CN111639466A - Cable-stayed bridge cable service life evaluation method based on monitoring data - Google Patents
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Abstract
The invention relates to the technical field of cable-stayed bridges, in particular to a cable-stayed bridge cable service life evaluation method based on monitoring data, which comprises the following steps: acquiring cable force monitoring data of a stay cable; dividing a state space of the inhaul cable; determining the state distribution of the inhaul cable; calculating a state transition probability matrix; calculating according to the probability matrix to obtain prediction data; and obtaining the residual service life of the stay cable according to the predicted data. The invention fully considers the massive cable force monitoring data of the health monitoring system, takes the Markov process theory as the basis, and timely grasps the development of the future state of the cable force of the stay cable through the improved Markov prediction model so as to timely maintain the stay cable, reduce the possibility of damage of the stay cable and ensure the safe operation of the cable-stayed bridge.
Description
Technical Field
The invention relates to the technical field of cable-stayed bridges, in particular to a cable-stayed bridge cable service life evaluation method based on monitoring data.
Background
The stay cable is a main bearing structure of the cable-stayed bridge, and transmits most of main beam dead load and live load to the cable tower in a self-tension mode so as to maintain the normal operation of a cable-stayed bridge system. The statistics of the breakage life of the well-known bridge guy cable at home and abroad is 2-16 years, rarely exceeds 20 years, and the average is about 1/10 of the service life of the bridge. The stay cable can better exert the working performance at the initial stage of bridge construction. However, as the service age of the bridge increases, the structure is inevitably aged, and the steel wire is likely to be corroded after the sheath is damaged, thereby losing the bearing capacity. Meanwhile, the traffic flow is increased day by day, and the stay cable is in a heavy-load traffic operation state for a long time, so that the service period of the stay cable is greatly discounted. If the stayed cable is damaged, the light guy cable abnormally vibrates to increase the psychological burden of the pedestrian; the heavy person will cause huge loss due to the broken cable.
In the prior art, defect equivalent treatment is mostly established based on the damage degree of an outer sheath for evaluating the service life of a stay cable, specifically, the stay cable is firstly detected, and the pitting defect of the damaged outer sheath stay cable and the position and the size of the defect are found out; then, based on equivalent treatment of the pitting defects, calculating the amplitude of the stress intensity factor by applying a fracture mechanics theory; finally, based on model tests, the influence factor coefficients under the alternating stress and different corrosion environments are solved, and the service life cycle of the stay cable under the coupling action of the alternating stress and the environmental corrosion is further quantitatively obtained;
however, the above-mentioned cable life detection method needs to be completed by manual operation when the positions and sizes of the defects are counted, but various errors always occur in the manual operation, and finally the accuracy of life detection is affected.
In view of the above problems, the designer actively makes research and innovation based on the practical experience and professional knowledge that the product engineering is applied for many years, so as to create a cable-stayed bridge cable service life evaluation method based on monitoring data, so that the method is more practical.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for evaluating the service life of the stay cable of the cable-stayed bridge based on the monitoring data is provided, and the service life evaluation of the stay cable based on cable force statistics is realized.
In order to achieve the purpose, the invention adopts the technical scheme that: a cable-stayed bridge cable service life evaluation method based on monitoring data comprises the following steps:
taking cable force monitoring data of a stay cable;
dividing a state space of the inhaul cable;
determining the state distribution of the inhaul cable;
calculating a state transition probability matrix;
calculating according to the probability matrix to obtain prediction data;
and obtaining the residual service life of the stay cable according to the predicted data.
Further, the stay cable force monitoring data are the annual average value of the stay cable force and the frequency of the stay cable force appearing in different state spaces.
Further, the inhaul cable state space is divided into three classes according to the fluctuation range of the annual average value of inhaul cable force.
Furthermore, the first class interval of the cable state space is 0-5% of the annual average cable force value fluctuation range of the cable, the second class interval is 5-10% of the annual average cable force value fluctuation range of the cable, and the third class interval is 10-100% of the annual average cable force value fluctuation range of the cable, wherein 5% of the three class intervals belong to the first class interval, and 10% of the three class intervals belong to the third class interval.
Further, the change process of the cable force is regarded as a homogeneous Markov process, and the probability matrix P is changed according to the state-1B, calculating future cable force change data;
p is a one-step state transition probability matrix of the stay cable changing from the state at the moment t to the state at the moment t + 1; let the state vector at time m1 be (a1, b1, c1), and the state vector at time m2 be (a2, b2, c2) … …
Then
P ═ a calculated at this time-1B is the change of state vector (a3, B3, c3) to at time m3
m4 is the one-step state transition probability matrix of the state vector (a4, b4, c 4).
Further, when calculating future cable force change data, the calculation result needs to be compared with the statistical structure to verify the accuracy of the calculation result, and if the comparison result is closer, the calculation is continued.
Further, when the calculation is performed, the latest value calculated is calculated as the last data.
Further, the criterion of the remaining life is that the frequency of occurrence of class one is not less than 85%.
The invention has the beneficial effects that: the invention fully considers the massive cable force monitoring data of the health monitoring system, takes the Markov process theory as the basis, and timely grasps the development of the future state of the cable force of the stay cable through the improved Markov prediction model so as to timely maintain the stay cable, reduce the possibility of damage of the stay cable and ensure the safe operation of the cable-stayed bridge.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a cable-stayed bridge cable service life evaluation method based on monitoring data in the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The method for evaluating the service life of the stay cable of the cable-stayed bridge based on the monitoring data, which is shown in figure 1, comprises the following steps:
s10: acquiring cable force monitoring data of a stay cable;
s20: dividing a state space of the inhaul cable;
s30: determining the state distribution of the inhaul cable;
s40: calculating a state transition probability matrix;
s50: calculating according to the probability matrix to obtain prediction data;
s60: and obtaining the residual service life of the stay cable according to the predicted data.
In step S10, the stay cable force monitoring data is the annual average value of the stay cable force and the frequency of occurrence in the different state spaces.
In step S20, the cable state space is classified into three categories according to the fluctuation range of the annual average value of the cable force.
Specifically, the first class interval of the cable state space is 0-5% of the annual average cable force value fluctuation range of the cable, the second class interval is 5-10% of the annual average cable force value fluctuation range of the cable, and the third class interval is 10-100% of the annual average cable force value fluctuation range of the cable, wherein 5% of the three class intervals belong to the first class interval, and 10% of the three class intervals belong to the third class interval.
In steps S30 and S40, the change process of the cable force is regarded as a homogeneous Markov process, and the probability matrix P ═ A of the state transition-1B, calculating future cable force change data;
p is a one-step state transition probability matrix of the stay cable changing from the state at the moment t to the state at the moment t + 1; let the state vector at time m1 be (a1, b1, c1), and the state vector at time m2 be (a2, b2, c2) … …
Then
P ═ a calculated at this time-1B is the change of state vector (a3, B3, c3) to at time m3
m4 is the one-step state transition probability matrix of the state vector (a4, b4, c 4).
In step S50, when calculating the future cable force variation data, the calculation result needs to be compared with the statistical structure to verify the accuracy, and if the comparison result is closer, the calculation is continued. It should be noted here that, in the case of the stay cable, the cable force tends to become large as the service life increases and the traffic flow increases. That is, the cable force in the interval (0, 5%) is small, and the cable force in the interval (5.10%) and (10%, 1) is large. This phenomenon becomes evident for a large number of monitoring statistics of the year. The verification given herein is intended to prove the rationality of the method, and if the difference between the calculation result and the actual statistical result is large, the statistical interval of the cable force can be enlarged, so that the change of the distribution of the cable force state is more obvious, and a more accurate result can be obtained. Here, the closer is to mean that two digits after the decimal point are the same.
Specifically, when the calculation is performed, the latest calculated value is used as the last data to perform the calculation.
In step S60, the determination criterion of the remaining life is that the frequency of occurrence of class one is not less than 85%.
In the embodiment, the development of the future state of the stay cable force of the stay cable is mastered in time through the improved Markov prediction model on the basis of the Markov process theory by fully considering the massive cable force monitoring data of the health monitoring system, so that the stay cable is maintained in time, the possibility of damage of the stay cable is reduced, and the safe operation of the cable-stayed bridge is ensured.
The specific evaluation steps are as follows:
(1) determining stay cable state space
In the maintenance regulations of large-span suspension bridges and cable-stayed bridges, the grades of the members are often classified into four categories, namely good, poor and bad. However, according to the actual cable force monitoring data of the sutong bridge, most cable forces fluctuate by about 5%, the cable force exceeding 10% is subdivided, the monitoring and evaluation significance is very small, and the occasional serious overrun cable force value may also influence the state evaluation of the cable. Therefore, the cable grades are divided into three classes, and the classification standard is as follows:
TABLE 1 stay cable class Classification
(2) Determining an initial distribution
And (3) fully utilizing the advantages of the massive cable force monitoring data of the Sootong bridge and selecting a probability statistical method.
(3) Determining a state transition probability matrix
Let the state vector of the stay at m time be A1＝(a1,b1,c1) The predicted state vector at time m +1 is A2＝(a2,b2,c2) The state transition probability matrix is
Then the state prediction at time m +1 is, according to markov theory:
a1p11+b1p21+c1p31＝a2(1)
a1p12+b1p22+c1p32＝b2(2)
a1p13+b1p23+c1p33＝c2(3)
the state prediction at time m +2 is:
a2p11+b2p21+c2p31＝a3(4)
a2p12+b2p22+c2p32＝b3(5)
a2p13+b2p23+c2p33＝c3(6)
the state prediction at time m +3 is:
a3p11+b3p21+c3p31＝a4(7)
a3p12+b3p22+c3p32＝b4(8)
a3p13+b3p23+c3p33＝c4(9)
recombining formulas (1), (4) and (7) to obtain:
a1p11+b1p21+c1p31＝a2
a2p11+b2p21+c2p31＝a3
a3p11+b3p21+c3p31＝a4
can observe from the above formula
Wherein A is3＝(a3,b3,c3)，
Similarly, combining (2), (5) and (8) is:
a1p12+b1p22+c1p32＝b2
a2p12+b2p22+c2p32＝b3
a3p12+b3p22+c3p32＝b4
observed to obtain
Wherein
By the same way, obtain
Wherein
Order to
[p1p2p3]＝P，[B1B2B3]B, then:
AP ═ B, a matrix is square and reversible, then P ═ a-1And B is the state transition probability matrix.
Through the three steps, the cable tension can be predicted, and statistical data of the Souton bridge in recent years are analyzed. The following table 2 lists the occurrence frequency of three types of cable forces in cable force statistics of SA18 cable 2015-2019 on the sutong bridge, and calculates out that the cable force average value in 2015 is 4640.05KN, thereby determining that the cable force interval in one type is (4408.05, 4872.06), the cable force interval in the second type is (4176.05, 4408.05) U (4872.06, 5104.06), and the cable force interval in the third type is (0, 4176.05) (U5104.06, +/-infinity).
Table 2 statistical data of cable force of SA18 cable 2015-2019 on Souton bridge
And (5) carrying out analysis by using the cable force data of 2015-2018.
As can be seen from the above table,
then
The state transition probability matrix is
That is, the cable force prediction result in 2019 is
f2019＝[0.953 0.045 0.002]P＝[0.951 0.046 0.003]The result is compared with 2019 actual statistics [ 0.9500.0470.003]Relatively close, it is believed that this method is capable of predicting the cable force.
The change process of the cable force is regarded as a homogeneous Markov process, and after the cable force can be accurately predicted by the method, the future cable force change condition is predicted by the same method. By mastering the long-term cable force development trend, the service life of the stay cable can be evaluated.
The cable force data of 2016-2019 cables of SA18 on the Souton bridge are used for analysis.
The results are shown in Table 2 below, in which,
through calculation, the prediction data of the cable force of 2020-2060 years are obtained and are listed in the following table 3.
TABLE 3 prediction data of cable tension in 2020-2060 year for sutong bridge
As can be seen from the table, the future cable force development condition is good. The service life of the Sutong bridge stay cable is 50 years, and the performance of the stay cable can decline along with the rapid increase of traffic flow and the gradual aging of materials. Therefore, from a strict viewpoint, a cable state is required at a guarantee rate of 85% in engineering. The occurrence frequency of the rope type predicted in 2050 in the table above is less than 85%, i.e. it can be considered that the remaining life of the SA18 cable on sutong bridges is at least 30 years in view of the current rope force situation. The evaluation result plus the service age of the sutong bridge for 12 years is still 50 years lower than the design service life of the inhaul cable, and the evaluation result is more conservative.
After the one-step prediction is performed, the history information is updated, the first data is removed, the newly predicted data is supplemented as the last data, and then the next data is predicted. The updating can keep the new information merged, keep the model parameters continuously updated to be closer to the true condition, improve the utilization rate of new information and improve the accuracy of prediction, namely, the improved Markov prediction model. The future development state of the stay cable force is predicted by using the improved Markov model, and the accuracy of the prediction result is improved. Namely, after the cable force statistical result in 2020 is obtained, 2016 cable force data is removed, and the cable force state in the future is predicted according to 2017-2020 cable force data, so that the predicted result is closer to the real situation of the structure.
Theory of markov process
A mathematical description of the markov process theory is given below:
if the system is described by values of state variables, the system is considered to complete a state transition when a variable of the system changes from a value of one state to a value of the next state. The transfer process has great randomness and no determined transfer rule, and the transfer is carried out with certain probability. When the system is at t0When the state of the time is known, the process is carried out at the time t > t0The distribution of the states and the system at the time t0This property is called markov, regardless of the state it was in. That is, the state change occurring in the future is not related to the past state, and the process having such a characteristic is a markov process.
When both time and state are discrete, the Markov process is called Markov chain model, and its mathematical language is described as assuming that the stochastic process { X (T) }, T ∈ T } satisfies the following condition:
(1) the time set is a nonnegative integer set T ═ 0,1,2, …, the state space corresponds to each time instant, and is also a discrete set denoted E ═ 0,1,2, …. I.e. x (t) is discrete in time discrete states;
(2) for any positive integer s, m, k, and any non-negative integer, js＞…＞j2＞j1(m＞js) Corresponding to the state im+k,im,ijs,…,ij2,ij1Has the following formula
P{X＝(m+k)＝im+k|x(m)＝im,X(js)＝js,…X(j2)＝j2,X(j1)＝j1}
＝P{X(m+k)＝im+k|x(m)＝im} (10)
Then { X (T) }, T ∈ T } is called a Markov chain.
Expression (10) indicates that X (t) is in a state of m + k ═ im+kOnly with the state x (m) i at time mmThis is a mathematical expression of the markov property regardless of the state before time m.
When k is 1, the right end of equation (10) is x (t) one-step state transition probability at time m, and then
P{Xm+1＝im+1|Xm＝im}＝P{Xm+1＝j|Xm＝i}＝pij(m) (11)
It represents the probability that the system is in state i at time m and in state j at time m + 1. Since a specific state in the space E is necessarily reached after a one-step transition from the state i, the one-step state transition matrix pij(m) the following conditions should be satisfied:
0≤pij(m)≤1,i,j∈E
if the time m ∈ T is fixed, the probability p is changed from one stepij(m) is a matrix of elements of
Referred to as a one-step state transition matrix at time m.
If Markov chain one-step state transition matrix pij(m) independently of m, i.e. at any time, starting from state i, the probability of reaching state j through one transition is equal, i.e. a homogeneous markov chain:
P{Xm+1＝j|Xm＝i}＝pij,(m＝0,1,2,…；i,j∈E) (12)
for a homogeneous Markov chain, one step of the transition matrix is
Meanwhile, the homogeneous markov chain has the following properties:
0≤pij≤1,i,j∈E
it will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. A cable-stayed bridge cable service life evaluation method based on monitoring data is characterized by comprising the following steps:
acquiring cable force monitoring data of a stay cable;
dividing a state space of the inhaul cable;
determining the state distribution of the inhaul cable;
calculating a state transition probability matrix;
calculating according to the probability matrix to obtain prediction data;
and obtaining the residual service life of the stay cable according to the predicted data.
2. The cable-stayed bridge cable service life evaluation method based on the monitoring data as claimed in claim 1, wherein the cable force monitoring data are the annual average value of the cable force and the frequency of occurrence in different state spaces.
3. The cable-stayed bridge cable service life evaluation method based on the monitoring data as claimed in claim 2, characterized in that the cable state space is divided into three classes according to the fluctuation range of the annual average value of the cable force of the cable.
4. The cable-stayed bridge cable service life evaluation method based on the monitoring data as claimed in claim 3, characterized in that the cable state space has a class I interval of 0-5% of the cable force annual average value fluctuation range, a class II interval of 5-10% of the cable force annual average value fluctuation range, and a class III interval of 10-100% of the cable force annual average value fluctuation range, wherein 5% of the class I interval and 10% of the class III interval.
5. The method as claimed in claim 4, wherein the change process of cable force is considered as homogeneous Markov process, and the cable force is estimated according to the state transition probability matrix P ═ A-1B, calculating future cable force change data;
p is a one-step state transition probability matrix of the stay cable changing from the state at the moment t to the state at the moment t + 1; let the state vector at time m1 be (a1, b1, c1), and the state vector at time m2 be (a2, b2, c2) … …
Then
P ═ a calculated at this time-1B is the change of state vector (a3, B3, c3) to at time m3
m4 is the one-step state transition probability matrix of the state vector (a4, b4, c 4).
6. The cable-stayed bridge cable service life evaluation method based on the monitoring data as claimed in claim 5, wherein when calculating future cable force change data, the calculation result is compared with the statistical structure to verify the accuracy of the calculation result, and if the comparison result is closer, the calculation is continued; otherwise, the selected space needs to be enlarged and then the verification is continued.
7. The method as claimed in claim 6, wherein the last data is the latest value calculated during calculation.
8. The cable-stayed bridge cable service life evaluation method based on the monitoring data as claimed in claim 7, wherein the judgment criterion of the remaining life is that the frequency of occurrence of class-class is not less than 85%.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113111415A (en) * | 2021-04-02 | 2021-07-13 | 中铁大桥勘测设计院集团有限公司 | Cable-stayed bridge cable force reliability assessment method considering partial cable failure |
| CN113570121A (en) * | 2021-07-09 | 2021-10-29 | 桂林理工大学 | Cable force trend prediction method for stayed cable based on convolutional neural network |
-
2020
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Non-Patent Citations (1)
| Title |
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| 龙凯: "斜拉桥的缺损状况评估方案及剩余寿命预测方法研究", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113111415A (en) * | 2021-04-02 | 2021-07-13 | 中铁大桥勘测设计院集团有限公司 | Cable-stayed bridge cable force reliability assessment method considering partial cable failure |
| CN113111415B (en) * | 2021-04-02 | 2022-06-03 | 中铁大桥勘测设计院集团有限公司 | Cable-stayed bridge cable force reliability assessment method considering partial cable failure |
| CN113570121A (en) * | 2021-07-09 | 2021-10-29 | 桂林理工大学 | Cable force trend prediction method for stayed cable based on convolutional neural network |
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