A tunnel surrounding rock strength prediction method and system based on penetration rock breaking test

Abstract

The present invention discloses a method and system for predicting the strength of tunnel surrounding rock based on a penetration rock breaking test. The method generates and obtains first data corresponding to the penetration rock breaking test and preprocesses the first data; constructs a data analysis model corresponding to the first data, and generates second data corresponding to the first data based on the data analysis model; generates third data corresponding to the rock based on the second data and in combination with the first data; and a system corresponding to the method can more accurately judge the strength of the tunnel surrounding rock. In other words, the scheme of the present invention can accurately capture the size dependence in the penetration test and explain the size effect with a minimum number of tests. At the same time, the penetration results affected by the size effect can be converted into values ​​corresponding to larger sample sizes, thereby improving the accuracy and reliability of the experimental results.

Description

Technical Field

[0001] The present invention belongs to the technical field of rock mechanical property testing and processing, and in particular relates to a method and system for predicting the strength of tunnel surrounding rock based on a penetration and rock breaking test. Background Art

[0002] Currently, in tunnel engineering, accurately evaluating the strength of tunnel surrounding rock is crucial for tunnel design, construction, and safety. Traditional methods for evaluating surrounding rock strength have certain limitations and cannot accurately consider the impact of factors such as size effect on rock strength.

[0003] Rock penetration tests are important laboratory methods for measuring material properties and assessing rock performance. However, laboratory testing is limited by specimen size, which can lead to potential size effects. Previous attempts to address size effects have not addressed frictional materials such as rock, and accounting for these effects with minimal testing and effective theoretical approaches remains a challenge.

[0004] Therefore, the above traditional surrounding rock strength evaluation methods have certain limitations. They cannot accurately consider the influence of factors such as size effect on rock strength. In addition, laboratory tests are limited by limited sample size, which will produce potential technical problems and defects of size effect. It is urgent to design and develop a tunnel surrounding rock strength prediction method and system based on penetration rock breaking test. Summary of the Invention

[0005] In order to overcome the above-mentioned deficiencies and difficulties in the prior art, the purpose of the present invention is to provide a method and system for predicting the strength of tunnel surrounding rock based on penetration and rock breaking tests, so as to more accurately judge the strength of tunnel surrounding rock.

[0006] The first purpose of the present invention is to provide a tunnel surrounding rock strength prediction method based on penetration rock breaking test; the second purpose of the present invention is to provide a tunnel surrounding rock strength prediction system based on penetration rock breaking test; the third purpose of the present invention is to provide a tunnel surrounding rock strength prediction platform based on penetration rock breaking test.

[0007] The first object of the present invention is achieved in that the method comprises the steps of:

[0008] Generate and obtain first data corresponding to the rock penetration test, and pre-process the first data; wherein the first data is load data, displacement data, and penetration pressure data corresponding to the rock penetration process;

[0009] Constructing a data analysis model corresponding to the first data, and generating second data corresponding to the first data based on the data analysis model; wherein the second data is damage zone evolution data corresponding to the rock;

[0010] Based on the second data and in combination with the first data, third data corresponding to the rock is generated; wherein the third data is tunnel surrounding rock strength prediction data.

[0011] Furthermore, the generating and acquiring first data corresponding to the rock penetration test and preprocessing the first data further includes:

[0012] Generate and set fourth data corresponding to the hydraulic servo control machine; wherein the fourth data is operating parameter data of the hydraulic servo control machine, including: displacement control mode data and piston constant speed data;

[0013] Based on the fourth data, fifth data and sixth data corresponding to the rock penetration test are sequentially generated and acquired; wherein the fifth data are load data, displacement data, and penetration pressure data corresponding to the first rock penetration process; and the sixth data are load data, displacement data, and penetration pressure data corresponding to the second rock penetration process;

[0014] filtering and removing the fifth data to generate seventh data corresponding to the fifth data; wherein the seventh data is the data obtained by removing obvious outliers and noise from the fifth data;

[0015] The sixth data is filtered and cleared; and eighth data corresponding to the sixth data is generated; wherein the eighth data is the data obtained by removing obvious outliers and noise from the sixth data.

[0016] Furthermore, the generating and acquiring first data corresponding to the rock penetration test and preprocessing the first data further includes:

[0017] Based on the first data, the penetration pressure data corresponding to the penetration rock breaking test is calculated and generated; wherein the calculation formula is as follows:

[0018]

[0019] Where: F is the load, R ind represents the radius of the spherical indenter tip and d is the penetration depth.

[0020] Furthermore, the step of constructing a data analysis model corresponding to the first data and generating second data corresponding to the first data based on the data analysis model further includes:

[0021] generating damage area evolution characterization data corresponding to the second data according to the data analysis model;

[0022] Based on the damage zone evolution characterization data, ninth data corresponding to the rock damage zone is generated; wherein the ninth data includes the change data of the damage zone radius and the expansion speed data of the damage zone.

[0023] Furthermore, the expression of the data analysis model is as follows:

[0024]

[0025] Where: d is the indentation depth, R ind is the radius of the spherical indenter tip, Indicates the proportional relationship between the indentation depth and the indenter radius, G is the shear modulus, which reflects the elastic properties of the material under shear stress, and μ is the Poisson's ratio; dimensionless damage zone radius Where R represents the actual radius of the damage area, a is a size parameter related to the damage area; k is a parameter related to the geometry of the cavity. When k = 1, it represents a cylindrical cavity, and when k = 2, it represents a spherical cavity; f(m), K p (φ), β(K p ) is a parameter related to the internal friction angle of the rock; where: Is a K p and the function calculated by the size parameter m; The internal friction angle K p The impact of is the size parameter, where R represents the actual radius of the damaged area, B is the radius of the rock specimen,

[0026] Where λ and K d Related to other physical properties of rocks; K d is the internal friction angle Related dimensionless parameters Where σ unl is the uniaxial ultimate strength of rock.

[0027] The changes in these parameters reflect the development of the damage zone during the indentation test. Where σ unl is the uniaxial ultimate strength of rock;

[0028] Is a K p and size parameter m; μ is Poisson's ratio; G is the shear modulus.

[0029] Furthermore, the step of generating third data corresponding to the rock based on the second data and in combination with the first data further includes:

[0030] respectively obtaining tenth data corresponding to each test and analyzing the sample size effect corresponding to the tenth data; wherein the tenth data includes peak penetration pressure data and sample size data;

[0031] Based on the second data and combined with the first data, the two data are compared and analyzed to generate corresponding eleventh data; wherein, the eleventh data is characterization data for verifying the accuracy and reliability of the data analysis model.

[0032] The second object of the present invention is achieved as follows: the system is applied to the tunnel surrounding rock strength prediction method based on the penetration rock breaking test, and the system includes:

[0033] a data generation and processing unit, configured to generate and obtain first data corresponding to the rock penetration test, and pre-process the first data; wherein the first data is load data, displacement data, and penetration pressure data corresponding to the rock penetration process;

[0034] a model building and generating unit, configured to build a data analysis model corresponding to the first data, and generate second data corresponding to the first data based on the data analysis model; wherein the second data is damage zone evolution data corresponding to the rock;

[0035] The prediction data generating unit is used to generate third data corresponding to the rock based on the second data and in combination with the first data; wherein the third data is the prediction data of the tunnel surrounding rock strength.

[0036] Furthermore, the data generation processing unit further includes:

[0037] a first data generating module, configured to generate and set fourth data corresponding to the hydraulic servo control machine; wherein the fourth data is operating parameter data of the hydraulic servo control machine, including displacement control mode data and piston constant speed data;

[0038] a second data generation module for sequentially generating and acquiring fifth and sixth data corresponding to the rock penetration test based on the fourth data; wherein the fifth data is load data, displacement data, and penetration pressure data corresponding to the first rock penetration process; and the sixth data is load data, displacement data, and penetration pressure data corresponding to the second rock penetration process;

[0039] a first data processing module, configured to filter, remove, and process the fifth data to generate seventh data corresponding to the fifth data; wherein the seventh data is the data obtained by removing obvious outliers and noise from the fifth data;

[0040] a second data processing module, configured to filter, remove, and process the sixth data; and generate eighth data corresponding to the sixth data; wherein the eighth data is the sixth data after removing obvious outliers and noise;

[0041] And / or, the model building and generating unit further includes:

[0042] a third data generating module, configured to generate damage area evolution characterization data corresponding to the second data according to the data analysis model;

[0043] a fourth data generating module, configured to generate ninth data corresponding to the rock damage zone based on the damage zone evolution characterization data; wherein the ninth data includes data on changes in the damage zone radius and data on the expansion speed of the damage zone;

[0044] And / or, the prediction data generating unit further includes:

[0045] a data acquisition and analysis module, configured to respectively acquire tenth data corresponding to each test and analyze a sample size effect corresponding to the tenth data; wherein the tenth data includes peak penetration pressure data and sample size data;

[0046] The fifth data generation module is used to compare and analyze the second data in combination with the first data and generate corresponding eleventh data; wherein the eleventh data is characterization data for verifying the accuracy and reliability of the data analysis model.

[0047] Furthermore, the data generation processing unit further includes:

[0048] The first calculation module is configured to calculate and generate penetration pressure data corresponding to the rock penetration test based on the first data; wherein the calculation formula is as follows:

[0049]

[0050] Where: F is the load, R ind represents the radius of the spherical indenter tip, and d is the penetration depth;

[0051] The expression of the data analysis model is as follows:

[0052]

[0053] Where: d is the indentation depth, R ind is the radius of the spherical indenter tip, Indicates the proportional relationship between the indentation depth and the indenter radius, G is the shear modulus, which reflects the elastic properties of the material under shear stress, and μ is the Poisson's ratio; dimensionless damage zone radius Where R represents the actual radius of the damage area, a is a size parameter related to the damage area; k is a parameter related to the geometry of the cavity. When k = 1, it represents a cylindrical cavity, and when k = 2, it represents a spherical cavity; f(m), K p (φ), β(K p ) is a parameter related to the internal friction angle of the rock; where: Is a K p and the function calculated by the size parameter m; The internal friction angle K p The impact of is the size parameter, where R represents the actual radius of the damaged area, B is the radius of the rock specimen,

[0054] Where λ and K d Related to other physical properties of rocks; K d is the internal friction angle Related dimensionless parameters Where σ unl is the uniaxial ultimate strength of rock.

[0055] The changes in these parameters reflect the development of the damage zone during the indentation test. Where σ unl is the uniaxial ultimate strength of rock;

[0056] Is a K p and size parameter m; μ is Poisson's ratio; G is the shear modulus.

[0057] The third object of the present invention is achieved as follows: it includes a processor, a memory and a tunnel surrounding rock strength prediction platform control program based on penetration rock breaking test; wherein the tunnel surrounding rock strength prediction platform control program based on penetration rock breaking test is executed in the processor, the tunnel surrounding rock strength prediction platform control program based on penetration rock breaking test is stored in the memory, and the tunnel surrounding rock strength prediction platform control program based on penetration rock breaking test realizes the tunnel surrounding rock strength prediction method based on penetration rock breaking test.

[0058] The present invention generates and obtains first data corresponding to the penetration and rock breaking test through a method, and preprocesses the first data; wherein, the first data is load data, displacement data and penetration pressure data corresponding to the rock penetration process; constructs a data analysis model corresponding to the first data, and based on the data analysis model, generates second data corresponding to the first data; wherein, the second data is damage zone evolution data corresponding to the rock; based on the second data and in combination with the first data, generates third data corresponding to the rock; wherein, the third data is tunnel surrounding rock strength prediction data, and a system corresponding to the method can more accurately judge the strength of the tunnel surrounding rock.

[0059] In other words, the present invention accurately captures the size dependence of penetration tests and accounts for the size effect with a minimum number of tests (at least two). Furthermore, it converts penetration results affected by the size effect to values ​​corresponding to larger specimen sizes, improving the accuracy and reliability of the experimental results. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0061] Figure 1 This is a schematic flow chart of the steps of a method for predicting tunnel surrounding rock strength based on a penetration rock breaking test according to the present invention;

[0062] Figure 2 This is a schematic diagram showing a case of a result curve obtained from a penetration test according to an embodiment of a method for predicting tunnel surrounding rock strength based on a penetration rock breaking test of the present invention;

[0063] Figure 3 Schematic diagram showing the difference between the first penetration point and the second penetration point during a penetration test in accordance with an embodiment of a method for predicting the surrounding rock strength of a tunnel based on a penetration rock breaking test of the present invention;

[0064] Figure 4 Schematic diagram of the overall test steps of a penetration test in accordance with an embodiment of a method for predicting tunnel surrounding rock strength based on a penetration rock breaking test of the present invention;

[0065] Figure 5 This is a schematic diagram of the operating steps of the first penetration test in an embodiment of a method for predicting the strength of surrounding rock of a tunnel based on a penetration rock breaking test of the present invention;

[0066] Figure 6This is a schematic diagram of the operating steps of a second penetration test in an embodiment of a method for predicting tunnel surrounding rock strength based on a penetration rock breaking test of the present invention;

[0067] Figure 7 This is a schematic diagram of the specific process steps of an embodiment of a method for predicting the strength of surrounding rock of a tunnel based on a penetration and rock breaking test of the present invention;

[0068] Figure 8 This is a schematic diagram of the architecture of a tunnel surrounding rock strength prediction system based on penetration and rock breaking tests according to the present invention;

[0069] Figure 9 This is a schematic diagram of the architecture of a tunnel surrounding rock strength prediction platform based on penetration and rock breaking tests in the present invention. DETAILED DESCRIPTION

[0070] In order to better understand the purpose, technical solutions and advantages of the present invention, the present invention is further described below with reference to the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification.

[0071] The present invention may also be implemented or applied through other different specific examples, and the details in this specification may also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0072] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0073] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. Secondly, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that ordinary technicians in this field can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0074] Preferably, the tunnel surrounding rock strength prediction method based on rock penetration testing of the present invention is applied to one or more terminals or servers. The terminal 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.

[0075] The terminal can be a computing device such as a desktop computer, notebook, PDA, cloud server, etc. The terminal can interact with the client through a keyboard, mouse, remote control, touchpad, or voice control device.

[0076] The present invention provides a method, system and platform for predicting the strength of tunnel surrounding rock based on penetration and rock breaking tests.

[0077] like Figure 1 , which is a flow chart of a method for predicting tunnel surrounding rock strength based on penetration and rock breaking test provided by an embodiment of the present invention.

[0078] In this embodiment, the tunnel surrounding rock strength prediction method based on penetration and rock breaking test can be applied to a terminal or fixed terminal with a display function. The terminal is not limited to a personal computer, a smart phone, a tablet computer, a desktop computer or an all-in-one computer equipped with a camera, etc.

[0079] The tunnel surrounding rock strength prediction method based on rock penetration testing can also be applied in a hardware environment consisting of a terminal and a server connected to the terminal via a network. The network includes, but is not limited to, a wide area network, a metropolitan area network, or a local area network. The tunnel surrounding rock strength prediction method based on rock penetration testing in this embodiment of the present invention can be executed by a server, a terminal, or both.

[0080] For example, for a terminal that needs to perform a tunnel surrounding rock strength prediction based on a penetration rock breaking test, the tunnel surrounding rock strength prediction function based on a penetration rock breaking test provided by the method of the present invention can be directly integrated on the terminal, or a client for implementing the method of the present invention can be installed. For another example, the method provided by the present invention can also be run on a server or other device in the form of a software development kit (SDK), and an interface for the tunnel surrounding rock strength prediction function based on a penetration rock breaking test is provided in the form of an SDK. The terminal or other device can implement the tunnel surrounding rock strength prediction function based on a penetration rock breaking test through the provided interface. The present invention is further explained below in conjunction with the accompanying drawings.

[0081] like Figure 1-Figure 7 As shown, the present invention provides a method for predicting the strength of tunnel surrounding rock based on a penetration rock breaking test, the method comprising the following steps:

[0082] S01. Generate and obtain first data corresponding to a rock penetration test, and pre-process the first data; wherein the first data is load data, displacement data, and penetration pressure data corresponding to a rock penetration process;

[0083] S02. Constructing a data analysis model corresponding to the first data, and generating second data corresponding to the first data based on the data analysis model; wherein the second data is damage zone evolution data corresponding to the rock;

[0084] S03. Generate third data corresponding to the rock based on the second data and in combination with the first data; wherein the third data is tunnel surrounding rock strength prediction data.

[0085] The generating and acquiring first data corresponding to the rock penetration test and preprocessing the first data further includes:

[0086] S011. Generate and set fourth data corresponding to the hydraulic servo control machine; wherein the fourth data is operating parameter data of the hydraulic servo control machine, including: displacement control mode data and piston constant speed data;

[0087] S012. Based on the fourth data, sequentially generate and acquire fifth and sixth data corresponding to the rock penetration test; wherein the fifth data is load data, displacement data, and penetration pressure data corresponding to the first rock penetration process; and the sixth data is load data, displacement data, and penetration pressure data corresponding to the second rock penetration process;

[0088] S013. Filter and remove the fifth data to generate seventh data corresponding to the fifth data; wherein the seventh data is the data obtained by removing obvious outliers and noise from the fifth data;

[0089] S014. Filter and clear the sixth data; generate eighth data corresponding to the sixth data; wherein the eighth data is the data obtained by removing obvious outliers and noise from the sixth data.

[0090] The generating and acquiring first data corresponding to the rock penetration test and preprocessing the first data further includes:

[0091] S015. Based on the first data, calculate and generate penetration pressure data corresponding to the rock penetration test; wherein the calculation formula is as follows:

[0092]

[0093] Where: F is the load, R ind represents the radius of the spherical indenter tip and d is the penetration depth.

[0094] The step of constructing a data analysis model corresponding to the first data and generating second data corresponding to the first data based on the data analysis model further includes:

[0095] S021. Generate damage area evolution characterization data corresponding to the second data according to the data analysis model;

[0096] S022. Based on the damage zone evolution characterization data, generate ninth data corresponding to the rock damage zone; wherein the ninth data includes change data of the damage zone radius and expansion speed data of the damage zone.

[0097] The expression of the data analysis model is as follows:

[0098]

[0099] Where: d is the indentation depth, R ind is the radius of the spherical indenter tip, Indicates the proportional relationship between the indentation depth and the indenter radius, G is the shear modulus, which reflects the elastic properties of the material under shear stress, and μ is the Poisson's ratio; dimensionless damage zone radius Where R represents the actual radius of the damage area, a is a size parameter related to the damage area; k is a parameter related to the geometry of the cavity. When k = 1, it represents a cylindrical cavity, and when k = 2, it represents a spherical cavity; f(m), K p (φ), β(K p ) is a parameter related to the internal friction angle of the rock; where: Is a K p and the function calculated by the size parameter m; The internal friction angle K p The impact of is the size parameter, where R represents the actual radius of the damaged area, B is the radius of the rock specimen,

[0100] Where λ and K d Related to other physical properties of rocks; K d is the internal friction angle Related dimensionless parameters Where σ unl is the uniaxial ultimate strength of rock.

[0101] The changes in these parameters reflect the development of the damage zone during the indentation test. Where σ unl is the uniaxial ultimate strength of rock;

[0102] Is a K p and size parameter m; μ is Poisson's ratio; G is the shear modulus.

[0103] The step of generating third data corresponding to the rock based on the second data and in combination with the first data further includes:

[0104] S031. Obtain tenth data corresponding to each test, and analyze the sample size effect corresponding to the tenth data; wherein the tenth data includes peak penetration pressure data and sample size data;

[0105] S032. Based on the second data and in combination with the first data, compare and analyze the two data and generate corresponding eleventh data; wherein the eleventh data is characterization data for verifying the accuracy and reliability of the data analysis model.

[0106] Specifically, in an embodiment of the present invention, this solution proposes a method for evaluating the strength of tunnel surrounding rock based on a penetration rock breaking test, including: considering the evolution of the damage zone according to a theoretical model, establishing the relationship between the penetration pressure, dimensionless damage zone radius, size parameters, parameters related to the internal friction angle, indentation depth, radius of the spherical indenter tip, shear modulus, uniaxial ultimate strength, and Poisson's ratio; conducting a penetration test according to the theoretical model, using a chamfered conical probe for a standard point load test, a wedge-shaped indenter, and performing the test at a constant piston speed in a displacement control mode; interpreting the penetration test results according to the theoretical model; and verifying the validity of the model by comparing the theoretical predictions with experimental data according to the theoretical model, and further understanding the behavior and strength characteristics of rock in the indentation test;

[0107] The theoretical model is:

[0108]

[0109] The evaluation method, which analyzes the evolution of the damage zone, is characterized by the use of some parameters: such as the dimensionless damage zone radius Among them, R represents the actual radius of the damage area, and a is a size parameter related to the damage area. As well as functions and parameters related to the damage area, such as:

[0110] etc. The changes of these parameters reflect the development of the damage zone during the indentation test; Where σ unl is the uniaxial ultimate strength of rock; Is a K p and the function calculated from the size parameter m; μ is the Poisson's ratio; G is the shear modulus; The internal friction angle K p the impact of; is the size parameter, where R represents the actual radius of the damage zone and B is the radius of the rock specimen.

[0111] The evaluation method comprises preparing samples of three different rocks, including granite, basalt, and sandstone, cutting the samples as required, and conducting a penetration test using a specific machine;

[0112] The evaluation method described above is based on the load-displacement curve obtained from the penetration test. We determine the penetration depth d by measuring the displacement, and the indentation pressure p is calculated based on the ratio of the applied load F to the projected area of ​​the indentation.

[0113]

[0114] Among them, R ind Indicates the radius of the spherical indenter tip.

[0115] The test method comprises conducting two consecutive rock penetration tests on the same specimen, wherein the first penetration is conducted at the center of the flat surface of the specimen, causing the specimen to split into two or three pieces, and the largest fragment is selected for a subsequent penetration test conducted at the center of the flat surface opposite to the first penetration point. After the second penetration test, the length of the second penetration point along the macro crack to the nearest free surface is measured;

[0116] In the theoretical model, the indentation depth is d and the radius of the spherical indenter tip is R ind , Indicates the proportional relationship between the indentation depth and the indenter radius; G is the shear modulus, which reflects the elastic properties of the material under shear stress; μ is the Poisson's ratio; f(m), K p (φ), β(K p ) These parameters are related to the internal friction angle of the rock, Is a K p and the function calculated by the size parameter m, The internal friction angle K p impact. is the size parameter, where R represents the actual radius of the damage zone and B is the radius of the rock specimen where λ and K d Related to other physical properties of rocks; K d is the internal friction angle Related dimensionless parameters The function, where σ unl is the uniaxial ultimate strength of rock;

[0117] The λ,

[0118] The theoretical model described above uses the fourth-order Runge-Kutta method to solve the model equations with the initial condition ξ R =1When δ=0,

[0119] The experimental method in the scheme of the present invention collects the peak penetration pressure and sample size of each test, analyzes the sample size effect, and compares the experimental data with the model prediction; the prediction method can effectively capture the size dependence in the rock penetration test and convert the penetration results affected by the size effect into values ​​corresponding to a larger sample size.

[0120] In other words, the innovation of the present invention lies in proposing a new theoretical model, which can more accurately describe the behavior of rock in the penetration and rock breaking test, thereby more accurately evaluating the strength of the tunnel surrounding rock.

[0121] The embodiments of the present application rely on rock penetration tests.

[0122] The experimental apparatus used in the present invention comprises a hydraulic servo-controlled machine equipped with an integrated load cell (with an accuracy better than 0.5%, 1000 kN) and a displacement sensor (with an accuracy better than 0.06 mm).

[0123] In the experiments, a wedge-shaped indenter with a conical tip was used for penetration tests, and all tests were performed on the machine in displacement control mode with a constant piston speed of 0.05 mm / min.

[0124] The experimental steps are as follows:

[0125] S1. Prepare specimens of three different rocks (granite, basalt, and sandstone) with a diameter of 50 mm and a height of 25 mm.

[0126] S2. Penetration tests were performed on a hydraulic servo-controlled machine using a wedge-shaped indenter of a standard point load device in displacement control mode with a constant piston speed of 0.05 mm / min.

[0127] S3. Two consecutive rock penetration tests are conducted on the same specimen. The first penetration is conducted at the center of the flat surface of the specimen, causing the specimen to split into two or three pieces. The largest fragment is selected for the subsequent penetration test, which is conducted at the center of the flat surface opposite to the first penetration point.

[0128] S4. After the second penetration test, measure the length from the (second) penetration point along the macro crack to the nearest free surface.

[0129] Data processing and analysis: The penetration depth d is determined by measuring the displacement according to the formula:

[0130]

[0131] Calculate the penetration pressure p, where R ind is the radius of the spherical indenter tip and F is the applied load.

[0132] Using a specific model:

[0133]

[0134] To interpret the penetration test results, the model relates the normalized penetration pressure p to the dimensionless damage zone radius of the specimen with radius B. Get in touch.

[0135] Among them, in the process of analyzing the evolution of the damage zone, some parameters are used to characterize it: such as the dimensionless damage zone radius Where R represents the actual radius of the damaged area, and a is a size parameter related to the damaged area. v are functions and parameters related to the damage area, such as:

[0136] The changes in these parameters reflect the development of the damage zone during the indentation test.

[0137] In the above theoretical model, the indentation depth is d and the radius of the spherical indenter tip is R ind , Indicates the proportional relationship between the indentation depth and the indenter radius, G is the shear modulus, which reflects the elastic properties of the material under shear stress, and μ is the Poisson's ratio. p (φ), β(K p ) These parameters are related to the internal friction angle of the rock,

[0138] Is a K p and the function calculated by the size parameter m, The internal friction angle K p The impact of is the size parameter, where R represents the actual radius of the damage zone, B is the radius of the rock specimen, Among them, λ and K d Related to other physical properties of rocks.

[0139] K d is the internal friction angle Related dimensionless parameters where σ unl is the uniaxial ultimate strength of rock.

[0140] The model equations are solved using the fourth-order Runge–Kutta method with the initial condition ξ R =1When δ=0,

[0141] Determine the parameters related to the evolution of the damage zone and substitute these determined parameters into the relevant formulas in the model. By analyzing the model calculation results, we can understand the development of the damage zone in the indentation test, including the change in the damage zone radius and the expansion speed of the damage zone.

[0142] The peak penetration pressure and sample size of each test were collected, the sample size effect was analyzed, and the damage zone evolution results calculated by the model were compared with the data observed in the actual penetration rock breaking test to verify the accuracy and reliability of the model.

[0143] Based on the evaluation results of the damaged zone evolution, its impact on rock strength is analyzed, and then the strength of the tunnel surrounding rock is judged.

[0144] Preferably, in the present invention, experimental materials were prepared using three different rocks from Hunan, China: granite, basalt, and sandstone. The rocks were cut and sawn into cylinders with a diameter of 50 mm and a height of 25 mm for subsequent penetration tests. Physical and strength parameters of the three rocks were measured and summarized, including density, connected porosity, P-wave velocity, internal friction angle, Young's modulus, Poisson's ratio, and uniaxial compressive strength. The average and standard deviation values, as well as the number of test specimens, are presented.

[0145] Experimental setup: Hydraulic servo control machine: model RUT-100, manufactured by Tuxin (Hunan); integrated load sensor: accuracy better than 0.5%, maximum measurement value 1000kN; displacement sensor: accuracy better than 0.06mm.

[0146] Place the machine on a stable, sturdy bench, ensuring it is level and stable. Connect the power and data cables according to the machine's installation manual.

[0147] The present invention proposes an experimental method considering the size effect in rock penetration test. The experimental steps are described as follows:

[0148] First Penetration Test: S1. Place a prepared specimen horizontally on the machine's worktable, ensuring the specimen surface is flat. S2. Adjust the indenter position so that it is aligned with the center of the specimen's flat surface. S3. Start the hydraulic servo control machine, set to displacement control mode, and maintain a constant piston speed of 0.05 mm / min. S4. Start loading the machine, and gradually press the indenter into the specimen until it splits into two or three pieces. S5. Record the load and displacement data during the penetration process.

[0149] Sample fragment selection: S6. Observe the sample after it is split and select the largest fragment.

[0150] Second penetration test: S7. Place the largest selected fragment horizontally on the workbench again, ensuring that the center of the flat surface opposite the first penetration point faces upward. S8. Adjust the indenter position so that it is aligned with the center of this surface. S9. Start the machine for the second penetration test using the same displacement control mode and constant piston speed (0.05 mm / min). S10. Record the load and displacement data until penetration is complete.

[0151] Measurement and recording: S11. After the second penetration test is completed, use a ruler to measure the length from the (second) penetration point along the macro crack to the nearest free surface and record the measurement value.

[0152] End of experiment: S12. Turn off the power of the hydraulic servo control machine. S13. Remove the specimen and indenter, and clean the experimental apparatus and work area.

[0153] Data collection and recording: Describe the data that needs to be collected during the experiment, such as load, displacement, penetration pressure, etc., as well as the data recording method and frequency.

[0154] During the experiment, the data that need to be collected include load, displacement and penetration pressure.

[0155] Load: The load value applied by the indenter on the specimen is measured in real time by the integrated load sensor equipped with the hydraulic servo control machine.

[0156] Displacement: Use the displacement sensor to monitor the displacement of the pressure head in real time.

[0157] Penetration pressure: Based on the measured displacement, according to the formula:

[0158]

[0159] Calculate the penetration pressure p, where F is the load and R ind is the radius of the spherical indenter tip and d is the displacement.

[0160] There are two common ways to record data: Spreadsheet recording: Use spreadsheet software (such as Excel) to create a detailed data record sheet. Each row corresponds to a penetration test, and each column records information such as the specimen number, rock type, load value, displacement value, calculated penetration pressure value, penetration time, and test phase (first or second penetration).

[0161] Data processing and analysis: First, the collected raw load and displacement data are screened and cleaned to remove obvious outliers and noise. Second, the penetration depth d is calculated based on the displacement data.

[0162] Finally, using the formula:

[0163]

[0164] Calculate the penetration pressure p, where F is the load and R ind Indicates the radius of the spherical indenter tip.

[0165] Using a newly developed and slightly modified model by the authors

[0166]

[0167] To explain the penetration test results.

[0168] Among them, in the process of analyzing the evolution of the damage zone, some parameters are used to characterize it: such as the dimensionless damage zone radius Where R represents the actual radius of the damaged area, and a is a size parameter related to the damaged area. v are functions and parameters related to the damage area, such as:

[0169] The changes in these parameters reflect the development of the damage zone during the indentation test. In the above theoretical model, the indentation depth is d and the radius of the spherical indenter tip is R ind , Indicates the proportional relationship between the indentation depth and the indenter radius, G is the shear modulus, which reflects the elastic properties of the material under shear stress, and μ is the Poisson's ratio. p (φ), β(K p ) These parameters are related to the internal friction angle of the rock; Is a K p and the function calculated by the size parameter m, The internal friction angle K p The impact of is the size parameter, where R represents the actual radius of the damage zone, B is the radius of the rock specimen, Among them, λ and K d Related to other physical properties of rocks. K d is the internal friction angle Related dimensionless parameters where σ unl is the uniaxial ultimate strength of rock;

[0170] The model equations are solved using the fourth-order Runge–Kutta method with the initial condition ξ R =1When δ=0,

[0171] Parameters related to damage zone evolution are determined and substituted into the relevant formulas in the model. Analysis of the model's calculated results reveals how the damage zone develops during the indentation test, including changes in the damage zone radius and its expansion rate. Peak penetration pressure and specimen size are collected for each test, and the effect of specimen size is analyzed. The damage zone evolution results calculated from the model are compared with data observed during actual penetration tests to verify the model's accuracy and reliability.

[0172] Data analysis was performed using the mathematical software MATLAB. Penetration tests on rock specimens of varying sizes were conducted and the experimental results were compared with simulations based on a specific theoretical model. The results showed a high degree of agreement. By incorporating parameters related to specimen size and accounting for the size-dependent variation of the material's mechanical properties, the model accurately predicted the mechanical response of specimens of varying sizes during penetration. Detailed analysis of the experimental data and simulation results for multiple specimens of varying sizes demonstrated that key indicators such as peak penetration pressure and penetration depth exhibited predictable trends with increasing or decreasing specimen size. This excellent agreement not only validated the effectiveness of the experimental method but, more importantly, demonstrated its ability to reliably account for size effects in rock penetration tests. This method holds important implications for accurately evaluating the penetration performance of rock specimens of varying sizes and optimizing construction processes and design parameters in practical engineering applications such as tunneling and rock drilling. Although uncertainties may exist in the experimental measurements and model assumptions, analysis has shown that these uncertainties do not significantly impact the assessment of size effects, further supporting the reliability and practicality of this experimental method.

[0173] Typically, during penetration, the force increases almost linearly to a distinct peak, at which point the specimen splits, resulting in a sudden loss of load-bearing capacity ( Figure 2 a) The penetration pressure curves show a similar trend. For all tests, the peak penetration pressure precedes the peak force, due to the onset of macrocracks before the final breakup of the specimen. The two penetration tests have similar slopes in these curves, but different peak values.

[0174] The proposed model enables the comparison of normalized penetration pressure with experimental data. Figure 2 As shown in (b), despite a slight deviation in the initial slope (attributable to the different specimen dimensions), there is good agreement between the two. In particular, the peak is well captured by the model.

[0175] The damage zone evolution results calculated by the model were compared with data observed during actual penetration tests to verify the model's accuracy and reliability. Based on the evaluation results of the damage zone evolution, its impact on rock strength was analyzed, and the strength of the tunnel surrounding rock was determined.

[0176] To achieve the above object, the present invention also provides a tunnel surrounding rock strength prediction system based on penetration rock breaking test, the system is applied to the tunnel surrounding rock strength prediction method based on penetration rock breaking test, such as Figure 8 As shown, the system specifically includes:

[0177] a data generation and processing unit, configured to generate and obtain first data corresponding to the rock penetration test, and pre-process the first data; wherein the first data is load data, displacement data, and penetration pressure data corresponding to the rock penetration process;

[0178] a model building and generating unit, configured to build a data analysis model corresponding to the first data, and generate second data corresponding to the first data based on the data analysis model; wherein the second data is damage zone evolution data corresponding to the rock;

[0179] The prediction data generating unit is used to generate third data corresponding to the rock based on the second data and in combination with the first data; wherein the third data is the prediction data of the tunnel surrounding rock strength.

[0180] The data generation processing unit further includes:

[0181] a first data generating module, configured to generate and set fourth data corresponding to the hydraulic servo control machine; wherein the fourth data is operating parameter data of the hydraulic servo control machine, including displacement control mode data and piston constant speed data;

[0182] a second data generation module for sequentially generating and acquiring fifth and sixth data corresponding to the rock penetration test based on the fourth data; wherein the fifth data is load data, displacement data, and penetration pressure data corresponding to the first rock penetration process; and the sixth data is load data, displacement data, and penetration pressure data corresponding to the second rock penetration process;

[0183] a first data processing module, configured to filter, remove, and process the fifth data to generate seventh data corresponding to the fifth data; wherein the seventh data is the data obtained by removing obvious outliers and noise from the fifth data;

[0184] a second data processing module, configured to filter, remove, and process the sixth data; and generate eighth data corresponding to the sixth data; wherein the eighth data is the sixth data after removing obvious outliers and noise;

[0185] And / or, the model building and generating unit further includes:

[0186] a third data generating module, configured to generate damage area evolution characterization data corresponding to the second data according to the data analysis model;

[0187] a fourth data generating module, configured to generate ninth data corresponding to the rock damage zone based on the damage zone evolution characterization data; wherein the ninth data includes data on changes in the damage zone radius and data on the expansion speed of the damage zone;

[0188] And / or, the prediction data generating unit further includes:

[0189] a data acquisition and analysis module, configured to respectively acquire tenth data corresponding to each test and analyze a sample size effect corresponding to the tenth data; wherein the tenth data includes peak penetration pressure data and sample size data;

[0190] The fifth data generation module is used to compare and analyze the second data in combination with the first data and generate corresponding eleventh data; wherein the eleventh data is characterization data for verifying the accuracy and reliability of the data analysis model.

[0191] The data generation processing unit further includes:

[0192] The first calculation module is configured to calculate and generate penetration pressure data corresponding to the rock penetration test based on the first data; wherein the calculation formula is as follows:

[0193]

[0194] Where: F is the load, R ind represents the radius of the spherical indenter tip, and d is the penetration depth;

[0195] The expression of the data analysis model is as follows:

[0196]

[0197] Where: d is the indentation depth, R ind is the radius of the spherical indenter tip, Indicates the proportional relationship between the indentation depth and the indenter radius, G is the shear modulus, which reflects the elastic properties of the material under shear stress, and μ is the Poisson's ratio; dimensionless damage zone radius Where R represents the actual radius of the damage area, a is a size parameter related to the damage area; k is a parameter related to the geometry of the cavity. When k = 1, it represents a cylindrical cavity, and when k = 2, it represents a spherical cavity; f(m), K p (φ), β(K p ) is a parameter related to the internal friction angle of the rock; where: Is a K p and the function calculated by the size parameter m; The internal friction angle K p The impact of is the size parameter, where R represents the actual radius of the damaged area, B is the radius of the rock specimen,

[0198] Where λ and K d Related to other physical properties of rocks; K d is the internal friction angle Related dimensionless parameters Where σ unl is the uniaxial ultimate strength of rock.

[0199] The changes in these parameters reflect the development of the damage zone during the indentation test. Where σ unl is the uniaxial ultimate strength of rock;

[0200] Is a K p and size parameter m; μ is Poisson's ratio; G is the shear modulus.

[0201] In the system solution embodiment of the present invention, the method steps involved in the prediction of tunnel surrounding rock strength based on penetration and rock breaking test have been described above in detail. That is to say, the functional modules in the system are used to implement the steps or sub-steps in the above method embodiment, which will not be repeated here.

[0202] To achieve the above objectives, the present invention also provides a tunnel surrounding rock strength prediction platform based on penetration rock breaking test, such as Figure 9 As shown, it includes a processor, a memory, and a tunnel surrounding rock strength prediction platform control program based on penetration rock breaking test; wherein, the processor executes the tunnel surrounding rock strength prediction platform control program based on penetration rock breaking test, the tunnel surrounding rock strength prediction platform control program based on penetration rock breaking test is stored in the memory, and the tunnel surrounding rock strength prediction platform control program based on penetration rock breaking test implements the tunnel surrounding rock strength prediction method steps based on penetration rock breaking test. For example:

[0203] S01. Generate and obtain first data corresponding to a rock penetration test, and pre-process the first data; wherein the first data is load data, displacement data, and penetration pressure data corresponding to a rock penetration process;

[0204] S02. Constructing a data analysis model corresponding to the first data, and generating second data corresponding to the first data based on the data analysis model; wherein the second data is damage zone evolution data corresponding to the rock;

[0205] S03. Generate third data corresponding to the rock based on the second data and in combination with the first data; wherein the third data is tunnel surrounding rock strength prediction data.

[0206] The specific details of the steps have been explained above and will not be repeated here.

[0207] In an embodiment of the present invention, the built-in processor of the tunnel surrounding rock strength prediction platform based on the penetration rock breaking test can be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor uses various interfaces and circuits to connect various components, and executes or executes programs or units stored in the memory, as well as calls data stored in the memory, to perform various functions and process data for the tunnel surrounding rock strength prediction based on the penetration rock breaking test.

[0208] The memory is used to store program codes and various data. It is installed in the tunnel surrounding rock strength prediction platform based on the penetration and rock breaking test, and realizes high-speed and automatic access to programs or data during operation.

[0209] The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electronically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0210] The present invention generates and obtains first data corresponding to a penetration rock breaking test through a method, and pre-processes the first data; wherein the first data is load data, displacement data, and penetration pressure data corresponding to the rock penetration process;

[0211] Constructing a data analysis model corresponding to the first data, and generating second data corresponding to the first data based on the data analysis model; wherein the second data is damage zone evolution data corresponding to the rock;

[0212] Based on the second data and in combination with the first data, third data corresponding to the rock is generated; wherein the third data is tunnel surrounding rock strength prediction data, and a system corresponding to the method can more accurately judge the strength of the tunnel surrounding rock.

[0213] In other words, the present invention accurately captures the size dependence of penetration tests and accounts for the size effect with a minimum number of tests (at least two). Furthermore, it converts penetration results affected by the size effect to values ​​corresponding to larger specimen sizes, improving the accuracy and reliability of the experimental results.

[0214] In other words, the present invention provides a method for evaluating the strength of tunnel surrounding rock based on a penetration rock breaking test. The method includes preparing samples of three types of rock: granite, basalt and sandstone, using a wedge-shaped indenter with a conical tip, and conducting two consecutive penetration tests on a hydraulic servo-controlled machine. After the first penetration causes the sample to split, the largest fragment is selected for the second penetration, and data such as the load-displacement curve is recorded. The penetration pressure is calculated using a specific formula, and a model is introduced to explain the test results and appropriately modified. The model links the penetration pressure to the dimensionless damage zone radius of the sample. The model equation is solved using the fourth-order Runge-Kutta method. Peak penetration pressure and sample size data are collected, and the sample size effect is analyzed. The present invention can effectively capture the size dependence in rock penetration tests, explain the size effect through a smaller number of tests, and convert the penetration results affected by the size effect into values ​​corresponding to a larger sample size, thereby improving the accuracy and reliability of the experimental results.

[0215] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for predicting tunnel surrounding rock strength based on penetration and rock breaking test, characterized in that: The method comprises the steps of: Generate and obtain first data corresponding to the rock penetration test, and pre-process the first data; wherein the first data is load data, displacement data, and penetration pressure data corresponding to the rock penetration process; Constructing a data analysis model corresponding to the first data, and generating second data corresponding to the first data based on the data analysis model; wherein the second data is damage zone evolution data corresponding to the rock; Generating third data corresponding to the rock based on the second data and in combination with the first data; wherein the third data is tunnel surrounding rock strength prediction data; The expression of the data analysis model is as follows: Where: d is the penetration depth, R ind is the radius of the spherical indenter tip, Indicates the proportional relationship between penetration depth and indenter radius, G is the shear modulus, which reflects the elastic properties of the material under shear stress, μ is the Poisson's ratio; dimensionless damage zone radius Where R represents the actual radius of the damage area, a is a size parameter related to the damage area; k is a parameter related to the geometry of the cavity. When k = 1, it represents a cylindrical cavity, and when k = 2, it represents a spherical cavity; f(m), K p (φ), β(K p ) is a parameter related to the internal friction angle of the rock; where: Is a K p and the function calculated by the size parameter m; The internal friction angle K p The impact of is the size parameter, where R represents the actual radius of the damaged area, B is the radius of the rock specimen, Where λ and K d Related to other physical properties of rocks; K d is a dimensionless parameter related to the internal friction angle φ Where σ unl is the uniaxial ultimate strength of rock; The changes in these parameters reflect the development of the damaged area during the indentation test. Where σ unl is the uniaxial ultimate strength of rock; Is a K p and size parameter m; μ is Poisson's ratio; G is the shear modulus.

2. The method for predicting tunnel surrounding rock strength based on penetration rock breaking test according to claim 1, characterized in that: The generating and acquiring first data corresponding to the rock penetration test and preprocessing the first data further includes: Generate and set fourth data corresponding to the hydraulic servo control machine; wherein the fourth data is operating parameter data of the hydraulic servo control machine, including: displacement control mode data and piston constant speed data; Based on the fourth data, fifth data and sixth data corresponding to the rock penetration test are sequentially generated and acquired; wherein the fifth data are load data, displacement data, and penetration pressure data corresponding to the first rock penetration process; and the sixth data are load data, displacement data, and penetration pressure data corresponding to the second rock penetration process; filtering and removing the fifth data to generate seventh data corresponding to the fifth data; wherein the seventh data is the data obtained by removing obvious outliers and noise from the fifth data; The sixth data is filtered and cleared; and eighth data corresponding to the sixth data is generated; wherein the eighth data is the data obtained by removing obvious outliers and noise from the sixth data.

3. A method for predicting tunnel surrounding rock strength based on penetration rock breaking test according to claim 1 or 2, characterized in that: The generating and acquiring first data corresponding to the rock penetration test and preprocessing the first data further includes: Based on the first data, the penetration pressure data corresponding to the penetration rock breaking test is calculated and generated; wherein the calculation formula is as follows: Where: F is the load, R ind represents the radius of the spherical indenter tip and d is the penetration depth.

4. The method for predicting tunnel surrounding rock strength based on penetration and rock breaking test according to claim 1, characterized in that: The step of constructing a data analysis model corresponding to the first data and generating second data corresponding to the first data based on the data analysis model further includes: generating damage area evolution characterization data corresponding to the second data according to the data analysis model; Based on the damage zone evolution characterization data, ninth data corresponding to the rock damage zone is generated; wherein the ninth data includes the change data of the damage zone radius and the expansion speed data of the damage zone.

5. The method for predicting tunnel surrounding rock strength based on penetration rock breaking test according to claim 1, characterized in that: The step of generating third data corresponding to the rock based on the second data and in combination with the first data further includes: respectively obtaining tenth data corresponding to each test and analyzing the sample size effect corresponding to the tenth data; wherein the tenth data includes peak penetration pressure data and sample size data; Based on the second data and combined with the first data, the two data are compared and analyzed to generate corresponding eleventh data; wherein, the eleventh data is characterization data for verifying the accuracy and reliability of the data analysis model.

6. A tunnel surrounding rock strength prediction system based on penetration and rock breaking test, characterized in that: The system is applied to the tunnel surrounding rock strength prediction method based on the penetration rock breaking test as claimed in any one of claims 1 to 5, and the system comprises: a data generation and processing unit, configured to generate and obtain first data corresponding to the rock penetration test, and pre-process the first data; wherein the first data is load data, displacement data, and penetration pressure data corresponding to the rock penetration process; a model building and generating unit, configured to build a data analysis model corresponding to the first data, and generate second data corresponding to the first data based on the data analysis model; wherein the second data is damage zone evolution data corresponding to the rock; The prediction data generating unit is used to generate third data corresponding to the rock based on the second data and in combination with the first data; wherein the third data is the prediction data of the tunnel surrounding rock strength.

7. The tunnel surrounding rock strength prediction system based on penetration rock breaking test according to claim 6, characterized in that: The data generation processing unit further includes: a first data generating module, configured to generate and set fourth data corresponding to the hydraulic servo control machine; wherein the fourth data is operating parameter data of the hydraulic servo control machine, including displacement control mode data and piston constant speed data; a second data generation module for sequentially generating and acquiring fifth and sixth data corresponding to the rock penetration test based on the fourth data; wherein the fifth data is load data, displacement data, and penetration pressure data corresponding to the first rock penetration process; and the sixth data is load data, displacement data, and penetration pressure data corresponding to the second rock penetration process; a first data processing module, configured to filter, remove, and process the fifth data to generate seventh data corresponding to the fifth data; wherein the seventh data is the data obtained by removing obvious outliers and noise from the fifth data; a second data processing module, configured to filter, remove, and process the sixth data; and generate eighth data corresponding to the sixth data; wherein the eighth data is the sixth data after removing obvious outliers and noise; And / or, the model building and generating unit further includes: a third data generating module, configured to generate damage area evolution characterization data corresponding to the second data according to the data analysis model; a fourth data generating module, configured to generate ninth data corresponding to the rock damage zone based on the damage zone evolution characterization data; wherein the ninth data includes data on changes in the damage zone radius and data on the expansion speed of the damage zone; And / or, the prediction data generating unit further includes: a data acquisition and analysis module, configured to respectively acquire tenth data corresponding to each test and analyze a sample size effect corresponding to the tenth data; wherein the tenth data includes peak penetration pressure data and sample size data; The fifth data generation module is used to compare and analyze the second data in combination with the first data and generate corresponding eleventh data; wherein the eleventh data is characterization data for verifying the accuracy and reliability of the data analysis model.

8. The tunnel surrounding rock strength prediction system based on penetration rock breaking test according to claim 7, characterized in that: The data generation processing unit further includes: The first calculation module is configured to calculate and generate penetration pressure data corresponding to the rock penetration test based on the first data; wherein the calculation formula is as follows: Where: F is the load, R ind represents the radius of the spherical indenter tip and d is the penetration depth.

9. A tunnel surrounding rock strength prediction platform based on penetration and rock breaking test, characterized by: It includes a processor, a memory and a tunnel surrounding rock strength prediction platform control program based on penetration rock breaking test; wherein, the tunnel surrounding rock strength prediction platform control program based on penetration rock breaking test is executed in the processor, the tunnel surrounding rock strength prediction platform control program based on penetration rock breaking test is stored in the memory, and the tunnel surrounding rock strength prediction platform control program based on penetration rock breaking test realizes the tunnel surrounding rock strength prediction method based on penetration rock breaking test as described in any one of claims 1 to 5.

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