A method and system for estimating bearing capacity of slope foundation considering slope stability

Abstract

The application discloses a slope foundation bearing capacity estimation method and system considering slope stability, and the method comprises the following steps: collecting geological data and environmental data of a target area and constructing a target area simulation model, simulating slope parameter changes of the target area under different environments and loads; determining a potential sliding surface and calculating a slope stability coefficient of the target area based on the simulated slope parameter changes, a preset sliding surface determination method and a preset slope stability coefficient calculation method; determining the geological risk grade of the target area, and when the geological risk grade is greater than a preset risk grade, collecting a slope surface image of the target area, calculating an earth parameter adjustment value; and correcting a foundation bearing capacity formula by using the slope stability coefficient or a slope stability coefficient recalculated according to the earth parameter adjustment value, and outputting a foundation bearing capacity estimation. The application can consider slope stability and more accurately obtain a slope foundation bearing capacity estimation under complex terrain conditions.

Description

Technical Field

[0001] This application relates to the technical field of foundation bearing capacity assessment, and in particular to a method and system for estimating the bearing capacity of adjacent foundations that takes into account slope stability. Background Technology

[0002] In the field of civil engineering, foundation bearing capacity assessment is one of the core steps in ensuring the safety and durability of building projects. With rapid urbanization and the gradual depletion of available land resources, construction projects are increasingly expanding into complex terrains, such as hillsides and river valley edges, creating a growing demand for development in these challenging environments. These areas often present higher geological risks, making the scientific and rational assessment of foundation bearing capacity crucial. Accurate and reliable foundation bearing capacity estimation not only provides a solid basis for building design to meet functional requirements but also effectively mitigates economic losses and even serious safety accidents caused by improper construction.

[0003] To address the need for foundation bearing capacity assessment under complex terrain conditions, the industry currently predominantly employs methodologies based on traditional soil mechanics theory. These methods primarily include, but are not limited to: obtaining soil physical property parameters (such as particle composition and porosity) through field exploration, determining shear strength indices through laboratory tests, and calculating allowable bearing capacity using empirical formulas recommended by standards to complete a preliminary assessment; simultaneously, finite element numerical simulation tools are used to analyze the stress-strain distribution characteristics within the strata under specific loading conditions to aid decision-making. However, most of these processes are based on the assumption of a flat ground surface and do not fully consider the impact of potential sloping surfaces on overall stability in the actual environment.

[0004] While some existing technologies have begun to address the impact of slope stability on foundation bearing capacity and have proposed corresponding correction methods, such as introducing slope stability coefficients for initial adjustments, the accuracy of the specific correction process still needs improvement due to a lack of sufficient refinement. Especially under complex natural conditions, existing correction methods struggle to fully capture the subtle influences of slope characteristics and their interaction with the surrounding environment, potentially leading to biased results. This means that in certain specific situations, particularly in foundation construction projects involving steep slopes, relying solely on data provided by current technology may be insufficient to fully guarantee the safe and smooth progress of the project. Therefore, there is an urgent need to further optimize and improve relevant assessment strategies. Summary of the Invention

[0005] In order to obtain a more accurate estimate of the bearing capacity of the foundation under complex terrain conditions, taking into account slope stability, this application provides a method and system for estimating the bearing capacity of the foundation under slope stability.

[0006] In a first aspect, this application provides a method for estimating the bearing capacity of a foundation on a slope that takes into account slope stability, including:

[0007] Collect geological and environmental data for the target area, which includes the slope.

[0008] A simulation model of the target area is constructed based on the collected geological and environmental data to simulate the changes in slope parameters under different environmental and load conditions. Potential sliding surfaces are determined based on the simulated changes in slope parameters and a pre-defined method for determining sliding surfaces. The slope stability coefficient of the target area is calculated based on the determined potential sliding surfaces and a pre-defined slope stability coefficient calculation method. The geological risk level of the target area is determined based on the potential sliding surfaces and the slope stability coefficient. When the geological risk level is greater than the pre-defined risk level, images of the slope surface in the target area are collected, and image analysis technology is used to identify cracks and vegetation coverage in the slope. Adjusted soil parameters are calculated based on these cracks and vegetation coverage.

[0009] The formula for foundation bearing capacity is corrected by using the slope stability coefficient or by recalculating the slope stability coefficient based on soil parameters, and the corrected foundation bearing capacity estimate is output.

[0010] By adopting the above scheme, geological and environmental data of the target area are collected, and the changes in slope parameters under different loads are analyzed by combining simulation models. Potential sliding surfaces are accurately located and stability coefficients are calculated. When the geological risk level is determined to be high, image analysis technology is introduced to quantitatively assess and calculate soil parameter adjustment values ​​for slope cracks and vegetation coverage. The obtained slope stability coefficient and related adjustment coefficients are used to revise the foundation bearing capacity formula to obtain a more accurate foundation bearing capacity estimate.

[0011] Preferred options also include:

[0012] Based on the collected geological and environmental data, the slope types within the target area are classified; the slope types include:

[0013] Homogeneous soil slopes, jointed / faulted rock slopes, slopes with high seepage or dynamic loads, and complex slopes with multi-field coupling;

[0014] Based on the slope type, obtain the corresponding preset target area simulation model type, preset sliding surface determination method, and preset slope stability coefficient calculation method; for homogeneous soil slopes, set up a matching single stress field simulation model, a sliding surface determination method based on stress field, and the limit equilibrium method; for jointed / faulted rock slopes, set up a matching single stress field simulation model, a sliding surface determination method based on intelligent optimization algorithm, and the strength reduction method; for slopes with high seepage or dynamic loads, set up a matching seepage-stress coupled field simulation model, a sliding surface determination method based on intelligent optimization algorithm, and the stress integration method; for complex slopes with multi-field coupling, set up a matching seepage-temperature-stress coupled field simulation model, a sliding surface determination method based on intelligent optimization algorithm, and an improved strength reduction method under coupled numerical conditions.

[0015] By adopting the above scheme, and selecting appropriate simulation models, sliding surface determination methods, and stability coefficient calculation methods according to the characteristics of different types of slopes, the stability of slopes under various complex terrain conditions can be reflected more accurately.

[0016] Preferred options also include:

[0017] Using a deep learning model, the rate of change of geological risk level in the target area is predicted based on the identified potential sliding surface and the calculated slope stability coefficient.

[0018] Compare the predicted rate of change of the geological risk level of the target area with the preset rate of change. If the rate of change of the geological risk level is greater than the preset rate of change, select to collect the slope surface image of the target area when the geological risk level is greater than the preset risk level, instead of selecting to collect the slope surface image of the target area when the geological risk level is greater than the preset risk level, and increase the frequency of collecting the slope surface image of the target area.

[0019] By adopting the above scheme, the changing trend of geological risk level is predicted based on the deep learning model, thereby predicting the possible deterioration of slope stability in advance, triggering the conditions for collecting slope surface images in advance, and increasing the collection frequency, making the foundation bearing capacity assessment more sensitive and accurate.

[0020] Preferred options also include:

[0021] The geometric parameters of the slope in the target area are quantized without dimension, and a geometric factor function of the slope in the target area is constructed that is associated with the parameter index in the foundation bearing capacity formula.

[0022] The geometric factor function of the slope in the target area is used as a new correction factor. The new correction factor is weighted and calculated with the slope stability coefficient or the slope stability coefficient recalculated based on the adjusted values ​​of soil parameters. The formula for foundation bearing capacity is then corrected based on the weighted calculation results.

[0023] By adopting the above scheme, in addition to considering the influence of the slope's own stability on the foundation bearing capacity, it is also necessary to consider the influence of the geometric characteristics of the slope in the target area on the foundation bearing capacity. The correction factor corresponding to the geometric factor function is combined with the slope stability coefficient for weighted calculation, further refining the correction process of the foundation bearing capacity formula, so that the evaluation results are closer to the actual situation.

[0024] Preferably, the weight ratio in the weighted calculation of the new correction factor and the slope stability coefficient or the slope stability coefficient recalculated based on the adjusted soil parameter values ​​is determined according to the geological risk level of the target area; if the geological risk level of the target area is greater than the preset geological risk level, the weight ratio of the new correction factor is less than the weight ratio of the slope stability coefficient or the slope stability coefficient recalculated based on the adjusted soil parameter values; otherwise, the weight ratio of the new correction factor is greater than the weight ratio of the slope stability coefficient or the slope stability coefficient recalculated based on the adjusted soil parameter values.

[0025] By adopting the above scheme, the adaptability and accuracy of the foundation bearing capacity assessment model can be effectively improved by flexibly adjusting the weight ratios of slope stability and slope geometric constraints according to different geological risk levels.

[0026] Preferred options also include:

[0027] The slope parameters of the target area are simulated and obtained under the effect of the estimated foundation bearing capacity. The slope stability coefficient of the target area is recalculated. The recalculated slope stability coefficient of the target area is compared with the safety factor of the target area. If it is less than the safety factor of the target area, the parameters of the modified foundation bearing capacity formula are adjusted.

[0028] Alternatively, the actual bearing capacity of the foundation can be collected, and the error between the actual bearing capacity and the estimated bearing capacity can be compared. If the error is greater than the preset error, the parameters of the modified bearing capacity formula can be adjusted.

[0029] By adopting the above scheme, the accuracy of the current modified foundation bearing capacity formula is verified using either forward or reverse verification methods. If the accuracy is insufficient, the parameters in the foundation bearing capacity formula are further optimized and adjusted to ensure the accuracy and reliability of the foundation bearing capacity estimation.

[0030] Preferably, the method for calculating the slope stability coefficient of the target area based on the potential sliding surface and slope stability coefficient includes:

[0031] Each slope in the target area is assigned a weighting coefficient according to an allocation rule; the allocation rule is determined based on the actual slope stability coefficient of each slope under different environmental and load conditions in history.

[0032] For each slope, the corresponding slope stability coefficient is calculated, and the weighted average is used to obtain the slope stability coefficient of the target area.

[0033] By adopting the above scheme, weight coefficients are assigned to different slopes based on their historical stability, and the overall slope stability coefficient of the target area is accurately calculated based on the calculation method of potential sliding surface and stability coefficient of each slope.

[0034] Secondly, this application provides a slope foundation bearing capacity estimation system that considers slope stability, including: a slope data acquisition module for acquiring geological and environmental data of a target area containing the slope;

[0035] The slope stability coefficient acquisition module is used to construct a simulation model of the target area based on the collected geological and environmental data, simulate the changes in slope parameters under different environments and loads, determine the potential sliding surface based on the simulated changes in slope parameters and the preset sliding surface determination method, and calculate the slope stability coefficient of the target area based on the determined potential sliding surface and the preset slope stability coefficient calculation method.

[0036] The soil parameter adjustment value acquisition module is used to determine the geological risk level of the target area based on the potential sliding surface and slope stability coefficient; when the geological risk level is greater than the preset risk level, the module selects to collect the slope surface image of the target area, uses image analysis technology to identify the cracks and vegetation coverage of the slope in the target area, and calculates the soil parameter adjustment value based on the cracks and vegetation coverage of the slope in the target area.

[0037] The foundation bearing capacity correction module is used to correct the foundation bearing capacity formula using the slope stability coefficient or the slope stability coefficient recalculated based on the soil parameters, and outputs the corrected foundation bearing capacity estimate.

[0038] By adopting the above scheme, geological and environmental data of the target area are comprehensively collected, and a simulation model of the target area is constructed to accurately simulate the changes in characteristic parameters of the slope under different environments and loads. Based on the simulation results, potential sliding surfaces are determined and slope stability coefficients are calculated, thereby accurately assessing the geological risk level of the target area. When the geological risk is high, slope images are collected and slope cracks and vegetation coverage are identified through image analysis technology, and then soil parameter adjustment values ​​are calculated, making the correction of the foundation bearing capacity formula more scientific and reasonable, and improving the accuracy and reliability of foundation bearing capacity assessment under complex terrain conditions.

[0039] Thirdly, this application provides a computer-readable storage medium including a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to perform the method described above.

[0040] Fourthly, this application provides a computer device, the computer device including a memory, a processor and a program stored in the memory and executable thereon, the program being executed by the processor to implement the steps of the method described above.

[0041] In summary, this application has the following beneficial effects:

[0042] 1. By collecting geological and environmental data of the target area and combining them with simulation models to determine the potential sliding surface and slope stability coefficient, it can accurately reflect the impact of slope stability on foundation bearing capacity under complex terrain conditions and judge the geological risk level. When the geological risk level is high, image analysis technology is introduced to identify slope cracks and vegetation coverage, and the soil parameter adjustment values ​​are further refined, making the foundation bearing capacity estimate closer to the actual situation and significantly improving the assessment accuracy.

[0043] 2. Considering applicability in various scenarios, appropriate simulation models and calculation methods are selected according to different types of slopes, which improves the accuracy of foundation bearing capacity estimation under complex terrain conditions;

[0044] 3. The formula comprehensively considers the influence of the geometric characteristics of the slope in the target area on the bearing capacity of the foundation, accurately reflects the constraints and influences of factors such as slope shape and size on the bearing capacity of the foundation, and combines the correction factor constructed by geometric parameters with the slope stability coefficient for weighted calculation, further refining the correction process of the foundation bearing capacity formula, making the estimation results more accurate. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the method for estimating the bearing capacity of a foundation on a slope that considers slope stability, as described in a specific embodiment.

[0046] Figure 2 This is a schematic diagram of the structure of the slope foundation bearing capacity estimation system considering slope stability as described in a specific embodiment. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0048] like Figure 1As shown in the figure, this application discloses a method for estimating the bearing capacity of a foundation on a slope that considers slope stability. The specific steps are as follows:

[0049] S1. Collect geological and environmental data of the target area containing the slope.

[0050] Specifically, geological and environmental data of the target area are collected using sensors, drilling equipment, or samplers installed in the target area. The geological data includes, but is not limited to, basic parameters such as soil type, moisture content, and density; and topographic parameters such as slope, slope height, slope toe distance, boreholes, geological profiles, soil parameters, rock and soil layer distribution, faults, and groundwater. The environmental data includes, but is not limited to, meteorological data such as rainfall, temperature, and humidity; seismic wave data; and data on surrounding traffic conditions.

[0051] S2. Construct a simulation model of the target area to simulate the changes in slope parameters within the target area under different environments and loads.

[0052] Specifically, a simulation model is established in the finite element software based on the collected geological and environmental data, and relevant parameters are set, including: defining the geometric conditions and dimensions of the target area model, defining material properties (such as permeability, porosity, elastic modulus, Poisson's ratio, cohesion, internal friction angle, density, etc.), setting initial and boundary conditions, such as initial temperature, stress state, and inlet and outlet conditions of the seepage field, and defining coupling relationships.

[0053] The finite element method (FEM) simulation is run to simulate the changes in slope parameters within the target area under different environments and loads, such as the changes in various physical fields (seepage field, temperature field, stress field). Key data are analyzed and extracted, such as seepage velocity, temperature value, stress distribution, and other slope parameters at specific locations. The loads include static loads, such as additional foundation loads (e.g., building self-weight), soil self-weight stress, and dynamic loads, such as seismic waves and traffic vibrations.

[0054] S3. Determine the potential sliding surface based on the simulated changes in slope parameters and the preset sliding surface determination method.

[0055] Specifically, a sliding surface criterion based on the stress field can be used to initially determine potential sliding surfaces. For example, the ratio of shear strength to shear stress can be calculated for each mesh element, using the following formula:

[0056]

[0057] In the formula, σ n For normal stress, For shear stress, cohesion c, and internal friction angle, ... For F local The region with a value less than 1 is preliminarily identified as a potential sliding surface.

[0058] Alternatively, by analyzing the equivalent shear strain contour map, strain concentration areas can be identified, and potential sliding surfaces can be preliminarily determined; or by using the plastic penetration criterion or displacement abrupt change analysis, potential sliding surfaces can be preliminarily determined, etc.

[0059] To further identify potential sliding surfaces, intelligent optimization algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO), or simulated annealing (SA), can be used to further evaluate the initially identified potential sliding surfaces. Generally, intelligent optimization algorithms are generated by training with historical slope parameter changes and the actual identified sliding surface data.

[0060] S4. Calculate the slope stability coefficient of the target area based on the determined potential sliding surface and the preset slope stability coefficient calculation method.

[0061] Specifically, the slope stability coefficient of the target area can be calculated by using preset slope stability coefficient calculation methods such as the limit equilibrium method, strength reduction method, stress integration method, and strength reduction method under coupled numerical conditions.

[0062] If the limit equilibrium method is used: import the stress field calculated by the finite element method into the limit equilibrium software, and use the Bishop method to calculate the three-dimensional safety factor;

[0063] Alternatively, the strength reduction method can be used to gradually reduce the soil shear strength parameters. Calculate the stability coefficient F until the finite element calculation fails to converge (instability criterion). s = Actual strength / Critical strength for instability;

[0064] Alternatively, the stability coefficient can be calculated using the finite element stress integral method, with the following formula:

[0065]

[0066] In the formula, the integration is performed along the potential sliding surface S.

[0067] Alternatively, improvements to the limit equilibrium method or strength reduction method under the numerical conditions of trampling coupling include:

[0068] Definition of multi-field coupling mechanism: The seepage field changes the effective stress (σ′=σ-u) through pore water pressure. w The temperature field is mediated by the coefficient of thermal expansion (α). TThis induces changes in soil volume, and the three factors satisfy the equations of energy conservation, mass conservation, and momentum conservation. Key coupling parameters are quantified, completing seepage-stress coupling, temperature-seepage coupling, and temperature-stress coupling. Coupled numerical acquisition includes: initializing the initial seepage field calculation; importing pore water pressure into the stress field and calculating the displacement field; updating the permeability and thermal conductivity; coupling the temperature field to calculate the thermal stress distribution, etc. The coupled numerical values ​​are used to improve the strength reduction method, including: substituting the pore water pressure distribution and temperature correction parameters obtained from numerical simulation into the correction formula. Actual shear stress is a measure of the shear stress that the slope soil actually bears under specific stress states and environmental conditions.

[0069] Furthermore, considering that there may be multiple slopes within the target area, in order to accurately calculate the slope stability coefficient of the target area, the slope stability coefficient calculation method based on potential sliding surface and slope stability coefficient includes: assigning a weight coefficient to each slope in the target area according to the allocation rules; the allocation rules include determining the actual slope stability coefficient of each slope under different environmental and load conditions in history, with a higher actual slope stability coefficient resulting in a higher weight; and calculating the slope stability coefficient of the target area by weighting the corresponding slope stability coefficients calculated for each slope.

[0070] S5. Determine the geological risk level of the target area and obtain the soil parameter adjustment values.

[0071] Specifically, the geological risk level of the target area is determined based on the identified potential sliding surface and the calculated slope stability coefficient. The geological risk level of the target area includes general, moderate, and medium, and each level is assigned a corresponding risk level value. The risk level can be determined by constructing a neural network model. The specific model is generated by training on the marked geological risk level of the target area in history and its corresponding risk level value, as well as the potential sliding surface and slope stability coefficient.

[0072] Considering the high risk of slope collapse during periods of high geological risk, it is necessary to estimate the foundation bearing capacity by combining actual monitoring data. Specifically, when the geological risk level exceeds a preset risk level, images of the slope surface in the target area should be collected. For example, UAV remote sensing technology can be introduced to monitor changes in the slope surface. Image analysis technology can be used to identify cracks and vegetation coverage in the target area, and soil parameters can be adjusted based on these parameters. The specific formula is as follows:

[0073] Crack index In the formula, L is the crack length, w is the crack width, α and β are weighting coefficients, and L max wcrit Critical threshold; coverage metric C v，ref For reference coverage, C v For coverage; using cohesion, it is corrected to c′=c·(1-k c I c ), where k c The coefficient is an empirical factor; the internal friction angle is corrected to... Where k v This is the gain coefficient.

[0074] S6. Modify the traditional foundation bearing capacity formula to obtain the revised foundation bearing capacity estimate.

[0075] Specifically, the formula for foundation bearing capacity is corrected by using the slope stability coefficient or by recalculating the slope stability coefficient based on soil parameters, and the corrected foundation bearing capacity estimate is output.

[0076] The slope stability coefficient recalculated based on the adjusted soil parameters refers to the coefficient calculated using the adjusted soil parameters (cohesion correction value c′, internal friction angle correction value). Substitute the values ​​into the constructed simulation model and recalculate to obtain F. s .

[0077] The revised formula for foundation bearing capacity is: q ult '=ηq ult =f(F s )q ult Among them, F is fitted through finite element parametric analysis. s Relationship with bearing capacity reduction:

[0078]

[0079] In the formula, F s，安全 The target safety threshold is typically set to 1.5; F s，临界 The instability threshold is typically set to 1.0, and a and b are determined through regression analysis; or k is the decay rate parameter.

[0080] In a specific embodiment, to more accurately reflect the slope stability under various complex terrain conditions, slope types are classified according to slope characteristics, and appropriate simulation models, sliding surface determination methods, and stability coefficient calculation methods are selected based on the slope type. The method further includes:

[0081] Based on the collected geological and environmental data, the slope types included in the target area are classified; the slope types include: homogeneous soil slopes, jointed / faulted rock slopes, slopes with high seepage or dynamic loads, and complex slopes with multi-field coupling.

[0082] Based on the slope type, obtain the corresponding preset target area simulation model type, preset sliding surface determination method, and preset slope stability coefficient calculation method; specifically including:

[0083] To effectively assess slope stability under simple soil conditions, a single stress field simulation model matching the homogeneous soil slope is set up, a method for determining the sliding surface based on the stress field is adopted, and the limit equilibrium method is used.

[0084] Considering the introduction of intelligent optimization algorithms to determine the sliding surface and the use of strength reduction methods to calculate the stability coefficient, it is helpful to capture the impact of the complexity of rock structures. For rock slopes with joints / faults, a single stress field simulation model matching it is set up, and the sliding surface is determined based on intelligent optimization algorithms and strength reduction methods are used.

[0085] To better address the effects of groundwater flow and dynamic loads, a matching seepage-stress coupling field simulation model, a sliding surface determination method based on intelligent optimization algorithms, and the stress integration method are established for slopes with high seepage or dynamic loads.

[0086] To accurately assess slope stability under extremely complex environments, a matching seepage-temperature-stress coupled field simulation model was established for complex slopes with multiple coupled fields. This model also includes a sliding surface determination method based on intelligent optimization algorithms and an improved strength reduction method under coupled numerical conditions. The seepage-temperature-stress coupled field simulation model satisfies the energy conservation, mass conservation, and momentum conservation equations for multiple coupled fields, including seepage-stress coupling. This coupling is achieved by linking pore water pressure and soil deformation through the effective stress principle and employing the Bishop formula for unsaturated soil strength. c′=c / F s , Temperature-seepage coupling: The temperature gradient causes a change in the seepage direction, and the permeability coefficient is corrected to: k T =k0exp(-γ T ΔT),γ T Temperature sensitivity coefficient; temperature-stress coupling: thermal expansion strain ε T =α T ΔT, α T The coefficient of thermal expansion leads to soil structural reorganization, and the elastic modulus E decreases exponentially with temperature. Fully coupled solution: Using multiphysics software such as ABAQUS, boundary conditions and material parameters are set, and coupled numerical results are obtained through an iterative process. The iterative process includes: initial seepage field calculation, importing pore water pressure into the stress field, calculating the displacement field, updating the permeability coefficient and thermal conductivity coefficient; calculating the thermal stress distribution in the temperature coupled field, performing convergence judgment, and continuing the iteration until convergence is completed.

[0087] In one specific embodiment, considering the presence of dynamic loads, a method based on a deep learning model to predict the changing trend of geological risk levels is selected to anticipate potential deterioration in slope stability. A multi-method comprehensive foundation bearing capacity assessment approach is employed to more accurately evaluate the long-term bearing capacity of the foundation. The method further includes:

[0088] Using a deep learning model, the rate of change of geological risk level in the target area is predicted based on the identified potential sliding surface and the calculated slope stability coefficient, i.e., the rate of change of geological risk level over a period of time.

[0089] By comparing the predicted rate of change of the geological risk level of the target area with the preset rate of change, if the rate of change of the geological risk level is greater than the preset rate of change, it indicates that the slope in the current target area is prone to rapid collapse and damage. Instead of selecting to collect the slope surface image of the target area when the geological risk level is higher than the preset risk level, we select to collect the slope surface image of the target area when the geological risk level is higher than the preset risk level. We also increase the frequency of collecting the slope surface image of the target area to accurately calculate the foundation bearing capacity in advance, so that users can adjust the foundation bearing capacity in time and avoid damage.

[0090] In one specific embodiment, the method comprehensively considers the influence of the geometric characteristics of the slope in the target area on the bearing capacity of the foundation, achieving a more accurate assessment of the bearing capacity of traditional foundations. The method further includes:

[0091] The geometric parameters of the slope in the target area are quantized dimensionlessly, and a geometric factor function of the slope in the target area is constructed that is associated with the parameter indices in the foundation bearing capacity formula, such as:

[0092]

[0093] λ=d / B

[0094] In the formula, a dimensionless distance ratio λ = d / B is introduced, where d is the distance from the foundation to the top of the slope and B is the foundation width; a hyperbolic function is used to describe the spatial attenuation effect, and a geometric factor parameter g(λ) is designed, where θ is the slope angle.

[0095] Using the geometric factor function of the slope in the target area as a new correction factor, the new correction factor is weighted and calculated with the slope stability coefficient or the slope stability coefficient recalculated based on the adjusted values ​​of soil parameters. The formula for foundation bearing capacity is then corrected using the weighted calculation result, as follows:

[0096] q ult '=ηq ult =(αf(F) s )+βg(λ))·q ult

[0097] In the formula, α and β are weighting coefficients; q ult For traditional load-bearing capacity.

[0098] The new correction factor g(λ) and the slope stability coefficient f(F) are mentioned. s Alternatively, the slope stability coefficient can be recalculated based on adjusted soil parameters, with the weighting ratio determined according to the geological risk level of the target area. Specifically, this includes:

[0099] If the geological risk level of the target area is greater than the preset geological risk level, it indicates a high risk of landslide, indicated by F. s If the design of the new correction factor is dominant, the weight ratio of the new correction factor should be less than that of the slope stability coefficient or the weight ratio of the slope stability coefficient recalculated based on the soil parameters, i.e., α>β; otherwise, it indicates that the current slope is relatively stable, and the focus is on geometric constraints, so the weight ratio of the new correction factor should be greater than that of the slope stability coefficient or the weight ratio of the slope stability coefficient recalculated based on the soil parameters, i.e., α<β.

[0100] Furthermore, to further improve the accuracy of the foundation bearing capacity estimation, the stress loss value on the slope side calculated by finite element method is considered to complete the stress coupling correction. The specific formulas include:

[0101]

[0102] In the formula, Δσ is the amount of horizontal stress reduction at the toe of the slope extracted by finite element method; H is the slope height;

[0103] Alternatively, considering the long-term stability impact, a time-varying factor can be introduced, taking into account the material (e.g., soil type) degradation coefficient m of the target area and the design life t, to obtain η. total =η·γ(t),

[0104] In one specific embodiment, to improve the accuracy and reliability of foundation bearing capacity assessment under complex terrain conditions and effectively avoid engineering safety hazards caused by assessment deviations, the method further includes:

[0105] The simulation obtains the changes in slope parameters of the target area under the estimated foundation bearing capacity. That is, the estimated foundation bearing capacity of the target area is applied to the finite element model, and the slope stability coefficient of the target area is recalculated. The recalculated slope stability coefficient of the target area is compared with the safety factor of the target area. If it is less than the safety factor of the target area, the parameters of the modified foundation bearing capacity formula are adjusted, such as adjusting the correction coefficient η, such as introducing a time-varying factor.

[0106] Alternatively, the actual bearing capacity of the foundation can be collected, and the error between the actual bearing capacity and the estimated bearing capacity can be compared. If the error is greater than the preset error, it indicates that the accuracy of the estimate is still lacking. The parameters of the modified bearing capacity formula can be adjusted to iteratively minimize the error between the actual bearing capacity and the estimated bearing capacity.

[0107] like Figure 2 As shown, a system for estimating the bearing capacity of a foundation on a slope that considers slope stability includes:

[0108] The slope data acquisition module 101 is used to collect geological and environmental data of the target area containing the slope.

[0109] The slope stability coefficient acquisition module 102 is used to construct a simulation model of the target area based on the collected geological and environmental data, simulate and acquire the changes in slope parameters of the target area under different environments and loads; determine the potential sliding surface based on the simulated changes in slope parameters and the preset sliding surface determination method; and calculate the slope stability coefficient of the target area based on the determined potential sliding surface and the preset slope stability coefficient calculation method.

[0110] The soil parameter adjustment value acquisition module 103 is used to determine the geological risk level of the target area based on the potential sliding surface and the slope stability coefficient; when the geological risk level is greater than the preset risk level, the module selects to collect the slope surface image of the target area, uses image analysis technology to identify the cracks and vegetation coverage of the slope in the target area, and calculates the soil parameter adjustment value based on the cracks and vegetation coverage of the slope in the target area.

[0111] The foundation bearing capacity correction module 104 is used to correct the foundation bearing capacity formula using the slope stability coefficient or the slope stability coefficient recalculated based on the soil parameter adjustment value, and output the corrected foundation bearing capacity estimate.

[0112] In one specific embodiment, the slope stability coefficient acquisition module 102 in the system is further used to classify the slope types included in the target area based on the collected geological and environmental data; the slope types include: homogeneous soil slopes, jointed / faulted rock slopes, slopes with high seepage or dynamic loads, and complex slopes with multi-field coupling; based on the slope type, it acquires the preset target area simulation model type, preset sliding surface determination method, and preset slope stability coefficient calculation method that match it; for homogeneous soil slopes, it sets up a matching single stress field simulation model and a base... Methods for determining the sliding surface in stress fields include the limit equilibrium method; for jointed / faulted rock slopes, a matching single stress field simulation model is set up, along with a sliding surface determination method based on intelligent optimization algorithms and the strength reduction method; for slopes with high seepage or dynamic loads, a matching seepage-stress coupled field simulation model is set up, along with a sliding surface determination method based on intelligent optimization algorithms and the stress integration method; for complex slopes with multi-field coupling, a matching seepage-temperature-stress coupled field simulation model is set up, along with a sliding surface determination method based on intelligent optimization algorithms and an improved strength reduction method under coupled numerical conditions.

[0113] In one specific embodiment, the soil parameter adjustment value acquisition module 103 in the system is further used to use a deep learning model to predict the rate of change of the geological risk level of the target area based on the determined potential sliding surface and the calculated slope stability coefficient; compare the predicted rate of change of the geological risk level of the target area with the preset rate of change; if the rate of change of the geological risk level is greater than the preset rate of change, then when the geological risk level is greater than the preset risk level, the slope surface image of the target area is selected to be collected instead of when the geological risk level is greater than the preset risk level, and the frequency of collecting the slope surface image of the target area is increased.

[0114] In one specific embodiment, the foundation bearing capacity correction module 104 in the system is further configured to include:

[0115] The geometric parameters of the slope in the target area are quantified dimensionlessly to construct a geometric factor function of the slope in the target area that is associated with the parameter indices in the foundation bearing capacity formula. Using this geometric factor function as a new correction factor, the new correction factor is weighted and calculated with the slope stability coefficient or the slope stability coefficient recalculated based on adjusted soil parameter values. The foundation bearing capacity formula is then corrected based on the weighted calculation result. The weight ratio in the weighted calculation of the new correction factor and the slope stability coefficient or the slope stability coefficient recalculated based on adjusted soil parameter values ​​is determined according to the geological risk level of the target area. If the geological risk level of the target area is greater than the preset geological risk level, the weight ratio of the designed new correction factor is less than the weight ratio of the slope stability coefficient or the slope stability coefficient recalculated based on adjusted soil parameter values; otherwise, the weight ratio of the designed new correction factor is greater than the weight ratio of the slope stability coefficient or the slope stability coefficient recalculated based on adjusted soil parameter values.

[0116] In one specific embodiment, the system further includes: a foundation bearing capacity correction verification module 105, used to simulate and acquire the slope parameter changes of the target area under the effect of the foundation bearing capacity estimate, recalculate the slope stability coefficient of the target area, compare whether the recalculated slope stability coefficient of the target area is less than the safety factor of the target area, if it is less, then continue to adjust the parameters of the corrected foundation bearing capacity formula; or collect the actual foundation bearing capacity, compare the error value between the actual foundation bearing capacity and the foundation bearing capacity estimate, if the error value is greater than the preset error value, then continue to adjust the parameters of the corrected foundation bearing capacity formula.

[0117] This application also discloses a computer-readable storage medium.

[0118] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and executed, such as the above-described method for estimating the bearing capacity of a foundation considering slope stability. The computer-readable storage medium includes, for example, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0119] This application also discloses a computer device.

[0120] Specifically, the computer device includes a memory and a processor, and the memory stores a computer program that can be loaded by the processor and executed to estimate the bearing capacity of the foundation of the adjacent slope that takes into account the stability of the slope.

[0121] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for estimating the bearing capacity of a foundation on a slope that considers slope stability, characterized in that, include: Collect geological and environmental data for the target area, which includes the slope. A simulation model of the target area is constructed based on the collected geological and environmental data to simulate and obtain the changes in slope parameters under different environmental and load conditions. The potential sliding surface is determined based on the simulated changes in slope parameters and the preset sliding surface determination method. The slope stability coefficient of the target area is calculated based on the determined potential sliding surface and the preset slope stability coefficient calculation method. The geological risk level of the target area is determined based on the potential sliding surface and slope stability coefficient; When the geological risk level is greater than the preset risk level, select to collect images of the slope surface in the target area, use image analysis technology to identify cracks and vegetation coverage in the slope in the target area, and calculate and adjust soil parameters based on cracks and vegetation coverage in the slope in the target area. The formula for foundation bearing capacity is corrected by using the slope stability coefficient or by recalculating the slope stability coefficient based on soil parameters, and the corrected foundation bearing capacity estimate is output.

2. The method for estimating the bearing capacity of a foundation on a slope considering slope stability according to claim 1, characterized in that, Also includes: Based on the collected geological and environmental data, the slope types included in the target area are classified. The slope types include: homogeneous soil slopes, jointed / faulted rock slopes, slopes with high seepage or dynamic loads, and complex slopes with multi-field coupling. Based on the slope type, obtain the corresponding preset target area simulation model type, preset sliding surface determination method, and preset slope stability coefficient calculation method; for homogeneous soil slopes, set up a matching single stress field simulation model, a sliding surface determination method based on stress field, and the limit equilibrium method; for jointed / faulted rock slopes, set up a matching single stress field simulation model, a sliding surface determination method based on intelligent optimization algorithm, and the strength reduction method; for slopes with high seepage or dynamic loads, set up a matching seepage-stress coupled field simulation model, a sliding surface determination method based on intelligent optimization algorithm, and the stress integration method; for complex slopes with multi-field coupling, set up a matching seepage-temperature-stress coupled field simulation model, a sliding surface determination method based on intelligent optimization algorithm, and an improved strength reduction method under coupled numerical conditions.

3. The method for estimating the bearing capacity of a foundation on a slope considering slope stability according to claim 1, characterized in that, Also includes: Using a deep learning model, the rate of change of geological risk level in the target area is predicted based on the identified potential sliding surface and the calculated slope stability coefficient. Compare the predicted rate of change of the geological risk level of the target area with the preset rate of change. If the rate of change of the geological risk level is greater than the preset rate of change, then select the method of collecting slope surface images of the target area when the geological risk level is greater than the preset risk level, instead of selecting the method of collecting slope surface images of the target area when the geological risk level is greater than the preset risk level, and increase the frequency of collecting slope surface images of the target area.

4. The method for estimating the bearing capacity of a foundation on a slope considering slope stability according to claim 1, characterized in that, Also includes: The geometric parameters of the slope in the target area are quantized without dimension, and a geometric factor function of the slope in the target area is constructed that is associated with the parameter index in the foundation bearing capacity formula. The geometric factor function of the slope in the target area is used as a new correction factor. The new correction factor is weighted and calculated with the slope stability coefficient or the slope stability coefficient recalculated based on the adjusted values ​​of soil parameters. The formula for foundation bearing capacity is then corrected based on the weighted calculation results.

5. The method for estimating the bearing capacity of a foundation on a slope considering slope stability according to claim 4, characterized in that, The weighting ratio in the weighted calculation of the new correction factor and the slope stability coefficient or the slope stability coefficient recalculated based on the adjusted soil parameter values ​​is determined according to the geological risk level of the target area. If the geological risk level of the target area is greater than the preset geological risk level, the weighting ratio of the new correction factor is less than the weighting ratio of the slope stability coefficient or the slope stability coefficient recalculated based on the adjusted soil parameter values; otherwise, the weighting ratio of the new correction factor is greater than the weighting ratio of the slope stability coefficient or the slope stability coefficient recalculated based on the adjusted soil parameter values.

6. The method for estimating the bearing capacity of a foundation on a slope considering slope stability according to claim 1, characterized in that, Also includes: The slope parameters of the target area are simulated and obtained under the effect of the estimated foundation bearing capacity. The slope stability coefficient of the target area is recalculated. The recalculated slope stability coefficient of the target area is compared with the safety factor of the target area. If it is less than the safety factor of the target area, the parameters of the modified foundation bearing capacity formula are adjusted. Alternatively, the actual bearing capacity of the foundation can be collected, and the error between the actual bearing capacity and the estimated bearing capacity can be compared. If the error is greater than the preset error, the parameters of the modified bearing capacity formula can be adjusted.

7. The method for estimating the bearing capacity of a foundation on a slope considering slope stability according to claim 1, characterized in that, The slope stability coefficient of the target area is calculated based on the potential sliding surface and slope stability coefficient calculation method, including: Each slope in the target area is assigned a weighting coefficient according to an allocation rule; the allocation rule is determined based on the actual slope stability coefficient of each slope under different environmental and load conditions in history. For each slope, the corresponding slope stability coefficient is calculated, and the weighted average is used to obtain the slope stability coefficient of the target area.

8. A system for estimating the bearing capacity of a foundation on a slope that considers slope stability, characterized in that, include: The slope data acquisition module is used to collect geological and environmental data of the target area containing the slope. The slope stability coefficient acquisition module is used to construct a simulation model of the target area based on the collected geological and environmental data, simulate the changes in slope parameters under different environments and loads, determine the potential sliding surface based on the simulated changes in slope parameters and the preset sliding surface determination method, and calculate the slope stability coefficient of the target area based on the determined potential sliding surface and the preset slope stability coefficient calculation method. The soil parameter adjustment value acquisition module is used to determine the geological risk level of the target area based on the potential sliding surface and slope stability coefficient; When the geological risk level is greater than the preset risk level, select to collect images of the slope surface in the target area, use image analysis technology to identify cracks and vegetation coverage in the slope in the target area, and calculate and adjust soil parameters based on cracks and vegetation coverage in the slope in the target area. The foundation bearing capacity correction module is used to correct the foundation bearing capacity formula using the slope stability coefficient or the slope stability coefficient recalculated based on the soil parameters, and outputs the corrected foundation bearing capacity estimate.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the method as described in any one of claims 1 to 7.

10. A computer device, characterized in that, The computer device includes a memory, a processor, and a program stored in and executable on the memory, the program being executed by the processor to implement the steps of the method as described in any one of claims 1 to 7.

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