Static rigidity measuring device and method for bridge erecting machine

Abstract

The application provides a bridge erecting machine static stiffness measuring device and method, strain data of a target bridge erecting machine under various levels of static load are used to determine strain contribution of the various levels of static load to the target bridge erecting machine; loss gradient of strain state in the target bridge erecting machine when the static load is increased is determined according to all strain contribution and structure characteristics of the target bridge erecting machine; dominant strain mode of the various levels of static load to the bridge erecting machine is determined based on the loss gradient of strain; inter-stage coupling residual error between each adjacent static load on the target bridge erecting machine is determined according to displacement data and strain data of the target bridge erecting machine under the various levels of static load; structure stiffness of the target bridge erecting machine under the various levels of static load is determined based on a static stiffness measuring model combined with all dominant strain modes and the inter-stage coupling residual error between each adjacent static load. The application can reduce the influence of displacement and strain coupling relationship when strain dynamically changes on measuring the static stiffness of the bridge erecting machine.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rigidity measurement, and more particularly to a bridge erecting machine static rigidity measurement device and method. BACKGROUND

[0002] Rigidity refers to the ability of a structure or component to resist deformation, and is a key index for measuring structural performance in the engineering field (such as mechanical manufacturing, aerospace, civil engineering, etc.). The core goal of rigidity measurement is to quantify the deformation law of a structure under a specific load through experimental or computational means, thereby providing data support for structural design, safety evaluation, fault diagnosis, and performance optimization.

[0003] With the continuous expansion of bridge construction projects, the structural rigidity of a bridge erecting machine directly affects the safety and construction accuracy of bridge erection. Existing methods for measuring the static rigidity of a bridge erecting machine rely on a single parameter (such as displacement or strain) for evaluation, which is difficult to fully reflect the true mechanical performance of the structure under complex load conditions. On the one hand, traditional methods do not fully consider the contribution differences of different parts of the structure during loading, and ignore the influence of strain distribution of key components on overall rigidity. On the other hand, they do not deeply analyze the coupling relationship between displacement and strain data, and cannot effectively identify rigidity degradation phenomena caused by material nonlinearity, contact effects, or local damage. In addition, there is a lack of quantitative analysis of the strain state change trend during the incremental process of static load, making it difficult to accurately capture the dynamic characteristics of structural rigidity changes with load, resulting in insufficient precision of measurement results. Therefore, how to reduce the influence of the coupling relationship between displacement and strain during strain dynamic changes on the measurement of the static rigidity of a bridge erecting machine has become a problem in the industry. SUMMARY

[0004] The present application provides a bridge erecting machine static rigidity measurement device and method, which can reduce the influence of the coupling relationship between displacement and strain during strain dynamic changes on the measurement of the static rigidity of a bridge erecting machine.

[0005] In a first aspect, the present application provides a bridge erecting machine static rigidity measurement method, wherein a sensor network is arranged on a target bridge erecting machine in advance, and a graded static load is applied to the target bridge erecting machine by a loading device. The method includes the following steps:

[0006] Strain data of the target bridge erecting machine under each level of static load is collected based on the sensor network;

[0007] The contribution of each level of static load to the strain of the target bridge erecting machine is determined through all the strain data;

[0008] gradient analysis is performed on the strain state of the static load on the target bridge machine according to all the strain contribution degrees and structural characteristics of the target bridge machine, to obtain a loss gradient of the strain state in the target bridge machine when the static load is incremented, and a dominant strain mode of each level of static load on the bridge machine is determined based on the loss gradient of the strain state;

[0009] displacement data of the target bridge machine under each level of static load is monitored, coupling analysis is performed on the displacement data and strain data under each level of static load, and then an inter-level coupling residual error between each adjacent static load on the target bridge machine is obtained;

[0010] A static stiffness measurement model of the target bridge machine is constructed, and the structural stiffness of the target bridge machine under each level of static load is determined based on the static stiffness measurement model, in combination with all the dominant strain modes and the inter-level coupling residual error between each adjacent static load.

[0011] In some embodiments, determining the strain contribution degree of each level of static load on the target bridge machine by all the strain data specifically includes:

[0012] A first level of static load is selected as a selected static load, and a contribution degree component of each sensor corresponding position in the target bridge machine under the selected static load is determined according to the strain data corresponding to the selected static load;

[0013] The strain contribution degree of the selected static load on the target bridge machine is determined by all the contribution degree components;

[0014] The strain contribution degree of the remaining static load on the target bridge machine is continuously determined.

[0015] In some embodiments, the gradient analysis on the strain state of the static load on the target bridge machine according to all the strain contribution degrees and the structural characteristics of the target bridge machine to obtain the loss gradient of the strain state in the target bridge machine when the static load is incremented specifically includes:

[0016] The structural characteristics of the target bridge machine are determined;

[0017] A plurality of local strain gradients of the static load on the target bridge machine are determined according to all the strain contribution degrees;

[0018] The loss gradient of the strain state in the target bridge machine when the static load is incremented is determined by all the local strain gradients.

[0019] In some embodiments, determining the dominant strain mode of each level of static load on the bridge machine based on the loss gradient of the strain state specifically includes:

[0020] The structural dynamics characteristics of the target bridge machine are obtained;

[0021] According to the loss gradient of the strain state and the structure dynamics characteristics, the strains under each level of static load are classified and analyzed to obtain a plurality of similar strain clusters;

[0022] The dominant strain modes of the bridge erecting machine under each level of static load are determined through all the similar strain clusters.

[0023] In some embodiments, the displacement data and the strain data under each level of static load are coupled and analyzed to obtain the inter-level coupling residual between each adjacent static load on the target bridge erecting machine, which specifically includes:

[0024] The displacement data and the strain data under each level of static load are matched and aligned to obtain displacement-strain matching data;

[0025] According to the displacement-strain matching data, the coupling degree characteristics between each adjacent static load on the target bridge erecting machine are determined;

[0026] According to all the coupling degree characteristics, the inter-level coupling residual between each adjacent static load on the target bridge erecting machine is determined.

[0027] In some embodiments, based on the static stiffness measurement model, all the dominant strain modes and the inter-level coupling residual between each adjacent static load, the structure stiffness of the target bridge erecting machine under each level of static load is determined, which specifically includes:

[0028] According to all the dominant strain modes, the main deformation region and the deformation trend of the target bridge erecting machine are determined;

[0029] Through the inter-level coupling residual between each adjacent static load, the coupling relationship coefficient of displacement and strain is determined;

[0030] The main deformation region, the deformation trend and the coupling relationship coefficient are input into the static stiffness measurement model, and the structure stiffness of the target bridge erecting machine under each level of static load is output through the static stiffness measurement model.

[0031] In some embodiments, the displacement data of the target bridge erecting machine under each level of static load is monitored by a laser displacement sensor.

[0032] In a second aspect, the present application provides a bridge erecting machine static stiffness measurement device, which comprises a stiffness measurement unit, and the stiffness measurement unit comprises:

[0033] The acquisition module is configured to acquire strain data of the target bridge erecting machine under each level of static load based on the sensor network;

[0034] The processing module is configured to determine the strain contribution of each level of static load to the target bridge erecting machine through all the strain data;

[0035] The processing module is further configured to perform gradient analysis on the strain state of the static load on the target bridge machine according to all the strain contribution degrees and structural characteristics of the target bridge machine, to obtain a loss gradient of the strain state in the target bridge machine when the static load is increased, and to determine the dominant strain mode of each level of static load on the bridge machine based on the loss gradient of the strain state.

[0036] The processing module is further configured to monitor displacement data of the target bridge machine under each level of static load, to perform coupling analysis on the displacement data and the strain data under each level of static load, and to further obtain inter-level coupling residual errors between each adjacent static load on the target bridge machine.

[0037] The execution module is configured to construct a static stiffness measurement model of the target bridge machine, to determine the structural stiffness of the target bridge machine under each level of static load based on the static stiffness measurement model in combination with all the dominant strain modes and the inter-level coupling residual errors between each adjacent static load.

[0038] In a third aspect, the present application provides a computer device, which comprises a memory and a processor, the memory stores a code, and the processor is configured to acquire the code and execute the bridge machine static stiffness measurement method described above.

[0039] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the bridge machine static stiffness measurement method described above.

[0040] The technical scheme provided by the embodiments of the present application has the following beneficial effects:

[0041] In the bridge machine static stiffness measurement device and method provided by the present application, first, strain data of a target bridge machine under each level of static load is collected based on the sensor network; strain contribution degrees of each level of static load on the target bridge machine are determined through all the strain data; a loss gradient of the strain state in the target bridge machine when the static load is increased is obtained by performing gradient analysis on the strain state of the static load on the target bridge machine according to all the strain contribution degrees and structural characteristics of the target bridge machine; the dominant strain mode of each level of static load on the bridge machine is determined based on the loss gradient of the strain state; displacement data of the target bridge machine under each level of static load is monitored, coupling analysis is performed on the displacement data and the strain data under each level of static load, and further, inter-level coupling residual errors between each adjacent static load on the target bridge machine are obtained; a static stiffness measurement model of the target bridge machine is constructed, and the structural stiffness of the target bridge machine under each level of static load is determined based on the static stiffness measurement model in combination with all the dominant strain modes and the inter-level coupling residual errors between each adjacent static load.

[0042] It can be seen that, in the static stiffness measurement process of the bridge erecting machine, first, the strain contribution of each level of static load to the target bridge erecting machine is determined through all strain data, to filter out secondary load interference, focus on the core load section that has a significant impact on the coupling relationship, reduce the noise interference of redundant data on the coupling analysis, and make the subsequent gradient analysis more accurately locate the dominant strain mode, thereby indirectly weakening the influence of displacement-strain nonlinear coupling under non-dominant load; second, gradient analysis is performed on the strain state of the static load on the target bridge erecting machine according to all strain contribution and the structural characteristics of the target bridge erecting machine, to obtain the loss gradient of the strain state of the target bridge erecting machine when the static load is increased, and to determine the dominant strain mode of the bridge erecting machine under each level of static load based on the loss gradient of the strain state, separate the dominant mode with weak displacement coupling through gradient analysis, establish a "pure" mapping relationship between load and strain, avoid the interference of displacement on complex strain mode in dynamic change, and provide characteristic variables for static stiffness measurement that are not polluted by the coupling relationship; third, the displacement data and strain data under each level of static load are analyzed for coupling, to obtain the inter-stage coupling residual error between each adjacent static load on the target bridge erecting machine, and to convert the coupling effect into a quantifiable correction parameter through residual error analysis, to provide error compensation basis for subsequent model; finally, the structural stiffness of the target bridge erecting machine under each level of static load is determined based on the static stiffness measurement model combined with all dominant strain modes and the inter-stage coupling residual error between each adjacent static load, the dominant strain mode and the inter-stage coupling residual error can eliminate the interference term introduced by displacement in strain dynamic change, so that the stiffness calculation only depends on the linear relationship between static load and strain, the influence of the coupling relationship on the measurement result is reduced from the model level, and finally the high-precision static stiffness measurement is realized. By using the above scheme, the influence of the displacement-strain coupling relationship on the static stiffness measurement of the bridge erecting machine when the strain is dynamically changing can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is an exemplary flowchart of a bridge erecting machine static stiffness measurement method according to some embodiments of the present application;

[0044] Figure 2 is a schematic diagram of a bridge erecting machine according to some embodiments of the present application;

[0045] Figure 3 is an exemplary flowchart of determining a loss gradient of a strain state according to some embodiments of the present application;

[0046] Figure 4 is a structural schematic diagram of a stiffness measurement unit according to some embodiments of the present application;

[0047] Figure 5 Fig. 1 is a structural schematic diagram of a computer device for implementing a method for measuring static rigidity of a bridge girder according to some embodiments of the present application. DETAILED DESCRIPTION

[0048] In order to better understand the technical solutions of the present application, the technical solutions of the present application will be described in detail below in combination with the accompanying drawings and specific embodiments.

[0049] Reference Figure 1 Fig. 1 is an exemplary flowchart of a method for measuring static rigidity of a bridge girder according to some embodiments of the present application, which mainly includes the following steps:

[0050] In some embodiments, the pre-arrangement of the sensor network on the target bridge girder can be achieved in the following manner, i.e., on the main girder, sensors (such as resistance strain gauges) are arranged at certain intervals (such as 2-5 meters) along the length direction and at the midspan and near the support point where the bending moment is large; on the support leg, sensors are installed at the upper and lower ends connected to the main girder and at the key sections where the stress is concentrated; for the cross beam, sensors are arranged at the connection nodes of the cross beam with the main girder and the support leg and at the middle part of the cross beam where deformation is prone to occur, while following the principle of symmetrical distribution to ensure that the sensors appear in pairs at corresponding positions on both sides of the bridge girder, forming a sensor network layout that fully covers the key stress areas and is correlated with each other, so as to ensure that the collected data can accurately reflect the overall strain condition of the bridge girder; and professional installation technology is adopted to ensure that the sensors are closely attached to the structure of the bridge girder and can accurately perceive the structural strain; in other embodiments, other arrangements can be adopted, which are not limited here.

[0051] In some embodiments, the application of the staged static load to the target bridge girder by the loading device can be achieved in the following manner, i.e., by installing a device (such as a hydraulic cylinder) to gradually increase the hydraulic pressure according to a certain load gradient (such as an increase of 5%-10% of the designed load each time), and applying the static load uniformly to the designated loading points of the bridge girder through the hydraulic cylinder, which are usually selected at the key stress positions such as the midspan of the main girder, the support point and the top of the support leg; in other embodiments, other loading methods can be adopted, which are not limited here.

[0052] In step 101, the strain data of the target bridge girder under each level of static load is collected based on the sensor network.

[0053] In practice, after the target bridge erecting machine completes the sensor network layout, each sensor is connected to the data acquisition instrument via a dedicated cable to form a complete data acquisition path. The data acquisition instrument is pre-set with a sampling frequency (e.g., 100Hz, which can be adjusted according to the deformation speed and measurement accuracy requirements of the bridge erecting machine) and a acquisition duration (1-2 minutes of acquisition after each load level is applied and stabilized). When the loading device applies each level of static load to the bridge erecting machine and stabilizes the load, the data acquisition instrument synchronously starts acquiring data from all sensors according to the set parameters, acquiring the strain data of the target bridge erecting machine under each level of static load. Other acquisition methods can also be used in other embodiments, which are not limited here.

[0054] It should be noted that the strain data in this application includes a set of all strain values. Each sensor collects the strain value at the corresponding location once under a first-level static load. The strain data represents the strain level of the target bridge erecting machine under the sub-static load and can be used to analyze the static stiffness of the target bridge erecting machine.

[0055] In some embodiments, reference Figure 2 As shown, this figure is a schematic diagram of a bridge erecting machine in some embodiments of this application, such as... Figure 2 As described, the transverse long beam component in the diagram is the main beam, used to bear the weight of the lifted beam and the load during its own operation. It is mostly made of steel and has high strength and rigidity. The structure supporting the main beam in the diagram is the outrigger, which plays the role of supporting and stabilizing the bridge erecting machine. It can be divided into front outrigger, middle outrigger, and rear outrigger. Different outriggers perform different functions during the longitudinal and lateral movement of the bridge erecting machine and during the beam erection operation. The lifting mechanism in the diagram includes the hook component (the part in the diagram with the hook suspending the heavy object). It realizes the vertical lifting and lowering of the beam through winches, wire ropes, etc., and is the key execution mechanism for the beam erection operation. The bridge erecting machine is supported on the pier or the already erected beam through the outrigger. It uses the lifting mechanism to lift the precast beam from the beam transport vehicle, and then accurately places the beam into the predetermined position on the pier through longitudinal and lateral movements to complete the bridge erection work.

[0056] In step 102, the contribution of each level of static load to the strain of the target bridge erecting machine is determined using all strain data.

[0057] In some embodiments, determining the contribution of each level of static load to the strain of the target bridge erecting machine using all strain data can be achieved through the following steps:

[0058] A first-level static load is selected as the selected static load. Based on the strain data corresponding to the selected static load, the contribution components of each sensor at the corresponding position in the target bridge erecting machine under the selected static load are determined.

[0059] The strain contribution of the selected static load to the target bridge erecting machine is determined by all contribution components;

[0060] Continue to determine the contribution of the remaining static load to the strain of the target bridge erecting machine.

[0061] It should be noted that, within the scope of elasticity, the strain of a bridge erecting machine structure is linearly related to the static load. That is, the strain generated by each load acting individually can be superimposed to obtain the total strain. Therefore, by separating the strain data corresponding to each load, the contribution ratio of each load to the total strain can be quantified.

[0062] In specific implementation, firstly, the strain data collected by each sensor in the target bridge erecting machine under the selected static load is normalized using a normalization algorithm in the existing technology. That is, the strain value of each sensor is divided by the sum of the strain values ​​of all sensors, and the normalized value of each sensor is used as the contribution component of the corresponding sensor position. This contribution component reflects the proportion of the contribution of each sensor position to the overall strain. Then, based on the weighted summation method in statistics, the contribution components of each sensor position are used as weights, combined with the importance coefficient of the structural part where the sensor is located (this coefficient can be preset according to the structural design specifications of the bridge erecting machine), and all contribution components are weighted and summed. The value obtained by weighted summation is used as the strain contribution of the selected static load to the target bridge erecting machine. Other methods can be used to determine this in other embodiments, which are not limited here.

[0063] It should be noted that the strain contribution rate in this application represents the parameter value of the degree of contribution of static load to the strain of the target bridge erecting machine, and can be used to analyze the strain state of the target bridge erecting machine.

[0064] In step 103, gradient analysis is performed on the strain state of the static load on the target bridge erecting machine based on all strain contributions and the structural characteristics of the target bridge erecting machine. The loss gradient of the strain state in the target bridge erecting machine is obtained as the static load increases. Based on the loss gradient of the strain state, the dominant strain modes of each level of static load on the bridge erecting machine are determined.

[0065] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flowchart for determining the loss gradient of the strain state in some embodiments of this application. In this embodiment, the strain state of the static load on the target bridge erecting machine is analyzed based on all strain contributions and the structural characteristics of the target bridge erecting machine. The loss gradient of the strain state in the target bridge erecting machine when the static load increases can be obtained by the following steps:

[0066] First, in step 1031, the structural characteristics of the target bridge erecting machine are determined;

[0067] Secondly, in step 1032, multiple local strain gradients of the static load on the target bridge erecting machine are determined based on all strain contributions;

[0068] Finally, in step 1033, the loss gradient of the strain state in the target bridge machine during the static load increment is determined by all the local strain gradients.

[0069] It should be noted that the strain gradient represents the strain distribution inside the structure, which is not only related to the current load, but also related to the strain gradient of the adjacent region. During the static load increment process, the stiffness difference and stress concentration factors of each part of the structure will lead to a decrease in strain transmission efficiency, forming a "loss" phenomenon. The structural characteristics of the target bridge machine (such as component size, connection method, material properties) determine the stress transmission path and strain distribution law of the target bridge machine, and the strain contribution degree reflects the strain proportion of different parts under each load level. The combination of the two can quantify the attenuation trend of the strain with the load increment, i.e. the loss gradient of the strain state in the target bridge machine during the static load increment.

[0070] In specific implementation, first, the structural characteristics of the target bridge machine are extracted from the design drawings of the target bridge machine, wherein the structural characteristics include the material properties (such as elastic modulus, yield strength) of the main beam and the key components of the legs of the bridge machine, the geometric size (cross-sectional shape, length), the connection method (welding, bolt connection) and the constraint condition (support type) of the bridge machine. In other embodiments, other ways can also be used to determine, which are not limited here.

[0071] In specific implementation, the determination of the plurality of local strain gradients of the static load on the target bridge machine according to all the strain contribution degrees can be realized in the following manner, i.e. for each level of static load, the finite difference method in the prior art is used in combination with the structural characteristics of the target bridge machine to divide the structure of the bridge machine into a plurality of micro-element segments, wherein the micro-element segment refers to the smallest analysis unit obtained after the discretization of the overall structure of the bridge machine, for example, the main beam and the key parts of the legs are divided according to a certain length (such as 1-2 meters) or according to a structural unit (such as each cross beam and each column segment), and a corresponding relationship between the micro-element segments and the sensor positions is established. From all the strain contribution degrees, the strain contribution degrees of the corresponding sensors of each micro-element segment under the adjacent two levels of static load are extracted. If a micro-element segment contains multiple sensors, the comprehensive strain contribution degree of the micro-element segment under each level of load is calculated by the weighted average method (the weight is set according to the importance of the sensor position or the coverage area). Then, for each micro-element segment, the comprehensive strain contribution degree under the next level of load is subtracted from the comprehensive strain contribution degree of the previous level, to obtain the strain contribution degree difference. At the same time, the difference between the values of the adjacent two levels of load is calculated as the load increment. Finally, the strain contribution degree difference of the micro-element segment is divided by the corresponding load increment, and the value obtained by the division is taken as the local strain gradient of the micro-element segment, thereby obtaining the plurality of local strain gradients of the static load on the target bridge machine, wherein the local strain gradient reflects the rate of change of the strain of different parts with the load; in other embodiments, other ways can also be used to determine, which are not limited here.

[0072] In a specific implementation, the loss gradient of the strain state in the target bridge machine when the static load is increased can be determined by all local strain gradients in the following manner: first, based on the design drawings of the target bridge machine, material properties and historical failure data, the importance and vulnerability of each structural part during loading are comprehensively evaluated, for example, the midspan of the main beam is determined to be a high importance part because it bears a large bending moment, and the connecting part of the leg is considered to be a high vulnerability part because it is prone to fatigue damage due to stress concentration, the loss gradient of the strain state in the target bridge machine when the static load is increased is calculated by establishing a weighted average model and combining the weights of different parts (the weight is related to the importance and vulnerability of the part in the structure), for example: the importance and vulnerability of each structural part are compared and scored by constructing a judgment matrix using the analytic hierarchy process, the weight coefficient of each structural part is determined by calculating the eigenvector of the matrix, for example, the weight coefficient ranges from 0 to 1, and the larger the weight coefficient, the greater the influence of the part in the structure, then, the local strain gradient of each microelement segment is multiplied by the weight coefficient of the corresponding structural part to obtain the weighted local strain gradient value of each microelement segment, finally, all weighted local strain gradient values are taken as the loss gradient of the strain state in the target bridge machine when the static load is increased; in other embodiments, other methods can also be used to determine the loss gradient, which is not limited here.

[0073] It should be noted that the loss gradient in the present application represents the loss degree of the strain state in the target bridge machine when the static load is increased, and can be used to quantify the change trend of the strain growth efficiency of the structure during loading.

[0074] In some embodiments, determining the dominant strain mode of each level of static load on the bridge machine based on the loss gradient of the strain state can be achieved in the following steps:

[0075] Obtaining the structural dynamics characteristics of the target bridge machine;

[0076] According to the loss gradient of the strain state and the structural dynamics characteristics, the strains under each level of static load are classified and analyzed to obtain a plurality of similar strain clusters;

[0077] Determining the dominant strain mode of each level of static load on the bridge machine by all similar strain clusters.

[0078] It should be noted that the loss gradient of the strain state reflects the attenuation trend of the strain growth efficiency of each part of the bridge machine when the static load is increased, and the dominant strain mode refers to the main deformation mode (such as bending, stretching, shearing or combined deformation) of the structure under a specific load. When the load is gradually increased, the difference in strain loss gradient of different parts will reveal the deformation characteristics: the high loss gradient area (slow strain growth) may show local deformation dominance due to the material entering the nonlinear stage or the structure stiffness degradation, and the low loss gradient area (stable strain growth) may maintain the dominant overall mode of elastic deformation, so the dominant strain mode of each level of static load on the bridge machine can be determined based on the loss gradient of the strain state.

[0079] In a specific implementation, first, the structural dynamics characteristics of the bridge machine, including natural frequency, mode shape, are calculated by a modal analysis algorithm combined with the detailed structural parameters (such as component size, material property, connection method) and boundary conditions of the target bridge machine. The structural dynamics characteristics reflect the deformation characteristics of the bridge machine in the dynamic state. Then, the existing clustering analysis algorithm (such as K-means clustering algorithm) is used to classify the strain under each level of static load combined with the structural dynamics characteristics. In the clustering process, the change trend of the loss gradient, the strain peak position, and the structural dynamics mode are used as clustering basis to divide the static loads with similar strain characteristics into the same class, thereby obtaining multiple same-class strain clusters. Each same-class strain cluster represents the strain mode of the bridge machine under a similar mechanical state. Finally, for each same-class strain cluster, the principal component analysis algorithm is used to extract the principal component of the strain in the same-class strain cluster from all strains in the same-class strain cluster. The strain pattern corresponding to the principal component of the strain is the dominant strain mode under the same class of static load, and the dominant strain mode of each level of static load on the target bridge machine is determined. In other embodiments, other methods can also be used to determine the dominant strain mode, which is not limited here.

[0080] It should be noted that the dominant strain mode in this application represents the dominant strain distribution pattern of the target bridge machine under the action of static load, which can be used for accurate identification of the main deformation mode of the bridge machine under different load conditions.

[0081] In step 104, the displacement data of the target bridge machine under each level of static load is monitored, and the displacement data and strain data under each level of static load are coupled and analyzed to obtain the inter-level coupling residual error between each adjacent static load on the target bridge machine.

[0082] In a specific implementation, the displacement data of the target bridge erecting machine under each level of static load can be obtained by the following method: displacement sensors (such as laser displacement sensors) are arranged at key positions of the target bridge erecting machine, such as the middle of the main beam, the ends of the support points, and the top and bottom of the support legs where significant displacement is likely to occur. When the sensors are installed, the reference points of the sensors need to be firmly connected to the structure of the bridge erecting machine, and the measurement direction needs to be consistent with the expected displacement direction. During the process of applying each level of static load to the bridge erecting machine by the loading device, the data acquisition system collects the signals output by the displacement sensors in real time at a fixed frequency (such as 10 times per second). After each level of load is applied and stabilized for 3-5 minutes, the displacement data of each displacement sensor under this level of load is recorded, and the corresponding load level and collection time are marked. The displacement data includes a set of multiple displacement values, and the displacement values in the displacement data represent the spatial movement distance of the corresponding position of each displacement sensor. In other embodiments, other methods can also be used for monitoring, which are not limited here.

[0083] In some embodiments, the displacement data and strain data under each level of static load are coupled and analyzed to obtain the inter-level coupling residual between each adjacent static load on the target bridge erecting machine by the following steps:

[0084] The displacement data and strain data under each level of static load are matched and aligned to obtain displacement-strain matching data.

[0085] The coupling degree characteristics between each adjacent static load on the target bridge erecting machine are determined according to the displacement-strain matching data.

[0086] The inter-level coupling residual between each adjacent static load on the target bridge erecting machine is determined according to all coupling degree characteristics.

[0087] It should be noted that under static load, the structure is in a balanced state, and the displacement increment and strain increment of adjacent load levels should satisfy the theoretical coupling relationship. The inter-level coupling residual is defined as the deviation of the measured displacement and strain increment from the theoretical coupling relationship, that is, the difference between the measured data is calculated to quantify this deviation.

[0088] In a specific implementation, first, the displacement data and the strain data under each level of static load are matched and aligned based on a load application sequence in combination with a data alignment algorithm (such as a dynamic time warping algorithm), and the result obtained through the matching and alignment is taken as displacement-strain matching data. For example, the dynamic time warping algorithm finds the best matching path of the displacement data and the strain data by calculating the similarity between two time series, eliminates the time deviation caused by the difference in sampling frequency or data collection delay, and thus obtains the displacement-strain matching data under each level of static load. The displacement-strain matching data includes a displacement data group and a strain data group, and represents the data of the matching of displacement and strain under each level of static load. Then, the correlation values between adjacent static loads are calculated by using a canonical correlation analysis algorithm in combination with the displacement data group and the strain data group in the displacement-strain matching data, and the correlation values are taken as the coupling degree features between the corresponding adjacent static loads. The coupling degree features represent the features of the correlation degree between displacement and strain between adjacent static loads. Finally, a coupling relationship model between adjacent load levels is constructed based on the least square method principle in combination with the coupling degree features under each level of static load, and the coupling degree under each level of adjacent static load is predicted through the coupling relationship model. For each level of adjacent static load, the sum of squares of the difference between the predicted coupling degree of each level of adjacent static load and the corresponding coupling degree feature is calculated, and the values obtained by all the sums of squares are normalized. Each value obtained by the normalization is taken as the inter-level coupling residual error between the corresponding adjacent static loads on the target bridge erecting machine. In other embodiments, the inter-level coupling residual error can also be determined in other ways, which is not limited here.

[0089] It should be noted that the inter-level coupling residual error in the present application represents a quantitative index of the deviation degree between the actual and theoretical coupling relationship between the measured displacement data and the strain data of the bridge erecting machine under the action of adjacent two levels of static load, and can be used to evaluate the stability and difference degree of the displacement and strain coupling relationship when the load changes.

[0090] In step 105, a static stiffness measurement model of the target bridge erecting machine is constructed, and the structural stiffness of the target bridge erecting machine under each level of static load is determined based on the static stiffness measurement model in combination with all the dominant strain modes and the inter-level coupling residual errors between the adjacent static loads.

[0091] In a specific implementation, the static stiffness measurement model of the target bridge erecting machine can be implemented in the following manner: according to the basic principles of material mechanics and structural mechanics, in combination with the actual structural parameters (such as the cross-sectional size of the main beam and the support leg, the material elastic modulus, the Poisson's ratio), the connection mode (welding, bolt connection, etc.), and the boundary conditions (the support form of the support leg) of the target bridge erecting machine, a static stiffness measurement model of the target bridge erecting machine is established, and a correction coefficient is introduced to optimize the static stiffness measurement model. The correction coefficient can be adjusted according to the deviation between the actual measurement data and the theoretical calculation result. Finally, the optimized static stiffness measurement model is used as the static stiffness measurement model of the mechanical behavior of the target bridge erecting machine under various levels of static load. The static stiffness measurement model represents the measured stiffness of the mechanical behavior of the target bridge erecting machine under various levels of static load.

[0092] In some embodiments, the determination of the structural stiffness of the target bridge erecting machine under various levels of static load based on the static stiffness measurement model in combination with all dominant strain modes and the inter-stage coupling residual between the various adjacent static loads can be implemented in the following steps:

[0093] determining the main deformation region and the deformation trend of the target bridge erecting machine according to all dominant strain modes;

[0094] determining the coupling relationship coefficient of displacement and strain through the inter-stage coupling residual between the various adjacent static loads;

[0095] inputting the main deformation region, the deformation trend, and the coupling relationship coefficient into the static stiffness measurement model, and outputting the structural stiffness of the target bridge erecting machine under various levels of static load through the static stiffness measurement model.

[0096] In a specific implementation, first, a pattern recognition algorithm is used to identify and analyze all dominant strain modes, so as to identify the region with the most concentrated strain distribution of the target bridge erecting machine under each level of static load, and take this region as the main deformation region of the target bridge erecting machine, and determine the deformation trend such as bending, stretching or shearing based on the modal superposition method in the finite element software and the direction of all dominant strain modes; then, the inter-level coupling residual error, the corresponding displacement increment and strain increment data under each level of static load are sorted to form a data set containing the inter-level coupling residual error, the displacement increment and the strain increment, and a linear combination (i.e. inter-level coupling residual error = a x displacement increment + b x strain increment + noise term) between the inter-level coupling residual error, the displacement increment and the strain increment is constructed according to a regression analysis model (such as a linear regression model), wherein a and b are coupling relationship coefficients to be solved, and the least square method is used to optimize the parameters (a and b) in the linear combination, and the optimized parameters are taken as the coupling relationship coefficients of displacement and strain, which represent the coupling degree coefficients between displacement and strain and can be used to correct the deviation of the displacement-strain theoretical relationship caused by structural nonlinearity and measurement error factors; finally, the main deformation region, the deformation trend and the coupling relationship coefficients are input into the static stiffness measurement model as input parameters, and the structural stiffness of the target bridge erecting machine under each level of static load is output by the static stiffness measurement model, so as to realize the accurate calculation and evaluation of the structural stiffness of the bridge erecting machine. In other embodiments, other ways can also be used to determine, which is not limited here.

[0097] In addition, another aspect of the present application, in some embodiments, the present application provides a bridge erecting machine static stiffness measurement device, which comprises a stiffness measurement unit, which is used to Figure 4 The figure is a structural schematic diagram of a stiffness measurement unit according to some embodiments of the present application. The stiffness measurement unit 400 comprises a collection module 401, a processing module 402 and an execution module 403, which are described as follows:

[0098] The collection module 401 is mainly used to collect strain data of the target bridge erecting machine under each level of static load based on the sensor network in the present application;

[0099] The processing module 402 is used to determine the strain contribution of each level of static load to the target bridge erecting machine through all strain data in the present application;

[0100] It should be noted that the processing module 402 is also used to perform gradient analysis on the strain state of the static load on the target bridge erecting machine according to all strain contribution degrees and the structural characteristics of the target bridge erecting machine, to obtain the loss gradient of the strain state in the target bridge erecting machine when the static load is increased, and to determine the dominant strain mode of the bridge erecting machine based on the loss gradient of the strain;

[0101] In addition, it should be noted that the processing module 402 is further configured to monitor displacement data of the target bridge girder under each level of static load, and perform coupling analysis on the displacement data and strain data under each level of static load, so as to obtain inter-stage coupling residual errors between each adjacent static load on the target bridge girder.

[0102] The execution module 403 is mainly configured to construct a static stiffness measurement model of the target bridge girder, and determine the structural stiffness of the target bridge girder under each level of static load based on the static stiffness measurement model, all dominant strain modes and the inter-stage coupling residual errors between each adjacent static load.

[0103] In addition, the present application further provides a computer device, which comprises a memory and a processor, the memory stores codes, and the processor is configured to acquire the codes and execute the above-mentioned bridge girder static stiffness measurement method.

[0104] In some embodiments, with reference to Figure 5 FIG. 1 is a structural schematic diagram of a computer device for implementing a bridge girder static stiffness measurement method according to some embodiments of the present application. The bridge girder static stiffness measurement method in the above-mentioned embodiments can be implemented by the computer device shown in FIG. 1. The computer device 500 comprises at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504. Figure 5

[0105] The processor 501 can be a general central processing unit (CPU) or an application specific integrated circuit (ASIC).

[0106] The communication bus 502 can be used to transmit information between the above-mentioned components.

[0107] ​The memory 503 can be a readonly memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM), or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable readonly memory (EEPROM), a compact disc readonly memory (CDROM) or other optical disk storage, a magneto-optical disk storage, a magnetic disk or other magnetic storage device, or any other medium capable of storing desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to this. The memory 503 can exist independently, and is connected to the processor 501 through the communication bus 502. The memory 503 can also be integrated with the processor 501.

[0108] The memory 503 is configured to store program codes for implementing the solutions of the present application, and the processor 501 is configured to control the execution of the program codes. The processor 501 is configured to execute the program codes stored in the memory 503. The program codes can include one or more software modules. The methods used in the above embodiments can be implemented by the processor 501 and one or more software modules in the program codes in the memory 503.

[0109] The communication interface 504 is configured to communicate with other devices or communication networks, such as an Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc., using any transceiver-like mechanism.

[0110] In specific implementations, as an example, the computer device can include multiple processors, each of which can be a single CPU processor or a multi-CPU processor. The processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0111] The computer device described above can be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device can be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of the present application do not limit the type of the computer device.

[0112] In addition, the present application also provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the bridge static stiffness measurement method described above.

[0113] Although the preferred embodiments of the present application have been described, those skilled in the art who are informed of the basic inventive concept can make additional changes and modifications to the embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0114] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A method of measuring the static stiffness of a bridge-launching machine, wherein, The method comprises the following steps: A sensor network is arranged on the target bridge machine in advance, and a graded static load is applied to the target bridge machine by a loading device, characterized in that the method comprises the following steps: Strain data of the target bridge machine under each level of static load is collected based on the sensor network; The strain contribution of each level of static load to the target bridge machine is determined based on all the strain data; Gradient analysis is performed on the strain state of the static load on the target bridge machine based on all the strain contribution and the structural characteristics of the target bridge machine, so as to obtain the loss gradient of the strain state in the target bridge machine when the static load is increased, and the dominant strain mode of each level of static load to the bridge machine is determined based on the loss gradient of the strain state; Displacement data of the target bridge machine under each level of static load is monitored, and coupling analysis is performed on the displacement data and the strain data under each level of static load, so as to obtain the inter-level coupling residual error between each adjacent static load on the target bridge machine; 2. The method of claim 1, wherein, A static stiffness measurement model of the target bridge machine is constructed, and the structural stiffness of the target bridge machine under each level of static load is determined based on the static stiffness measurement model, all the dominant strain modes and the inter-level coupling residual error between each adjacent static load. The strain contribution of each level of static load to the target bridge machine is determined based on all the strain data, specifically including: A first level of static load is selected as a selected static load, and the contribution component of each sensor corresponding position in the target bridge machine under the selected static load is determined based on the strain data corresponding to the selected static load; The strain contribution of the selected static load to the target bridge machine is determined based on all the contribution components; 3. The method of claim 1, wherein, The strain contribution of the remaining static load to the target bridge machine is determined. Gradient analysis is performed on the strain state of the static load on the target bridge machine based on all the strain contribution and the structural characteristics of the target bridge machine, so as to obtain the loss gradient of the strain state in the target bridge machine when the static load is increased, specifically including: The structural characteristics of the target bridge machine are determined; A plurality of local strain gradients of the static load on the target bridge machine are determined based on all the strain contribution; 4. The method of claim 1, wherein, The loss gradient of the strain state in the target bridge machine when the static load is increased is determined based on all the local strain gradients. The dominant strain mode of each level of static load to the bridge machine is determined based on the loss gradient of the strain state, specifically including: The structural dynamics characteristics of the target bridge machine are obtained; The strain under each level of static load is classified and analyzed based on the loss gradient of the strain state and the structural dynamics characteristics, so as to obtain a plurality of similar strain clusters; 5. The method of claim 1, wherein, The dominant strain mode of each level of static load to the bridge machine is determined based on all the similar strain clusters. Coupling analysis is performed on the displacement data and the strain data under each level of static load, so as to obtain the inter-level coupling residual error between each adjacent static load on the target bridge machine, specifically including: The displacement data and the strain data under each level of static load are matched and aligned to obtain displacement-strain matching data; The coupling degree characteristics between each adjacent static load on the target bridge machine are determined based on the displacement-strain matching data; The inter-level coupling residual error between each adjacent static load on the target bridge machine is determined based on all the coupling degree characteristics.

6. The method of claim 1, wherein, The structural stiffness of the target bridge machine under each level of static load is determined based on the static stiffness measurement model in combination with all dominant strain modes and inter-stage coupling residuals between the respective adjacent static loads, and specifically includes: The main deformation region and deformation trend of the target bridge machine are determined according to all dominant strain modes; The coupling relationship coefficient of displacement and strain is determined through the inter-stage coupling residuals between the respective adjacent static loads; The main deformation region, the deformation trend, and the coupling relationship coefficient are input into the static stiffness measurement model, and the structural stiffness of the target bridge machine under each level of static load is output by the static stiffness measurement model.

7. The method of claim 1, wherein, The displacement data of the target bridge machine under each level of static load is monitored by a laser displacement sensor.

8. A bridge girder static rigidity measuring apparatus comprising a rigidity measuring unit, wherein, A sensor network is arranged in advance on the target bridge machine, and a grading static load is applied to the target bridge machine by a loading device, and the stiffness measurement unit includes: A collection module is configured to collect strain data of the target bridge machine under each level of static load based on the sensor network; A processing module is configured to determine the strain contribution degree of each level of static load to the target bridge machine through all strain data; The processing module is further configured to perform gradient analysis on the strain state of the static load on the target bridge machine according to all strain contribution degrees and the structural characteristics of the target bridge machine, to obtain a loss gradient of the strain state in the target bridge machine when the static load is increased, and to determine the dominant strain mode of the target bridge machine under each level of static load based on the loss gradient of the strain state; The processing module is further configured to monitor the displacement data of the target bridge machine under each level of static load, to perform coupling analysis on the displacement data and the strain data under each level of static load, and to further obtain the inter-stage coupling residuals between the respective adjacent static loads on the target bridge machine; An execution module is configured to construct a static stiffness measurement model of the target bridge machine, to determine the structural stiffness of the target bridge machine under each level of static load based on the static stiffness measurement model in combination with all dominant strain modes and the inter-stage coupling residuals between the respective adjacent static loads.

9. A computer device, comprising: The computer device includes a memory and a processor, the memory stores code, and the processor is configured to acquire the code and execute the bridge machine static stiffness measurement method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the bridge machine static stiffness measurement method according to any one of claims 1 to 7.

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