Semi-quantitative evaluation method and device for bias pressure of tunnel portal section and storage medium

Abstract

The invention provides a tunnel portal section bias voltage semi-quantitative evaluation method and device and a storage medium, and relates to the technical field of tunnel bias voltage calculation. The method comprises the following steps: calculating and determining bias coefficients of a plurality of tunnel portal sections; determining a plurality of bias levels based on the values of the plurality of bias coefficients; determining the influence of a plurality of bias voltage influence factors on the bias voltage coefficient under each bias voltage level based on numerical simulation, so as to determine one or more bias voltage influence factors with the maximum influence on the bias voltage coefficient as target influence factors; and obtaining one or more target influence factors of the target tunnel portal section, and generating a semi-quantitative evaluation result of the target tunnel portal section based on the target influence factors. According to the method, the core index of the bias voltage coefficient method is introduced, the complex mechanical behavior is converted into a quantifiable evaluation benchmark, and the evaluation efficiency of the bias voltage of the tunnel portal section can be greatly improved while certain precision is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel bias calculation, in particular to a semi-quantitative evaluation method, device and storage medium for tunnel portal section bias. BACKGROUND

[0002] In the field of tunnel engineering, accurately evaluating the bias condition of the portal section is a key link to ensure construction and operation safety. The traditional evaluation method usually has the problem of low evaluation efficiency, which is mainly due to its inherent analysis mode.

[0003] The traditional technical method often relies on detailed quantitative calculation and mechanical analysis of all geological and topographic influencing factors. This method attempts to accurately simulate the mechanical response of the tunnel by constructing a complex numerical model, considering the interaction of multiple parameters such as slope gradient, overburden thickness, and rock mass properties. However, this all-in-one analysis approach results in a very complex calculation model that requires a large number of input rock and soil parameters, and the data acquisition and processing process itself is very time-consuming. In addition, due to the concealment and uncertainty of actual engineering geological conditions, it is extremely difficult to obtain accurate values for all parameters, which makes comprehensive quantitative analysis either difficult due to insufficient data or affects the reliability of the results due to model simplification.

[0004] Therefore, in engineering practice, in order to cope with the difficulties of comprehensive analysis, the evaluation work often relies too much on the personal experience of engineers for qualitative judgment. This experience-based mode, although direct, lacks systematicness and unified standards, and the conclusions drawn by different engineers may differ greatly, making it difficult to form objective and reproducible evaluation results, thus having obvious deficiencies in efficiency and objectivity. SUMMARY

[0005] The present application provides a semi-quantitative evaluation method, device and storage medium for tunnel portal section bias, which converts complex mechanical behavior into a quantifiable evaluation benchmark by introducing the core index of bias coefficient method. In actual application, only a few key factors of the target tunnel portal section need to be obtained to generate a semi-quantitative evaluation result, which can greatly improve the evaluation efficiency of the tunnel portal section bias while ensuring a certain accuracy.

[0006] In a first aspect, the present invention provides a semi-quantitative evaluation method for bias pressure in a tunnel portal section, comprising: calculating and determining bias pressure coefficients for multiple tunnel portal sections; wherein the bias pressure coefficients are obtained based on the ratio of the stress difference between the surrounding rock on the left and right sides of the tunnel to the average stress of the surrounding rock on both sides; determining multiple bias pressure levels based on the values ​​of the multiple bias pressure coefficients; determining the influence of multiple bias pressure influencing factors on the bias pressure coefficients at each bias pressure level based on numerical simulation, and determining one or more bias pressure influencing factors that have the greatest influence on the bias pressure coefficients as target influencing factors; obtaining one or more target influencing factors for the target tunnel portal section, and generating a semi-quantitative evaluation result for the target tunnel portal section based on the target influencing factors.

[0007] According to one embodiment of the present invention, determining multiple bias levels based on the values ​​of multiple bias coefficients includes: obtaining multiple engineering critical conditions of the tunnel portal section and establishing a tunnel model corresponding to each of the engineering critical conditions; performing excavation simulation on each tunnel model based on numerical simulation to calculate multiple critical bias coefficients; and determining multiple bias levels using the critical bias coefficients as critical values.

[0008] According to one embodiment of the present invention, the tunnel entrance section is located in a soil slope or a rock slope; when the tunnel entrance section is located in a soil slope, the bias pressure influencing factors include slope gradient, tunnel depth, and tunnel surrounding rock stiffness; when the tunnel entrance section is located in a rock slope, the bias pressure influencing factors include slope gradient, tunnel depth, structural surface dip angle, structural surface strength, rock layer thickness, and location of weak structural surfaces.

[0009] According to one embodiment of the present invention, the step of determining the influence of multiple bias influencing factors on the bias coefficient at each bias level based on numerical simulation, and determining one or more bias influencing factors that have the greatest influence on the bias coefficient as target influencing factors, includes: when the tunnel entrance section is located on a soil slope, performing numerical simulation of the bias influencing factors based on the finite difference method to determine one or more bias influencing factors that have the greatest influence on the bias coefficient as target influencing factors; and when the tunnel entrance section is located on a rock slope, performing numerical simulation of the bias influencing factors based on the discrete element method to determine one or more bias influencing factors that have the greatest influence on the bias coefficient as target influencing factors.

[0010] According to one embodiment of the present invention, the target influencing factors include slope gradient and tunnel depth.

[0011] According to one embodiment of the present invention, the bias level includes a first level, a second level and a third level, wherein the first level indicates that the bias coefficient is less than 0.3, the second level indicates that the bias coefficient is greater than or equal to 0.3 and less than 0.5, and the third level indicates that the bias coefficient is greater than or equal to 0.5.

[0012] According to one embodiment of the present invention, the step of obtaining one or more target influencing factors of a target tunnel portal section and generating a semi-quantitative evaluation result of the target tunnel portal section based on the target influencing factors includes: generating a semi-quantitative evaluation table of the target tunnel portal section based on the slope and tunnel burial depth; wherein the semi-quantitative evaluation table includes a first marker, a second marker, and a third marker, the first marker indicating that the tunnel's bias state is at a first level, the second marker indicating that the tunnel's bias state is at a second level, and the third marker indicating that the tunnel's bias state is at a third level.

[0013] According to one embodiment of the present invention, the bias coefficient is the bias coefficient at the tunnel arch foot.

[0014] In a second aspect, the present invention also provides a computer device, the computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method of the above embodiments.

[0015] Thirdly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0016] Compared with existing technologies, the advantages of this application are as follows: By introducing the core index of the bias coefficient method, complex mechanical behavior is transformed into a quantifiable evaluation benchmark. Bias levels are classified based on multiple coefficient values, establishing a grading system from mild to severe, providing a classification basis for bias conditions of different severity. Furthermore, by using numerical simulation, the influence of various factors on the bias coefficient at different levels is analyzed, thereby identifying key target influencing factors. Finally, in practical applications, only a few key factors of the target tunnel portal section need to be obtained to generate semi-quantitative evaluation results. This method avoids cumbersome full-parameter calculations and significantly improves the evaluation efficiency of bias at tunnel portal sections while ensuring a certain level of accuracy. Attached Figure Description

[0017] Figure 1 A schematic diagram illustrating the steps of a semi-quantitative evaluation method for bias pressure at the tunnel entrance section provided in this application embodiment.

[0018] Figure 2 A schematic diagram of a numerical calculation model for shallow-buried biased tunnel design provided for embodiments of this application.

[0019] Figure 3 This is a schematic diagram of a numerical calculation model for a biased tunnel that requires ground reinforcement, provided as an embodiment of this application.

[0020] Figure 4 This is a schematic diagram of the tunnel bias analysis points provided in an embodiment of this application.

[0021] Figure 5 This is a schematic diagram of the Mohr-Coulomb yield criterion in FLAC3D as shown in an embodiment of this application.

[0022] Figure 6 This is a schematic diagram of a numerical calculation model for different slope gradients provided in the embodiments of this application.

[0023] Figure 7 The maximum compressive stress cloud map of the surrounding rock of the tunnel under different slopes is provided for the embodiments of this application.

[0024] Figure 8 This is a schematic diagram of the variation curves of slope gradient and bias coefficient provided in the embodiments of this application.

[0025] Figure 9 A schematic diagram of a numerical calculation model for different tunnel burial depths provided in the embodiments of this application.

[0026] Figure 10 The maximum compressive stress cloud map of the surrounding rock of the tunnel at different burial depths is provided for the embodiments of this application.

[0027] Figure 11 This is a schematic diagram of the variation curves of tunnel burial depth and bias coefficient provided in the embodiments of this application.

[0028] Figure 12 This is a schematic diagram of a numerical calculation model for the stiffness of the surrounding rock of a tunnel, provided in an embodiment of this application.

[0029] Figure 13 The maximum compressive stress cloud map of the tunnel surrounding rock under different elastic moduli of the surrounding rock is provided for the embodiments of this application.

[0030] Figure 14 This is a schematic diagram of the variation curves of tunnel surrounding rock stiffness and bias coefficient provided in the embodiments of this application.

[0031] Figure 15 This is a schematic diagram of a numerical calculation model for different slope gradients under a rock slope provided in an embodiment of this application.

[0032] Figure 16 The maximum compressive stress cloud map of the surrounding rock of the tunnel under different slopes of the rock slope provided in the embodiments of this application.

[0033] Figure 17 This is a schematic diagram of the variation curves of slope gradient and bias coefficient of a rock slope provided in the embodiments of this application.

[0034] Figure 18 A schematic diagram of a numerical calculation model for tunnels at different burial depths under a rock slope, provided in an embodiment of this application.

[0035] Figure 19 The maximum compressive stress cloud map of the surrounding rock of tunnels at different tunnel burial depths under the rock slope provided in the embodiments of this application.

[0036] Figure 20 This is a schematic diagram of the variation curves of tunnel burial depth and bias coefficient under rock slope provided in the embodiments of this application.

[0037] Figure 21 This is a schematic diagram of a numerical calculation model for different structural surface dip angles under a rock slope, provided in an embodiment of this application.

[0038] Figure 22 The maximum compressive stress cloud map of the surrounding rock of the tunnel under different structural surface dip angles provided in the embodiments of this application.

[0039] Figure 23 This is a schematic diagram of the variation curves of the dip angle and bias coefficient of the structural surface under the rock slope provided in the embodiments of this application.

[0040] Figure 24 A schematic diagram of a numerical calculation model for the structural surface strength under a rock slope provided in an embodiment of this application.

[0041] Figure 25 The maximum compressive stress cloud map of the tunnel surrounding rock with different internal friction angles under different structural planes under the rock slope provided in the embodiments of this application.

[0042] Figure 26 This is a schematic diagram showing the variation curves of the internal friction angle and bias coefficient of the structural surface under the rock slope provided in this application embodiment.

[0043] Figure 27 This is a schematic diagram of a numerical calculation model for different rock layer thicknesses under a rock slope, provided in an embodiment of this application.

[0044] Figure 28 The maximum compressive stress cloud map of the surrounding rock of the tunnel with different rock layer thicknesses under the rock slope provided in the embodiments of this application.

[0045] Figure 29 This is a schematic diagram of the variation curves of rock layer thickness and bias coefficient under a rock slope provided in an embodiment of this application.

[0046] Figure 30This is a schematic diagram of a numerical calculation model for the location of different weak structural surfaces under a rock slope, as provided in an embodiment of this application.

[0047] Figure 31 This is a schematic diagram of the variation curves of the weak structural surface and the bias coefficient under the rock slope provided in the embodiments of this application.

[0048] Figure 32 The stress distribution cloud map provided in the embodiments of this application is shown before the excavation of a tunnel under a rock slope.

[0049] Figure 33 The slope displacement cloud map after tunnel excavation is provided for the embodiments of this application.

[0050] Figure 34 This is a schematic diagram of the eccentric deformation trend after tunnel excavation, provided in an embodiment of this application.

[0051] Figure 35 This is a schematic diagram showing the variation curve of the bias coefficient at the arch foot of the soil slope under different tunnel depths and slopes, provided for embodiments of this application.

[0052] Figure 36 This is a schematic diagram illustrating the qualitative judgment of the degree of bias pressure in tunnels under different combinations of burial depth and slope in soil surrounding rock, as provided in the embodiments of this application.

[0053] Figure 37 This is a schematic diagram showing the variation of the arch foot bias coefficient with slope under different burial depths in rock slopes, as provided in the embodiments of this application.

[0054] Figure 38 This is a schematic diagram illustrating the qualitative judgment of the degree of bias pressure of tunnels under different burial depths and slopes in rock slopes, as provided in the embodiments of this application. Detailed Implementation

[0055] The present application will now be described in further detail with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the subject matter of the present application to the following embodiments. All technologies implemented based on the content of the present application fall within the scope of protection of the present application.

[0056] Please refer to Figure 1 , Figure 1 A schematic diagram illustrating the steps of the semi-quantitative evaluation method for tunnel portal bias pressure provided in this application embodiment. The semi-quantitative evaluation method for tunnel portal bias pressure may include: S1. Calculate and determine the bias coefficients of multiple tunnel entrance sections.

[0057] S2. Determine multiple bias levels based on the values ​​of multiple bias coefficients.

[0058] S3. Based on numerical simulation, determine the influence of multiple bias influencing factors on the bias coefficient at each bias level, and identify one or more bias influencing factors that have the greatest impact on the bias coefficient as target influencing factors.

[0059] S4. Obtain one or more target influencing factors of the target tunnel portal section, and generate a semi-quantitative evaluation result of the target tunnel portal section based on the target influencing factors.

[0060] In this embodiment, the bias coefficient is a dimensionless index used to quantify the degree of unevenness in the bias load on the tunnel structure. It is a numerical result obtained through a specific calculation formula, based on the ratio of the stress difference between the left and right sides of the tunnel's surrounding rock to the average stress of the surrounding rock on both sides. Specifically, the formula for calculating the bias coefficient is:

[0061] in, C This is the bias coefficient. The deep-buried lateral pressure stress on the same horizontal line of the tunnel surrounding rock. This refers to the shallow-buried lateral pressure stress along the same horizontal line of the tunnel surrounding rock. The bias coefficient describes the degree of stress imbalance between the two sides of the tunnel surrounding rock along the same horizontal line, with a value ranging from 0 to 2. The larger the value, the more severe the bias stress in the tunnel. When the bias coefficient is 0, At this point, the forces on both sides of the tunnel are uniform, with no bias pressure affecting the tunnel; when the bias pressure coefficient is 2, At this point, the tunnel is under extreme bias pressure.

[0062] Bias level refers to a series of discrete bias degree ranges defined based on the results of a large number of numerical simulations. That is, it divides the bias coefficients into different severity levels, such as "slight bias," "moderate bias," and "severe bias," with each level corresponding to different engineering treatment requirements. Specifically, this application can obtain critical models from a large number of typical engineering conditions or specifications. By statistically analyzing this dataset and setting critical thresholds for classifying bias degrees based on engineering significance, a hierarchical system is ultimately established that maps continuous coefficient values ​​to a finite number of discrete severity levels.

[0063] Bias pressure influencing factors refer to various engineering geological and geometric parameters that may affect the bias pressure coefficient value in numerical simulation studies. These include variables such as slope gradient, tunnel depth, tunnel surrounding rock stiffness, structural plane dip angle, structural plane strength, rock layer thickness, and the location of weak structural planes. Target influencing factors are one or more key parameters that have the most significant impact on the bias pressure coefficient, selected through systematic numerical simulation analysis from among numerous bias pressure influencing factors. When determining target influencing factors, numerical simulation software such as FLAC3D and 3DEC can be used to gradually change one influencing factor (e.g., slope gradient from 10 degrees to 50 degrees) while keeping other parameters constant. The resulting trend and level of bias pressure coefficient change at different levels can be observed and recorded. By comparing the sensitivity and magnitude of the influence of all factors on the coefficient at different levels, one or more parameters with the greatest influence can be identified as target influencing factors. For example, tunnel depth can be used as the target influencing factor, or both slope gradient and tunnel depth can be used as target influencing factors.

[0064] After identifying the target influencing factors, when faced with a specific tunnel portal project to be evaluated, one can survey or obtain data on its key target influencing factors, such as tunnel depth data or slope and tunnel depth data. Then, by querying the semi-quantitative evaluation table constructed from the research results of step S3 (which uses slope and depth as row and column indexes and directly gives the corresponding bias level), one can quickly obtain a graded evaluation result with clear engineering guidance significance regarding the bias level of the tunnel portal, thereby quickly completing the semi-quantitative evaluation.

[0065] The following is an embodiment of this application that determines multiple bias levels based on the values ​​of multiple bias coefficients.

[0066] The steps for determining multiple bias levels based on the numerical values ​​of multiple bias coefficients may include obtaining multiple engineering critical conditions for the tunnel portal section, establishing a tunnel model corresponding to each engineering critical condition; performing excavation simulation on each tunnel model based on numerical simulation to calculate multiple critical bias coefficients; and using the critical bias coefficients as critical values ​​to determine multiple bias levels.

[0067] Please refer to Table 1. The values ​​listed in Table 1 are the abutment overburden thicknesses required for shallow-buried, bias-pressure tunnel design. When the ground is inclined and the abutment overburden thickness is less than the values ​​listed in Table 1, the design should be based on shallow-buried, bias-pressure tunnels. Several examples are selected to establish calculation models, such as... Figure 2 As shown.

[0068] Table 1. Limits (m) for the thickness of the abutment overburden layer required for shallow-buried, bias-pressure tunnel design.

[0069] Figure 2 A schematic diagram of a numerical calculation model for shallow-buried biased tunnel design provided for embodiments of this application. Figure 2 The models shown are a collection of representative models established for various critical conditions in Table 1, each corresponding to a specific set of conditions in Table 1. For example, models (a), (b), and (c) simulate the critical state of Class III surrounding rock at slopes of 1:0.75, 1:1.25, and 1:2, with overburden thicknesses of 20, 15, and 10 meters respectively. The other models also correspond to critical states at multiple slopes under Class IV or V surrounding rock levels. The model's shape clearly shows that its terrain surface is set as an inclined slope with a constant gradient to simulate real biased terrain. The tunnel structure is embedded in the mountain, and the burial depth of its arch shoulder is controlled within the critical thickness values ​​specified in Table 1. The model's establishment considers the physical and mechanical parameters of the surrounding rock, which strictly correspond to the surrounding rock levels in the table, ensuring the realism of the simulation. The purpose of this study is to simulate tunnel excavation for these models that are at the design critical state. By calculating the stress in key components such as the tunnel arch foot after excavation and substituting these values ​​into the formula for calculating the bias coefficient, a series of corresponding critical bias coefficients are obtained. These coefficient values ​​are the core basis for determining the subsequent bias level.

[0070] Table 2 lists the minimum thicknesses of the strata above the tunnel arch that require reinforcement. When the ground is inclined and the thickness of the strata above the arch is less than the values ​​listed in Table 1, engineering measures should be taken to reinforce the surface hillside. This provides a clear quantitative standard for determining when engineering reinforcement measures for the surface hillside are necessary under biased terrain. Several examples are selected to establish calculation models, such as... Figure 3 As shown.

[0071] Table 2. Limits (m) for the thickness of strata above the arch of biased tunnels requiring reinforcement.

[0072] Figure 3 This is a schematic diagram of a numerical calculation model for a biased tunnel that requires ground reinforcement, provided as an embodiment of this application. Figure 3The models shown represent a set of specific critical conditions from Table 2. For example, models (a) and (b) simulate the extreme conditions of Class III surrounding rock with slopes of 1:1.25 and 1:1.5, respectively, and the thickness of the strata above the arch crown being 4 meters and 3 meters. The other models also correspond to the critical conditions of the strata thickness above the arch crown under Class IV or V surrounding rock. It is clear from the model construction that the terrain surface is set as an inclined slope with a constant gradient to simulate real biased terrain. The tunnel structure is embedded in the mountain, and the thickness of the rock and soil mass directly above its arch crown is controlled to the critical thickness value specified in Table 2. This thickness value is usually much smaller than the arch shoulder overburden thickness limit in Table 1, indicating that the model is in a more dangerous state.

[0073] Please refer to Figure 4 , Figure 4 This is a schematic diagram of the tunnel bias analysis points provided in the embodiments of this application. During the research process, the applicant found that the bias coefficient at the arch foot was the largest under various combinations of surface slope and burial depth, indicating that the bias was most severe at the arch foot. In actual highway biased tunnels, the bias degree at the arch foot should be given priority consideration. When calculating the bias coefficients at the arch foot, arch waist, and arch shoulder, since the calculation adopted a geological structure model that considered the coordinated deformation of the surrounding rock and lining system, the deformation at the arch foot was minimal and the stress release was the least after being subjected to bias load. The variation law of the bias coefficient at the arch foot is the most representative and can reflect the overall bias degree of the tunnel. Therefore, the stress at the arch foot was used for calculation. The parameters and results used in these calculation models are shown in Tables 3 to 5.

[0074] Table 3. Physical and mechanical parameters used in the calculation of surrounding rock at various levels.

[0075] Table 4 Calculation results of the bias coefficient required for shallow buried biased tunnel design

[0076] Table 5. Calculation results of the bias coefficient for formations requiring engineering reinforcement.

[0077] By simulating tunnel excavation under these critical conditions that require reinforcement, the stress in key parts of the tunnel after excavation was calculated, and a series of corresponding critical bias coefficients were obtained. As shown in the calculation results in Table 5, these coefficient values ​​(such as the minimum value of about 0.56) are generally higher than the values ​​in Table 4. Therefore, from the perspective of engineering safety, a bias coefficient of 0.5 is set as the critical value for judging whether engineering reinforcement measures are needed.

[0078] Analysis of the calculation results listed in Tables 3 and 4 shows that the bias coefficient is significantly affected by the cross slope and burial depth, specifically exhibiting a pattern where the steeper the slope and the shallower the burial depth, the larger the bias coefficient. The minimum bias coefficient listed in Table 3 is approximately 0.30, and most model calculations show results slightly higher than 0.30; therefore, a bias coefficient of 0.30 can be considered the critical value for considering tunnel bias. The minimum bias coefficient listed in Table 4 is 0.56; for engineering safety considerations, a bias coefficient of 0.50 can be considered the critical value for requiring engineering reinforcement measures for the strata. Based on the above conclusions, the bias coefficient can be defined as follows: C <0.3 represents the first level of bias coefficient, with a bias degree of "slight bias"; 0.3≤ C A value of <0.5 indicates a bias coefficient for the second level, which represents a "medium bias". C A bias coefficient of ≥0.5 is the third level, and its bias degree is "severe bias".

[0079] The following is an embodiment of this application that uses numerical simulation to determine the influence of multiple bias influencing factors on the bias coefficient at each bias level, so as to identify one or more bias influencing factors that have the greatest impact on the bias coefficient as target influencing factors.

[0080] The tunnel portal section may be located within a soil slope or a rock slope. To study the influence of the soil slope on tunnel bias pressure, FLAC3D numerical simulation software was used based on the finite difference method to numerically simulate the factors influencing bias pressure, identifying one or more bias pressure influencing factors with the greatest impact on the bias pressure coefficient as target influencing factors. Specifically, calculation models were established using FLAC3D numerical simulation software for different cross slope gradients, tunnel depths, and surrounding rock moduli, and single-factor analyses were performed for the same variable. Based on the results of the single-factor analyses, variables with significant influence were selected for multi-factor analyses to derive the general law governing the bias pressure caused by the overburden soil slope on the tunnel. Since the surrounding rock at the portal of shallow-buried tunnels is mostly fractured and loose, it was considered as an unfavorable condition and analyzed as Class V surrounding rock. The calculation parameter values ​​are shown in Table 6.

[0081] Table 6. Physical and mechanical parameters used in FLAC3D calculations

[0082] In this embodiment of the application, considering that the calculation mainly involves the mechanical characteristic analysis of the tunnel passing through the surrounding soil and rock, the three-dimensional finite difference method program FLAC3D7.0 is used for numerical simulation. In the calculation model, the surrounding rock material is simulated using a Mohr-Coulomb elastoplastic constitutive model, and the tunnel lining is simulated using an elastic constitutive model.

[0083] The elastic constitutive model in FLAC3D is a spatial problem with 15 basic equations and 15 unknown functions. The number of basic equations and the number of unknown variables are equal. With appropriate boundary conditions, it can be solved.

[0084] The equilibrium differential equation for the spatial problem is:

[0085] The equilibrium differential equations for spatial problems describe the static equilibrium conditions that must be satisfied between the stress state and the applied forces at any point inside an object. Among them, , and These represent the three components of the normal stress at that point, namely the stresses acting perpendicular to the x-axis, y-axis, and z-axis. , The symbols represent shear stress components. The first subscript indicates the direction of the normal to the surface where the stress acts, and the second subscript indicates the direction of the stress. X, Y, and Z represent the components of the body force per unit volume of an object in the x, y, and z directions, respectively.

[0086] The geometric equation for the spatial problem is:

[0087] The geometric equations of spatial problems establish the geometric relationship between an object before and after deformation, that is, they describe the relationship between strain and displacement. , and These represent the normal strain along the x, y, and z directions at that point, i.e., the relative elongation or shortening of the length. , , The equations represent the shear strain components and describe the change in angle. u, v, and w represent the displacement components of that point in the x, y, and z directions, respectively. The geometric equations of the spatial problem explicitly state through partial derivatives that strain is essentially the gradient of the displacement field as a function of space. Therefore, knowing the displacement of every point within the object allows for the complete determination of its strain state using these geometric equations.

[0088] The constitutive equation for the spatial problem is:

[0089] Constitutive equations for spatial problems describe the mechanical properties of materials and reveal the physical relationship between stress and strain. Here, E is the elastic modulus; the larger the value, the less easily the material deforms. Poisson's ratio is the ratio of the absolute values ​​of transverse strain to axial strain when a material is under uniaxial tension or compression. The left side of the equation represents the strain components, and the right side represents the corresponding stress component combination. This represents the shear stress component acting in a plane perpendicular to the y-axis and along the z-axis. This represents the shear stress component acting in a plane perpendicular to the z-axis and along the x-axis. This represents the shear stress component acting in a plane perpendicular to the x-axis and along the y-axis. For normal strain, it depends not only on the normal stress in the same direction but also on the coupling effect of the normal stress in the perpendicular direction, due to the Poisson effect. For shear strain, it is only proportional to the corresponding shear stress component.

[0090] Please refer to Figure 5 , Figure 5 This is a schematic diagram of the Mohr-Coulomb yield criterion in FLAC3D, as illustrated in an embodiment of this application. The failure envelope of the Mohr-Coulomb elastoplastic constitutive model conforms to the Mohr-Coulomb criterion with a tensile intercept (i.e., the shear yield function). The position of the stress point on the envelope is jointly controlled by the non-associated flow law of shear failure and the associated flow law of tensile failure. Principal stresses exist in the criterion. The relationship, yield criterion and Define and establish on The stress plane, and the envelope function of failure, are: . For the maximum principal stress, The intermediate principal stress, It is the minimum principal stress.

[0091] A ray originating from point B and passing through point A can be defined as the envelope of the Mohr-Coulomb violation criterion. The expression is:

[0092] A ray originating from point B and passing through point C can be defined as the envelope of the tensile failure criterion. The expression is:

[0093] in, , For cohesion, It is the internal friction angle. This refers to tensile strength.

[0094] The tensile strength of the material must not exceed that at point D. Value, that is, the straight line and The maximum value of the intersection point can be calculated by the following formula:

[0095] Potential function is used and It is described by two functions. The corresponding non-associated flow rule has the following form:

[0096] in, Ψ For shear expansion angle, conforms to .

[0097] Corresponding to the related flow rule, it has the following form:

[0098] straight line and The angle bisector divides the region above the envelope into two regions, such as... Figure 5 As shown. When the stress point falls within region 1, shear failure occurs; when the stress point falls within region 2, tensile failure occurs.

[0099] When the tunnel entrance is located on a soil slope, the factors affecting the bias pressure include the slope gradient, tunnel depth, and the stiffness of the surrounding rock.

[0100] Please refer to Figure 6 , Figure 6 This is a schematic diagram of a numerical calculation model for different slope gradients provided in the embodiments of this application. Figure 6 The paper shows that the fixed tunnel burial depth is 10m, and nine numerical calculation models with different cross slopes from 10° to 50° are established. Figure 7 The maximum compressive stress cloud map of the tunnel surrounding rock under different slopes is provided in the embodiments of this application. Figure 7 Each maximum compressive stress contour plot and Figure 6 Each numerical calculation model corresponds one-to-one. The color of the maximum compressive stress contour map changes from blue to red, indicating that the compressive stress value gradually increases.

[0101] Based on nine numerical calculation models, the influence of slope gradient on the bias coefficient can be obtained. Please refer to [the relevant documentation]. Figure 8 , Figure 8 This is a schematic diagram illustrating the variation curves of slope gradient and bias coefficient provided in the embodiments of this application. It can be seen that as the cross slope gradient increases, the bias coefficient at the arch foot increases, reaching a maximum value around 45°. When it exceeds 45°, the bias coefficient at the arch foot drops sharply, while the bias coefficient at the arch shoulder increases. This is because when the slope is too steep, the direction of pressure within the slope gradually becomes orthogonal to the tunnel arch shoulder. However, in reality, soil slopes exceeding 45° are rare. Therefore, it can be considered that the bias coefficient is directly proportional to the slope, meaning that the greater the slope, the more severe the tunnel bias.

[0102] Please refer to Figure 9 ,Figure 9 A schematic diagram of a numerical calculation model for different tunnel burial depths provided in the embodiments of this application. Figure 9 The image shows 12 calculation models with a fixed cross slope of 25° set in this application, and different tunnel burial depths ranging from 5m to 70m. Figure 10 The maximum compressive stress cloud map of the surrounding rock of the tunnel at different burial depths is provided in the embodiments of this application. Figure 10 Each maximum compressive stress contour plot and Figure 9 Each numerical calculation model corresponds one-to-one. The color of the maximum compressive stress contour map changes from blue to red, indicating that the compressive stress value gradually increases.

[0103] Based on 12 numerical calculation models, the influence relationship between tunnel burial depth and bias coefficient can be obtained. Please refer to [the relevant documentation]. Figure 11 , Figure 11 This is a schematic diagram illustrating the relationship between tunnel burial depth and bias coefficient provided in an embodiment of this application. It can be seen that as the tunnel burial depth increases, the bias coefficient at the arch foot and arch waist shows the same decreasing trend, while the bias at the arch shoulder remains insignificant. Within a burial depth of 20m, the rate of change of the bias coefficient is relatively large. When the burial depth exceeds 45m, the bias coefficient is generally less than 0.1 and does not change significantly. At this point, the stress on both sides of the tunnel is relatively balanced, and the tunnel is not affected by bias. This also explains why shallow-buried tunnels are more prone to bias failure at the tunnel entrance.

[0104] Please refer to Figure 12 , Figure 12 This is a schematic diagram of a numerical calculation model for the stiffness of the surrounding rock of a tunnel, provided in an embodiment of this application. Figure 12 The diagram illustrates a calculation model with a fixed cross slope of 25° and a tunnel depth of 10m, as set in this application. The elastic modulus of the surrounding rock is set to 0.1GPa, 0.3GPa, 0.5GPa, 0.7GPa, and 1.0GPa, and the bias coefficient is calculated for each. The specific element division of the calculation model and the calculation results are also presented. Figure 13 As shown. Figure 13 This application provides cloud diagrams of the maximum compressive stress in the surrounding rock of a tunnel under different elastic moduli. Figure 13 The diagram shows the maximum compressive stress contour plots with the surrounding rock elastic modulus parameters set to 0.1 GPa, 0.3 GPa, 0.5 GPa, 0.7 GPa, and 1.0 GPa. The color of the maximum compressive stress contour plots changes from blue to red, indicating that the compressive stress value gradually increases.

[0105] Based on multiple maximum compressive stress contour maps, the influence of tunnel surrounding rock stiffness on the bias coefficient can be obtained. Please refer to [link / reference needed]. Figure 14 , Figure 14This is a schematic diagram illustrating the variation curves of tunnel surrounding rock stiffness and bias coefficient provided in an embodiment of this application. It can be seen that as the elastic modulus of the surrounding rock increases, the bias coefficient decreases. When the elastic modulus of the surrounding rock is below 0.5 GPa, the rate of change of the bias coefficient is relatively large; when it is above 0.5 GPa, the rate of change of the bias coefficient is small or even essentially unchanged. This indicates that in hard rock with high stiffness, the bias coefficient hardly changes with stiffness, which also explains why tunnel damage under bias typically occurs in surrounding rock of grades IV, V, and below.

[0106] When the tunnel entrance section is located on a rock slope, the factors affecting the bias pressure include the slope gradient, tunnel depth, dip angle of the structural surface, strength of the structural surface, thickness of the rock strata, and location of the weak structural surface.

[0107] To investigate the influence of rock slopes on tunnel bias pressure, 3DEC numerical simulation software was used based on the discrete element method to numerically simulate the bias pressure influencing factors. This identified one or more bias pressure influencing factors with the greatest impact on the bias pressure coefficient as the target influencing factors. Specifically, calculation models were established using 3DEC numerical simulation software for different cross slope gradients, tunnel depths, structural surface dip angles, structural surface strengths, and rock layer thicknesses. Single-factor analyses were performed on the same variable for each. Based on the single-factor analysis results, multi-factor analyses were conducted on variables with significant influence to derive the general law governing the bias pressure caused by rock slopes on tunnels. Since the surrounding rock at the entrance of shallow-buried tunnels is mostly fractured and loose, it was considered as an unfavorable condition and analyzed as Class V surrounding rock. The calculation parameter values ​​are shown in Tables 7 and 8.

[0108] Table 7. Physical and mechanical parameters used in 3DEC calculations

[0109] Table 8. Physical and mechanical parameters of the structural surfaces used in the 3DEC calculation.

[0110] In this embodiment, considering that the calculation mainly involves the mechanical characteristic analysis of the tunnel traversing multi-jointed rock, the three-dimensional discrete single-method program 3DEC7.0 is used for numerical simulation. In the calculation model, the surrounding rock material is simulated using a Mohr-Coulomb elastoplastic constitutive model, and the tunnel lining is simulated using an elastic constitutive model.

[0111] Please refer to Figure 15 , Figure 15 This is a schematic diagram of a numerical calculation model for different slope gradients under a rock slope provided in an embodiment of this application. Figure 15 The paper shows that the fixed tunnel burial depth is 10m, the structural surface inclination angle is 30°, the rock layer thickness is 5m, and nine calculation models with different slopes from 10° to 50° are established.Figure 16 The maximum compressive stress cloud map of the surrounding rock of the tunnel under different slope gradients is provided in the embodiments of this application. Figure 16 Each maximum compressive stress contour plot and Figure 15 Each numerical calculation model corresponds one-to-one. The color of the maximum compressive stress contour map changes from blue to red, indicating that the compressive stress value gradually increases.

[0112] Based on nine numerical calculation models, the influence of the slope gradient on the bias coefficient of a rock slope can be obtained. Please refer to [the relevant documentation]. Figure 17 , Figure 17 This is a schematic diagram illustrating the variation curves of slope gradient and bias coefficient under a rock slope provided in this application embodiment. It can be seen that as the slope increases, the bias coefficient at the arch foot increases, reaching a maximum value around 45 degrees. When the slope exceeds 45 degrees, the bias coefficient at the arch foot decreases. Compared to soil slopes, bedding rock slopes, due to the presence of structural surfaces, have a relatively fixed bias direction on the tunnel. Therefore, there is no sudden drop in the bias coefficient at the arch foot and an increase in the bias coefficient at the arch shoulder under steep slopes. It can be considered that the bias coefficient is directly proportional to the slope; that is, the greater the slope, the more severe the bias pressure on the tunnel.

[0113] Please refer to Figure 18 , Figure 18 A schematic diagram of a numerical calculation model for tunnels at different burial depths under a rock slope, provided in an embodiment of this application. Figure 18 The paper shows that the fixed slope of this application is 25°, the inclination angle of the structural surface is 30°, the rock layer thickness is 5m, and 12 calculation models with different slopes from 5m to 100m are established. Figure 19 The maximum compressive stress cloud map of the surrounding rock of tunnels at different tunnel burial depths under rock slopes provided in this application embodiment. Figure 19 Each maximum compressive stress contour plot and Figure 18 Each numerical calculation model corresponds one-to-one. The color of the maximum compressive stress contour map changes from blue to red, indicating that the compressive stress value gradually increases.

[0114] Based on 12 numerical calculation models, the influence of the slope gradient on the bias coefficient of a rock slope can be obtained. Please refer to [the relevant documentation]. Figure 20 , Figure 20 This is a schematic diagram illustrating the variation curves of tunnel burial depth and bias coefficient under rock slopes provided in this application embodiment. It can be seen that as the tunnel burial depth increases, the bias coefficient at the arch foot and sidewalls shows the same decreasing trend, while the bias at the arch shoulder remains insignificant. Within a burial depth of 20m, the rate of change of the bias coefficient is relatively large. When the burial depth exceeds 45m, the bias coefficient is generally less than 0.2 and does not change significantly. At this point, the stress on both sides of the tunnel is relatively balanced, and the tunnel is not affected by bias. This also explains why shallow-buried tunnels are more prone to bias failure at the tunnel entrance.

[0115] Please refer to Figure 21 ,Figure 21 This is a schematic diagram of a numerical calculation model for different structural surface dip angles under a rock slope, provided in an embodiment of this application. Figure 21 The paper shows that the fixed slope angle is 25°, the tunnel depth is 10m, the rock layer thickness is 5m, and six calculation models with different structural surface inclination angles from 10° to 60° are established. Figure 22 The maximum compressive stress cloud map of tunnel surrounding rock with different structural surface dip angles under rock slopes provided in this application embodiment. Figure 22 Each maximum compressive stress contour plot and Figure 21 Each numerical calculation model corresponds one-to-one. The color of the maximum compressive stress contour map changes from blue to red, indicating that the compressive stress value gradually increases.

[0116] Based on six numerical calculation models, the influence of the dip angle of the structural surface under the rock slope on the bias coefficient can be obtained. Please refer to [link / reference]. Figure 23 , Figure 23 This is a schematic diagram illustrating the relationship between the dip angle of the structural surface and the bias coefficient under a rock slope, as provided in an embodiment of this application. It can be seen that when the dip angle of the structural surface is less than 40°, the bias coefficient increases slightly with increasing dip angle. When the dip angle of the structural surface is greater than 40°, the bias coefficient decreases slightly with increasing dip angle. The overall change in the bias coefficient is not significant with changes in the dip angle of the structural surface, indicating that the change in the dip angle has little impact on the bias coefficient.

[0117] Please refer to Figure 24 , Figure 24 A schematic diagram of a numerical calculation model for the structural surface strength under a rock slope provided in an embodiment of this application. Figure 24 The diagram shows a calculation model with a fixed cross slope of 25°, a tunnel depth of 10m, a rock layer thickness of 5m, a structural surface inclination of 30°, a structural surface cohesion of 10kPa, and an internal friction angle of 10°~50°. Figure 25 The maximum compressive stress cloud map of tunnel surrounding rock with different internal friction angles under rock slopes provided in this application embodiment is an example of this application. Figure 25 The diagram shows the maximum compressive stress contour plots with the surrounding rock elastic modulus parameter set to an internal friction angle of 10°, 20°, 30°, 40°, and 50°. The colors of the maximum compressive stress contour plots change from blue to red, indicating that the compressive stress value gradually increases.

[0118] Based on multiple maximum compressive stress contour maps, the influence of the internal friction angle of different structural planes on the bias coefficient under rock slopes can be obtained. Please refer to [link / reference needed]. Figure 26 , Figure 26This is a schematic diagram illustrating the relationship between the internal friction angle and the bias coefficient of a structural surface under a rock slope, as provided in an embodiment of this application. It can be seen that as the internal friction angle increases, the bias coefficient decreases. However, when the internal friction angle increases beyond the slope angle of the structural surface, the decreasing trend of the bias coefficient slows significantly. This is because as the internal friction angle increases, the sliding tendency of the block weakens, and the stress on the deeply buried side of the tunnel decreases, leading to a decrease in the bias coefficient. However, when the internal friction angle is greater than the slope angle, the structural surface strength can be fully utilized, and the bias coefficient will no longer change sensitively with the internal friction angle. Overall, when the structural surface strength is sufficiently high, the bias coefficient does not change with the structural surface strength; when the structural surface strength is too low, the bias coefficient is more sensitive to changes in the structural surface strength.

[0119] Please refer to Figure 27 , Figure 27 This is a schematic diagram of a numerical calculation model for different rock layer thicknesses under a rock slope, provided in an embodiment of this application. Figure 27 The paper shows nine calculation models with a fixed tunnel burial depth of 10m, a structural surface inclination angle of 25°, a cross slope of 25°, and different rock layer thicknesses ranging from 1m to 5m, as set in this application. Figure 28 The maximum compressive stress cloud map of tunnel surrounding rock with different rock layer thicknesses under rock slopes provided in the embodiments of this application. Figure 28 Each maximum compressive stress contour plot and Figure 27 Each numerical calculation model corresponds one-to-one. The color of the maximum compressive stress contour map changes from blue to red, indicating that the compressive stress value gradually increases.

[0120] Based on nine numerical calculation models, the influence of the thickness of the underlying rock layer on the bias coefficient of a rock slope can be obtained. Please refer to [the relevant documentation]. Figure 29 , Figure 29 This is a schematic diagram illustrating the variation curves of rock stratum thickness and bias coefficient under a rock slope provided in an embodiment of this application. It can be seen that the bias coefficient decreases slightly with increasing rock stratum thickness. This is because when the rock stratum thickness is small, there are more structural surfaces passing through the tunnel, which easily leads to stress concentration in the surrounding rock, increasing stress and making bias more likely. However, overall, the bias coefficient does not change significantly with increasing rock stratum thickness; therefore, the influence of rock stratum thickness on bias is relatively small.

[0121] Please refer to Figure 30 , Figure 30 This is a schematic diagram of a numerical calculation model for the location of different weak structural surfaces under a rock slope, as provided in an embodiment of this application. Figure 30 The paper shows six calculation models with a fixed cross slope of 25°, a tunnel burial depth of 10m, and a weak structural surface at different locations with an inclination angle of 30°.

[0122] Based on six numerical calculation models, the influence relationship between the weak structural surface and the bias coefficient under a rock slope can be obtained. Please refer to [link / reference]. Figure 31 , Figure 31 This is a schematic diagram illustrating the variation curves of the weak structural surface and the bias coefficient under a rock slope, provided in an embodiment of this application. It can be seen that when the weak structural surface is located above the tunnel arch, the structural surface has almost no effect on the bias of the tunnel; when the weak structural surface crosses the tunnel, it causes stress concentration at the crossing point, increasing the degree of bias; when the weak structural surface passes below the tunnel arch, the bias coefficient begins to decrease slowly. Overall, when the weak structural surface is above the tunnel, it does not cause bias to the tunnel; the bias effect is greatest when it crosses the tunnel; and when it is below the tunnel, the bias effect gradually weakens with increasing burial depth.

[0123] Please refer to Figure 32 , Figure 32 This application provides a stress distribution cloud map of a tunnel under a rock slope before excavation. The entire cloud map uses color variations to characterize stress magnitude; blue generally represents lower compressive stress, green and yellow represent moderate compressive stress, and red and orange represent higher compressive stress. Parts (a) and (b) of the first group fix the tunnel's burial depth and primarily study the influence of slope gradient on stress distribution. Part (a) shows the stress state at a slope of 10 degrees. Due to its relatively gentle slope and high topographic symmetry, the stress distribution is relatively uniform and symmetrical. It can be seen that the isostress lines inside the mountain are roughly horizontally layered, and the stress gradient around the tunnel body changes gently, without severe stress concentration. This indicates that under gentle slope conditions, the slope has minimal interference with the initial stress field at the tunnel location. In part (b), the slope reaches 40 degrees. The steep terrain drastically alters the mountain's stress pattern, leading to a significant redistribution of stress. The stress distribution exhibits strong asymmetry. On the shallow-buried side of the slope, stress is released more fully due to the influence of the free face, resulting in a large blue low-stress zone. On the deep-buried side, however, due to the enormous constraint of the mountain rock strata, stress cannot be released to the free face, causing a high concentration of stress and forming a significant red high-stress zone. The tunnel axis is no longer located in a symmetrical stress field; the initial stress level on its right side (deep-buried side) is much higher than that on its left side (shallow-buried side). This inherent stress difference is a direct manifestation of the "biased pressure" effect before excavation.

[0124] The second group, sections (c) and (d), fixed the slope conditions and primarily studied the impact of tunnel depth on stress distribution. Section (c) shows the stress state at a depth of ten meters. Because the tunnel is very close to the slope, its overlying rock thickness is very thin, resulting in a low overall stress level around the tunnel, with large areas appearing blue. Stress contour lines closely surround the tunnel arch and sidewalls. Section (d) shows the stress state at a depth of thirty meters. With increasing depth, the overlying rock thickness above the tunnel increases significantly, and the self-weight of the rock mass leads to a significant increase in the overall stress level of the entire area, with the warm-colored area in the figure becoming significantly larger. The tunnel needs to withstand greater initial ground stress.

[0125] It can be seen that the initial geostress field before tunnel excavation exhibits a layered distribution parallel to the slope, thus the stress on the deeply buried side is greater than that on the shallowly buried side. After tunnel excavation, a new free face is formed, causing drastic changes in the stress field near the free face. Because the original geostress on the deeply buried side is greater than that on the shallowly buried side, the stress release is more significant, resulting in greater displacement of the lining and surrounding rock. The tunnel structure is prone to eccentric failure. As the slope increases, the stress difference between the two sides before tunnel excavation will increase due to the layered distribution of the initial stress field parallel to the slope, thus the degree of eccentricity will become more severe with increasing slope. However, as the burial depth increases, the ratio of the initial stress fields on both sides before tunnel excavation will decrease, thus the eccentricity effect caused by topography will decrease to almost nothing with increasing burial depth.

[0126] After tunnel excavation, a new free face is created. The high active earth pressure on the deeply buried side can easily lead to significant deformation of the tunnel structure on the deeply buried side, resulting in eccentric deformation. Please refer to [link / reference needed]. Figure 33 and Figure 34 , Figure 33 The slope displacement cloud map after tunnel excavation is provided for the embodiments of this application. Figure 34 This is a schematic diagram illustrating the eccentric deformation trend after tunnel excavation, provided in an embodiment of this application. From... Figure 33 As can be seen, (a) shows the complex deformation pattern caused by tunnel excavation under these unfavorable terrain conditions. The displacement influence range is extremely wide, not only limited to the area around the tunnel but also extending to the entire slope surface. The color transitions from yellowish-green inside the slope to red near the toe, indicating that the displacement gradually increases from the inside to the outside. (b) shows a deeply buried, gently sloping slope. Due to the greater burial depth and gentle slope, the bias pressure effect brought about by the terrain is significantly weakened. The displacement cloud map shows that the high displacement area (yellowish-green) is closely concentrated in a relatively small area directly above the tunnel arch, while the slope as a whole shows almost no obvious deformation. The displacement vector arrows are almost all vertically downward pointing to the tunnel, which clearly shows that the dominant deformation mechanism under this condition is the loosening and failure caused by tunnel excavation, mainly vertical settlement, rather than the overall instability of the slope.

[0127] A multi-factor coupled analysis was conducted considering both slope and tunnel depth. Comparison of the eccentricity coefficient variation curves at three characteristic points—the tunnel shoulder, arch waist, and arch foot—revealed that, when considering the coordinated deformation of the tunnel and surrounding rock system, the shoulder and arch waist experience significant displacement due to eccentric loads, affecting the variation of the eccentricity coefficient. The arch foot typically does not experience significant eccentric deformation, and its eccentricity coefficient variation is relatively clear. Therefore, the eccentricity coefficient at the arch foot best reflects the degree of eccentricity influence on the tunnel as a whole under different conditions.

[0128] After completing the calculations for all working conditions, the results of all bias coefficients for the soil slope can be systematically organized into a matrix table (Table 9).

[0129] Table 9 Calculation results of bias pressure coefficient for tunnels on soil slopes

[0130] Please refer to Figure 35 , Figure 35 This is a schematic diagram showing the variation curves of the bias pressure coefficient at the arch foot of a soil slope under different tunnel depths and slopes, provided in the embodiments of this application. It can be seen that in soil slopes, tunnel depth and slope gradient have the greatest impact on the degree of bias pressure. Based on the research results on the bias pressure law of soil slopes, a qualitative evaluation table of bias pressure at the tunnel entrance can be derived.

[0131] In some embodiments, a semi-quantitative evaluation table for the target tunnel portal section can be generated based on the slope gradient and tunnel depth. The semi-quantitative evaluation table includes a first marker, a second marker, and a third marker. The first marker indicates that the tunnel's bias state is at a first level, the second marker indicates that the tunnel's bias state is at a second level, and the third marker indicates that the tunnel's bias state is at a third level. Specifically, the first marker can be blue, the second marker can be yellow, and the third marker can be red. In practical applications, the first, second, and third markers can also be other colors, graphics, or combinations of colors and graphics.

[0132] For example, please see Figure 36 , Figure 36 This is a schematic diagram illustrating the qualitative judgment of the degree of bias pressure in tunnels under different combinations of burial depth and slope in soil surrounding rock, as provided in the embodiments of this application.

[0133] Under a certain combination of burial depth and slope, when the bias coefficient is less than 0.3, it is displayed in blue in the table, and the degree of bias is "slight bias"; when the bias coefficient is greater than or equal to 0.3 but less than 0.5, it is displayed in yellow in the table, indicating that bias needs to be considered in the tunnel design, and the degree of bias is "moderate bias"; when the bias coefficient is greater than or equal to 0.5, it is displayed in red in the table, indicating that anti-slide support is required for the slope, otherwise bias failure may occur, and the degree of bias is "severe bias".

[0134] After completing the calculations for all working conditions, the results of all bias coefficients for the rock slope can be systematically organized into a matrix table, Table 10.

[0135] Table 10 Calculation results of bias pressure coefficient for tunnels on rock slopes

[0136] Please refer to Figure 37 , Figure 37 This is a schematic diagram illustrating the variation of the arch foot bias pressure coefficient with slope at different burial depths under a rock slope, provided as an embodiment of this application. It can be seen that in rock slopes, both tunnel burial depth and slope gradient have the greatest impact on the degree of bias pressure. Based on the research results on the bias pressure law of rock slopes, a qualitative evaluation table of tunnel entrance bias pressure can be derived. Please refer to... Figure 38 , Figure 38 This diagram illustrates the qualitative assessment of the bias pressure degree under different combinations of burial depth and slope for tunnels on rock slopes, as provided in this embodiment of the application. It can be seen that when the tunnel depth exceeds 45m, the bias pressure coefficient is less than 0.3 regardless of slope variation, and the increase in the bias pressure coefficient with increasing slope is very small. The tunnel height used in the numerical simulation is 10m; therefore, it can be determined that when the tunnel depth exceeds approximately 4.5 times the tunnel height, the influence of bias pressure can be ignored.

[0137] In the semi-quantitative evaluation method for tunnel portal bias provided in this application embodiment, the complex mechanical behavior is transformed into a quantifiable evaluation benchmark by introducing the bias coefficient method as a core index. Based on multiple coefficient values, bias levels are classified, establishing a grading system from slight to severe, providing a classification basis for bias conditions of different severity. Furthermore, numerical simulation is used to analyze the influence of various factors on the bias coefficient at different levels, thereby identifying key target influencing factors. Finally, in practical applications, only a few key factors of the target tunnel portal section need to be obtained to generate semi-quantitative evaluation results. This method avoids cumbersome full-parameter calculations, significantly improving evaluation efficiency while ensuring a certain level of accuracy, and providing strong support for rapid risk assessment and targeted design in tunnel engineering.

[0138] Based on the same concept, embodiments of this application also provide a computer device, which may include a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the methods described above.

[0139] Based on the same concept, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0140] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A semi-quantitative evaluation method for bias pressure at tunnel portal sections, characterized in that, include: The bias pressure coefficients of multiple tunnel portal sections are calculated and determined; wherein the bias pressure coefficients are obtained based on the ratio of the stress difference between the surrounding rock on the left and right sides of the tunnel to the average stress of the surrounding rock on both sides. Multiple bias levels are determined based on the values ​​of multiple bias coefficients; Based on numerical simulation, the influence of multiple bias influencing factors on the bias coefficient at each bias level is determined, and one or more bias influencing factors that have the greatest impact on the bias coefficient are identified as target influencing factors. Obtain one or more of the target influencing factors for the target tunnel portal section, and generate a semi-quantitative evaluation result for the target tunnel portal section based on the target influencing factors.

2. The method according to claim 1, characterized in that, Multiple bias levels are determined based on the values ​​of multiple bias coefficients, including: Obtain multiple critical engineering conditions for the tunnel entrance section and establish a tunnel model corresponding to each critical engineering condition; Excavation simulations were performed on each tunnel model based on numerical simulations, and multiple critical bias coefficients were calculated. Multiple bias levels are determined by using the critical bias coefficient as the critical value.

3. The method according to claim 1, characterized in that, The tunnel entrance section is located within a soil slope or a rock slope. When the tunnel entrance section is located on a soil slope, the factors influencing the bias pressure include the slope gradient, tunnel depth, and surrounding rock stiffness. When the tunnel entrance section is located on a rock slope, the factors influencing the bias pressure include the slope gradient, tunnel depth, structural surface dip angle, structural surface strength, rock layer thickness, and location of weak structural surfaces.

4. The method according to claim 3, characterized in that, The method of determining the influence of multiple bias influencing factors on the bias coefficient at each bias level based on numerical simulation, and identifying one or more bias influencing factors with the greatest influence on the bias coefficient as target influencing factors, includes: When the tunnel entrance section is located on a soil slope, numerical simulation of the bias pressure influencing factors is performed based on the finite difference method to determine one or more bias pressure influencing factors that have the greatest impact on the bias pressure coefficient as the target influencing factors. When the tunnel entrance section is located on a rock slope, numerical simulation of the bias influencing factors is performed based on the discrete element method to determine one or more of the bias influencing factors that have the greatest impact on the bias coefficient as the target influencing factors.

5. The method according to claim 1, characterized in that, The target influencing factors include slope gradient and tunnel depth.

6. The method according to claim 1, characterized in that, The bias levels include a first level, a second level, and a third level. The first level indicates that the bias coefficient is less than 0.3, the second level indicates that the bias coefficient is greater than or equal to 0.3 and less than 0.5, and the third level indicates that the bias coefficient is greater than or equal to 0.

5.

7. The method according to claim 1, characterized in that, The step of acquiring one or more target influencing factors of the target tunnel portal section, and generating a semi-quantitative evaluation result of the target tunnel portal section based on the target influencing factors, includes: A semi-quantitative evaluation table for the target tunnel portal section is generated based on the slope gradient and tunnel burial depth; wherein, the semi-quantitative evaluation table includes a first marker, a second marker, and a third marker, the first marker indicating that the tunnel's bias state is at a first level, the second marker indicating that the tunnel's bias state is at a second level, and the third marker indicating that the tunnel's bias state is at a third level.

8. The method according to any one of claims 1-7, characterized in that, The bias coefficient is the bias coefficient at the tunnel arch foot.

9. A computer device, characterized in that, The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 8.

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