A test method for optimizing anchoring of a dip slope based on in-situ response characteristics

Abstract

The application discloses a kind of based on in-situ response characteristic's order of slope anchoring optimization test method, belong to anchoring test technical field.The method first obtains the multi-dimensional in-situ response time series data of anchoring system under hydraulic coupling condition;Second, through space-time matching, the local failure characteristic node caused by pore water splitting is identified, and the dynamic attenuation feature of frictional resistance is extracted;Then, with the dynamic window monitoring triggered by failure node, the strain peak shift rate is calculated, and the spatial evolution trajectory of stress peak value transfer to deep part is constructed;Finally, the attenuation feature and evolution trajectory are combined to determine the cooperative convergence state, the dynamic effective anchoring length is quantified, and the anchoring parameters are optimized.The application accurately restores the hydraulic micro-damage mechanism, realizes the real-time tracking of interface deterioration and stress transfer evolution law, scientifically quantifies the dynamic effective anchoring length under long-term seepage pressure, avoids overestimating the uplift performance, and significantly improves the authenticity and reliability of anchoring optimization test.

Description

Technical Field

[0001] This invention relates to the field of anchorage testing technology, and in particular to a test method for optimal anchorage of dip-slopes based on in-table response characteristics. Background Technology

[0002] Dip-slopes, controlled by bedding planes or weak interlayers, are highly susceptible to sudden bedding-slip under rainfall or excavation disturbances, making them a frequent and serious disaster in engineering. Their anchoring stress mechanism is exceptionally complex. The anchoring system must not only resist shear sliding forces but also overcome interlayer slippage and void deformation, often bearing complex alternating tensile and shear stresses, making it highly prone to yielding failure due to stress concentration.

[0003] In existing technologies for optimizing anchorage in dip-side slopes, the first step is typically to investigate and analyze the engineering geological conditions of the slope, including parameters such as rock strata dip, slope structural surface development, rock mass integrity, groundwater distribution, and slope geometry. This aims to identify potential slippage modes and key factors affecting stability, and to obtain physical and mechanical parameters of the soil and rock mass as the basis for the experiment. Subsequently, a physical or numerical model of the dip-side slope is established based on similarity theory. This model simulates the characteristics of layered rock masses and weak structural surfaces to recreate the failure mechanism of strata-side slope slippage. After model construction, multiple different anchorage schemes are set up. By changing parameters such as anchor length, spacing, inclination angle, prestress, and anchorage position, different test conditions are created, and comparative tests are conducted under the same boundary conditions. During the test, gravity loading, slope top loading, rainfall infiltration, or vibration disturbance are typically used to simulate the actual engineering stress environment. Simultaneously, displacement gauges, strain gauges, pressure sensors, fiber optic monitoring equipment, or digital image correlation technology are used to monitor slope deformation, crack propagation, anchor axial force changes, and local stress response in real time. After the experiment, a comprehensive comparative analysis will be conducted on the displacement changes, failure modes, slip development characteristics, anchor stress state, and ultimate failure load of the slope under different anchoring schemes. The reinforcement effect of each scheme will be evaluated through indicators such as stability, deformation control effect, and economy, so as to select the optimal combination of anchoring parameters suitable for the treatment of dip slopes. The experimental results will be further verified by theoretical analysis or numerical calculation, providing a basis for the design of slope anchoring in engineering projects.

[0004] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0005] Current technologies for optimizing anchorage in dip-side slopes primarily focus on macroscopic phenomena such as increased slope weight and softened rock masses when simulating water-rock coupling effects. They fail to adequately reflect microscopic damage mechanisms, such as groundwater seepage erosion and pore water fracturing, under conditions of sudden water level drops or continuous heavy rainfall. Due to the lack of in-depth simulation of these microscopic damage mechanisms, experiments often struggle to accurately capture the "in-surface response" of the anchorage system. This makes it difficult to track the dynamic attenuation of the anchorage interface friction under water-rock coupling in real time, and to determine the evolution of stress peaks shifting deeper into the anchorage after front-end interface failure. This insufficient characterization of interface dynamic degradation and in-surface stress redistribution can lead to idealized assessments of the long-term pull-out resistance of the anchorage interface. Consequently, the anchorage length optimized based on such experiments may not fully meet engineering expectations in long-term complex seepage pressure environments, thus affecting the accuracy and long-term engineering applicability of the optimized anchorage parameters for dip-side slopes. Summary of the Invention

[0006] To address the technical problem that existing technologies, due to their limitation of simulating water-rock coupling effects to macroscopic appearances and failure to address microscopic damage mechanisms such as seepage erosion and pore water fracturing, cannot capture the dynamic attenuation of anchorage interface friction and the redistribution of stress within the surface in real time. This results in inaccurate test results due to insufficient effective anchorage lengths under long-term seepage pressure. Therefore, this invention provides a method for optimizing anchorage in dip-slope slopes based on surface response characteristics. The technical solution is as follows:

[0007] A method for optimal anchorage testing of dip-slopes based on in-table response characteristics is proposed. The method includes: acquiring multidimensional in-table response time-series data of the dip-slope anchorage system under hydraulic coupling conditions containing pulsating water pressure characteristics. The multidimensional in-table response time-series data includes axial strain sequences, interfacial micro-permeability sequences, and interfacial shear stress sequences generated from the axial strain sequences. The hydraulic coupling conditions are used to induce seepage erosion and pore water fracturing damage at the interface. After spatial gradient analysis of the axial strain sequences and interfacial shear stress sequences, spatiotemporal matching is performed with the interfacial micro-permeability sequences to identify failure characteristic nodes of the local interface caused by pore water fracturing under pulsating water pressure, and to extract the dynamic attenuation characteristic data of interfacial frictional resistance with the time history of the hydraulic coupling conditions. Using the failure characteristic nodes as triggering conditions, the method applies full loading... The axial strain sequence of the process is monitored dynamically via a window. The spatial coordinates of the axial strain peak value within each time window are extracted, and the dynamic rate at which the axial strain peak value moves deeper into the rod as the front-end interface fails is calculated, thus constructing the spatial evolution trajectory of the axial strain peak value transfer. The dynamic attenuation characteristic data and the spatial evolution trajectory are jointly mapped and analyzed. When the dynamic attenuation characteristic data is lower than the preset attenuation threshold and the dynamic rate is lower than the preset stability threshold, the current anchoring system is determined to have entered a state of coordinated convergence. Using the depth coordinates of the axial strain peak value when the coordinated convergence state is reached as the reference point, the distance from the peak value to the end of the anchor rod is quantified as the dynamic effective anchoring segment length under the current working condition. Anchoring parameter combinations that satisfy the coordinated convergence state and whose dynamic effective anchoring segment length meets the ultimate safety threshold are selected, and the optimal anchoring result is output.

[0008] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following:

[0009] Compared with existing technologies, this invention significantly improves the realism and precision of simulating water-rock coupling effects on dip slopes. Addressing the experimental distortion caused by existing technologies' limitation to macroscopic simulation, it achieves a leap from "macroscopic simulation" to "microscopic mechanism analysis," and from "static parameter assumptions" to "dynamic evolution tracking," resulting in the following beneficial effects: First, at the coupling simulation level, it breaks through the limitations of existing macroscopic simulations, realistically reproducing the microscopic hydraulic damage mechanism. Existing technologies for simulating water-rock coupling effects are mostly limited to the macroscopic appearance of increasing slope self-weight and softening rock, failing to address microscopic damage. This invention, however, by acquiring multidimensional intrasurface response time-series data including pulsating water pressure under hydraulic coupling conditions, realistically simulates the seepage erosion and pore water fracturing effect of groundwater on the anchorage interface under scenarios of sudden water level drops or heavy rainfall, laying a realistic hydraulic boundary and data foundation for accurately capturing intrasurface responses.

[0010] Secondly, at the attenuation tracking level, this invention overcomes the shortcomings of existing technologies in accurately capturing the in-surface response, achieving precise quantification of the dynamic attenuation of interfacial friction. The distortion in existing macroscopic simulations makes it difficult to drive precise capture of the in-surface response; however, this invention, based on the restoration of the microscopic hydraulic environment, performs spatial gradient analysis of axial strain and shear stress, and innovatively combines interfacial microscopic seepage pressure for spatiotemporal matching. This accurately identifies the local failure characteristic nodes caused by pore water fracturing, thereby extracting the dynamic attenuation characteristics of friction over time, and intuitively revealing the age-related damage and deterioration law of the anchoring interface.

[0011] Furthermore, at the stress evolution level, this invention fills the gap in existing technologies regarding the failure to detect the stress redistribution pattern within the surface, and elucidates the intrinsic laws governing the transfer of stress peak values. Existing technologies are in a blind spot regarding the transfer of stress to deeper layers after failure of the front-end interface; this invention uses the aforementioned identified failure nodes as triggering conditions, introduces a dynamic window monitoring mechanism, calculates the dynamic rate at which the axial strain peak value moves deeper into the rod, and constructs a spatial evolution trajectory. It dynamically and continuously depicts the spatiotemporal evolution path of stress redistribution within the anchoring system after local failure, clarifying the intrinsic dynamic laws of load transfer.

[0012] Ultimately, at the optimal decision-making level, this invention abandons the static overestimation of long-term pull-out resistance found in existing technologies and scientifically quantifies the dynamic effective anchorage segment. Existing technologies, due to their failure to consider the dynamic degradation of the interface and the stress redistribution, severely overestimate long-term pull-out resistance, resulting in a significant deficiency in the actual effective anchorage segment under long-term seepage pressure. This invention, by jointly mapping attenuation characteristics and evolution trajectories, scientifically defines the "co-convergence state" where the system tends to stabilize, and quantifies the "dynamic effective anchorage segment length" using the strain peak depth in this state. This truly reflects the effective bearing range under long-term seepage pressure, fundamentally avoiding the insufficient anchorage length and failure risk caused by static estimation. It greatly improves the reliability of anchorage optimization tests on dip slopes and provides a scientific and safe parameter basis for practical engineering. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A flowchart of an optimal test method for anchorage of dip-slope based on in-table response characteristics provided in this application embodiment;

[0015] Figure 2This is a diagram illustrating the overall logical architecture of a test method for optimal anchorage of dip slopes based on in-table response characteristics, provided in an embodiment of this application. Detailed Implementation

[0016] Embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present disclosure are shown in the drawings, it should be understood that embodiments of the present disclosure may be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure.

[0017] It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure. In the description of the embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "this embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] With the large-scale construction of transportation, water conservancy, and mining projects in mountainous areas, dip-side slopes, due to their structural plane dip aligning with the slope surface dip, are prone to slippage and instability along weak structural planes under the influence of rainfall, groundwater fluctuations, and external loads, making them a key focus of slope engineering for prevention and control. Anchor bolt (cable) support, due to its convenient construction and strong active reinforcement capabilities, is widely used in dip-side slope stability control. The rational determination of anchorage length and parameters directly affects the long-term service safety of the slope. Therefore, conducting optimal anchorage tests for dip-side slopes is of great significance for improving the stability of the anchorage system and the safety of the project.

[0020] Existing optimal anchorage tests for dip-side slopes mostly simulate water-rock coupling using static loading or simple softening treatments. These tests typically focus only on macroscopic responses such as slope strength attenuation, rock softening, and overall displacement changes, lacking effective simulation of microscopic damage processes caused by groundwater seepage erosion, pore water fracturing, and interface bond degradation at the anchorage interface under complex conditions. Particularly under conditions of continuous rainwater infiltration, sudden drops in water level, and periodic groundwater fluctuations, localized pressure concentration and crack propagation easily occur at the interface, leading to a continuous decrease in the anchor-soil interface friction and inducing changes in load transfer paths and a shortening of the effective anchorage section. However, existing test methods struggle to capture these dynamic evolution processes in real time, resulting in significant biases in the assessment of the long-term pull-out resistance of the anchorage system.

[0021] Meanwhile, existing technologies do not pay enough attention to the "in-surface response" characteristics within the anchoring system, lacking coordinated analysis of multi-dimensional response parameters such as axial strain, interfacial seepage pressure, and interfacial shear stress. This makes it impossible to reveal the pattern of stress peak migration to deeper parts of the rod after interfacial failure, and also makes it difficult to accurately determine the dynamic effective anchoring length. This experimental method, lacking analysis of dynamic interfacial degradation and stress redistribution, easily overestimates the long-term stability of the anchoring interface, leading to the optimized anchoring length failing to meet the safety requirements of actual engineering projects under long-term seepage pressure conditions.

[0022] Therefore, there is an urgent need to provide a test method for optimizing anchorage in dip-slopes that can realistically simulate the microscopic damage mechanism of the interface under hydraulic coupling and dynamically capture the evolution law of the surface response of the anchorage system. This method would enable accurate analysis of the interface friction attenuation, stress migration, and changes in the effective anchorage section, thereby improving the scientific validity and engineering applicability of the anchorage parameter optimization results.

[0023] In the above context, such as Figure 1 The diagram shows a flowchart of a test method for optimal anchorage of a dip-slope based on in-table response characteristics, provided in an embodiment of this application. Figure 1 It should also be understood that this invention, by acquiring multidimensional in-table response time-series data of the slope anchoring system under hydraulic coupling conditions including pulsating water pressure characteristics, can simultaneously reflect the stress state and seepage damage evolution process of the anchor-soil interface, truly capture the microscopic influence of pore water fracturing and seepage erosion on the anchoring interface, ensure the integrity and authenticity of the experimental data, and provide a reliable basis for interface failure identification. Furthermore, by performing spatial gradient analysis on the axial strain sequence and the interface shear stress sequence, and combining it with the interface micro-seepage pressure sequence for spatiotemporal matching, it can accurately identify local interface failure characteristic nodes, quantitatively reveal the dynamic attenuation law of interface friction, achieve early perception of the initiation stage of anchoring interface instability, and improve the accuracy of failure analysis. Simultaneously, by monitoring the peak value of axial strain through a dynamic window and constructing its spatial evolution trajectory as it transfers to the deeper parts of the anchor, it can reveal the stress redistribution and load transfer path changes of the anchor, dynamically reflect the process of interface failure extending from shallow to deep, provide a basis for real-time determination of the effective anchoring section, and improve the ability to assess the stress state of the anchoring structure. Furthermore, by jointly analyzing the dynamic attenuation characteristics of interfacial friction and the spatial evolution trajectory of axial strain peak, it can be determined that the anchoring system has entered a state of coordinated convergence, and the length of the dynamically effective anchoring segment can be accurately determined, thereby selecting the optimal combination of anchoring parameters that meets the ultimate safety conditions. The method of this invention comprehensively considers the synergistic relationship between interfacial degradation and stress redistribution, improving the scientific nature and safety of anchoring length design, avoiding the problem of insufficient effective anchoring segments under long-term seepage pressure, and providing a reliable basis for anchoring parameters in practical engineering.

[0024] refer to Figure 1Specifically, the method includes the following steps: First, in step one, multidimensional in-table response time-series data of the dip-side slope anchorage system under hydraulic coupling conditions containing pulsating water pressure characteristics are obtained. The multidimensional in-table response time-series data includes axial strain sequence, interface micro-permeability sequence, and interface shear stress sequence generated by analyzing the axial strain sequence. The hydraulic coupling condition is used to induce seepage erosion and pore water fracturing damage at the interface. Compared with existing technologies that only perform macroscopic simulation by increasing slope load or softening rock parameters, this step can realistically simulate microscopic damage mechanisms such as groundwater seepage erosion and pore water fracturing, achieving a precise perception of the internal mechanical-seepage coupling process of the anchorage system. This improves the realism and engineering adaptability of the dip-side slope anchorage test to complex water-rock coupling effects, providing a reliable data foundation for subsequent interface damage identification and anchorage performance analysis.

[0025] Next, in step two, after performing spatial gradient analysis on the axial strain sequence and the interface shear stress sequence, spatiotemporal matching is performed in conjunction with the interface micro-permeability sequence to identify the failure characteristic nodes of the local interface caused by pore water fracturing under pulsating water pressure, and to extract the dynamic attenuation characteristic data of the interface friction resistance with the time history of the hydraulic coupling condition. This step, through spatial gradient analysis and spatiotemporal matching of multidimensional response parameters, can accurately identify the location and evolution process of local interface failure caused by pore water fracturing, and realize real-time perception of the initiation stage of anchorage interface instability. At the same time, it can quantitatively reveal the dynamic degradation law of interface friction resistance under hydraulic coupling, and improve the accuracy of long-term stability analysis of the anchorage interface.

[0026] Secondly, in step three, using the failure characteristic node as the trigger condition, dynamic window monitoring is performed on the axial strain sequence throughout the entire loading process; the spatial coordinates of the axial strain peak value within each time window are extracted, and the dynamic rate at which the axial strain peak value moves deeper into the rod as the front-end interface fails is calculated, thus constructing the spatial evolution trajectory of the axial strain peak value transfer; compared with existing technologies that only rely on overall displacement or static mechanical indicators for analysis, this step can dynamically reveal the load transfer path and stress redistribution law within the anchoring system, intuitively reflecting the process of the effective stress area migrating to the depth of the anchor rod after the front-end interface fails, thereby improving the accuracy of judging the internal stress state and evolution law of the effective anchoring zone of the anchoring system.

[0027] Further, in step four, the dynamic attenuation characteristic data and spatial evolution trajectory are jointly mapped and analyzed. When the dynamic attenuation characteristic data is lower than the preset attenuation threshold and the dynamic rate is lower than the preset stability threshold, the current anchoring system is determined to have entered a coordinated convergence state. Using the depth coordinates of the axial strain peak at the time of coordinated convergence as the reference point, the distance from it to the end of the anchor bolt is quantified as the dynamic effective anchoring segment length under the current working condition. Anchoring parameter combinations that meet the coordinated convergence state and whose dynamic effective anchoring segment length meets the ultimate safety threshold are selected, and the optimal anchoring result is output. This step can avoid the problem of overestimation of long-term pull-out performance caused by the use of a single static index in the existing technology, and realize the dynamic and accurate assessment of the effective anchoring capacity under long-term seepage pressure conditions. At the same time, by optimizing the anchoring parameters through the coordinated convergence judgment mechanism, the scientificity and reliability of the determination of the anchoring length and parameter combination can be improved, avoiding the risk of slope instability caused by insufficient dynamic effective anchoring segment.

[0028] In addition to the above, it is also necessary to add, such as Figure 2 The diagram shown is an overall logical architecture diagram of a test method for optimal anchorage of dip-slopes based on in-table response characteristics provided in an embodiment of this application. Figure 2 It should also be understood that the various steps of this invention, through close correlation and coordination, form a complete closed-loop logical chain of "multi-field coupled sensing - accurate damage identification - dynamic evolution tracking - convergence state determination and optimization," producing significant beneficial effects. First, the multi-dimensional in-table response time-series data obtained in step one provides a spatiotemporal matching basis for step two. The two work together to precisely link "hydraulic causes" with "mechanical responses," enabling the removal of failure feature nodes caused by pore water fracturing from a complex coupling background, thus improving the accuracy of damage cause identification. Second, the failure feature nodes identified in step two directly trigger the dynamic window monitoring in step three, constructing a "fixed-point triggering - full-domain tracking" mechanism. This ensures that the spatial evolution trajectory constructed in step three is driven by real failure events, effectively revealing the spatiotemporal transmission law from local damage initiation to overall anchorage degradation. Third, step three... The provided dynamic rate data and the dynamic attenuation characteristic data extracted in step two are jointly mapped and analyzed in step four. This achieves dual verification of damage rate attenuation in the time dimension and stress migration rate stability in the spatial dimension, overcoming the limitations of single index evaluation and objectively reflecting whether the anchoring system has truly reached a stable equilibrium state. Finally, the coordinated operation of the entire process transforms real working condition data into key indicators reflecting the degradation of anchoring performance, enabling the optimized anchoring parameter combination to meet the needs of resisting initial hydraulic damage and maintaining long-term stable reserves, significantly improving the engineering design reliability of dip slopes under complex hydraulic coupling conditions.

[0029] Furthermore, it should be noted that the "internal response" of this invention refers to the real-time physical response of the microscopic mechanical and seepage fields within the anchoring system (including the anchor rod body, grouting body, and the soil-rock interface). This is an internal state representation at the physical quantity level, rather than referring to the "tabular presentation" or "form-based recording" of monitoring data. Specifically, "internal" is relative to the "external" macroscopic manifestations such as the macroscopic surface or slope displacement of the slope. The multidimensional internal response time-series data of this invention specifically refers to the axial strain sequence collected by distributed fiber optic sensors embedded within the anchor body, the interface microscopic seepage pressure sequence collected by an array of pore water pressure gauges deployed along the anchoring interface, and the interface shear stress sequence generated from their analysis. By capturing these microscopic evolution characteristics within the anchoring system, this method can fundamentally analyze the microscopic damage mechanisms of hydraulic fracturing and stress transfer, possessing a completely different technical connotation and physical boundary from conventional external macroscopic deformation monitoring.

[0030] As a further solution, the specific implementation method for step one, "obtaining multidimensional in-table response time series data of the dip slope anchorage system under hydraulic coupling conditions containing pulsating water pressure characteristics," is as follows:

[0031] First, a physical model test platform for a dip-slope was constructed, comprising a model box, a servo loading system, and a water pressure control module. The servo loading system applies periodically alternating water pressure to the dip-slope physical model. Specifically, the servo loading system is connected to a water supply pipeline at the rear of the model box or a water pressure loading device installed within the slope. Based on the designed rainfall or reservoir water level changes, the servo system outputs a pulsating water flow with a preset waveform (such as a sine wave, triangular wave, or a random wave simulating actual hydrological conditions) and frequency. By adjusting the opening of the servo valve, the amplitude and periodic rate of change of the water pressure are precisely controlled, causing dynamic fluctuations in the pore water pressure within the model. This method, through periodic loading and unloading cycles, repeatedly generates shear and tension effects at the anchoring interface, thereby constructing a severe hydraulic coupling condition capable of inducing interfacial seepage erosion and pore water fracturing damage, providing a realistic physical environment for subsequent data acquisition.

[0032] Secondly, during continuous loading under hydraulic coupling conditions, the following data reading operations were simultaneously performed using a high-precision data acquisition system: Specifically, in the test preparation phase, distributed fiber optic sensors (such as fiber Bragg grating (FBG) sensors or BOTDA sensing cables based on Brillouin optical time-domain analysis) were laid along the entire axial length of the anchor bolt. To ensure sensor survival and testing accuracy, axial grooves were created on the surface of the bolt, the optical fiber was adhered to the grooves and encapsulated with epoxy resin for protection, and then implanted into the anchor body for grouting. During the test, the data acquisition system read the monitoring data from the distributed fiber optic sensors in real time at a sampling frequency of no less than 50Hz to obtain the axial deformation values ​​distributed along the entire length of the anchor bolt. These data constitute a data set including spatial location... and time Axial strain sequence of information At the anchorage interface around the anchor bolt, an array of miniature pore water pressure gauges is installed at different depths along the bolt's axial direction (e.g., one every 0.5m), ensuring that the gauge probes are in close contact with the borehole wall soil. The data acquisition system simultaneously reads the real-time readings of each gauge, obtaining monitoring data on the pore water pressure at the anchorage interface as a function of depth and time, and records this as a microscopic permeability sequence at the interface. .

[0033] Next, based on the axial strain sequence obtained above... The data processing terminal performs analytical calculations to generate an interface shear stress sequence. The specific analytical logic is as follows:

[0034] First, according to the formulas of mechanics of materials The axial strain sequence is converted into an axial force sequence. ;in, The elastic modulus of the anchor rod. The first is the cross-sectional area of ​​the anchor bolt; the second is the axial force sequence. Along depth coordinates Find the first derivative. In data processing, since the data is a discrete sequence, the central difference method or other numerical differentiation algorithms are used to calculate the gradient variation of the axial force along the rod length. Finally, based on the mechanical equilibrium relationship of the anchor bolt segments, the formula is used... The shear stress values ​​at different depths and times of the anchorage interface are calculated, thereby generating an interface shear stress sequence. In the formula, The diameter of the anchor rod. This is the perimeter of the anchor bolt.

[0035] Through the specific implementation methods described above, this step successfully obtained multidimensional in-table response time-series data including axial strain, interfacial micro-permeability, and interfacial shear stress under pulsating water pressure characteristics, realizing a fine perception of the internal state of the anchoring system under water-rock coupling.

[0036] Further details are needed regarding the implementation of step two, "identifying failure characteristic nodes" and "extracting dynamic decay characteristic data," which are primarily executed by the data processing terminal. This involves analyzing the multi-dimensional in-table response time-series data obtained in the preceding steps, as detailed below:

[0037] First, identify the failure characteristic nodes; the data processing terminal receives the axial strain sequence. and interfacial micro-osmotic pressure sequence The process involves the following sub-steps:

[0038] Specifically, generating a spatial strain gradient sequence: for each timestamp The system along the anchor depth direction The axial strain sequence is differentially analyzed. Specifically, the difference between two adjacent monitoring points is calculated. and Axial strain difference at the location And this difference is compared with the physical distance between monitoring points. By performing a quotient, the spatial strain gradient is obtained. This calculation is performed on all monitoring points to generate a spatial strain gradient sequence that varies with time and space. This is then mapped to an interface shear stress sequence: the system calls pre-stored anchor body physical parameters, including the elastic modulus of the anchor bolt. and cross-sectional area and anchor bolt perimeter Based on the equilibrium relationships in mechanics of materials, using the formula... The spatial strain gradient sequence is mapped to the interface shear stress sequence. Dual-sequence conditional traversal and locking: The system will use the interface shear stress sequence With interfacial micro-osmotic pressure sequence Precise alignment is performed based on timestamps. Then, a double-sequence conditional traversal algorithm is executed for each spatial coordinate. and time The combination is used for judgment:

[0039] (1) Hydraulic condition determination: detection interface micro-permeability sequence At the present moment Whether the value is greater than other values ​​within its preset time window (e.g., 5 seconds before and after), that is, whether a local extreme value (sudden increase) has occurred, in order to identify pore water fracturing events; (2) Mechanical condition determination: synchronously detect the corresponding interface shear stress sequence At any moment Did a fall occur, and calculate the percentage of the fall magnitude? .like If the percentage exceeds a preset threshold (e.g., 10%), it is determined that the interfacial adhesion has been substantially lost.

[0040] When the system locks the same spatial coordinates and timestamp When both the above hydraulic and mechanical conditions are met, the spatial coordinates are... With timestamp Jointly extract and label as "failure feature nodes".

[0041] Secondly, dynamic decay feature data is extracted; after identifying the failure feature nodes, the system further performs the following operations to quantify the degradation of interface performance:

[0042] Specifically, extract local time-series data: using the timestamps corresponding to the failure feature nodes. Starting from the interface shear stress sequence Extract the spatial coordinates of the node. By loading shear stress data throughout the entire process, local interface shear stress time-series data fragments can be obtained. ,in Nonlinear fitting: Call a nonlinear decay curve fitting algorithm (such as least squares fitting) with time... As the independent variable, shear stress Using the dependent variable, the extracted data segment is fitted. In this implementation, the exponential decay model is preferred. Perform fitting, where This represents the initial strength loss. The decay rate coefficient, Residual strength. Attenuation coefficient extraction: Extract the attenuation rate coefficient from the parameters of the fitted curve. This is defined as dynamic decay characteristic data. This coefficient directly quantifies the rate at which the interfacial interlocking friction at the characteristic node is lost over time as water and rock interact after pore water fracturing occurs.

[0043] It should be understood that the dual-sequence spatiotemporal matching logic of "sudden increase in osmotic pressure" and "shrinkage in shear stress" not only identified the failure but also clarified that the cause of the failure was "pore water fracturing." This causal relationship locking effectively eliminated interference signals caused solely by mechanical load fluctuations, ensuring that the failure characteristic nodes have clear physical meaning.

[0044] Furthermore, by introducing a nonlinear attenuation curve fitting and extracting the attenuation coefficient, the change in interfacial friction is transformed from a static numerical value to a dynamic process description. This allows the present invention not only to assess the current degree of damage but also to predict the rate of damage development, providing a quantitative indicator for evaluating the durability of anchoring systems under long-term hydraulic coupling.

[0045] Further details are needed regarding the implementation of step three: "performing dynamic window monitoring of the axial strain sequence throughout the entire loading process" and "constructing the spatial evolution trajectory of the axial strain peak transfer." These steps are executed by the data processing terminal starting from the identified failure characteristic nodes and include the following steps:

[0046] First, dynamic window monitoring is performed; after confirming the failure characteristic node, the data processing terminal immediately starts the dynamic monitoring program for the subsequent loading process. The specific operation is as follows: Extracting the pulsating water pressure cycle: The terminal system retrieves the interface micro-permeability sequence. Time-domain analysis is performed. By calculating the time interval between adjacent seepage pressure peaks or analyzing the dominant frequency of the spectrum using Fast Fourier Transform (FFT), the fluctuation period time value of the pulsating water pressure characteristics is accurately extracted. Constructing a sliding data window: The system constructs a sliding data window along the timeline of the entire loading process, starting from the timestamp corresponding to the failure characteristic node and ending with the end of loading. Configure the sliding step size of the sliding data window. (For example, set to a multiple of the sampling period or a fixed duration).

[0047] To effectively filter out transient strain fluctuations caused by high-frequency water pressure pulses (i.e., to remove the interference of water hammer effect on strain peak determination), the system adjusts the window time width of the sliding data window. Dynamically configured as fluctuation period time value Integer multiples of, i.e. ,in This is a positive integer, typically k=1 or k=2. This setting ensures that the window contains the complete water pressure fluctuation cycle.

[0048] For each time period covered by the sliding data window The system performs the following calculations: (1) Time-averaging calculation: axial strain sequence within the time period The time-averaged axial strain within this window is obtained by integrating and averaging along the time axis. (2) Smoothing filter: for time-averaged axial strain Along spatial depth coordinates The dimensions are smoothed using a filtering process (such as a five-point moving average or Gaussian filtering) to remove sensor measurement noise, generating a smooth curve characterizing the strain distribution state within that time period, i.e., the spatial strain distribution envelope. .

[0049] The system traverses the spatial strain distribution envelope. The search term involves finding the local maximum value. This point is then designated as the peak value of the axial strain within the current sliding data window. Record the anchor depth coordinates corresponding to the maximum point. .

[0050] As a further step, after obtaining the peak data for all time windows, the system analyzes its spatial migration patterns through the following steps:

[0051] First, calculate the dynamic shift rate: extract the first... The sliding data window and the first Depth coordinates corresponding to adjacent sliding data windows and Calculate the coordinate change of spatial depth. The change in this coordinate The time difference between the two windows (i.e., the sliding step) The dynamic rate at which the peak axial strain moves deeper into the rod was calculated. .

[0052] Secondly, generate trajectory curves: based on the time flow of the full loading process. The horizontal axis represents the depth coordinates of the extreme points extracted in each time window. Construct a coordinate system with the vertical axis as the dimension. Utilize a continuous interpolation algorithm (such as cubic spline interpolation) to represent all discrete data points throughout the process. A smooth connection is made to generate a continuous spatial evolution trajectory curve of axial strain peak transfer. This curve intuitively reflects the spatial path of the anchor bolt axial strain concentration point (i.e., the main stress section) continuously migrating deeper as the front-end interface fails.

[0053] By dynamically configuring the width of the sliding data window to an integer multiple of the pulsating water pressure period and performing time-averaging processing, transient strain fluctuations caused by high-frequency water pressure pulses were successfully filtered out. This enables the system to extract the dominant trend of anchorage interface failure evolution from the wildly fluctuating raw data, avoiding misjudgments caused by water hammer effects and significantly improving the reliability of monitoring data.

[0054] Meanwhile, the constructed spatial evolution trajectory curve clearly illustrates the "movement" trajectory of the effective anchorage section of the anchor bolt. This not only confirms the adaptive adjustment mechanism of the anchorage system in progressive failure, but also provides a visual basis for determining whether the anchorage length is sufficient and whether the anchorage needs to be deepened.

[0055] Furthermore, the specific implementation process of "jointly mapping and analyzing the dynamic attenuation characteristic data with the spatial evolution trajectory" and "outputting the optimal anchoring result" in step four is executed by the data processing terminal, and specifically includes the following steps:

[0056] First, joint mapping analysis and collaborative convergence state determination are performed. The data processing terminal fuses the dynamic decay feature data (curve decay coefficient) extracted in the previous steps with the pushing dynamic rate in the spatial evolution trajectory for analysis. The specific implementation is as follows: Constructing two-dimensional feature state data pairs: The system synchronously extracts the same time-series nodes according to the time axis. Dynamic decay characteristic data The dynamic rate at which the peak axial strain pushes deeper into the rod The two are combined to form a two-dimensional feature state data pair. Real-time monitoring and judgment: The system monitors the two-dimensional feature state data in real time. The trajectory of change on a two-dimensional plane.

[0057] The judgment logic is as follows: when the system detects data pairs... When the following dual conditions are met within a continuous time period: (1) Dynamic decay characteristic data The value is continuously lower than the preset attenuation threshold. (2) Dynamic rate under the same time sequence The value is continuously lower than the preset stability threshold. Furthermore, the cumulative time span during which the above two conditions are continuously met exceeds the pre-set residence time length. (For example, set to 10 consecutive sampling points or 5 minutes). If all the above conditions are met, the system determines that the damage evolution of the anchoring interface has become gradual and the migration of stress concentration locations has become stagnant, thus outputting a judgment command that the current anchoring system has entered a "cooperative convergence state".

[0058] It should be added that the judgment threshold is configured through the following physical parameter calibration steps: Obtain the preset attenuation threshold: Based on the shear stress-displacement curve of the interface shear test, extract the tangent modulus attenuation rate of the softening stage after the peak, convert it into the benchmark attenuation slope under hydraulic coupling, and configure it as the preset attenuation threshold. Obtaining a preset stability threshold: Based on the creep characteristics of the anchor body material, a macroscopic maximum safe deformation rate is established. With the peak shift rate of micro-strain Geometric conversion model: ,in To account for the spatial transfer reduction factor of the stiffness ratio between the anchor rod and the soil / rock mass, the calculated... That is, configured to a preset stability threshold. .

[0059] Furthermore, when the system receives a determination instruction regarding the collaborative convergence state, it performs the following calculation and filtering operations: Calculate the dynamic effective anchorage segment length: The system locks the time when the determination instruction is issued. The spatial depth coordinates of the corresponding peak axial strain Using this depth coordinate as the dividing point, calculate the distance to the end of the anchor bolt (coordinate). The straight-line distance of ), i.e. This distance is quantified into the dynamic effective anchorage length under the current working conditions. The system captures and records the dynamic effective anchorage length data under the current working conditions and compares it with the theoretical basic anchorage length required for slope engineering design. Perform a difference comparison and calculate the length redundancy value. The system is pre-configured with extreme safety thresholds. When the length redundancy value If the value is greater than or equal to the limit safety threshold, it is determined that the anchoring parameter combination (including anchor length, grouting material type, etc.) corresponding to the current test model still has sufficient safety reserve under the current working conditions, and it is marked as a qualified solution.

[0060] Finally, the optimal anchoring result is output; the system summarizes all anchoring parameter combinations that have been deemed qualified under multiple tests or different parameter combinations. For each qualified combination, the system extracts its corresponding length redundancy value (as a safety evaluation index), construction cost parameter (as an economic evaluation index), and water-rock degradation resistance assessment score calculated based on the aforementioned dynamic attenuation characteristic data (as a durability evaluation index), which together constitute the evaluation dimension set. The water-rock degradation resistance assessment score can be obtained by normalizing the integral area of ​​the dynamic attenuation characteristic data over the entire time period.

[0061] Multi-objective optimization algorithms (such as weighted summation, analytic hierarchy process, or Pareto optimality search algorithm) are used to optimize the aforementioned multi-dimensional evaluation data. Based on the optimization results, the system selects the parameter configuration with the best comprehensive evaluation score. This configuration specifically includes the optimal anchor length, prestress level, and grouting material type. Finally, the system generates and outputs an experimental evaluation report containing visual charts (such as parameter performance radar charts and cost-safety scatter plots) to intuitively display the advantages and disadvantages of each scheme and the final recommendation result.

[0062] It should also be understood that by jointly mapping data from two dimensions—"attenuation characteristics" and "spatial evolution rate"—and introducing "dwelling time length" as a time constraint, misjudgments caused by transient interference are effectively avoided. Convergence is only determined when interface damage slows down and stress migration rate remains stable over a long period, ensuring an accurate assessment of the true safety state of the anchoring system.

[0063] Meanwhile, a multi-objective optimization algorithm comprehensively considers safety, cost, and durability to output the optimal combination of anchorage parameters. This not only ensures the long-term stability of the slope under hydraulic coupling conditions but also avoids the waste caused by blindly increasing the anchorage length, achieving a balance between safety and economy and providing scientific decision support for engineering design.

[0064] Overall, this invention constructs a multi-dimensional in-table response time-series data system encompassing axial strain, interfacial micro-permeability, and interfacial shear stress, enabling dynamic perception of seepage erosion, pore water fracturing, and interfacial friction attenuation processes at the anchorage interface under hydraulic coupling. Furthermore, through interface failure feature identification, strain peak migration trajectory construction, and collaborative convergence state determination, it reveals the stress redistribution within the anchorage system and the dynamic evolution of the effective anchorage segment. Compared to existing technologies that only simulate macroscopic water-rock coupling and fail to reflect the micro-damage mechanism of the interface, this invention can more realistically simulate the service state of the anchorage system under long-term permeability conditions, avoiding the problem of insufficient effective anchorage segments in anchorage length design. This improves the scientific rigor, accuracy, and engineering applicability of the anchorage parameter optimization results, providing reliable experimental evidence and technical support for dip-slope anchorage engineering.

[0065] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the above functions can be divided into different functional modules to complete all or part of the functions described above.

[0066] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0067] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units, located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0068] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0069] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the solution, or all or part of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0070] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A test method for optimal anchorage of dip-slope based on in-table response characteristics, characterized in that, Includes the following steps: Multidimensional in-table response time series data of a dip slope anchorage system under hydraulic coupling conditions containing pulsating water pressure characteristics are obtained. The multidimensional in-table response time series data includes axial strain sequence, interface micro-permeability sequence, and interface shear stress sequence generated by axial strain sequence analysis. The hydraulic coupling condition is used to induce seepage erosion and pore water fracturing damage at the interface. After performing spatial gradient analysis on the axial strain sequence and the interface shear stress sequence, spatiotemporal matching was performed by combining the interface micro-permeability sequence to identify the failure characteristic nodes of the local interface caused by pore water splitting under the drive of pulsating water pressure, and the dynamic attenuation characteristic data of interface friction with the time history of hydraulic coupling were extracted. Using failure feature nodes as triggering conditions, dynamic window monitoring is performed on the axial strain sequence throughout the entire loading process; the spatial coordinates of the axial strain peak value within each time window are extracted, and the dynamic rate at which the axial strain peak value moves deeper into the rod as the front-end interface fails is calculated, thus constructing the spatial evolution trajectory of the axial strain peak value transfer. The dynamic attenuation characteristic data and spatial evolution trajectory are jointly mapped and analyzed. When the dynamic attenuation characteristic data is lower than the preset attenuation threshold and the dynamic rate is lower than the preset stability threshold, the current anchoring system is determined to have entered a coordinated convergence state. Taking the depth coordinate of the axial strain peak when the coordinated convergence state is reached as the reference point, the distance from it to the end of the anchor is quantified as the dynamic effective anchoring section length under the current working condition. The anchoring parameter combination that satisfies the coordinated convergence state and the dynamic effective anchoring section length meets the limit safety threshold is selected, and the optimal anchoring result is output. The identification of failure feature nodes of local interfaces caused by pore water fracturing under pulsating water pressure specifically includes: Calculate the data difference of the axial strain sequence of adjacent monitoring points at the same time stamp, and divide it by the physical distance between the corresponding monitoring points to generate a spatial strain gradient sequence. The spatial strain gradient sequence is mapped to the interface shear stress sequence by calling the preset parameters of the anchor body's elastic modulus, cross-sectional area, and cross-sectional perimeter. The interface shear stress sequence and the interface micro-pressure sequence are aligned according to the timestamp, and a dual-sequence conditional traversal is performed. When the interface micro-pressure sequence under the same spatial coordinate suddenly increases and a local extreme value appears, and the corresponding interface shear stress sequence drops synchronously and the drop amplitude exceeds the preset ratio, the spatial coordinate and the corresponding timestamp are jointly extracted as failure feature nodes.

2. The experimental method for optimal anchorage of dip-slope based on in-table response characteristics as described in claim 1, characterized in that, The acquisition of multidimensional in-table response time-series data of the slope anchorage system under hydraulic coupling conditions containing pulsating water pressure characteristics specifically includes: The servo loading system is controlled to apply periodic alternating water pressure to the physical model of the dip slope in order to construct a hydraulic coupling condition; During the hydraulic coupling loading process, the strain data of the distributed optical fiber sensors embedded in the anchor body are read synchronously as the axial strain sequence, and the water pressure monitoring data of the pore water pressure gauge array deployed along the anchor interface is read as the interface micro-permeability sequence. Based on the axial strain sequence, the interface shear stress sequence is generated.

3. The experimental method for optimal anchorage of dip-slope based on in-table response characteristics as described in claim 1, characterized in that, The extracted dynamic attenuation characteristic data of interfacial frictional resistance over time under hydraulic coupling conditions specifically includes: Using the timestamp corresponding to the failure feature node as the starting point for data extraction, local interface shear stress time series data under the subsequent loading process in the spatial monitoring coordinates are extracted; A nonlinear attenuation curve fitting algorithm was used to fit the time series data of the local interface shear stress after the truncation was performed. The attenuation coefficient of the fitted curve is extracted as dynamic attenuation feature data. The curve attenuation coefficient is used to quantify the rate at which the interfacial interlocking friction at the feature node is lost over time due to the interaction between water and rock.

4. The experimental method for optimal anchorage of dip-slope based on in-table response characteristics as described in claim 1, characterized in that, The dynamic window monitoring of the axial strain sequence throughout the entire loading process specifically includes: Time-domain analysis was performed on the interfacial micro-permeability pressure sequence to extract the fluctuation period time value of the pulsating water pressure characteristics; A sliding data window is constructed on the data time axis of the entire loading process. The sliding step size of the sliding data window is configured to slide forward along the time axis. The window time width of the sliding data window is dynamically configured to be an integer multiple of the fluctuation period time value in order to filter out transient strain fluctuations caused by high-frequency water pressure pulses. Within the time period covered by each sliding data window, the axial strain sequence is subjected to time averaging and smoothing filtering calculations along the spatial monitoring coordinate dimension to generate the spatial strain distribution envelope corresponding to the current window. Traverse the data points of the spatial strain distribution envelope, extract the local maximum point with the largest strain value, locate it as the axial strain peak value within the sliding data window, and record the spatial depth coordinate data corresponding to the maximum point.

5. The experimental method for optimal anchorage of dip-slope based on in-table response characteristics as described in claim 4, characterized in that, The construction of the spatial evolution trajectory of the axial strain peak transfer specifically includes: Extract the spatial depth coordinates corresponding to the maximum points within adjacent sliding data windows, and calculate the change in spatial depth coordinates; The ratio of coordinate change to sliding step size is used as the dynamic rate at which the peak axial strain moves deeper into the rod. Using time flow as the horizontal axis and the spatial depth coordinates corresponding to the extreme points as the vertical axis, a continuous interpolation algorithm is used to connect the data points of the entire loading process to generate the spatial evolution trajectory curve of the axial strain peak transfer.

6. The experimental method for optimal anchorage of dip-slope based on in-table response characteristics as described in claim 1, characterized in that, The joint mapping analysis of dynamic decay characteristic data and spatial evolution trajectory specifically includes: Extract the dynamic decay feature data and the dynamic rate of movement under the same time node to construct a two-dimensional feature state data pair; Real-time monitoring of the changing trajectory of two-dimensional feature state data pairs; When the dynamic decay feature data in the two-dimensional feature state data pair is continuously lower than the preset decay threshold, and the dynamic rate in the same time series is continuously lower than the preset stability threshold, and the time span of satisfying the above two conditions exceeds the preset dwell time series length, a judgment command is output that the current anchoring system has entered the collaborative convergence state.

7. The experimental method for optimal anchorage of dip-slope based on in-table response characteristics as described in claim 6, characterized in that, The steps for obtaining the preset attenuation threshold and the preset stability threshold include: Shear test records of the contact interface between the rock mass and the grouting body were obtained before the test. Based on the shear stress-displacement curve of the shear test records, the tangent modulus attenuation rate of the softening stage after the peak was extracted, converted into the benchmark attenuation slope under hydraulic coupling, and configured as the preset attenuation threshold. The creep test characteristic data of the anchor body material are obtained, and combined with the maximum allowable safe deformation rate index of the slope engineering, the safe critical rate of strain peak displacement in the deep part of the rod is calculated and configured as the preset stability threshold.

8. The experimental method for optimal anchorage of dip-slope based on in-table response characteristics as described in claim 1, characterized in that, The selection of anchorage parameter combinations that satisfy the collaborative convergence state and whose dynamically effective anchorage segment length meets the ultimate safety threshold specifically includes: When a determination instruction for the collaborative convergence state is received, the dynamic effective anchorage length data under the current working condition is frozen. The length of the frozen dynamic effective anchorage segment is compared with the theoretical anchorage length required for slope design to obtain the length redundancy value. When the length redundancy value is greater than or equal to the configured limit safety threshold, the anchoring parameter combination corresponding to the current model is determined to be a qualified solution.

9. The experimental method for optimal anchorage of dip-slope based on in-table response characteristics as described in claim 8, characterized in that, The preferred anchoring result output specifically includes: Summarize all anchoring parameter combinations that are deemed qualified, and extract the length redundancy value, construction cost parameters, and water and rock degradation resistance assessment scores corresponding to each qualified scheme as evaluation dimensions. A multi-objective optimization algorithm is used to perform optimization calculations on multi-dimensional evaluation data, select the parameter combination of anchor length, prestress level and grouting material type with the best comprehensive evaluation score, and output an experimental evaluation report including visualization charts.

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