Bridge bearing capacity assessment method based on multistage fuzzy comprehensive assessment method

Abstract

The invention discloses a large transport vehicle bridge safety assessment method based on fuzzy comprehensive evaluation, which comprises the following steps: constructing a bridge bearing capacity reduction coefficient model, integrating four factors of material performance degradation, structural damage, environmental corrosion and load combination, and determining index weights by adopting an analytic hierarchy process; and a multi-stage evaluation system is established in combination with a fuzzy mathematics theory. A prestressed concrete continuous beam bridge in Jiangsu section of Jinghai highway is taken as an object, finite element simulation is used for analyzing the load effect of large vehicles under independent and mixed passing conditions, and verification shows that when the large vehicles pass at the mixing rate of 20%, the most unfavorable section safety coefficient of the bridge reaches 1.35, and the standard requirements are met. According to the method, fuzzy mathematics and structural mechanics analysis are creatively fused, a standardized process of parameter collection, model calculation and safety judgment is established, the efficiency is improved by 40% or above compared with a traditional static load test, the method is applied to transportation examination and approval of five bridges in Jiangsu province, a scientific basis is provided for selection of large transportation channels, and the risk of bridge damage is effectively prevented.

Description

Technical Field

[0001] The present invention belongs to the field of bridge engineering and traffic infrastructure safety assessment, and particularly relates to a dynamic assessment method for bridge bearing capacity combining fuzzy comprehensive evaluation theory and Monte Carlo simulation, which is applicable to the safety assessment of large-piece transport vehicles passing through bridges. Technical Background

[0002] With the rapid development of the economic society, the infrastructure construction has been continuously promoted. As a key component in the traffic network, bridges are undertaking increasingly heavy traffic tasks. Especially with the increase in large-piece transport tasks, higher requirements are put forward for the safe passage of bridges. However, there are many limitations in the existing bridge bearing capacity assessment methods, and it is difficult to comprehensively consider various uncertain factors that bridges are subjected to during actual use. The discreteness of material properties, the randomness of loads, environmental factors, the uncertainty of vehicle loads, as well as bridge aging and damage, etc. These factors lead to the decline of bridge bearing capacity and increase potential safety hazards.

[0003] Traditional assessment methods such as code-based checking methods, on-site loading test methods, and numerical simulation methods, although to a certain extent can provide assessment results of bridge bearing capacity, often have difficulty in providing accurate and reliable assessments when facing complex factors and uncertainties. Code-based checking methods usually adopt deterministic calculation formulas and are difficult to consider the discreteness of material properties and the randomness of loads; on-site loading test methods can directly obtain the actual response data of bridges, but the test process is complex, costly, and interferes with the normal passage of bridges; numerical simulation methods rely on accurate bridge models and material parameters, but in practical applications, due to model simplification and parameter uncertainties, the simulation results may deviate from the actual situation.

[0004] Therefore, there is an urgent need for a bridge bearing capacity assessment method that can comprehensively consider various adverse factors and whose assessment results are more scientific and accurate. This new assessment method should be able to effectively handle the uncertainties and ambiguities in the assessment process, and by comprehensively considering various influencing factors, provide more scientific and accurate assessment results, providing reliable guarantees for the safe passage of bridges. The present invention aims to provide a bridge bearing capacity assessment method based on the multi-level fuzzy comprehensive evaluation method to solve the deficiencies in the prior art and meet the high requirements for bridge bearing capacity assessment in practical engineering. Summary of the Invention

[0005] The present invention aims to provide a bridge bearing capacity assessment method based on the multi-level fuzzy comprehensive evaluation method, which solves problems such as insufficient assessment accuracy and failure to consider parameter randomness in the prior art by constructing a multi-factor dynamic weight system and combining Monte Carlo stochastic simulation, and improves the safety assessment efficiency and reliability of large-piece transport vehicles passing through bridges.

[0006] To solve the above technical problems, the present invention models a bridge bearing capacity evaluation model, including the following steps:

[0007] S1. Bridge load distribution and coefficient evaluation

[0008] S2. Establish a multi-level fuzzy comprehensive evaluation system;

[0009] S3. Determine the calculation method of the comprehensive influence coefficient;

[0010] S4. Finite element rapid verification: Based on Midas Civil, construct a single beam model, apply the large piece transportation and mixed traffic flow loads, calculate the extreme values of the load effects (bending moment, shear force), and compare them with the structural resistance (bending moment, shear force);

[0011] S5. Compare the load effect with the bearing capacity to judge whether the large piece transportation vehicle can pass safely.

[0012] S1. Bridge load distribution and coefficient evaluation specifically includes the following steps:

[0013] S101. Determine the transverse distribution coefficient

[0014] According to the statistical analysis of the currently in-service highway bridges in Jiangsu Province, it is found that their main structural forms are hollow slab bridges and composite box girder bridges with multi-beam structures. To simplify the structural analysis process and improve work efficiency, when calculating the large piece transportation load, the load transverse distribution coefficient is usually used to simplify the complex spatial bridge model into a single beam model. As Figure 1 shown, when calculating the transverse distribution coefficient at the mid-span, the hinged plate (beam) method is usually used for the hollow slab bridge, while the rigid plate (beam) method is selected for the composite box girder.

[0015] S102. Determine the load partial coefficient

[0016] The "General Code for Design of Highway Bridges and Culverts" (JTG D60-2015) stipulates that when the highway bridge and culvert structure is designed according to the ultimate limit state of bearing capacity, the live load partial coefficient is 1.4, and the corresponding load combination formula is shown in Equation 1.

[0017] S = 1.2S G + 1.4m c (1 + μ)S Q (1)

[0018] The code stipulates that the bridge load partial coefficient is 1.1 when the motor vehicle is overloaded. In view of the weak correlation between the technical condition and the bearing capacity in the existing evaluation system, and the occasional nature of the large piece transportation vehicle being similar to the trailer load in the "89 Code", the present invention simplifies the evaluation process and uniformly sets the load partial coefficient of the large piece transportation vehicle to 1.1, which is consistent with the current code.

[0019] S2. Construct a multi-level fuzzy comprehensive evaluation model, which specifically includes the following steps:

[0020] S201. Construct the first-level indicators of the multi-level fuzzy comprehensive evaluation system:

[0021] {U1, U2, U3} = {bearing capacity inspection coefficient, bearing capacity deterioration coefficient, concrete section reduction coefficient}. Since the cross-section of steel bars usually does not experience significant loss within the design life, the cross-section reduction of steel bars is not considered.

[0022] Further refine the evaluation indicators and construct the second-level indicators of the multi-level fuzzy comprehensive evaluation system:

[0023] S1 = {crack width s1, ratio of crack length to cross-section size s2, surface damage rate s3, concrete strength coefficient s4};

[0024] S2 = {crack width s1, ratio of crack length to cross-section size s2, surface damage rate s3, concrete strength coefficient s4, concrete carbonation coefficient s5, cover thickness coefficient s6, chloride ion content s7, concrete resistivity s8, steel bar corrosion potential s9};

[0025] S3 = {surface damage rate s3, concrete carbonation coefficient s5, ratio of concrete spalling depth to minimum cross-section size s10}.

[0026] S202. Determine the weight set using the AHP method

[0027] Regarding the influence of different disease parameters of in-service bridges on their bearing capacity, using the analytic hierarchy process method and in accordance with the specifications, the corresponding weight values of each index element in the factor set are determined as: S1 = (0.2, 0.2, 0.2, 0.4); S2 = (0.08, 0.08, 0.16, 0.05, 0.20, 0.12, 0.15, 0.05, 0.11): S3 = (0.44, 0.17, 0.39).

[0028] S203. Determine the evaluation set and design the membership function: According to the specifications, divide the index grade interval (such as from grade I to grade V), and use the gradient membership function to quantify the fuzzy boundary.

[0029] The evaluation set V = {v1, v2,..., vm}. Following the bridge technical condition assessment standard in the specifications, the evaluation set V is divided into five grades, namely: V = {I, II, III, IV, V}.

[0030] Use the fuzzy evaluation method based on gradient membership. Determine the fuzzy subset of factor indicators by the membership function, \(r_i = \{r_{i1}, r_{i2}, \ldots, r_{im}\}\), where \(r_{ij}\) is the membership degree of factor \(s_i\) in the corresponding evaluation set \(V\) for level \(j\). Construct the fuzzy relation matrix \(R\) of the checking coefficients of each bearing capacity item of the object to be evaluated. \(R\) is an \(n\times m\) matrix, and each row of \(R\) is the fuzzy subset corresponding to the factor of each checking coefficient item. Based on the factor evaluation set \(V\), referring to the grading of the influence of each disease index on the bearing capacity in the "Technical Condition Assessment Standard for Highway Bridges" and the "Code for Inspection and Assessment of Highway Bridge Bearing Capacity" (JTG / T J21 - 2011), according to the fuzzy division principle, obtain the fuzzy boundaries of each factor indicator under different levels. As shown in Table 1.

[0031] Table 1 Grading of Evaluation Index

[0032]

[0033] According to the weight set \(W\) and the fuzzy relation matrix \(R\), calculate the fuzzy comprehensive evaluation set \(B\) of each checking coefficient by \(B = W\times R=(b_1, b_2, b_3, \ldots, b m )). To make full use of the information contained in the evaluation set \(B\) and highlight the dominant levels, use formula 2b i The quadratic power weighted average method is used to determine

[0034]

[0035] In the formula, \(D\): The comprehensive evaluation value of the object to be evaluated; \(v t : The evaluation level corresponding to the element \(b i \) in \(B\). Calculate the comprehensive evaluation value \(D\), and use linear interpolation to calculate the checking coefficients \(Z_1\), \(\xi e and \(\xi c values.

[0036] S3. Determine the calculation method of the comprehensive influence coefficient, which specifically includes the following steps:

[0037] The random uncertainty of the indicators determines the random uncertainty of the checking coefficients \(Z_1\), \(\xi e and \(\xi c . In the calculation of the bearing capacity reduction coefficient, based on the bridge inspection data, use Monte Carlo random simulation: conduct 10,000 random samplings of the checking coefficients to obtain the parameter statistical results of each bearing capacity checking coefficient.

[0038] For the parameters that cannot be directly obtained, according to the bridge technical condition score and the code values, the coefficient of variation is set to 0.05.

[0039] Use the formula \(\gamma_0S\leq\leq R(f d , \xi c as ,ξ s a s )Z1(1-ξ e ) When evaluating the bearing capacity of a bridge, four check coefficients and actual load calculations are combined. In order to quickly evaluate the passing capacity of large vehicles, the present invention combines the "Highway Reinforced Concrete and Prestressed Concrete Bridge and Culvert Design Specifications" (JTG3362-2018), combines the four coefficients, achieves the dimensionality reduction goal, and obtains the comprehensive influence coefficient X as a single reduction index. In this process, the product of certain coefficients is taken as the cross-sectional reduction coefficient on a safer basis. The comprehensive influence coefficient X of the bridge bearing capacity can be expressed as:

[0040] X=ξZ1(1-ξ e ) (3)

[0041] In order to facilitate calculation and result judgment, the relevant formulas are combined to obtain the load combination formula for large-scale transport vehicles traveling alone (Formula 4) and mixed traveling (Formula 5):

[0042] γ0S / X=γ0(1.2×S G +1.1S T ) / X≤R(f d , a dc , a ds ) (4)

[0043] γ0S / X=γ0(1.2×S G +1.1S T +1.4m c (1+μ)S Q ) / X≤R(f d , a dc , a ds ) (5)

[0044] Where: S is the effect function of the action combination; SG is the standard value of the permanent action; μ is the impact coefficient; SQ is the standard value of the vehicle load; ST is the verification load of the large-scale transport vehicle; X is the comprehensive influence coefficient of the bearing capacity calculated by the fuzzy comprehensive evaluation method based on the random simulation theory.

[0045] S4. Finite element rapid verification: A single beam model was constructed based on Midas Civil, and the loads of large-scale transportation and mixed traffic flow were loaded. The extreme values of load effects (bending moment, shear force) were calculated and compared with the structural resistance.

[0046] S5. Compare the load effect with the carrying capacity to determine whether the large-scale transport vehicle can pass safely. The specific steps include:

[0047] Compare the calculated extreme values of the load effects of large-piece transportation vehicles with the structural resistance to determine whether the requirements for checking the passage of large-piece vehicles are met under the ultimate bearing state. The following criteria are formulated for the assessment and determination:

[0048] K = H_large - H_resistance (6)

[0049] In the formula: H_large——Load effect of large-piece transportation vehicle;

[0050] H_resistance——Calculation result of resistance.

[0051] The discrimination criterion is as follows:

[0052] When K ≤ 0, the bridge meets the requirement that it has not reached the ultimate state under the action of large-piece load and is allowed to pass;

[0053] When K > 0, measures need to be taken to further determine the bearing capacity of the bridge. If the K value in the analysis result of the load test is still negative, large-piece vehicles are not released before the bridge is jacked up. Description of the Drawings

[0054] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments. The drawings are only for reference and illustration, and are not intended to limit the present invention.

[0055] Figure 1 It is a longitudinal bridge direction value-taking diagram of the lateral distribution coefficient in the bridge load distribution and coefficient evaluation in the first stage of the present invention;

[0056] Figure 2 It is a probability distribution fitting curve diagram of the comprehensive influence coefficient in the calculation method for determining the comprehensive influence coefficient in the third stage of the present invention;

[0057] Figure 3 It is a schematic diagram of the finite element model in the finite element rapid verification in the fourth stage of the present invention;

[0058] Figure 4 It is a flowchart for the bearing capacity evaluation of the present invention. Specific Embodiments

[0059] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments. The drawings are only for reference and illustration, and are not intended to limit the present invention.

[0060] The present invention aims to provide a bridge bearing capacity evaluation method based on the multi-level fuzzy comprehensive evaluation method, which solves the problems of insufficient evaluation accuracy and unconsidered parameter randomness in the prior art by constructing a multi-factor dynamic weight system and combining the Monte Carlo stochastic simulation, and improves the safety evaluation efficiency and reliability of large-piece transportation vehicles passing through bridges.

[0061] In this study, a bridge located in a certain section of the Beijing-Shanghai Expressway in Jiangsu Province that has undergone destructive load tests was selected as the example calculation object. The bridge was built in 1999, and the center pile number is K784+092. The superstructure is a 6×13m prestressed concrete hollow slab, the bridge deck is a 10cm cast-in-place concrete layer and a 9cm asphalt concrete pavement. The hollow slab is a simply supported structure with continuous bridge decks. When checking, the total width of the bridge is 28m, and the transverse direction of a single-span bridge is composed of 13 slab beams; the substructure is a double-column pier with a bored pile foundation.

[0062] To solve the above technical problems, the present invention models the bridge bearing capacity evaluation model, including the following steps:

[0063] S1. Bridge load distribution and coefficient evaluation

[0064] S2. Establish a multi-level fuzzy comprehensive evaluation system;

[0065] S3. Determine the calculation method of the comprehensive influence coefficient;

[0066] S4. Finite element rapid verification;

[0067] S5. Compare the load effect with the bearing capacity to judge whether the large-piece transportation vehicle can pass safely.

[0068] S1. Bridge load distribution and coefficient evaluation, specifically including the following steps:

[0069] S101. Determine the transverse distribution coefficient

[0070] According to the statistical analysis of the currently in-service highway bridges in Jiangsu Province, it is found that their main structural forms are hollow slab bridges and composite box girder bridges with multi-beam structures. In order to simplify the structural analysis process and improve work efficiency, when calculating the large-piece transportation load, the load transverse distribution coefficient is usually used to simplify the complex spatial bridge model into a single-beam model. As Figure 1 shown, when calculating the transverse distribution coefficient at the mid-span, the articulated plate (beam) method is usually used for the analysis of hollow slab bridges, while the rigid plate (beam) method is selected for composite box girders. For the hollow slab beam bridge selected in this scheme, the specific parameters are shown in Table 1.

[0071] Table 2 Transverse distribution coefficient calculation results

[0072]

[0073] S102. Determine the load partial coefficient

[0074] The load partial coefficient of the large-piece transportation vehicle is uniformly set to 1.1, which is consistent with the current specification.

[0075] S2. Build a multi-level fuzzy comprehensive evaluation model, specifically including the following steps:

[0076] S201. Construct the first-level indicators of the multi-level fuzzy comprehensive evaluation system:

[0077] {U1, U2, U3} = {bearing capacity inspection coefficient, bearing capacity deterioration coefficient, concrete section reduction coefficient}. Since the cross-section of steel bars usually does not experience significant loss within the design life, the cross-section reduction of steel bars is not considered.

[0078] Further refine the evaluation indicators and construct the second-level indicators of the multi-level fuzzy comprehensive evaluation system:

[0079] S1 = {crack width s1, ratio of crack length to cross-section size s2, surface damage rate s3, concrete strength coefficient s4};

[0080] S2 = {crack width s1, ratio of crack length to cross-section size s2, surface damage rate s3, concrete strength coefficient s4, concrete carbonation coefficient s5, cover thickness coefficient s6, chloride ion content s7, concrete resistivity s8, steel bar corrosion potential s9};

[0081] S3 = {surface damage rate s3, concrete carbonation coefficient s5, ratio of concrete spalling depth to minimum cross-section size s10}.

[0082] S202. Use the AHP method to determine the weight set

[0083] Regarding the influence of different disease parameters of in-service bridges on their bearing capacity, using the analytic hierarchy process method, according to the specifications, the weight values corresponding to each index element in the factor set are determined as: S1 = (0.2, 0.2, 0.2, 0.4); S2 = (0.08, 0.08, 0.16, 0.05, 0.20, 0.12, 0.15, 0.05, 0.11); S3 = (0.44, 0.17, 0.39).

[0084] S203. Determine the evaluation set and design the membership function: According to the specifications, divide the index grade interval (such as from grade I to grade V), and use the gradient membership function to quantify the fuzzy boundary.

[0085] The evaluation set V = {v1, v2,..., vm}. Following the bridge technical condition assessment standard in the specifications, the evaluation set V is divided into five grades, namely: V = {I, II, III, IV, V}.

[0086] Use the fuzzy evaluation method based on gradient membership. Determine the fuzzy subset of the factor index from the membership function, ri = {ri1, ri2,..., rim}, where rij is the membership degree of factor si in the corresponding evaluation set V to grade j. According to the weight set W and the fuzzy relation matrix R, B = W × R = (b1, b2, b3,..., b m)Calculate the fuzzy comprehensive evaluation set B of each partial coefficient. To make full use of the information contained in the evaluation set B and highlight the dominant levels, the quadratic power weighted average method is used with the formula to determine

[0087]

[0088] where D is the comprehensive evaluation value of the object to be evaluated; v i : the evaluation level corresponding to the element b i in B. Calculate the comprehensive evaluation value D, and the partial check coefficients Z1, ξ e and ξ c values can be calculated using linear interpolation.

[0089] According to the example bridge detection data, the calculation of the parameter distribution is shown in Table 3.

[0090] Table 3 Statistical parameters of each factor

[0091]

[0092] S3. Determine the calculation method of the comprehensive influence coefficient, which specifically includes the following steps:

[0093] The random uncertainty of the index determines the random uncertainty of the partial check coefficients Z1, ζ e and ζ c . In the calculation of the load-carrying capacity reduction coefficient, based on the bridge detection data, Monte Carlo random simulation is used: 10,000 random samplings are carried out on the partial coefficients, and the parameter statistical results of each load-carrying capacity check coefficient are shown in Table 4.

[0094] Table 4 Statistical parameters of the load-carrying capacity check coefficient

[0095]

[0096] According to the parameter calculation results of each check coefficient, according to formula 8

[0097]

[0098] After 10,000 random simulation calculations. Analyzing the calculation result X, it is found that the probability statistical curve of the comprehensive influence coefficient is high in the middle and decreases synchronously on both sides, which is highly similar to the functional form of the theoretical lognormal distribution. Fitting the calculation result, it is considered that it approximately follows a lognormal distribution with μ = 0.0937 and δ = 0.0107 (as Figure 2 ), and the corresponding probability distribution function is:

[0099]

[0100] When different guarantee rates are taken, the comparison between the corresponding bearing capacity values and the measured resistance coefficients is shown in Table 5.

[0101] Table 5 Reduced resistance coefficients under different guarantee rates

[0102]

[0103] The results of random sampling show that the resistance coefficients of the bridge after reduction are all greater than 1, indicating that the current state of the bridge has additional bearing capacity compared with the design value. Compared with the resistance coefficients measured by actual load tests, there is a safety margin of more than 10%. The measured bearing capacity reaches more than 1.2 times the design value, meeting the safety factor requirements for material strength. According to the specifications, the safety factors for prestressed steel strands, ordinary steel bars, concrete, and steel are 1.2, 1.1, 1.45, and 1.1 respectively. Therefore, the measured resistance coefficient should be between 1.1 and 1.45. When the design safety level is Class I, the structural importance coefficient is taken as 1.1. Considering comprehensively, the reduced resistance coefficient of 1.0791 under a 95% guarantee rate is selected as the key parameter for load effect checking.

[0104] S4. Quick verification by finite element, which specifically includes the following steps:

[0105] Use Midas Civil to quickly build a finite element model. Considering that large-piece transport vehicles drive in the middle of the bridge deck according to regulations and the load mainly acts on the beam body at the middle of the transverse bridge direction, the transverse distribution coefficient is introduced, and the calculation object of the load effect is focused on a single beam through the transverse distribution reduction of the load. For the convenience of quickly building the model to achieve quick evaluation, in this paper, the 7# slab beam is selected as the object for load effect analysis to build a finite element model, as Figure 3 shown.

[0106] The basic information of the finite element model is as follows: Number of elements: 13; Component type: partially prestressed Class A; Self-weight: The dead load generated by the automatic loading of the structural material in the model, and the concrete unit weight is calculated according to 26 kN / m3; Secondary dead load: 5.2 kN / m; Vehicle load: Lane loading defined in the model based on the actual vehicle data obtained; Influence of concrete shrinkage and creep: Relative humidity 80%, and the calculation parameters are taken according to the "Code for Design of Highway Reinforced Concrete and Prestressed Concrete Bridges and Culverts" (JTG 3362-2018), and the calculation time is 3650 days; Temperature load: Overall temperature rise of 20 °C, overall temperature drop of 20 °C; Temperature gradient: Adopted according to Article 4.3.12 of JTG D60-2015; Construction stage: Divided into 5 construction stages and 1 operation stage according to the construction sequence.

[0107] By applying the verification load for mixed traffic of heavy-haul vehicles on the finite element model, the maximum flexural bearing capacity of the mid-span section and the shear bearing capacity results of the elements at both side supports under the ultimate limit state of bearing capacity are obtained, as shown in Tables 6 and 7. The columns in the tables are, in sequence, the finite element model element numbers, the cross-sections at both ends of the element, the maximum and minimum values of the load effects at the cross-section, the verification load effect, and the structural resistance.

[0108] Table 6 Results of Flexural Bearing Capacity Verification

[0109]

[0110] Table 7 Results of Shear Bearing Capacity Verification

[0111]

[0112] S5. Compare the load effect with the bearing capacity to determine whether heavy-haul vehicles can pass safely. The specific steps are as follows:

[0113] The analysis in Table 6 shows that under the mixed traffic scenario, the flexural bearing capacity of the mid-span section of the finite element model is less than the structural resistance, meeting the passing requirements. Table 7 shows that under the same passing scenario, the shear bearing capacity of the control section is also less than the structural resistance, satisfying the safe passing conditions. Based on the above calculation results of the ultimate limit state of bearing capacity, it can be determined that when heavy-haul vehicles and conventional vehicles travel mixed, the maximum value of the load effect is still less than the structural resistance, and the bridge meets the requirements for safe passage.

Claims

1. A bridge bearing capacity evaluation method based on the multi-level fuzzy comprehensive evaluation method, characterized in that, It includes the following steps: S1. Bridge load distribution and coefficient evaluation: Simplify the spatial bridge model into a single-beam model through the lateral distribution coefficient, and determine that the partial coefficient of the heavy-haul vehicle load is 1.

1. S2. Construct a multi-level fuzzy comprehensive evaluation system: Establish a multi-level evaluation model including primary indicators (bearing capacity inspection coefficient, deterioration coefficient, concrete section reduction coefficient) and secondary indicators, use the analytic hierarchy process (AHP) to determine the weight set, and construct a fuzzy relation matrix based on the gradient membership function. S3. Determine the calculation method of the comprehensive influence coefficient: Sample the partial verification coefficients through Monte Carlo random simulation, and calculate the comprehensive influence coefficient X = ξZ1(1 - ξ e ); S4. Finite element rapid verification: Based on Midas Civil, construct a single-beam model, apply the heavy-haul and mixed traffic flow loads, calculate the extreme values of the load effects and compare them with the structural resistance. S5. Safe passage judgment: By the formula K = H 大件 -H 抗力 Judge whether the bridge meets the passage requirements for large vehicles. If K ≤ 0, passage is allowed.

2. The method according to claim 1, wherein In the step S1: The lateral distribution coefficient is calculated by the hinged plate method (hollow slab bridge) or the rigid plate method (composite box girder bridge). The partial coefficient of the load is set to 1.1 according to the General Code for Design of Highway Bridges and Culverts (JTG D60-2015).

3. The method according to claim 1, characterized in that In the step S2: The secondary indicators include: S1 = {crack width, ratio of crack length to cross-sectional dimension, surface damage rate, concrete strength coefficient}; S2 = {crack width, ratio of crack length to cross-sectional dimension, surface damage rate, concrete strength coefficient, concrete carbonation coefficient, cover thickness coefficient, chloride content, concrete resistivity, steel corrosion potential}; S3 = {surface damage rate, concrete carbonation coefficient, ratio of concrete spalling depth to minimum cross-sectional dimension}; The weight set is set as: S1＝(0.2,0.2,0.2,0.4) S2＝(0.08,0.08,0.16,0.05,0.20,0.12,0.15,0.05,0.11) S3＝(0.44,0.17,0.39)。 4. The method according to claim 1, characterized in that In the step S3: The number of sampling times for Monte Carlo random simulation is 10,000 times. The comprehensive influence coefficient X follows a lognormal distribution, and its probability density function is:

5. The method according to claim 1, characterized in that In the step S4: When constructing the finite element model, select the beam body at the center of the transverse bridge as the analysis object, and the loading methods include: The load of the heavy-haul vehicle driving in the center; The mixed traffic flow load (including the live load of conventional vehicles); The temperature load (overall temperature rise and fall of ±20°C and gradient temperature); The concrete shrinkage and creep effect.

6. The method according to claim 1, characterized in that In the step S5: When K > 0, it is necessary to further verify through a load test. If the load test result still does not satisfy K ≤ 0, passage is prohibited until the bridge is strengthened.

7. The method according to any one of claims 1-6, characterized in that The method is applicable to the safety assessment of the passage of heavy-haul vehicles on multi-beam structure hollow slab bridges and composite box girder bridges.

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