Method and device for evaluating long-term stability of rock mass engineering

Abstract

The embodiment of the present application provides a kind of rock mass engineering long-term stability evaluation method and related equipment, can improve the prediction precision of rock mass engineering long-term safety and failure probability.The method comprises: the target rock mass engineering corresponding to target rock mass is carried out the elastic-plastic analysis of the randomness and variability of mechanical parameter, to obtain the predicted deformation field, predicted stress field and predicted damage zone of target rock mass;According to the predicted deformation field, the deformation failure tendency of the target rock mass engineering is determined;According to the predicted stress field, the stress failure tendency of the target rock mass engineering is determined;According to the predicted damage zone, the cracking failure tendency of the target rock mass engineering is determined;According to the deformation failure tendency, the stress failure tendency and the cracking failure tendency, the multi-factor comprehensive early warning evaluation model of the target rock mass engineering is determined;According to the multi-factor comprehensive early warning evaluation model, the long-term safety and failure probability of the target rock mass engineering are determined.

Description

Technical Field

[0001] This invention relates to the field of rock mass engineering, and more particularly to a method and apparatus for assessing the long-term stability of rock mass engineering projects. Background Technology

[0002] Long-term stability assessment of rock mass engineering is a hot topic in rock mechanics and engineering, and a key issue affecting disaster early warning and prevention during the operational period of rock mass engineering projects. Currently, two main approaches are commonly used for long-term stability assessment of rock mass engineering projects: First, in-situ testing methods, such as borehole ultrasonic testing, borehole imaging observation, borehole elastic modulus testing, and multi-point displacement gauge monitoring, are used to assess the stability and evolution of the surrounding rock based on long-term field observation results. Second, numerical simulation methods are employed, using 3D continuum mechanics or discontinuous media mechanics numerical simulations to simulate the long-term mechanical response during the operational period of the project. Evaluation indicators considering the plastic zone or the degradation of unit strength or mechanical parameters are used to assess the safety status and long-term stability of the surrounding rock. Examples include strength utilization coefficient, failure proximity, rock fracturing degree, and elastic modulus degradation degree.

[0003] For large-scale deep underground engineering projects, the scale often ranges from several meters to hundreds of meters, with interconnected chambers and enormous size spans. At this scale, evaluating global stability based on in-situ testing methods is extremely difficult, while numerical simulation methods suffer from sensitivity to abnormal rock mass mechanical parameters. Some scholars have attempted to combine in-situ testing techniques with numerical simulation methods to address these challenges.

[0004] However, as rock mass engineering projects become increasingly deep and large-scale, and the environment becomes more complex, it is inevitable that using a single indicator such as deformation, stress, or cracking to evaluate their long-term safety status in isolation will be one-sided, thus hindering the accurate assessment of the long-term safety and stability of the project and the rational formulation of disaster prevention and control measures. Summary of the Invention

[0005] This invention provides a method and apparatus for assessing the long-term stability of rock mass engineering, which can improve the accuracy of predicting the long-term safety and failure probability of rock mass engineering.

[0006] The first aspect of this invention provides a method for assessing the long-term stability of rock mass engineering, the method comprising:

[0007] Elastoplastic analysis of the randomness and variability of mechanical parameters of the target rock mass is performed on the target rock mass engineering to obtain the predicted deformation field, predicted stress field and predicted damage zone of the target rock mass;

[0008] The deformation and failure tendency of the target rock mass engineering is determined based on the predicted deformation field;

[0009] The stress failure tendency of the target rock mass engineering is determined based on the predicted stress field;

[0010] The cracking and failure tendency of the target rock mass engineering is determined based on the predicted damage zone;

[0011] A multi-factor comprehensive early warning evaluation model for the target rock mass engineering is determined based on the deformation failure tendency, the stress failure tendency, and the cracking failure tendency.

[0012] The long-term safety and failure probability of the target rock mass engineering are determined based on the multi-factor comprehensive early warning evaluation model.

[0013] A second aspect of the present invention provides an apparatus for assessing the long-term stability of rock mass engineering, comprising:

[0014] The analysis module is used to perform elastoplastic analysis on the randomness and variability of mechanical parameters of the target rock mass engineering corresponding to the target rock mass, so as to obtain the predicted deformation field, predicted stress field and predicted damage zone of the target rock mass;

[0015] The first determining module is used to determine the deformation and failure tendency of the target rock mass engineering based on the predicted deformation field;

[0016] The second determining module is used to determine the stress failure tendency of the target rock mass engineering based on the predicted stress field;

[0017] The third determining module is used to determine the cracking and failure tendency of the target rock mass engineering based on the predicted damage zone;

[0018] The fourth determining module is used to determine a multi-factor comprehensive early warning evaluation model for the target rock mass engineering based on the deformation failure tendency, the stress failure tendency, and the cracking failure tendency.

[0019] The fifth determination module is used to determine the long-term safety and failure probability of the target rock mass project based on the multi-factor comprehensive early warning evaluation model.

[0020] In one possible design, the first determining module is specifically used for:

[0021] The deformation and failure tendency of the target rock mass engineering is determined by the following formula:

[0022]

[0023] Among them, D tendency For the aforementioned tendency to deform and fail, D isp Let D be the rock mass deformation at any point in the predicted deformation field. max D represents the maximum deformation of the rock mass in the predicted deformation field.min The minimum deformation of the rock mass in the predicted deformation field.

[0024] In one possible design, the second determining module is specifically used for:

[0025] The stress failure tendency of the target rock mass engineering is determined by the following formula:

[0026]

[0027] Among them, S tendency For the stress failure tendency, S tres S represents the rock mass stress storage degree at any point in the predicted stress field. max S is the maximum stored stress of the rock mass in the predicted stress field. min The minimum stored stress of the rock mass in the predicted stress field is S; determined by the following formula. tres :

[0028]

[0029] Where, σ x σ y σ z τ xy τ xz τ yz Let σ be the stress component of the stress vector σ at any point in the predicted stress field.

[0030] In one possible design, the third determining module is specifically used for:

[0031] The tendency of the target rock mass to crack and fail is determined by the following formula:

[0032]

[0033] Among them, F tendency E represents the tendency to crack and fail. 0 E represents the elastic modulus of the target rock mass in its initial state. r The elastic modulus of the target rock mass under excavation conditions. The confining pressure of the target rock mass in its initial state. The confining pressure of the target rock mass during excavation. The volume of the target rock mass in its initial state. Let be the rock mass volume under the excavation state of the target rock mass, k and η be constant parameters obtained by fitting experimental data, and ζ be a preset constant.

[0034] In one possible design, the multi-factor comprehensive early warning evaluation model includes comprehensive early warning evaluation indicators and comprehensive weight coefficients, and the fourth determining module is specifically used for:

[0035] The comprehensive early warning evaluation index is constructed using the following formula:

[0036] DSF=f1·D tendency +f2·S tendency +f3F tendency

[0037] Wherein, DSF is the comprehensive early warning evaluation index, f1 is the deformation failure tendency weight coefficient, f2 is the stress failure tendency weight coefficient, and f3 is the crack failure tendency weight coefficient.

[0038] The comprehensive weighting coefficient is determined using the following formula:

[0039]

[0040] Among them, f i s f is the first weight coefficient determined by the triangular fuzzy number-analytic hierarchy process (AHP) combined weighting method. i k The second weighting coefficient is determined by the CRITIC objective weighting method, and both the first weighting coefficient and the second weighting coefficient are the comprehensive weighting coefficient.

[0041] In one possible design, the fifth determining module is specifically used for:

[0042] The long-term bearing capacity limit state equation of the target rock mass engineering is determined based on the multi-factor comprehensive early warning evaluation model.

[0043] Based on the long-term bearing limit state equation, the long-term safety and failure probability of the target rock mass project are calculated using the least squares support vector machine method.

[0044] In one possible design, the fifth determining module determines the long-term bearing capacity limit state equation of the target rock mass engineering according to the multi-factor comprehensive early warning evaluation model, including:

[0045] The long-term load-bearing ultimate limit state equation is determined by the following formula:

[0046] F z =DSF lim -DSF t ;

[0047] Among them, F z The long-term bearing limit state equation is given by DSF. limThe critical warning value of the comprehensive early warning evaluation index is DSF. t Let be the predicted value of the comprehensive early warning evaluation index at time t.

[0048] In one possible design, the fifth determining module, based on the long-term bearing capacity limit state equation, calculates the long-term safety and failure probability of the target rock mass engineering using the least squares support vector machine method, including:

[0049] The function values ​​of each sample point corresponding to the target rock mass engineering are solved based on the long-term bearing limit state equation.

[0050] By fitting the sample points and the functional values, the long-term load-bearing limit state equation is reconstructed to obtain an approximate response surface function.

[0051] The long-term security and the failure probability are determined based on the approximate response surface function.

[0052] In one possible design, the fifth determining module solves for the function values ​​of each sample point corresponding to the target rock mass engineering based on the long-term bearing capacity limit state equation, including:

[0053] The function value of each sample point is calculated using the following formula:

[0054]

[0055] Where n is the number of sample points corresponding to the target rock mass engineering;

[0056] Determining the long-term security and the failure probability based on the approximate response surface function includes:

[0057] The long-term security and the failure probability are determined by the following formula:

[0058]

[0059] Where, β k p is the reliability index for the long-term security. f Let Γ be the failure probability, and Γ be the response surface function. The failure domain, X, is a column vector associated with DSF, which is determined by the following formula:

[0060] X i =Φ -1 [F(DSF)];

[0061] Where, Φ -1 [] is the inverse cumulative probability distribution function of the quasi-normal distribution, and F[] is the cumulative probability distribution function of the original non-normal distribution.

[0062] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the processor is used to execute a computer management program stored in the memory to implement the steps of the long-term stability assessment method for rock mass engineering as described in the first aspect above.

[0063] A fourth aspect of the present invention provides a computer-readable storage medium having a computer management program stored thereon, wherein when the computer management program is executed by a processor, it implements the steps of the method for evaluating the long-term stability of rock mass engineering as described in the first aspect above.

[0064] In summary, it can be seen that in the embodiments provided in this application, when conducting long-term stability assessment of the target rock mass engineering, the deformation failure tendency, stress failure tendency, and crack failure tendency are comprehensively considered. Based on the deformation failure tendency, stress failure tendency, and crack failure tendency, a multi-factor comprehensive early warning evaluation model is constructed. Therefore, when determining the long-term safety and failure probability of the target rock mass engineering through the multi-factor comprehensive early warning evaluation model, the prediction accuracy of the long-term safety and failure probability of the rock mass engineering can be improved. Attached Figure Description

[0065] Figure 1 A flowchart illustrating the method for assessing the long-term stability of rock mass engineering provided in this embodiment of the invention;

[0066] Figure 2 A virtual structural schematic diagram of the rock mass engineering long-term stability assessment device provided in an embodiment of the present invention;

[0067] Figure 3 A schematic diagram of the hardware structure of a device for evaluating the long-term stability of rock mass engineering provided in an embodiment of the present invention;

[0068] Figure 4 A schematic diagram illustrating an embodiment of an electronic device provided by an embodiment of the present invention;

[0069] Figure 5 This is a schematic diagram of an embodiment of a computer-readable storage medium provided in this invention. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] In the following description, specific embodiments of the invention will be illustrated with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be referred to several times as being performed by a computer, and computer execution as referred to herein includes operations by a computer processing unit representing electronic signals of data in a structured format. This operation transforms the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise alter the operation of the computer in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the invention described above are not intended to be limiting, and those skilled in the art will understand that many of the steps and operations described below can also be implemented in hardware.

[0072] The principles of this invention are applied using many other general-purpose or purpose-specific computing, communication environments, or configurations. Examples of well-known computing systems, environments, and configurations suitable for use with this invention include (but are not limited to) handheld phones, personal computers, servers, multiprocessor systems, microcomputer-based systems, mainframe computers, and distributed computing environments, including any of the aforementioned systems or devices.

[0073] The terms "first," "second," and "third," etc., used in this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0074] The following section describes the evaluation method for the long-term stability of rock mass engineering, using an evaluation device for long-term stability of rock mass engineering. This evaluation device can be a server or a service unit within a server; there are no specific limitations.

[0075] Please see Figure 1 , Figure 1 A flowchart illustrating the method for assessing the long-term stability of rock mass engineering provided in this embodiment of the invention includes:

[0076] 101. Perform elastoplastic analysis on the randomness and variability of mechanical parameters of the target rock mass engineering corresponding to the target rock mass to obtain the predicted deformation field, predicted stress field and predicted damage zone of the target rock mass.

[0077] In this embodiment, the long-term stability assessment device for rock mass engineering can perform elastoplastic analysis on the randomness and variability of mechanical parameters of the target rock mass engineering corresponding to the target rock mass, thereby obtaining the predicted deformation field, predicted stress field and predicted damage zone of the target rock mass.

[0078] It should be noted that when performing elastoplastic analysis of the randomness and variability of mechanical parameters in rock engineering, the following factors need to be considered:

[0079] Randomness of mechanical parameters: The mechanical parameters of rock masses, such as elastic modulus, Poisson's ratio, and yield stress, can vary randomly due to the inhomogeneity of the material's microstructure and the influence of factors such as manufacturing processes, heat treatment, and usage conditions. This randomness causes elastoplastic materials to exhibit different behaviors under different testing conditions and usage environments. Therefore, the randomness of mechanical parameters needs to be considered when analyzing the stability of rock mass engineering.

[0080] The variability of mechanical parameters in rock masses is mainly reflected in the statistical characteristics of their distribution. Statistical analysis can be used to obtain parameters such as the mean, standard deviation, and coefficient of variation of these parameters, thus describing their variability. Variability reflects the degree of dispersion of mechanical parameters, i.e., the differences in mechanical parameter values ​​between different material samples. Therefore, the variability of mechanical parameters needs to be considered when analyzing the stability of rock mass engineering.

[0081] Elastoplastic constitutive model: When conducting elastoplastic analysis in rock engineering, it is necessary to establish an elastoplastic constitutive model containing random parameters to more accurately describe the mechanical properties of materials and provide a more reliable basis for the design and use of rock engineering.

[0082] 102. Determine the deformation and failure tendency of the target rock mass engineering based on the predicted deformation field.

[0083] In this embodiment, after determining the predicted deformation field, the long-term stability assessment device for rock mass engineering can determine the deformation and failure tendency of the target rock mass engineering based on the predicted deformation field. Specifically, the deformation and failure tendency of the target rock mass engineering can be determined by the following formula:

[0084]

[0085] Among them, D tendency For deformation and failure tendency, D isp To predict the rock mass deformation at any point in the deformation field, D max To predict the maximum deformation of the rock mass in the deformation field, D min Predict the minimum deformation of the rock mass in the deformation field.

[0086] 103. Determine the stress failure tendency of the target rock mass engineering based on the predicted stress field.

[0087] In this embodiment, after determining the predicted stress field, the long-term stability assessment device for rock mass engineering can determine the stress failure tendency of the target rock mass engineering based on the predicted stress field. Specifically, the stress failure tendency of the target rock mass engineering can be determined by the following formula:

[0088]

[0089] Among them, S tendency For stress failure tendency, S tres To predict the stress storage degree of rock mass at any point in the stress field, S max To predict the maximum stored stress in the rock mass within the stress field, S min To predict the minimum stored stress in the rock mass within the stress field; where S is determined by the following formula. tres :

[0090]

[0091] Where, σ x σ y σ z τ xy τ xz τ yz To predict the stress components of the stress vector σ at any point in the stress field.

[0092] 104. Determine the cracking and failure tendency of the target rock mass engineering based on the predicted damage zone.

[0093] In this embodiment, after determining the predicted damage zone, the rock mass engineering long-term stability assessment device can determine the cracking and failure tendency of the target rock mass engineering based on the predicted damage zone. Specifically, the cracking and failure tendency of the target rock mass engineering can be determined by the following formula:

[0094]

[0095] Among them, F tendency E is prone to cracking and damage. 0 E represents the elastic modulus of the target rock mass in its initial state. r The elastic modulus of the target rock mass under excavation conditions. The confining pressure of the target rock mass in its initial state. The confining pressure of the target rock mass during excavation. The volume of the target rock mass in its initial state. Let ζ be the volume of the rock mass under the excavation state of the target rock mass, k and η be constant parameters obtained by fitting experimental data, and ζ be a preset constant (the preset constant can be, for example, 1.0 to 1.1, and can also be adjusted according to the actual situation, without being limited to any specific value).

[0096] It should be noted that step 102 can determine the deformation and failure tendency of the target rock mass project, step 103 can determine the stress failure tendency of the target rock mass project, and step 104 can determine the cracking failure tendency of the target rock mass project. However, there is no restriction on the order of execution of these three steps. Step 102 can be executed first, or step 103 can be executed first, or step 104 can be executed first, or they can be executed simultaneously. There is no specific limitation.

[0097] 105. Determine a multi-factor comprehensive early warning evaluation model for target rock mass engineering based on deformation failure tendency, stress failure tendency, and cracking failure tendency.

[0098] In this embodiment, the multi-factor comprehensive early warning evaluation model includes comprehensive early warning evaluation indicators and comprehensive weight coefficients. The assessment device for the long-term stability of rock mass engineering can obtain the comprehensive early warning evaluation indicators using the following formula:

[0099] DSF=f1·D tendency +f2·S tendency +f3F tendency

[0100] Wherein, DSF is the comprehensive early warning evaluation index, f1 is the weight coefficient of the deformation failure tendency, f2 is the weight coefficient of the stress failure tendency, and f3 is the weight coefficient of the crack failure tendency.

[0101] The comprehensive weighting coefficient is determined using the following formula:

[0102]

[0103] Among them, f i s f is the first weight coefficient determined by the triangular fuzzy number-analytic hierarchy process (AHP) combined weighting method. i k The second weighting coefficient is determined by the CRITIC objective weighting method, and both the first weighting coefficient and the second weighting coefficient are the comprehensive weighting coefficient.

[0104] 106. Determine the long-term safety and failure probability of the target rock mass engineering based on the multi-factor comprehensive early warning evaluation model.

[0105] In this embodiment, after the multi-factor comprehensive early warning evaluation model is constructed, the long-term stability assessment device for rock mass engineering can determine the long-term safety and timeliness probability of the target rock mass engineering based on the multi-factor comprehensive early warning evaluation model.

[0106] In one embodiment, the long-term stability assessment device for rock mass engineering determines the long-term safety and failure probability of the target rock mass engineering based on a multi-factor comprehensive early warning evaluation model, including:

[0107] The long-term bearing capacity limit state equation of the target rock mass engineering is determined based on the multi-factor comprehensive early warning evaluation model;

[0108] Based on the long-term bearing limit state equation, the long-term safety and failure probability of the target rock mass engineering are calculated by the least squares support vector machine method.

[0109] In this embodiment, the long-term stability assessment device for rock mass engineering can determine the long-term bearing limit state equation using the following formula:

[0110] F z =DSF lim -DSF t ;

[0111] Among them, F z For the long-term bearing limit state equation, DSF lim The critical warning value for the comprehensive early warning evaluation indicators, DSF t Let F be the predicted value of the comprehensive early warning evaluation index at time t. z When F > 0, it indicates that the target rock mass engineering is in a reliable state. z When <0, it indicates that the target rock mass engineering is in a state of failure.

[0112] Next, assume the initial points (sample points) required for the iteration of the Least Squares Support Vector Machine (LSSVM algorithm). The long-term stability assessment device for rock mass engineering solves for the function values ​​of each sample point corresponding to the target rock mass engineering based on the long-term bearing limit state equation. Specifically, the function values ​​of each sample point can be calculated using the following formula:

[0113] Where n is the number of sample points corresponding to the target rock mass engineering;

[0114] Then, the sample points and the function values ​​are fitted to reconstruct the long-term load-bearing state equation and obtain an approximate response surface function.

[0115] Finally, based on the approximate response surface function, the long-term safety index and failure probability of the target rock mass engineering are determined using the following formula:

[0116]

[0117] Where, β k p is the reliability index for the long-term security. f Let Γ be the failure probability, and Γ be the response surface function. The failure domain, X, is a column vector associated with DSF, which is determined by the following formula:

[0118] X i =Φ -1 [F(DSF)];

[0119] Where, Φ -1 [] is the inverse cumulative probability distribution function of the quasi-normal distribution, and F[] is the cumulative probability distribution function of the original non-normal distribution.

[0120] In summary, it can be seen that in the embodiments provided in this application, when conducting long-term stability assessment of the target rock mass engineering, the deformation failure tendency, stress failure tendency, and crack failure tendency are comprehensively considered. Based on the deformation failure tendency, stress failure tendency, and crack failure tendency, a multi-factor comprehensive early warning evaluation model is constructed. Therefore, when determining the long-term safety and failure probability of the target rock mass engineering through the multi-factor comprehensive early warning evaluation model, the prediction accuracy of the long-term safety and failure probability of the rock mass engineering can be improved.

[0121] The embodiments of the present invention have been described above from the perspective of the evaluation method for the long-term stability of rock mass engineering. The embodiments of the present invention will now be described below from the perspective of the evaluation device for the long-term stability of rock mass engineering.

[0122] Please see Figure 2 A virtual structural diagram of a rock mass engineering long-term stability assessment device 200 is shown in this embodiment of the invention. The rock mass engineering long-term stability assessment device 200 includes:

[0123] Analysis module 201 is used to perform elastoplastic analysis on the randomness and variability of mechanical parameters of the target rock mass corresponding to the target rock mass, so as to obtain the predicted deformation field, predicted stress field and predicted damage zone of the target rock mass.

[0124] The first determining module 202 is used to determine the deformation and failure tendency of the target rock mass engineering based on the predicted deformation field;

[0125] The second determining module 203 is used to determine the stress failure tendency of the target rock mass engineering based on the predicted stress field;

[0126] The third determining module 204 is used to determine the cracking and failure tendency of the target rock mass engineering based on the predicted damage zone;

[0127] The fourth determining module 205 is used to determine a multi-factor comprehensive early warning evaluation model for the target rock mass engineering based on the deformation failure tendency, the stress failure tendency, and the cracking failure tendency.

[0128] The fifth determining module 206 is used to determine the long-term safety and failure probability of the target rock mass project based on the multi-factor comprehensive early warning evaluation model.

[0129] In one possible design, the first determining module 202 is specifically used for:

[0130] The deformation and failure tendency of the target rock mass engineering is determined by the following formula:

[0131]

[0132] Among them, D tendency For the aforementioned tendency to deform and fail, D isp Let D be the rock mass deformation at any point in the predicted deformation field. max D represents the maximum deformation of the rock mass in the predicted deformation field. min The minimum deformation of the rock mass in the predicted deformation field.

[0133] In one possible design, the second determining module 203 is specifically used for:

[0134] The stress failure tendency of the target rock mass engineering is determined by the following formula:

[0135]

[0136] Among them, S tendency For the stress failure tendency, S tres S represents the rock mass stress storage degree at any point in the predicted stress field. max S is the maximum stored stress of the rock mass in the predicted stress field. min The minimum stored stress of the rock mass in the predicted stress field is S; determined by the following formula. tres :

[0137]

[0138] Where, σ x σ y σ z τ xy τ xz τ yz Let σ be the stress component of the stress vector σ at any point in the predicted stress field.

[0139] In one possible design, the third determining module 204 is specifically used for:

[0140] The tendency of the target rock mass to crack and fail is determined by the following formula:

[0141]

[0142] Among them, F tendency E represents the tendency to crack and fail. 0E represents the elastic modulus of the target rock mass in its initial state. r The elastic modulus of the target rock mass under excavation conditions. The confining pressure of the target rock mass in its initial state. The confining pressure of the target rock mass during its excavation state. The volume of the target rock mass in its initial state. Let be the rock mass volume under the excavation state of the target rock mass, k and η be constant parameters obtained by fitting experimental data, and ζ be a preset constant.

[0143] In one possible design, the multi-factor comprehensive early warning evaluation model includes comprehensive early warning evaluation indicators and comprehensive weight coefficients, and the fourth determining module 205 is specifically used for:

[0144] The comprehensive early warning evaluation index is constructed using the following formula:

[0145] DSF=f1·D tendency +f2·S tendency +f3F tendency

[0146] Wherein, DSF is the comprehensive early warning evaluation index, f1 is the deformation failure tendency weight coefficient, f2 is the stress failure tendency weight coefficient, and f3 is the crack failure tendency weight coefficient.

[0147] The comprehensive weighting coefficient is determined using the following formula:

[0148]

[0149] Among them, f i s f is the first weight coefficient determined by the triangular fuzzy number-analytic hierarchy process (AHP) combined weighting method. i k The second weighting coefficient is determined by the CRITIC objective weighting method, and both the first weighting coefficient and the second weighting coefficient are the comprehensive weighting coefficient.

[0150] In one possible design, the fifth determining module 206 is specifically used for:

[0151] The long-term bearing capacity limit state equation of the target rock mass engineering is determined based on the multi-factor comprehensive early warning evaluation model.

[0152] Based on the long-term bearing limit state equation, the long-term safety and failure probability of the target rock mass project are calculated using the least squares support vector machine method.

[0153] In one possible design, the fifth determination module 206 determines the long-term bearing capacity limit state equation of the target rock mass engineering according to the multi-factor comprehensive early warning evaluation model, including:

[0154] The long-term load-bearing ultimate limit state equation is determined by the following formula:

[0155] F z =DSF lim -DSF t ;

[0156] Among them, F z The long-term bearing limit state equation is given by DSF. lim The critical warning value of the comprehensive early warning evaluation index is DSF. t Let be the predicted value of the comprehensive early warning evaluation index at time t.

[0157] In one possible design, the fifth determining module 206 calculates the long-term safety and failure probability of the target rock mass engineering project based on the long-term bearing limit state equation using the least squares support vector machine method, including:

[0158] The function values ​​of each sample point corresponding to the target rock mass engineering are solved based on the long-term bearing limit state equation.

[0159] By fitting the sample points and the functional values, the long-term load-bearing limit state equation is reconstructed to obtain an approximate response surface function.

[0160] The long-term security and the failure probability are determined based on the approximate response surface function.

[0161] In one possible design, the fifth determining module 206 solves for the function values ​​of each sample point corresponding to the target rock mass engineering according to the long-term bearing limit state equation, including:

[0162] The function value of each sample point is calculated using the following formula:

[0163]

[0164] Where n is the number of sample points corresponding to the target rock mass engineering;

[0165] Determining the long-term security and the failure probability based on the approximate response surface function includes:

[0166] The long-term security and the failure probability are determined by the following formula:

[0167]

[0168] Where, βk p is the reliability index for the long-term security. f Let Γ be the failure probability, and Γ be the response surface function. The failure domain, X, is a column vector associated with DSF, which is determined by the following formula:

[0169] X i =Φ -1 [F(DSF)];

[0170] Where, Φ -1 [] is the inverse cumulative probability distribution function of the quasi-normal distribution, and F[] is the cumulative probability distribution function of the original non-normal distribution.

[0171] above Figure 2 The long-term stability assessment device for rock mass engineering in this embodiment of the invention has been described from the perspective of modular functional entities. The following is a detailed description of the long-term stability assessment device for rock mass engineering in this embodiment of the invention from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 A schematic diagram of an embodiment of the long-term stability assessment device 300 for rock mass engineering in this invention is shown. The long-term stability assessment device 300 for rock mass engineering includes:

[0172] Input device 301, output device 302, processor 303, and memory 304 (where the number of processors 303 can be one or more). Figure 3 (Taking a processor 303 as an example). In some embodiments of the present invention, the input device 301, the output device 302, the processor 303, and the memory 304 may be connected via a communication bus or other means, wherein... Figure 3 Take the China-Israel communication bus connection as an example.

[0173] Specifically, by calling the operation instructions stored in memory 304, processor 303 executes the following steps:

[0174] Elastoplastic analysis of the randomness and variability of mechanical parameters of the target rock mass is performed on the target rock mass engineering to obtain the predicted deformation field, predicted stress field and predicted damage zone of the target rock mass;

[0175] The deformation and failure tendency of the target rock mass engineering is determined based on the predicted deformation field;

[0176] The stress failure tendency of the target rock mass engineering is determined based on the predicted stress field;

[0177] The cracking and failure tendency of the target rock mass engineering is determined based on the predicted damage zone;

[0178] A multi-factor comprehensive early warning evaluation model for the target rock mass engineering is determined based on the deformation failure tendency, the stress failure tendency, and the cracking failure tendency.

[0179] The long-term safety and failure probability of the target rock mass engineering are determined based on the multi-factor comprehensive early warning evaluation model.

[0180] By calling the operation instructions stored in memory 304, processor 303 is also used to execute... Figure 1 Any of the methods in the corresponding embodiments.

[0181] Please see Figure 4 , Figure 4 A schematic diagram of an embodiment of the electronic device provided in this invention.

[0182] like Figure 4 As shown, this embodiment of the invention provides an electronic device, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, it performs the following steps:

[0183] Elastoplastic analysis of the randomness and variability of mechanical parameters of the target rock mass is performed on the target rock mass engineering to obtain the predicted deformation field, predicted stress field and predicted damage zone of the target rock mass;

[0184] The deformation and failure tendency of the target rock mass engineering is determined based on the predicted deformation field;

[0185] The stress failure tendency of the target rock mass engineering is determined based on the predicted stress field;

[0186] The cracking and failure tendency of the target rock mass engineering is determined based on the predicted damage zone;

[0187] A multi-factor comprehensive early warning evaluation model for the target rock mass engineering is determined based on the deformation failure tendency, the stress failure tendency, and the cracking failure tendency.

[0188] The long-term safety and failure probability of the target rock mass engineering are determined based on the multi-factor comprehensive early warning evaluation model.

[0189] In practical implementation, when the processor 420 executes the computer program 411, it can achieve... Figure 1 Any of the corresponding implementation methods in the embodiments.

[0190] Since the electronic device described in this embodiment is the device used to implement the long-term stability assessment device for rock mass engineering in this embodiment of the present invention, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in this embodiment of the present invention. Therefore, how the electronic device implements the method in this embodiment of the present invention will not be described in detail here. Any device used by those skilled in the art to implement the method in this embodiment of the present invention is within the scope of protection of this invention.

[0191] Please see Figure 5 , Figure 5 This is a schematic diagram of an embodiment of a computer-readable storage medium provided in this invention.

[0192] like Figure 5 As shown, this embodiment of the invention also provides a computer-readable storage medium 500, on which a computer program 511 is stored. When the computer program 511 is executed by a processor, it performs the following steps:

[0193] Elastoplastic analysis of the randomness and variability of mechanical parameters of the target rock mass is performed on the target rock mass engineering to obtain the predicted deformation field, predicted stress field and predicted damage zone of the target rock mass;

[0194] The deformation and failure tendency of the target rock mass engineering is determined based on the predicted deformation field;

[0195] The stress failure tendency of the target rock mass engineering is determined based on the predicted stress field;

[0196] The cracking and failure tendency of the target rock mass engineering is determined based on the predicted damage zone;

[0197] A multi-factor comprehensive early warning evaluation model for the target rock mass engineering is determined based on the deformation failure tendency, the stress failure tendency, and the cracking failure tendency.

[0198] The long-term safety and failure probability of the target rock mass engineering are determined based on the multi-factor comprehensive early warning evaluation model.

[0199] In the specific implementation process, the computer program 511 is executed by the processor to achieve... Figure 1 Any of the corresponding implementation methods in the embodiments.

[0200] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0201] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0202] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0203] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0204] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0205] This invention also provides a computer program product comprising computer software instructions that, when executed on a processing device, cause the processing device to perform actions such as... Figure 2 The process in the corresponding embodiment.

[0206] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0207] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0208] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0209] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0210] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0211] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0212] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing the long-term stability of rock mass engineering, characterized in that, include: Elastoplastic analysis of the randomness and variability of mechanical parameters of the target rock mass is performed on the target rock mass engineering to obtain the predicted deformation field, predicted stress field and predicted damage zone of the target rock mass; The deformation and failure tendency of the target rock mass engineering is determined based on the predicted deformation field; The stress failure tendency of the target rock mass engineering is determined based on the predicted stress field; The cracking and failure tendency of the target rock mass engineering is determined based on the predicted damage zone; A multi-factor comprehensive early warning evaluation model for the target rock mass engineering is determined based on the deformation failure tendency, the stress failure tendency, and the cracking failure tendency. The long-term safety and failure probability of the target rock mass engineering are determined based on the multi-factor comprehensive early warning evaluation model. The determination of the deformation and failure tendency of the target rock mass engineering based on the predicted deformation field includes: The deformation and failure tendency of the target rock mass engineering is determined by the following formula: ； in, For the aforementioned tendency to deform and fail, Let be the rock mass deformation at any point in the predicted deformation field. The maximum deformation of the rock mass in the predicted deformation field is given. The minimum deformation of the rock mass in the predicted deformation field; The determination of the stress failure tendency of the target rock mass engineering based on the predicted stress field includes: The stress failure tendency of the target rock mass engineering is determined by the following formula: ； in, For the aforementioned stress failure tendency, The rock mass stress storage degree at any point in the predicted stress field. The maximum stored stress in the rock mass within the predicted stress field. The minimum stored stress of the rock mass in the predicted stress field is determined by the following formula. : ； in, The stress vector at any point in the predicted stress field Stress components; The step of determining the cracking and failure tendency of the target rock mass engineering based on the predicted damage zone includes: The tendency of the target rock mass to crack and fail is determined by the following formula: ； in, For the aforementioned tendency to crack and fail, The elastic modulus of the target rock mass in its initial state. The elastic modulus of the target rock mass under excavation conditions. The confining pressure of the target rock mass in its initial state. The confining pressure of the target rock mass during its excavation state. The volume of the target rock mass in its initial state. The volume of the target rock mass during its excavation state. and These are constant parameters obtained by fitting experimental data. This is a preset constant.

2. The method according to claim 1, characterized in that, The multi-factor comprehensive early warning evaluation model includes comprehensive early warning evaluation indicators and comprehensive weight coefficients. The multi-factor comprehensive early warning evaluation model for determining the target rock mass engineering based on the deformation failure tendency, the stress failure tendency, and the cracking failure tendency includes: The comprehensive early warning evaluation index is constructed using the following formula: in, The comprehensive early warning evaluation index is... The weighting coefficient for the deformation and failure tendency is... This is the stress failure tendency weighting coefficient. The weighting coefficient for cracking and failure tendency; The comprehensive weighting coefficient is determined using the following formula: ； in, The first weight coefficient is determined by the triangular fuzzy number-analytic hierarchy process (AHP) combined weighting method. The second weighting coefficient is determined by the CRITIC objective weighting method, and both the first weighting coefficient and the second weighting coefficient are the comprehensive weighting coefficient.

3. The method according to any one of claims 1 to 2, characterized in that, The determination of the long-term safety and failure probability of the target rock mass engineering based on the multi-factor comprehensive early warning evaluation model includes: The long-term bearing capacity limit state equation of the target rock mass engineering is determined based on the multi-factor comprehensive early warning evaluation model. Based on the long-term bearing limit state equation, the long-term safety and failure probability of the target rock mass project are calculated using the least squares support vector machine method.

4. The method according to claim 3, characterized in that, The determination of the long-term bearing capacity limit state equation of the target rock mass engineering based on the multi-factor comprehensive early warning evaluation model includes: The long-term load-bearing ultimate limit state equation is determined by the following formula: ； in, This is the long-term bearing capacity limit state equation. This is the critical warning value of the comprehensive early warning evaluation index. Let be the predicted value of the comprehensive early warning evaluation index at time t.

5. The method according to claim 4, characterized in that, The calculation of the long-term safety and failure probability of the target rock mass engineering project based on the long-term bearing limit state equation using the least squares support vector machine method includes: The function values ​​of each sample point corresponding to the target rock mass engineering are solved based on the long-term bearing limit state equation. By fitting the sample points and the functional values, the long-term load-bearing limit state equation is reconstructed to obtain an approximate response surface function. The long-term security and the failure probability are determined based on the approximate response surface function.

6. The method according to claim 5, characterized in that, The process of solving for the function values ​​of each sample point corresponding to the target rock mass engineering based on the long-term bearing capacity limit state equation includes: The function value of each sample point is calculated using the following formula: ； Where n is the number of sample points corresponding to the target rock mass engineering; Determining the long-term security and the failure probability based on the approximate response surface function includes: The long-term security and the failure probability are determined by the following formula: ； in, This is a reliability indicator for the aforementioned long-term security. The failure probability is... For the response surface function The failure domain of 0, The column vector X associated with DSF is determined by the following formula: ； in, It is the inverse function of the cumulative probability distribution of the quasi-normal distribution. is the cumulative probability distribution function of the original non-normal distribution.

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