Roadway surrounding rock stability evaluation method and system based on optical fiber monitoring

Abstract

The invention relates to the technical field of surrounding rock stability evaluation, in particular to a roadway surrounding rock stability evaluation method and system based on optical fiber monitoring. The method comprises the following steps: acquiring time sequence monitoring data of a plurality of excavated sections in a deep high ground stress roadway surrounding rock; based on the time sequence monitoring data, the final deformation of the surrounding rock of the excavated section is obtained through a time sequence prediction model; utilizing the final deformation of the surrounding rock to invert mechanical parameters of the excavated section, and constructing a finite element model of the surrounding rock of the deep high ground stress roadway according to the mechanical parameters; and completing the stability evaluation of the roadway surrounding rock based on optical fiber monitoring through the finite element model. According to the method, by inverting the mechanical parameters of the surrounding rock, the finite element model is constructed to accurately and effectively evaluate the stability of the surrounding rock of the deep high-ground-stress roadway, a basis is provided for formulating a construction scheme, it is ensured that a supporting structure can effectively control deformation and damage of the surrounding rock, and construction safety and engineering quality are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of surrounding rock stability evaluation, and in particular to a tunnel surrounding rock stability evaluation method and system based on optical fiber monitoring. Background Art

[0002] With the increasing depletion of shallow coal resources, most major mines have entered the kilometer mining range, while deep mining causes large disturbances and high ground stress, which is very likely to cause disasters such as dynamic impact. The premise of ensuring coal mine output is to ensure the safety of coal mine tunnels, that is, to ensure the safety of the life passages of construction workers. The safety of tunnels is closely related to the strength of support, so choosing a reasonable support method is one of the important issues that need to be considered in coal mine production operations.

[0003] In the past, the support method of surrounding rock of coal mine tunnels was mainly determined by the observation of technical personnel, and the influence of some potential factors was often ignored, resulting in the use of wrong support methods, resulting in waste of support materials or threatening the personal safety of construction workers. Therefore, when designing the tunnel support plan, modern emerging technologies should be combined to make accurate and effective assessments of the surrounding rock stability of coal mine tunnels, and then judge the difficulty of construction and optimize the support structure design, provide a basis for the formulation of the construction plan, and ensure that the support structure can effectively control the deformation and damage of the surrounding rock, and ensure construction safety and project quality. Summary of the invention

[0004] In view of the shortcomings of existing methods and the needs of practical applications, in order to solve the problem of accurately and effectively evaluating the stability of the surrounding rock of deep high-in-situ stress tunnels. On the one hand, the present invention provides a tunnel surrounding rock stability evaluation method based on optical fiber monitoring, comprising the following steps: obtaining time-series monitoring data of multiple excavated sections in the surrounding rock of deep high-in-situ stress tunnels; based on the time-series monitoring data, obtaining the final deformation of the surrounding rock of the excavated section through a time-series prediction model; using the final deformation of the surrounding rock to invert the mechanical parameters of the excavated section, and constructing a finite element model of the surrounding rock of the deep high-in-situ stress tunnel according to the mechanical parameters; completing the tunnel surrounding rock stability evaluation based on optical fiber monitoring through the finite element model. The present invention inverts the mechanical parameters of the surrounding rock and then constructs a finite element model to accurately and effectively evaluate the stability of the surrounding rock of deep high-in-situ stress tunnels, providing a basis for the formulation of construction plans, ensuring that the support structure can effectively control the deformation and damage of the surrounding rock, and ensuring construction safety and engineering quality.

[0005] Optionally, the tunnel surrounding rock stability evaluation method based on optical fiber monitoring further includes the following steps: The time series monitoring data is subjected to isometric interpolation processing; and the final deformation of the surrounding rock is corrected. The present invention is beneficial to improving the accuracy of the present invention by preprocessing the time series monitoring data and correcting the final deformation of the surrounding rock.

[0006] Optionally, the final deformation of the surrounding rock is corrected to satisfy the following formula: in, represents the final deformation of the surrounding rock after correction, represents the final deformation of the surrounding rock before correction, represents the displacement release rate, Indicates the surrounding rock grade. The present invention corrects the final deformation of the surrounding rock based on the displacement release rate and the surrounding rock grade, eliminates the influence of the surrounding rock deformation before monitoring, and improves the rationality of the present invention.

[0007] Optionally, the inversion of the mechanical parameters of the excavated section using the final deformation of the surrounding rock comprises the following steps: Based on the value range of the mechanical parameters, multiple mechanical parameter matrices are constructed; according to the mechanical parameter matrices, a surrounding rock deformation data set is obtained; an improved sparrow search algorithm is introduced to optimize the hyperparameters of the learning network model, and the optimized learning network model is trained using the surrounding rock deformation data set; the mechanical parameters of the excavated section are obtained through the trained learning network model. The present invention uses an improved sparrow search algorithm to optimize the hyperparameters of the learning network model, which is conducive to quickly and accurately training the learning network model, thereby improving the accuracy of the present invention.

[0008] Optionally, the introducing of the improved sparrow search algorithm to optimize the hyperparameters of the learning network model comprises the following steps: Through the first mapping model, a sparrow population with the hyperparameters as sparrow individuals is randomly generated; the discoverer position update formula, the follower position update formula and the sentinel position update formula are improved, and the improved sparrow search algorithm is used to iteratively optimize to obtain the optimal hyperparameters of the learning network model.

[0009] Optionally, the first mapping model satisfies the following formula: in, Represents the first values, Represents the first values, The present invention generates an initial sparrow population through the first mapping model, and the population is highly uniform and reasonable, which is conducive to improving iteration efficiency.

[0010] Optionally, the improved discoverer location update formula satisfies the following formula: in, Indicates The sparrow discovered The updated position after iterations, Indicates The sparrow discovered The position at the iteration, represents the maximum number of iterations, express A random number, Indicates belonging to The warning value of Indicates belonging to The warning threshold, represents random numbers that follow a standard normal distribution, Indicates that the element is 1 matrix, Indicates the spatial dimension of the sparrow's position. The present invention introduces As a search direction, it is helpful to improve the search ability of the discoverer.

[0011] Optionally, the follower position update formula is improved to satisfy the following formula: in, Indicates Follower Sparrow in the The updated position after iterations, represents random numbers that follow a standard normal distribution, Indicated in The global worst position at the iteration, Indicates Follower Sparrow in the The position at the iteration, Indicates the number of sparrows, represents the follower learning factor and satisfies , represents the maximum number of iterations, Indicated in The global optimal position at the iteration is represents the follower's adaptive inertia weight factor and satisfies The present invention further improves the search efficiency of the present invention by improving the learning factor of the follower adaptation and introducing an adaptive inertia weight factor.

[0012] Optionally, the sentinel position update formula is improved to satisfy the following formula: in, Indicates The Vigilant Sparrow is in The updated position after iterations, represents the maximum number of iterations, Indicates A vigilante sparrow is in The position at the iteration, Indicated in The global optimal position at the iteration is Indicated in The global worst position at the iteration, express The present invention utilizes the spiral jump method to increase the search range of the sentinel, avoids premature convergence, and further improves the accuracy of the present invention.

[0013] In the second aspect, in order to be able to efficiently execute the tunnel surrounding rock stability evaluation method based on optical fiber monitoring provided by the present invention, the present invention also provides a tunnel surrounding rock stability evaluation system based on optical fiber monitoring, including a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the tunnel surrounding rock stability evaluation method based on optical fiber monitoring as described in the first aspect of the present invention. The tunnel surrounding rock stability evaluation system based on optical fiber monitoring of the present invention has a compact structure and stable performance, and can stably execute the tunnel surrounding rock stability evaluation method based on optical fiber monitoring provided by the present invention, further improving the overall applicability and practical application capabilities of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart of a tunnel surrounding rock stability evaluation method based on optical fiber monitoring provided by an embodiment of the present invention; Figure 2 A framework diagram of a tunnel surrounding rock stability evaluation system based on optical fiber monitoring provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a tunnel surrounding rock stability evaluation device based on optical fiber monitoring provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0016] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0017] See also Figure 1 In order to solve the problem of accurately and effectively evaluating the stability of surrounding rock of deep high-stress tunnels, the present invention provides a tunnel surrounding rock stability evaluation method based on optical fiber monitoring, such as Figure 1 As shown, in one embodiment, the method includes the following steps: S1. Obtain time-series monitoring data of multiple excavated sections in the surrounding rock of deep high-in-situ stress tunnels.

[0018] In the field of mine research, deep usually refers to mines with deeper coal seams. Different countries have different definition standards for deep mines. For example, in Japan, a depth of about 700m is regarded as the limit of deep mines, while in South Africa, Germany, Canada and other countries, 800m~1000m is used as the standard for deep mines; high ground stress refers to the high stress state of the tunnel surrounding rock, which is usually the result of multiple factors such as crustal movement and the rock's own gravity; tunnel surrounding rock refers to the surrounding rock or soil used to support the tunnel after the tunnel is excavated; the excavated section refers to the cross-sectional shape and size of the underground space formed on a certain horizontal or vertical plane after the engineering excavation activity is completed; time series monitoring data refers to data recorded according to timestamps. Each data point contains a timestamp and corresponding data value, which is used to describe and analyze dynamic changes.

[0019] In this embodiment, strain monitoring data of multiple excavated sections of surrounding rock of deep high-ground stress tunnels are obtained based on optical fiber sensing technology. Specifically, a number of rigid anchor rods, a first optical fiber for detecting strain, and a second optical fiber for detecting temperature are evenly anchored into the surrounding rock on both sides and the top of the tunnel, and a temperature measuring optical fiber channel penetrating along the axial direction is provided on the rigid anchor rod, and the second optical fiber passes forward from the rear end of the rigid anchor rod into the temperature measuring optical fiber channel, and then passes backward from the front end of the rigid anchor rod through the temperature measuring optical fiber channel, and each first optical fiber is connected in series in sequence, and each second optical fiber is connected in series in sequence; then the first first optical fiber on the series line is respectively connected to the first light source and the distributed optical fiber temperature measurement system, and the first second optical fiber on the series line is respectively connected to the second light source and the distributed optical fiber temperature measurement system.

[0020] The first light source emits a detection light into the first optical fiber, and the optical signal reflected by the first optical fiber is processed by the distributed optical fiber strain sensing system to obtain the strain data of the monitored object; the second light source emits a detection light into the second optical fiber, and the optical signal reflected by the second optical fiber is processed by the distributed optical fiber temperature measurement system to obtain the temperature data of the monitored object; based on the obtained temperature data, the first optical fiber strain data caused by temperature is obtained from the relationship between temperature and optical fiber strain, and then the strain data processed by the distributed optical fiber strain sensing system is revised according to the first optical fiber strain data caused by temperature to obtain the final surrounding rock strain monitoring data. In other embodiments, data can also be directly retrieved from the relevant database.

[0021] Furthermore, after obtaining the time series monitoring data of multiple excavated sections in the surrounding rock of deep high-stress tunnels, the time series monitoring data is also subjected to equidistant interpolation processing. It should be understood that the continuity of deformation data is the premise for ensuring the accuracy of time series prediction. In engineering, different monitoring frequencies are often selected for deformation monitoring based on the deformation rate of the surrounding rock. The measured deformation values ​​are often discontinuous and difficult to meet the requirements of time series prediction.

[0022] Equidistant interpolation processing refers to inserting new data points between known data points according to the principle of equidistant. These new data points are calculated by a certain interpolation method, aiming to make the intervals between data points uniform. The interpolation method can be selected according to specific needs, such as linear interpolation, cubic spline interpolation, piecewise cubic Hermite interpolation, polynomial interpolation, etc.

[0023] Linear interpolation is to calculate the value of the insertion point based on the straight line relationship between two known data points. The calculation is simple, but it may not accurately reflect the nonlinear changes of the data.

[0024] Cubic spline interpolation is to construct a cubic polynomial between each adjacent known data point, so that these polynomials are continuous and smooth on the data points, which can better reflect the nonlinear changes of the data and the interpolation results are smooth.

[0025] Piecewise cubic Hermite interpolation polynomial interpolation is similar to cubic spline interpolation, but it focuses more on maintaining local features of the data, such as monotonicity and concavity, and is suitable for scenarios where local features of the data need to be maintained.

[0026] S2. Based on the time series monitoring data, the final deformation of the surrounding rock of the excavated section is obtained through a time series prediction model.

[0027] It is understandable that it is difficult to obtain the final deformation value of the surrounding rock in a short time during construction. Therefore, a sufficient number of measured deformation data in the early stage can be used to better predict the final deformation value of the surrounding rock.

[0028] Time series forecasting refers to making scientific inferences and judgments on the future development trends or states of specific objects based on the development trends and changing laws of objective things. The basic principle of the time series forecasting model is based on the continuity of the development of things, using historical time series data for statistics and analysis, mining the changing laws of things, and exploring the future existence forms of things based on the changing laws.

[0029] Time series prediction models include traditional time series prediction models and machine learning time series prediction models.

[0030] Commonly used traditional time series forecasting models include mean regression, autoregressive integrated moving average (ARIMA) model, exponential smoothing forecasting method, etc. These methods are usually of low complexity and fast calculation speed, and have great advantages in dealing with single variable forecasting problems. However, if the forecasting problem is more complex or there are too many variables, traditional time series forecasting models may easily have shortcomings.

[0031] Machine learning time series prediction models include LSTM (Long Short-Term Memory Network), Transformer, CNN (Convolutional Neural Network), etc. Different deep learning model architectures can also be designed through combination to achieve more complex prediction model design.

[0032] Specifically, the final deformation of the surrounding rock includes the settlement of the arch crown, the horizontal convergence of the upper step, the horizontal convergence of the middle step, and the horizontal convergence of the lower step.

[0033] Furthermore, the quantity and timeliness of monitoring data are the prerequisites for ensuring the accuracy of time series prediction. On the basis of ensuring that the time series prediction model is fully learned, it is also necessary to continuously update the samples through the single-step rolling prediction method.

[0034] Furthermore, tunnel construction will disturb the surrounding rock in front of the face, causing the surrounding rock in front of the face to pre-converge before excavation. The displacement obtained through monitoring is only a part of the actual deformation displacement of the surrounding rock. Therefore, after obtaining the final deformation of the surrounding rock of the excavated section through the time series prediction model, the final deformation of the surrounding rock needs to be corrected.

[0035] Specifically, the final deformation of the surrounding rock is corrected to satisfy the following formula: in, represents the final deformation of the surrounding rock after correction, represents the final deformation of the surrounding rock before correction, represents the displacement release rate, Indicates the grade of surrounding rock.

[0036] The surrounding rock grade is to divide the infinite rock sequence into a finite number of categories with different degrees of stability based on factors such as rock integrity, rock strength, degree of development of joints and fissures, groundwater conditions and rock stability.

[0037] The surrounding rock grade is generally divided into six levels, namely Ⅰ, Ⅱ, Ⅲ, Ⅳ, Ⅴ, Ⅵ, corresponding to The number is 1-6. The smaller the number, the better the surrounding rock properties and the higher the stability. The specific classification standards are as follows: Level Ⅰ (stable): The rock is fresh and complete, slightly affected by geological structures, with undeveloped or slightly developed joints and fissures, mostly closed and not extending long; there is no or only weak structural surface with a width of generally less than 0.1m; groundwater activity is slight; Level II (basically stable): The rock is fresh or slightly weathered, generally affected by geological structures, with slightly developed or developed joints and fissures, and a few weak structural surfaces with a width of no more than 0.5-0.6m; the cave wall is damp with water seepage or dripping; Level III (poor stability): The rock is slightly or weakly weathered, seriously affected by geological structure, with developed joints and fissures, some of which are open and filled with mud; there are many weak structural surfaces with a width of less than 1.0m; there is significant groundwater activity; Level IV (poor stability): The rock mass state is the same as that of Level III, but there are more weak structural planes with a width of less than 2.0m; the local structure is crushed like gravel; and the groundwater activity is significant; Level V (extremely unstable): Strongly weathered or fully weathered rock mass, seriously affected by geological structure, with extremely developed joints and fissures; the width of the fault fracture zone is greater than 2m, and the fissures are mostly filled with mud; the groundwater activity is strong, with a large amount of water inflow; Grade VI (soft soil): mainly soft soil surrounding rock, such as loose soil layer, sand layer, etc.

[0038] S3. Invert the mechanical parameters of the excavated section using the final deformation of the surrounding rock, and construct a finite element model of the surrounding rock of the deep high-in-situ stress tunnel based on the mechanical parameters.

[0039] In an embodiment, the inversion of the mechanical parameters of the excavated section using the final deformation of the surrounding rock comprises the following steps: S31. Constructing a plurality of mechanical parameter matrices based on the value range of the mechanical parameters.

[0040] The mechanical parameters include elastic modulus, Poisson's ratio, internal friction angle, shear dilation angle, cohesion and plastic strain. It should be understood that under the action of high ground stress, the surrounding rock of a tunnel with a large burial depth gradually changes from an elastic state to a plastic state, and a certain range of plastic deformation occurs. In the elastic deformation stage, the surrounding rock conforms to the linear elastic model, and the factors affecting the deformation of the surrounding rock are mainly elastic modulus and Poisson's ratio; in the plastic deformation stage, the factors affecting the deformation of the surrounding rock are internal friction angle, shear dilation angle, cohesion and plastic strain.

[0041] Specifically, the value ranges of each mechanical parameter are divided equally, and orthogonal design is performed according to the results of the division to obtain multiple mechanical parameter matrices. It should be noted that the division according to the value ranges of each mechanical parameter needs to be determined according to the accuracy requirements and computing power, and five equal divisions are sufficient in the embodiment.

[0042] S32. Obtain a surrounding rock deformation data set according to the mechanical parameter matrix.

[0043] Specifically, the mechanical parameters are assigned to the finite element simulation software, and a joint model is established to establish a numerical model of tunnel excavation for calculation, so as to obtain the final deformation of the surrounding rock predicted by the model. Then, based on the mechanical parameters and the corresponding predicted final deformation of the surrounding rock, a surrounding rock deformation data set is constructed and divided into a data training set and a data verification set according to a certain ratio.

[0044] S33, introducing an improved sparrow search algorithm to optimize the hyperparameters of the learning network model, and using the surrounding rock deformation data set to train the optimized learning network model.

[0045] In an embodiment, the learning network model is a machine learning model such as an extreme learning machine, a support vector machine, and a neural network. The hyperparameters of the machine learning model are parameters that need to be set before training the model. They are not obtained through training, but need to be set manually, and have an important impact on the performance of the model.

[0046] Furthermore, the step S33 of introducing the improved sparrow search algorithm to optimize the hyperparameters of the learning network model includes the following steps: S331. Randomly generate a sparrow population with the hyperparameters as sparrow individuals through a first mapping model.

[0047] Specifically, one or more chaotic sequences are generated through the first mapping model, and the generated chaotic sequences are mapped to the solution space of the problem, including scaling the values ​​of the chaotic sequences to between the upper and lower limits of the solution space and converting them into corresponding solutions, and finally the mapped solutions are used as individuals of the initial population.

[0048] Furthermore, the first mapping model satisfies the following formula: in, Represents the first values, Represents the first values, Represents a random number in [0,1].

[0049] S332, improving the discoverer position update formula, the follower position update formula and the sentinel position update formula, and performing iterative optimization through the improved sparrow search algorithm to obtain the optimal hyperparameters of the learning network model.

[0050] Specifically, the improved sparrow search algorithm is used to perform iterative optimization to obtain the optimal hyperparameters of the learning network model. It is necessary to define an objective function, namely a fitness function. In this embodiment, the objective function is a performance indicator of the model, namely, accuracy. Secondly, it is necessary to determine the number of sparrows and set a reasonable search range for each hyperparameter. Then, the fitness is calculated based on the initialized population, including training the learning network model with the current hyperparameter value, evaluating the performance of the model on the validation set, and calculating the fitness value. Iteratively update the sparrow position, and in each iteration, record the current optimal individual, namely, the optimal hyperparameter combination, until a predetermined number of iterations is reached or a certain stop condition is met, such as the fitness no longer improving.

[0051] Furthermore, the improved finder location update formula satisfies the following formula: in, Indicates The sparrow discovered The updated position after iterations, Indicates The sparrow discovered The position at the iteration, represents the maximum number of iterations, express A random number, Indicates belonging to The warning value of Indicates belonging to The warning threshold, represents random numbers that follow a standard normal distribution, Indicates that the element is 1 matrix, Represents the spatial dimension of the sparrow's position.

[0052] In yet another embodiment, the follower position update formula is improved to satisfy the following formula: in, Indicates Follower Sparrow in the The updated position after iterations, represents random numbers that follow a standard normal distribution, Indicated in The global worst position at the iteration, Indicates Follower Sparrow in the The position at the iteration, Indicates the number of sparrows, represents the follower learning factor and satisfies , represents the maximum number of iterations, Indicated in The global optimal position at the iteration is represents the follower's adaptive inertia weight factor and satisfies .

[0053] In an embodiment, the alerter position update formula is improved to satisfy the following formula: in, Indicates The Vigilant Sparrow is in The updated position after iterations, represents the maximum number of iterations, Indicates The Vigilant Sparrow is in The position at the iteration, Indicated in The global optimal position at the iteration is Indicated in The global worst position at the iteration, express A random number.

[0054] In some other embodiments, during each iteration, the ratio of discoverers, followers, and sentinels is dynamically adjusted to balance the search focus in the early and late stages of the iteration.

[0055] Furthermore, the optimized learning network model trained using the surrounding rock deformation data set needs to be objectively evaluated for its calculation accuracy through root mean square error, mean relative error and symmetric mean absolute percentage error.

[0056] S34. Obtain the mechanical parameters of the excavated section through the trained learning network model.

[0057] Specifically, by learning a large number of representative samples, a nonlinear mapping relationship between surrounding rock deformation and mechanical parameters is finally established. Then, the measured deformation values ​​of the monitoring points of the sections near the surrounding rock section whose mechanical parameters are to be inverted are used as the input values ​​of the learning network model, and the mean of the output values ​​is the mechanical parameters of the surrounding rock section whose mechanical parameters are to be inverted.

[0058] S35. Construct a finite element model of the surrounding rock of the deep high-in-situ stress tunnel according to the mechanical parameters.

[0059] Specifically, the geological parameters of the tunnel surrounding rock are collected, including the compressive strength, tensile strength, weight of the surrounding rock, and the magnitude and direction of the ground stress at the depth of the tunnel, especially the magnitude and ratio of vertical stress and horizontal stress. The shape, size, direction of the tunnel and the rock constraints around the tunnel, such as fixed boundaries, free boundaries or boundaries with specific stresses, and other factors such as groundwater, temperature, rock joints and faults are also included. The finite element model is established in combination with the mechanical parameters obtained through the above steps.

[0060] S4. Complete the tunnel surrounding rock stability evaluation based on optical fiber monitoring through the finite element model.

[0061] Specifically, set the analysis type such as static analysis, dynamic analysis and solver parameters in the finite element model, and then run the finite element analysis. During the analysis, the finite element model will solve the discretized mathematical equations to obtain the displacement, stress and other distributions of the tunnel surrounding rock. By analyzing the displacement distribution of the tunnel surrounding rock, the convergence deformation of the tunnel is evaluated; by analyzing the stress distribution of the tunnel surrounding rock, especially the main stress concentration area and the shear stress concentration area, it is evaluated whether the tunnel surrounding rock has stress concentration and whether the stress concentration exceeds the bearing capacity of the rock.

[0062] Furthermore, based on the analysis results of displacement and stress, the stability of the tunnel surrounding rock is comprehensively evaluated. If the displacement and stress distribution of the tunnel surrounding rock are within a reasonable range and there is no obvious stress concentration phenomenon, the tunnel surrounding rock stability is considered to be good; on the contrary, if the displacement and stress distribution of the tunnel surrounding rock are abnormal, or there is obvious stress concentration phenomenon, the tunnel surrounding rock stability is considered to be poor, and corresponding reinforcement measures need to be taken. Furthermore, based on the results of the finite element model analysis, the design and support scheme of the tunnel are optimized and improved, for example, the geometric size and shape of the tunnel are adjusted, the strength and stiffness of the support structure are increased, the support scheme is optimized, etc., to improve the stability of the tunnel surrounding rock.

[0063] See also Figure 2 In an embodiment, in order to efficiently execute the tunnel surrounding rock stability evaluation method based on optical fiber monitoring provided by the present invention, the present invention also provides a tunnel surrounding rock stability evaluation system based on optical fiber monitoring, including: an input device, an output device, a processor, and a memory, wherein the input device, the output device, the processor, and the memory are interconnected, and the memory contains program instructions, and the program instructions are used for the steps of the tunnel surrounding rock stability evaluation method based on optical fiber monitoring. The tunnel surrounding rock stability evaluation system based on optical fiber monitoring of the present invention has a compact structure and stable performance, and can stably execute the tunnel surrounding rock stability evaluation method based on optical fiber monitoring of the present invention, further improving the overall applicability and practical application ability of the present invention.

[0064] In an embodiment, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. An input device may be used to obtain data information. An output device may be used to output the results obtained by storing program instructions contained in a computer program in a memory provided by the present invention. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory.

[0065] In yet another alternative embodiment, see Figure 3In order to efficiently execute the tunnel surrounding rock stability evaluation method based on optical fiber monitoring provided by the present invention, this embodiment also provides a tunnel surrounding rock stability evaluation device based on optical fiber monitoring, such as Figure 3 As shown, including: The memory 10 is used to store computer programs; the processor 20 is used to execute the computer programs to implement the above-mentioned tunnel surrounding rock stability evaluation method based on optical fiber monitoring. The memory 10, the processor 20, the communication interface 31 and the communication bus 32. The memory 10, the processor 20 and the communication interface 31 all communicate with each other through the communication bus 32.

[0066] In an embodiment, the memory 10 is used to store one or more program instructions. The memory 10 may store program instructions for implementing the following functions: Acquire time-series monitoring data of multiple excavated sections in the surrounding rock of a deep high-in-situ stress tunnel; obtain the final deformation of the surrounding rock of the excavated section through a time-series prediction model based on the time-series monitoring data; invert the mechanical parameters of the excavated section using the final deformation of the surrounding rock, and construct a finite element model of the surrounding rock of the deep high-in-situ stress tunnel based on the mechanical parameters; and complete the tunnel surrounding rock stability evaluation based on optical fiber monitoring through the finite element model.

[0067] In one possible implementation, the memory 10 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, and at least one application required for a function, etc.; the data storage area may store data created during use. In addition, the memory 10 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0068] The processor 20 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic device, and the processor 20 may be a microprocessor or any conventional processor, etc. The processor 20 may call a program stored in the memory 10. The communication interface 31 may be an interface of a communication module, and is used to connect with other devices or systems.

[0069] Of course, it should be noted that Figure 3The structure shown does not constitute a limitation on the tunnel surrounding rock stability evaluation device based on optical fiber monitoring in this embodiment. In actual applications, the tunnel surrounding rock stability evaluation device based on optical fiber monitoring may include Figure 3 More or fewer components than shown, or combinations of certain components.

[0070] The embodiment further provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned method for evaluating the stability of surrounding rock of a tunnel based on optical fiber monitoring are implemented.

[0071] The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0072] In summary, the present invention inverts the mechanical parameters of the surrounding rock and then constructs a finite element model to accurately and effectively evaluate the stability of the surrounding rock of deep high-stress tunnels, providing a basis for the formulation of construction plans, ensuring that the support structure can effectively control the deformation and damage of the surrounding rock, and ensuring construction safety and engineering quality.

[0073] Therefore, the present invention effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope recorded in the present invention.

Claims

1. A tunnel surrounding rock stability evaluation method based on optical fiber monitoring, characterized in that: The tunnel surrounding rock stability evaluation method based on optical fiber monitoring comprises the following steps: Obtain time-series monitoring data of multiple excavated sections in the surrounding rock of deep high-in-situ stress tunnels; Based on the time series monitoring data, a final deformation of the surrounding rock of the excavated section is obtained through a time series prediction model; Inverting the mechanical parameters of the excavated section using the final deformation of the surrounding rock, and constructing a finite element model of the surrounding rock of the deep high-in-situ stress tunnel according to the mechanical parameters; The finite element model is used to complete the evaluation of tunnel surrounding rock stability based on optical fiber monitoring.

2. The tunnel surrounding rock stability evaluation method based on optical fiber monitoring according to claim 1 is characterized in that: The following steps are also included: Performing equidistant interpolation processing on the time series monitoring data; The final deformation of the surrounding rock is corrected.

3. The tunnel surrounding rock stability evaluation method based on optical fiber monitoring according to claim 2 is characterized in that: The final deformation of the surrounding rock is corrected to satisfy the following formula: in, represents the final deformation of the surrounding rock after correction, represents the final deformation of the surrounding rock before correction, represents the displacement release rate, Indicates the grade of surrounding rock.

4. The tunnel surrounding rock stability evaluation method based on optical fiber monitoring according to claim 1 is characterized in that: The method of inverting the mechanical parameters of the excavated section using the final deformation of the surrounding rock comprises the following steps: Based on the value range of the mechanical parameters, construct a plurality of mechanical parameter matrices; According to the mechanical parameter matrix, a surrounding rock deformation data set is obtained; An improved sparrow search algorithm is introduced to optimize the hyperparameters of the learning network model, and the optimized learning network model is trained using the surrounding rock deformation data set; The mechanical parameters of the excavated section are obtained through the trained learning network model.

5. The tunnel surrounding rock stability evaluation method based on optical fiber monitoring according to claim 4 is characterized in that: The method of introducing the improved sparrow search algorithm to optimize the hyperparameters of the learning network model includes the following steps: By means of the first mapping model, a sparrow population is randomly generated with the hyperparameters as sparrow individuals; The discoverer position update formula, the follower position update formula and the sentinel position update formula are improved, and the optimal hyperparameters of the learning network model are obtained by iterative optimization through the improved sparrow search algorithm.

6. The tunnel surrounding rock stability evaluation method based on optical fiber monitoring according to claim 5 is characterized in that: The first mapping model satisfies the following formula: in, Represents the first values, Represents the first values, Represents a random number in [0,1].

7. The tunnel surrounding rock stability evaluation method based on optical fiber monitoring according to claim 5 is characterized in that: The improved discoverer position update formula satisfies the following formula: in, Indicates The discoverer sparrow The updated position after iterations, Indicates The discoverer sparrow The position at the iteration, represents the maximum number of iterations, express A random number, Indicates belonging to The warning value of Indicates belonging to The warning threshold, represents random numbers that follow a standard normal distribution, Indicates that the element is 1 matrix, Represents the spatial dimension of the sparrow's position.

8. The tunnel surrounding rock stability evaluation method based on optical fiber monitoring according to claim 5 is characterized in that: Improve the follower position update formula to satisfy the following formula: in, Indicates Follower Sparrow in the The updated position after iterations, represents random numbers that follow a standard normal distribution, Indicated in The global worst position at the iteration, Indicates Follower Sparrow in the The position at the iteration, Indicates the number of sparrows, represents the follower learning factor and satisfies , represents the maximum number of iterations, Indicated in The global optimal position at the iteration is represents the follower's adaptive inertia weight factor and satisfies .

9. The tunnel surrounding rock stability evaluation method based on optical fiber monitoring according to claim 5 is characterized in that: Improve the sentinel position update formula to satisfy the following formula: in, Indicates A vigilante sparrow is in The updated position after iterations, represents the maximum number of iterations, Indicates A vigilante sparrow is in The position at the iteration, Indicated in The global optimal position at the iteration is Indicated in The global worst position at the iteration, express A random number.

10. The tunnel surrounding rock stability evaluation system based on optical fiber monitoring is characterized by: The tunnel surrounding rock stability evaluation system based on optical fiber monitoring includes: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory includes program instructions, and the program instructions are used to execute the tunnel surrounding rock stability evaluation method based on optical fiber monitoring according to any one of claims 1 to 9.

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