CN111274544B - Early warning threshold setting method based on mobile interval relevance trend - Google Patents
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Abstract
The invention discloses an early warning threshold setting method based on mobile interval relevance trend, which utilizes stable and sensitive relevance relation among monitoring data, adopts relevance mobile interval probability statistical characteristics to obtain relevance trend lines and structural response normal value envelope lines, and then obtains red early warning limit values according to a certain guarantee rate on the basis of the normal value envelope lines based on reliability theory, thereby establishing a relevance early warning mechanism of 'two lines and three regions', and realizing relevance early warning of a system. Compared with a single time course early warning threshold value with fixed structural response data, the method provided by the invention belongs to a comprehensive early warning system, and can fully utilize massive monitoring data of the bridge health monitoring system and grasp the inherent change rule. The relevance trend early warning threshold changes along with relevance characteristic changes, and the dynamic threshold has higher precision and sensitivity, so that the system can perform real-time early warning more scientifically. The method is realized by programming, is simple and quick to operate, and has wide engineering application value.
Description
Technical Field
The invention relates to a method for setting an early warning threshold value based on a correlation trend of a moving interval, and belongs to the technical field of bridge structure data analysis and research.
Background
At present, the research of the bridge early warning system has important significance for bridge safety evaluation, and is also the core content of the operation monitoring early warning evaluation system. For an operation monitoring system, whether early warning evaluation can be accurately and timely carried out directly relates to whether adverse effects caused by events can be reduced to the greatest extent when some special events occur, and structural safety is guaranteed.
In recent years, with the development of operation monitoring systems and the continuous improvement of related technologies, various early warning systems of bridge health monitoring systems are also widely applied to engineering practice. The early warning threshold is core content of the early warning system, and is related to whether an operation monitoring system can reflect real-safe state scientific early warning of a structure, so that missing report and false report of a major event are prevented, and the acquisition of the scientific and accurate early warning threshold is an important technical problem to be solved in the bridge health monitoring early warning system.
At present, a common early warning threshold setting method of a bridge health monitoring system comprises the following steps: (1) The standard limit value is obtained based on the standard and is generally applied to conventional bridges on the basis of a large number of experiments, however, the standard limit value is not suitable for special bridges and large-span bridges due to self deformation and stress characteristics, meanwhile, the standard limit value often has a certain conservation type, actual measurement data is difficult to reach the limit value for effective alarm, and when the structural response reaches the standard limit value, the structure is often destroyed and timely early warning cannot be achieved for processing. (2) The method for setting the early warning threshold is based on bridge structure finite element analysis, and the response value of the measuring point position under the least adverse working condition is extracted by considering all working conditions, so that the early warning threshold is widely applied and can be used as a system early warning threshold, however, the scientificity of the early warning threshold obtained based on the structure finite element analysis depends on the accuracy of model establishment, and meanwhile, the least adverse load working condition is difficult to appear in the normal operation process of the bridge, and the set early warning threshold has certain conservation. (3) The statistics value of the monitoring data is based on a probability statistics method, the early warning threshold setting method has a certain statistical significance according to the bit dividing value extracted by a certain guarantee rate on the premise that the monitoring system collects sufficient data, and the early warning threshold setting is single.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the invention provides a method for setting an early warning threshold based on a mobile interval relevance trend, which utilizes a stable and sensitive relevance relation among monitoring data, adopts relevance mobile interval probability statistical characteristics to acquire a relevance trend line and a structural response normal value envelope line, and then obtains a red early warning limit based on a reliability theory and a certain guarantee rate on the basis of the normal value envelope line, thereby establishing a relevance early warning mechanism of 'two lines and three areas', and realizing relevance early warning of a system.
The technical scheme is as follows: in order to solve the technical problems, the invention adopts the following technical scheme:
a warning threshold setting method based on a mobile interval relevance trend comprises the following steps:
obtaining self-variable data X with relevance relation in monitoring data n Strain data Y n N is a number n=1, 2,3,..n;
according to self-variable data X n Strain data Y n Calculating section early warning trend value data mid,i ,i＝1,2,...,M；
Calculating dependent variable data Y n And early warning trend sequence A mid,n Difference data E between n ,n＝1,2,...,N；
Calculating difference data E n A split upper limit value and a split lower limit value under a certain guarantee rate alpha;
and calculating the lower limit value and the upper limit value of the normal value of the blue early warning of the strain quantity.
Preferably, the method further comprises: and calculating a lower limit value and an upper limit value of the red early warning of the dependent variable.
Preferably, the section early warning trend value data mid,i The calculation steps are as follows:
with self-variable data X n As reference, L step Dividing a moving interval for step length to obtain M independent variable intervals, MThe dependent variable data corresponding to each independent variable interval is f data,i Wherein i=1, 2, where, M, m=int ((max (X) n )-min(X n ))/L step )+1；
F corresponding to M independent variable intervals respectively data,i Subdividing into m sections, and counting the data frequency value p on each section k ＝data k /n，data k F corresponding to the ith independent variable interval data,i Frequency number in kth zone, n is f corresponding to ith independent variable interval data,i The total number of data is calculated, k=1, 2,. -%, m;
calculating the data frequency value p on each section k Integral value fre of probability density of distribution function over the length of the segment k Residual r between k Is the sum of the squares of (c),r k ＝p k -fre k k=1, 2..m, taking the probability distribution corresponding to the minimum value of a as the optimal probability distribution, and obtaining f corresponding to the ith independent variable interval data,i The strain data corresponding to the probability maximum value under the optimal probability distribution is used as the interval early warning trend value data mid,i ,i＝1,2,...,M。
Preferably, the difference data E n The calculation steps are as follows:
according to the strain data Y n The number N of the data pairs of the interval early warning trend value data mid,i Interpolation is carried out on M to obtain an early warning trend sequence A, wherein i=1, 2 mid,n N=1, 2, where, N, according to E n ＝Y n -A mid,n Calculating difference data E n ,n＝1,2,...,N。
Preferably, the calculation step of the upper limit value and the lower limit value is as follows:
with difference data E n As reference, L step Dividing moving intervals for step length to obtain M difference intervals, and respectively calculating difference subsequences E based on random variable distribution functions i I=1, 2, where, M is respectively used as the normal upper limit value diff of the difference subsequence at the upper limit value and the lower limit value of the dividing position under a certain guarantee rate alpha i,u Lower limit value diff in general i,d ，i＝1,2,...,M；
Wherein F is -1 (. Cndot.) the inverse of the random variable distribution function.
As a preferable scheme, the calculation steps of the lower limit value and the upper limit value of the normal value of the strain quantity blue early warning are as follows:
respectively superposing the normal upper limit value and the normal lower limit value of the difference subsequence with the interval early warning trend value according toObtaining the upper limit value I of the blue early warning normal value of the strain quantity i,up Blue early warning normal value lower limit value I of strain quantity i,down ,i＝1,2,...,M。
As a preferable scheme, the calculation steps of the lower limit value and the upper limit value of the red early warning of the strain quantity are as follows:
based on the finite element model, calculating the response maximum value S of the strain data under the least adverse load working condition FEM,max Statistical strain data Y n Maximum S Monitoring,max Calculating a reliability coefficient r=s FEM,max /S Monitoring,max According toCalculating the red early warning lower limit value R of the dependent variable i,down Upper limit value R i,up ,i＝1,2,...,M。
The beneficial effects are that: the early warning threshold setting method based on the correlation trend of the moving interval provides powerful technical support for analysis of follow-up structure response monitoring data and safety state assessment work of bridge structures, is efficient and accurate, and can be widely applied to engineering practice.
Compared with the prior art, the invention has the following advantages:
1. compared with a single time course early warning threshold value with fixed structural response data, the method belongs to a comprehensive early warning system, and can fully utilize massive monitoring data of the bridge health monitoring system and grasp the inherent change rule.
2. The relevance trend early warning threshold changes along with relevance characteristic changes, and the dynamic threshold has higher precision and sensitivity, so that the system can perform real-time early warning more scientifically.
3. The method is realized by programming, is simple and quick to operate, and has wide engineering application value.
Drawings
Fig. 1 is a graph showing a correlation between vehicle speed and acceleration peak.
Fig. 2 is a probability distribution of acceleration peaks corresponding to a vehicle speed section.
FIG. 3 is a graph of peak acceleration difference time.
FIG. 4 shows a two-line three-zone vehicle speed-acceleration peak warning mechanism.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
The invention discloses a method for setting an early warning threshold based on a correlation trend of a mobile zone, which comprises the following steps:
the first step: and acquiring self-variable data and dependent variable data with association relation in the monitoring data.
Recording the self-variable data in the monitoring data as X n The strain data in the monitoring data is Y n N is a number n=1, 2,3,..n.
And a second step of: according to self-variable data X n Strain data Y n Calculating section early warning trend value data mid,i I=1, 2,..m. With self-variable data X n As reference, L step Dividing a moving interval for step length to obtain M independent variable intervals, wherein the dependent variable data corresponding to the M independent variable intervals is f data,i Wherein i=1, 2, where, M, m=int ((max (X) n )-min(X n ))/L step )+1；
F corresponding to M independent variable intervals respectively data,i Subdividing into m sections, and counting the data frequency value p on each section k ＝data k /n，data k F corresponding to the ith independent variable interval data,i Frequency number in kth zone, n is f corresponding to ith independent variable interval data,i Total data, k=1, 2,..m. Iterative call MATLAB statistics tool box, and based on least square algorithm, calculating data frequency value p on each section k Integral value fre of probability density of distribution function over the length of the segment k Residual r between k Is the sum of the squares of (c),r k ＝p k -fre k k=1, 2..m, taking the probability distribution corresponding to the minimum value of a as the optimal probability distribution, and obtaining f corresponding to the ith independent variable interval data,i The strain data corresponding to the probability maximum value under the optimal probability distribution is used as the interval early warning trend value data mid,i ,i＝1,2,...,M。
And a third step of: calculating dependent variable data Y n And early warning trend sequence A mid,n Difference data E between n N=1, 2,..n. According to the strain data Y n The number N of the data pairs of the interval early warning trend value data mid,i I=1, 2, M performs linear interpolation, two-point interpolation, cubic interpolation, etc., to obtain an early warning trend sequence a mid,n N=1, 2, N, calculating difference data E n ,n＝1,2,...,N。
E n ＝Y n -A mid,n
Fourth step: calculating difference data E n The upper limit value and the lower limit value of the dividing position under a certain guarantee rate alpha.
With difference data E n As reference, L step Dividing moving intervals for step length to obtain M difference intervals, and respectively calculating difference subsequences E based on random variable distribution functions i I=1, 2, where, M is respectively used as the normal upper limit value diff of the difference subsequence at the upper limit value and the lower limit value of the dividing position under a certain guarantee rate alpha i,u Lower limit value diff in general i,d ，i＝1,2,...,M。
Wherein F is -1 (. Cndot.) the inverse of the random variable distribution function.
Such as: based on step length L step Data E of difference value n Dividing M sub-sequences E i I=1, 2, M, calculating a difference subsequence E based on normal distribution i The upper limit value and the lower limit value of the division at the guaranteed rate of α=99.90% are respectively used as the normal upper limit value and the normal lower limit value of the difference subsequence.
P(diff i,d ＜E i ＜diff i,u )＝1-0.0005×2＝99.90％
diff i,u ＝F -1 (1-0.0005)，diff i,d ＝F -1 (0.0005)
Wherein F is -1 (. Cndot.) fitting the inverse of the normal distribution function.
Fifth step: and calculating the lower limit value and the upper limit value of the normal value of the blue early warning of the strain quantity.
The normal upper limit value and the normal lower limit value of the difference subsequence are respectively overlapped with the interval early warning trend value to obtain the upper limit value I of the blue early warning normal value of the dependent variable i,up Blue early warning normal value lower limit value I of strain quantity i,down And (i=1, 2, and the term M is used for respectively connecting the upper limit value and the lower limit value of the normal value of the blue early warning of the strain quantity to obtain the normal upper limit and the normal lower limit of the blue early warning of the strain quantity.
Sixth step: and calculating a lower limit value and an upper limit value of the red early warning of the dependent variable.
Based on the finite element model, calculating the response maximum value S of the strain data under the least adverse load working condition FEM,max Statistical strain data Y n Maximum S Monitoring,max The reliability coefficient is calculated as r=s FEM,max /S Monitoring,max The red early warning lower limit value R of the strain quantity i,down Upper limit value R i,up I=1, 2,..m, the calculation formula is as follows:
respectively red early warning lower limit value R of strain quantity i,down Upper limit value R i,up And connecting lines to obtain an upper early warning limit of the strain quantity red and a lower early warning limit of the strain quantity red.
Through the set blue early warning normal upper limit, normal lower limit, red early warning upper limit and early warning lower limit, a two-line three-zone early warning mechanism is established, wherein a blue early warning limit value region represents a normal response zone of a structure under the normal operation condition of a bridge, a region between the blue early warning normal limit value and the red early warning limit value represents that the structure is possibly damaged, important attention is required, and a region beyond the red early warning limit value represents that the structure is damaged and is required to be managed and maintained in a targeted manner.
Examples:
the Nanjing Dashenguan Changjiang bridge is a Beijing Shanghai line speed railway and a Huhan Rong railway on a Jiang river crossing channel of Nanjing Yangtze river, the Nanjing double-line subway is carried, and the driving speed is 300km/h for a six-line high speed railway bridge. The length of the main bridge is 1615.0m, and a 2-link (84+84) m continuous steel truss plus (108+192+336+336+192+108) m six-span continuous steel truss arch structure is adopted. The rise of the main span 336m steel truss arch is 84m, the rise ratio is 1/4, the truss height at the arch crown is 12m, and the truss height at the arch foot is 53m; the truss height of the side span continuous steel truss girder is 16m, and the internode length is 12m. The operation monitoring system of the Nanjing Dasheng Guangguan bridge structure is used for implementing long-term on-line monitoring on key structure parts of a main bridge, 138 measuring points are provided, and the monitoring content is mainly as follows: bridge site environment monitoring, steel structure dynamic stress monitoring, dynamic response monitoring, displacement deformation monitoring, special part monitoring and driving monitoring. The method comprises the following steps of using the relationship between vehicle speed and main beam acceleration peak of the Min Dasheng Guanghong Gang of Jiang Daqiao 2016 years as a typical analysis early warning threshold value setting step, selecting measuring point JSD-11-04 main beam acceleration peak (16 carriage trains on the Jinghu line side travel from north to south) data:
the first step: and acquiring the vehicle speed and the corresponding acceleration peak value of the main beam during the passing of the vehicle in the association relation.
Recording the speed self-variable data as X n The peak strain data of the acceleration of the main beam during the driving is Y n N is a number of data, n=1, 2,3,..n.
And a second step of: calculating section early warning trend value data mid,i ,i＝1,2,...,M。
Based on the relation of the relevance between X and Y, as shown in figure 1, the vehicle speed X is used for controlling the speed of the vehicle n The vehicle speed range under the working condition in 2016 years is [165.49km/h,281.66km/h]The interval is divided into 190 sections, L is taken step = (281.66-165.49)/190 = 0.61km/h, corresponding to a main beam acceleration peak of f data,i Wherein i=1, 2..190, subdividing the main beam acceleration peak values of 190 sections into m sections respectively, and calculating the residual r of the m sections in each section k Is equal to the sum of squares of [195.38km/h,195.99km/h ] at the 50 th vehicle speed interval]Taking the corresponding main beam acceleration peak value data as an example, solving a residual error r k Sum of squares according toSequencing from small to large, wherein the first 4 optimal probability distributions are shown in fig. 2, probability statistical information is shown in table 1, it can be seen that the optimal probability distribution of the corresponding main beam acceleration peak value in the vehicle speed interval is normal distribution, and the corresponding probability maximum value is the interval early warning trend value data mid,50 ＝15.57cm/s 2 . With the interval moving from front to back, 190 early warning trend values data exist under 190 intervals mid,i ,i＝1,2,...,190。
TABLE 1 optimal probability statistics
And a third step of: calculating the acceleration peak Yn and the early warning trend sequence A of the main beam mid,n Difference data E between n ,n＝1,2,...,N。
According to the peak value Y of the acceleration of the main beam n The number N of the data pairs of the interval early warning trend value data mid,i I=1, 2, 190 performs linear interpolation to obtain an early warning trend sequence a mid,n N=1, 2, N, calculating difference data E n ,n＝1,2,...,N，The delta data time course is shown in fig. 3.
E n ＝Y n -A mid,n
Fourth step: dividing the difference data into 190 sub-sequences and calculating a difference sub-sequence E i I=1, 2,..190 is a split upper limit and a split lower limit at 99.90% assurance rate.
Based on step length L step Let difference e=0.61 km/h n Dividing 190 sub-sequences E i I=1, 2,..190, calculating a difference subsequence E based on a normal distribution i The upper limit value and the lower limit value of the dividing position under the 99.90% guarantee rate are respectively used as the normal upper limit value and the normal lower limit value of the difference subsequence.
Then there are:
P(diff i,d ＜E i ＜diff i,u )＝1-0.0005×2＝99.90％
diff i,u ＝F -1 (1-0.0005)，diff i,d ＝F -1 (0.0005)
wherein F is -1 (. Cndot.) fitting the inverse of the normal distribution function.
Fifth step: and calculating the lower limit value and the upper limit value of the blue early warning normal value of the acceleration peak value of the main beam.
The normal upper limit value and the normal lower limit value of the difference subsequence are respectively overlapped with the interval early warning trend value to obtain the upper limit value I of the blue early warning normal value of the dependent variable i,up Blue early warning normal value lower limit value I of strain quantity i,down The method comprises the steps of carrying out a first treatment on the surface of the Then there are:
and respectively connecting the upper limit value and the lower limit value of the normal value of the blue early warning of the strain quantity to obtain the normal upper limit and the normal lower limit of the blue early warning of the strain quantity.
Sixth step: and calculating a lower limit value and an upper limit value of the red early warning of the dependent variable. Based on bridge finite element calculation results, calculating the maximum value S of the acceleration peak value of the main beam under the least adverse load working condition according to the position structure response of the measuring point FEM,max Counting the maximum value S of the acceleration peak value of the main beam of the measured data of the measuring point Monitoring,max (at least one year of monitoring data), the reliability coefficient is r=s FEM /S Monitoring,max Obtaining the red early warning lower limit value R of the bridge structure relativity i,down Upper limit value R i,up 。
Respectively red early warning lower limit value R of strain quantity i,down Upper limit value R i,up And connecting lines to obtain an upper early warning limit of the strain quantity red and a lower early warning limit of the strain quantity red.
For early warning of correlation between vehicle speed and main beam acceleration peak value, finite element calculation measuring point position structure response is at main beam acceleration peak value maximum value S under the most unfavorable load working condition FEM,max ＝51.566cm/s 2 Counting the peak value S of the acceleration of the main beam of the 2016-year measured data of the measuring point Monitoring,max ＝41.253cm/s 2 The reliability coefficient r=1.25, and the safety coefficient at this time is 1/0.75=1.33, so that a part of space is reserved to judge whether the operation state monitoring result of the structural member needs to be subjected to detailed safety evaluation. On the basis, a two-line three-region early warning mechanism is established, a blue early warning limit value region represents a normal response region of the structure under the normal operation condition of the bridge, a region between the blue early warning normal limit value and the red early warning limit value represents that the structure is possibly damaged, important attention is required, and a region beyond the red early warning limit value represents that the structure is damaged, and targeted management and maintenance are required.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (1)
1. A method for setting an early warning threshold based on a correlation trend of a mobile interval is characterized by comprising the following steps: the method comprises the following steps:
obtaining self-variable data X with relevance relation in monitoring data n Strain data Y n N is a number n=1, 2,3,..n;
according to self-variable data X n Strain data Y n Calculating section early warning trend value data mid,i ,i＝1,2,...,M；
Calculating dependent variable data Y n And corresponding early warning trend sequence A mid,n Difference data E between n ,n＝1,2,...,N；
Calculating difference data E n A split upper limit value and a split lower limit value under a certain guarantee rate alpha;
calculating the lower limit value and the upper limit value of a normal value of the blue early warning of the dependent variable;
the section early warning trend value data mid,i The calculation steps are as follows:
with self-variable data X n As reference, L step Dividing a moving interval for step length to obtain M independent variable intervals, wherein the dependent variable data corresponding to the M independent variable intervals is f data,i Wherein i=1, 2, where, M, m=int ((max (X) n )-min(X n ))/L step )+1；
F corresponding to M independent variable intervals respectively data,i Subdividing into m sections, and counting the data frequency value p on each section k ＝data k /n，data k F corresponding to the ith independent variable interval data,i Frequency number in kth zone, n is f corresponding to ith independent variable interval data,i The total number of data is calculated, k=1, 2,. -%, m;
calculating the data frequency value p on each section k Integral value fre of probability density of distribution function over the length of the segment k Residual r between k Is the sum of the squares of (c),r k ＝p k -fre k k=1, 2..m, taking the probability distribution corresponding to the minimum value of a as the optimal probability distribution, and obtaining f corresponding to the ith independent variable interval data,i Optimum probability scoreThe strain data corresponding to the maximum probability value is used as the interval early warning trend value data mid,i ,i＝1,2,...,M；
The difference data E n The calculation steps are as follows:
according to the strain data Y n The number N of the data pairs of the interval early warning trend value data mid,i Interpolation is carried out on M to obtain an early warning trend sequence A, wherein i=1, 2 mid,n N=1, 2, where, N, according to E n ＝Y n -A mid,n Calculating difference data E n ,n＝1,2,...,N；
The calculation steps of the split upper limit value and the split lower limit value are as follows:
with difference data E n As reference, L step Dividing moving intervals for step length to obtain M difference intervals, and respectively calculating difference subsequences E based on random variable distribution functions i I=1, 2, where, M is respectively used as the normal upper limit value diff of the difference subsequence at the upper limit value and the lower limit value of the dividing position under a certain guarantee rate alpha i,u Lower limit value diff in general i,d ，i＝1,2,...,M；
Wherein F is -1 (-) inverse of the random variable distribution function;
the calculation steps of the lower limit value and the upper limit value of the normal value of the strain quantity blue early warning are as follows:
respectively superposing the normal upper limit value and the normal lower limit value of the difference subsequence with the interval early warning trend value according toObtaining the upper limit value I of the blue early warning normal value of the strain quantity i,up Blue early warning normal value lower limit value I of strain quantity i,down ,i＝1,2,...,M；
Further comprises: calculating a lower limit value and an upper limit value of the red early warning of the dependent variable;
the calculation steps of the lower limit value and the upper limit value of the strain red early warning are as follows:
based on the finite element model, calculating the response maximum value S of the strain data under the least adverse load working condition FEM,max Statistical strain data Y n Maximum S Monitoring,max Calculating a reliability coefficient r=s FEM,max /S Monitoring,max According toCalculating the red early warning lower limit value R of the dependent variable i,down Upper limit value R i,up ,i＝1,2,...,M。
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| CN109992815A (en) * | 2018-12-24 | 2019-07-09 | 中铁大桥（南京）桥隧诊治有限公司 | A kind of support accumulative displacement calculation method based on relevance rain-flow counting |
| CN110208019A (en) * | 2019-05-14 | 2019-09-06 | 北京博华信智科技股份有限公司 | A kind of dynamic equipment condition monitoring dynamic threshold method for early warning |
| CN110598265A (en) * | 2019-08-16 | 2019-12-20 | 中铁大桥（南京）桥隧诊治有限公司 | Bridge health data abnormity correction method and system based on wavelet analysis |
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