A method for evaluating roadway roof stability based on drilling parameter response characteristics

Abstract

This application relates to a method for evaluating the stability of roadway roof based on the response characteristics of drilling parameters. The method includes: selecting any point on the roof within the roadway and continuously collecting drilling parameters during the drilling process using sensors; using singular value decomposition to denoise the drilling parameters to obtain a denoised drilling dataset, analyzing the energy consumption per unit volume of rock during drilling, and obtaining the uniaxial compressive strength of the rock based on the energy consumption per unit volume of rock; analyzing the thickness, number of fractures, and width of intact rock strata in the roadway roof based on drilling energy consumption or borehole images at different drilling depths; analyzing the cumulative percentage of intact rock strata in the roadway roof based on the thickness of intact rock strata; and determining the stability evaluation result of the roadway roof based on the uniaxial compressive strength of the rock, the thickness of intact rock strata, the number of fractures, the width of fractures, and the cumulative percentage of intact rock strata. This allows for a simple and rapid effective rating and evaluation of roof stability.

Description

Technical Field

[0001] This application relates to the field of underground engineering tunnel analysis technology, and in particular to a method for evaluating the stability of tunnel roof based on the response characteristics of drilling parameters. Background Technology

[0002] In shallow and deep tunnel engineering projects (such as subways, tunnels, and coal mines), if the exposed tunnel roof is not reinforced in a timely manner, the surrounding rock is highly likely to collapse or spall in a short period of time, causing large deformations or even instability and failure of the tunnel. Real-time and rapid reliable evaluation of tunnel roof stability not only helps to rationally select support parameters but also avoids over- or under-support, thereby improving the economy and safety of tunnel engineering. Traditional methods for evaluating tunnel roof stability require core drilling to calculate rock quality indicators. Relatively intact core samples must be sent to a laboratory for processing into standard specimens and mechanical testing. Furthermore, statistical analysis of the development of fractures in the roof strata is required using methods such as borehole inspection. This method is complex, has a long experimental cycle, and exhibits a certain degree of lag. Summary of the Invention

[0003] Therefore, it is necessary to provide a roadway roof stability evaluation method based on drilling parameter response characteristics that can simply and quickly evaluate the roadway roof stability in a way that addresses the aforementioned technical problems.

[0004] A method for evaluating roadway roof stability based on drilling parameter response characteristics, the method comprising:

[0005] Step S1: Select any point on the roof of the tunnel and continuously collect the drilling parameters during the drilling process of the drilling rig on the roof of the tunnel through the sensor. The drilling parameters include drilling torque M, drill bit speed N, thrust F, drilling speed V, drilling depth h and drilling time t.

[0006] Step S2: Use singular value decomposition to denoise the drilling parameters to obtain the denoised drilling dataset X(B).

[0007] Step S3: Based on the denoised drilling dataset X(B), analyze the energy consumption E of drilling per unit volume of rock, and obtain the uniaxial compressive strength UCS of the rock based on the energy consumption E of drilling per unit volume of rock.

[0008] Step S4: Based on the drilling energy consumption or borehole images at different drilling depths, analyze the thickness l, number n, and width b of the intact rock strata in the roof of the tunnel.

[0009] Step S5: Analyze the cumulative percentage of intact rock strata (RQD) in the tunnel roof based on the thickness l of the intact rock strata.

[0010] Step S6: Determine the stability evaluation result of the roadway roof based on the uniaxial compressive strength (UCS) of the rock in the roadway roof, the thickness (l) of the intact rock strata, the number (n) of the fractures, the width (b) of the fractures, and the cumulative percentage (RQD) of the intact rock strata.

[0011] In one embodiment, step S2 includes:

[0012] Step 1.1: Mark the zero-value points and significant mutation points in the drilling data caused by human factors during the drilling process. Filter and delete the drilling data corresponding to the zero-value points and significant mutation points to obtain the filtered drilling dataset X(A). Wherein, if the abnormal data evaluation index T=0 at a certain moment or the fluctuation range of adjacent drilling torque data is ≥25%, the corresponding drilling data at that moment needs to be deleted. The expression for the abnormal data evaluation index is:

[0013]

[0014] In the formula, T represents the evaluation index for abnormal data; X M X N X F X V These represent the drilling torque, drill bit speed, thrust, and drilling speed of the drilling rig at a given moment, respectively.

[0015] Step 1.2: The filtered drilling dataset X(A) = {a1, a2, a3, ..., aA} is decomposed and reconstructed using singular value decomposition (SVD) to transform it into a singular value matrix H. The transformation process is as follows:

[0016]

[0017] In the formula, D is the effective molecular space of the noisy drilling signal, W is the noise molecular space, i is the number of the drilling parameters, and j is the number of sampling points;

[0018] Step 1.3: Determine the singular values ​​of the singular value matrix H, separate the effective drilling signal components from the noise components, and obtain the denoised drilling dataset X(B) through inverse transformation.

[0019] In one embodiment, the analytical formula for the energy consumption E per unit volume of rock drilling is:

[0020]

[0021] Where N is the drill bit rotation speed; M is the drilling torque; F is the propulsion force; μ is the friction coefficient between the drill bit and the rock contact surface; Rs represents the drill bit radius; L is the length of the cutting edge; and V is the drilling speed.

[0022] In one embodiment, the analytical formula for the uniaxial compressive strength (UCS) of the rock is:

[0023]

[0024] UCS stands for uniaxial compressive strength of rock.

[0025] In one embodiment, step S4 includes:

[0026] Establish the relationship curve between the energy consumption E per unit volume of rock drilling and the drilling depth h, and determine the stable drilling section and the abnormal data stage of the relationship curve.

[0027] Based on the smooth drilling section and the abnormal data stage, determine the thickness l, the number n, and the width b of the intact rock strata in the tunnel roof;

[0028] Alternatively, after drilling is completed, borehole images corresponding to different depths of the borehole inner wall can be obtained using a borehole inspection instrument, and the thickness l of the intact rock strata, the number n of fractures, and the width b of the fractures inside the borehole can be analyzed using image processing software.

[0029] In one embodiment, the analytical formula for the cumulative intact rock strata percentage (RQD) is:

[0030]

[0031] In the formula, lq represents the thickness of the qth complete rock stratum; L represents the total length of the borehole; and Q is the total number of complete rock strata in the roof of the tunnel.

[0032] In one embodiment, determining the thickness l, number n, and width b of the intact rock strata in the tunnel roof based on the smooth drilling section and the data anomaly stage includes:

[0033] Each smooth drilling section corresponds to a complete rock stratum. The difference between the drilling depth at the beginning and the drilling depth at the end of the smooth drilling section is the thickness l of the corresponding complete rock stratum.

[0034] Each data anomaly stage corresponds to a fracture. The difference between the drilling depth at the start and end of the data anomaly stage corresponds to the width b of the fracture. The number of data anomaly stages is determined as the number of fractures n.

[0035] In one embodiment, step S6 includes:

[0036] Determine whether the roadway roof meets the Class I roof stability classification standard. If the roadway roof meets the Class I roof stability classification standard, then the stability evaluation result of the roadway roof is determined to be a Class I roof. The Class I roof stability classification standard is as follows: uniaxial compressive strength of rock UCS ≥ 90 MPa, cumulative intact rock layer ratio RQD ≥ 0.95, thickness of each intact rock layer l > 100 cm, number of fractures n ≤ 1 and width of each fracture b < 1 cm.

[0037] If the roadway roof does not meet the Class I roof stability classification standard, determine whether the roadway roof meets the Class II roof stability classification standard. If the roadway roof meets the Class II roof stability classification standard, then the stability evaluation result of the roadway roof is determined to be a Class II roof. The Class II roof stability classification standard is: uniaxial compressive strength of rock UCS ≥ 60MPa, cumulative intact rock layer RQD ≥ 0.85, thickness of each intact rock layer l > 50cm, number of fractures n ≤ 2 and width of each fracture b < 2cm.

[0038] If the roadway roof does not meet the Class II roof stability classification standard, determine whether the roadway roof meets the Class III roof stability classification standard. If the roadway roof meets the Class III roof stability classification standard, then the stability evaluation result of the roadway roof is determined to be a Class III roof. The Class III roof stability classification standard is as follows: uniaxial compressive strength of rock UCS ≥ 30 MPa, cumulative intact rock layer RQD ≥ 0.75, thickness of each intact rock layer l > 30 cm, number of fractures n ≤ 3 and width of each fracture b < 5 cm.

[0039] If the roadway roof does not meet the Class III roof stability classification standard, determine whether the roadway roof meets the Class IV roof stability classification standard. If the roadway roof meets the Class IV roof stability classification standard, then the stability evaluation result of the roadway roof is determined to be a Class IV roof. The Class IV roof stability classification standard is: uniaxial compressive strength of rock UCS ≥ 5MPa, cumulative intact rock layer ratio RQD ≥ 0.65, thickness of each intact rock layer l > 10cm, number of fractures n ≤ 5 and width of each fracture b < 10cm.

[0040] If the roadway roof does not meet the Class IV roof stability classification standard, then the stability evaluation result of the roadway roof is determined to be Class V roof.

[0041] The aforementioned method for evaluating roadway roof stability based on drilling parameter response characteristics involves selecting any point on the roadway roof and continuously collecting drilling parameters during the drilling process using sensors. These parameters include drilling torque, drill bit speed, thrust, drilling speed, drilling depth, and drilling time. The parameters are then denoised using singular value decomposition (SVD) to obtain a denoised drilling dataset. Based on this denoised dataset, the energy consumption per unit volume of rock during drilling is analyzed, and the energy consumption per unit volume of rock during drilling is inverted to obtain... The uniaxial compressive strength of the rock is obtained; based on the drilling energy consumption or borehole images at different drilling depths, the thickness, number of fractures, and width of the intact rock strata in the roadway roof are analyzed; based on the thickness of the intact rock strata, the cumulative proportion of intact rock strata in the roadway roof is analyzed; based on the uniaxial compressive strength of the rock in the roadway roof, the thickness of the intact rock strata, the number of fractures, the width of the fractures, and the cumulative proportion of intact rock strata, the stability evaluation result of the roadway roof is determined, thereby enabling a simple and rapid effective rating evaluation of roof stability. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating a method for evaluating roadway roof stability based on drilling parameter response characteristics in one embodiment.

[0043] Figure 2 This is a schematic diagram showing the relationship between energy consumption E per unit volume of rock drilling and drilling depth h in one embodiment. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0045] In one embodiment, such as Figure 1 As shown, a method for evaluating the stability of roadway roof based on the response characteristics of drilling parameters is provided, including the following steps:

[0046] Step S1: Select any point on the roof in the roadway and continuously collect the drilling parameters during the drilling process of the drilling rig on the roadway roof through the sensor. The drilling parameters include drilling torque M, drill bit speed N, thrust F, drilling speed V, drilling depth h and drilling time t.

[0047] Step S2: Use singular value decomposition to denoise the drilling parameters to obtain the denoised drilling dataset X(B).

[0048] Step S3: Based on the denoised drilling dataset X(B), analyze the energy consumption E of drilling per unit volume of rock, and obtain the uniaxial compressive strength UCS of the rock based on the energy consumption E of drilling per unit volume of rock.

[0049] Step S4: Based on the drilling energy consumption or borehole images at different drilling depths, analyze the thickness l, number n, and width b of the intact rock strata in the tunnel roof.

[0050] Step S5: Analyze the cumulative percentage of intact rock strata (RQD) in the tunnel roof based on the thickness l of the intact rock strata.

[0051] Step S6: Determine the stability evaluation result of the roadway roof based on the uniaxial compressive strength (UCS) of the rock, the thickness (l) of the intact rock strata, the number (n) of fractures, the width (b) of fractures, and the cumulative percentage (RQD) of intact rock strata.

[0052] The aforementioned method for evaluating roadway roof stability based on drilling parameter response characteristics involves selecting any point on the roof within the roadway and continuously collecting drilling parameters during the drilling process using sensors. These parameters include drilling torque, drill bit speed, thrust, drilling speed, drilling depth, and drilling time. The parameters are then denoised using singular value decomposition (SVD) to obtain a denoised drilling dataset. Based on the denoised dataset X(B), the energy consumption per unit volume of rock is analyzed, and the uniaxial compressive strength of the rock is obtained by inversion based on this energy consumption. The thickness, number of fractures, and width of intact rock strata in the roadway roof are analyzed based on drilling energy consumption or borehole images at different drilling depths. The cumulative percentage of intact rock strata in the roadway roof is analyzed based on the thickness of intact rock strata. Finally, the stability evaluation results of the roadway roof are determined based on the uniaxial compressive strength, thickness, number of fractures, width of fractures, and cumulative percentage of intact rock strata. This allows for a simple and rapid effective rating and evaluation of roof stability.

[0053] In one embodiment, step S2 includes:

[0054] Step 1.1: Mark the zero-value points and significant mutation points in the drilling data caused by human factors during the drilling process. Filter and delete the drilling data corresponding to the zero-value points and significant mutation points to obtain the filtered drilling dataset X(A). Wherein, if the abnormal data evaluation index T=0 at a certain moment or the fluctuation range of adjacent drilling torque data is ≥25%, the corresponding drilling data at that moment needs to be deleted. The expression for the abnormal data evaluation index is:

[0055]

[0056] In the formula, T represents the evaluation index for abnormal data; X M X N XF X V These represent the drilling torque, drill bit speed, thrust, and drilling speed of the drilling rig at a given moment, respectively.

[0057] Step 1.2: The filtered drilling dataset X(A) = {a1, a2, a3, ..., aA} is decomposed and reconstructed using singular value decomposition (SVD) to transform it into a singular value matrix H. The transformation process is as follows:

[0058]

[0059] In the formula, D is the effective molecular space of the noisy drilling signal, W is the noise molecular space, i is the number of drilling parameters, and j is the number of sampling points;

[0060] Step 1.3: Determine the singular values ​​of the singular value matrix H, separate the effective drilling signal components from the noise components, and obtain the denoised drilling dataset X(B) through inverse transformation.

[0061] Among them, human factors that cause zero points and significant abrupt changes in drilling data can include factors such as the working environment, stuck drill pipe, and drill pipe replacement.

[0062] In one embodiment, the analytical formula for the energy consumption E per unit volume of rock drilling is:

[0063]

[0064] Where N is the drill bit rotation speed in r / min; M is the drilling torque in N•m; F is the propulsion force in N•m; μ is the coefficient of friction between the drill bit and the rock contact surface; Rs represents the drill bit radius in mm; L is the length of the cutting edge in mm; and V is the drilling speed in mm / s.

[0065] In one embodiment, the analytical formula for the uniaxial compressive strength (UCS) of rock is:

[0066]

[0067] UCS stands for uniaxial compressive strength of rock.

[0068] In one embodiment, step S4 includes:

[0069] Establish the relationship curve between energy consumption E per unit volume of rock drilling and drilling depth h, and determine the stable drilling section and the data anomaly stage of the relationship curve.

[0070] Based on the stable drilling section and the abnormal data stage, determine the thickness l, the number n, and the width b of the intact rock strata in the roadway roof;

[0071] Alternatively, after drilling is completed, borehole images corresponding to different depths of the borehole wall can be obtained using a borehole inspection instrument, and the thickness l of the intact rock strata, the number n of fractures, and the width b of the fractures can be analyzed using image processing software.

[0072] When analyzing the width of a crack using image processing software, the maximum width value within the crack can be taken as the crack width.

[0073] The curve showing the relationship between energy consumption E per unit volume of rock drilling and drilling depth h includes the initial steep ascent section, the steady drilling section, and the data anomaly stage.

[0074] The initial steep ascent stage is characterized by a sharp increase in slope from a drilling depth of 0, as shown in the curve of energy consumption E per unit volume of rock drilling versus drilling depth h. This is because the drill bit transitions from its initial idle state to rotary cutting and breaking through the rock surface and penetrating into the rock mass. This stage is generally completed within a drilling depth of 2-3 mm.

[0075] During the stable drilling phase, the relationship between energy consumption per unit volume of rock drilled, E, and drilling depth, h, is characterized by a stable curve with slight fluctuations, approximating a straight line parallel to the horizontal axis. With the drill bit rotation speed N and drilling speed V remaining constant, the relationship curve becomes relatively stable with slight fluctuations after the drill bit has fully entered the rock formation. Within this range, the difference between the maximum and minimum energy consumption, Emax and Emin, is ≤5%.

[0076] In the abnormal data phase, the relationship between the energy consumption per unit volume of rock drilling (E) and the drilling depth (h) is shown in the curve. After a period of stable drilling, the energy consumption per unit volume of rock drilling (E) suddenly drops to 0 and then increases rapidly. This is because after the drill bit enters the original fractures in the top rock layer, it is initially in an idle state, and then part of the drill bit rotates and cuts into the rock mass. At this time, the data of energy consumption per unit volume of rock drilling (E) fluctuates greatly.

[0077] In one embodiment, the analytical formula for the cumulative intact rock strata percentage (RQD) is:

[0078]

[0079] In the formula, lq represents the thickness of the qth complete rock stratum; L represents the total length of the borehole; and Q is the total number of complete rock strata in the tunnel roof.

[0080] In one embodiment, determining the thickness l, number n, and width b of the intact rock strata in the tunnel roof based on the smooth drilling phase and the data anomaly phase includes:

[0081] Each smooth drilling section corresponds to a complete rock stratum. The difference between the drilling depth at the beginning and the drilling depth at the end of the smooth drilling section is the thickness l of the corresponding complete rock stratum.

[0082] Each data anomaly stage corresponds to a fracture. The difference between the drilling depth at the start and end of the data anomaly stage corresponds to the width b of the fracture. The number of data anomaly stages is determined as the number of fractures n.

[0083] In one embodiment, step S6 includes:

[0084] To determine whether the roadway roof meets the Class I roof stability classification criteria, if the roadway roof meets the Class I roof stability classification criteria, then the stability evaluation result of the roadway roof is determined to be a Class I roof. The Class I roof stability classification criteria are: uniaxial compressive strength of rock UCS ≥ 90 MPa, cumulative intact rock layer ratio RQD ≥ 0.95, thickness of each intact rock layer l > 100 cm, number of fractures n ≤ 1 and width of each fracture b < 1 cm;

[0085] If the roadway roof does not meet the Class I roof stability classification criteria, determine whether the roadway roof meets the Class II roof stability classification criteria. If the roadway roof meets the Class II roof stability classification criteria, the stability evaluation result of the roadway roof is determined to be a Class II roof. The Class II roof stability classification criteria are: uniaxial compressive strength of rock UCS ≥ 60 MPa, cumulative intact rock layer RQD ≥ 0.85, thickness of each intact rock layer l > 50 cm, number of fractures n ≤ 2 and width of each fracture b < 2 cm;

[0086] If the roadway roof does not meet the Class II roof stability classification criteria, determine whether the roadway roof meets the Class III roof stability classification criteria. If the roadway roof meets the Class III roof stability classification criteria, the stability evaluation result of the roadway roof is determined to be a Class III roof. The Class III roof stability classification criteria are: uniaxial compressive strength of rock UCS ≥ 30 MPa, cumulative intact rock layer RQD ≥ 0.75, thickness of each intact rock layer l > 30 cm, number of fractures n ≤ 3 and width of each fracture b < 5 cm;

[0087] If the roadway roof does not meet the Class III roof stability classification standard, determine whether the roadway roof meets the Class IV roof stability classification standard. If the roadway roof meets the Class IV roof stability classification standard, the stability evaluation result of the roadway roof is determined to be a Class IV roof. The Class IV roof stability classification standard is: uniaxial compressive strength of rock UCS ≥ 5MPa, cumulative intact rock layer RQD ≥ 0.65, thickness of each intact rock layer l > 10cm, number of fractures n ≤ 5 and width of each fracture b < 10cm;

[0088] If the roadway roof does not meet the Class IV roof stability classification criteria, the stability evaluation result of the roadway roof is determined to be Class V roof.

[0089] The aforementioned method for evaluating roadway roof stability based on the response characteristics of drilling parameters overcomes the shortcomings of traditional roadway stability evaluation methods, such as being time-consuming, cumbersome, and lagging behind on-site conditions. Through real-time drilling parameter inversion, it can quickly provide a quantitative and graded evaluation of roof stability, facilitating the rational selection of support parameters on-site and avoiding over- or under-support, thereby improving the economy and safety of roadway engineering. Furthermore, by continuously collecting drilling parameters during the roof anchoring drilling process, and based on the response characteristics of these parameters, it inverts and predicts factors such as the uniaxial compressive strength of the roof rock, the thickness of intact rock strata, and fracture development, thus achieving an accurate evaluation of roof stability. This method is highly operable and offers comprehensive and reliable evaluation indicators.

[0090] In one embodiment, a quantitative evaluation of roadway roof stability based on drilling parameter response characteristics is conducted, using a coal mine roadway as the application scenario. First, high-precision sensors are used to continuously collect different types of drilling parameters during the drilling process. Then, using singular value decomposition (SVD) noise reduction, noise reduction is applied to the drilling torque M, drill bit speed N, thrust F, drilling speed V, drilling depth h, and drilling time t, as follows:

[0091] First, mark the zero-value points and significant abrupt changes in the drilling data caused by human factors such as the working environment, stuck drill pipe, and drill pipe replacement during the drilling process. Then, filter and delete the corresponding drilling data. The filtering rules are as follows:

[0092]

[0093] In the formula, X M X N X F X V These represent the drilling torque, drill bit speed, thrust, and drilling speed of the drilling rig at a given moment. If T=0 or the fluctuation range of adjacent data of the drilling torque is ≥25%, the corresponding data for that moment should be deleted.

[0094] Secondly, taking the drilling torque M in the drilling parameters as an example, for the drilling dataset X(MA) = {a1, a2, a3, ..., ai} after deleting zero values ​​and significant outliers, a 600×400 singular value matrix H is constructed, and the boundary singular value is determined to be 0.1534. Then, the reconstructed drilling dataset X(MB) = {a1, a2, a3, ..., ai} is obtained through inverse transformation. To quantitatively evaluate the denoising effect, the mean squared error is used to compare the dispersion of the torque dataset before and after denoising, which are 47.25% and 9.17%, respectively. It can be seen that the singular value denoising method has a significant denoising effect on the drilling data.

[0095] For the other types of drilling parameters, such as drill bit rotation speed N, thrust F, and drilling speed V, singular value denoising method is used for data preprocessing.

[0096] Then, the noise-reduced drilling data is substituted into the following formula to calculate the energy consumption E per unit volume of rock drilling:

[0097]

[0098] In the formula, N is the drill bit rotation speed, r / min; M is the drilling torque, N•m; F is the propulsion force, kN; μ is the friction coefficient between the drill bit and the rock contact surface, and the friction coefficient between the metal drill bit and the rock contact surface is taken as 0.15; Rs represents the radius of the drill bit, mm, which is taken as 14 mm here; L is the length of the cutting edge, mm, which is taken as 2 mm here; V is the drilling speed, mm / s.

[0099] The uniaxial compressive strength (UCS) of rock is calculated using the following formula:

[0100]

[0101] Based on the above calculation results, a curve showing the relationship between energy consumption E per unit volume of rock drilling and drilling depth h is plotted, as shown in the attached figure. Figure 2 As shown, statistical analysis reveals three abrupt change points within the 10 m deep top plate, forming three data anomaly stages and corresponding to three fissures. The fissure widths b1, b2, and b3 are 7.31 cm, 2.04 cm, and 9.65 cm, respectively. The energy consumption per unit volume of rock during drilling along the drilling depth h indicates four stable drilling sections (l1, l2, l3, l4), with corresponding intact rock layer thicknesses of 0.95 m, 2.79 m, 2.93 m, and 3.08 m, respectively, and an RQD value of 0.87. Furthermore, the calculated uniaxial compressive strength of the rock is 57.35 MPa. By comparing with the top plate stability classification standard, this top plate is classified as Class IV.

[0102] As can be seen from the above embodiments, the method proposed in this application can predict and invert multiple evaluation indicators in the roof stability evaluation standard based on the response characteristics of drilling parameters. The roof stability evaluation results obtained by this method are scientific and accurate, and the evaluation process is convenient and highly operable.

[0103] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0104] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0105] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for evaluating the stability of roadway roof based on drilling parameter response characteristics, characterized in that, The method includes: Step S1: Select any point on the roof of the tunnel and continuously collect the drilling parameters during the drilling process of the drilling rig on the roof of the tunnel through the sensor. The drilling parameters include drilling torque M, drill bit speed N, thrust F, drilling speed V, drilling depth h and drilling time t. Step S2: Use singular value decomposition to denoise the drilling parameters to obtain the denoised drilling dataset X(B); Step S3: Based on the denoised drilling dataset X(B), analyze the energy consumption E of drilling per unit volume of rock, and obtain the uniaxial compressive strength UCS of the rock based on the energy consumption E of drilling per unit volume of rock. Step S4: Based on the drilling energy consumption or borehole images at different drilling depths, analyze the thickness l, number n, and width b of the intact rock strata in the tunnel roof. Step S5: Analyze the cumulative percentage of intact rock strata (RQD) in the tunnel roof based on the thickness l of the intact rock strata. Step S6: Determine the stability evaluation result of the roadway roof based on the uniaxial compressive strength (UCS) of the rock in the roadway roof, the thickness (l) of the intact rock strata, the number (n) of the fractures, the width (b) of the fractures, and the cumulative percentage (RQD) of the intact rock strata. Step S2 includes: Step 1.1: Mark the zero-value points and significant abrupt changes in the drilling data caused by human factors during the drilling process. Filter and delete the drilling data corresponding to the zero-value points and significant abrupt changes to obtain the filtered drilling dataset X(A). Wherein, if the abnormal data evaluation index T = 0 at a certain moment or the fluctuation range of adjacent drilling torque data is ≥ 25%, the corresponding drilling data at that moment needs to be deleted. The expression for the abnormal data evaluation index is: T=X M ·X N ·X F ·X V In the formula, T represents the evaluation index for abnormal data; X M X N X F X V These represent the drilling torque, drill bit speed, thrust, and drilling speed of the drilling rig at a given moment, respectively. Step 1.2: Apply singular value decomposition to the filtered drilling dataset X(A) = {a1, a2, a3, ..., a...} A The matrix is ​​decomposed and reconstructed into a singular value matrix H. The transformation process is as follows: In the formula, D is the effective molecular space of the noisy drilling signal, W is the noise molecular space, i is the number of the drilling parameters, and j is the number of sampling points; Step 1.3: Determine the singular values ​​of the singular value matrix H, separate the effective drilling signal components from the noise components, and obtain the denoised drilling dataset X(B) through inverse transformation; The analytical formula for the energy consumption E per unit volume of rock drilling is as follows: Where N is the drill bit rotation speed; M is the drilling torque; F is the propulsion force; μ is the coefficient of friction between the drill bit and the rock contact surface; R s The radius of the drill bit is represented by L; the length of the cutting edge is represented by V; and the drilling speed is represented by V. The analytical formula for the uniaxial compressive strength (UCS) of the rock is as follows: UCS = 0.2938E + 9.718 Wherein, UCS is the uniaxial compressive strength of rock; Step S4 includes: Establish the relationship curve between the energy consumption E per unit volume of rock drilling and the drilling depth h, and determine the stable drilling section and the abnormal data stage of the relationship curve. Based on the smooth drilling section and the abnormal data stage, determine the thickness l, the number n, and the width b of the intact rock strata in the tunnel roof; Alternatively, after drilling is completed, borehole images corresponding to different depths of the borehole inner wall can be obtained using a borehole inspection instrument, and the thickness l of the intact rock strata, the number n of fractures, and the width b of the fractures inside the borehole can be analyzed using image processing software. The analytical formula for the cumulative intact rock strata percentage (RQD) is as follows: In the formula, l q L represents the thickness of the qth complete rock stratum; L represents the total length of the borehole; and Q represents the total number of complete rock strata in the roof of the tunnel.

2. The method according to claim 1, characterized in that, The step of determining the thickness l, number n, and width b of the intact rock strata in the tunnel roof based on the stable drilling section and the abnormal data stage includes: Each smooth drilling section corresponds to a complete rock stratum. The difference between the drilling depth at the beginning and the drilling depth at the end of the smooth drilling section is the thickness l of the corresponding complete rock stratum. Each data anomaly stage corresponds to a fracture. The difference between the drilling depth at the start and end of the data anomaly stage corresponds to the width b of the fracture. The number of data anomaly stages is determined as the number of fractures n.

3. The method according to claim 1, characterized in that, Step S6 includes: Determine whether the roadway roof meets the Class I roof stability classification standard. If the roadway roof meets the Class I roof stability classification standard, then the stability evaluation result of the roadway roof is determined to be a Class I roof. The Class I roof stability classification standard is as follows: uniaxial compressive strength of rock UCS ≥ 90 MPa, cumulative intact rock layer ratio RQD ≥ 0.95, thickness of each intact rock layer l > 100 cm, number of fractures n ≤ 1 and width of each fracture b < 1 cm. If the roadway roof does not meet the Class I roof stability classification standard, determine whether the roadway roof meets the Class II roof stability classification standard. If the roadway roof meets the Class II roof stability classification standard, then the stability evaluation result of the roadway roof is determined to be a Class II roof. The Class II roof stability classification standard is: uniaxial compressive strength of rock UCS ≥ 60MPa, cumulative intact rock layer RQD ≥ 0.85, thickness of each intact rock layer l > 50cm, number of fractures n ≤ 2 and width of each fracture b < 2cm. If the roadway roof does not meet the Class II roof stability classification standard, determine whether the roadway roof meets the Class III roof stability classification standard. If the roadway roof meets the Class III roof stability classification standard, then the stability evaluation result of the roadway roof is determined to be a Class III roof. The Class III roof stability classification standard is as follows: uniaxial compressive strength of rock UCS ≥ 30 MPa, cumulative intact rock layer RQD ≥ 0.75, thickness of each intact rock layer l > 30 cm, number of fractures n ≤ 3 and width of each fracture b < 5 cm. If the roadway roof does not meet the Class III roof stability classification standard, determine whether the roadway roof meets the Class IV roof stability classification standard. If the roadway roof meets the Class IV roof stability classification standard, then the stability evaluation result of the roadway roof is determined to be a Class IV roof. The Class IV roof stability classification standard is: uniaxial compressive strength of rock UCS ≥ 5MPa, cumulative intact rock layer ratio RQD ≥ 0.65, thickness of each intact rock layer l > 10cm, number of fractures n ≤ 5 and width of each fracture b < 10cm. If the roadway roof does not meet the Class IV roof stability classification standard, then the stability evaluation result of the roadway roof is determined to be Class V roof.

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