting of the rock quality pre-evaluation value and the geophysical detection value.

6. The method for advanced prediction of rock mass quality according to claim 5, characterized in that: The step of determining the rock mass quality advance prediction value of the point to be predicted based on the rock mass quality fitting curve and the large rock mass quality advance prediction model and the geophysical exploration result of the point to be predicted in front of the tunnel face comprises: Performing geophysical exploration on the point to be predicted in front of the tunnel face to obtain a geophysical exploration result of the point to be predicted; Determine the geophysical detection value of the point to be predicted based on the geophysical detection result of the point to be predicted, and obtain the rock mass quality pre-evaluation value of the point to be predicted corresponding to the geophysical detection value of the point to be predicted based on the large model for advanced prediction of rock mass quality and the rock mass quality fitting curve; Acquiring the stress state of the point to be predicted, and determining the stress reduction coefficient of the point to be predicted according to the stress state; The ratio of the preliminary evaluation value of the rock mass quality of the point to be predicted to the stress reduction coefficient is calculated to obtain the advanced prediction value of the rock mass quality of the point to be predicted.

7. The method for advanced prediction of rock mass quality according to claim 1, characterized in that: The known target body includes at least one of a goaf, a water-bearing body and a fault fracture zone, and the detection points are evenly staggered on the tunnel face facing the intended excavation direction.

8. A rock mass quality advance prediction device, characterized in that: The device comprises: The test module is used to conduct geophysical exploration and comparison tests on known targets to determine the noise threshold of the test environment and optimize the inversion algorithm; a processing module for performing geophysical detection on a plurality of detection points arranged on the tunnel face in the intended excavation direction to obtain multi-point geophysical detection results, performing data processing on the multi-point geophysical detection results according to the noise threshold and the optimized inversion algorithm to obtain a three-dimensional modeling diagram of the geophysical detection results, and slicing the three-dimensional modeling diagram of the geophysical detection results to generate a two-dimensional diagram of the geophysical detection results; A calculation module is used to obtain engineering geological survey parameters of a plurality of field survey points arranged correspondingly in the two-dimensional map of the geophysical exploration results, and calculate a rock mass quality preliminary evaluation value of each field survey point based on the engineering geological survey parameters of each field survey point; A determination module is used to obtain the geophysical detection value of each of the on-site investigation points in the two-dimensional diagram of the geophysical detection results, establish a large model for advanced prediction of rock quality based on the rock quality pre-evaluation value and the geophysical detection value of each of the on-site investigation points, and draw a rock quality fitting curve. According to the rock quality fitting curve and the large model for advanced prediction of rock quality, the advanced prediction value of the rock quality of the point to be predicted is determined by the geophysical detection result of the point to be predicted in front of the tunnel face.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for advanced prediction of rock mass quality according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for advanced prediction of rock mass quality according to any one of claims 1 to 7 are implemented.

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