Bridge suspender force adjusting method based on arch bridge vehicle load big data

Abstract

The invention provides a bridge suspender force adjusting method based on arch bridge vehicle load big data. The bridge suspender force adjusting method comprises the steps that dynamic response data and suspender force data of an arch bridge under vehicle load are obtained according to a sensor arranged in advance; carrying out load identification on the collected dynamic response data, and determining load characteristics; according to the suspender force data and the corresponding load characteristics, a response model of the load characteristics and suspender force is established, and the suspender force of the arch bridge under the target load condition is predicted; and inputting the suspender force under the target load condition into the suspender force adjustment model, and determining suspender force adjustment data. By implementing the method, the accuracy and high efficiency of arch bridge supervision can be met, meanwhile, the bearing capacity and durability of the bridge are improved by optimizing the prestress of the suspender, the service life of the bridge is prolonged, the long-term maintenance cost is reduced, the safety of the bridge under various load conditions is ensured, and the risk of accidents is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of bridge construction, and in particular relates to a bridge hanger force adjustment method based on arch bridge vehicle load big data. Background Art

[0002] With the rapid development of bridge construction in my country, arch bridges, as an important type of bridge structure, are widely used in urban transportation and infrastructure construction. When an arch bridge bears vehicle loads, the magnitude of its suspender force is directly related to the stability and safety of the bridge. However, traditional manual detection and empirical judgment methods have limitations when processing a large amount of vehicle load data, and it is difficult to meet the requirements of modern bridge engineering for accuracy and efficiency. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a bridge hanger force adjustment method and device based on arch bridge vehicle load big data to meet the requirements of accuracy and efficiency.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] According to the first aspect, the present invention provides a bridge hanger force adjustment method based on arch bridge vehicle load big data, including: acquiring dynamic response data and hanger force data of the arch bridge under vehicle load according to pre-deployed sensors; performing load identification on the collected dynamic response data to determine the load characteristics; establishing a response model of load characteristics and hanger force according to the hanger force data and the corresponding load characteristics to predict the hanger force of the arch bridge under the target load condition; inputting the hanger force under the target load condition into the hanger force adjustment model to determine the hanger force adjustment data.

[0006] Optionally, a response model of load characteristics and hanger force is established based on the hanger force data and the corresponding load characteristics to predict the hanger force of the arch bridge under the target load conditions, including: dividing the vehicle load of the arch bridge into a light load area, a medium load area and a heavy load area according to the load characteristics; in the light load area, a linear fitting method is used to construct a response model of load characteristics and hanger force; in the medium load area, a quadratic fitting method is used to construct a response model of load characteristics and hanger force; in the heavy load area, a finite element analysis method is used to construct a response model of load characteristics and hanger force.

[0007] Optionally, the method for establishing the response model of load characteristics and hanger force includes: based on big data technology, obtaining response data of load characteristics and hanger force of multiple arch bridges that are similar to the arch bridge size, span and service life; using a regression statistical model to establish the response model of load characteristics and hanger force according to the relationship between load characteristics and hanger force.

[0008] Optionally, the hanger force under the target load condition is input into the hanger force adjustment model to determine the hanger force adjustment data, including: applying the hanger force of the arch bridge under the target load condition to the corresponding hangers in the first bridge model, analyzing whether the mid-span displacement of the tie beam is within the maximum deformation range of the bridge, and the first bridge model is a model established according to the actual parameters of the arch bridge; when it exceeds the maximum deformation range of the bridge, the target load of the arch bridge is used as the applied load and input into the second bridge model to determine the bending energy of each hanger under the load condition, and the bending stiffness of the arch rib and the tie beam in the second bridge model is reduced to a target multiple; with the minimum bending energy as the goal, the hanger prestress is changed to obtain the optimal prestress of the hanger; the hanger force of the arch bridge under the target load is applied to the corresponding hangers in the third bridge model, analyzing whether the mid-span displacement of the tie beam is within the maximum deformation range of the bridge, and when it is within the maximum range, the optimal prestress of the hanger is output, the bending stiffness of the arch rib and the tie beam in the third bridge model is restored to the actual level, and the hanger prestress is adjusted to the optimal prestress of the hanger.

[0009] Optionally, the bending energy construction process includes: obtaining the bending moment, axial force and shear force of the main beam section, arch rib section and hanger section of the arch bridge under a preset hanger force; determining the internal force of the main beam section according to the bending moment, axial force and shear force of the main beam section; determining the internal force of the arch rib section according to the bending moment, axial force and shear force of the arch rib section; determining the internal force of the hanger section according to the bending moment, axial force and shear force of the hanger section, wherein the bending moment, axial force and shear force of the hanger section are determined by the prestress of the hanger; determining the strain energy of the main beam, the arch rib strain energy and the hanger strain energy according to the internal force of the main beam section, the arch rib section internal force, the hanger section internal force, the main beam cross-sectional area, the arch rib interface area and the hanger cross-sectional area; determining the bending energy according to the strain energy of the main beam, the arch rib strain energy and the hanger strain energy.

[0010] Optionally, the internal force of the hanger cross-section is determined according to the bending moment, axial force and shear force of the hanger cross-section, including: according to the finite element analysis method, the target load is added to each hanger of the arch bridge, and the hanger force change value of the target load hanger and the hanger force change value of other hangers caused by the target load are determined; the influence of the load on each hanger is determined according to the hanger force change value of the target load hanger and the hanger force change value of other hangers caused by the target load; according to the ranking result of the influence of each hanger, a target number of hangers are selected, and the internal force of the cross-section of the selected hanger is determined according to the bending moment, axial force and shear force of the cross-section of the selected hanger; with the minimum bending energy as the goal, the prestress of the hanger is changed to obtain the optimal prestress of the hanger, including: changing the hanger prestress of the selected hanger to obtain the optimal prestress of the selected hanger.

[0011] Optionally, the bending energy is calculated as follows:

[0012]

[0013] Among them, U1 represents the strain energy of the main beam, U2 represents the strain energy of the arch rib, U3 represents the strain energy of the hanger, E1 represents the elastic modulus of the main beam, M1 represents the bending moment of the main beam section, N1 represents the axial force of the main beam section, Q1 represents the shear force of the main beam section, I1 represents the bending moment of the main beam, G1 represents the shear modulus of the main beam, S1 represents the cross-sectional area of ​​the main beam, E2 represents the elastic modulus of the arch rib section, M2 represents the bending moment of the arch rib section, N2 represents the axial force of the arch rib section, Q2 represents the shear force of the arch rib section, I2 represents the bending moment of the arch rib, G2 represents the shear modulus of the arch rib, S2 represents the cross-sectional area of ​​the arch rib, E3 represents the elastic modulus of the hanger section, M ext Indicates the bending moment caused by the external load of the hanger, N ext represents the axial force generated by the external load of the hanger, Q ext It represents the shear force generated by the external load of the hanger, I3 is the bending moment of inertia of the hanger, G3 is the shear modulus of the hanger, S3 is the cross-sectional area of ​​the hanger, P represents the prestress of the hanger, and e represents the distance from the prestressing point to the neutral axis of the section.

[0014] According to the second aspect, the present invention provides a bridge hanger force adjustment device based on arch bridge vehicle load big data, including: a data acquisition module, used to obtain the dynamic response data and hanger force data of the arch bridge under vehicle load according to pre-deployed sensors; a load characteristic determination module, used to perform load identification on the collected dynamic response data and determine the load characteristics; a prediction module, used to establish a response model of load characteristics and hanger force according to the hanger force data and the corresponding load characteristics, and predict the hanger force of the arch bridge under the target load condition; a hanger force adjustment module, used to input the hanger force under the target load condition into the hanger force adjustment model, and determine the hanger force adjustment data.

[0015] According to the third aspect, an embodiment of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the steps of the bridge hanger force adjustment method based on arch bridge vehicle load big data as described in the first aspect or any embodiment of the first aspect.

[0016] According to the fourth aspect, an embodiment of the present invention provides a computer storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of the bridge hanger force adjustment method based on arch bridge vehicle load big data as described in the first aspect or any embodiment of the first aspect.

[0017] The present embodiment provides a bridge hanger force adjustment method based on arch bridge vehicle load big data. By performing load identification on dynamic response data, the load characteristics acting on the bridge can be understood more accurately, and a response model of load characteristics and hanger force can be established. The hanger force under specific load conditions is predicted based on actual data, and the predicted hanger force is input into the hanger force adjustment model to optimize the prestress of the hanger. It does not rely on manual monitoring and judgment, and can meet the accuracy and efficiency of arch bridge supervision. At the same time, by optimizing the hanger prestress, the bearing capacity and durability of the bridge are improved, the service life of the bridge is extended, and the long-term maintenance cost is reduced, thereby ensuring the safety of the bridge under various load conditions and reducing the risk of accidents.

[0018] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art may be taught from the practice of the present invention. The objectives and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:

[0020] Figure 1 A specific example flow chart of a bridge hanger force adjustment method based on arch bridge vehicle load big data in the present invention;

[0021] Figure 2 It is a principle block diagram of a specific example of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, it can also be the internal connection of two components, it can be a wireless connection, or it can be a wired connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0025] The embodiment of the present invention provides a bridge suspender force adjustment method based on arch bridge vehicle load big data, such as Figure 1 As shown, including:

[0026] S101, obtaining dynamic response data and suspender force data of the arch bridge under vehicle load according to pre-deployed sensors;

[0027] S102, performing load identification on the collected dynamic response data to determine load characteristics;

[0028] S103, establishing a response model of load characteristics and hanger force according to the hanger force data and the corresponding load characteristics, and predicting the hanger force of the arch bridge under the target load condition;

[0029] S104, inputting the boom force into a boom force adjustment model to determine boom force adjustment data.

[0030] For example, the method proposed in this embodiment can be applied to simulate the change of the load type or load amount borne by the arch bridge, such as the increase of traffic flow or the frequent passage of large vehicles, by adjusting the suspension rod force to adapt to the new load conditions. This scenario is used as an example to describe the specific implementation of this solution.

[0031] The dynamic response data of the bridge under vehicle load is collected by pre-installing target sensors on the arch bridge with heavy traffic. The target sensors can be accelerometers, displacement sensors, etc. The dynamic response data may include information such as vibration, displacement, and stress of the bridge. The collected data is analyzed to identify the load characteristics, which include the size of the load, the frequency of the load, and the load distribution. Specifically, useful features such as frequency, amplitude, and duration are first extracted from the pre-processed data to help identify the type and size of the load. Then, the load can be classified by statistical or machine learning methods, such as cluster analysis, support vector machines, neural networks, etc. to distinguish the load distribution and the frequency of the load. The load distribution may include uniform distribution, concentrated load, etc.

[0032] According to the multiple collection of the hanger force data of the arch bridge and the corresponding load characteristic analysis, the relationship between the load characteristic and the hanger force is established as a response model of the load characteristic and the hanger force. Specifically, the response model of the load characteristic and the hanger force can be constructed based on the consideration of the geometry, material characteristics, load distribution and other factors of the bridge combined with the principle of structural dynamics. For example, the relationship between the load characteristic and the hanger force can be analyzed by finite element analysis to form a corresponding response model. In order to save computing resources, the relationship between the collected hanger force data and the corresponding load characteristics can also be simply analyzed to determine the relationship between the hanger force and the load characteristics of each hanger, and the determined relationship between the hanger force and the load characteristics is used as the response model of the load characteristic and the hanger force.

[0033] This response model can predict the hanger force of the arch bridge under any load condition. For example, when an arch bridge is expected to serve as an important transportation hub at a certain important time node and will increase the target traffic flow, the load characteristics within this time node can be used as input. The response model of load characteristics and hanger force can predict the hanger force required under the expected load characteristics.

[0034] The above steps determine the level to which the arch bridge suspender force should be adjusted, but it is necessary to further determine whether the suspender force meets the safety requirements to ensure that the bridge will not exceed the design stress and deformation limits under the expected load, thereby avoiding structural damage or failure. Therefore, this embodiment also needs to input the suspender force into the suspender force adjustment model to determine the suspender force adjustment data. The suspender force adjustment model takes into account the material properties of the suspender, the structural properties of the bridge, environmental factors (such as temperature changes, wind force, etc.) and possible maintenance or repair needs. The method for the suspender force adjustment model to determine the suspender force adjustment data can be based on a finite element analysis of the digital twin model of the arch bridge to analyze whether the suspender force determined by S103 causes a safety hazard to the bridge. Specifically, the mid-span displacement of the bridge tie beam of the suspender force can be used to determine whether it is within the maximum deformation range of the bridge. If it is, the suspender force determined by S103 above is used as the suspender force adjustment data. If it exceeds this range, a secondary adjustment is performed.

[0035] The present embodiment provides a bridge hanger force adjustment method based on arch bridge vehicle load big data. By performing load identification on dynamic response data, the load characteristics acting on the bridge can be understood more accurately, and a response model of load characteristics and hanger force can be established. The hanger force under specific load conditions is predicted based on actual data, and the predicted hanger force is input into the hanger force adjustment model to optimize the prestress of the hanger, thereby improving the bearing capacity and durability of the bridge, extending the service life of the bridge, and reducing long-term maintenance costs, ensuring the safety of the bridge under various load conditions, and reducing the risk of accidents.

[0036] As an optional implementation, a response model of load characteristics and hanger force is established according to the hanger force data and the corresponding load characteristics to predict the hanger force of the arch bridge under the target vehicle load, including: dividing the arch bridge vehicle load into a light load area, a medium load area and a heavy load area according to the load characteristics; in the light load area, a linear fitting method is used to construct a response model of load characteristics and hanger force; in the medium load area, a quadratic fitting method is used to construct a response model of load characteristics and hanger force; in the heavy load area, a finite element analysis method is used to construct a response model of load characteristics and hanger force.

[0037] For example, in the light load area, the vehicle load is relatively small and has little impact on the bridge structure. In this case, the present embodiment uses a linear fitting method to construct a response model of load characteristics and hanger force. Linear fitting is simple and intuitive, with a small amount of calculation. It is suitable for situations where the load variation range is not large and the relationship between the load and the hanger force is relatively linear. The construction of the model is usually based on a large amount of historical data, and the linear relationship between the load and the hanger force is determined by the least squares method or other linear regression methods.

[0038] The load in the medium load area is between light load and heavy load, and the relationship between load and boom force is more complex, and the linear model cannot accurately describe this relationship. Therefore, this embodiment uses a quadratic fitting method to construct a model. The quadratic fitting model can better capture the nonlinear relationship between load and boom force, and fit the data through a quadratic polynomial equation to provide a more accurate prediction. The parameters of the model are usually determined by the least squares method or other optimization algorithms, which are not limited in this embodiment.

[0039] In the heavy-load area, the vehicle load is large and has a significant impact on the bridge structure, so a more accurate analysis method is needed to predict the hanger force. As a powerful numerical analysis tool, finite element analysis can simulate the behavior of complex structures under various load conditions. By discretizing the bridge structure into multiple small units, finite element analysis can calculate the stress, strain, displacement and other parameters of each unit under a specific load, and then predict the hanger force. The finite element analysis method can take into account the complex geometry, material properties and boundary conditions of the structure, and provide highly accurate response results.

[0040] The bridge hanger force adjustment method based on arch bridge vehicle load big data provided in this embodiment can achieve precise prediction and management of the hanger force by dividing the arch bridge vehicle load into light load, medium load and heavy load areas and adopting different modeling methods for different areas, thereby reducing the amount of data processing while ensuring the accuracy of the response model.

[0041] As an optional implementation, the pre-established load characteristic and suspension rod force response model is established in the following manner:

[0042] Based on big data technology, the response data of load characteristics and hanger force of multiple arch bridges that are similar to the arch bridge size, span and service life are obtained; the regression statistical model is used to establish the response model of load characteristics and hanger force according to the relationship between load characteristics and hanger force.

[0043] Exemplarily, in this embodiment, in order to increase the relationship data between load characteristics and hanger force, big data technology is used to obtain response data of load characteristics and hanger force of multiple arch bridges that are similar to the arch bridge size, span and service life. A regression statistical model is used to establish a response model of load characteristics and hanger force according to the relationship between load characteristics and hanger force. The regression model can be linear regression, ridge regression, LASSO regression or support vector regression (SVR), etc.

[0044] The bridge hanger force adjustment method based on arch bridge vehicle load big data provided in this embodiment can obtain more approximate response data of multiple arch bridge load characteristics and hanger forces through big data, which is beneficial to improving the accuracy of the load characteristics and hanger force response model.

[0045] As an optional implementation, the suspender force of the arch bridge under the vehicle load is input into the suspender force adjustment model to determine the suspender force adjustment data, including:

[0046] Apply the hanger force of the arch bridge under the target load to the corresponding hangers in the first completed bridge model to analyze whether the mid-span displacement of the tie beam is within the maximum deformation range of the bridge. The first completed bridge model is a model established according to the actual parameters of the arch bridge.

[0047] When the maximum deformation range of the bridge is exceeded, the target load of the arch bridge is used as the applied load and input into the second completed bridge model to determine the bending energy of each hanger under the load condition. The bending stiffness of the arch rib and tie beam in the second completed bridge model is reduced to the target multiple.

[0048] Taking the minimum bending energy as the goal, the prestress of the hanger rod is changed to obtain the optimal prestress of the hanger rod;

[0049] The hanger force of the arch bridge under the target load is applied to the corresponding hangers in the third completed bridge model, and it is analyzed whether the mid-span displacement of the tie beam is within the maximum deformation range of the bridge. When it is within the maximum range, the optimal prestress of the hanger is output, and the bending stiffness of the arch rib and tie beam in the third completed bridge model is restored to the actual level, and the hanger prestress is adjusted to the optimal prestress of the hanger.

[0050] Exemplarily, the first completed bridge model is a model established according to the actual parameters of the arch bridge. The actual parameters of the arch bridge may include the size of the bridge, the material properties of each part of the bridge, structural details, loads and environmental conditions, etc. The size may include the type of arch axis (such as arc, parabola, catenary, etc.), arch span length, arch height, span-rise ratio, cross-sectional size and shape of arch ribs, width, thickness and cross-sectional shape of tie beams (bridge deck), layout, number and length of hangers. Material properties may include the material type of arch ribs, tie beams and hangers (such as steel, concrete, etc.), elastic modulus, yield strength, tensile strength and density of materials, durability and aging characteristics of materials. Structural details may include the type and layout of connectors, such as bolts, welds, anchoring systems, etc., the type of bearings (such as fixed bearings, sliding bearings, rubber bearings, etc.), design parameters of arch feet and abutments, etc.

[0051] The suspender force of the arch bridge under the target load is applied to the corresponding suspenders in the first completed bridge model. If the mid-span displacement of the tie beam exceeds the maximum deformation range, it indicates that the current suspender force distribution is unreasonable. The target load is input into the second completed bridge model as the applied load, and the bending stiffness of the arch rib and tie beam is reduced to the target multiple in the second completed bridge model. When performing structural analysis, by reducing the stiffness first, the changes of the structure under the action of prestress can be more intuitively observed, and then the stiffness can be restored to verify the rationality of the analysis results. By reducing the bending stiffness, the performance of the bridge under the most unfavorable conditions can be simulated to ensure that the bridge can still meet safety requirements even when the stiffness is reduced.

[0052] As an optional implementation, the bending energy construction process includes: obtaining the bending moment, axial force, and shear force of the arch bridge main beam section, arch rib section, and hanger section under a preset hanger force; determining the internal force of the main beam section according to the bending moment, axial force, and shear force of the main beam section; determining the internal force of the arch rib section according to the bending moment, axial force, and shear force of the arch rib section; determining the internal force of the hanger section according to the bending moment, axial force, and shear force of the hanger section, wherein the bending moment, axial force, and shear force of the hanger section are determined by the hanger prestress; determining the main beam strain energy, arch rib strain energy, and hanger strain energy according to the internal force of the main beam section, the internal force of the arch rib section, the internal force of the hanger section, the main beam cross-sectional area, the arch rib interface area, and the hanger cross-sectional area; determining the bending energy according to the main beam strain energy, the arch rib strain energy, and the hanger strain energy.

[0053] The specific calculation formula is as follows:

[0054]

[0055] Among them, U1 represents the strain energy of the main beam, U2 represents the strain energy of the arch rib, U3 represents the strain energy of the hanger, E1 represents the elastic modulus of the main beam, M1 represents the bending moment of the main beam section, N1 represents the axial force of the main beam section, Q1 represents the shear force of the main beam section, I1 represents the bending moment of the main beam, G1 represents the shear modulus of the main beam, S1 represents the cross-sectional area of ​​the main beam, E2 represents the elastic modulus of the arch rib section, M2 represents the bending moment of the arch rib section, N2 represents the axial force of the arch rib section, Q2 represents the shear force of the arch rib section, I2 represents the bending moment of the arch rib, G2 represents the shear modulus of the arch rib, S2 represents the cross-sectional area of ​​the arch rib, E3 represents the elastic modulus of the hanger section, M ext Indicates the bending moment caused by the external load of the hanger, N ext represents the axial force generated by the external load of the hanger, Q ext It represents the shear force generated by the external load of the hanger, I3 is the bending moment of inertia of the hanger, G3 is the shear modulus of the hanger, S3 is the cross-sectional area of ​​the hanger, P represents the prestress of the hanger, and e represents the distance from the prestressing point to the neutral axis of the section.

[0056] Bending energy is the energy stored in a structure when it is deformed under external loads. Minimizing bending energy means reducing deformation and stress concentration of the structure as much as possible while meeting functional requirements, thereby improving the durability and reliability of the structure. This embodiment minimizes bending energy and changes the prestress of the hanger to obtain the optimal prestress of the hanger.

[0057] As an optional implementation, the internal force of the hanger cross-section is determined according to the bending moment, axial force and shear force of the hanger cross-section, including: according to the finite element analysis method, the target load is added to each hanger of the arch bridge, and the hanger force change value of the target load hanger and the hanger force change value of other hangers caused by the target load are determined; the influence of the load on each hanger is determined according to the hanger force change value of the target load hanger and the hanger force change value of other hangers caused by the target load; according to the ranking result of the influence of each hanger, a target number of hangers are selected, and the internal force of the cross-section of the selected hanger is determined according to the bending moment, axial force and shear force of the cross-section of the selected hanger; with the minimum bending energy as the goal, the prestress of the hanger is changed to obtain the optimal prestress of the hanger, including: changing the hanger prestress of the selected hanger to obtain the optimal prestress of the selected hanger.

[0058] For example, in the finite element model, target loads are applied to the various suspenders of the arch bridge, and these loads represent the expected maximum loads, such as vehicle weight, wind load, or other external factors that may affect the bridge structure. Through finite element analysis, the changes in the internal forces (such as tension or pressure) of the suspenders under the target load are calculated, including the direct response of the suspenders with the target load and the indirect response of other suspenders due to structural interaction. Based on the direct response of the suspenders with the target load and the indirect response of other suspenders due to structural interaction, the influence of the load on each suspender is determined, which can be specifically:

[0059]

[0060] Among them, C i represents the influence of the boom i, I ii It represents the change value of the boom force of boom i when the target load is applied to boom i, I ij It represents the change of the boom force of boom j when the target load is applied to boom i, and N represents the total number of booms.

[0061] The influence degree can be relative as described above, indicating the relative magnitude of the force change of each suspender rod, or it can be absolute, indicating the absolute value of the force change of the suspender rod. According to the ranking result of the influence degree, a certain number of suspenders with a large influence degree are selected for bending energy calculation, so as to determine the optimal prestress of the selected suspenders.

[0062] The bridge hanger force adjustment method based on arch bridge vehicle load big data provided in this embodiment reduces the amount of hanger calculation and improves the calculation efficiency of bending energy by pre-selecting several hangers with large influences to calculate the bending energy, because the influence degree characterizes the effect of the hanger on the bridge.

[0063] This embodiment provides a bridge suspender force adjustment device based on arch bridge vehicle load big data, including:

[0064] The data acquisition module is used to obtain the dynamic response data of the arch bridge under vehicle load and the hanger force data according to the pre-deployed sensors; please refer to the corresponding steps of the method embodiment for details, which will not be repeated here.

[0065] The load characteristic determination module is used to perform load identification on the collected dynamic response data and determine the load characteristics; please refer to the corresponding steps of the method embodiment for details, which will not be repeated here.

[0066] The prediction module is used to establish a response model of load characteristics and hanger force according to the hanger force data and the corresponding load characteristics, and predict the hanger force of the arch bridge under the target load condition; please refer to the corresponding steps of the method embodiment for details, which will not be repeated here.

[0067] The boom force adjustment module is used to input the boom force under the target load condition into the boom force adjustment model to determine the boom force adjustment data. For details, please refer to the corresponding steps of the method embodiment, which will not be repeated here.

[0068] The present application also provides an electronic device, such as Figure 2 As shown, a processor 501 and a memory 502, wherein the processor 501 and the memory 502 may be connected via a bus or other means.

[0069] The processor 501 may be a central processing unit (CPU). The processor 501 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0070] The memory 502 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the bridge suspender force adjustment method based on arch bridge vehicle load big data in the embodiment of the present invention. The processor executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory.

[0071] The memory 502 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 502 may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0072] The one or more modules are stored in the memory 502, and when executed by the processor 501, the following is performed: Figure 1 The bridge hanger force adjustment method based on arch bridge vehicle load big data in the illustrated embodiment.

[0073] For details of the above electronic equipment, please refer to Figure 2 The corresponding related descriptions and effects in the illustrated embodiments can be understood and will not be repeated here.

[0074] This embodiment also provides a computer storage medium, which stores computer executable instructions, and the computer executable instructions can execute the bridge suspender force adjustment method based on arch bridge vehicle load big data in any of the above method embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memory.

[0075] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A bridge suspender force adjustment method based on arch bridge vehicle load big data, characterized in that: include: The dynamic response data of the arch bridge under vehicle load and the hanger force data are obtained based on the pre-deployed sensors; Carry out load identification on the collected dynamic response data and determine the load characteristics; According to the hanger force data and the corresponding load characteristics, a response model of load characteristics and hanger force is established to predict the hanger force of the arch bridge under the target load condition; The boom force under the target load condition is input into the boom force adjustment model to determine the boom force adjustment data.

2. The bridge suspender force adjustment method based on arch bridge vehicle load big data according to claim 1 is characterized in that: According to the hanger force data and the corresponding load characteristics, a response model of load characteristics and hanger force is established to predict the hanger force of the arch bridge under the target load condition, including: According to the load characteristics, the vehicle load of the arch bridge is divided into light load area, medium load area and heavy load area; In the light load area, a linear fitting method is used to construct the response model of load characteristics and hanger force; In the medium load region, the response model of load characteristics and hanger force is constructed by quadratic fitting method; In the heavy load area, the finite element analysis method is used to construct the response model of load characteristics and hanger force.

3. The bridge suspender force adjustment method based on arch bridge vehicle load big data according to claim 1 is characterized in that: The response model of load characteristics and hanger force is established in the following ways: Based on big data technology, multiple arch bridge load characteristics and suspender force response data similar to the arch bridge size, span and service life are obtained; The response model of load characteristics and hanger force is established based on the relationship between load characteristics and hanger force by using regression statistical model.

4. The bridge suspender force adjustment method based on arch bridge vehicle load big data according to claim 1 is characterized in that: Input the boom force under the target load condition into the boom force adjustment model to determine the boom force adjustment data, including: Apply the hanger force of the arch bridge under the target load to the corresponding hangers in the first completed bridge model to analyze whether the mid-span displacement of the tie beam is within the maximum deformation range of the bridge. The first completed bridge model is a model established according to the actual parameters of the arch bridge. When the maximum deformation range of the bridge is exceeded, the target load of the arch bridge is used as the applied load and input into the second completed bridge model to determine the bending energy of each hanger under the load condition. The bending stiffness of the arch rib and tie beam in the second completed bridge model is reduced to the target multiple. Taking the minimum bending energy as the goal, the prestress of the hanger rod is changed to obtain the optimal prestress of the hanger rod; The hanger force of the arch bridge under the target load is applied to the corresponding hangers in the third completed bridge model, and it is analyzed whether the mid-span displacement of the tie beam is within the maximum deformation range of the bridge. When it is within the maximum range, the optimal prestress of the hanger is output, and the bending stiffness of the arch rib and tie beam in the third completed bridge model is restored to the actual level, and the hanger prestress is adjusted to the optimal prestress of the hanger.

5. The bridge suspender force adjustment method based on arch bridge vehicle load big data according to claim 4 is characterized in that: Bending energy construction process, including: Obtain the bending moment, axial force and shear force of the arch bridge main beam section, arch rib section and hanger section under the preset hanger force; Determine the internal force of the main beam section according to the bending moment, axial force and shear force of the main beam section; Determine the internal force of the arch rib section according to the bending moment, axial force and shear force of the arch rib section; Determine the internal force of the truncated surface of the suspender rod according to the bending moment, axial force and shear force of the truncated surface of the suspender rod, wherein the bending moment, axial force and shear force of the truncated surface of the suspender rod are determined by the prestress of the suspender rod; Determine the strain energy of the main beam, the strain energy of the arch rib and the strain energy of the hanger rod according to the internal force of the main beam section, the internal force of the arch rib section, the internal force of the hanger rod section, the cross-sectional area of ​​the main beam, the interface area of ​​the arch rib and the cross-sectional area of ​​the hanger rod; The bending energy is determined based on the strain energy of the main beam, the strain energy of the arch rib and the strain energy of the hanger.

6. The bridge suspender force adjustment method based on arch bridge vehicle load big data according to claim 5 is characterized in that: The internal forces of the suspender section are determined based on the bending moment, axial force and shear force of the suspender section, including: According to the finite element analysis method, the target load is added to each suspender of the arch bridge, and the change value of the suspender force of the target load suspender and the change value of the suspender force of other suspenders caused by the target load are determined; Determine the influence of the load on each boom according to the boom force change value of the target load boom and the boom force change values ​​of other booms caused by the target load; According to the ranking results of the influence of each suspender, a target number of suspenders are selected, and the internal force of the selected suspender section is determined according to the bending moment, axial force and shear force of the selected suspender section; With the goal of minimizing bending energy, the prestress of the hanger is changed to obtain the optimal prestress of the hanger, including: The prestress of the selected hanger is changed to obtain the optimal prestress of the selected hanger.

7. The bridge suspender force adjustment method based on arch bridge vehicle load big data according to claim 5 is characterized in that: The calculation formula of bending energy is as follows: Among them, U1 represents the strain energy of the main beam, U2 represents the strain energy of the arch rib, U3 represents the strain energy of the hanger, E1 represents the elastic modulus of the main beam, M1 represents the bending moment of the main beam section, N1 represents the axial force of the main beam section, Q1 represents the shear force of the main beam section, I1 represents the bending moment of the main beam, G1 represents the shear modulus of the main beam, S1 represents the cross-sectional area of ​​the main beam, E2 represents the elastic modulus of the arch rib section, M2 represents the bending moment of the arch rib section, N2 represents the axial force of the arch rib section, Q2 represents the shear force of the arch rib section, I2 represents the bending moment of the arch rib, G2 represents the shear modulus of the arch rib, S2 represents the cross-sectional area of ​​the arch rib, E3 represents the elastic modulus of the hanger section, M ext Indicates the bending moment caused by the external load of the hanger, N ext represents the axial force generated by the external load of the hanger, Q ext It represents the shear force generated by the external load of the hanger, I3 is the bending moment of inertia of the hanger, G3 is the shear modulus of the hanger, S3 is the cross-sectional area of ​​the hanger, P represents the prestress of the hanger, and e represents the distance from the prestressing point to the neutral axis of the section.

8. A bridge suspender force adjustment device based on arch bridge vehicle load big data, characterized in that: include: A data acquisition module, used to acquire dynamic response data of the arch bridge under vehicle load and suspension rod force data according to pre-deployed sensors; A load characteristic determination module is used to perform load identification on the collected dynamic response data and determine the load characteristics; A prediction module is used to establish a response model of load characteristics and hanger force based on the hanger force data and the corresponding load characteristics, and predict the hanger force of the arch bridge under the target load condition; The boom force adjustment module is used to input the boom force under the target load condition into the boom force adjustment model to determine the boom force adjustment data.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the steps of the bridge hanger force adjustment method based on arch bridge vehicle load big data as described in any one of claims 1-7.

10. A computer storage medium having computer instructions stored thereon, characterized in that: When the instruction is executed by the processor, the steps of the bridge hanger force adjustment method based on arch bridge vehicle load big data as described in any one of claims 1-7 are implemented.

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