A structural reliability assessment and prediction method and system based on long-term monitoring data

Abstract

The present invention discloses a structural reliability assessment and prediction method and system based on long-term monitoring data, which relates to the field of civil structure monitoring, and fully utilizes massive monitoring data to analyze monitored components and unmonitored components respectively; establishes a connection between the two through a finite element model of the structure: applies the monitored load to the structural finite element model to obtain the load effect of the unmonitored component, so that the reliability of all components of the entire structure can be analyzed; fully utilizes massive health monitoring data to more accurately and comprehensively assess and predict the reliability of civil structures: in addition to the degradation of structural strength, the influence of long-term changes in load effects on the structural reliability assessment results is also considered; and simultaneously utilizes the overall and local response data of the structure to assess the structural reliability from two levels, namely, the component and structural system.

Description

Technical Field

[0001] The present disclosure relates to the field of civil engineering structure monitoring, and in particular to a method and system for evaluating and predicting structural reliability based on long-term monitoring data. Background Art

[0002] The statements in this section merely provide background art related to the present disclosure and do not necessarily constitute prior art.

[0003] Structural health monitoring technology is widely used in the field of civil engineering. The system has accumulated a huge amount of data over the years, including loads such as traffic, wind, and temperature, and acceleration, displacement, and strain structural response data. However, analyzing the safe service status of the structure based on these data and effectively guiding daily management and maintenance work have become practical problems that need to be solved urgently.

[0004] Uncertainty is inevitable in structural status assessment, such as loads, load effects, component materials, geometric characteristics, and structural degradation mechanisms are all random variables. To ensure the accuracy of the assessment results, it is necessary to quantify these random variables. Structural reliability theory provides a scientific method to describe the inherent uncertainty in engineering. It studies the failure probability of a structure through the probability distribution of resistance and load effects, usually expressed as a reliability index. Health monitoring data can be used to obtain the probability distribution of load effects and provide necessary information for resistance distribution.

[0005] The inventors found that in the currently used prediction method, the resistance distribution function of the structure is updated by the stiffness attenuation coefficient under the influence of load, material shrinkage and creep, so as to obtain the evolution law of structural reliability and the remaining service life of the structure. However, the two major elements of structural reliability assessment are not only resistance but also load effect. This method focuses on the degradation of resistance, and does not consider the changes in load effects during the service life of the structure; another evaluation method evaluates the stiffness reliability of the bridge by performing mathematical statistics on the deflection data. However, the deflection response is the overall response of the bridge, and it is not very sensitive to local damage to the structure. When the change in the safety status of the bridge is detected from the deflection response, the damage to the structure is likely to be very serious and has posed a huge threat to the safety of people's lives and property. Summary of the invention

[0006] The purpose of the present invention is to address the defects of the prior art and provide a structural reliability assessment and prediction method and system based on long-term monitoring data, which makes full use of massive health monitoring data to more accurately and comprehensively assess and predict the reliability of civil structures: in addition to the degradation of structural strength, the influence of long-term changes in load effects on the structural reliability assessment results is also considered; at the same time, the overall and local response data of the structure are used to evaluate the structural reliability from two levels: component and structural system.

[0007] The first object of the present disclosure is to provide a structural reliability assessment and prediction method based on long-term monitoring data, comprising the following steps:

[0008] Obtain long-term monitoring data for preprocessing to obtain the annual probability distribution and extreme value distribution of load effects, establish a finite element model for the monitoring structure and calibrate it;

[0009] The probability distribution and extreme value distribution of the load effects of unmonitored components are simulated and determined annually through the finite element model;

[0010] Calculate the annual reliability index using the probability distribution of annual load effects of components and structures, evaluate the reliability, and update the annual reliability index of components and structures;

[0011] The full life cycle degradation model for resistance is determined based on the extreme value distribution of load effects mentioned above to predict the reliability indicators of components and structures.

[0012] Furthermore, long-term health monitoring data of civil structures are obtained and denoised, the signal sources corresponding to various loads in the load effect signal are separated, the data are converted into structural engineering physical quantities, and mathematical statistics are performed on various load data to obtain the annual probability distribution and overall extreme value distribution.

[0013] Furthermore, mathematical statistics are performed on the total load effect and various types of load effects to obtain their annual probability distribution and extreme value distribution respectively.

[0014] Furthermore, the finite element model is calibrated using various load data, probability distributions and population extreme value distributions.

[0015] Furthermore, the probability distribution of the initial resistance of each component and structure is obtained, the service time of the structure is discretized into years, and the annual reliability index is calculated.

[0016] Furthermore, the load effect data are classified according to the local response of the structure and the overall response of the structure, and the annual load effect probability distribution of each component and the annual load probability distribution of the structure as a whole are obtained respectively.

[0017] Furthermore, for updating the annual reliability index, the probability distribution function of resistance is updated based on the maximum load effect data and the maintenance and upkeep records of the structure. In combination with the historical monitoring data, the annual probability distribution parameters are updated and the annual reliability index of the structure and components is recalculated.

[0018] Furthermore, it also includes conducting risk analysis, setting target reliability of each component within a certain reference period, taking the target reliability as a benchmark, and specifying inspection and maintenance strategies according to component reliability assessment results.

[0019] The second objective of the present disclosure is to provide a structural reliability assessment and prediction system based on long-term monitoring data, which adopts the following technical solutions:

[0020] Preprocessing module: It is configured to obtain long-term monitoring data and preprocess them to obtain the probability distribution and extreme value distribution of load effects every year;

[0021] Finite element module: configured to establish and calibrate a finite element model, and use the finite element model to simulate and determine the probability distribution and extreme value distribution of the load effect of the unmonitored component each year;

[0022] Evaluation module: configured to calculate the annual reliability index using the probability distribution of the annual load effects of the components and structures, and to evaluate; and to update the annual reliability index of the components and structures;

[0023] Prediction module: It is configured to determine the full life cycle degradation model of resistance, relying on the above-mentioned load effect extreme value distribution, and predicts the reliability indicators of components and structures.

[0024] Furthermore, the evaluation module updates the probability distribution function of resistance based on the maximum load effect data and the maintenance and upkeep records of the structure, updates the annual probability distribution parameters in combination with the historical monitoring data, and recalculates and updates the annual reliability index of the structure and components.

[0025] Compared with the prior art, the advantages and positive effects of the present disclosure are:

[0026] (1) Making full use of massive health monitoring data can more accurately and comprehensively evaluate and predict the reliability of civil structures: In addition to the degradation of structural strength, the impact of long-term changes in load effects on the structural reliability assessment results is also considered; at the same time, the overall and local response data of the structure are used to evaluate the structural reliability from the component and structural system levels respectively;

[0027] (2) Fully tap the potential of massive amounts of structural health monitoring data, incorporate the concept of the entire life cycle into structural condition evaluation, more comprehensively and accurately assess and predict the reliability of civil structures, and provide quantitative reference indicators for scientific and economical maintenance and repair work;

[0028] (3) Based on long-term monitoring data, the probability distribution of structural resistance and load effects required for structural reliability can be estimated and predicted more accurately. Moreover, it can not only grasp the service safety status of the structure as a whole, but also grasp the reliability of each component and rank them, which helps to arrange the management, maintenance and repair of the structure more reasonably within a limited economic budget, and also facilitates the early detection of structural abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0030] Figure 1 It is a flowchart of the evaluation and prediction process in Examples 1 and 2 of the present disclosure. DETAILED DESCRIPTION

[0031] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0032] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "include" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or their combinations;

[0033] For the convenience of description, if the words "up", "down", "left" and "right" appear in the present disclosure, they only indicate that they are consistent with the up, down, left and right directions of the drawings themselves, and do not limit the structure. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present disclosure.

[0034] As introduced in the background technology, the existing technology focuses on the degradation of resistance, but does not consider the changes in load effects during the service life of the structure; the stiffness reliability of the bridge is evaluated by mathematical statistics on the deflection data, but the deflection response is the overall response of the bridge, which is not very sensitive to local damage to the structure; in response to the above problems, the present disclosure proposes a structural reliability assessment and prediction method and system based on long-term monitoring data.

[0035] Example 1

[0036] In a typical embodiment of the present disclosure, Figure 1 As shown in the figure, a structural reliability assessment and prediction method based on long-term monitoring data is proposed.

[0037] Make full use of massive monitoring data to analyze monitored components and unmonitored components respectively;

[0038] The connection between the two is established through the finite element model of the structure: the monitored load is applied to the finite element model of the structure to obtain the load effect of the unmonitored components, so that the reliability of all components of the entire structure can be analyzed.

[0039] The specific workflow is as follows:

[0040] Preprocessing of long-term monitoring data, derivation of structural engineering physical quantities and mathematical statistics;

[0041] A refined finite element model of the structure is established and calibrated based on the monitoring data, through which the probability distribution of the load effects of the unmonitored components is obtained;

[0042] Evaluate and update the annual structural reliability of each component and the structure as a whole;

[0043] Predict the evolution of component and structure reliability over the entire life cycle;

[0044] Establish target reliability for each component and formulate corresponding inspection, maintenance and repair strategies based on reliability analysis results.

[0045] Specifically, the detailed working process of this embodiment is described in conjunction with the accompanying drawings:

[0046] 1. Preprocess and perform mathematical statistics on long-term monitoring data. Including:

[0047] (1-1) Use time-frequency analysis and other methods to process long-term monitoring data, effectively remove invalid information such as noise, spikes, trends, etc. in the signal, and separate the signal sources corresponding to various types of loads in the load effect signal;

[0048] (1-2) Convert monitoring data into structural engineering physical quantities through physical analysis, such as wind pressure, vehicle load, lane load and other load effects, as well as axial force, bending moment, shear force and other load effects;

[0049] (1-3) Perform mathematical statistics on various load data after preprocessing to obtain their annual probability distribution and overall extreme value distribution;

[0050] (1-4) Mathematical statistics are performed on the total load effect and its components, i.e., various types of load effects, to obtain their annual probability distribution and extreme value distribution.

[0051] 2. Build and calibrate a detailed finite element model of the structure based on monitoring data.

[0052] (2-1) Use finite element software such as ABAQUS to establish a detailed finite element model of the structure;

[0053] (2-2) Carry out load test on the structure according to the regulations;

[0054] (2-3) Apply the test load to the finite element model of the structure and obtain the corresponding structural response;

[0055] (2-4) Compare the static and dynamic structural responses measured by the model and the monitoring system, and calibrate the finite element model.

[0056] 3. The annual probability distribution and overall extreme value distribution of the load effect of unmonitored components are obtained through the finite element model. Including:

[0057] (3-1) Through the finite element model, the probability distribution of various load effects obtained by (1-4) is used to infer the probability distribution of unmeasured loads;

[0058] (3-2) Apply various load spectra, including those directly measured and those obtained by inverse calculation (3-1), to the structural finite element model, and compare the load effects of the monitored components with the measured data to further verify and calibrate the accuracy of the finite element model;

[0059] (3-3) The annual probability distribution and extreme value distribution of the load effect of unmonitored components are determined based on the finite element simulation results.

[0060] 4. Evaluate and update the annual structural reliability of each component and the structure as a whole.

[0061] (4-1) Obtain the probability distribution of the initial resistance of each component and structure through design data and the material and geometric properties of the components;

[0062] (4-2) Discretize the service life of the structure into years, thereby transforming the time-varying reliability problem into a time-invariant problem for study;

[0063] (4-3) Analyze the annual probability distribution of load effects on components and structures based on health monitoring data and finite element simulation;

[0064] Specifically include:

[0065] (4-3a) Classify the load effect data according to the local and global responses of the structure;

[0066] (4-3b) Using the annual local response data of the structure, such as strain, the probability distribution of the annual load effect of each component is obtained;

[0067] (4-3c) Using the annual structural overall response data, such as displacement or deflection, the probability distribution of the annual load effect of the structure is obtained.

[0068] (4-4) Calculate the annual reliability index of the structure and each component by analytical methods such as the second-order moment method or numerical methods such as the Monte Carlo method;

[0069] (4-5) Combined with historical monitoring data, the probability distribution of resistance and load effects is updated, thereby continuously updating the annual reliability indicators of structures and components.

[0070] Specifically include:

[0071] (4-5a) Update the probability distribution function of resistance based on the maximum load effect data and the repair and maintenance records of the structure;

[0072] (4-5b) Integrate all the historical data and update the annual probability distribution parameters of the load effect. All the new and old data can be put together and updated using the probability theory method, or the probability distribution parameters can be regarded as variables and the new data can be used to update each parameter using the Bayesian method;

[0073] (4-5c) The annual structural reliability of structures and components is recalculated using the updated probability distribution of resistance and load effects to make the assessment results more reasonable.

[0074] 5. Predict the evolution of component and structure reliability over the entire life cycle.

[0075] (5-1) Determine the full life cycle degradation model of resistance, which can be based on previous research results or accelerated degradation tests of structures and components;

[0076] (5-2) Based on the full life cycle degradation model of resistance and the extreme value distribution of load effects obtained from long-term monitoring data, the reliability indicators of structures and components in the future years are predicted and their evolution laws are analyzed.

[0077] 6. Establish target reliability for each component and formulate corresponding inspection, maintenance and repair strategies.

[0078] (6-1) Under the premise of ensuring structural safety, risk analysis is carried out based on the principle of economic optimization, and the target reliability of each component within a certain reference period is set;

[0079] (6-2) Based on the target reliability, formulate corresponding inspection, maintenance and repair strategies according to the reliability assessment results of the components: if the assessed reliability is higher than the target reliability, the assessment is passed and general inspection of the components is sufficient; if the component reliability is lower than the target reliability, special inspection, maintenance or repair is carried out according to the degree.

[0080] Based on long-term monitoring data, the structural resistance and probability distribution of load effects required for structural reliability can be estimated and predicted more accurately. Moreover, it can not only grasp the service safety status of the structure as a whole, but also grasp the reliability of each component and rank them, which helps to arrange the management, maintenance and repair of the structure more reasonably under a limited economic budget, and also facilitates the early detection of structural abnormalities.

[0081] Example 2

[0082] In another typical embodiment of the present disclosure, Figure 1 As shown in the figure, a structural reliability assessment and prediction system based on long-term monitoring data is proposed.

[0083] include:

[0084] Preprocessing module: It is configured to obtain long-term monitoring data and preprocess them to obtain the probability distribution and extreme value distribution of load effects every year;

[0085] Finite element module: configured to establish and calibrate a finite element model, and use the finite element model to simulate and determine the probability distribution and extreme value distribution of the load effect of the unmonitored component each year;

[0086] Evaluation module: configured to calculate the annual reliability index using the probability distribution of the annual load effects of the components and structures, and to evaluate; and to update the annual reliability index of the components and structures;

[0087] Prediction module: It is configured to determine the full life cycle degradation model of resistance, relying on the above-mentioned load effect extreme value distribution, and predicts the reliability indicators of components and structures.

[0088] The evaluation module updates the probability distribution function of resistance based on the maximum load effect data and the maintenance and upkeep records of the structure, updates the annual probability distribution parameters in combination with the historical monitoring data, and recalculates and updates the annual reliability index of the structure and components.

[0089] The specific configuration of the system in this embodiment is the same as that in Embodiment 1 and will not be described in detail here.

[0090] By making full use of massive health monitoring data, the reliability of civil structures can be evaluated and predicted more accurately and comprehensively: in addition to the degradation of structural strength, the impact of long-term changes in load effects on the structural reliability assessment results is also considered; at the same time, the overall and local response data of the structure are used to evaluate the structural reliability from two levels: component and structural system.

[0091] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A structural reliability assessment and prediction method based on long-term monitoring data, characterized in that: The following steps are involved: Obtain long-term monitoring data for preprocessing to obtain the annual probability distribution and extreme value distribution of load effects, establish a finite element model for the monitoring structure and calibrate it; The probability distribution and extreme value distribution of the load effects of unmonitored components are simulated and determined annually through the finite element model; Calculate the annual reliability index using the probability distribution of annual load effects of components and structures, evaluate the reliability, and update the annual reliability index of components and structures; Determine the full life cycle degradation model of resistance, relying on the extreme value distribution of load effects on components and structures, and predict the reliability indicators of components and structures; The specific method for obtaining long-term monitoring data for preprocessing is: obtaining long-term health monitoring data of civil structures, and performing denoising, separating the signal sources corresponding to various loads in the load effect signal, converting the data into structural engineering physical quantities, performing mathematical statistics on various load data, and obtaining the annual probability distribution and overall extreme value distribution; performing mathematical statistics on the total load effect and various load effects, and obtaining their annual probability distribution and extreme value distribution respectively; Among them, simulating and determining the annual probability distribution and extreme value distribution of the load effect of the unmonitored components through the finite element model specifically includes: using the probability distribution of various load effects obtained through the finite element model to infer the probability distribution of the unmeasured load; applying various load spectra, including directly measured and inversely inferred, on the finite element model, comparing the load effect of the monitored component with the measured data, and further verifying and calibrating the accuracy of the finite element model; determining the annual probability distribution and extreme value distribution of the load effect of the unmonitored component from the finite element simulation results; The specific method for calculating the annual reliability index using the annual load effect probability distribution of components and structures is as follows: obtaining the probability distribution of the initial resistance of each component and structure; discretizing the service time of the structure into units of years, and converting the time-varying reliability problem into a time-invariant problem; classifying the load effect data according to the local and overall responses of the structure, and obtaining the annual load effect probability distribution of each component and the annual load probability distribution of the structure as a whole, respectively, and calculating the annual reliability index of the structure and each component by an analytical method; The specific method for updating the annual reliability index of components and structures is as follows: based on the maximum load effect data and the maintenance and upkeep records of the structure, the probability distribution function of the resistance is updated, and in combination with the historical monitoring data, the annual probability distribution parameters of the load effect are updated, and the annual reliability index of the structure and components is recalculated using the updated probability distribution of resistance and load effect; The method also includes conducting risk analysis, setting a target reliability for each component within a certain reference period, and specifying inspection and maintenance strategies based on the target reliability and component reliability assessment results: if the assessed reliability is higher than the target reliability, the assessment is passed, and a general inspection of the component is sufficient; if the component reliability is lower than the target reliability, special inspections, maintenance, or repairs are performed according to the degree.

2. The structural reliability assessment and prediction method based on long-term monitoring data according to claim 1 is characterized in that: Calibrate finite element models using various types of load data, probability distributions, and population extreme value distributions.

3. A structural reliability assessment and prediction system based on long-term monitoring data, characterized in that: include: Preprocessing module: It is configured to obtain long-term monitoring data and preprocess them to obtain the probability distribution and extreme value distribution of load effects every year; Finite element module: configured to establish and calibrate a finite element model, and use the finite element model to simulate and determine the probability distribution and extreme value distribution of the load effect of the unmonitored component each year; Evaluation module: configured to calculate the annual reliability index using the probability distribution of annual load effects of components and structures and to perform evaluation; and update the annual reliability indicators of components and structures; Prediction module: It is configured to determine the full life cycle degradation model of resistance, relying on the extreme value distribution of load effects on components and structures, and predicting the reliability indicators of components and structures; The specific method for obtaining long-term monitoring data for preprocessing is: obtaining long-term health monitoring data of civil structures, and performing denoising, separating the signal sources corresponding to various loads in the load effect signal, converting the data into structural engineering physical quantities, performing mathematical statistics on various load data, and obtaining the annual probability distribution and overall extreme value distribution; performing mathematical statistics on the total load effect and various load effects, and obtaining their annual probability distribution and extreme value distribution respectively; Among them, simulating and determining the annual probability distribution and extreme value distribution of the load effect of the unmonitored components through the finite element model specifically includes: using the probability distribution of various load effects obtained through the finite element model to infer the probability distribution of the unmeasured load; applying various load spectra, including directly measured and inversely inferred, on the finite element model, comparing the load effect of the monitored component with the measured data, and further verifying and calibrating the accuracy of the finite element model; determining the annual probability distribution and extreme value distribution of the load effect of the unmonitored component from the finite element simulation results; The specific method for calculating the annual reliability index using the annual load effect probability distribution of components and structures is as follows: obtaining the probability distribution of the initial resistance of each component and structure; discretizing the service time of the structure into units of years, and converting the time-varying reliability problem into a time-invariant problem; classifying the load effect data according to the local and overall responses of the structure, and obtaining the annual load effect probability distribution of each component and the annual load probability distribution of the structure as a whole, respectively, and calculating the annual reliability index of the structure and each component by an analytical method; The specific method for updating the annual reliability index of components and structures is as follows: based on the maximum load effect data and the maintenance and curing records of the structure, the probability distribution function of the resistance is updated, and in combination with the historical monitoring data, the annual probability distribution parameters of the load effect are updated, and the annual reliability index of the structure and components is recalculated using the updated probability distribution of resistance and load effect; The method also includes conducting risk analysis, setting a target reliability for each component within a certain reference period, and specifying inspection and maintenance strategies based on the target reliability and component reliability assessment results: if the assessed reliability is higher than the target reliability, the assessment is passed, and a general inspection of the component is sufficient; if the component reliability is lower than the target reliability, special inspections, maintenance, or repairs are performed according to the degree.

4. The structural reliability assessment and prediction system based on long-term monitoring data as claimed in claim 3, characterized in that: The evaluation module updates the probability distribution function of resistance based on the maximum load effect data and the maintenance and upkeep records of the structure, updates the annual probability distribution parameters in combination with the historical monitoring data, and recalculates and updates the annual reliability index of the structure and components.

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