Bridge component extreme effect short-time evaluation method based on vehicle distribution on bridge deck

Abstract

The application relates to a kind of component extreme effect short-time evaluation methods based on bridge deck vehicle distribution, comprising: collecting and statistics the vehicle flow and vehicle data information of specific bridge site, obtaining the influence line of bridge structure to be evaluated effect;Vehicle flow data is loaded to the influence line of to be evaluated effect, and extreme value scene sample is obtained;The Poisson parameter of each lane heavy vehicle distribution position and the Gaussian mixture distribution parameter of total weight of heavy vehicle are counted;Based on non-stationary Poisson distribution, heavy vehicle position simulation is carried out, and based on Nataf transformation, total weight of heavy vehicle is simulated, to obtain load simulation extreme scene under to be evaluated effect;Using load simulation extreme scene under different effects, the complex spatial distribution of heavy vehicle on bridge deck is simulated.Compared with the prior art, the application can quickly and accurately simulate the heavy vehicle distribution on the bridge deck that causes the extreme value of various effects of the bridge, thereby ensuring the efficiency and reliability of the bridge component extreme effect evaluation.

Description

Technical Field

[0001] This invention relates to the field of bridge structural performance evaluation technology, and in particular to a short-time evaluation method for extreme effects of components based on vehicle distribution on the bridge deck. Background Technology

[0002] When evaluating the performance of bridge structures, it is necessary to know the actual resistance and load effects of the components. Vehicle load is one of the most important factors to consider in the evaluation, as it reflects the actual situation and development trend of bridge transport load. An accurate and reasonable extreme value probability model of vehicle load effect is an important prerequisite for ensuring the reliability assessment of bridge structures. Due to the high degree of randomness in the space and time of vehicle loads on bridges, there are numerous loading scenarios. However, traditional design codes do not fully consider the spatiotemporal variability of vehicle loads, and the traditional traffic load models specified in these codes are mainly developed for small- and medium-span bridges, including D60, US standards, British standards, and European standards. Vehicle load models for small- and medium-span bridges may not be suitable for direct application to large-span bridges, or at least need to be updated regularly according to current traffic characteristics before they can be applied to large-span bridges. Furthermore, with the rapid development of the current transportation industry, evaluating bridge structures based on traffic flow characteristics at the bridge site has become essential.

[0003] Currently, domestic and international vehicle load models have limitations in quantitatively representing the complex spatial distribution of traffic loads on bridge decks. Traditional location-specific traffic flow simulation methods can only reconstruct traffic flow information and cannot incorporate structural features for scenario simulation. In reality, the spatial distribution of vehicles on the bridge deck that triggers the maximum load effect is crucial for assessing bridge safety and helps understand the bridge's performance under vehicle loads. Furthermore, focusing solely on traffic flow simulation while ignoring traffic scenarios that may lead to extreme response values ​​makes traditional simulation methods inefficient and computationally intensive in obtaining extreme effects over long time periods. Adopting a method that traverses all possible vehicle spatial arrangements on the bridge deck can lead to combinatorial explosion, and it is difficult to calculate the empirical probability of each possible spatial vehicle distribution pattern. All of these factors contribute to the inability to quickly and accurately assess the extreme effects of bridge components, making it difficult to guarantee the reliability of the assessment results. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art by providing a short-time evaluation method for extreme effects of bridge components based on the distribution of vehicles on the bridge deck. This method can quickly model complex spatial distributions of vehicles on the bridge deck and realistically simulate vehicle scenarios under extreme response events, thereby ensuring the efficiency and reliability of the evaluation of extreme effects of bridge components.

[0005] The objective of this invention can be achieved through the following technical solution: a short-time evaluation method for extreme value effects of components based on vehicle distribution on a bridge deck, comprising the following steps:

[0006] S1. Collect and statistically analyze traffic flow and vehicle data at a specific bridge site, including but not limited to vehicle total weight, vehicle length, vehicle speed, and vehicle arrival time.

[0007] S2. Construct a finite element model of the bridge structure and obtain the influence lines of the effects to be evaluated on the bridge structure;

[0008] S3. Based on the total weight and length of the vehicles, filter out the length information of heavy vehicles and determine the length of the bridge deck cell accordingly.

[0009] S4. Based on the influence line of the effect to be evaluated on the bridge structure, load the traffic flow data onto the influence line of the effect to be evaluated according to the set time step to obtain extreme value scenario samples.

[0010] S5. Based on extreme value scenario samples, calculate the Poisson parameters of the distribution location of heavy vehicles in each lane and the Gaussian mixture distribution parameters of the total weight of heavy vehicles in each lane.

[0011] S6. Based on the data obtained in step S5, simulate the position of heavy vehicles based on non-stationary Poisson distribution and simulate the total weight of heavy vehicles based on Nataf transformation to obtain the extreme scenario of load simulation under the current effect to be evaluated.

[0012] S7. Repeat steps S2 to S6 to obtain extreme load simulation scenarios under different effects, so as to simulate the complex spatial distribution of heavy vehicles on the bridge surface and determine the current extreme value effect evaluation results of the bridge components.

[0013] Furthermore, step S1 specifically involves collecting WIM (Weigh in Motion) data at a specific bridge site and performing data cleaning operations to statistically analyze the corresponding traffic flow and vehicle data.

[0014] Furthermore, in step S3, the heavy vehicle specifically refers to a vehicle with a total weight of 10 tons or more.

[0015] Furthermore, in step S3, the length of the bridge deck cell is greater than the average length of heavy vehicles, and the difference between the two is within a set threshold range.

[0016] Furthermore, step S4 specifically includes the following steps:

[0017] S41. Restore the WIM data to the traffic flow sequence with a set bridge length, then load it onto the influence line of the effect to be evaluated, and calculate multiple structural effect values ​​in sequence according to the set time step, which are the obtained samples.

[0018] S42. Select the group of samples with the largest effect value from the multiple groups of samples each day, and use it as the extreme value scenario sample.

[0019] Furthermore, step S5 specifically includes the following steps:

[0020] S51. Based on extreme value scenario samples, extract Poisson distribution parameters from the statistical information of the number of heavy vehicles in each lane;

[0021] S52. Based on extreme value scenario samples, extract Gaussian mixture distribution parameters from the total weight statistics of heavy vehicles in each lane.

[0022] Furthermore, the Poisson distribution parameters in step S51 are specifically as follows:

[0023]

[0024] Where Pr(·) is the probability of the event occurring, and λ is the average probability of a cell being occupied by a heavy vehicle. For non-stationary events, λ varies along the lane.

[0025] Furthermore, the Gaussian mixture distribution parameters in step S52 are specifically as follows:

[0026]

[0027]

[0028] 0≤π k ≤1

[0029] Wherein, N(χ|μ k , ∑ k ) is based on μ k and ∑ k Let π be the probability density function (PDF) of the k-th Gaussian distribution with mean and covariance matrices. k The weight is the weight of the k-th Gaussian distribution component.

[0030] Furthermore, step S6 specifically includes the following steps:

[0031] S61. A longitudinal non-stationary Poisson distribution method is adopted, and the Poisson distribution parameters are used to simulate the bridge deck position of heavy vehicles in extreme scenarios.

[0032] S62. Based on the Gaussian mixture distribution of the total weight of heavy vehicles, the correlation of the total weight of heavy vehicles along the lane direction is simulated through Nataf transformation.

[0033] Furthermore, the expression for the longitudinal non-stationary Poisson distribution in step S61 is specifically as follows:

[0034] λ(l)=λ0(l)·θ

[0035] Where λ0(l) is the average level of occupied cells in the entire lane, and θ is the variation law of the probability of occupancy along the lane.

[0036] Compared with the prior art, the present invention has the following advantages:

[0037] I. This invention collects and statistically analyzes traffic flow and vehicle data at a specific bridge site, loads the traffic flow data onto the influence line of the effect to be evaluated to obtain extreme scenario samples. Based on these extreme scenario samples, it statistically analyzes the Poisson parameters of the distribution locations of heavy vehicles in each lane and the Gaussian mixture distribution parameters of the total weight of heavy vehicles in each lane. Finally, it simulates the positions of heavy vehicles based on the non-stationary Poisson distribution and the total weight of heavy vehicles based on the Nataf transform to obtain extreme load simulation scenarios under the effect to be evaluated. Thus, based on extreme load simulation scenarios under different effects, it simulates the complex spatial distribution of heavy vehicles on the bridge deck and determines the extreme effect evaluation results of the current bridge components. This allows for the rapid and accurate simulation of the distribution of heavy vehicles on the bridge deck that causes extreme values ​​of various bridge effects, thereby effectively improving the efficiency and reliability of extreme effect evaluation of bridge components.

[0038] Second, this invention is aimed at simulating the position of heavy vehicles. It uses a non-stationary Poisson process to simulate the uneven position distribution of heavy vehicles on the lane and assumes that they are located in cells on the bridge surface. It can accurately simulate the position of heavy vehicles on the bridge surface under extreme scenarios.

[0039] Third, this invention is aimed at simulating the total weight of heavy vehicles. It uses a Gaussian mixture distribution to describe and simulate the total weight of heavy vehicles on the bridge deck, and uses Nataf transformation to consider the correlation between the total weights of adjacent heavy vehicles, which can accurately simulate the total weight of heavy vehicles in extreme scenarios. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0041] Figure 2 This is a schematic diagram of an extreme scenario for a long-span bridge in the embodiment.

[0042] Figure 3 The example shows the influence line of the longest cable axial force effect in the long-span cable-stayed bridge.

[0043] Figures 4a-4d This is an example of a simulation sample of the extreme value scenario of the cable axial force effect in the embodiment;

[0044] Figure 5 This is a schematic diagram showing the extrapolated response values ​​to the standard ratio for the five effects simulating extreme scenarios in the example. Detailed Implementation

[0045] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0046] Example

[0047] like Figure 1 As shown, a short-time evaluation method for the extreme value effect of components based on vehicle distribution on the bridge deck includes the following steps:

[0048] S1. Collect and statistically analyze traffic flow and vehicle data at a specific bridge site, including but not limited to vehicle total weight, vehicle length, vehicle speed, and vehicle arrival time.

[0049] S2. Construct a finite element model of the bridge structure and obtain the influence lines of the effects to be evaluated on the bridge structure;

[0050] S3. Based on the total weight and length of the vehicles, filter out the length information of heavy vehicles and determine the length of the bridge deck cell accordingly.

[0051] S4. Based on the influence line of the effect to be evaluated on the bridge structure, load the traffic flow data onto the influence line of the effect to be evaluated according to the set time step to obtain extreme value scenario samples.

[0052] S5. Based on extreme value scenario samples, calculate the Poisson parameters of the distribution location of heavy vehicles in each lane and the Gaussian mixture distribution parameters of the total weight of heavy vehicles in each lane.

[0053] S6. Based on the data obtained in step S5, simulate the position of heavy vehicles based on non-stationary Poisson distribution and simulate the total weight of heavy vehicles based on Nataf transformation to obtain the extreme scenario of load simulation under the current effect to be evaluated.

[0054] S7. Repeat steps S2 to S6 to obtain extreme load simulation scenarios under different effects, so as to simulate the complex spatial distribution of heavy vehicles on the bridge surface and determine the current extreme value effect evaluation results of the bridge components.

[0055] The specific application process of the above technical solution includes:

[0056] (1) Collect and analyze traffic flow information at specific locations

[0057] Collect WIM traffic flow data at specific locations and perform preliminary data cleaning and other preliminary work. Vehicle data should include information such as vehicle gross weight, vehicle length, vehicle speed, and vehicle arrival time.

[0058] (2) Obtaining the influence lines of specific effects based on the finite element model

[0059] Construct a finite element model of the bridge structure to obtain the influence lines of the effects of interest.

[0060] (3) Bridge deck cell division based on heavy vehicle length statistics

[0061] In this embodiment, vehicles weighing 10 tons or more are defined as heavy vehicles. Therefore, the length of the bridge deck cell is determined by statistically analyzing the length information of heavy vehicles weighing 10 tons or more. The length of the bridge deck cell is slightly longer than the average length of heavy vehicles, mainly to take into account factors such as the front and rear trailers and necessary driving distances.

[0062] (4) Obtaining extreme scenarios of specific effects

[0063] Based on the influence line of a specific effect, this embodiment sets the time step to 1 second and reconstructs the WIM data as traffic flow loaded onto the influence line of that effect. Then, the group with the largest effect among the 86,400 daily samples is selected as the sample for the extreme value scenario.

[0064] (5) Statistics on non-stationary information of heavy vehicle position in extreme scenarios of specific effects

[0065] Based on the above extreme scenario samples, the Poisson parameters of the distribution locations of heavy vehicles in each lane were statistically analyzed.

[0066] (6) Statistics on Gaussian mixture distribution of heavy vehicle weight in extreme scenarios of specific effects

[0067] Based on the above extreme scenario samples, the Gaussian mixture distribution parameters of the total weight of heavy vehicles in each lane were statistically analyzed.

[0068] (7) Heavy vehicle position simulation based on non-stationary Poisson distribution

[0069] Based on the statistical information of the location distribution of heavy vehicles under the aforementioned specific effects, the Poisson distribution is applied to simulate the bridge deck positions of heavy vehicles in extreme scenarios. The formula for the Poisson distribution is as follows:

[0070]

[0071] In the formula, Pr(·) is the probability of the event occurring, and λ represents the average probability of a cell being occupied by a heavy vehicle. For non-stationary events, λ varies along the lane.

[0072] The non-stationarity of heavy vehicle distribution can be expressed by the following formula:

[0073] λ(l)=λ0(l)·θ

[0074] In the formula, λ0(l) represents the average level of occupied cells in the entire lane, and θ represents the variation of the probability of occupancy along the lane.

[0075] (8) Simulation of gross vehicle weight based on Gaussian mixture distribution using Nataf transform

[0076] The total vehicle weight is a key variable in vehicle load and has significant uncertainty, requiring simulation using probabilistic methods. Furthermore, the total vehicle weight exhibits a multimodal distribution; this technical solution employs a Gaussian mixture distribution, which accurately captures this information. In addition, combining the Gaussian mixture distribution with the Nataf transform method further incorporates the correlation of the total vehicle weight into the simulation.

[0077] Currently, the Gaussian mixture distribution is widely used in various fields and exhibits good performance in multimodal simulations. For a random variable χ, the Gaussian mixture distribution is expressed as:

[0078]

[0079] Wherein, N(χ|μ k , ∑ k ) is based on μ k and ∑ k Let π be the PDF of the k-th Gaussian distribution with mean and covariance matrix. k The weight of the k-th Gaussian distribution component is given by the following condition: and 0≤π k ≤1.

[0080] When applying Gaussian mixture distributions, selecting an appropriate number of components is crucial. Increasing the number of components improves the accuracy of the Gaussian mixture distribution but reduces efficiency. In practical applications, several entropy criteria can be used to determine the number of components, including the AIC (Akaike Information Criterion) method and the BIC (Bayesian Information Criterion) method.

[0081] This technical solution also considers that several consecutive heavy vehicles in a lane are a key factor causing extreme load effects, threatening the safety of the bridge structure. Therefore, for extreme scenarios, it is necessary to assume that there is a correlation between heavy vehicles in adjacent positions in the same lane. The purpose of setting this parameter is to describe the correlation between adjacent heavy vehicles in the same lane in extreme scenarios, which is reflected by a correlation function. This embodiment uses the Pearson correlation coefficient to characterize the correlation of the total weight of heavy vehicles, as shown below:

[0082]

[0083] Where, ρj，k α represents the correlation coefficient between the total weight of heavy vehicles at adjacent positions j and k. j，k Corresponding to the covariance between adjacent total weights, λ j and λ k ρ represents the standard deviation of the gross weight at points j and k, respectively. The correlation coefficient ρ between the gross weights of two adjacent heavy vehicles... j，k With distance |l j -l k The correlation coefficient (ρ) weakens as the ρ increases. Referring to engineering cases in related research, this embodiment uses an exponential correlation function to express ρ. j，k :

[0084]

[0085] Where D is a parameter representing the relevant length, for distance |l j -l k |, then the centroid distance between two adjacent cells is taken.

[0086] The Nataf transformation can be used to convert any random variable into a standard Gaussian distribution, an n-dimensional correlated random vector. The marginal cumulative density function is F j (β j The correlation coefficient matrix is ​​K = [K j，k =κ j，k Through Nataf transformation, It can be converted into an independent standard normal variable.

[0087] The Nataf transformation process is as follows:

[0088] Relevant standard normal variables It can be obtained from the following formula Transformation:

[0089] ξ j =Φ -1 (F j (β j ))

[0090] Where, Φ -1 (·) is the marginal cumulative density function of the inverse of the standard Gaussian distribution. The correlation coefficient matrix is ​​assumed to be K′=[κ′ j，k ], κ j，k and κ′ j，k The relationship between them is shown in the following formula:

[0091]

[0092] Where, ω(β) j ,βk ;κ′ j，k The following equations give the mean and standard deviation of the variable, E(·) and SD(·), respectively:

[0093]

[0094] Among them, κ′ j，k via κ j，k The calculation shows that the relationship between the two can be simplified by the following formula:

[0095] κ′ j，k =P j，k ·κ j，k

[0096] P j，k The polynomial approximation is then given by the following formula:

[0097]

[0098] Among them, parameters p1 to p4 can be calculated using the Monte Carlo method. Because The covariance matrix is ​​positive definite, therefore K′ can be decomposed into a lower triangular matrix and an upper triangular matrix using Cholesky decomposition, as shown in the following equation:

[0099]

[0100] Here, A is a lower triangular matrix. Therefore, the variable... Represented as A and independent standard normal variables The product of is shown in the following formula:

[0101]

[0102] Independent standard normal variables Related variables The relationship is then established as an equation:

[0103]

[0104] Where Φ(·) is the marginal cumulative density function of the standard Gaussian distribution. Then, the Monte Carlo method is used to analyze the extreme load scenario, where the total weight of the relevant heavy vehicle is simulated according to the following formula:

[0105]

[0106] (9) Monte Carlo simulation of extreme scenarios for specific effects

[0107] To obtain sufficient sample data to illustrate the possibility of extreme load responses within the structural design reference period, the Monte Carlo method was employed. Based on multiple simulations, extreme load simulation scenarios under various effects were generated. Representative simulation samples in this embodiment are as follows: Figure 2 As shown, the height of the bar chart represents the total weight of each heavy vehicle, and the horizontal and vertical axes represent the layout of the bridge structure and deck cells, respectively. Figure 2 It can be seen that this technical solution can effectively simulate the complex spatial distribution of heavy vehicles on the bridge surface.

[0108] To further verify the effectiveness of this technical solution, this embodiment evaluates the extreme value effect of components in a long-span cable-stayed bridge. The main contents include:

[0109] The cable-stayed bridge in this embodiment is a typical six-lane, double-tower, long-span cable-stayed bridge. The bridge's loaded length is 2088m, and the maximum span is 1088m. The WIM data is 423 days long and was collected from a six-lane highway. After cleaning, the raw data showed a daily traffic flow of approximately 16,500-50,000 vehicles, with a total of 11,055,095 vehicle flow data points collected (including small and heavy vehicles). The WIM data collected represents the free-flowing traffic conditions, which serves as the basis for extreme scenario simulations.

[0110] Reconstructing WIM data into a traffic flow sequence across a certain bridge length requires several assumptions: first, that the data monitoring point is located 100m from one side of the bridge; and second, that vehicles maintain a constant speed while crossing the bridge. Based on the arrival time and speed of a vehicle, and the location of the monitoring station (the location recording the vehicle's arrival time), the time history of that vehicle crossing the bridge can be obtained. Using the influence line method, the load effect on long-span bridges can be calculated based on the data collected by the WIM system. In this embodiment, the structural effect is calculated once per second for 423 consecutive days. The maximum response from the daily sample of 86,400 seconds is selected as a sample for this model. Figure 2 This shows an example of an extreme value scenario, with a total of 423 samples.

[0111] According to statistics, the average length of large vehicles is 15.13m, and the peak total wheelbase (distance from the first axle to the rear axle) of multi-axle vehicles is 17.5m. Considering the influence of the front and rear suspensions and the distance between vehicles during travel, the length of each cell should be slightly longer than the wheelbase. Therefore, for the bridge in this embodiment, each lane is divided into 105 cells, with a cell length of 20m. The influence lines of load effects are obtained using the finite element method. Figure 3 The side elevations of the bridge and the influence lines of the axial force of the longest main cable are depicted.

[0112] The Poisson parameters of the non-stationary distribution of the loaded vehicle position under extreme cable force scenarios are shown in Table 1. The Gaussian mixture distribution parameters of the vehicle weight under extreme scenarios are also shown in Table 2. The optimal number of components is determined to be 4 using the AIC method.

[0113] Table 1

[0114]

[0115] The outer lanes include lanes 1 and 6, and the inner lanes include lanes 2 to 5.

[0116] Table 2

[0117] Gaussian mixture distribution components Peak value of outer lane / ton Peak value of inner lane / tons 1 16.98 17.02 2 29.60 29.56 3 60.80 60.66 4 99.71 101.13

[0118] To obtain sufficient sample data to illustrate the extreme values ​​of the effect within a specific return period, the Monte Carlo method was employed. Based on 10... 5 This simulation generated simulated extreme value scenarios. This embodiment selects several sets of simulated extreme value scenarios as examples, such as... Figures 4a-4d As shown.

[0119] This embodiment applies the extreme scenario simulation method described above to various effects on the studied bridge and compares it with the response obtained from the existing standard D60. The responses calculated in the extreme scenarios are all extrapolated to the response results for a specific reference period. According to D60, the load response should have a 95% guarantee rate within a 100-year design reference period, meaning there is a 5% probability of exceeding the limit within 100 years. This is equivalent to the return period of 1 / (1-0.95^(1 / 100)) = 1950. The responses calculated from the simulated extreme scenario loading data and their extrapolated curves are plotted on Gumbel probability paper, as shown below. Figure 5 As shown, the simulated sample is fitted using the GEV-based extrapolation method. The fitted GEV distribution parameters have a certain confidence level. Strictly speaking, the extrapolated values ​​can be values ​​within the 95% confidence interval. Figure 5 The horizontal axis represents the ratio of the response to several effects to the D60 normative value. The formula for calculating the regression period value in 1950 is:

[0120] -log(-log(1-1 / (1950×365)))=13.48

[0121] The shape of the influence line for each effect is also plotted on Figure 5 As can be seen, under extreme scenarios, the response ratios for effects 3 and 5 are 0.628 and 0.575, respectively, far lower than the standard values. The response ratio for effect 4 is 2.363, far higher than the standard value. Different effects exhibit varying degrees of sensitivity to the standard load model, indicating that constructing load models for different effects based on actual vehicle scenarios with extreme response events is crucial.

[0122] In summary, to simulate the distribution of heavy vehicles on bridge decks, this technical solution sets the data foundation as follows: Based on WIM (Weigh In Motion) or other traffic flow data at a specific bridge site, traffic flow and vehicle information are collected and statistically analyzed; based on the influence lines and surfaces of the bridge structure's effects to be evaluated, information on the non-stationary distribution of vehicles on the bridge deck under extreme scenarios is obtained; the bridge deck vehicle scenario simulated in this technical solution is an extreme scenario, i.e., established for specific structural effects, and the simulation object is the distribution of heavy vehicles on the bridge deck that can produce extreme effects. The simulation basis is the traffic flow information at a specific bridge site and location; the simulated scenario is only responsible for its data foundation.

[0123] The basic assumptions of this technical solution are: 1) The simulation object is a heavy vehicle with a total weight of more than 10 tons; 2) Based on the statistical information of the length of heavy vehicles, the bridge deck is divided into cells of a specific length, and it is reasonably assumed that the center of gravity of the simulated heavy vehicles is located in the center of the cell.

[0124] When simulating the position of heavy vehicles, Poisson distribution parameters are extracted based on the statistical information of the number of heavy vehicles in each lane under extreme scenarios, and the position of heavy vehicles in each lane is simulated.

[0125] When simulating the weight of heavy vehicles, Gaussian mixture distribution parameters are extracted based on the statistical information of the total weight of heavy vehicles in each lane under extreme scenarios, and the total weight of heavy vehicles in each lane is simulated by combining Nataf transformation.

[0126] This technical solution enables a rapid and realistic simulation of extreme vehicle load scenarios on bridges, effectively simulating the complex spatial distribution of heavy vehicles on the bridge deck, thereby ensuring the efficiency and reliability of extreme value effect assessment of bridge components.

Claims

1. A short-time evaluation method for the extreme value effect of components based on vehicle distribution on bridge deck, characterized in that, Includes the following steps: S1. Collect and statistically analyze traffic flow and vehicle data at a specific bridge site, including but not limited to vehicle total weight, vehicle length, vehicle speed, and vehicle arrival time. S2. Construct a finite element model of the bridge structure and obtain the influence lines of the effects to be evaluated on the bridge structure; S3. Based on the total weight and length of the vehicles, filter out the length information of heavy vehicles and determine the length of the bridge deck cell based on this. The length of the bridge deck cell is greater than the average length of heavy vehicles, and the difference between the two is within the set threshold range. S4. Based on the influence line of the effect to be evaluated on the bridge structure, load the traffic flow data onto the influence line of the effect to be evaluated according to the set time step to obtain extreme value scenario samples. S5. Based on extreme value scenario samples, calculate the Poisson parameters of the distribution location of heavy vehicles in each lane and the Gaussian mixture distribution parameters of the total weight of heavy vehicles in each lane. Step S5 specifically includes the following steps: S51. Based on extreme value scenario samples, extract Poisson distribution parameters from the statistical information of the number of heavy vehicles in each lane; S52. Based on extreme value scenario samples, extract Gaussian mixture distribution parameters from the total weight statistics of heavy vehicles in each lane; The Poisson distribution parameters in step S51 are as follows: Where Pr(·) is the probability of the event occurring. Let be the average probability of a cell being occupied by heavy vehicles. For non-stationary events, Change along the lane; The Gaussian mixture distribution parameters in step S52 are as follows: 0≤ ≤1 in, For and The first and second mean and covariance matrices are... k A Gaussian distributed PDF, For the first k The weights of the Gaussian distribution components; S6. Based on the data obtained in step S5, simulate the position of heavy vehicles based on non-stationary Poisson distribution and simulate the total weight of heavy vehicles based on Nataf transformation to obtain the extreme scenario of load simulation under the current effect to be evaluated. Step S6 specifically includes the following steps: S61. A longitudinal non-stationary Poisson distribution method is adopted, and the Poisson distribution parameters are used to simulate the bridge deck position of heavy vehicles in extreme scenarios. S62. Based on the Gaussian mixture distribution of the total weight of heavy vehicles, the correlation of the total weight of heavy vehicles along the lane direction is simulated through Nataf transformation. S7. Repeat steps S2 to S6 to obtain extreme load simulation scenarios under different effects, so as to simulate the complex spatial distribution of heavy vehicles on the bridge surface and determine the current extreme effect evaluation results of the bridge components.

2. The method for short-time evaluation of component extreme effects based on bridge deck vehicle distribution according to claim 1, characterized in that, Step S1 specifically involves collecting WIM data for a specific bridge site and performing data cleaning to statistically analyze the corresponding traffic flow and vehicle data.

3. The method for short-time evaluation of component extreme value effects based on bridge deck vehicle distribution according to claim 1, characterized in that, In step S3, heavy vehicles specifically refer to vehicles with a total weight of 10 tons or more.

4. The method for short-time evaluation of component extreme value effects based on bridge deck vehicle distribution according to claim 1, characterized in that, Step S4 specifically includes the following steps: S41. Restore the WIM data to the traffic flow sequence with a set bridge length, then load it onto the influence line of the effect to be evaluated, and calculate multiple structural effect values ​​in sequence according to the set time step, which are the obtained samples. S42. Select the group of samples with the largest effect value from the multiple groups of samples each day, and use it as the extreme value scenario sample.

5. The method for short-time evaluation of component extreme value effects based on bridge deck vehicle distribution according to claim 1, characterized in that, The specific expression for the longitudinal non-stationary Poisson distribution in step S61 is as follows: in, The average level of occupied cells across the entire lane. This represents the changing pattern of the probability of lane occupancy.

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