Smart maintenance platform for providing bridge structure related data and bridge structure maintenance method therewith

Abstract

A smart maintenance platform and a method for maintaining bridge structures using the same are provided, which provide data and analysis algorithms related to bridge structures and can construct a bridge structure maintenance database by organizing big data based on bridge-related inspection and diagnosis reports, environmental information, and measurement information, and can provide services required by management entities, the inspection and diagnosis industry, research institutes, and academia in a modular form based on this, and can also predict damage and aging necessary for the preventive maintenance of bridges and provide integrated data and analysis algorithms related to bridge structures suitable for users, and can also provide comparative data to verify the measurement and evaluation results of a target bridge based on data measuring vibration, displacement, cracks, and temperature / humidity for more than one year for multiple representative bridges according to the type and environmental conditions of representative bridges when performing safety evaluations of general bridges where long-term measurement systems are not installed.

Description

Technology Field

[0001] The present invention relates to a smart maintenance platform that provides data and analysis algorithms related to bridge structures (hereinafter also simply referred to as the "bridge structure smart maintenance platform"), and more specifically, as a utilization technology for the smart maintenance platform for aging bridge structures, the invention relates to a smart maintenance platform that provides data and analysis algorithms related to bridge structures and a method for maintaining bridge structures using the same, which can construct big data based on inspection and diagnosis reports, environmental information, and measurement information related to bridges, and provide services required by management entities, the inspection and diagnosis industry, research institutes, and academia in a modular form based on this data. Background Technology

[0002] Generally, facilities are structures created through construction work and their ancillary facilities, and are classified into Class 1 facilities and Class 2 facilities. For example, Class 1 facilities refer to facilities that require special management to ensure public convenience and safety, such as bridges, roads, railways, ports, dams, tunnels, and buildings, or facilities that are recognized as requiring advanced technology for structural maintenance. Class 2 facilities refer to facilities other than Class 1 facilities.

[0003] Facility safety inspections and precise safety diagnoses are necessary to promptly and accurately investigate and evaluate inherent risk factors, functional and performance degradation, and conditions of such facilities, take appropriate safety measures to prevent disasters and calamities, and enhance the utility of facilities by supplementing and preserving their safety and functionality, while also systematizing scientific maintenance management.

[0004] In particular, among these facilities, domestic bridge structures that have been completed for more than 30 years account for 17.5% of the total as of the end of 2019, and with this figure expected to exceed 48% in 10 years, it is necessary to devise long-term maintenance plans for bridge structures.

[0005] For example, while inspections and diagnoses by personnel have been the primary method for evaluating the condition of existing bridge structures, the reality is that it is difficult to maintain bridge structures using these existing methods due to the recent shortage of specialized personnel and insufficient maintenance budgets resulting from the rapid increase in the number of aging bridges.

[0006] Specifically, even for the same bridge type, the degree and type of deterioration vary depending on the installation environment; however, there is a problem in that there are clear limitations to efficient maintenance because bridge maintenance is carried out based on a grading system using existing manual inspection and diagnosis methods.

[0007] Figure 1 is a diagram schematically illustrating the maintenance of facilities by evaluating the level of deterioration of facilities using inspection / diagnosis by personnel.

[0008] As shown in Fig. 1, the existing method for evaluating the level of deterioration of facilities does not provide an accurate evaluation because it evaluates the degree of deterioration of facilities based only on the service life calculated at the time of design.

[0009] Meanwhile, as prior art for solving the aforementioned problems, Korean Registered Patent No. 10-2124062, filed and registered by the applicant of the present invention, discloses an invention titled "System for Evaluating the Level of Aging of Facilities," which will be explained with reference to FIGS. 2 and FIGS. 3.

[0010] FIG. 2 is a drawing for explaining a method for evaluating the level of aging of facilities according to conventional technology, and

[0011] Figure 3 is a configuration diagram of a facility aging level evaluation system according to conventional technology.

[0012] A system (10) for evaluating the level of deterioration of facilities according to conventional technology can evaluate the level of deterioration of facilities, including bridges, tunnels, retaining walls, and buildings, more accurately by calculating the level of deterioration of facilities using a deterioration level evaluation method rather than the existing service life standard, as shown in FIG. 2.

[0013] Specifically, the facility aging level evaluation system (10) according to the prior art can evaluate the aging level of a facility by measuring facility detailed indicators, collecting detailed indicator measurement values, calculating the degree of aging by component and the overall degree of aging according to the detailed indicator weights and the aging acceleration index (AAI), and then evaluating the aging level of a facility through a relative comparison between the calculated overall degree of aging and the actual service life of the facility.

[0014] For example, the calculated overall deterioration of a facility can be expressed as the years of deterioration by comparing it with the design life, and the level of deterioration of the facility can be evaluated by comparing the years of deterioration with the years of service of the facility at the time of evaluation.

[0015] Specifically, referring to FIG. 3, a facility aging level evaluation system (10) according to conventional technology,

[0016] A system for evaluating the level of deterioration of facilities (20), including bridges, tunnels, retaining walls, and buildings, is configured to include a deterioration-related data collection unit (11), a deterioration-related data analysis unit (12), a detailed indicator weight setting unit (13), a detailed indicator deterioration acceleration index setting unit (14), a component-specific deterioration calculation unit (15), a component-specific weight setting unit (16), a comprehensive deterioration calculation unit (17), a deterioration years and level evaluation unit (18), and a deterioration-related DB (19).

[0017] The aging-related data collection unit (11) collects detailed indicator measurements of the facility (20) by measuring the detailed indicator items of the members (21, 22, 23) related to deterioration or damage over time for the facility (20).

[0018] The aging-related data analysis unit (12) analyzes the level of aging of the detailed indicator measurements according to the initial state (design value) and limit state of the component.

[0019] The detailed indicator weight setting unit (13) sets the detailed indicator weight, and the detailed indicator aging acceleration index setting unit (14) sets the detailed indicator aging acceleration index (AAI). At this time, the detailed indicator aging acceleration index can be calculated based on the measurement values ​​of the detailed indicators collected periodically according to the corresponding measurement cycle.

[0020] The component-specific aging degree calculation unit (15) calculates the aging degree of each component of the facility (20) for the aging level evaluation of the analyzed detailed indicator measurements according to the detailed indicator weight and the detailed indicator aging acceleration index (AAI). Here, the detailed indicator items include the carbonation depth of the concrete, concrete surface strength, concrete crack width, deflection of the member, and inclination of the member, and the aging acceleration index (AAI) represents the change in the rate of aging progression based on the time history data of the corresponding detailed indicator.

[0021] The weight setting unit (16) sets weights for the members (21, 22, 23) so that the degree of deterioration of the member can be calculated by combining data from the analysis of the measured values ​​of detailed indicators related to the degree of deterioration of each member.

[0022] The comprehensive aging calculation unit (17) calculates the comprehensive aging of the facility by combining the aging of each component.

[0023] The aging years and level evaluation department (18) evaluates the level of aging of the facility by comparing the overall aging calculation value with the actual service years of the facility.

[0024] Accordingly, the overall deterioration of the facility calculated above is expressed as the years of deterioration by comparing it with the design life, and the years of deterioration and the level of deterioration can be evaluated by comparing the years of deterioration with the years of service of the facility at the time of evaluation.

[0025] The aging-related DB (19) stores the initial state and limit state of the component, and stores the set weight and aging acceleration index.

[0026] According to a system for evaluating the level of deterioration of facilities based on conventional technology, the degree of deterioration of facilities, including bridges, tunnels, retaining walls, and buildings, can be evaluated more accurately by calculating the degree of deterioration based on measurement data of the facilities rather than based on the service life standard.

[0027] In addition, by setting weights for facility detailed indicator items and a detailed indicator aging acceleration index, and calculating the degree of aging by component and the overall degree of aging, the level of aging of the facility can be evaluated through a relative comparison with the actual years of service of the facility.

[0028] In addition, it is possible to establish maintenance plans based on the level of facility deterioration, and it can be utilized as basic data for analyzing the causes of facility deterioration and preparing countermeasures.

[0029] However, conventional systems for evaluating the level of deterioration of facilities calculate the degree of deterioration based on an assessment method rather than the service life standard. To evaluate the level of deterioration, weights, detailed indicators, the deterioration acceleration index, deterioration by component, and the overall deterioration must be calculated, and in particular, they fail to reflect the deterioration environment specific to the regions where the bridge structures were constructed.

[0030] Accordingly, there is a need to easily establish a maintenance management system for facilities, particularly bridge structures, by reflecting the deterioration environment of each region where the bridge structures were constructed.

[0031] Meanwhile, as a related technology, Korean published patent number 2023-85364, which was filed and disclosed by the applicant of the present invention, discloses an invention titled "System for maintenance of bridge structures using an artificial intelligence-based deterioration model and method thereof," which is described with reference to FIG. 4 and is referenced within this specification and forms part of the present invention.

[0032] Figure 4 is a diagram of a bridge structure maintenance system utilizing an artificial intelligence-based deterioration model according to conventional technology.

[0033] Referring to FIG. 4, a maintenance system (40) for a bridge structure utilizing an artificial intelligence-based deterioration model according to conventional technology comprises a data collection unit (41), an artificial intelligence-based deterioration model generation unit (42), a deterioration curve generation unit (43), a deterioration environment information matching unit (44), a target area deterioration curve derivation unit (45), and a maintenance decision unit (46).

[0034] The data collection unit (41) collects inspection / diagnosis data, experimental data, deterioration data, and deterioration environment data for each of the bridge structures (50) constructed in multiple regions via wired or wireless means, and, for example, the deterioration data may include service years and carbonation depth.

[0035] The artificial intelligence-based deterioration model generation unit (42) generates artificial intelligence learning data according to the collected data and generates a deterioration model by learning artificial intelligence-based data by years of service.

[0036] Specifically, the AI-based degradation model generation unit (42) includes an AI learning data generation unit (42a) and an AI-based data learning unit by years of service (42b). The AI ​​learning data generation unit (42a) generates AI learning data, and the AI-based data learning unit by years of service (42b) generates an AI-based degradation model by performing learning by years of service according to the generated learning data.

[0037] In addition, the degradation curve generation unit (43) generates time series data according to the degradation model generated by the artificial intelligence-based degradation model generation unit (42) and derives a degradation curve.

[0038] The deterioration environment information matching unit (44) performs 2D contour mapping based on GPS to obtain quantitative values ​​for the deterioration environment of each bridge.

[0039] At this time, the deterioration environment may be a single environment or a complex environment, and the above deterioration environment information may be determined by deterioration environment data regarding the number of days of freeze-thaw cycles, the number of days of de-icing agent use, the airborne salt environment, and the presence or absence of industrial complexes, but is not limited thereto.

[0040] The target area deterioration curve derivation unit (45) performs AI-based regional data learning according to the input target area to generate a deterioration curve for each target area deterioration index.

[0041] The maintenance decision-making unit (46) makes a decision to allocate a budget for the maintenance of each bridge structure according to the deterioration curve derived from the target area deterioration curve derivation unit (45).

[0042] Accordingly, according to a maintenance system for bridge structures utilizing an artificial intelligence-based deterioration model based on conventional technology, in generating deterioration curves based on time series for each deterioration indicator of a bridge structure, artificial intelligence technology is applied to learn time series data for each deterioration indicator and deterioration environment data to generate prediction data, and deterioration curves for each deterioration indicator considering the deterioration environment can be derived.

[0043] In particular, deterioration curves for each deterioration indicator of bridge structures are generated, and deterioration consideration factors are set as freeze-thaw cycles, days of de-icing agent use, and airborne salt, and quantitative values ​​for each deterioration environment can be obtained through 2D contour mapping based on GPS.

[0044] Meanwhile, as prior art, Korean published patent number 2022-93935 discloses an invention titled "AI-based learning data construction system for detecting defects in tunnel and bridge facilities and defect data trading platform," which will be explained with reference to FIG. 5.

[0045] Figure 5 is a configuration diagram of a learning data construction system for detecting defects in tunnel and bridge facilities based on artificial intelligence according to conventional technology.

[0046] Referring to FIG. 5, a learning data construction system for detecting defects in tunnel and bridge facilities based on artificial intelligence according to conventional technology comprises: a memory in which a learning data construction program for detecting defects in tunnel and bridge facilities based on artificial intelligence is stored; and a processor for executing said program.

[0047] The processor builds training data for artificial intelligence algorithms using images of tunnels and bridge facilities.

[0048] In addition, the processor collects images of tunnel and bridge facilities from data providers and supplies the results processed with an AI-based auto-labeling tool on a platform that generates and trades defect data to data demand agencies.

[0049] In addition, the processor provides a labeling function that indicates the location of cracks and defects, and a deep learning-based auto-labeling function that automatically recommends candidates for bounding boxes.

[0050] In addition, the processor provides cloud-based deployment capabilities that enable multiple labelers and quality managers to access and work remotely simultaneously. It also offers an auto-labeling function that increases labeling speed by recommending multiple objects with crack and defect probabilities exceeding a set value, and an auto-labeling function that improves learning speed through meta-learning algorithms.

[0051] In addition, the processor provides functions for registering and managing labeler tasks, statistical processing, and visualizing the results of statistical processing.

[0052] According to a conventional AI-based training data construction system for detecting defects in tunnel and bridge facilities, by providing a cloud-based AI training data construction system for detecting defects in public facilities that allows multiple labelers and multiple quality managers to simultaneously access and work remotely using a specialized labeling tool for cracks and defects in tunnel and bridge facilities, the system increases the labeling speed by recommending multiple candidates for cracks and defects through an auto-labeling function, and enables even labelers without specialized knowledge of cracks and defects to perform labeling work conveniently and accurately.

[0053] In addition, the defective data generation and trading platform resolves the problem of underutilized AI training data previously generated under the leadership of the government and local governments, and can reduce training data production costs for both suppliers and consumers by facilitating the reprocessing and trading of training data.

[0054] Meanwhile, for special bridges with a main span of 200m or more, such as cable-stayed bridges and suspension bridges, a measurement system is operated using various sensors for vibration, displacement, temperature, wind speed, etc., and for other large bridges, a measurement system is operated to collect bridge behavior data and evaluate safety.

[0055] However, for most of the more than 30,000 general bridges nationwide, it is difficult to install measurement sensors on all bridges and analyze them due to issues such as cost and a shortage of management personnel.

[0056] In particular, for most bridges with individual spans of 50m or less, there is insufficient comparative measurement data for safety analysis, so safety evaluations are conducted based on independent single measurement results, which may make it difficult to ensure reliability.

[0057] Meanwhile, Fig. 6 is a diagram illustrating the concept of a smart bridge management system according to conventional technology.

[0058] As shown in Fig. 6, a smart maintenance platform is being developed that provides various services necessary for the maintenance of bridge structures, including the condition assessment and life prediction of bridge structures using artificial intelligence algorithms based on data acquired from over 5 million pieces of data related to bridge aging through IoT sensors, field surveys, long-term data, and the use of drones.

[0059] In response to this, there is a need for a bridge maintenance platform capable of providing integrated bridge maintenance data and analysis algorithms by concretizing the services required by management entities, the inspection and diagnosis industry, research institutes, and academia in a modular form. Prior art literature

[0060] Republic of Korea Registered Patent No. 10-2124062 (Registration Date: June 11, 2020), Title of Invention: "System for Evaluating the Level of Aging of Facilities" Republic of Korea Published Patent No. 2022-93935 (Publication Date: July 5, 2022), Title of Invention: "System for Constructing Learning Data for Detecting Defects in Tunnel and Bridge Facilities Based on Artificial Intelligence and Defect Data Trading Platform" Republic of Korea Published Patent No. 2023-85364 (Publication Date: June 14, 2023), Title of Invention: "System for Maintaining Bridge Structures Using an AI-Based Deterioration Model and Method Thereof" Republic of Korea Published Patent No. 2023-94074 (Publication Date: June 27, 2023), Title of Invention: "System for Estimating Load-Bearing Performance of Small and Medium-Sized Aging Bridges and Method Thereof" Japanese Published Patent No. Patent No. 2008-291440 (Publication Date: December 4, 2008), Title of Invention: "System for Calculating Structural Deterioration Curve and Method for Evaluating Life Cycle Costs" The problem to be solved

[0061] The technical objective of the present invention to solve the aforementioned problems is to provide a smart maintenance platform that provides data and analysis algorithms related to bridge structures, which can construct a bridge structure maintenance database by configuring big data based on inspection and diagnosis reports, environmental information, and measurement information related to the amount, and provide services required by management entities, the inspection and diagnosis industry, research institutes, and academia in a modular form based on the same, and a method for maintaining bridge structures using the same.

[0062] Another technical objective of the present invention is to provide a smart maintenance platform that provides bridge structure-related data and analysis algorithms, which utilizes DNA (Data, Network and AI) technology to predict damage and aging necessary for the preventive maintenance of bridges and can integrally provide bridge structure-related data and analysis algorithms suitable for the user, and a bridge structure maintenance method using the same.

[0063] Another technical objective of the present invention is to provide a smart maintenance platform that provides bridge structure-related data and analysis algorithms, which can be used as objective basic data for calculating bridge maintenance costs by utilizing artificial intelligence technology in the analysis of aging data to estimate the future damage state of the bridge to be analyzed, and a method for maintaining bridge structures using the same. means of solving the problem

[0064] As a means to achieve the aforementioned technical challenge, a smart maintenance platform according to the present invention that provides data and analysis algorithms related to bridge structures comprises: a data collection module that collects and inputs bridge measurement data, extracted information from bridge inspection / diagnosis reports, chloride data, deterioration information on concrete specimens, and Korea Meteorological Administration data; a numerical model creation module that creates a numerical model for the data collected and input by the data collection module; a bridge structure maintenance DB constructed from the data collected and input by the data collection module for the maintenance of bridge structures; an analysis algorithm provision module that provides an analysis algorithm related to bridge maintenance to a user; a bridge maintenance data provision module that provides bridge maintenance data based on the data collected by the data collection module and, by extending this, provides comparison data to verify the measured and evaluated results of the target bridge; and an application service provision module that provides application services to a user terminal by utilizing the constructed numerical model and database.It provides bridge structure-related data and analysis algorithms including, wherein the data collection module collects and inputs: 1) bridge measurement data measuring vibration, displacement, crack width, and temperature / humidity measured for more than one year at approximately 100 bridges; 2) information extracted from more than 10,000 bridge inspection / diagnosis reports; 3) chloride data collected from approximately 100 environmental information collection devices; 4) deterioration information from concrete specimens investigated for more than 20 years; and 5) data from the Korea Meteorological Administration. 5) Collecting and inputting data from the Korea Meteorological Administration, the bridge maintenance data provision module comprises: a measurement data-based management standard setting unit that provides trends in data to be measured over the next year using AI technology based on static and dynamic data measured at a representative bridge and data measured over the past year; a bridge deterioration environment evaluation unit that evaluates the comprehensive deterioration environment by deriving a deterioration evaluation formula according to a deterioration environment evaluation algorithm and determining the evaluation grade and weight for each grade of each deterioration environment; a data-based small and medium-sized bridge load-carrying performance estimation unit that provides an estimated load-carrying performance of a bridge according to a bridge load-carrying performance prediction model when specifications and environmental information of a specific bridge are input based on a data set from which data outliers based on bridge inspection / diagnosis results have been removed; and a measurement / environment data-based bridge aging evaluation unit that provides aging evaluation results according to a measurement / environment aging evaluation algorithm when specifications and environmental information of a specific bridge are input based on a bridge aging model including representative bridge and environmental information.It includes an AI-based deterioration model accuracy verification unit that verifies the accuracy of AI-generated data for sections where actual data has not been collected; a measurement data-deterioration model linkage unit that links the deterioration model to the measurement data to reflect deterioration environment data; and an aging evaluation unit by evaluation indicator that evaluates the quantification of deterioration damage of bridge structures over time through the subdivision of evaluation indicators, weight adjustment, and the generation of aging curves by evaluation indicator at the member unit level, wherein the AI-based deterioration model accuracy verification unit performs accuracy verification by comparing the verification data group, obtained by distinguishing between the training data group and the verification data group in the actual data, with the AI-generated data.

[0065] Meanwhile, as another means for achieving the aforementioned technical task, a method for maintaining a bridge structure using a smart maintenance platform that provides data related to a bridge structure and an analysis algorithm according to the present invention comprises: a) a step in which a data collection module of the bridge structure smart maintenance platform collects and inputs data related to a bridge structure from a data provider; b) a step in which a numerical model creation module of the bridge structure smart maintenance platform creates a numerical model for the received data; c) a step in which a bridge structure maintenance DB is constructed from the received data; d) a step in which an analysis algorithm provision module of the bridge structure smart maintenance platform provides an analysis algorithm to a user in the form of a service module through a user terminal; e) a step in which a bridge maintenance data provision module of the bridge structure smart maintenance platform provides bridge maintenance data to a user in the form of a service module through a user terminal; and f) a step in which an application service is provided by utilizing the constructed numerical model and the bridge structure maintenance DB. and g) a step in which an application service provision module of a smart maintenance platform for bridge structures recycles the analyzed data when providing the application service to circulate and accumulate data; wherein big data is constructed based on inspection and diagnosis reports, environmental information, and measurement information related to bridges, and based thereon, services required by management entities, the inspection and diagnosis industry, research institutes, and academia are provided in the form of modules. Effects of the invention

[0066] According to the present invention, a bridge structure maintenance database is constructed by organizing big data based on inspection and diagnosis reports, environmental information, and measurement information related to bridges, and based on this, services required by management entities, the inspection and diagnosis industry, research institutes, and academia can be provided in a modular form.

[0067] According to the present invention, by utilizing DNA (Data, Network and AI) technology, damage and aging necessary for the preventive maintenance of bridges can be predicted, and bridge structure-related data and analysis algorithms suitable for the user can be provided in an integrated manner.

[0068] According to the present invention, when performing a safety evaluation of a general bridge in which a long-term measurement system is not installed, comparative data can be provided to verify the measurement and evaluation results of the target bridge based on data obtained by measuring vibration, displacement, cracks, and temperature / humidity for a number of representative bridges for more than one year according to the type and environmental conditions of the representative bridge.

[0069] According to the present invention, by utilizing artificial intelligence technology in the analysis of aging data to estimate the future damage state of a bridge to be analyzed, the data is used as objective basic data for calculating bridge maintenance costs, thereby significantly contributing to the preventive maintenance of bridge structures and reducing large-scale maintenance costs that may occur in the future. Brief explanation of the drawing

[0070] Figure 1 is a diagram schematically illustrating the maintenance of facilities by evaluating the level of deterioration of facilities using inspection / diagnosis by personnel. Figure 2 is a diagram illustrating a method for evaluating the level of deterioration of facilities according to conventional technology. Figure 3 is a configuration diagram of a facility aging level evaluation system according to conventional technology. Figure 4 is a diagram of a bridge structure maintenance system utilizing an artificial intelligence-based deterioration model according to conventional technology. Figure 5 is a configuration diagram of a learning data construction system for detecting defects in tunnel and bridge facilities based on artificial intelligence according to conventional technology. Figure 6 is a diagram illustrating the concept of a smart bridge management system. FIG. 7 is a diagram showing a smart maintenance platform system that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. FIG. 8 is a configuration diagram of a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. FIG. 9 is a diagram specifically illustrating an application service in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. FIG. 10 is a diagram showing the architecture of a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. FIG. 11 is a diagram specifically illustrating the setting of measurement data management standards in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. FIG. 12 is a diagram illustrating a deterioration environment evaluation algorithm in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. FIG. 13 is a diagram showing a data-based load-bearing performance estimation dataset for small and medium-sized bridges in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. FIG. 14 is a diagram showing a bridge load-bearing performance prediction model through correlation analysis in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. FIG. 15 is a diagram illustrating a bridge aging assessment algorithm based on measurement / environmental data in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. FIG. 16 is a diagram showing an artificial intelligence-based deterioration model accuracy verification system in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. FIG. 17 is a diagram showing a measurement data-deterioration model linkage system in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. FIG. 18 is a diagram for specifically explaining a bridge aging evaluation algorithm by evaluation indicator in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. FIG. 19 is a flowchart illustrating a bridge structure maintenance method using a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. FIG. 20 is a diagram for specifically explaining a data circulation accumulation system in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention. Specific details for implementing the invention

[0071] Embodiments of the present invention are described below with reference to the attached drawings so that those skilled in the art can easily implement the invention. However, the present invention may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification are denoted by similar reference numerals.

[0072] Throughout the specification, when a part is described as "comprising" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Furthermore, terms such as "…part" as used in the specification refer to a unit that processes at least one function or operation, and this may be implemented in hardware, software, or a combination of hardware and software.

[0073] [Smart maintenance platform (100) that provides data and analysis algorithms related to bridge structures]

[0074] FIG. 7 is a diagram showing a smart maintenance platform system that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention.

[0075] A smart maintenance platform providing data and analysis algorithms related to bridge structures according to an embodiment of the present invention can predict damage and aging necessary for the preventive maintenance of bridges by utilizing DNA (Data, Network and AI) technology.

[0076] For the proactive management of such aging bridges, it is possible to understand the performance degradation characteristics that differ for each bridge through the accumulation of extensive and strategic data, and based on this, predict the degree of bridge deterioration.

[0077] Accordingly, data directly or indirectly related to bridge aging can be constructed, and changes in the spread of bridge damage over time can be predicted through AI learning based on the constructed data.

[0078] In addition, data reliability can be improved by securing bridge site data utilizing IoT technology and experimental data considering domestic environmental conditions.

[0079] In addition, artificial intelligence technology related to facility maintenance has so far focused on the field of estimating damage types based on images, but

[0080] A smart maintenance platform providing bridge structure-related data and analysis algorithms according to an embodiment of the present invention utilizes artificial intelligence technology in the analysis of aging data to estimate the future damage state of a bridge to be analyzed, in the bridge aging level prediction technology.

[0081] Therefore, it can be utilized as objective basic data for calculating bridge maintenance costs, significantly contributing to the preventive maintenance of bridge structures and enabling the reduction of large-scale maintenance costs that may occur in the future.

[0082] Specifically, as illustrated in FIG. 7, the smart maintenance platform for bridge structures according to an embodiment of the present invention is,

[0083] When performing a safety assessment of a general bridge where a long-term monitoring system is not installed, comparative data is provided based on data obtained by measuring vibration, displacement, cracks, and temperature / humidity for over one year for about 100 representative bridges according to typical bridge types and environmental conditions, and extending this to verify the measured and evaluated results of the target bridge.

[0084] In other words, the smart maintenance platform for bridge structures according to an embodiment of the present invention is a platform system that organizes big data based on inspection and diagnosis reports, environmental information, and measurement information related to bridges, and provides services required by management entities, the inspection and diagnosis industry, research institutes, and academia based on this data.

[0085] Meanwhile, FIG. 8 is a configuration diagram of a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention.

[0086] Referring to FIG. 8, a smart maintenance platform (100) that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention comprises a data collection module (110), a numerical model creation module (120), a bridge structure maintenance DB (130), an analysis algorithm provision module (140), a bridge maintenance data provision module (150), and an application service provision module (160).

[0087] The data collection module (110) collects and inputs bridge measurement data, extracted information from bridge inspection / diagnosis reports, chloride data, deterioration information on concrete specimens, and meteorological agency data.

[0088] For example, the data collection module (110) above is,

[0089] 1) Bridge measurement data of vibration, displacement, crack width, and temperature / humidity measured for over one year at approximately 100 bridges,

[0090] 2) Information extracted from over 10,000 bridge inspection / diagnosis reports,

[0091] 3) Chloride data collected from approximately 100 environmental information collection devices, 4) Deterioration information from concrete specimens investigated over a long period of 20 years or more, and 5) Meteorological Agency data can be collected and input.

[0092] The numerical model creation module (120) creates a numerical model for the data collected and input by the data collection module (110).

[0093] The bridge structure maintenance DB (130) is constructed with data collected and input from the data collection module (110) for the maintenance of multiple bridge structures (200).

[0094] The analysis algorithm providing module (140) provides the user with an analysis algorithm related to bridge maintenance.

[0095] For example, the algorithm provided by the analysis algorithm providing module (140) may include, but is not limited to, a bridge deterioration environment evaluation algorithm, a bridge load-bearing performance prediction model, and a bridge aging evaluation algorithm.

[0096] The bridge maintenance data provision module (150) provides bridge maintenance data based on the data collected by the data collection module (110), and extends this to provide comparison data that can be verified with the measured and evaluated results of the target bridge.

[0097] That is, the bridge maintenance data provision module (150) provides comparative data that can be verified with the measured and evaluated results of the target bridge by extending the data based on data measured for more than one year for vibration, displacement, cracks, and temperature / humidity for about 100 representative bridges according to the type and environmental conditions of the representative bridge when performing safety evaluation of a general bridge where a long-term measurement system is not installed.

[0098] Specifically, the bridge maintenance data provision module (150) is,

[0099] It may include a measurement data-based management standard value setting unit (151), a bridge deterioration environment evaluation unit (152), a data-based small and medium bridge load-bearing performance estimation unit (153), a measurement / environment data-based bridge aging evaluation unit (154), an artificial intelligence-based deterioration model accuracy verification unit (155), a measurement data-deterioration model linkage unit (156), and an aging evaluation unit (157) for each evaluation indicator.

[0100] The measurement data-based management standard value setting unit (151) provides a trend of data to be measured over the next year using AI technology based on static and dynamic data measured at a representative bridge and data measured over the past year.

[0101] The bridge deterioration environment evaluation unit (152) derives a deterioration evaluation formula according to the deterioration environment evaluation algorithm and evaluates the comprehensive deterioration environment by determining the evaluation grade and grade-specific weights for each deterioration environment.

[0102] At this time, the bridge deterioration environment evaluation unit (152) can derive each deterioration evaluation formula for coastal airborne salt, de-icing agent airborne salt, offshore bridge airborne salt, and the East Sea environment according to the deterioration environment evaluation algorithm.

[0103] The data-based small and medium-sized bridge load-bearing performance estimation unit (153) provides an estimated load-bearing performance of a bridge according to a bridge load-bearing performance prediction model when the specifications and environmental information of a specific bridge are input based on a data set from which data outliers are removed according to the bridge inspection / diagnosis results.

[0104] The measurement / environmental data-based bridge aging evaluation unit (154) provides an aging evaluation result according to the measurement / environmental aging evaluation algorithm when the specifications and environmental information of a specific bridge are input based on a bridge aging model including representative bridge and environmental information.

[0105] At this time, the measurement / environmental aging evaluation algorithm of the measurement / environmental data-based bridge aging evaluation unit (154) may include an aging evaluation algorithm for RCS, RA, and PSC I bridge types that reflects the weighting of member-unit damage types and damage diffusion (heterogeneous damage) to improve accuracy based on measurement / environmental data.

[0106] The AI-powered degradation model accuracy verification unit (155) verifies the accuracy of the AI-powered generated data for the section where actual data was not collected.

[0107] At this time, the AI-powered degradation model accuracy verification unit (155) can perform accuracy verification by comparing the verification data group, which is distinguished from the training data group and the verification data group in the actual data, with the AI-powered generated data.

[0108] The measurement data-deterioration model linkage unit (156) links the deterioration model to the measurement data to reflect the deterioration environment data.

[0109] The aging evaluation unit (157) evaluates the quantification of deterioration damage of bridge structures over time by subdividing the evaluation indicators, adjusting weights, and generating an aging curve for each evaluation indicator at the member unit level.

[0110] Referring again to FIG. 8, the application service providing module (160) provides application services to the user terminal (400) by utilizing the constructed numerical model and database.

[0111] Accordingly, in the case of a smart maintenance platform (100) that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention, the bridge structure maintenance DB (130) forms big data based on inspection and diagnosis reports, environmental information, and measurement information related to the bridge, and

[0112] Based on this, the above application service provision module (160) can provide services required by management entities, inspection and diagnosis industries, research institutes, and academia in the form of modules.

[0113] Meanwhile, FIG. 9 is a diagram specifically illustrating an application service in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention.

[0114] As illustrated in FIG. 9, the application service provision module (160) in the smart maintenance platform providing bridge structure-related data and analysis algorithms according to an embodiment of the present invention is largely composed of, as illustrated in FIG. 9, a maintenance policy establishment unit (161), a bridge performance degradation level information provision unit (162), a repair and reinforcement maintenance information provision unit (163), a bridge performance impact indicator information provision unit (164), and a bridge inspection / diagnosis task support unit (165).

[0115] The maintenance policy formulation department (161) provides support for establishing bridge management implementation plans, evaluating the feasibility of road facility performance improvement projects, supporting the establishment of medium-term financial plans for road facilities, and supporting the management of repair and reinforcement history information.

[0116] The bridge performance degradation level information provision unit (162) provides assessment and prediction of the degree of deterioration, estimation of the bridge load-bearing performance, estimation of the bridge seismic performance, estimation of safety based on bridge measurement data, provision of information on the degree of environmental impact, and prediction of the spread of heterogeneous damage to the bridge.

[0117] The repair and reinforcement maintenance information provision unit (163) provides information on repair and reinforcement methods and costs, provides maintenance scenarios for each bridge member, and provides maintenance scenario information for each major durability item of the bridge.

[0118] The bridge performance impact indicator information provision unit (164) provides estimation of the amount of salt in the atmosphere, provision of regional deterioration environment information, provision of regional deterioration environment data, estimation of the amount of chloride infiltration by deterioration environment, and estimation of the amount of heavy vehicle traffic.

[0119] The bridge inspection / diagnosis support department (165) provides support for operating the IoT sensor-based bridge measurement system, reading damage photos (damage and damage volume), providing bridge substructure routing information, setting bridge measurement data management standards, and supporting input of the bridge exterior network diagram.

[0120] Meanwhile, FIG. 10 is a diagram showing the architecture of a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention.

[0121] As shown in Fig. 10, 23 application services are provided by utilizing the constructed numerical model and database. Data analyzed in the application service algorithm is converted back into a database and used as input values ​​in all other service algorithms.

[0122] Meanwhile, FIG. 11 is a diagram specifically illustrating the setting of measurement data management standards in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention.

[0123] As illustrated in Fig. 11, setting management criteria based on measurement data: Based on static and dynamic data measured at a representative bridge, AI technology is utilized to provide trends in data to be measured over the next year.

[0124] Meanwhile, FIG. 12 is a diagram illustrating a deterioration environment evaluation algorithm in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention.

[0125] As shown in FIG. 12, the bridge deterioration environment evaluation algorithm includes a comprehensive deterioration environment evaluation algorithm that derives a deterioration evaluation formula for coastal airborne salt, de-icing agent airborne salt, offshore bridge airborne salt, and East Sea environment, and determines the evaluation grade and grade-specific weight for each deterioration environment.

[0126] Meanwhile, FIG. 13 is a diagram showing a data-based load-bearing performance estimation dataset for small and medium-sized bridges in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention, and

[0127] FIG. 14 is a diagram showing a bridge load-bearing performance prediction model through correlation analysis in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention.

[0128] As illustrated in Fig. 13, data-based estimation of load-bearing performance of small and medium-sized bridges: When specifications and environmental information of a specific bridge are input based on a data set from which data outliers have been removed based on over 4,000 inspection / diagnosis results, an estimate of the load-bearing performance of the bridge is provided.

[0129] Specifically, a dataset is created by extracting data through document parsing and removing data outliers. In addition, correlation analysis and multiple regression analysis are performed with respect to the common load-carrying rate for each collected indicator.

[0130] For example, the accuracy of determining load-carrying performance for 164 data sets was verified, and as shown in Figure 14, the accuracy of the bridge load-carrying performance prediction model through correlation analysis was 97%.

[0131] Meanwhile, FIG. 15 is a diagram illustrating a bridge aging assessment algorithm based on measurement / environmental data in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention.

[0132] As shown in FIG. 15, the bridge aging evaluation algorithm based on measurement / environmental data includes an aging evaluation algorithm for RCS, RA, and PSC I bridge types that reflects damage diffusion (heterogeneous damage) and weight adjustment of member-unit damage types to improve accuracy based on measurement / environmental data.

[0133] At this time, when the specifications and environmental information of a specific bridge are input based on a bridge aging model that includes representative bridges and environmental information, the aging assessment results are provided.

[0134] Meanwhile, FIG. 16 is a diagram showing an artificial intelligence-based deterioration model accuracy verification system in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention, and

[0135] FIG. 17 is a diagram showing a measurement data-deterioration model linkage system in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention.

[0136] As illustrated in Fig. 16, verification of the accuracy of the degradation model using artificial intelligence: the accuracy of the data generated using artificial intelligence for sections where actual data was not collected is verified.

[0137] Accuracy verification is performed by distinguishing between the training and validation data sets in real data and comparing the validation data set with AI-generated data.

[0138] As illustrated in FIG. 17, the measurement data-degradation model linkage system can link the degradation model to the measurement data to reflect the degradation environment data.

[0139] Meanwhile, FIG. 18 is a diagram for specifically explaining a bridge aging evaluation algorithm by evaluation indicator in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention.

[0140] As illustrated in Fig. 18, the aging evaluation process according to the bridge aging evaluation algorithm by evaluation indicator performs the quantification of structural deterioration damage over time in the order of evaluation indicator → evaluation module → member unit → bridge unit through the subdivision of evaluation indicators, weight adjustment, and generation of aging curves by evaluation indicator at the member unit level.

[0141] In other words, the smart maintenance platform (100) that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention is a platform built on a cloud basis that takes into account the openness and connectivity of the smart bridge maintenance service.

[0142] At this time, the smart maintenance platform (100) providing bridge structure-related data and analysis algorithms according to an embodiment of the present invention includes measurement / diagnosis / environmental / weather data and can provide various maintenance services by utilizing the data.

[0143] In addition, the data calculated from the smart maintenance platform (100) that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention is collected again as data and constructed in the form of a circular data analysis platform to improve precision.

[0144] In addition, the smart maintenance platform (100) providing bridge structure-related data and analysis algorithms according to an embodiment of the present invention is an algorithm-extended platform and can provide services including various developers' maintenance algorithms necessary for performing bridge maintenance tasks.

[0145] Ultimately, according to an embodiment of the present invention, the bridge smart maintenance platform can enhance service accessibility by providing services required by management entities, the inspection and diagnosis industry, research institutes, and academia in a modular form based on data related to bridges.

[0146] [Bridge Structure Maintenance Method Using a Smart Maintenance Platform Providing Data and Analysis Algorithms Related to Bridge Structures]

[0147] FIG. 19 is a flowchart illustrating a bridge structure maintenance method using a smart maintenance platform that provides bridge structure-related data and an analysis algorithm according to an embodiment of the present invention, and

[0148] FIG. 20 is a diagram for specifically explaining a data circulation accumulation system in a smart maintenance platform that provides bridge structure-related data and analysis algorithms according to an embodiment of the present invention.

[0149] Referring to FIG. 19, a bridge structure maintenance method using a smart maintenance platform that provides bridge structure-related data and an analysis algorithm according to an embodiment of the present invention is,

[0150] First, the data collection module of the bridge structure smart maintenance platform collects and inputs data from a data provider in relation to the bridge structure (S110).

[0151] For example,

[0152] The above 1) bridge measurement data, including vibration, displacement, crack width, and temperature / humidity measured for more than one year at approximately 100 bridges,

[0153] 2) Information extracted from over 10,000 bridge inspection / diagnosis reports,

[0154] 3) Chloride data collected from approximately 100 environmental information collection devices,

[0155] 4) Deterioration information and for concrete specimens investigated over a long period of more than 20 years

[0156] 5) Collect and input data from the Korea Meteorological Administration.

[0157] Next, the numerical model creation module of the smart maintenance platform for bridge structures creates a numerical model for the provided data (S120).

[0158] Next, a bridge structure maintenance DB (130) is constructed from the provided data (S130).

[0159] Next, the analysis algorithm providing module (140) of the bridge structure smart maintenance platform (100) provides the analysis algorithm to the user in the form of a service module (S140).

[0160] Here, the algorithm provided by the analysis algorithm providing module (140) may include, but is not limited to, a bridge deterioration environment evaluation algorithm, a bridge load-bearing performance prediction model, and a bridge aging evaluation algorithm.

[0161] Next, the bridge maintenance data provision module (150) of the bridge structure smart maintenance platform (100) provides bridge maintenance data to the user in the form of a service module (S150).

[0162] At this time, when the bridge maintenance data provision module (150) performs a safety evaluation of a general bridge in which a long-term measurement system is not installed, it provides comparison data based on data measuring vibration, displacement, cracks, and temperature / humidity for about 100 representative bridges for more than one year according to the type and environmental conditions of the representative bridge, and extends this to verify the measurement and evaluation results of the target bridge.

[0163] Specifically, the bridge maintenance data provision module (150) can set management standards based on measurement data, evaluate the bridge deterioration environment, estimate the load-carrying performance of small and medium-sized bridges based on data, evaluate the degree of bridge deterioration based on measurement / environmental data, verify the accuracy of the deterioration model using artificial intelligence, link the measurement data with the deterioration model, and evaluate the degree of deterioration by evaluation indicator.

[0164] Next, an application service is provided using the established numerical model and bridge structure maintenance DB (130) (S160).

[0165] Specifically, the application service provision module (160) may include a maintenance policy establishment unit (161), a bridge performance degradation level information provision unit (162), a repair and reinforcement maintenance information provision unit (163), a bridge performance impact indicator information provision unit (164), and a bridge inspection / diagnosis work support unit (165).

[0166] Next, the application service provision module (160) of the bridge structure smart maintenance platform (100) recycles the data analyzed when providing the application service and accumulates the data in a circular manner (S170).

[0167] Specifically, as a data circulation accumulation system in a smart maintenance platform providing bridge structure-related data and analysis algorithms according to an embodiment of the present invention,

[0168] As illustrated in FIG. 20, the results produced from all services provided by the bridge structure smart maintenance platform (100) are circulated and accumulated in the form of numerical models and databases, and the circulated accumulated data can be utilized to provide improved results for other service algorithms.

[0169] Accordingly, big data is constructed based on bridge-related inspection and diagnosis reports, environmental information, and measurement information, and based on this, services required by management entities, the inspection and diagnosis industry, research institutes, and academia can be provided in a modular form.

[0170] Ultimately, according to an embodiment of the present invention, a bridge structure maintenance database is constructed by organizing big data based on bridge-related inspection and diagnosis reports, environmental information, and measurement information, and based on this, services required by management entities, the inspection and diagnosis industry, research institutes, and academia can be provided in a modular form.

[0171] In addition, by utilizing DNA (Data, Network, and AI) technology, it is possible to predict damage and deterioration necessary for the preventive maintenance of bridges and to comprehensively provide users with bridge structure-related data and analysis algorithms suitable for their needs.

[0172] In addition, when performing a safety assessment of a general bridge where a long-term monitoring system is not installed, based on data measuring vibration, displacement, cracks, and temperature / humidity for more than one year for about 100 representative bridges according to the type and environmental conditions of the representative bridge, comparative data can be provided to verify the measurement and evaluation results of the target bridge by extending this data.

[0173] Furthermore, by utilizing artificial intelligence technology in the analysis of aging data to estimate the future damage status of the bridge to be analyzed, this serves as objective basic data for calculating bridge maintenance costs. This significantly contributes to the preventive maintenance of bridge structures and can reduce large-scale maintenance costs that may occur in the future.

[0174] The foregoing description of the present invention is for illustrative purposes only, and those skilled in the art will understand that other specific forms can be easily modified without altering the technical spirit or essential features of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single unit may be implemented in a distributed manner, and components described as distributed may likewise be implemented in a combined form.

[0175] The scope of the present invention is defined by the claims set forth below rather than by the detailed description above, and all modifications or variations derived from the meaning and scope of the claims and equivalent concepts thereof should be interpreted as being included within the scope of the present invention. Explanation of the symbols

[0176] 100: Smart Maintenance Platform for Bridge Structures 110: Data Collection Module 120: Numerical Model Creation Module 130: Bridge Structure Maintenance DB 140: Analysis Algorithm Provision Module 150: Bridge Maintenance Data Provision Module 151: Measurement Data-Based Management Standard Setting Section 152: Bridge Deterioration Environment Assessment Department 153: Data-based Load-carrying Performance Estimation Section for Small and Medium-sized Bridges 154: Measurement / Environmental Data-Based Bridge Deterioration Assessment Division 155: AI-based Degradation Model Accuracy Verification Unit 156: Measurement Data-Degradation Model Linkage 157: Deterioration Evaluation Section by Evaluation Indicator 160: Application Service Provision Module 161: Maintenance Policy Formulation Department 162: Bridge Performance Degradation Level Information Provision Section 163: Repair, Reinforcement, and Maintenance Information Provision Department 164: Bridge Performance Impact Indicator Information Provision Section 165: Bridge Inspection / Diagnosis Support Department 200: Bridge structures 300: Data provider 400: User terminal

Claims

Claim 1 A data collection module (110) that collects and inputs bridge measurement data, extracted information from bridge inspection / diagnosis reports, chloride data, deterioration information for concrete specimens, and meteorological agency data; a numerical model creation module (120) that creates a numerical model for the data collected and input by the data collection module (110); a bridge structure maintenance DB (130) that is constructed using the data collected and input by the data collection module (110) for the maintenance of a bridge structure (200); an analysis algorithm provision module (140) that provides an analysis algorithm related to bridge maintenance to a user; a bridge maintenance data provision module (150) that provides bridge maintenance data based on the data collected by the data collection module (110), and provides comparison data to expand upon this to verify the measured and evaluated results of the target bridge; and an application service provision module (160) that provides application services to a user terminal (400) by utilizing the constructed numerical model and database.It provides bridge structure-related data and analysis algorithms including, wherein the data collection module (110) collects and inputs: 1) bridge measurement data measuring vibration, displacement, crack width, and temperature / humidity measured for more than one year at approximately 100 bridges; 2) information extracted from more than 10,000 bridge inspection / diagnosis reports; 3) chloride data collected from approximately 100 environmental information collection devices; 4) deterioration information on concrete specimens investigated for more than 20 years; and 5) Korea Meteorological Administration data. 5) Data from the Korea Meteorological Administration is collected and input, and the bridge maintenance data provision module (150) comprises: a measurement data-based management standard value setting unit (151) that provides a trend of data to be measured over the next year using AI technology based on static and dynamic data measured at a representative bridge and data measured over the past year; a bridge deterioration environment evaluation unit (152) that evaluates the comprehensive deterioration environment by deriving a deterioration evaluation formula according to a deterioration environment evaluation algorithm and determining the evaluation grade and weight for each grade of each deterioration environment; a data-based small and medium-sized bridge load-bearing performance estimation unit (153) that provides an estimated load-bearing performance of the bridge according to a bridge load-bearing performance prediction model when specifications and environmental information of a specific bridge are input based on a data set from which data outliers have been removed according to bridge inspection / diagnosis results; and a measurement / environment data-based bridge aging evaluation unit (154) that provides an aging evaluation result according to a measurement / environment aging evaluation algorithm when specifications and environmental information of a specific bridge are input based on a bridge aging model including representative bridge and environmental information.A smart maintenance platform comprising: an AI-based deterioration model accuracy verification unit (155) that verifies the accuracy of AI-based generated data for sections where actual data is not collected; a measurement data-deterioration model linkage unit (156) that links the deterioration model to the measurement data to reflect the deterioration environment data; and an evaluation indicator-specific aging evaluation unit (157) that evaluates the quantification of deterioration damage of bridge structures over time through the subdivision of evaluation indicators, weight adjustment, and the generation of an aging curve for each evaluation indicator at the member unit level, wherein the AI-based deterioration model accuracy verification unit (155) performs accuracy verification by comparing the verification data group, which is separated into a training data group and a verification data group in the actual data, with the AI-based generated data. Claim 2 A smart maintenance platform providing bridge structure-related data and analysis algorithms, characterized in that, in claim 1, the bridge structure maintenance DB (130) forms big data based on inspection and diagnosis reports, environmental information, and measurement information related to bridges, and based on this, the application service provision module (160) provides services required by management entities, the inspection and diagnosis industry, research institutes, and academia in the form of modules. Claim 3 delete Claim 4 delete Claim 5 In claim 1, the algorithm provided by the analysis algorithm providing module (140) is a smart maintenance platform that provides bridge structure-related data and analysis algorithms, including a bridge deterioration environment evaluation algorithm, a bridge load-bearing performance prediction model, and a bridge aging evaluation algorithm. Claim 6 A smart maintenance platform that provides bridge structure-related data and analysis algorithms, wherein the bridge maintenance data provision module (150) in claim 1 is characterized by setting management standards based on measurement data, evaluating the bridge deterioration environment, estimating the load-bearing performance of small and medium-sized bridges based on data, evaluating the degree of bridge deterioration based on measurement / environmental data, verifying the accuracy of the deterioration model using artificial intelligence, linking the measurement data and the deterioration model, and evaluating the degree of deterioration by evaluation indicator. Claim 7 delete Claim 8 A smart maintenance platform that provides bridge structure-related data and analysis algorithms, wherein the bridge deterioration environment evaluation unit (152) is characterized by deriving each deterioration evaluation formula for coastal airborne salt, de-icing agent airborne salt, offshore bridge airborne salt, and East Sea environment according to a deterioration environment evaluation algorithm. Claim 9 In claim 1, the measurement / environmental aging evaluation algorithm of the measurement / environmental data-based bridge aging evaluation unit (154) is a smart maintenance platform that provides bridge structure-related data and analysis algorithms, including an aging evaluation algorithm for RCS, RA, and PSC I bridge types that reflects the weighting of member-unit damage types and damage diffusion (heterogeneous damage) to improve accuracy based on measurement / environmental data. Claim 10 delete Claim 11 In paragraph 1, the application service provision module (160) comprises: a maintenance policy establishment unit (161) that provides support for establishing a bridge management execution plan, evaluating the feasibility of road facility performance improvement projects, supporting the establishment of a medium-term financial plan for road facilities, and supporting the management of repair and reinforcement history information; a bridge performance degradation level information provision unit (162) that provides assessment and prediction of deterioration, estimation of bridge load-bearing performance, estimation of bridge seismic performance, safety estimation based on bridge measurement data, provision of environmental impact information, and prediction of the spread of heterogeneous damage to the bridge; a repair and reinforcement maintenance information provision unit (163) that provides information on repair and reinforcement methods and costs, maintenance scenarios for each bridge member, and maintenance scenario information for each major durability item of the bridge; and a bridge performance impact indicator information provision unit (164) that provides estimation of atmospheric salinity, provision of regional deterioration environment information, provision of regional deterioration environment data, estimation of chloride infiltration amount by deterioration environment, and estimation of heavy vehicle traffic volume. A smart maintenance platform that provides bridge structure-related data and analysis algorithms, including a bridge inspection / diagnosis work support unit (165) that supports operation of an IoT sensor-based bridge measurement system, interpretation of damage photographs (damage and volume of damage), provision of bridge substructure routing information, setting of bridge measurement data management standards, and support for inputting a bridge exterior network diagram. Claim 12 A method for maintaining a bridge structure using a smart maintenance platform that provides data and analysis algorithms related to a bridge structure using the smart maintenance platform of claim 1, comprising: a) a step in which a data collection module (110) of the bridge structure smart maintenance platform (100) collects and inputs data from a data provider (300) in relation to a bridge structure (200); b) a step in which a numerical model creation module (120) of the bridge structure smart maintenance platform (100) creates a numerical model for the provided data; c) a step in which a bridge structure maintenance DB (130) is constructed from the provided data; d) a step in which an analysis algorithm provision module (140) of the bridge structure smart maintenance platform (100) provides an analysis algorithm to a user in the form of a service module through a user terminal (400); e) a step in which a bridge maintenance data provision module (150) of the bridge structure smart maintenance platform (100) provides bridge maintenance data to a user in the form of a service module through a user terminal (400); f) a pre-constructed numerical model and bridge structure maintenance A method for maintaining a bridge structure using a smart maintenance platform that provides data and analysis algorithms related to a bridge structure, comprising: a step of providing application services using a DB; and g) a step in which an application service provision module (160) of a smart maintenance platform (100) for a bridge structure recycles the data analyzed when providing the application services to circulate and accumulate data; wherein big data is constructed based on inspection and diagnosis reports, environmental information, and measurement information related to the bridge, and services required by management entities, inspection and diagnosis industries, research institutes, and academia are provided in the form of modules based on this. Claim 13 delete Claim 14 delete Claim 15 delete Claim 16 delete Claim 17 delete Claim 18 delete Claim 19 delete Claim 20 In claim 12, the application service provision module (160) of step g) above comprises: a maintenance policy establishment unit (161) that provides support for establishing a bridge management execution plan, evaluating the feasibility of road facility performance improvement projects, supporting the establishment of a medium-term financial plan for road facilities, and supporting the management of repair and reinforcement history information; a bridge performance degradation level information provision unit (162) that provides assessment and prediction of deterioration, estimation of bridge load-bearing performance, estimation of bridge seismic performance, safety estimation based on bridge measurement data, provision of environmental impact information, and prediction of the spread of heterogeneous damage to the bridge; a repair and reinforcement maintenance information provision unit (163) that provides information on repair and reinforcement methods and costs, maintenance scenarios by bridge member, and maintenance scenario information by major durability item of the bridge; and a bridge performance impact indicator information provision unit (164) that provides estimation of atmospheric salinity, provision of regional deterioration environment information, provision of regional deterioration environment data, estimation of chloride infiltration amount by deterioration environment, and estimation of heavy vehicle traffic volume. A method for maintaining a bridge structure using a smart maintenance platform that provides bridge structure-related data and analysis algorithms, including a bridge inspection / diagnosis work support unit (165) that supports the operation of an IoT sensor-based bridge measurement system, the interpretation of damage-taking photos (damage and volume of damage), the provision of bridge substructure routing information, the setting of bridge measurement data management standards, and the input of a bridge exterior network diagram. Claim 21 A method for maintaining a bridge structure using a smart maintenance platform that provides bridge structure-related data and analysis algorithms, characterized in that, in step g) above, the results produced from all services provided by the bridge structure smart maintenance platform (100) are included again in the bridge structure smart maintenance platform (100) in the form of the numerical model and database and are circulatedly accumulated, and the circulatedly accumulated data is utilized to provide improved results of other service algorithms.

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