CN110765679B - Dam monitoring web display method based on finite element model and SVM regression algorithm - Google Patents
Dam monitoring web display method based on finite element model and SVM regression algorithm Download PDFInfo
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Abstract
The invention discloses a dam monitoring web display method based on a finite element model and an SVM regression algorithm, which comprises the steps of selecting a typical section of a target dam, introducing the selected typical section of the dam into Hypermesh for grid division, and classifying and storing node unit information in each grid; searching the profile outline through the corresponding information between the nodes and the units: forming a complete closed graph by using the edges of each grid once, wherein the closed graph can be used as the outline of the profile of the dam; acquiring target measuring points and target date data from a monitoring platform database in real time; performing secondary processing on the independent variable, and expanding independent variable information; constructing an SVM regressive, selecting a relaxation variable, and adjusting SVM parameters aiming at a training set; preprocessing the coordinates of the finite element nodes and calculating target values, and diffusing the measurement point information to the full section; and interpolating to draw a cloud image of the measured value of the cross section of the dam body, and displaying the drawn contour line in real time. The monitoring result of the invention has better timeliness.
Description
Technical Field
The invention relates to the technical field of dam detection, in particular to a dam monitoring web display method based on a finite element model and an SVM regression algorithm.
Background
With the water conservancy project playing an increasingly important role in the Chinese energy structure, the safety problem of the dam is also increasingly prominent, so the field of dam safety monitoring is born. The specific mode is that specific monitoring instruments are installed in the dam body or on the outer surface of the dam body in the construction process and after the construction is completed, and the specific monitoring instruments are respectively responsible for different monitoring objects, such as horizontal displacement of the surface of the dam body in the parallel direction, horizontal displacement of the surface of the dam body in the transverse direction, settlement of the measuring point position of the dam body in the vertical direction and the like.
The existing monitoring data analysis means mainly comprises offline mode, namely half a year or one year is taken as a period, the data collected by the monitoring instrument are uniformly subjected to characteristic value analysis, future development trend is predicted, and the safety state of the building is judged. However, this approach has a certain post ductility and cannot be used for real-time analysis of the dam conditions. The analysis algorithm is mainly a multiple linear regression method, has simple principle and easy application, has a certain physical meaning, and can be used for amplitude analysis of measured values. However, multiple regression algorithms have gradually lost their feasibility under the combined effects of increasingly complex environmental factors and changes in dam behavior. There is an urgent need for better performing algorithms to address these problems.
The traditional monitoring analysis often breaks away from the building body, simply performs data analysis, is not closely related to engineering, and performs modeling calculation on single monitoring point position data, and although the analysis method accurately reflects the change trend of the position of the measuring point, the analysis method fails to consider the relation and the behavior change of the whole building. Because of high cost and the need of ensuring the safety of the structure, the position arrangement of the measuring points in the dam body and on the surface is extremely sparse, so that the single-measuring-point model cannot meet the evaluation of the overall safety state of the dam.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a dam monitoring web display method based on a finite element model and an SVM regression algorithm, which solves the technical problem of inaccurate evaluation of the overall safety state of a dam in the prior art.
In order to achieve the above object, the present invention adopts the following technical scheme:
a dam monitoring web presentation method based on a finite element model and an SVM regression algorithm comprises the following steps:
selecting a typical section of a target dam, introducing the selected typical section of the dam into Hypermesh for grid division, and classifying and storing node unit information in each grid;
searching the profile outline through the corresponding information between the nodes and the units: forming a complete closed graph by using the edges of each grid once, wherein the closed graph can be used as the outline of the profile of the dam;
acquiring target measuring points and target date data from a monitoring platform database in real time; performing secondary processing on the independent variable, and expanding independent variable information;
constructing an SVM regressive, selecting a relaxation variable, and adjusting SVM parameters aiming at a training set;
preprocessing the coordinates of the finite element nodes and calculating target values, and diffusing the measurement point information to the full section;
and interpolating to draw a cloud image of the measured value of the cross section of the dam body, and displaying the drawn contour line in real time.
As a preferable scheme of the invention, the dam monitoring web display method based on the finite element model and the SVM regression algorithm adopts quadrilateral grids for grid division.
As a preferable scheme of the invention, the dam monitoring web display method based on the finite element model and the SVM regression algorithm stores node number coordinates and unit numbers as a folder; the constituent nodes are saved sequentially as another folder.
As a preferred scheme of the invention, the dam monitoring web display method based on the finite element model and the SVM regression algorithm lists the edges of all grids, counts the edges forming only one grid as 1, counts the edges forming two grids as 2, deletes the edges counted as 2, sorts all the edges counted as 1 through the number of the head node and the tail node to form a complete closed graph, and the closed graph can be used as the outline of the section of the dam.
As a preferred scheme of the present invention, in the dam monitoring web display method based on the finite element model and the SVM regression algorithm, the first column of the independent variables is the X-coordinate of the measuring point, the first independent variable is the X, the second column is the y-coordinate of the measuring point, the second independent variable is the X, the independent variables are combined by the square, the square of the X-coordinate is generated, the third independent variable is the X, the square of the y-coordinate is the fourth independent variable is the X, and the X y of each measuring point is the fifth independent variable is the X.
As a preferable scheme of the invention, the dam monitoring web display method based on the finite element model and the SVM regression algorithm comprises the following steps of: and introducing the independent variable X as data into an SVM regressive device, iteratively correcting coefficients in the SVM with the dependent variable as a target, and reducing the output error of the regressive device. In actual engineering, a specific mapping relation exists between an independent variable X and a dependent variable y in a function space, so that an X state can only correspond to a result state y, but the mapping relation is mostly complex and changeable in a real state, and the function relation is difficult to accurately describe through the existing linear analysis means.
As a preferable scheme of the invention, the dam monitoring web display method based on the finite element model and the SVM regression algorithm has the range of 0.01-1000 of selected relaxation variables.
As a preferred scheme of the invention, the dam monitoring web showing method based on the finite element model and the SVM regression algorithm enables the coordinate system of the finite element model to be consistent with the actual engineering coordinate, outputs the node coordinate of the finite element model, constructs five columns of data through a method of expanding independent variable information, takes the independent variable as the independent variable of a test set, inputs the independent variable into the SVM regression which is built and completed in the middle, generates a prediction target value, and expands a small amount of measuring point displacement and measuring value information in a typical section to the whole section.
As a preferable scheme of the invention, the dam monitoring web display method based on the finite element model and the SVM regression algorithm adopts the griddata function in python to interpolate the predicted value in the whole section, forms a continuously-changing target value on the typical section, clears redundant interpolation points through the searched section outline, and finally draws a corresponding cloud image and a contour image.
As a preferred scheme of the invention, the dam monitoring web display method based on the finite element model and the SVM regression algorithm selects a relaxation variable equal to 1000.
The invention has the beneficial effects that:
the method monitors the typical section of the dam in real time through the finite element model and the nonlinear algorithm, the monitoring result has good timeliness, and the monitoring structure can be displayed in real time by drawing the cloud chart of the measured value of the section of the dam body.
The method can preprocess the finite element coordinates selected from the typical section of the dam and calculate the target value, combines the first-order coordinates to rise into the second-order coordinates, expands independent variable information and spreads the measuring point information to the full section.
Drawings
FIG. 1 is a general flow chart of the present invention;
FIG. 2 is a full-section contour map of the present method.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
As shown in fig. 1: the embodiment discloses a dam monitoring web display method based on a finite element model and an SVM regression algorithm, which comprises the steps of firstly selecting a typical section of a target dam, wherein the selection of the typical section can be performed according to an engineering drawing of the dam. The typical profile referred to herein may be understood as a "dangerous profile", i.e., a profile where under normal conditions, assuming a dam crossing, damage occurs first, or a profile representative of monitoring. The selected typical section of the dam is led into Hypermesh for grid division, and as a plane is selected, the grid (unit) type on the plane mainly comprises triangles and quadrilaterals, and the quadrangular grid is preferably adopted in the embodiment, namely, each unit has four nodes. Then classifying and storing node unit information in each grid; storing the node number coordinates and the unit numbers in the model as a folder; and sequentially storing the component nodes as another folder, and placing the folder into the back end for subsequent data processing.
Searching the profile outline through the corresponding information between the nodes and the units: the whole section consists of a plurality of quadrangles (units), so that the outline of the section is formed by connecting edge edges in the edge quadrangles end to end, and the outline of the edge edges of the edge quadrangles can be used as the section outline of the dam.
The specific practice of this embodiment is: listing the sides of all grids, counting the sides forming only one grid to be 1, counting the sides forming two grids to be 2, wherein the sides with the counted number of 1 are at the edge, deleting the sides with the counted number of 2, and sequencing all the sides with the counted number of 1 through the head-tail node number to form a complete closed graph which can be used as the outline of the section of the dam.
Acquiring target measuring points and target date data from a monitoring platform database in real time; and performing secondary processing on the independent variable, and expanding independent variable information.
Because the typical cross-section of the intercept dam is planar, the calculation uses a biaxial coordinate system, the x-axis and the y-axis, respectively. A quadratic combination of the coordinates is calculated. The following are provided:
x=point_data [: 0] # measurement point x-direction coordinate
y=point_data [: 1] # measurement point y-direction coordinate
row＝x.shape[0]
Square of x_2=pow (x, 2) # x coordinate
Square of y_2=pow (y, 2) # y coordinates
Combination of xy=x×y#x and y coordinates
one＝np.ones((row,1))
# are combined into a high-dimensional independent variable X as input data of the SVM
X＝np.column_stack((np.double(one),np.double(x),np.double(y),np.double(z),np.double(x_2),np.double(y_2),np.double(xy))
And measuring the value of the # measuring point, which is used as training target data of the SVM.
disp＝point_data[:,3]
The variable point_data stores four rows of data, wherein the first row is the X-coordinate of a measuring point, the first row is the first independent variable of X, the second row is the y-coordinate of the measuring point, the second independent variable of X is the second independent variable of X, the independent variables are combined by the square, the square of the X-coordinate is generated, the third independent variable of X is the square of the y-coordinate is the fourth independent variable of X, and the X-y of each measuring point is the fifth independent variable of X. The combined X is the independent variable training set of the SVM regressor. The fourth column in point_data is a measurement point target date measurement value disp, and is used as a target variable training set of the SVM regressor.
And constructing an SVM regressive, selecting a relaxation variable, training an internal parameter gradient of the SVM according to a training data set, and adjusting a functional relation between a model input variable and an output variable of the SVM. And introducing the independent variable X as data into an SVM regressive device, iteratively correcting coefficients in the SVM by taking the dependent variable as a target, reducing the output error of the regressive device, and approximating the implicit functional relation between the independent variable and the dependent variable. The value of the relaxation variable C is determined according to trial calculation, after the relaxation variable C is manually selected before the training phase begins, the parameter is kept unchanged in the training process, the span range is 0.01-1000, and the value of C corresponding to the minimum output error is finally selected to be 1000 in the embodiment. And after training, generating a target model regressor.
Preprocessing the coordinates of the finite element nodes and calculating target values, and diffusing the measurement point information to the full section; the method specifically comprises the following steps: the coordinate system of the finite element model is kept consistent with the actual engineering coordinate, the node coordinate of the finite element model is output, five columns of data (five independent variables of X) are constructed through the method for expanding independent variable information, the independent variables are used as independent variables of a test set, the independent variables are input into an SVM regressor which is built in the middle, a prediction target value is generated, and a small amount of measuring point displacement and measuring value information in a typical section are reasonably expanded to the whole section.
Interpolating to draw a cloud image of the measured value of the cross section of the dam body, and displaying the drawn contour line in real time; interpolation is carried out on the predicted value in the whole section by adopting a griddata function in python, a continuously-changing target value is formed on a typical section, redundant interpolation points are cleared through the searched section outline, namely, redundant interpolation points outside the section outline are deleted, and finally, a corresponding cloud image and a contour image are drawn, as shown in fig. 2.
The method monitors the typical section of the dam in real time through the finite element model and the nonlinear algorithm, the monitoring result has good timeliness, and the monitoring structure can be displayed in real time by drawing the cloud chart of the measured value of the section of the dam body.
The method can preprocess the finite element coordinates selected from the typical section of the dam and calculate the target value, combines the first-order coordinates to rise into the second-order coordinates, expands independent variable information and spreads the measuring point information to the full section.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (10)
1. A dam monitoring web display method based on a finite element model and an SVM regression algorithm is characterized in that:
selecting a typical section of a target dam, introducing the selected typical section of the dam into Hypermesh for grid division, and classifying and storing node unit information in each grid;
searching the profile outline through the corresponding information between the nodes and the units: forming a complete closed figure by using the edges which are used once in each grid, wherein the closed figure is taken as the outline of the profile of the dam;
acquiring target measuring points and target date data from a monitoring platform database in real time; performing secondary processing on the independent variable, and expanding independent variable information;
constructing an SVM regressive, selecting a relaxation variable, and adjusting SVM parameters aiming at a training set;
preprocessing the coordinates of the finite element nodes and calculating target values, and diffusing the measurement point information to the full section;
and interpolating to draw a cloud image of the measured value of the cross section of the dam body, and displaying the drawn contour line in real time.
2. The dam monitoring web presentation method based on the finite element model and the SVM regression algorithm as claimed in claim 1, wherein the method comprises the following steps: the grid division adopts quadrilateral grids.
3. The dam monitoring web presentation method based on the finite element model and the SVM regression algorithm as claimed in claim 2, wherein the method comprises the following steps: the node unit information storage is to store the node number coordinates and the unit numbers as a folder; the constituent nodes are saved sequentially as another folder.
4. The dam monitoring web presentation method based on the finite element model and the SVM regression algorithm according to claim 1 or 2, wherein the dam monitoring web presentation method is characterized in that: listing the sides of all grids, counting the sides forming only one grid as 1, counting the sides forming two grids as 2, deleting the sides with the counts as 2, and sequencing all sides with the counts as 1 through the number of the head and tail nodes to form a complete closed graph which is taken as the profile of the dam section.
5. The dam monitoring web presentation method based on the finite element model and the SVM regression algorithm as claimed in claim 1, wherein the method comprises the following steps: the first column of arguments is the X-coordinate of the measurement point, as the first argument of X, the second column is the y-coordinate of the measurement point, as the second argument of X, combining the arguments by a square yields the square of the X-coordinate, as the third argument of X, the square of the y-coordinate as the fourth argument of X, and the X y of each measurement point, as the fifth argument of X.
6. The dam monitoring web presentation method based on the finite element model and the SVM regression algorithm according to claim 5, wherein the dam monitoring web presentation method is characterized in that: the method for constructing the SVM regressive device comprises the following steps: introducing the independent variable X as data into an SVM regressive, iteratively correcting coefficients in the SVM with the dependent variable as a target, and reducing the output error of the regressive; in actual engineering, a specific mapping relation exists between an independent variable X and an independent variable y in a function space, so that one X state can only correspond to one result state y, but the mapping relation is complex and changeable in a real state, and the function relation is difficult to accurately describe by the existing linear analysis means.
7. The dam monitoring web presentation method based on the finite element model and the SVM regression algorithm according to claim 6, wherein the dam monitoring web presentation method is characterized in that: the selected relaxation variable ranges from 0.01 to 1000.
8. The dam monitoring web presentation method based on the finite element model and the SVM regression algorithm according to claim 5, wherein the dam monitoring web presentation method is characterized in that: the method comprises the steps of enabling a finite element model coordinate system to be consistent with an actual engineering coordinate, outputting a node coordinate of a finite element model, constructing five-column data through a method of expanding independent variable information, taking the five-column data as an independent variable of a test set, inputting the independent variable into an SVM regressor with built-in structure, generating a prediction target value, and expanding a small amount of measuring point displacement and measuring value information in a typical section to the whole section.
9. The dam monitoring web presentation method based on the finite element model and the SVM regression algorithm according to claim 8, wherein the dam monitoring web presentation method is characterized in that: interpolating the predicted value in the whole section by adopting a griddata function in python, forming a continuously-changing target value on a typical section, clearing redundant interpolation points through the searched section outline, and finally drawing a corresponding cloud image and a contour image.
10. The dam monitoring web presentation method based on the finite element model and the SVM regression algorithm according to claim 7, wherein the dam monitoring web presentation method is characterized in that: the selected relaxation variable is equal to 1000.
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