Bridge structure fatigue crack propagation prediction method and system

Abstract

The invention provides a bridge structure fatigue crack propagation prediction method and system, and relates to the technical field of artificial intelligence. According to the method, structural parameter information of a bridge is collected, a three-dimensional model of the bridge structure is established through finite element analysis, and a fatigue crack propagation prediction model is established based on the fracture mechanics theory and a fatigue damage accumulation model; performing expansion simulation on preset cracks in the three-dimensional model of the bridge structure, calculating a crack expansion rate and predicting a crack expansion path, correcting a crack expansion simulation result in combination with an actual working environment and a load condition of the bridge structure, and calculating a crack expansion simulation result according to the corrected crack expansion simulation result. And predicting crack propagation information of the bridge structure in the preset time period. The method can accurately predict the expansion path, rate and future state of the crack under a complex working condition, thereby providing a scientific basis for maintenance and repair of the bridge, realizing real-time feedback and dynamic adjustment, ensuring high efficiency and accuracy of a prediction result, and providing a scientific basis for maintenance management of the bridge.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to the field of intelligent inspection, which can automatically perform inspection tasks at engineering sites, and specifically to a method and system for predicting fatigue crack propagation of a bridge structure. Background Art

[0002] With the increasing demand for transportation and logistics, bridges, as important transportation infrastructure, bear heavy vehicle loads and environmental pressures. Long-term loads, temperature changes, humidity and other environmental factors cause the bridge structure to gradually age and fatigue, making it prone to crack propagation. In particular, some early-constructed bridges lack adequate maintenance and monitoring, which increases the risk of crack propagation. The expansion of fatigue cracks not only affects the safety of the bridge, but also significantly affects its service life. Therefore, accurate prediction of crack propagation is particularly important.

[0003] At present, traditional crack propagation prediction methods mainly rely on laboratory tests, empirical formulas and finite element analysis. Although these methods can provide predictions of bridge crack propagation to a certain extent, they all have some limitations. Laboratory tests cannot fully simulate the actual working environment of bridges, the prediction accuracy of empirical formulas is limited by the availability and representativeness of test data, and although finite element analysis is accurate, it has a large amount of calculations and is time-consuming. In addition, when dealing with complex structures, the prediction accuracy may not be high.

[0004] Therefore, the existing fatigue crack prediction methods still have the disadvantage of being difficult to predict. There is an urgent need for a more accurate, flexible and efficient crack propagation prediction method to better assess the health of bridge structures, formulate scientific repair and maintenance strategies, extend the service life of bridges and ensure bridge safety. Summary of the invention

[0005] In view of this, the purpose of the present invention is to propose a method and system for predicting fatigue crack propagation of bridge structures. By taking into account factors such as dynamic load changes, environmental factors, fatigue damage accumulation, and the like, combined with an improved fatigue damage accumulation model, the method and system can accurately predict the crack propagation path, rate, and future state under complex working conditions, thereby providing a scientific basis for the maintenance and repair of bridges, achieving real-time feedback and dynamic adjustment, ensuring the efficiency and accuracy of the prediction results, and providing a scientific basis for the maintenance and management of bridges.

[0006] To achieve the above object, the present invention provides the following technical solutions: Based on the above objectives, in a first aspect, the present invention provides a method for predicting fatigue crack growth of a bridge structure, comprising the following steps: Collect structural parameter information of the bridge, including bridge material, structural dimensions, load type and historical load records; Based on the structural parameter information, a three-dimensional model of the bridge structure is established using finite element analysis, and the crack position and size are preset to simulate the actual crack occurrence; A fatigue damage accumulation model of the bridge structure is constructed based on fracture mechanics. The fatigue damage accumulation model is optimized based on load fluctuations, temperature and humidity changes, and material aging effect data. The expansion of the preset cracks in the three-dimensional model of the bridge structure is simulated. Calculate the crack growth rate and predict the crack growth path based on the stress amplitude, strain, temperature and humidity data monitored in real time on the bridge structure; Combined with the actual working environment and load conditions of the bridge structure, the crack growth simulation results are corrected to obtain the corrected crack growth simulation results; According to the corrected crack propagation simulation results, the crack propagation information of the bridge structure within a preset time period is predicted. The predicted crack propagation information includes crack length, depth, location and propagation speed.

[0007] As a further solution of the present invention, based on the structural parameter information, a three-dimensional model of the bridge structure is established using finite element analysis, comprising the following steps: According to the collected bridge materials, structural dimensions, load types and historical load records, a geometric model of the bridge is established in the finite element analysis software. The geometric model includes the beams, supports, plates and truss structural components of the bridge. Use beam elements, shell elements, and solid elements to construct the geometric model of the bridge, and divide the bridge structure into several finite element units to generate a mesh model; In combination with the bridge material information, corresponding material properties are assigned to each structural component, wherein the material properties include elastic modulus, Poisson's ratio, yield strength and fatigue limit; Apply boundary conditions to each structural component in the model, and apply traffic loads and environmental loads under normal working conditions of the bridge; By simulating the deformation of the bridge under load, a three-dimensional structural model of the bridge is established.

[0008] As a further solution of the present invention, the preset crack position and size include the following steps: Determine the initial location of the crack based on the bridge's working environment, stress concentration areas, and historical damage records; Set the initial size of the crack, including crack length and crack depth; calculate the crack growth rate using fracture mechanics theory, and simulate the crack propagation path in the finite element model.

[0009] As a further solution of the present invention, based on the load fluctuation, temperature and humidity change and material aging effect data, the fatigue damage accumulation model is optimized, and the expansion simulation of the preset crack in the three-dimensional model of the bridge structure is performed, including the following steps: Establish a three-dimensional finite element model of the bridge structure and preset initial cracks at key locations; Apply boundary conditions and loads to the bridge, taking into account load fluctuations, temperature and humidity changes, and material aging effects; Simulate the crack growth rate based on stress intensity factor and Paris law; The fatigue damage accumulation is calculated using Koers law and the remaining life of crack growth is predicted.

[0010] As a further solution of the present invention, the fatigue damage accumulation model is expressed by the following formula: in, Indicates at time Cumulative fatigue damage of moment cracks, is the number of load cycles experienced by the bridge structure within a time interval, is the stress amplitude, is the temperature, For humidity, For the The fatigue life parameters corresponding to the load cycle are optimized based on stress amplitude, temperature and humidity. For the The number of load cycles, expressed in time Moment of experience The number of stress changes during a load cycle.

[0011] As a further solution of the present invention, when calculating the crack growth rate, the formula for calculating the crack growth rate using the Paris law is: in, is the crack growth rate, which indicates the crack extension length in each load cycle. is the material constant; is the magnitude of the stress intensity factor used to describe the stress state at the crack tip, A material index that describes the relationship between crack growth rate and stress intensity factor amplitude.

[0012] As a further embodiment of the present invention, the stress intensity factor amplitude The calculation formula is: in, is a dimensionless geometric factor, which represents the effect of crack geometry on the stress intensity factor; is the stress amplitude, is the crack length, which indicates the distance from the crack tip to the longest direction of the crack surface.

[0013] As a further solution of the present invention, when the crack propagation simulation result is corrected, the crack propagation path correction formula is: in, is the crack propagation path, which indicates the distance the crack propagates in each load cycle; is the path correction coefficient; is the increment of the number of load cycles, indicating the increment of load cycles during crack propagation.

[0014] As a further solution of the present invention, when using the Kuhn's law to calculate the accumulation of fatigue damage and predict the remaining life of crack growth, the dynamic response of the stress amplitude is calculated based on the actual traffic flow and load distribution of the bridge, and the load fluctuation effect ignored in the crack growth simulation is corrected. When the temperature and humidity are corrected, the crack growth rate is adjusted by the temperature and humidity correction factor: In the formula, is the temperature and humidity correction factor; When corrosion correction is performed, the corrosion thickness Through the corrosion rate and the age of the bridge Make an estimate: The corrected crack growth rate is: The calculation formula for predicting the remaining life of crack growth is: in, represents the cumulative damage calculation result of fatigue damage, For the The number of loading cycles at each stress amplitude is is the number of failure cycles under this stress amplitude; Correction factor for fatigue life loss caused by temperature, humidity and corrosion; Predict the remaining life of bridge structures based on the calculation results of crack growth rate and cumulative damage; By calculating the damage value when the crack grows to a critical size , and obtain the fatigue life of the bridge.

[0015] As a further solution of the present invention, when predicting crack extension information according to the corrected crack extension simulation results, crack extension is predicted based on the actual working environment and load conditions, and the prediction step includes calculation of crack length, depth, position and extension speed, wherein the corrected crack extension rate It is used to predict the expansion of cracks in the future and obtain the change of crack length over time through the integration method.

[0016] In a second aspect, the present invention provides a bridge structure fatigue crack growth prediction system, comprising: Data acquisition module, used to collect structural parameter information of bridges, including bridge materials, structural dimensions, load types and historical load records; A modeling and simulation module is used to establish a three-dimensional model of the bridge structure based on the structural parameter information using finite element analysis, and preset the crack position and size to simulate the actual crack occurrence; Fatigue damage accumulation calculation module, which is used to construct a fatigue damage accumulation model of bridge structure based on fracture mechanics, optimize the fatigue damage accumulation model based on load fluctuation, temperature and humidity changes and material aging effect data, and simulate the expansion of preset cracks in the three-dimensional model of the bridge structure; The expansion rate calculation module is used to calculate the crack expansion rate and predict the crack expansion path based on the stress amplitude, strain, temperature and humidity data monitored in real time on the bridge structure; A simulation correction module is used to correct the crack propagation simulation results in combination with the actual working environment and load conditions of the bridge structure to obtain corrected crack propagation simulation results; The crack prediction module is used to predict the crack propagation information of the bridge structure within a preset time period based on the corrected crack propagation simulation results. The predicted crack propagation information includes crack length, depth, location and propagation speed.

[0017] In another aspect of the present invention, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, any one of the above-mentioned methods for predicting fatigue crack propagation of bridge structures according to the present invention is executed.

[0018] In another aspect of the present invention, a computer-readable storage medium is provided, storing computer program instructions, which, when executed, implement any of the above-mentioned methods for predicting fatigue crack growth in bridge structures according to the present invention.

[0019] Compared with the prior art, the method and system for predicting fatigue crack growth of a bridge structure proposed in the present invention have the following beneficial effects: 1. The present invention establishes a comprehensive crack propagation prediction model by comprehensively utilizing the structural parameter information, real-time monitoring data and environmental factors of the bridge, which can effectively predict the fatigue crack propagation of the bridge structure. Among them, finite element analysis is used to establish a three-dimensional model to accurately simulate the working state of the bridge structure; combined with fracture mechanics, a fatigue damage accumulation model is constructed to consider load fluctuations, temperature and humidity changes and material aging effects; real-time feedback of real-time monitoring data improves the accuracy and applicability of the prediction results.

[0020] 2. The present invention can accurately predict the crack growth rate, crack path, crack location, etc. based on structural parameters, crack initial state and real-time environmental data. Through temperature and humidity correction factors, load fluctuation correction and fatigue damage accumulation correction, key parameters such as crack growth rate, crack depth, crack length, etc. can be accurately calculated, and the prediction results can flexibly reflect different working environments and usage conditions.

[0021] 3. The present invention accumulates fatigue damage through a model based on fracture mechanics and Miners' law. Taking into account the multiple loads of the bridge under different working cycles and the influence of external factors such as temperature and humidity on fatigue damage, the present invention can dynamically update the damage state and reflect the actual fatigue life of the structure. Moreover, the present invention can receive monitoring data (such as stress, strain, temperature and humidity, etc.) from the data acquisition module in real time, and dynamically adjust the prediction model so that the simulation results are more in line with the actual working environment; the simulation correction module corrects the crack extension simulation results according to external environmental factors such as load fluctuations, temperature and humidity changes, and material aging effects, further improving the accuracy of the prediction results.

[0022] 4. The present invention can also accurately predict the crack expansion trend of the bridge structure during the future use period by presetting different crack positions and sizes in the model and performing simulations within a preset period of time, providing a scientific basis for the life management and maintenance strategy of the bridge, thereby improving the safety and reliability of the bridge. Not only can the long-term crack expansion of the bridge be predicted, but real-time analysis and early warning can also be performed in combination with real-time monitoring data. When the crack expansion exceeds the preset safety range, the system can issue a warning in time, which is convenient for maintenance personnel to take measures in advance to prevent serious failures or accidents of the bridge.

[0023] In summary, the fatigue crack propagation prediction method and system of bridge structures provided by the present invention can accurately predict the fatigue crack propagation process of bridge structures and provide accurate crack propagation information, including crack length, depth, location and propagation speed, by integrating advanced finite element analysis, fracture mechanics model, fatigue damage accumulation, real-time data monitoring and correction mechanism. The implementation of this method and system can not only improve the safety and reliability of bridges, but also optimize maintenance strategies and reduce maintenance costs, laying the foundation for the intelligent development of bridge engineering.

[0024] These and other aspects of the present invention will be more clearly understood in the following description of the embodiments. It should be understood that the above general description and the following detailed description are only exemplary and explanatory and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following briefly introduces the drawings required for use in the exemplary embodiments or related technical descriptions. The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings: Figure 1 The present invention is a flowchart of a method for predicting fatigue crack growth in a bridge structure according to an embodiment of the present invention.

[0026] Figure 2 The present invention is a flowchart of using finite element analysis to establish a three-dimensional model of a bridge structure in a method for predicting fatigue crack growth of a bridge structure according to an embodiment of the present invention.

[0027] Figure 3 The present invention is a flowchart of presetting crack positions and sizes in a method for predicting fatigue crack growth in a bridge structure according to an embodiment of the present invention.

[0028] Figure 4 The present invention is a flowchart of simulating the expansion of a preset crack in a method for predicting fatigue crack expansion of a bridge structure according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0030] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0031] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are intended to distinguish two non-identical entities or non-identical parameters with the same name. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, other steps or units inherent to a process, method, system, product or device that includes a series of steps or units.

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0033] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0034] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0035] Aiming at the inadequacy of crack propagation prediction in the prior art, the present invention proposes a method and system for predicting fatigue crack propagation of bridge structures, which can accurately predict the crack propagation path, rate and future state under complex working conditions by combining factors such as dynamic load changes, environmental factors, fatigue damage accumulation, etc., and an improved fatigue damage accumulation model, thereby providing a scientific basis for the maintenance and repair of bridges, achieving real-time feedback and dynamic adjustment, ensuring the efficiency and accuracy of the prediction results, and providing a scientific basis for the maintenance and management of bridges.

[0036] See also Figure 1 As shown, an embodiment of the present invention provides a method for predicting fatigue crack growth of a bridge structure, the method comprising the following steps: Step S10: Collect the structural parameter information of the bridge, including bridge material, structural size, load type and historical load records.

[0037] In this step, parameter information related to the bridge structure is collected and organized for subsequent analysis and modeling, including: Bridge material: The type of building materials used in the bridge, such as concrete, steel, composite materials, etc., which affects the durability, strength and other mechanical properties of the bridge.

[0038] Structural dimensions: including geometric parameters such as bridge span, deck width, slab thickness, and support positions.

[0039] Load type: including static load (such as deadweight) and dynamic load (such as traffic load, wind load, etc.). Different load types affect the stress state of the bridge.

[0040] Historical load records: Records of historical load data of a bridge during operation, usually from long-term monitoring data of the bridge or past load test data, are used to assess the cumulative fatigue damage of the bridge.

[0041] For example, if a steel structure highway bridge is collected, the structural parameter information of the bridge includes: the span is 50 meters, the width is 10 meters, and it mainly bears dynamic loads from vehicles. In the historical load records, the load data when heavy trucks pass through was recorded. The material of the bridge is Q345 steel, and the yield strength is 345 MPa.

[0042] Step S20: Based on the structural parameter information, a three-dimensional model of the bridge structure is established using finite element analysis, and the crack position and size are preset to simulate the actual crack occurrence.

[0043] In this step, see Figure 2 As shown, based on the structural parameter information, a three-dimensional model of the bridge structure is established using finite element analysis, including the following steps: Step S201: Based on the collected bridge material, structural dimensions, load type and historical load records, a geometric model of the bridge is established in a finite element analysis software. The geometric model includes beams, supports, plates and truss structural components of the bridge.

[0044] Step S202: Use beam units, shell units, and solid units to construct a geometric model of the bridge, and divide the bridge structure into a number of finite element units to generate a mesh model.

[0045] Step S203: In combination with the bridge material information, corresponding material properties are assigned to each structural component, wherein the material properties include elastic modulus, Poisson's ratio, yield strength and fatigue limit.

[0046] Step S204: applying boundary conditions to each structural component in the model, and applying traffic loads and environmental loads under normal working conditions of the bridge.

[0047] Step S205: Establish a three-dimensional structural model of the bridge by simulating the deformation of the bridge under the load.

[0048] For example, for the aforementioned steel structure highway bridge, the geometric model needs to include the main beam, support and lane slab. The position of the support is set according to the design drawings, and the length and cross-sectional size of the beam are determined based on the structural design. When establishing the geometric model of the bridge in the finite element analysis software, the steel beam is divided into multiple 1-meter-long beam units, and the bridge deck is divided using shell units, and the supports and connecting parts are simulated using solid units. Through meshing, the deformation and stress distribution of each component under load can be captured. Combined with the material information of the bridge, the corresponding material properties are specified for each structural component, including: Modulus of elasticity: determines the stiffness of the material; Poisson's ratio: reflects the lateral deformation of the material when subjected to force; Yield Strength: The critical strength of a material at which it undergoes permanent deformation when subjected to stress; Fatigue limit: The critical load at which a material undergoes fatigue failure under repeated loads.

[0049] Among them, it is assumed that the material properties of Q345 steel are specified for the steel beam part, with an elastic modulus of 210 GPa, a Poisson's ratio of 0.3, a yield strength of 345 MPa, and a fatigue limit of 160 MPa. When applying boundary conditions and loads, the load conditions are set to a load amplitude of 200 kN and a period of 10 seconds caused by heavy trucks passing through the bridge each time, and the corresponding traffic load mode is set. Temperature changes can also be simulated as environmental loads. The stress distribution diagram of the bridge under load shows that the center of the bridge is subjected to the greatest force, indicating that this area may be the key area for crack propagation.

[0050] In this embodiment, see Figure 3 As shown, the preset crack position and size include the following steps: Step S211, determining the initial position of the crack according to the working environment, stress concentration area and historical damage records of the bridge; Step S212: setting the initial size of the crack, including the crack length and crack depth; calculating the crack growth rate using fracture mechanics theory, and simulating the crack growth path in a finite element model.

[0051] For example, based on the actual working environment of the bridge, such as the road sections where heavy vehicles pass, and historical damage records, it is assumed that cracks may occur in the central beam of the bridge due to long-term load action, and the initial crack size is set to 2 mm in depth and 5 mm in length. When setting the crack propagation model, for the above-set cracks, the Paris law (used to describe the fatigue crack propagation rate) is used to simulate the crack propagation. It is assumed that the crack propagates to a depth of 5 mm after 10,000 load actions and begins to affect the structural safety of the bridge.

[0052] Step S30: construct a fatigue damage accumulation model of the bridge structure according to fracture mechanics, optimize the fatigue damage accumulation model based on load fluctuations, temperature and humidity changes, and material aging effect data, and simulate the expansion of preset cracks in the three-dimensional model of the bridge structure.

[0053] In this step, see Figure 4 As shown, based on the load fluctuation, temperature and humidity changes and material aging effect data, the fatigue damage accumulation model is optimized, and the expansion simulation of the preset cracks in the three-dimensional model of the bridge structure is carried out, including the following steps: Step S301, establishing a three-dimensional finite element model of the bridge structure, and presetting initial cracks at key locations; Step S302: applying the boundary conditions and loads of the bridge, taking into account load fluctuations, temperature and humidity changes, and material aging effects; Step S303, simulating the crack growth rate according to the stress intensity factor and Paris law; Step S304: Use Kuhn's law to calculate the accumulation of fatigue damage and predict the remaining life of crack growth.

[0054] The fatigue damage accumulation model is expressed by the following formula: in, Indicates at time Cumulative fatigue damage of moment cracks, is the number of load cycles experienced by the bridge structure within a time interval, is the stress amplitude, is the temperature, For humidity, For the The fatigue life parameters corresponding to the load cycle are optimized based on stress amplitude, temperature and humidity. For the The number of load cycles, expressed in time Moment of experience The number of stress changes during a load cycle.

[0055] For example, assume that the middle of the main beam of a steel bridge shows signs of fatigue damage in past monitoring data, so an initial crack with a depth of 2mm and a length of 5mm is preset at this location. The area where the bridge is located has seasonal temperature fluctuations, with temperatures reaching 40°C in summer and dropping to -10°C in winter. Load fluctuations are simulated based on historical traffic flow and vehicle type data. The material aging effect adjusts the fatigue limit of the material based on the year the bridge was built and the maintenance record. The Paris law is used to simulate the crack growth rate. The Paris law is a classic formula that describes the relationship between the crack growth rate and the amplitude of the stress intensity factor: When simulating the crack growth rate, the crack growth constant of the bridge is assumed to be , ; If the stress intensity factor amplitude is 15MPa in a certain load cycle , then the crack growth rate is: For a certain load cycle, if the stress amplitude is 100 MPa, the temperature is 30°C, and the humidity is 60%, the fatigue life of the load cycle is 50,000 cycles. If 10,000 cycles are performed under this load amplitude, the cumulative damage is: When the cumulative damage reaches 1, it means that the crack will cause the structure to fail. At this point, the remaining life of the crack can be predicted.

[0056] Step S40: Calculate the crack growth rate and predict the crack growth path based on the stress amplitude, strain, temperature and humidity data monitored in real time on the bridge structure.

[0057] In this step, when calculating the crack growth rate, the formula for calculating the crack growth rate using the Paris law is: in, is the crack growth rate, which indicates the crack extension length in each load cycle. is the material constant; is the magnitude of the stress intensity factor used to describe the stress state at the crack tip, A material index that describes the relationship between crack growth rate and stress intensity factor amplitude.

[0058] Among them, the stress intensity factor amplitude The calculation formula is: in, is a dimensionless geometric factor, which represents the effect of crack geometry on the stress intensity factor; is the stress amplitude, is the crack length, which indicates the distance from the crack tip to the longest direction of the crack surface.

[0059] For example, for a depth of Crack, load amplitude , geometric factor The magnitude of the stress intensity factor is: During the operation of the bridge, the monitoring data will be continuously updated, and the real-time measured stress amplitude, temperature and humidity data will be input into the fatigue damage model to update the crack growth rate and path, and then predict the remaining life of the bridge and the possible failure location. For example: if the real-time monitoring data shows that the temperature rises to 40°C and the stress amplitude increases to 200 MPa, the crack growth rate will change accordingly, and the crack growth prediction will be updated through the Paris rule.

[0060] This example describes in detail the whole process of simulating and predicting crack growth in bridge structures based on fracture mechanics and fatigue damage accumulation models, combined with factors such as load fluctuations, temperature and humidity changes, and material aging effects. Through real-time monitoring data and cumulative damage calculations, the crack growth model can be dynamically updated to more accurately predict the remaining life of the bridge and possible structural failure, helping to make effective structural maintenance and reinforcement decisions.

[0061] Step S50: correct the crack growth simulation result in combination with the actual working environment and load conditions of the bridge structure to obtain a corrected crack growth simulation result.

[0062] In this step, when the crack propagation simulation results are corrected, the crack propagation path correction formula is: in, is the crack propagation path, which indicates the distance the crack propagates in each load cycle; is the path correction coefficient; is the increment of the number of load cycles, indicating the increment of load cycles during crack propagation.

[0063] In this embodiment, when using the Kuhn's law to calculate the accumulation of fatigue damage and predict the remaining life of crack growth, the dynamic response of the stress amplitude is calculated based on the actual traffic flow and load distribution of the bridge, and the load fluctuation effect ignored in the crack growth simulation is corrected. When the temperature and humidity are corrected, the crack growth rate is adjusted by the temperature and humidity correction factor: In the formula, is the temperature and humidity correction factor; When corrosion correction is performed, the corrosion thickness Through the corrosion rate and the age of the bridge Make an estimate: The corrected crack growth rate is: The calculation formula for predicting the remaining life of crack growth is: in, represents the cumulative damage calculation result of fatigue damage, For the The number of loading cycles at each stress amplitude is is the number of failure cycles under this stress amplitude; Correction factor for fatigue life loss caused by temperature, humidity and corrosion; Predict the remaining life of bridge structures based on the calculation results of crack growth rate and cumulative damage; By calculating the damage value when the crack grows to a critical size , and obtain the fatigue life of the bridge.

[0064] The present invention can also accurately predict the crack expansion trend of the bridge structure during future use by presetting different crack positions and sizes in the model and performing simulations within a preset time period, providing a scientific basis for the life management and maintenance strategy of the bridge, thereby improving the safety and reliability of the bridge. Not only can the long-term crack expansion of the bridge be predicted, but real-time analysis and early warning can also be performed in combination with real-time monitoring data. When the crack expansion exceeds the preset safety range, the system can issue a warning in time, making it easier for maintenance personnel to take measures in advance to prevent serious failures or accidents of the bridge.

[0065] Step S60: predicting crack growth information of the bridge structure within a preset period of time according to the corrected crack growth simulation result, wherein the predicted crack growth information includes crack length, depth, position and growth speed.

[0066] In this step, when predicting crack growth information based on the corrected crack growth simulation results, crack growth is predicted based on the actual working environment and load conditions. The prediction step includes calculation of crack length, depth, position and growth rate. The corrected crack growth rate It is used to predict the expansion of cracks in the future and obtain the change of crack length over time through the integration method.

[0067] The present invention can accurately predict the crack growth rate, crack path, crack position, etc. according to structural parameters, crack initial state and real-time environmental data. Through temperature and humidity correction factors, load fluctuation correction and fatigue damage accumulation correction, key parameters such as crack growth rate, crack depth, crack length, etc. can be accurately calculated, and the prediction results can flexibly reflect different working environments and usage conditions.

[0068] The fatigue crack propagation prediction method of the bridge structure of the present invention establishes a comprehensive crack propagation prediction model by comprehensively utilizing the structural parameter information, real-time monitoring data and environmental factors of the bridge, and can effectively predict the fatigue crack propagation of the bridge structure. Among them, finite element analysis is used to establish a three-dimensional model to accurately simulate the working state of the bridge structure; combined with fracture mechanics, a fatigue damage accumulation model is constructed to consider load fluctuations, temperature and humidity changes and material aging effects; real-time feedback of real-time monitoring data improves the accuracy and applicability of the prediction results.

[0069] The present invention accumulates fatigue damage by using a model based on fracture mechanics and the Mines' law. Taking into account the multiple loads of the bridge under different working cycles and the influence of external factors such as temperature and humidity on fatigue damage, the present invention can dynamically update the damage state and reflect the actual fatigue life of the structure. Moreover, the present invention can receive monitoring data (such as stress, strain, temperature and humidity, etc.) from the data acquisition module in real time, and dynamically adjust the prediction model so that the simulation results are more in line with the actual working environment; the simulation correction module corrects the crack extension simulation results according to external environmental factors such as load fluctuations, temperature and humidity changes, and material aging effects, further improving the accuracy of the prediction results.

[0070] It should be noted that the above figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0071] It should be understood that, although described in a certain order, these steps are not necessarily performed in sequence in the above order. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, a part of the steps of the present embodiment may include a plurality of steps or a plurality of stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a part of the steps or stages in other steps or other steps.

[0072] In a second aspect of the embodiments of the present invention, the present invention further provides a bridge structure fatigue crack growth prediction system, comprising: Data acquisition module, used to collect structural parameter information of bridges, including bridge materials, structural dimensions, load types and historical load records; A modeling and simulation module is used to establish a three-dimensional model of the bridge structure based on the structural parameter information using finite element analysis, and preset the crack position and size to simulate the actual crack occurrence; Fatigue damage accumulation calculation module, which is used to construct a fatigue damage accumulation model of bridge structure based on fracture mechanics, optimize the fatigue damage accumulation model based on load fluctuation, temperature and humidity changes and material aging effect data, and simulate the expansion of preset cracks in the three-dimensional model of the bridge structure; The expansion rate calculation module is used to calculate the crack expansion rate and predict the crack expansion path based on the stress amplitude, strain, temperature and humidity data monitored in real time on the bridge structure; A simulation correction module is used to correct the crack propagation simulation results in combination with the actual working environment and load conditions of the bridge structure to obtain corrected crack propagation simulation results; The crack prediction module is used to predict the crack propagation information of the bridge structure within a preset time period based on the corrected crack propagation simulation results. The predicted crack propagation information includes crack length, depth, location and propagation speed.

[0073] Through the above detailed steps, the bridge structure fatigue crack growth prediction system of the present invention is used to execute the steps of the bridge structure fatigue crack growth prediction method in the above embodiment, which will not be repeated here.

[0074] The bridge structure fatigue crack growth prediction system of the present invention can accurately predict the fatigue crack growth process of the bridge structure by integrating advanced finite element analysis, fracture mechanics model, fatigue damage accumulation, real-time data monitoring and correction mechanism, and provide accurate crack growth information, including crack length, depth, location and growth rate. The implementation of this method and system can not only improve the safety and reliability of the bridge, but also optimize the maintenance strategy and reduce the maintenance cost, laying the foundation for the intelligent development of bridge engineering.

[0075] According to a third aspect of an embodiment of the present invention, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method of any one of the above embodiments is implemented.

[0076] The computer device includes a processor and a memory, and may also include an input system and an output system. The processor, memory, input system and output system may be connected via a bus or other means, and the input system may receive input digital or character information and generate signal input related to the migration of fatigue crack propagation prediction of bridge structures. The output system may include display devices such as display screens.

[0077] As a non-volatile computer-readable storage medium, the memory can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as the program instructions / modules corresponding to the fatigue crack growth prediction system for bridge structures in the embodiment of the present invention. The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created by the use of the fatigue crack growth prediction system for bridge structures, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the local module 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.

[0078] The processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor is generally used to control the overall operation of the computer device. In this embodiment, the processor is used to run the program code stored in the memory or process data. The processors of multiple computer devices of the computer device of this embodiment execute various functional applications and data processing of the server by running non-volatile software programs, instructions and modules stored in the memory, that is, implementing the steps of the bridge structure fatigue crack growth prediction method of the above method embodiment.

[0079] It should be understood that, without conflicting with each other, all the embodiments, features and advantages described above for the bridge structure fatigue crack growth prediction method according to the present invention are also applicable to the bridge structure fatigue crack growth prediction method and storage medium according to the present invention.

[0080] It will also be appreciated by those skilled in the art that various exemplary logic blocks, modules, circuits and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, a general description has been given to the functions of various schematic components, blocks, modules, circuits and steps. Whether this function is implemented as software or hardware depends on specific applications and the design constraints imposed on the entire system. Those skilled in the art can implement the function in various ways for each specific application, but this implementation decision should not be interpreted as causing a departure from the disclosed scope of the embodiments of the present invention.

[0081] Finally, it should be noted that the computer-readable storage medium (e.g., memory) herein may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. As an example and not by way of limitation, a non-volatile memory may include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. A volatile memory may include a random access memory (RAM), which may act as an external cache memory. As an example and not by way of limitation, RAM may be obtained in a variety of forms, such as synchronous RAM (DRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The storage devices of the disclosed aspects are intended to include, but are not limited to, these and other suitable types of memory.

[0082] The various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure herein may be implemented or executed using the following components designed to perform the functions herein: a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP, and / or any other such configuration.

[0083] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope disclosed in the embodiments of the present invention as defined in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless explicitly limited to the singular.

[0084] It should be understood that, as used herein, the singular form "a" or "an" is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations of one or more of the items listed in association. The serial numbers of the embodiments disclosed in the above embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments.

[0085] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the above embodiments of the present invention, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. A method for predicting fatigue crack growth of a bridge structure, characterized in that: The method comprises the following steps: Collect structural parameter information of the bridge, including bridge material, structural dimensions, load type and historical load records; Based on the structural parameter information, a three-dimensional model of the bridge structure is established using finite element analysis, and the crack position and size are preset to simulate the actual crack occurrence; A fatigue damage accumulation model of the bridge structure is constructed based on fracture mechanics. The fatigue damage accumulation model is optimized based on load fluctuations, temperature and humidity changes, and material aging effect data. The expansion of the preset cracks in the three-dimensional model of the bridge structure is simulated. Calculate the crack growth rate and predict the crack growth path based on the stress amplitude, strain, temperature and humidity data monitored in real time on the bridge structure; Combined with the actual working environment and load conditions of the bridge structure, the crack growth simulation results are corrected to obtain the corrected crack growth simulation results; According to the corrected crack propagation simulation results, the crack propagation information of the bridge structure within a preset time period is predicted. The predicted crack propagation information includes crack length, depth, location and propagation speed.

2. The bridge structure fatigue crack growth prediction method according to claim 1, characterized in that: Based on the structural parameter information, a three-dimensional model of the bridge structure is established using finite element analysis, including the following steps: According to the collected bridge materials, structural dimensions, load types and historical load records, a geometric model of the bridge is established in the finite element analysis software. The geometric model includes the beams, supports, plates and truss structural components of the bridge. Use beam elements, shell elements, and solid elements to construct the geometric model of the bridge, and divide the bridge structure into several finite element units to generate a mesh model; In combination with the bridge material information, corresponding material properties are assigned to each structural component, wherein the material properties include elastic modulus, Poisson's ratio, yield strength and fatigue limit; Apply boundary conditions to each structural component in the model, and apply traffic loads and environmental loads under normal working conditions of the bridge; By simulating the deformation of the bridge under load, a three-dimensional structural model of the bridge is established.

3. The bridge structure fatigue crack growth prediction method according to claim 2, characterized in that: Preset the crack location and size, including the following steps: Determine the initial location of the crack based on the bridge's working environment, stress concentration areas, and historical damage records; Set the initial size of the crack, including crack length and crack depth; calculate the crack growth rate using fracture mechanics theory, and simulate the crack propagation path in the finite element model.

4. The bridge structure fatigue crack growth prediction method according to claim 3, characterized in that: Based on the load fluctuation, temperature and humidity changes and material aging effect data, the fatigue damage accumulation model is optimized to simulate the expansion of the preset cracks in the three-dimensional model of the bridge structure, including the following steps: Establish a three-dimensional finite element model of the bridge structure and preset initial cracks at key locations; Apply boundary conditions and loads to the bridge, taking into account load fluctuations, temperature and humidity changes, and material aging effects; Simulate the crack growth rate based on stress intensity factor and Paris law; The fatigue damage accumulation is calculated using Koers law and the remaining life of crack growth is predicted.

5. The bridge structure fatigue crack growth prediction method according to claim 4, characterized in that: The fatigue damage accumulation model is expressed by the following formula: in, Indicates at time Cumulative fatigue damage of moment cracks, is the number of load cycles experienced by the bridge structure within a time interval, is the stress amplitude, is the temperature, For humidity, For the The fatigue life parameters corresponding to the load cycle are optimized based on stress amplitude, temperature and humidity. For the The number of load cycles, expressed in time Moment of experience The number of stress changes during a load cycle.

6. The method for predicting fatigue crack growth of a bridge structure according to claim 5, characterized in that: When calculating the crack growth rate, the formula for calculating the crack growth rate using the Paris law is: in, is the crack growth rate, which indicates the crack extension length in each load cycle. is the material constant; is the magnitude of the stress intensity factor used to describe the stress state at the crack tip, A material index that describes the relationship between crack growth rate and stress intensity factor amplitude.

7. The bridge structure fatigue crack growth prediction method according to claim 6, characterized in that: Stress intensity factor amplitude The calculation formula is: in, is a dimensionless geometric factor, which represents the effect of crack geometry on the stress intensity factor; is the stress amplitude, is the crack length, which indicates the distance from the crack tip to the longest direction of the crack surface.

8. The bridge structure fatigue crack growth prediction method according to claim 7, characterized in that: When correcting the crack propagation simulation results, the crack propagation path correction formula is: in, is the crack propagation path, which indicates the distance the crack propagates in each load cycle; is the path correction coefficient; is the increment of the number of load cycles, indicating the increment of load cycles during crack propagation.

9. The bridge structure fatigue crack growth prediction method according to claim 8, characterized in that: When using the Kuhn's law to calculate the accumulation of fatigue damage and predict the remaining life of crack growth, the dynamic response of the stress amplitude is calculated based on the actual traffic flow and load distribution of the bridge, and the load fluctuation effect ignored in the crack growth simulation is corrected. When the temperature and humidity are corrected, the crack growth rate is adjusted by the temperature and humidity correction factor: In the formula, is the temperature and humidity correction factor; When corrosion correction is performed, the corrosion thickness Through the corrosion rate and the age of the bridge Make an estimate: The corrected crack growth rate is: The calculation formula for predicting the remaining life of crack growth is: in, represents the cumulative damage calculation result of fatigue damage, For the The number of loading cycles at each stress amplitude is is the number of failure cycles under this stress amplitude; Correction factor for fatigue life loss caused by temperature, humidity and corrosion; Predict the remaining life of bridge structures based on the calculation results of crack growth rate and cumulative damage; By calculating the damage value when the crack grows to a critical size , and obtain the fatigue life of the bridge.

10. A bridge structure fatigue crack growth prediction system, characterized in that: The system is used to execute the bridge structure fatigue crack growth prediction method according to any one of claims 1 to 9, comprising: Data acquisition module, used to collect structural parameter information of bridges, including bridge materials, structural dimensions, load types and historical load records; A modeling and simulation module is used to establish a three-dimensional model of the bridge structure based on the structural parameter information using finite element analysis, and preset the crack position and size to simulate the actual crack occurrence; Fatigue damage accumulation calculation module, which is used to construct a fatigue damage accumulation model of bridge structure based on fracture mechanics, optimize the fatigue damage accumulation model based on load fluctuation, temperature and humidity changes and material aging effect data, and simulate the expansion of preset cracks in the three-dimensional model of the bridge structure; The expansion rate calculation module is used to calculate the crack expansion rate and predict the crack expansion path based on the stress amplitude, strain, temperature and humidity data monitored in real time on the bridge structure; A simulation correction module is used to correct the crack propagation simulation results in combination with the actual working environment and load conditions of the bridge structure to obtain corrected crack propagation simulation results; The crack prediction module is used to predict the crack propagation information of the bridge structure within a preset time period based on the corrected crack propagation simulation results. The predicted crack propagation information includes crack length, depth, location and propagation speed.

Images