A slope group anchor load full-field detection method based on sparse benchmark in-situ calibration

Abstract

The application discloses a kind of slope group anchor load full-field detection methods based on sparse reference in-situ calibration, comprising: step S1: the slope is divided into several detection regions;In each region, anchor cable is divided into two categories of reference anchor cable and to-be-measured anchor cable;Step S2: when carrying out on-site inspection, the operator carries portable magnetic flux detector, and the exposed section of reference anchor cable is observed and the real load value of magnetic flux observation value is collected;Step S3: based on the magnetic flux observation value and the real load value obtained, an in-situ dynamic calibration model is established;Step S4: the to-be-measured anchor cable in the region is scanned and detected one by one, and the real load of the to-be-measured anchor cable is calculated inversely;Step S5: generate the group anchor load nephogram of the whole slope, and identify the stress abnormal concentration area, and issue a graded early warning signal.The application uses the existing few dynamometers on site as a dynamic standard source, and calibrates the portable magnetic flux detector in real time, so as to realize high-precision, low-cost general survey of unmonitored anchor cables.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering safety monitoring and non-destructive testing technology, and in particular to a method for full-field detection of slope group anchor load based on in-situ calibration of sparse benchmarks. Background Technology

[0002] Anchoring engineering is a core method for slope stabilization and underground engineering support, and its long-term safety mainly depends on the effective maintenance of the prestress in the anchor cables. Currently, the prestress detection and evaluation technology for anchored slopes has the following three main limitations:

[0003] First, online monitoring coverage is low, resulting in blind spots. Existing long-term monitoring mainly relies on anchor cable force gauges (such as vibrating wire or resistance sensors). However, due to the high cost of force gauges, complex installation processes, and difficult line maintenance, in engineering practice, only 5%-10% of the total number of anchor cables are typically selected as monitoring points, leaving more than 90% of anchor cables in a "no data" state. This "point-to-surface" approach based on sparse samples ignores the spatial variability of soil and rock properties and the dispersion of construction quality, leading to insufficient sample representativeness and difficulty in detecting potential group anchor failure risks in non-monitored areas.

[0004] Secondly, existing non-destructive testing technologies have limited accuracy and poor field adaptability. For anchor cables without force gauges, current technologies often employ magnetic flux cable force detectors based on the magnetoelastic effect. However, in actual geotechnical anchoring projects, the magnetoelastic effect depends not only on the stress level but also on the nonlinear coupling interference from multiple factors, such as localized corrosion of the steel strand surface, temperature gradient fluctuations in the soil and rock mass, and slight differences in materials between different batches. Existing equipment calibration largely relies on controlled laboratory environments, and these idealized calibration parameters often fail to accurately reflect the complex field service conditions, resulting in significant errors in direct field testing (generally around 15%-20%), which cannot meet the accuracy requirements for structural safety evaluation.

[0005] Finally, high-precision testing methods are difficult to implement and cannot cover the entire field. Although the "lift-off test" is currently recognized as the "gold standard" for obtaining the true load of anchor cables, it falls under the category of destructive testing. This method is cumbersome (requiring the removal of anchor head protection devices and the erection of high-altitude platforms) and costly to implement; more seriously, repeated lift-off operations can damage the original permanent anti-corrosion sealing system of the anchor head and may cause mechanical damage to the wedges and steel strands, inducing stress corrosion or wire breakage risks.

[0006] In summary, lift-off tests can only serve as a small-scale sampling method, while conventional magnetic flux detection is limited by on-site environmental interference. Therefore, how to effectively decouple and in-situ compensate for the complex "environmental background noise" of slopes without damaging the existing anchoring system, and thus achieve high-precision, low-cost, full-field non-destructive testing and early warning of slope group anchors, is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for full-field detection of slope group anchor load based on sparse benchmark in-situ calibration. It utilizes a small number of existing force gauges as dynamic standard sources to calibrate portable magnetic flux detectors in real time, thereby achieving high-precision and low-cost general survey and full-field safety assessment of unmonitored anchor cables.

[0008] The objective of this invention is achieved through the following technical solution: a method for full-field detection of slope group anchor load based on sparse benchmark in-situ calibration, comprising the following steps:

[0009] Step S1: Based on the geological conditions and support structure of the slope, divide the slope into several testing areas; within each area, classify the anchor cables into two categories: reference anchor cables and anchor cables to be tested;

[0010] Step S2: During on-site inspection, the operator carries a portable magnetic flux detector to collect magnetic flux observation values ​​and actual load values ​​of the exposed steel strand of the reference anchor cable.

[0011] Step S3: Based on the obtained magnetic flux observation value and the actual load value, establish an in-situ dynamic calibration model; and utilize the characteristic that the reference anchor cable and the anchor cable under test are in the same field physical environment to eliminate environmental system errors in real time through the in-situ dynamic calibration model.

[0012] Step S4: Scan and test each anchor cable in the area to calculate the actual load of the anchor cable.

[0013] Step S5: Generate a group anchor load cloud map of the entire slope, and identify areas of stress anomaly concentration based on the spatial aggregation characteristics of the load cloud map, and issue graded early warning signals.

[0014] The beneficial effects of the present invention are: (1) High precision: The "in-situ real-time calibration" effectively eliminates the systematic errors caused by steel strand corrosion, temperature changes and material differences.

[0015] (2) High efficiency and low cost: No need to install sensors for each anchor cable; only a portable device is needed to achieve full-field detection, which greatly reduces the cost of engineering monitoring.

[0016] (3) Comprehensive data: It has achieved a leap from "single-point monitoring" to "full-field assessment", effectively eliminating safety blind spots. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention;

[0018] Figure 2 This is a schematic diagram of the slope group anchor load cloud map and graded early warning reconstructed using the Kriging algorithm in an embodiment of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0020] like Figure 1 As shown, a method for full-field detection of slope group anchor load based on sparse benchmark in-situ calibration includes the following steps:

[0021] Step S1: Based on the geological conditions and support structure of the slope, divide the slope into several testing areas; within each area, classify the anchor cables into two categories: reference anchor cables and anchor cables to be tested;

[0022] The reference anchor cable refers to an anchor cable that has been equipped with a permanent force gauge and is functioning properly; or it refers to an anchor cable selected for a lift-off test to obtain the actual load.

[0023] The anchor cable to be tested refers to an anchor cable that does not have a force gauge installed or whose force gauge is damaged and requires non-destructive testing.

[0024] Step S2: During on-site inspection, the operator carries a portable magnetic flux detector to collect magnetic flux observation values ​​and actual load values ​​of the exposed steel strand of the reference anchor cable.

[0025] Step S2 includes:

[0026] During on-site inspections, operators carried portable magnetic flux detectors to collect magnetic flux signals from the exposed steel strands of the reference anchor cable, obtaining the observed magnetic flux value M. base ;

[0027] At the same moment the magnetic flux signal is acquired, the actual load value F of the reference anchor cable is recorded. real ;

[0028] The observed magnetic flux values ​​are paired with the corresponding actual load values ​​to form a data pair (M). base , F real ).

[0029] When determining the actual load value of the reference anchor cable, if the reference anchor cable is equipped with a force gauge, the force gauge value is read directly; if the reference anchor cable does not have a force gauge, a lift-off test is conducted on-site to obtain the locking load value.

[0030] Step S3: Based on the obtained magnetic flux observation values ​​and actual load values, establish an in-situ dynamic calibration model;

[0031] In step S3, the environmental system error is automatically and in real time eliminated by utilizing the characteristics of the reference anchor cable and the anchor cable to be tested being in the same field physical environment (i.e., the same temperature, similar corrosion environment, and similar material batches).

[0032] The in-situ dynamic calibration model is a mapping relationship model F=f(M), where M is the magnetic flux observation value collected in real time by the portable magnetic flux detector;

[0033] The mapping relationship model adopts a linear correction model F=F real + α(MM base Alternatively, a nonlinear mapping model F=F represented by a polynomial can be used. real + β1(MM base ) + β2(MM base ) 2 , where α, β1, and β2 are environmental calibration coefficients obtained by fitting the reference anchor cable data.

[0034] Step S4: Scan and test each anchor cable in the area to calculate the actual load of the anchor cable.

[0035] Step S4 includes:

[0036] The magnetic flux detector was used to scan and test each anchor cable under test in the area.

[0037] For any anchor cable under test, the magnetic flux signal M of the exposed section of the anchor cable is measured. target By inputting the data into the in-situ calibration model, the actual load F of the anchor cable under test can be calculated in real time. target .

[0038] Step S5: Generate a group anchor load cloud map of the entire slope, and identify areas of stress anomaly concentration based on the spatial aggregation characteristics of the load cloud map, and issue graded early warning signals.

[0039] Step S5 includes:

[0040] (1) The measured load value of the reference anchor cable and the inverted value of the actual load of the anchor cable to be tested are linked to their physical spatial coordinates on the slope:

[0041] The measured load values ​​of the reference anchor cable and the actual load inversion values ​​of the anchor cable to be tested are uniformly mapped to the two-dimensional physical space coordinate system of the slope (where the X-axis represents the slope direction length and the Y-axis represents the slope elevation) or the three-dimensional physical space coordinate system (where the X-axis represents the slope direction length, the Y-axis represents the slope elevation, and the Z-axis represents the depth direction pointing into the slope body).

[0042] (2) The Kriging spatial interpolation algorithm is used to process the bound spatial data lattice and reconstruct the continuous group anchor load cloud map. This algorithm can fully consider the spatial variability and local discreteness of the properties of the soil and rock, thereby transforming the discrete anchor load lattice into a smooth and continuous group anchor load distribution surface.

[0043] (3) The prestress loss rate is obtained by comparing the actual load with the anchor cable design locking load. When the group anchor load cloud map shows that the loads of multiple adjacent anchor cables are lower than the set allowable threshold ratio and a low-stress closed loop is formed on the cloud map, it is determined to be the stress anomaly concentration area with local sliding risk, and a graded warning is triggered. Among them, the multiple adjacent anchor cables refer to at least 3 anchor cables that are continuously distributed in spatial topology; the low-stress closed loop refers to the continuous closed geometric area enclosed by contour lines equal to the allowable threshold ratio on the group anchor load cloud map.

[0044] In the generated cloud map, different geometric symbols can be used to distinguish the data source (e.g., triangles represent reference force gauge data, and dots represent inversion data), and the prestress level can be represented by warm and cool color bars to visually display the abnormal closed loop.

[0045] The above scheme avoids an overly simplistic early warning mechanism that is limited to specific projects. Instead, it integrates with current geotechnical engineering standards, introducing a two-dimensional judgment logic of "parametric threshold" and "spatial clustering." In the embodiments of this application, the specific judgment is as follows:

[0046] 1. Parametric reduction determination based on standard benchmarks: Instead of using the absolute value of a fixed load, the real-time inverted load is compared with the "design locking load" of the anchor cable to calculate its "prestress loss rate". Referring to the allowable values ​​for steel strand relaxation and rock mass creep set in current engineering specifications, static warning thresholds are defined (e.g., a yellow concern zone is set if the loss rate exceeds the standard allowable value; a red severe loss zone is set if the load is 70% lower than the design value).

[0047] 2. A two-dimensional hierarchical early warning strategy combining "prestress loss rate" and "spatial concentration":

[0048] The system does not rely solely on the absolute load value of a single anchor cable, but rather determines the risk level through two-dimensional cross-logic, specifically including:

[0049] Level 3 Warning (Yellow Attention Zone - Isolated Point Downgrade): If the cloud map shows that the load of a single or two adjacent anchor cables is lower than 70% of the design value (triggering a high loss rate), but the aggregation condition of at least three cables is not met, and a low-stress closed loop is not formed, the system determines it as a construction quality defect or local wedge failure of an individual anchor cable. This does not trigger a regional surface instability warning; it is only recorded and downgraded to a Level 3 warning.

[0050] Level 2 Warning (Orange Alert Zone - Early Aggregated Rheology): If at least 3 adjacent anchor cables are present in the cloud map, with their loads between 70% and 90% of the design value, and a preliminary low-stress closed loop has formed on the cloud map, the system determines that the rock mass in this area may undergo overall early rheology, triggering a Level 2 warning and prompting an increase in the detection frequency in this area.

[0051] Level 1 Warning (Red Danger Zone - Aggregation Escalation): If the cloud map shows at least 3 adjacent anchor cables simultaneously under low load, forming a "low-stress closed loop" enclosed by contour lines representing 70% of the design value, the system automatically utilizes this spatial aggregation characteristic to determine that the rock mass in this area has undergone overall rheological changes or structural plane displacement, posing a significant risk of local sliding. This immediately escalates to the highest level, Level 1 Warning, and guides immediate on-site in-situ reinforcement (as described in the example of the abnormal area in the middle of the slope).

[0052] In an embodiment of this application, taking a high slope project as an example, the physical dimensions of the slope detection sub-area are 100m long and 50m high, and the design locking load of the group anchors in the area is 1500 kN.

[0053] Step 1: Benchmark Selection and Collaborative Data Acquisition. Based on the geological conditions and support structure of the slope, the anchor cables in the area are divided into two categories: benchmark anchor cables and anchor cables to be measured. A small number of anchor cables with installed force gauges, evenly distributed on the slope surface, are selected. Figure 2 The triangular marker (marked at the reference point) serves as the baseline anchor cable. Inspection personnel use a portable magnetic flux detector on-site to collect the magnetic signal of the reference anchor cable and simultaneously record its actual load value.

[0054] Step 2: Dynamic Calibration and Full-Field Testing. Based on the collected data pairs, an in-situ dynamic calibration model is established under the current environment to automatically eliminate environmental system errors introduced by factors such as temperature and corrosion. Subsequently, the inspection personnel use the calibrated equipment to test the remaining anchor cables (such as...) in the area that do not have force gauges. Figure 2 The test points (marked by dots in the diagram) are scanned one by one, and the actual load of each anchor cable to be tested is calculated in real time.

[0055] Step 3: Cloud Map Generation (Load Field Reconstruction) This step combines the measured values ​​of the reference anchor cable and the inverted values ​​of the anchor cable to be measured with their physical spatial coordinates (X-axis: 0-100m, Y-axis: 0-50m) on the slope. Using the Kriging interpolation algorithm, spatial gridding is performed to reconstruct a smooth and continuous slope group anchor load cloud map. For example... Figure 2 As shown, the color gradient on the right represents the magnitude of the anchor cable load, with values ​​ranging from 800 kN to over 1500 kN.

[0056] Step 4: Risk Identification and Graded Early Warning. Based on the generated load cloud map, the system introduces a parameterized graded early warning mechanism. The threshold for "Level 1 Early Warning" for this slope is set as follows: the actual load of the anchor cable is less than 70% of the design value (i.e., 1500 × 70% = 1050 kN).

[0057] A visual analysis of the load field in the attached diagram revealed a distinct dark blue "low-stress closed loop" in the middle of the slope (approximately 45m-55m X-coordinate and 20m-35m Y-coordinate). Within this area, the load on multiple adjacent anchor cables had decreased to around 800-900 kN, forming an abnormal stress concentration zone. Since the load in this area had exceeded the first-level warning threshold of 1050 kN, the system determined that there was a serious loss of prestress and a risk of localized slippage, automatically issuing a graded warning signal to prompt engineers to immediately implement in-situ reinforcement measures in this area.

[0058] The foregoing description illustrates and describes a preferred embodiment of the present invention. However, as previously stated, it should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the inventive concept described herein through the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for full-field detection of slope group anchor load based on sparse benchmark in-situ calibration, characterized in that: Includes the following steps: Step S1: Based on the geological conditions and support structure of the slope, divide the slope into several testing areas; within each area, classify the anchor cables into two categories: reference anchor cables and anchor cables to be tested; Step S2: During on-site inspection, the operator carries a portable magnetic flux detector to collect magnetic flux observation values ​​and actual load values ​​of the exposed steel strand of the reference anchor cable. Step S3: Based on the obtained magnetic flux observation value and the actual load value, establish an in-situ dynamic calibration model; and utilize the characteristic that the reference anchor cable and the anchor cable under test are in the same field physical environment to eliminate environmental system errors in real time through the in-situ dynamic calibration model. Step S4: Scan and test each anchor cable in the area to calculate the actual load of the anchor cable. Step S5: Generate a group anchor load cloud map of the entire slope, and identify areas of stress anomaly concentration based on the spatial aggregation characteristics of the load cloud map, and issue graded early warning signals.

2. The method for full-field detection of slope group anchor load based on sparse benchmark in-situ calibration according to claim 1, characterized in that: The reference anchor cable refers to an anchor cable that has been equipped with a permanent force gauge and is functioning properly; or it refers to an anchor cable selected for a lift-off test to obtain the actual load.

3. The method for full-field detection of slope group anchor load based on sparse benchmark in-situ calibration according to claim 1, characterized in that: The anchor cable to be tested refers to an anchor cable that does not have a force gauge installed or whose force gauge is damaged and requires non-destructive testing.

4. The method for full-field detection of slope group anchor load based on sparse benchmark in-situ calibration according to claim 1, characterized in that: Step S2 includes: During on-site inspections, operators carried portable magnetic flux detectors to collect magnetic flux signals from the exposed steel strands of the reference anchor cable, obtaining the observed magnetic flux value M. base ; At the same moment the magnetic flux signal is acquired, the actual load value F of the reference anchor cable is recorded. real ; The observed magnetic flux values ​​are paired with the corresponding actual load values ​​to form a data pair (M). base , F real ).

5. The method for full-field detection of slope group anchor load based on sparse benchmark in-situ calibration according to claim 4, characterized in that: When determining the actual load value of the reference anchor cable, if the reference anchor cable is equipped with a force gauge, the force gauge value is read directly; if the reference anchor cable does not have a force gauge, a lift-off test is conducted on-site to obtain the locking load value.

6. The method for full-field detection of slope group anchor load based on sparse benchmark in-situ calibration according to claim 1, characterized in that: In step S3, the environmental system error includes errors introduced by temperature gradient, steel strand corrosion, and material differences. The in-situ dynamic calibration model is a mapping relationship model F=f(M), where M is the magnetic flux observation value collected in real time by the portable magnetic flux detector; The mapping relationship model adopts a linear correction model F=F real + α(MM base Alternatively, a nonlinear mapping model F=F represented by a polynomial can be used. real + β1(MM base ) + β2(MM base ) 2 , where α, β1, and β2 are environmental calibration coefficients obtained by fitting the reference anchor cable data.

7. The method for full-field detection of slope group anchor load based on sparse benchmark in-situ calibration according to claim 6, characterized in that: Step S4 includes: The magnetic flux detector was used to scan and test each anchor cable under test in the area. For any anchor cable under test, the magnetic flux signal M of the exposed section of the anchor cable is measured. target By inputting the data into the in-situ calibration model, the actual load F of the anchor cable under test can be calculated in real time. target .

8. The method for full-field detection of slope group anchor load based on sparse benchmark in-situ calibration according to claim 1, characterized in that: Step S5 includes: The measured load value of the reference anchor cable and the inverted value of the actual load of the anchor cable to be tested are bound to its physical spatial coordinates on the slope. The bound spatial data point matrix is ​​processed using the Kriging spatial interpolation algorithm to reconstruct and generate a continuous group anchor load cloud map. The prestress loss rate is obtained by comparing the actual load with the anchor cable design locking load. When the group anchor load cloud map shows that the loads of multiple adjacent anchor cables are lower than the set allowable threshold ratio and form a low-stress closed loop on the cloud map, it is determined to be a stress anomaly concentration area with local sliding risk, and a graded warning is triggered. Here, the multiple adjacent anchor cables refer to at least 3 anchor cables that are continuously distributed in spatial topology; the low-stress closed loop refers to a continuous closed geometric area on the group anchor load cloud map enclosed by contour lines equal to the allowable threshold ratio.

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