A Reliability Assessment Method and System for Highway Bridge Components

Abstract

The present invention belongs to the technical field of structural engineering, and specifically discloses a reliability assessment method and system for highway bridge components, including the following steps: S1. Obtain bridge data for assessing the technical state of components; S2. Use the membership function in fuzzy mathematics to represent the uncertainty of the flexural bearing capacity calculation parameters in the technical state of components; S3. Use the residence time of different component technical states to solve the semi-Markov probability transition matrix through the probability density function and survival function of the Weibull distribution, and obtain the probability distribution of the component technical state varying with time. By adopting the above-mentioned reliability assessment method and system for highway bridge components, the present invention combines the membership function with real bridge data to obtain the flexural bearing capacity calculation parameter values, and then obtains the mean value and standard deviation of the flexural bearing capacity, solving the problems of predicting the time-varying reliability of structures and obtaining the mean value and standard deviation of the time-varying flexural bearing capacity in the highway network.

Description

Technical Field

[0001] The invention relates to the technical field of structural engineering, and in particular to a reliability assessment method and system for highway bridge components. Background Art

[0002] In the field of structural engineering, the performance degradation of highway bridge components will cause the reliability of bridges to decrease over time. Reliability index is an important parameter for evaluating bridge performance, and the maintenance time point can be determined based on the reliability index. In bridge engineering, it is very important to obtain reliability index parameters. The traditional bridge time-varying reliability analysis method obtains the time-varying reliability of the structure, which requires obtaining two parameters, namely the mean and variance of the bearing capacity, and the mean and variance of the load effect value.

[0003] In the prior art, the statistical values ​​of structural material parameters are substituted into the structural bearing capacity calculation formula. The statistical values ​​of material parameters are mostly obtained by laboratory values, and the mean and variance of resistance have always been a difficult problem. The actual operating status of the bridge structure is not considered, and the evaluation is not based on the actual bridge material parameters. The bridge status data cannot evaluate the structural reliability index, which leads to a certain deviation between the results of the time-varying reliability analysis method and the actual situation. Summary of the invention

[0004] The purpose of the present invention is to provide a reliability assessment method and system for highway bridge components. By combining the membership function with real bridge data, the bending bearing capacity calculation parameter values ​​are obtained, and then the mean and standard deviation of the bending bearing capacity are obtained, thereby solving the problems of predicting the time-varying reliability of structures in highway networks and obtaining the mean and standard deviation of the time-varying bending bearing capacity of structures.

[0005] To achieve the above object, the present invention provides a highway bridge component reliability assessment method, comprising the following steps:

[0006] S1. Obtain bridge data for assessing the technical status of components;

[0007] S2. Use the membership function in fuzzy mathematics to express the uncertainty of the calculation parameters of the bending bearing capacity in the technical status of the component;

[0008] S3. Using the residence time of different component technical states, the semi-Markov probability transfer matrix is ​​solved through the probability density function and survival function of the Weibull distribution to obtain the probability distribution of the component technical state that changes with time;

[0009] S4. Based on the calculation formula of bending capacity, input the design parameter value of the component section, use the membership function combined with the semi-Markov process to obtain the result value of the variable parameter of the bending capacity calculation parameter changing with time, then solve the bending capacity of the component multiple times through Monte Carlo simulation, and perform statistics on the bending capacity of the component to obtain its mean and standard deviation;

[0010] S5. Based on the limit state equation, input the mean and standard deviation of the load effect value, input the mean and standard deviation of the bending bearing capacity obtained in S4, and use Monte Carlo simulation to obtain the time-varying reliability index of the component.

[0011] Preferably, the technical status of the component includes i Status level, i =1, 2, 3...n, where n is an integer, and the first status level indicates that the bridge is in a brand new state with complete functions.

[0012] Preferably, according to i The residence time of the status level is divided into degradation rate levels, and the degradation rate levels include very slow, slow, medium, fast and very fast.

[0013] Preferably, in S2, the membership function in fuzzy mathematics is used to represent the uncertainty of the bending bearing capacity calculation parameters in the technical state of the component, specifically:

[0014] Calculation formula for bending bearing capacity:

[0015] ;

[0016] in, is the compressive strength of concrete, is the tensile strength of the steel bar, is the cross-sectional area of ​​the tensile reinforcement, is the width of the component, is the effective height of the component;

[0017] The bending bearing capacity calculation parameters include , and , , and are the variable parameters of the components that change with time. and is a non-variable parameter of the component;

[0018] According to the material damage interval of the bending bearing capacity calculation parameters, the i The fuzzy degree interval of the state level change is calculated, and then the time-varying value of the component's bending bearing capacity is obtained.

[0019] Preferably, S3 is as follows:

[0020] The properties of a Markov chain are expressed as:

[0021] ;

[0022] Among them, P is the conditional probability, for t The time corresponding to i Status level;

[0023] In a Markov chain, the single-step transition probability of its transition process refers to the transition from the first i Status level transferred to j The probability of the state level is expressed as:

[0024] ,where ;

[0025] Among them, M is the first i Status level / j the number of status levels;

[0026] The difference between Markov process and semi-Markov process lies in the time dependence of the state transition mechanism. The semi-Markov probability transition matrix P' is:

[0027] and ;

[0028] but t The moment i The state level probability is:

[0029] ;

[0030] in, is at the time t The semi-Markov probability transition matrix for the year, for t -1 year probability distribution for each status level;

[0031] No. i Status level over time t The probability density function of and the survival function Respectively expressed as:

[0032]

[0033] ;

[0034] in, and In thei The scale parameter and shape parameter of the Weibull distribution at the state level, for The original function of

[0035] Survival function via Weibull distribution and the probability density function Calculated, each time node t、 The state transition probability between two adjacent state levels , expressed as:

[0036] ;

[0037] in, is the cumulative stay time The probability density function of and The residence time and The survival function of The value is 1 year;

[0038] At the same time, i Status level over time t Weibull distribution survival function Close to the level of the component, over time t Keep the same i Probability of status level , the difference between the two is expressed as:

[0039] ;

[0040] By solving the minimization problem, we can calculate any i Scale parameter in Weibull distribution with state rank and shape parameters , expressed as:

[0041] ;

[0042] in, is Always keep the same i The probability of the state level, For the i The duration of the status level.

[0043] Preferably, in S4, Added into the calculation formula of the bending bearing capacity of the component, then:

[0044] ;

[0045] The loss of concrete strength, steel bar tensile strength, and steel bar cross section are expressed as:

[0046]

[0047]

[0048] ;

[0049] in, for t Moment i State level probability, For the i State grade steel bar section loss rate, For the i State grade steel bar tensile strength loss rate, For the i Strength loss value of concrete in different condition grades.

[0050] Preferably, in S5, the limit state equation is:

[0051] ;

[0052] in, is the structural reliability function, is the bending bearing capacity, is the load effect value.

[0053] Preferably, time-varying reliability index It is expressed as:

[0054] ;

[0055] in, for t Time component reliability index, , They are t Mean and standard deviation of the time-varying bending capacity of components at each moment; , are the mean and standard deviation of the time-varying load effect values ​​of the components, respectively.

[0056] To achieve the above object, the present invention also provides a highway bridge component reliability assessment system, comprising:

[0057] A bridge status data storage module is used to store or update the actually collected bridge status data;

[0058] Status data processing module, used for i Classification of residence time under state level and probability distribution under degradation rate, fuzzy state processing of bending capacity calculation parameters;

[0059] The calculation and analysis module is used to use load information and component section design parameter values ​​as input data, obtain time-varying reliability indicators as output data, and simulate the time-varying reliability of components.

[0060] Therefore, the present invention adopts the above-mentioned highway bridge component reliability assessment method and system, and the beneficial effects are as follows:

[0061] (1) The present invention obtains the material parameter values ​​of the flexural bearing capacity by combining the membership function with the real bridge data, and then obtains the mean and standard deviation of the time-varying flexural bearing capacity, thereby solving the problem of predicting the time-varying reliability of the structure in the highway network and obtaining the mean and standard deviation of the time-varying flexural bearing capacity of the structure.

[0062] (2) The present invention is based on the components i The residence time of the status level is used to determine the degradation rate level of the component. By inputting the initial calculation parameters of the component's flexural bearing capacity, the mean and standard deviation of the component's time-varying flexural bearing capacity can be obtained. The obtained flexural bearing capacity value is used to solve the component's time-varying reliability index to ensure that the reliability analysis conforms to the actual degradation process. At the same time, relevant load information is input to judge the bridge performance.

[0063] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a flow chart of an embodiment of a reliability assessment method for a highway bridge component of the present invention;

[0065] Figure 2 This is a first embodiment of a highway bridge component reliability assessment method of the present invention. i Status level ( i =1, 2, 3, 4, 5), where (a) is the concrete strength loss rate, (b) is the steel bar tensile strength loss rate, and (c) is the steel bar section loss rate;

[0066] Figure 3 This is a first embodiment of a highway bridge component reliability assessment method of the present invention. i Status level ( i =1, 2, 3, 4, 5) cumulative residence time probability density function;

[0067] Figure 4 This is a first embodiment of a highway bridge component reliability assessment method of the present invention. i Status level ( i =1, 2, 3, 4) cumulative survival function value;

[0068] Figure 5It is a time-varying value diagram of the bending bearing capacity of a component in an embodiment of a reliability assessment method for a highway bridge component of the present invention;

[0069] Figure 6 It is a component reliability index value of an embodiment of a highway bridge component reliability assessment method of the present invention. DETAILED DESCRIPTION

[0070] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0071] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0072] Embodiment 1

[0073] like Figure 1 As shown, a reliability assessment method for highway bridge components comprises the following steps:

[0074] S1. Obtain bridge data for evaluating the technical status of components. The technical status of components includes i Status level, according to i The residence time of the state level is divided into degradation rate levels, where i =1, 2, 3...n, where n is an integer.

[0075] As one embodiment of the present invention, the degradation rate levels include very slow, slow, medium, fast, and very fast.

[0076] Bridge data includes the routine inspection and damage inspection of bridges by bridge management units, detailed disease detection of bridge components, and the evaluation of bridges based on appearance conditions. i Status level.

[0077] Each state level has a certain stay time. According to the collected bridge data, the i The residence time range of the state level is divided into several different degradation rate levels according to the residence time range, so that the first i The dwell time value of the status level.

[0078] S2. Use the membership function in fuzzy mathematics to represent the uncertainty of the calculation parameters of the bending capacity in the technical status of the component.

[0079] According to i The status level cannot obtain an accurate bridge bearing capacity, but the technical status information of the component contains the content of the calculation parameters related to the bending bearing capacity. The membership function in fuzzy mathematics is used to simulate the interval range value of the bending bearing capacity calculation parameters.

[0080] Specifically, the Gaussian membership function in fuzzy mathematics is used to simulate the variables of the bending bearing capacity calculation parameters.

[0081] Calculation formula for bending bearing capacity:

[0082] ;

[0083] in, is the compressive strength of concrete, is the tensile strength of the steel bar, is the cross-sectional area of ​​the tensile reinforcement, is the width of the component, is the effective height of the component;

[0084] The calculation parameters of the bending capacity include , and , , and is the variable parameter of the component that changes with time. and It is a non-variable parameter of the component, that is, it does not change with time.

[0085] The Gaussian membership function is:

[0086] .

[0087] in, , , D L The lower limit of the fuzzy state interval, D U is the upper limit of the fuzzy state interval.

[0088] Expressed as the confidence level of the loss range, The larger the value, the higher the reliability.

[0089] , and These three parameters are related to the component technical status test data. It is difficult for bridge inspectors to directly evaluate the performance or bearing capacity of the bridge using bridge data. i The condition level gives a fuzzy range of bridge performance. Using the fuzzy range of bridge performance in the reliability analysis of bridge components is of great significance for bridge performance evaluation and determination of maintenance time nodes.

[0090] According to the material damage range given by the bridge information, the time-varying value of the material performance is obtained, and then the time-varying value of the structural resistance performance is solved, which is later used in the reliability analysis to evaluate the reliability of the bridge.

[0091] According to the steel corrosion assessment standard and concrete strength assessment standard, the actual bridge performance range of each scale is given.

[0092] The steel bar corrosion assessment standard is shown in Table 1, and the concrete strength assessment standard is shown in Table 2. i The fuzzy degree interval of the state level change is shown in Table 3, and the distribution is as follows Figure 2 shown.

[0093] Table 1 Assessment criteria for steel bar corrosion

[0094]

[0095] Table 2 Concrete strength assessment standards

[0096]

[0097] Table 3 Fuzzy degree range

[0098]

[0099] S3. The bending bearing capacity calculation parameters in the technical status of the component are converted into the probability distribution of the status level over time through a semi-Markov process. Based on the residence time of the component status level, the probability density function and survival function of the Weibull distribution are used to solve the semi-Markov probability transfer matrix.

[0100] The properties of a Markov chain are expressed as:

[0101] ;

[0102] Among them, P is the conditional probability, for t The time corresponding to i Status level.

[0103] In a Markov chain, the single-step transition probability function (also called the transition probability matrix) describes the probability of transitioning from the current state to the next state, expressed as:

[0104] ,where ;

[0105] Where M is the number of i-th state level / j-th state level in the degradation process.

[0106] The difference between the Markov process and the semi-Markov process lies in the time dependence of the state transition mechanism. The probability transition matrix P' of the semi-Markov process is:

[0107] and ;

[0108] The sum of the probabilities of each row of the matrix is ​​1, and the probability transition matrix of the semi-Markov process 'It changes over time.

[0109] During the bridge degradation process assessment, components cannot be completely degraded from the first i Status level jumps to i +2 status level. Also, without repair, the component cannot be i Status level becomes i- 1 status level.

[0110] In a specific embodiment, the semi-Markov probability transfer matrix is:

[0111] .

[0112] t The first time solution i The state level probability distribution is:

[0113] ;

[0114] in, is at the time t The semi-Markov probability transition matrix of the year, a( t-1 )for t -1 year probability distribution for each status level.

[0115] The Weibull distribution is suitable for describing all the i The probability of the state level residence time can more accurately predict the degradation process of the component. i Status level over time t The probability density function of and the survival function Respectively expressed as:

[0116]

[0117] ;

[0118] in, and In the i The scale parameter and shape parameter of the Weibull distribution at the state level, for The original function of .

[0119] Survival function via Weibull distribution and the probability density function Calculate each time node t、 The state transition probability between two adjacent state levels , expressed as:

[0120] ;

[0121] in, is the cumulative stay time The probability density function of and The residence time and The survival function of The value is 1 year.

[0122] At the same time, i Status level over time t Weibull distribution survival function Close to the level of the component, over time t Keep the same i Probability of status level , the difference between the two is expressed as:

[0123] ;

[0124] By solving the minimization problem, we can calculate any i Scale parameter in Weibull distribution with state rank and shape parameters , expressed as:

[0125] ;

[0126] in, is Always keep the same i The probability of the state level, For the i The duration of stay at the status level.

[0127] No. t The state level probability distribution of the year can be obtained from the probability transfer matrix of the semi-Markov process and the state probability distribution, which is determined by the following formula:

[0128]

[0129] Where: It's in timet The semi-Markov process probability transition matrix for years, for t-1 Probability distribution of status levels in the year.

[0130] As one of the embodiments of the present invention, ,in, Expressed as t Time node, probability of the first state level. Similar to the above, For t The probability of the second state level at the time node. It means there are five states, that is, five levels.

[0131] S4. Based on the calculation formula of bending capacity, input the initial design parameter value of the component section, and determine the variable parameters and non-variable parameters. The membership function is combined with the semi-Markov probability transfer matrix to obtain the result value of the variable parameters of the bending capacity calculation parameters changing with time, and the mean and standard deviation of the variable parameters calculated at each time node are obtained. Then, the bending capacity of the component is solved multiple times through Monte Carlo simulation, and the bending capacity of the component is statistically analyzed to obtain its mean and standard deviation.

[0132] The three material properties of concrete compressive strength, steel bar tensile strength and tensile steel bar cross-sectional area are time-varying values, so the bending bearing capacity calculation formula is:

[0133] ;

[0134] Will Added into the calculation formula of the bending bearing capacity of the component, the variable parameters are expressed as:

[0135]

[0136]

[0137] ;

[0138] in, for t Moment i State level probability, For the i State grade steel bar section loss rate, For the i State grade steel bar tensile strength loss rate, For the i Strength loss value of concrete in different condition grades.

[0139] S5. Based on the limit state equation of structural reliability, the mean and standard deviation of the load effect value and the mean and standard deviation of the bending bearing capacity obtained in S4 are input, and the time-varying reliability index of the component is obtained by Monte Carlo simulation.

[0140] The limit state equation is Z=RS ,in, S is the load effect value, R is the bending capacity. The load effect value includes the dead load and vehicle load , which is obtained by designing drawings and establishing finite element models.

[0141] Time-varying reliability index It is expressed as:

[0142] ;

[0143] in, for t Time structure reliability index, , They are t Mean and standard deviation of the time-varying bending capacity of the structure at each moment; , are the mean and standard deviation of the time-varying load effect on the structure, respectively.

[0144] In this embodiment, the bridge is the main beam, and the technical status of the bridge components is divided into 5 levels. The first level of status indicates that the bridge is in a brand new state and has good functions, and the fifth level of status indicates that the bridge components are seriously damaged and the bridge cannot be used normally. The definition and inspection specifications of the bridge status refer to (JTG / T H21-2011) "Highway Bridge Technical Condition Assessment Standard".

[0145] In actual scenarios, the speed of degradation is divided according to the total stay interval of the bridge condition interval, and the stay time statistics of different states are shown in Table 4.

[0146] Table 4 Statistics of residence time in different states

[0147]

[0148] Taking the slow degradation rate as an example, according to all i The probability density function and survival function of the residence time of the state level are calculated. From Table 4, it can be seen that the residence time from the first state level to the second state level is 20 years under the slow degradation rate, so , . After getting i After the Weibull distribution under the state level, it can be expressed as:

[0149]

[0150] Calculate the scale parameter at each residence time and shape parameters , as shown in Table 5. i The probability density function of the cumulative stay time under the state level (i=1, 2, 3, 4, 5) is as follows Figure 3 As shown. i Status level ( i =1, 2, 3, 4) The cumulative survival function value is as follows Figure 4 shown.

[0151] Table 5 Scaling parameters and shape parameters

[0152]

[0153] The semi-Markov probability transfer matrix is ​​solved through the survival function and probability density function values.

[0154] Set the initial probability distribution of each state level of the component , at this time, the probability that the component state is in the first state level is 1. The state level probability distribution of each time node is calculated by the semi-Markov formula , the calculation parameters of bending bearing capacity are obtained through fuzzy mathematical simulation.

[0155]

[0156]

[0157]

[0158] Where: is the time t i Status level probability, there are 5 probabilities in total. is the steel section loss rate of each i-th state level, For each i State grade steel bar tensile strength loss rate, For each i Strength loss rate of concrete in different condition grades.

[0159] Calculation formula for bending bearing capacity:

[0160]

[0161] Through Monte Carlo sampling, 100,000 samples are taken for each variable parameter in each year to calculate the structural flexural bearing capacity, and the mean and standard deviation of the calculated parameters are obtained by statistical calculation. The mean and standard deviation of the flexural bearing capacity calculation results are obtained, as shown in Table 6 (the data is too large, so only part of the data is listed), and then the time-varying mean of the component flexural bearing capacity is obtained, as shown in Figure 5 shown.

[0162] Dead load on bridge girder It is composed of the self-weight of the main beam components and the self-weight of the bridge deck paving materials. The statistical parameters are shown in Table 7. Vehicle load during the design reference period The maximum value distribution parameters of are shown in Table 8. Then the time-varying reliability index value of the component is obtained, as shown in Figure 6 shown.

[0163] Table 6 Calculation data of mean and standard deviation of bending bearing capacity

[0164]

[0165] Table 7 Constant load statistical parameters

[0166]

[0167] Note: Gk is the effect value calculated from the standard weight of the component or the standard weight of the bridge deck specified in the code.

[0168] Table 8 Vehicle load statistical parameters

[0169]

[0170] Effect values ​​calculated for standard vehicle loads.

[0171] Embodiment 2

[0172] A highway bridge component reliability assessment system comprises a bridge status data storage module for storing or updating actually collected bridge status data.

[0173] Status data processing module, used for i The classification of residence time under state level and the probability distribution under degradation rate are used to perform fuzzy state processing on the calculation parameters of flexural bearing capacity.

[0174] The calculation and analysis module is used to use load information and component section design parameter values ​​as input data, obtain time-varying reliability indicators as output data, and simulate the time-varying reliability of components.

[0175] Therefore, the present invention adopts the above-mentioned highway bridge component reliability assessment method and system, obtains the bearing capacity material parameter value through the membership function combined with the real bridge status data, and then obtains the mean and variance of the bridge resistance, thereby solving the problem of predicting the time-varying reliability of the structure in the highway network and obtaining the mean and variance of the time-varying bearing capacity of the structure.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A reliability assessment method for highway bridge components, characterized in that: The following steps are involved: S1. Obtain bridge data for evaluating the technical status of components, wherein the technical status of components includes Status level, =1, 2, 3...n, where n is an integer; S2. Use the membership function in fuzzy mathematics to express the uncertainty of the calculation parameters of the bending bearing capacity in the technical status of the component; S3. Using the residence time of different component technical states, the semi-Markov probability transfer matrix is ​​solved through the probability density function and survival function of the Weibull distribution to obtain the probability distribution of the component technical state that changes with time; S4. Based on the calculation formula of bending capacity, input the design parameter value of the component section, use the membership function combined with the semi-Markov process to obtain the result value of the variable parameter of the bending capacity calculation parameter changing with time, then solve the bending capacity of the component multiple times through Monte Carlo simulation, and perform statistics on the bending capacity of the component to obtain its mean and standard deviation; S5. Based on the limit state equation, input the mean and standard deviation of the load effect value, input the mean and standard deviation of the bending bearing capacity obtained in S4, and use Monte Carlo simulation to obtain the time-varying reliability index of the component; In S2, the membership function in fuzzy mathematics is used to represent the uncertainty of the variable parameters in the bending bearing capacity calculation parameters in the technical status of the component, specifically: Calculation formula for bending bearing capacity: ； in, is the compressive strength of concrete, is the tensile strength of the steel bar, is the cross-sectional area of ​​the tensile reinforcement, is the component width, is the effective height of the component; The bending bearing capacity calculation parameters include , and , , and are the variable parameters of the components that change with time. and is a non-variable parameter of the component; According to the material damage interval of the bending bearing capacity calculation parameters, the The fuzzy degree interval of the state level change is used to obtain the time-varying value of the calculation parameters of the component's bending bearing capacity; In S3, ； in, is at the time t The semi-Markov probability transition matrix for the year, for t -1 year probability distribution for each status level; In S4, the three material properties of concrete compressive strength, steel bar tensile strength and tensile steel bar cross-sectional area are time-varying values, so the bending bearing capacity calculation formula is: ； Will Added into the calculation formula of the bending bearing capacity of the component, the variable parameters are expressed as: ； ； ； in, for t Moment State level probability, For the State grade steel bar section loss rate, For the State grade steel bar tensile strength loss rate, For the Strength loss value of concrete in different condition grades.

2. A highway bridge component reliability assessment method according to claim 1, characterized in that: In S1, the first condition level means that the bridge is in a new condition with complete functions.

3. A highway bridge component reliability assessment method according to claim 2, characterized in that: According to The residence time of the status level is divided into degradation rate levels, and the degradation rate levels include very slow, slow, medium, fast and very fast.

4. A highway bridge component reliability assessment method according to claim 3, characterized in that: S3 is as follows: The properties of a Markov chain are expressed as: ； Among them, P is the conditional probability, for t The state level corresponding to the moment; In a Markov chain, the single-step transition probability of its transition process refers to the transition from the first Status level transferred to The probability of the state level is expressed as: ,in ; Among them, M is the first Status level / the number of status levels; The difference between Markov process and semi-Markov process lies in the time dependence of the state transition mechanism. The semi-Markov probability transition matrix P' is: ,and ; but t The moment The state level probability is: ； in, is at the time t The semi-Markov probability transition matrix for the year, for t -1 year probability distribution for each status level; No. Status level over time t The probability density function of and the survival function Respectively expressed as: ； ； in, and In the The scale parameter and shape parameter of the Weibull distribution at the state level, for The original function of Survival function via Weibull distribution and the probability density function Calculated, each time node t、 The state transition probability between two adjacent state levels , expressed as: ； in, is the cumulative stay time The probability density function of and The residence time and The survival function of The value is 1 year; At the same time, Status level over time t Weibull distribution survival function Close to the level of the component, over time t Keep the same Probability of status level , the difference between the two is expressed as: ； By solving the minimization problem, we can calculate any Scale parameter in Weibull distribution with state rank and shape parameters , expressed as: ； in, is Always keep the same The probability of the state level, For the The duration of the status level.

5. A highway bridge component reliability assessment method according to claim 1, characterized in that: In S5, the limit state equation is: ； in, is the structural reliability function, is the bending bearing capacity, is the load effect value.

6. A highway bridge component reliability assessment method according to claim 5, characterized in that: Time-varying reliability index It is expressed as: ； in, , They are t Mean and standard deviation of the time-varying bending capacity of components at each moment; , are the mean and standard deviation of the time-varying load effect values ​​of the components, respectively.

7. A highway bridge component reliability assessment system implemented by using a highway bridge component reliability assessment method according to any one of claims 1 to 6, characterized in that: include: A bridge status data storage module is used to store or update the actually collected bridge status data; Status data processing module, used for i Classification of residence time under state level and probability distribution under degradation rate, fuzzy state processing of bending capacity calculation parameters; The calculation and analysis module is used to use load information and component section design parameter values ​​as input data, obtain time-varying reliability indicators as output data, and simulate the time-varying reliability of components.

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