Failure monitoring and alarm method for damper at tower-beam junction of long-span bridges

Abstract

The present invention discloses a method for monitoring and alarming the failure of a damper at the joint of a tower and beam of a large-span bridge. According to the method, the correlation between the speed of the tower and beam damper of the large-span bridge under the action of wind load and the pulsating wind is an important indicator for judging the performance of the damper. By monitoring and analyzing the characteristic frequency of the longitudinal velocity of the tower and beam, the square of the wind speed power spectrum amplitude corresponding to the characteristic frequency is obtained, the order of the wind speed data is improved, and the data sensitivity is enhanced. The wind speed data is used as an equivalent pulsating wind load, and then a correlation model of the equivalent pulsating wind load and the speed is established. The performance failure of the tower and beam damper can be quickly and sensitively monitored and alarmed, so that maintenance personnel can check the use status of the tower and beam damper in time.

Description

Technical Field

[0001] The invention relates to the field of bridge structure performance monitoring, and in particular to a method for monitoring and alarming failure of a damper at a tower-beam junction of a large-span bridge. Background Art

[0002] Dampers are usually installed between the tower and the main beam of a cable-stayed bridge to improve the dynamic performance of the bridge. However, the working environment of the damper is relatively harsh. It is in an open-air environment, repeatedly affected by wind, and subjected to alternating loads. Therefore, the tower-beam damper is easily damaged and cannot work normally. For large-span cable-stayed bridges, the damper at the tower-beam junction is very important. It improves the dynamic performance of the bridge, thus affecting the comfort of the bridge. At the same time, it can make the bridge more stable under wind loads, and the safety performance of the bridge is also enhanced. Therefore, it is necessary to explore new technologies that use multi-source response data of bridges to perceive the state of the damper at the tower-beam junction, so as to realize abnormal state monitoring of the damper at the tower-beam junction.

[0003] The main dynamic force on the bridge damper is the wind load. After the tower-beam junction damper is installed, the relative movement between the main tower and the main beam under the action of wind load will produce a specific high-order correlation with the wind speed at the bridge site. It is very important to express the correlation between wind speed and the high-order state of damper displacement. At the same time, it is also necessary to eliminate the influence of ambient temperature on the displacement between towers and beams. Therefore, using the relative displacement data and wind speed data of the tower-beam junction of cable-stayed bridges to perceive the bridge state will definitely bring positive benefits. However, in the existing technology, there is no mature precedent for using the displacement data and wind speed data of the tower-beam junction damper to warn of abnormal damper status. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a method for monitoring and alarming the failure of the damper at the tower-beam junction of a large-span bridge, which solves the problem that the displacement data and wind speed data of the damper at the tower-beam junction have not been used in the prior art to warn of the abnormal state of the damper. The present invention uses the high-order information of the relative displacement between the tower and the main beam of the cable-stayed bridge and the wind speed data of the bridge deck to establish a high-order correlation model, extract parameters that express the performance of the damper at the tower-beam junction, and establish the warning threshold of the abnormal warning parameters of the tower-beam damper, thereby realizing real-time failure monitoring of the damper.

[0005] Technical solution: The present invention provides a method for monitoring and alarming the failure of a damper at the tower-beam junction of a large-span bridge, comprising the following steps:

[0006] (1) A damper and a longitudinal displacement sensor are installed at the junction of the tower and the main beam of the cable-stayed bridge. The longitudinal displacement sensor collects the time history data of the longitudinal relative displacement response of the tower and the main beam, i.e., the time history data of the longitudinal relative displacement response of the tower and the beam. An anemometer is installed at the mid-span position of the main beam to collect the time history data of the wind speed on the bridge deck.

[0007] (2) Perform wavelet decomposition on the collected tower-beam longitudinal relative displacement response time history data to remove the tower-beam longitudinal static displacement caused by temperature and extract the time history data of the tower-beam longitudinal relative dynamic displacement under wind load;

[0008] (3) Performing first-order differentiation on the time history data of the longitudinal relative dynamic displacement of the tower beam to obtain the time history data of the longitudinal relative displacement velocity of the tower beam;

[0009] (4) calculating the power spectrum of the longitudinal relative displacement velocity of the tower beam according to the time history data of the longitudinal relative displacement velocity of the tower beam, with the first preset time as the first time interval, and extracting the characteristic frequency f corresponding to the maximum amplitude of the power spectrum in each first time interval;

[0010] (5) calculating the power spectrum of the bridge deck wind speed time history data collected by the anemometer according to the second preset time as the second time interval, and extracting the wind speed power spectrum amplitude A corresponding to the characteristic frequency f in each second time interval;

[0011] (6) Establish the square of the wind speed power spectrum amplitude A 2 Linear correlation model A of the characteristic frequency f of the tower-beam relative velocity 2 =kf+a, where k is the slope of the linear correlation model, a is a constant term, and the correlation slope k′ of each numerical point is extracted according to the linear correlation model;

[0012] (7) calculating the correlation slope k″ based on the monitoring data of the bridge monitoring system, and calculating the average value of the correlation slope k″ based on the monitoring data of the bridge obtained by the bridge monitoring system for one year after the bridge is completed as a benchmark value;

[0013] (8) When the correlation slope k′ of each monitored numerical point is less than the warning threshold of the reference value, it indicates that the damper fails and an alarm is issued.

[0014] Furthermore, the first preset time is the same as the second preset time.

[0015] Furthermore, the warning threshold of the benchmark value is 80% of the benchmark value.

[0016] Furthermore, the first preset time and the second preset time are 1 minute.

[0017] Furthermore, the monitoring data in step (7) refers to the square of the wind speed power spectrum amplitude A obtained by the same method as steps (1) to (6) retained by the bridge monitoring system. 2 Data on the characteristic frequency f of the tower-beam relative velocity.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] (1) High-order expression: The power spectrum of wind speed is squared, thus establishing a high-order correlation expression between the tower-beam relative displacement and wind speed, making the parameters extracted by the correlation model more sensitive to the working state of the damper;

[0020] (2) Strong specificity: The relative displacement between the tower and the beam is differentiated to the first order, and the displacement is converted into velocity, which is consistent with the unit of the collected bridge deck wind speed, making the model highly specific and less prone to misreporting. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of the method of the present invention;

[0022] Figure 2 The collected wind speed time history data and the time history data of the longitudinal relative displacement response of the tower beam;

[0023] Figure 3 is the time history data of the longitudinal relative dynamic displacement of the tower and beam obtained after wavelet decomposition;

[0024] Figure 4 is the time history data of the longitudinal relative displacement velocity of the tower and beam obtained after the first-order differential;

[0025] Figure 5 The power spectrum obtained by power spectrum analysis using the time history data of the longitudinal relative displacement velocity of the tower beam, and the schematic diagram of the characteristic frequency f corresponding to the maximum amplitude;

[0026] Figure 6 is the power spectrum obtained by power spectrum analysis using the bridge deck wind speed time history data, and the power spectrum amplitude A corresponding to the characteristic frequency f;

[0027] Figure 7 To use the f and A of one year 2 The list of k″ values ​​for one year and the average of k″ values ​​obtained by linear regression analysis of time series data;

[0028] Figure 8 In order to use the real-time k value calculation to provide early warning of abnormal damper status. DETAILED DESCRIPTION

[0029] The present invention will be further described below in conjunction with embodiments:

[0030] The present invention utilizes the relative displacement between the tower and the main beam of a cable-stayed bridge and the bridge deck wind speed data to perform damper state perception and failure warning at the junction of the tower and the main beam. Specifically, a high-order correlation model is constructed based on the bridge deck wind speed and tower-beam relative displacement data of the cable-stayed bridge, and the tower-beam damper state identification parameters are extracted using the correlation model, and tower-beam damper failure monitoring is performed based on the identification parameters.

[0031] The technical solution adopted by the present invention is: installing a wind speed sensor on the deck of a cable-stayed bridge, collecting wind speed data, collecting data on the relative displacement of the tower and beam at the tower-beam junction, unifying the dimensions, establishing a correlation model between wind speed and displacement, and using the model to extract judgment parameters and complete abnormal event warning.

[0032] like Figure 1 As shown, the present invention provides a method for monitoring and alarming failure of a damper at a tower-beam junction of a large-span bridge, comprising the following steps:

[0033] (1) A damper and a longitudinal displacement sensor are installed at the junction of the tower and the main beam of the cable-stayed bridge. The longitudinal displacement sensor collects the time history data of the longitudinal relative displacement response of the tower and the main beam, i.e., the time history data of the longitudinal relative displacement response of the tower and the beam. An anemometer is installed at the mid-span position of the main beam to collect the time history data of the wind speed on the bridge deck.

[0034] (2) performing wavelet decomposition on the collected tower-beam longitudinal relative displacement response time history data, removing the tower-beam longitudinal static displacement caused by temperature, and extracting the time history data of the tower-beam longitudinal relative dynamic displacement under wind load; this step eliminates the influence of ambient temperature on the displacement between the tower and the beam;

[0035] (3) Performing the first-order differentiation on the time-history data of the longitudinal relative dynamic displacement of the tower beam, the time-history data of the longitudinal relative displacement velocity of the tower beam is obtained; performing the first-order differentiation on the time-history data of the longitudinal relative dynamic displacement of the tower beam converts the displacement data into displacement velocity data, thus achieving the dimensional unification of wind speed and displacement;

[0036] (4) calculating the power spectrum of the longitudinal relative displacement velocity of the tower beam according to the time history data of the longitudinal relative displacement velocity of the tower beam, with the first preset time as the first time interval, and extracting the characteristic frequency f corresponding to the maximum amplitude of the power spectrum in each first time interval;

[0037] (5) According to the bridge deck wind speed time history data collected by the anemometer, the power spectrum of the bridge deck wind speed time history data is calculated according to the second preset time as the second time interval, and the wind speed power spectrum amplitude A corresponding to the characteristic frequency f in each second time interval is extracted; the first preset time is the same as the second preset time, preferably 1 minute; when extracting the wind speed power spectrum value in this step, the wind speed characteristic frequency extracted in step 4 is used, so the correlation between the two can be incorporated;

[0038] (6) Establish the square of the wind speed power spectrum amplitude A 2 Linear correlation model A of the characteristic frequency f of the tower-beam relative velocity 2=kf+a, where k is the slope of the linear correlation model, a is a constant term, and the correlation slope k′ of each numerical point is extracted according to the linear correlation model; the wind speed power spectrum value is squared to achieve a high-order correlation expression between wind speed and relative displacement, so that the parameters extracted by the correlation model are more sensitive to the working state of the damper;

[0039] (7) Calculate the correlation slope k″ based on the monitoring data of the bridge monitoring system, and calculate the average value of the correlation slope k″ based on the monitoring data of the bridge obtained by the bridge monitoring system for one year as the benchmark value; since the working condition of the bridge remains basically unchanged within one year after completion and one year includes a comprehensive time period, the data within one year after completion for calculating the benchmark value is the preferred choice considering the mechanical properties of the bridge and the actual engineering practice; the monitoring data refers to the square A of the wind speed power spectrum amplitude obtained by the same method as steps (1) to (6) retained by the bridge monitoring system 2 The data of the characteristic frequency f of the tower-beam relative velocity are then used to establish the square of the wind speed power spectrum amplitude A 2 Linear correlation model A of the characteristic frequency f of the tower-beam relative velocity 2 =kf+a, where k is the slope of the linear correlation model, a is a constant term, and the correlation slope k″ of each numerical point is extracted according to the linear correlation model; and the monitoring data of the bridge one year after completion is the square of the wind speed power spectrum amplitude A after completion of the bridge one year 2 The data of characteristic frequency f of tower-beam relative velocity are obtained, and the average value of correlation slope k″ is calculated as the reference value;

[0040] (8) When the correlation slope k′ of each monitored numerical point is less than the warning threshold of the benchmark value, it indicates that the damper fails and an alarm is issued. Existing technologies can be used to issue an alarm, such as: the alarm light of the damper flashes or the buzzer of the damper works and makes a sound. The warning threshold needs to be formulated according to the characteristics of different bridges and needs to be adjusted according to the needs of the actual project. The preferred warning threshold of the benchmark value is 80% of the benchmark value.

[0041] The present invention monitors and analyzes the characteristic frequency of the longitudinal velocity of the tower beam, obtains the square of the wind speed power spectrum amplitude corresponding to the characteristic frequency, improves the order of wind speed data, and enhances data sensitivity. It is used as an equivalent pulsating wind load, and then a correlation model between the equivalent pulsating wind load and velocity is established. The performance failure of the tower beam damper can be quickly and sensitively monitored and alarmed.

[0042] Example 1

[0043] Based on the wind speed data at the bridge site and the relative displacement data of the tower-beam junction collected by the monitoring system of the Huanggang highway-railway dual-purpose cable-stayed bridge, the specific implementation process of the present invention is explained.

[0044] (1) Dampers and longitudinal displacement sensors are installed at the junction of the tower and the main beam of the Huanggang highway-railway dual-use cable-stayed bridge. The longitudinal displacement sensors collect the time-history data of the longitudinal relative displacement response of the tower and beam, and an anemometer is installed at the mid-span of the main beam to collect the time-history data of the wind speed on the bridge deck. These data are the basis for subsequent work, such as Figure 2 The collected wind speed time history data and the time history data of the longitudinal relative displacement response of the tower beam;

[0045] (2) The collected tower-beam longitudinal relative displacement response time history data are subjected to wavelet decomposition to remove the temperature component in the tower-beam relative displacement and obtain the time history data of the tower-beam longitudinal relative dynamic displacement under wind load, as shown in Figure 3 As shown;

[0046] (3) In order to make the dimension of dynamic displacement consistent with that of wind speed, the time history data of the longitudinal relative dynamic displacement of the tower and beam are differentiated by the first order to obtain the time history data of the longitudinal relative displacement velocity of the tower and beam, as follows: Figure 4 As shown;

[0047] (4) According to the time history data of the longitudinal relative displacement velocity of the tower beam, the power spectrum of the longitudinal relative displacement velocity of the tower beam is calculated at a time interval of 1 min, and the characteristic frequency f corresponding to the maximum amplitude of the power spectrum in each time interval is extracted, as follows: Figure 5 As shown;

[0048] (5) Based on the bridge deck wind speed time history data collected by the anemometer, the power spectrum of the bridge deck wind speed time history data is calculated at a time interval of 1 min, and the wind speed power spectrum amplitude A corresponding to the characteristic frequency f in each time interval is extracted, as shown in Figure 6 As shown;

[0049] (6) Square A and record it as A 2 , each A 2 Perform linear regression analysis with the numerical points of f to establish the square of the wind speed power spectrum amplitude A 2 Linear correlation model A of the characteristic frequency f of the tower-beam relative velocity 2 =kf+a, where k is the slope of the linear correlation model, a is a constant term, and the correlation slope k′ of each numerical point is extracted according to the linear correlation model;

[0050] (7) The correlation slope k″ is calculated based on the monitoring data of the bridge monitoring system, and the average value of the correlation slope k″ is calculated based on the monitoring data of the bridge obtained by the bridge monitoring system after one year of completion as the benchmark value; the average value of k″ is 2.90, such as Figure 7 As shown;

[0051] (8) When the correlation slope k′ of each monitored numerical point is less than 80% of the baseline value, it indicates that the damper fails and an alarm is issued; 80% of the average k value is 2.32, which is the monitoring warning threshold, such as Figure 8 As shown, it is a schematic diagram of real-time warning of tower beam damper using real-time k value and warning value.

[0052] The above embodiments are only further specific descriptions and examples of the scheme of the present invention. After reading the embodiments of the present invention, ordinary technicians in this field may make various equivalent modifications and substitutions to the present invention, which all fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for monitoring and alarming the failure of a damper at the tower-beam junction of a long-span bridge, characterized in that: The following steps are involved: (1) A damper and a longitudinal displacement sensor are installed at the junction of the tower and the main beam of the cable-stayed bridge. The longitudinal displacement sensor collects the time history data of the longitudinal relative displacement response of the tower and the main beam, i.e., the time history data of the longitudinal relative displacement response of the tower and the beam. An anemometer is installed at the mid-span position of the main beam to collect the time history data of the wind speed on the bridge deck. (2) Perform wavelet decomposition on the collected tower-beam longitudinal relative displacement response time history data to remove the tower-beam longitudinal static displacement caused by temperature and extract the time history data of the tower-beam longitudinal relative dynamic displacement under wind load; (3) Performing first-order differentiation on the time history data of the longitudinal relative dynamic displacement of the tower beam to obtain the time history data of the longitudinal relative displacement velocity of the tower beam; (4) calculating the power spectrum of the longitudinal relative displacement velocity of the tower beam according to the time history data of the longitudinal relative displacement velocity of the tower beam, with the first preset time as the first time interval, and extracting the characteristic frequency f corresponding to the maximum amplitude of the power spectrum in each first time interval; (5) calculating the power spectrum of the bridge deck wind speed time history data collected by the anemometer according to the second preset time as the second time interval, and extracting the wind speed power spectrum amplitude A corresponding to the characteristic frequency f in each second time interval; (6) Establish the square of the wind speed power spectrum amplitude A 2 Linear correlation model A of the characteristic frequency f of the tower-beam relative velocity 2 =kf+a, where k is the slope of the linear correlation model, a is a constant term, and the correlation slope k′ of each numerical point is extracted according to the linear correlation model; (7) calculating the correlation slope k″ based on the monitoring data of the bridge monitoring system, and calculating the average value of the correlation slope k″ based on the monitoring data of the bridge obtained by the bridge monitoring system for one year after the bridge is completed as a benchmark value; (8) When the correlation slope k′ of each monitored numerical point is less than the warning threshold of the reference value, it indicates that the damper fails and an alarm is issued.

2. The method for monitoring and alarming the failure of a damper at the tower-beam junction of a long-span bridge according to claim 1 is characterized in that: The first preset time is the same as the second preset time.

3. The method for monitoring and alarming the failure of a damper at the tower-beam joint of a long-span bridge according to claim 1 is characterized in that: The warning threshold of the reference value is 80% of the reference value.

4. The method for monitoring and alarming the failure of a damper at the tower-beam joint of a long-span bridge according to claim 2 is characterized in that: The first preset time and the second preset time are 1 minute.

5. The method for monitoring and alarming the failure of a damper at the tower-beam joint of a long-span bridge according to claim 1 is characterized in that: The monitoring data in step (7) refers to the square of the wind speed power spectrum amplitude A obtained by the same method as steps (1) to (6) retained by the bridge monitoring system. 2 Data on the characteristic frequency f of the tower-beam relative velocity.

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