Model material preferred method and system for comprehensive quantitative evaluation of strength and deformation indicators

Abstract

The application provides a model material optimization method and system for comprehensive quantitative evaluation of strength and deformation indexes, and the method comprises the following steps: obtaining two types of samples and performing uniaxial compression experiments on the two types of samples, the model material samples comprising multiple groups of model material samples; obtaining a strength response deviation of the model material samples based on uniaxial compression test results of the two types of samples and similarity theory; obtaining stress-strain curves of corresponding samples based on the uniaxial compression test results of the two types of samples, and then determining multiple characteristic points, the characteristic points comprising a peak stress point and multiple deformation characteristic points; obtaining a deformation index deviation of the model material samples based on the compressive strength of each characteristic point and the slope of a linear fitting curve between any two characteristic points by using the compressive strength of each characteristic point; obtaining a comprehensive deviation of each group of model material samples based on the strength response deviation and the deformation index deviation, and selecting a group of model material samples corresponding to the minimum comprehensive deviation as optimal model material for building a physical similar model.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering laboratory physical model material development technology, and in particular to a method and system for the comprehensive quantitative evaluation of strength and deformation indices for the selection of model materials. Background Technology

[0002] Physical similarity simulation experiments are a common research method in geotechnical engineering fields such as underground coal mine excavation. By establishing a scaled-down engineering model based on similarity principles, the mechanical state of the prototype can be studied after disturbances. Therefore, the mechanical properties of the materials used to create the physical model directly affect the experimental results.

[0003] Currently, the selection of physical and mechanical parameters for model materials mainly relies on the similarity scale conversion relationship based on the principle of similarity. After determining several key physical and mechanical parameters of the model material according to experimental requirements, the closest value to the scaled-down version of each parameter is selected to obtain the optimal model material for building a physically similar model. However, it is difficult to meet the accuracy requirements of the experiment if the model material and the target rock mass only meet the similarity relationship in terms of physical and strength indicators. It is equally important that the model material and the target rock mass have a relatively consistent deformation similarity relationship. At present, the deformation similarity of model materials is mainly controlled by the elastic modulus, or by a rough comparison of the deformation curves of the model material and the target rock mass. There is a lack of a quantitative optimization method for deformation similarity. Moreover, the existing model material optimization methods fail to effectively integrate both strength and deformation indicators for screening, making the selection of model materials not precise enough. Therefore, it is necessary to establish a quantitative optimization method for model materials with dual similarity in strength and deformation. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this invention is to propose a method for the optimal selection of model materials by comprehensively and quantitatively evaluating strength and deformation indices. The main objective is to screen materials by combining both strength and deformation indices to select model materials more accurately.

[0006] The second objective of this invention is to propose a model material selection system for comprehensive quantitative evaluation of strength and deformation indices.

[0007] The third objective of this invention is to provide an electronic device.

[0008] The fourth objective of this invention is to provide a computer-readable storage medium.

[0009] To achieve the above objectives, a first aspect of the present invention provides a method for the optimal selection of model materials based on a comprehensive quantitative evaluation of strength and deformation indices, comprising:

[0010] Two types of samples were obtained and uniaxial compression tests were performed on the two types of samples. The two types of samples included target rock mass samples and model material samples. The model material samples included multiple sets of model material specimens.

[0011] The strength response deviation of the model material sample is obtained based on the uniaxial compression test results of the two types of samples and similarity theory.

[0012] Based on the uniaxial compression test results of the two types of samples, the stress-strain curves of the corresponding types of samples are obtained, and then multiple characteristic points are determined, including peak stress points and multiple deformation characteristic points.

[0013] Based on all feature points, the deviation of the deformation index of the model material sample is obtained by using the compressive strength of each feature point and the slope of the linear fitting curve of the stress-strain curve segment between any two feature points.

[0014] Based on the strength response deviation and the deformation index deviation, the comprehensive deviation of each group of model material samples in the model material sample is obtained, and the model material sample of the group with the smallest comprehensive deviation is selected as the optimal model material for building a physical similarity model.

[0015] In the method of the first aspect of the present invention, the intensity response deviation satisfies:

[0016] A = |σ cM / σ cS -C σ |+|E cM / E cS -C E |

[0017] In the formula, A is the intensity response deviation, and σ is the intensity response deviation. cS σ represents the measured uniaxial compressive strength of any group of model material samples. cM C represents the uniaxial compressive strength of the target rock mass sample. σ For stress similarity scale, E cS E represents the measured elastic modulus of any set of model material samples in the model material sample set. cM C represents the elastic modulus of the target rock mass sample. E It is a scale for elastic modulus similarity.

[0018] In the method of the first aspect of the present invention, the deviation of the deformation index includes the deviation of the deformation feature point and the deviation of the lateral axis deformation ratio.

[0019] In the method of the first aspect of the present invention, the overall deviation satisfies: Q = a / 10 OM+1A+b(B1+B2), where Q is the overall deviation, a is the weight of the strength response deviation, b is the weight of the deformation index deviation, OM is the similarity scale order of magnitude, A is the strength response deviation, B1 is the deformation feature point deviation, and B2 is the lateral axis deformation ratio deviation.

[0020] In the method of the first aspect of the present invention, the deformation feature points include, but are not limited to, crack closure points, damage stress points, crack fracture points, and stable failure points.

[0021] In the method of the first aspect of the present invention, each deformation feature point is located based on the compressive strength corresponding to each deformation feature point and the peak stress point, and then the deviation of each deformation feature point is calculated.

[0022] In the method of the first aspect of the present invention, the positioning of each deformation feature point includes: calculating the percentage of the compressive strength of each deformation feature point to the compressive strength of the peak stress point in various types of samples to obtain the positioning position of each deformation feature point, and then obtaining the deviation of the deformation feature points of each group of model material samples in the model material samples.

[0023] In the method of the first aspect of the present invention, the deviation of the lateral deformation ratio of each group of model material specimens in the model material sample is obtained based on the slope of the linear fitting curve between any two consecutive deformation feature points in various types of samples.

[0024] To achieve the above objectives, a second aspect of the present invention provides a model material selection system for comprehensive quantitative evaluation of strength and deformation indices, comprising:

[0025] The uniaxial compression test module is used to acquire two types of samples and perform uniaxial compression tests on the two types of samples. The two types of samples include target rock mass samples and model material samples. The model material samples include multiple sets of model material specimens.

[0026] The strength response deviation calculation module is used to obtain the strength response deviation of the model material sample based on the uniaxial compression test results of the two types of samples and similarity theory.

[0027] The deformation index deviation calculation module is used to obtain the stress-strain curve of the corresponding sample based on the uniaxial compression test results of the two types of samples, and then determine multiple feature points, including peak stress points and multiple deformation feature points; it is also used to obtain the deformation index deviation of the model material sample based on all feature points, using the compressive strength of each feature point, and the slope of the linear fitting curve of the stress-strain curve segment between any two feature points.

[0028] The comprehensive deviation calculation module is used to obtain the comprehensive deviation of each group of model material samples in the model material sample based on the strength response deviation and the deformation index deviation.

[0029] The selection module is used to select the model material sample of the group with the smallest comprehensive deviation as the optimal model material for building the physical similarity model.

[0030] To achieve the above objectives, a third aspect of the present invention provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method proposed in the first aspect of the present invention.

[0031] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method proposed in the first aspect of the present invention.

[0032] This invention provides a method, system, electronic device, and storage medium for the comprehensive quantitative evaluation of strength and deformation indices in the selection of optimal model materials. The method involves acquiring two types of samples and conducting uniaxial compression tests on them. The two types of samples include target rock mass samples and model material samples, with the model material samples comprising multiple groups of model material specimens. Based on the uniaxial compression test results of the two types of samples and similarity theory, the strength response deviation of the model material samples is obtained. Based on the uniaxial compression test results of the two types of samples, stress-strain curves for the corresponding sample types are obtained, thereby determining multiple characteristic points, including peak stress points and multiple deformation characteristic points. Based on all characteristic points, the deformation index deviation of the model material samples is obtained using the compressive strength of each characteristic point and the slope of the linear fitting curve of the stress-strain curve segment between any two characteristic points. Based on the strength response deviation and deformation index deviation, the comprehensive deviation of each group of model material specimens in the model material samples is obtained, and the model material specimens of the group with the smallest comprehensive deviation are selected as the optimal model materials for building a physical similarity model. This approach overcomes the shortcomings of existing model material selection methods that neglect deformation indices, enables quantitative similarity assessment of model material deformation indices, and integrates both strength and deformation indices for screening, making model material selection more accurate.

[0033] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0034] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0035] Figure 1 This is a flowchart illustrating a method for the optimal selection of model materials for a comprehensive quantitative evaluation of strength and deformation indices, as provided in an embodiment of the present invention.

[0036] Figure 2 This is a schematic diagram of the deformation characteristic points of the uniaxial compressive stress-strain curve of the specimen provided in the embodiment of the present invention;

[0037] Figure 3 This is a schematic diagram of the uniaxial compression side shaft deformation ratio curve and fitting curve of the specimen provided in the embodiment of the present invention;

[0038] Figure 4 This is a block diagram of a model material selection system for comprehensive quantitative evaluation of strength and deformation indices, provided in an embodiment of the present invention. Detailed Implementation

[0039] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0040] The following describes, with reference to the accompanying drawings, a method and system for the comprehensive quantitative evaluation of strength and deformation indices of the model material according to embodiments of the present invention.

[0041] This invention provides a method for the optimal selection of model materials by comprehensively and quantitatively evaluating strength and deformation indices. The main purpose is to screen materials by combining strength and deformation indices to select model materials more accurately.

[0042] Figure 1 This is a flowchart illustrating a method for the optimal selection of model materials for a comprehensive quantitative evaluation of strength and deformation indices, as provided in an embodiment of the present invention.

[0043] like Figure 1 As shown, the method for optimizing model materials through a comprehensive quantitative evaluation of strength and deformation indices includes the following steps:

[0044] Step S101: Obtain two types of samples and conduct uniaxial compression tests on the two types of samples. The two types of samples include target rock mass samples and model material samples. The model material samples include multiple sets of model material specimens.

[0045] In step S101, the target rock mass sample is the rock mass specimen to be studied using physical similarity simulation tests. The target rock mass sample includes several groups of rock mass specimens; the selection method of one group of target rock mass specimens will be used as an example for explanation.

[0046] In step S101, the model material samples include multiple groups of model material specimens, wherein the material parameters of different groups of model material specimens are different. Each group of model material specimens is a candidate model material specimen.

[0047] In step S101, both types of samples can be processed into a preset shape of a preset size in order to conduct a uniaxial compression experiment.

[0048] Specifically, in step S101, a target rock mass sample to be studied using physical similarity simulation testing is prepared, along with a model material sample. Both types of samples are processed into standard cylindrical specimens with a length of 100 mm and a diameter of 50 mm to facilitate uniaxial compression testing. After both the target rock mass sample and the model material sample are prepared, a uniaxial compression test is conducted according to national standard methods, wherein the loading rate of the testing machine can be appropriately reduced based on the strength of the model material.

[0049] Step S102: Obtain the strength response deviation of the model material sample based on the uniaxial compression test results of the two types of samples and similarity theory.

[0050] In step S102, the uniaxial compression test results of the two types of samples are recorded and analyzed. The uniaxial compression test results include the uniaxial compressive strength and elastic modulus of the two types of samples. The strength parameters such as the uniaxial compressive strength and elastic modulus of the two types of samples are statistically analyzed. Based on similarity theory, the conversion is performed according to the similarity scale. Specifically, a calculation index is established around the similarity scale and taking into account the elastic modulus and uniaxial compressive strength. The calculation formula for the strength response deviation A is as follows:

[0051] A = |σ cM / σ cS -C σ |+|E cM / E cS -C E |

[0052] In the formula, σ cS σ represents the measured uniaxial compressive strength of any group of model material samples. cM C represents the uniaxial compressive strength of the target rock mass sample. σ For stress similarity scale, E cS E represents the measured elastic modulus of any set of model material samples in the model material sample set. cM C represents the elastic modulus of the target rock mass sample. E It is a scale for elastic modulus similarity.

[0053] Step S103: Based on the uniaxial compression test results of the two types of samples, obtain the stress-strain curves of the corresponding types of samples, and then determine multiple characteristic points, including peak stress points and multiple deformation characteristic points.

[0054] In step S103, multiple characteristic points are determined for any group of specimens from the two types of samples. These multiple characteristic points include multiple deformation characteristic points and peak stress points. The multiple deformation characteristic points include, but are not limited to, crack closure points, damage stress points, crack fracture points, and stable failure points. Specifically, the crack fracture point is the starting point of the post-peak stress drop, characterizing the instability of the main crack propagation and accelerated failure; the stable failure point is the ending point of the post-peak stress drop, characterizing the beginning of stable failure after the peak, with the failure rate slowing down until the load-bearing capacity is lost.

[0055] Figure 2 This is a schematic diagram of the deformation characteristic points of the uniaxial compressive stress-strain curve of the specimen provided in an embodiment of the present invention. Figure 2 As shown, stress-strain curves for various samples are plotted, and deformation characteristic points in the stress-strain curves are determined. For example, based on the variation law of the stress-strain curves, the crack closure point σ of each sample in the stage before the axial stress peak is obtained. cc Damage stress point σ cd and the fracture point σ in the post-peak stage. cv Stability failure point σ cz .

[0056] In step S103, the peak stress point is the stress point where the axial stress reaches its peak value.

[0057] Step S104: Based on all feature points, the deviation of the deformation index of the model material sample is obtained by using the compressive strength of each feature point and the slope of the linear fitting curve of the stress-strain curve segment between any two feature points.

[0058] In step S104, the deviation of the deformation index includes the deviation of the deformation feature point and the deviation of the lateral axis deformation ratio.

[0059] In step S104, based on the compressive strength corresponding to each deformation feature point and peak stress point, each deformation feature point is located, and then the deviation of each deformation feature point is calculated. The location of each deformation feature point includes: calculating the percentage of the compressive strength of each deformation feature point to the compressive strength of the peak stress point in various types of samples to obtain the location of each deformation feature point, thereby obtaining the deviation of the deformation feature points of each group of model material samples.

[0060] Specifically, the method for locating each deformation feature point in the stress-strain curve is as follows: based on the peak compressive strength of each group of specimens, the quotient of the compressive strength value corresponding to a certain deformation feature point in the curve and the peak compressive strength of the curve is the location of that deformation feature point. By obtaining the location of each deformation feature point in the curve of each group of specimens one by one, a comparison is made between the target rock mass sample and each group of model material specimens.

[0061] A similarity calculation formula based on deformation feature points is established, and the points are located using stress-strain curves, with the peak compressive strength σ of the corresponding specimen as the reference. c Based on the baseline quantification calculation, the peak compressive strength σ is used for each characteristic point. c Expressed as a percentage. The specific calculation method is as follows: Based on the experimental requirements, several key deformation feature points are determined. The formula for calculating the deviation B1 of these deformation feature points is:

[0062]

[0063] In the formula, σ Si For any deformation feature point in any set of model material specimens, σ represents the peak compressive strength σ of the corresponding specimen. c Percentage, σ Mi This represents the percentage of the peak compressive strength of the target rock mass sample corresponding to the deformation feature points, where n is the number of deformation feature points. The deformation feature points are categorized by fracture closure point σ. cc Damage stress point σ cd σ, fracture point cv Stability failure point σ cz When assembling the model, n is taken as 4. The deviation of the deformation characteristic points of each group of model material specimens is obtained using this formula.

[0064] In step S104, the deviation of the lateral deformation ratio of each group of model material specimens in the model material sample can be obtained based on the slope of the linear fitting curve between each deformation feature point and the peak stress point in various types of samples. Alternatively, the deviation of the lateral deformation ratio of each group of model material specimens in the model material sample can be obtained based on the slope of the linear fitting curve between any two consecutive deformation feature points in various types of samples. The curve segment enclosed by any two consecutive deformation feature points can be selected according to experimental requirements.

[0065] Figure 3 This is a schematic diagram of the uniaxial compression side shaft deformation ratio curve and fitting curve of the specimen provided in the embodiment of the present invention.

[0066] Specifically, the lateral deformation ratio is defined. Poisson's ratio mainly reflects the relationship between axial and lateral deformation in the elastic stage. To further analyze the coordination relationship between lateral and axial strains of each group of specimens throughout the experiment, the quotient of axial strain divided by lateral strain is defined as the lateral deformation ratio, and it is represented by a more intuitive line graph, such as... Figure 3 As shown, M-1 is the target rock mass sample, and M-2 is any set of model material samples. Any point on curves M-1 and M-2 represents the lateral deformation ratio of the corresponding sample. The lateral deformation ratio reflects the degree of coordination between axial and lateral deformation during the sample's deformation under stress.

[0067] The quantitative calculation method is to calculate the fracture closure point σ. cc and peak stress point σ c0The loading stage curves are fitted, and the deviation of the growth rate (i.e., the deviation of the lateral deformation ratio) between each group of model material samples and the target rock mass sample is compared. The similarity of the lateral deformation characteristics of each group of model material samples and the target rock mass sample is determined by the fitting formula. Taking a linear function as an example, such as... Figure 3 As shown, the dashed line in the M-1 linear fitting represents the fracture closure point σ of the target rock mass sample. cc The slope of the linear fitting curve between the peak stress point and the peak stress point, and the dashed line of the M-2 linear fitting represent the crack closure point σ of any set of model material specimens. cc The slope of the linear fitting curve between the peak stress point and the peak stress point. The slope k of the linear fitting curve represents the growth rate, and the formula for calculating the deviation B2 of the lateral deformation ratio is:

[0068] B2=|k S / k M -1|

[0069] In the formula, k S Let k be the slope of the pre-peak growth stage of any set of model material samples. M This represents the slope of the pre-peak growth stage of the target rock mass sample. The deviation of the lateral deformation ratio for each group of model material samples was obtained using this formula.

[0070] In other embodiments, in addition to using the deviation of deformation index to quantitatively achieve the quantitative similarity assessment of the deformation index of the model material, it also includes a qualitative description of deformation characteristics. Specifically, after obtaining the stress-strain curves of the corresponding sample type, the uniaxial compression deformation characteristics are analyzed, and the curves are divided into four typical stages: the crack compaction stage, the elastic deformation stage, the crack development stage, and the post-peak stage. The deformation characteristics of the model material and the target rock mass sample at different stages are compared, thereby qualitatively describing the similarity relationship between the two types of samples at each deformation stage.

[0071] Step S105: Based on the deviation of strength response and the deviation of deformation index, obtain the comprehensive deviation of each group of model material samples in the model material sample, and select the model material sample of the group with the smallest comprehensive deviation as the optimal model material for building the physical similarity model.

[0072] In step S105, a comprehensive deviation Q calculation method considering both sample strength and deformation similarity is established. Since the strength deviation is strongly correlated with the similarity scale order OM (i.e., a power of 10), and considering the reasonable weighting of strength and deformation deviations, the calculated strength deviation is divided by 10 raised to the power of OM+1 for averaging. Simultaneously, weighting adjustment parameters a and b are introduced as weighting factors for strength and deformation indices, respectively. The weights for strength and deformation need to be comprehensively determined based on the actual similarity simulation test requirements. The comprehensive deviation satisfies:

[0073]

[0074] In the formula, Q represents the overall deviation, a is the weight of the strength response deviation, b is the weight of the deformation index deviation, OM is the similarity scale order of magnitude, A is the strength response deviation, B1 is the deviation of the deformation characteristic point, and B2 is the deviation of the lateral deformation ratio. The similarity scale order of magnitude OM ranges from integers greater than 0, and the sum of the weight adjustment parameters a and b equals 1. By calculating the overall deviation Q value of each group of model material samples, the smaller the overall deviation Q value, the lower the similarity deviation, that is, the higher the similarity with the target rock mass sample in terms of strength and deformation. The model material sample corresponding to the group with the smallest overall deviation is selected as the optimal model material for building the physical similarity model.

[0075] Taking four groups of physically similar simulated materials as examples, the following describes the specific process of the model material selection method based on a comprehensive quantitative evaluation of strength and deformation indices:

[0076] To conduct a physical similarity simulation test for a rock mass excavation project, samples of the target rock mass were taken at the project site, and standard uniaxial compression test specimens were prepared for use. Simultaneously, four sets (N, X, Z, Y) of model materials were prepared and processed into standard uniaxial compression test specimens. The geometric similarity scale C of the similar physical model test is... l The value is determined to be 20, and the similarity scale of the bulk density is C. γ The value is set to 1, and the calculated elastic modulus similarity scale C is obtained. E Similarity scale C to stress σ Both are 20.

[0077] Implementation step 201: Measure the measured uniaxial compressive strength and measured elastic modulus of the target rock mass sample.

[0078] Implementation step 202: Based on the measured elastic modulus and measured uniaxial compressive strength obtained from the uniaxial compression test of the four groups of model material specimens, calculate the strength response deviation A value of each group of model material specimens: Group N is 21.2, Group X is 17.2, Group Z is 33.8, and Group Y is 16.0.

[0079] Implementation step 301: Plot the stress-strain curves of the target rock mass sample and the four sets of model material samples, and classify the curves of each sample into four typical stages: fracture compaction stage, elastic deformation stage, fracture development stage and post-peak stage. Compare the deformation characteristics of the four sets of model materials and the target rock mass sample at different stages, and determine the set of model materials with the highest similarity to the target rock mass.

[0080] Implementation step 401: Mark the crack closure point σ on the stress-strain curves of each group of specimens. cc Damage stress point σ cd σ, fracture point cv and the stable failure point σcz The location of different deformation characteristic points in each group of samples was calculated. The location of the four deformation characteristic points of the target rock mass sample was found to be 31%, 85%, 89%, and 75%, respectively.

[0081] Implementation step 402: The positioning position is obtained by calculating each deformation characteristic point of the four groups of model material specimens one by one, and the deviation B1 value of the deformation characteristic points of each group is calculated. The smaller the B1 value, the lower the similarity deviation. This experiment focuses on the crack closure point σ before the peak strength. cc and damage stress point σ cd The B1 values ​​of the four model material samples were found to be 0.130, 0.080, 0.060, and 0.065, respectively.

[0082] Implementation step 501: Plot the lateral deformation ratio curves of the target rock mass sample and the four sets of model material samples, and analyze the loading stage (such as the fracture closure point σ). cc and peak stress point σ c0 Linear fitting is performed on the curve between (between) and the slope of the fitted curve is obtained.

[0083] Step 502: Using the slope of the stage fitting curve of the target rock mass sample as a benchmark, calculate the deviation B2 value of the lateral deformation ratio of the four sets of model material samples. The B2 values ​​of the four sets of model material samples are 0.430, 0.858, 0.079 and 0.199, respectively.

[0084] Step 601: Based on the calculation results of the above steps, calculate the comprehensive deviation Q value of the four groups of model material samples, where the similarity scale order OM is 10 to the power of 1, and the strength and deformation weight factors a and b are respectively taken as 0.5. The comprehensive deviation Q values ​​for the four groups (N, X, Z, Y) of model material samples are 0.386, 0.555, 0.239, and 0.212, respectively, and are ordered from largest to smallest as X group > N group > Z group > Y group. The material mix of group Y is determined to be the optimal proposed scheme (i.e., the optimal model material for building a physically similar model).

[0085] To achieve the above embodiments, the present invention also proposes a model material selection system for comprehensive quantitative evaluation of strength and deformation indices.

[0086] Figure 4 This is a block diagram of a model material selection system for comprehensive quantitative evaluation of strength and deformation indices, provided in an embodiment of the present invention.

[0087] like Figure 4As shown, the model material selection system for comprehensive quantitative evaluation of strength and deformation indices includes a uniaxial compression test module 11, a strength response deviation calculation module 12, a deformation index deviation calculation module 13, a comprehensive deviation calculation module 14, and a selection module 15, wherein:

[0088] The uniaxial compression test module 11 is used to acquire two types of samples and perform uniaxial compression tests on the two types of samples. The two types of samples include target rock mass samples and model material samples. The model material samples include multiple sets of model material specimens.

[0089] The strength response deviation calculation module 12 is used to obtain the strength response deviation of the model material sample based on the uniaxial compression test results of two types of samples and similarity theory.

[0090] The deformation index deviation calculation module 13 is used to obtain the stress-strain curve of the corresponding sample based on the uniaxial compression test results of the two types of samples, and then determine multiple feature points, including peak stress points and multiple deformation feature points; it is also used to obtain the deformation index deviation of the model material sample based on all feature points, using the compressive strength of each feature point, and the slope of the linear fitting curve of the stress-strain curve segment between any two feature points.

[0091] The comprehensive deviation calculation module 14 is used to obtain the comprehensive deviation of each group of model material samples in the model material sample based on the strength response deviation and deformation index deviation.

[0092] Selection module 15 is used to select the model material sample of the group with the smallest comprehensive deviation as the optimal model material for building the physical similarity model.

[0093] Furthermore, in one possible implementation of the present invention, the deformation index deviation calculation module 13 includes, but is not limited to, crack closure points, damage stress points, crack fracture points, and stable failure points.

[0094] Furthermore, in one possible implementation of this invention, the deformation index deviation calculation module 13 includes the deformation feature point deviation and the lateral axis deformation ratio deviation.

[0095] Furthermore, in one possible implementation of this invention, the deformation index deviation calculation module 13 locates each deformation feature point based on the compressive strength corresponding to each deformation feature point and peak stress point, and then calculates the deviation of each deformation feature point.

[0096] Furthermore, in one possible implementation of the present invention, the deformation index deviation calculation module 13 locates each deformation feature point, including: in various types of samples, calculating the percentage of the compressive strength of each deformation feature point to the compressive strength of the peak stress point to obtain the location of each deformation feature point, and then obtaining the deviation of the deformation feature points of each group of model material samples in the model material samples.

[0097] Furthermore, in one possible implementation of this invention, the deformation index deviation calculation module 13 obtains the lateral deformation ratio deviation of each group of model material samples in the model material sample based on the slope of the linear fitting curve between any two continuous deformation feature points in various types of samples.

[0098] Furthermore, in one possible implementation of this invention, in the comprehensive deviation calculation module 14, the comprehensive deviation satisfies: Q = a / 10 OM+1 A+b(B1+B2), where Q is the overall deviation, a is the weight of the strength response deviation, b is the weight of the deformation index deviation, OM is the similarity scale order of magnitude, A is the strength response deviation, B1 is the deformation feature point deviation, and B2 is the lateral axis deformation ratio deviation.

[0099] It should be noted that the explanation of the aforementioned embodiment of the model material selection method for comprehensive quantitative evaluation of strength and deformation indices also applies to the model material selection system for comprehensive quantitative evaluation of strength and deformation indices in this embodiment, and will not be repeated here.

[0100] In this embodiment of the invention, two types of samples are acquired and subjected to uniaxial compression tests. The two types of samples include target rock mass samples and model material samples, with the model material samples comprising multiple sets of model material specimens. Based on the uniaxial compression test results of the two types of samples and similarity theory, the strength response deviation of the model material samples is obtained. Based on the uniaxial compression test results of the two types of samples, stress-strain curves for the corresponding sample types are obtained, thereby determining multiple characteristic points, including peak stress points and multiple deformation characteristic points. Based on all characteristic points, the deformation index deviation of the model material samples is obtained using the compressive strength of each characteristic point and the slope of the linear fitting curve of the stress-strain curve segment between any two characteristic points. Based on the strength response deviation and the deformation index deviation, the comprehensive deviation of each set of model material specimens in the model material samples is obtained, and the model material specimens of the set with the smallest comprehensive deviation are selected as the optimal model material for building a physical similarity model. This overcomes the deficiency of neglecting deformation indices in existing model material selection methods, achieves quantitative similarity assessment of model material deformation indices, and integrates both strength and deformation indices for screening, making model material selection more accurate.

[0101] The method of this invention mainly includes preparing uniaxial compression test specimens of target rock mass samples and model materials and conducting tests; calculating the strength response deviation; plotting stress-strain curves to compare deformation similarity characteristics at typical stages; defining curve deformation characteristic points and calculating their positioning positions, and calculating the deviation of deformation characteristic points; defining the lateral axis deformation ratio and calculating the lateral axis deformation ratio deviation from the slope of the fitted curve; calculating the comprehensive deviation index value considering both strength and deformation similarity, and determining the optimal model material specimen group corresponding to the minimum index value. This method overcomes the technical challenge of lacking a comprehensive quantitative optimization method for the two mechanical parameters of model materials—strength and deformation—and achieves quantitative calculation of the similarity between the strength and deformation indices of model materials and the mechanical indices of the target rock mass, solving the problem of quantitative optimization of model materials considering both strength and deformation indices. It compensates for the deficiency of neglecting deformation indices in existing model material selection methods, realizes quantitative similarity assessment of model material deformation indices, establishes a model material similarity assessment method that comprehensively evaluates strength and deformation indices, and provides an important and intuitive basis for model material selection, significantly improving the reliability and accuracy of physical similarity simulation tests.

[0102] To implement the above embodiments, the present invention also proposes an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0103] To implement the above embodiments, the present invention also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0104] To implement the above embodiments, the present invention also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0105] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0106] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0107] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0109] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0110] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0111] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0112] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for optimal selection of model materials based on a comprehensive quantitative evaluation of strength and deformation indices, characterized in that, include: Two types of samples were obtained and uniaxial compression tests were performed on the two types of samples. The two types of samples included target rock mass samples and model material samples. The model material samples included multiple sets of model material specimens. The strength response deviation of the model material sample is obtained based on the uniaxial compression test results of the two types of samples and similarity theory. Based on the uniaxial compression test results of the two types of samples, the stress-strain curves of the corresponding types of samples are obtained, and then multiple characteristic points are determined, including peak stress points and multiple deformation characteristic points. Based on all feature points, the deformation index deviation of the model material sample is obtained by using the compressive strength of each feature point and the slope of the linear fitting curve of the stress-strain curve segment between any two feature points. The deformation index deviation includes the deviation of deformation feature points and the deviation of lateral axis deformation ratio. Based on the compressive strength corresponding to each deformation feature point and the peak stress point, the deformation feature points are located, and the deviation of each deformation feature point is calculated. Based on the slope of the linear fitting curve between any two consecutive deformation feature points in various types of samples, the deviation of the lateral deformation ratio of each group of model material samples in the model material samples is obtained. Based on the strength response deviation and the deformation index deviation, the comprehensive deviation of each group of model material samples in the model material sample is obtained, and the model material sample of the group with the smallest comprehensive deviation is selected as the optimal model material for building the physical similarity model. Wherein, the comprehensive deviation satisfies: In the formula, Q For the overall deviation, a As the weight of the intensity response deviation, b The weight of the deviation of the deformation index. OM The similarity scale is on the order of magnitude. A For intensity response deviation, B 1 represents the deviation of the deformation feature point. B 2 represents the deviation of the side axis deformation ratio.

2. The method for optimal selection of model materials based on the comprehensive quantitative evaluation of strength and deformation indices according to claim 1, characterized in that, The intensity response deviation satisfies: In the formula, A For intensity response deviation, σ cS The measured uniaxial compressive strength of any group of model material samples. σ cM The uniaxial compressive strength of the target rock mass sample. C σ For stress similarity scale, E cS Let be the measured elastic modulus of any group of model material samples. E cM The elastic modulus of the target rock mass sample. C E It is a scale for elastic modulus similarity.

3. The method for optimal selection of model materials based on the comprehensive quantitative evaluation of strength and deformation indices according to claim 1, characterized in that, The process of locating each deformation feature point includes: In various types of samples, the percentage of the compressive strength of each deformation feature point to the compressive strength of the peak stress point is calculated to obtain the location of each deformation feature point, and then the deviation of the deformation feature points of each group of model material samples is obtained.

4. A model material selection system for comprehensive quantitative evaluation of strength and deformation indices, characterized in that, The system is used to implement the model material selection method for comprehensive quantitative evaluation of strength and deformation indices as described in claim 1, and the system includes: The uniaxial compression test module is used to acquire two types of samples and perform uniaxial compression tests on the two types of samples. The two types of samples include target rock mass samples and model material samples. The model material samples include multiple sets of model material specimens. The strength response deviation calculation module is used to obtain the strength response deviation of the model material sample based on the uniaxial compression test results of the two types of samples and similarity theory. The deformation index deviation calculation module is used to obtain the stress-strain curve of the corresponding sample based on the uniaxial compression test results of the two types of samples, and then determine multiple feature points, including peak stress points and multiple deformation feature points; it is also used to obtain the deformation index deviation of the model material sample based on all feature points, using the compressive strength of each feature point, and the slope of the linear fitting curve of the stress-strain curve segment between any two feature points. The comprehensive deviation calculation module is used to obtain the comprehensive deviation of each group of model material samples in the model material sample based on the strength response deviation and the deformation index deviation. The selection module is used to select the model material sample of the group with the smallest comprehensive deviation as the optimal model material for building the physical similarity model.

5. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-3.

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