Bridge network maintenance decision-making method and system based on bridge technical condition index

Abstract

The present application relates to the field of bridge maintenance, in particular to a bridge network maintenance decision method and system based on a bridge technical condition index. The bridge network maintenance decision method based on the bridge technical condition index comprises the following steps: obtaining the average service time of the bridge under different state levels by using bridge service data; obtaining the time-varying relationship of the bridge technical condition index within the full life service cycle according to the first corresponding relationship and the average service time; setting the bridge maintenance parameters within the full life service cycle; obtaining the carbonation coefficients of the bridge before and after the maintenance node according to the second corresponding relationship and the bridge technical condition indexes before and after the maintenance node; setting a carbonation depth threshold, predicting the total carbonation depth of the bridge under the maintenance measures according to the carbonation coefficients of the bridge before and after the maintenance node, and adjusting the bridge maintenance parameters according to the comparison result of the carbonation depth threshold and the total carbonation depth. The present application can provide a suitable opportunity for the bridge to implement maintenance measures by taking the bridge technical condition index as a reference.

Description

Technical Field

[0001] This invention relates to the field of bridge maintenance, and specifically to a bridge network maintenance decision-making method and system based on a bridge technical condition index. Background Technology

[0002] As bridges age, their service conditions deteriorate, leading to increased "aging" and prominent durability issues, resulting in a rise in safety accidents. As critical nodes in road infrastructure networks, the smooth operation and safe passage of the road network largely depend on the safety and durability of bridges.

[0003] Currently, bridge maintenance management strategies are usually "reactive maintenance" strategies, and research on the timing and benefits of preventive maintenance for bridges only considers the laws of bridge performance degradation or durability deterioration, without considering the correlation between the laws of bridge performance degradation and durability deterioration.

[0004] In bridge maintenance and management, it is generally required that medium and small bridges undergo routine maintenance and minor repairs without major or medium-sized overhauls to ensure that their technical condition meets the safety requirements for normal use within their service life. Therefore, choosing the right time to take appropriate preventive maintenance measures for bridges to ensure that their performance and durability are always in good condition is of great significance for operation and management. Summary of the Invention

[0005] In response to the shortcomings of existing technologies and the needs of practical applications, this invention provides a bridge network maintenance decision-making method and system based on the bridge technical condition index. It aims to provide effective preventive maintenance strategies and management decision-making tools for bridge maintenance management, so as to improve the level of bridge maintenance management and enhance the safety, durability and sustainable development capabilities of transportation infrastructure.

[0006] In a first aspect, the bridge network maintenance decision-making method based on the bridge technical condition index provided by the present invention includes the following steps: acquiring bridge service data, wherein the bridge service data includes the service duration of different bridges under different condition levels, and using the bridge service data to obtain the average service duration of bridges under different condition levels; acquiring a first correspondence between the bridge technical condition index and the bridge condition level, and combining the first correspondence with the average service duration of bridges under different condition levels to obtain the time-varying relationship of the bridge technical condition index over the entire service life; and setting bridge maintenance parameters over the entire service life, wherein the bridge maintenance parameters include multiple maintenance nodes and maintenance... The bridge technical condition index before and after the maintenance node is determined. A second correspondence between the bridge technical condition index and the bridge carbonization coefficient is obtained through the time-varying relationship of the bridge technical condition index. This second correspondence is then combined with the bridge technical condition index before and after the maintenance node to obtain the bridge carbonization coefficient before and after the maintenance node. A carbonization depth threshold is set. Combining the bridge carbonization coefficient before and after the maintenance node with the maintenance duration between the maintenance nodes, the total carbonization depth of the bridge under maintenance measures is predicted over its entire service life. Based on the comparison between the carbonization depth threshold and the total carbonization depth, the bridge maintenance parameters are adjusted so that the total carbonization depth over the entire service life is less than the carbonization depth threshold.

[0007] The bridge network maintenance decision-making method based on the bridge technical condition index provided by this invention has the following advantages: This invention uses the bridge technical condition index as a reference to provide a suitable time for implementing bridge maintenance measures, thereby changing the current passive "reactive maintenance" strategy and realizing proactive "preventive maintenance" measures to ensure that the bridge's performance and durability are always in good condition, thereby reducing operation and management costs and extending the safe service life of the bridge.

[0008] Optionally, the bridge maintenance parameters also include maintenance costs. Adjusting the bridge maintenance parameters further includes the following steps: setting a maintenance cost threshold; obtaining the total maintenance cost of the bridge over its entire service life based on the maintenance costs of each maintenance node; and adjusting the bridge maintenance parameters based on the comparison between the maintenance cost threshold and the total maintenance cost, so that the total maintenance cost over the entire service life is less than the maintenance cost threshold. The bridge network maintenance decision-making method based on the bridge technical condition index provided by this invention effectively balances the relationship between maintenance effectiveness, economic costs, and resource utilization efficiency by setting a maintenance cost threshold and adjusting maintenance parameters. This not only improves the scientificity and accuracy of bridge maintenance management but also enables cost control and optimization throughout the bridge's entire life cycle, providing important support and guarantees for the safe operation and sustainable development of transportation infrastructure.

[0009] Optionally, obtaining bridge service data includes the following steps: assessing the status level of different bridges at different service durations.

[0010] Further optionally, the assessment of the condition level of different bridges under different service durations includes the following steps: assessing the first component technical condition index of each component in the bridge deck system based on the apparent damage of each component, and obtaining the bridge deck system component technical condition index by combining the first component technical condition index of each component with its corresponding first component weight; assessing the second component technical condition index of each component in the superstructure based on the apparent damage of each component, and obtaining the bridge deck system component technical condition index by combining the second component technical condition index of each component with its corresponding first component weight. The technical condition index of the superstructure components is obtained by weighting the corresponding second component. Based on the apparent damage of each component in the substructure, the technical condition index of the third component in the substructure is assessed. Combining the technical condition index of the third component in the substructure with its corresponding weight, the technical condition index of the substructure components is obtained. Finally, the bridge technical condition index of the entire bridge is obtained by weighting the technical condition indices of the bridge deck components with their respective weights, the superstructure components with their respective weights, and the substructure components with their respective weights. This bridge network maintenance decision-making method based on bridge technical condition indices provides an overall bridge technical condition index by weighted summation of the technical condition indices of the bridge deck components, superstructure components, and substructure components with their respective weights. This assessment method considers the importance and contribution of each component in the bridge, and can objectively and accurately reflect the overall technical condition of the bridge.

[0011] Optionally, obtaining the average service life of a bridge under different condition levels using the bridge service data includes the following steps: using the bridge service data, fitting a Weiper distribution probability density function of the bridge's service life under different condition levels, and obtaining the Weiper distribution shape parameter and Weiper distribution size parameter through the Weiper distribution probability density function; based on the Weiper distribution shape parameter and Weiper distribution size parameter, constructing a prediction model for the average service life under different condition levels, wherein the average service life prediction model satisfies the following equation: Among them, E(T) i T represents the average service life of the bridge at condition level i. i This represents the service life data of the bridge within condition level i, where e represents the natural constant, and β represents the service life data. i η represents the shape parameter of the Weiber distribution corresponding to the probability density function of the service duration at state level i. iThe Weber distribution size parameter represents the probability density function of the Weber distribution corresponding to the service duration at state level i. Using the average service duration prediction model, the average service duration of the bridge at different state levels is obtained. The bridge network maintenance decision-making method based on the bridge technical condition index provided by this invention utilizes the average service duration prediction model to obtain the average service duration of the bridge at different state levels, providing crucial information for formulating maintenance strategies and decisions.

[0012] Optionally, the step of combining the first correspondence with the average service time of the bridge under different state levels to obtain the time-varying relationship of the bridge technical condition index throughout its entire service life includes the following steps: obtaining the starting bridge technical condition index and the ending bridge technical condition index corresponding to any bridge state level, and obtaining the starting time point and the ending time point of any bridge state level within its entire service life based on the average service time of the bridge under different state levels; using the starting bridge technical condition index and the starting time point as the starting point of the bridge under the corresponding state level, and using the ending bridge technical condition index and the ending time point as the ending point of the bridge under the corresponding state level, linearly fitting the time-varying relationship of the bridge technical condition index under the corresponding state level through the starting point and the ending point; summarizing the time-varying relationships of the bridge technical condition index under different state levels throughout its entire service life to obtain the time-varying relationship of the bridge technical condition index throughout its entire service life. The bridge network maintenance decision-making method based on the bridge technical condition index provided by this invention obtains the time-varying relationship of the bridge technical condition index throughout its entire service life by summarizing the time-varying relationships of the technical condition indices of all bridges under different state levels. This relationship comprehensively considers the evolution trajectory of bridges under different condition levels, and can fully depict the changing pattern of bridge technical condition over time, providing important time-series information for maintenance decisions.

[0013] Optionally, the step of obtaining a second correspondence between the bridge technical condition index and the bridge carbonization coefficient through the time-varying relationship of the bridge technical condition index, and combining the second correspondence with the bridge technical condition index before and after the maintenance node to obtain the bridge carbonization coefficient before and after the maintenance node, includes the following steps: obtaining the carbonization coefficient of the bridge at different state levels, and obtaining the bridge technical condition index interval corresponding to any bridge state level according to the time-varying relationship of the bridge technical condition index; summarizing the bridge technical condition index interval and carbonization coefficient corresponding to any bridge state level to obtain a second correspondence between the bridge technical condition index and the carbonization coefficient; and combining the second correspondence with the bridge technical condition index before and after the maintenance node to obtain the bridge carbonization coefficient before and after the maintenance node. The bridge network maintenance decision-making method based on the bridge technical condition index provided by this invention uses the relationship between the bridge technical condition index and the carbonization coefficient, combined with the bridge technical condition index before and after the maintenance node, to predict the bridge carbonization coefficient before and after the maintenance node, thus providing a basis for maintenance management decisions.

[0014] Optionally, the step of predicting the total carbonization depth of a bridge under maintenance measures over its entire service life by combining the bridge carbonization coefficient before and after a maintenance node with the maintenance duration between maintenance nodes includes the following steps: obtaining a first duration and a first carbonization depth between the bridge technical condition index decaying from its maximum value to the bridge technical condition index marked by the maintenance node; obtaining a second duration and a second carbonization depth between the bridge technical condition index decaying after the maintenance node and the bridge technical condition index marked by the maintenance node; obtaining the number of maintenance operations within the entire service life based on the first and second durations; and obtaining the total carbonization depth of the bridge under maintenance measures within the entire service life based on the number of maintenance operations and the first and second carbonization depths. The bridge network maintenance decision-making method based on the bridge technical condition index provided by this invention provides an important quantitative analysis tool for maintenance management decisions by analyzing the correlation between changes in the bridge technical condition index and carbonization depth, which helps maintenance managers formulate targeted maintenance plans.

[0015] Alternatively, the total carbonization depth of the bridge under maintenance measures over its entire service life can be calculated using the following model: Where X represents the total carbonization depth, X1 represents the first carbonization depth, X2 represents the second carbonization depth, Ω represents the number of maintenance cycles during the entire service life, and θ represents the slope of the linear fit between the bridge technical condition index and the bridge carbonization coefficient. The service duration of the bridge technical condition index after it decays from its maximum value to the maintenance node is represented by Υ, where Υ represents the maintenance duration of the last maintenance node within the entire service life, W represents the total service life, BCI(t) represents the time-varying relationship of the bridge technical condition index within the entire service life, t represents the service duration, α represents the environmental impact index, T1 represents the first duration, and T2 represents the second duration. The total carbonization depth calculation model proposed in this invention comprehensively considers factors such as changes in the bridge technical condition index, the number of maintenance operations, and environmental impacts, and can effectively predict the changes in carbonization depth of bridges within their entire service life under preventative maintenance measures.

[0016] Secondly, the bridge network maintenance decision-making system based on the bridge technical condition index provided by the present invention includes an input device, a processor, a memory, and an output device. The input device, the processor, the memory, and the output device are interconnected. The memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the bridge network maintenance decision-making method based on the bridge technical condition index.

[0017] The bridge network maintenance decision-making system based on the bridge technical condition index provided by this invention has the following advantages: Through the execution of computer programs and processors, the bridge network maintenance decision-making system based on the bridge technical condition index provided by this invention realizes the automation and intelligence of the bridge maintenance decision-making process, improves the accuracy and efficiency of decision-making, and can help maintenance managers formulate reasonable maintenance strategies, extend the service life of bridges, and improve safety and reliability. Attached Figure Description

[0018] Figure 1 The first flowchart of the bridge network maintenance decision-making method based on the bridge technical condition index;

[0019] Figure 2 A schematic diagram illustrating the time-varying relationship of the bridge's technical condition index before and after maintenance throughout its entire service life.

[0020] Figure 3 The second flowchart of the bridge network maintenance decision-making method based on the bridge technical condition index is shown.

[0021] Figure 4 This is a structural diagram of a bridge network maintenance decision-making system based on a bridge technical condition index. Detailed Implementation

[0022] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0023] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0024] In one embodiment, see Figure 1 , Figure 1 This is a first flowchart of the bridge network maintenance decision-making method based on the bridge technical condition index provided in an embodiment of the present invention. Figure 1 As shown in the embodiments of the present invention, the bridge network maintenance decision-making method based on the bridge technical condition index includes the following steps:

[0025] S01. Obtain bridge service data, which includes the service duration of different bridges under different state levels, and use the bridge service data to obtain the average service duration of the bridges under different state levels.

[0026] In this embodiment, in order to implement preventive maintenance measures for bridges in the target area, the bridge service data obtained in step S01 comes from the bridge network of the target area.

[0027] In one or more other embodiments, in order to implement preventive maintenance measures for bridges in other target areas, the bridge service data obtained in step S01 comes from the bridge network of the corresponding target area.

[0028] It is understood that the bridge network refers to a network formed by bridges connecting various regions or locations. The bridge network specifically includes, but is not limited to, bridges based on reinforced concrete components such as highway bridges, urban road bridges, railway bridges, urban rail transit bridges, viaducts, and pedestrian overpasses.

[0029] Furthermore, the acquisition of bridge service data described in step S01 specifically includes the following step: assessing the condition level of different bridges at different service durations. The assessment criteria for evaluating the bridge condition level include, but are not limited to, existing assessment standards and self-defined assessment standards based on actual conditions.

[0030] In this embodiment, the evaluation criteria for the selected bridge condition level are based on the "Technical Standard for Urban Bridge Maintenance" (CJJ99-2017). Further, the evaluation steps for the condition level of any bridge are as follows:

[0031] S0111. Based on the apparent damage of each component in the bridge deck system, evaluate the technical condition index of the first component of each component in the bridge deck system, and combine the technical condition index of the first component of each component in the bridge deck system with the corresponding weight of the first component to obtain the technical condition index of the bridge deck system components.

[0032] The bridge deck system, located above the bridge structure, directly bears the loads of vehicles and pedestrians. Its main function is to provide a flat surface for vehicular and pedestrian traffic and to transfer loads to other parts of the bridge. The bridge deck system primarily includes components such as pavement, expansion joints, sidewalks, railings, and drainage systems.

[0033] In this embodiment, the technical condition index of the bridge deck components satisfies the following calculation model: Where, ∑BCI i，bds β represents the technical condition index of the bridge deck system of the i-th bridge in the bridge network, m represents the total number of components in the bridge deck system of the i-th bridge in the bridge network, and β i，j，bds BCI represents the weight of the first component in the deck system of the i-th bridge in the bridge network. i，j，bds This represents the technical condition index of the first component in the deck system of the i-th bridge in the bridge network.

[0034] It is understandable that the first component technical condition index is an index derived from an assessment of the damage status of each component in the bridge deck system. The first component technical condition index reflects the health status of each component in the bridge deck system, and is usually expressed in numerical form. The higher the value, the better the component condition, and the lower the value, the worse the component condition.

[0035] S0112. Based on the apparent damage of each component in the superstructure, assess the technical condition index of the second component of each component in the superstructure, and combine the technical condition index of the second component of each component in the superstructure with the corresponding weight of the second component to obtain the technical condition index of the superstructure components.

[0036] The superstructure refers to the part of a bridge structure located below the bridge deck system, directly supporting and transferring loads to the substructure. The various components of the superstructure are primarily responsible for bearing traffic loads and other external loads, and transferring these loads to the bridge foundation. The superstructure mainly includes main beams, diaphragms, and supports.

[0037] In this embodiment, the technical condition index of the superstructure components satisfies the following calculation model: Where, ∑BCI i，sps β represents the technical condition index of the superstructure components of the i-th bridge in the bridge network, n represents the total number of components in the superstructure of the i-th bridge in the bridge network, and β i，j，sps BCI represents the weight of the second component in the superstructure of the i-th bridge in the bridge network, specifically the weight of the j-th component. i，j，sps It represents the technical condition index of the second component in the superstructure of the j-th component of the i-th bridge in the bridge network.

[0038] Understandably, the second component technical condition index is an index derived from an assessment of the damage status of each component in the superstructure. The second component technical condition index reflects the health status of each component in the superstructure. Similarly, the second component technical condition index is expressed in numerical form; a higher value indicates a better component condition, and a lower value indicates a worse component condition.

[0039] S0113. Based on the apparent damage of each component in the substructure, assess the technical condition index of the third component of each component in the substructure, and combine the technical condition index of the third component of each component in the substructure with the corresponding weight of the third component to obtain the technical condition index of the substructure components.

[0040] The substructure refers to the part of a bridge structure that supports the superstructure and transfers loads to the foundation. The substructure mainly includes components such as piers, abutments, pier and abutment foundations, and wing walls.

[0041] In this embodiment, the technical condition index of the substructure components satisfies the following calculation model: Where, ∑BCI i，sbs Let represent the technical condition index of the substructure components of the i-th bridge in the bridge network, ο represent the total number of components in the substructure of the i-th bridge in the bridge network, and β represent the technical condition index of the substructure components of the i-th bridge in the bridge network. i，j，sbs BCI represents the weight of the third component in the substructure of the i-th bridge in the bridge network. i，j，sbs This represents the technical condition index of the third component of the j-th component in the superstructure of the i-th bridge in the bridge network.

[0042] It is understood that the third component technical condition index is an index derived from an assessment of the damage status of each component in the substructure. The third component technical condition index reflects the health status of each component in the substructure. Similarly, the third component technical condition index is expressed in numerical form; a higher value indicates a better component condition, and a lower value indicates a worse component condition.

[0043] S0114. The bridge technical condition index of the entire bridge is obtained by superimposing the product of the technical condition index of the bridge deck components and the weight of the bridge deck components, the product of the technical condition index of the superstructure components and the weight of the superstructure components, and the product of the technical condition index of the substructure components and the weight of the substructure components.

[0044] In this embodiment, the bridge technical condition index (BCI) of the entire bridge satisfies the following calculation model: BCI i =α bds ∑BCI i，bds +α sps ∑BCI i，sps +α sbs ∑BCI i，sbs Among them, BCI i α represents the bridge technical condition index of the i-th bridge in the bridge network. bds The weights of the bridge deck components are represented by ∑BCI. i，bds α represents the technical condition index of the bridge deck system of the i-th bridge in the bridge network. sps ∑BCI represents the weight of the superstructure components. i，sps α represents the technical condition index of the superstructure components of the i-th bridge in the bridge network. sbs ∑BCI represents the weight of the substructure components. i，sbs This represents the technical condition index of the substructure components of the i-th bridge in the bridge network.

[0045] S0115. Based on the bridge maintenance technical standards, assess the bridge's condition level using the bridge technical condition index.

[0046] In this embodiment, the correspondence between the bridge technical condition index and the condition level is shown in the table below:

[0047] Status Level A B C D E

[0048] As shown in the table above, when the bridge's technical condition index is within [90, 100], the bridge's condition level is A; when the bridge's technical condition index is within [80, 90), the bridge's condition level is B; when the bridge's technical condition index is within [66, 80), the bridge's condition level is C; when the bridge's technical condition index is within [50, 66), the bridge's condition level is D; and when the bridge's technical condition index is within [0, 50), the bridge's condition level is E.

[0049] Typically, the technical condition of bridges in a bridge network is assessed periodically or irregularly as needed, and the assessment results are stored in corresponding storage devices using paper or electronic records. It is understood that these assessment results constitute the bridge service data required by this invention.

[0050] Furthermore, in this embodiment, the step S01 of obtaining the average service life of the bridge under different condition levels using the bridge service data includes the following steps:

[0051] S0121. Using the bridge service data, fit the Weber distribution probability density function of the bridge service duration under different state levels, and obtain the Weber distribution shape parameter and Weber distribution size parameter through the Weber distribution probability density function.

[0052] The Weiber probability density function satisfies the following model: Where, f(T) i ) represents the Weiber probability density function of the service duration at state level i in the bridge network, and β i η represents the shape parameter of the Weiber distribution corresponding to the probability density function of the service duration at state level i. i T represents the Weiber distribution size parameter corresponding to the Weiber distribution probability density function of service duration at state level i. i This represents the service life data of the bridge within state level i.

[0053] S0122. Based on the shape parameter and scale parameter of the Weber distribution, construct a prediction model for the average service duration under different state levels.

[0054] Furthermore, the average service duration prediction model satisfies the following equation: Among them, E(T) i T represents the average service life of the bridge at condition level i. i This represents the service life data of the bridge within condition level i, where e represents the natural constant, and β represents the service life data. i η represents the shape parameter of the Weiber distribution corresponding to the probability density function of the service duration at state level i.i The Weber distribution size parameter represents the probability density function of the Weber distribution corresponding to the service duration at state level i.

[0055] S0123. Using the average service life prediction model, the average service life of the bridge under different state levels is obtained.

[0056] In one or more other embodiments, the step S01, which involves using the bridge service data to obtain the average service time of a bridge under different state levels, is achieved through the following steps: selecting a landmark bridge from the bridge network; obtaining the service time of the landmark bridge under different state levels based on the bridge service data of the landmark bridge; and using the service time of the landmark bridge under different state levels to characterize the service time of each bridge in the bridge network corresponding to different state levels.

[0057] In another embodiment or several embodiments, the step S01 of obtaining the average service time of bridges under different state levels using the bridge service data is achieved through the following steps: obtaining the service time of each bridge in the bridge network under different state levels based on the bridge service data of each bridge in the bridge network; summarizing the service time of each bridge under different state levels, and averaging the service time under different state levels in the summarization results to obtain the service time of the bridges in the bridge network corresponding to different state levels.

[0058] S02. Obtain the first correspondence between the bridge technical condition index and the bridge condition level, and combine the first correspondence with the average service time of the bridge under different condition levels to obtain the time-varying relationship of the bridge technical condition index throughout the entire service life.

[0059] It is understandable that the first correspondence between the bridge technical condition index obtained in step S02 and the bridge condition level is related to the evaluation criteria of the bridge technical condition index used to evaluate the bridge in step S01.

[0060] In this embodiment, the first correspondence between the bridge technical condition index and the bridge condition level is shown in the table above. Further, step S02, which combines the first correspondence with the average service life of the bridge under different condition levels to obtain the time-varying relationship of the bridge technical condition index over its entire service life, includes the following steps:

[0061] S021. Obtain the starting bridge technical condition index and the ending bridge technical condition index corresponding to any bridge condition level, and obtain the starting time point and ending time point of any bridge condition level in the whole life service cycle based on the average service time of the bridge under different condition levels.

[0062] In this embodiment, the starting bridge technical condition index for bridge status level A is 100, and the ending bridge technical condition index is 90. The starting time for bridge status level A is year 1, and the ending time is year 13. The starting bridge technical condition index for bridge status level B is 90, and the ending bridge technical condition index is 80. The starting time for bridge status level B is year 14, and the ending time is year 28. The starting bridge technical condition index for bridge status level C is 80, and the ending bridge technical condition index is 66. The starting time for bridge status level C is year 29, and the ending time is year 49. The starting bridge technical condition index for bridge status level D is 66, and the ending bridge technical condition index is 50. The starting time for bridge status level D is year 50, and the ending time is year 70. The starting bridge technical condition index for bridge status level E is 50, and the ending bridge technical condition index is 0. The starting time for bridge status level E is year 71, and the ending time is year 100.

[0063] S022. Using the starting bridge technical condition index and the starting time point as the starting point of the bridge under the corresponding condition level, and the ending bridge technical condition index and the ending time point as the ending point of the bridge under the corresponding condition level, the time-varying relationship of the bridge technical condition index under the corresponding condition level is linearly fitted through the starting point and the ending point.

[0064] In this embodiment, (1, 100) and (13, 90) are used to linearly fit the time-varying relationship of the bridge technical condition index corresponding to state level A; (14, 90) and (28, 80) are used to linearly fit the time-varying relationship of the bridge technical condition index corresponding to state level B; (29, 80) and (49, 66) are used to linearly fit the time-varying relationship of the bridge technical condition index corresponding to state level C; (50, 66) and (70, 50) are used to linearly fit the time-varying relationship of the bridge technical condition index corresponding to state level D; and (71, 50) and (100, 0) are used to linearly fit the time-varying relationship of the bridge technical condition index corresponding to state level E.

[0065] S023. Summarize the time-varying relationship of bridge technical condition index under different state levels throughout the entire service life of the bridge, and obtain the time-varying relationship of bridge technical condition index throughout the entire service life.

[0066] Typically, the service life of a bridge is 50 to 100 years. In this embodiment, the service life is 100 years, and the duration corresponding to the time-varying relationship of the bridge's technical condition index for any given period includes the entire time from the year corresponding to the start time to the year corresponding to the end time. Please refer to [link / reference]. Figure 2 , Figure 2Figure (1) is a schematic diagram of the time-varying relationship of the bridge technical condition index of the bridge before maintenance during the entire service life of the bridge provided in the embodiment of the present invention. Figure 2 The horizontal axis represents the service time, and the vertical axis represents the bridge's technical condition index.

[0067] S03. During the entire service life, bridge maintenance parameters are set, including multiple maintenance nodes and bridge technical condition indices before and after the maintenance nodes.

[0068] The bridge maintenance parameters described in this invention are various parameters or indicators used to guide and manage bridge maintenance work. In this embodiment, a maintenance node refers to a maintenance marker point throughout the entire service life of the bridge, specifically the bridge technical condition index. When the bridge technical condition index decays to the level corresponding to the maintenance marker point, maintenance measures are implemented on the bridge. After the maintenance measures are implemented, the bridge technical condition index will rebound, and based on the rebounded bridge technical condition index, it will decay again with increasing service time.

[0069] Understandably, the change in the bridge's technical condition index before and after a maintenance milestone is determined by the specific maintenance measures, and different maintenance measures correspond to different maintenance costs. Generally, the higher the maintenance cost, the greater the change in the bridge's technical condition index, meaning the higher the bridge's technical condition index after maintenance. Furthermore, the initial values ​​of bridge maintenance parameters can be set by relevant personnel based on experience or other methods.

[0070] S04. By using the time-varying relationship of the bridge technical condition index, obtain the second correspondence between the bridge technical condition index and the bridge carbonization coefficient, and combine the second correspondence with the bridge technical condition index before and after the maintenance node to obtain the bridge carbonization coefficient before and after the maintenance node.

[0071] Further, step S04, which involves obtaining a second correspondence between the bridge technical condition index and the bridge carbonization coefficient through the time-varying relationship of the bridge technical condition index, and combining the second correspondence with the bridge technical condition index before and after the maintenance node to obtain the bridge carbonization coefficient before and after the maintenance node, includes the following steps:

[0072] S041. Obtain the carbonization coefficient of the bridge under different condition levels, and obtain the bridge technical condition index interval corresponding to any bridge condition level according to the time-varying relationship of the bridge technical condition index.

[0073] The carbonation coefficient is a comprehensive parameter characterizing the carbonation rate of reinforced concrete. In this embodiment, the carbonation coefficient is used to measure the durability of reinforced concrete bridges. Further, obtaining the carbonation coefficient of the bridge under different condition levels in step S041 includes the following steps:

[0074] S0411. Obtain bridge carbonization depth detection data, which includes carbonization depth data of different bridges under different service durations.

[0075] Typically, the carbonation depth of the bridge can be measured by drilling holes in the reinforced concrete components of the bridge and using the carbonation depth of the concrete in one hole or the average of the carbonation depths in multiple holes as the data for bridge carbonation depth testing.

[0076] S0412. Using the bridge carbonization depth detection data, fit the carbonization coefficient of different bridges under different service durations.

[0077] Based on the service life of the bridge during the inspection and the corresponding bridge carbonization depth test data, the carbonization coefficient of different bridges at different service lives can be fitted.

[0078] In this embodiment, the carbonization coefficient satisfies the following fitting model: k ε =X / (t) α Where X represents the carbonization depth, k ε denoted by α, where t represents the carbonization coefficient, α represents the service life, and α represents the environmental impact index.

[0079] Furthermore, if the reinforced concrete components of the bridge do not develop cracks, the environmental impact index is less than 0.5; if the reinforced concrete components of the bridge have developed cracks, the environmental impact index is greater than 0.5; if the reinforced concrete components of the bridge are in an ideal situation with a uniform pore structure and an unchanged external environment, the environmental impact index is 0.5.

[0080] Furthermore, the fitted carbonization coefficient can be achieved using data processing software such as Excel and MATLAB, as well as related programming tools such as Python, combined with the corresponding fitting model and bridge carbonization depth detection data.

[0081] S0413. Combining the carbonization coefficient and condition level of different bridges under different service durations, obtain the carbonization coefficient of the bridge under different condition levels. The carbonization coefficient of the bridge under any condition level is the average carbonization coefficient of different bridges under the corresponding condition level.

[0082] In one or more other embodiments, to avoid further drilling that could damage the reinforced concrete components of the bridge, the step S041 of obtaining the carbonation coefficient of the bridge under different condition levels is implemented through the following steps:

[0083] S00411. Obtain bridge compressive strength test data, which includes compressive strength data of different bridges under different service durations.

[0084] The compressive strength of reinforced concrete is a major indicator reflecting the mechanical properties of concrete. It can comprehensively reflect the combined effects of different factors such as water-cement ratio, cement type, aggregate type, cement dosage, construction quality and curing conditions on the concrete quality value. Moreover, it is easier to measure than the carbonation depth of bridges.

[0085] S00412. Using the bridge compressive strength test data, fit the carbonization coefficient of different bridges under different service durations.

[0086] In this embodiment, the carbonization coefficient satisfies the following fitting model: k ε = a / f+b, where X represents carbonization depth, a represents the first regression parameter, b represents the second regression parameter, f represents compressive strength, and α represents the environmental impact index.

[0087] Furthermore, for the first regression parameter 'a' and the second regression parameter 'b' of the model, existing bridge carbonization depth detection data can be used, combined with corresponding bridge compressive strength detection data and service duration at the time of detection, to obtain the following fitted model: X = (a / f + b) · (t) α Where X represents carbonization depth, a represents the first regression parameter, b represents the second regression parameter, f represents compressive strength, t represents service life, and α represents the environmental impact index.

[0088] S00413. Combining the carbonization coefficient and condition level of different bridges under different service durations, obtain the carbonization coefficient of the bridge under different condition levels. The carbonization coefficient of the bridge under any condition level is the average carbonization coefficient of different bridges under the corresponding condition level.

[0089] In one or more other embodiments, based on the time-varying characteristics of the carbonization coefficient, the step S041 of obtaining the carbonization coefficient of the bridge under different state levels further includes the following step: using the average carbonization coefficient and average service time of the bridge under different state levels, the carbonization coefficient of the bridge under different state levels is corrected by a carbonization coefficient correction model, wherein the carbonization coefficient correction model satisfies the following equation: K' N = Where N is a positive integer, N represents the condition level of the bridge throughout its entire service life, K'1 represents the instantaneous average carbonization coefficient of the bridge at condition level 1, K1 represents the average carbonization coefficient of the bridge at condition level 1, and K' N K represents the instantaneous average carbonization coefficient of a bridge at condition level N. N K' represents the average carbonation coefficient of a bridge at condition level N. j This represents the instantaneous average carbonization coefficient of the bridge at condition level j. α represents the average service life of a bridge at state level i with state level N, and α represents the environmental impact index.

[0090] Specifically, the condition levels throughout the entire service life are classified as A, B, C, D, and E. Grade A corresponds to bridges that are functionally intact, while the degree of damage increases progressively with grade B, C, D, and E. When a bridge is in Grade A condition, its instantaneous average carbonization coefficient is: K' A =K A , where K' A This represents the instantaneous average carbonization coefficient of the bridge at condition level A.

[0091] When the bridge's condition rating is Class B, its instantaneous average carbonization coefficient is: Among them, K' A K' represents the instantaneous average carbonization coefficient of a bridge in condition class A. B K represents the instantaneous average carbonization coefficient of a bridge in condition class B. B This represents the average carbonation coefficient of the bridge at condition level B. This represents the average service life of bridges in state level B at state level i. This represents the average service life of a bridge in state B when it was in state A.

[0092] When the bridge's condition rating is Class C, its instantaneous average carbonization coefficient is: Among them, K' j K represents the instantaneous average carbonization coefficient of a bridge at condition level j. C This represents the average carbonation coefficient of the bridge at condition level C. This represents the average service life of a bridge in state level C at state level i.

[0093] When the bridge's condition rating is Class D, its instantaneous average carbonization coefficient is: Among them, K' j K represents the instantaneous average carbonization coefficient of a bridge at condition level j. D This represents the average carbonation coefficient of the bridge at condition level D. This represents the average service life of a bridge at state level i when it is in state level D.

[0094] When the bridge's condition rating is Class E, its instantaneous average carbonization coefficient is: Among them, K' j K represents the instantaneous average carbonization coefficient of a bridge at condition level j. E This represents the average carbonation coefficient of the bridge at condition level E. This represents the average service life of a bridge at state level i when it is at state level E.

[0095] S042. Summarize the bridge technical condition index range and carbonization coefficient corresponding to any bridge condition level to obtain the second correspondence between the bridge technical condition index and the carbonization coefficient.

[0096] In this embodiment, any bridge technical condition index within a bridge technical condition index range of a condition level corresponds to the instantaneous average carbonization coefficient under that condition level.

[0097] In one or more other optional embodiments, the step of summarizing the bridge technical condition index interval and carbonization coefficient corresponding to any bridge condition level to obtain the second correspondence between the bridge technical condition index and the carbonization coefficient includes the following steps: obtaining the median of the bridge technical condition index interval under any condition level, and combining the median with the instantaneous average carbonization coefficient under the corresponding condition level to obtain the second correspondence data sample; summarizing the second correspondence data samples under different condition levels, and linearly fitting the second correspondence through the second correspondence data samples.

[0098] Furthermore, by linearly fitting the second correspondence data sample, the second correspondence is satisfied with the following characterization model: k = (100 - BCI) / θ, where k ≥ 0, k represents the instantaneous carbonization coefficient, BCI represents the bridge technical condition index, and θ represents the fitting slope.

[0099] S043. Based on the second correspondence and the bridge technical condition index before and after the maintenance node, obtain the bridge carbonization coefficient before and after the maintenance node.

[0100] S05. Set a carbonization depth threshold, combine the bridge carbonization coefficient before and after the maintenance node with the maintenance time between the maintenance nodes, predict the total carbonization depth of the bridge under maintenance measures during its entire service life, and adjust the bridge maintenance parameters according to the comparison result between the carbonization depth threshold and the total carbonization depth, so that the total carbonization depth during the entire service life is less than the carbonization depth threshold.

[0101] It is understandable that the carbonization depth threshold refers to the maximum permissible carbonization depth value set during the entire service life of a bridge. Specifically, the carbonization depth threshold is determined based on the bridge materials and design requirements.

[0102] Furthermore, step S05, which combines the bridge carbonization coefficient before and after the maintenance node with the maintenance duration between the maintenance nodes to predict the total carbonization depth of the bridge over its entire service life under maintenance measures, includes the following steps:

[0103] S051. Obtain the first duration and first carbonization depth of the bridge technical condition index from its maximum value to the bridge technical condition index at the maintenance node mark.

[0104] In this embodiment, the maximum value of the bridge technical condition index is 100.

[0105] Furthermore, the first time interval between the decay of the bridge technical condition index from its maximum value to the bridge technical condition index marked by the maintenance node can be obtained based on the time-varying relationship of the bridge technical condition index over the entire service life obtained in step S02.

[0106] Furthermore, the first carbonization depth between the bridge technical condition index decaying from its maximum value to the bridge technical condition index marked by the maintenance node can be obtained through the second correspondence between the bridge technical condition index and the bridge carbonization coefficient obtained in step S04.

[0107] Specifically, the first carbonization depth satisfies the following calculation model: Where X1 represents the first carbonization depth between the bridge technical condition index decaying from its maximum value to the bridge technical condition index marked by the maintenance node, θ represents the slope of the linear fit between the bridge technical condition index and the bridge carbonization coefficient, and BCI(t) represents the time-varying relationship of the bridge technical condition index over its entire service life. This indicates the service life when the bridge's technical condition index reaches its maximum value. In this embodiment, The service life of the bridge technical condition index is represented by t, which indicates the service life of the bridge technical condition index as it decays from its maximum value to the maintenance node mark. α represents the environmental impact index.

[0108] S052, the second duration and second carbonization depth between the bridge technical condition index decaying after the maintenance node and the bridge technical condition index marked by the maintenance node.

[0109] Similarly, the second time interval between the decay of the bridge technical condition index after the maintenance node and the bridge technical condition index marked by the maintenance node can also be obtained based on the time-varying relationship of the bridge technical condition index during the entire service life obtained in step S02.

[0110] Similarly, the second carbonization depth between the bridge technical condition index after the maintenance node decays and the bridge technical condition index marked by the maintenance node can also be obtained through the second correspondence between the bridge technical condition index and the bridge carbonization coefficient obtained in step S04.

[0111] Specifically, the second carbonization depth satisfies the following calculation model: X2 represents the second carbonization depth between the bridge technical condition index after the maintenance node and the bridge technical condition index marked by the maintenance node. This indicates the service life of the bridge technical condition index after it decays from its maximum value to the maintenance node.

[0112] S053. Based on the first duration and the second duration, obtain the number of maintenance operations during the entire service life, and based on the number of maintenance operations, obtain the total carbonization depth of the bridge under maintenance measures during the entire service life based on the first carbonization depth and the second carbonization depth.

[0113] Furthermore, the number of maintenance operations during the entire service life is calculated according to the following model: Ω=[(W-T1) / T2]+1, where Ω represents the number of maintenance operations during the entire service life, W represents the duration of the entire service life, T1 represents the first duration, T2 represents the second duration, and [(W-T1) / T2] represents the integer part of (W-T1) / T2.

[0114] Furthermore, the total carbonization depth of the bridge under maintenance measures throughout its entire service life satisfies the following calculation model: Υ=W-T1-(Ω-1)T2, where X represents the total carbonization depth of the bridge during its entire service life under maintenance measures, and Υ represents the maintenance duration of the last maintenance node during its entire service life.

[0115] Specifically, please see Figure 2 , Figure 2 Figures (2) to (4) are schematic diagrams illustrating the time-varying relationship of the bridge technical condition index of the bridge after maintenance during its entire service life, as provided in the embodiments of the present invention. Figure 2 The bridge technical condition index marked by the maintenance node in Figure (2) is 90, and the bridge technical condition index after maintenance is 100. Figure 2 The bridge technical condition index marked by the maintenance node in Figure (3) is 80, and the bridge technical condition index after maintenance is 90. Figure 2 The bridge technical condition index of the maintenance node in Figure (4) is 66, and the bridge technical condition index after maintenance is 80.

[0116] Further, the comparison result between the carbonization depth threshold and the total carbonization depth mentioned in step S05 is: the total carbonization depth of the bridge during its entire service life does not exceed the set carbonization depth threshold; or the total carbonization depth of the bridge during its entire service life exceeds the set carbonization depth threshold. When the total carbonization depth of the bridge during its entire service life exceeds the set carbonization depth threshold, the bridge maintenance parameters are further adjusted so that the total carbonization depth during its entire service life is less than the carbonization depth threshold. In this embodiment, adjusting the bridge maintenance parameters specifically refers to adjusting the magnitude of the bridge technical condition index of the maintenance node marker.

[0117] In one embodiment, see Figure 3 , Figure 3This is a second flowchart of the bridge network maintenance decision-making method based on the bridge technical condition index provided in an embodiment of the present invention. Figure 3 As shown in the embodiments of the present invention, the bridge network maintenance decision-making method based on the bridge technical condition index further includes the following steps:

[0118] S06. Set a maintenance cost threshold, obtain the total maintenance cost of the bridge over its entire service life based on the maintenance cost of each maintenance node, and adjust the bridge maintenance parameters based on the comparison result between the maintenance cost threshold and the total maintenance cost, so that the total maintenance cost over the entire service life is less than the maintenance cost threshold.

[0119] Specifically, the total maintenance cost is the sum of all maintenance costs throughout the entire service life. Further, the comparison between the maintenance cost threshold and the total maintenance cost is determined as follows: either the total maintenance cost does not exceed the set maintenance cost threshold; or the total maintenance cost exceeds the set maintenance cost threshold.

[0120] When the total maintenance cost of a bridge over its entire service life exceeds a set maintenance cost threshold, the bridge maintenance parameters are further adjusted so that the total maintenance cost over its entire service life is less than the maintenance cost threshold. In this embodiment, adjusting the bridge maintenance parameters specifically refers to adjusting the magnitude of the bridge technical condition index of the maintenance node marker.

[0121] In one embodiment, see Figure 4 , Figure 4 This is a structural diagram of a bridge network maintenance decision-making system based on a bridge technical condition index, provided in an embodiment of the present invention. Figure 4 As shown, the bridge network maintenance decision system based on the bridge technical condition index provided in this embodiment of the invention includes an input device, a processor, a memory, and an output device.

[0122] In this embodiment, the input device, the processor, the memory, and the output device are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions to execute a bridge network maintenance decision-making method based on a bridge technical condition index.

[0123] Furthermore, the input device is used to receive data, parameters, and conditions input by the user for the system to make bridge network maintenance decisions. Specifically, the input device may be a keyboard and mouse, a touch screen, a sensor, a voice recognition device, a scanner, or other hardware devices.

[0124] Furthermore, the processor is responsible for executing instructions in a computer program. Specifically, the processor can be a processing device that integrates or sets up one or more existing processors, such as CPU, GPU, dedicated processor, cloud processor, and edge computing processor, based on the method of the present invention.

[0125] Furthermore, the memory is used to store computer programs, data, and intermediate results, providing persistent storage of information. Specifically, the memory can be one or more types of existing storage devices that can be used to store data, such as RAM, HDD, SDD, and cloud storage systems, integrated or configured as a processing device based on the method of the present invention.

[0126] Furthermore, the output device is used to display the calculation results to the user or transmit them to other systems through charts, text reports, etc. Specifically, the output device can be one or more existing output devices such as displays, printers, alarms, and data export interfaces, and the output device can be integrated or designed based on the present invention.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A bridge network maintenance decision-making method based on bridge technical condition index, characterized in that, Includes the following steps: Acquire bridge service data, which includes the service duration of different bridges under different condition levels, and use the bridge service data to obtain the average service duration of bridges under different condition levels. Obtain the first correspondence between the bridge technical condition index and the bridge condition level, and combine the first correspondence with the average service time of the bridge under different condition levels to obtain the time-varying relationship of the bridge technical condition index throughout the entire service life. Throughout the entire service life of a bridge, bridge maintenance parameters are set, including multiple maintenance nodes and bridge technical condition indices before and after the maintenance nodes. By using the time-varying relationship of the bridge technical condition index, a second correspondence between the bridge technical condition index and the bridge carbonization coefficient is obtained. Then, by combining the second correspondence with the bridge technical condition index before and after the maintenance node, the bridge carbonization coefficient before and after the maintenance node is obtained. A carbonization depth threshold is set, and the total carbonization depth of the bridge under maintenance measures is predicted by combining the bridge carbonization coefficient before and after the maintenance node with the maintenance time between the maintenance nodes. Based on the comparison between the carbonization depth threshold and the total carbonization depth, the bridge maintenance parameters are adjusted so that the total carbonization depth during the entire service life is less than the carbonization depth threshold. The step of using the bridge service data to obtain the average service life of the bridge under different condition levels includes the following steps: Using the bridge service data, a Weber distribution probability density function of the service duration of the bridge under different state levels is fitted, and the Weber distribution shape parameter and Weber distribution scale parameter are obtained through the Weber distribution probability density function. Based on the shape parameter and size parameter of the Weber distribution, an average service duration prediction model is constructed for different state levels. The average service duration prediction model satisfies the following equation: ,in, This represents the average service life of the bridge at condition level i. This represents the service life data of the bridge within state level i. Represents the natural constant. The shape parameter of the Weipah distribution corresponding to the probability density function of the service duration at state level i is denoted by . The Weiber distribution size parameter represents the probability density function of the Weiber distribution corresponding to the service duration at state level i. Using the aforementioned average service life prediction model, the average service life of bridges under different state levels is obtained; The step of obtaining a second correspondence between the bridge technical condition index and the bridge carbonization coefficient through the time-varying relationship of the bridge technical condition index, and combining the second correspondence with the bridge technical condition index before and after the maintenance node to obtain the bridge carbonization coefficient before and after the maintenance node, includes the following steps: Obtain the carbonization coefficient of the bridge under different condition levels, and obtain the bridge technical condition index range corresponding to any bridge condition level based on the time-varying relationship of the bridge technical condition index. By summing up the bridge technical condition index range and carbonization coefficient corresponding to any bridge condition level, a second correspondence between the bridge technical condition index and the carbonization coefficient is obtained. By combining the second correspondence with the bridge technical condition index before and after the maintenance node, the bridge carbonization coefficient before and after the maintenance node is obtained. The method of combining the bridge carbonization coefficient before and after the maintenance node with the maintenance duration between the maintenance nodes to predict the total carbonization depth of the bridge over its entire service life under maintenance measures includes the following steps: Acquire the first duration and first carbonization depth of the bridge technical condition index as it decays from its maximum value to the maintenance node marker. The second duration and second carbonization depth between the bridge technical condition index after obtaining the maintenance node and the bridge technical condition index marked by the maintenance node. Based on the first duration and the second duration, the number of maintenance operations during the entire service life is obtained, and based on the number of maintenance operations, the total carbonization depth of the bridge under maintenance measures during the entire service life is obtained according to the first carbonization depth and the second carbonization depth. The first carbonization depth satisfies the following calculation model: ,in, The first carbonization depth represents the bridge technical condition index as it decays from its maximum value to the maintenance node marker. This represents the slope of the linear fit between the bridge technical condition index and the bridge carbonization coefficient. This indicates the time-varying relationship of the bridge's technical condition index throughout its entire service life. This indicates the service life of a bridge when its technical condition index reaches its maximum value. The service life of a bridge technical condition index, which indicates the rate at which the bridge technical condition index decays from its maximum value to the maintenance milestone. Indicates service duration. Indicates the environmental impact index; The second carbonization depth satisfies the following calculation model: ,in, This represents the second carbonization depth between the bridge technical condition index after the maintenance node and the bridge technical condition index marked by the maintenance node. This indicates the service life of the bridge technical condition index after it decays from its maximum value to the maintenance node; The total carbonization depth of a bridge under maintenance measures over its entire service life is calculated using the following model: , ,in, Indicates the total carbonization depth. Indicates the first carbonization depth. Indicates the second carbonization depth. This indicates the number of maintenance cycles throughout the entire service life. This represents the slope of the linear fit between the bridge technical condition index and the bridge carbonization coefficient. This indicates the service life of a bridge after its technical condition index decays from its maximum value to a maintenance threshold. This indicates the duration of maintenance at the last maintenance point during the entire service life cycle. Indicates the total service life duration. This indicates the time-varying relationship of the bridge's technical condition index throughout its entire service life. Indicates service duration. Indicates the environmental impact index. Indicates the first duration. Indicates the second duration; The number of maintenance operations during the entire service life is calculated according to the following model: ,in, This indicates the number of maintenance cycles throughout the entire service life. Indicates the total service life duration. Indicates the first duration. Indicates the second duration. Indicates to Round down.

2. The bridge network maintenance decision-making method based on bridge technical condition index according to claim 1, characterized in that, It also includes the following steps: A maintenance cost threshold is set, and the total maintenance cost of the bridge over its entire service life is obtained based on the maintenance cost of each maintenance node. The bridge maintenance parameters are then adjusted based on the comparison between the maintenance cost threshold and the total maintenance cost, so that the total maintenance cost over the entire service life is less than the maintenance cost threshold.

3. The bridge network maintenance decision-making method based on bridge technical condition index according to any one of claims 1-2, characterized in that, The process of acquiring bridge service data includes the following steps: assessing the status level of different bridges at different service durations.

4. The bridge network maintenance decision-making method based on bridge technical condition index according to claim 3, characterized in that, The assessment of the condition level of different bridges under different service durations includes the following steps for assessing the condition level of any given bridge: Based on the apparent damage of each component in the bridge deck system, the technical condition index of the first component of each component in the bridge deck system is evaluated, and the technical condition index of the bridge deck system components is obtained by combining the technical condition index of the first component of each component in the bridge deck system and the corresponding weight of the first component. Based on the apparent damage of each component in the superstructure, the technical condition index of the second component of each component in the superstructure is evaluated, and the technical condition index of the superstructure components is obtained by combining the technical condition index of the second component of each component in the superstructure and the corresponding weight of the second component. Based on the apparent damage of each component in the substructure, the technical condition index of the third component of each component in the substructure is evaluated, and the technical condition index of the substructure components is obtained by combining the technical condition index of the third component of each component in the substructure and the corresponding third component weight. The bridge technical condition index of the entire bridge is obtained by superimposing the product of the technical condition index of the bridge deck components and the weight of the bridge deck components, the product of the technical condition index of the superstructure components and the weight of the superstructure components, and the product of the technical condition index of the substructure components and the weight of the substructure components.

5. The bridge network maintenance decision-making method based on bridge technical condition index according to any one of claims 1-2, characterized in that, The step of combining the first correspondence with the average service life of the bridge under different condition levels to obtain the time-varying relationship of the bridge's technical condition index throughout its entire service life includes the following steps: Obtain the starting bridge technical condition index and the ending bridge technical condition index corresponding to any bridge condition level, and obtain the starting time point and ending time point of any bridge condition level in the whole service life cycle based on the average service time of the bridge under different condition levels. Using the starting bridge technical condition index and the starting time point as the starting point of the bridge under the corresponding condition level, and the ending bridge technical condition index and the ending time point as the ending point of the bridge under the corresponding condition level, the time-varying relationship of the bridge technical condition index under the corresponding condition level is linearly fitted through the starting point and the ending point. The time-varying relationship of bridge technical condition index under different condition levels during the entire service life of bridges is summarized to obtain the time-varying relationship of bridge technical condition index during the entire service life of bridges.

6. A bridge network maintenance decision-making system based on a bridge technical condition index, characterized in that, The system includes an input device, a processor, a memory, and an output device, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the bridge network maintenance decision-making method based on the bridge technical condition index as described in any one of claims 1 to 5.

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