Dam displacement monitoring index drawing method and device

Abstract

The invention provides a dam displacement monitoring index setting method and device, and relates to the technical field of dam safety monitoring. The method comprises the steps that a finite element model of a dam to be monitored is established, and target material parameters are determined based on the finite element model; random field simulation is conducted on target material parameters, random finite element analysis is conducted on parameter random fields obtained through simulation, and dam body displacement probability density distribution under different water levels is obtained; determining a displacement statistical value and a displacement confidence interval under each water level according to the displacement probability density distribution of each dam body; and fitting based on the displacement statistical value and the displacement confidence interval to obtain a water pressure component and an interval value thereof, constructing a dam displacement monitoring hybrid model in combination with the temperature component and the aging component, and calculating a dam displacement monitoring index based on the dam displacement monitoring hybrid model and the water pressure component interval value. According to the method, the dam displacement monitoring index is drawn up by considering the uncertainty of the dam body material, and dynamic monitoring and early warning of the dam can be accurately realized.

Description

Technical Field

[0001] The present invention relates to the technical field of dam safety monitoring, and in particular to a method and device for formulating dam displacement monitoring indicators. Background Art

[0002] With the gradual acceleration of dam construction, it is particularly important to carry out in-depth dam safety monitoring, fully explore the hidden information contained in the safety monitoring data, and quickly and accurately grasp the dam operation status. Among them, dam displacement monitoring can more intuitively and accurately reflect the safety status of the dam structure. Therefore, displacement monitoring is one of the most important monitoring items in dam safety monitoring.

[0003] When monitoring dam displacement, the ability of the dam to resist possible loads can be evaluated and predicted by formulating monitoring indicators, so as to determine the warning value and extreme value of the effect quantity under the load combination. At present, the commonly used methods for formulating dam displacement monitoring indicators include the confidence interval method and the typical small probability method. Among them, the confidence interval method and the typical small probability method belong to mathematical statistics methods. They are based on statistical probability theory and evaluate dam monitoring indicators based on existing monitoring data. They have the advantages of simple methods and easy operation. However, in the early stage of project operation, when there is little monitoring data on dam displacement and the most unfavorable load conditions have not been experienced, the monitoring indicators formulated by the confidence interval method and the typical small probability method are not the extreme value states in reality. At the same time, the monitoring indicators established based on mathematical statistics methods are not related to the mechanical characteristics and deformation mechanism of the dam structure, and their physical concepts are unclear, which are not suitable for the formulation of dam monitoring indicators in the early stage of operation.

[0004] Therefore, there is an urgent need to provide a new scheme for formulating dam displacement monitoring indicators to accurately and comprehensively reflect the actual operating performance of the dam.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] The purpose of the embodiments of the present disclosure is to provide a method and device for formulating dam displacement monitoring indicators, thereby at least to a certain extent solving the problem in the related art that it is difficult to accurately and comprehensively reflect the actual operating status of the rockfill dam due to deviations in the formulated dam displacement monitoring indicators.

[0007] According to a first aspect of an embodiment of the present disclosure, a method for formulating a dam displacement monitoring index is provided, comprising: Establishing a finite element model of the dam to be monitored, and determining target material parameters based on the finite element model; Performing random field simulation on the target material parameters, and performing random finite element analysis on the simulated parameter random fields at different water levels to obtain probability density distribution of dam body displacement at each water level; Determining the displacement statistics and displacement confidence interval at each water level according to the probability density distribution of the dam body displacement; Fitting the water pressure component and the water pressure component interval value based on the displacement statistical value and the displacement confidence interval; A dam displacement monitoring hybrid model is constructed by combining the temperature component, the time-effect component and the water pressure component, and a dam displacement monitoring index is calculated based on the dam displacement monitoring hybrid model and the water pressure component interval value.

[0008] In an exemplary embodiment of the present disclosure, the random field simulation is performed on the target material parameters, and the random finite element analysis is performed on the simulated parameter random field at different water levels to obtain the probability density distribution of the dam body displacement at each water level, including: Performing random field simulation on target material parameters of each material partition in the dam to be monitored to obtain a corresponding simulated random field; Assigning each of the simulated random fields to the corresponding dam body unit in the finite element model to obtain a parameter random field of the dam to be monitored; The parameter random field is subjected to random finite element analysis at different water levels to obtain the displacement of the dam body measuring points at each water level, and the probability density distribution of the dam body displacement at each water level is obtained by statistically analyzing the displacement of the dam body measuring points.

[0009] In an exemplary embodiment of the present disclosure, the simulated random field comprises a correlated log-normal random field; The random field simulation of the target material parameters of each material partition in the dam to be monitored to obtain the corresponding simulated random field includes: generating a standard Gaussian random field according to the statistical distribution characteristics of the target material parameters and the spatial correlation defined by the Gaussian autocorrelation function; The random variable correlation coefficient matrix in the standard Gaussian random field is decomposed, and the relevant standard Gaussian random field is calculated according to the decomposition result and the relevant standard normal random sample matrix, and the relevant standard Gaussian random field is subjected to equal probability transformation processing to generate the relevant log-normal random field.

[0010] In an exemplary embodiment of the present disclosure, the dam body measuring point displacement includes a first dam body measuring point displacement, a second dam body measuring point displacement, a third dam body measuring point displacement and a fourth dam body measuring point displacement; The random finite element analysis of the parameter random field under different water levels is performed to obtain the displacement of the dam body measuring points under each water level, and the probability density distribution of the dam body displacement under each water level is obtained by statistically analyzing the displacement of the dam body measuring points, including: Performing random finite element analysis on the parameter random field at different water levels to obtain the displacement of the first dam body measuring point at each water level; A random simulation proxy model is established using the target material parameters and the first dam body measurement point displacement, and the random simulation proxy model is used to fit the second dam body measurement point displacement at each water level; comparing the statistical characteristic value error between the displacement of the first dam body measuring point and the displacement of the second dam body measuring point; If the statistical characteristic value error is greater than the preset difference, the number of the parameter random fields is increased, and random finite element analysis is performed on the parameter random fields after the adjustment under different water levels to obtain the displacement of the third dam body measuring point; Reconstructing and fitting a random simulation proxy model based on the displacement of the third dam body measuring point to obtain the displacement of the fourth dam body measuring point; If the statistical characteristic value error between the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point is less than or equal to the preset difference, the probability density distribution of the dam body displacement at each water level is obtained by statistically analyzing the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point.

[0011] In an exemplary embodiment of the present disclosure, the probability density distribution of dam body displacement at each water level is obtained by counting the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point, including: Using the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point at each water level to perform stability detection, and obtain the target random field simulation number of the dam body measuring point displacement being in a stable state; The displacements of the dam body measuring points corresponding to the target random field simulation times are counted to obtain the probability density distribution of the dam body displacements at each water level.

[0012] In an exemplary embodiment of the present disclosure, the displacement statistics include a displacement mean and a displacement standard deviation; Determining the displacement statistics and displacement confidence interval at each water level according to the probability density distribution of the dam body displacements includes: Performing statistical analysis on the probability density distribution of the displacement of each dam body, and calculating the corresponding displacement mean and displacement standard deviation; The limit values ​​of the displacement confidence interval are determined based on the displacement mean and displacement standard deviation and combined with the significance level.

[0013] In an exemplary embodiment of the present disclosure, the fitting based on the displacement statistics and the displacement confidence interval to obtain the water pressure component and the water pressure component interval value includes: The displacement mean under different water levels is fitted with a polynomial to generate the water pressure component related to the water level; Polynomial fitting is used to fit the limit values ​​of the displacement confidence interval under different water levels to generate the corresponding water pressure component interval values.

[0014] In an exemplary embodiment of the present disclosure, the dam displacement monitoring hybrid model includes an original dam displacement monitoring hybrid model and a target dam displacement monitoring hybrid model; The dam displacement monitoring hybrid model is constructed by combining the temperature component, the time-effect component and the water pressure component, and the dam displacement monitoring index is calculated based on the dam displacement monitoring hybrid model and the water pressure component interval value, including: The original dam displacement monitoring hybrid model is constructed by taking the environmental quantities corresponding to the temperature component, the time-effect component and the water pressure component as independent variables and the displacement of the dam body measuring point as the dependent variable; Using the displacement monitoring data of the dam to be monitored and the environmental monitoring data, fitting and analyzing each component in the original dam displacement monitoring hybrid model is performed to obtain the undetermined coefficients in the original dam displacement monitoring hybrid model; A target dam displacement monitoring hybrid model is obtained based on the undetermined coefficients, and the water pressure component interval value is substituted into the target dam displacement monitoring hybrid model to obtain the dam displacement monitoring index of the dam to be monitored at a preset significance level.

[0015] In an exemplary embodiment of the present disclosure, the step of establishing a finite element model of the dam to be monitored and determining target material parameters based on the finite element model includes: Acquiring engineering data of the dam to be monitored, and establishing a finite element model of the dam to be monitored according to the engineering data; The constitutive model used for each material partition divided in the finite element model is determined, and target material parameters are selected from the constitutive model.

[0016] According to a second aspect of an embodiment of the present disclosure, a device for formulating a dam displacement monitoring index is provided, comprising: A material parameter determination module, used to establish a finite element model of the dam to be monitored and determine target material parameters based on the finite element model; A parameter random simulation module is used to perform random field simulation on the target material parameters, and perform random finite element analysis on the simulated parameter random field at different water levels to obtain the probability density distribution of dam body displacement at each water level; A probability density statistics module, used to determine the displacement statistics and displacement confidence interval at each water level according to the probability density distribution of the dam body displacement; A water pressure component determination module, used for obtaining a water pressure component and a water pressure component interval value based on the displacement statistics and the displacement confidence interval fitting; The monitoring index determination module is used to construct a dam displacement monitoring hybrid model by combining the temperature component, the time-effect component and the water pressure component, and calculate the dam displacement monitoring index based on the dam displacement monitoring hybrid model and the water pressure component interval value.

[0017] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, any step of the method for formulating dam displacement monitoring indicators described in the first aspect is implemented.

[0018] According to a fourth aspect of the present disclosure, there is provided an electronic device, including: A processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any step of the method for formulating dam displacement monitoring indicators described in the first aspect is implemented.

[0019] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: The method for formulating dam displacement monitoring indicators provided in the example embodiments of the present disclosure firstly utilizes finite element models and random field simulations, taking into account the spatial uncertainty of the dam body material, to obtain the displacement distribution under different water levels, which can effectively capture the random characteristics of the dam body displacement, thereby more accurately predicting the displacement behavior of the dam. Secondly, by analyzing the probability density distribution of displacements under various water levels, the displacement mean and confidence interval are obtained, which dynamically reflects the impact of different water levels on the dam body displacement, and can provide accurate displacement range and uncertainty assessment for real-time monitoring. Furthermore, the water pressure component is obtained by fitting the displacement statistics and confidence intervals, and a hybrid model for dam displacement monitoring is constructed in combination with temperature and aging factors. The monitoring indicators calculated based on the model and the water pressure component interval can accurately and comprehensively reflect the true operating performance of the dam, and provide reliable early warning indicators to timely identify abnormal dam body displacements.

[0020] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0022] Figure 1 A system architecture diagram of a dam displacement monitoring indicator formulation method to which the embodiment of the present disclosure can be applied is shown.

[0023] Figure 2 A schematic flow chart of a method for formulating dam displacement monitoring indicators in an embodiment of the present disclosure is shown.

[0024] Figure 3 A schematic diagram of a cross section of a finite element model and material partitioning of a rockfill dam in an embodiment of the present disclosure is shown.

[0025] Figure 4 A schematic diagram of a flow chart for statistically analyzing the probability density distribution of dam body displacement at various water levels in an embodiment of the present disclosure is shown.

[0026] Figure 5 Another flow chart of statistically analyzing the probability density of dam body displacement at various water levels in an embodiment of the present disclosure is shown.

[0027] Figure 6 A schematic diagram of a process for determining a dam displacement monitoring index in an embodiment of the present disclosure is shown.

[0028] Figure 7 A flow chart of another method for formulating dam displacement monitoring indicators in an embodiment of the present disclosure is shown.

[0029] Figure 8 A schematic diagram of a dam displacement monitoring index formulation device in an embodiment of the present disclosure is shown.

[0030] Figure 9 A schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure is shown.

[0031] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0032] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a", "the" and "the" used in this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0033] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0034] Figure 1 A schematic diagram of the system architecture of a method for formulating dam displacement monitoring indicators to which the embodiment of the present disclosure can be applied is shown.

[0035] like Figure 1 As shown, the system architecture 100 may include one or more of terminal devices such as a smart phone 101, a portable computer 102, a desktop computer 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the terminal device and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0036] The terminal device may be any electronic device with data processing function, which has a display screen for displaying to the user the finite element model of the dam to be monitored, the distribution of dam displacement monitoring points, random field simulation results, water pressure components and water pressure component intervals, and dam displacement monitoring indicators, etc. The electronic device includes but is not limited to the above-mentioned desktop computers, portable computers, smart phones, tablet computers, etc.

[0037] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. According to the implementation requirements, there may be any number of terminal devices, networks and servers. For example, the server 105 may be a server cluster composed of multiple servers.

[0038] The dam displacement monitoring index formulation method provided in the embodiment of the present disclosure can be executed by the terminal device, and accordingly, the dam displacement monitoring index formulation device can be set in the terminal device. However, it is easy for those skilled in the art to understand that the dam displacement monitoring index formulation method provided in the embodiment of the present disclosure can also be executed by the server 105, and accordingly, the dam displacement monitoring index formulation device can also be set in the server 105, which is not particularly limited in this exemplary embodiment.

[0039] It should be noted that the method used to formulate dam displacement monitoring indicators also includes structural analysis methods. This method uses the finite element method to establish a model to simulate the force characteristics of the dam. It has clear physical concepts and can calculate the deformation and stress changes of the dam body under various working conditions, and then simulate load conditions that have never been encountered. Therefore, it is more suitable for the formulation of dam displacement monitoring indicators in the initial operation stage.

[0040] Taking rockfill dam as an example, the mechanical parameters of rockfill materials can be used to determine displacement monitoring indicators. For example, the parameters of indoor geotechnical test are used for structural calculations, and the water pressure components are determined accordingly. However, in actual engineering, due to factors such as geological surveys, geotechnical tests, design assumptions, construction technology and construction conditions, the physical properties (density, porosity, average particle size, etc.) and mechanical parameters of rockfill materials have certain uncertainties and random distribution characteristics. If these uncertainties are ignored in safety monitoring and evaluation, it will have a serious impact on the deformation prediction and safety analysis of the dam, resulting in deviations in the dam displacement monitoring indicators, making it difficult to accurately and comprehensively reflect the true operating state of the dam.

[0041] Based on one or more of the above problems, the embodiments of the present disclosure provide a method for formulating dam displacement monitoring indicators. By formulating dam displacement monitoring indicators taking into account the uncertainty of dam body materials, dynamic monitoring and early warning of the dam can be accurately achieved. The following uses server execution as an example to illustrate this method. Figure 2 As shown, the method may include steps S210 to S250: Step S210, establishing a finite element model of the dam to be monitored, and determining target material parameters based on the finite element model; Step S220, performing random field simulation on the target material parameters, and performing random finite element analysis on the simulated parameter random field at different water levels to obtain a probability density distribution of dam body displacement at each water level; Step S230, determining the displacement statistics and displacement confidence interval at each water level according to the probability density distribution of the dam body displacement; Step S240, obtaining a water pressure component and a water pressure component interval value by fitting based on the displacement statistics and the displacement confidence interval; Step S250, constructing a dam displacement monitoring hybrid model by combining the temperature component, the time-effect component and the water pressure component, and calculating the dam displacement monitoring index based on the dam displacement monitoring hybrid model and the water pressure component interval value.

[0042] The method for formulating dam displacement monitoring indicators provided by the present disclosure is implemented. First, the finite element model and random field simulation are used to consider the spatial uncertainty of the dam material to obtain the displacement distribution under different water levels, which can effectively capture the random characteristics of the dam displacement, thereby more accurately predicting the displacement behavior of the dam. Secondly, the displacement mean and confidence interval are obtained by analyzing the probability density distribution of displacement under each water level, which dynamically reflects the impact of different water levels on the dam displacement, and can provide accurate displacement range and uncertainty assessment for real-time monitoring. Furthermore, the water pressure component is obtained by fitting the displacement statistics and confidence intervals, and a hybrid model for dam displacement monitoring is constructed in combination with temperature and aging factors. The monitoring indicators calculated based on the model and the water pressure component interval can accurately and comprehensively reflect the real operating state of the dam, and provide reliable early warning indicators to timely identify abnormal dam displacement.

[0043] Next, the method for formulating the dam displacement monitoring index in this example embodiment will be described in detail.

[0044] In step S210, a finite element model of the dam to be monitored is established, and target material parameters are determined based on the finite element model.

[0045] In the exemplary implementation manner of the present disclosure, the dam to be inspected may be a homogeneous earth dam, a clay core wall earth dam, a clay inclined wall dam, an asphalt concrete core wall, a rockfill dam, etc. The present disclosure does not limit the type of the dam to be inspected.

[0046] For example, the engineering data of the dam to be monitored can be obtained, and a finite element model of the dam to be monitored can be established based on the engineering data. Then, the constitutive model used for each material partition divided in the finite element model is determined, and the target material parameters are selected from the constitutive model. Among them, the constitutive model includes a linear elastic constitutive model, a nonlinear elastic constitutive model, a plastic constitutive model, a viscoelastic constitutive model, an elastoplastic constitutive model, a damage constitutive model, etc.

[0047] The dam to be tested is a concrete face rockfill dam (abbreviated as rockfill dam) as an example. The engineering data of the rockfill dam includes the engineering plan layout, dam profile design, dam deformation monitoring layout, dam construction progress report, etc. According to the engineering data of the rockfill dam, the partition of different materials inside the dam, the layered rolling construction, and the layout and installation of the dam displacement monitoring points can be determined, so that a finite element model of the rockfill dam that conforms to the actual engineering can be established.

[0048] The calculation units in the finite element model of the dam to be monitored can be quadrilateral units or hexahedral units. For example, the size of the unit grid is 1 / 20~1 / 30 of the dam height and should not exceed 10m. A fixed boundary is set at the bottom of the finite element model, and a hydraulic boundary is set at the upstream panel. At the same time, a layered filling method is set to simulate the construction steps, and a hydraulic load loading method is set to simulate the phased water storage.

[0049] refer to Figure 3 As shown, a cross section of a finite element model of a rockfill dam is schematically given. The interior of the dam body is divided according to the actual project. The material divisions of the finite element model include concrete face plate, toe plate, extrusion wall, cushion layer, transition layer, upstream rockfill area, main rockfill area and downstream rockfill area. It can be understood that a plurality of dam body displacement monitoring points corresponding to the layout positions of the dam body displacement monitoring points on the rockfill dam are provided on the finite element model to facilitate the screening of monitoring data.

[0050] In an exemplary implementation, the constitutive model used for each material partition is the Duncan-Zhang EB model in the nonlinear elastic constitutive model. Specifically, the material parameters in the Duncan-Zhang EB model include cohesion C , initial internal friction angle f 0. Internal friction angle increment △ f , initial tangent modulus intercept K , initial tangent modulus slope n , tangent bulk modulus intercept K b , tangent bulk modulus slope m , unloading modulus intercept K ur and destruction ratio R f Among them, the cohesion of the rockfill C Generally 0, for removing cohesion C The remaining material parameters, tangent bulk modulus intercept K b , initial internal friction angle f 0. Initial tangent modulus slope n The influence on the displacement of the dam body is significant, and these three material parameters are the target material parameters. Therefore, in order to improve the analysis efficiency, the target material parameters of each material partition of the dam body are only analyzed in the exemplary embodiment of the present disclosure. K b , f 0. n Random field simulation was performed, and the remaining material parameters were experimental values.

[0051] In step S220, random field simulation is performed on the target material parameters, and random finite element analysis is performed on the simulated parameter random field at different water levels to obtain the probability density distribution of dam body displacement at each water level.

[0052] It should be noted that since the displacement change of the dam body is mainly caused by the displacement of the rockfill material, in the exemplary embodiment of the present disclosure, only the target material parameters of the three material partitions of the upstream rockfill area, the main rockfill area and the downstream rockfill area are calculated. K b , f 0. n Random field simulation is performed respectively. Random field simulation refers to the use of probability statistics methods to generate variable distributions with randomness and spatial correlation within a space or time range to simulate physical parameters with uncertainty and spatial variability in the real world. The embodiment of the present disclosure is used to simulate the parameter variability and correlation of soil and stone materials within a spatial range.

[0053] In one example implementation, reference is made to Figure 4 As shown, step S220 may further include steps S410 to S430: Step S410, performing random field simulation on target material parameters of each material partition in the dam to be monitored to obtain a corresponding simulated random field.

[0054] For the three material zones of upstream rockfill area, main rockfill area and downstream rockfill area, it is assumed that the target material parameters in each material zone are K b , f 0. n The numerical distribution of all obeys the lognormal distribution ln X ~ N ( m 1, s 1 2 ). For example, the mean of material parameters in the standard normal distribution space is m 1 Using the indoor geotechnical test parameter values ​​of each material, the coefficient of variation of the material parameters in the upstream rockfill area and the main rockfill area is 0.1, and the coefficient of variation of the material parameters in the downstream rockfill area is 0.2. The standard deviation s 1= m 1 C v , C v is the coefficient of variation of material parameters. Among them, the autocorrelation distance of rockfill material is 10m (vertical) and 100m (horizontal and cross-river direction).

[0055] In an example implementation, the simulated random field obtained by random field simulation includes a correlated log-normal random field. For example, a standard Gaussian random field can be generated based on the statistical distribution characteristics of the target material parameters and the spatial correlation defined by the Gaussian autocorrelation function, and then the random variable correlation coefficient matrix in the standard Gaussian random field is decomposed, and the correlated standard Gaussian random field is calculated based on the decomposition result and the correlated standard normal random sample matrix, and the correlated standard Gaussian random field is subjected to equal probability transformation to generate a correlated log-normal random field. The statistical distribution characteristics of the target material parameters may include the material parameter mean value. m 1. Standard Deviation s 1 and coefficient of variation of material parameters C v ,It should be noted that these three physical quantities are also the ,characteristic values ​​of the random field parameters of the target material ,parameters.

[0056] Specifically, the random field simulation method based on Cholesky decomposition (square root method) can be used to perform U random field simulations on the target material parameters in each material partition. In order to simulate the parameter uncertainty and correlation of rockfill materials in the spatial range, the correlation degree between two points in the space of a soil parameter is expressed by the autocorrelation coefficient. r It is used to describe the autocorrelation between soil parameters at different spatial locations in the random field: (1) in, , The center point coordinates are , COV( ) is the covariance of the grid parameter distribution, and Var( ) is the variance of the grid parameter distribution.

[0057] It should be noted that the autocorrelation function shown in formula (1) assumes that the random field follows the stationary hypothesis or quasi-stationary hypothesis, that is, the mean and variance of the soil parameters are independent of the spatial position of the random field, and the autocorrelation function is only related to the relative distance between two points in space. Due to the lack of field measured data, the theoretical correlation function can be used to describe the autocorrelation between material parameters. Among them, the Gaussian correlation function has better stationarity, so the autocorrelation coefficient r It can also be expressed as: (2) in, is the horizontal relative distance, , is the vertical relative distance, , is the horizontal relative distance, , , , They are the fluctuation ranges in three directions respectively.

[0058] In addition to the autocorrelation of the same soil parameters, there is also cross-correlation between different soil parameters, which can also be calculated through the Gaussian correlation function shown in formula (2), thereby obtaining the autocorrelation coefficient matrix and the cross-correlation coefficient matrix.

[0059] It should be noted that when using lognormal distribution to describe the distribution of geotechnical mechanics parameters, it is necessary to pass Nataf The transformation generates a correlated log-normal random field to represent the autocorrelation and cross-correlation between soil parameters. Nataf Transformation refers to converting the original random variable into a standard normal random variable. During the random field transformation process, the autocorrelation coefficient matrix decomposition and the mutual correlation coefficient matrix decomposition are both performed in the standard normal space. Since the equal probability transformation is a nonlinear transformation process in different spaces, the correlation coefficients between soil parameters will change during the transformation process, so it is necessary to calculate the equivalent correlation coefficient in the standard normal space: (3) in, is the equivalent autocorrelation coefficient of the standard normal random variable, SN (Standard Normal) represents the standard normal space, is the autocorrelation coefficient of the original random variable, is the equivalent cross-correlation coefficient of the standard normal random variables, is the cross-correlation coefficient of the original random variables, , The center point coordinates are and The eigenvalues ​​of the random field unit grid parameter distribution, and The coordinates of the center point are and The Gaussian random field simulation value of the unit parameter, is the equivalent mutual correlation coefficient The two-dimensional joint probability density function of m H , s H are the mean and standard deviation of the random field, k i,j is the autocorrelation correction coefficient. Since the difference between the equivalent autocorrelation coefficient and the original autocorrelation coefficient has little effect on the calculation result and can be ignored, it can be set k i,j =1.

[0060] Based on the equivalent correlation coefficients in the standard normal space, the equivalent mutual correlation coefficient matrices can be obtained respectively And the equivalent autocorrelation coefficient matrix Then, perform Cholesky decomposition on the two, namely: , , we can get the lower triangular matrix L 1. L 2. Furthermore, an independent standard normal random sample matrix of soil parameters is established x , the lower triangular matrix L 1. L 2 respectively with x Multiplying them together, we can get the relevant standard Gaussian random field ,Right now: (4) in, is the associated standard normal random sample matrix.

[0061] Finally, the relevant standard Gaussian random field is transformed by the equal probability transformation method. Taking the exponential, we get the associated lognormal random field: (5) in, is a normal variable The standard deviation of , is a normal variable The mean of , is a lognormal variable X i The standard deviation of is a lognormal variable X i The mean of .

[0062] Step S420, assigning each of the simulated random fields to the corresponding dam body units in the finite element model to obtain the parameter random field of the dam to be monitored.

[0063] Among them, the material parameters generated in the simulated random field are assigned to each dam unit of the finite element model, so that the material parameters of each unit are no longer fixed values, but have spatial randomness, so as to more realistically reflect the uncertainty and spatial variability of the dam material.

[0064] For example, the dam body units in the finite element model are first identified, and the random field coordinates are matched with the finite element mesh coordinates to ensure that each unit can receive the correct material parameter values. Then, the material parameter values ​​of the corresponding positions in the simulated random field are assigned to the finite element mesh units so that the material parameters of each unit (such as the initial internal friction angle, the initial tangent modulus slope, etc.) conform to the statistical distribution characteristics of the random field. After the assignment, the finite element model of the entire dam is no longer a deterministic model, but a parameter random field with spatial random characteristics, which can be used for subsequent random finite element analysis.

[0065] Step S430, performing random finite element analysis on the parameter random field at different water levels to obtain the displacement of the dam body measuring points at each water level, and obtaining the probability density distribution of the dam body displacement at each water level by counting the displacement of the dam body measuring points.

[0066] The dam body measuring point displacement is taken as the horizontal displacement at the apex of the maximum cross-section of the face rockfill dam (such as denoted as measuring point B) as an example for explanation. In an exemplary implementation, the dam body measuring point displacement includes the first dam body measuring point displacement, the second dam body measuring point displacement, the third dam body measuring point displacement and the fourth dam body measuring point displacement. Among them, the first dam body measuring point displacement, the second dam body measuring point displacement, the third dam body measuring point displacement and the fourth dam body measuring point displacement are respectively the horizontal displacement values ​​of different measuring points calculated at measuring point B.

[0067] refer to Figure 5 As shown, the process for determining the probability density distribution of the dam displacement at each water level at the measuring point specifically includes steps S510 to S560: Step S510, performing random finite element analysis on the parameter random field at different water levels to obtain the displacement of the first dam body measuring point at each water level.

[0068] In this step, the displacement of the first dam body measuring point corresponding to different water levels under the random field of group U parameters is obtained by random finite element calculation. The displacement of the first dam body measuring point can be statistically analyzed to preliminarily obtain the probability density distribution of the dam body displacement. The random finite element method used in the embodiment of the present disclosure separates the traditional deterministic finite element analysis from the random analysis process, which can obtain accurate and reliable structural numerical simulation results while simplifying the amount of calculation. In addition, in order to match the finite element mesh of the dam body with the random field unit mesh, the random field unit in the embodiment of the present disclosure uses the same number and size as the finite element mesh.

[0069] Step S520: establishing a random simulation proxy model using the target material parameters and the first dam body measurement point displacement, and fitting the random simulation proxy model to obtain the second dam body measurement point displacement at each water level.

[0070] The random finite element analysis in step S510 is regarded as a random simulator, the input variable of which is the random field parameter characteristic value of the target material parameter, and the output variable is the displacement of the dam body measuring point. In the example implementation of the present disclosure, the random polynomial chaos expansion method can be used to simulate the nonlinear relationship between the target material parameter and the displacement of the first dam body measuring point and the displacement probability density distribution, that is, the random polynomial chaos expansion method is used to establish a proxy model of the random simulator, that is, to obtain a random simulation proxy model. In this example, an approximate calculation model is established to replace the random finite element analysis with high calculation cost, so as to improve the calculation efficiency and ensure the accuracy at the same time.

[0071] Specifically, the process of establishing a random simulation agent model is as follows: For the probability density function f X Describes and has independent components A random variable, express M dimensional real number set, which can establish a mapping relationship based on the finite variance calculation model ,but M ( X ) is expanded into a polynomial chaos: (6) in, It is about the probability density function f X Orthogonal multivariate polynomials, is a multivariate index to label the components of the multivariate polynomial , are the coefficients corresponding to the polynomial, represents a real number set. In the embodiment of the present disclosure, the polynomial in formula (6) can be truncated and converted into a sum of finite terms to improve the feasibility of the analysis. Therefore, formula (6) can also be written as: (7) in, is the truncated polynomial chaos expansion, is the selected set of multivariate indices for the multivariate polynomial, express M dimensional set of non-negative integers.

[0072] From a probability perspective, Y x It can be considered as a conditional random variable Assumptions represents the associated cumulative distribution function, which can be used to transform any continuous random variable into Z Transform to the desired distribution ,Right now: (8) in, is a random variable Z The cumulative distribution function, symbol " " represents the distribution form, that is, the random variables on both sides of formula (8) follow the same distribution, and the right side of the formula is x and Z Therefore, when assuming Y When the variance is finite, the random polynomial chaos expansion method can be realized ( X , Z ) The distribution in the variable space is equal as shown in formula (9): (9) In addition, in order to improve numerical stability, an additional noise variable is introduced e , and define the random replacement ( is the truncated set, express M +1-dimensional non-negative integer set), then formula (8) can be expressed as: (10) Among them, the noise variable e The standard deviation is s 2-centered Gaussian random variable, that is .

[0073] Next, we estimate the coefficients c α With noise variables e Standard Deviation s 2.

[0074] Based on material parameters, water level and dam body displacement data , the coefficient matrix can be fitted using maximum likelihood estimation c ={ c α}: (11) in, is the coefficient matrix c The estimated value of l For the coefficient matrix c The likelihood function of is the numerical approximation of the one-dimensional integral, N is the number of polynomial terms after truncation, x and y are input data and output data, namely material parameters, water level height and measuring point displacement, s 3 is the standard deviation of displacement.

[0075] The likelihood function in formula (11) is: (12) The likelihood function in formula (11) can be obtained by analytical calculation, where y is the actual displacement value of the dam body measuring point, is the approximate value of the displacement of the dam body measuring point after polynomial truncation, s 3 is the displacement standard deviation, D Z Representation variables Z The domain of f ( z )for Z Therefore, the derivative-based BFGS quasi-Newton method can be used to solve the optimization problem.

[0076] Standard Deviation s 2 can be used N cv -fold cross validation (nested cross validation) selection s The best value of 2 is the maximum cross-validation score of formula (11), which is calculated by derivative-free Bayesian optimization. s The search interval of 2 is , then the optimal s The 2 value is the final alternative established in equation (11) using all available data.

[0077] In the exemplary embodiment of the present disclosure, the random field parameter characteristic value of the target material parameter is used as the input variable The displacement of the first dam body measuring point corresponding to different water levels under the random field of the obtained U group of parameters is the output variable , according to formula (6) to formula (12), the random simulation agent model is constructed and fitted, and the fitting value of the random simulation agent model is obtained , That is the displacement of the second dam measuring point under each water level.

[0078] The fitting accuracy of the random simulation proxy model can be verified later. When the accuracy meets the simulation requirements, the random simulation proxy model is used to output the displacement values ​​of multiple dam body measuring points. Step S530: comparing the statistical characteristic value error between the displacement of the first dam body measuring point and the displacement of the second dam body measuring point.

[0079] The statistical characteristic values ​​include but are not limited to the mean and standard deviation. For example, the displacement of the first dam body measurement point can be compared. The mean value of the displacement of the second dam body measuring point The fitting accuracy of the random simulation proxy model can be determined based on the error between the two. Of course, the displacement of the first dam body measurement point can also be compared. The standard deviation of the displacement of the second dam body measuring point The fitting accuracy of the random simulation agent model is determined by the standard deviation of , which is not limited in the present disclosure.

[0080] Step S540: if the statistical characteristic value error is greater than a preset difference, the number of parameter random fields is increased, and random finite element analysis is performed on the parameter random fields after the adjustment under different water levels to obtain the displacement of the third dam body measuring point.

[0081] For example, the preset difference is set to 5%. Of course, the specific value of the preset difference can also be set according to actual application needs, which is not limited here. The mean value of the displacement of the second dam body measuring point If the relative error between the means of is less than or equal to 5%, it indicates that the fitting accuracy of the random simulation proxy model is high, and the next step of analysis can be carried out, that is, the probability density distribution of the dam body displacement under each water level is obtained by statistically analyzing the displacement of the first dam body measuring point and the displacement of the second dam body measuring point, and step S230 is directly executed.

[0082] If the displacement of the first dam measuring point The mean value of the displacement of the second dam body measuring point If the relative error between the means of is greater than 5%, the number of parameter random fields in step S410 is increased. In fact, the number of parameter random fields is increased by increasing the number of random field simulations U, and random finite element calculations are performed again to obtain larger-scale dam displacement data, that is, to obtain the displacement of the third dam body measuring point.

[0083] Step S550: reconstructing and fitting a random simulation proxy model based on the displacement of the third dam body measuring point to obtain the displacement of the fourth dam body measuring point.

[0084] In this step, the random simulation proxy model is recalculated and optimized using the new calculation data. Specifically, the displacement of the third dam body measuring point is used as the output variable to reconstruct and fit the random simulation proxy model to obtain the displacement of the fourth dam body measuring point.

[0085] Step S560: if the statistical characteristic value error between the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point is less than or equal to a preset difference, the probability density distribution of the dam body displacement under each water level is obtained by statistically calculating the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point.

[0086] Among them, if the statistical characteristic value error between the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point is less than the preset difference, such as the relative error between the mean of the displacement of the third dam body measuring point and the mean of the displacement of the fourth dam body measuring point is less than or equal to 5%, the error between the two meets the requirement. The random simulation agent model is used to output multiple dam body measuring point displacement values, such as generating the displacement of the fourth dam body measuring point corresponding to each water level under the random field of the U' group parameters.

[0087] Finally, the probability density distribution of dam displacement under different water levels is obtained by statistically analyzing the displacement of the third and fourth dam measuring points, that is, the probability density distribution of horizontal displacement of measuring point B under different water levels is formed. Among them, since the calculated value of dam displacement under parameter random field has a certain randomness, the stability of the calculation result needs to be tested.

[0088] Exemplarily, the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point of measuring point B at each water level are used to perform stability detection, and the target random field simulation times at which the displacement of the dam body measuring point is in a stable state are obtained. Specifically, the horizontal displacement of measuring point B at a certain water level is counted to obtain the change of the sliding mean of the horizontal displacement with the number of random field simulations. When the displacement mean is basically stable, the corresponding target random field simulation times are determined. For example, when the number of random field simulations reaches 400 times, the displacement calculation results are in a stable state, that is, under 400 random field simulations, the displacement calculation results have good stability and representativeness, so the first 400 groups of displacement calculation results can be used to construct the water pressure component in the future.

[0089] Then, by selecting the sample size corresponding to the stable mean displacement, the probability density distribution of the displacement of the dam body measuring point under this sample size is obtained. Specifically, by counting the displacement of the first 400 groups of dam body measuring points under the target random field simulation times, the probability density distribution of the dam body displacement under this water level can be obtained. Similarly, the probability density distribution of the dam body displacement under each water level is obtained.

[0090] It can be understood that if the statistical eigenvalue error between the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point is still greater than the preset difference, steps S540 and S550 can be continuously repeated until the statistical eigenvalue error between the latest dam body measuring point displacement obtained by random finite element calculation and the latest dam body measuring point displacement obtained by fitting the random simulation proxy model meets the requirements, and finally the probability density distribution of the dam body displacement at each water level is obtained based on the statistics of the two.

[0091] In step S230, the displacement statistics and displacement confidence intervals at each water level are determined according to the probability density distribution of the dam body displacements.

[0092] The displacement statistics include the displacement mean and displacement standard deviation. For example, the probability density distribution of the displacement of each dam body is statistically analyzed to calculate the corresponding displacement mean and displacement standard deviation. Furthermore, the limit value of the displacement confidence interval is determined based on the displacement mean and displacement standard deviation and combined with the significance level.

[0093] For example, for the first 400 sets of displacement calculation results, the corresponding probability density distribution of dam displacement is analyzed and the mean displacement under each water level is calculated. And the significant level α = Limits of the 5% displacement confidence interval , ,have: (13) in, , are the upper and lower limits of the displacement confidence interval at each water level, m 2. s 2 are the mean and standard deviation of the probability density distribution of dam displacement at each water level, is the standard score, the significant level α =5% .

[0094] In step S240, a water pressure component and a water pressure component interval value are obtained by fitting based on the displacement statistics and the displacement confidence interval.

[0095] In this step, the displacement statistic is the displacement mean. Specifically, a polynomial fitting is used for the displacement mean under different water levels to generate a water pressure component related to the water level. Similarly, a polynomial fitting is used for the limit value of the displacement confidence interval under different water levels to generate the corresponding water pressure component interval value.

[0096] Exemplarily, the expressions of the obtained water pressure components and water pressure component interval values ​​are: (14) in, Based on the displacement mean The water pressure component, and Based on the confidence interval limits , The upper and lower limits of the water pressure component, H m The upstream water depth, m represents the power exponent of the water level, M is the number of polynomial terms, usually 3 or 4, a m 、a’ m、a’’ m is the coefficient to be determined.

[0097] In step S250, a dam displacement monitoring hybrid model is constructed by combining the temperature component, the time-effect component and the water pressure component, and a dam displacement monitoring index is calculated based on the dam displacement monitoring hybrid model and the water pressure component interval value.

[0098] The dam displacement monitoring hybrid model includes the original dam displacement monitoring hybrid model and the target dam displacement monitoring hybrid model. Figure 6 As shown, step S250 may further include steps S610 to S630: Step S610, constructing the original dam displacement monitoring hybrid model by taking the environmental quantities corresponding to the temperature component, the time-effect component and the water pressure component as independent variables and the displacement of the dam body measuring point as the dependent variable; In the exemplary implementation of the present disclosure, the environmental quantities corresponding to the temperature component, the time-effect component and the water pressure component are used as independent variables, and the displacement of the dam body measuring point is used as the dependent variable to construct the original dam displacement monitoring hybrid model, which is: (15) in, is the displacement of the measuring point on the dam body, such as the horizontal displacement of measuring point B, are the displacement components of the dam body in three directions, d H is the water pressure component, i.e., the displacement of the dam body caused by the change of upstream water depth, d H The corresponding environmental quantity is the upstream water depth, d T is the temperature component, i.e. the displacement of the dam body due to temperature change, d T The corresponding environmental quantity is temperature, d θ is the time-dependent component, i.e., the displacement of the dam body due to time change, d θ The corresponding environmental quantity is time.

[0099] The temperature component in formula (15) d T and time-dependent d θ Determine according to formula (16): (16) in, T p The day before the observation p The average temperature of the day, t is the time from a certain day (days), m1. m 2 are the temperature factor coefficient and the period coefficient, which are generally 9~10. i = Project operation time (days) / 100, b p , b 1p , b 2p 、c 1. c 2 are all unknown coefficients.

[0100] However, in actual projects, due to the influence of fault replacement, installation time, earthquake or human factors on monitoring equipment, its monitoring benchmark does not match the initial displacement value of the dam, resulting in a certain difference between the measured displacement data and the displacement value obtained by finite element calculation. Therefore, when establishing the original dam displacement monitoring hybrid model, the embodiment of the present disclosure can add a correction parameter to the deterministic component (i.e., water pressure component) obtained by finite element calculation. L To make appropriate adjustments, at this time, formula (15) can be adjusted to: (17) Step S620, using the displacement monitoring data of the dam to be monitored and the environmental monitoring data to perform fitting analysis on each component in the original dam displacement monitoring hybrid model, to obtain the undetermined coefficients in the original dam displacement monitoring hybrid model; Still taking the measured horizontal displacement of measuring point B at the top of the maximum cross-section of the panel dam as an example, when establishing the displacement monitoring index of this measuring point, the horizontal displacement and environmental quantity monitoring data of this measuring point can be sorted out, among which the environmental quantity monitoring data include upstream water depth, temperature, etc.

[0101] For example, a total of 1700 sets of “displacement-environmental quantity” monitoring data are obtained, and the “displacement-environmental quantity” monitoring data are divided into a fitting sample set and a test sample set, where the fitting sample set contains 1600 sets of monitoring data and the test sample set contains 100 sets of monitoring data. Then, the fitting sample set is substituted into the original dam displacement monitoring hybrid model for partial least squares fitting, and the undetermined coefficients in each component are calculated, including all the undetermined coefficients shown in formulas (14) and (16).

[0102] Step S630, obtaining a target dam displacement monitoring hybrid model based on the undetermined coefficients, and substituting the water pressure component interval value into the target dam displacement monitoring hybrid model to obtain the dam displacement monitoring index of the dam to be monitored at a preset significance level.

[0103] It can be understood that if all the unknown coefficients shown in formulas (14) and (16) have been determined, the fitted target dam displacement monitoring hybrid model can be obtained. Then, the upper limit of the water pressure component obtained in step S240 is Lower limit of water pressure component Substituting the expression into the hybrid model for displacement monitoring of the target dam, the preset significance level is obtained as α =5% dam displacement monitoring index.

[0104] In this embodiment, the hybrid model for monitoring the displacement of the target dam has the advantages of small prediction residual, high prediction accuracy, good prediction stability, etc., and the correlation between the predicted value and the measured value is high, and the distribution form similarity is high. Therefore, the hybrid model for monitoring the displacement of the target dam has high feasibility and superiority in the prediction and analysis of dam displacement.

[0105] In the disclosed embodiment, the dam body structural mechanics and deformation characteristics are linked, and the random finite element method and the corresponding proxy model are used to simulate the water pressure component on the basis of the traditional structural analysis method to consider the uncertainty of the dam body material parameters, so as to more truly and accurately reflect the dam body deformation mechanism. When there is less monitoring data in the initial operation, the disclosed method can directly determine the corresponding monitoring indicators by combining the displacement statistical characteristics under different water levels and based on the hybrid model of target dam displacement monitoring. At the same time, the monitoring indicators are dynamically adjusted as the input environmental quantity changes, and the theoretical basis is more sufficient and has higher rationality.

[0106] In one example implementation, reference is made to Figure 7 As shown, a flow chart of another method for formulating dam displacement monitoring indicators is provided, and the process includes steps S701 to S712: Step S701, determining the random parameter characteristic values ​​of the target material parameters: establishing a finite element model of the dam to be monitored, determining the constitutive model used for each material partition divided in the finite element model, selecting the target material parameters from the constitutive model, and then determining the random parameter characteristic values ​​of the target material parameters; Step S702, performing random field simulation on target material parameters to obtain parameter random fields; Step S703, performing random finite element analysis to obtain the displacement of the dam body measurement points; Step S704, establishing a random simulation proxy model to replace the random finite element analysis, and outputting new displacements of the dam body measurement points; Step S705, determining whether the fitting accuracy requirement is met, if so, executing step S706, otherwise, increasing the number of random field simulations, and repeatedly executing steps S702 to S705 until the random simulation proxy model meets the fitting accuracy requirement; Step S706, obtaining the displacement probability density distribution at each water level according to the displacement of the dam body measurement point; Step S707, calculating the displacement mean at each water level; Step S708, calculating the displacement confidence interval at each water level; Step S709, performing polynomial fitting on the displacement mean at each water level to obtain the water pressure component; Step S710, performing polynomial fitting on the displacement confidence interval at each water level to obtain the water pressure component interval boundary value; Step S711, constructing a hybrid model for dam displacement monitoring: using the environmental quantities corresponding to the temperature component, the time-effect component and the water pressure component as independent variables, and using the displacement of the dam body measuring point as the dependent variable, to construct a hybrid model for dam displacement monitoring; Step S712, determine the undetermined coefficients of the model, and obtain the fitted hybrid model for dam displacement monitoring: use the displacement monitoring data of the dam to be monitored and the environmental monitoring data to fit and analyze the hybrid model for dam displacement monitoring, calculate the undetermined coefficients in the model, and obtain the hybrid model for dam displacement monitoring in which the undetermined coefficients in each component are determined. Then, substitute the water pressure component interval boundary value obtained in step S710 into the fitted hybrid model for dam displacement monitoring, and finally obtain the dam displacement monitoring index.

[0107] In this example implementation, a random field is used to simulate the uncertainty of the mechanical parameters of the dam material, and the water pressure component is determined based on the random simulation agent model, thereby establishing a hybrid model for dam displacement monitoring. Furthermore, the limit value of the displacement confidence interval is calculated based on the probability density distribution of the dam displacement at each water level, and then the dam displacement monitoring index is formulated. In this process, the meaning of each physical factor of the monitoring model is clear, the prediction error of the displacement at different measuring points changes little, the prediction stability is strong, and it has high accuracy and applicability. Moreover, by linking the structural mechanics and deformation characteristics of the dam body, and using the random finite element method combined with the random simulation agent model to simulate the water pressure component to consider the uncertainty of the dam material parameters, the deformation mechanism of the dam body is reflected more realistically and accurately. Therefore, the displacement monitoring index proposed in the present disclosure has higher accuracy and adaptability, sufficient theoretical basis, and better early warning effect.

[0108] In this exemplary embodiment, a device for formulating dam displacement monitoring indicators is also provided. Figure 8 As shown, the device may include a material parameter determination module 810, a parameter random simulation module 820, a probability density statistics module 830, a water pressure component determination module 840 and a monitoring index determination module 850, wherein: A material parameter determination module 810 is used to establish a finite element model of the dam to be monitored and determine target material parameters based on the finite element model; The parameter random simulation module 820 is used to perform random field simulation on the target material parameters, and perform random finite element analysis on the simulated parameter random field at different water levels to obtain the probability density distribution of dam body displacement at each water level; The probability density statistics module 830 is used to determine the displacement statistics and displacement confidence interval at each water level according to the probability density distribution of the dam body displacement; A water pressure component determination module 840, configured to obtain a water pressure component and a water pressure component interval value based on the displacement statistics and the displacement confidence interval fitting; The monitoring index determination module 850 is used to construct a dam displacement monitoring hybrid model by combining the temperature component, the time-effect component and the water pressure component, and calculate the dam displacement monitoring index based on the dam displacement monitoring hybrid model and the water pressure component interval value.

[0109] In an exemplary embodiment of the present disclosure, when the parameter random simulation module 820 performs random field simulation on the target material parameters and performs random finite element analysis on the simulated parameter random field at different water levels to obtain the probability density distribution of dam body displacement at each water level, it is specifically used to: Performing random field simulation on target material parameters of each material partition in the dam to be monitored to obtain a corresponding simulated random field; Assigning each of the simulated random fields to the corresponding dam body unit in the finite element model to obtain a parameter random field of the dam to be monitored; The parameter random field is subjected to random finite element analysis at different water levels to obtain the displacement of the dam body measuring points at each water level, and the probability density distribution of the dam body displacement at each water level is obtained by statistically analyzing the displacement of the dam body measuring points.

[0110] In an exemplary embodiment of the present disclosure, the simulated random field includes a correlated log-normal random field; when the parameter random simulation module 820 performs random field simulation on the target material parameters of each material partition in the dam to be monitored to obtain the corresponding simulated random field, it is specifically used to: generating a standard Gaussian random field according to the statistical distribution characteristics of the target material parameters and the spatial correlation defined by the Gaussian autocorrelation function; The random variable correlation coefficient matrix in the standard Gaussian random field is decomposed, and the relevant standard Gaussian random field is calculated according to the decomposition result and the relevant standard normal random sample matrix, and the relevant standard Gaussian random field is subjected to equal probability transformation processing to generate the relevant log-normal random field.

[0111] In an exemplary embodiment of the present disclosure, the dam body measuring point displacement includes a first dam body measuring point displacement, a second dam body measuring point displacement, a third dam body measuring point displacement and a fourth dam body measuring point displacement; the parameter random simulation module 820 performs random finite element analysis on the parameter random field at different water levels to obtain the dam body measuring point displacement at each water level, and obtains the dam body displacement probability density distribution at each water level by statistically analyzing the dam body measuring point displacement, specifically for: Performing random finite element analysis on the parameter random field at different water levels to obtain the displacement of the first dam body measuring point at each water level; A random simulation proxy model is established using the target material parameters and the first dam body measurement point displacement, and the random simulation proxy model is used to fit the second dam body measurement point displacement at each water level; comparing the statistical characteristic value error between the displacement of the first dam body measuring point and the displacement of the second dam body measuring point; If the statistical characteristic value error is greater than the preset difference, the number of the parameter random fields is increased, and random finite element analysis is performed on the parameter random fields after the adjustment under different water levels to obtain the displacement of the third dam body measuring point; Reconstructing and fitting a random simulation proxy model based on the displacement of the third dam body measuring point to obtain the displacement of the fourth dam body measuring point; If the statistical characteristic value error between the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point is less than or equal to the preset difference, the probability density distribution of the dam body displacement at each water level is obtained by statistically analyzing the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point.

[0112] In an exemplary embodiment of the present disclosure, when the parameter random simulation module 820 obtains the probability density distribution of the dam body displacement at each water level by counting the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point, it is specifically used to: Using the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point at each water level to perform stability detection, and obtain the target random field simulation number of the dam body measuring point displacement being in a stable state; The displacements of the dam body measuring points corresponding to the target random field simulation times are counted to obtain the probability density distribution of the dam body displacements at each water level.

[0113] In an exemplary embodiment of the present disclosure, the displacement statistics include a displacement mean and a displacement standard deviation; when the probability density statistics module 830 determines the displacement statistics and displacement confidence interval at each water level according to the probability density distribution of each dam body displacement, it is specifically used to: Performing statistical analysis on the probability density distribution of the displacement of each dam body, and calculating the corresponding displacement mean and displacement standard deviation; The limit values ​​of the displacement confidence interval are determined based on the displacement mean and displacement standard deviation and combined with the significance level.

[0114] In an exemplary embodiment of the present disclosure, when the water pressure component determination module 840 performs fitting based on the displacement statistics and the displacement confidence interval to obtain the water pressure component and the water pressure component interval value, it is specifically used to: The displacement mean under different water levels is fitted with a polynomial to generate the water pressure component related to the water level; Polynomial fitting is used to fit the limit values ​​of the displacement confidence interval under different water levels to generate the corresponding water pressure component interval values.

[0115] In an exemplary embodiment of the present disclosure, the dam displacement monitoring hybrid model includes an original dam displacement monitoring hybrid model and a target dam displacement monitoring hybrid model; The monitoring index determination module 850 is specifically used to: The original dam displacement monitoring hybrid model is constructed by taking the environmental quantities corresponding to the temperature component, the time-effect component and the water pressure component as independent variables and the displacement of the dam body measuring point as the dependent variable; Using the displacement monitoring data of the dam to be monitored and the environmental monitoring data, fitting and analyzing each component in the original dam displacement monitoring hybrid model is performed to obtain the undetermined coefficients in the original dam displacement monitoring hybrid model; A target dam displacement monitoring hybrid model is obtained based on the undetermined coefficients, and the water pressure component interval value is substituted into the target dam displacement monitoring hybrid model to obtain the dam displacement monitoring index of the dam to be monitored at a preset significance level.

[0116] In an exemplary embodiment of the present disclosure, when the material parameter determination module 810 establishes a finite element model of the dam to be monitored and determines the target material parameters based on the finite element model, it is specifically used to: Acquiring engineering data of the dam to be monitored, and establishing a finite element model of the dam to be monitored according to the engineering data; The constitutive model used for each material partition divided in the finite element model is determined, and target material parameters are selected from the constitutive model.

[0117] The specific details of each module of the above-mentioned dam displacement monitoring index formulation device have been described in detail in the corresponding dam displacement monitoring index formulation method, so they will not be repeated here.

[0118] The exemplary embodiments of the present disclosure also provide a computer-readable storage medium on which a program product capable of implementing the above-mentioned method of the present specification is stored. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code, and when the program product is run on an electronic device, the program code is used to enable the electronic device to perform the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned "Exemplary Method" section of the present specification.

[0119] The program product may be in the form of a portable compact disk read-only memory (CD-ROM) and include program code, and may be run on an electronic device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0120] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0121] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0122] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0123] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C#, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0124] In addition, an exemplary embodiment of the present disclosure also provides an electronic device capable of implementing the above-mentioned method for formulating dam displacement monitoring indicators.

[0125] Refer to the following Figure 9 hereinafter describes an electronic device 900 according to such an embodiment of the present disclosure. Figure 9 The electronic device 900 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0126] like Figure 9 As shown, the electronic device 900 is in the form of a general computing device. The components of the electronic device 900 may include but are not limited to: at least one processing unit 910, at least one storage unit 920, a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910), and a display unit 940.

[0127] The storage unit 920 stores program codes, which can be executed by the processing unit 910, so that the processing unit 910 performs the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. For example, the processing unit 910 can perform the method steps in the exemplary embodiments of the present disclosure.

[0128] The storage unit 920 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 921 and / or a cache memory unit (Cache) 922 , and may further include a read-only memory unit (ROM) 923 .

[0129] The storage unit 920 may also include a program / utility 924 having a set (at least one) of program modules 925, such program modules 925 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0130] Bus 930 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0131] The electronic device 900 may also communicate with one or more external devices 970 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 900, and / or communicate with any device that enables the electronic device 900 to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface 950. In addition, the electronic device 900 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 960. As shown, the network adapter 960 communicates with other modules of the electronic device 900 through a bus 930. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0132] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0133] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0134] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0135] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0136] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for formulating dam displacement monitoring indicators, characterized in that: include: Establishing a finite element model of the dam to be monitored, and determining target material parameters based on the finite element model; Performing random field simulation on the target material parameters, and performing random finite element analysis on the simulated parameter random fields at different water levels to obtain probability density distribution of dam body displacement at each water level; Determining the displacement statistics and displacement confidence interval at each water level according to the probability density distribution of the dam body displacement; Fitting the water pressure component and the water pressure component interval value based on the displacement statistical value and the displacement confidence interval; A dam displacement monitoring hybrid model is constructed by combining the temperature component, the time-effect component and the water pressure component, and a dam displacement monitoring index is calculated based on the dam displacement monitoring hybrid model and the water pressure component interval value.

2. The method for formulating dam displacement monitoring indicators according to claim 1, characterized in that: The random field simulation of the target material parameters is performed, and the random finite element analysis of the simulated parameter random field is performed at different water levels to obtain the probability density distribution of the dam body displacement at each water level, including: Performing random field simulation on target material parameters of each material partition in the dam to be monitored to obtain a corresponding simulated random field; Assigning each of the simulated random fields to the corresponding dam body unit in the finite element model to obtain a parameter random field of the dam to be monitored; The parameter random field is subjected to random finite element analysis at different water levels to obtain the displacement of the dam body measuring points at each water level, and the probability density distribution of the dam body displacement at each water level is obtained by statistically analyzing the displacement of the dam body measuring points.

3. The method for formulating dam displacement monitoring indicators according to claim 2 is characterized in that: The simulated random field includes a correlated log-normal random field; The random field simulation of the target material parameters of each material partition in the dam to be monitored to obtain the corresponding simulated random field includes: generating a standard Gaussian random field according to the statistical distribution characteristics of the target material parameters and the spatial correlation defined by the Gaussian autocorrelation function; The random variable correlation coefficient matrix in the standard Gaussian random field is decomposed, and the relevant standard Gaussian random field is calculated according to the decomposition result and the relevant standard normal random sample matrix, and the relevant standard Gaussian random field is subjected to equal probability transformation processing to generate the relevant log-normal random field.

4. The method for formulating dam displacement monitoring indicators according to claim 2 is characterized in that: The dam body measuring point displacement includes the first dam body measuring point displacement, the second dam body measuring point displacement, the third dam body measuring point displacement and the fourth dam body measuring point displacement; The random finite element analysis of the parameter random field under different water levels is performed to obtain the displacement of the dam body measuring points under each water level, and the probability density distribution of the dam body displacement under each water level is obtained by statistically analyzing the displacement of the dam body measuring points, including: Performing random finite element analysis on the parameter random field at different water levels to obtain the displacement of the first dam body measuring point at each water level; A random simulation proxy model is established using the target material parameters and the first dam body measurement point displacement, and the random simulation proxy model is used to fit the second dam body measurement point displacement at each water level; comparing the statistical characteristic value error between the displacement of the first dam body measuring point and the displacement of the second dam body measuring point; If the statistical characteristic value error is greater than the preset difference, the number of the parameter random fields is increased, and random finite element analysis is performed on the parameter random fields after the adjustment under different water levels to obtain the displacement of the third dam body measuring point; Reconstructing and fitting a random simulation proxy model based on the displacement of the third dam body measuring point to obtain the displacement of the fourth dam body measuring point; If the statistical characteristic value error between the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point is less than or equal to the preset difference, the probability density distribution of the dam body displacement under each water level is obtained by statistically analyzing the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point.

5. The method for formulating dam displacement monitoring indicators according to claim 4 is characterized in that: The method of obtaining the probability density distribution of the dam body displacement at each water level by counting the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point includes: Using the displacement of the third dam body measuring point and the displacement of the fourth dam body measuring point at each water level to perform stability detection, and obtain the target random field simulation number of the dam body measuring point displacement being in a stable state; The displacements of the dam body measuring points corresponding to the target random field simulation times are counted to obtain the probability density distribution of the dam body displacements at each water level.

6. The method for formulating dam displacement monitoring indicators according to claim 1, characterized in that: The displacement statistics include a displacement mean and a displacement standard deviation; Determining the displacement statistics and displacement confidence interval at each water level according to the probability density distribution of the dam body displacements includes: Performing statistical analysis on the probability density distribution of the displacement of each dam body, and calculating the corresponding displacement mean and displacement standard deviation; The limit values ​​of the displacement confidence interval are determined based on the displacement mean and displacement standard deviation and combined with the significance level.

7. The method for formulating dam displacement monitoring indicators according to claim 6, characterized in that: The fitting based on the displacement statistics and the displacement confidence interval to obtain the water pressure component and the water pressure component interval value includes: The displacement mean under different water levels is fitted with a polynomial to generate the water pressure component related to the water level; Polynomial fitting is used to fit the limit values ​​of the displacement confidence interval under different water levels to generate the corresponding water pressure component interval values.

8. The method for formulating dam displacement monitoring indicators according to claim 1, characterized in that: The dam displacement monitoring hybrid model includes an original dam displacement monitoring hybrid model and a target dam displacement monitoring hybrid model; The dam displacement monitoring hybrid model is constructed by combining the temperature component, the time-effect component and the water pressure component, and the dam displacement monitoring index is calculated based on the dam displacement monitoring hybrid model and the water pressure component interval value, including: The original dam displacement monitoring hybrid model is constructed by taking the environmental quantities corresponding to the temperature component, the time-effect component and the water pressure component as independent variables and the displacement of the dam body measuring point as the dependent variable; Using the displacement monitoring data of the dam to be monitored and the environmental monitoring data, fitting and analyzing each component in the original dam displacement monitoring hybrid model is performed to obtain the undetermined coefficients in the original dam displacement monitoring hybrid model; A target dam displacement monitoring hybrid model is obtained based on the undetermined coefficients, and the water pressure component interval value is substituted into the target dam displacement monitoring hybrid model to obtain the dam displacement monitoring index of the dam to be monitored at a preset significance level.

9. The method for formulating dam displacement monitoring indicators according to claim 1, characterized in that: The step of establishing a finite element model of the dam to be monitored and determining target material parameters based on the finite element model includes: Acquiring engineering data of the dam to be monitored, and establishing a finite element model of the dam to be monitored according to the engineering data; The constitutive model used for each material partition divided in the finite element model is determined, and target material parameters are selected from the constitutive model.

10. A dam displacement monitoring index formulation device, characterized in that: include: A material parameter determination module, used to establish a finite element model of the dam to be monitored and determine target material parameters based on the finite element model; A parameter random simulation module is used to perform random field simulation on the target material parameters, and perform random finite element analysis on the simulated parameter random field at different water levels to obtain the probability density distribution of dam body displacement at each water level; A probability density statistics module, used to determine the displacement statistics and displacement confidence interval at each water level according to the probability density distribution of the dam body displacement; A water pressure component determination module, used for obtaining a water pressure component and a water pressure component interval value based on the displacement statistics and the displacement confidence interval fitting; The monitoring index determination module is used to construct a dam displacement monitoring hybrid model by combining the temperature component, the time-effect component and the water pressure component, and calculate the dam displacement monitoring index based on the dam displacement monitoring hybrid model and the water pressure component interval value.

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