Bridge life prediction method, device and electronic equipment for aircraft load

Abstract

The application discloses a kind of aircraft load bridge life prediction method, device and electronic equipment, it relates to bridge digital monitoring technical field, when monitoring aircraft to slide through aircraft load bridge, the bridge strain data monitored by sensor array is acquired, the bridge finite element model is corrected based on the bridge strain data monitored by sensor array, and the aircraft event sequence in the preset time period is determined based on airport flight traffic forecast data prediction, the stress-time history when aircraft slides through bridge in the preset time period is calculated based on aircraft event sequence using the updated bridge finite element model, stress-time history is decomposed into Z separate closed stress cycles, the damage value brought to bridge by each closed stress cycle is calculated, and then based on the damage value brought to bridge by all closed stress cycles, the damage brought to bridge by aircraft event sequence in the preset time period is calculated, accurate prediction of aircraft load bridge life is realized.

Description

Technical Field

[0001] This invention relates to the field of bridge digital monitoring technology, specifically to a method, device, and electronic equipment for predicting the lifespan of bridges under aircraft loads. Background Technology

[0002] Aircraft load bridges are crucial transportation infrastructure within airports and vital structures for the safe operation of flight zones; their functionality and safety are paramount. Unlike ordinary highway bridges, taxiway bridges bear the world's heaviest traffic loads—aircraft. Aircraft can weigh hundreds of tons, and their load is transferred to the bridge deck through complex landing gear wheel assemblies. This load is characterized by "high total weight, high wheel pressure, dense wheel assembly, and concentrated action," posing an extremely severe fatigue test to the bridge structure, especially the bridge deck and local supporting components. Therefore, accurate fatigue damage assessment and life prediction of aircraft load bridge structures are essential prerequisites for ensuring the safe and efficient operation of airports.

[0003] The core of fatigue damage assessment and life prediction is accurately obtaining the stress history of a structure under cyclic loading. For aircraft load bridges, this stress history is entirely determined by aircraft takeoff, landing, and taxiing events. An aircraft landing gear system typically consists of multiple wheel sets (e.g., including four main landing gears, for a total of 16 main wheels), with very small tire spacing within each wheel set. When an aircraft taxis over the bridge, these dense wheel pressures generate a complex superimposed stress field within the bridge deck, which is the key and challenging aspect of taxiway bridge fatigue problems. Furthermore, the stress history of each aircraft load bridge is closely related to airport operational scheduling, exhibiting significant variability and distinct characteristics.

[0004] Existing methods typically employ the simplified load method to assess fatigue damage and predict the lifespan of aircraft-loaded bridges. This method simplifies the landing gear load into several concentrated forces with standard load values ​​and spacing, based on a standard aircraft model provided by relevant design specifications. These forces are applied to the most unfavorable location on the structure for static analysis, and then multiplied by a fixed impact coefficient to account for dynamic effects. The fatigue damage and lifespan prediction are then based on these dynamic effects. However, this method uses a standardized design aircraft and a fixed impact coefficient, which fails to reflect the diverse aircraft types, varying takeoff weights, and actual taxiing conditions (speed, trajectory) encountered in real-world operations. It is only suitable for design verification; using it to assess cumulative fatigue damage under actual operational conditions will result in significant errors. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method, apparatus and electronic device for predicting the lifespan of bridges under aircraft loads, so as to provide a high-precision scheme for predicting the lifespan of bridges under aircraft loads.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0007] A method for predicting the lifespan of a bridge under aircraft load includes:

[0008] Obtain the finite element model of the bridge;

[0009] The finite element model of the bridge is updated based on the bridge strain data monitored by the sensor array when the aircraft taxis over the bridge. The sensor array is set under the bridge.

[0010] Obtain airport flight traffic forecast data;

[0011] Based on the airport flight traffic prediction data, a sequence of aircraft events within a preset time period is determined. Each aircraft event in the sequence includes aircraft information, trajectory information, tire contact force distribution, and taxiing speed.

[0012] The updated bridge finite element model was used to calculate the stress-time history of the aircraft as it passed over the bridge within a preset time period based on the aircraft event sequence.

[0013] The stress-time history is decomposed into Z types of individual closed stress cycles. Different closed stress cycles correspond to different bridge stress amplitudes. Z is the total number of closed stress cycles corresponding to aircraft events within a preset time period.

[0014] Obtain the damage value of the bridge for each closed stress cycle;

[0015] Based on the damage values ​​of all closed stress cycles to the bridge, the damage to the bridge caused by the sequence of aircraft events within a preset time period is calculated.

[0016] Optionally, in the above-mentioned method for predicting bridge life under aircraft load, the bridge finite element model is updated based on the bridge strain data monitored by the sensor array when the aircraft taxis over the bridge, including:

[0017] When the target aircraft is detected to be skidding over the bridge, the bridge strain data is acquired through the sensor array and recorded as the measured bridge strain data.

[0018] Obtain trajectory information of the target aircraft as it skids over the bridge;

[0019] The measured bridge strain data and trajectory information are matched with the bridge strain data stored in the aircraft information and bridge strain information database. The aircraft information and bridge strain information database stores the bridge strain data, trajectory information and aircraft information of each target aircraft when it passes over the bridge.

[0020] Obtain the aircraft information and taxiing trajectory matched with the measured bridge strain data;

[0021] The aircraft information was used to simulate the finite element model of the bridge, and the simulated strain value at each sensor location in the sensor array was calculated.

[0022] The simulated strain value is compared with the measured value of the sensor in the sensor array, and the bridge finite element model is updated.

[0023] Optionally, in the above-mentioned method for predicting the lifespan of bridges under aircraft loads, updating the finite element model of the bridge includes:

[0024] An extended Kalman filter is used to organically fuse the measured values ​​and simulated strain values ​​of the sensor, and the bridge finite element model is updated based on the fusion result.

[0025] Optionally, in the above-mentioned method for predicting bridge life under aircraft load, before matching the measured bridge strain data and trajectory information with the bridge strain data stored in the aircraft information and bridge strain information database, the method further includes:

[0026] Acquire bridge strain data and trajectory information when a target aircraft skids over a bridge, wherein the target aircraft is any type of aircraft that needs to be monitored and analyzed.

[0027] Obtain the total weight of the target aircraft;

[0028] The bridge strain data is split into the bridge's own strain data and the aircraft-excited strain data.

[0029] The strain data of the bridge itself and the strain data of the aircraft excitation are correlated with the aircraft information of the target aircraft and stored in the aircraft information and bridge strain information database.

[0030] Optionally, in the above method for predicting bridge lifespan based on aircraft load, obtaining the total weight of the target aircraft includes:

[0031] Calculate the total weight of the target aircraft based on its own weight, passenger capacity, and fuel consumption.

[0032] Optionally, in the above method for predicting bridge lifespan based on aircraft load, obtaining airport flight traffic prediction data includes:

[0033] Airport flight traffic forecast data is obtained by predicting future flight information based on flight information in historical flight databases.

[0034] Optionally, in the above method for predicting bridge life under aircraft load, the damage value of a single aircraft taxiing event to the bridge corresponding to each bridge stress amplitude is obtained, including:

[0035] Based on relationships The damage value of a single aircraft taxiing event to the bridge corresponding to each bridge stress amplitude was calculated. ;

[0036] Where, N i Let i be the fatigue life of the bridge under the bridge stress amplitude corresponding to aircraft event i. Let i be the number of times each aircraft event i occurs.

[0037] An aircraft load bridge life prediction device includes:

[0038] Model elements are used to obtain the finite element model of the bridge.

[0039] The model correction unit is used to update the finite element model of the bridge based on the bridge strain data monitored by the sensor array when the aircraft skids over the bridge. The sensor array is located under the bridge.

[0040] The prediction unit is used to acquire airport flight traffic prediction data;

[0041] The aircraft event sequence extraction unit is used to determine the aircraft event sequence within a preset time period based on the airport flight traffic prediction data. Each aircraft event in the aircraft event sequence includes aircraft information, trajectory information, tire contact force distribution, and taxiing speed.

[0042] The stress-time history plotting unit is used to calculate the stress-time history of an aircraft skidding across a bridge within a preset time period based on the aircraft event sequence using the updated bridge finite element model.

[0043] The stress decomposition unit is used to decompose the stress-time history into Z types of individual closed stress cycles. Different closed stress cycles correspond to different bridge stress amplitudes, where Z is the total number of closed stress cycles corresponding to aircraft events within a preset time period.

[0044] A single-event damage calculation unit is used to obtain the damage value of the bridge for each closed stress cycle.

[0045] The total event damage calculation unit is used to calculate the damage to the bridge caused by the sequence of aircraft events within a preset time period, based on the damage values ​​caused to the bridge by all closed stress cycles.

[0046] An electronic device includes at least one processing device and a storage device connected to the processing device, wherein:

[0047] The storage device is used to store computer programs;

[0048] The processing device is used to execute the computer program so that the electronic device can implement any of the above-described methods for predicting the lifespan of bridges under aircraft loads.

[0049] Optionally, the above-mentioned electronic device further includes: a sensor array disposed under the bridge; and a wheel monitor or video camera used to monitor the trajectory information of the target aircraft as it skids across the bridge.

[0050] Based on the above technical solution, the solution provided in this embodiment of the invention acquires bridge strain data monitored by a sensor array when an aircraft is detected taxiing over an aircraft-loaded bridge. The bridge finite element model is then corrected based on this data. Furthermore, an aircraft event sequence within a preset time period is predicted based on airport flight traffic forecast data. The updated bridge finite element model is then used to calculate the stress-time history of the aircraft taxiing over the bridge within the preset time period based on the aircraft event sequence. This stress-time history is decomposed into Z individual closed stress cycles, and the damage value of each closed stress cycle to the bridge is calculated. Finally, based on the damage values ​​of all closed stress cycles, the damage to the bridge caused by the aircraft event sequence within the preset time period is calculated. In this process, the bridge finite element model is continuously corrected based on the actual measurement results of the sensor array, resulting in highly accurate calculation results and thus achieving accurate prediction of the lifespan of the aircraft-loaded bridge. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0052] Figure 1 A flowchart illustrating the method for predicting bridge life under aircraft load provided in this application embodiment;

[0053] Figure 2 A flowchart illustrating the method for updating the finite element model of a bridge provided in this application embodiment;

[0054] Figure 3 This is a schematic diagram illustrating the creation process of the aircraft information and bridge strain information database disclosed in the embodiments of this application;

[0055] Figure 4 This is a schematic diagram of the structure of the aircraft load bridge life prediction device disclosed in the embodiments of this application. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] See Figure 1 This embodiment discloses a method for predicting the lifespan of a bridge under aircraft load, including:

[0058] Step S101: Obtain the finite element model of the bridge.

[0059] A high-precision finite element model of the bridge (aircraft-loaded bridge) is established based on various parameters (including bridge length, building material parameters, etc.), and the relevant physical parameters are stored in the digital twin bridge model.

[0060] Step S102: Based on the bridge strain data monitored by the sensor array when the aircraft skids over the bridge, update the finite element model of the bridge. The sensor array is located under the bridge.

[0061] In this scheme, a sensor array is deployed below the bridge deck along the aircraft's taxiing direction and the transverse direction. The sensor array consists of a matrix of strain sensors. When an aircraft taxis over the bridge, the bridge deforms, and this deformation is measured by the sensors in the array. In this embodiment, when an aircraft is detected taxiing over the bridge, the bridge strain data output by the sensor array is acquired. Simultaneously, a finite element model of the bridge is used to simulate the bridge strain data during the aircraft's passage, obtaining the simulated strain force. Based on the comparison between the bridge strain data output by the sensor array and the simulated strain force, the digital bridge structural parameters in the finite element model are corrected and updated.

[0062] Step S103: Obtain airport flight traffic forecast data.

[0063] Based on historical flight databases, airport flight traffic data is predicted for a certain period of time in the future, and is denoted as airport flight traffic prediction data. The historical flight database stores airport flight traffic data for various time periods, including at least data such as the type of aircraft taking off and landing, and passenger capacity.

[0064] Step S104: Determine the sequence of aircraft events within a preset time period based on the airport flight traffic prediction data. Each aircraft event includes aircraft information, trajectory information, tire contact force distribution, and taxiing speed.

[0065] In this step, based on the various aircraft events stored in the aircraft information and bridge strain information database, a sequence of aircraft events within a preset time period is randomly generated according to the airport flight traffic prediction data. Each aircraft event may include parameters such as aircraft information, trajectory information (the trajectory when taxiing on the bridge), tire ground contact distribution, and taxiing speed.

[0066] Step S105: Using the updated bridge finite element model, calculate the stress-time history of each aircraft skidding across the bridge within a preset time period based on the aircraft event sequence.

[0067] Using the updated finite element model of the bridge, the stress-time history of aircraft skidding across the bridge during each aircraft event in the aircraft event sequence is quickly calculated using the influence surface method. The formula is expressed as:

[0068]

[0069] Indicating an airplane incident, This indicates the updated digital bridge structural parameters. This represents the amplitude of the bridge's stress response at time t during an aircraft skidding across the bridge, as calculated by the bridge's finite element model.

[0070] Step S106: Decompose the stress-time history into Z types of individual closed stress cycles. Different closed stress cycles correspond to different bridge stress amplitudes. Z is the total number of closed stress cycles corresponding to aircraft events within a preset time period.

[0071] The stress-time history of all aircraft events within a preset time period is analyzed using the rainflow counting method. Based on the magnitude of the bridge stress amplitude monitored when each aircraft event taxis on the bridge, the stress-time history of the preset time period is decomposed into several individual closed stress cycles. Based on the decomposition results, the closed stress cycles are classified to obtain Z types of individual closed stress cycles (each aircraft event corresponds to one closed stress cycle, and one closed stress cycle can correspond to multiple aircraft events). In aircraft events corresponding to the same type of closed stress cycle, the bridge stress amplitude generated when the aircraft taxis over the bridge is the same. Then, the frequency of occurrence of this type of closed stress cycle within the preset time period is determined. For example, if the stress-time history includes Z types of closed stress cycles, the Z1 closed stress cycle appears once, the Z2 closed stress cycle appears twice, and the Z3 closed stress cycle appears three times.

[0072] Step S107: Obtain the damage value of the bridge for each closed stress cycle.

[0073] The amplitude of the stress response (stress monitored when the aircraft taxis over the bridge) based on each closed stress cycle. and their corresponding frequency of occurrence (The total number of aircraft events corresponding to the i-th type of closed stress cycle), where i represents the i-th type of closed stress cycle, and the equivalent stress amplitude is calculated using the following formula:

[0074]

[0075] in, The slope parameter of the SN curve for the material. This refers to the number of reference cycles within a single aircraft taxiing event.

[0076] The amplitude of the stress response for each closed stress cycle is determined. and their corresponding frequency of occurrence Subsequently, based on the preset amplitude With fatigue life The correspondence between them, and the acquisition of the stress response amplitude Corresponding fatigue life Based on this mapping relationship, the amplitude of each stress response is determined to be... Damage to bridges caused by aircraft skidding events due to closed stress cycles: 1 / Based on Corresponding frequency Determine the total damage to the bridge caused by this type of closed stress cycle within a preset time period: .

[0077] Step S108: Based on the damage values ​​of all closed stress cycles to the bridge, calculate the damage to the bridge caused by the sequence of aircraft events within a preset time period.

[0078] Based on the predicted flight traffic data, the damage values ​​to the bridge caused by all closed stress cycles within a preset time period are determined, and the damage values ​​are linearly accumulated. ,when When the bridge reaches its fatigue limit, which is the predicted lifespan of the bridge, it can be accurately predicted to withstand aircraft loads.

[0079] In this scheme, when an aircraft is detected taxiing over an aircraft-loaded bridge, bridge strain data monitored by a sensor array is acquired. Based on the bridge strain data monitored by the sensor array, the bridge finite element model is corrected. Based on airport flight traffic prediction data, an aircraft event sequence within a preset time period is predicted and determined. Using the updated bridge finite element model, the stress-time history of the aircraft taxiing over the bridge within the preset time period is calculated based on the aircraft event sequence. The stress-time history is decomposed into Z individual closed stress cycles, and the damage value of each closed stress cycle to the bridge is calculated. Then, based on the damage value of all closed stress cycles to the bridge, the damage to the bridge caused by the aircraft event sequence within the preset time period is calculated. In this process, the bridge finite element model is continuously corrected based on the actual measurement results of the sensor array, so that the bridge finite element model has high-precision calculation results, thereby realizing accurate prediction of the life of the aircraft-loaded bridge.

[0080] This embodiment discloses a specific modification scheme for a bridge finite element model. For details, please refer to... Figure 2 Based on the bridge strain data monitored by the sensor array when the aircraft taxis over the bridge, the finite element model of the bridge is updated, including:

[0081] Step S201: When the target aircraft is detected to be skidding over the bridge, the bridge strain data is acquired through the sensor array and recorded as the measured bridge strain data.

[0082] In this step, when the target aircraft is detected to be skidding over the bridge, a set of real-time spatiotemporal strain response data is collected by the sensor array under the bridge. This response data is the bridge strain data, which is recorded as the measured bridge strain data in this scheme.

[0083] Step S202: Obtain the trajectory information of the target aircraft as it skids over the bridge;

[0084] By deploying wheel monitors or video cameras at the bridge entrances / exits as an aid, the trajectory information of the target aircraft as it taxis across the bridge can be obtained through the wheel monitors or video cameras.

[0085] Step S203: Match the measured bridge strain data and trajectory information with the bridge strain data stored in the aircraft information and bridge strain information database;

[0086] The aircraft information and bridge strain information database stores bridge strain data, trajectory information, and aircraft information of each target aircraft as it passes over the bridge.

[0087] Step S204: Obtain the aircraft information and taxiing trajectory matched with the measured bridge strain data.

[0088] In steps S203 and S204, the collected measured bridge strain data is used as a "fingerprint to be investigated." This "fingerprint to be investigated" is then matched against all standard fingerprints in the aircraft model fingerprint database (aircraft information and bridge strain information database) using a pattern matching algorithm (such as cross-correlation analysis, Euclidean distance, or a CNN classifier). The standard fingerprint with the highest matching degree is determined, along with the corresponding aircraft information (including aircraft model) and approximate taxiing trajectory. This information is then used as the aircraft model and initial trajectory for this aircraft event, matching the measured bridge strain data and trajectory information.

[0089] In this scheme, spatial distribution information is provided by a sensor array. Different aircraft have different wheel layouts, and the "footprints" (response fingerprints) they leave on the sensor array are highly distinguishable. Therefore, by matching the measured bridge strain data with the aircraft model fingerprint database, the information of the aircraft that skidded across the bridge can be identified. The content of the aircraft information can be set according to design requirements, and it includes at least the aircraft model.

[0090] Step S205: Use the aircraft information to simulate the finite element model of the bridge and calculate the simulated strain value at each sensor position in the sensor array.

[0091] In this step, after determining the aircraft information, the aircraft model is extracted based on the aircraft information, the aircraft parameters are determined based on the aircraft model, and the bridge finite element model is simulated based on the aircraft parameters. The simulation calculates the simulated strain value at each sensor position in the sensor array (i.e., the strain value generated at the sensor when a unit load is applied at any position (x,y) on the bridge deck).

[0092] Step S206: Compare the simulated strain value with the measured value of the sensor in the sensor array, and update the bridge finite element model.

[0093] The simulated strain values ​​obtained from the simulation were compared with the measured strain data of the bridge, and an extended Kalman filter was used to organically fuse the two. Based on the fusion result, the finite element model of the bridge was updated, thereby realizing the dynamic correction of the bridge finite element model. Specifically:

[0094] The prediction steps are as follows:

[0095]

[0096]

[0097] The update steps are as follows:

[0098]

[0099]

[0100]

[0101] In the above formula, This represents the state vector composed of parameters in the finite element method, where This represents the state vector obtained from the last update. This represents the state vector predicted through finite element simulation. This is the updated current state vector; Let represent the covariance matrix of the finite element parameters, where The covariance matrix obtained from the last update. The predicted covariance matrix, This is the updated covariance matrix; Indicates Kalman gain; This is the covariance matrix of the process noise, used to quantify the covariance relationship between the deviation between the finite element model and the actual bridge behavior; The covariance matrix for measurement noise is used to represent the covariance relationship of noise in the strain sensor measurement data; This represents the strain data measured by the sensor; This represents the state transition process, i.e., the parameter changes obtained through finite element simulation; This refers to the observation process, specifically the bridge strain data obtained through finite element simulation. Represents the state transition process Jacobian matrix; Indicates the observation process The Jacobian matrix.

[0102] The iterative process provided by the above formula forms a feedback loop, enabling real-time correction of the physical parameters in the bridge finite element model. This ensures that the physical parameters in the bridge finite element model are consistent with the actual physical structure of the bridge, ultimately improving the accuracy of bridge damage status assessment and life prediction.

[0103] In this embodiment, a database of aircraft information and bridge strain information can be established using historically collected data. For details, see [link to documentation]. Figure 3 Before matching the measured bridge strain data and trajectory information with the bridge strain data stored in the aircraft information and bridge strain information database, the process further includes:

[0104] Step S301: Obtain bridge strain data and trajectory information when the target aircraft skids over the bridge. The target aircraft can be any type of aircraft that needs to be monitored and analyzed.

[0105] Step S302: Obtain the total weight of the target aircraft;

[0106] Step S303: Decompose the bridge strain data into the bridge's own strain data and the aircraft-excited strain data;

[0107] Step S304: Match the bridge's own strain data and the aircraft's excitation strain data with the target aircraft's aircraft information and store them in the aircraft information and bridge strain information database.

[0108] In the above process, each aircraft type required for data entry is designated as the target aircraft. For each target aircraft, bridge strain data generated by the sensor array and aircraft trajectory information collected by wheel monitors or video cameras are recorded as it taxis across the bridge. Simultaneously, information such as the aircraft's weight, passenger capacity, and fuel consumption are obtained through data querying and retrieval. The weight of the target aircraft is calculated based on this weight information. The obtained bridge strain data is then processed using Principal Component Analysis (PCA) or an autoencoder to separate the bridge's inherent strain from the strain under aircraft excitation. The separated bridge strain data, along with corresponding aircraft information (including at least aircraft type, weight, and trajectory information), is stored in the aircraft information and bridge strain information database, forming a "response fingerprint database."

[0109] In this embodiment, when obtaining airport flight traffic prediction data, future flight information can be predicted based on flight information in the historical flight database to obtain airport flight traffic prediction data.

[0110] Corresponding to the above method, this embodiment discloses an aircraft load-bearing bridge life prediction device. For the specific operation of each unit in the device, please refer to the content of the above method embodiment. The aircraft load-bearing bridge life prediction device provided in this embodiment is described below. The aircraft load-bearing bridge life prediction device described below can be referred to in conjunction with the aircraft load-bearing bridge life prediction method described above. See also... Figure 4 The aircraft load bridge life prediction device includes:

[0111] Model element 10 is used to obtain the finite element model of the bridge.

[0112] Model correction unit 20 is used to update the finite element model of the bridge based on the bridge strain data monitored by the sensor array when the aircraft skids over the bridge. The sensor array is set under the bridge.

[0113] Prediction unit 30 is used to acquire airport flight traffic prediction data;

[0114] The aircraft event sequence extraction unit 40 is used to determine the aircraft event sequence within a preset time period based on the airport flight traffic prediction data. Each aircraft event in the aircraft event sequence includes aircraft information, trajectory information, tire contact force distribution, and taxiing speed.

[0115] The stress-time history plotting unit 50 is used to calculate the stress-time history of the aircraft skidding over the bridge within a preset time period based on the aircraft event sequence using the updated bridge finite element model.

[0116] The stress decomposition unit 60 is used to decompose the stress-time history into Z types of individual closed stress cycles. Different closed stress cycles correspond to different bridge stress amplitudes, and Z is the total number of closed stress cycles corresponding to aircraft events within a preset time period.

[0117] The single-event damage calculation unit 70 is used to obtain the damage value of the bridge caused by each closed stress cycle.

[0118] The total event damage calculation unit 80 is used to calculate the damage to the bridge caused by the sequence of aircraft events within a preset time period based on the damage values ​​caused to the bridge by all closed stress cycles.

[0119] Corresponding to the above method, this application also provides an electronic device, which includes at least one processing device and a storage device connected to the processing device, wherein: the storage device is used to store a computer program; the processing device is used to execute the computer program to enable the electronic device to implement any of the aircraft load bridge life prediction methods described above. The electronic device further includes: a sensor array disposed under the bridge; and a wheel monitor or video camera used to monitor the trajectory information of a target aircraft as it passes over the bridge.

[0120] All information and data involved in this application (including but not limited to data used for analysis, stored data, and displayed data) are information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0121] For ease of description, the above system is described by dividing it into various modules based on their functions. Of course, in implementing this invention, the functions of each module can be implemented in one or more software and / or hardware components.

[0122] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0123] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0124] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0125] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0126] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the lifespan of a bridge under aircraft load, characterized in that, include: Obtain the finite element model of the bridge; The finite element model of the bridge is updated based on the bridge strain data monitored by the sensor array when the aircraft taxis over the bridge. The sensor array is set under the bridge. Obtain airport flight traffic forecast data; Based on the airport flight traffic prediction data, a sequence of aircraft events within a preset time period is determined. Each aircraft event in the sequence includes aircraft information, trajectory information, tire contact force distribution, and taxiing speed. The updated bridge finite element model was used to calculate the stress-time history of the aircraft as it passed over the bridge within a preset time period based on the aircraft event sequence. The stress-time history is decomposed into Z types of individual closed stress cycles. Different closed stress cycles correspond to different bridge stress amplitudes. Z is the total number of closed stress cycles corresponding to aircraft events within a preset time period. Obtain the damage value of the bridge for each closed stress cycle; Based on the damage values ​​of all closed stress cycles to the bridge, the damage to the bridge caused by the sequence of aircraft events within a preset time period is calculated.

2. The method for predicting bridge lifespan under aircraft load according to claim 1, characterized in that, Based on the bridge strain data monitored by the sensor array when the aircraft taxis over the bridge, the finite element model of the bridge is updated, including: When the target aircraft is detected to be skidding over the bridge, the bridge strain data is acquired through the sensor array and recorded as the measured bridge strain data. Obtain trajectory information of the target aircraft as it skids over the bridge; The measured bridge strain data and trajectory information are matched with the bridge strain data stored in the aircraft information and bridge strain information database. The aircraft information and bridge strain information database stores the bridge strain data, trajectory information and aircraft information of each target aircraft when it passes over the bridge. Obtain the aircraft information and taxiing trajectory matched with the measured bridge strain data; The aircraft information was used to simulate the finite element model of the bridge, and the simulated strain value at each sensor location in the sensor array was calculated. The simulated strain value is compared with the measured value of the sensor in the sensor array, and the bridge finite element model is updated.

3. The method for predicting bridge lifespan under aircraft load according to claim 2, characterized in that, Updating the finite element model of the bridge includes: An extended Kalman filter is used to organically fuse the measured values ​​and simulated strain values ​​of the sensor, and the bridge finite element model is updated based on the fusion result.

4. The method for predicting bridge lifespan under aircraft load according to claim 2, characterized in that, Before matching the measured bridge strain data and trajectory information with the bridge strain data stored in the aircraft information and bridge strain information database, the process also includes: Acquire bridge strain data and trajectory information when a target aircraft skids over a bridge, wherein the target aircraft is any type of aircraft that needs to be monitored and analyzed. Obtain the total weight of the target aircraft; The bridge strain data is split into the bridge's own strain data and the aircraft-excited strain data. The strain data of the bridge itself and the strain data of the aircraft excitation are correlated with the aircraft information of the target aircraft and stored in the aircraft information and bridge strain information database.

5. The method for predicting bridge lifespan under aircraft load according to claim 4, characterized in that, Obtain the total weight of the target aircraft, including: Calculate the total weight of the target aircraft based on its own weight, passenger capacity, and fuel consumption.

6. The method for predicting bridge lifespan under aircraft load according to claim 1, characterized in that, Obtaining airport flight traffic forecast data includes: Airport flight traffic forecast data is obtained by predicting future flight information based on flight information in historical flight databases.

7. The method for predicting bridge lifespan under aircraft load according to claim 1, characterized in that, Obtain the damage value of a single aircraft taxiing event to the bridge corresponding to each bridge stress amplitude, including: Based on relationships The damage value of a single aircraft taxiing event to the bridge corresponding to each bridge stress amplitude was calculated. ; Where, N i Let i be the fatigue life of the bridge under the bridge stress amplitude corresponding to aircraft event i. Let i be the number of times each aircraft event i occurs.

8. A device for predicting the lifespan of a bridge under aircraft load, characterized in that, include: Model elements are used to obtain the finite element model of the bridge. The model correction unit is used to update the finite element model of the bridge based on the bridge strain data monitored by the sensor array when the aircraft skids over the bridge. The sensor array is located under the bridge. The prediction unit is used to acquire airport flight traffic prediction data; The aircraft event sequence extraction unit is used to determine the aircraft event sequence within a preset time period based on the airport flight traffic prediction data. Each aircraft event in the aircraft event sequence includes aircraft information, trajectory information, tire contact force distribution, and taxiing speed. The stress-time history plotting unit is used to calculate the stress-time history of an aircraft skidding across a bridge within a preset time period based on the aircraft event sequence using the updated bridge finite element model. The stress decomposition unit is used to decompose the stress-time history into Z types of individual closed stress cycles. Different closed stress cycles correspond to different bridge stress amplitudes, where Z is the total number of closed stress cycles corresponding to aircraft events within a preset time period. A single-event damage calculation unit is used to obtain the damage value of the bridge for each closed stress cycle. The total event damage calculation unit is used to calculate the damage to the bridge caused by the sequence of aircraft events within a preset time period, based on the damage values ​​caused to the bridge by all closed stress cycles.

9. An electronic device, characterized in that, It includes at least one processing device and a storage device connected to the processing device, wherein: The storage device is used to store computer programs; The processing device is used to execute the computer program so that the electronic device can implement the aircraft load bridge life prediction method as described in any one of claims 1 to 7.

10. The electronic device according to claim 9, characterized in that, The electronic device further includes: a sensor array disposed under the bridge; A wheel monitor or video camera is used to monitor the trajectory information of a target aircraft as it skids over a bridge.

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