Hydropower station dam safety monitoring index tolerance analysis method
By constructing a non-probabilistic target reliability analysis model and setting dynamic limits, the problem of insufficient uncertainty identification in existing hydropower station dam safety monitoring was solved, thereby improving the sensitivity and early warning capability of dam safety monitoring.
Patent Information
- Application Number
- CN202511223468.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-29
- Publication Date
- 2026-01-20
Smart Images
Figure CN121365537A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of hydraulic engineering safety monitoring, and particularly relates to a safety monitoring index tolerance limit analysis method for a dam of a hydropower station. BACKGROUND
[0002] As important basic water conservancy facilities, the long-term stable operation of a dam of a hydropower station is directly related to the safety of people's lives and property and the energy supply guarantee of a region. In order to ensure the safe operation of the dam structure in the whole life cycle, multiple types of monitoring equipment are usually arranged to continuously monitor the operating state of the key parts of the dam, and the monitoring content mainly includes dam body deformation, seepage pressure, concrete or rock stress and strain, temperature change, water level and the like. By collecting and analyzing the above monitoring data, the structural behavior characteristics of the dam can be identified, the stability thereof can be evaluated, and data support can be provided for operation and maintenance decision, risk early warning and dispatching management.
[0003] In the prior art, the safety monitoring method commonly used in engineering practice for the dam of the hydropower station depends on experience or a statistical analysis method to set a warning threshold, such as regression analysis through historical monitoring data, setting a fixed deviation band, using a multivariate linear model for stability evaluation and the like. These methods can identify abnormal change trends of the dam to some extent, but have obvious limitations. On the one hand, the experience-based limiting method cannot comprehensively reflect the uncertainty faced by the dam in the operation process, especially in the risk evolution process of extreme working conditions, material degradation or slope instability; on the other hand, the traditional reliability analysis method mostly uses probability modeling, and a large number of samples are required to support the statistical distribution assumption, but in a complex hydraulic structure, it is often difficult to obtain sufficient data samples to support, which easily leads to large deviation of the analysis result and untimely risk identification.
[0004] Therefore, there is an urgent need for a new safety monitoring method for the dam of the hydropower station to improve the early warning capability of dam safety monitoring. SUMMARY
[0005] In view of at least one of the above technical problems, the present application provides a safety monitoring index tolerance limit analysis method for a dam of a hydropower station, which uses a non-probabilistic target reliability analysis model and sets an initial tolerance limit of a monitoring index to enhance the early warning capability of dam safety monitoring.
[0006] According to a first aspect of the present application, a safety monitoring index tolerance limit analysis method for a dam of a hydropower station is provided, comprising the following steps:
[0007] obtaining historical monitoring data of the dam of the hydropower station, the historical monitoring data including temperature data, deformation data, seepage data and stress and strain data;
[0008] The historical monitoring data is preprocessed by denoising, smoothing and normalization, and based on the processed historical monitoring data, a monitoring index law of dam body changing with service time is analyzed to identify a dam safety monitoring risk factor, the risk factor is determined by establishing a dam foundation surface instability function function, and the function function is established according to the anti-sliding force analysis rule;
[0009] A non-probabilistic target reliability analysis model is established, which is used to describe the uncertain influencing factors in dam safety monitoring, and is verified to be compatible with the probabilistic model, and the conversion relationship between the non-probabilistic reliability index and the probabilistic reliability index can be established;
[0010] The non-probabilistic reliability index of the hydropower station dam is calculated by the non-probabilistic target reliability analysis model, which includes obtaining the upper and lower limits of the structure function function, and taking the ratio of the function function mean value and the deviation as the structure reliability index;
[0011] Based on the non-probabilistic reliability index and the identified risk factor, the initial deviation range of the hydropower station dam monitoring index is determined;
[0012] Combined with the non-probabilistic target reliability analysis model, the deviation range is dynamically adjusted to adapt to different service periods and operating conditions of the hydropower station dam, and a deviation calculation method based on the actual monitoring data of the hydropower station dam and the non-probabilistic reliability analysis is formed.
[0013] In some embodiments of the present application, the identification of the dam safety monitoring risk factor further comprises using detection means to test the analyzed dam risk points, and recalculating and reviewing the strength of the dam body and the bank slope.
[0014] In some embodiments of the present application, after analyzing the monitoring index law of dam body changing with service time and identifying the dam safety monitoring risk factor, a comprehensive evaluation index system of hydropower station dam structure safety is established, which divides the hydropower station dam into three first-level evaluation index parts of dam body and dam foundation, near-dam area and environmental quantity monitoring, and establishes respective second-level evaluation indexes for each first-level evaluation index part.
[0015] In some embodiments of the present application, the second-level evaluation indexes of the dam body and the dam foundation include dam body and dam foundation deformation evaluation results, seepage evaluation results, internal observation evaluation results and patrol evaluation results; the second-level evaluation indexes of the near-dam area include near-dam area deformation evaluation results, groundwater evaluation results and patrol evaluation results; and the second-level evaluation indexes of the environmental quantity monitoring include environmental quantity monitoring quantities.
[0016] In some embodiments of the present application, the establishment of the non-probabilistic target reliability analysis model comprises:
[0017] research and analyze uncertain influence factors of a gravity dam;
[0018] determine a non-probabilistic model describing the uncertain influence factors of the gravity dam, the non-probabilistic model being used for quantifying the uncertain factors;
[0019] verify compatibility of the selected non-probabilistic model and a probabilistic model.
[0020] In some embodiments of the present application, the non-probabilistic reliability index of the hydropower station dam is calculated by analyzing the structure performance function by a response surface method, the response surface method being a response surface finite element method based on a quadratic polynomial, the response surface finite element method comprising the following steps:
[0021] a plurality of uncertain variables affecting the safety of the dam structure are selected, including dam material performance parameters, dam foundation rock and soil physical and mechanical parameters, water level variation parameters, temperature parameters, and operation state characteristic parameters extracted from historical monitoring data;
[0022] the mean values of the above variables in their uncertainty intervals are determined as the center points of the response surface;
[0023] around the center points, intermediate perturbation points are set in the upper and lower intervals of each variable respectively to generate a plurality of variable combinations;
[0024] the working conditions under each variable combination are simulated by a finite element model to obtain corresponding structure response results;
[0025] the obtained response results are constructed into a mathematical equation, and the least square method is used to solve the equation to determine the values of the undetermined coefficients in the structure performance function, and an initial response surface function is established.
[0026] In some embodiments of the present application, the structure performance function construction process comprises fitting the function multiple times by multiple rounds of variable perturbation point combinations and response calculations, and setting different perturbation step lengths in different fitting stages.
[0027] In some embodiments of the present application, the calculation of the non-probabilistic reliability index further comprises applying an improved global optimal solution method: solving a plurality of local minimum points of the structure limit state function, performing dimension reduction and variable range determination, converting into a unary function and solving the real roots, and taking the absolute value of the real roots and the minimum value of the roots of the limit state function as the non-probabilistic reliability index.
[0028] In some embodiments of the present application, after the response surface method is used to analyze the structural function function, whether the calculation result converges is determined by iterative calculation of the response surface function, if the result converges, the result is taken as the final calculation result, if the result does not converge, linear interpolation is performed and iterative calculation is continued until the result converges.
[0029] In some embodiments of the present application, the determination of the initial limit range of the monitoring index of the hydropower station dam specifically comprises: determining a reasonable limit range of the monitoring index according to the structural characteristics of the dam, risk factors and reliability analysis results, and the reliability analysis results are obtained based on non-probabilistic reliability analysis.
[0030] The present application has the advantages that: by constructing a non-probabilistic target reliability analysis model, the complex uncertain influencing factors faced by the dam in the service process are described, and the dependence on a large amount of statistical data is avoided. At the same time, the initial limit of the monitoring index is set combining the historical monitoring data, risk factor identification and structural function analysis results, and the dynamic adjustment of the limit is realized combining the service stage and operating conditions. Compared with the prior art, the sensitivity and early warning ability of dam safety monitoring are enhanced. BRIEF DESCRIPTION OF DRAWINGS
[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.
[0032] Fig. 1 The step flow chart of the hydropower station dam safety monitoring index limit analysis method in the embodiments of the present application is shown in the figure.
[0033] Fig. 2 The step flow chart of establishing a non-probabilistic target reliability analysis model in the embodiments of the present application is shown in the figure.
[0034] Fig. 3 The step flow chart of the response surface finite element method in the embodiments of the present application is shown in the figure. DETAILED DESCRIPTION
[0035] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments.
[0036] It should be understood that when an element as a layer, region or plate is referred to as being "on" another element, it can be directly on the other element or intervening elements can also be present. In addition, it should be understood that when an element is referred to as being "connected", "coupled", "attached" or "linked" to another element, it can be directly connected, coupled, attached or linked to the other element or intervening elements can also be present. The terms "vertical", "horizontal", "left", "right" and similar expressions as used herein are for illustrative purposes only and are not intended to be limiting.
[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for describing particular embodiments only and is not intended to be limiting of the application. The use herein of the terms "and / or" includes a set of one or more associated listed items.
[0038] As Figs. 1 to 3 The dam safety monitoring index tolerance limit analysis method of a hydropower station shown in the figure comprises the following steps:
[0039] Obtain historical monitoring data of the dam of the hydropower station, the historical monitoring data including temperature data, deformation data, seepage data and stress-strain data;
[0040] Preprocess the historical monitoring data by denoising, smoothing and normalizing, and based on the processed historical monitoring data, analyze the monitoring index law of the dam body changing with service time to identify dam safety monitoring risk factors, the risk factors being determined by establishing a dam foundation surface instability function function, the function function being established according to the anti-sliding force analysis rule;
[0041] Establish a non-probabilistic target reliability analysis model, the non-probabilistic target reliability analysis model being used to describe uncertain influencing factors in dam safety monitoring, and being verified to be compatible with a probabilistic model and capable of establishing a conversion relationship between non-probabilistic reliability indexes and probabilistic reliability indexes;
[0042] Calculate non-probabilistic reliability indexes of the dam of the hydropower station through the non-probabilistic target reliability analysis model, the calculation including obtaining upper and lower limits of the structure function function, and taking the ratio of the mean value of the function function to the deviation as the structure reliability index;
[0043] Based on the non-probabilistic reliability indexes and the identified risk factors, determine the initial tolerance limit range of the monitoring indexes of the dam of the hydropower station;
[0044] In combination with the non-probabilistic target reliability analysis model, dynamically adjust the tolerance limit range to adapt to different service periods and operating conditions of the dam of the hydropower station, and form a tolerance limit calculation method based on actual monitoring data of the dam of the hydropower station and non-probabilistic reliability analysis.
[0045] As Fig. 1The dam historical monitoring data are obtained from the dam monitoring system of the hydropower station. These data should include temperature, deformation, seepage, stress and strain, and other monitoring indicators. The data can be collected by various sensors installed at different parts of the dam. The collected historical data often have noise and incompleteness, so the data must be preprocessed, such as denoising, smoothing, and normalization. It should be noted that wavelet transform, Kalman filter, and other methods can be used for data denoising, sliding average method for smoothing, and minimum-maximum normalization method for normalization. Based on the preprocessed data, the safety monitoring risk factors of the dam are identified by analyzing the monitoring index law of the dam with the service time. For this purpose, regression analysis, time series analysis, and other data analysis methods can be used to explore the trend of the dam deformation, seepage, and other monitoring indicators. By establishing a function function based on the anti-sliding force analysis of the dam foundation surface instability, the potential risk factors affecting the safety of the dam are identified. The function function is established according to the anti-sliding force analysis, which can be referred to the "Concrete Gravity Dam Design Specification" to select the dam foundation surface instability function function. The safety monitoring risk factors include material degradation, extreme working conditions, etc., which are input into the subsequent analysis. In order to consider the uncertainty factors such as material property uncertainty and environmental factors, a non-probabilistic target reliability analysis model needs to be established. Here, fuzzy mathematics, interval analysis, and other non-probabilistic theories can be used to describe the uncertainty in the monitoring indicators. In order to ensure the effectiveness and accuracy of the non-probabilistic reliability analysis model, its compatibility with the traditional probabilistic model needs to be verified. Through simulation experiments, the calculation results of the non-probabilistic target reliability analysis model and the probabilistic model are compared to verify whether the reliability evaluation results of the two models are consistent when dealing with the same uncertainty factors. In practical applications, the non-probabilistic reliability analysis results need to be compared and converted with the traditional probabilistic reliability indicators, and the conversion relationship between the non-probabilistic reliability indicators and the probabilistic reliability indicators needs to be established for application in the existing safety evaluation system. Specifically, by comparing the ratio of the mean value to the deviation of the dam function function, the non-probabilistic reliability indicators can be converted into the corresponding probabilistic reliability indicators. Compared with the traditional probabilistic model, the non-probabilistic target reliability analysis model can better handle the uncertainty and fuzziness in the dam monitoring data. After establishing the non-probabilistic reliability analysis model, the non-probabilistic reliability indicators of the hydropower station dam are calculated. In the calculation process, the upper and lower limits of the structure function function are solved, and the reliability indicators of the structure are determined by the ratio of the mean value to the deviation of the function function. Based on the above non-probabilistic reliability indicator analysis results and the identified risk factors, the initial deviation range of the dam monitoring indicators can be determined. This deviation range reflects the fluctuation range of the monitoring data during the normal operation of the dam, which is used to determine whether there is an anomaly. With the change of the dam service period, the monitoring data may be affected by different operating conditions, so the deviation range also needs to be dynamically adjusted.In combination with the non-probabilistic reliability analysis model, the tolerance range is dynamically updated through periodic analysis of the latest monitoring data and risk factors. Ultimately, through the above steps, in combination with the actual monitoring data of the dam and the non-probabilistic target reliability analysis model, a tolerance calculation method based on the actual monitoring data of the hydropower station dam and the non-probabilistic reliability analysis is formed. In the embodiment, not only the historical data of the dam are considered, but also the changes of the uncertainty factors and the operating conditions, so that the operating state of the dam is more accurate.
[0046] In the above embodiment, the present application describes the complex uncertain influencing factors faced by the dam in the service process by constructing a non-probabilistic target reliability analysis model, avoiding the dependence on a large amount of statistical data. At the same time, in combination with the historical monitoring data, the risk factor identification and the analysis results of the structure function, the initial tolerance of the monitoring index is set, and the dynamic adjustment of the tolerance is realized in combination with the service stage and the operating conditions. Compared with the prior art, the sensitivity and early warning ability of the dam safety monitoring are enhanced.
[0047] In the embodiment of the present application, identifying the dam safety monitoring risk factor further comprises using detection means to test the analyzed dam risk points, and recalculating and reviewing the strength of the dam as a whole and the reservoir bank slope. Through the previous data analysis, potential risk points are identified, and the risk points usually appear abnormal deformation, seepage or stress and strain detection data fluctuations at the dam body, dam foundation, reservoir bank slope and the like. It should be noted that the detection means of these monitoring points can be realized by means of field measurement, sensor detection, remote sensing technology and the like. On the basis of the dam monitoring data and the field detection results, the strength of the dam as a whole and the reservoir bank slope needs to be recalculated and reviewed to evaluate its stability. In the evaluation of the overall strength of the dam, in combination with the actual stress and strain data, material properties, numerical methods such as finite element analysis are used to simulate the stress distribution and possible failure mode of the dam under different working conditions, and the structural strength of the dam is evaluated. In the strength evaluation of the reservoir bank slope, it is evaluated whether there is a potential landslide or instability risk, considering the shear strength of the soil or rock mass, pore water pressure, load distribution and other factors, and the stability of the slope is recalculated. According to the results of recalculation and review, the safety state of the dam and the reservoir bank slope is evaluated. If it is found that the strength of the dam structure or the reservoir bank slope is lower than the safety standard, or the monitoring data shows that there is a potential instability risk, appropriate engineering measures should be taken in time. In the embodiment, by periodically performing the above detection, calculation and evaluation, the safety of the dam under different working conditions and service stages is ensured.
[0048] In an embodiment of the present application, after analyzing the regularity of monitoring indexes of dam body changing with service time and identifying dam safety monitoring risk factors, a comprehensive evaluation index system for the safety of the dam structure of the hydropower station is established. The system divides the dam of the hydropower station into three first-level evaluation index parts of dam body and dam foundation, near-dam area and environmental quantity monitoring, and establishes respective second-level evaluation indexes for each first-level evaluation index part. According to different structure and function areas of the dam, the dam of the hydropower station is divided into three first-level evaluation index parts, which are: dam body and dam foundation, near-dam area and environmental quantity monitoring. The dam body and dam foundation part mainly focuses on the overall structure of the dam, including the dam body itself and its foundation, such as soil or rock layer, etc. The near-dam area part refers to the environmental area around the dam, including the reservoir bank around the dam, the dam and its supporting structure, the settlement condition, the seepage condition, etc. The environmental quantity monitoring part involves factors related to environmental changes during the operation of the dam, such as water level, temperature, precipitation, earthquake, etc. natural factors. Respective second-level evaluation indexes are established for the above three first-level evaluation index parts, and each second-level index represents a specific aspect in the first-level evaluation index.
[0049] Further, the second-level evaluation indexes of the dam body and dam foundation include dam body and dam foundation deformation evaluation results, seepage evaluation results, internal observation evaluation results and patrol evaluation results. By installing displacement sensors, settlement gauges and other equipment, the deformation of the dam at different parts of the dam body is measured regularly. These deformation monitoring data can effectively evaluate the possible settlement, cracking or deformation phenomena of the dam during long-term operation. By laying seepage meters, pressure gauges and other equipment, the seepage conditions inside the dam and around the dam foundation are monitored. Observation includes the inspection of the internal structure of the dam, especially the observation of the core part of the dam. For example, by using ultrasonic, acoustic and other technical means to check the cracks, cavities and other internal structures of the dam body, the integrity of the internal structure is ensured. The patrol content includes the surface cracks, settlement, equipment operation condition, etc. of the dam body. The second-level evaluation indexes of the near-dam area include near-dam area deformation evaluation results, groundwater evaluation results and patrol evaluation results. Near-dam area deformation refers to the deformation of soil or rock around the dam caused by external force or environmental change. By installing ground deformation monitoring equipment, the settlement, displacement, cracking and other conditions around the dam area are monitored in real time. By installing groundwater level gauges and seepage monitoring instruments, the changes of groundwater in the near-dam area are evaluated, especially in the soil or rock mass near the dam, the fluctuation of groundwater level may cause soil softening or rock structure loosening, thereby affecting the safety of the dam. Similar to the dam body and dam foundation part, the near-dam area also needs regular patrol, including slope stability, geological changes, seepage path, etc. The second-level evaluation indexes of the environmental quantity monitoring include environmental quantity monitoring. Environmental quantity monitoring includes water level monitoring, temperature monitoring, precipitation monitoring and earthquake monitoring. In this embodiment, the second-level evaluation index system conducts comprehensive safety monitoring and evaluation of the dam, improving the accuracy of dam monitoring.
[0050] In embodiments of the present invention, establishing a non-probabilistic target reliability analysis model includes:
[0051] Study and analyze the uncertain influencing factors of gravity dams;
[0052] A nonprobabilistic model is determined to describe the uncertain influencing factors of gravity dams. The nonprobabilistic model is used to quantify the uncertain factors.
[0053] Verify the compatibility between the selected non-probabilistic model and the probabilistic model.
[0054] like Fig. 2 As shown, the uncertain influencing factors of gravity dams include material properties, load conditions, geometric parameters, hydrological and environmental conditions, and construction and aging processes. The variations of these factors are often difficult to describe accurately using traditional probability distributions; therefore, non-probabilistic methods are needed for analysis. Non-probabilistic models can include convex set models, interval analysis models, and fuzzy set theory. A suitable non-probabilistic model is selected to model the range and volatility of the uncertain factors, and these uncertain factors are incorporated into the dam reliability analysis. To ensure the validity of the non-probabilistic reliability analysis results, the compatibility between the selected non-probabilistic model and the traditional probabilistic model needs to be verified. The verification process includes model comparison experiments, sensitivity analysis, error analysis, and data verification. In the model comparison experiments, appropriate sample data is selected, and calculations are performed using both the non-probabilistic model and the traditional probabilistic model, comparing the results. The focus is on verifying whether the reliability indices of the non-probabilistic model and the probabilistic model are similar within the range of uncertain factor variations. In the sensitivity analysis, sensitivity analysis is performed on different uncertain factors to assess their impact on the overall reliability of the dam. Error analysis compares the calculation results of the two models to check whether the non-probabilistic model's results can tolerate a certain range of error. If the error is within an acceptable range, it proves that the non-probabilistic model and the probabilistic model are compatible. Finally, data validation is performed. The calculation results are compared with field monitoring data and historical data to verify the applicability and predictive accuracy of the non-probabilistic model in actual engineering. In this embodiment, it is ensured that the selected non-probabilistic model can be effectively compared and integrated with the probabilistic model, thereby providing reliable evaluation results in environments with high uncertainty.
[0055] In an embodiment of the present invention, the nonprobabilistic reliability index of the hydropower station dam is calculated by structural function analysis using the response surface methodology. The response surface methodology is a response surface finite element method based on quadratic polynomials, and the response surface finite element method includes the following steps:
[0056] Several uncertain variables affecting the structural safety of the dam were selected, including dam material performance parameters, dam foundation soil and rock physical and mechanical parameters, water level change parameters, temperature parameters, and operational status characteristic parameters extracted from historical monitoring data.
[0057] Determine the mean of the above variables within their uncertainty interval as the center point for constructing the response surface;
[0058] Around the center point, intermediate perturbation points are set in the upper and lower intervals of each variable to generate multiple variable combinations;
[0059] The working conditions under various combinations of variables are simulated using a finite element model to obtain the corresponding structural response results;
[0060] The obtained response results are used to construct mathematical equations, and the least squares method is used to solve the equations to determine the values of the undetermined coefficients in the structure-function and establish the initial response surface function.
[0061] like Fig. 3 As shown, in the step of determining the mean of the uncertain variables and setting the center point, the mean of the uncertain variables within the uncertainty interval is determined, and this mean is used as the center point for constructing the response surface function. The mean can be determined through experimental data; based on historical monitoring data of the dam, the historical average value of the required variables is calculated as their mean. When historical data is insufficient, the mean of the required variables can be determined based on work experience or expert knowledge. Around the center point, intermediate perturbation points are set within the upper and lower intervals of each variable, forming multiple variable combinations. Based on the physical characteristics, historical data, or expert estimates of each variable, the uncertainty interval of each variable is determined; within the uncertainty interval of each variable, several perturbation points are set; through combinations of different variables, a series of simulated operating conditions are generated as inputs. Subsequently, using the generated multiple variable combinations, the dam structure under different operating conditions is simulated and calculated using a finite element model. Based on the actual structure, geometric dimensions, and material properties of the dam, a finite element analysis model of the dam is established; the perturbation points of each variable are adjusted to simulate the dam's response under different operating conditions; the finite element model will provide structural response data for each operating condition. In the solution process, based on the results of finite element analysis, a mathematical equation describing the dam structure response is constructed. A mathematical model is established between the influence of variables and the structural response, based on a quadratic polynomial form. The mathematical equation is fitted using the least squares method, and the undetermined coefficients in the equation are solved by minimizing the fitting error. The obtained coefficients are used to establish an initial response surface function. Using the response surface method, the behavior of dam structures under complex conditions can be predicted efficiently.
[0062] In the embodiment of the present application, the structural function function construction process includes multiple fitting of the function through multiple rounds of variable disturbance point combination and response calculation, and setting different disturbance step lengths in different fitting stages. In the multiple rounds of variable disturbance point combination and response calculation, the preliminary disturbance points are selected based on the mean value and the upper and lower intervals of the uncertainty variables; in each round of fitting, a new set of variable combinations is generated by disturbing the upper and lower intervals of each variable; the dam is simulated and calculated using the finite element model to obtain the dam response under each set of variable combinations. Then multiple fitting is performed, after the first round of disturbance point combination and response calculation, according to the obtained response data, preliminary fitting is performed through a quadratic polynomial or other appropriate mathematical model; the deviation between the fitting equation and the actual response data; adjust the position and number of the disturbance points to generate a new set of disturbance points in the next round of calculation. In order to ensure the efficiency and accuracy of the fitting, different disturbance step lengths can be set at different fitting stages. In the initial stage of fitting, a larger disturbance step length can be set; in the subsequent fitting stage, when the preliminary response surface function has been basically established, the disturbance step length can be gradually reduced; with the progress of the fitting process, the adjustment of the step length should be determined dynamically according to the error of the fitting result, if the error is larger or there is a significant fitting deviation in a certain round of fitting, the disturbance step length can be appropriately increased to make more extensive disturbance and correct the error of the model, while in the case of smaller error, the disturbance step length is gradually reduced to improve the accuracy of the fitting. Through multiple rounds of disturbance point combination and response calculation, multiple fitting results are finally generated. In this embodiment, through the analysis of the response surface function, the structural safety of the dam under different environmental and operating conditions is evaluated to provide a prediction basis for the long-term stability of the dam.
[0063] Further, the calculation of the non-probabilistic reliability index also includes applying an improved global optimal solution method: solving multiple local minimum points of the limit state function of the structure, performing dimension reduction and variable range determination, converting to a unary function and solving its real roots, and taking the absolute value of the real root and the minimum value of the root value of the limit state function as the non-probabilistic reliability index. Based on the structural characteristics, material properties, uncertainty factors, etc. of the dam, the limit state function of the dam is established. Multiple local minimum points of the limit state function are found through multiple iterations of numerical methods. The multi-dimensional problem is converted into a one-dimensional problem, which can be reduced by principal component analysis, feature selection or variable importance analysis, etc. The goal of dimension reduction is to retain the information that best reflects the safety of the structure while reducing the amount of calculation and complexity. According to the physical properties, historical data or expert estimates of the uncertainty variables, the variation range of each variable is determined. A key variable after dimension reduction is selected to construct a unary limit state function, and the input of the unary function is the value of the variable after dimension reduction, and the output is the corresponding limit state value. The real roots of the unary limit state function are solved by numerical methods. The root value represents the critical safety state of the dam under certain working conditions, indicating whether the dam is in a safe or failed state. The real root value of the unary function solved represents the limit state point. By taking the absolute value of the real root, the safety measure of the dam structure under the current working condition can be obtained. If the real root value is small, it indicates that the safety of the dam is high; if the real root value is large, it indicates that the dam may be in an unsafe state. At the same time, by comparing each local minimum point, the minimum value of the root value is calculated. This minimum value corresponds to the worst safety state of the dam under all possible working conditions, reflecting the most serious risk situation. Finally, by comparing the absolute value of the real root with the minimum root value of the limit state function, the non-probabilistic reliability index is determined. In this embodiment, the method effectively obtains the non-probabilistic reliability index of the hydropower station dam, improving the accuracy of the dam safety analysis.
[0064] In the embodiment of the present application, after analyzing the structural function function based on the response surface method, whether the calculation result converges is judged by iterative calculation of the response surface function, if the result converges, the result is taken as the final calculation result, if the result does not converge, linear interpolation is carried out, and iterative calculation is continued until the result converges. In this embodiment, the structural function function of the dam of the hydropower station is analyzed based on the response surface method, and the structural response model of the dam is gradually optimized by iterative calculation. After the response surface function is initially established, multiple rounds of iterative calculation are carried out, and whether the result converges is judged after each round of calculation. The convergence judgment is based on the change amount of the calculation results of the previous and next rounds, if the change amount is less than the set allowable error, it is considered that the calculation result has converged, if the result does not converge, the response surface function is adjusted by linear interpolation method, a new calculation result is generated, and iterative calculation is continued. Linear interpolation generates an intermediate value through the difference between the calculation results of the previous and next rounds to accelerate the convergence process, so as to ensure that the response surface function can accurately reflect the structural response of the dam. The stability and reliability of the dam under different working conditions are evaluated by this method.
[0065] In the embodiment of the present application, determining the initial limit range of the monitoring index of the dam of the hydropower station specifically includes: determining the reasonable limit range of the monitoring index according to the structural characteristics of the dam, the risk factors and the reliability analysis result, and the reliability analysis result is obtained based on non-probabilistic reliability analysis. The initial limit range of the monitoring index of the dam of the hydropower station is determined by combining the structural characteristics of the dam, the risk factors and the non-probabilistic reliability analysis result. In the implementation process, the structural characteristics of the dam need to be analyzed first, such as dam body material, dam foundation characteristics, design type, etc., these characteristics directly affect the stability of the dam and the performance of the monitoring index. Identify the risk factors that affect the safety of the dam, including material degradation, extreme working conditions, construction quality problems and changes in operating state. Then, through non-probabilistic reliability analysis, the safety of the dam under different working conditions is evaluated. Combined with the results of non-probabilistic reliability analysis, the initial limit range of the monitoring index of the dam is set. The limit range takes into account the actual design and operating conditions of the dam, and can effectively cope with the influence of different risk factors on the stability of the dam. In this embodiment, the scientificity and effectiveness of the dam safety management are ensured, which is helpful for effective risk control of the dam in long-term operation.
[0066] Those skilled in the art should understand that the present application is not limited by the above embodiments, the above embodiments and descriptions in the specification are only to illustrate the principles of the present application, and various changes and improvements can be made without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.
Claims
1. A method for analyzing the limit difference of safety monitoring indexes of a hydropower station dam, characterized in that, The method comprises the following steps: obtaining historical monitoring data of a hydropower station dam, the historical monitoring data comprising temperature data, deformation data, seepage data and stress-strain data; performing denoising, smoothing and normalization preprocessing on the historical monitoring data, and analyzing the monitoring index law of the dam body with service time based on the processed historical monitoring data, to identify dam safety monitoring risk factors, the risk factors being determined by establishing a dam foundation surface instability function function, the function function being established according to the anti-sliding force analysis rule; establishing a non-probabilistic target reliability analysis model, the non-probabilistic target reliability analysis model being used to describe the uncertain influencing factors in dam safety monitoring, and being verified to be compatible with the probabilistic model, and being capable of establishing a conversion relationship between the non-probabilistic reliability index and the probabilistic reliability index; calculating the non-probabilistic reliability index of the hydropower station dam through the non-probabilistic target reliability analysis model, the calculation comprising obtaining the upper and lower limits of the structure function function, and taking the ratio of the mean value and the deviation of the function function as the structure reliability index; determining the initial limit deviation range of the monitoring index of the hydropower station dam based on the non-probabilistic reliability index and the identified risk factors; combining the non-probabilistic target reliability analysis model, dynamically adjusting the limit deviation range to adapt to different service periods and operating conditions of the hydropower station dam, and forming a limit deviation calculation method based on the actual monitoring data of the hydropower station dam and the non-probabilistic reliability analysis.
2. The method for analyzing the safety monitoring index tolerance of a hydropower dam according to claim 1, characterized in that, The identification of dam safety monitoring risk factors further comprises using detection means to test the dam risk points analyzed, and recalculating and reviewing the strength of the dam body and the bank slope.
3. The method for analyzing the safety monitoring index tolerance of a hydropower dam according to claim 1, characterized in that, After analyzing the monitoring index law of the dam body with service time and identifying the dam safety monitoring risk factors, a comprehensive evaluation index system of the structure safety of the hydropower station dam is established, the system divides the hydropower station dam into three first-level evaluation index parts of dam body and dam foundation, near-dam area and environmental quantity monitoring, and establishes respective second-level evaluation indexes for each first-level evaluation index part.
4. The method for analyzing the safety monitoring index tolerance of a hydropower dam according to claim 3, characterized in that, The second-level evaluation indexes of the dam body and dam foundation include dam body and dam foundation deformation evaluation results, seepage evaluation results, internal observation evaluation results and patrol evaluation results; the second-level evaluation indexes of the near-dam area include near-dam area deformation evaluation results, groundwater evaluation results and patrol evaluation results; and the second-level evaluation indexes of the environmental quantity monitoring include environmental quantity monitoring quantities.
5. The method for analyzing the safety monitoring index tolerance of a hydropower dam according to claim 1, characterized in that, The establishment of the non-probabilistic target reliability analysis model comprises: researching and analyzing the uncertain influencing factors of the gravity dam; determining a non-probabilistic model for describing the uncertain influencing factors of the gravity dam, the non-probabilistic model being used for quantifying the uncertain factors; verifying the compatibility of the selected non-probabilistic model and the probabilistic model.
6. The method for analyzing the safety monitoring index tolerance of a hydropower dam according to claim 1, characterized in that, The calculation of the non-probabilistic reliability index of the hydropower station dam is performed by the response surface method for analyzing the structure function function, the response surface method being a response surface finite element method based on a quadratic polynomial, the response surface finite element method comprising the following steps: Select a plurality of uncertainty variables affecting the safety of the dam structure, including dam material performance parameters, dam foundation rock and soil physical and mechanical parameters, water level variation parameters, temperature parameters, and operating state characteristic parameters extracted from historical monitoring data; Determine the mean value of the above variables within their uncertainty intervals as the center point of the response surface; Around the center point, set intermediate disturbance points in the upper and lower intervals of each variable respectively to generate a plurality of variable combinations; Simulate the working conditions under each variable combination through a finite element model to obtain the corresponding structural response results; Construct a mathematical equation with the obtained response results and solve the equation using the least squares method to determine the values of the undetermined coefficients in the structural function function and establish an initial response surface function.
7. The method for analyzing the safety monitoring index tolerance of a hydropower dam according to claim 6, characterized in that, The structural function function construction process includes multiple fitting of the function through multiple rounds of variable disturbance point combinations and response calculations, and setting different disturbance step sizes at different fitting stages.
8. The method for analyzing the safety monitoring index tolerance of a hydropower dam according to claim 7, characterized in that, The calculation of the non-probabilistic reliability index also includes applying an improved global optimal solution method: solving multiple local minimum points of the structural limit state function, performing dimension reduction and variable range determination, converting to a unary function and solving its real roots, and taking the absolute value of the real root and the minimum value of the root value of the limit state function as the non-probabilistic reliability index.
9. The method for analyzing the safety monitoring index tolerance of a hydropower dam according to claim 6, characterized in that, After analyzing the structural function function based on the response surface method, the response surface function is iteratively calculated to determine whether the calculation result converges, if it converges, the result is taken as the final calculation result; if it does not converge, linear interpolation is performed and iterative calculation is continued until the result converges.
10. The method for analyzing the safety monitoring index tolerance of a hydropower dam according to claim 1, characterized in that, The initial limit range of the monitoring index of the hydropower station dam specifically includes: determining the reasonable limit range of the monitoring index according to the structural characteristics of the dam, risk factors and reliability analysis results, and the reliability analysis results are obtained based on non-probabilistic reliability analysis.