Method for predicting residual life of reinforced concrete bridge based on monitoring data

Abstract

The invention discloses a method for predicting the residual life of a reinforced concrete bridge based on monitoring data, and belongs to the technical field of bridge life prediction. The method comprises the steps of obtaining a residual life prediction result of a target bridge before reinforcement, and obtaining a reinforcement scheme and a predicted life prolonging effect adopted when the target bridge is reinforced; determining a plurality of to-be-monitored key parameter types through the reinforcement scheme, and generating a corresponding monitoring scheme according to the plurality of key parameter types for deployment of monitoring equipment to complete acquisition of key parameters; and acquiring a key parameter standard value corresponding to the predicted life prolonging effect during numerical simulation, performing numerical comparison on the key parameter standard value and a plurality of acquired key parameters, correcting the predicted life prolonging effect according to a comparison result, and prolonging a residual life prediction result according to a correction result to obtain the residual life of the reinforced concrete bridge. The method can reduce the dependence on the finite element model during prediction of the residual life of the reinforced concrete bridge.

Description

Technical Field

[0001] The present invention belongs to the technical field of bridge life prediction, and in particular relates to a method for predicting the remaining life of a reinforced concrete bridge based on monitoring data. Background Art

[0002] In recent years, with the continuous development of the economy, the concept of urbanization has gradually taken shape. However, in the process of promoting urbanization, due to the gradual increase in the permanent population of each city, in order to alleviate the pressure on urban traffic caused by the increase in the permanent population, each city has vigorously carried out the construction of municipal bridges. To date, major cities have built thousands of municipal bridges. Among the newly built municipal bridges, the majority are small and medium-span concrete bridges. As a key component of transportation infrastructure, these concrete bridges are inevitably affected by various factors such as vehicle loads, environmental corrosion, and material aging during their long-term service life, resulting in the gradual degradation of structural performance. To ensure traffic safety and increase the service life of bridges, reinforcement treatment has become a necessary measure. However, reinforced concrete bridges still face complex service environments. How to accurately predict their remaining life after reinforcement is of great significance to the scientific operation and maintenance of bridges, maintenance decisions, and the sustainable development of transportation systems.

[0003] Currently, when it comes to predicting the remaining life of reinforced concrete bridges, analysis and evaluation are usually carried out by constructing finite element models based on monitoring data. However, the establishment of finite element models is a complex process with multiple links and multiple factors coupled, which requires a high level of relevant experience from the staff responsible for establishing the model. When a large number of reinforced concrete bridges need to be predicted at the same time, it is difficult for relevant departments to dispatch enough staff with sufficient experience to quickly predict each concrete bridge, which greatly delays the completion cycle and affects the time when the reinforced concrete bridges can be reopened to traffic. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method for predicting the remaining service life of reinforced concrete bridges based on monitoring data, so as to solve the above technical problems.

[0005] In order to achieve the above object, the present invention provides the following technical solutions: A method for predicting the remaining life of reinforced concrete bridges based on monitoring data includes: Obtain the remaining life prediction results of the target bridge before reinforcement, as well as the reinforcement scheme adopted during the reinforcement of the target bridge and the expected life extension effect; Determine multiple key parameter types to be monitored through the reinforcement plan, and generate corresponding monitoring plans based on multiple key parameter types for monitoring equipment deployment to complete the collection of key parameters; Obtain the standard values ​​of key parameters corresponding to the expected life extension effect during numerical simulation, perform numerical comparison with multiple key parameters collected, complete the correction of the expected life extension effect based on the comparison results, and extend the remaining life prediction result based on the correction result to obtain the remaining life of the reinforced concrete bridge.

[0006] Furthermore, the remaining life prediction results of the target bridge before reinforcement are obtained, including: Obtain the unique identifier of the target bridge, which is used to enter the bridge management system for retrieval to obtain the latest historical inspection report of the target bridge before reinforcement; Extract the remaining life prediction results from the latest historical inspection report and record them as initial results; Get the test date of the latest historical test report and record it as the initial time node; Calculate the time difference between the initial time node and the current time node, and record it as the transition time; By calculating the difference between the initial result and the transition time, the remaining life prediction result of the target bridge before reinforcement is obtained.

[0007] Furthermore, a method for predicting the remaining service life of a reinforced concrete bridge based on monitoring data also includes: Obtain all historical inspection reports of the target bridge before reinforcement, and extract N historical inspection reports in reverse order of inspection date as target reports; among them, the first target report extracted in reverse order The latest historical test report; right N Sort the historical test reports by test date to get a report set ; Get report collection M The remaining life prediction results of each target report ; Calculated The unit deviation coefficient of any two adjacent remaining life prediction results in is calculated as follows: in, Indicates the N The remaining life prediction results of the first target report are consistent with those of the N-1 Unit deviation coefficient of the remaining life prediction results of the target report, Indicates the N The test date of the first target report is the same as the N-1 The difference between the detection dates of the target reports; Get all unit deviation coefficients and generate difference series ; The autoregressive model is fitted by the difference sequence, and the autocorrelation coefficient of the remaining life prediction results of the target bridge before reinforcement is calculated. ; According to the autocorrelation coefficient , initial results and transition time, and calculate the remaining life prediction result of the target bridge before reinforcement after eliminating the prediction error. The calculation method is: in, The unit deviation coefficient between the remaining life prediction result of the first target report and the remaining life prediction result corresponding to the current time node, Indicates the transition time, It represents the remaining life prediction result of the target bridge before reinforcement after eliminating the prediction error.

[0008] Furthermore, the reinforcement scheme adopted during the reinforcement of the target bridge and the expected life extension effect are obtained, including: Obtaining the unique identifier of the target bridge, which is used to retrieve the reinforcement design file used when reinforcing the target bridge through the bridge management system; Extract the reinforcement scheme and expected life extension effect contained in the reinforcement design document; the expected life extension effect is obtained through numerical simulation of the finite element model during the reinforcement design.

[0009] Furthermore, multiple key parameter types to be monitored are determined through the reinforcement plan, and corresponding monitoring plans are generated based on the multiple key parameter types for monitoring equipment deployment to complete the collection of key parameters, including: Obtain a reinforcement plan, determine multiple key parameter types related to the bridge life in the reinforcement plan based on fuzzy theory, and determine the weight value corresponding to each key parameter type related to the bridge life; According to multiple key parameter types and their corresponding weight values, the pre-built monitoring solution database is screened to obtain a matching monitoring solution for the deployment of monitoring equipment and determination of parameter collection conditions; Through the deployment of monitoring equipment, key parameters can be collected when the parameter collection conditions are met.

[0010] Furthermore, the standard values ​​of key parameters corresponding to the expected life extension effect during the numerical simulation are obtained, and numerical comparisons are performed with the multiple key parameters obtained. Based on the comparison results, the expected life extension effect is corrected, including: Obtaining standard values ​​of multiple key parameters corresponding to the expected life extension effect when performing numerical simulations using a finite element model, and obtaining parameter weights when determining the types of multiple key parameters using fuzzy theory; The ratio of each key parameter to the corresponding key parameter standard value is calculated as the benchmark value; wherein, the calculation principle follows the same direction processing of indicators; Obtain all benchmark values ​​and corresponding parameter weights for weighted average calculation to obtain the correction coefficient for the expected life extension effect; The correction result is obtained by multiplying the correction factor and the expected life extension effect.

[0011] Furthermore, the remaining life prediction results are extended based on the correction results to obtain the remaining life of the reinforced concrete bridge, including: Obtaining a correction result; wherein the correction result is the estimated life extension effect after correction; Based on the type characteristics of the correction results, the adaptive operation mode is matched to calculate the correction results and the remaining life prediction results of the target bridge before reinforcement, and the remaining life of the reinforced concrete bridge is obtained. Among them, the type characteristics of the correction result include numerical type and percentage type. The operation mode corresponding to the numerical type is addition operation, and the operation mode corresponding to the percentage type is multiplication operation.

[0012] The beneficial effects of the present invention are: The present invention discloses a method for predicting the remaining life of reinforced concrete bridges based on monitoring data. By integrating the estimated remaining life values ​​in the historical monitoring data of concrete bridges, combining the design parameters of the reinforcement scheme with the real-time monitoring data after reinforcement, a dynamic correction model is constructed to achieve accurate prediction of the remaining life of the reinforced bridge. This method breaks through the reliance on traditional finite element models and does not require complex mechanical modeling and parameter calibration. Through a data-driven adaptive correction strategy, it effectively solves the problems of low computational efficiency and strong manual dependence in the prediction of reinforced concrete bridges in traditional methods. In particular, for the rapid assessment of large-scale bridge groups, this method can significantly reduce the dependence on professional technicians, greatly improve prediction efficiency, and provide efficient and reliable technical support for urban bridge operation and maintenance management.

[0013] Other advantages, objectives, and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or may be taught by those skilled in the art from the practice of the present invention. The purposes and other advantages of the present invention may be realized and obtained through the structures particularly pointed out in the written description and the accompanying drawings.

[0014] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of a method for predicting the remaining service life of a reinforced concrete bridge based on monitoring data in an embodiment of the present invention; Figure 2 This is a flow chart of a method for obtaining a prediction result of the remaining life of a bridge before reinforcement in a method for predicting the remaining life of a reinforced concrete bridge based on monitoring data in an embodiment of the present invention; Figure 3 This is a flow chart of a method for correcting a result of a bridge's remaining life prediction before reinforcement in a method for predicting the remaining life of a reinforced concrete bridge based on monitoring data in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0017] like Figure 1 As shown, the present invention proposes a method for predicting the remaining life of reinforced concrete bridges based on monitoring data, comprising: S101. Obtain the remaining life prediction result of the target bridge before reinforcement, and obtain the reinforcement scheme adopted during the reinforcement of the target bridge and the expected life extension effect; S102: Determine multiple key parameter types to be monitored through the reinforcement plan, and generate corresponding monitoring plans based on the multiple key parameter types for deploying monitoring equipment to complete the collection of key parameters; S103, obtaining standard values ​​of key parameters corresponding to the expected life extension effect during the numerical simulation, comparing the values ​​with the multiple key parameters obtained, and revising the expected life extension effect based on the comparison results; S104. Extend the remaining life prediction result according to the correction result to obtain the remaining life of the reinforced concrete bridge; The working principle and beneficial effects of the above technical solution are as follows: Currently, due to the large number of concrete bridges in cities and the fact that most of them are in the maintenance stage after being built, existing technologies generally use reinforcement to extend the service life of concrete bridges. Common reinforcement methods include increasing the cross-section, external steel reinforcement, prestressed reinforcement, and carbon fiber reinforced concrete structures. After the reinforcement is completed, to determine the remaining life of the reinforced concrete bridge, the existing technology adopts the method of obtaining a reinforcement plan, determining a monitoring plan based on the reinforcement plan, and finally constructing a finite element model based on the monitoring data obtained from the monitoring plan for finite element analysis to determine the remaining life of the current concrete bridge. However, the establishment of a finite element model is a complex process with multiple links and multiple factors coupled, which requires a high level of relevant experience from the staff responsible for establishing the model. When a large number of reinforced concrete bridges need to be predicted at the same time, it is difficult for the relevant departments to dispatch enough staff with sufficient relevant experience to perform rapid predictions for each concrete bridge. At the time of reinforcement completion, the completion cycle is greatly delayed, affecting the time when the reinforced concrete bridge can be reopened to traffic. Based on this, this application proposes a method for predicting the remaining life of reinforced concrete bridges based on monitoring data to solve the above technical problems. Before elaborating on the technical solution of this application, it is necessary to explain the entire process of bridge reinforcement to facilitate understanding by technical personnel in the same field. Normally, before a concrete bridge is reinforced, an assessment of its remaining life is conducted to determine whether reinforcement is necessary. If the assessment indicates that reinforcement is necessary, the reinforcement design will be based on the actual needs of the municipal authorities (the need to extend the remaining life of the concrete bridge) to ensure that the reinforced concrete bridge meets these needs and to determine the expected life extension effect. It is worth noting that the above two assessments are usually determined by finite element analysis using a finite element model; If the present application is not adopted, the existing technology will also use the finite element model to perform finite element analysis on the reinforced concrete bridge when evaluating it, so as to obtain the remaining life prediction results under the actual monitoring data. The technical solution provided by the present application does not directly use the finite element model to perform finite element analysis when evaluating the reinforced concrete bridge. Instead, it integrates the remaining life estimation values ​​in the historical monitoring data of the concrete bridge through the results of the previous two finite element analyses, combines the design parameters of the reinforcement scheme with the real-time monitoring data after reinforcement, and constructs a dynamic correction model to achieve accurate prediction of the remaining life of the reinforced bridge. Compared with the existing technology, there is one less finite element model establishment process in the overall evaluation process of the bridge. When predicting the remaining life of the reinforced concrete bridge alone, it breaks through the traditional finite element model dependence. In the scenario of rapid evaluation of large-scale bridge groups, it significantly reduces the dependence on professional and technical personnel, and is suitable for large-scale reinforcement scenarios of municipal bridges. Specifically, the technical solution of this application is as follows: First, obtain the remaining life prediction result of the target bridge before reinforcement. This remaining life prediction result can be obtained through finite element model analysis or other reliable methods. Then, obtain the reinforcement scheme adopted during the reinforcement of the target bridge and the expected life extension effect. This expected life extension effect is obtained during the reinforcement scheme design. Similarly, the method adopted in the reinforcement scheme design is not limited to the finite element model method. Then, the reinforcement plan is used to determine multiple key parameter types to be monitored. These multiple key parameter types are strongly correlated with the expected life extension effect. The specific acquisition method can be screened using fuzzy theory and assigned corresponding weights based on the correlation strength. After determining multiple key parameter types, a corresponding monitoring plan is generated and corresponding monitoring equipment is set up for data monitoring. The key parameter collection is completed within the expected time. The selection of the expected time is related to the monitoring plan. During the key parameter collection process, multiple standard values ​​of key parameters corresponding to the numerical simulation of the finite element model used in the reinforcement scheme design are obtained. These multiple standard values ​​of key parameters are strongly correlated with the expected life extension effect. After the key parameter collection is completed, the obtained multiple standard values ​​of key parameters are numerically compared with them, and a correction model is generated based on the comparison results to complete the correction of the expected life extension effect; Finally, the remaining life prediction results are extended according to the correction results to obtain the remaining life of the reinforced concrete bridge.

[0018] like Figure 2 As shown, in one embodiment, obtaining the remaining life prediction result of the target bridge before reinforcement includes: S201. Obtain a unique identifier of a target bridge, and input it into a bridge management system to retrieve the latest historical inspection report of the target bridge before reinforcement. S202, extracting the remaining life prediction result from the latest historical inspection report and recording it as the initial result; S203. Obtain the test date of the latest historical test report and record it as the initial time node; S204: Calculate the time difference between the initial time node and the current time node, and record it as the transition time; S205, calculating the difference between the initial result and the transition time, and obtaining the remaining life prediction result of the target bridge before reinforcement; The working principle of the above technical solution is as follows: this application is mainly aimed at the prediction of the remaining life of municipal bridges after reinforcement. Since in the municipal bridge management, each concrete bridge has its corresponding unique identifier, in this technical solution, when obtaining the remaining life prediction result of the target bridge before reinforcement, it is first necessary to clarify the unique identifier of the current target bridge and input it into the bridge management system for retrieval through a portable computer device to obtain the latest historical inspection report of the target bridge before reinforcement, that is, to determine the life assessment report before reinforcement; then extract the latest historical The remaining life prediction result in the inspection report is recorded as the initial result. At the same time, the inspection time of the latest historical inspection report is obtained and recorded as the initial inspection time node. The time difference between the initial time node and the real-time current time node is calculated and recorded as the transition time. For example, if the initial time node is February 15, 21 and the current time node is February 15, 22, then the transition time is 1 year. Finally, the remaining life prediction result of the target bridge before reinforcement is obtained by calculating the difference after subtracting the transition time from the initial result. That is, when the initial result is 5 years, the remaining life prediction result of the target bridge before reinforcement is 4 years. The beneficial effect of the above technical solution is that through the above technical solution, the remaining life prediction result of the target bridge before reinforcement is accurately calculated, which is beneficial to provide reliable data support for the remaining life prediction of the subsequent reinforced concrete bridge.

[0019] like Figure 3 As shown, in one embodiment, a method for predicting the remaining service life of a reinforced concrete bridge based on monitoring data further includes: S301, obtain all historical inspection reports of the target bridge before reinforcement, and extract N historical inspection reports in reverse order of inspection date as target reports; among them, the first target report extracted in reverse order The latest historical test report; S302, yes NSort the historical test reports by test date to get a report set ; S303. Obtaining a report set M The remaining life prediction results of each target report ; S304, calculated The unit deviation coefficient of any two adjacent remaining life prediction results in is calculated as follows: in, Indicates the N The remaining life prediction results of the first target report are consistent with those of the N-1 Unit deviation coefficient of the remaining life prediction results of the target report, Indicates the N The test date of the first target report is the same as the N-1 The difference between the detection dates of the target reports; S305. Obtain all unit deviation coefficients and generate a differential sequence ; S306. Fit the autoregressive model using the difference sequence and calculate the autocorrelation coefficient of the remaining life prediction result of the target bridge before reinforcement. ; Among them, the autocorrelation coefficient The value of can be directly estimated by the least square method after the autoregressive model AR is fitted. The specific estimation method is relatively mature in the existing technology and will not be described in detail here. S307, according to the autocorrelation coefficient , initial results and transition time, and calculate the remaining life prediction result of the target bridge before reinforcement after eliminating the prediction error. The calculation method is: in, The unit deviation coefficient between the remaining life prediction result of the first target report and the remaining life prediction result corresponding to the current time node, Indicates the transition time, It represents the remaining life prediction result of the target bridge before reinforcement after eliminating the prediction error; The beneficial effects of the above technical solution are: compared with the simple method of directly subtracting the transition time from the initial calculation result to obtain the predicted value of the remaining life before reinforcement, this technical solution innovatively introduces a prediction error estimation mechanism. By performing a systematic error analysis on each remaining life prediction result of the target bridge before reinforcement, the random error of the prediction result changing with time is determined, which is used to calculate multiple unit deviation coefficients, and based on this, a difference sequence is generated to fit the autoregressive model. Finally, the autocorrelation coefficient of the remaining life prediction result of the target bridge before reinforcement is calculated according to the AR model. , complete the dynamic optimization of the remaining life prediction results of the target bridge before reinforcement, thereby effectively eliminating the prediction deviation caused by data fluctuations, model simplification and other factors in each remaining life prediction; this method not only provides more realistic and accurate results for the remaining life prediction before reinforcement, but also lays a solid data foundation for the subsequent remaining life prediction of the bridge after reinforcement, ensures the continuity and reliability of the full-cycle life assessment, avoids the subsequent prediction inaccuracy caused by early data errors, and provides a scientific and reliable decision-making basis for bridge operation and maintenance management.

[0020] In one embodiment, obtaining the reinforcement scheme adopted during the reinforcement of the target bridge and the expected life extension effect includes: Obtain the unique identifier of the target bridge, which is used to retrieve the reinforcement design documents used when the target bridge is reinforced through the bridge management system. The specific method of searching through the unique identifier has been described in the aforementioned technical solution and will not be repeated here. It is worth noting that the reinforcement design documents used during the reinforcement will also be saved through the municipal bridge management system to facilitate future traceability. Extract the reinforcement scheme and expected life extension effect contained in the reinforcement design document; the expected life extension effect is obtained through numerical simulation of the finite element model during the reinforcement design, which is used to determine whether the reinforcement scheme meets the actual needs of the municipal department; The beneficial effects of the above technical solution are: through the above technical solution, the reinforcement solution and the expected life extension effect data can be accurately obtained, which is beneficial to building a reliable data foundation for the subsequent remaining life prediction; secondly, this technical solution innovatively proposes the "expected life extension effect" indicator. This indicator is different from the traditional remaining life prediction results. It focuses on quantifying the direct gain effect of the current reinforcement solution on the bridge life, and can intuitively reflect the degree of improvement of the reinforcement measures on the bridge structure performance; by taking the expected life extension effect as the basic value of the remaining life prediction in subsequent calculations, it effectively connects the reinforcement solution and the bridge life assessment system, which not only improves the accuracy and interpretability of the prediction results, but also provides an intuitive and quantitative reinforcement effect evaluation basis for bridge operation and maintenance decisions.

[0021] In one embodiment, a reinforcement scheme is used to determine multiple key parameter types to be monitored, and corresponding monitoring schemes are generated based on the multiple key parameter types for deploying monitoring equipment to complete the collection of key parameters, including: Obtain a reinforcement plan, determine multiple key parameter types related to the bridge life in the reinforcement plan based on fuzzy theory, and determine the weight value corresponding to each key parameter type and the bridge life; the specific determination method has been described in the previous plan and will not be repeated here; Based on multiple key parameter types and their corresponding weight values, a pre-built monitoring solution database is screened to obtain a matching monitoring solution for monitoring equipment deployment and parameter collection conditions; parameter collection conditions include monitoring equipment setting parameters and collection time settings; Through the deployed monitoring equipment, key parameters are collected when the parameter collection conditions are met. It is worth noting that the collected parameters are expressed by calculating the value of all parameters of each parameter type according to the parameter type to obtain a final value. The expression value calculation can be performed using mean calculation or variance calculation. The beneficial effects of the above technical solution are: through the above technical solution, the collection of key parameters is completed, which is beneficial to providing reliable data support for the remaining life prediction of concrete bridges after subsequent reinforcement. At the same time, corresponding monitoring plans are generated according to multiple key parameter types for monitoring equipment deployment, which is beneficial to improve the targetedness of monitoring data collection and improve the reliability of monitoring data collection.

[0022] In one embodiment, the standard values ​​of key parameters corresponding to the expected life extension effect during the numerical simulation are obtained, and numerical comparisons are performed with the collected multiple key parameters. The expected life extension effect is corrected according to the comparison results, including: Obtaining standard values ​​of multiple key parameters corresponding to the expected life extension effect when performing numerical simulation using a finite element model, and obtaining parameter weights when determining the types of multiple key parameters using fuzzy theory; wherein the fuzzy theory is preferably used to determine the parameter weights when determining the types of multiple key parameters using a fuzzy comprehensive evaluation method. The specific evaluation method is described in the prior art and is not repeated here; the sum of all parameter weights is 1; The ratio of each key parameter to the corresponding key parameter standard value is calculated as the benchmark value; wherein, the calculation principle follows the same-direction processing of indicators; the same-direction processing principle of indicators is that when the value corresponding to a key parameter type is larger, it indicates that the effect on extending the life of the bridge is better. In this case, when calculating the ratio, the key parameter standard value of the key parameter type is used as the denominator; conversely, when the value corresponding to a key parameter type is larger, it indicates that the effect on extending the life of the bridge is worse. In this case, when calculating the ratio, the key parameter value of the key parameter type is used as the denominator. Obtain all benchmark values ​​and corresponding parameter weights for weighted average calculation to obtain the correction coefficient for the expected life extension effect; The correction factor and the expected life extension effect are multiplied to obtain the correction result; The beneficial effect of the above technical solution is: through the above technical solution, the key parameter ratio calculation and weighted average calculation are used to realize the correction of the expected life extension effect. Compared with directly using the expected life extension effect designed in the reinforcement scheme for subsequent calculations, it is beneficial to provide reliable data support for the subsequent remaining life prediction of concrete bridges after reinforcement, and improve the accuracy of the remaining life prediction of concrete bridges after reinforcement.

[0023] In one embodiment, the remaining life prediction result is extended according to the correction result to obtain the remaining life of the reinforced concrete bridge, including: Obtaining a correction result; wherein the correction result is the estimated life extension effect after correction; Based on the type characteristics of the correction results, the adaptive operation mode is matched to calculate the correction results and the remaining life prediction results of the target bridge before reinforcement, and the remaining life of the reinforced concrete bridge is obtained. The type characteristics of the correction result include numerical type and percentage type. The operation mode corresponding to the numerical type is addition operation, and the operation mode corresponding to the percentage type is multiplication operation. The beneficial effect of the above technical solution is: through the above technical solution, a matching adaptive operation mode is adopted to meet the calculation requirements under different calculation scenarios, which is beneficial to improving the reliability of the calculation results of the remaining life of reinforced concrete bridges.

[0024] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A method for predicting the remaining life of reinforced concrete bridges based on monitoring data, characterized in that: include: Obtain the remaining life prediction results of the target bridge before reinforcement, as well as the reinforcement scheme adopted during the reinforcement of the target bridge and the expected life extension effect; Determine multiple key parameter types to be monitored through the reinforcement plan, and generate corresponding monitoring plans based on multiple key parameter types for monitoring equipment deployment to complete the collection of key parameters; Obtain the standard values ​​of key parameters corresponding to the expected life extension effect during numerical simulation, perform numerical comparison with multiple key parameters collected, complete the correction of the expected life extension effect based on the comparison results, and extend the remaining life prediction result based on the correction result to obtain the remaining life of the reinforced concrete bridge.

2. The method for predicting the remaining service life of reinforced concrete bridges based on monitoring data according to claim 1 is characterized in that: Obtain the remaining life prediction results of the target bridge before reinforcement, including: Obtain the unique identifier of the target bridge, which is used to enter the bridge management system for retrieval to obtain the latest historical inspection report of the target bridge before reinforcement; Extract the remaining life prediction results from the latest historical inspection report and record them as initial results; Get the test date of the latest historical test report and record it as the initial time node; Calculate the time difference between the initial time node and the current time node, and record it as the transition time; By calculating the difference between the initial result and the transition time, the remaining life prediction result of the target bridge before reinforcement is obtained.

3. The method for predicting the remaining service life of reinforced concrete bridges based on monitoring data according to claim 2 is characterized in that: Also includes: Obtain all historical inspection reports of the target bridge before reinforcement, and extract N historical inspection reports in reverse order of inspection date as target reports; among them, the first target report extracted in reverse order The latest historical test report; right N Sort the historical test reports by test date to get a report set ; Get report collection M The remaining life prediction results of each target report ; Calculated The unit deviation coefficient of any two adjacent remaining life prediction results in is calculated as follows: in, Indicates the N The remaining life prediction results of the first target report are consistent with those of the N-1 Unit deviation coefficient of the remaining life prediction results of the target report, Indicates the N The test date of the first target report is the same as the N-1 The difference between the detection dates of the target reports; Get all unit deviation coefficients and generate difference series ; The autoregressive model is fitted by the difference sequence, and the autocorrelation coefficient of the remaining life prediction results of the target bridge before reinforcement is calculated. ; According to the autocorrelation coefficient , initial results and transition time, and calculate the remaining life prediction result of the target bridge before reinforcement after eliminating the prediction error. The calculation method is: in, The unit deviation coefficient between the remaining life prediction result of the first target report and the remaining life prediction result corresponding to the current time node, Indicates the transition time, It represents the remaining life prediction result of the target bridge before reinforcement after eliminating the prediction error.

4. The method for predicting the remaining service life of reinforced concrete bridges based on monitoring data according to claim 1 is characterized in that: Obtain the reinforcement scheme adopted for the target bridge and the expected life extension effect, including: Obtaining the unique identifier of the target bridge, which is used to retrieve the reinforcement design file used when reinforcing the target bridge through the bridge management system; Extract the reinforcement scheme and expected life extension effect contained in the reinforcement design document; the expected life extension effect is obtained through numerical simulation of the finite element model during the reinforcement design.

5. The method for predicting the remaining service life of reinforced concrete bridges based on monitoring data according to claim 1 is characterized in that: The reinforcement plan determines multiple key parameter types to be monitored, and generates corresponding monitoring plans based on these key parameter types for monitoring equipment deployment and key parameter collection, including: Obtain a reinforcement plan, determine multiple key parameter types related to the bridge life in the reinforcement plan based on fuzzy theory, and determine the weight value corresponding to each key parameter type related to the bridge life; According to multiple key parameter types and their corresponding weight values, the pre-built monitoring solution database is screened to obtain a matching monitoring solution for the deployment of monitoring equipment and determination of parameter collection conditions; Through the deployment of monitoring equipment, key parameters can be collected when the parameter collection conditions are met.

6. The method for predicting the remaining service life of reinforced concrete bridges based on monitoring data according to claim 1 is characterized in that: Obtain the standard values ​​of key parameters corresponding to the expected life extension effect during numerical simulation, compare them with the collected values ​​of multiple key parameters, and complete the correction of the expected life extension effect based on the comparison results, including: Obtaining standard values ​​of multiple key parameters corresponding to the expected life extension effect when performing numerical simulations using a finite element model, and obtaining parameter weights when determining the types of multiple key parameters using fuzzy theory; The ratio of each key parameter to the corresponding key parameter standard value is calculated as the benchmark value; wherein, the calculation principle follows the same direction processing of indicators; Obtain all benchmark values ​​and corresponding parameter weights for weighted average calculation to obtain the correction coefficient for the expected life extension effect; The correction result is obtained by multiplying the correction factor and the expected life extension effect.

7. The method for predicting the remaining service life of reinforced concrete bridges based on monitoring data according to claim 1 is characterized in that: The remaining life prediction results are extended based on the correction results to obtain the remaining life of the reinforced concrete bridge, including: Obtaining a correction result; wherein the correction result is the estimated life extension effect after correction; Based on the type characteristics of the correction results, the adaptive operation mode is matched to calculate the correction results and the remaining life prediction results of the target bridge before reinforcement, and the remaining life of the reinforced concrete bridge is obtained. Among them, the type characteristics of the correction result include numerical type and percentage type. The operation mode corresponding to the numerical type is addition operation, and the operation mode corresponding to the percentage type is multiplication operation.

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