Tunnel anchor safety performance evaluation method, system and storage medium based on cloud computing

Abstract

The present invention discloses a cloud computing-based tunnel anchor safety performance assessment method, system, and storage medium. The method includes: analyzing the conductive force of the main cable of the suspension bridge corresponding to the tunnel anchor; obtaining the rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor, and analyzing the anchor site risk parameters corresponding to the tunnel anchor based on the rock mass characteristic parameters; obtaining the anchor plug parameters of the tunnel anchor, and analyzing the pull-out bearing capacity of the tunnel anchor based on the anchor plug parameters; evaluating and processing the conductive force, pull-out bearing capacity, and anchor site risk parameters based on the central node in cloud computing to generate a safety assessment coefficient, and evaluating the safety performance of the tunnel anchor based on the safety assessment coefficient. The present invention evaluates the safety performance of the tunnel anchor, fully considering various factors affecting the safety performance of the tunnel anchor, and improving the accuracy of the assessment; at the same time, distributed processing through cloud computing improves the efficiency of the analysis, thereby achieving a rapid and accurate assessment of the safety performance of the tunnel anchor.

Description

Technical Field

[0001] The present invention relates to the technical field of suspension bridges, and in particular to a cloud computing-based tunnel anchor safety performance evaluation method, system, and storage medium. Background Art

[0002] Suspension bridges are one of the most common bridge types currently used in bridge engineering. Their main components include the main girder, suspenders, main cables, main towers, and anchors. The main girder primarily bears the weight of the bridge deck and is mostly constructed of steel. The suspenders are the force-transmitting components that suspend the main girder from the main cables. The main cables, as the primary load-bearing components of the entire bridge, determine the load and span capacity of the suspension bridge. The main towers are the primary load-bearing components for the entire bridge's vertical loads. Anchors are key components that anchor the main cables at both ends, securely securing the weight carried by the main cables and preventing them from being pulled up or moved.

[0003] Generally speaking, the anchoring methods for the main cables of suspension bridges can be divided into self-anchoring and ground-anchoring. The self-anchoring method directly anchors the main cables to the stiffening beams. The ground-anchoring method is further divided into gravity anchoring and tunnel anchoring. Gravity anchoring is achieved by excavating a foundation pit and pouring a large volume of concrete to form an anchor, which relies on its own gravity to resist the tension of the main cables. Tunnel anchoring is achieved by excavating a tunnel in the rock mass and pouring concrete inside the tunnel. The concrete, in combination with the surrounding rock, forms an anchor plug, which in turn resists the tension of the main cables.

[0004] Compared to gravity anchors, tunnel anchors are only 20% to 25% as large and have less impact on the surrounding environment, leading to a growing use of them. When designing tunnel anchors to anchor suspension bridges, safely and stably anchoring the main cables is crucial for the bridge's normal operation, necessitating a thorough assessment of the safety performance of tunnel anchors. However, current safety performance assessments of tunnel anchors are relatively inaccurate and difficult to quickly assess due to the numerous parameters involved. To mitigate risks, higher parameters are often used for assessments. While this results in higher safety, it also leads to issues with overdesign, such as overly large or long anchor plugs. Therefore, how to quickly and accurately assess the safety performance of tunnel anchors is a pressing technical challenge. Summary of the Invention

[0005] The main purpose of the present invention is to provide a cloud computing-based tunnel anchor security performance evaluation method, system and storage medium, aiming to solve the technical problem of how to quickly and accurately evaluate the security performance of tunnel anchors.

[0006] To achieve the above objectives, the present invention provides a cloud computing-based tunnel anchor security performance evaluation method, wherein the tunnel anchor security performance evaluation method includes:

[0007] analyzing, based on the first cloud node in the cloud computing, the conductive force of the main cable of the suspension bridge corresponding to the tunnel anchor;

[0008] Obtaining rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor based on the second cloud node in the cloud computing, and analyzing anchor site risk parameters corresponding to the tunnel anchor based on the rock mass characteristic parameters;

[0009] acquiring anchor plug body parameters of the tunnel anchor based on a third cloud node in the cloud computing, and analyzing the pull-out bearing capacity of the tunnel anchor based on the anchor plug body parameters;

[0010] The conductive force, pull-out bearing capacity and anchor site risk parameters are evaluated and processed based on the central node in the cloud computing to generate a safety assessment coefficient, and the safety performance of the tunnel anchor is evaluated based on the safety assessment coefficient.

[0011] Preferably, the step of analyzing the conductive force of the main cable of the suspension bridge corresponding to the tunnel anchor based on the first cloud node in the cloud computing includes:

[0012] Reading the bridge body conductive force corresponding to the main cable of the suspension bridge based on the first cloud node, and obtaining historical climate data of the environment in which the main cable of the suspension bridge is located;

[0013] According to the change trend of wind data in the historical climate data, the maximum wind force and wind direction within a preset time period are predicted, and the wind force consistent with the direction of the main cable of the suspension bridge is calculated based on the maximum wind force and wind direction. The calculation formula is:

[0014]

[0015] Among them, Fa is the wind force, Fmax is the maximum wind force, The angle between the wind direction and the direction of the main cable of the suspension bridge k1 is the wind force level coefficient, is the changing wind direction coefficient, Used to calculate the bending vibration frequency of the main cable of the suspension bridge, L is the height of the main cable of the suspension bridge, E is the elastic modulus of the main cable of the suspension bridge, I is the section polar moment of inertia of the main cable of the suspension bridge, A is the cross-sectional area of ​​the main cable of the suspension bridge, is the annual coefficient corresponding to the maximum wind speed;

[0016] The conductive force of the bridge body is corrected according to the wind force to obtain the conductive force.

[0017] Preferably, the step of obtaining rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor based on the second cloud node in the cloud computing, and analyzing the anchor site risk parameters corresponding to the tunnel anchor based on the rock mass characteristic parameters includes:

[0018] Determining, based on the second cloud node, an extended area corresponding to the anchor area, and obtaining rock mass characteristic parameters corresponding to the extended area, the rock mass characteristic parameters including rock mass type, rock mass morphology, a first proportion corresponding to each of the rock mass types, and a second proportion corresponding to each of the rock mass morphologies;

[0019] The first proportions and the second proportions are transmitted to a preset model for calculation and processing to obtain the minimum fracture force of the rock mass in the extended area. The calculation formula is:

[0020] ;

[0021] Where W is the minimum breaking force, For the first The connection weights from the hidden layer to the output layer, For each first proportion, For each second proportion, is the input function, The preset model Layer hidden layer output;

[0022] Obtaining historical geological disaster data of the anchor site area, and predicting the probability of geological disasters occurring in the anchor site area within a preset time period and the type of geological disasters that occur based on the displayed historical geological disaster data;

[0023] According to the probability and type, a maximum probability within a preset time period is generated, and the maximum probability and the minimum rupture force are generated as anchor site risk parameters.

[0024] Preferably, the anchor plug body parameters include the front end surface area, rear end surface area, axial length, anchor plug body inclination angle, concrete coefficient, anchor plug body positioning coefficient, anchor plug body contact area, and anchor plug body wedge angle of the anchor plug body corresponding to the tunnel anchor;

[0025] The step of analyzing the pull-out bearing capacity of the tunnel anchor based on the anchor plug body parameters includes:

[0026] Calculating the volume of the anchor plug body according to the front end surface area, the rear end surface area and the axial length; and determining the gravity of the anchor plug body according to the volume;

[0027] According to the inclination angle of the anchor plug body and the wedge angle of the anchor plug body, the gravity is divided into a first gravity component along the direction of the main cable of the suspension bridge and a second gravity component perpendicular to the outer surface of the anchor plug body;

[0028] Obtaining a friction coefficient corresponding to the anchor plug body, and generating a rock resistance according to the second gravity component and the friction coefficient; dividing the rock resistance into a first rock resistance along the direction of the main cable of the suspension bridge and a second rock resistance perpendicular to the direction of the main cable of the suspension bridge according to the inclination angle of the anchor plug body and the wedge angle of the anchor plug body;

[0029] Obtaining a gravity component coefficient corresponding to the first gravity component and a resistance component coefficient corresponding to the rock mass resistance;

[0030] The pull-out bearing capacity is calculated based on the first gravity component, the first rock mass resistance, the second rock mass resistance, the gravity component coefficient, the resistance component coefficient, the concrete coefficient, the anchor body positioning coefficient, and the anchor body contact area. The calculation formula is:

[0031] ;

[0032] in, For pull-out bearing capacity, is the concrete coefficient, is the anchor plug positioning coefficient, is the first gravity component, is the gravity component coefficient, is the first rock mass resistance, is the second rock mass resistance, is the contact area of ​​the anchor plug, is the resistance component coefficient.

[0033] Preferably, the step of evaluating and processing the conductivity, pull-out bearing capacity and anchor risk parameters based on the central node in the cloud computing to generate a safety assessment coefficient includes:

[0034] performing a difference calculation on the conductive force and the pull-out bearing capacity based on the central node to obtain a calculation result, and determining a safety factor to be evaluated corresponding to the calculation result according to a correspondence between a preset difference interval and a safety factor;

[0035] The safety factor to be evaluated is evaluated according to the anchor site risk parameter to generate the safety evaluation coefficient.

[0036] Preferably, the step of evaluating the safety performance of the tunnel anchor according to the safety evaluation coefficient includes:

[0037] evaluating, based on the safety assessment coefficient, whether the safety performance of the tunnel anchor meets a preset condition;

[0038] If the preset conditions are met, the safety performance evaluation of the tunnel anchor is completed;

[0039] If the preset conditions are not met, a prompt message for adjusting the anchor plug body parameters of the tunnel anchor is output, so that the safety performance of the tunnel anchor can be re-evaluated after the adjustment is completed.

[0040] Preferably, the step of obtaining the rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor based on the second cloud node in the cloud computing, and analyzing the anchor site risk parameters corresponding to the tunnel anchor based on the rock mass characteristic parameters includes:

[0041] Acquire a plurality of sample data, and divide the plurality of sample data into training samples and test samples, wherein the sample data is a mapping relationship between the proportion of rock mass types and the proportion of rock mass morphology and fracture force;

[0042] Training a preset initial model based on the training samples, and when the training reaches a first preset end condition, testing the preset initial model a preset number of times based on the test samples to obtain a preset number of test results;

[0043] According to the test results of the preset number of times, the loss function value of the preset initial model is calculated, and based on the loss function value, the preset initial model is generated as a preset model. The calculation formula of the loss function value is:

[0044] ;

[0045] in, is the loss function value, is the weight factor of the preset initial model, For the The test results generated by the test samples are For the The minimum breaking force corresponding to the test specimen.

[0046] Preferably, the step of generating the preset initial model into a preset model based on the loss function value includes:

[0047] Determining whether the loss function value satisfies a second preset end condition, and if so, ending the training of the preset initial model and generating the preset initial model as a preset model;

[0048] If the loss function value does not meet the second preset end condition, the model weight of the preset initial model is adjusted, and the adjustment formula is:

[0049] ;

[0050] in, The first step to preset the initial model The model weight adjustment value of the middle layer, Preset initial model The error value of the test results of the middle layer, Indicates the preset number of times. To preset the initial model Layer The actual output value of the test result, To preset the initial model Layer Theoretical test results, To preset the initial model The connection weights of the layer, To preset the initial model The input data of the layer;

[0051] The step of training the preset initial model based on the training sample is performed based on the adjusted model parameters until the loss function value meets a second preset end condition, thereby generating the preset model.

[0052] Furthermore, to achieve the above-mentioned object, the present invention also provides a cloud computing-based tunnel anchor security performance evaluation system, the cloud computing-based tunnel anchor security performance evaluation system comprising: a memory, a processor, a communication bus, and a control program stored in the memory:

[0053] The communication bus is used to realize the connection and communication between the processor and the memory;

[0054] The processor is used to execute the control program to implement the steps of the cloud computing-based tunnel anchor security performance evaluation method as described above.

[0055] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a storage medium, on which a control program is stored. When the control program is executed by a processor, the steps of the above-mentioned cloud computing-based tunnel anchor security performance evaluation method are implemented.

[0056] The present invention provides a cloud computing-based tunnel anchor safety performance assessment method, system, and storage medium. The cloud computing system includes a first cloud node, a second cloud node, a third cloud node, and a central node. The first cloud node analyzes the conductive force of the suspension bridge main cable corresponding to the tunnel anchor; the second cloud node obtains rock mass characteristic parameters of the anchor site corresponding to the tunnel anchor, and analyzes the anchor site risk parameters corresponding to the tunnel anchor based on these rock mass characteristic parameters; the third cloud node obtains the anchor plug parameters of the tunnel anchor, and analyzes the pull-out bearing capacity of the tunnel anchor based on the anchor plug parameters. The central node then evaluates the conductive force, pull-out bearing capacity, and anchor site risk parameters to generate a safety assessment coefficient, and evaluates the safety performance of the tunnel anchor based on the safety assessment coefficient. In this way, the safety performance of the tunnel anchor is assessed by analyzing the conductive force of the suspension bridge main cable, the pull-out bearing capacity of the tunnel anchor itself, and the anchor site risk parameters of the tunnel anchor site that may affect the safety performance of the tunnel anchor. This fully considers all factors affecting the safety performance of the tunnel anchor and improves the accuracy of the assessment. At the same time, by dividing cloud computing into the first node, the second node, the third node and the central node for distributed processing, the conductivity, anchor site risk parameters and pull-out bearing capacity can be analyzed simultaneously, which improves the efficiency of the analysis and realizes the rapid and accurate evaluation of the safety performance of tunnel anchors. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of a first embodiment of a tunnel anchor safety performance assessment method based on cloud computing according to the present invention;

[0058] Figure 2 This is a flow chart of a second embodiment of a tunnel anchor safety performance assessment method based on cloud computing according to the present invention;

[0059] Figure 3 This is a flow chart of a third embodiment of a tunnel anchor safety performance assessment method based on cloud computing according to the present invention;

[0060] Figure 4 This is a structural diagram of the hardware operating environment involved in an embodiment of a tunnel anchor safety performance evaluation system based on cloud computing of the present invention.

[0061] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0062] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0063] The present invention provides a tunnel anchor safety performance evaluation method based on cloud computing, please refer to Figure 1 , Figure 1This is a flow chart of the first embodiment of the tunnel anchor security performance evaluation method based on cloud computing of the present invention.

[0064] The present invention provides an embodiment of a cloud computing-based tunnel anchor security performance assessment method. It should be noted that although the flowchart shows a logical order, in some cases, the steps shown or described may be performed in a different order. Specifically, the tunnel anchor security performance assessment in this embodiment includes:

[0065] Step S10: Analyze the conductive force of the main cable of the suspension bridge corresponding to the tunnel anchor based on the first cloud node in the cloud computing.

[0066] The cloud computing of this embodiment is a distributed system consisting of at least a first node, a second node, a third node, and a central node. The first node, the second node, and the third node are each configured to communicate with the central node. A control center is also provided to control the first node, the second node, the third node, and the central node. The cloud computing-based tunnel anchor security performance assessment method of this embodiment is applied to this control center.

[0067] Understandably, tunnel anchors are used to anchor suspension bridges and withstand the tension of the suspension bridge's main cables. This tension primarily consists of the weight of the main beam, the main cable, the weight of vehicles traveling on the main beam, and the weight of ancillary facilities such as slings, main cable protection facilities, bridge deck pavement, and guardrails. Typically, when designing a suspension bridge, the bridge deck width and load capacity are designed based on traffic volume, thereby determining the bridge's own weight, the weight of the main cables, and the weight of related ancillary facilities. This weight can be used to determine the tension of the suspension bridge's main cables that the tunnel anchor must withstand. This embodiment uses the first cloud node to obtain this data for analysis, and obtains the corresponding tension of the suspension bridge's main cables as the conductive force of the suspension bridge's main cables corresponding to the tunnel anchor. It should be noted that if any of this data is modified during the suspension bridge design process, the corresponding first node can obtain and analyze the modified and updated data to generate a new conductive force.

[0068] Furthermore, considering that suspension bridges are inevitably subject to strong winds during use, strong winds will affect the suspension bridge, thereby increasing the force transmitted to the tunnel anchor through the suspension bridge main cable. Therefore, in order to ensure the accuracy of the tunnel anchor safety performance assessment, it is necessary to correct the tension of the suspension bridge main cable based on wind force to obtain accurate transmission force. Specifically, the step of analyzing the transmission force of the suspension bridge main cable corresponding to the tunnel anchor based on the first cloud node in the cloud computing includes:

[0069] Step S11: reading the bridge body conductive force corresponding to the main cable of the suspension bridge based on the first cloud node, and obtaining historical climate data of the environment in which the main cable of the suspension bridge is located;

[0070] Step S12, predicting the maximum wind force and wind direction within a preset time period based on the change trend of the wind force data in the historical climate data, and calculating the wind force consistent with the direction of the main cable of the suspension bridge based on the maximum wind force and wind direction;

[0071] Step S13: Correcting the bridge body conductive force according to the wind force to obtain the conductive force.

[0072] Furthermore, the first cloud node converts the various weights transmitted by the suspension bridge main cables into bridge body conduction forces corresponding to the suspension bridge main cables, and obtains historical climate data for the environment in which the suspension bridge main cables are located. The historical climate data can be set to a specific period of time, such as the past 5, 10, 20, or 50 years.

[0073] Furthermore, wind data is filtered from historical climate data, and the trend of each wind data set is determined based on the period in which it was generated. This wind data trend can be generated based on consecutive periods, such as a 12-month trend, or based on the same period, such as a trend for March within 10 years, or both. Based on the wind data trend, the maximum wind speed and direction within a preset time period are predicted. The preset time period is a pre-set period based on the service life of the suspension bridge, such as 80 years or 100 years.

[0074] Understandably, the wind direction is usually inconsistent with the direction of the suspension bridge's main cables. Therefore, after predicting the maximum wind force and wind direction, the wind force along the suspension bridge's main cables can be calculated based on these two factors. This wind force is usually superimposed on the suspension bridge's main cables and transmitted to the tunnel anchor. The specific calculation formula is shown in Equation (1).

[0075] (1);

[0076] Among them, Fa is the wind force, Fmax is the maximum wind force, is the angle between the wind direction and the main cable direction of the suspension bridge, k1 is the wind force level coefficient, is the changing wind direction coefficient, Used to calculate the bending vibration frequency of the main cable of the suspension bridge, L is the height of the main cable of the suspension bridge, E is the elastic modulus of the main cable of the suspension bridge, I is the section polar moment of inertia of the main cable of the suspension bridge, A is the cross-sectional area of ​​the main cable of the suspension bridge, is the annual coefficient corresponding to the maximum wind speed.

[0077] Different wind speed intervals are pre-set to correspond to different wind speed level coefficients, and the greater the wind speed in the wind speed interval, the greater the corresponding wind speed level coefficient. Compare the determined maximum wind speed with each wind speed interval, determine the target wind speed interval where the maximum wind speed is located, and find the wind speed level coefficient corresponding to the target wind speed interval as k1. At the same time, A coefficient pre-set for the possibility of wind direction change according to the predicted maximum wind force. is the fatigue coefficient pre-set for the service life of the suspension bridge. The value in the above formula (1) is the fatigue coefficient for the predicted years when the maximum wind force occurs.

[0078] Furthermore, after the wind force is calculated by formula (1), the wind force is added to the bridge body conduction force to achieve the correction of the bridge body conduction force by the wind force, and obtain the accurate conduction force transmitted from the main cable of the suspension bridge to the tunnel anchor.

[0079] Step S20: obtaining rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor based on the second cloud node in the cloud computing, and analyzing anchor site risk parameters corresponding to the tunnel anchor based on the rock mass characteristic parameters.

[0080] Understandably, the area where the tunnel anchor is set is the anchor site area. Different anchor sites have different types and forms of rock masses, and the proportion of each rock mass is also different, resulting in different characteristics of different anchor sites and different risks to the tunnel anchor. For example, one anchor site area mainly contains sandstone, conglomerate, limestone, etc., while another anchor site area mainly includes mudstone, claystone, shale, slate and other mudstones. After the anchor site area is selected, geological surveys and other methods are used to determine the rock mass type of the anchor site area, the proportion of each type of rock mass, and various parameters such as the block structure, layered structure, fragmented structure, and dispersed structure formed by each type of rock mass. These parameters are obtained through the second node in the cloud computing as the rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor. These rock mass characteristic parameters are analyzed to obtain the anchor site risk parameters corresponding to the tunnel anchor. The anchor site risk parameters are used to reflect the risk posed by the rock mass structure of the anchor site area to the tunnel anchor. Specifically, the step of obtaining the rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor based on the second cloud node in the cloud computing, and analyzing the anchor site risk parameters corresponding to the tunnel anchor based on the rock mass characteristic parameters includes:

[0081] Step S21: determining an extended area corresponding to the anchor area based on the second cloud node, and obtaining rock mass characteristic parameters corresponding to the extended area, the rock mass characteristic parameters including rock mass type, rock mass morphology, a first proportion corresponding to each rock mass type, and a second proportion corresponding to each rock mass morphology;

[0082] Step S22, transmitting each of the first proportions and each of the second proportions to a preset model for calculation and processing to obtain the minimum fracture force of the rock mass in the expansion area;

[0083] Step S23, obtaining historical geological disaster data of the anchor site area, and predicting the probability of geological disasters occurring in the anchor site area within a preset time period and the type of geological disasters occurring based on the historical geological disaster data;

[0084] Step S24: generating a maximum probability within a preset time period according to the probability and type, and generating the maximum probability and the minimum fracture force as anchor site risk parameters.

[0085] Furthermore, while tunnel anchors share the tension of the suspension bridge's main cables through the anchor plug and the surrounding rock mass, the surrounding rock mass supporting this tension does not extend infinitely. For example, for a given tunnel anchor, the surrounding rock mass within 5 meters of the anchor plug can share the tension of the suspension bridge's main cables, but the surrounding rock mass within 10 meters cannot. Furthermore, after the suspension bridge design is finalized, the tension of the main cables is also determined accordingly. To support this tension, the dimensional parameters of the anchor plug in the tunnel anchor are also determined accordingly. Anchor plugs of different sizes correspond to different dimensional regions of the surrounding rock mass that share the tension of the suspension bridge's main cables. A preset expansion ratio is pre-set between the anchor plug size and the surrounding rock mass size. For anchor plugs with predetermined dimensional parameters, the second cloud node determines the expansion region corresponding to the anchor site based on this preset expansion ratio.

[0086] Furthermore, based on geological exploration data, each rock mass type and its respective first proportion, as well as each rock mass morphology and its respective second proportion in the extended area, are analyzed. Furthermore, a pre-trained preset model is pre-trained, and the first and second proportions obtained from the analysis are transferred to the preset model for calculation and processing to obtain the minimum fracture force of the rock mass in the extended area. This minimum fracture force reflects the force corresponding to the most fracture-prone rock mass in the extended area. As long as the load is greater than the fracture force, the most fracture-prone rock mass will fracture. The calculation formula for this minimum fracture force is shown in the following formula (2).

[0087] (2);

[0088] Where W is the minimum breaking force, For the first The connection weights from the hidden layer to the output layer, For each first proportion, For each second proportion, is the input function, The preset model Layer hidden layer output.

[0089] Understandably, geological disasters such as earthquakes can affect the rock mass surrounding the tunnel anchor, potentially causing changes in rock mass morphology. Therefore, to determine whether the rock mass within the expansion area is susceptible to earthquakes, this embodiment also acquires historical geological disaster data for the anchor area. This historical geological disaster data, in addition to earthquake data, may also include debris flow and landslide data. This historical geological disaster data is analyzed for regularities, and based on these regularities, the probability and type of geological disasters occurring within the anchor area within a preset time period are predicted. Because the expansion area is adjacent to the anchor area, these predicted probabilities and types are effectively the same as those for geological disasters occurring within the expansion area.

[0090] Furthermore, different geological disasters have different degrees of damage to rock masses. Among earthquakes, debris flows and landslides, earthquakes are the most destructive. Therefore, for the predicted probability and type, it is necessary to combine the two to generate the maximum probability within a preset time period. Different correction coefficients are set in advance for different types of geological disasters. The correction coefficient corresponding to earthquakes is the largest. The predicted probability is corrected by different correction coefficients to form a final probability, and then the final probabilities of various types of geological disasters are compared to determine the maximum probability. This maximum probability represents the possibility of the occurrence of a geological disaster with the greatest harm. Thereafter, the generated minimum fracture force and maximum probability are jointly formed into an anchor site risk parameter, which is used to reflect the force of fracture of the rock mass around the anchor plug and the risk of a geological disaster.

[0091] Step S30: acquiring the anchor plug body parameters of the tunnel anchor based on the third cloud node in the cloud computing, and analyzing the pull-out bearing capacity of the tunnel anchor based on the anchor plug body parameters.

[0092] Furthermore, the bearing capacity of a tunnel anchor includes both the bearing capacity of its anchor body and the bearing capacity of the surrounding rock mass. The bearing capacity of the anchor body is primarily related to its size, length, wedge angle, and other factors. The bearing capacity of the surrounding rock mass is related to its type, shape, and contact with the anchor body. This embodiment uses a third cloud node in cloud computing to obtain these parameters as anchor body parameters for analysis, and obtains the bearing capacity of the tunnel anchor and its surrounding rock mass for carrying the tension of the suspension bridge main cable, and uses this bearing capacity as the pull-out bearing capacity.

[0093] Step S40: evaluating and processing the conductivity, pull-out bearing capacity and anchor site risk parameters based on the central node in the cloud computing to generate a safety assessment coefficient, and evaluating the safety performance of the tunnel anchor according to the safety assessment coefficient.

[0094] Furthermore, after obtaining the conductivity, anchor risk parameters, and pull-out bearing capacity through the first cloud node, the second cloud node, and the third cloud node, respectively, the first cloud node, the second cloud node, and the third cloud node are controlled to transmit the generated conductivity, anchor risk parameters, and pull-out bearing capacity to a central node in the cloud computing, which then performs evaluation and processing to generate a safety assessment coefficient. Specifically, the step of evaluating and processing the conductivity, pull-out bearing capacity, and anchor risk parameters based on the central node in the cloud computing to generate a safety assessment coefficient includes:

[0095] Step S41: performing a difference operation on the conductive force and the pull-out bearing capacity based on the central node to obtain a calculation result, and determining a safety factor to be evaluated corresponding to the calculation result based on a preset correspondence between a difference interval and a safety factor;

[0096] Step S42: Evaluate the safety factor to be evaluated based on the anchor site risk parameter to generate the safety evaluation factor.

[0097] Furthermore, the transmission force is the maximum force transmitted by the suspension bridge's main cable to the tunnel anchor, while the pullout bearing capacity is the combined bearing capacity provided by the anchor plug and the surrounding rock mass. The relationship between the two reflects whether the tunnel anchor is sufficient to withstand the tensile force transmitted by the suspension bridge's main cable. Therefore, a difference calculation is performed between the two to obtain a difference result. Furthermore, a correspondence between multiple difference intervals and safety factors is pre-set, where the larger the difference between the difference intervals, the larger the safety factor, indicating greater safety for the tunnel anchor. The difference calculation can be set to calculate the pullout bearing capacity minus the transmission force. If the result is a negative value, it indicates that the tunnel anchor is insufficient to withstand the tensile force transmitted by the suspension bridge's main cable. The safety factor is directly set to a negative value, and an alarm is issued. If the result is a positive value, the calculation result is compared with each difference interval to determine the target difference interval within which the calculation result falls. Then, the safety factor corresponding to the target difference interval is found and used as the safety factor to be evaluated for the calculation result.

[0098] Understandably, although the calculation result is a positive value, indicating that the tunnel anchor is sufficient to bear the tension transmitted by the main cable of the suspension bridge, risks may still occur due to the influence of external factors, especially when the pull-out bearing capacity is not much greater than the conductive force. To further ensure safety, this embodiment also evaluates the safety factor to be assessed using the anchor site risk parameter to form a final safety assessment factor. Among them, the anchor site risk parameter includes at least the minimum breaking force indicating that the surrounding rock area is most likely to break and the maximum probability indicating the possibility of the most serious geological disaster. Once the most serious geological disaster occurs, the minimum breaking force will be further reduced, and the pull-out bearing capacity may be less than the conductive force.

[0099] Furthermore, a correction coefficient corresponding to the minimum fracture force and maximum probability is pre-set. The larger the maximum probability, the smaller the minimum fracture force, and the higher the likelihood of a reduction in the minimum fracture force, so the correction coefficient is set smaller. Conversely, the smaller the maximum probability, the larger the minimum fracture force, and the lower the likelihood of a reduction in the minimum fracture force, so the correction coefficient is set larger. The correction coefficients for other situations lie between these two values. A correction coefficient corresponding to the anchor site risk parameter generated by the second cloud node is determined, and this correction coefficient is used to correct the safety factor to be assessed, ultimately forming a more accurate safety assessment factor.

[0100] Furthermore, after the final safety assessment coefficient is formed, the safety performance of the tunnel anchor can be evaluated based on it. Specifically, the step of evaluating the safety performance of the tunnel anchor based on the safety assessment coefficient includes:

[0101] Step S43: evaluating whether the safety performance of the tunnel anchor meets a preset condition based on the safety evaluation coefficient;

[0102] Step S44: If the preset conditions are met, the safety performance evaluation of the tunnel anchor is completed;

[0103] Step S45: If the preset conditions are not met, a prompt message is outputted to adjust the parameters of the anchor plug of the tunnel anchor, so that the safety performance of the tunnel anchor can be re-evaluated after the adjustment is completed.

[0104] Furthermore, a preset condition indicating that the tunnel anchor has good safety performance is pre-set, and the preset condition may be a safety reference coefficient. The generated safety assessment coefficient is compared with the safety reference coefficient to evaluate whether the safety performance of the tunnel anchor meets the preset condition. If the comparison determines that the safety assessment coefficient is greater than the safety reference coefficient, it means that the safety performance of the tunnel anchor meets the preset condition, and construction can be carried out according to the currently designed tunnel anchor, thereby completing the safety performance evaluation of the tunnel anchor. On the contrary, if the comparison determines that the safety assessment coefficient is less than or equal to the safety reference coefficient, it means that there may be risks in the tunnel anchor, and it is determined that the safety performance of the tunnel anchor does not meet the preset condition. At this time, a prompt message for adjusting the anchor plug parameters of the tunnel anchor is output to prompt adjustment of parameters such as the size and length of the tunnel anchor. After the adjustment is completed, the safety performance evaluation request can be triggered again to re-evaluate the safety performance of the tunnel anchor based on the adjusted anchor plug parameters to ensure the safety performance of the tunnel anchor.

[0105] This cloud computing-based tunnel anchor safety performance assessment method includes a first cloud node, a second cloud node, a third cloud node, and a central node. The first cloud node analyzes the conductive force of the suspension bridge main cable corresponding to the tunnel anchor. The second cloud node obtains rock mass characteristic parameters of the anchor site corresponding to the tunnel anchor and, based on these rock mass characteristic parameters, analyzes the anchor site risk parameters corresponding to the tunnel anchor. Furthermore, the third cloud node obtains the anchor plug parameters of the tunnel anchor and, based on these parameters, analyzes the pullout bearing capacity of the tunnel anchor. The central node then evaluates the conductive force, pullout bearing capacity, and anchor site risk parameters to generate a safety assessment coefficient. The safety performance of the tunnel anchor is then assessed based on the safety assessment coefficient. This method analyzes the conductive force of the suspension bridge main cable, the pullout bearing capacity of the tunnel anchor itself, and the anchor site risk parameters of the tunnel anchor site that may affect the safety performance of the tunnel anchor. This method comprehensively considers all factors affecting the safety performance of the tunnel anchor and improves the accuracy of the assessment. At the same time, by dividing cloud computing into the first node, the second node, the third node and the central node for distributed processing, the conductivity, anchor site risk parameters and pull-out bearing capacity can be analyzed simultaneously, which improves the efficiency of the analysis and realizes the rapid and accurate evaluation of the safety performance of tunnel anchors.

[0106] For further information, please refer to Figure 2 Based on the first embodiment of the tunnel anchor security performance evaluation method based on cloud computing of the present invention, a second embodiment of the tunnel anchor security performance evaluation method based on cloud computing of the present invention is proposed.

[0107] The second embodiment of the cloud computing-based tunnel anchor safety performance evaluation method differs from the first embodiment of the cloud computing-based tunnel anchor safety performance evaluation method in that the anchor plug body parameters include the front end face area, rear end face area, axial length, anchor plug body inclination angle, concrete coefficient, anchor plug body positioning coefficient, anchor plug body contact area, and anchor plug body wedge angle of the anchor plug body corresponding to the tunnel anchor;

[0108] The step of analyzing the pull-out bearing capacity of the tunnel anchor based on the anchor plug body parameters includes:

[0109] Step S31, calculating the volume of the anchor plug body according to the front end surface area, the rear end surface area and the axial length; and determining the gravity of the anchor plug body according to the volume;

[0110] Step S32, dividing the gravity into a first gravity component along the main cable direction of the suspension bridge and a second gravity component perpendicular to the outer surface of the anchor plug body according to the inclination angle and wedge angle of the anchor plug body;

[0111] Step S33, obtaining a friction coefficient corresponding to the anchor plug body, and generating rock resistance according to the second gravity component and the friction coefficient;

[0112] Step S34, dividing the rock resistance into a first rock resistance along the main cable direction of the suspension bridge and a second rock resistance perpendicular to the main cable direction of the suspension bridge according to the anchor plug body inclination angle and the anchor plug body wedge angle;

[0113] Step S35, obtain the gravity component coefficient corresponding to the first gravity component and the resistance component coefficient corresponding to the rock resistance, and calculate the pull-out bearing capacity based on the first gravity component, the first rock resistance, the second rock resistance, the gravity component coefficient, the resistance component coefficient, the concrete coefficient, the anchor body positioning coefficient and the anchor body contact area.

[0114] Furthermore, the anchor body parameters of this embodiment include parameters related to the size of the anchor body. The anchor body is usually a structure that is narrow at the top and wide at the bottom. When subjected to tension, the wide lower structure generates an extrusion force with the surrounding rock mass in the direction of tension. Such size-related parameters are related to the amount of tension that the anchor body can withstand, and include at least the front end face area, rear end face area, axial length, and anchor body wedge angle of the anchor body. At the same time, the anchor body parameters also include parameters related to the formation of the anchor body. Such parameters are also related to the force on the anchor body, and include at least the anchor body inclination angle relative to the horizontal plane, the concrete coefficient forming the anchor body, the anchor body positioning coefficient used for positioning during the formation of the anchor body, and the anchor body contact area where the anchor body contacts the surrounding rock mass.

[0115] Furthermore, when analyzing the pullout bearing capacity of a tunnel anchor using its parameters, the volume of the anchor is first calculated based on the front face area, rear face area, and axial length of the anchor. The required cubic volume of concrete is then determined based on the volume of the anchor. This cubic volume, combined with the expected concrete strength grade, is used to calculate the weight of the concrete forming the anchor and convert it into the anchor's gravity. The anchor's gravity can then be decomposed into a first component along the main cable of the suspension bridge and a second component perpendicular to the outer surface of the anchor, based on the anchor's inclination angle and wedge angle.

[0116] Furthermore, during the tensioning process, the anchor plug generates friction with the surrounding rock mass. The friction coefficient between the surrounding rock mass and the concrete forming the anchor plug is pre-measured and stored. This friction coefficient is then multiplied by the second gravity component to obtain the rock resistance. Subsequently, based on the anchor plug's inclination angle and its wedge angle, the rock resistance is divided into a first rock resistance along the main cable of the suspension bridge and a second rock resistance perpendicular to the main cable.

[0117] Furthermore, different coefficients are pre-set for different forces to reflect the magnitude of the effects of different forces against the tension of the main cable of the suspension bridge. For the first gravity component, the corresponding gravity component coefficient is obtained, and for the rock resistance, the corresponding resistance component coefficient is obtained. Then, based on the first gravity component, the first rock resistance, the second rock resistance, the gravity component coefficient, the resistance component coefficient, the concrete coefficient, the anchor body positioning coefficient, and the anchor body contact area, the pull-out bearing capacity of the anchor body is calculated. The calculation formula is shown in the following formula (3):

[0118] (3);

[0119] in, For pull-out bearing capacity, is the concrete coefficient, is the anchor plug positioning coefficient, is the first gravity component, is the gravity component coefficient, is the first rock mass resistance, is the second rock mass resistance, is the contact area of ​​the anchor plug, is the resistance component coefficient.

[0120] It should be noted that the anchor body formed by concrete of different strength grades has different strengths and can withstand different tensions of the main cable of the suspension bridge. Therefore, different concrete coefficients are pre-set for concrete of different strength grades. The calculated force is corrected by the concrete coefficient, which can make the final calculated pull-out bearing capacity of the anchor body more accurate. At the same time, the anchor body is usually built one by one by multiple sections. The installation and positioning accuracy of the next section relative to the previous section has a significant impact on the mechanical properties of the entire anchor body. The possible error size of the installation between each section is determined in advance through the test model, and the anchor body positioning coefficient that represents the overall error size is set for each error size. The calculated force is corrected by the anchor body positioning coefficient and the concrete coefficient, which further improves the accuracy of the anchor body pull-out bearing capacity.

[0121] This embodiment calculates the pull-out bearing capacity of the anchor plug body by comprehensively considering various factors that affect the pull-out bearing capacity of the anchor plug body, making the calculation more accurate, thereby improving the accuracy of the tunnel anchor safety performance assessment.

[0122] For further information, please refer to Figure 3 Based on the first and second embodiments of the tunnel anchor security performance evaluation method based on cloud computing of the present invention, a third embodiment of the tunnel anchor security performance evaluation method based on cloud computing of the present invention is proposed.

[0123] The third embodiment of the cloud computing-based tunnel anchor safety performance assessment method differs from the first and second embodiments of the cloud computing-based tunnel anchor safety performance assessment method in that, before the step of obtaining rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor based on the second cloud node in the cloud computing and analyzing the anchor site risk parameters corresponding to the tunnel anchor based on the rock mass characteristic parameters, the following steps are included:

[0124] Step S50: Acquire multiple sample data and divide the multiple sample data into training samples and test samples, wherein the sample data is a mapping relationship between the proportion of rock mass types and the proportion of rock mass morphology and fracture force;

[0125] Step S60, training a preset initial model based on the training samples, and when the training reaches a first preset end condition, testing the preset initial model a preset number of times based on the test samples to obtain a preset number of test results;

[0126] Step S70 , calculating the loss function value of the preset initial model according to the test results of the preset number of times, and generating the preset initial model as a preset model based on the loss function value.

[0127] To improve the accuracy of the minimum fracture force analysis within the anchor risk parameters, this embodiment pre-trains a pre-set model using a large amount of sample data. Specifically, the sample data is a mapping relationship between rock mass data and minimum fracture force. The rock mass data includes the proportions of different rock mass types and rock mass morphologies. For example, for a rock mass W represented by a sample data item, it includes a rock mass type proportion A, a rock mass morphology proportion B, and a minimum fracture force C. This indicates that for rock mass W, the distribution ratio of its rock mass types is A, the rock mass morphology is B, and the minimum fracture force required to cause it to fracture is C.

[0128] Furthermore, after acquiring a large amount of such sample data, each sample data is divided into training samples for training and test samples for testing. Furthermore, a preset initial model for training and a first preset termination condition indicating the end of training are pre-set. The preset initial model is trained using the divided training sample data, and a determination is made as to whether the training has met the first preset termination condition. If the first preset termination condition has been met, the preset initial model is tested a preset number of times using the test sample data to obtain the preset number of test results. The first preset termination condition can be set as a duration or a number of times; for example, the training duration can be set to 1 hour, 2 hours, 3 hours, etc., or the number of times the training can be set to 5, 10, 20, etc. The preset number of times is also pre-set based on requirements. For example, if the accuracy rate is higher after 5 tests, the preset number of times can be set to 5; if the accuracy rate is higher after 10 tests, the preset number of times can be set to 10, etc.

[0129] Furthermore, a loss function is pre-set for the preset initial model, and the loss function of the preset initial model is calculated using the obtained test results for the preset number of times to obtain the loss function value. The calculation formula for calculating the loss function value is specifically shown in the following formula (4).

[0130] (4);

[0131] in, is the loss function value, is the weight factor of the preset initial model, For the The test results generated by the test samples are For the The minimum breaking force corresponding to the test specimen.

[0132] Furthermore, the loss function value reflects the performance of the preset initial model after training. The smaller the loss function value, the smaller the loss of the preset initial model and the better the performance. Conversely, the performance is worse. Therefore, the preset initial model can be generated as the preset model based on the size of the preset loss function value. Specifically, the step of generating the preset initial model as the preset model based on the loss function value includes:

[0133] Step S71, determining whether the loss function value satisfies a second preset end condition; if so, terminating the training of the preset initial model and generating the preset initial model as a preset model;

[0134] Step S72: If the loss function value does not satisfy the second preset end condition, the model weight of the preset initial model is adjusted;

[0135] Step S73: Based on the adjusted model parameters, the step of training the preset initial model based on the training sample is executed until the loss function value meets the second preset end condition, thereby generating the preset model.

[0136] Furthermore, a second preset termination condition is pre-set, and the second preset termination condition is preferably a preset value representing the magnitude of the loss function value. The calculated loss function value is compared with the preset value to determine whether the loss function value satisfies the second preset termination condition. If the comparison determines that the loss function value is less than the preset value, it indicates that the loss function value is small and the preset initial model has achieved good performance after training. Therefore, it can be determined that the loss function value satisfies the second preset termination condition, and the preset initial model is generated as the preset model.

[0137] On the other hand, if the loss function value is determined to be no less than the preset value after comparison, it means that the loss of the preset initial model is large and training optimization is still required. In this case, the model weights of the preset initial model are first adjusted, and after the adjustment, the preset initial model with the new model parameters is iteratively trained again using the training sample parameters. This cycle is repeated, and the loss function value is calculated each time until the calculated loss function value meets the second preset end condition, completing the model training and generating the preset initial model as the preset model. The formula for adjusting the model parameters is shown in the following formula (5).

[0138] (5);

[0139] in, The first step to preset the initial model The model weight adjustment value of the middle layer, Preset initial model The error value of the test results of the middle layer, Indicates the preset number of times. To preset the initial model Layer The actual output value of the test result, To preset the initial model Layer Theoretical test results, To preset the initial model The connection weights of the layer, To preset the initial model The input data of the layer.

[0140] This embodiment pre-forms a preset model for analyzing the minimum breaking force through training with a large amount of sample data, and optimizes the training through the first preset end condition, the second preset end condition, the loss function value and the model weight, so that the model training is more accurate, thereby improving the accuracy of the analysis of the minimum breaking force.

[0141] In addition, the embodiment of the present invention also provides a tunnel anchor security performance evaluation system based on cloud computing. Figure 4 , Figure 4 This is a structural diagram of the equipment hardware operating environment involved in the cloud computing-based tunnel anchor safety performance evaluation system embodiment of the present invention.

[0142] like Figure 4 As shown, the cloud computing-based tunnel anchor security performance assessment system may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display and an input unit, such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory, such as a disk storage device. The memory 1005 may also be a storage device independent of the processor 1001.

[0143] Those skilled in the art will understand that Figure 4 The hardware structure of the cloud computing-based tunnel anchor safety performance evaluation system shown in the figure does not constitute a limitation of the cloud computing-based tunnel anchor safety performance evaluation system, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0144] like Figure 4 As shown, memory 1005, a storage medium, may include an operating system, a network communication module, a user interface module, and a control program. The operating system is a program that manages and controls the cloud computing-based tunnel anchor security performance assessment system and software resources, supporting the operation of the network communication module, the user interface module, the control program, and other programs or software. The network communication module is used to manage and control the network interface 1004; and the user interface module is used to manage and control the user interface 1003.

[0145] exist Figure 4In the hardware structure of the cloud computing-based tunnel anchor security performance evaluation system shown, the network interface 1004 is mainly used to connect to the backend server and communicate data with the backend server; the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client; the processor 1001 can call the control program stored in the memory 1005 and perform the following operations:

[0146] analyzing, based on the first cloud node in the cloud computing, the conductive force of the main cable of the suspension bridge corresponding to the tunnel anchor;

[0147] Obtaining rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor based on the second cloud node in the cloud computing, and analyzing anchor site risk parameters corresponding to the tunnel anchor based on the rock mass characteristic parameters;

[0148] acquiring anchor plug body parameters of the tunnel anchor based on a third cloud node in the cloud computing, and analyzing the pull-out bearing capacity of the tunnel anchor based on the anchor plug body parameters;

[0149] The conductive force, pull-out bearing capacity and anchor site risk parameters are evaluated and processed based on the central node in the cloud computing to generate a safety assessment coefficient, and the safety performance of the tunnel anchor is evaluated based on the safety assessment coefficient.

[0150] Furthermore, the step of analyzing the conductive force of the main cable of the suspension bridge corresponding to the tunnel anchor based on the first cloud node in the cloud computing includes:

[0151] Reading the bridge body conductive force corresponding to the main cable of the suspension bridge based on the first cloud node, and obtaining historical climate data of the environment in which the main cable of the suspension bridge is located;

[0152] According to the change trend of wind data in the historical climate data, the maximum wind force and wind direction within a preset time period are predicted, and the wind force consistent with the direction of the main cable of the suspension bridge is calculated based on the maximum wind force and wind direction. The calculation formula is:

[0153]

[0154] Among them, Fa is the wind force, Fmax is the maximum wind force, is the angle between the wind direction and the main cable direction of the suspension bridge, k1 is the wind force level coefficient, is the changing wind direction coefficient, Used to calculate the bending vibration frequency of the main cable of the suspension bridge, L is the height of the main cable of the suspension bridge, E is the elastic modulus of the main cable of the suspension bridge, I is the section polar moment of inertia of the main cable of the suspension bridge, A is the cross-sectional area of ​​the main cable of the suspension bridge, is the annual coefficient corresponding to the maximum wind speed;

[0155] The conductive force of the bridge body is corrected according to the wind force to obtain the conductive force.

[0156] Furthermore, the step of obtaining rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor based on the second cloud node in the cloud computing, and analyzing the anchor site risk parameters corresponding to the tunnel anchor based on the rock mass characteristic parameters includes:

[0157] Determining, based on the second cloud node, an extended area corresponding to the anchor area, and obtaining rock mass characteristic parameters corresponding to the extended area, the rock mass characteristic parameters including rock mass type, rock mass morphology, a first proportion corresponding to each of the rock mass types, and a second proportion corresponding to each of the rock mass morphologies;

[0158] The first proportions and the second proportions are transmitted to a preset model for calculation and processing to obtain the minimum fracture force of the rock mass in the extended area. The calculation formula is:

[0159] ;

[0160] Where W is the minimum breaking force, For the first The connection weights from the hidden layer to the output layer, For each first proportion, For each second proportion, is the input function, The preset model Layer hidden layer output.

[0161] Obtaining historical geological disaster data of the anchor site area, and predicting the probability of geological disasters occurring in the anchor site area within a preset time period and the type of geological disasters that occur based on the displayed historical geological disaster data;

[0162] According to the probability and type, a maximum probability within a preset time period is generated, and the maximum probability and the minimum rupture force are generated as anchor site risk parameters.

[0163] Furthermore, the anchor plug body parameters include the front end face area, rear end face area, axial length, anchor plug body inclination angle, concrete coefficient, anchor plug body positioning coefficient, anchor plug body contact area, and anchor plug body wedge angle of the anchor plug body corresponding to the tunnel anchor;

[0164] The step of analyzing the pull-out bearing capacity of the tunnel anchor based on the anchor plug body parameters includes:

[0165] Calculating the volume of the anchor plug body according to the front end surface area, the rear end surface area and the axial length; and determining the gravity of the anchor plug body according to the volume;

[0166] According to the inclination angle of the anchor plug body and the wedge angle of the anchor plug body, the gravity is divided into a first gravity component along the direction of the main cable of the suspension bridge and a second gravity component perpendicular to the outer surface of the anchor plug body;

[0167] Obtaining a friction coefficient corresponding to the anchor plug body, and generating a rock resistance according to the second gravity component and the friction coefficient; dividing the rock resistance into a first rock resistance along the direction of the main cable of the suspension bridge and a second rock resistance perpendicular to the direction of the main cable of the suspension bridge according to the inclination angle of the anchor plug body and the wedge angle of the anchor plug body;

[0168] Obtaining a gravity component coefficient corresponding to the first gravity component and a resistance component coefficient corresponding to the rock mass resistance;

[0169] The pull-out bearing capacity is calculated based on the first gravity component, the first rock mass resistance, the second rock mass resistance, the gravity component coefficient, the resistance component coefficient, the concrete coefficient, the anchor body positioning coefficient, and the anchor body contact area. The calculation formula is:

[0170] ;

[0171] in, For pull-out bearing capacity, is the concrete coefficient, is the anchor plug positioning coefficient, is the first gravity component, is the gravity component coefficient, is the first rock mass resistance, is the second rock mass resistance, is the contact area of ​​the anchor plug, is the resistance component coefficient.

[0172] Furthermore, the step of evaluating and processing the conductivity, pull-out bearing capacity, and anchor risk parameters based on the central node in the cloud computing to generate a safety assessment coefficient includes:

[0173] performing a difference calculation on the conductive force and the pull-out bearing capacity based on the central node to obtain a calculation result, and determining a safety factor to be evaluated corresponding to the calculation result according to a correspondence between a preset difference interval and a safety factor;

[0174] The safety factor to be evaluated is evaluated according to the anchor site risk parameter to generate the safety evaluation coefficient.

[0175] Furthermore, the step of evaluating the safety performance of the tunnel anchor according to the safety evaluation coefficient includes:

[0176] evaluating, based on the safety assessment coefficient, whether the safety performance of the tunnel anchor meets a preset condition;

[0177] If the preset conditions are met, the safety performance evaluation of the tunnel anchor is completed;

[0178] If the preset conditions are not met, a prompt message for adjusting the anchor plug body parameters of the tunnel anchor is output, so that the safety performance of the tunnel anchor can be re-evaluated after the adjustment is completed.

[0179] Furthermore, before the step of obtaining the rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor based on the second cloud node in the cloud computing, and analyzing the anchor site risk parameters corresponding to the tunnel anchor based on the rock mass characteristic parameters, the following steps are included:

[0180] Acquire a plurality of sample data, and divide the plurality of sample data into training samples and test samples, wherein the sample data is a mapping relationship between the proportion of rock mass types and the proportion of rock mass morphology and fracture force;

[0181] Training a preset initial model based on the training samples, and when the training reaches a first preset end condition, testing the preset initial model a preset number of times based on the test samples to obtain a preset number of test results;

[0182] According to the test results of the preset number of times, the loss function value of the preset initial model is calculated, and based on the loss function value, the preset initial model is generated as a preset model. The calculation formula of the loss function value is:

[0183] ;

[0184] in, is the loss function value, is the weight factor of the preset initial model, For the The test results generated by the test samples are For the The minimum breaking force corresponding to the test specimen.

[0185] Furthermore, the step of generating the preset initial model into a preset model based on the loss function value includes:

[0186] Determining whether the loss function value satisfies a second preset end condition, and if so, ending the training of the preset initial model and generating the preset initial model as a preset model;

[0187] If the loss function value does not meet the second preset end condition, the model weight of the preset initial model is adjusted, and the adjustment formula is:

[0188] ;

[0189] in, The first step to preset the initial model The model weight adjustment value of the middle layer, Used to calculate the preset initial model The error value of the test results of the middle layer, Indicates the preset number of times. To preset the initial model Layer The actual output value of the test result, To preset the initial model Layer Theoretical test results, To preset the initial model The connection weights of the layer, To preset the initial model The input data of the layer;

[0190] The step of training the preset initial model based on the training sample is performed based on the adjusted model parameters until the loss function value meets a second preset end condition, thereby generating the preset model.

[0191] The specific implementation of the cloud computing-based tunnel anchor security performance evaluation system of the present invention is basically the same as the various embodiments of the cloud computing-based tunnel anchor security performance evaluation method described above, and will not be repeated here.

[0192] An embodiment of the present invention further provides a storage medium having a control program stored thereon, which, when executed by a processor, implements the steps of the cloud computing-based tunnel anchor security performance assessment method.

[0193] The storage medium of the present invention may be a computer-readable storage medium, and its specific implementation is substantially the same as that of the above-mentioned cloud computing-based tunnel anchor security performance evaluation method embodiments, and will not be described in detail here.

[0194] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims. Any equivalent structure or equivalent process transformation made using the contents of the description and drawings of the present invention, or directly or indirectly used in other related technical fields, all fall within the protection of the present invention.

Claims

1. A cloud computing-based tunnel anchor security performance evaluation method, characterized in that: The tunnel anchor safety performance evaluation method includes: analyzing, based on the first cloud node in the cloud computing, the conductive force of the main cable of the suspension bridge corresponding to the tunnel anchor; Obtaining rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor based on the second cloud node in the cloud computing, and analyzing anchor site risk parameters corresponding to the tunnel anchor based on the rock mass characteristic parameters; acquiring anchor plug body parameters of the tunnel anchor based on a third cloud node in the cloud computing, and analyzing the pull-out bearing capacity of the tunnel anchor based on the anchor plug body parameters; The conductive force, pull-out bearing capacity and anchor site risk parameters are evaluated and processed based on the central node in the cloud computing to generate a safety assessment coefficient, and the safety performance of the tunnel anchor is evaluated based on the safety assessment coefficient.

2. The tunnel anchor safety performance evaluation method according to claim 1, characterized in that: The step of analyzing the conductive force of the main cable of the suspension bridge corresponding to the tunnel anchor based on the first cloud node in the cloud computing includes: Reading the bridge body conductive force corresponding to the main cable of the suspension bridge based on the first cloud node, and obtaining historical climate data of the environment in which the main cable of the suspension bridge is located; According to the change trend of wind data in the historical climate data, the maximum wind force and wind direction within a preset time period are predicted, and the wind force consistent with the direction of the main cable of the suspension bridge is calculated based on the maximum wind force and wind direction. The calculation formula is: ； Among them, Fa is the wind force, Fmax is the maximum wind force, is the angle between the wind direction and the main cable direction of the suspension bridge, k1 is the wind force level coefficient, is the changing wind direction coefficient, Used to calculate the bending vibration frequency of the main cable of the suspension bridge, L is the height of the main cable of the suspension bridge, E is the elastic modulus of the main cable of the suspension bridge, I is the section polar moment of inertia of the main cable of the suspension bridge, A is the cross-sectional area of ​​the main cable of the suspension bridge, is the annual coefficient corresponding to the maximum wind speed; The conductive force of the bridge body is corrected according to the wind force to obtain the conductive force.

3. The tunnel anchor safety performance evaluation method according to claim 1, characterized in that: The step of obtaining rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor based on the second cloud node in the cloud computing, and analyzing the anchor site risk parameters corresponding to the tunnel anchor based on the rock mass characteristic parameters includes: Determining, based on the second cloud node, an extended area corresponding to the anchor area, and obtaining rock mass characteristic parameters corresponding to the extended area, the rock mass characteristic parameters including rock mass type, rock mass morphology, a first proportion corresponding to each of the rock mass types, and a second proportion corresponding to each of the rock mass morphologies; The first proportions and the second proportions are transmitted to a preset model for calculation and processing to obtain the minimum fracture force of the rock mass in the extended area. The calculation formula is: ； Where W is the minimum breaking force, For the first The connection weights from the hidden layer to the output layer, For each first proportion, For each second proportion, is the input function, The preset model Layer hidden layer output; Obtaining historical geological disaster data of the anchor site area, and predicting the probability of geological disasters occurring in the anchor site area within a preset time period and the type of geological disasters that occur based on the displayed historical geological disaster data; According to the probability and type, a maximum probability within a preset time period is generated, and the maximum probability and the minimum rupture force are generated as anchor site risk parameters.

4. The tunnel anchor safety performance evaluation method according to claim 1, characterized in that: The anchor plug body parameters include the front end face area, rear end face area, axial length, anchor plug body inclination angle, concrete coefficient, anchor plug body positioning coefficient, anchor plug body contact area, and anchor plug body wedge angle corresponding to the tunnel anchor; The step of analyzing the pull-out bearing capacity of the tunnel anchor based on the anchor plug body parameters includes: Calculating the volume of the anchor plug body according to the front end surface area, the rear end surface area and the axial length; and determining the gravity of the anchor plug body according to the volume; According to the inclination angle of the anchor plug body and the wedge angle of the anchor plug body, the gravity is divided into a first gravity component along the direction of the main cable of the suspension bridge and a second gravity component perpendicular to the outer surface of the anchor plug body; Obtaining a friction coefficient corresponding to the anchor plug body, and generating a rock resistance according to the second gravity component and the friction coefficient; dividing the rock resistance into a first rock resistance along the direction of the main cable of the suspension bridge and a second rock resistance perpendicular to the direction of the main cable of the suspension bridge according to the inclination angle of the anchor plug body and the wedge angle of the anchor plug body; Obtaining a gravity component coefficient corresponding to the first gravity component and a resistance component coefficient corresponding to the rock mass resistance; The pull-out bearing capacity is calculated based on the first gravity component, the first rock mass resistance, the second rock mass resistance, the gravity component coefficient, the resistance component coefficient, the concrete coefficient, the anchor body positioning coefficient, and the anchor body contact area. The calculation formula is: ； in, For the pull-out bearing capacity, is the concrete coefficient, is the anchor plug positioning coefficient, is the first gravity component, is the gravity component coefficient, is the first rock mass resistance, is the second rock mass resistance, is the contact area of ​​the anchor plug, is the resistance component coefficient.

5. The tunnel anchor safety performance evaluation method according to claim 1, characterized in that: The step of evaluating and processing the conductivity, pull-out bearing capacity, and anchor risk parameters based on the central node in the cloud computing to generate a safety assessment coefficient includes: performing a difference calculation on the conductive force and the pull-out bearing capacity based on the central node to obtain a calculation result, and determining a safety factor to be evaluated corresponding to the calculation result according to a correspondence between a preset difference interval and a safety factor; The safety factor to be evaluated is evaluated according to the anchor site risk parameter to generate the safety evaluation coefficient.

6. The tunnel anchor safety performance evaluation method according to any one of claims 1 to 5, characterized in that: The step of evaluating the safety performance of the tunnel anchor according to the safety evaluation coefficient includes: evaluating, based on the safety assessment coefficient, whether the safety performance of the tunnel anchor meets a preset condition; If the preset conditions are met, the safety performance evaluation of the tunnel anchor is completed; If the preset conditions are not met, a prompt message for adjusting the anchor plug body parameters of the tunnel anchor is output, so that the safety performance of the tunnel anchor can be re-evaluated after the adjustment is completed.

7. The tunnel anchor safety performance evaluation method according to any one of claims 1 to 5, characterized in that: The step of obtaining the rock mass characteristic parameters of the anchor site area corresponding to the tunnel anchor based on the second cloud node in the cloud computing, and analyzing the anchor site risk parameters corresponding to the tunnel anchor based on the rock mass characteristic parameters includes: Acquire a plurality of sample data, and divide the plurality of sample data into training samples and test samples, wherein the sample data is a mapping relationship between the proportion of rock mass types and the proportion of rock mass morphology and fracture force; Training a preset initial model based on the training samples, and when the training reaches a first preset end condition, testing the preset initial model a preset number of times based on the test samples to obtain a preset number of test results; According to the test results of the preset number of times, the loss function value of the preset initial model is calculated, and based on the loss function value, the preset initial model is generated as a preset model. The calculation formula of the loss function value is: ； in, is the loss function value, is the weight factor of the preset initial model, For the The test results generated by the test samples are For the The minimum breaking force corresponding to the test specimen.

8. The tunnel anchor safety performance evaluation method according to claim 7, characterized in that: The step of generating the preset initial model into a preset model based on the loss function value includes: Determining whether the loss function value satisfies a second preset end condition, and if so, ending the training of the preset initial model and generating the preset initial model as a preset model; If the loss function value does not meet the second preset end condition, the model weight of the preset initial model is adjusted, and the adjustment formula is: ； in, The first step to preset the initial model The model weight adjustment value of the middle layer, Used to calculate the preset initial model The error value of the test results of the middle layer, Indicates the preset number of times. To preset the initial model Layer The actual output value of the test result, To preset the initial model Layer Theoretical test results, To preset the initial model The connection weights of the layer, To preset the initial model The input data of the layer; The step of training the preset initial model based on the training sample is performed based on the adjusted model parameters until the loss function value meets a second preset end condition, thereby generating the preset model.

9. A cloud computing-based tunnel anchor safety performance evaluation system, characterized in that: The cloud computing-based tunnel anchor security performance evaluation system includes: a memory, a processor, a communication bus, and a control program stored in the memory: The communication bus is used to realize the connection and communication between the processor and the memory; The processor is used to execute the control program to implement the steps of the cloud computing-based tunnel anchor security performance evaluation method according to any one of claims 1 to 8.

10. A storage medium, characterized in that: The storage medium stores a control program, and when the control program is executed by the processor, the steps of the cloud computing-based tunnel anchor security performance evaluation method according to any one of claims 1 to 8 are implemented.

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