Intelligent sensing and self-adaptive estimation method for bearing character of pressure type anchor rod

Abstract

The invention discloses a pressure type anchor rod bearing property intelligent sensing and self-adaptive estimation method, and relates to the technical field of geotechnical engineering.The method comprises the following steps that key nodes of a pressure type anchor rod are obtained, and bearing property influence data of the pressure type anchor rod are obtained; constructing a bearing character influence coefficient model based on a machine learning algorithm according to the bearing character influence data; parameters in the bearing character influence model are pre-estimated based on linear regression analysis, and self-adaptive pre-estimation is carried out by inputting the pre-estimated parameters; according to the method, the bearing character influence model is constructed, so that the construction risk is reduced, and the project quality and safety are improved.

Description

Technical Field

[0001] The present invention relates to the field of geotechnical engineering technology, and more specifically, to an intelligent perception and adaptive prediction method for the bearing characteristics of a pressure anchor rod. Background Art

[0002] In modern civil engineering, especially in the fields of slope support and underground engineering, anchor rods are a key support component, and the stability and reliability of their bearing performance are crucial. Pressure anchor rods, with their unique support performance, transfer the pressure of the slope rock mass to the stable rock or soil layer through the pressure system formed by the anchor rod and the grouting body, providing stable support for the slope. However, the bearing properties of anchor rods are affected by geological conditions, engineering factors and the characteristics of the anchor rods themselves, which makes the prediction and analysis of anchor rod bearing capacity a complex and critical issue.

[0003] The above disclosed technical solutions have at least the following technical problems: However, traditional monitoring methods often rely on manual inspections and regular testing, which have problems such as low monitoring frequency, discontinuous data, and inability to monitor in real time. In addition, due to the complexity and uncertainty of geotechnical engineering, traditional empirical formulas and calculation methods are often difficult to accurately estimate the bearing capacity of anchor rods. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an intelligent perception and adaptive prediction method of the bearing characteristics of a pressure-type anchor rod, which solves the problem of reducing construction risks and improving project quality and safety by constructing a bearing characteristic influence model.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for intelligently sensing and adaptively estimating the bearing characteristics of a pressure-type anchor, comprising obtaining key nodes of the pressure-type anchor and obtaining bearing characteristic influencing data of the pressure-type anchor; constructing a bearing characteristic influencing coefficient model based on a machine learning algorithm according to the bearing characteristic influencing data; estimating parameters in the bearing characteristic influencing model based on linear regression analysis, and performing adaptive estimation by inputting the estimated parameters.

[0006] In a preferred embodiment, the bearing property influencing data includes an anchoring force influence coefficient, and the steps for obtaining the anchoring force influence coefficient are as follows: obtain the diameter of the anchor rod and the depth of the anchor rod buried in the rock and soil medium; collect the pressure data borne by the anchor rod, pre-process the pressure data borne by the anchor rod, remove abnormal values, and average the pre-processed pressure data borne by the anchor rod to obtain the average pressure borne by the anchor rod; select multiple groups of anchor rods of the same material, perform limit tests on each group of anchor rods, obtain the maximum bearing pressure of each group of anchor rods, average the maximum bearing pressure data of multiple groups of anchor rods, obtain the maximum bearing pressure average, and use the maximum bearing pressure average as the standard value; select a group of anchor rods to be tested, apply pressure to the anchor rods, and the pressure continues to increase, and record the friction force exerted on the anchor rods under different pressures. The relationship between pressure and friction is analyzed linearly to obtain the material property index and the friction coefficient of the anchor surface; the type, density and water content of the rock and soil in the area where the anchor is used are obtained, and the friction stress of the rock and soil medium and the shear strength of the rock are obtained through a direct shear test; the lowest temperature and the highest temperature of the area to be tested in previous years are obtained to obtain a temperature range, and the expansion change data of the anchor material is recorded in the temperature range. The relationship between temperature and expansion change is analyzed based on the linear analysis method to obtain the temperature influence coefficient; the anchor force influence coefficient is calculated based on the preset anchor force influence coefficient formula according to the diameter of the anchor, the depth of the anchor buried in the rock and soil medium, the pressure borne by the anchor, the maximum bearing pressure, the material property index, the friction coefficient of the anchor surface, the friction stress of the rock and soil medium, the shear strength of the rock and the temperature influence coefficient.

[0007] In a preferred embodiment, the bearing property influencing data includes the influence coefficient of rock and soil medium characteristics, and the steps for obtaining the influence coefficient of rock and soil medium characteristics are as follows: conduct a triaxial shear test on the test area, record the shear strength of the soil under different stress conditions, fit the obtained shear strength data, and obtain the internal friction angle of the rock and soil medium and the current friction angle of the rock and soil medium; obtain the soil in the test area, perform a vibration table density test on the soil, and obtain the density of the rock and soil medium; obtain a piece of soil in the test area again, perform a dry density test in the laboratory, and obtain the standard density of the soil; obtain the soil in the test area based on the triaxial undrained shear test, apply different stresses to the soil, and record the cohesion value of the soil under different stresses, and obtain the cohesion of the soil under the maximum stress condition from the cohesion value data of the soil; obtain different densities and friction The test data of the bearing capacity of the anchor rod under the friction angle condition is obtained, and the test data is preprocessed, and the preprocessed data is subjected to regression analysis to obtain the influence coefficient of density and the influence coefficient of friction angle; multiple groups of soils with different cohesion are collected, and the bearing capacity of the soil under different cohesion is tested, and the bearing capacity of the soil under different cohesion is obtained, and the influence coefficient of the nonlinear relationship of the soil cohesion and the influence coefficient of the nonlinear relationship of the soil bearing capacity are obtained based on the fitting method; according to the internal friction angle of the rock and soil medium, the friction angle of the current rock and soil medium, the density of the rock and soil medium, the standard density of the soil, the cohesion of the soil under the maximum stress condition, the influence coefficient of density, the influence coefficient of friction angle, the influence coefficient of the nonlinear relationship of the soil cohesion and the influence coefficient of the nonlinear relationship of the soil bearing capacity are calculated based on the preset rock and soil medium characteristic influence coefficient formula to obtain the rock and soil medium characteristic influence coefficient.

[0008] In a preferred embodiment, the bearing property influencing data includes an external load influence coefficient, and the steps for obtaining the external load influence coefficient are as follows: obtaining characteristic parameters of the external load, the characteristic parameters of the external load include seismic load, wind load and mechanical load; calculating the maximum bearing capacity of the anchor rod without external load, that is, the critical load, through static analysis; obtaining the historical response data of the anchor rod load, filtering the historical response data of the load, removing abnormal values, analyzing and processing the processed data, and obtaining the load action time; selecting multiple groups of anchor rods to be tested of the same material, applying external loads to the anchor rods until the anchor rods are damaged, and recording the time the anchor rods bear under the external load, and adding and averaging the time the multiple groups of anchor rods bear as the maximum action time of the external load; experimentally measuring the bearing capacity of the anchor rods under different load action times, obtaining the relationship data between the load time and the bearing capacity, and performing regression analysis on the relationship data between the load time and the bearing capacity to obtain the influence index of the load time on the bearing capacity; through The frequency spectrum of the load signal is analyzed to obtain the frequency spectrum of the load, and thus the time interval of the load change is obtained, which is the external load cycle; the anchor behavior under different periodic loads is simulated to obtain the bearing capacity of the anchor under different periods, and the influence index of the period on the bearing capacity is obtained based on regression analysis; a group of anchors to be tested are selected, different degrees of external loads are applied to the anchors, and the change data of the bearing capacity of the anchors under different external loads are recorded, and the coefficient of the external load intensity is obtained based on linear analysis; regression analysis is performed through the data relationship between the load action time, period and bearing capacity to obtain the coefficient of the external load action time and the coefficient of the external load cycle; the external load influence coefficient is calculated based on the preset external load influence coefficient formula according to the critical load, external load, load action time, maximum external load action time, load time influence index on bearing capacity, external load cycle, cycle influence index on bearing capacity, external load intensity coefficient, external load action time coefficient and external load cycle coefficient.

[0009] In a preferred embodiment, the parameters in the bearing behavior influence model are estimated based on linear regression analysis, and adaptive estimation is performed by inputting the estimated parameters, specifically as follows: key characteristic parameters are extracted from the bearing behavior influence data, and the key characteristic parameters include anchor diameter, anchor depth buried in the rock and soil medium, maximum bearing pressure of the anchor, shear strength of rock, friction angle of the rock and soil medium, cohesion of soil and external load; the key characteristic parameters are estimated, and the estimated parameters are input into the bearing behavior influence coefficient model to obtain the estimated bearing behavior influence coefficient; if the estimated bearing behavior influence coefficient is lower than the preset value, the construction is stopped.

[0010] Technical effects and advantages of the intelligent perception and adaptive prediction method of the bearing characteristics of pressure anchor rods of the present invention: 1. The intelligent sensing and prediction system for the bearing properties of pressure-type anchor rods of the present invention comprises an intelligent sensing unit, a data acquisition and processing module, an adaptive prediction model and a user interaction interface. The data acquisition and processing module integrates high-precision sensors and intelligent data processing algorithms to achieve accurate monitoring of the stress state and deformation characteristics of the anchor rods, thereby improving the accuracy and reliability of the data.

[0011] 2. The intelligent sensing and prediction system for the bearing properties of pressure-type anchor rods of the present invention comprises an intelligent sensing unit, a data acquisition and processing module, an adaptive prediction model and a user interaction interface, wherein the intelligent sensing unit monitors the bearing properties of the anchor rods in real time, promptly discovers potential safety hazards and reduces risks during the construction process.

[0012] 3. The intelligent perception and prediction system of the bearing characteristics of pressure-type anchor rods of the present invention includes an intelligent perception unit, a data acquisition and processing module, an adaptive prediction model and a user interaction interface. Based on the prediction results of the adaptive prediction model, the design parameters of the anchor rods are optimized and adjusted to improve the scientificity and rationality of the engineering design. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention is a schematic diagram of the structure of a method for intelligently sensing and adaptively estimating the bearing characteristics of a pressure-type anchor rod. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] In modern civil engineering, especially in the fields of slope support and underground engineering, anchor rods are a key support component, and the stability and reliability of their bearing performance are crucial. Pressure anchor rods, with their unique support performance, transfer the pressure of the slope rock mass to the stable rock or soil layer through the pressure system formed by the anchor rod and the grouting body, providing stable support for the slope. However, the bearing properties of anchor rods are affected by geological conditions, engineering factors and the characteristics of the anchor rods themselves, which makes the prediction and analysis of anchor rod bearing capacity a complex and critical issue.

[0016] Traditional monitoring methods often rely on manual inspections and regular testing, which have problems such as low monitoring frequency, discontinuous data, and inability to monitor in real time. In addition, due to the complexity and uncertainty of geotechnical engineering, traditional empirical formulas and calculation methods often have difficulty in accurately estimating the bearing capacity of anchor bolts.

[0017] Embodiment 1, Figure 1 The present invention provides a method for intelligently sensing and adaptively estimating the bearing characteristics of a pressure anchor, comprising the following steps: S1, obtain the sensor node of the pressure anchor and collect the bearing property influence data through the sensor.

[0018] In this embodiment, the sensor node of the pressure anchor is obtained, and the bearing property influence data is collected through the sensor, as follows: The bearing behavior influencing data include the anchoring force influencing coefficient, the rock and soil medium characteristic influencing coefficient, and the external load influencing coefficient, which are as follows: Anchoring force influence coefficient is a complex coefficient that comprehensively considers the interaction between the anchor and the rock and soil medium, the anchoring depth, the anchoring method and various physical parameters. It is calculated by a specific formula, involving factors such as the anchor diameter, anchoring length, anchor force, maximum bearing pressure, rock and soil medium friction stress and rock shear strength. In addition, the material correlation coefficient, friction coefficient and material property index, as well as the relationship between the anchoring depth and the bearing characteristics and the temperature influence coefficient are also considered. This coefficient is of great significance for evaluating the anchoring effect of the anchor.

[0019] The steps for obtaining the anchoring force influence coefficient are as follows: Obtain the diameter of the anchor rod and the depth of the anchor rod buried in the rock and soil medium; Collect the pressure data of the anchor rod, pre-process the pressure data of the anchor rod, remove the abnormal values, and average the pre-processed pressure data of the anchor rod to obtain the mean pressure of the anchor rod; Select multiple groups of anchor rods of the same material, perform limit tests on each group of anchor rods, obtain the maximum bearing pressure of each group of anchor rods, average the maximum bearing pressure data of multiple groups of anchor rods, obtain the mean value of the maximum bearing pressure, and use the mean value of the maximum bearing pressure as the standard value; Select a group of anchor rods to be tested, apply pressure to the anchor rods, and the pressure is continuously increasing. Record the friction force of the anchor rods under different pressures. By linearly analyzing the relationship between pressure and friction force, obtain the material property index and the friction coefficient of the anchor surface. Obtain the rock and soil type, density and water content in the area where the anchor is used, and obtain the friction stress of the rock and soil medium and the shear strength of the rock through direct shear tests; Obtain the lowest and highest temperatures of the area to be tested in previous years to obtain a temperature range, record the expansion change data of the anchor material in the temperature range, analyze the relationship between temperature and expansion change based on the linear analysis method, and obtain the temperature influence coefficient; The anchoring force influence coefficient is calculated based on a preset anchoring force influence coefficient formula according to the diameter of the anchor rod, the depth of the anchor rod buried in the rock and soil medium, the pressure borne by the anchor rod, the maximum bearing pressure, the material property index, the friction coefficient of the anchoring surface, the friction stress of the rock and soil medium, the shear strength of the rock and the temperature influence coefficient.

[0020] The calculation formula of the anchoring force influence coefficient is as follows:

[0021] In the formula, is the anchoring force influence coefficient, is the anchor diameter, is the depth of the anchor buried in the rock and soil medium. is the friction coefficient of the anchor surface, is the material property index, is the pressure on the anchor rod. is the maximum bearing pressure, is the adjustment factor, is the material correlation coefficient, is the depth correlation coefficient, is the temperature influence coefficient, is the friction stress of the rock medium, is the shear strength of rock.

[0022] The influence coefficient of geotechnical medium characteristics is a parameter that comprehensively considers the influence of factors such as geotechnical medium hardness, friction coefficient and density on the bearing performance of anchor rods. This coefficient is calculated through a complex mathematical formula, involving the difference between the internal friction angle of the geotechnical medium and the standard internal friction angle, the ratio of the geotechnical density to the standard density, and the ratio of soil cohesion to the maximum cohesion.

[0023] The steps for obtaining the influence coefficient of the rock and soil medium characteristics are specifically as follows: Carry out triaxial shear test on the tested area, record the shear strength of soil under different stress conditions, fit the obtained shear strength data, and obtain the internal friction angle of rock and soil medium and the current friction angle of rock and soil medium; Obtain soil from the area to be tested, perform a vibration table density test on the soil, and obtain the density of the rock and soil medium; Re-obtain a sample of soil from the area to be tested, conduct a dry density test in the laboratory, and obtain the standard density of the soil; Obtain the soil in the test area based on the triaxial undrained shear test, apply different stresses to the soil, and record the cohesion values ​​of the soil under different stresses, and obtain the cohesion of the soil under the maximum stress condition from the cohesion value data of the soil; Obtain the test data of anchor bearing capacity under different density and friction angle conditions, preprocess the test data, perform regression analysis on the preprocessed data, and obtain the influence coefficient of density and the influence coefficient of friction angle; Collect multiple groups of soils with different cohesion, conduct bearing capacity tests on the soils, obtain the bearing capacity of soils under different cohesion, and obtain the influence coefficient of the nonlinear relationship of soil cohesion and the influence coefficient of the nonlinear relationship of soil bearing capacity based on the fitting method; The influence coefficient of the rock and soil medium characteristics is calculated based on the internal friction angle of the rock and soil medium, the friction angle of the current rock and soil medium, the density of the rock and soil medium, the standard density of the soil, the cohesion of the soil under the maximum stress condition, the influence coefficient of the density, the influence coefficient of the friction angle, the influence coefficient of the nonlinear relationship of the soil cohesion and the influence coefficient of the nonlinear relationship of the soil bearing capacity based on the preset rock and soil medium characteristics influence coefficient formula to obtain the rock and soil medium characteristics influence coefficient.

[0024] The calculation formula of the influence coefficient of rock and soil medium characteristics is as follows:

[0025] In the formula, is the influence coefficient of geotechnical medium characteristics, is the internal friction angle of the rock medium, is the friction angle of the current rock and soil medium, is the influence coefficient of the friction angle, is the influence coefficient of density, is the density of the rock medium, is the standard density, is the influence index of density on bearing capacity, is the influence coefficient of the nonlinear relationship of soil cohesion, is the influence coefficient of the nonlinear relationship of soil bearing capacity, is the cohesion of the soil, It is the cohesion of soil under maximum stress conditions.

[0026] External load influence coefficient, the external load influence coefficient is a parameter to measure the influence of external loads (such as earthquakes, wind or mechanical loads, etc.) on the bearing capacity of anchors. Its calculation formula combines the influence of the intensity, action time and load cycle of external loads on the performance of anchors. Specifically, the intensity of the external load is reflected by the ratio of load to critical load, and its influence is controlled by coefficients and exponents; the action time of the load is described by the time influence index, and the influence of the load cycle is characterized by the period coefficient. The combined effect of the external load influence coefficient reveals the dynamic change law of load intensity, duration and cycle on the bearing capacity of anchors. This coefficient is of great significance in the design and evaluation of the safety of anchors, especially when considering complex environmental factors (such as earthquakes and storms).

[0027] The steps for obtaining the external load influence coefficient are specifically as follows: Acquiring characteristic parameters of external loads, wherein the characteristic parameters of external loads include earthquake loads, wind loads, and mechanical loads; The maximum load-bearing capacity of the anchor rod without external load, i.e. the critical load, is calculated through static analysis; Obtain the historical response data of the anchor load, filter the historical response data, remove abnormal values, analyze and process the processed data, and obtain the load action time; Select multiple groups of anchor rods to be tested of the same material, apply external loads to the anchor rods until the anchor rods are damaged, and then stop. Record the time the anchor rods withstand the external loads, and add up the average of the times the multiple groups of anchor rods withstand as the maximum action time of the external loads. The bearing capacity of anchor rods under different load action times was experimentally measured to obtain the relationship data between load time and bearing capacity. The influence index of load time on bearing capacity was obtained by regression analysis of the relationship data between load time and bearing capacity. The frequency spectrum of the load is obtained by spectrum analysis of the load signal, thereby obtaining the time interval of load change, which is the external load cycle; Simulate the behavior of anchor rods under different cycle loads, obtain the bearing capacity of anchor rods under different cycles, and obtain the influence index of cycle on bearing capacity based on regression analysis; Select a group of anchor rods to be tested, apply different degrees of external loads to the anchor rods, and record the change data of the anchor rod bearing capacity under different external loads, and obtain the coefficient of external load intensity based on linear analysis; Through the data relationship between load action time, cycle and bearing capacity, regression analysis is performed to obtain the coefficient of external load action time and the coefficient of external load cycle; The external load influence coefficient is calculated based on the preset external load influence coefficient formula according to the critical load, external load, load action time, maximum external load action time, load time influence index on bearing capacity, external load cycle, cycle influence index on bearing capacity, external load intensity coefficient, external load action time coefficient and external load cycle coefficient.

[0028] The calculation formula of the external load influence coefficient is as follows:

[0029] In the formula, is the external load influence coefficient, is the external load, is the critical load, is the load action time, is the maximum action time of the external load, is the influence index of load time on bearing capacity, is the coefficient of the external load period, is the coefficient of external load intensity, is the coefficient of the external load action time, is the period of the external load, is the reference cycle, It is the index of the impact of the cycle on the bearing capacity.

[0030] S2, constructing a bearing trait influence coefficient model based on the bearing trait influence data and a machine learning algorithm.

[0031] In this embodiment, the bearing characteristic influence coefficient model is constructed based on the bearing characteristic influence data and the machine learning algorithm, as follows: The calculation formula of the bearing property influence coefficient model is as follows:

[0032] In the formula, is the bearing characteristics influence coefficient, is the anchoring force influence coefficient, is the influence coefficient of geotechnical medium characteristics, is the external load influence coefficient.

[0033] It should be noted that the influence coefficient of bearing characteristics reflects the overall strength and stability of the bearing characteristics of the pressure anchor. If the influence coefficient of bearing characteristics is high, it means that the bearing capacity of the anchor is affected by many factors such as anchoring force, soil characteristics, external loads and environmental factors, and the bearing capacity is strong; on the contrary, if the influence coefficient of bearing characteristics is low, it may indicate that certain factors (such as poor soil conditions, excessive external loads or harsh environment) have a negative impact on the bearing capacity of the anchor, which may lead to poor performance or failure of the anchor.

[0034] S3, estimates the parameters in the bearing trait influence model based on linear regression analysis, and performs adaptive estimation by inputting the estimated parameters.

[0035] In this embodiment, the parameters in the bearing trait influence model are estimated based on linear regression analysis, and adaptive estimation is performed by inputting the estimated parameters, as follows: Extracting key characteristic parameters from the bearing behavior influencing data, the key characteristic parameters include anchor diameter, anchor buried depth in rock and soil medium, anchor maximum bearing pressure, rock shear strength, rock friction angle, soil cohesion and external load; The key characteristic parameters are estimated, and the estimated parameters are input into the bearing trait influence coefficient model to obtain the estimated bearing trait influence coefficient; If the estimated bearing behavior influence coefficient is lower than the preset value, construction will be stopped.

[0036] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0037] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0038] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0039] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0040] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0041] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for intelligent perception and adaptive prediction of bearing characteristics of pressure anchor bolts, characterized in that: The following steps are involved: Obtain the key nodes of the pressure anchor and the bearing characteristics influencing data of the pressure anchor; Constructing a bearing trait influence coefficient model based on the bearing trait influence data and a machine learning algorithm; The parameters in the bearing trait influencing model are estimated based on linear regression analysis, and adaptive estimation is performed by inputting the estimated parameters.

2. According to claim 1, a method for intelligently sensing and adaptively estimating the bearing characteristics of a pressure anchor bolt is characterized in that: The bearing behavior influencing data includes an anchoring force influencing coefficient, and the steps for obtaining the anchoring force influencing coefficient are as follows: Obtain the diameter of the anchor rod and the depth of the anchor rod buried in the rock and soil medium; Collect the pressure data of the anchor rod, pre-process the pressure data of the anchor rod, remove the abnormal values, and average the pre-processed pressure data of the anchor rod to obtain the mean pressure of the anchor rod; Select multiple groups of anchor rods of the same material, perform limit tests on each group of anchor rods, obtain the maximum bearing pressure of each group of anchor rods, average the maximum bearing pressure data of multiple groups of anchor rods, obtain the mean value of the maximum bearing pressure, and use the mean value of the maximum bearing pressure as the standard value; Select a group of anchor rods to be tested, apply pressure to the anchor rods, and the pressure is continuously increasing. Record the friction force of the anchor rods under different pressures. By linearly analyzing the relationship between pressure and friction force, obtain the material property index and the friction coefficient of the anchor surface. Obtain the rock and soil type, density and water content in the area where the anchor is used, and obtain the friction stress of the rock and soil medium and the shear strength of the rock through direct shear tests; Obtain the lowest and highest temperatures of the area to be tested in previous years to obtain a temperature range, record the expansion change data of the anchor material in the temperature range, analyze the relationship between temperature and expansion change based on the linear analysis method, and obtain the temperature influence coefficient; The anchoring force influence coefficient is calculated based on a preset anchoring force influence coefficient formula according to the diameter of the anchor rod, the depth of the anchor rod buried in the rock and soil medium, the pressure borne by the anchor rod, the maximum bearing pressure, the material property index, the friction coefficient of the anchoring surface, the friction stress of the rock and soil medium, the shear strength of the rock and the temperature influence coefficient.

3. The method for intelligently sensing and adaptively estimating the bearing characteristics of a pressure anchor according to claim 2, characterized in that: The bearing behavior influencing data includes the rock and soil medium characteristic influencing coefficient, and the steps for obtaining the rock and soil medium characteristic influencing coefficient are as follows: Carry out triaxial shear test on the tested area, record the shear strength of soil under different stress conditions, fit the obtained shear strength data, and obtain the internal friction angle of rock and soil medium and the current friction angle of rock and soil medium; Obtain soil from the area to be tested, perform a vibration table density test on the soil, and obtain the density of the rock and soil medium; Re-obtain a sample of soil from the area to be tested, conduct a dry density test in the laboratory, and obtain the standard density of the soil; Obtain the soil in the test area based on the triaxial undrained shear test, apply different stresses to the soil, and record the cohesion values ​​of the soil under different stresses, and obtain the cohesion of the soil under the maximum stress condition from the cohesion value data of the soil; Obtain the test data of anchor bearing capacity under different density and friction angle conditions, preprocess the test data, perform regression analysis on the preprocessed data, and obtain the influence coefficient of density and the influence coefficient of friction angle; Collect multiple groups of soils with different cohesion, conduct bearing capacity tests on the soils, obtain the bearing capacity of soils under different cohesion, and obtain the influence coefficient of the nonlinear relationship of soil cohesion and the influence coefficient of the nonlinear relationship of soil bearing capacity based on the fitting method; The influence coefficient of the rock and soil medium characteristics is calculated based on the internal friction angle of the rock and soil medium, the friction angle of the current rock and soil medium, the density of the rock and soil medium, the standard density of the soil, the cohesion of the soil under the maximum stress condition, the influence coefficient of the density, the influence coefficient of the friction angle, the influence coefficient of the nonlinear relationship of the soil cohesion and the influence coefficient of the nonlinear relationship of the soil bearing capacity based on the preset rock and soil medium characteristics influence coefficient formula to obtain the rock and soil medium characteristics influence coefficient.

4. The method for intelligently sensing and adaptively estimating the bearing characteristics of a pressure anchor according to claim 3 is characterized in that: The bearing property influence data includes an external load influence coefficient, and the steps for obtaining the external load influence coefficient are as follows: Acquiring characteristic parameters of external loads, wherein the characteristic parameters of external loads include earthquake loads, wind loads, and mechanical loads; The maximum load-bearing capacity of the anchor rod without external load, i.e. the critical load, is calculated through static analysis; Obtain the historical response data of the anchor load, filter the historical response data, remove abnormal values, analyze and process the processed data, and obtain the load action time; Select multiple groups of anchor rods to be tested of the same material, apply external loads to the anchor rods until the anchor rods are damaged, and then stop. Record the time the anchor rods withstand the external loads, and add up the average of the times the multiple groups of anchor rods withstand as the maximum action time of the external loads. The bearing capacity of anchor rods under different load action times was experimentally measured to obtain the relationship data between load time and bearing capacity. The influence index of load time on bearing capacity was obtained by regression analysis of the relationship data between load time and bearing capacity. The frequency spectrum of the load is obtained by spectrum analysis of the load signal, thereby obtaining the time interval of load change, which is the external load cycle; Simulate the behavior of anchor rods under different cycle loads, obtain the bearing capacity of anchor rods under different cycles, and obtain the influence index of cycle on bearing capacity based on regression analysis; Select a group of anchor rods to be tested, apply different degrees of external loads to the anchor rods, and record the change data of the anchor rod bearing capacity under different external loads, and obtain the coefficient of external load intensity based on linear analysis; Through the data relationship between load action time, cycle and bearing capacity, regression analysis is performed to obtain the coefficient of external load action time and the coefficient of external load cycle; The external load influence coefficient is calculated based on the preset external load influence coefficient formula according to the critical load, external load, load action time, maximum external load action time, load time influence index on bearing capacity, external load cycle, cycle influence index on bearing capacity, external load intensity coefficient, external load action time coefficient and external load cycle coefficient.

5. The method for intelligently sensing and adaptively estimating the bearing characteristics of a pressure anchor according to claim 4, characterized in that: The parameters in the bearing trait influence model are estimated based on linear regression analysis, and adaptive estimation is performed by inputting the estimated parameters, as follows: Extracting key characteristic parameters from the bearing behavior influencing data, the key characteristic parameters include anchor diameter, anchor buried depth in rock and soil medium, anchor maximum bearing pressure, rock shear strength, rock friction angle, soil cohesion and external load; The key characteristic parameters are estimated, and the estimated parameters are input into the bearing trait influence coefficient model to obtain the estimated bearing trait influence coefficient; If the estimated bearing behavior influence coefficient is lower than the preset value, construction will be stopped.

6. The method for intelligently sensing and adaptively estimating the bearing characteristics of a pressure anchor according to claim 5, characterized in that: The calculation formula of the anchoring force influence coefficient is as follows: In the formula, is the anchoring force influence coefficient, is the anchor diameter, is the depth of the anchor buried in the rock and soil medium. is the friction coefficient of the anchor surface, is the material property index, is the pressure on the anchor rod. is the maximum bearing pressure, is the adjustment factor, is the material correlation coefficient, is the depth correlation coefficient, is the temperature influence coefficient, is the friction stress of the rock medium, is the shear strength of rock.

7. The method for intelligently sensing and adaptively estimating the bearing characteristics of a pressure anchor according to claim 6, characterized in that: The calculation formula of the influence coefficient of rock and soil medium characteristics is as follows: In the formula, is the influence coefficient of geotechnical medium characteristics, is the internal friction angle of the rock medium, is the friction angle of the current rock and soil medium, is the influence coefficient of the friction angle, is the influence coefficient of density, is the density of the rock medium, is the standard density, is the influence index of density on bearing capacity, is the influence coefficient of the nonlinear relationship of soil cohesion, is the influence coefficient of the nonlinear relationship of soil bearing capacity, is the cohesion of the soil, It is the cohesion of soil under maximum stress conditions.

8. The method for intelligently sensing and adaptively estimating the bearing characteristics of a pressure anchor according to claim 7, characterized in that: The calculation formula of the external load influence coefficient is as follows: In the formula, is the external load influence coefficient, is the external load, is the critical load, is the load action time, is the maximum action time of the external load, is the influence index of load time on bearing capacity, is the coefficient of the external load period, is the coefficient of external load intensity, is the coefficient of the external load action time, is the period of the external load, is the reference cycle, It is the index of the impact of the cycle on the bearing capacity.

9. The method for intelligently sensing and adaptively estimating the bearing characteristics of a pressure anchor according to claim 8, characterized in that: The calculation formula of the bearing property influence coefficient model is as follows: In the formula, is the bearing characteristics influence coefficient, is the anchoring force influence coefficient, is the influence coefficient of geotechnical medium characteristics, is the external load influence coefficient.

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