Rock burst risk monitoring method and system in tunnel construction period

Abstract

The invention relates to a rockburst risk monitoring method and system in a tunnel construction period, and relates to the technical field of tunnel construction, and the method comprises the steps: obtaining micro-seismic monitoring data and geological parameters of a tunnel; calculating a rockburst risk index of the tunnel based on the micro-seismic monitoring data and the geological parameters; determining a dynamic stress field analysis result of the tunnel through the dynamic stress field evolution model; based on the rockburst risk index and the dynamic stress field analysis result, dangerous area distribution of the tunnel is obtained; evaluating the rockburst risk grade of the tunnel in the next construction period in combination with the rockburst risk index, the dangerous area distribution and the to-be-performed construction process, and dynamically adjusting the early warning threshold value of the rockburst risk index to obtain a dynamic early warning threshold value; and when the rockburst risk index of the tunnel exceeds a dynamic early warning threshold value, generating a risk assessment result, sending out early warning of a corresponding level, and giving a prevention and control suggestion of construction according to the risk assessment result. The tunnel rockburst risk monitoring accuracy can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel construction, and in particular to a rockburst risk monitoring method, system, electronic equipment and non-transient computer-readable storage medium during a tunnel construction period. Background Art

[0002] During tunnel construction, rockburst is a common and dangerous geological disaster. To deal with the risk of rockburst, existing monitoring methods mainly rely on geological exploration and empirical judgment. Usually, the construction team obtains geological parameters of the rock mass around the tunnel, such as rock strength, joint distribution, and ground stress state, through geological drilling, sonic logging, etc., and conducts a preliminary assessment of the rockburst risk based on historical data and expert experience. In addition, some tunnel projects have introduced microseismic monitoring systems in local areas to infer the possibility of rockburst by monitoring the frequency and energy of microseismic events.

[0003] However, the single geological exploration and empirical judgment method lacks real-time monitoring of the rock mass stress state during the construction process, and it is difficult to accurately capture the dynamic changes in the internal stress of the rock mass, which may lead to missed reports or false reports. Even when using a microseismic monitoring system, traditional microseismic event analysis methods usually only rely on simple statistical models, ignoring the combined influence of multiple factors such as geological conditions and construction procedures, resulting in inaccurate assessment results of rockburst risks. In addition, the warning thresholds in existing methods are usually fixed, which is difficult to adapt to changes in different geological conditions and construction environments, resulting in insufficient sensitivity and reliability of the early warning system.

[0004] Faced with the above problems, the existing methods are incapable of coping with the high complexity and high risk of deep buried tunnel construction. There is an urgent need for a more comprehensive, dynamic and accurate tunnel rockburst risk monitoring method. Summary of the invention

[0005] In view of the technical problems existing in the prior art, the present invention provides a rock burst risk monitoring method, system, electronic device and non-transient computer-readable storage medium within a tunnel construction period, which can improve the accuracy of tunnel rock burst risk monitoring.

[0006] The technical solution of the present invention to solve the above technical problems is as follows:

[0007] The present invention provides a method for monitoring rock burst risk during a tunnel construction period, the method comprising:

[0008] Acquiring microseismic monitoring data of the tunnel and geological parameters of the rock mass surrounding the tunnel;

[0009] Calculating a rockburst risk index of the tunnel based on the microseismic monitoring data and the geological parameters;

[0010] Determine microseismic positioning results according to the microseismic monitoring data, and construct a dynamic stress field evolution model of the rock mass around the tunnel based on the microseismic positioning results, and determine the dynamic stress field analysis results of the tunnel through the dynamic stress field evolution model;

[0011] Based on the rockburst risk index and the dynamic stress field analysis result, the surrounding rock mass of the tunnel is divided into different rockburst risk level areas to obtain the dangerous area distribution of the tunnel;

[0012] In combination with the rockburst risk index, the distribution of the dangerous area and the upcoming construction process, the rockburst risk level of the tunnel in the next construction period is evaluated, and the warning threshold of the rockburst risk index is dynamically adjusted to obtain a dynamic warning threshold;

[0013] When the rock burst risk index of the tunnel exceeds the dynamic warning threshold, a risk assessment result is generated, a warning of a corresponding level is issued, and construction prevention and control suggestions are given according to the risk assessment result.

[0014] Optionally, the calculating the rockburst risk index of the tunnel based on the microseismic monitoring data and the geological parameters includes:

[0015] Obtaining the total energy released by the microseismic events in the tunnel per unit time and the corresponding reference energy value;

[0016] Obtaining the frequency of microseismic events in the tunnel per unit time and the corresponding reference frequency value;

[0017] Obtaining the maximum horizontal principal stress of the tunnel and the uniaxial compressive strength of rock;

[0018] Obtaining a joint density factor reflecting the complexity of the rock mass structure of the tunnel and an excavation speed factor reflecting the impact of the construction progress on the rock mass stability;

[0019] The rock burst risk index of the tunnel is calculated based on the total energy released by microseismic events in the tunnel per unit time, the frequency of microseismic events, the reference energy value, the reference frequency value, the maximum horizontal principal stress, the uniaxial compressive strength of the rock, the physical density factor and the excavation speed factor.

[0020] Optionally, the rock burst risk index of the tunnel is expressed as:

[0021]

[0022] Among them, RRI is the rock burst risk index, E ms is the total energy released by the microseismic event per unit time, E0 is the reference energy value, and f ms is the frequency of microseismic events per unit time, f0 is the reference frequency value, σh is the maximum horizontal principal stress, σ c is the uniaxial compressive strength of rock, D j is the joint density factor, V e is the excavation speed factor, α, β, γ, δ, λ, and μ are the first weight, second weight, third weight, fourth weight, fifth weight, and sixth weight, respectively.

[0023] Optionally, after obtaining the microseismic monitoring data of the tunnel, the method further includes:

[0024] The microseismic monitoring data is automatically identified and classified, and different types of microseismic events are distinguished using a machine learning algorithm; the microseismic events include rock burst events, blasting events, and equipment noise events.

[0025] Optionally, constructing a dynamic stress field evolution model of the rock mass surrounding the tunnel based on the microseismic positioning result includes:

[0026] Obtaining an initial stress field of the tunnel;

[0027] Obtaining the stress amplitude of each microseismic event;

[0028] Obtaining the distance from any spatial coordinate point in the tunnel to the microseismic source of each microseismic event;

[0029] Obtaining the stress attenuation characteristic length, stress wave frequency, phase angle and time corresponding to the stress wave of each microseismic event;

[0030] A dynamic stress field evolution model of the rock mass surrounding the tunnel is constructed based on the initial stress field of the tunnel, the stress amplitude of each microseismic event, the stress attenuation characteristic length corresponding to the stress wave, the stress wave frequency, phase angle and time, and the distance from any spatial coordinate point in the tunnel to the microseismic source of each microseismic event.

[0031] Optionally, the expression of the dynamic stress field evolution model is:

[0032]

[0033] Among them, σ dyn (x, y, z, t) is the dynamic stress field of the tunnel, σ ini (x, y, z) is the initial stress field of the tunnel, A i is the stress amplitude of the ith microseismic event, (x, y, z) is the spatial coordinate point, r i is the distance from any spatial coordinate point (x, y, z) to the i-th microseismic source, R0 is the stress attenuation characteristic length, ω i is the stress wave frequency, is the phase angle and t is the time.

[0034] Optionally, the dynamically adjusting the warning threshold of the rockburst risk index to obtain a dynamic warning threshold includes:

[0035] Obtaining a basic warning threshold of the rockburst risk index;

[0036] Counting the accuracy of early warning for the rockburst risk index in a period of time before the current moment, and determining the change in the early warning accuracy in the period of time before the current moment, and the total number of early warnings;

[0037] Acquire a first adjustment coefficient and a second adjustment coefficient for adjusting the basic warning threshold;

[0038] The basic warning threshold is dynamically adjusted according to the change in warning accuracy in a period of time before the current moment, the total number of warnings, the first adjustment coefficient and the second adjustment coefficient to obtain the dynamic warning threshold.

[0039] Optionally, the expression of the dynamic warning threshold is:

[0040]

[0041] Among them, RRI threshold (t) is the dynamic warning threshold, RRI base is the basic warning threshold, △N(t) is the change in warning accuracy in the most recent period before the current moment, and N total is the total number of warnings, ∈ is the first adjustment coefficient, and η is the second adjustment coefficient.

[0042] Optionally, the rockburst risk level areas are divided into high-risk areas, medium-risk areas and low-risk areas.

[0043] The present invention also provides a rock burst risk monitoring system during a tunnel construction period, the system comprising:

[0044] A data acquisition module, used to acquire microseismic monitoring data of the tunnel and geological parameters of the rock mass surrounding the tunnel;

[0045] A risk calculation module, used to calculate the rockburst risk index of the tunnel based on the microseismic monitoring data and the geological parameters;

[0046] A stress analysis module, used to determine a microseismic positioning result according to the microseismic monitoring data, and to construct a dynamic stress field evolution model of the rock mass surrounding the tunnel based on the microseismic positioning result, and to determine a dynamic stress field analysis result of the tunnel through the dynamic stress field evolution model;

[0047] A hazard identification module, used to divide the surrounding rock mass of the tunnel into different rock burst risk level areas based on the rock burst risk index and the dynamic stress field analysis result, and obtain the dangerous area distribution of the tunnel;

[0048] A threshold adjustment module is used to evaluate the rockburst risk level of the tunnel in the next construction period in combination with the rockburst risk index, the distribution of the dangerous area and the upcoming construction process, and dynamically adjust the warning threshold of the rockburst risk index to obtain a dynamic warning threshold;

[0049] The risk monitoring module is used to generate risk assessment results, issue warnings of corresponding levels, and provide construction prevention and control suggestions based on the risk assessment results when the rockburst risk index of the tunnel exceeds the dynamic warning threshold.

[0050] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing a rock burst risk monitoring method within a tunnel construction cycle as described above.

[0051] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, a rock burst risk monitoring method within a tunnel construction cycle as described above is implemented.

[0052] The beneficial effects of the present invention are:

[0053] (1) The present invention combines microseismic monitoring, geological parameter collection and construction parameter recording, and automatically identifies and classifies microseismic events through machine learning algorithms, effectively distinguishing different types of events such as rockburst, blasting and equipment noise. By introducing the rockburst risk index (RRI) calculation formula, it is possible to comprehensively consider multiple factors such as microseismic energy, event frequency, ground stress and rock mass characteristics, thereby more accurately quantifying rockburst risk. This comprehensive analysis of multiple factors greatly improves the accuracy of rockburst risk assessment and can better guide construction decisions.

[0054] (2) The present invention constructs a dynamic stress field evolution model and uses microseismic positioning results to monitor and predict the stress changes in the rock mass in real time. The model can reflect the dynamic evolution of stress in the rock mass around the tunnel, allowing the construction team to identify potential high-risk areas and take corresponding prevention and control measures. This real-time dynamic monitoring capability significantly enhances the early warning capability, enabling earlier identification of rock burst risks and intervention.

[0055] (3) The present invention dynamically adjusts the warning threshold of RRI through an adaptive algorithm, and can continuously optimize the warning standard according to historical rock burst events and false alarms. This dynamic adjustment mechanism enables the warning system to flexibly respond to different geological conditions and construction conditions, reduce false alarms and missed alarms, and improve the accuracy and reliability of warnings. This advantage is particularly important in tunnel construction with complex geological conditions, and can effectively reduce construction risks.

[0056] (4) When the RRI exceeds the dynamic warning threshold, the present invention can not only issue a warning, but also give targeted prevention and control suggestions based on the specific risk assessment results, such as adjusting support parameters, taking pre-cracking measures, or adjusting the construction sequence. These suggestions help the construction team take timely measures to prevent rock bursts, thereby ensuring construction safety.

[0057] (5) After each construction cycle, the present invention will compare the warning results with the actual situation and continuously optimize the various parameters of the risk assessment model through a feedback mechanism. This feedback optimization mechanism ensures that the model can continuously adapt to the actual situation on site, gradually improve the accuracy of the warning and the overall performance of the system, thereby providing more reliable technical support for the construction of deep buried tunnel groups.

[0058] In summary, the present invention provides more accurate, comprehensive and reliable health monitoring and assessment through methods such as multi-parameter fusion, data calibration, error assessment and personalized processing, providing strong support for users' health management and intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A scene diagram of a rock burst risk monitoring method within a tunnel construction period provided by the present invention;

[0060] Figure 2 A flow chart of a method for monitoring rock burst risks during a tunnel construction period provided by the present invention;

[0061] Figure 3 A schematic diagram of the structure of a rockburst risk monitoring system during a tunnel construction period provided by the present invention;

[0062] Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0063] Figure 5 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0065] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0066] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0067] See also Figure 1 , Figure 1 This is a scene diagram of a rock burst risk monitoring method during a tunnel construction period provided by the present invention. Figure 1 As shown, the terminal and the server are connected via a network, such as a wired or wireless network connection. The terminal may include but is not limited to portable terminals such as mobile phones and tablets installed with various network platform applications, as well as fixed terminals such as computers, query machines, and advertising machines. The server provides users with various business services, including service push servers, user recommendation servers, etc.

[0068] It should be noted that Figure 1The scenario diagram of a method for monitoring rock burst risks during a tunnel construction period shown is only an example. The terminal, server, and application scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not generate limitations on the technical solution provided by the embodiment of the present invention. A person of ordinary skill in the art can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0069] Among them, the terminal can be used for:

[0070] Acquiring microseismic monitoring data of the tunnel and geological parameters of the rock mass surrounding the tunnel;

[0071] Calculating a rockburst risk index of the tunnel based on the microseismic monitoring data and the geological parameters;

[0072] Determine microseismic positioning results according to the microseismic monitoring data, and construct a dynamic stress field evolution model of the rock mass around the tunnel based on the microseismic positioning results, and determine the dynamic stress field analysis results of the tunnel through the dynamic stress field evolution model;

[0073] Based on the rockburst risk index and the dynamic stress field analysis result, the surrounding rock mass of the tunnel is divided into different rockburst risk level areas to obtain the dangerous area distribution of the tunnel;

[0074] In combination with the rockburst risk index, the distribution of the dangerous area and the upcoming construction process, the rockburst risk level of the tunnel in the next construction period is evaluated, and the warning threshold of the rockburst risk index is dynamically adjusted to obtain a dynamic warning threshold;

[0075] When the rock burst risk index of the tunnel exceeds the dynamic warning threshold, a risk assessment result is generated, a warning of a corresponding level is issued, and construction prevention and control suggestions are given according to the risk assessment result.

[0076] See also Figure 2 , provides a flow chart of a method for monitoring rockburst risk during a tunnel construction period of the present invention, comprising the following steps:

[0077] Step 201: Acquire microseismic monitoring data of a tunnel and geological parameters of the rock mass surrounding the tunnel.

[0078] In some embodiments, the microseismic monitoring data is acquired by a microseismic monitoring system arranged around the tunnel, which generally includes an all-fiber sensor, a data acquisition device, and a signal processing system.

[0079] In some embodiments, multiple microseismic sensors can be arranged around the tunnel to form a monitoring network covering the entire construction area. These sensors are usually buried in the ground or on the tunnel wall and can sense tiny vibrations inside the rock mass.

[0080] The placement and density of sensors should be optimized according to the geological conditions and construction of the tunnel to ensure that the monitoring system can capture sufficient microseismic signals, especially those that may indicate rockburst events.

[0081] In some embodiments, microseismic sensors can monitor the vibration of the rock mass around the tunnel in real time and transmit these signals to the data acquisition device. The data acquisition device processes the received microseismic signals, filters out environmental noise, and extracts valid data of the microseismic event. The collected data usually includes information such as the time, amplitude, frequency, energy, and source location of the microseismic event. These data can reflect the changes in stress in the rock mass and the release process of energy, and are key indicators for assessing rock burst risks.

[0082] Signal processing and analysis techniques are used to identify and classify different types of microseismic events (such as rockbursts, blasting, equipment noise, etc.). This classification helps to distinguish between vibrations caused by construction activities and potential rockbursts. Data analysis also includes statistics on the frequency and energy release of microseismic events to assess the stress accumulation inside the rock mass during tunnel construction.

[0083] In some embodiments, core samples can be obtained through drilling technology to analyze the mineral composition, structure, crack distribution and other characteristics of the core. Drilling can provide detailed rock strength data, such as uniaxial compressive strength and joint density factor. Geological radar is used to perform non-invasive detection of the rock mass around the tunnel to obtain information on the internal structure, crack distribution and stratum changes of the rock mass. Geological radar can quickly provide the overall situation of the underground rock mass and help identify potential high-risk areas for rock bursts.

[0084] During the tunnel construction process, the in-situ stress state inside the rock mass, especially the maximum horizontal principal stress, is measured by setting up stress gauges and other equipment. In-situ stress measurement usually includes field tests and laboratory analysis to determine the stress distribution of the rock mass in its natural state. By placing an acoustic logging instrument in the borehole, parameters such as the velocity of sound, elastic modulus and density of the rock are measured. These parameters can help calculate the stress state and deformation characteristics of the rock. During the construction process, engineering and technical personnel observe and record the rock mass in the tunnel, and collect information about the distribution of rock strata, the development of joints, the integrity of the rock mass, etc. These field records help to correct and supplement geological exploration and measurement data.

[0085] Through microseismic monitoring data, the construction team can grasp the dynamic stress changes of the rock mass around the tunnel in real time. Microseismic data provides direct evidence for rockburst risk assessment and helps predict the time and location of rockburst. Geological parameters reflect the physical and mechanical properties of the rock mass around the tunnel and are an important basis for rockburst risk assessment. By combining these parameters, the stress state of the rock mass can be simulated more accurately, potential danger areas can be identified, and effective prevention and control measures can be formulated.

[0086] By acquiring and analyzing the microseismic monitoring data of the tunnel and the geological parameters of the surrounding rock mass, the construction team was able to build a comprehensive rockburst risk monitoring and early warning system to effectively prevent rockburst accidents that may occur during the construction process.

[0087] In some embodiments, the present invention may further include:

[0088] The microseismic monitoring data is automatically identified and classified, and different types of microseismic events are distinguished using a machine learning algorithm.

[0089] Among them, microseismic events can include rock burst events, blasting events and equipment noise events.

[0090] In some embodiments, the microseismic monitoring data collected from the sensor can be preprocessed, including denoising, signal enhancement and standardization. Denoising is to remove background noise in the monitoring data, such as environmental noise and instrument noise, so as to improve the signal-to-noise ratio of the data. Signal enhancement and standardization help to extract clearer features for subsequent identification and classification.

[0091] In some embodiments, key parameters that can reflect the characteristics of microseismic events can be extracted from the preprocessed data, such as vibration amplitude, frequency, energy, waveform shape, duration, and source location. These characteristics provide a basis for distinguishing different types of microseismic events. For example, rock burst events are usually accompanied by high energy release and specific frequency characteristics, while equipment noise may have a more regular waveform.

[0092] In some embodiments, the most effective features for distinguishing different types of microseismic events can be selected from the extracted features through a feature selection algorithm. Feature selection can reduce redundant features and improve the recognition efficiency and accuracy of the model.

[0093] Once feature extraction and selection are completed, the next step is to classify microseismic events using machine learning algorithms. Machine learning algorithms can learn the characteristic patterns of different types of microseismic events through training on a large amount of historical data, and automatically classify newly collected microseismic data based on them.

[0094] Before the system is deployed, the machine learning model needs to be trained using labeled historical microseismic data. This data includes known categories of microseismic events, such as rockbursts, blasting events, and equipment noise events. During the training process, the model will gradually learn the characteristic patterns of these events.

[0095] When new data arrives, after feature extraction, the machine learning model will classify the event based on the previously learned feature patterns. The model will assign microseismic events to different categories, such as rockburst events, blasting events, or equipment noise events.

[0096] Among them, rockburst events usually have high energy release, short-term high-frequency vibration, and the source location is mostly inside the rock mass. Rockburst events are caused by the sudden rupture of the rock mass after the stress accumulation in the rock mass reaches a critical value, which is a direct indicator of rockburst risk.

[0097] Blasting events are usually triggered by the use of explosives during construction, with instantaneous high energy release and obvious source location. The vibration frequency and waveform are different from rockburst events. Due to its human-controlled nature, it usually occurs in concentrated periods of blasting.

[0098] Equipment noise events are usually generated by the operation of tunnel construction equipment, with a relatively stable vibration frequency, a long duration, and a relatively fixed source location. The vibration characteristics of such events are significantly different from those of rock burst events, but in a noisy environment, they may be confused with low-energy microseismic events.

[0099] Accurately classifying different types of microseismic events can help improve the accuracy of rockburst risk monitoring systems. By distinguishing rockburst events from other vibration sources (such as blasting and equipment noise), the system can focus on signals related to rockburst, thereby reducing false alarms and improving the reliability of early warnings. During tunnel construction, real-time classification of microseismic events can help construction teams identify potential rockburst risks in a timely manner and take corresponding preventive measures, such as adjusting support plans or suspending construction to ensure construction safety.

[0100] The present invention automatically identifies and classifies microseismic monitoring data and uses machine learning algorithms to accurately distinguish different types of microseismic events. The construction team can better understand the stress changes in the tunnel rock mass and effectively prevent and control rock burst risks.

[0101] Step 202: Calculate the rockburst risk index of the tunnel based on the microseismic monitoring data and the geological parameters.

[0102] In some embodiments, step 202 may include:

[0103] Obtaining the total energy released by the microseismic events in the tunnel per unit time and the corresponding reference energy value;

[0104] Obtaining the frequency of microseismic events in the tunnel per unit time and the corresponding reference frequency value;

[0105] Obtaining the maximum horizontal principal stress of the tunnel and the uniaxial compressive strength of rock;

[0106] Obtaining a joint density factor reflecting the complexity of the rock mass structure of the tunnel and an excavation speed factor reflecting the impact of the construction progress on the rock mass stability;

[0107] The rock burst risk index of the tunnel is calculated based on the total energy released by microseismic events in the tunnel per unit time, the frequency of microseismic events, the reference energy value, the reference frequency value, the maximum horizontal principal stress, the uniaxial compressive strength of the rock, the physical density factor and the excavation speed factor.

[0108] In some embodiments, the rockburst risk index of a tunnel is expressed as:

[0109]

[0110] Among them, RRI is the rock burst risk index, E ms is the total energy released by the microseismic event per unit time, E0 is the reference energy value, and f ms is the frequency of microseismic events per unit time, f0 is the reference frequency value, σ h is the maximum horizontal principal stress, σ c is the uniaxial compressive strength of rock, D j is the joint density factor, V e is the excavation speed factor, α, β, γ, δ, λ, and μ are the first weight, second weight, third weight, fourth weight, fifth weight, and sixth weight, respectively.

[0111] In specific implementation, RRI is the Rockburst Risk Index, which indicates the possibility of rockburst under specific construction conditions and geological conditions. The higher the RRI value, the greater the risk of rockburst. RRI is a comprehensive indicator that combines multiple influencing factors to help the construction team assess the risk of rockburst during tunnel construction and take corresponding preventive measures.

[0112] E ms It is the total energy released by microseismic events per unit time. The energy release of microseismic events is closely related to the stress accumulation inside the rock mass. Higher energy release usually indicates that the stress state inside the rock mass is more active and may cause rock bursts.

[0113] E0 is the reference energy value used for standardization processing E ms To eliminate the dimension difference. E0 provides a benchmark to compare the microseismic energy release in different construction environments or at different times.ms Normalization can make the calculation of RRI more universal and adaptable.

[0114] f ms It is the frequency of microseismic events per unit time, which is the number or frequency of microseismic events monitored around the tunnel. The frequency of microseismic events reflects the frequency of stress state changes in the rock mass. A higher frequency usually means more energy accumulation or release events in the rock mass, indicating a higher risk of rock burst.

[0115] f0 is the reference frequency value used for standardization processing f ms , f0 provides a reference frequency, through f ms Normalization is performed to make the calculation of RRI more comparable and universal.

[0116] σ h It is the maximum horizontal principal stress, which is an important parameter affecting the stability of the rock mass. Higher horizontal principal stress means that the shear stress in the rock mass is greater, increasing the possibility of rock burst.

[0117] σ c It is the uniaxial compressive strength of rock. The compressive strength of rock is closely related to its fracture characteristics. Lower compressive strength may cause rock mass to fracture more easily under high stress conditions, causing rock bursts.

[0118] D j It is the joint density factor. The greater the joint density, the more weak planes there are inside the rock mass. These weak planes are prone to cause rupture when stress is concentrated, increasing the possibility of rock burst.

[0119] V e is the excavation speed factor. The excavation speed directly affects the redistribution of rock mass stress. A faster excavation speed may cause stress to be quickly concentrated in certain areas, thereby increasing the risk of rock burst.

[0120] α, β, γ, δ, λ, and μ are the first weight, second weight, third weight, fourth weight, fifth weight, and sixth weight, respectively. By adjusting these weight coefficients, the influence of various factors on RRI can be flexibly adjusted according to specific construction and geological conditions, making the calculation of RRI closer to the actual situation.

[0121] The formula calculates a comprehensive rockburst risk index (RRI) by combining multiple factors such as microseismic energy, event frequency, ground stress state, rock strength, joint density and construction speed. Through standardization and weighting, the formula can accurately reflect the rockburst risk under specific conditions and provide a scientific basis for construction safety.

[0122] RRI is used to monitor and assess rockburst risks in real time during tunnel construction. By calculating RRI, the construction team can understand the current rockburst risk in a timely manner and take corresponding preventive measures, such as adjusting the support plan and controlling the excavation speed.

[0123] Compared with a single indicator or empirical judgment, the RRI calculation method of this scheme comprehensively considers multiple influencing factors and flexibly adjusts them through weight coefficients, making the rockburst risk assessment more accurate and comprehensive.

[0124] Through the above formula, the construction team can scientifically quantify and manage rockburst risks, thereby improving the safety and efficiency of tunnel construction.

[0125] Step 203: determine a microseismic positioning result according to the microseismic monitoring data, and construct a dynamic stress field evolution model of the rock mass surrounding the tunnel based on the microseismic positioning result, and determine a dynamic stress field analysis result of the tunnel through the dynamic stress field evolution model.

[0126] In some embodiments, step 203 may include:

[0127] Obtaining an initial stress field of the tunnel;

[0128] Obtaining the stress amplitude of each microseismic event;

[0129] Obtaining the distance from any spatial coordinate point in the tunnel to the microseismic source of each microseismic event;

[0130] Obtaining the stress attenuation characteristic length, stress wave frequency, phase angle and time corresponding to the stress wave of each microseismic event;

[0131] A dynamic stress field evolution model of the rock mass surrounding the tunnel is constructed based on the initial stress field of the tunnel, the stress amplitude of each microseismic event, the stress attenuation characteristic length corresponding to the stress wave, the stress wave frequency, phase angle and time, and the distance from any spatial coordinate point in the tunnel to the microseismic source of each microseismic event.

[0132] In some embodiments, the expression of the dynamic stress field evolution model is:

[0133]

[0134] Among them, σ dyn (x, y, z, t) is the dynamic stress field of the tunnel, σ ini (x, y, z) is the initial stress field of the tunnel, Ai 为 The stress amplitude of the i-th microseismic event, (x, y, z) is the spatial coordinate point, r is the distance from any spatial coordinate point (x, y, z) to the i-th microseismic source, R0 is the stress attenuation characteristic length, ωi is the stress wave frequency, is the phase angle and t is the time.

[0135] In the specific implementation, σ dyn (x, y, z, t) is the dynamic stress field of the tunnel, which represents the dynamic stress field of the rock mass around the tunnel at any time t and spatial point (x, y, z). This dynamic stress field reflects the changes in rock stress caused by microseismic events, construction activities, etc. during the construction process. It is an important indicator for real-time monitoring of the internal stress state of the rock mass.

[0136] σ ini (x, y, z) is the initial stress field of the tunnel, which indicates the initial stress field distribution of the rock mass around the tunnel before construction. It reflects the natural stress state inside the rock mass when it is not disturbed by construction. The initial stress field is the basis of the dynamic stress field and indicates the baseline value of rock mass stress when there is no microseismic event.

[0137] A i is the stress amplitude of the ith microseismic event, indicating the maximum disturbance intensity of the event on the rock mass stress field. The larger the amplitude, the more significant the stress impact of the microseismic event on the surrounding rock mass.

[0138] r i is the distance from any spatial coordinate point (x, y, z) to the i-th microseismic source, R0 is the stress attenuation characteristic length, ω i is the stress wave frequency, is the phase angle, x, y, z are the spatial coordinates, and t is the time.

[0139] Specifically, This part is the accumulation of stress disturbances caused by microseismic events. It consists of the stress amplitude, spatial attenuation function, time-dependent stress wave and phase angle of each microseismic event.

[0140] is an exponential decay function, which indicates the degree to which the stress wave caused by the microseismic event decays with increasing distance. The farther away from the microseismic source, the more significant the attenuation of the disturbance stress.

[0141] is a time-dependent stress wave, which describes the periodic variation of stress disturbances caused by microseismic events over time. The stress wave frequency determines the speed of the oscillation, while the phase angle determines the starting point of the stress wave.

[0142] The expression of the dynamic stress field evolution model is the superposition result of the initial stress field and the stress disturbance caused by all microseismic events. It comprehensively considers the impact of each microseismic event, including spatial attenuation, temporal oscillation and initial stress state. Through this formula, the dynamic changes of internal stress of the rock mass during tunnel construction can be monitored and predicted in real time, thereby identifying and warning of potential rock burst risks.

[0143] The dynamic stress field evolution model is mainly used for dynamic analysis and monitoring of the stress state of the rock mass around the deep-buried tunnel during tunnel construction. It can help the construction team understand the distribution of the current stress field, predict future stress changes, and take timely prevention and control measures. Compared with traditional static stress field analysis, the dynamic stress field model of the present invention can more accurately reflect the real-time changes in the internal stress of the rock mass during the construction process, and provide more reliable risk assessment and early warning. Through this model, the construction team can accurately grasp the changes in rock mass stress during the construction process, thereby effectively reducing the risk of rock bursts and improving construction safety.

[0144] Step 204: Based on the rockburst risk index and the dynamic stress field analysis result, the surrounding rock mass of the tunnel is divided into different rockburst risk level areas to obtain the dangerous area distribution of the tunnel.

[0145] Among them, rockburst risk level areas can be divided into high-risk areas, medium-risk areas and low-risk areas. The rockburst risk index (RRI) is a comprehensive indicator used to quantify the possibility of rockburst during tunnel construction. The calculation of RRI takes into account several key factors, such as the energy and frequency of microseismic events, the ground stress state of the rock mass, the compressive strength of the rock, and construction parameters (such as excavation speed). A high RRI value indicates a high risk of rockburst at a certain location in the tunnel, while a low RRI value indicates a low risk.

[0146] Dynamic stress field analysis is a model built based on microseismic monitoring data and initial geological stress field, which can reflect the stress changes in the rock mass around the tunnel in real time. Through this analysis, the distribution and change trend of stress in the rock mass can be determined, especially the stress concentration areas can be identified, which may be the potential occurrence points of rock burst.

[0147] High-risk areas are those with high RRI values ​​and where dynamic stress field analysis shows that stress is highly concentrated or changes dramatically. The internal stress of the rock mass in these areas is close to or exceeds the compressive strength of the rock, and microseismic events occur frequently, with significant energy release. Such areas are most likely to experience rockbursts, which are usually concentrated in certain special parts of the tunnel, such as corners or intersections with other geological structures. The construction team should focus on monitoring high-risk areas, and may need to strengthen support, slow down excavation speed, or even take pre-cracking measures to release stress and prevent rockbursts.

[0148] Medium-risk areas refer to areas with moderate RRI values, where the dynamic stress field shows relatively stable stress distribution, but still have a certain risk of rock burst. These areas may be on the stress transfer path or close to high-risk areas, and the frequency of microseismic events and energy release are not as obvious as in high-risk areas, but attention is still required. In medium-risk areas, the construction team should continue to monitor stress changes and remain vigilant, and appropriately strengthen support or adjust the construction sequence when necessary to avoid aggravating stress concentration.

[0149] Low-risk areas are areas with low RRI values ​​and dynamic stress fields showing relatively uniform stress and small changes. These areas are usually far away from stress concentration zones, with few microseismic events and low energy release, and minimal rockburst risk. In low-risk areas, construction can proceed as planned, but regular monitoring is still required to respond to possible stress changes.

[0150] By classifying the risk levels of the rock mass around the tunnel, the distribution of dangerous areas in the tunnel is finally formed. This distribution map is crucial for construction safety. It can intuitively present the rockburst risk areas of different levels in the plane map or three-dimensional model of the tunnel, so that the construction team can understand the risk situation of each area at a glance. The distribution map of dangerous areas provides a basis for construction decisions, such as adjusting the construction sequence, support design, excavation speed, etc. The construction team can give priority to high-risk areas and dynamically adjust the construction plan based on real-time monitoring results. According to the distribution of dangerous areas, emergency plans for different risk levels can be formulated. In high-risk areas, the construction team should prepare emergency measures to quickly respond to sudden rockburst incidents.

[0151] The present invention divides the rock mass around the tunnel into different risk level areas based on the rockburst risk index (RRI) and dynamic stress field analysis results, and forms a dangerous area distribution, so that the construction team can accurately evaluate and manage the rockburst risk that may occur during the tunnel construction process. This method not only improves the safety of tunnel construction, but also optimizes the construction process and reduces the impact of emergencies on construction progress and costs.

[0152] Step 205: In combination with the rockburst risk index, the distribution of the dangerous areas and the upcoming construction process, the rockburst risk level of the tunnel in the next construction period is evaluated, and the warning threshold of the rockburst risk index is dynamically adjusted to obtain a dynamic warning threshold.

[0153] In some embodiments, step 205 may include:

[0154] Obtaining a basic warning threshold of the rockburst risk index;

[0155] Counting the accuracy of early warning for the rockburst risk index in a period of time before the current moment, and determining the change in the early warning accuracy in the period of time before the current moment, and the total number of early warnings;

[0156] Acquire a first adjustment coefficient and a second adjustment coefficient for adjusting the basic warning threshold;

[0157] The basic warning threshold is dynamically adjusted according to the change in warning accuracy in a period of time before the current moment, the total number of warnings, the first adjustment coefficient and the second adjustment coefficient to obtain the dynamic warning threshold.

[0158] In some embodiments, the expression of the dynamic warning threshold is:

[0159]

[0160] Among them, RRI threshold (t) is the dynamic warning threshold, RRI base is the basic warning threshold, △N(t) is the change in warning accuracy in the most recent period before the current moment, and N total is the total number of warnings, ∈ is the first adjustment coefficient, and η is the second adjustment coefficient.

[0161] In the specific implementation, RRI threshold (t) is the dynamic warning threshold, which is the dynamic warning threshold of the rockburst risk index set by the system. This dynamic warning threshold is used to determine whether the current rockburst risk index (RRI) exceeds the acceptable safety limit of the system. When the RRI exceeds this threshold, the system will trigger an early warning and may take corresponding prevention and control measures.

[0162] RRI base The basic warning threshold is a fixed reference value, which indicates the warning threshold at the initial setting of the system and provides a reference for dynamic adjustment. The dynamic warning threshold is based on this basic value and adapts to changes in real-time conditions by adjusting the coefficient.

[0163] △N(t) is the change in the accuracy of the early warning system in the most recent period before the current moment. It reflects the improvement or regression of the early warning effect of the system over a period of time. The change in the accuracy of the early warning is an important indicator for measuring the performance of the system. If the accuracy of the early warning is significantly improved over a period of time, it means that the threshold of the system may be too low and the threshold needs to be appropriately increased; conversely, if the accuracy decreases, the threshold may need to be lowered in order to more sensitively capture potential risks.

[0164] N totalis the total number of warnings issued by the system up to the current moment. The total number of warnings provides a standardized reference for measuring the relative impact of changes in warning accuracy. toal It can prevent excessive impact on system thresholds caused by a small number of warning events in a short period of time, making threshold adjustments more robust.

[0165] ∈ is the first adjustment coefficient, which is the adjustment coefficient for controlling the dynamic adjustment range of the warning threshold. The size of ∈ determines the sensitivity of the system to changes in the accuracy of the warning. A larger ∈ value will lead to a larger adjustment of the threshold, while a smaller ∈ value will result in a smaller adjustment range. By adjusting ∈, the reaction sensitivity of the system can be controlled.

[0166] η is the second adjustment coefficient, which is the adjustment coefficient that affects the threshold adjustment range of the change in warning accuracy. It is used to adjust the impact of the change in warning accuracy. A larger η value will amplify the impact of the change in warning accuracy, resulting in a more obvious threshold adjustment, while a smaller η value will make the threshold adjustment more conservative. By adjusting η, the system's adjustment strategy when facing accuracy changes can be affected.

[0167] is a hyperbolic tangent function, indicating that the warning accuracy changes between η and N total Adjustment factor under normalization. The value of the hyperbolic tangent function varies between -1 and 1, ensuring that the change of the adjustment factor is smooth and limited, without excessive fluctuations. This function makes the system sensitive to small changes, but the response tends to be stable when the change approaches the extreme value.

[0168] In the above manner, the present invention can dynamically adjust the warning threshold to adapt to real-time changes in construction and geological conditions. The basic warning threshold provides a benchmark value, and △N(t) reflects the changes in the warning effect of the system in the recent period of time. Through the adjustment of the hyperbolic tangent function, the system can respond appropriately to changes in the accuracy of the warning, and the adjustment coefficients ∈ and η allow the reaction sensitivity of the system to be controlled. Ultimately, this formula ensures that the warning threshold can be adaptively adjusted according to the actual situation, thereby improving the accuracy and reliability of the warning.

[0169] This dynamic warning threshold is used to adjust the sensitivity of the warning system in real time during tunnel construction. By continuously adjusting the threshold, the system can adapt to different geological conditions and construction conditions, ensuring that warnings are issued at the most appropriate time and reducing false alarms and missed alarms. Compared with the warning system with a fixed threshold, the dynamic threshold adjustment mechanism can better cope with complex and changing construction environments, thereby providing more reliable safety guarantees. Through the above formula, the construction team can more accurately judge the risk of rock burst, optimize prevention and control measures, and ensure the safety and efficiency of the construction process.

[0170] Step 206: When the rockburst risk index of the tunnel exceeds the dynamic warning threshold, a risk assessment result is generated, a warning of a corresponding level is issued, and construction prevention and control suggestions are given according to the risk assessment result.

[0171] The Rockburst Risk Index (RRI) is a comprehensive indicator used to quantify the likelihood of rockburst during tunnel construction. The higher the RRI value, the greater the risk of rockburst.

[0172] The dynamic warning threshold is a benchmark value that the system dynamically adjusts based on historical data and real-time monitoring conditions, and is used to determine whether the current RRI is within a safe range.

[0173] During the construction process, the system calculates the RRI in real time and compares it with the dynamic warning threshold. If the RRI exceeds the dynamic warning threshold, it means that the rock burst risk has reached a level that cannot be ignored and further action is needed to prevent possible accidents.

[0174] In some embodiments, the evaluation results include the following:

[0175] Risk level: Based on the extent to which the RRI exceeds the warning threshold, the risk is divided into different levels, such as "low risk", "medium risk", "high risk" or "extremely high risk". Each risk level corresponds to different construction response measures.

[0176] Risk source analysis: The assessment results also include analysis of the causes of the current high RRI value, such as frequent microseismic events, high energy release, or stress concentration in a specific area. This analysis helps the construction team understand the source of risk and take targeted prevention and control measures.

[0177] Potentially affected areas: Based on the risk assessment results, the system will map out the affected areas, indicating which parts of the tunnel are at high risk and may require immediate action.

[0178] Based on the risk assessment results generated, the system will issue a warning of the corresponding level. The level of the warning is usually directly related to the risk level and includes the following levels:

[0179] Information warning (low risk): If the RRI exceeds the threshold but the risk is relatively low, the system may issue an information warning to alert the construction team to potential risks, but no immediate major action is required.

[0180] Attention Alert (Medium Risk): For medium risks, the system will issue an attention alert, advising the construction team to conduct more intensive monitoring and analysis, and possibly start preparing emergency measures.

[0181] Warning (high risk): If the risk assessment results show that the risk is high, the system will issue a warning and require the construction team to immediately take certain preventive measures, such as adjusting the construction sequence, strengthening support, etc.

[0182] Emergency warning (extremely high risk): When the RRI significantly exceeds the threshold and shows an extremely high risk of rock burst, the system will issue an emergency warning and construction may need to be stopped immediately for comprehensive risk assessment and control measures.

[0183] In some embodiments, based on the risk assessment results, the system will automatically generate targeted construction prevention and control suggestions to help the construction team reduce rock burst risks and ensure construction safety. Prevention and control suggestions include but are not limited to the following:

[0184] (1) Adjust support parameters:

[0185] Strengthen support: In high-risk areas, strengthen tunnel support, such as increasing support density, improving the strength of support materials, or using more stable support forms, such as steel arches, concrete linings, etc.

[0186] Adjust the support method: If the original support method may not be able to withstand the current stress, it is recommended to replace it with a more suitable support method to cope with the new stress distribution.

[0187] (2) Adjustment of construction sequence:

[0188] Prioritize high-risk areas: In the construction plan, prioritize the identified high-risk areas, which may require changing the original planned construction sequence to reduce the overall rockburst risk.

[0189] Reduce excavation speed: In high-risk areas, it is recommended to reduce the tunnel excavation speed to reduce stress concentration and sudden release of energy, thereby reducing the possibility of rockburst.

[0190] (3) Pre-cracking measures:

[0191] Controlled blasting: In areas of stress concentration, small-scale controlled blasting is carried out to release some stress in advance, thereby reducing the possibility of subsequent rock bursts.

[0192] Advance support: In areas where rock bursts may occur, support is set up in advance to enhance the stability of the rock mass and avoid sudden damage.

[0193] (4) Intensive monitoring and data analysis:

[0194] Increase monitoring frequency: In high-risk areas, increase the frequency of microseismic monitoring to track stress changes and the occurrence of microseismic events in real time.

[0195] Regular risk assessment: During the construction process, regularly update and evaluate RRI and dynamic stress fields, and adjust prevention and control strategies in a timely manner.

[0196] In the present invention, when the rockburst risk index (RRI) of a tunnel exceeds the dynamic warning threshold, the system generates risk assessment results, issues warnings of corresponding levels, and gives specific construction prevention and control suggestions based on the risk assessment results, providing a comprehensive risk management solution for the construction team. This mechanism can identify and respond to potential rockburst risks in a timely manner, ensuring the safety and efficiency of tunnel construction.

[0197] See also Figure 3 , Figure 3 A structural schematic diagram of a rock burst risk monitoring system within a tunnel construction period provided by the present invention.

[0198] like Figure 3 As shown, a rockburst risk monitoring system during a tunnel construction period proposed in an embodiment of the present invention includes:

[0199] The data acquisition module 301 is used to acquire microseismic monitoring data of the tunnel and geological parameters of the rock mass surrounding the tunnel;

[0200] A risk calculation module 302, configured to calculate a rockburst risk index of the tunnel based on the microseismic monitoring data and the geological parameters;

[0201] A stress analysis module 303 is used to determine a microseismic positioning result according to the microseismic monitoring data, and to construct a dynamic stress field evolution model of the rock mass around the tunnel based on the microseismic positioning result, and to determine a dynamic stress field analysis result of the tunnel through the dynamic stress field evolution model;

[0202] A hazard identification module 304 is used to divide the surrounding rock mass of the tunnel into different rock burst risk level areas based on the rock burst risk index and the dynamic stress field analysis result, and obtain the dangerous area distribution of the tunnel;

[0203] The threshold adjustment module 305 is used to evaluate the rockburst risk level of the tunnel in the next construction period in combination with the rockburst risk index, the distribution of the dangerous area and the upcoming construction process, and dynamically adjust the warning threshold of the rockburst risk index to obtain a dynamic warning threshold;

[0204] The risk monitoring module 306 is used to generate a risk assessment result, issue a warning of a corresponding level, and provide construction prevention and control suggestions based on the risk assessment result when the rock burst risk index of the tunnel exceeds the dynamic warning threshold.

[0205] See also Figure 4 , Figure 4Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, 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, the following steps are implemented:

[0206] Acquiring microseismic monitoring data of the tunnel and geological parameters of the rock mass surrounding the tunnel;

[0207] Calculating a rockburst risk index of the tunnel based on the microseismic monitoring data and the geological parameters;

[0208] Determine microseismic positioning results according to the microseismic monitoring data, and construct a dynamic stress field evolution model of the rock mass around the tunnel based on the microseismic positioning results, and determine the dynamic stress field analysis results of the tunnel through the dynamic stress field evolution model;

[0209] Based on the rockburst risk index and the dynamic stress field analysis result, the surrounding rock mass of the tunnel is divided into different rockburst risk level areas to obtain the dangerous area distribution of the tunnel;

[0210] In combination with the rockburst risk index, the distribution of the dangerous area and the upcoming construction process, the rockburst risk level of the tunnel in the next construction period is evaluated, and the warning threshold of the rockburst risk index is dynamically adjusted to obtain a dynamic warning threshold;

[0211] When the rock burst risk index of the tunnel exceeds the dynamic warning threshold, a risk assessment result is generated, a warning of a corresponding level is issued, and construction prevention and control suggestions are given according to the risk assessment result.

[0212] See also Figure 5 , Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 5 As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented:

[0213] Acquiring microseismic monitoring data of the tunnel and geological parameters of the rock mass surrounding the tunnel;

[0214] Calculating a rockburst risk index of the tunnel based on the microseismic monitoring data and the geological parameters;

[0215] Determine microseismic positioning results according to the microseismic monitoring data, and construct a dynamic stress field evolution model of the rock mass around the tunnel based on the microseismic positioning results, and determine the dynamic stress field analysis results of the tunnel through the dynamic stress field evolution model;

[0216] Based on the rockburst risk index and the dynamic stress field analysis result, the surrounding rock mass of the tunnel is divided into different rockburst risk level areas to obtain the dangerous area distribution of the tunnel;

[0217] In combination with the rockburst risk index, the distribution of the dangerous area and the upcoming construction process, the rockburst risk level of the tunnel in the next construction period is evaluated, and the warning threshold of the rockburst risk index is dynamically adjusted to obtain a dynamic warning threshold;

[0218] When the rock burst risk index of the tunnel exceeds the dynamic warning threshold, a risk assessment result is generated, a warning of a corresponding level is issued, and construction prevention and control suggestions are given according to the risk assessment result.

[0219] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0220] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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.

[0221] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0222] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0223] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0224] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0225] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for monitoring rockburst risk during a tunnel construction period, characterized in that: The method comprises: Acquiring microseismic monitoring data of the tunnel and geological parameters of the rock mass surrounding the tunnel; Calculating a rockburst risk index of the tunnel based on the microseismic monitoring data and the geological parameters; Determine microseismic positioning results according to the microseismic monitoring data, and construct a dynamic stress field evolution model of the rock mass around the tunnel based on the microseismic positioning results, and determine the dynamic stress field analysis results of the tunnel through the dynamic stress field evolution model; Based on the rockburst risk index and the dynamic stress field analysis result, the surrounding rock mass of the tunnel is divided into different rockburst risk level areas to obtain the dangerous area distribution of the tunnel; In combination with the rockburst risk index, the distribution of the dangerous area and the upcoming construction process, the rockburst risk level of the tunnel in the next construction period is evaluated, and the warning threshold of the rockburst risk index is dynamically adjusted to obtain a dynamic warning threshold; When the rock burst risk index of the tunnel exceeds the dynamic warning threshold, a risk assessment result is generated, a warning of a corresponding level is issued, and construction prevention and control suggestions are given according to the risk assessment result.

2. The rockburst risk monitoring method during the tunnel construction period according to claim 1 is characterized in that: The calculating the rockburst risk index of the tunnel based on the microseismic monitoring data and the geological parameters includes: Obtaining the total energy released by the microseismic events in the tunnel per unit time and the corresponding reference energy value; Obtaining the frequency of microseismic events in the tunnel per unit time and the corresponding reference frequency value; Obtaining the maximum horizontal principal stress of the tunnel and the uniaxial compressive strength of rock; Obtaining a joint density factor reflecting the complexity of the rock mass structure of the tunnel and an excavation speed factor reflecting the impact of the construction progress on the rock mass stability; The rock burst risk index of the tunnel is calculated based on the total energy released by microseismic events in the tunnel per unit time, the frequency of microseismic events, the reference energy value, the reference frequency value, the maximum horizontal principal stress, the uniaxial compressive strength of the rock, the physical density factor and the excavation speed factor.

3. The rockburst risk monitoring method during the tunnel construction period according to claim 2 is characterized in that: The rockburst risk index of the tunnel is expressed as: Among them, RRI is the rock burst risk index, E ms is the total energy released by the microseismic event per unit time, E0 is the reference energy value, and f ms is the frequency of microseismic events per unit time, f0 is the reference frequency value, σ h is the maximum horizontal principal stress, σ c is the uniaxial compressive strength of rock, D j is the joint density factor, V e is the excavation speed factor, α, β, γ, δ, λ, and μ are the first weight, second weight, third weight, fourth weight, fifth weight, and sixth weight, respectively.

4. The rockburst risk monitoring method during the tunnel construction period according to claim 3 is characterized in that: After obtaining the microseismic monitoring data of the tunnel, the method further includes: The microseismic monitoring data is automatically identified and classified, and different types of microseismic events are distinguished using a machine learning algorithm; the microseismic events include rock burst events, blasting events, and equipment noise events.

5. The rockburst risk monitoring method during the tunnel construction period according to claim 4 is characterized in that: The step of constructing a dynamic stress field evolution model of the rock mass surrounding the tunnel based on the microseismic positioning result includes: Obtaining an initial stress field of the tunnel; Obtaining the stress amplitude of each microseismic event; Obtaining the distance from any spatial coordinate point in the tunnel to the microseismic source of each microseismic event; Obtaining the stress attenuation characteristic length, stress wave frequency, phase angle and time corresponding to the stress wave of each microseismic event; A dynamic stress field evolution model of the rock mass surrounding the tunnel is constructed based on the initial stress field of the tunnel, the stress amplitude of each microseismic event, the stress attenuation characteristic length corresponding to the stress wave, the stress wave frequency, phase angle and time, and the distance from any spatial coordinate point in the tunnel to the microseismic source of each microseismic event.

6. The method for monitoring rockburst risk during tunnel construction period according to claim 5, characterized in that: The expression of the dynamic stress field evolution model is: Among them, σ dyn (x, y, z, t) is the dynamic stress field of the tunnel, σ ini (x, y, z) is the initial stress field of the tunnel, A i is the stress amplitude of the ith microseismic event, (x, y, z) is the spatial coordinate point, r i is the distance from any spatial coordinate point (x, y, z) to the i-th microseismic source, R0 is the stress attenuation characteristic length, ω i is the stress wave frequency, is the phase angle and t is the time.

7. The rockburst risk monitoring method during tunnel construction period according to claim 6, characterized in that: The dynamically adjusting the warning threshold of the rockburst risk index to obtain a dynamic warning threshold includes: Obtaining a basic warning threshold of the rockburst risk index; Counting the accuracy of early warning for the rockburst risk index in a period of time before the current moment, and determining the change in the early warning accuracy in the period of time before the current moment, and the total number of early warnings; Acquire a first adjustment coefficient and a second adjustment coefficient for adjusting the basic warning threshold; The basic warning threshold is dynamically adjusted according to the change in warning accuracy in a period of time before the current moment, the total number of warnings, the first adjustment coefficient and the second adjustment coefficient to obtain the dynamic warning threshold.

8. The rockburst risk monitoring method during tunnel construction period according to claim 7, characterized in that: The expression of the dynamic warning threshold is: Among them, RRI threshold (t) is the dynamic warning threshold, RRI base is the basic warning threshold, △N(t) is the change in warning accuracy in the most recent period before the current moment, and N total is the total number of warnings, ∈ is the first adjustment coefficient, and η is the second adjustment coefficient.

9. The method for monitoring rockburst risk during a tunnel construction period according to any one of claims 1 to 8, characterized in that: The rock burst risk level areas are divided into high risk areas, medium risk areas and low risk areas.

10. A rockburst risk monitoring system during a tunnel construction period, characterized in that: The system comprises: A data acquisition module, used to acquire microseismic monitoring data of the tunnel and geological parameters of the rock mass surrounding the tunnel; A risk calculation module, used to calculate the rockburst risk index of the tunnel based on the microseismic monitoring data and the geological parameters; A stress analysis module, used to determine a microseismic positioning result according to the microseismic monitoring data, and to construct a dynamic stress field evolution model of the rock mass surrounding the tunnel based on the microseismic positioning result, and to determine a dynamic stress field analysis result of the tunnel through the dynamic stress field evolution model; A hazard identification module, used to divide the surrounding rock mass of the tunnel into different rock burst risk level areas based on the rock burst risk index and the dynamic stress field analysis result, and obtain the dangerous area distribution of the tunnel; A threshold adjustment module is used to evaluate the rockburst risk level of the tunnel in the next construction period in combination with the rockburst risk index, the distribution of the dangerous area and the upcoming construction process, and dynamically adjust the warning threshold of the rockburst risk index to obtain a dynamic warning threshold; The risk monitoring module is used to generate risk assessment results, issue warnings of corresponding levels, and provide construction prevention and control suggestions based on the risk assessment results when the rockburst risk index of the tunnel exceeds the dynamic warning threshold.

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