CN111553003A - Visual large-span cable-stayed bridge cable evaluation method based on data driving - Google Patents
Visual large-span cable-stayed bridge cable evaluation method based on data driving Download PDFInfo
- Publication number
- CN111553003A CN111553003A CN202010260401.6A CN202010260401A CN111553003A CN 111553003 A CN111553003 A CN 111553003A CN 202010260401 A CN202010260401 A CN 202010260401A CN 111553003 A CN111553003 A CN 111553003A
- Authority
- CN
- China
- Prior art keywords
- cable
- data
- cable force
- degree value
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L5/00—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes
- G01L5/04—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes for measuring tension in flexible members, e.g. ropes, cables, wires, threads, belts or bands
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
- G01M5/0008—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings of bridges
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/60—Editing figures and text; Combining figures or text
Abstract
The invention discloses a visualized large-span cable-stayed bridge cable evaluation method based on data drive, which comprises the steps of firstly obtaining cable force, temperature, humidity, wind power, wind speed, deflection and other data based on time distribution by a health monitoring system aiming at cables, then calculating the most relevant factors influencing the cable force to serve as next evaluation indexes, drawing and reflecting the evaluation indexes of five dimensions of the regression coefficient of the most sensitive environmental factor, the change of the cable force, the change of the standard difference of the deflection, the similarity degree of the time distribution of the cable force and the similarity degree of the time distribution of the symmetric cable force on the same radar map, simply and clearly presenting the overall state of the cables in two compared time periods and the independent change degrees of the five evaluation indexes, determining whether the cables need to be subjected to manual retest according to the graph, and determining the reason causing radar image abnormity, the current cable state is truly reflected, and the evaluation model of the radar map is flexible and can be used in other space-time distribution scenes.
Description
Technical Field
The invention relates to the technical field of bridge health monitoring and assessment. More specifically, the invention relates to a visualized large-span cable-stayed bridge cable evaluation method based on data driving.
Background
The bridge health monitoring is an important means for ensuring the safe operation of the bridge, and the health monitoring system of the bridge comprises various sensors and other strong data acquisition capabilities which can acquire mass data including temperature, humidity, wind speed, wind direction, displacement, stress, strain and the like. The data looks like disorder and is mixed with a large amount of errors and noises, but contains a large amount of information in the bridge operation process, so that after the data is refined and mined, on one hand, the health state of the bridge can be known, and on the other hand, decision basis can be provided for operation and maintenance. Especially for a cable-stayed bridge, a stay cable is a key part of the bridge, a large amount of documents and actual work show that the stay cable is most easily damaged in the using process of the cable-stayed bridge, but a difficult problem exists in how to evaluate the state of the stay cable by utilizing the existing actual monitoring data.
Disclosure of Invention
An object of the present invention is to solve at least the above problems and to provide at least the advantages described later.
The invention also aims to provide a data-driven visual large-span cable-stayed bridge cable evaluation method to solve the technical problems of complicated modeling process, no clear and visual contrast and poor authenticity of the reflected state of the existing bridge evaluation method.
To achieve these objects and other advantages in accordance with the present invention, there is provided a data-driven visual large-span cable-stayed bridge cable evaluation method, comprising the steps of:
firstly, respectively acquiring data of six parameters of cable force, temperature, humidity, wind power, wind speed and deflection of a large-span cable-stayed bridge cable to be monitored as original data;
step two, data preprocessing, namely performing noise reduction processing on the original data acquired in the step one and then storing the original data;
thirdly, analyzing and calculating by using the data stored in the second step, calculating Pearson correlation coefficients of the cable force and four parameter data of temperature, humidity, wind speed and wind power respectively, comparing the Pearson correlation coefficients, selecting the parameter with the maximum Pearson correlation coefficient as the environmental factor with the strongest sensitivity, setting a time scale, calculating regression coefficients of the cable force and the environmental factor with the strongest sensitivity in two time periods with the same time scale respectively, calculating a difference value to obtain a regression coefficient change degree value, calculating a cable force change degree value and a deflection standard deviation change degree value respectively according to the data of the two time periods selected when the regression coefficient change degree is calculated, and calculating the cable force time distribution similarity degree value and the symmetrical cable force time distribution similarity degree value in the same time period of different years by using a DTW algorithm;
step four, carrying out data normalization processing on the regression coefficient change degree value, the cable force change degree value, the deflection standard deviation change degree value, the cable force time distribution similarity degree value and the symmetric cable force time distribution similarity degree value obtained by calculation in the step three;
and step five, drawing a visual radar chart according to the data subjected to normalization processing in the step four.
Preferably, in the second step, the noise reduction processing is to process an empty value and an abnormal value.
Preferably, the null value is processed by directly deleting the null value, and the abnormal value is processed by using the Lauder criterion.
Preferably, the specific steps of the ralada criterion used when processing the abnormal value are: (1) calculating a first quantile Q1 and a third quantile Q3 of the data sample, (2) calculating IQR from Q3 to Q1, (3) comparing all data in the data sample with ± 1.5IQR, and recognizing data exceeding ± 1.5IQR as an abnormal value, (4) deleting the abnormal value and replacing by an interval median.
Preferably, in step three, the Pearson correlation coefficient R is calculated by the formula
Xi、YiRespectively variable X, Y corresponds to the monitored value at point i,
is the average number of X samples and,
is the average number of Y samples and n is the sample volume.
Preferably, when calculating the change degree value of the cable force in the third step, the formula is used
Calculation of where x1、x2Is the average of the data over two different time periods.
Preferably, the DTW algorithm in step three comprises the following steps:
(1) setting the cable force data in the first time period as a time series R, R ═ { R (1), R (2), … R (m) }, and setting the cable force data in the second time period as a cable force time series T, T ═ { T (1), T (2), … R (n) }, where m and n are not necessarily equal;
(2) calculating a distance matrix of R and T;
(3) the process of finding the best path of the smallest accumulated distance in the distance matrix and searching the path, wherein the available previous lattice point (n, m) can only be (n-1, m), (n-1, m-1), (n, m-1);
(4) the final cumulative distance is D (T (n)), R (m)) + min { D (T (n-1), R (m)), + D (T (n-1), R (n-1)), D (T (n), R (m-1)) }.
Preferably, in step four, the normalization process is performed by a Softmax function, wherein the Softmax formula is
j＝1,…5,zjAnd step three, obtaining a regression coefficient change degree value, a cable force change degree value, a deflection standard deviation change degree value, a cable force time distribution similarity degree value and a symmetric cable force time distribution similarity degree value.
The invention at least comprises the following beneficial effects: the invention relates to a visualized large-span cable-stayed bridge cable evaluation method based on data driving, which comprises the steps of firstly obtaining cable force, temperature, humidity, wind power, wind speed, deflection and other data based on time distribution by aiming at each health monitoring system, namely a sensor arranged on a cable, then calculating the most relevant factors influencing the cable force to serve as next evaluation indexes, simply and clearly presenting the integral state of the cable and the independent change degrees of the five evaluation indexes in two compared time periods by drawing and reflecting the evaluation indexes of five dimensions of the regression coefficient of the most sensitive environmental factor, the change of the cable force, the change of the standard difference of the deflection, the time distribution similarity degree of the cable force and the time distribution similarity degree of the symmetric cable force on the same radar map, determining whether the cable needs to be subjected to manual retesting according to the map, and further determining which reasons are image abnormity caused by sensor faults, the state of the current stay cable can be reflected more truly, and the evaluation model of the radar map is very flexible and can be used in other space-time distribution scenes.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a time profile of a cable force according to an embodiment of the present invention;
fig. 3 is a radar chart of an embodiment of the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
It is to be noted that the experimental methods described in the following embodiments are all conventional methods unless otherwise specified, and the reagents and materials, if not otherwise specified, are commercially available; in the description of the present invention, the terms "lateral", "longitudinal", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
As shown in the flowchart of fig. 1, the invention provides a data-driven visualized evaluation method for a cable of a long-span cable-stayed bridge, which comprises the following steps:
firstly, respectively acquiring data of six parameters of cable force, temperature, humidity, wind power, wind speed and deflection of a large-span cable-stayed bridge cable to be monitored as original data;
step two, data preprocessing, namely performing noise reduction processing on the original data acquired in the step one and then storing the original data;
thirdly, analyzing and calculating by using the data stored in the second step, calculating Pearson correlation coefficients of the cable force and four parameter data of temperature, humidity, wind speed and wind power respectively, comparing the Pearson correlation coefficients, selecting the parameter with the maximum Pearson correlation coefficient as the environmental factor with the strongest sensitivity, setting a time scale, calculating regression coefficients of the cable force and the environmental factor with the strongest sensitivity in two time periods with the same time scale respectively, calculating a difference value to obtain a regression coefficient change degree value, calculating a cable force change degree value and a deflection standard deviation change degree value respectively according to the data of the two time periods selected when the regression coefficient change degree is calculated, and calculating the cable force time distribution similarity degree value and the symmetrical cable force time distribution similarity degree value in the same time period of different years by using a DTW algorithm;
step four, carrying out data normalization processing on the regression coefficient change degree value, the cable force change degree value, the deflection standard deviation change degree value, the cable force time distribution similarity degree value and the symmetric cable force time distribution similarity degree value obtained by calculation in the step three;
and step five, drawing a visual radar chart according to the data subjected to normalization processing in the step four.
In the prior art, deflection sensors are arranged at different sections of a bridge, an environment temperature and humidity monitoring sensor and a wind speed and direction sensor are arranged at the top of the tower, a cable force sensor and the like are arranged at a cable, the health state of a bridge cable is monitored through various sensors, and data obtained by the sensors are generally in normal distribution. Combining with the flow chart shown in fig. 1, the cable evaluation method of the invention firstly obtains six parameters of cable force, temperature, humidity, wind power, wind speed and deflection of the monitored large-span cable-stayed bridge cable as original data, wherein the temperature, the humidity, the wind power and the wind speed are environmental influence factors, then carries out noise reduction treatment, removes abnormal values such as blank and obviously deviates from normal distribution trend, then saves the abnormal values, then selects a calculation sample from the data for calculation, calculates Pearson correlation coefficients of the cable force and the four parameter data of the temperature, the humidity, the wind speed and the wind power respectively and compares the Pearson correlation coefficients, and the maximum value of the correlation coefficient is the correlation with the cable forceThe method includes the steps of setting a time scale, calculating regression coefficients of the cable force of the first quarter of the present year and the first quarter of the last year or the first quarter and the second quarter of a certain year and the environmental factor with the highest sensitivity in two time periods with the same time scale by taking a month, a season and a year as units, calculating a difference value to obtain a regression coefficient change degree value, which is equivalent to obtaining the change situation of the environmental factor and the cable force correlation degree in different time periods, quantifying the sensitivity degree through the regression coefficients, and calculating the regression coefficients by adopting the following formula: regression coefficient
Wherein, yiFor the ith prediction value of the sample,
the ith value is predicted for the regression,
calculating the change degree value of the cable force and the standard deviation change degree value of the deflection according to the data of two time periods selected when the change degree of the regression coefficient is calculated, calculating the time distribution similarity degree value of the cable force and the time distribution similarity degree value of the symmetric cable force in the same time period of different years by using a DTW algorithm, wherein the cumulative distance D (T (n) and R (m) obtained by the DTW algorithm are the time distribution similarity degree value and the time distribution similarity degree value of the symmetric cable force which are obtained by calculating the corresponding data, and the standard deviation can adopt a formula
xiThe ith value of the sample, x is the average value of the sample, n is the capacity of the sample, then 5-dimensional data of the obtained regression coefficient change degree value, the change degree value of the cable force, the standard deviation change degree value of the deflection, the time distribution similarity degree value of the cable force and the time distribution similarity degree value of the symmetric cable force are normalized and a visual radar graph is drawn,the symmetric cable force is the cable force symmetrically arranged on two sides of the cable-stayed bridge in the transverse bridge direction.
The invention relates to a visualized large-span cable-stayed bridge cable evaluation method based on data driving, which comprises the steps of firstly obtaining cable force, temperature, humidity, wind power, wind speed, deflection and other data based on time distribution by aiming at each health monitoring system, namely a sensor arranged on a cable, then calculating the most relevant factors influencing the cable force to serve as next evaluation indexes, drawing and reflecting evaluation indexes of five dimensions, namely regression coefficients of environment factors with the strongest sensitivity, cable force change, deflection standard deviation change, cable force time distribution similarity and symmetric cable force time distribution similarity on the same radar map, simply and clearly presenting the compared integral states of the cable in two time periods and the independent change degrees of the five evaluation indexes, determining whether the cable needs to be subjected to manual retest according to the graph, and further determining which reasons, such as abnormal or missing of monitoring data caused by sensor failure or obvious sensitivity reduction The image is abnormal, the current cable state can be reflected more truly, and the evaluation model of the radar map is very flexible and can be used in other space-time distribution scenes.
In another technical scheme, in the step two, the noise reduction processing is to process an empty value and an abnormal value. And the empty value and the abnormal value are processed, so that the integrity of the acquired data is ensured, and the accuracy of data calculation is prevented from being influenced.
In another technical scheme, the vacancy value is processed in a mode of directly deleting the vacancy value, and an abnormal value is processed by adopting a Lauda criterion. When the sample capacity is large, the processing error of the abnormal value by adopting the Laplace criterion is smaller.
In another technical scheme, the Laplace criterion adopted when processing the abnormal value comprises the following specific steps: (1) calculating a first quantile Q1 and a third quantile Q3 of the data sample, (2) calculating IQR from Q3 to Q1, (3) comparing all data in the data sample with ± 1.5IQR, and recognizing data exceeding ± 1.5IQR as an abnormal value, (4) deleting the abnormal value and replacing by an interval median. The first quantile Q1, the second quantile Q2 and the third quantile Q3 of the data sample are calculated by using the Lauda criterion, the first quantile Q1 and the third quantile Q3 are taken for subsequent calculation, the Lauda criterion is transformed, the coefficient is changed from 3 to 1.5, the data range is reduced, a better data processing effect can be obtained, the data calculation precision is higher, and the error is smaller.
In another technical scheme, in the third step, the calculation formula of the Pearson correlation coefficient R is
Xi、YiRespectively variable X, Y corresponds to the monitored value at point i,
is the average number of X samples and,
is the average number of Y samples and n is the sample volume. The calculation mode is relatively simple and flexible, and the data calculation error is small.
In another technical scheme, when the change degree value of the cable force is calculated in the third step, a formula is utilized
Calculation of where x1、x2Is the average of the data over two different time periods. The degree value values of the five evaluation indexes are close to facilitate subsequent normalization processing and observation on a radar map.
In another technical solution, the DTW algorithm in step three includes the following steps:
(1) setting the cable force data in the first time period as a time series R, R ═ { R (1), R (2), … R (m) }, and setting the cable force data in the second time period as a cable force time series T, T ═ { T (1), T (2), … R (n) }, where m and n are not necessarily equal;
(2) calculating a distance matrix of R and T;
(3) the process of finding the best path of the smallest accumulated distance in the distance matrix and searching the path, wherein the available previous lattice point (n, m) can only be (n-1, m), (n-1, m-1), (n, m-1);
(4) the final cumulative distance is D (T (n)), R (m)) + min { D (T (n-1), R (m)), + D (T (n-1), R (n-1)), D (T (n), R (m-1)) }. The calculation method is applied to calculation of the time distribution similarity degree value of the cable force and the time distribution similarity degree value of the symmetric cable force, the similarity values are optimized, and data calculation efficiency and data processing capacity are improved.
In another technical solution, in step four, the normalization processing is performed by a Softmax function, where the Softmax formula is
j＝1,…5,zjAnd step three, obtaining a regression coefficient change degree value, a cable force change degree value, a deflection standard deviation change degree value, a cable force time distribution similarity degree value and a symmetric cable force time distribution similarity degree value. The evaluation indexes of the five dimensions are normalized by utilizing a softmax formula, so that the characteristics of the five dimensions have visual comparability on the numerical value, and the accuracy of data mining analysis is greatly improved.
Example (b):
the method comprises the steps of testing a certain cable-stayed bridge in Wuhan, wherein the width of a main bridge of the certain cable-stayed bridge is 46m (including a tuyere) and is a five-span one-linkage double-tower double-cable-plane steel box girder semi-floating body system cable-stayed bridge, the span is arranged to be 100+275+760+275+100m and the total length is 1510m, selecting a test cable, selecting a deflection measuring point closest to the current test cable as a deflection judgment standard point, assembling an environment temperature and humidity monitoring sensor and a wind speed and direction sensor on the tower top of the bridge, installing a cable force sensor at the test cable, monitoring the cable force, temperature, humidity, wind power, wind speed and deflection data of the cable through the sensors, and obtaining the time interval of the data which is generally set to be more than 1. And calculating the correlation coefficient of the cable force and the environmental factors according to a Pearson correlation coefficient calculation formula, wherein the Pearson correlation coefficients of the cable force, the temperature, the humidity, the wind speed and the wind power are respectively 0.86, -0.061, -0.15 and-0.083, and the maximum linear correlation between the cable force and the temperature is obtained, so that the temperature is selected as an evaluation index for subsequent calculation.
Selecting 7 months-12 months in 2018 as a first time period, selecting 6 months in 1 month-2019 as a second time period, calculating to obtain a regression coefficient of 0.70 in the first time period and a regression coefficient of 0.73 in the second time period according to the data of cable force and temperature in the two time periods, and calculating to obtain a regression coefficient change degree value of 0.03; calculating the standard deviation of deflection, wherein the variance of the first time period is 0.01, the variance of the second time period is 0.00, and the variation degree value is 0.01;
calculating the cable force variation degree, statistically calculating to obtain the cable force average value of 4678.82kN in the first time period and the cable force average value of 4652.47kN in the second time period, and calculating the cable force variation degree through a formula
Calculating the change degree value, wherein the result is 0.01;
when calculating the similarity of the cable force time distribution according to the DTW algorithm, the selected time periods are the first quarter of 2018 and the first quarter of 2019 because the data of 2019 is up to the first quarter at that time, and the DTW distance is calculated by python programming, and the core codes are as follows:
from dtw import dtw
#use L2 norm as the element comparison distance
l2_norm＝lambda x,y:np.abs(x-y)/x
dist,cost_matrix,acc_cost_matrix,path＝dtw(x,y,dist＝l2_norm)
wherein the time distribution graph of the cable force of the Wuhan cable-stayed bridge is shown in FIG. 2, wherein s1 represents the first quarter of 2018, s2 represents the first quarter of 2019, and the similarity is calculated to be 0.27;
since the cable-stayed bridge has no symmetric cable force data, it is defaulted to 0 here.
The five indexes are normalized through a softmax function, data conversion is realized through a python programming language, and core codes are as follows:
and finally, drawing the normalized data into a radar map for visual analysis, wherein the radar map of the embodiment is shown in fig. 3.
As can be seen from fig. 2 and 3, the indexes of all dimensions of the cable are relatively stable, but the time distribution has a relatively prominent change, and from the view point of the figure, the data in 19 years is more stable, and the data fluctuation of 18 data is large and basically higher than 19 years.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (8)
1. A visualized large-span cable-stayed bridge cable evaluation method based on data driving is characterized by comprising the following steps:
firstly, respectively acquiring data of six parameters of cable force, temperature, humidity, wind power, wind speed and deflection of a large-span cable-stayed bridge cable to be monitored as original data;
step two, data preprocessing, namely performing noise reduction processing on the original data acquired in the step one and then storing the original data;
thirdly, analyzing and calculating by using the data stored in the second step, calculating Pearson correlation coefficients of the cable force and four parameter data of temperature, humidity, wind speed and wind power respectively, comparing the Pearson correlation coefficients, selecting the parameter with the maximum Pearson correlation coefficient as the environmental factor with the strongest sensitivity, setting a time scale, calculating regression coefficients of the cable force and the environmental factor with the strongest sensitivity in two time periods with the same time scale respectively, calculating a difference value to obtain a regression coefficient change degree value, calculating a cable force change degree value and a deflection standard deviation change degree value respectively according to the data of the two time periods selected when the regression coefficient change degree is calculated, and calculating the cable force time distribution similarity degree value and the symmetrical cable force time distribution similarity degree value in the same time period of different years by using a DTW algorithm;
step four, carrying out data normalization processing on the regression coefficient change degree value, the cable force change degree value, the deflection standard deviation change degree value, the cable force time distribution similarity degree value and the symmetric cable force time distribution similarity degree value obtained by calculation in the step three;
and step five, drawing a visual radar chart according to the data subjected to normalization processing in the step four.
2. The visualization large-span cable-stayed bridge cable evaluation method based on data driving as claimed in claim 1, wherein in the second step, the noise reduction processing is processing of blank values and abnormal values.
3. The visualized large-span cable-stayed bridge cable evaluation method based on data driving as claimed in claim 2, wherein the vacancy value is processed by directly deleting the vacancy value, and the abnormal value is processed by using a Layouda criterion.
4. The visualized large-span cable-stayed bridge cable evaluation method based on data driving as claimed in claim 3, wherein the specific steps of the Lauda criterion adopted when processing the abnormal value are as follows: (1) calculating a first quantile Q1 and a third quantile Q3 of the data sample, (2) calculating IQR from Q3 to Q1, (3) comparing all data in the data sample with ± 1.5IQR, and recognizing data exceeding ± 1.5IQR as an abnormal value, (4) deleting the abnormal value and replacing by an interval median.
5. The visualization large-span cable-stayed bridge cable evaluation method based on data driving as claimed in claim 1, wherein in step three, the calculation formula of Pearson correlation coefficient R is
Xi、YiRespectively variable X, Y corresponds to the monitored value at point i,
is the average number of X samples and,
is the average number of Y samples and n is the sample volume.
6. The visual large-span cable-stayed bridge cable evaluation method based on data driving as claimed in claim 1, wherein the formula is utilized when calculating the change degree value of the cable force in the third step
Calculation of where x1、x2Is the average of the data over two different time periods.
7. The visualization large-span cable-stayed bridge cable evaluation method based on data driving as claimed in claim 1, wherein the DTW algorithm in step three comprises the following calculation steps:
(1) setting the cable force data in the first time period as a time series R, R ═ { R (1), R (2), … R (m) }, and setting the cable force data in the second time period as a cable force time series T, T ═ { T (1), T (2), … R (n) }, where m and n are not necessarily equal;
(2) calculating a distance matrix of R and T;
(3) the process of finding the best path of the smallest accumulated distance in the distance matrix and searching the path, wherein the available previous lattice point (n, m) can only be (n-1, m), (n-1, m-1), (n, m-1);
(4) the final cumulative distance is D (T (n)), R (m)) + min { D (T (n-1), R (m)), + D (T (n-1), R (n-1)), D (T (n), R (m-1)) }.
8. The data-driven visualization large-span cable-stayed bridge cable evaluation method based on the claim 7, wherein in the fourth step, the normalization process is performed by a Softmax function, wherein the Softmax formula is
zjAnd step three, obtaining a regression coefficient change degree value, a cable force change degree value, a deflection standard deviation change degree value, a cable force time distribution similarity degree value and a symmetric cable force time distribution similarity degree value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010260401.6A CN111553003B (en) | 2020-04-03 | 2020-04-03 | Visual large-span cable-stayed bridge cable evaluation method based on data driving |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010260401.6A CN111553003B (en) | 2020-04-03 | 2020-04-03 | Visual large-span cable-stayed bridge cable evaluation method based on data driving |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111553003A true CN111553003A (en) | 2020-08-18 |
| CN111553003B CN111553003B (en) | 2023-01-31 |
Family
ID=71998309
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010260401.6A Active CN111553003B (en) | 2020-04-03 | 2020-04-03 | Visual large-span cable-stayed bridge cable evaluation method based on data driving |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111553003B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112381345A (en) * | 2020-10-10 | 2021-02-19 | 广东工业大学 | Clock model based method and system for visualizing pipeline health evaluation result |
| CN114169374A (en) * | 2021-12-10 | 2022-03-11 | 湖南工商大学 | Cable-stayed bridge stay cable damage identification method and electronic equipment |
| CN115035694A (en) * | 2022-06-08 | 2022-09-09 | 连云港职业技术学院 | Cable-stayed bridge wind pressure monitoring and early warning system based on BIM technology and management and control method |
| CN116300574A (en) * | 2023-01-30 | 2023-06-23 | 江苏海盟金网信息技术有限公司 | Industrial control information mixed control system and method based on big data |
| CN117876969A (en) * | 2024-03-11 | 2024-04-12 | 贵州省公路建设养护集团有限公司 | Safety monitoring method and system for bridge construction |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105975702A (en) * | 2016-05-11 | 2016-09-28 | 石家庄铁道大学 | Cable stayed bridge health monitoring sensor optimization layout method and system |
| CN106482917A (en) * | 2016-10-11 | 2017-03-08 | 广州大学 | A kind of detection method of cable-stayed bridge main-beam dynamic deflection |
| WO2018001147A1 (en) * | 2016-06-29 | 2018-01-04 | 深圳市智能机器人研究院 | Optimized tensioned cord model-based method and system for monitoring bridge cable |
| WO2018000561A1 (en) * | 2016-06-30 | 2018-01-04 | 东南大学 | Bridge testing and evaluation method and apparatus based on impact vibration |
| CN108319767A (en) * | 2018-01-22 | 2018-07-24 | 健研检测集团有限公司 | The method of suspension bridge sunpender stress assessment based on traveling load |
| CN110132511A (en) * | 2019-05-30 | 2019-08-16 | 山东省建筑科学研究院 | A kind of bridge structure monitoring and assessing method based on dynamic deflection attenuation law |
-
2020
- 2020-04-03 CN CN202010260401.6A patent/CN111553003B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105975702A (en) * | 2016-05-11 | 2016-09-28 | 石家庄铁道大学 | Cable stayed bridge health monitoring sensor optimization layout method and system |
| WO2018001147A1 (en) * | 2016-06-29 | 2018-01-04 | 深圳市智能机器人研究院 | Optimized tensioned cord model-based method and system for monitoring bridge cable |
| WO2018000561A1 (en) * | 2016-06-30 | 2018-01-04 | 东南大学 | Bridge testing and evaluation method and apparatus based on impact vibration |
| CN106482917A (en) * | 2016-10-11 | 2017-03-08 | 广州大学 | A kind of detection method of cable-stayed bridge main-beam dynamic deflection |
| CN108319767A (en) * | 2018-01-22 | 2018-07-24 | 健研检测集团有限公司 | The method of suspension bridge sunpender stress assessment based on traveling load |
| CN110132511A (en) * | 2019-05-30 | 2019-08-16 | 山东省建筑科学研究院 | A kind of bridge structure monitoring and assessing method based on dynamic deflection attenuation law |
Non-Patent Citations (2)
| Title |
|---|
| WANG GAOXING: "Localization and quantification of partial cable damage in the long-span cable-stayed bridge using the abnormal variation of temperature-induced girder deflection", 《STRUCTURAL CONTROL & HEALTH MONITORING》 * |
| 刘小玲等: "斜拉桥监测索力的时空评估方法", 《哈尔滨工业大学学报》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112381345A (en) * | 2020-10-10 | 2021-02-19 | 广东工业大学 | Clock model based method and system for visualizing pipeline health evaluation result |
| CN114169374A (en) * | 2021-12-10 | 2022-03-11 | 湖南工商大学 | Cable-stayed bridge stay cable damage identification method and electronic equipment |
| CN114169374B (en) * | 2021-12-10 | 2024-02-20 | 湖南工商大学 | Cable-stayed bridge stay cable damage identification method and electronic equipment |
| CN115035694A (en) * | 2022-06-08 | 2022-09-09 | 连云港职业技术学院 | Cable-stayed bridge wind pressure monitoring and early warning system based on BIM technology and management and control method |
| CN116300574A (en) * | 2023-01-30 | 2023-06-23 | 江苏海盟金网信息技术有限公司 | Industrial control information mixed control system and method based on big data |
| CN116300574B (en) * | 2023-01-30 | 2023-10-24 | 江苏海盟金网信息技术有限公司 | Industrial control information mixed control system and method based on big data |
| CN117876969A (en) * | 2024-03-11 | 2024-04-12 | 贵州省公路建设养护集团有限公司 | Safety monitoring method and system for bridge construction |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111553003B (en) | 2023-01-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111553003B (en) | Visual large-span cable-stayed bridge cable evaluation method based on data driving | |
| CN109164343B (en) | Transformer fault diagnosis method based on characteristic information quantization and weighted KNN | |
| Sarmadi et al. | Bridge health monitoring in environmental variability by new clustering and threshold estimation methods | |
| CN116756595B (en) | Conductive slip ring fault data acquisition and monitoring method | |
| CN116592951A (en) | Intelligent cable data acquisition method and system | |
| CN114328075A (en) | Intelligent power distribution room sensor multidimensional data fusion abnormal event detection method and system and computer readable storage medium | |
| CN117074961A (en) | Variation self-encoder power battery abnormality detection method, system, device and storage medium | |
| Guo et al. | A Hybrid Prognosis Scheme for Rolling Bearings Based on a Novel Health Indicator and Nonlinear Wiener Process | |
| US7043401B2 (en) | Multivariate data analysis method and uses thereof | |
| CN113887866A (en) | Method and device for generating human living environment evaluation index | |
| CN111695176A (en) | Method and device for evaluating condition of stay cable of large-span cable-stayed bridge | |
| CN113449809A (en) | Cable insulation on-line monitoring method based on KPCA-NSVDD | |
| WO2020183781A1 (en) | Abnormality diagnosis device | |
| CN110990916A (en) | Evaluation and prediction integration method for long-term operation safety of dam considering hysteresis effect | |
| CN116226767B (en) | Automatic diagnosis method for experimental data of power system | |
| CN117421616B (en) | Mine shaft detection system and method | |
| WO2023181230A1 (en) | Model analysis device, model analysis method, and recording medium | |
| CN117824588A (en) | Sampling optimization and early warning method for building safety monitoring inclination sensor | |
| CN116882814A (en) | Reinforced concrete structure health evaluation method based on multidimensional cloud model reasoning technology | |
| CN117349921A (en) | Bridge safety early warning control diagram method and device based on principal component analysis | |
| CN116894037A (en) | Multidimensional feature updating method based on space-time data dictionary database | |
| CN117909777A (en) | Industrial multivariable time sequence anomaly detection method and device | |
| CN117592863A (en) | Robot polishing production line reliability assessment method based on Stacking integrated learning framework | |
| CN116720080A (en) | Homologous meteorological element fusion inspection method | |
| Yao et al. | Cross-Correlation and Causality between Physicochemical Properties and Wine Quality |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210907 Address after: 430048 No. 11 Jinyinhu Road, Dongxihu District, Wuhan City, Hubei Province Applicant after: CCCC SECOND HARBOR ENGINEERING Co.,Ltd. Applicant after: CCCC HIGHWAY BRIDGES NATIONAL ENGINEERING RESEARCH CENTER Co.,Ltd. Address before: 430048 No. 11 Jinyinhu Road, Dongxihu District, Wuhan City, Hubei Province Applicant before: CCCC SECOND HARBOR ENGINEERING Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |