Steel bridge deck fatigue crack propagation evaluation method and device, storage medium and product

Abstract

The invention discloses a steel bridge deck fatigue crack propagation evaluation method and device, a storage medium and a product. The method comprises the steps that equivalent stress range prediction models of different vehicle types are constructed according to a probability model of actually measured traffic loads; according to the actually measured traffic load probability model and the equivalent stress range prediction model, constructing a steel bridge deck weld fatigue stress spectrum probability model; constructing an equivalent stress strength factor range prediction model of the crack tip and the crack edge; according to the initial crack depth, the form ratio, the probability model of the material related parameters, the stress spectrum probability model and the equivalent stress strength factor range prediction model of the crack tip and the crack edge, performing crack random propagation analysis; and probability evolution analysis of different sensitivity parameters and reliability evaluation of the steel bridge deck are carried out according to the distribution characteristics of the sensitivity parameters in the crack propagation process. According to the invention, the evaluation accuracy is improved, the evaluation time consumption is reduced, and the evaluation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of bridge structure safety, and in particular relates to a method, device, storage medium and product for evaluating fatigue crack propagation of a steel bridge deck based on measured traffic flow. Background Art

[0002] Due to the complex structure, numerous welds and high residual stress of steel bridge decks, fatigue cracks initiate and rapidly expand at the weld defect sites under the action of wheel loads, resulting in a large number of fatigue cracks in a short service life of a large number of steel bridge decks. These cracks are highly concealed and difficult to detect. Once discovered, they penetrate the top plate, resulting in reduced local stiffness of the bridge deck and damage to the pavement layer, which in turn causes a series of problems such as driving safety and corrosion. The degree of harm is high and the repair cost is high. In addition to the structural defects of the steel bridge deck itself, there are also special problems such as widespread overloading and rapid growth in traffic volume, which also aggravate the fatigue problem of existing steel bridge decks. Steel bridge decks are affected by complex factors of multi-source uncertainty, and crack propagation is highly random, making fatigue assessment of steel bridge decks difficult. At present, the random propagation assessment method for fatigue cracks in steel bridge deck welds remains to be explored.

[0003] Traditional fatigue assessment methods for steel bridge decks include SN curves (i.e., stress-life curves), fracture mechanics methods, and direct probability methods. The SN curve ignores the dynamic changes of the crack propagation process and actual traffic loads. The fracture mechanics method requires a lot of mesh re-division work during the crack propagation process, which increases the computational complexity and complicates the probabilistic simulation. The above-mentioned geometry-based methods are relatively conservative and cannot fully reflect the randomness of fatigue cracking. Direct probability methods include probabilistic stress-life (i.e., PSN) curves and probabilistic fatigue crack propagation methods based on fracture mechanics. PSN curves usually need to be constructed through fatigue tests, which is costly and inefficient. In addition, deterministic fracture mechanics analysis is used, which contradicts the nature of random fatigue crack propagation and overestimates fatigue life. The probabilistic fatigue crack propagation method based on fracture mechanics considers the random propagation behavior of fatigue cracks and is generally accepted, but it relies on the analytical solution or interpolation of deterministic finite element results, resulting in the flexibility and accuracy of the finite element-based fracture mechanics analysis method not being fully utilized, and it is difficult to fundamentally solve the problem of time-consuming calculations caused by the uncertainty of fatigue crack propagation.

[0004] On the other hand, the uncertainty of traffic flow will directly affect the accuracy of fatigue crack growth analysis of steel bridge deck welds. However, there is currently insufficient research on the direct application of measured traffic flow to fatigue crack growth analysis. In addition, existing research has mainly focused on estimating fatigue damage of steel bridge decks by measuring stress ranges. However, the essence of steel bridge deck degradation is the process of cumulative damage caused by fatigue crack growth, which has significant uncertainty. Summary of the invention

[0005] The purpose of the present invention is to provide a method, equipment, storage medium and product for evaluating fatigue crack propagation of steel bridge decks, so as to solve the problem that fatigue cracks in welds of steel bridge decks are affected by complex uncertain factors of multiple sources (such as base material defects, weld defects, residual stress and manufacturing errors) and the randomness of traffic loads, resulting in a highly random crack propagation process, which in turn leads to low accuracy in fatigue crack propagation evaluation of steel bridge decks, and the need to perform cycle-by-cycle growth analysis and stress intensity factor calculation on each sample during fatigue crack propagation evaluation, resulting in low efficiency in crack propagation evaluation.

[0006] The present invention solves the above technical problems through the following technical solutions: A method for evaluating fatigue crack growth of a steel bridge deck, comprising:

[0007] Acquire measured traffic flow data, perform statistical analysis and fitting on the measured traffic flow data, and obtain a probability model of the measured traffic load;

[0008] Constructing an equivalent stress range prediction model for different vehicle models according to the probability model of the measured traffic load;

[0009] According to the probability model of the measured traffic load and the prediction model of the equivalent stress range of different vehicle models, a probability model of fatigue stress spectrum of steel bridge deck welds is constructed;

[0010] Construct a prediction model for the range of equivalent stress intensity factors at the crack tip and crack edge;

[0011] Probabilistic models for obtaining initial crack depth, morphology ratio, and material-related parameters;

[0012] Based on the probability model of initial crack depth, morphology ratio and material-related parameters, the probability model of fatigue stress spectrum of steel bridge deck welds, and the range prediction model of equivalent stress intensity factor at crack tip and crack edge, the random crack growth analysis is carried out to obtain the distribution characteristics of sensitivity parameters during the random crack growth process;

[0013] According to the distribution characteristics of sensitivity parameters during the random crack propagation process, the probability evolution analysis of different sensitivity parameters and the reliability evaluation of steel bridge deck are carried out.

[0014] Furthermore, a prediction model for the equivalent stress range of different vehicle models is constructed based on the probability model of the measured traffic load, including:

[0015] Designing a plurality of first sub-samples according to the probability model of the measured traffic load, and constructing a first sub-sample data set according to the plurality of first sub-samples; wherein each vehicle type corresponds to a first sub-sample data set, and each first sub-sample in each first sub-sample data set includes a vehicle load and a lateral offset distance of a wheel track of the corresponding vehicle type;

[0016] Construct finite element models of steel bridge decks;

[0017] For each first sub-sample data set, the operation of the vehicle in the finite element model of the steel bridge deck is simulated according to each first sub-sample of the first sub-sample data set to generate a stress history curve corresponding to the first sub-sample; the stress history curve is converted into multiple stress ranges and their corresponding number of cycles using a rain flow counting method; the equivalent stress range is calculated according to the multiple stress ranges of each first sub-sample and their corresponding number of cycles; the first sample data set corresponding to the vehicle type is constructed according to each first sub-sample of the first sub-sample data set and its equivalent stress range; wherein each first sample of the first sample data set includes the vehicle load, wheel track lateral offset distance and equivalent stress range of the corresponding vehicle type;

[0018] constructing a first prediction model;

[0019] For each first sample data set, the first prediction model is trained and verified using the first sample data set to obtain an equivalent stress range prediction model for the corresponding vehicle model.

[0020] Preferably, the equivalent stress range is calculated according to the multiple stress ranges of each first sub-sample and the corresponding number of cycles, and the specific formula is:

[0021] ΔS eq =(∑ΔS i m n si ) 1 / m ;

[0022] Among them, ΔS eq Indicates the equivalent stress range, ΔS i represents the ith stress range, n si represents the number of cycles, and m represents material-related parameters.

[0023] Furthermore, according to the probability model of the measured traffic load and the prediction model of the equivalent stress range of different vehicle models, a probability model of fatigue stress spectrum of steel bridge deck welds is constructed, including:

[0024] Extracting a plurality of second samples from the probability model of the measured traffic load; wherein each second sample includes vehicle loads and lateral wheel track offset distances of different vehicle types;

[0025] Using the equivalent stress range prediction model of different vehicle models to predict the second sample of the corresponding vehicle model, the equivalent stress range of the corresponding second sample is obtained;

[0026] Statistical analysis and fitting are performed on multiple equivalent stress ranges to obtain the probability model of fatigue stress spectrum of steel bridge deck welds.

[0027] Furthermore, a prediction model for the range of equivalent stress intensity factors at the crack tip and crack edge is constructed, including:

[0028] Step 4.1: constructing a third sample data set and a fourth sample data set; wherein each third sample in the third sample data set includes a crack parameter and an equivalent stress intensity factor range at the crack tip; and each fourth sample in the fourth sample data set includes a crack parameter and an equivalent stress intensity factor range at the crack edge;

[0029] Step 4.2: Construct a second prediction model, and use the third sample data set and the fourth sample data set to train and verify the second prediction model respectively, to obtain a prediction model for the equivalent stress intensity factor range of the crack tip and the crack edge.

[0030] Furthermore, constructing a third sample data set and a fourth sample data set includes:

[0031] Step 4.11: Import the finite element model of the steel bridge deck into FRANC3D software, embed fatigue cracks at the welds between the top plate and the longitudinal ribs of the steel bridge deck, and construct a solid sub-model containing fatigue cracks;

[0032] Step 4.12: Apply a deterministic load to the solid sub-model containing fatigue cracks, and perform simulation calculations to obtain stress intensity factor history curves at the crack tip and crack edge;

[0033] Step 4.13: using a rain flow counting method to convert the stress intensity factor history curve at the crack tip into multiple stress intensity factor ranges at the crack tip and their corresponding number of cycles;

[0034] The stress intensity factor history curve of the crack edge is converted into a plurality of stress intensity factor ranges of the crack edge and their corresponding cyclic action times by using a rain flow counting method;

[0035] Step 4.14: Calculate the equivalent stress intensity factor range at the crack tip according to the multiple stress intensity factor ranges at the crack tip and the corresponding number of cycles;

[0036] Calculate the equivalent stress intensity factor range of the crack edge according to multiple stress intensity factor ranges of the crack edge and their corresponding cyclic action times;

[0037] Step 4.15: Repeat steps 4.11 to 4.14 to obtain the equivalent stress intensity factor range of each fatigue crack and its crack tip and crack edge;

[0038] Step 4.16: Construct a third sample data set based on the range of equivalent stress intensity factors of all fatigue cracks and their crack tips; construct a fourth sample data set based on the range of equivalent stress intensity factors of all fatigue cracks and their crack edges.

[0039] Preferably, the equivalent stress intensity factor range at the crack tip or crack edge is calculated according to multiple stress intensity factor ranges at the crack tip or crack edge and their corresponding number of cycles, and the specific formula is:

[0040] ΔK eq =(∑ΔK ri m n Ki ) 1 / m ;

[0041] Among them, ΔK eq Indicates the range of equivalent stress intensity factors at the crack tip or crack edge, ΔK ri represents the range of the i-th stress intensity factor at the crack tip or crack edge, n Ki represents the number of ith cycles of action at the crack tip or crack edge, and m represents material-related parameters.

[0042] Furthermore, based on the probability model of initial crack depth, morphology ratio and material-related parameters, the probability model of fatigue stress spectrum of steel bridge deck welds, and the prediction model of equivalent stress intensity factor range at crack tip and crack edge, random crack propagation analysis is carried out, including:

[0043] Step 6.1: extract multiple fifth samples from the probability model of initial crack depth, morphology ratio and material-related parameters, and the probability model of fatigue stress spectrum of steel bridge deck weld; wherein each fifth sample corresponds to a crack, and each fifth sample includes the initial crack depth, crack semi-major axis length, material-related parameters and equivalent stress range; the crack semi-major axis length is calculated based on the initial crack depth and morphology ratio;

[0044] Step 6.2: For each fifth sample, the initial crack depth and crack semi-major axis length of the fifth sample are predicted using the equivalent stress intensity factor range prediction model of the crack tip and crack edge to obtain the equivalent stress intensity factor range of the crack tip and crack edge under the deterministic load;

[0045] Step 6.3: Obtain the equivalent stress range under the deterministic load; calculate the equivalent stress intensity factor range of the crack tip and crack edge under the measured traffic flow according to the equivalent stress range under the deterministic load, the equivalent stress range of the fifth sample, and the equivalent stress intensity factor range of the crack tip and crack edge under the deterministic load;

[0046] Step 6.4: define the crack depth expansion increment, and calculate the crack semi-major axis length expansion increment and the corresponding load cycle number increment of the fifth sample according to the crack depth expansion increment, the material-related parameters of the fifth sample, and the equivalent stress intensity factor range of the crack tip and the crack edge under the measured traffic flow;

[0047] Step 6.5: Crack extension is performed according to the crack depth extension increment, the crack semi-major axis length extension increment and the corresponding load cycle number increment to obtain the current crack depth, the current crack semi-major axis length and the current load cycle number;

[0048] Step 6.6: Determine whether to terminate the crack extension based on the current crack depth and the critical crack depth; if so, obtain the sensitivity parameters of the corresponding crack random extension process; if not, proceed to step 6.2 based on the current crack depth, the current crack semi-major axis length and the current number of load cycles.

[0049] Preferably, the calculation formula for the equivalent stress intensity factor range of the crack tip or crack edge under the measured traffic flow is:

[0050]

[0051] Where p represents the crack tip, e represents the crack edge; ΔK i_aeq Indicates the range of equivalent stress intensity factors at the crack tip or crack edge under measured traffic flow; ΔK i_EC1 Indicates the range of equivalent stress intensity factors at the crack tip or crack edge under deterministic load, ΔS eq represents the equivalent stress range of the fifth sample (i.e. the equivalent stress range under the measured traffic flow), ΔS EC1 Indicates the equivalent stress range under deterministic load.

[0052] Furthermore, the sensitivity parameters in the crack random extension process include the crack semi-major axis length, morphology ratio, number of load cycles and equivalent stress intensity factor range of the crack tip and crack edge at each crack depth; the probability evolution analysis includes equivalent stress intensity factor range evolution analysis, crack morphology evolution analysis, load cycle number evolution analysis and residual fatigue life evolution analysis;

[0053] The analysis of the evolution of the equivalent stress intensity factor range includes:

[0054] The equivalent stress intensity factor ranges of each crack at the crack tip and crack edge at each crack depth are statistically analyzed and fitted, and the probability evolution models of the equivalent stress intensity factor ranges at the crack tip and crack edge are obtained respectively.

[0055] The crack morphology evolution analysis includes:

[0056] The crack semi-major axis length and morphology ratio of each crack at each crack depth are statistically analyzed to obtain the crack semi-major axis evolution and crack morphology evolution respectively;

[0057] The load cycle number evolution analysis includes:

[0058] The load cycle number of each crack at each crack depth is statistically analyzed and fitted to obtain the load cycle number probability evolution model;

[0059] The remaining fatigue life evolution analysis includes:

[0060] The residual fatigue life of each crack at each crack depth is statistically analyzed and fitted to obtain the residual fatigue life probability evolution model;

[0061] The calculation formula for the reliability of the steel bridge deck is:

[0062] β=norminv(1-P f );

[0063]

[0064] Among them, β represents the fatigue reliability index, norminv represents the inverse function of the normal distribution function, P f represents the failure probability of the steel bridge deck, a(t) represents the crack depth, and a f represents the critical crack depth, I[] i Indicates the true or false indicator of the fifth sample of the i-th order, n MCS Indicates the number of the fifth sample.

[0065] Based on the same concept, the present invention also provides an electronic device, including a memory, a processor, and a computer program / instructions stored in the memory, wherein the processor executes the computer program / instructions to implement the steel bridge deck fatigue crack growth assessment method as described above.

[0066] Based on the same concept, the present invention also provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steel bridge deck fatigue crack growth assessment method as described above.

[0067] Based on the same concept, the present invention also provides a computer program product, including a computer program / instruction, which implements the above-mentioned steel bridge deck fatigue crack growth assessment method when executed by a processor.

[0068] Beneficial Effects

[0069] Compared with the prior art, the advantages of the present invention are:

[0070] The present invention adopts a method of constructing a random fatigue crack propagation model, fully considering the uncertainties in the crack structure material properties, loads, crack sizes, etc., to obtain the statistical characteristics of the crack propagation sensitivity parameters of the steel bridge deck under the measured traffic flow, and establishes its probabilistic evolution model based on this; based on the measured traffic flow data, it accurately captures the outstanding uncertainties in the measured traffic loads, ensuring the accuracy of the random fatigue crack propagation analysis; the random fatigue crack propagation analysis is carried out by replacing the time-consuming finite element model with a prediction model, achieving a delicate balance between accuracy, efficiency and flexibility, and solving the problem of low efficiency in crack propagation assessment caused by the need to perform cycle-by-cycle growth analysis and stress intensity factor calculation on each sample when using the finite element model for fatigue crack propagation assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only one embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0072] Figure 1 is a flow chart of a method for evaluating fatigue crack growth of a steel bridge deck in an embodiment of the present invention;

[0073] Figure 2 is a schematic diagram of a suspension bridge structure according to an embodiment of the present invention;

[0074] Figure 3 is the probability model of the gross vehicle weight of the C6 model in the embodiment of the present invention;

[0075] Figure 4 is the probability model of the average daily traffic volume in the embodiment of the present invention;

[0076] Figure 5 is a probability model of the lateral offset distance of the wheel track in the embodiment of the present invention;

[0077] Figure 6 is a finite element model of a steel bridge deck in an embodiment of the present invention;

[0078] Figure 7 is the RF model based on the bagging algorithm in the embodiment of the present invention;

[0079] Figure 8 is a schematic diagram of the verification results of the equivalent stress range prediction model of the C6 vehicle model in an embodiment of the present invention;

[0080] Fig. 9 is a probability model of fatigue stress spectrum of steel bridge deck weld in an embodiment of the present invention;

[0081] Fig.10 is a flowchart of the interactive use of ABAQUS and FRANC3D in an embodiment of the present invention;

[0082] Fig.11 is a schematic diagram of the verification results of the equivalent stress intensity factor range prediction model at the crack tip in an embodiment of the present invention;

[0083] Fig.12 is a schematic diagram of the verification results of the equivalent stress intensity factor range prediction model of the crack edge in an embodiment of the present invention;

[0084] Fig.13 is a probability evolution model of the equivalent stress intensity factor range at the fatigue crack tip in the embodiment of the present invention;

[0085] Fig.14 is a probability evolution model of the equivalent stress intensity factor range of the fatigue crack edge in the embodiment of the present invention;

[0086] Fig.15 Schematic diagram of the evolution of the semi-major axis length of a crack in an embodiment of the present invention;

[0087] Fig.16 Schematic diagram of crack morphology evolution in an embodiment of the present invention;

[0088] Fig.17 is a probability evolution model of the number of fatigue load cycles in an embodiment of the present invention;

[0089] Fig.18 is the probability evolution model of the remaining fatigue life (or remaining extended life) in the embodiment of the present invention; wherein E(1 / 6.63×10 7 ) indicates that the expectation is 1 / 6.63×10 7 The exponential distribution of LogN(19.50,1.00 2 ) represents a lognormal distribution with a mean of 19.5 and a standard deviation of 1;

[0090] Fig.19 is the fatigue reliability index curve under the influence of the average daily traffic volume growth in the embodiment of the present invention; wherein, α 1 represents the overall growth factor of average daily traffic volume;

[0091] Fig. 20 is the fatigue reliability index curve under the influence of the vehicle gross weight growth in the embodiment of the present invention; wherein, α 2 Represents the overall growth factor of the gross vehicle weight. DETAILED DESCRIPTION

[0092] The following is a clear and complete description of the technical solutions in the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0093] The technical solution of the present application is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0094] like Figure 1 As shown, a steel bridge deck fatigue crack growth assessment method provided by an embodiment of the present invention comprises the following steps:

[0095] Step 1: Obtain measured traffic flow data, perform statistical analysis and fitting on the measured traffic flow data, and obtain a probability model of the measured traffic load.

[0096] The present invention obtains the measured traffic flow data of the suspension bridge based on a WIM (Weight-in-Motion) system. Figure 2 The schematic diagram of the suspension bridge structure is shown in FIG. Figure 2 (a) is the elevation view of the suspension bridge. Figure 2 (b) is a schematic diagram of the cross section of the steel box girder. Figure 2 (c) shows the details of the steel bridge deck segment and welding structure. The WIM system has been serving the Figure 2 The suspension bridge shown records the measured traffic flow data, performs statistical classification on the measured traffic flow data, first obtains the occupancy rate of each vehicle type, then obtains the probability distribution characteristics of the total vehicle weight of each vehicle type and the average daily traffic volume, and aligns and fits them, so as to obtain the probability models of different vehicle types, the probability models of the total vehicle weight of different vehicle types, and the probability model of the average daily traffic volume. That is, the probability model of the measured traffic load of this embodiment includes the occupancy rate of different vehicle types, the probability model of the total vehicle weight of different vehicle types, and the probability model of the average daily traffic volume.

[0097] In this embodiment, six types of vehicle models can be obtained through the measured traffic flow data recorded by the WIM system, and the occupancy rate of each type of vehicle model in all vehicle models is shown in Table 1.

[0098] Table 1 Different models and their occupancy rates

[0099]

[0100] The gross vehicle weight of each vehicle model corresponds to a probability model. In this embodiment, a Gaussian mixture model is used to fit the gross vehicle weight data of each vehicle model to obtain the probability model of the gross vehicle weight of each vehicle model. Taking the C6 model in Table 1 as an example, its probability model is as follows: Figure 3 As shown, Figure 3 In the above figure, GMM stands for Gaussian mixture model, and the subscripts 1, 2, and 3 indicate that a Gaussian mixture model containing three Gaussian functions is used for fitting. i represents the weight of the i-th Gaussian function in the Gaussian mixture model, μ i represents the mean of the i-th Gaussian function, σ i represents the standard deviation of the i-th Gaussian function, i = 1, 2, 3. This embodiment uses the normal distribution function to fit the average daily traffic volume data to obtain the probability model of the average daily traffic volume, such as Figure 4 shown. Figure 4 In, N(3824,178 2 ) represents the normal distribution function with a mean of 3824 and a standard deviation of 178.

[0101] The probability model of the measured traffic load also includes a probability model of the lateral offset distance of the wheel track. By obtaining the probability distribution characteristics of the lateral offset distance of the wheel track and fitting it, the probability model of the lateral offset distance of the wheel track can be obtained. There are two ways to obtain the probability distribution characteristics of the lateral offset distance of the wheel track: one is to obtain it from the measured traffic flow data, and the other is to obtain it from the prior art. The probability distribution characteristics of the lateral offset distance of the wheel track in this embodiment are obtained according to the Eurcode1 specification. The Eurcode1 specification can be specifically found in: European committee for standardization. EN 1991: Eurocode 1-Actions on structures-part 2: traffic load on bridges. Brussels, Belgium: CEN; 2003. This embodiment uses a normal distribution function to fit the probability distribution characteristics of the lateral offset distance of the wheel track, and the obtained probability model of the lateral offset distance of the wheel track is as follows: Figure 5 As shown, EC1 represents the Eurcode1 standard.

[0102] The probability model of the measured traffic load (i.e., the measured traffic load model) is composed of the probability models of the occupancy rates of different types of vehicles, the gross vehicle weights of different types of vehicles, the average daily traffic volume, and the lateral offset distance of the wheel tracks. The probability model of the measured traffic load integrates the conditional probability relationship between various traffic variables, and the number of axles and wheelbase are regarded as deterministic variables directly related to the vehicle configuration.

[0103] Compared with relying on fatigue design specifications to establish a fatigue vehicle model, the present invention fully considers the uncertainty of traffic loads during the service life of the bridge and establishes a measured traffic load model, which can accurately simulate the crack propagation performance of the actual steel bridge deck under the measured traffic flow.

[0104] Step 2: Construct an equivalent stress range prediction model for different vehicle types based on the probability model of measured traffic loads.

[0105] The influence line of the steel bridge deck weld is shorter than the axle spacing. Each axle passing through the weld will induce a fatigue stress peak. At the same time, the number of axles corresponding to different vehicle models is also different. Therefore, it is necessary to establish a corresponding equivalent stress range prediction model according to the vehicle model. In a specific embodiment of the present invention, an equivalent stress range prediction model for different vehicle models is constructed based on the probability model of the measured traffic load, including:

[0106] Step 2.1: Design a plurality of first sub-samples according to the probability model of the measured traffic load, and construct a first sub-sample data set according to the plurality of first sub-samples.

[0107] For each vehicle model, the uniform design method is used to design N from the probability model of vehicle gross weight and wheel track lateral offset distance. 11 The total vehicle weight and lateral offset distance of the wheel track of the model are calculated, so that the total vehicle weight of the model corresponds to a lateral offset distance of the wheel track, thereby obtaining a first sub-sample, which is composed of N 11 The first sub-samples constitute the first sub-sample data set of the vehicle model. That is, each vehicle model corresponds to a first sub-sample data set, and each first sub-sample in each first sub-sample data set includes the gross vehicle weight and the lateral offset distance of the wheel track of the corresponding vehicle model.

[0108] Step 2.2: Construct the finite element model of the steel bridge deck.

[0109] based on Figure 2 Based on the structural design drawings of the suspension bridge, a three-dimensional multi-scale finite element model of the steel bridge deck is established, including the overall model of the steel bridge deck segment and the local sub-model of the detail area, such as Figure 6 As shown. In the finite element model of the steel bridge deck, the shell element is simulated using SR4, with a total of 43,596 elements; for the key areas, the local solid sub-model is refined, and the solid element is simulated using C3D8R, with a total of 62,460 elements. In order to balance the calculation accuracy and efficiency, the mesh size of the weld area in the local solid sub-model is finely set to 1mm, and the mesh size of other non-critical areas is 4mm. For the overall segment model, the mesh size is uniformly set to 80mm. In addition, the finite element model of the steel bridge deck imposes necessary constraints on the three degrees of freedom, longitudinal, transverse and vertical, to ensure the accuracy of the analysis results.

[0110] Since the steel bridge deck finite element model does not directly include the pavement unit, a 45° expansion angle can be used to apply a vertically uniformly distributed wheel load on the pavement. Specifically, the pavement thickness is 67 mm, the tire contact area on the front axle side is 300 mm × 200 mm (width and length), and the tire contact area on the rear axle side is 600 mm × 200 mm. By calculating the load diffusion effect under the influence of the pavement, the corrected load areas are set to 434 mm × 334 mm and 734 mm × 334 mm, respectively.

[0111] Step 2.3: For each first sub-sample data set, simulate the operation of a vehicle on a steel bridge deck finite element model according to each first sub-sample of the first sub-sample data set to generate a stress history curve corresponding to the first sub-sample.

[0112] According to each first sub-sample of the first sub-sample data set, the total vehicle weight and the lateral offset distance of the wheel track are set in the Dload subroutine in the ABAQUS software, and the vehicle operation is simulated to generate the stress history.

[0113] Step 2.4: Use the rain flow counting method to convert the stress history curve into multiple stress ranges and their corresponding number of cycles.

[0114] Step 2.5: Calculate the equivalent stress range according to the multiple stress ranges of each first sub-sample and the corresponding number of cycles. The specific calculation formula is:

[0115] ΔS eq =(∑ΔS i m n si ) 1 / m (1)

[0116] Among them, ΔS eq Indicates the equivalent stress range, ΔS i represents the ith stress range, n si Expressed with ΔS i The corresponding number of cycles is the i-th cycle, and m represents the material-related parameters, which is usually taken as 3.

[0117] Step 2.6: Construct a first sample data set corresponding to the vehicle type according to each first sub-sample of the first sub-sample data set and its equivalent stress range.

[0118] For the first sub-sample data set of each vehicle model, the equivalent stress range of each first sub-sample can be obtained according to steps 2.3 to 2.6, and the first sample data set of the corresponding vehicle model is constructed according to each first sub-sample (including the gross vehicle weight and lateral wheel track offset distance of the corresponding vehicle model) and its equivalent stress range. Therefore, each first sample of the first sample data set of each vehicle model includes the gross vehicle weight, lateral wheel track offset distance and equivalent stress range of the vehicle model.

[0119] Step 2.7: Build the first prediction model.

[0120] In a specific implementation of the present invention, the first prediction model uses a random forest model (i.e., RF model). Figure 7 As shown in the figure, the RF model, as an advanced model based on the ensemble learning strategy of the bagging algorithm, shows broad application prospects in the field of bridge engineering. By constructing multiple decision trees as weak learners and integrating their prediction results, the generalization ability and prediction accuracy of the overall model are enhanced. During the training process, each decision tree randomly selects some samples and features for learning. This double randomness not only ensures the diversity and independence between the weak learners, but also promotes the effective training of the model, so that the model can show stronger robustness and lower random errors when facing unknown sample sets.

[0121] Step 2.8: For each first sample data set, the first prediction model is trained and verified using the first sample data set to obtain an equivalent stress range prediction model for the corresponding vehicle model.

[0122] For each vehicle model, the vehicle gross weight and wheel track lateral offset distance of each first sample of the first sample data set of the vehicle model are used as input, and the corresponding equivalent stress range is used as output. The first prediction model is trained and verified, and the equivalent stress range prediction model of the vehicle model can be obtained. At the same time, the adjusted determination coefficient (AdjustedR 2 ) is 0.9996, indicating that the trained equivalent stress range prediction model has good generalization ability and prediction accuracy. Figure 8 shown.

[0123] Each vehicle model corresponds to an equivalent stress range prediction model.

[0124] Step 3: Based on the probability model of measured traffic loads and the prediction model of equivalent stress range for different vehicle models, a probability model of fatigue stress spectrum of steel bridge deck welds is constructed.

[0125] In order to provide a large number of random samples for subsequent fatigue crack random propagation analysis, the present invention also constructs a probability model of fatigue stress spectrum of steel bridge deck welds. In a specific embodiment of the present invention, a probability model of fatigue stress spectrum of steel bridge deck welds is constructed based on the probability model of measured traffic loads and the equivalent stress range prediction model of different vehicle models, including:

[0126] Step 3.1: Draw a plurality of second samples from the probability model of the measured traffic load.

[0127] Suppose we need to extract N 2 The second sample, according to the occupancy rate and N in Table 1 2 Calculate the number of second samples required for each vehicle model, and then use Monte Carlo sampling to extract the corresponding number of vehicle gross weights from the probability model of the vehicle gross weight of the model, and the gross weight of all vehicle models constitutes N 2 The total weight of the vehicle is 1.45×10.4. Monte Carlo sampling is used to extract N from the probability model of the wheel track lateral offset distance. 2 One wheel track lateral offset distance corresponds to one vehicle gross weight, thus N is obtained. 2 That is, each second sample includes the gross vehicle weight and the lateral offset distance of the wheel track of the corresponding vehicle model.

[0128] Step 3.2: Use the equivalent stress range prediction model of different vehicle models to predict the second sample of the corresponding vehicle model to obtain the equivalent stress range of the corresponding second sample.

[0129] The equivalent stress range prediction model corresponding to the vehicle model of the second sample is used to predict the second sample, so that the equivalent stress range of the second sample can be predicted.

[0130] Step 3.3: Perform statistical analysis and fitting on the equivalent stress range of all second samples to obtain the probability model of fatigue stress spectrum of steel bridge deck welds.

[0131] The equivalent stress ranges of all second samples were statistically analyzed and fitted using different fitting functions, e.g. Fig. 9 As shown in the figure, the log-normal distribution function can best fit the probability model of fatigue stress spectrum of steel bridge deck welds.

[0132] Step 4: Construct a prediction model for the equivalent stress intensity factor range at the crack tip and crack edge.

[0133] In a specific embodiment of the present invention, a prediction model for the equivalent stress intensity factor (SIF) range of the crack tip and the crack edge is constructed, including:

[0134] Step 4.1: Construct the third sample data set and the fourth sample data set.

[0135] In a specific embodiment of the present invention, constructing the third sample data set and the fourth sample data set includes:

[0136] Step 4.11: Import the finite element model of the steel bridge deck constructed in step 2.2 into the FRANC3D software, embed fatigue cracks at the welds between the top plate and the longitudinal ribs of the steel bridge deck, and construct a solid sub-model containing fatigue cracks, such as Fig.10 shown.

[0137] Fig.10 In the figure, a represents the crack depth and c represents the semi-major axis length of the crack. The joint solution method of ABAQUS and FRANC3D has an adaptive re-meshing technology. The crack tip can be meshed and re-meshed by singular wedge units or degenerate hexahedral units to ensure high-quality meshes around the crack front and achieve accurate prediction of SIF and crack propagation process. That is, after the initial crack is embedded in the weld between the top plate and the longitudinal rib of the steel bridge deck, the model is adaptively re-meshed to obtain a solid sub-model containing the initial crack.

[0138] Step 4.12: Apply a deterministic load to the solid sub-model containing fatigue cracks, and perform simulation calculations to obtain the stress intensity factor history curves at the crack tip and crack edge.

[0139] This example applies a deterministic fatigue vehicle model (i.e., Eurcode1 specification), through Fig.10 The ABAQUS and FRANC3D interactive technology shown in the figure is used to obtain the stress intensity factors at the crack tip and crack edge when the vehicle load acts on different positions of the steel bridge deck, thereby obtaining the stress intensity factor history curves at the crack tip and crack edge.

[0140] The M-integral method can be used to solve the stress intensity factors at the crack tip and crack edge.

[0141] Step 4.13: Using the rain flow counting method, the stress intensity factor history curve at the crack tip is converted into multiple stress intensity factor ranges at the crack tip and their corresponding number of cycles;

[0142] The rain flow counting method is used to convert the stress intensity factor history curve of the crack edge into multiple stress intensity factor ranges of the crack edge and their corresponding number of cycles.

[0143] Step 4.14: Calculate the equivalent stress intensity factor range at the crack tip based on multiple stress intensity factor ranges at the crack tip and their corresponding number of cycles; calculate the equivalent stress intensity factor range at the crack edge based on multiple stress intensity factor ranges at the crack edge and their corresponding number of cycles.

[0144] In this embodiment, the equivalent stress intensity factor range at the crack tip or crack edge is calculated according to multiple stress intensity factor ranges at the crack tip or crack edge and their corresponding number of cycles. The specific formula is:

[0145] ΔK eq =(∑ΔK ri m n Ki ) 1 / m (2)

[0146] Among them, ΔK eq Indicates the range of equivalent stress intensity factors at the crack tip or crack edge, ΔK ri represents the range of the i-th stress intensity factor at the crack tip or crack edge, n Ki represents the number of ith cycles of action at the crack tip or crack edge, and m represents material-related parameters.

[0147] According to steps 4.11 to 4.14, a fatigue crack (including crack depth and crack semi-major axis length) and the equivalent stress intensity factor range of the crack tip and crack edge of the fatigue crack can be obtained.

[0148] Step 4.15: Repeat steps 4.11 to 4.14 to obtain the equivalent stress intensity factor range of each fatigue crack and its crack tip and crack edge.

[0149] In order to obtain the equivalent stress intensity factor range of multiple fatigue cracks and their crack tips and crack edges, steps 4.11 to 4.14 are repeated. In this embodiment, Latin hypercube sampling is used according to the critical crack size and the reasonable crack extension range, and the number of fatigue cracks extracted is 80.

[0150] Step 4.16: Construct a third sample data set based on the range of equivalent stress intensity factors of all fatigue cracks and their crack tips; construct a fourth sample data set based on the range of equivalent stress intensity factors of all fatigue cracks and their crack edges.

[0151] Each third sample in the third sample data set includes a crack parameter and an equivalent stress intensity factor range at the crack tip; each fourth sample in the fourth sample data set includes a crack parameter and an equivalent stress intensity factor range at the crack edge. In this embodiment, the crack parameters include crack depth and crack semi-major axis length, and the number of samples in the third sample data set and the fourth sample data set are both 80.

[0152] During the fatigue crack propagation process, its morphological evolution is often maintained within a specific range. Therefore, this embodiment considers that the extracted fatigue cracks are evenly distributed within a reasonable crack propagation range.

[0153] Step 4.2: Construct a second prediction model, and use the third sample data set and the fourth sample data set to train and verify the second prediction model, respectively, to obtain a prediction model for the equivalent stress intensity factor range of the crack tip and the crack edge.

[0154] In this embodiment, the second prediction model uses a random forest model.

[0155] The crack depth and crack semi-major axis length of each third sample in the third sample data set are used as input, and the corresponding equivalent stress intensity factor range of the crack tip is used as output. The second prediction model is trained and verified, and the equivalent stress intensity factor range prediction model of the crack tip can be obtained. At the same time, the adjusted determination coefficient (Adjusted R 2 ) is 0.99238, indicating that the trained prediction model of equivalent stress intensity factor range at crack tip has good generalization ability and prediction accuracy. Fig.11 As shown, where c represents the crack semi-major axis length and a represents the crack depth.

[0156] The crack depth and crack semi-major axis length of each fourth sample in the fourth sample data set are used as input, and the corresponding equivalent stress intensity factor range of the crack edge is used as output. The second prediction model is trained and verified, and the equivalent stress intensity factor range prediction model of the crack edge can be obtained. At the same time, the adjusted determination coefficient (Adjusted R 2 ) is 0.99612, indicating that the trained equivalent stress intensity factor range prediction model of crack edge has good generalization ability and prediction accuracy. Fig.12 shown.

[0157] Step 5: Obtain the probability model of initial crack depth, morphology ratio and material related parameters.

[0158] In order to provide a large number of random samples for subsequent fatigue crack random extension analysis, it is also necessary to obtain the probability model of the initial crack depth, morphology ratio and material-related parameters, and the probability model of these parameters can be obtained through existing technologies. This embodiment obtains the probability model of the initial crack depth and morphology ratio from the existing literature (Kountouris IS, Baker MJ. Defect assessment: analysis of the dimensions of defects detected by ultrasonic inspection in an offshore structure, CESLIC Report OR8. London, UK: Imperial College of Science and Technology.). The probability model of material-related parameters is obtained from the BS7910 standard.

[0159] Step 6: Based on the probability model of initial crack depth, morphology ratio and material-related parameters, the probability model of fatigue stress spectrum of steel bridge deck welds, and the equivalent stress intensity factor range prediction model of crack tip and crack edge, a random crack propagation analysis is performed to obtain the distribution characteristics of sensitivity parameters during the random crack propagation process.

[0160] In a specific embodiment of the present invention, a random crack extension analysis is performed based on a probability model of initial crack depth, morphology ratio and material-related parameters, a probability model of fatigue stress spectrum of steel bridge deck welds, and a prediction model of equivalent stress intensity factor range at crack tip and crack edge, specifically including:

[0161] Step 6.1: Extract a plurality of fifth samples from the probability model of initial crack depth, morphology ratio and material-related parameters, and the probability model of fatigue stress spectrum of steel bridge deck weld.

[0162] Each fifth sample corresponds to a crack, and each fifth sample includes an initial crack depth, a crack semi-major axis length, material-related parameters, and an equivalent stress range; the crack semi-major axis length is calculated based on the initial crack depth and the morphology ratio.

[0163] Step 6.2: For each fifth sample, the initial crack depth and crack semi-major axis length of the fifth sample are predicted using the equivalent stress intensity factor range prediction model of the crack tip and crack edge to obtain the equivalent stress intensity factor range of the crack tip and crack edge under the deterministic load.

[0164] The initial crack depth and crack semi-major axis length of the fifth sample are input into the equivalent stress intensity factor range prediction model of the crack tip to obtain the equivalent stress intensity factor range of the crack tip of the fifth sample under the deterministic load; the initial crack depth and crack semi-major axis length of the fifth sample are input into the equivalent stress intensity factor range prediction model of the crack edge to obtain the equivalent stress intensity factor range of the crack edge of the fifth sample under the deterministic load. Since the deterministic load is applied to the entity sub-model containing fatigue cracks when constructing the equivalent stress intensity factor range prediction model of the crack tip and crack edge, the equivalent stress intensity factor range of the crack tip and crack edge of the fifth sample calculated at this time is the equivalent stress intensity factor range under the deterministic load.

[0165] Step 6.3: Obtain the equivalent stress range of the steel bridge deck weld under the deterministic load; calculate the equivalent stress intensity factor range of the crack tip and crack edge of the fifth sample under the measured traffic flow based on the equivalent stress range under the deterministic load, the equivalent stress range of the fifth sample, and the equivalent stress intensity factor range of the crack tip and crack edge under the deterministic load.

[0166] Using the fatigue vehicle model specified in Eurcode1 as the vehicle load, the equivalent stress range of the steel bridge deck weld under deterministic load can be obtained by following steps 2.3 to 2.6. The equivalent stress intensity factor range of the crack tip or crack edge under deterministic load can be expressed as:

[0167]

[0168] Where p represents the crack tip, e represents the crack edge; ΔK i_EC1 Indicates the range of equivalent stress intensity factors at the crack tip or crack edge under deterministic load; Y i (a,c) represents the correction factor at the crack tip or crack edge, depending on the geometry and crack shape of the detail of interest; ΔS EC1 represents the equivalent stress range under deterministic load, a represents the crack depth, and c represents the semi-major axis length of the crack.

[0169] By introducing the stress ratio relationship between the measured traffic flow and the fatigue vehicle model specified in Eurcode1, formula (3) is rewritten as:

[0170]

[0171] Among them, ΔK i_aeq Indicates the range of equivalent stress intensity factors at the crack tip or crack edge under measured traffic flow, ΔS eq represents the equivalent stress range of the fifth sample (i.e. the equivalent stress range under the measured traffic flow), ΔS EC1 Indicates the equivalent stress range under deterministic load.

[0172] For the same morphological crack in the same detail, the SIF range is proportional to the stress range. The essence of the equivalent stress intensity factor range prediction model for the crack tip and edge is to predict the equivalent stress intensity factor range of the fatigue crack edge and tip of different sizes (crack depth and crack semi-major axis length) under a certain deterministic vehicle load, and then solve the equivalent stress intensity factor range of the fatigue crack edge and crack tip under the measured traffic flow through the stress ratio relationship between the deterministic vehicle load and the measured traffic flow. Therefore, according to formula (4), the equivalent stress intensity factor range of the crack tip and crack edge of each fifth sample (i.e. each crack) under the measured traffic flow can be calculated.

[0173] Step 6.4: Define the crack depth expansion increment. According to the crack depth expansion increment, the material parameters of the fifth sample, and the equivalent stress intensity factor range of the crack tip and crack edge under the measured traffic flow, calculate the crack semi-major axis length expansion increment and the corresponding load cycle number increment of the fifth sample.

[0174] A crack growth model is selected to characterize the development relationship between the stress intensity factor range and the crack growth rate. Crack growth models include Pairs model, Walker model and Forman model, etc. In this embodiment, the Pairs model is selected to characterize the relationship between the stress intensity factor range and the crack growth rate, specifically:

[0175]

[0176] Where a and c represent crack depth and semi-major axis length, respectively, N represents the number of load cycles, and C and m represent material-related parameters. According to BS7910 standard, m of Q345qD steel is set to 3, ΔK p and ΔK e They represent the range of stress intensity factors at the crack tip and crack edge as the number of loading cycles changes.

[0177] Based on formula (5) and formula (6), the crack semi-major axis length and the increment of the number of load cycles are solved for a given crack depth extension increment. The specific formula is:

[0178]

[0179] Among them, Δa represents the crack depth expansion increment, Δc represents the crack semi-major axis expansion increment, and ΔN represents the load cycle increment. Substitute the defined crack depth expansion increment, the material parameters of the fifth sample, and the equivalent stress intensity factor range of the crack tip under the measured traffic flow into formula (7) to calculate the load cycle increment; then substitute the load cycle increment, the material parameters of the fifth sample, and the equivalent stress intensity factor range of the crack edge under the measured traffic flow into formula (8) to calculate the crack semi-major axis expansion increment.

[0180] Step 6.5: Crack extension is performed according to the crack depth extension increment, the crack semi-major axis length extension increment and the corresponding load cycle number increment to obtain the current crack depth, the current crack semi-major axis length and the current load cycle number.

[0181] The random crack extension process is discretized into a process of gradually superimposing the crack depth and semi-major axis length. The current crack depth, the current crack semi-major axis length and the current load cycle number are calculated based on the crack depth extension increment, the crack semi-major axis length extension increment and the corresponding load cycle number increment. In order to ensure the calculation accuracy, the crack depth extension increment is set to 1% of the initial crack depth.

[0182] Step 6.6: Determine whether to terminate the crack extension based on the current crack depth and the critical crack depth; if so, obtain the distribution characteristics of the sensitivity parameters in the corresponding crack random extension process; if not, proceed to step 6.2 based on the current crack depth, the current crack semi-major axis length and the current number of load cycles.

[0183] When the current crack depth is less than or equal to the critical crack depth, the crack continues to expand, that is, based on the current crack depth, the current crack semi-major axis length and the current number of load cycles, the process goes to step 6.2; when the current crack depth is greater than the critical crack depth, the crack expansion is terminated, and the sensitivity parameters of the crack during its expansion process are obtained.

[0184] According to steps 6.2 to 6.6, a crack random propagation analysis is performed on the crack corresponding to each fifth sample to obtain the distribution characteristics of the sensitivity parameters during the random propagation process of each crack.

[0185] The present invention introduces the RF model to predict the equivalent stress range and the equivalent stress intensity factor range. There is no need to use a finite element model for crack propagation, and there is no need to perform cycle-by-cycle growth analysis and stress intensity factor calculation on each sample when evaluating fatigue crack propagation. This reduces time consumption while ensuring propagation accuracy and improves crack propagation efficiency.

[0186] Compared with the analysis of fatigue crack random propagation based on finite element model alone or analytical solution or interpolation based on deterministic finite element results, the present invention introduces RF model for crack random propagation, which greatly improves the calculation efficiency. Under the same hardware configuration conditions, the calculation efficiency of the present invention is improved by more than 1400 times.

[0187] Step 7: Perform probability evolution analysis and steel bridge deck reliability assessment based on the distribution characteristics of sensitivity parameters during random crack propagation.

[0188] In a specific embodiment of the present invention, the sensitivity parameters during the random crack propagation process include the crack semi-major axis length, the shape ratio, the number of load cycles, and the equivalent stress intensity factor range of the crack tip and the crack edge for each crack depth.

[0189] The probability evolution analysis includes the evolution analysis of equivalent stress intensity factor range, crack morphology, load cycle number and residual fatigue life.

[0190] In order to evaluate the reliability of steel bridge deck, the critical crack depth is used as the structural failure criterion and the limit state function is established:

[0191] G(t)=a(t)-a f (9)

[0192] Among them, a f represents the critical crack depth, t represents the service life of the steel bridge deck, G represents the limit state function of fatigue failure of the steel bridge deck weld, and a represents the crack depth. Based on formula (9), the failure probability of the steel bridge deck can be derived as:

[0193] P f =P[G(t)≥0]=P[a(t)-a f ≥0] (10)

[0194] Among them, P f represents the failure probability of steel bridge deck.

[0195] According to the random crack growth results of the fifth sample and combined with the average daily traffic volume probability model, the P corresponding to each service time point of the steel bridge deck is solved. f :

[0196]

[0197] Among them, I[] i Indicates the true or false indicator of the fifth sample of the i-th order, n MCS Indicates the number of the fifth sample.

[0198] The ability of a structure to perform its expected functions within the specified operational service life and under the specified use conditions can be evaluated by the reliability index. According to the geometric meaning of the reliability index, the relationship between the failure probability of a steel bridge deck and the reliability index is:

[0199] β=norminv(1-P f ) (12)

[0200] Among them, β represents the fatigue reliability index, and norminv represents the inverse function of the normal distribution function.

[0201] The equivalent stress intensity factor range of the crack tip corresponding to each crack depth is statistically analyzed and fitted to obtain the probability evolution model of the equivalent stress intensity factor range at the crack tip point (only four crack depths of 2mm, 4mm, 6mm and 8mm are selected in this example), as shown in Fig.13 As shown, Kp represents the crack tip, a i represents the crack depth of the ith extension, c i Represents the semi-major axis length of the crack that expands for the i-th time. The equivalent stress intensity factor range of each crack edge under the measured traffic flow at each crack depth is statistically analyzed and fitted to obtain the probability evolution model of the equivalent stress intensity factor range of the crack tip edge, as shown in Fig.14 As shown, where Ke represents the crack edge.

[0202] according to Figure 13 to Figure 14 It can be seen that the lognormal distribution can accurately fit the probability evolution model of the equivalent SIF range. The mean value of the equivalent SIF range at the crack edge is always higher than that at the crack tip. When fatigue failure occurs, the mean value of the equivalent SIF range at the crack edge is 124.52 MPa·mm 1 / 2 , which is 52.21% higher than the crack tip.

[0203] The crack semi-major axis length corresponding to each crack depth is fitted to obtain the crack semi-major axis evolution process, such as Fig.15 As shown; the morphological ratio of each crack at each crack depth is fitted to obtain the crack morphological evolution process, as shown in Fig.16 As shown, in this example, only eight crack depths ranging from 1 mm to 8 mm are selected.

[0204] according to Fig.15 and Fig.16 It can be seen that as the crack expands, the discreteness and average value of the crack semi-major axis will increase, and its growth rate will continue to accelerate even after fatigue failure; the change trend of the crack morphology ratio is the opposite. Once the crack depth exceeds the critical value, the crack morphology ratio will stabilize in a very small range.

[0205] The load cycle number corresponding to each crack depth is statistically analyzed and fitted to obtain the load cycle number probability evolution model, such as Fig.17 As shown; the remaining fatigue life of each crack depth is statistically analyzed and fitted to obtain the remaining fatigue life probability evolution model, as shown in Fig.18 shown.

[0206] according to Fig.17 and Fig.18 It can be seen that the probability evolution model of the number of load cycles can be well described by the lognormal distribution, while the remaining fatigue life follows a mixed distribution of the lognormal distribution and the exponential distribution. It is worth noting that this distribution transition occurs when the crack depth is 5 mm, at which time the deterioration rate of the welded joint is significantly accelerated.

[0207] according to Fig.19 and Fig. 20 It can be seen that the reliability index decreases with the increase of traffic volume and gross vehicle weight, among which the influence of gross vehicle weight is more significant. Specifically, when the traffic volume increases by 40%, the reliability index decreases from 2.46 to 1.87 in the 100th year, while when the gross vehicle weight increases by 20%, the reliability index decreases from 2.46 to 1.68 in the 100th year. For a steel bridge with an expected design life of 100 years, a 40% increase in traffic volume will shorten the fatigue life to 99 years, while a 20% increase in gross vehicle weight will shorten the fatigue life to 82 years.

[0208] In the process of random fatigue crack propagation, the present invention fully considers the uncertainties of crack structure material properties, load, crack size, etc., obtains the statistical characteristics of the crack propagation sensitivity parameters of the steel bridge deck under the measured traffic flow, and conducts probabilistic evolution analysis based on this. The limit state function is established with the critical crack depth as the structural failure criterion, and then the evaluation of structural reliability is more reasonable.

[0209] Example 2

[0210] An embodiment of the present invention also provides an electronic device, which includes: a memory, a processor, and a computer program / instructions stored in the memory, and the processor executes the computer program / instructions to implement the steel bridge deck fatigue crack propagation assessment method in the embodiment of the present application.

[0211] Although not shown, the electronic device includes a processor that can perform various appropriate operations and processes based on programs and / or data stored in a read-only memory (ROM) or programs and / or data loaded from a storage portion into a random access memory (RAM). The processor can be a multi-core processor or can include multiple processors. In some embodiments, the processor can include a general-purpose main processor and one or more special coprocessors, such as a central processing unit, a graphics processing unit (GPU), a neural network processor (NPU), a digital signal processor (DSP), etc. In the RAM, there is also a

[0212] Stores various programs and data required for device operation. The processor, ROM, and RAM are connected to each other through the bus. The input / output (I / O) interface is also connected to the bus.

[0213] The processor and memory are used together to execute the program / instructions stored in the memory. When the program / instructions are executed by the computer, the methods, steps or functions described in the above embodiments can be implemented.

[0214] Although not shown, an embodiment of the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steel bridge deck fatigue crack propagation assessment method in the embodiment of the present application.

[0215] Storage media in embodiments of the present invention include permanent and non-permanent, removable and non-removable items that can be implemented by any method or technology to store information. Examples of storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0216] Readable storage media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0217] Although not shown, an embodiment of the present invention further provides a computer program product, including: a computer program / instructions, which, when executed by a processor, implements the steel bridge deck fatigue crack propagation assessment method in the embodiment of the present application.

[0218] What is disclosed above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or modifications within the technical scope disclosed in the present invention, which should be covered within the protection scope of the present invention.

Claims

1. A method for evaluating fatigue crack growth of a steel bridge deck, characterized in that: The evaluation methods include: Acquire measured traffic flow data, perform statistical analysis and fitting on the measured traffic flow data, and obtain a probability model of the measured traffic load; Constructing an equivalent stress range prediction model for different vehicle models according to the probability model of the measured traffic load; According to the probability model of the measured traffic load and the prediction model of the equivalent stress range of different vehicle models, a probability model of fatigue stress spectrum of steel bridge deck welds is constructed; Construct a prediction model for the range of equivalent stress intensity factors at crack tip and crack edge; Probabilistic models for obtaining initial crack depth, morphology ratio, and material-related parameters; Based on the probability model of initial crack depth, morphology ratio and material-related parameters, the probability model of fatigue stress spectrum of steel bridge deck welds, and the range prediction model of equivalent stress intensity factor at crack tip and crack edge, the random crack growth analysis is carried out to obtain the distribution characteristics of sensitivity parameters during the random crack growth process; According to the distribution characteristics of sensitivity parameters during the random crack propagation process, the probability evolution analysis of different sensitivity parameters and the reliability evaluation of steel bridge deck are carried out.

2. The method for evaluating fatigue crack growth of a steel bridge deck according to claim 1, characterized in that: According to the probability model of the measured traffic load, an equivalent stress range prediction model for different vehicle models is constructed, including: Designing a plurality of first sub-samples according to the probability model of the measured traffic load, and constructing a first sub-sample data set according to the plurality of first sub-samples; wherein each vehicle type corresponds to a first sub-sample data set, and each first sub-sample in each first sub-sample data set includes a vehicle load and a lateral offset distance of a wheel track of the corresponding vehicle type; Construct finite element models of steel bridge decks; For each first sub-sample data set, the operation of the vehicle in the finite element model of the steel bridge deck is simulated according to each first sub-sample of the first sub-sample data set to generate a stress history curve corresponding to the first sub-sample; the stress history curve is converted into multiple stress ranges and their corresponding number of cycles using a rain flow counting method; the equivalent stress range is calculated according to the multiple stress ranges of each first sub-sample and their corresponding number of cycles; the first sample data set corresponding to the vehicle type is constructed according to each first sub-sample of the first sub-sample data set and its equivalent stress range; wherein each first sample of the first sample data set includes the vehicle load, wheel track lateral offset distance and equivalent stress range of the corresponding vehicle type; constructing a first prediction model; For each first sample data set, the first prediction model is trained and verified using the first sample data set to obtain an equivalent stress range prediction model for the corresponding vehicle model; Preferably, the equivalent stress range is calculated according to the multiple stress ranges of each first sub-sample and the corresponding number of cycles, and the specific formula is: ΔS eq =(∑ΔS i m n si ) 1 / m ; Among them, ΔS eq Indicates the equivalent stress range, ΔS i represents the ith stress range, n si represents the number of cycles, and m represents material-related parameters.

3. The method for evaluating fatigue crack growth of a steel bridge deck according to claim 1, characterized in that: According to the probability model of the measured traffic load and the prediction model of the equivalent stress range of different vehicle models, a probability model of fatigue stress spectrum of steel bridge deck welds is constructed, including: Extracting a plurality of second samples from the probability model of the measured traffic load; wherein each second sample includes vehicle loads and lateral wheel track offset distances of different vehicle types; Using the equivalent stress range prediction model of different vehicle models to predict the second sample of the corresponding vehicle model, the equivalent stress range of the corresponding second sample is obtained; Statistical analysis and fitting are performed on multiple equivalent stress ranges to obtain the probability model of fatigue stress spectrum of steel bridge deck welds.

4. The method for evaluating fatigue crack growth of a steel bridge deck according to claim 1, characterized in that: Construct a prediction model for the equivalent stress intensity factor range at the crack tip and crack edge, including: Step 4.1: constructing a third sample data set and a fourth sample data set; wherein each third sample in the third sample data set includes a crack parameter and an equivalent stress intensity factor range at the crack tip; and each fourth sample in the fourth sample data set includes a crack parameter and an equivalent stress intensity factor range at the crack edge; Step 4.2: Construct a second prediction model, and use the third sample data set and the fourth sample data set to train and verify the second prediction model respectively, to obtain a prediction model for the equivalent stress intensity factor range of the crack tip and the crack edge.

5. The method for evaluating fatigue crack growth of a steel bridge deck according to claim 4, characterized in that: Constructing the third sample data set and the fourth sample data set includes: Step 4.11: Import the finite element model of the steel bridge deck into FRANC3D software, embed fatigue cracks at the welds between the top plate and the longitudinal ribs of the steel bridge deck, and construct a solid sub-model containing fatigue cracks; Step 4.12: Apply a deterministic load to the solid sub-model containing fatigue cracks, and perform simulation calculations to obtain stress intensity factor history curves at the crack tip and crack edge; Step 4.13: using a rain flow counting method to convert the stress intensity factor history curve at the crack tip into multiple stress intensity factor ranges at the crack tip and their corresponding number of cycles; The stress intensity factor history curve of the crack edge is converted into a plurality of stress intensity factor ranges of the crack edge and their corresponding cyclic action times by using a rain flow counting method; Step 4.14: Calculate the equivalent stress intensity factor range at the crack tip according to the multiple stress intensity factor ranges at the crack tip and the corresponding number of cycles; Calculate the equivalent stress intensity factor range of the crack edge according to multiple stress intensity factor ranges of the crack edge and their corresponding cyclic action times; Step 4.15: Repeat steps 4.11 to 4.14 to obtain the equivalent stress intensity factor range of each fatigue crack and its crack tip and crack edge; Step 4.16: construct a third sample data set based on the range of equivalent stress intensity factors of all fatigue cracks and their crack tips; construct a fourth sample data set based on the range of equivalent stress intensity factors of all fatigue cracks and their crack edges; Preferably, the equivalent stress intensity factor range at the crack tip or crack edge is calculated according to multiple stress intensity factor ranges at the crack tip or crack edge and their corresponding number of cycles, and the specific formula is: ΔK eq =(∑ΔK ri m n Ki ) 1 / m ; Among them, ΔK eq Indicates the range of equivalent stress intensity factors at the crack tip or crack edge, ΔK ri represents the range of the i-th stress intensity factor at the crack tip or crack edge, n Ki represents the number of ith cycles of action at the crack tip or crack edge, and m represents material-related parameters.

6. The method for evaluating fatigue crack growth of a steel bridge deck according to any one of claims 1 to 5, characterized in that: According to the probability model of initial crack depth, morphology ratio and material related parameters, the probability model of fatigue stress spectrum of steel bridge deck weld, and the prediction model of equivalent stress intensity factor range of crack tip and crack edge, the random crack propagation analysis is carried out, including: Step 6.1: extract multiple fifth samples from the probability model of initial crack depth, morphology ratio and material-related parameters, and the probability model of fatigue stress spectrum of steel bridge deck weld; wherein each fifth sample corresponds to a crack, and each fifth sample includes the initial crack depth, crack semi-major axis length, material-related parameters and equivalent stress range; the crack semi-major axis length is calculated based on the initial crack depth and morphology ratio; Step 6.2: For each fifth sample, the initial crack depth and crack semi-major axis length of the fifth sample are predicted using the equivalent stress intensity factor range prediction model of the crack tip and crack edge to obtain the equivalent stress intensity factor range of the crack tip and crack edge under the deterministic load; Step 6.3: Obtain the equivalent stress range under the deterministic load; calculate the equivalent stress intensity factor range of the crack tip and crack edge under the measured traffic flow according to the equivalent stress range under the deterministic load, the equivalent stress range of the fifth sample, and the equivalent stress intensity factor range of the crack tip and crack edge under the deterministic load; Step 6.4: define the crack depth expansion increment, and calculate the crack semi-major axis length expansion increment and the corresponding load cycle number increment of the fifth sample according to the crack depth expansion increment, the material-related parameters of the fifth sample, and the equivalent stress intensity factor range of the crack tip and the crack edge under the measured traffic flow; Step 6.5: Crack extension is performed according to the crack depth extension increment, the crack semi-major axis length extension increment and the corresponding load cycle number increment to obtain the current crack depth, the current crack semi-major axis length and the current load cycle number; Step 6.6: Determine whether to terminate the crack extension based on the current crack depth and the critical crack depth; if yes, obtain the distribution characteristics of the sensitivity parameters in the random crack extension process; if no, proceed to step 6.2 based on the current crack depth, the current crack semi-major axis length and the current number of load cycles; Preferably, the calculation formula for the equivalent stress intensity factor range of the crack tip or crack edge under the measured traffic flow is: Where p represents the crack tip, e represents the crack edge; ΔK i_aeq Indicates the range of equivalent stress intensity factors at the crack tip or crack edge under measured traffic flow; ΔK i_EC1 Indicates the range of equivalent stress intensity factors at the crack tip or crack edge under deterministic load, ΔS eq Indicates the equivalent stress range of the fifth sample, ΔS EC1 Indicates the equivalent stress range under deterministic load.

7. The method for evaluating fatigue crack growth of a steel bridge deck according to any one of claims 1 to 5, characterized in that: The sensitivity parameters in the crack random extension process include the crack semi-major axis length, shape ratio, number of load cycles and equivalent stress intensity factor range of the crack tip and crack edge at each crack depth; the probability evolution analysis includes equivalent stress intensity factor range evolution analysis, crack shape evolution analysis, load cycle number evolution analysis and residual fatigue life evolution analysis; The analysis of the evolution of the equivalent stress intensity factor range includes: The equivalent stress intensity factor ranges of each crack at the crack tip and crack edge at each crack depth are statistically analyzed and fitted, and the probability evolution models of the equivalent stress intensity factor ranges at the crack tip and crack edge are obtained respectively. The crack morphology evolution analysis includes: The crack semi-major axis length and morphology ratio of each crack at each crack depth are statistically analyzed to obtain the crack semi-major axis evolution and crack morphology evolution respectively; The load cycle number evolution analysis includes: The load cycle number of each crack at each crack depth is statistically analyzed and fitted to obtain the load cycle number probability evolution model; The remaining fatigue life evolution analysis includes: The residual fatigue life of each crack at each crack depth is statistically analyzed and fitted to obtain the residual fatigue life probability evolution model; The calculation formula for the reliability of the steel bridge deck is: β=norminv(1-P f ); Among them, β represents the fatigue reliability index, norminv represents the inverse function of the normal distribution function, P f represents the failure probability of the steel bridge deck, a(t) represents the crack depth of the steel bridge deck after service time t, and a f represents the critical crack depth, I[] i Indicates the true or false indicator of the fifth sample of the i-th order, n MCS Indicates the number of the fifth sample.

8. An electronic device comprising a memory, a processor, and a computer program / instruction stored in the memory, characterized in that: The processor executes the computer program / instructions to implement the steel bridge deck fatigue crack growth assessment method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the method for evaluating fatigue crack growth of a steel bridge deck according to any one of claims 1 to 7 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method for evaluating fatigue crack growth of a steel bridge deck according to any one of claims 1 to 7 is implemented.

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