A method and system for predicting the shear strength of structural surfaces under non-uniformly distributed loads

Abstract

This invention provides a method and system for predicting the shear strength of a structural surface under non-uniformly distributed loads, belonging to the field of rock mechanics. The method includes: acquiring the surface roughness parameters of the target structural surface and determining the distribution of the non-uniformly distributed normal loads acting on the structural surface and the magnitude of the average normal stress; establishing a shear analysis model of the structural surface and obtaining shear response data of the structural surface under non-uniformly distributed normal loads; determining the peak shear strength and residual shear strength of the structural surface; constructing a shear strength prediction model of the structural surface and outputting the predicted shear strength results of the structural surface based on the shear strength prediction model. This invention achieves a unified characterization of the actual stress state of the structural surface and a highly consistent prediction of shear strength by simultaneously introducing the surface roughness characteristics and the non-uniformly distributed normal load mechanism during the shear strength prediction process.

Description

Technical Field

[0001] This invention relates to the field of rock mechanics, specifically to a method and system for predicting the shear strength of a structural surface under non-uniformly distributed load. Background Technology

[0002] In rock engineering, structural surfaces of varying scales and morphologies are commonly found. The presence of these surfaces significantly influences the overall mechanical behavior of the rock mass, especially under shear conditions. The shear strength of these structural surfaces is a key parameter in slope stability analysis, underground engineering support design, and rock engineering safety assessment. Existing research typically obtains the shear strength of structural surfaces through laboratory direct shear tests or numerical analysis, generally assuming a uniform distribution of the normal load along the surface direction. However, under actual engineering conditions, due to uneven overburden load distribution, complex structural surface geometry, and varying boundary constraints, the normal load on the structural surface often exhibits significant non-uniform distribution characteristics. This leads to significant differences in the stress state at different locations on the structural surface, altering the shear failure process and strength evolution patterns.

[0003] Current research on the shear behavior of structural surfaces under non-uniformly distributed normal loads relies heavily on extensive laboratory experiments or numerical simulations to obtain shear response data under different load distributions. While these methods can reflect the shear mechanical properties of structural surfaces to some extent, the experimental and simulation processes are typically costly, involve complex parameter combinations, and are difficult to rapidly obtain shear strength parameters under different conditions during the engineering design phase. Furthermore, existing methods primarily focus on the analysis of the shear response process, lacking systematic modeling and expression of the quantitative predictive relationships between non-uniformly distributed normal loads, structural surface roughness, peak shear strength, and residual shear strength. This makes it difficult to meet the need for rapid prediction of structural surface shear strength in engineering applications. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for predicting the shear strength of a structural surface under non-uniformly distributed loads, so as to at least solve the problem in the prior art that it is difficult to obtain the shear strength parameters of a structural surface under non-uniformly distributed normal loads.

[0005] To achieve the above objectives, a first aspect of the present invention provides a method for predicting the shear strength of a structural surface under non-uniformly distributed loads. The method includes: acquiring the surface roughness parameters of a target structural surface and determining the distribution pattern of the non-uniformly distributed normal loads acting on the structural surface and the magnitude of the average normal stress; constructing a shear analysis model of the structural surface based on the surface roughness parameters, the distribution pattern of the non-uniformly distributed normal loads, and the magnitude of the average normal stress; obtaining shear response data of the structural surface under non-uniformly distributed normal loads based on the shear analysis model; determining the peak shear strength and residual shear strength of the structural surface based on the shear response data; constructing a shear strength prediction model of the structural surface based on the surface roughness parameters, the distribution pattern of the non-uniformly distributed normal loads, the magnitude of the average normal stress, the peak shear strength, and the residual shear strength; and outputting the predicted shear strength result of the structural surface based on the shear strength prediction model.

[0006] Optionally, obtaining the surface roughness parameters of the target structural surface and determining the non-uniformly distributed normal load distribution and the magnitude of the average normal stress acting on the structural surface includes: obtaining structural surface morphology data of the target structural surface and extracting surface roughness parameters to characterize the undulation features of the structural surface based on the structural surface morphology data; obtaining normal load loading condition information acting on the structural surface and identifying the distribution difference characteristics of the normal load in the length direction of the structural surface based on the normal load loading condition information to determine the corresponding non-uniformly distributed normal load distribution form; and performing equivalent processing on the normal load within the structural surface range based on the normal load loading condition information to calculate the magnitude of the average normal stress corresponding to the non-uniformly distributed normal load distribution form.

[0007] Optionally, based on the structural surface roughness parameters, the non-uniformly distributed normal load distribution, and the average normal stress magnitude, a structural surface shear analysis model is constructed, including: constructing a structural surface representation within the analysis domain to characterize the geometric undulations of the structural surface based on the structural surface roughness parameters; setting normal load application rules that vary along the length direction of the structural surface within the spatial range corresponding to the structural surface representation, based on the non-uniformly distributed normal load distribution; applying amplitude constraints to the normal load application rules based on the average normal stress magnitude to ensure that the overall normal stress level in the structural surface shear analysis model remains consistent with the average normal stress magnitude; and constructing a structural surface shear analysis model to describe the shear behavior of the structural surface under the joint constraints of the structural surface representation and the normal load application rules.

[0008] Optionally, obtaining shear response data of the structural surface under non-uniformly distributed normal load based on the structural surface shear analysis model includes: in the structural surface shear analysis model, applying shear displacement loading to the structural surface while keeping the non-uniformly distributed normal load application state unchanged; during the shear displacement loading process, acquiring the corresponding shear force response information of the structural surface in real time along the shear direction, and synchronously recording the shear displacement change process; constructing the response relationship between shear force and shear displacement of the structural surface based on the shear force response information and the shear displacement change process; and using the response relationship between shear force and shear displacement as the shear response data of the structural surface under non-uniformly distributed normal load.

[0009] Optionally, determining the peak shear strength and residual shear strength of the structural surface based on the shear response data includes: identifying the maximum shear force value that occurs during the change of shear force with shear displacement based on the shear response data; determining the shear strength corresponding to the maximum shear force value as the peak shear strength of the structural surface; and, as the shear displacement continues to increase, filtering the shear force data located after the peak shear strength in the shear response data based on a preset residual strength determination rule, and determining the residual shear strength of the structural surface based on the filtered shear force data.

[0010] Optionally, the preset residual strength determination rule is as follows: based on the shear response data, the direction of shear force change corresponding to adjacent sampling points is determined along the direction of shear displacement increase; the shear displacement point in the shear response data where the direction of shear force change from increasing to non-increasing for the first time is determined as the starting point of the residual shear stage; the shear displacement point in the shear response data where the direction of shear force change from increasing to non-increasing for the last time is determined as the ending point of the residual shear stage; within the residual shear stage, the corresponding shear force value is determined as the residual shear strength of the structural surface.

[0011] Optionally, based on the structural surface roughness parameters, the non-uniform normal load distribution, the average normal stress, the peak shear strength, and the residual shear strength, a structural surface shear strength prediction model is constructed, including: obtaining a training sample dataset containing the structural surface roughness parameters, the non-uniform normal load distribution, the average normal stress, the peak shear strength, and the residual shear strength; based on the training sample dataset, establishing a prediction model for mapping the structural surface roughness parameters, the non-uniform normal load distribution, and the average normal stress to the peak shear strength and the residual shear strength, and training the prediction model using reserved test samples to obtain the structural surface shear strength prediction model.

[0012] Optionally, the shear strength prediction result of the structural surface is output based on the structural surface shear strength prediction model, including: obtaining the structural surface roughness parameters, non-uniformly distributed normal load distribution form, and average normal stress magnitude of the structural surface to be predicted; inputting the structural surface roughness parameters, the non-uniformly distributed normal load distribution form, and the average normal stress magnitude into the structural surface shear strength prediction model to obtain the corresponding peak shear strength prediction value and residual shear strength prediction value, and outputting the peak shear strength prediction value and the residual shear strength prediction value as the shear strength prediction result of the structural surface.

[0013] A second aspect of the present invention provides a system for predicting the shear strength of a structural surface under non-uniformly distributed loads. The system includes: a data acquisition unit for acquiring surface roughness parameters of a target structural surface and determining the distribution pattern of the non-uniformly distributed normal load acting on the structural surface and the magnitude of the average normal stress; a processing unit for constructing a shear analysis model of the structural surface based on the surface roughness parameters, the distribution pattern of the non-uniformly distributed normal load, and the magnitude of the average normal stress, and obtaining shear response data of the structural surface under non-uniformly distributed normal loads based on the shear response data; a mapping unit for determining the peak shear strength and residual shear strength of the structural surface based on the shear response data; and a result output unit for constructing a shear strength prediction model of the structural surface based on the surface roughness parameters, the distribution pattern of the non-uniformly distributed normal load, the magnitude of the average normal stress, the peak shear strength, and the residual shear strength, and outputting the predicted shear strength result of the structural surface based on the shear strength prediction model.

[0014] On the other hand, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described method for predicting the shear strength of a non-uniformly distributed load structure.

[0015] Through the above technical solution, this invention effectively characterizes the stress characteristics of a structural surface under non-uniformly distributed load conditions by simultaneously introducing structural surface roughness parameters, non-uniformly distributed normal load distribution, and average normal stress magnitude during shear strength analysis. Furthermore, by obtaining corresponding shear response data through shear analysis, peak shear strength and residual shear strength are extracted as key mechanical parameters. Based on these parameters, a shear strength prediction model is established to predict the shear strength of the structural surface under different non-uniformly distributed load conditions. This technical solution avoids the limitations of obtaining shear strength parameters solely based on the assumption of uniformly distributed loads, making the process of obtaining structural surface shear strength more consistent with the non-uniformly distributed load conditions in actual engineering, and improving the applicability of shear strength parameters in engineering analysis.

[0016] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the steps of a method for predicting the shear strength of a non-uniformly distributed load structure provided by one embodiment of the present invention. Figure 2 This is a detailed flowchart of step S10 of a method for predicting the shear strength of a non-uniformly distributed load structure according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the shear crack evolution characteristics of a structural surface under a normal load distribution pattern of 123, provided by one embodiment of the present invention. Figure 4 This is a schematic diagram of the shear crack evolution characteristics of a structural surface under a normal load distribution pattern of 132, provided by one embodiment of the present invention. Figure 5 This is a schematic diagram of the shear crack evolution characteristics of a structural surface under a normal load distribution pattern of 213, provided by one embodiment of the present invention. Figure 6 This is a schematic diagram of the evolution characteristics of shear cracks on a structural surface under a normal load distribution pattern of 231, provided by one embodiment of the present invention. Figure 7 This is a schematic diagram of the evolution characteristics of shear cracks on a structural surface under a normal load distribution pattern of 312, provided by one embodiment of the present invention. Figure 8 This is a schematic diagram of the evolution characteristics of shear cracks on a structural surface under a normal load distribution pattern of 321, provided by one embodiment of the present invention. Figure 9 This is a flowchart of step S30 of the method for predicting the shear strength of a non-uniformly distributed load structure surface provided in one embodiment of the present invention. Figure 10 This is a diagram showing the prediction results of peak shear strength and residual shear strength of the structural surface based on a BP neural network according to one embodiment of the present invention; Figure 11 This is a prediction result of the peak shear strength and residual shear strength of the structural surface based on the MPA-BP neural network provided by one embodiment of the present invention; Figure 12 This is a system structure diagram of a non-uniformly distributed load structural surface shear strength prediction system provided in one embodiment of the present invention; Figure 13 This is an internal structural diagram of a computer device provided in one embodiment of the present invention. Detailed Implementation

[0018] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0019] like Figure 1 As shown, embodiments of the present invention provide a method for predicting the shear strength of a structural surface under non-uniformly distributed loads, the method comprising: Step S10: Obtain the surface roughness parameters of the target structural surface, and determine the non-uniformly distributed normal load distribution form and the magnitude of the average normal stress acting on the structural surface.

[0020] Specifically, by acquiring the surface roughness parameters of the target structural surface and simultaneously determining the distribution of non-uniform normal loads acting on the structural surface and the magnitude of the average normal stress, the geometric characteristics of the structural surface and the external stress conditions are uniformly characterized within the same analytical framework. The surface roughness parameters reflect the undulations of the structural surface and their influence on shear behavior, while the non-uniform normal load distribution characterizes the uneven action of the normal load along the structural surface under actual engineering conditions. The magnitude of the average normal stress is used to constrain the overall stress level. The acquisition and determination of these parameters provide clear and consistent input conditions for subsequent shear behavior analysis and shear strength calculation, avoiding the deviations caused by using only uniform normal loads or single mechanical parameters to describe the stress state of the structural surface, making the description of the stress characteristics of the structural surface closer to actual engineering conditions. Specifically, such as... Figure 2 Step S10 includes the following steps: Step S101: Obtain the structural surface morphology data of the target structural surface, and extract the structural surface roughness parameters to characterize the undulation features of the structural surface based on the structural surface morphology data.

[0021] Specifically, morphological data is collected from the target structural surface to obtain morphological data that reflects the geometric undulation characteristics of the surface. This morphological data can be obtained through 3D laser scanning, photogrammetry, profilometry, or other methods that can acquire surface elevation information. The collection range covers the effective area of ​​the target structural surface that participates in the shear analysis. The collected morphological data is stored as a discrete point set or a continuous surface, where each sampling point corresponds to specific spatial location and elevation information.

[0022] After acquiring the structural surface morphology data, the data is preprocessed, including outlier removal, data smoothing, and coordinate unification, to eliminate the impact of measurement errors on subsequent analysis. Based on this, the surface undulation characteristics are analyzed using the processed data. By comprehensively characterizing the elevation variation amplitude, undulation frequency, and spatial distribution characteristics of the structural surface, surface roughness parameters reflecting the overall undulation characteristics are extracted.

[0023] The surface roughness parameter is used as an input to describe the geometric properties of the surface in subsequent steps, so that the shear behavior analysis of the surface can fully take into account the influence of the actual geometry of the surface on the shear response.

[0024] Step S102: Obtain the normal load loading condition information acting on the structural surface, and identify the distribution difference characteristics of the normal load in the length direction of the structural surface based on the normal load loading condition information, so as to determine the corresponding non-uniformly distributed normal load distribution form.

[0025] Specifically, by analyzing the stress conditions of the engineering environment in which the structural surface is located, information on the normal load conditions acting on the structural surface is obtained. This information includes the load source from the rock mass or structure above the structural surface, the load transfer path, and boundary constraints. Because the normal load on the structural surface in actual engineering projects is usually affected by uneven distribution of overlying loads, variations in the geometric shape of the structural surface, and differences in support or constraint conditions, the normal load along the length of the structural surface often exhibits a significant non-uniform distribution.

[0026] After obtaining the information on the normal load loading conditions, the normal force conditions at different locations on the structural surface are compared and analyzed to identify the variation patterns and differences in the normal load along the length of the structural surface. By analyzing these differences, the normal load on the structural surface is distinguished from the idealized uniform distribution state, thereby determining the non-uniformly distributed normal load distribution form that reflects the actual stress characteristics.

[0027] The non-uniformly distributed normal load distribution form is used to describe the variation of the normal load along the length of the structural surface and serves as an important input condition for subsequent shear analysis, thereby avoiding the deviation caused by the uniformly distributed load assumption in the shear behavior analysis.

[0028] In another possible implementation, under the cyclical loads during construction, excavation unloading, or operation phases, the normal forces at different locations on the structural surface are not statically constant but evolve slowly over time. Based on this, the normal load loading condition information obtained at different time points for the same structural surface is compared and analyzed to extract the temporal variation trend of the normal forces along the length of the structural surface. This temporal variation trend is then jointly characterized with spatial distribution characteristics to determine the corresponding temporal non-uniformly distributed normal load distribution form.

[0029] The distribution pattern is used to simultaneously reflect the non-uniform action characteristics of the normal load in both spatial location and temporal evolution, so that subsequent shear analysis can take into account the cumulative effect of long-term stress evolution on the shear behavior of the structural surface.

[0030] Step S103: Based on the normal load loading condition information, perform equivalent processing on the normal load within the structural surface range, and calculate the average normal stress corresponding to the non-uniformly distributed normal load distribution form.

[0031] Specifically, based on the determination of the non-uniformly distributed normal load distribution, to ensure the comparability of the overall stress level of the structural surface under different working conditions in subsequent analyses, the normal load within the structural surface area is treated equivalently. The equivalent treatment, based on the obtained normal load loading condition information, uniformly represents the normal load borne by the entire structural surface while maintaining the non-uniformly distributed normal load distribution.

[0032] In practice, based on the effective area of ​​the structural surface participating in the shear analysis, the normal loads acting at each location on the structural surface are summarized. Combined with the effective area of ​​the structural surface, the normal loads are equivalently converted to obtain the average normal stress, which reflects the overall stress level. The average normal stress is used to characterize the overall normal stress level borne by the structural surface under non-uniformly distributed normal load conditions and serves as an important constraint parameter for subsequent shear analysis model construction.

[0033] By introducing the average normal stress, we can conduct a unified analysis of the shear behavior under different structural surfaces or different loading conditions while considering the differences in the spatial distribution of normal loads, thus providing a consistent stress basis for subsequent shear response acquisition and shear strength prediction.

[0034] In another possible implementation, based on the obtained structural surface morphology data, and combined with the contact point distribution of the upper and lower plates under initial stress, the actual contact density differences in different regions of the structural surface are identified, and these contact density differences are introduced as a correction factor into the normal load distribution identification process. In this way, the normal load distribution not only reflects the external loading conditions but also simultaneously reflects the local contact strengthening or weakening effects caused by the geometric undulations of the structural surface.

[0035] In this implementation, the surface roughness parameter is no longer merely used to describe the geometry, but further participates in the determination of the non-uniformly distributed normal load distribution. This results in rough, protruding areas corresponding to a higher equivalent normal load proportion, while concave or weakly contacted areas correspond to a lower equivalent normal load proportion. Based on this, the modified normal load distribution is subjected to overall equivalent processing, and the average normal stress corresponding to the combined distribution is calculated. Through this method, the coupled characterization of the structural surface geometry and the normal stress state is achieved, making the determined non-uniformly distributed normal load distribution more closely resemble the actual contact stress state, thus providing more physically meaningful input conditions for subsequent shear behavior analysis.

[0036] Step S20: Based on the surface roughness parameters, the non-uniform normal load distribution, and the magnitude of the average normal stress, construct a shear analysis model for the surface, and obtain the shear response data of the surface under non-uniform normal load based on the shear analysis model.

[0037] Specifically, based on the surface roughness parameters, the non-uniformly distributed normal load distribution, and the average normal stress magnitude, a structural surface shear analysis model is constructed, including: constructing a structural surface representation within the analysis domain to characterize the geometric undulations of the structural surface based on the surface roughness parameters; setting normal load application rules that vary along the length of the structural surface within the spatial range corresponding to the structural surface representation, based on the non-uniformly distributed normal load distribution; applying amplitude constraints to the normal load application rules based on the average normal stress magnitude to ensure that the overall normal stress level in the structural surface shear analysis model remains consistent with the average normal stress magnitude; and constructing a structural surface shear analysis model to describe the shear behavior of the structural surface under the joint constraints of the structural surface representation and the normal load application rules.

[0038] Furthermore, obtaining shear response data of the structural surface under non-uniformly distributed normal load based on the structural surface shear analysis model includes: in the structural surface shear analysis model, under the condition of keeping the non-uniformly distributed normal load application state unchanged, applying shear displacement loading to the structural surface; during the shear displacement loading process, acquiring the corresponding shear force response information of the structural surface in real time along the shear direction, and synchronously recording the shear displacement change process; based on the shear force response information and the shear displacement change process, constructing the response relationship between the shear force and shear displacement of the structural surface; and using the response relationship between the shear force and shear displacement as the shear response data of the structural surface under non-uniformly distributed normal load.

[0039] In this embodiment of the invention, a structural surface shear analysis model is constructed based on the surface roughness parameters, the non-uniformly distributed normal load distribution, and the magnitude of the average normal stress. Shear response data of the structural surface under non-uniformly distributed normal loads are then obtained based on this model. This process is used to characterize the coupling effect of the geometric undulations of the structural surface and the non-uniformly distributed normal stress conditions on the shear behavior of the structural surface within a unified analysis framework, providing a foundation for determining and predicting the shear strength parameters of the structural surface.

[0040] Among them, the surface roughness parameter is used to characterize the surface undulation characteristics and spatial complexity of the surface. In this invention, the surface roughness parameter can be characterized by roughness indices obtained from the statistics of the surface geometry, such as using the JRC classification method of joint roughness coefficient to quantitatively describe the surface roughness. By introducing the surface roughness parameter, the shear analysis model can reflect the changes in contact state, local constraint enhancement, and failure mode evolution caused by differences in geometric undulations during shearing of surface roughness with different roughness, thereby avoiding the distortion of the shear response description caused by simplifying the surface as an ideal smooth plane.

[0041] Based on the surface roughness parameters, a surface representation is constructed within the analysis domain to characterize the geometric undulations of the surface. This surface representation describes the distribution of geometric undulations along the length and within local regions of the surface during shear analysis, enabling the shear analysis model to reflect the interlocking effect, contact closure effect, and local geometric barrier effect of the rough protrusions during shearing. Through this surface representation, the differences in mechanical response exhibited by surface roughnesses during shearing can be reflected at the model level.

[0042] Based on the constructed structural surface representation, and using a non-uniformly distributed normal load distribution, rules for applying normal loads are set within the spatial range corresponding to the structural surface representation. The non-uniformly distributed normal load distribution describes the loading state where the normal load distribution varies along the length of the structural surface or at different locations, reflecting the objective situation that the normal constraints on the structural surface are not uniformly distributed under actual engineering conditions. By introducing a non-uniformly distributed normal load distribution into the shear analysis model, different locations on the structural surface experience varying degrees of normal constraints during shearing, thereby affecting the stress transfer path, local contact state, and failure initiation location during shearing.

[0043] Furthermore, based on the magnitude of the average normal stress, amplitude constraints are applied to the normal loads according to rules to ensure that the overall normal stress level in the structural shear analysis model remains consistent with the magnitude of the average normal stress. The magnitude of the average normal stress is used to impose an overall constraint on the non-uniformly distributed normal load distribution, making different normal load distributions comparable under the same overall normal stress level. Through this amplitude constraint method, while maintaining the differences in normal load distribution, the interference of differences in the overall normal stress level on the shear response analysis results can be eliminated, thereby highlighting the influence of the non-uniform distribution characteristics themselves on shear behavior.

[0044] Under the joint constraints of structural surface representation and normal load application rules, a structural surface shear analysis model is constructed to describe the shear behavior of the structural surface. This model comprehensively reflects the coupling effect of structural surface roughness characteristics, non-uniformly distributed normal load distribution, and the magnitude of the average normal stress on the structural surface shear behavior. This allows the model to describe the overall mechanical response characteristics of the structural surface during shearing, from initial stress and shear resistance to the gradual evolution into a stable shearing stage.

[0045] After constructing the structural surface shear analysis model, shear response data of the structural surface under non-uniformly distributed normal loads were obtained based on the model. Specifically, in the structural surface shear analysis model, shear displacement loading was applied to the structural surface while keeping the non-uniformly distributed normal load application state unchanged. The shear displacement loading was carried out along the shear direction of the structural surface to simulate the process of relative slippage of the structural surface under actual shear conditions.

[0046] During shear displacement loading, shear force response information corresponding to the structural surfaces is acquired along the shear direction, and the shear displacement change process is recorded simultaneously. The shear force response information characterizes the change in the shear resistance of the structural surfaces during shearing, while the shear displacement change process describes the development state of the relative slip of the structural surfaces. By simultaneously acquiring the shear force response information and the shear displacement change process, the shear response behavior of the structural surfaces under non-uniformly distributed normal load constraints can be fully characterized.

[0047] Based on shear force response information and shear displacement variation, a response relationship between shear force and shear displacement of the structural surface is constructed. This response relationship comprehensively reflects the overall characteristics of the evolution of the structural surface's shear capacity with displacement during shearing. It not only reflects the growth and decay trends of shear capacity during shearing but also the moderating effect of surface roughness and non-uniformly distributed normal stress conditions on the shear response path. Using the response relationship between shear force and shear displacement as shear response data of the structural surface under non-uniformly distributed normal loads provides fundamental data support for the subsequent determination of the peak shear strength and residual shear strength of the structural surface.

[0048] In another possible implementation, a shear displacement partitioning loading mechanism based on the difference in normal constraints is introduced during the construction of the structural surface shear analysis model.

[0049] Specifically, while maintaining the non-uniformly distributed normal load distribution and the magnitude of the average normal stress, the structural surface is divided into multiple normal constraint segments along its length, based on the differences in the distribution of the non-uniformly distributed normal load along its length. Each segment corresponds to a different level of normal constraint. When applying shear displacement loading to the structural surface, local coordination rules for shear displacement are set according to the differences in constraint strength among the normal constraint segments, allowing for slight differences in displacement response between high and low normal constraint segments during the development of shear displacement.

[0050] This approach enables the shear analysis model to simultaneously reflect the coupling effect between the differences in normal constraints and the characteristics of shear displacement transmission during shearing. This more realistically reflects the mechanical evolution of the structural surface under non-uniform normal loads, from local slip initiation to the gradual expansion of the shear band and eventual overall shear stability. The shear force and shear displacement response relationship obtained based on the aforementioned shear displacement partitioning loading mechanism is used as the shear response data of the structural surface under non-uniform normal loads for subsequent analysis steps, making the shear response data more closely resemble the actual shear behavior characteristics of the structural surface at the spatial resolution level.

[0051] In one specific implementation, a set of JRC10-12 structural surfaces are selected as target structural surfaces. A structural surface representation is constructed based on the surface roughness parameters, and a non-uniformly distributed normal load distribution is set on this representation to obtain shear response data. To facilitate the description of the non-uniformly distributed normal load distribution, the structural surface is divided into three segments from left to right: segment 1, segment 2, and segment 3. The numbers 1, 2, and 3 represent the relative amplitude levels of the normal load within each segment, from low to high. Each distribution is scaled down under the constraint of the average normal stress magnitude to ensure a consistent overall normal stress level. Subsequently, shear displacement is applied while maintaining the normal load application state, and the structural surface deformation and crack evolution when the shear displacement reaches the preset analysis position are extracted as shear response data. Green represents shear cracks, and red represents tensile cracks.

[0052] When the normal load pattern is 123, the normal loads in sections 1, 2, and 3 increase from low to high, with the high-load zone located in section 3. Figure 3 The crack-dense area shown is closer to the side of section 3, and is mainly composed of shear cracks that develop along the structural plane. This indicates that the normal constraint in the high-load area makes the local contact more sufficient, and the shear band is more likely to penetrate near the high-load area.

[0053] When the normal load pattern is 132, section 1 is a low load, section 2 is a high load, section 3 is a medium load, and the high load zone is located in section 2. Figure 4 The concentrated location of crack evolution shown is relatively offset towards the middle of the structural surface. Shear cracks and tensile cracks exhibit more obvious spatial partitioning characteristics, reflecting the guiding role of high load in the middle on the crack propagation path.

[0054] When the normal load type is 213, section 1 is a medium load, section 2 is a low load, and section 3 is a high load. The high load zone is still located in section 3. Figure 5 The cracks shown are still more active mainly on side 3 of section, but compared with... Figure 3 Compared to the more dispersed distribution of cracks in segments 1 and 2, this reflects that the moderate normal constraint in segment 1 will change the starting position of crack initiation and the way it expands and connects.

[0055] When the normal load pattern is 231, section 1 is a medium load, section 2 is a high load, section 3 is a low load, and the high load zone is located in section 2. Figure 6 The crack-dense area and local failure shown are closer to the middle of the structural surface. At the same time, the crack propagation on the three sides of the section is more likely to show a branching pattern that extends along the top of the structural surface. This indicates that the low-load area at the end provides a larger local deformation space, making it easier to trigger tensile-shear combined failure.

[0056] When the normal load pattern is 312, section 1 is a high load, section 2 is a low load, and section 3 is a medium load. The high load zone is located in section 1. Figure 7 The main control region of crack evolution shown migrates to the side of section 1, and more local crack clusters appear at the undulations of the structural surface, indicating that the high load in the early stage will cause the stress concentration in the shear process to form earlier near the loading end, and constrain the subsequent shear band expansion direction.

[0057] When the normal load pattern is 321, segment 1 is a high load, segment 2 is a medium load, and segment 3 is a low load. The high load zone is also located in segment 1, but the normal constraint of segment 2 is higher than that of segment 2. Figure 7 Corresponding working conditions, Figure 8 The crack propagation continuity is stronger, and the shear cracks are more connected along the structural plane. The crack distribution on the third side of the section is relatively convergent, which shows that after the normal constraint in the middle is improved, the shear band tends to dominate the penetration on the high load side and suppress the free propagation at the end.

[0058] To quantify crack evolution, this embodiment statistically analyzes the proportion of shear cracks at the preset analysis location, and the results are shown in Table 1.

[0059] Table 1. Percentage of shear cracks on the surface of JRC10-12 structure under different normal load distributions. Table 1 shows that the proportion of shear cracks varies under different normal load conditions, and that under the same roughness parameters, the crack concentration zone migrates with the location of the high-load zone. Figures 3 to 8 Together with Table 1, they constitute the shear response data, which are used for the subsequent determination of peak shear strength and residual shear strength, as well as the construction of a shear strength prediction model.

[0060] Step S30: Based on the shear response data, determine the peak shear strength and residual shear strength of the structural surface.

[0061] Specifically, based on the shear response data, the peak shear strength and residual shear strength of the structural surface are determined. The shear response data reflects the overall process of shear force evolution with shear displacement under non-uniformly distributed normal load constraints. By analyzing the response relationship between shear force and shear displacement, the transition characteristic of shear capacity from a gradually increasing to a decreasing stage during shearing can be identified, and the peak shear strength corresponding to the structural surface can be determined accordingly. Simultaneously, after the shear displacement continues to increase and enters the stable shear stage, the shear force response tends to be relatively stable, and its corresponding shear capacity can be used to characterize the residual shear strength of the structural surface. Specifically, as... Figure 9 Step S30 includes the following steps: Step S301: Based on the shear response data, identify the maximum shear force value that occurs during the change of shear force with shear displacement.

[0062] Specifically, shear response data is used to describe the entire process of shear force evolution with shear displacement during shear loading under non-uniformly distributed normal load constraints. In the initial stage of shear displacement loading, since the structural surface has not yet experienced significant slippage or failure, the shear force shows a continuous increasing trend with increasing shear displacement, reflecting the gradual activation of the structural surface's shear resistance under the action of rough protrusion interlocking and normal constraints. As the shear displacement further increases, relative slippage, protrusion shearing, or contact state reorganization begin to occur in local areas of the structural surface, and the growth trend of the shear force gradually slows down and eventually reaches an extreme value.

[0063] In this step, the shear response data is traversed along the direction of increasing shear displacement, and the shear force value corresponding to each sampling point is compared to identify the maximum shear force value that occurs during the change of shear force with shear displacement. The maximum shear force value corresponds to the state point in the shear response curve where the shear capacity reaches its highest level, which is represented by a turning point in the curve shape where the continuous increase turns into a decrease or fluctuation. Since the shear response data is obtained under the condition of keeping the non-uniformly distributed normal load application state unchanged, this maximum shear force value can comprehensively reflect the combined influence of structural surface roughness characteristics, the non-uniformly distributed normal load distribution, and the average normal stress level on the upper limit of shear capacity. Identifying the maximum shear force value in this way avoids subjective point selection or experience-based judgment, allowing the determination of the peak state to be based on the objective evolution characteristics of the shear response data itself.

[0064] Step S302: Determine the shear strength corresponding to the maximum shear force value as the peak shear strength of the structural surface.

[0065] Specifically, after identifying the maximum shear force value corresponding to the shear response data, the shear capacity state reflected by this maximum shear force value is determined as the peak shear strength of the structural surface. The peak shear strength is used to characterize the maximum shear capacity level that the structural surface can withstand under non-uniformly distributed normal load constraints before significant overall slippage or through-failure occurs.

[0066] During the shear response process, the state corresponding to the peak shear strength typically occurs when the rough protrusions on the structural surface can still maintain effective engagement, and the local contact has not yet completely destabilized. At this point, the shear force reaches its maximum value throughout the entire process. Defining the shear strength in this state as the peak shear strength can reflect, in an engineering sense, the critical shear capacity of the structural surface as it transitions from "constrained deformation" to "failure-dominated deformation." Since the maximum shear force value is automatically identified based on complete shear response data, the corresponding peak shear strength does not depend on manually set displacement positions or empirical thresholds, but is determined by the evolution characteristics of the shear response curve itself.

[0067] By directly using the maximum shear force value as the peak shear strength, it is possible to ensure that the peak shear strength has a consistent determination criterion under different roughness structural surfaces, different non-uniformly distributed normal load distribution forms, and different average normal stress levels. This provides a unified strength parameter input for subsequent comparative analysis between different working conditions and the construction of shear strength prediction models.

[0068] Step S303: As the shear displacement continues to increase, based on the preset residual strength determination rule, the shear force data located after the peak shear strength in the shear response data is filtered, and the residual shear strength of the structural surface is determined based on the filtered shear force data.

[0069] Specifically, the preset residual strength determination rule is as follows: based on the shear response data, the direction of shear force change corresponding to adjacent sampling points is determined along the direction of shear displacement increase; the shear displacement point in the shear response data where the direction of shear force change from increasing to non-increasing for the first time is determined as the starting point of the residual shear stage; the shear displacement point in the shear response data where the direction of shear force change from increasing to non-increasing for the last time is determined as the ending point of the residual shear stage; within the residual shear stage, the corresponding shear force value is determined as the residual shear strength of the structural surface.

[0070] Specifically, after the peak shear strength is determined, as the shear displacement further increases, the structural surface gradually enters the post-peak shear stage. At this point, the overall shear resistance of the structural surface no longer continues to increase, but instead decreases or fluctuates within a certain range. To objectively determine the residual shear strength of the structural surface during this stage, this invention employs a residual strength determination rule based on the evolution direction of shear response data for screening.

[0071] The residual strength determination rule includes: judging the direction of shear force change at each adjacent sampling point along the direction of increasing shear displacement. When the direction of shear force change with shear displacement changes from "increasing" to "non-increasing" for the first time, the corresponding shear displacement point is determined as the starting point of the residual shear stage; when the direction of shear force change with shear displacement changes from "increasing" to "non-increasing" for the last time, the corresponding shear displacement point is determined as the ending point of the residual shear stage. The shear displacement interval between the above starting point and ending point is used to define the residual shear stage where the structural surface enters a relatively stable shear state after the peak.

[0072] During the residual shear stage, the overall failure mechanism of the structural surface has shifted from processes such as convex shearing and local instability to a shear state dominated by continuous slip and friction control, and the corresponding shear force level no longer rises significantly. By screening shear response data during this stage and determining the screened shear force values ​​as the residual shear strength of the structural surface, the random influence of characterizing the residual state with only a single point value can be avoided, making the determination of the residual shear strength more consistent with the overall evolution characteristics of the shear response curve.

[0073] By using the above residual strength determination rules, the residual shear stage can be objectively identified from the shear response data without introducing additional empirical parameters, and the residual shear strength of the structural surface can be determined accordingly. This, together with the peak shear strength, constitutes a complete structural surface shear strength characterization result.

[0074] In another possible implementation, based on determining the start and end points of the residual shear stage according to the direction of shear force change, a stability criterion for the rate of change of shear force is introduced to further quantify and confirm the residual shear stage. Specifically, let the shear displacement corresponding to the i-th sampling point in the shear response data be... The corresponding shear force is The rate of change of shear force between adjacent sampling points can be expressed as: in, This represents the rate of change of shear force within the i-th sampling interval. This represents the shear force corresponding to the i-th sampling point. This represents the shear force corresponding to the previous sampling point. This represents the shear displacement corresponding to the i-th sampling point. This represents the shear displacement corresponding to the previous sampling point.

[0075] After the shear displacement corresponding to the peak shear strength, for all Point-by-point calculations were performed, and a sequence of shear force change rates was constructed. When the rate of change sequence satisfies the following equation over multiple consecutive sampling intervals: Where ε is a preset threshold for the rate of change of shear force, used to characterize the criterion that the shear force tends to stabilize with the change of shear displacement, and the corresponding shear displacement interval is determined as the candidate residual shear stage.

[0076] Furthermore, the candidate residual shear stages are intersected with the residual shear stages determined based on the direction of shear force change to obtain the final residual shear stage interval. Within the final residual shear stage interval, the shear force data is statistically processed, and the average shear force within this interval is expressed as the residual shear strength of the structural surface. Its expression is: in, Represents the residual shear strength of the structural surface. This represents the shear force corresponding to the j-th sampling point within the final residual shear stage, and n represents the number of sampling points included in the final residual shear stage.

[0077] By introducing the stability criterion of shear force change rate, the determination of residual shear strength not only depends on the direction of shear force change, but also simultaneously considers the mechanical characteristics after the shear response enters the stable friction control stage, thereby further enhancing the objectivity and consistency of the residual shear strength determination results.

[0078] Step S40: Based on the surface roughness parameters, the non-uniformly distributed normal load distribution, the average normal stress magnitude, the peak shear strength, and the residual shear strength, construct a structural surface shear strength prediction model, and output the structural surface shear strength prediction result based on the structural surface shear strength prediction model.

[0079] Specifically, a training sample dataset is obtained, which includes the structural surface roughness parameters, the non-uniformly distributed normal load distribution, the average normal stress magnitude, the peak shear strength, and the residual shear strength. Based on the training sample dataset, a prediction model is established to map the structural surface roughness parameters, the non-uniformly distributed normal load distribution, and the average normal stress magnitude to the peak shear strength and the residual shear strength. The prediction model is then trained using reserved test samples to obtain a structural surface shear strength prediction model.

[0080] Furthermore, the shear strength prediction result of the structural surface is output based on the structural surface shear strength prediction model, including: obtaining the structural surface roughness parameters, non-uniformly distributed normal load distribution form, and average normal stress magnitude of the structural surface to be predicted; inputting the structural surface roughness parameters, the non-uniformly distributed normal load distribution form, and the average normal stress magnitude into the structural surface shear strength prediction model to obtain the corresponding peak shear strength prediction value and residual shear strength prediction value, and outputting the peak shear strength prediction value and the residual shear strength prediction value as the shear strength prediction result of the structural surface.

[0081] In this embodiment of the invention, a training sample dataset is obtained, comprising the structural surface roughness parameters, the non-uniformly distributed normal load distribution, the average normal stress magnitude, the peak shear strength, and the residual shear strength. Each sample in the training sample dataset originates from the shear response data obtained through the structural surface shear analysis model under different structural surface roughness conditions, different non-uniformly distributed normal load distributions, and different average normal stress levels in the preceding steps, and the obtained peak shear strength and residual shear strength are further determined. In this way, each set of samples in the training sample dataset corresponds to a specific set of input parameters and output strength parameters, wherein the input parameters characterize the geometric features and stress state of the structural surface, and the output parameters characterize the shear strength characteristics of the structural surface.

[0082] When constructing the training sample dataset, the surface roughness parameter reflects the degree of geometric undulation of the surface, the non-uniformly distributed normal load distribution reflects the difference in the distribution of normal load along the length of the surface, the average normal stress reflects the overall normal constraint level, and the peak shear strength and residual shear strength reflect the maximum shear capacity of the surface during shearing and the shear capacity during the stable shearing stage, respectively. By organizing the above parameters into a unified training sample dataset, the prediction model can simultaneously learn the combined influence of surface geometry and stress factors on shear strength.

[0083] After obtaining the training sample dataset, a prediction model is established based on the training sample dataset to map the structural surface roughness parameters, the non-uniformly distributed normal load distribution, and the average normal stress magnitude to the peak shear strength and the residual shear strength. The prediction model is established using the training sample dataset as input. By analyzing the mapping relationship between the input parameters and the corresponding output strength parameters, a prediction relationship reflecting the variation law of the structural surface shear strength is formed. Since the peak shear strength and residual shear strength in the training sample dataset are determined based on shear response data according to uniform rules, the mapping relationship learned by the prediction model can reflect the objective trend of the structural surface shear strength changing with roughness characteristics, non-uniformly distributed load characteristics, and normal stress level.

[0084] During the prediction model establishment process, a portion of samples can be reserved from the training dataset as test samples, which are then used to train and validate the prediction model. By comparing the output of the prediction model with the corresponding peak shear strength and residual shear strength in the test samples, the stability and applicability of the prediction model can be verified, and the model can be adjusted accordingly to ensure that it can output reasonable shear strength prediction results under different input conditions. Through the above training process, a structural surface shear strength prediction model is obtained for subsequent predictions.

[0085] After constructing the structural surface shear strength prediction model, the shear strength prediction results of the structural surface are further output based on the model. Specifically, the structural surface roughness parameters, non-uniformly distributed normal load distribution, and average normal stress magnitude of the structural surface to be predicted are first obtained. The structural surface to be predicted can be a target structural surface that has not yet undergone shear analysis or testing. Its structural surface roughness parameters can be obtained by extracting structural surface morphology data, while the non-uniformly distributed normal load distribution and average normal stress magnitude can be determined according to the actual engineering stress conditions or design conditions.

[0086] Furthermore, the surface roughness parameters, the non-uniformly distributed normal load distribution, and the average normal stress magnitude are input into the structural surface shear strength prediction model. The prediction model processes the input parameters to obtain the corresponding peak shear strength prediction value and residual shear strength prediction value. The peak shear strength prediction value is used to characterize the maximum shear capacity level that the structural surface to be predicted may reach under non-uniformly distributed normal load constraints, and the residual shear strength prediction value is used to characterize the shear capacity level of the structural surface to be predicted after entering the stable shear stage.

[0087] Furthermore, the predicted peak shear strength and the predicted residual shear strength are output as the shear strength prediction results of the structural surface. Through this prediction process, the shear strength characteristics of the structural surface can be predicted based on known surface roughness features and stress conditions without directly obtaining shear response data, thus providing a reference for engineering design, stability analysis, and risk assessment. Since the prediction model is trained based on the aforementioned shear analysis model and the systematic construction of shear response data, the output shear strength prediction results have the same physical meaning and engineering interpretation basis as the actual shear behavior of the structural surface under non-uniformly distributed normal load conditions.

[0088] In one specific embodiment, in the non-uniformly distributed load structural shear strength prediction method of the present invention, the construction and training of the structural shear strength prediction model are not limited to a specific model form or training algorithm. Based on the mapping relationship between structural surface roughness parameters, non-uniformly distributed normal load distribution, and the magnitude of average normal stress with the peak shear strength and residual shear strength of the structural surface, various data-driven modeling methods can be used to train the prediction model. Those skilled in the art can select different training algorithms or parameter optimization methods according to sample size, data characteristics, and application requirements; all of these should be considered as optional implementations of the method of the present invention.

[0089] In optional embodiments, the structural shear strength prediction model can be constructed using a neural network model based on the backpropagation mechanism, such as a backpropagation neural network (BP neural network). Alternatively, different parameter optimization algorithms can be introduced to optimize the model weights and thresholds based on the basic neural network model, such as genetic algorithm-optimized BP neural network (GA-BP), marine predator algorithm-optimized BP neural network (MPA-BP), moth-flame optimization-optimized BP neural network (MFO-BP), and octopus optimization algorithm-optimized BP neural network (OOA-BP). The above training algorithms and optimization methods are merely illustrative examples and do not constitute a limitation of the present invention.

[0090] In one specific implementation, the structural surface shear strength prediction model is trained and validated based on sample data obtained from the aforementioned direct shear numerical simulation experiment. The sample data includes structural surface roughness parameters, normal load distribution, and average normal stress as input features, and peak shear strength and residual shear strength extracted from shear response data as output features. Before model training, the sample data is randomly divided into a training sample set and a test sample set. The training sample set is used for model training, and the test sample set is used to independently validate the trained prediction model to evaluate its predictive consistency and generalization ability.

[0091] For ease of explanation, this embodiment selects two of the various optional prediction models mentioned above as examples for demonstration. For instance... Figure 10 The figure shows the prediction results of the structural surface shear strength prediction model constructed using a backpropagation neural network (BP neural network) on the test samples, with comparisons between the predicted values ​​and corresponding experimental values ​​of peak shear strength and residual shear strength. Figure 10 It can be seen that the BP neural network model can reflect the overall trend of structural shear strength as a function of sample conditions.

[0092] like Figure 11 The image shows the prediction results of a structural surface shear strength prediction model constructed using the marine predator algorithm to optimize a backpropagation neural network (MPA-BP neural network) on the same test samples. Figure 10 Compared with the BP neural network prediction results shown, the MPA-BP neural network exhibits smaller deviations in both peak shear strength and residual shear strength predictions, and the fitting degree between the prediction curve and the experimental curve is further improved. This indicates that training the prediction model by introducing a parameter optimization algorithm helps to improve the prediction accuracy of the structural surface shear strength under non-uniformly distributed normal load conditions.

[0093] Furthermore, to quantitatively evaluate the performance of prediction models corresponding to different training algorithms, evaluation indicators such as the coefficient of determination, root mean square error, and mean absolute error can be used to compare and analyze the model output results, as shown in Table 2. Table 2 shows that different prediction models differ in prediction accuracy, but overall they can all effectively predict the peak shear strength and residual shear strength of the structural surface under non-uniformly distributed normal load conditions.

[0094] Table 2 Evaluation indicators of peak intensity prediction results of different models As can be seen from the above embodiments, the method of the present invention can flexibly adopt different prediction models to model and predict the shear strength of structural surfaces without limiting the specific model training algorithm, and has strong versatility and scalability.

[0095] like Figure 12 As shown, this invention provides a system for predicting the shear strength of a structural surface under non-uniform loads. The system includes: a data acquisition unit for acquiring the surface roughness parameters of a target structural surface and determining the distribution of the non-uniform normal load and the magnitude of the average normal stress acting on the structural surface; a processing unit for constructing a shear analysis model of the structural surface based on the surface roughness parameters, the distribution of the non-uniform normal load, and the magnitude of the average normal stress, and obtaining shear response data of the structural surface under non-uniform normal loads based on the shear analysis model; a mapping unit for determining the peak shear strength and residual shear strength of the structural surface based on the shear response data; and a result output unit for constructing a shear strength prediction model of the structural surface based on the surface roughness parameters, the distribution of the non-uniform normal load, the magnitude of the average normal stress, the peak shear strength, and the residual shear strength, and outputting the predicted shear strength result of the structural surface based on the shear strength prediction model. The present invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described method for predicting the shear strength of a non-uniformly distributed load structure.

[0096] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 13 As shown, the computer device includes a processor A01, a network interface A02, memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used for communication with external terminals via a network connection. When the processor A01 executes the computer program B02, it implements a method for predicting the shear strength of a non-uniformly distributed load structure.

[0097] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes 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.

[0098] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0099] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A method for predicting the shear strength of a structural surface under non-uniformly distributed load, characterized in that, The method includes: Obtain the surface roughness parameters of the target structural surface, and determine the non-uniformly distributed normal load distribution and the magnitude of the average normal stress acting on the structural surface; Based on the surface roughness parameters, the non-uniform normal load distribution, and the average normal stress, a shear analysis model for the surface is constructed, and shear response data of the surface under non-uniform normal load is obtained based on the shear analysis model. Based on the shear response data, the peak shear strength and residual shear strength of the structural surface are determined; Based on the surface roughness parameters, the non-uniform normal load distribution, the average normal stress, the peak shear strength, and the residual shear strength, a structural surface shear strength prediction model is constructed, and the predicted shear strength of the structural surface is output based on the structural surface shear strength prediction model.

2. The method for predicting the shear strength of a non-uniformly distributed load structure according to claim 1, characterized in that, Obtain the surface roughness parameters of the target structural surface, and determine the non-uniformly distributed normal load distribution and the magnitude of the average normal stress acting on the structural surface, including: Obtain the structural surface morphology data of the target structural surface, and extract the structural surface roughness parameters to characterize the undulation features of the structural surface based on the structural surface morphology data; Information on the loading conditions of the normal load acting on the structural surface is obtained, and the distribution difference characteristics of the normal load in the length direction of the structural surface are identified based on the information on the loading conditions of the normal load, so as to determine the corresponding non-uniform normal load distribution form. Based on the normal load loading condition information, the normal load within the structural surface area is equivalently processed to calculate the average normal stress corresponding to the non-uniformly distributed normal load distribution.

3. The method for predicting the shear strength of a non-uniformly distributed load structure according to claim 1, characterized in that, Based on the surface roughness parameters, the non-uniformly distributed normal load distribution, and the magnitude of the average normal stress, a shear analysis model for the surface is constructed, including: Based on the surface roughness parameters, a surface representation for characterizing the geometric undulations of the surface is constructed within the analysis domain. Based on the aforementioned non-uniform normal load distribution, within the spatial range corresponding to the structural surface, a normal load application rule that varies along the length of the structural surface is set. Based on the magnitude of the average normal stress, a rule is applied to constrain the amplitude of the normal load so that the overall normal stress level in the structural shear analysis model is consistent with the magnitude of the average normal stress. Under the joint constraints of the structural surface representation and the normal load application rules, a structural surface shear analysis model is constructed to describe the shear behavior of the structural surface.

4. The method for predicting the shear strength of a non-uniformly distributed load structure according to claim 3, characterized in that, Based on the aforementioned structural shear analysis model, shear response data of the structural surface under non-uniformly distributed normal loads are obtained, including: In the structural surface shear analysis model, under the condition that the non-uniformly distributed normal load is applied unchanged, a shear displacement load is applied to the structural surface. During the shear displacement loading process, the shear force response information of the structural surface is acquired in real time along the shear direction, and the shear displacement change process is recorded synchronously. Based on the shear force response information and the shear displacement change process, the response relationship between the shear force and shear displacement of the structural surface is constructed. The response relationship between shear force and shear displacement is used as the shear response data of the structural surface under non-uniformly distributed normal load.

5. The method for predicting the shear strength of a non-uniformly distributed load structure according to claim 4, characterized in that, Based on the shear response data, the peak shear strength and residual shear strength of the structural surface are determined, including: Based on the shear response data, the maximum shear force value that occurs during the change of shear force with shear displacement is identified; The shear strength corresponding to the maximum shear force value is determined as the peak shear strength of the structural surface; As the shear displacement continues to increase, based on a preset residual strength determination rule, the shear force data located after the peak shear strength in the shear response data is filtered, and the residual shear strength of the structural surface is determined based on the filtered shear force data.

6. The method for predicting the shear strength of a non-uniformly distributed load structure according to claim 5, characterized in that, The preset residual strength determination rule is: Based on the shear response data, the direction of shear force change corresponding to adjacent sampling points is determined along the direction of shear displacement increase; The point in the shear response data where the direction of shear force change from increasing to non-increasing for the first time is determined as the starting point of the residual shear stage. The point where the direction of shear force change from increasing to non-increasing for the last time in the shear response data is determined as the termination point of the residual shear stage. During the residual shear stage, the corresponding shear force value is determined as the residual shear strength of the structural surface.

7. The method for predicting the shear strength of a non-uniformly distributed load structure according to claim 1, characterized in that, Based on the surface roughness parameters, the non-uniform normal load distribution, the average normal stress magnitude, the peak shear strength, and the residual shear strength, a surface shear strength prediction model is constructed, including: Obtain a training sample dataset containing the surface roughness parameters, the non-uniformly distributed normal load distribution, the average normal stress magnitude, the peak shear strength, and the residual shear strength. Based on the training sample dataset, a prediction model is established to map the surface roughness parameters, the non-uniformly distributed normal load distribution, and the average normal stress magnitude to the peak shear strength and the residual shear strength. The prediction model is then trained using reserved test samples to obtain a surface shear strength prediction model.

8. The method for predicting the shear strength of a non-uniformly distributed load structure according to claim 7, characterized in that, Based on the structural shear strength prediction model, the output of the structural shear strength prediction results includes: Obtain the surface roughness parameters, non-uniformly distributed normal load distribution form, and average normal stress magnitude of the structural surface to be predicted; The surface roughness parameters, the non-uniformly distributed normal load distribution, and the magnitude of the average normal stress are input into the surface shear strength prediction model to obtain the corresponding peak shear strength prediction value and residual shear strength prediction value. The peak shear strength prediction value and the residual shear strength prediction value are then output as the shear strength prediction result of the surface.

9. A system for predicting the shear strength of a structural surface under non-uniformly distributed load, characterized in that, The system includes: The acquisition unit is used to acquire the surface roughness parameters of the target structural surface and determine the non-uniformly distributed normal load distribution and the magnitude of the average normal stress acting on the structural surface. The processing unit is used to construct a structural surface shear analysis model based on the structural surface roughness parameters, the non-uniform normal load distribution form, and the magnitude of the average normal stress, and to obtain shear response data of the structural surface under non-uniform normal load based on the structural surface shear analysis model. A mapping unit is used to determine the peak shear strength and residual shear strength of the structural surface based on the shear response data. The result output unit is used to construct a structural surface shear strength prediction model based on the structural surface roughness parameters, the non-uniformly distributed normal load distribution, the average normal stress magnitude, the peak shear strength, and the residual shear strength, and to output the structural surface shear strength prediction result based on the structural surface shear strength prediction model.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the method for predicting the shear strength of a non-uniformly distributed load structure as described in any one of claims 1-8.

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