Construction method of bridge member safety knowledge graph based on multi-category monitoring information and hidden danger information

Abstract

The invention discloses a construction method of a bridge member safety knowledge graph based on multi-category monitoring information and hidden danger information, and relates to the technical field of bridge structure safety monitoring, and the method comprises the steps: determining a structure reliability index as a representation index of bridge structure safety, building a two-dimensional collaborative analysis system based on monitoring items and hidden danger items, and obtaining a bridge structure safety knowledge graph; building a multi-source data fusion structure reliability analysis framework by taking a structure reliability index as a final analysis purpose; based on a structure reliability analysis framework, a four-layer ontology model comprising a structure physical layer, a damage monitoring layer, a reliability assessment layer and an auxiliary decision-making layer is designed through Protege software, a knowledge graph is formed, a knowledge representation path of bridge structure safety assessment decision is established, and timely dynamic update and reliable maintenance decision of a bridge structure safety state are realized. According to the method, the knowledge graph is constructed through the four-layer ontology model with the causal conduction mechanism, and a knowledge representation path capable of systematically analyzing the safety of the bridge structure is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge structure safety monitoring, and specifically to a method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information. Background Art

[0002] In modern transportation infrastructure construction, bridges are critical hubs, and their structural safety and service life are directly related to the safety of people's lives and property, as well as social and economic stability. With the rapid development of the Internet of Things, sensor technology, and intelligent detection equipment, bridge structural health monitoring systems have been able to deploy a large number of high-precision monitoring devices, such as strain sensors, displacement gauges, and accelerometers, capable of collecting real-time data on structural response and environmental loads. At the same time, the inspection and maintenance process also accumulates a wealth of unstructured data, such as disease records and maintenance history. Statistics show that a large bridge can generate up to GB of monitoring data in a single day, encompassing a variety of modalities, including time series data, image data, and text reports.

[0003] However, there are significant technical bottlenecks in the current field of bridge health monitoring and digital management and maintenance. From a data perspective, there is an "information island" phenomenon in massive monitoring data: multi-source heterogeneous data such as equipment monitoring data (such as vibration frequency, stress and strain), inspection and maintenance data (such as crack width records, corrosion degree assessment), and environmental data (such as temperature and humidity, wind speed and direction) lack unified semantic association and structured integration. For example, the real-time vibration data collected by the sensor cannot be directly matched with the description of local damage in the inspection report, resulting in the potential causal relationship between the data being severed, making it difficult to construct a complete structural safety analysis model. In addition, existing data analysis mostly uses statistical methods based on a single data source, such as threshold alarms based on strain data, which cannot comprehensively consider the impact of the coupling of multiple factors on structural safety, making it difficult to fully tap the value of massive dynamic monitoring data and difficult to identify structural safety hazards in a timely manner.

[0004] From the perspective of structural safety analysis, bridge components, as the basic units of load-bearing structures, require accurate assessment of their safety status to ensure overall structural stability. However, traditional monitoring methods focus only on the overall response of the bridge, neglecting detailed analysis at the component level. For example, local damage, such as critical welds in bridge steel box girders and crack development in concrete piers, is difficult to directly diagnose using overall monitoring data. Furthermore, there are technical gaps in the integration and interpretation of multi-source monitoring data at the component scale, making it impossible to effectively quantify the degradation process and remaining life of components. This situation results in a lack of scientific basis for bridge maintenance decisions, and the frequent occurrence of "over-maintenance" or "delayed maintenance" issues, which not only wastes resources but also increases structural safety risks.

[0005] In the field of digital maintenance, existing systems primarily focus on data storage and simple queries, lacking in-depth analysis of the correlation between monitoring data and maintenance strategies. For example, maintenance plan development still relies on manual experience and regular inspection results, failing to integrate real-time monitoring data with structural performance assessment and maintenance plan optimization into a closed-loop management system. Furthermore, the massive amount of data covering the entire lifecycle of bridges has yet to be organized into a structured knowledge system, making it impossible to provide managers with intuitive and explainable decision-making support, making it difficult to meet the development needs of intelligent and scientific bridge operation and maintenance.

[0006] In summary, it has become an urgent need to improve the level of bridge operation and maintenance to build a systematic knowledge representation path, take bridge structural components as the core carrier, realize the deep integration and semantic association of multi-source monitoring data, and then break through the technical barriers between health monitoring and digital management and maintenance. Summary of the Invention

[0007] In response to the needs and shortcomings of current technological development, the present invention provides a method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information, so as to form a data application and analysis system from raw dynamic monitoring data to structural safety assessment decision-making through data fusion and knowledge representation.

[0008] The present invention provides a method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information, and adopts the following technical solutions to solve the above technical problems:

[0009] A method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information includes the following steps:

[0010] S1. Determine the structural reliability index as the indicator of bridge structural safety, establish a two-dimensional collaborative analysis system based on monitoring items and potential hazards, take the structural reliability index as the final analysis goal, and build a structural reliability analysis framework that integrates multi-source data;

[0011] S2. A structural reliability analysis framework based on multi-source data fusion is designed using Protege software. The four-layer ontology model includes the structural physics layer, damage monitoring layer, reliability assessment layer, and auxiliary decision-making layer. This forms a knowledge graph and establishes a knowledge representation path for bridge structure safety assessment and decision-making, thus achieving timely dynamic updates of the safety status of bridge structures and reliable maintenance decisions.

[0012] Optionally, step S1 is executed to study the possibility of the structure completing the intended function within the specified time and conditions;

[0013] When analyzing structural reliability, first determine the probability distribution of random variables in structural resistance and load effects, and use the limit state equation Z = RS ≤ 0 to determine whether the structure has failed, where R represents structural resistance and S represents load effect. Then calculate the failure probability of Z ≤ 0, and subtract the failure probability of Z ≤ 0 from 1 to obtain the structural reliability. The structural reliability is quantified using the structural reliability index β. The larger the index value, the more reliable the structure.

[0014] Execute step S1 to sort out the variables in the structural resistance and load effect of the structure based on the continuously and dynamically updated monitoring item data and hidden danger item data, and build a structural reliability analysis framework based on multi-source data fusion data.

[0015] Optionally, execute step S2 to design the structural physical layer using Protege software, and use components as carriers to implement the force analysis transmission path of structural resistance and load effects. This process requires creating a component type class and associating and binding and defining geometric properties and spatial topological relationship properties, including:

[0016] S2.1.1. Set geometric properties, define the geometric properties of different component types in the component type class, and define the subclass inheritance relationship of the component type class; use the component ID attribute to achieve a one-to-one mapping between the BIM model geometric properties and the geometric properties of the component type class subclass instance. Each subclass instance obtains unique structural spatial coordinates, providing data support for the establishment of spatial topological relationships between different components;

[0017] S2.1.2. Based on the obtained structural spatial coordinates, set the spatial topological association attributes for each subclass instance of the component type class; by setting the spatial topological relationship of each subclass instance, the linkage effect of the force analysis between different components is achieved, thereby providing a data transmission path for the calculation of local structural reliability analysis or overall structural reliability analysis.

[0018] Optionally, step S2 is performed to design a damage monitoring layer using Protege software, associate different components with monitoring items and potential hazards, and calculate the component damage type and damage degree caused by the monitoring items or potential hazards. This process specifically includes:

[0019] S2.2.1. Establish a dual-mode damage triggering mechanism in the damage monitoring layer;

[0020] S2.2.2. Based on the associated monitoring items and hidden danger items under the component type subclass instance, the dual-mode damage trigger mechanism calculates the damage type and its damage confidence of each subclass instance, and transmits them to the reliability assessment layer through damage attributes.

[0021] Preferably, the damage attribute is a structured data including key fields such as damage type, confidence level, occurrence location, involved component number and first detection time;

[0022] After calculating the damage type and damage confidence of each subclass instance, they are encapsulated in the form of damage attributes and transmitted to the reliability assessment layer in real time through a standardized data interface.

[0023] Preferably, the dual-mode damage trigger mechanism establishes attribute associations between monitoring items, hidden danger items and damage type confidence levels through a monitoring trigger mechanism and a hidden danger level assessment mechanism, wherein:

[0024] The monitoring trigger mechanism establishes the judgment logic of "monitoring value abnormality → triggering the over-limit threshold → calculating the confidence level of the damage type":

[0025] If the monitoring value X> the set threshold α,

[0026] Then activate the preset damage type confidence index calculation rules;

[0027] The hidden danger level assessment mechanism establishes the judgment logic of "hidden danger item exists → trigger hidden danger level assessment → calculate damage type confidence level":

[0028] If hidden danger item Y∈ a certain hidden danger level interval,

[0029] Then activate the preset damage type confidence index calculation rules.

[0030] Preferably, the construction method involved includes three types of damage: apparent damage, stress risk and structural displacement.

[0031] Optionally, step S2 is performed to design a reliability assessment layer using Protege software to calculate reliability indices from individual components to the entire bridge structure. This process specifically includes:

[0032] S2.3.1. Develop a reliability assessment rule base in the reliability assessment layer;

[0033] S2.3.2. Calculate the structural resistance and load effects of individual components based on the rules in the reliability assessment rule base. Further calculate the local structural resistance and load effects caused by the component and its adjacent components based on the load transfer path of the component. Evaluate the entire bridge, integrate the data of each component, and calculate the structural resistance and load effects of the entire bridge structure.

[0034] S2.3.3. Based on the rules in the reliability assessment rule base and utilizing expertise in material mechanics and engineering mechanics, design damage evolution rules for the three damage types of apparent damage, stress risk, and structural displacement. Furthermore, determine the resistance reduction factor and load magnification factor of the component under different damage types to quantify the impact of damage on structural performance.

[0035] S2.3.4. Substitute the calculated structural resistance, load effect, and resistance reduction factor and load magnification factor determined by the damage evolution rule into the limit state equation for calculation. Use the limit state equation to determine the failure probability of the structure, and then clarify the reliability index of the structure to complete the safety assessment of individual components, local structures, or the entire bridge structure.

[0036] Optionally, step S2 is performed to design an auxiliary decision layer using Protege software to provide recommendations for preventive maintenance decisions of the bridge using dynamically updated structural reliability indicators. This process specifically includes: S2.4.1. Comprehensively collect technical condition indicators related to the structure and determine the reliability indicators therefrom;

[0037] S2.4.2. Based on professional knowledge in material mechanics and structural mechanics, analyze the impact mechanism of technical condition indicators on reliability indicators and establish quantitative or qualitative correlation rules between technical condition indicators and reliability indicators through experimental research, numerical simulation, or statistical analysis of historical data;

[0038] S2.4.3. Classify preventive maintenance recommendations into different types, clarifying the applicable conditions and maintenance objectives for each type; determine the corresponding reliability index range for different structural technical conditions, combining the developed association relationship rules; match the determined reliability index range with the preventive maintenance recommendation type to establish a one-to-one correspondence;

[0039] S2.4.4. Obtain information on the actual technical condition of the structure through on-site testing or monitoring data collection, and evaluate the structural technical condition according to pre-established standards and methods to draw maintenance conclusions. Based on the maintenance conclusions, find the correspondence between the established technical condition, reliability indicators, and preventive maintenance recommendations, and determine preventive maintenance recommendations that match the current structural technical condition and reliability indicators, providing a scientific basis for structural maintenance decisions.

[0040] Compared with the prior art, the method of constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information has the following beneficial effects:

[0041] 1. The present invention can form a data application and analysis system from original dynamic monitoring data (including equipment monitoring data and inspection hidden danger data) to structural safety assessment and decision-making through data fusion and knowledge representation;

[0042] 2. The present invention integrates multi-source heterogeneous data and constructs a knowledge graph through a four-layer ontology model with a causal transmission mechanism, forming a knowledge representation path that can conduct a systematic analysis of bridge structure safety, thereby realizing timely dynamic assessment of bridge structure safety and achieving the purpose of intelligent bridge management and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Attachment Figure 1 is a flowchart of a method implementation of an embodiment of the present invention;

[0044] Attachment Figure 2 This is a structural reliability analysis framework diagram constructed using a beam bridge as an example in an embodiment of the present invention;

[0045] Attachment Figure 3 This is a schematic diagram of a core classification system of the structural physical layer designed using a beam bridge as an example in an embodiment of the present invention;

[0046] Attachment Figure 4 This is a schematic diagram of associated attributes of a structural physical layer subclass instance designed using a beam bridge as an example in an embodiment of the present invention;

[0047] Attachment Figure 5 This is a schematic diagram of a core classification system for damage monitoring layers designed using a beam bridge as an example in an embodiment of the present invention;

[0048] Attachment Figure 6 This is a schematic diagram of associated attributes of a damage monitoring layer designed using a beam bridge as an example in an embodiment of the present invention;

[0049] Attachment Figure 7 This is a schematic diagram of a core classification system for reliability assessment layers designed using a beam bridge as an example in an embodiment of the present invention;

[0050] Attachment Figure 8 This is a schematic diagram of associated attributes of key items of reliability index analysis of a single component in a reliability assessment layer designed for a beam bridge in an embodiment of the present invention;

[0051] Attachment Figure 9 This is a schematic diagram of associated attributes of key items of reliability index analysis of a local structure or an overall structure of a reliability assessment layer designed for a beam bridge in an embodiment of the present invention;

[0052] Attachment Figure 10 This is a schematic diagram of the core classification system of the auxiliary decision-making layer designed by taking a beam bridge as an example in an embodiment of the present invention;

[0053] Attachment Figure 11This is a schematic diagram of associated attributes of a preventive maintenance suggestion subclass of an auxiliary decision-making layer designed for a beam bridge in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the technical solution, the technical problems solved and the technical effects of the present invention more clear, the technical solution of the present invention is clearly and completely described below in conjunction with specific embodiments.

[0055] Example:

[0056] Combined with attachment Figure 1 This embodiment proposes a method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information, which includes the following steps:

[0057] S1. Determine the structural reliability index β as the characterization indicator of bridge structural safety, establish a two-dimensional collaborative analysis system based on monitoring items and hidden danger items, take the structural reliability index β as the final analysis goal, and build a structural reliability analysis framework with multi-source data fusion.

[0058] Structural reliability studies the possibility of a structure completing its intended function within specified time and conditions;

[0059] When analyzing structural reliability, first determine the probability distribution of random variables in the structural resistance R and load effect S, use the limit state equation Z = RS ≤ 0 to determine whether the structure has failed, then calculate the failure probability of Z ≤ 0, and subtract the failure probability of Z ≤ 0 from 1 to obtain the structural reliability. The structural reliability index β is used to quantify the structural reliability. The larger the value of the structural reliability index β, the more reliable the structure.

[0060] When executing step S1, based on the continuously and dynamically updated monitoring item data and hidden danger item data, the variables in the structural resistance R and load effect S of the structure are sorted out, and a structural reliability analysis framework based on multi-source data fusion is established.

[0061] S2. A structural reliability analysis framework based on multi-source data fusion is designed using Protege software. The four-layer ontology model includes the structural physics layer, damage monitoring layer, reliability assessment layer, and auxiliary decision-making layer. This forms a knowledge graph and establishes a knowledge representation path for bridge structure safety assessment and decision-making, thus achieving timely dynamic updates of the safety status of bridge structures and reliable maintenance decisions.

[0062] S2.1. Use Protege software to design the structural physical layer, using components as carriers to implement the force analysis and transmission path of structural resistance and load effects. This process requires the creation of component type classes, the association and binding of geometric properties, and the definition of spatial topological relationship properties, including:

[0063] S2.1.1. Set geometric properties, define the geometric properties of different component types in the component type class, and define the subclass inheritance relationship of the component type class; use the component ID attribute to achieve a one-to-one mapping between the BIM model geometric properties and the geometric properties of the component type class subclass instance. Each subclass instance obtains unique structural spatial coordinates, providing data support for the establishment of spatial topological relationships between different components;

[0064] S2.1.2. Based on the obtained structural spatial coordinates, set spatial topological association attributes for each subclass instance of the component type class, such as component adjacency relationship, load transfer path, etc.; by setting the spatial topological relationship of each subclass instance, the linkage effect of force analysis between different components is achieved, thereby providing a data transfer path for the calculation of local structural reliability analysis or overall structural reliability analysis.

[0065] S2.2. Design a damage monitoring layer using Protege software to associate different components with monitoring items and potential hazards. Calculate the component damage type and damage degree caused by monitoring items or potential hazards. This process specifically includes:

[0066] S2.2.1. Establish a dual-mode damage trigger mechanism in the damage monitoring layer. The dual-mode damage trigger mechanism establishes attribute associations between monitoring items, hidden danger items, and damage type confidence levels through the monitoring trigger mechanism and the hidden danger level assessment mechanism.

[0067] The monitoring trigger mechanism establishes the judgment logic of "monitoring value abnormality → triggering the over-limit threshold → calculating the confidence level of the damage type":

[0068] If the monitoring value X> the set threshold α,

[0069] Then activate the preset damage type confidence index calculation rules;

[0070] The hidden danger level assessment mechanism establishes the judgment logic of "hidden danger item exists → trigger hidden danger level assessment → calculate damage type confidence level":

[0071] If hidden danger item Y∈ a certain hidden danger level interval,

[0072] Then activate the preset damage type confidence index calculation rules;

[0073] S2.2.2. Based on the associated monitoring items and hidden danger items under the component type subclass instance, the dual-mode damage trigger mechanism calculates the damage type and its damage confidence of each subclass instance, and transmits them to the reliability assessment layer through damage attributes.

[0074] Damage attributes are structured data that include key fields such as damage type, confidence level, occurrence location, component number involved, and first detection time. After calculating the damage type and damage confidence level of each subclass instance, they are encapsulated in the form of damage attributes and transmitted to the reliability assessment layer in real time through a standardized data interface.

[0075] Damage types include apparent damage, stress risk and structural displacement.

[0076] It should be added that the following contents can be clarified through the above two-layer ontology model design of the structural physical layer and the damage monitoring layer:

[0077] (1) Through the core classification system of the damage monitoring layer and the setting of associated attributes under different instances, a data basis can be provided for the calculation of structural resistance R and load effect S in the reliability assessment layer;

[0078] (2) Based on the setting of spatial topological attributes such as load transfer paths and adjacency relationships of each component instance in the structural physical layer, the structural transfer path of force data can be provided for the analysis and calculation of structural reliability in the reliability assessment layer.

[0079] S2.3. Design the reliability assessment layer using Protege software to calculate the reliability index β from individual components to the overall bridge structure. This process specifically includes:

[0080] S2.3.1. Develop a reliability assessment rule base in the reliability assessment layer;

[0081] S2.3.2. Calculate the structural resistance R and load effect S of each individual component based on the rules in the reliability assessment rule base. Further calculate the local structural resistance R and load effect S caused by the component and its adjacent components based on the load transfer path of the component. Evaluate the entire bridge, integrate the data of each component, and calculate the structural resistance R and load effect S of the entire bridge structure.

[0082] S2.3.3. Based on the rules in the reliability assessment rule base and utilizing expertise in material mechanics and engineering mechanics, design damage evolution rules for the three damage types of apparent damage, stress risk, and structural displacement. Furthermore, determine the resistance reduction factor and load magnification factor of the component under different damage types to quantify the impact of damage on structural performance.

[0083] S2.3.4. Substitute the calculated structural resistance R, load effect S, and the resistance reduction factor and load magnification factor determined by the damage evolution rule into the limit state equation for calculation. Use the limit state equation to determine the failure probability of the structure, and then determine the reliability index β of the structure to complete the safety assessment of a single component, local structure, or the entire bridge structure.

[0084] It should be added that the reliability assessment layer obtains the apparent damage confidence, stress damage confidence, and displacement damage confidence of each component based on the damage monitoring layer. Combined with the adjacency relationship and load transfer path between different components, the reliability assessment rules are used to analyze the structural resistance R and load effect S of all components that trigger the damage confidence calculation rules. Furthermore, based on the limit state equation Z = RS and the load transfer path of each component, the reliability index β of the damaged component and its local structure, as well as the overall bridge structure, is calculated to achieve reliability analysis of the bridge structure.

[0085] S2.4. Design a decision-making support layer using Protege software to provide recommendations for preventive maintenance decisions based on dynamically updated structural reliability indicators. This process specifically includes:

[0086] S2.4.1. Comprehensively collect technical condition indicators related to the structure and determine reliability indicators from them;

[0087] S2.4.2. Based on professional knowledge in material mechanics and structural mechanics, analyze the impact mechanism of technical condition indicators on reliability indicators and establish quantitative or qualitative correlation rules between technical condition indicators and reliability indicators through experimental research, numerical simulation, or statistical analysis of historical data;

[0088] S2.4.3. Classify preventive maintenance recommendations into different types, clarifying the applicable conditions and maintenance objectives for each type; determine the corresponding reliability index range for different structural technical conditions, combining the developed association relationship rules; match the determined reliability index range with the preventive maintenance recommendation type to establish a one-to-one correspondence;

[0089] S2.4.4. Obtain information on the actual technical condition of the structure through on-site testing or monitoring data collection, and evaluate the structural technical condition according to pre-established standards and methods to draw maintenance conclusions. Based on the maintenance conclusions, find the correspondence between the established technical condition, reliability index β, and preventive maintenance recommendations, and determine preventive maintenance recommendations that match the current structural technical condition and reliability index β, providing a scientific basis for structural maintenance decisions.

[0090] It should be added that the auxiliary decision-making layer refers to the "Highway Bridge and Culvert Maintenance Code" (JTG 5120-2021) to describe and define the preventive maintenance recommendations corresponding to different technical condition index value intervals, and based on the association rules between the technical condition index and the reliability index β, combined with project reality and engineering experience, the technical condition index and the reliability index of the individual component, the reliability index of the local structure to which it belongs, and the reliability index of the overall structure are associated. Furthermore, the component type (such as superstructure, substructure, bridge deck system) to which different components belong is clarified based on the component ID, so that preventive maintenance recommendations based on the reliability of the individual component, the reliability of the local structure, and the reliability of the overall structure can be obtained respectively, realizing intelligent maintenance at the component level of the beam bridge.

[0091] For the above technical solution, take a beam bridge as an example:

[0092] When executing step S1, taking the structural resistance R including material properties R1, geometric parameters R2 and structural connection mode R3, and the load effect S including dead load effect S1 and live load effect S2 as an example, the structural reliability analysis framework is constructed as follows: Figure 2 shown.

[0093] Specifically, when executing step S2.1, the geometric properties of the main beam, hanging beam, pier, abutment, bridge deck and other component types are defined, and the following are obtained: Figure 3 The schematic diagram of the core classification system of the structural physical layer is shown in the figure, and the corresponding schematic diagram of the associated attributes of the structural physical layer subclass instance is shown in the figure. Figure 4 As shown, Figure 4 Medium grey represents subclass ontology instances, and white represents the attributes of the instances.

[0094] When executing step S2.2, taking the main beam as an example, the core classification system of the designed damage monitoring layer is as follows: Figure 5 As shown, the corresponding damage monitoring layer instance association attribute diagram is as follows Figure 6 As shown, Figure 6 Medium grey represents subclass ontology instances, and white represents the attributes of the instances.

[0095] It should be noted that when executing step S2.2, monitoring items are not limited to concrete component temperature, component lateral displacement, component vertical displacement, beam end longitudinal displacement, key section static strain, component vibration acceleration, pier settlement, structural cracks, vehicle load, wind speed and pressure, wind direction, etc.; hidden danger items are not limited to beam body cracks, beam end displacement, platform cap cracks, cap beam cracks, pier cracks, exposed reinforcement corrosion, etc.; damage types refer to the damage type information in the "Highway Bridge Technical Condition Assessment Standard" (JTG / TH21-2011) and the "Urban Bridge Maintenance Technical Standard" (CJJ 99-2017), and are further sorted and summarized into three categories: apparent damage, stress risk, and structural displacement. The damage confidence level for different damage levels can be set based on the actual project and standard requirements.

[0096] When executing step S2.3, based on the damage monitoring layer, the reliability assessment layer from single component to local structure and then to the overall bridge structure is further realized by taking the structural resistance R and load effect S as an example. The core classification system is as follows: Figure 7 As shown in the figure, the corresponding single component reliability index analysis key item correlation attribute diagram is as follows Figure 8 As shown in the figure, the associated attributes of the key items of the reliability index analysis of the local structure or the overall structure are shown in Figure 9 As shown, Figure 8 and 9 Medium grey represents analysis key items, and white represents the attributes of the key items.

[0097] When executing step S2.4, based on the damage monitoring layer and reliability assessment layer, the auxiliary decision layer is further designed. Its core classification system is as follows: Figure 10 As shown, the corresponding associated attribute diagram of the preventive maintenance suggestion subclass is as follows Figure 11 As shown, Figure 11 Medium gray represents sub-category items, and white represents attributes of sub-category items.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements 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.

Claims

1. A method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information, characterized in that: The steps include: S1. Determine the structural reliability index as the indicator of bridge structural safety, establish a two-dimensional collaborative analysis system based on monitoring items and potential hazards, take the structural reliability index as the final analysis goal, and build a structural reliability analysis framework that integrates multi-source data; S2. A structural reliability analysis framework based on multi-source data fusion is designed using Protege software. The four-layer ontology model includes the structural physics layer, damage monitoring layer, reliability assessment layer, and auxiliary decision-making layer. This forms a knowledge graph and establishes a knowledge representation path for bridge structure safety assessment and decision-making, thus achieving timely dynamic updates of the safety status of bridge structures and reliable maintenance decisions.

2. The method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information according to claim 1 is characterized in that: Structural reliability studies the possibility of a structure completing its intended function within specified time and conditions; When analyzing structural reliability, first determine the probability distribution of random variables in structural resistance and load effects, and use the limit state equation Z = RS ≤ 0 to determine whether the structure has failed, where R represents structural resistance and S represents load effect. Then calculate the failure probability of Z ≤ 0, and subtract the failure probability of Z ≤ 0 from 1 to obtain the structural reliability. The structural reliability is quantified using the structural reliability index β. The larger the index value, the more reliable the structure. Execute step S1 to sort out the variables in the structural resistance and load effect of the structure based on the continuously and dynamically updated monitoring item data and hidden danger item data, and build a structural reliability analysis framework based on multi-source data fusion data.

3. The method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information according to claim 1, characterized in that: Execute step S2 and design the structural physical layer using Protege software. Use components as carriers to implement the force analysis transmission path of structural resistance and load effects. This process requires creating component type classes and associating and defining geometric properties and spatial topological relationship properties. Specifically, it includes: S2.1.

1. Set geometric properties, define the geometric properties of different component types in the component type class, and define the subclass inheritance relationship of the component type class; use the component ID attribute to achieve a one-to-one mapping between the BIM model geometric properties and the geometric properties of the component type class subclass instance. Each subclass instance obtains unique structural spatial coordinates, providing data support for the establishment of spatial topological relationships between different components; S2.1.

2. Based on the obtained structural spatial coordinates, set the spatial topological association attributes for each subclass instance of the component type class; by setting the spatial topological relationship of each subclass instance, the linkage effect of the force analysis between different components is achieved, thereby providing a data transmission path for the calculation of local structural reliability analysis or overall structural reliability analysis.

4. The method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information according to claim 3 is characterized in that: Execute step S2 to design a damage monitoring layer using Protege software, associate different components with monitoring items and potential hazards, and calculate the component damage type and damage degree caused by monitoring items or potential hazards. This process specifically includes: S2.2.

1. Establish a dual-mode damage triggering mechanism in the damage monitoring layer; S2.2.

2. Based on the associated monitoring items and hidden danger items under the component type subclass instance, the dual-mode damage trigger mechanism calculates the damage type and its damage confidence of each subclass instance, and transmits them to the reliability assessment layer through damage attributes.

5. The method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information according to claim 4 is characterized in that: Damage attributes are structured data that include key fields such as damage type, confidence level, location, involved component number, and first detection time. After calculating the damage type and damage confidence of each subclass instance, they are encapsulated in the form of damage attributes and transmitted to the reliability assessment layer in real time through a standardized data interface.

6. The method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information according to claim 4, characterized in that: The dual-mode damage trigger mechanism establishes attribute associations between monitoring items, hidden danger items and damage type confidence levels through the monitoring trigger mechanism and the hidden danger level assessment mechanism, wherein: The monitoring trigger mechanism establishes the judgment logic of "monitoring value abnormality → triggering the over-limit threshold → calculating the confidence level of the damage type": If the monitoring value X> the set threshold α, Then activate the preset damage type confidence index calculation rules; The hidden danger level assessment mechanism establishes the judgment logic of "hidden danger item exists → trigger hidden danger level assessment → calculate damage type confidence level": If hidden danger item Y∈ a certain hidden danger level interval, Then activate the preset damage type confidence index calculation rules.

7. The method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information according to claim 6, characterized in that: The method includes three types of damage: apparent damage, stress risk and structural displacement.

8. The method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information according to claim 7 is characterized in that: Execute step S2 and design the reliability assessment layer using Protege software to calculate the reliability index from individual components to the entire bridge structure. This process specifically includes: S2.3.

1. Develop a reliability assessment rule base in the reliability assessment layer; S2.3.

2. Calculate the structural resistance and load effects of individual components based on the rules in the reliability assessment rule base. Further calculate the local structural resistance and load effects caused by the component and its adjacent components based on the load transfer path of the component. Evaluate the entire bridge, integrate the data of each component, and calculate the structural resistance and load effects of the entire bridge structure. S2.3.

3. Based on the rules in the reliability assessment rule base and utilizing expertise in material mechanics and engineering mechanics, design damage evolution rules for the three damage types of apparent damage, stress risk, and structural displacement. Furthermore, determine the resistance reduction factor and load magnification factor of the component under different damage types to quantify the impact of damage on structural performance. S2.3.

4. Substitute the calculated structural resistance, load effect, and resistance reduction factor and load magnification factor determined by the damage evolution rule into the limit state equation for calculation. Use the limit state equation to determine the failure probability of the structure, and then clarify the reliability index of the structure to complete the safety assessment of individual components, local structures, or the entire bridge structure.

9. The method for constructing a bridge component safety knowledge graph based on multi-category monitoring information and hidden danger information according to claim 8, characterized in that: Execute step S2 and design a decision-making support layer using Protege software to provide recommendations for preventive maintenance decisions for bridges using dynamically updated structural reliability indicators. This process specifically includes: S2.4.

1. Comprehensively collect technical condition indicators related to the structure and determine the reliability indicators from them; S2.4.

2. Based on professional knowledge in material mechanics and structural mechanics, analyze the impact mechanism of technical condition indicators on reliability indicators and establish quantitative or qualitative correlation rules between technical condition indicators and reliability indicators through experimental research, numerical simulation, or statistical analysis of historical data; S2.4.

3. Classify preventive maintenance recommendations into different types, clarifying the applicable conditions and maintenance objectives for each type; determine the corresponding reliability index range for different structural technical conditions, combining the developed association relationship rules; match the determined reliability index range with the preventive maintenance recommendation type to establish a one-to-one correspondence; S2.4.

4. Obtain information on the actual technical condition of the structure through on-site testing or monitoring data collection, and evaluate the structural technical condition according to pre-established standards and methods to draw maintenance conclusions. Based on the maintenance conclusions, find the correspondence between the established technical condition, reliability indicators, and preventive maintenance recommendations, and determine preventive maintenance recommendations that match the current structural technical condition and reliability indicators, providing a scientific basis for structural maintenance decisions.

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