A method and system for analyzing water pressure at monitoring points of tunnel lining in goaf areas under heavy rainfall

Abstract

The present invention provides a method and system for analyzing water pressure at monitoring points of tunnel linings in goaf areas under heavy rainfall, and relates to the technical field of lining water pressure monitoring. The specific steps include: S1. Collecting parameters that affect the safety and stability of the lining and the stress field parameters around the goaf tunnel at different monitoring points, wherein the parameters that affect the safety and stability of the lining include water pressure data, groundwater level data, and seepage flow data, and the parameters that affect the stress field around the goaf tunnel include stress data, displacement data, and deformation data; S2. Analyzing water pressure data at different locations, evaluating the water pressure distribution around the tunnel lining, and generating a water pressure risk coefficient based on the water pressure distribution around the lining. The present invention can comprehensively evaluate the stability of the tunnel lining and the surrounding rock and soil; at the same time, it uses a comprehensive coefficient to divide high, medium, and low risk areas, and focuses on analyzing the water pressure data in high-risk areas, giving priority to high-risk monitoring points to ensure the safe operation of the tunnel.

Description

Technical Field

[0001] The invention relates to the technical field of lining water pressure monitoring, and in particular to a method and system for analyzing water pressure at a lining monitoring point of a goaf tunnel under heavy rainfall. Background Art

[0002] With the rapid development of transportation construction in my country, a large number of extra-long highway (railway) tunnels will be built, which will inevitably pass through or have already passed through coal mines, metal or non-metallic mines and other goafs. Shanxi is a province with rich coal mineral resources, and its geological conditions are special. During the long-term exploitation and utilization of natural resources, a large number of goafs have been formed. During the construction and operation of tunnels, due to the occurrence of extreme weather, heavy rainfall is often encountered. Under the conditions of heavy rainfall infiltration, the stability of the tunnel surrounding rock and support structure becomes extremely fragile. The existence of goafs also makes the tunnel seepage field more complicated, and the safety of tunnel construction and later operation is facing huge challenges.

[0003] In the prior art, during the construction and operation of tunnels in goaf areas, heavy rainfall will significantly affect the stability of tunnel linings and surrounding rock and soil, leading to safety hazards. Conventional tunnel monitoring methods usually only consider single factors, such as water pressure, groundwater level, etc., while ignoring the combined effects of multiple parameters, resulting in an incomplete and inaccurate assessment of tunnel lining safety. In addition, due to the special geological conditions of goaf areas, the stress field and seepage field around the tunnel are more complex, and traditional methods are difficult to effectively identify high-risk areas and provide timely warnings. Therefore, there is an urgent need for a method that can comprehensively evaluate the safety of tunnel linings with multiple parameters, especially under heavy rainfall conditions, by analyzing the water pressure of the monitoring points of the goaf tunnel lining to obtain the risk level of the monitoring points.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a method and system for analyzing water pressure at monitoring points of tunnel lining in goaf areas under heavy rainfall, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for analyzing water pressure at monitoring points of tunnel lining in goaf areas under heavy rainfall, the specific steps comprising:

[0008] S1. Collect parameters affecting the safety and stability of the lining and stress field parameters around the goaf tunnel at different monitoring points, wherein the parameters affecting the safety and stability of the lining include water pressure data, groundwater level data and seepage flow data, and the parameters affecting the stress field around the goaf tunnel include stress data, displacement data and deformation data;

[0009] S2. Analyze water pressure data at different locations, evaluate water pressure distribution around tunnel lining, generate water pressure risk coefficient based on water pressure distribution around lining, analyze groundwater level data at different locations, evaluate groundwater level depth, generate groundwater level risk coefficient based on groundwater level depth, analyze seepage flow data at different locations, evaluate seepage flow of water flow, generate seepage flow coefficient based on seepage flow of water flow;

[0010] S3. Process and analyze the stress data, displacement data and deformation data to generate a stress concentration factor around the tunnel in the goaf;

[0011] S4. Input the risk coefficient of water pressure and the risk coefficient of groundwater level into the safety analysis model of goaf tunnel lining to obtain the safety factor of lining structure, input the permeability coefficient of seepage flow and the stress concentration coefficient around goaf tunnel into the coupling model of stress field and seepage field around tunnel to generate the stability coefficient of rock and soil around tunnel, and input the safety factor of lining structure and the stability factor of rock and soil around tunnel to generate the comprehensive coefficient for evaluating tunnel lining risk;

[0012] S5. According to the interval range of the comprehensive coefficient, the high-risk, medium-risk and low-risk areas around the tunnel are divided, and the comprehensive coefficient of the high-risk area and the water pressure data of different monitoring points are processed and analyzed to generate the actual water pressure data of different monitoring points;

[0013] S6. Compare the actual water pressure data of monitoring points in high-risk areas, determine the risk level, and give priority to water pressure at monitoring points with high risks.

[0014] Furthermore, the water pressure data at different locations are analyzed to evaluate the water pressure distribution around the tunnel lining. Based on the water pressure distribution around the lining, the process of generating the risk coefficient of water pressure is as follows:

[0015]

[0016] Among them, R w is the risk factor of water pressure, which represents the comprehensive risk of assessing the water pressure around the tunnel lining, W i is the weight factor of the i-th monitoring point when collecting water pressure data, indicating the influence of this point on the overall water pressure assessment, P iis the water pressure data of the i-th monitoring point, indicating the actual water pressure value measured at this point, α i and β i is the adjustment coefficient of the ith monitoring point when collecting water pressure data, n is the total number of monitoring points, indicating the number of monitoring points involved in the assessment, δ is the influencing factor when collecting water pressure data, and adjusts the contribution of water pressure fluctuation to the risk coefficient. is the average water pressure of all monitoring points, indicating the overall level of water pressure, and γ is the adjustment coefficient when collecting water pressure data, which controls the impact of water pressure fluctuations on the risk factor.

[0017] Furthermore, the groundwater level data at different locations are analyzed to evaluate the depth of the groundwater level. According to the depth of the groundwater level, the process of generating the risk coefficient of the groundwater level is as follows:

[0018]

[0019] Among them, R d is the risk coefficient of the groundwater level, which indicates the comprehensive risk of assessing the depth of the groundwater level, V i is the weight factor of the ith monitoring point when collecting groundwater level data, indicating the influence of this point on the overall groundwater level assessment, H i is the groundwater level depth data of the i-th monitoring point, indicating the actual groundwater level height measured at the point, μ i and σ i is the adjustment coefficient of the ith monitoring point when collecting groundwater level data, n is the total number of monitoring points, indicating the number of monitoring points involved in the assessment, φ is the influencing factor when collecting groundwater level data, adjusting the contribution of groundwater level data fluctuation to the risk coefficient, ψ is the adjustment coefficient when collecting groundwater level data, adjusting the contribution of groundwater level fluctuation to the risk coefficient, is the average groundwater level of all monitoring points, indicating the overall level of groundwater.

[0020] Furthermore, the infiltration flow data at different locations are analyzed to evaluate the infiltration flow of the water flow. According to the infiltration flow of the water flow, the process of generating the permeability coefficient of the infiltration flow is as follows:

[0021]

[0022] Among them, R f is the permeability coefficient of the permeate flow, K i is the weight factor of the i-th monitoring point when collecting infiltration flow data, indicating the influence of this point on the overall infiltration flow assessment, Q i is the infiltration flow data of the ith monitoring point, and ω iis the adjustment coefficient of the ith monitoring point when collecting infiltration flow data, n is the total number of monitoring points, ξ is the influencing factor when collecting infiltration flow data, and adjusts the contribution of infiltration flow fluctuation to the permeability coefficient. is the average infiltration flow rate of all monitoring points, indicating the overall level of infiltration flow rate, and θ is the adjustment coefficient when collecting infiltration flow data, which controls the influence of infiltration flow fluctuation on permeability coefficient.

[0023] Furthermore, the stress data, displacement data and deformation data are processed and analyzed to generate the stress concentration factor around the tunnel in the goaf area, based on the following formula:

[0024]

[0025] Among them, K s is the stress concentration factor, η i is the weight factor of the i-th sampling point, ρ i is the stress sensitivity factor of the i-th sampling point, Δσ i is the stress change at the i-th sampling point, u i is the displacement data of the i-th sampling point, is the average displacement data of all sampling points, σ u is the standard deviation of the displacement data of all sampling points, π i is the deformation sensitivity factor of the i-th sampling point, ε i is the deformation data of the i-th sampling point, is the average value of deformation data of all sampling points, σ ε is the standard deviation of the deformation data of all sampling points, and n is the total number of sampling points.

[0026] Furthermore, the risk coefficient of water pressure and the risk coefficient of groundwater level are input into the safety analysis model of tunnel lining in goaf area to obtain the safety factor of lining structure, based on the following formula:

[0027]

[0028] Among them, S c is the safety factor of the lining structure, λ1 is the weight coefficient of the water pressure risk factor, λ2 is the weight coefficient of the groundwater level risk factor, R w is the water pressure risk coefficient, R d is the groundwater level risk coefficient, λ1>λ2>0, and λ1+λ2=1.

[0029] Furthermore, the permeability coefficient of the seepage flow and the stress concentration coefficient around the tunnel in the goaf are input into the coupling model of the stress field and seepage field around the tunnel to generate the stability coefficient of the rock and soil around the tunnel. The formula is as follows:

[0030]

[0031] Among them, S t is the stability coefficient of the rock and soil around the tunnel, ν1 is the permeability coefficient R of the seepage flow f The weight of ν2 is the stress concentration factor K around the tunnel in the goaf area. s weights, ν1>ν2>0, and ν1+ν2=1.

[0032] Furthermore, the safety factor of the lining structure and the stability factor of the rock mass around the tunnel are combined to generate a comprehensive factor for assessing the risk of the tunnel lining, based on the following formula:

[0033]

[0034] Among them, S r is a comprehensive coefficient used to assess the risk of tunnel lining, S c is the safety factor of the lining structure, S t is the stability coefficient of the rock and soil around the tunnel, κ1 is the weight of the safety factor of the lining structure, κ2 is the weight of the stability coefficient of the rock and soil around the tunnel, and κ1=κ2=0.5 is set.

[0035] Furthermore, according to the range of the comprehensive coefficient, the high-risk, medium-risk and low-risk areas around the tunnel are divided, and the process of giving priority to the high-risk areas is as follows:

[0036] When 0.55≤S r <0.7, low risk area;

[0037] When 0.7≤S r <0.85, medium risk area;

[0038] When 0.85≤S r <1, high-risk area;

[0039] The process of comparing the actual water pressure data of monitoring points in high-risk areas, judging the risk level, and giving priority to water pressure at high-risk monitoring points is as follows:

[0040] SP j =P i ·S r

[0041] Among them, SP j is the actual water pressure data of the monitoring points in the high-risk areas, P i is the water pressure data of the monitoring points in the high-risk area, j is the number of monitoring points in the high-risk area, <i;

[0042] The actual water pressure data of the monitoring points in the j high-risk areas are compared, and the water pressure of the monitoring point with the largest actual water pressure data is processed first.

[0043] A system for analyzing water pressure at monitoring points of tunnel lining in goaf area under heavy rainfall, the system being used to execute any of the above-mentioned methods for analyzing water pressure at monitoring points of tunnel lining in goaf area under heavy rainfall, comprising:

[0044] A data acquisition module is used to collect parameters affecting the safety and stability of the lining and the stress field parameters around the goaf tunnel at different monitoring points. The parameters affecting the safety and stability of the lining include water pressure data, groundwater level data and seepage flow data. The parameters affecting the stress field around the goaf tunnel include stress data, displacement data and deformation data.

[0045] The first data processing module is used to analyze water pressure data at different locations, evaluate the water pressure distribution around the tunnel lining, generate a water pressure risk coefficient based on the water pressure distribution around the lining, analyze groundwater level data at different locations, evaluate the depth of the groundwater level, generate a groundwater level risk coefficient based on the depth of the groundwater level, analyze infiltration flow data at different locations, evaluate the infiltration flow of water flow, and generate a permeability coefficient of the infiltration flow based on the infiltration flow of water flow;

[0046] The second data processing module is used to process and analyze the stress data, displacement data and deformation data to generate a stress concentration factor around the tunnel in the goaf;

[0047] The first data analysis module is used to input the risk coefficient of water pressure and the risk coefficient of groundwater level into the safety analysis model of goaf tunnel lining to obtain the safety factor of the lining structure, input the permeability coefficient of the seepage flow and the stress concentration coefficient around the goaf tunnel into the coupling model of the stress field and seepage field around the tunnel to generate the stability coefficient of the rock and soil around the tunnel, and generate a comprehensive coefficient for evaluating the risk of the tunnel lining by combining the safety factor of the lining structure and the stability factor of the rock and soil around the tunnel;

[0048] The second data analysis module is used to divide the high-risk, medium-risk and low-risk areas around the tunnel according to the interval range of the comprehensive coefficient, and to process and analyze the comprehensive coefficient of the high-risk area and the water pressure data of different monitoring points to generate the actual water pressure data of different monitoring points;

[0049] The execution module is used to compare the actual water pressure data of different monitoring points, determine the risk level, and give priority to the water pressure of monitoring points with high risks.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention generates comprehensive risk assessment indicators by collecting and analyzing a variety of parameters that affect the safety and stability of tunnel linings, such as water pressure, groundwater level, seepage flow, stress, displacement and deformation data; by combining the risk coefficient of water pressure, the risk coefficient of groundwater level, the permeability coefficient of seepage flow and the stress concentration coefficient around the tunnel in the goaf area, a set of coupling models is established to comprehensively evaluate the stability of the tunnel lining and the surrounding rock and soil; at the same time, the comprehensive coefficient is used to divide high, medium and low risk areas, and the water pressure data in high-risk areas is analyzed in detail, and high-risk monitoring points are processed first to ensure the safe operation of the tunnel. This method significantly improves the accuracy and early warning capability of tunnel safety assessment, effectively solves the technical problem that traditional methods are difficult to comprehensively evaluate multiple parameters, and enhances the accuracy and efficiency of judging the risk level of monitoring points in tunnels under heavy rainfall conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0053] Figure 2 This is a block diagram of the module composition of the present invention. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0055] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0056] Example:

[0057] See also Figure 1 , the present invention provides a technical solution:

[0058] A method for analyzing water pressure at monitoring points of tunnel lining in goaf areas under heavy rainfall, the specific steps comprising:

[0059] S1. Collect parameters affecting the safety and stability of the lining and stress field parameters around the goaf tunnel at different monitoring points, wherein the parameters affecting the safety and stability of the lining include water pressure data, groundwater level data and seepage flow data, and the parameters affecting the stress field around the goaf tunnel include stress data, displacement data and deformation data;

[0060] S2. Analyze water pressure data at different locations, evaluate water pressure distribution around tunnel lining, generate water pressure risk coefficient based on water pressure distribution around lining, analyze groundwater level data at different locations, evaluate groundwater level depth, generate groundwater level risk coefficient based on groundwater level depth, analyze seepage flow data at different locations, evaluate seepage flow of water flow, generate seepage flow coefficient based on seepage flow of water flow;

[0061] S3. Process and analyze the stress data, displacement data and deformation data to generate a stress concentration factor around the tunnel in the goaf;

[0062] S4. Input the risk coefficient of water pressure and the risk coefficient of groundwater level into the safety analysis model of goaf tunnel lining to obtain the safety factor of lining structure, input the permeability coefficient of seepage flow and the stress concentration coefficient around goaf tunnel into the coupling model of stress field and seepage field around tunnel to generate the stability coefficient of rock and soil around tunnel, and input the safety factor of lining structure and the stability factor of rock and soil around tunnel to generate the comprehensive coefficient for evaluating tunnel lining risk;

[0063] S5. According to the interval range of the comprehensive coefficient, the high-risk, medium-risk and low-risk areas around the tunnel are divided, and the comprehensive coefficient of the high-risk area and the water pressure data of different monitoring points are processed and analyzed to generate the actual water pressure data of different monitoring points;

[0064] S6. Compare the actual water pressure data of monitoring points in high-risk areas, determine the risk level, and give priority to water pressure at monitoring points with high risks.

[0065] In this embodiment, when the water pressure data, groundwater level data and seepage flow data change, the lining safety stability will change. The specific reasons are as follows:

[0066] As the water pressure increases, the external pressure exerted by water on the tunnel lining structure will also increase. Excessive water pressure may cause the lining structure to deform or even become unstable. The increased pore water pressure of groundwater on the rock and soil will reduce the effective stress of the soil, weaken the shear strength of the rock and soil, and lead to reduced stability of the surrounding rock. Changes in water pressure will also lead to stress redistribution of the lining and surrounding rock, causing stress concentration and increasing the risk of structural instability.

[0067] Rising groundwater levels will increase water pressure, impose additional loads on the lining structure, and increase the risk of structural instability. At the same time, high water levels may cause groundwater to flow into the tunnel, causing water accumulation and related problems. Rising groundwater levels will also increase seepage pressure, prompting water to penetrate through tiny cracks or defects in the lining, leading to internal erosion and deterioration of the structure. Changes in groundwater levels will also cause changes in the hydrological environment, affecting the physical properties of the rock and soil around the tunnel, such as saturation and density, thereby affecting its stability.

[0068] The increase in seepage flow will increase the scouring and erosion of water on the tunnel lining structure, causing the lining material to gradually deteriorate and its strength to decrease. When the seepage flow is large, the rock and soil around the tunnel may be eroded by the water flow, forming cavities or loose areas, weakening the overall stability of the surrounding rock. Changes in seepage flow will also cause changes in the water pressure gradient, causing local instability or damage to the lining structure.

[0069] In addition to their individual effects, water pressure, groundwater level and seepage flow usually interact with each other and jointly affect the safety and stability of tunnel linings. The following is a specific discussion:

[0070] A higher groundwater level will lead to higher water pressure, while a higher infiltration flow rate may be accompanied by both higher water pressure and a higher groundwater level. For example, a rising groundwater level will cause an increase in water pressure and an increase in infiltration flow rate, which will have a combined impact on the lining structure.

[0071] In this embodiment, when the stress data, displacement data and deformation data change, the stability of the rock and soil around the tunnel will change. The specific reasons are as follows:

[0072] Tunnel excavation will cause the original stress field to be redistributed. If the stress is concentrated in certain areas, it may cause damage or instability of the rock and soil in these areas. When the local stress exceeds the strength limit of the rock and soil material, it will cause the rock and soil to rupture, slide or collapse. The stress release during the excavation process will cause deformation of the rock and soil around the tunnel. If this deformation exceeds a certain limit, it will also affect its stability.

[0073] The displacement changes of the rock and soil around the tunnel will directly reflect the deformation of the rock and soil. Excessive displacement indicates that there may be strong stress concentration or softening inside the rock and soil. Excessive displacement differences at different positions may cause cracking inside the rock and soil, further reducing its stability.

[0074] Plastic deformation indicates that the rock and soil mass has entered the irreversible deformation stage. Excessive plastic deformation may cause permanent damage to the rock and soil structure. Although elastic deformation is recoverable, excessive elastic deformation may also cause stress concentration and subsequent damage.

[0075] In addition to their individual effects, the three factors of stress, displacement and deformation usually interact and jointly affect the stability of the rock mass around the tunnel:

[0076] Changes in stress can cause displacement and deformation, and changes in displacement and deformation can in turn affect the distribution of stress. For example, local stress concentration can cause displacement and deformation of the soil mass, which in turn can cause new stress concentration.

[0077] In this embodiment, the method for obtaining water pressure data, groundwater level data and infiltration flow data is as follows:

[0078] Water pressure data around the tunnel can be measured using water pressure sensors (such as piezoelectric pressure sensors), and the sensors are usually buried in the tunnel lining or surrounding rock and soil. Water level meters (such as resistive, water level probes or piezoresistive water level meters) are used in monitoring wells around the tunnel to measure groundwater level data. Flow meters (such as electromagnetic flow meters or ultrasonic flow meters) can be used to measure the infiltration flow data of groundwater around the tunnel.

[0079] The method of obtaining stress data, displacement data and deformation data is as follows:

[0080] Stress sensors (such as strain gauges, fiber optic strain gauges or pressure cells) are used to measure stress changes in the rock and soil around the tunnel, and the elastic modulus and strain of the material are used to calculate stress data. Displacement sensors (such as laser rangefinders) can be used to measure the displacement data of the rock and soil or lining around the tunnel. Deformation sensors (such as fiber optic sensors and strain gauges) can be used to measure the deformation data of the tunnel lining and surrounding rock and soil.

[0081] On the basis of the above embodiment, the water pressure data at different positions are analyzed to evaluate the water pressure distribution around the tunnel lining. According to the water pressure distribution around the lining, the process of generating the risk coefficient of water pressure is as follows:

[0082]

[0083] Among them, R w is the risk factor of water pressure, which represents the comprehensive risk of assessing the water pressure around the tunnel lining, W i is the weight factor of the i-th monitoring point when collecting water pressure data, indicating the influence of this point on the overall water pressure assessment, P i is the water pressure data of the i-th monitoring point, indicating the actual water pressure value measured at this point, α i and β iis the adjustment coefficient of the ith monitoring point when collecting water pressure data, which is used to adjust the influence of water pressure data in the summation and normalization processes, n is the total number of monitoring points, indicating the number of monitoring points involved in the assessment, δ is the influencing factor when collecting water pressure data, which adjusts the contribution of water pressure fluctuations to the risk coefficient, is the average water pressure of all monitoring points, indicating the overall level of water pressure, and γ is the adjustment coefficient when collecting water pressure data, which controls the impact of water pressure fluctuations on the risk factor.

[0084] Interaction rules in formulas

[0085] P i When increasing, and will increase accordingly, affecting the overall risk factor R w The change of adjustment coefficient α i and β i The existence of makes the water pressure at different monitoring points have different degrees of influence on the overall assessment, thus reflecting the interactive relationship between the data.

[0086] P i and The difference reflects the fluctuation of water pressure at each monitoring point, and the influencing factor δ and the adjustment coefficient γ adjust their contribution to the risk coefficient.

[0087] The range of the risk factor of water pressure R w The range is set to [0,10].

[0088] When R w Values ​​in the range [0,3] indicate low water pressure risk and high tunnel lining structural stability;

[0089] When R w Values ​​in the range [3,7] indicate that the water pressure risk is moderate and the tunnel lining structure needs to be monitored and preventive reinforcement measures should be taken;

[0090] When R w When the value is in the range of [7,10], it means that the water pressure risk is high and there is a great safety hazard in the tunnel lining structure, and reinforcement measures need to be taken immediately.

[0091] Based on the above embodiment, the process of analyzing the groundwater level data at different locations, evaluating the depth of the groundwater level, and generating the risk coefficient of the groundwater level according to the depth of the groundwater level is as follows:

[0092]

[0093] Among them, R d is the risk coefficient of the groundwater level, which indicates the comprehensive risk of assessing the depth of the groundwater level, Vi is the weight factor of the ith monitoring point when collecting groundwater level data, indicating the influence of this point on the overall groundwater level assessment, H i is the groundwater level depth data of the i-th monitoring point, indicating the actual groundwater level height measured at the point, μ i and σ i is the adjustment coefficient of the ith monitoring point when collecting groundwater level data, which is used to adjust the impact of groundwater level data in the summation and normalization processes, n is the total number of monitoring points, indicating the number of monitoring points involved in the assessment, φ is the influencing factor when collecting groundwater level data, which adjusts the contribution of groundwater level data fluctuation to the risk coefficient, ψ is the adjustment coefficient when collecting groundwater level data, which is used to adjust the contribution of groundwater level fluctuation to the risk coefficient, is the average groundwater level of all monitoring points, indicating the overall level of groundwater.

[0094] Interaction rules in formulas

[0095] H i When increasing, and will increase accordingly, affecting the overall risk factor R d The change of adjustment coefficient μ i and σ i The existence of makes the groundwater level at different monitoring points have different influences on the overall assessment, thus reflecting the interactive relationship between the data.

[0096] H i and The difference reflects the fluctuation of groundwater level at each monitoring point, and the influencing factor φ and adjustment coefficient ψ adjust their contribution to the risk coefficient.

[0097] The value range R of the risk factor of groundwater level d The range is set to [0,10].

[0098] When R d The value is in the range of [0,3], indicating that the groundwater level risk is low and the tunnel structure stability is high;

[0099] When R d Values ​​in the range [3,7] indicate that the groundwater level risk is moderate and the tunnel structure needs to be monitored and preventive reinforcement measures should be taken;

[0100] When R d When the value is in the range of [7,10], it means that the groundwater level risk is high and the tunnel structure has a great safety hazard, and it is necessary to take immediate safety reinforcement measures.

[0101] Based on the above embodiment, similarly, the process of analyzing the infiltration flow data at different positions, evaluating the infiltration flow of the water flow, and generating the permeability coefficient of the infiltration flow according to the infiltration flow of the water flow is as follows:

[0102]

[0103] Among them, R f is the permeability coefficient of the permeate flow, K i is the weight factor of the i-th monitoring point when collecting infiltration flow data, indicating the influence of this point on the overall infiltration flow assessment, Q i is the infiltration flow data of the ith monitoring point, and ω i is the adjustment coefficient of the ith monitoring point when collecting infiltration flow data, which is used to adjust the influence of infiltration flow data in the summation and normalization process, n is the total number of monitoring points, ξ is the influencing factor when collecting infiltration flow data, which is used to adjust the contribution of infiltration flow fluctuation to the permeability coefficient, is the average infiltration flow rate of all monitoring points, indicating the overall level of infiltration flow rate, and θ is the adjustment coefficient when collecting infiltration flow data, which controls the influence of infiltration flow fluctuation on permeability coefficient.

[0104] Interaction rules in formulas

[0105] Q i When increasing, and will increase accordingly, affecting the overall permeability coefficient R f The change of adjustment coefficient ζ i and ω i The existence of makes the infiltration flow at different monitoring points have different influences on the overall assessment, thus reflecting the interactive relationship between the data.

[0106] Q i and The difference reflects the fluctuation of the seepage flow rate at each monitoring point, and the influencing factor ξ and the adjustment coefficient θ adjust their contribution to the permeability coefficient.

[0107] The value range of the permeability coefficient of the infiltration flow is set to [0,10].

[0108] When R f The value is in the range of [0,3], indicating that the risk of infiltration flow is low and the tunnel structure stability is high.

[0109] When R f Values ​​in the range [3,7] indicate that the risk of infiltration flow is moderate and the tunnel structure needs to be monitored and preventive reinforcement measures need to be taken.

[0110] When R fWhen the value is in the range of [7,10], it means that the risk of infiltration flow is high and there is a great safety hazard in the tunnel structure, and reinforcement measures need to be taken immediately.

[0111] On the basis of the above embodiment, the stress data, displacement data and deformation data are processed and analyzed to generate the stress concentration factor around the tunnel in the goaf, according to the following formula:

[0112]

[0113] Among them, K s is the stress concentration factor, η i is the weight factor of the i-th sampling point, ρ i is the stress sensitivity factor of the i-th sampling point, Δσ i is the stress change at the i-th sampling point, u i is the displacement data of the i-th sampling point, is the average displacement data of all sampling points, σ u is the standard deviation of the displacement data of all sampling points, π i is the deformation sensitivity factor of the i-th sampling point, ε i is the deformation data of the i-th sampling point, is the average value of deformation data of all sampling points, σ ε is the standard deviation of the deformation data of all sampling points, and n is the total number of sampling points.

[0114] On the basis of the above embodiment, the risk coefficient of water pressure and the risk coefficient of groundwater level are input into the safety analysis model of tunnel lining in goaf area to obtain the safety factor of lining structure, according to the following formula:

[0115]

[0116] Among them, S c is the safety factor of the lining structure, λ1 is the weight coefficient of the water pressure risk factor, λ2 is the weight coefficient of the groundwater level risk factor, R w is the water pressure risk coefficient, R d is the groundwater level risk coefficient, λ1>λ2>0, and λ1+λ2=1.

[0117] Since water pressure is directly applied to the tunnel lining structure, it may cause structural deformation, leakage or even damage. The direct mechanical effect is often more obvious and dangerous than the change of groundwater level. In addition, water pressure will cause the seepage pressure of the soil around the tunnel to change, further affecting the stability of the tunnel. The increase in seepage pressure will lead to a decrease in the effective stress of the soil, thereby weakening the bearing capacity of the soil and increasing the risk of tunnel instability. Under the action of long-term high water pressure, the tunnel lining structure may suffer from fatigue and material damage, resulting in a decrease in the bearing capacity of the structure. In comparison, the change of groundwater level has a relatively small effect on structural fatigue and damage. Therefore, λ1>λ2>0 is set, and λ1+λ2=1 is set without considering other parameters.

[0118] The linear combination formula provides a simple and effective way. Since the safety of the tunnel lining structure is affected by many factors, water pressure and groundwater level are two important risk factors. By introducing weight coefficients λ1 and λ2, the different degrees of influence of these two factors on safety are comprehensively considered, and the two risk coefficients are combined into a comprehensive indicator to express the safety level of the lining structure.

[0119] On the basis of the above embodiment, the permeability coefficient of the seepage flow and the stress concentration coefficient around the tunnel in the goaf are input into the coupling model of the stress field and seepage field around the tunnel to generate the stability coefficient of the rock and soil around the tunnel, according to the following formula:

[0120]

[0121] Among them, S t is the stability coefficient of the rock and soil around the tunnel, ν1 is the permeability coefficient R of the seepage flow f The weight of ν2 is the stress concentration factor K around the tunnel in the goaf area. s weights, ν1>ν2>0, and ν1+ν2=1.

[0122] Because the seepage flow may cause greater water pressure, resulting in seepage deformation and decreased stability of the soil, high seepage pressure may cause the soil around the tunnel to loosen and collapse, which is a direct and fatal threat. The seepage flow may also cause the loss of soil particles, forming cavities and pipe effects, thereby weakening the support around the tunnel. Long-term seepage flow will gradually weaken the rock and soil structure around the tunnel, resulting in decreased structural stability. Therefore, ν1>ν2>0 is set, and ν1+ν2=1 is set without considering other parameters.

[0123] Similarly, the linear combination formula provides a simple and effective way. Since the stability of the rock and soil around the tunnel is affected by many factors, among which the infiltration of water flow and the stress around the tunnel in the goaf are two important risk factors, by introducing weight coefficients ν1 and ν2, the different degrees of influence of these two factors on safety are comprehensively considered, and the two risk coefficients are combined into a comprehensive indicator to express the stability of the rock and soil around the tunnel.

[0124] On the basis of the above embodiment, the safety factor of the lining structure and the stability factor of the rock mass around the tunnel are used to generate a comprehensive factor for assessing the risk of the tunnel lining, according to the following formula:

[0125]

[0126] Among them, S r is a comprehensive coefficient used to assess the risk of tunnel lining, S c is the safety factor of the lining structure, S t is the stability coefficient of the rock and soil around the tunnel, κ1 is the weight of the safety factor of the lining structure, κ2 is the weight of the stability coefficient of the rock and soil around the tunnel, and κ1=κ2=0.5 is set.

[0127] In order to specifically illustrate the functional relationship between the safety factor of the lining structure, the stability factor of the rock and soil around the tunnel and the comprehensive coefficient of the tunnel lining risk, at the same time, to illustrate the functional relationship between the safety factor of the lining structure and the risk factor of the water pressure, the risk factor of the groundwater level, the functional relationship between the stability factor of the rock and soil around the tunnel and the permeability coefficient of the seepage flow, and the stress concentration factor around the tunnel in the goaf, a data table is listed to express the functional relationship between the above coefficients.

[0128] Table 1 is a table showing the comprehensive coefficient of tunnel lining risk and the safety factor of the lining structure.

[0129]

[0130] From the above table, we can know the comprehensive coefficient S of tunnel lining risk: r With the safety factor S of the lining structure c and the stability factor S of the rock mass around the tunnel t The safety factor S of the lining structure increases with the increase of c The risk factor R of water pressure w and the risk factor R of the groundwater level d The stability factor S of the rock and soil around the tunnel increases with the increase of t As the permeability coefficient R d and the stress concentration factor R around the tunnel in the goaff Therefore, the comprehensive coefficient S of tunnel lining risk is r and the safety factor S of the lining structure c , the stability factor S of the rock and soil around the tunnel t are positively correlated, and the safety factor S of the lining structure c and water pressure risk factor R w , risk factor R of groundwater level d are positively correlated, and the stability coefficient S of the rock and soil around the tunnel t and the permeability coefficient R of the permeability flow d , stress concentration factor R around the tunnel in the goaf f All are positively correlated.

[0131] On the basis of the above embodiment, according to the interval range of the comprehensive coefficient, the process of dividing the high-risk, medium-risk and low-risk areas around the tunnel and giving priority to the high-risk areas is as follows:

[0132] When 0.55≤S r <0.7, which is a low-risk area. The first row of data in the table (S r =0.65) shows typical data for low-risk areas, where both the safety factor of the lining structure and the stability factor of the rock mass are low;

[0133] When 0.7≤S r <0.85, indicating a medium risk area. The data from the 2nd to the 5th row in the table (S r =0.72, 0.78, 0.81, 0.84) illustrate typical data for medium risk areas, where the safety factor of the lining structure and the stability factor of the rock mass are at a medium level;

[0134] When 0.85≤S r <1, it is a high-risk area. The data from rows 6 to 10 in the table (S r =0.87, 0.88, 0.90, 0.92, 0.94) illustrate typical data for high-risk areas, where both the safety factor of the lining structure and the stability factor of the rock mass are high.

[0135] On the basis of the above embodiment, the comprehensive coefficient of the high-risk area and the water pressure data of the monitoring point are processed and analyzed to generate the actual water pressure data of different monitoring points, according to the following formula:

[0136] SP j =P i ·S r

[0137] Among them, SP jis the actual water pressure data of the monitoring points in the high-risk areas, P i is the water pressure data of the monitoring points in the high-risk area, j is the number of monitoring points in the high-risk area, <i。

[0138] Compare the actual water pressure data of j monitoring points in high-risk areas, and give priority to the water pressure of the monitoring point with the largest actual water pressure data, such as strengthening drainage.

[0139] W in the formula i , α i , β i , δ, γ, μ i , K i 、V i , σ i ,φ,ψ, ω i , ξ, θ, η i , i and π i The specific value of is generally determined by those skilled in the art according to actual conditions. The essence of this formula is a comprehensive analysis by weighted summation. The technicians in this field collect multiple groups of sample data, and set corresponding preset coefficients or weight factors for each group of sample data. The preset coefficients or weight factors and the collected sample data are substituted into the formula. Through repeated experiments and parameter adjustments, the accuracy of the model output and the rationality of the results are observed, and these coefficients or weight factors are gradually adjusted. The performance and effect of the model under different parameter settings are compared to find the optimal coefficient combination. The calculated factor coefficients are screened and averaged to obtain W. i , α i , β i , δ, γ, μ i , K i 、V i , σ i ,φ,ψ, ω i , ξ, θ, η i , i and π i The value of .

[0140] In addition, the size of the preset coefficient or weight factor is a specific numerical value obtained by quantifying each parameter. In order to facilitate subsequent comparison, the size of the preset coefficient or weight factor depends on the amount of sample data and the preliminary setting of the corresponding preset coefficient or weight factor for each group of sample data by technical personnel in this field. It is not unique, as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0141] See also Figure 2 , the present invention also provides a technical solution:

[0142] A system for analyzing water pressure at monitoring points of tunnel lining in goaf area under heavy rainfall, the system being used to execute any of the above-mentioned methods for analyzing water pressure at monitoring points of tunnel lining in goaf area under heavy rainfall, comprising:

[0143] A data acquisition module is used to collect parameters affecting the safety and stability of the lining and the stress field parameters around the goaf tunnel at different monitoring points. The parameters affecting the safety and stability of the lining include water pressure data, groundwater level data and seepage flow data. The parameters affecting the stress field around the goaf tunnel include stress data, displacement data and deformation data.

[0144] The first data processing module is used to analyze water pressure data at different locations, evaluate the water pressure distribution around the tunnel lining, generate a water pressure risk coefficient based on the water pressure distribution around the lining, analyze groundwater level data at different locations, evaluate the depth of the groundwater level, generate a groundwater level risk coefficient based on the depth of the groundwater level, analyze infiltration flow data at different locations, evaluate the infiltration flow of water flow, and generate a permeability coefficient of the infiltration flow based on the infiltration flow of water flow;

[0145] The second data processing module is used to process and analyze the stress data, displacement data and deformation data to generate a stress concentration factor around the tunnel in the goaf;

[0146] The first data analysis module is used to input the risk coefficient of water pressure and the risk coefficient of groundwater level into the safety analysis model of goaf tunnel lining to obtain the safety factor of the lining structure, input the permeability coefficient of the seepage flow and the stress concentration coefficient around the goaf tunnel into the coupling model of the stress field and seepage field around the tunnel to generate the stability coefficient of the rock and soil around the tunnel, and generate a comprehensive coefficient for evaluating the risk of the tunnel lining by combining the safety factor of the lining structure and the stability factor of the rock and soil around the tunnel;

[0147] The second data analysis module is used to divide the high-risk, medium-risk and low-risk areas around the tunnel according to the interval range of the comprehensive coefficient, and to process and analyze the comprehensive coefficient of the high-risk area and the water pressure data of different monitoring points to generate the actual water pressure data of different monitoring points;

[0148] The execution module is used to compare the actual water pressure data of different monitoring points, determine the risk level, and give priority to the water pressure of monitoring points with high risks.

[0149] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0150] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0151] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0152] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for analyzing water pressure at monitoring points of tunnel lining in goaf areas under heavy rainfall, characterized in that: The specific steps include: S1. Collect parameters affecting the safety and stability of the lining and stress field parameters around the goaf tunnel at different monitoring points, wherein the parameters affecting the safety and stability of the lining include water pressure data, groundwater level data and seepage flow data, and the parameters affecting the stress field around the goaf tunnel include stress data, displacement data and deformation data; S2. Analyze water pressure data at different locations, evaluate water pressure distribution around tunnel lining, generate water pressure risk coefficient based on water pressure distribution around lining, analyze groundwater level data at different locations, evaluate groundwater level depth, generate groundwater level risk coefficient based on groundwater level depth, analyze seepage flow data at different locations, evaluate seepage flow of water flow, generate seepage flow coefficient based on seepage flow of water flow; S3. Process and analyze the stress data, displacement data and deformation data to generate a stress concentration factor around the tunnel in the goaf; S4. Input the risk coefficient of water pressure and the risk coefficient of groundwater level into the safety analysis model of goaf tunnel lining to obtain the safety factor of lining structure, input the permeability coefficient of seepage flow and the stress concentration coefficient around goaf tunnel into the coupling model of stress field and seepage field around tunnel to generate the stability coefficient of rock and soil around tunnel, and input the safety factor of lining structure and the stability factor of rock and soil around tunnel to generate the comprehensive coefficient for evaluating tunnel lining risk; S5. According to the interval range of the comprehensive coefficient, the high-risk, medium-risk and low-risk areas around the tunnel are divided, and the comprehensive coefficient of the high-risk area and the water pressure data of different monitoring points are processed and analyzed to generate the actual water pressure data of different monitoring points; S6. Compare the actual water pressure data of monitoring points in high-risk areas, determine the risk level, and prioritize the water pressure of monitoring points with high risks; The process of analyzing water pressure data at different locations and evaluating the water pressure distribution around the tunnel lining and generating the risk factor of water pressure based on the water pressure distribution around the lining is as follows: Among them, R w is the risk factor of water pressure, which represents the comprehensive risk of assessing the water pressure around the tunnel lining, W i is the weight factor of the i-th monitoring point when collecting water pressure data, indicating the influence of this point on the overall water pressure assessment, P i is the water pressure data of the i-th monitoring point, indicating the actual water pressure value measured at this point, α i and β i is the adjustment coefficient of the ith monitoring point when collecting water pressure data, n is the total number of monitoring points, indicating the number of monitoring points involved in the assessment, δ is the influencing factor when collecting water pressure data, and adjusts the contribution of water pressure fluctuation to the risk coefficient. is the average water pressure of all monitoring points, indicating the overall level of water pressure, and γ is the adjustment coefficient when collecting water pressure data, which controls the impact of water pressure fluctuations on the risk factor; The process of analyzing the groundwater level data at different locations, evaluating the depth of the groundwater level, and generating the risk coefficient of the groundwater level according to the depth of the groundwater level is as follows: Among them, R d is the risk coefficient of groundwater level, which indicates the comprehensive risk of assessing the depth of groundwater level, V i is the weight factor of the ith monitoring point when collecting groundwater level data, indicating the influence of this point on the overall groundwater level assessment, H i is the groundwater level depth data of the i-th monitoring point, indicating the actual groundwater level height measured at the point, μ i and σ i is the adjustment coefficient of the ith monitoring point when collecting groundwater level data, n is the total number of monitoring points, indicating the number of monitoring points involved in the assessment, φ is the influencing factor when collecting groundwater level data, adjusting the contribution of groundwater level data fluctuation to the risk coefficient, ψ is the adjustment coefficient when collecting groundwater level data, adjusting the contribution of groundwater level fluctuation to the risk coefficient, is the average groundwater level of all monitoring points, indicating the overall level of groundwater; The process of analyzing the infiltration flow data at different locations, evaluating the infiltration flow of water flow, and generating the permeability coefficient of the infiltration flow according to the infiltration flow of water flow is as follows: Among them, R f is the permeability coefficient of the permeate flow, K i is the weight factor of the i-th monitoring point when collecting infiltration flow data, indicating the influence of this point on the overall infiltration flow assessment, Q i is the infiltration flow data of the ith monitoring point, and ω i is the adjustment coefficient of the ith monitoring point when collecting infiltration flow data, n is the total number of monitoring points, ξ is the influencing factor when collecting infiltration flow data, and adjusts the contribution of infiltration flow fluctuation to the permeability coefficient. is the average infiltration flow rate of all monitoring points, indicating the overall level of infiltration flow rate, θ is the adjustment coefficient when collecting infiltration flow rate data, controlling the influence of infiltration flow rate fluctuation on permeability coefficient; The stress data, displacement data and deformation data are processed and analyzed to generate the stress concentration factor around the tunnel in the goaf area. The formula is as follows: Among them, K s is the stress concentration factor, η i is the weight factor of the i-th sampling point, ρ i is the stress sensitivity factor of the i-th sampling point, Δσ i is the stress change at the i-th sampling point, u i is the displacement data of the i-th sampling point, is the average displacement data of all sampling points, σ u is the standard deviation of the displacement data of all sampling points, π i is the deformation sensitivity factor of the i-th sampling point, ε i is the deformation data of the i-th sampling point, is the average value of deformation data of all sampling points, σ ε is the standard deviation of the deformation data of all sampling points, and n is the total number of sampling points; The risk coefficient of water pressure and the risk coefficient of groundwater level are input into the safety analysis model of tunnel lining in goaf area to obtain the safety factor of lining structure, based on the following formula: Among them, S c is the safety factor of the lining structure, λ1 is the weight coefficient of the water pressure risk coefficient, λ2 is the weight coefficient of the groundwater level risk coefficient, λ1>λ2>0, and λ1+λ2=1; The permeability coefficient of the seepage flow and the stress concentration coefficient around the tunnel in the goaf are input into the coupling model of the stress field and seepage field around the tunnel to generate the stability coefficient of the rock and soil around the tunnel. The formula is as follows: Among them, S t is the stability coefficient of the rock and soil around the tunnel, ν1 is the permeability coefficient R of the seepage flow f The weight of ν2 is the stress concentration factor K around the tunnel in the goaf area. s The weight of , ν1>ν2>0, and ν1+ν2=1; The safety factor of the lining structure and the stability factor of the rock mass around the tunnel are combined to generate a comprehensive factor for assessing the risk of the tunnel lining, based on the following formula: Among them, S r is a comprehensive coefficient used to assess the risk of tunnel lining, κ1 is the weight of the safety factor of the lining structure, κ2 is the weight of the stability factor of the rock and soil around the tunnel, and κ1=κ2=0.5 is set.

2. The method for analyzing water pressure at monitoring points of tunnel lining in goaf areas under heavy rainfall according to claim 1 is characterized by: According to the range of the comprehensive coefficient, the process of dividing the high-risk, medium-risk and low-risk areas around the tunnel and giving priority to the high-risk areas is as follows: When 0.55≤S r <0.7, low risk area; When 0.7≤S r <0.85, medium risk area; When 0.85≤S r <1, high-risk area; The comprehensive coefficient of the high-risk area and the water pressure data of the monitoring points are processed and analyzed to generate the actual water pressure data of different monitoring points. The formula is as follows: SP j =P i ·S r Among them, SP j is the actual water pressure data of the monitoring points in the high-risk areas, P i is the water pressure data of the monitoring points in the high-risk area, j is the number of monitoring points in the high-risk area, <i; The actual water pressure data of the monitoring points in the j high-risk areas are compared, and the water pressure of the monitoring point with the largest actual water pressure data is processed first.

3. A system for analyzing water pressure at monitoring points of tunnel lining in goaf areas under heavy rainfall, the system being used to execute the method for analyzing water pressure at monitoring points of tunnel lining in goaf areas under heavy rainfall as claimed in any one of claims 1 to 2, characterized in that: include: A data acquisition module is used to collect parameters affecting the safety and stability of the lining and the stress field parameters around the goaf tunnel at different monitoring points. The parameters affecting the safety and stability of the lining include water pressure data, groundwater level data and seepage flow data. The parameters affecting the stress field around the goaf tunnel include stress data, displacement data and deformation data. The first data processing module is used to analyze water pressure data at different locations, evaluate the water pressure distribution around the tunnel lining, generate a water pressure risk coefficient based on the water pressure distribution around the lining, analyze groundwater level data at different locations, evaluate the depth of the groundwater level, generate a groundwater level risk coefficient based on the depth of the groundwater level, analyze infiltration flow data at different locations, evaluate the infiltration flow of water flow, and generate a permeability coefficient of the infiltration flow based on the infiltration flow of water flow; The second data processing module is used to process and analyze the stress data, displacement data and deformation data to generate a stress concentration factor around the tunnel in the goaf; The first data analysis module is used to input the risk coefficient of water pressure and the risk coefficient of groundwater level into the safety analysis model of goaf tunnel lining to obtain the safety factor of the lining structure, input the permeability coefficient of the seepage flow and the stress concentration coefficient around the goaf tunnel into the coupling model of the stress field and seepage field around the tunnel to generate the stability coefficient of the rock and soil around the tunnel, and generate a comprehensive coefficient for evaluating the risk of the tunnel lining by combining the safety factor of the lining structure and the stability factor of the rock and soil around the tunnel; The second data analysis module is used to divide the high-risk, medium-risk and low-risk areas around the tunnel according to the interval range of the comprehensive coefficient, and to process and analyze the comprehensive coefficient of the high-risk area and the water pressure data of different monitoring points to generate the actual water pressure data of different monitoring points; The execution module is used to compare the actual water pressure data of different monitoring points, determine the risk level, and give priority to the water pressure of monitoring points with high risks.

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