Suspendome structure stability detection method and system based on prestress analysis

Abstract

The invention provides a suspended dome structure stability detection method and system based on pre-stress analysis, and relates to the technical field of structural mechanics analysis, and the method comprises the steps: carrying out the key point analysis of a suspended dome structure, configuring a sensor group, and building a sensor network; establishing a structural state vector; performing reason backtracking according to a real-time prestress loss analysis result, and establishing a loss identifier; carrying out finite element model correction by utilizing the loss identifier and the structural state vector, carrying out buckling analysis and nonlinear time-history analysis, and establishing a stability critical value prediction result; and generating a stability detection result. According to the method and the device, the technical problem that the stability detection result of the suspended dome structure is affected due to the fact that the finite element model is generally based on idealized hypothesis and has deviation from the actual situation is solved, the suspended dome structure is monitored and analyzed in real time, the finite element model is dynamically corrected, the actual adaptability of the finite element model is improved, and the stability detection result of the suspended dome structure is improved. Therefore, the accuracy of stability detection results is improved.

Description

Technical Field

[0001] This application relates to the technical field of structural mechanics analysis, and particularly to a method and system for detecting the stability of a prestressed cable-supported dome structure in prestress analysis. Background Art

[0002] The cable-supported dome structure is a typical lightweight long-span spatial structure, which is composed of an arched support system combined with suspension cables, and has the advantages of material conservation, large span, and unique shape. At present, in order to ensure the safe operation of the cable-supported dome structure, two types of stability detection methods are mainly adopted in engineering practice: static detection and dynamic detection. The static detection method mainly focuses on on-site measurement of key parameters, and evaluates the health status of the structure by monitoring the structural deformation and stress state; the dynamic detection method mainly uses modal analysis, combined with finite element analysis and nonlinear time history analysis, to evaluate the structure. However, since the construction of the finite element model usually relies on idealized assumptions and does not take into account the complex factors in actual engineering, there is a large deviation between the simulation results and the actual structural state, which directly affects the accuracy of the stability detection of the cable-supported dome structure.

[0003] In summary, there is a technical problem in the prior art that due to the fact that the finite element model is generally based on idealized assumptions, there is a deviation from the actual situation, which affects the stability detection results of the cable-supported dome structure. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for detecting the stability of a prestressed cable-supported dome structure in prestress analysis, so as to solve the technical problem in the prior art that due to the fact that the finite element model is generally based on idealized assumptions, there is a deviation from the actual situation, which affects the stability detection results of the cable-supported dome structure.

[0005] In view of the above problems, this application provides a method and system for detecting the stability of a prestressed cable-supported dome structure in prestress analysis.

[0006] In a first aspect, the present application provides a stability detection method for a suspension-dome structure with prestress analysis, and the stability detection method for a suspension-dome structure with prestress analysis is implemented by a stability detection system for a suspension-dome structure with prestress analysis, wherein the stability detection method for a suspension-dome structure with prestress analysis comprises: after performing a key point analysis on the suspension-dome structure, configuring a sensor group at the corresponding key point, the sensor group comprising a fiber grating sensor, a laser displacement sensor and a strain gauge, and establishing a sensor network according to the layout position of the sensor group; after time alignment of the monitoring data of the sensor network, multi-source data fusion of the cable force, displacement and strain data in the monitoring data is performed to establish a structural state vector; real-time prestress loss analysis is performed according to the structural state vector, and the cause of the loss is traced back according to the prestress loss analysis result to establish a loss mark; the finite element model is modified using the loss mark and the structural state vector, and the modified finite element model is used to perform buckling analysis and nonlinear time history analysis to establish a stability critical value prediction result; and a stability detection result is generated based on the stability critical value prediction result.

[0007] Optionally, environmental monitoring of the suspend-dome structure is performed to establish an environmental monitoring data set; after the environmental monitoring data set is associated with the structural state vector mapping, time domain features, frequency domain features and environmental coupling features are extracted according to the mapping association results; after optimizing the basic event threshold of the dynamic fault tree using the environmental monitoring data set, a feature vector is established according to the time domain features, the frequency domain features and the environmental coupling features, and the feature vector is used as input data to perform event triggering analysis through the optimized dynamic fault tree; after verifying the time-space correlation of the triggered event, a confidence assessment is performed, and a loss mark is generated using the confidence assessment result.

[0008] Optionally, basic event determination constraints are established, and the basic event determination constraints include cable force sudden drop event constraints, displacement jump event constraints, strain gradient abnormality event constraints, and high humidity corrosion risk event constraints; event triggering analysis of the feature vector is performed through the basic event determination constraints, and event triggering analysis results are established; intermediate event logic calculations are performed using the event triggering analysis results to establish intermediate event triggering results, and the intermediate event triggering results include anchor slip events, corrosion damage events, and material relaxation events.

[0009] Optionally, establish a cable tension drop event constraint as follows: ;in, , represents the rate of change of cable force, Characterization The change of prestress of the cable, Characterizes the monitoring data sampling time interval, Characterizes the temperature-dependent cable force loss rate threshold, , where characterizes the environmental temperature value.

[0010] Optionally, establish a displacement sudden jump event constraint as follows: ; where is a flag variable characterizing the displacement sudden jump event, characterizes the displacement change amount of the th key point actually monitored, characterizes the displacement change amount threshold of the th key point specified by the design; establish a strain gradient anomaly event constraint and a high humidity corrosion risk event constraint as follows: , ; where characterizes the strain gradient, representing the rate of change of strain with position at the th monitoring point, characterizes the relative humidity.

[0011] Optionally, when the cable force sudden drop event and the displacement sudden jump event are triggered simultaneously, and the correlation coefficient of the prestress change amount and the displacement change amount satisfies a preset correlation threshold, establish the triggering result of the anchor slip intermediate event.

[0012] Optionally, when the strain gradient anomaly event and the high humidity corrosion risk event are triggered simultaneously, and the causal association degree between the strain amount and the relative humidity is higher than a preset association threshold, establish the triggering result of the corrosion damage event.

[0013] Optionally, determine whether there is no cable force sudden drop event. If there is no cable force sudden drop event, calculate the time change rate of the prestress value; if the time change rate of the prestress value is lower than a preset rate threshold, establish the triggering result of the material relaxation event.

[0014] Optionally, execute the stability critical value prediction result for abnormal level triggering analysis, establish the abnormal level triggering analysis result; perform time series balance fitting according to the abnormal level triggering analysis result, and generate a stability detection result through the time series balance fitting result.

[0015] Second aspect, the present application also provides a stability detection system for prestressed analysis of a cable-supported dome structure, which is used to execute the stability detection method for prestressed analysis of a cable-supported dome structure as described in the first aspect. Among them, the stability detection system for prestressed analysis of a cable-supported dome structure includes: a sensor layout module, which is used to perform key point analysis on the cable-supported dome structure, configure a sensor group at the corresponding key points, the sensor group includes fiber Bragg grating sensors, laser displacement sensors and strain gauges, and establish a sensor network according to the layout positions of the sensor group; a data fusion module, which is used to align the monitoring data of the sensor network in time, perform multi-source data fusion on the cable force, displacement and strain data in the monitoring data, and establish a structural state vector; a loss analysis module, which is used to perform real-time prestressed loss analysis according to the structural state vector, trace back the loss cause according to the prestressed loss analysis result, and establish a loss identifier; a critical value prediction module, which is used to correct the finite element model by using the loss identifier and the structural state vector, and perform buckling analysis and nonlinear time history analysis by using the corrected finite element model, and establish a stability critical value prediction result; a detection result generation module, which is used to generate a stability detection result based on the stability critical value prediction result.

[0016] One or more technical solutions provided in the present application have at least the following beneficial effects: After performing key point analysis on the cable-supported dome structure, configure a sensor group at the corresponding key points. The sensor group includes fiber Bragg grating sensors, laser displacement sensors and strain gauges, and establish a sensor network according to the layout positions of the sensor group; align the monitoring data of the sensor network in time, perform multi-source data fusion on the cable force, displacement and strain data in the monitoring data, and establish a structural state vector; perform real-time prestressed loss analysis according to the structural state vector, trace back the loss cause according to the prestressed loss analysis result, and establish a loss identifier; correct the finite element model by using the loss identifier and the structural state vector, and perform buckling analysis and nonlinear time history analysis by using the corrected finite element model, and establish a stability critical value prediction result; generate a stability detection result based on the stability critical value prediction result. That is to say, by configuring mechanical sensors at the key points of the cable-supported dome structure for real-time monitoring, establishing a structural state vector, performing prestressed analysis at the same time, identifying the reasons for prestressed changes, and establishing a loss identifier, correcting the finite element model according to the structural state vector and the loss identifier, reducing the idealized assumptions in the model, making the model closer to the actual state of the structure, and performing stability detection by using the corrected finite element model, thereby improving the accuracy and reliability of the stability detection result of the cable-supported dome structure.

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the content of the specification. Moreover, in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specific embodiments of this application are specifically given. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of this application, nor is it used to limit the scope of this application. Other features of this application will become easily understandable through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary, and for those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0019] Figure 1 It is a schematic flowchart of the method for detecting the stability of a cable-supported dome structure for prestress analysis in this application; Figure 2 It is a schematic structural diagram of the system for detecting the stability of a cable-supported dome structure for prestress analysis in this application.

[0020] Description of reference numerals: Sensor layout module 11, data fusion module 12, loss analysis module 13, critical value prediction module 14, detection result generation module 15. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] By providing a method and system for detecting the stability of a cable-supported dome structure for prestress analysis, this application solves the technical problem in the prior art that due to the fact that the finite element model is generally based on idealized assumptions, there is a deviation from the actual situation, thus affecting the detection results of the stability of the cable-supported dome structure. By configuring mechanical sensors at key points of the cable-supported dome structure for real-time monitoring, establishing a structural state vector, simultaneously performing prestress analysis, identifying the reasons for prestress changes, and establishing a loss identifier, the finite element model is corrected according to the structural state vector and the loss identifier, reducing the idealized assumptions in the model and making the model closer to the actual state of the structure. The stability of the cable-supported dome structure is detected through the corrected finite element model, thereby improving the accuracy and reliability of the detection results of the stability of the cable-supported dome structure.

[0022] Next, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without making creative efforts belong to the scope of protection of the present application.

[0023] Example 1. Please refer to the attached Figure 1 , the present application provides a method for detecting the stability of a prestressed cable-supported dome structure by prestress analysis. Among them, the method for detecting the stability of a prestressed cable-supported dome structure by prestress analysis is executed by a detection system for the stability of a prestressed cable-supported dome structure by prestress analysis. The method for detecting the stability of a prestressed cable-supported dome structure by prestress analysis specifically includes the following steps: S100: After performing key point analysis on the cable-supported dome structure, a sensor group is configured at the corresponding key points. The sensor group includes fiber Bragg grating sensors, laser displacement sensors, and strain gauges, and a sensor network is established according to the layout positions of the sensor group.

[0024] Specifically, performing key point analysis on the cable-supported dome structure means performing mechanical analysis on this structure to identify local areas with stress concentration, large deformation, or potential safety hazards in the structure. Key points refer to points that bear the main load or play a decisive role in the structural stability, such as arch feet, cable connection points, and mid-span areas, etc. Generally, establishing an accurate cable-supported dome structure model should be able to reflect the actual geometric shape, material properties, and boundary conditions of the structure. Apply various possible loads to the structure, including dead loads, live loads, and extreme loads (such as wind loads, snow loads, etc.) to simulate the stress conditions of the structure during actual use. Calculate the structure model through existing finite element analysis software, and analyze the responses of the structure under various loads, including stress, strain, and displacement, etc. According to the results of the finite element analysis, identify the key points in the structure, usually the points with the maximum stress, the most significant deformation, or the most critical to the overall stability.

[0025] Configure a sensor group at the determined key points to monitor the structural state in real time. The sensor group at each key point usually includes fiber Bragg grating sensors, laser displacement sensors, and strain gauges, and monitors the state of the key points from multiple angles simultaneously. Among them, the fiber Bragg grating sensor is used to measure local strain and temperature changes; the laser displacement sensor is used to detect small displacements and deformations; the strain gauge is a resistive sensor. When the structure deforms, the resistance value of the strain gauge changes, and the strain of the material is sensed by measuring the resistance change.

[0026] According to the layout positions of the sensor groups, a sensor network covering the key parts of the whole suspended-dome is constructed. For example, in a suspended-dome project of a stadium, a total of 12 key nodes are deployed, and each node is configured with a sensor group. The data is transmitted to the central data processing platform through a wireless transmission gateway, realizing the collection and upload of data per second, and real-time monitoring and storage. In a certain arch-foot area, the fiber optic sensor detects that the strain fluctuation range is ±1.5 με, the laser displacement sensor records the displacement between 0.1 and 0.15 mm, and the strain value detected by the strain gauge is between 950 and 1050 με. Through key-point analysis, the monitoring work is concentrated on the areas that have the greatest impact on the structural stability, ensuring that the monitoring resources are concentrated on high-risk parts. Using multiple sensor groups of fiber Bragg gratings, laser displacement sensors and strain gauges, all-round real-time monitoring of parameters such as strain, displacement and temperature is realized. The data verify each other, effectively reducing the errors that may occur in a single sensor, helping to detect potential problems in a timely manner, and significantly improving the stability detection accuracy and structural safety of the suspended-dome structure as a whole.

[0027] S200: After time-aligning the monitoring data of the sensor network, perform multi-source data fusion on the cable force, displacement, and strain data in the monitoring data to establish a structural state vector.

[0028] Specifically, the suspended-dome structure is monitored through the sensor network arranged at key points, and real-time data is collected, including parameters such as the cable force, displacement, and strain of each key point. In the sensor network, since each sensor (such as fiber Bragg grating, laser displacement sensor, and strain gauge) has a different sampling frequency (for example, the sampling frequency of the fiber Bragg grating is 100 Hz, while the sampling frequency of the laser displacement sensor is 200 Hz), the collected data is not completely consistent in time stamps. In order to enable these data to be compared and fused at the same time point, time alignment is required. Generally, linear interpolation (such as spline interpolation) is used to resample the high-frequency data to the low-frequency data, or interpolate the low-frequency data to the high-frequency time points. Time alignment is to make all data points match on the same time basis through methods such as time stamp synchronization and interpolation processing, so as to facilitate subsequent data fusion.

[0029] The cable force can be indirectly measured by fiber Bragg grating strain sensors installed on the cable anchor head and the cable length adjusting rod. The stress-strain diagrams of the cable body, the ear plate, and the cable length adjusting rod show a linear distribution. Therefore, the cable force can be deduced by measuring the strain of these components. For example, when the cable is stressed, the strain change in the optical fiber is converted into the displacement of the reflected wavelength, and the cable force value can be obtained after calibration. The laser displacement sensor measures the displacement by emitting a laser beam and receiving the reflected light signal and calculating the optical path difference. The strain gauge obtains strain data by measuring the deformation generated by the material when it is stressed. Although its sensitivity is high, it is vulnerable to electromagnetic interference and environmental factors and is not as stable as the fiber Bragg grating sensor. The strain gauge and the fiber Bragg grating sensor are combined to improve the reliability and accuracy of detection.

[0030] For each data point collected by the sensor, its precise timestamp is recorded. Then, data preprocessing is performed on the monitoring data of the sensor network to remove noise and outliers. According to the collected timestamps, the data of different sensors are adjusted to a unified time reference. If some sensors have no data at a specific time point, interpolation methods are used to estimate the data values at these time points, that is, the data at the current time node is calculated through the data at the previous and subsequent moments, and the calculation can be carried out according to the law of the monitoring data, such as calculating the average value of the data at the previous and subsequent moments.

[0031] After the time alignment is completed, it is necessary to fuse the data of multiple sensors to establish a structural state vector. The monitoring data after time alignment is normalized to ensure that data with different dimensions can be compared and fused. The aligned cable force, displacement, and strain data are integrated into a multi-dimensional vector, and each dimension represents the measurement value of a sensor. Multi-source data fusion refers to integrating data from different sensors to improve the accuracy, stability, and robustness of the monitoring data. Multi-source data fusion methods include weighted average method, Kalman filtering method, principal component analysis method, etc. For example, the cable force can be calculated by the fiber Bragg grating sensor, the displacement can be measured by the laser displacement sensor, and the strain can be obtained by the strain gauge. Since each sensor may have errors or noise, by fusing multiple data sources, more reliable structural state information can be obtained. The structural state vector is a mathematical expression used to describe the health state of the cable-supported dome structure, including multiple monitoring parameters (such as cable force, displacement, strain, etc.), and can reflect the overall stress state of the structure.

[0032] Exemplarily, the structural state vector can be expressed as [F, d, ε], where F represents the cable force, d represents the displacement, and ε represents the strain. For example, at the moment of 1.0 second, the cable force data obtained after alignment is 211.5 kN, the displacement data is 2.3 mm, and the strain data is 1220 με. The cable force shown by the fiber Bragg grating sensor after calculation is 211.5 kN, and the error range is ±3 kN; at the same time, the cable force value is also deduced by reversing the measurement data of the strain gauge after conversion, which is about 209.0 kN, and the error is relatively small, ±2 kN; by assigning different weights (such as 0.6 and 0.4), the cable force after calculation and fusion is 0.6×211.5 kN + 0.4×209.0 kN = 210.6 kN. Similarly, the displacement data and strain data are also weighted respectively, and finally all data are expressed in a unified form as a structural state vector, that is, [210.6 kN, 2.3 mm, 1220 με].

[0033] By performing time alignment and multi-source data fusion on various data collected by the sensor network, it is possible to effectively eliminate the data misalignment caused by the sampling time difference of different sensors, thereby achieving the temporal consistency of the data. At the same time, through the fusion process, the error and noise of a single sensor can be significantly reduced, making the accuracy of the fused data significantly improved.

[0034] S300: Perform real-time prestress loss analysis according to the structural state vector, and trace back the cause of the loss according to the prestress loss analysis result, and establish a loss identifier.

[0035] Specifically, according to the structural state vector, perform real-time prestress loss analysis to analyze whether the current structural state vector triggers an abnormal event, that is, compare the structural state vector with the designed value to determine whether there is an abnormality, and obtain the prestress loss analysis result. By comparing the monitoring data and the structural behavior model, analyze the possible causes of prestress loss, such as anchor slip events, corrosion damage events, and material relaxation events. For example, if the prestress loss shows a slow and continuous downward trend, and the displacement and strain changes are relatively stable, it may be caused by creep or material relaxation; if the prestress suddenly drops significantly, accompanied by violent changes in local displacement and strain, it may be related to anchor loosening or local damage; if the environmental temperature and humidity are high, accompanied by abnormal strain gradients, it may imply that corrosion damage affects the prestress.

[0036] After obtaining the real-time prestress loss analysis results, determine the reasons for the prestress decline. Use the time domain, frequency domain, and environmental coupling characteristics extracted from the mapping correlation between environmental monitoring data and structural state vectors in the early stage, as well as the basic event thresholds optimized by the dynamic fault tree, to conduct event trigger analysis. Check whether other basic events (such as sudden displacement jumps, abnormal strains, etc.) are detected simultaneously, and compare the current prestress loss curve with historical data and standard models (such as material relaxation, anchor slip, corrosion loss, etc.). Compare, cluster, and match the structural state vector with the basic events in the dynamic fault tree model, attribute the normal patterns, and thus trace back the possible reasons for the prestress loss.

[0037] According to the backtracking results of the loss reasons, convert the attribution results into intuitive loss identifiers according to the preset generation rules. The specific process is explained in detail in the corresponding dependent claims. For the sake of simplicity of the specification, it will not be elaborated here. Define the categories and levels of the loss identifiers according to the degree and reasons of the prestress loss. The loss identifiers should include information such as the location, degree, reason, and potential impact of the loss. Through the prestress loss analysis, the real-time monitoring and analysis of the prestress state of the cable-supported dome structure can timely detect and diagnose the reasons for the prestress loss, and thus take corresponding maintenance measures to ensure the safe operation of the structure.

[0038] Furthermore, step S300 of the present application includes: Perform environmental monitoring on the cable-supported dome structure to establish an environmental monitoring data set; after mapping and correlating the environmental monitoring data set with the structural state vector, extract time domain characteristics, frequency domain characteristics, and environmental coupling characteristics according to the mapping correlation results; after optimizing the basic event thresholds of the dynamic fault tree using the environmental monitoring data set, establish a feature vector based on the time domain characteristics, the frequency domain characteristics, and the environmental coupling characteristics, and use the feature vector as input data to conduct event trigger analysis through the optimized dynamic fault tree; after performing spatio-temporal correlation verification on the triggered events, conduct confidence evaluation, and generate loss identifiers using the confidence evaluation results.

[0039] Furthermore, the present application further includes the following steps: Establish basic event determination constraints, where the basic event determination constraints include cable force sudden drop event constraints, displacement sudden jump event constraints, strain gradient anomaly event constraints, and high humidity corrosion risk event constraints; conduct event trigger analysis of the feature vector through the basic event determination constraints to establish event trigger analysis results; use the event trigger analysis results to perform intermediate event logic calculations to establish intermediate event trigger results, and the intermediate event trigger results include anchor slip events, corrosion damage events, and material relaxation events.

[0040] Specifically, environmental monitoring of the cable-supported dome structure is carried out through environmental sensors (such as thermometers, hygrometers, anemometers, rainfall sensors, etc.) arranged around and inside the cable-supported dome structure to obtain an environmental monitoring data set, which usually includes timestamps and corresponding environmental parameter values. The environmental monitoring data set is aligned with the previously established structural state vector using the timestamps. The correlation between the two is analyzed to establish a mapping relationship. That is, a mapping relationship is established between the environmental monitoring data and the structural state data according to the same time base and spatial position to reveal how environmental conditions affect the structural state, such as whether an increase in temperature or an increase in wind speed will cause changes in cable forces or displacements. The spatial position is the position of each key point. Since each key point is equipped with a sensor group, the environmental monitoring data set can be corresponded to the structural state vector at each key point position.

[0041] Using statistical analysis and signal processing methods, features are extracted from the mapped and associated data, including time-domain features, frequency-domain features, and environmental coupling features. Among them, time-domain features refer to the features directly extracted from time-series data, such as mean value, standard deviation, rise time, duration, etc., which are used to describe the change trend of data over time; frequency-domain features are the features obtained through spectral analysis such as Fourier transform of time-series data, such as main frequency, harmonic components, spectral energy distribution, etc., which are used to reveal information such as periodicity and vibration modes; usually, the frequency-domain conversion is carried out through the discrete Fourier transform formula to obtain spectral data, and the spectral data is usually represented in complex form, where the amplitude represents the intensity of the frequency component and the phase represents the phase information of the frequency component. Environmental coupling features refer to the features that describe the interaction between environmental variables (such as wind speed, temperature) and structural responses, such as the correlation coefficient between temperature change and strain change.

[0042] Calculate indicators such as the mean value, standard deviation, maximum value, and minimum value of the structural state data in the time domain. For example, within a continuous 1-hour period, the mean cable force at a certain key point may be 210 kN, and the standard deviation is 2 kN; secondly, by performing Fourier transform on the displacement or strain data, the main frequency and spectral energy distribution are extracted. For example, if the main vibration frequency is found to be 0.5 Hz, it indicates that the structure has periodic vibration; or the vibration harmonic components are detected, and the spectral energy distribution shows that the energy is mainly concentrated in the range of 0.5 Hz to 1 Hz. Finally, the coupling degree between the environmental variables and the structural responses is evaluated through correlation analysis. For example, by calculating the Pearson correlation coefficient between the wind speed and the displacement, a high correlation of 0.85 is obtained, indicating that the fluctuation of the wind speed has a significant impact on the structural displacement.

[0043] Dynamic fault tree is a reliability analysis method that considers the dynamic evolution of fault events over time. It not only focuses on the logical relationships between basic events but also takes into account the time sequence and persistence of events, enabling a more accurate simulation of the fault propagation process in complex systems. Dynamic fault tree includes basic events, intermediate events, and top events. Among them, basic events include sudden cable force drop events, sudden displacement jump events, abnormal strain gradient events, and high humidity corrosion risk events. Intermediate events include anchor slip events, corrosion damage events, and material relaxation events. The top event refers to the ultimate manifestation of prestress loss.

[0044] Optimize the threshold values of basic events in the dynamic fault tree using environmental monitoring data sets. The threshold value of a basic event refers to the judgment criterion or critical value for each basic event (such as an abnormal situation detected by a certain sensor) in the dynamic fault tree model, which is used to determine when the basic event is considered to occur. Adjust and optimize the threshold values of each basic event in the dynamic fault tree using actual environmental monitoring data to make it more in line with the actual engineering situation and reduce the risk of false alarms or missed alarms. For example, in an engineering experiment, the monitoring data shows that the normal fluctuation range of the cable force at a certain key point is 208 kN to 213 kN under normal conditions. Therefore, the threshold value of the cable force abnormal basic event can be set above 213 kN. Similarly, for displacement and strain, reasonable threshold values can also be reset according to the statistical results of environmental data.

[0045] Combine time-domain features, frequency-domain features, and environmental coupling features in a certain order to form a vector for comprehensively describing the multi-dimensional characteristics of the structure and environmental state. For example, the feature vector = [cable force mean 210.6 kN, cable force standard deviation 2 kN, displacement mean 2.3 mm, displacement standard deviation 0.1 mm, main vibration frequency 0.5 Hz, displacement spectrum energy 0.6, wind speed-displacement correlation coefficient 0.85, temperature-strain correlation coefficient 0.65].

[0046] Use the constructed feature vector as input data and input it into the dynamically fault tree model optimized by threshold values for event trigger analysis. The optimized dynamic fault tree can judge whether the current monitoring state triggers basic fault events based on the feature vector.

[0047] Based on factors such as the design specifications, material properties, and environmental conditions of the cable-supported dome structure, establish basic event judgment constraints, including cable force sudden drop event constraints, displacement sudden jump event constraints, strain gradient anomaly event constraints, and high humidity corrosion risk event constraints, to determine whether the structure has abnormal conditions that may lead to failures. The cable force sudden drop event constraint is used to detect the situation where the cable force (tension) in the cable-supported dome suddenly decreases, indicating the possible risk of prestress loss or cable breakage; the displacement sudden jump event constraint is used to identify the phenomenon that the displacement data of key parts of the structure suddenly changes violently, which may mean local instability or loosening of the structure connection; the strain gradient anomaly event constraint is used to judge whether the strain change rate on the component exceeds the normal range, reflecting abnormal local stress distribution, which may indicate local damage; the high humidity corrosion risk event constraint is used to detect whether the environmental humidity exceeds the set threshold and reaches the level that may cause corrosion.

[0048] For example, if at a certain key point, the cable force suddenly drops from the reference value of 210 kN to below 180 kN, it can be considered that the cable force sudden drop event is triggered; another example is that if the displacement data suddenly jumps from 2.3 mm to above 2.8 mm, it is determined as the displacement sudden jump event; at the same time, if the local strain gradient rises sharply and the change rate exceeds the predetermined range of 0.5% per second, the strain gradient anomaly event is triggered; furthermore, in environmental monitoring, if the humidity remains above 80% for a long time, the high humidity corrosion risk event may be triggered.

[0049] According to the basic event judgment constraints, conduct event trigger analysis on the feature vectors through a dynamic fault tree to form the event trigger analysis results, that is, which feature vectors meet the conditions for triggering events, which basic event is triggered, and the trigger time point. Conduct logical combination and statistical analysis on the trigger results of multiple basic events to infer higher-level fault states (intermediate events). Intermediate events not only reflect single anomalies but also consider the mutual relationships and correlations among multiple basic events, providing a deeper basis for fault diagnosis and early warning.

[0050] The trigger results of intermediate events include anchor slip events, corrosion damage events, and material relaxation events. Among them, the anchor slip event refers to the situation where the anchor in the cable-supported dome structure slides along the cable, which may lead to structural instability; the corrosion damage event refers to the corrosion damage of the structural material caused by environmental factors (such as humidity, chemical substances, etc.); the material relaxation event refers to the performance degradation of the structural material caused by long-term stress or environmental factors. If (the cable force sudden drop event is triggered and the displacement sudden jump event is triggered and the correlation coefficient is greater than the preset threshold), then the anchor slip event is triggered. If (the strain gradient anomaly event and the high humidity corrosion risk event and the causal association degree is greater than the preset association threshold), then the corrosion damage event is triggered. If (the cable force sudden drop event is not triggered and the prestress change rate is less than the preset rate threshold), then the material relaxation event is triggered.

[0051] Through intermediate time logic calculation, the triggering situations of multiple basic events are combined with the correlation analysis results, realizing the comprehensive determination of complex fault modes, accurately distinguishing acute faults (such as sudden drop in cable force) from long-term cumulative effects (such as material relaxation), and improving the accuracy and response speed of early warning.

[0052] Perform spatio-temporal correlation verification on the events after trigger analysis. That is, check the events triggered in terms of space and time, and cluster similar events occurring within a similar time and space range. Compare the events triggered at different monitoring points and time periods in terms of space and time to verify whether they are consistent and continuous, so as to exclude isolated abnormal data. Conduct a reliability assessment on the triggered events to quantify the probability of the events actually occurring. According to the confidence assessment results, generate loss indicators to visually represent the types and severity levels of possible losses suffered by the structure (such as anchor slip, corrosion damage, material relaxation). For example, use statistical methods (such as Kalman filtering, Bayesian inference) to calculate the comprehensive confidence of these events, obtaining a value between 0 and 1, representing the reliability of the events.

[0053] Time-domain verification is carried out by checking whether multiple sensors simultaneously or continuously trigger similar basic events within a similar time period. Space verification is carried out by comparing the geographical locations of different sensors to determine whether the events are concentrated in a certain area. If multiple sensors in a certain area report abnormalities, it proves that the events have spatial correlation. Calculate the correlation scores for the time-domain and space data respectively, and integrate them through a weighted average method to form an overall spatio-temporal correlation score. The higher the score, the more concentrated and consistent the triggered events are in space and time, increasing the credibility of the events. For example, if multiple adjacent monitoring points detect similar abnormalities at similar times, it is considered that these events are consistent in space and time, enhancing the authenticity of the events; on the contrary, if the events are scattered and uncorrelated in different regions or times, they may be isolated noise or false alarms.

[0054] Based on historical monitoring data and previous fault cases, construct a statistical model or adopt the Bayesian inference method, taking various indicators of the triggered events (such as abnormal amplitude, duration, spatio-temporal correlation score) as input parameters. After model calculation, a probability value is obtained, which is between 0 and 1, representing the credibility of the current triggered event being a real abnormality. For example, if the calculated confidence is 0.92, it means that there is a 92% probability that the event is a real existing abnormality rather than caused by random fluctuations or noise. Through spatio-temporal correlation verification, the interference caused by isolated and sporadic noise is eliminated, so that high-confidence results are only generated when multiple indicators are abnormally consistent. The generated loss indicators visually reflect the fault risk, enabling timely warning to maintenance personnel, thereby reducing the accident risk and ensuring the structural safety.

[0055] Further, the present application further includes the following steps: Establish a constraint for sudden cable force drop events as follows: ; where , representing the cable force change rate, represents the prestress change of the th stay cable, represents the sampling time interval of the monitoring data, represents the temperature-dependent cable force loss rate threshold, , where represents the ambient temperature value.

[0056] Specifically, cable force data is collected through sensors (such as fiber Bragg grating sensors) installed on the stay cables, and the cable force values of each stay cable at each sampling time are recorded. According to the cable force values at two sampling time points and the sampling time interval, the cable force change rate is calculated. For example, if the prestress of the ith stay cable drops from 210 kN to 190 kN within 1 minute, the cable force change rate E1 is 20 kN / min. The temperature-dependent cable force loss rate threshold changes with the ambient temperature T. For example, when the current ambient temperature value is 25 °C, according to calculate α(25) = 10% / min - e 0.1 = 11.05% / min, that is, the maximum allowable cable force loss ratio per minute is 11.05%. According to the constraint for sudden cable force drop events, the cable force drop change rate corresponding to the cable force loss rate threshold is 23.2 kN / min, and the cable force change rate of 20 kN / min is less than the threshold, so the sudden cable force drop event is not triggered.

[0057] By introducing a temperature-dependent threshold, the determination of cable force drop is more accurate and in line with the actual working conditions at different ambient temperatures, effectively reducing the false alarm rate and missed alarm rate. Using real-time monitoring, the sudden cable force drop phenomenon can be quickly identified, providing a warning signal for maintenance personnel to prevent structural safety problems caused by the loss of prestress.

[0058] Further, the present application further includes the following steps: Establish a constraint for sudden displacement jump events as follows: ; where represents the flag variable for sudden displacement jump events, represents the displacement change of the th key point actually monitored, represents the displacement change threshold of the th key point specified by the design; establish a constraint for abnormal strain gradient events and a constraint for high humidity corrosion risk events as follows: , ; where represents the strain gradient, indicating the The rate of change of strain with position at a monitoring point characterizes the relative humidity.

[0059] Specifically, by preset conditions, it is determined whether sudden and abnormal displacement changes occur in the key parts of the structure, and a displacement jump event constraint is established to timely capture the instantaneous displacement anomalies that occur in the structure under the influence of loads or other external factors. The displacement data is collected in real time through displacement sensors (such as laser displacement sensors) installed at key nodes, and the displacement change amount of each key point within the set time is calculated , according to the engineering design documents, each key point has a predetermined displacement change threshold , which means that under normal working conditions, the displacement change of this key point should not exceed this value. If the actual displacement change amount of a certain key point exceeds 1.2 times the displacement change amount threshold specified by the design, the displacement jump flag variable is set to 1, indicating that a displacement jump event has been triggered. Otherwise, it remains 0. The advantage of this judgment method is that it takes into account the design margin and prevents misjudging abnormal events due to minor fluctuations or measurement errors. is the flag variable of the displacement jump event. Generally, it is defined that 1 indicates that a displacement jump event is detected, and 0 indicates that no displacement anomaly is detected, serving as a discrete signal for judging whether a displacement anomaly is triggered. For example, the design displacement change threshold for a certain key point is specified as 2.0 mm, and the actually monitored displacement change amount is 2.5 mm. 2.5 mm > 1.2 × 2.0 = 2.4 mm. At this time, is set to 1, indicating that a displacement jump has occurred at this key point.

[0060] The strain data of each key point is collected in real time through strain sensors (such as fiber Bragg grating sensors and strain gauges) deployed on the cable-supported dome structure. By calculating the difference in strain between adjacent monitoring points and dividing it by the distance between them, the strain gradient can be obtained. The strain gradient characterizes the rate of change of strain with position at the kth monitoring point, with the unit of microstrain per meter, reflecting the non-uniformity of the stress distribution in the local area of the structure. Being higher than the set threshold may indicate the existence of local damage, cracks, or stress concentration problems. At the same time, the relative humidity H of the current environment is monitored using an environmental sensor (such as a hygrometer). When the environmental humidity exceeds 80%, the risk of structural corrosion will be further aggravated, and there is a high humidity corrosion risk. For example, the strains recorded at two points separated by 2 meters are 250 and 370 , the calculated strain gradient is 60 , exceeding the normal range of 50 specified by the design, indicating the existence of abnormal strain gradient.

[0061] By establishing the constraints of strain gradient anomaly events and high humidity corrosion risk events, it can detect and judge in real time whether there are structural anomalies and potential corrosion risks in key parts, significantly improving the monitoring accuracy and overall safety of the cable-strut dome structure.

[0062] Furthermore, the present application further includes the following steps: When the cable force sudden drop event and the displacement sudden jump event are triggered simultaneously, and the correlation coefficient of the prestress change amount and the displacement change amount meets the preset correlation threshold, then the triggering result of the anchor slip intermediate event is established.

[0063] Specifically, according to the above event constraint formula for analysis, when the cable force sudden drop event and the displacement sudden jump event are triggered simultaneously, that is, the tension borne by the cable suddenly drops significantly, and at the same time, the displacement data of the key point shows a sudden jump that significantly exceeds the normal fluctuation range within a short period of time, the correlation analysis of the prestress change amount and the displacement change amount during this period will be carried out. For example, when the prestress drops from 100 kN to 70 kN within 1 minute, the cable force sudden drop event is triggered; when the designed displacement change threshold is specified as 2.0 mm and the actually monitored displacement change amount is 2.5 mm, the displacement sudden jump event is triggered.

[0064] By calculating the Pearson correlation coefficient, the correlation coefficient of the prestress change amount and the displacement change amount is obtained, that is, the linear correlation degree between the two change amounts, with a value range from -1 to 1. The higher the value, the more consistent the change trends of the two. Usually, the prestress change amounts and displacement change amounts of multiple monitoring cycles are collected to obtain two data sets, calculate the means of the two data sets, so as to calculate the covariance according to the existing formula, then calculate the standard deviation, and obtain the correlation coefficient based on the standard deviations of the two data sets.

[0065] A correlation coefficient threshold is preset according to historical data, test results or expert experience. If the actually calculated correlation coefficient is higher than this threshold, it indicates that there is a significant positive correlation between the prestress change and the displacement change. For example, if the correlation coefficient of the prestress change amount and the displacement change amount is 0.85 and the preset correlation threshold is 0.8, it is considered that there is a high positive correlation between the two changes, indicating that these two anomalies may be caused by anchor slip, and thus the triggering result of the anchor slip intermediate event is established.

[0066] Exemplarily, assume that the data for 5 monitoring periods are as follows: the change in prestress (kN) is 30, 28, 32, 31, 29, and the change in displacement (mm) is 2.5, 2.4, 2.6, 2.55, 2.45. The mean values of the two sets of data are calculated as 30 kN and 2.5 mm, and the standard deviation is obtained as 0.0791 mm, thereby obtaining a correlation coefficient of 1, indicating a perfect positive correlation between the change in prestress and the change in displacement in this set of data. By combining the two basic events of sudden drop in cable force and sudden jump in displacement, the correlation coefficient is further used to verify the coupling effect between the two, ensuring that the anchor slip is determined only when the abnormal changes are highly consistent, and reducing the misjudgment rate.

[0067] Furthermore, the present application further includes the following steps: When the strain gradient anomaly event and the high-humidity corrosion risk event are simultaneously triggered, and the causal correlation degree between the strain and the relative humidity is higher than the preset correlation threshold, a corrosion damage event trigger result is established.

[0068] Specifically, when the strain gradient anomaly event (such as the strain gradient between adjacent points exceeding 50 μϵ / m) and the high-humidity corrosion risk event (environmental humidity greater than 80%) are simultaneously triggered, the causal correlation degree between the strain and the relative humidity is calculated simultaneously. If it is higher than the preset threshold (such as 0.7), a corrosion damage event trigger result is established. After collecting the strain data and the corresponding humidity data over a period of time, statistical methods are used to evaluate the causal correlation degree between the strain change and the humidity change. By collecting data at multiple moments within a monitoring period, a linear regression model is established. The mean values of the strain and the relative humidity are calculated respectively, and then the regression coefficient slope and intercept are obtained by the least squares method. Substitute the slope and intercept of the regression coefficient into the linear regression model, calculate the predicted value and the residual (the difference between the actual value and the predicted value) of each data point, and then calculate the sum of squares. Calculate the ratio of the total sum of squares to the sum of squares of the residuals, and 1 minus this ratio is the coefficient of determination. Compare this coefficient of determination with the preset correlation threshold to determine whether the causal correlation degree is higher than the preset correlation threshold. When both the strain gradient anomaly event and the high-humidity corrosion risk event are detected simultaneously, and the causal correlation degree exceeds the preset correlation threshold, it is comprehensively determined that the structure may be threatened by corrosion damage at this time.

[0069] Exemplarily, assume that the following data are collected: the strain values are 200, 210, 220, 215, 225, and the relative humidity is 70, 75, 80, 85, 90. The mean values are calculated as 214 μϵ and 80%. The coefficient of determination obtained according to the above calculation process is 0.82. Assume that the preset correlation threshold is 0.8. If it is comprehensively determined that the structure may be threatened by corrosion damage at this time, a corrosion damage event trigger result is established. By simultaneously monitoring the strain gradient and the environmental humidity and performing causal correlation analysis, the risk area can be identified in advance before obvious corrosion damage occurs to the structure.

[0070] Furthermore, the present application further includes the following steps: Determine whether there is no sudden drop event of cable force. If there is no sudden drop event of cable force, calculate the time change rate of the prestress value. If the time change rate of the prestress value is lower than the preset rate threshold, establish the triggering result of the material relaxation event.

[0071] Specifically, the prestress data of each stay cable are collected in real time, and it is determined whether there is a sudden drop event of cable force by detecting through the established judgment constraints. If a sudden drop event of cable force is detected, it indicates that the structure may have an acute failure, and at this time, no further judgment of the material relaxation event is performed. When there is no sudden drop event of cable force, calculate the time change rate of the prestress value. The time change rate of the prestress value refers to the percentage change of the prestress value within a period of time. For example, if the prestress value drops by 0.5% per day, the time change rate is -0.5% / day. The negative sign indicates that the prestress value is decreasing.

[0072] If the change rate is lower than the preset rate threshold, it is considered that the material may be relaxed due to the long-term load effect, and then the triggering result of the material relaxation event is established. For example, the change rate of the prestress value with time is less than -0.5% / day, that is, the prestress value decreases at a rate of at least 0.5% per day over time. Only when there is no sudden drop event of cable force and the time change rate of the prestress value is less than -0.5% / day, the material relaxation event will be triggered. When the time change rate of the prestress value is lower than the preset threshold and no sudden drop event of cable force is triggered, it is determined that the material may be in a relaxed state, thus triggering the material relaxation event.

[0073] By determining whether there is a sudden drop event of cable force, it is ensured that the judgment of material relaxation can only be carried out when there is no acute failure, so as to effectively distinguish short-term anomalies and long-term relaxation phenomena, calculate the time change rate of the prestress, and capture in advance the risk of gradual relaxation of the material under long-term load, so as to provide timely warning for maintenance personnel and prevent the decline of structural performance.

[0074] S400: Use the loss identifier and the structural state vector to correct the finite element model, and use the corrected finite element model to perform buckling analysis and nonlinear time history analysis to establish the prediction result of the stability critical value.

[0075] Specifically, analyze the information provided by the loss identification, including the location, degree, and cause of the prestress loss, etc., to determine how these losses affect the suspended-dome structure. Correspond the real-time monitoring data in the structural state vector with the parameters in the finite element model, that is, feedback the actually monitored prestress loss situation on-site into the finite element model and adjust the model parameters. For example, if the prestress loss of a certain cable is monitored, then reduce the prestress value of this cable correspondingly in the model. Verify the accuracy of the model by comparing the predicted results of the corrected model with the actual monitoring data. If there is a large deviation, further adjust the model parameters until the model prediction is consistent with the actual monitoring data. For example, assume that the prestress of a certain cable in the original model is set to 1000 kN, but the monitoring data shows that it continuously drops to 950 kN and there are signs of loosening of the anchor. During the correction process, update the initial prestress value of this cable to 950 kN and reduce the stiffness parameter of the anchorage node to reflect the actual situation.

[0076] Conduct buckling analysis and nonlinear time history analysis using the corrected finite element model. Buckling analysis is a linear or nonlinear static analysis method used to determine the stability limit of a structure under compressive, wind, or other loadings, that is, to find the critical load when the structure buckles. Nonlinear time history analysis performs dynamic simulation by applying time-history loads (such as earthquake, wind loads), considering geometric nonlinear and material nonlinear effects, predicting the response of the structure under extreme conditions, and evaluating its stability and instability risk.

[0077] Buckling analysis is used to analyze the critical state of the suspended-dome structure before instability under compressive or other loadings. Usually, one or more critical load values and corresponding buckling modes are obtained, and these results are used to judge the stability margin of the structure. In the corrected finite element model, first apply static loads (such as compressive load, wind pressure, or snow load, etc.), gradually increase the load value until the model buckles. Solve the critical buckling load and corresponding buckling mode of the structure through finite element software. For example, in the corrected model, the original designed buckling load is 1500 kN. After being corrected by the real-time monitoring data, the buckling analysis shows that the critical buckling load of the structure drops to 1400 kN, indicating that the safety margin of the suspended-dome structure has decreased.

[0078] Nonlinear time history analysis is used to analyze factors such as geometric nonlinearity, material nonlinearity, and contact nonlinearity of the suspended-dome structure, simulate the response process of the structure under dynamic loads, and obtain the displacement, strain, stress evolution curves, and energy dissipation characteristics of the suspended-dome structure under dynamic actions. Apply dynamic loads (such as typical earthquake records or wind load time histories) to the modified finite element model, and simulate the response of the structure through nonlinear time history analysis. During the analysis process, factors such as geometric nonlinearity (large displacement, large deformation), material nonlinearity (steel yield, concrete cracking), and contact nonlinearity of the structure need to be considered. The analysis results will show the displacement, strain, and stress time history curves of the structure under dynamic loads, as well as dynamic characteristics such as energy dissipation. For example, the simulation results may show that under earthquake action, the maximum displacement of the structure reaches the design warning value, and the strain at a certain key node exceeds the safety limit, further verifying the influence of prestress loss on the overall stability in the model.

[0079] Comprehensively evaluate the critical load obtained from static buckling analysis and the dynamic response indicators obtained from nonlinear time history analysis to form the prediction results of the stability critical value, including the critical buckling load, the maximum allowable displacement or strain of the key nodes, the dynamic safety margin index, etc., indicating the instability state of the structure under extreme loads or long-term service conditions. By using the on-site monitoring data (structural state vector) and loss identification to correct the finite element model, the model can accurately reflect the current state of the suspended-dome structure, conduct buckling analysis and nonlinear time history analysis, calculate the critical state of the suspended-dome structure under extreme loads, integrate and form the prediction results of the stability critical value, and timely discover and address potential problems of the suspended-dome structure to ensure the safety and reliability of the structure.

[0080] S500: Generate a stability detection result based on the prediction results of the stability critical value.

[0081] Furthermore, S500 of the present application includes: Execute the prediction results of the stability critical value to conduct an abnormal level trigger analysis, establish the results of the abnormal level trigger analysis; conduct a time series balance fitting according to the results of the abnormal level trigger analysis, and generate a stability detection result through the results of the time series balance fitting.

[0082] Specifically, according to the prediction results of the stability critical value, an abnormal level trigger analysis is carried out. The prediction results of the stability critical value obtained by using the modified finite element model are compared with the design standard or safety margin. Warning rules are set. For example, if the predicted critical buckling load is lower than 90% of the design requirement, or the predicted maximum displacement exceeds 110% of the design warning value, an abnormality is triggered. According to the deviation degree of each index in the prediction results of the stability critical value, the abnormal state is divided into different levels. For example, according to the deviation degree between the predicted value and the design requirement, the structural state is divided into normal, slightly abnormal, moderately abnormal or severely abnormal. According to the determined abnormal level, the detailed information of each key point is recorded, including the trigger time, level, possible impacts, etc.

[0083] Collect the abnormal level data over a period of time, which reflects the abnormal degree of the structure in different monitoring periods. Fit the time series data of the abnormal level, and use methods such as the least squares method, exponential smoothing or autoregressive model to obtain a trend model. Judge whether the abnormal level tends to be stable, rising or falling through the results of time series balance fitting. If the fitting curve shows that the abnormal level shows a continuous upward trend, it indicates that the structural safety risk is intensifying; on the contrary, if the abnormal level tends to be stable or decreasing, it may indicate that the problem has been alleviated.

[0084] Time series balance fitting refers to the trend analysis and fitting of the time series data composed of the results of the abnormal level trigger analysis. The purpose is to smooth short-term fluctuations, eliminate the influence of accidental noise, extract the long-term trend, and predict the future development of the abnormal level. Preprocess the original abnormal level data, such as noise filtering and missing value filling, to ensure the data quality. Select a suitable time series model according to the change characteristics of the abnormal level data. For example, if the data shows an obvious linear trend, a linear regression model can be used; if the data has periodic and random fluctuation characteristics, an ARIMA model or exponential smoothing method can be selected. According to the preprocessed time series data of the abnormal level, use the least squares method or maximum likelihood estimation to determine the model parameters. Use statistical software to fit the data to the selected model to obtain the fitting curve and prediction error. The fitting result will show the trend of the abnormal level over time (such as rising, stable or falling). According to the fitting curve, balance the short-term noise fluctuations so that the model can better reflect the long-term trend, calculate the fitting residuals, and adjust the model parameters according to the residuals to ensure that the fitting curve follows the overall trend of the data as smoothly as possible. Analyze the slope and shape of the fitting curve to determine the long-term change trend of the abnormal level. If the fitting curve shows that the abnormal level is gradually rising, it indicates that the structural risk may be intensifying; if it shows a downward trend, it may indicate that the structural state is improving.

[0085] The results of the abnormal level trigger analysis are combined with the trend data of the time series balance fitting to obtain the final stability test results. According to the preset rules, if the abnormal level is high and shows an upward trend, a high-risk stability test result is generated; if the abnormal level is low and tends to be stable, the structural state is considered safe. The stability test results include abnormal level, stability trend, risk warning, etc., which clearly show the stability state of the suspensory dome structure at different time points. Through abnormal level trigger analysis and time series balance fitting, the abnormal changes and development trends of the structural state can be accurately identified, the stability test results can be generated, and the structural safety status can be intuitively displayed through loss identification.

[0086] In summary, the stability detection method of the suspension-dome structure based on prestressed analysis provided in this application has the following beneficial effects: After analyzing the key points of the susceptor-dome structure, a sensor group is configured at the corresponding key points, the sensor group includes a fiber grating sensor, a laser displacement sensor and a strain gauge, and a sensor network is established according to the layout position of the sensor group; after time alignment of the monitoring data of the sensor network, multi-source data fusion is performed on the cable force, displacement and strain data in the monitoring data to establish a structural state vector; real-time prestress loss analysis is performed according to the structural state vector, and the cause of loss is traced back according to the prestress loss analysis result to establish a loss mark; the finite element model is corrected using the loss mark and the structural state vector, and the corrected finite element model is used to perform buckling analysis and nonlinear time history analysis to establish a stability critical value prediction result; and a stability detection result is generated based on the stability critical value prediction result. That is to say, by configuring mechanical sensors at the key points of the suspensory dome structure for real-time monitoring, the structural state vector is established, and prestress analysis is performed at the same time. The cause of the prestress change is identified, and a loss mark is established. The finite element model is corrected according to the structural state vector and the loss mark, the idealized assumptions in the model are reduced, and the model is closer to the actual state of the structure. The stability test is performed through the corrected finite element model, thereby improving the accuracy and reliability of the stability test results of the suspensory dome structure.

[0087] Embodiment 2, based on the same inventive concept as the method for detecting the stability of a suspensory dome structure by prestress analysis in the aforementioned embodiment 1, the present application also provides a system for detecting the stability of a suspensory dome structure by prestress analysis, please refer to the attached Figure 2 The stability detection system of the suspensory dome structure of the prestressed analysis comprises: The sensor layout module 11 is used to configure a sensor group at corresponding key points after analyzing the key points of the cable-supported dome structure. The sensor group includes fiber Bragg grating sensors, laser displacement sensors, and strain gauges, and a sensor network is established according to the layout positions of the sensor group. The data fusion module 12 is used to align the monitoring data of the sensor network in time, and then perform multi-source data fusion on the cable force, displacement, and strain data in the monitoring data to establish a structural state vector. The loss analysis module 13 is used to perform real-time prestress loss analysis based on the structural state vector, trace back the loss causes according to the prestress loss analysis results, and establish a loss identifier. The critical value prediction module 14 is used to correct the finite element model by using the loss identifier and the structural state vector, and perform buckling analysis and nonlinear time history analysis by using the corrected finite element model to establish a stability critical value prediction result. The detection result generation module 15 is used to generate a stability detection result based on the stability critical value prediction result.

[0088] Further, the loss analysis module 13 in the stability detection system of the cable-supported dome structure for prestress analysis is further used to: perform environmental monitoring on the cable-supported dome structure to establish an environmental monitoring data set; after mapping and associating the environmental monitoring data set with the structural state vector, extract time domain features, frequency domain features, and environmental coupling features according to the mapping and association results; optimize the basic event threshold of the dynamic fault tree by using the environmental monitoring data set, and then establish a feature vector according to the time domain features, the frequency domain features, and the environmental coupling features, and use the feature vector as input data to perform event trigger analysis through the optimized dynamic fault tree; after performing spatio-temporal correlation verification on the triggered events, perform confidence evaluation, and generate a loss identifier by using the confidence evaluation result.

[0089] Further, the loss analysis module 13 in the stability detection system of the cable-supported dome structure for prestress analysis is further used to: establish basic event determination constraints, where the basic event determination constraints include cable force sudden drop event constraints, displacement sudden jump event constraints, strain gradient anomaly event constraints, and high humidity corrosion risk event constraints; perform event trigger analysis on the feature vector through the basic event determination constraints to establish an event trigger analysis result; perform intermediate event logic calculation by using the event trigger analysis result to establish an intermediate event trigger result, where the intermediate event trigger result includes anchor slip event, corrosion damage event, and material relaxation event.

[0090] Further, the loss analysis module 13 in the stability detection system of the cable-supported dome structure for prestress analysis is further used to: establish cable force sudden drop event constraints as follows: ; where , representing the cable force change rate, represents the The prestress variation of the root cable Characterize the sampling time interval of the monitoring data Characterize the threshold of cable force loss rate dependent on temperature , where Characterize the environmental temperature value

[0091] Furthermore, the loss analysis module 13 in the stability detection system of the cable-supported dome structure for prestress analysis is further configured to: establish a displacement sudden jump event constraint as follows: ; where A flag variable characterizing the displacement sudden jump event Characterize the displacement variation of the th key point actually monitored Characterize the displacement variation threshold of the th key point specified by the design; establish a strain gradient anomaly event constraint and a high humidity corrosion risk event constraint as follows: , ; where Characterize the strain gradient, representing the rate of change of strain with position at the th monitoring point Characterize the relative humidity

[0092] Furthermore, the loss analysis module 13 in the stability detection system of the cable-supported dome structure for prestress analysis is further configured to: when the cable force sudden drop event and the displacement sudden jump event are both triggered, and the correlation coefficient between the prestress variation and the displacement variation satisfies a preset correlation threshold, establish a trigger result of the anchor slip intermediate event

[0093] Furthermore, the loss analysis module 13 in the stability detection system of the cable-supported dome structure for prestress analysis is further configured to: when the strain gradient anomaly event and the high humidity corrosion risk event are both triggered, and the causal association degree between the strain and the relative humidity is higher than a preset association threshold, establish a trigger result of the corrosion damage event

[0094] Furthermore, the loss analysis module 13 in the stability detection system of the cable-supported dome structure for prestress analysis is further configured to: determine whether there is no cable force sudden drop event. If there is no cable force sudden drop event, calculate the time change rate of the prestress value; if the time change rate of the prestress value is lower than a preset rate threshold, establish a trigger result of the material relaxation event

[0095] Further, the detection result generation module 15 in the stability detection system of the prestressed cable-supported dome structure for prestress analysis is further configured to: perform abnormal level trigger analysis on the stability critical value prediction result to establish an abnormal level trigger analysis result; perform time series balance fitting according to the abnormal level trigger analysis result, and generate a stability detection result through the time series balance fitting result.

[0096] The various embodiments in this specification are described in a progressive manner, and the key point of each embodiment is the difference from other embodiments. The Figure 1 The prestress analysis method and specific examples of the cable-supported dome structure stability detection in the first embodiment are equally applicable to the prestress analysis cable-supported dome structure stability detection system in this embodiment. Through the detailed description of the prestress analysis method of the cable-supported dome structure stability detection above, those skilled in the art can clearly know the prestress analysis cable-supported dome structure stability detection system in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0097] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0098] Obviously, for those skilled in the art, without departing from the principle of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the protection scope of the present application.

Claims

1. A stability detection method for a prestressed cable-supported dome structure in prestress analysis, characterized in that include: After analyzing the key points of the suspensory dome structure, a sensor group is configured at the corresponding key points, wherein the sensor group includes a fiber grating sensor, a laser displacement sensor and a strain gauge, and a sensor network is established according to the layout positions of the sensor group; After the monitoring data of the sensor network are time-aligned, the cable force, displacement and strain data in the monitoring data are multi-source fused to establish a structural state vector; Performing real-time prestress loss analysis according to the structural state vector, and tracing back the cause of the loss according to the prestress loss analysis result to establish a loss mark; Using the loss identifier and the structural state vector to modify the finite element model, and using the modified finite element model to perform buckling analysis and nonlinear time history analysis to establish a critical stability value prediction result; A stability detection result is generated based on the stability critical value prediction result.

2. The stability detection method of the prestressed analysis cable-supported dome structure according to claim 1, characterized in that, The real-time prestress loss analysis is performed according to the structural state vector, and the cause of the loss is traced back according to the prestress loss analysis result to establish a loss mark, including: Perform environmental monitoring of the suspension-dome structure and establish an environmental monitoring data set; After mapping and associating the environmental monitoring data set with the structural state vector, extracting time domain features, frequency domain features and environmental coupling features according to the mapping association results; After optimizing the basic event threshold of the dynamic fault tree using the environmental monitoring data set, a feature vector is established according to the time domain feature, the frequency domain feature and the environmental coupling feature, and the feature vector is used as input data to perform event triggering analysis through the optimized dynamic fault tree; After verifying the temporal and spatial correlation of the triggered events, a confidence assessment is performed and a loss mark is generated using the confidence assessment result.

3. The stability detection method for prestressed analysis of a cable-supported dome structure according to claim 2, characterized in that, The event triggering analysis is performed through the optimized dynamic fault tree, including: Establishing basic event determination constraints, wherein the basic event determination constraints include cable force sudden drop event constraints, displacement sudden jump event constraints, strain gradient abnormal event constraints, and high humidity corrosion risk event constraints; Perform event trigger analysis on the feature vector through the basic event determination constraint to establish an event trigger analysis result; The event trigger analysis results are used to perform intermediate event logic calculations to establish intermediate event trigger results, which include anchor slip events, corrosion damage events, and material relaxation events.

4. The stability detection method of the prestressed cable-supported dome structure for prestress analysis according to claim 3, characterized in that The establishing of basic event determination constraints includes: Establish the cable force drop event constraint as follows: ； Among them, , representing the cable force change rate, representing the th change in the prestress of the cable, representing the sampling time interval of the monitoring data, representing the temperature-dependent cable force loss rate threshold, , where represents the ambient temperature value.

5. The stability detection method of the prestressed analysis of the cable-supported dome structure according to claim 4, characterized in that, The establishing of basic event determination constraints further includes: Create a displacement jump event constraint as follows: ； Among them, a flag variable characterizing the displacement jump event, characterizing the displacement change of the th key point actually monitored, characterizing the displacement change threshold of the th key point specified by the design; The strain gradient anomaly event constraints and high humidity corrosion risk event constraints are established as follows: ， ； Among them, characterizes the strain gradient, indicating the change rate of the strain with respect to position at the th monitoring point, and characterizes the relative humidity.

6. The stability detection method for prestressed analysis of a cable-supported dome structure according to claim 5, characterized in that The method of performing intermediate event logic calculation using the event trigger analysis result to establish the intermediate event trigger result includes: When the cable force drop event and the displacement jump event are triggered at the same time, and the correlation coefficient between the prestress change and the displacement change meets the preset correlation threshold, the anchor slip intermediate event trigger result is established.

7. The stability detection method of the prestressed cable-supported dome structure for prestress analysis according to claim 6, characterized in that, The method of performing intermediate event logic calculation using the event trigger analysis result to establish the intermediate event trigger result also includes: When a strain gradient anomaly event and a high humidity corrosion risk event are triggered simultaneously, and the causal correlation degree between the strain and the relative humidity is higher than the preset correlation threshold, a corrosion damage event trigger result is established.

8. The stability detection method of the prestressed cable-supported dome structure for prestress analysis according to claim 7, characterized in that, The step of using the event trigger analysis result to perform intermediate event logic calculation and establish an intermediate event trigger result further includes: Determine whether there is no sudden drop in cable force event. If there is no sudden drop in cable force event, calculate the time change rate of the prestress value; If the time change rate of the prestress value is lower than the preset rate threshold, establish a material relaxation event trigger result.

9. The stability detection method of the prestressed cable-supported dome structure for prestress analysis according to claim 1, characterized in that, The step of generating a stability detection result based on the stability critical value prediction result includes: Execute the stability critical value prediction result to perform abnormal level trigger analysis and establish an abnormal level trigger analysis result; According to the abnormal level trigger analysis result, perform time series balance fitting, and generate a stability detection result through the time series balance fitting result.

10. A stability detection system for a prestressed cable-supported dome structure in prestress analysis, characterized in that, Steps of a stability detection method for a cable-supported dome structure for implementing the prestress analysis according to any one of claims 1 to 9. The stability detection system for the cable-supported dome structure of the prestress analysis includes: A sensor layout module, configured to, after performing key point analysis on the cable-supported dome structure, configure a sensor group at corresponding key points. The sensor group includes fiber Bragg grating sensors, laser displacement sensors, and strain gauges, and establish a sensor network according to the layout positions of the sensor group; A data fusion module, configured to align the monitoring data of the sensor network in time, and perform multi-source data fusion on the cable force, displacement, and strain data in the monitoring data to establish a structural state vector; A loss analysis module, configured to perform real-time prestress loss analysis according to the structural state vector, and trace back the loss cause according to the prestress loss analysis result to establish a loss identifier; A critical value prediction module, configured to use the loss identifier and the structural state vector to correct a finite element model, and perform buckling analysis and nonlinear time history analysis using the corrected finite element model to establish a stability critical value prediction result; A detection result generation module, configured to generate a stability detection result based on the stability critical value prediction result.

Images