Steel bridge deck system disease detection method and system and construction method thereof

Abstract

The invention relates to the technical field of steel bridge deck system disease detection, in particular to a steel bridge deck system disease detection method and system and a construction method of the steel bridge deck system disease detection method and system. Attributes of a steel bridge deck system entity are mapped into a digital twinborn model of a virtual space by using a digital means, so that steel bridge deck system disease data are analyzed more comprehensively; the virtual service state analysis of the current steel bridge deck system entity and the prediction of the formation and development degree of the steel bridge deck system diseases are realized through the Internet of Things technology and big data processing. In the process of predicting the formation and development degrees of steel bridge deck system diseases, steel bridge deck system entity monitoring data are updated in a digital twinborn model and a steel bridge deck system twinborn database in real time by using the Internet of Things technology, so that accurate establishment of the digital twinborn model and accurate mapping of total data are realized; meanwhile, the virtual test model accurately simulates the service state of the steel bridge deck system, and it is ensured that the steel bridge deck system disease development model has credibility and reference in predicting the formation and development degree of the steel bridge deck system diseases.

Description

Technical Field

[0001] The present invention relates to the technical field of steel bridge deck defect detection, and in particular to a steel bridge deck defect detection method, system and construction method thereof. Background Art

[0002] In the past two decades, my country's steel bridge deck construction has experienced a leapfrog development process. With the continuous extension of the service life of in-service steel bridge decks and the increasing traffic volume, the fatigue damage problem of orthotropic slabs and bridge deck pavement has become increasingly prominent. In particular, the proportion of heavy-loaded freight vehicles is high, and the operation status of large-span steel bridge decks is not very ideal. Fatigue diseases have begun to appear. Under heavy-load conditions, the service life cannot be guaranteed to be more than 15 years, and there are very few steel bridge decks that have not been overhauled for 10 years. Some bridges are about to or have entered the centralized maintenance period. The fatigue damage problem of orthotropic bridge decks and bridge deck pavement will also become increasingly prominent. Therefore, timely detection of steel bridge deck system diseases and taking timely and correct maintenance measures are of great significance to extending the performance of pavement.

[0003] Traditional steel bridge deck inspection has the following problems: (1) It is impossible to predict and judge in advance whether steel bridge deck system defects will occur and the extent of the defects; (2) The static model for steel bridge deck system inspection established based on historical data will continue to lose accuracy as the steel bridge deck is put into use, and will eventually become completely ineffective. Summary of the invention

[0004] The present invention provides a steel bridge deck defect detection method, system and construction method thereof, which can effectively solve the problems in the background technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: A method for detecting defects in a steel bridge deck system comprises the following steps: Establish the mapping relationship between the steel bridge deck entity and the steel bridge deck model, and construct a digital twin model of the steel bridge deck characteristics; Based on the digital twin model and in combination with the real-time working parameters of the steel bridge deck entity, a virtual test model of the steel bridge deck characteristics is constructed and driven; Based on the virtual test model and the historical working condition parameters of the steel bridge deck entity, a steel bridge deck disease development model is constructed; The formation and development degree of steel bridge deck disease are predicted by the steel bridge deck disease development model; The physical information of the steel bridge deck system and the digital twin model are displayed in real time based on the visual twin service.

[0006] Further, the digital twin model includes constructing and associating a geometric-physical model of a steel bridge deck system, a twin database of a steel bridge deck system, and a rule attribute model of a steel bridge deck system; The geometric-physical model of the steel bridge deck system is used to associate the geometric information of the steel bridge deck system with basic mechanical properties through modeling software; The steel bridge deck twin database includes IoT sensing system monitoring data recorded in time series, and bridge deck disease data manually detected in the corresponding time series; The steel bridge deck system rule attribute model is established according to the standards and specifications in the field of steel bridge deck systems, and reflects the rule attributes of steel bridge deck system defects.

[0007] Furthermore, in the IoT sensing system, the temperature parameters of the steel bridge deck system in service are monitored by a temperature sensor, and the structural response parameters of the steel bridge deck system in service are monitored by a strain sensor and a deflection sensor; The weight and number of axles of passing vehicles are detected through the bridge weighing system and video monitoring system respectively, and the axle load parameters of the steel bridge deck system in service are monitored in conjunction with load sensors.

[0008] Further, the steel bridge deck system disease data is monitored manually and periodically, and the type information, location information and degree information of the detected disease are input into the steel bridge deck system twin database; A corresponding relationship is established between the performance status data of different pavement layers of the steel bridge deck system and the structural coordinates of the digital twin model, and then the disease data of the steel bridge deck system is integrated into the digital twin model to establish a mapping relationship.

[0009] Furthermore, the steel bridge deck system twin database is classified into real-time data, historical data and statistical data, the real-time data is obtained through the IoT sensing system and regular manual detection, the historical data is stored in time series, and the statistical data is obtained by regularly running a statistical program; Based on the digital twin model and in combination with real-time data, a virtual test model of steel bridge deck characteristics is constructed and driven, and the virtual test model is corrected according to the real-time data, and a steel bridge deck disease development model is constructed based on the corrected virtual test model in combination with historical data and statistical data; Furthermore, in the process of establishing the mapping relationship between the steel bridge deck system entity and the steel bridge deck system model, the steel bridge deck system twin database is used as a transit to perform mapping interaction; An interactive interface between the temperature, axle load and structural response parameters and the steel bridge deck system model based on a Redis API database driver is set up to read the perception data, unified fusion data and structural response related data in the steel bridge deck system twin database in real time to drive the virtual test model; A data transmission interface between the temperature, axle load and structural response parameters and the steel bridge deck system entity based on the MQTT protocol is set to update the digital twin model and correct the steel bridge deck system disease development model.

[0010] A steel bridge deck defect detection system, applied to the above steel bridge deck defect detection method, comprising a steel bridge deck digital module, a steel bridge deck working condition update module, a steel bridge deck defect prediction module, and a steel bridge deck information visualization module; The steel bridge deck system digital module is used to construct a digital twin model and a virtual test model of the steel bridge deck system characteristics; The steel bridge deck system working condition update module uses the Internet of Things technology to automatically update the physical condition information of the steel bridge deck system to the steel bridge deck system twin database; The steel bridge deck system disease prediction module is used to calibrate and construct a steel bridge deck system disease development model to predict the formation and development degree of steel bridge deck system diseases; The steel bridge deck information visualization module provides a visualization twin service for real-time display of the steel bridge deck entity information and the digital twin model.

[0011] Furthermore, the steel bridge deck disease prediction module automatically updates the digital twin model and the steel bridge deck twin database with the physical disease status information of the steel bridge deck system according to the manually entered disease data, links the steel bridge deck system rule attribute model, utilizes environmental simulation technology, simulates the operation status of the bridge under different load conditions, and constructs a steel bridge deck disease development model to predict the formation and development degree of steel bridge deck diseases.

[0012] A method for constructing a steel bridge deck defect detection system, applied to the above-mentioned steel bridge deck defect detection system, comprises the following steps: Establish the digital module of steel bridge deck system: Based on Revit 3D modeling and rendering software, a lightweight model of the entire bridge structure was constructed according to the bridge design drawings. Then, a layered modeling method was used to model the upper pavement layer, lower pavement layer, bonding layer, bridge deck, U-rib, diaphragm, etc. of the steel bridge deck. The mechanical properties of the steel bridge deck were dynamically simulated by mathematical approximation through the modeling software to reflect the spatial conditions and physical and mechanical characteristics of the steel bridge deck system. Based on the rules summarized from historical correlation data, including the standards and specifications in the field of steel bridge deck systems, a twin rule attribute model for steel bridge deck systems is established; Establish the steel bridge deck system working condition update module: Use the Internet of Things technology to automatically update the digital twin model and the steel bridge deck twin database with the monitoring data of the steel bridge deck entity; manually detect the disease data and automatically update the digital twin model and the steel bridge deck twin database with the information on the disease status of the steel bridge deck entity; Establish a steel bridge deck system disease prediction module: Manually input the disease data, automatically update the digital twin model and the twin database of the steel bridge deck system with the physical disease status information of the steel bridge deck system, connect the steel bridge deck system rule attribute model, use environmental simulation technology to simulate the operation status of the bridge under different load conditions, and build a steel bridge deck system disease development model to determine the location coordinates of the steel bridge deck system disease formation and the severity of the existing disease development; Establishing the steel bridge deck information visualization module: A corresponding relationship is established between the performance status data of different pavement layers of the steel bridge deck system and the structural coordinates of the digital twin model, and the type information, location information and degree information of the defect are integrated into the digital twin model to achieve accurate mapping of the performance data of the steel bridge deck system in time and space dimensions. Three-dimensional rendering software such as U3D and Unreal Engine are used to visualize the defect data and reproduce the service status of the real bridge in the digital twin model.

[0013] Furthermore, when the physical disease information of the steel bridge deck system is different from the disease information of the steel bridge deck system disease development model, the rule attribute comparison mechanism is triggered; When there is a significant difference between the manually detected bridge deck disease data and the disease information of the steel bridge deck disease development model, prior knowledge is used for analysis to evaluate the physical disease status of the steel bridge deck and correct the virtual test model to form a logical closed loop of the steel bridge deck disease detection system.

[0014] The beneficial effects of the present invention are: In the present invention, a geometric-physical model of the steel bridge deck system entity and a steel bridge deck system twin database are established based on digital twins, and the correlation mapping of data between the entity model and the digital model is performed, and the correlation mapping of axle load-temperature-structural response data, steel bridge deck disease data, and detection history data is analyzed; based on the established digital twin model, combined with the real-time data of the steel bridge deck system entity, the service status of the steel bridge deck system is analyzed and studied, a virtual test model of the steel bridge deck system characteristics is constructed and driven, and a steel bridge deck system disease development model is further constructed in combination with the detection history data, and the steel bridge deck system disease development model is used to predict the location coordinates of the steel bridge deck system disease formation and the severity of the existing disease development; In the process of predicting the formation and development degree of steel bridge deck diseases, the Internet of Things technology is used to update the digital twin model and the steel bridge deck twin database with the physical monitoring data of the steel bridge deck system in real time, so as to realize the accurate establishment of the digital twin model and the accurate mapping of the full amount of data. At the same time, the virtual test model can accurately simulate the service status of the steel bridge deck system, ensuring that the steel bridge deck disease development model can predict the formation and development degree of steel bridge deck diseases with credibility and reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a schematic diagram of the framework of the steel bridge deck disease detection method of the present invention; Figure 2 It is a schematic diagram of a steel bridge deck disease detection system according to Embodiment 2 of the present invention; Figure 3 It is the relevant professional prior knowledge in the rule attributes of the digital twin system for steel bridge deck detection in Example 2 of the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0018] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only implementation method.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0020] like Figures 1 to 3 A steel bridge deck defect detection method, system and construction method thereof are shown, which use digital means to map the attributes of the steel bridge deck entity into a digital twin model in a virtual space, so that the steel bridge deck defect data can be analyzed more comprehensively. Through the Internet of Things technology and big data processing, the virtual service status analysis of the current steel bridge deck entity and the prediction of the formation and development degree of steel bridge deck defects can be realized.

[0021] Embodiment 1 The invention discloses a method for detecting defects in a steel bridge deck system, comprising the following steps: A mapping relationship between the steel bridge deck entity and the steel bridge deck model is established to construct a digital twin model of the steel bridge deck characteristics. Based on the digital twin model and in combination with the real-time operating parameters of the steel bridge deck entity, a virtual test model of the steel bridge deck characteristics is constructed and driven. Based on the virtual test model and the historical operating parameters of the steel bridge deck entity, a steel bridge deck disease development model is constructed. The formation and development degree of steel bridge deck diseases are predicted through the steel bridge deck disease development model. The steel bridge deck entity information and digital twin model are displayed in real time based on the visual twin service.

[0022] Among them, the digital twin model includes the construction and association of the geometric-physical model of the steel bridge deck system, the twin database of the steel bridge deck system and the rule attribute model of the steel bridge deck system; The geometric-physical model of the steel bridge deck system associates the geometric information of the steel bridge deck system with the basic mechanical properties through modeling software; the twin database of the steel bridge deck system includes the monitoring data of the Internet of Things perception system recorded in time series, and the bridge deck system disease data manually detected in the corresponding time series; the regular attribute model of the steel bridge deck system is established according to the standards and specifications in the field of steel bridge deck systems, reflecting the regular attributes of steel bridge deck system diseases.

[0023] In the present invention, a geometric-physical model of the steel bridge deck entity and a steel bridge deck twin database are established based on digital twins, and the data association mapping between the entity model and the digital model is performed; based on the established digital twin model, combined with the real-time data of the steel bridge deck entity, the service status of the steel bridge deck is analyzed and studied, a virtual test model of the steel bridge deck characteristics is constructed and driven, and a steel bridge deck disease development model is constructed by further analyzing the historical detection data of the steel bridge deck entity through big data, and the location coordinates of the formation of steel bridge deck disease and the severity of the existing disease development are predicted through the steel bridge deck disease development model; In the process of predicting the formation and development degree of steel bridge deck diseases, the Internet of Things technology is used to update the digital twin model and the steel bridge deck twin database with the physical monitoring data of the steel bridge deck system in real time, so as to realize the accurate establishment of the digital twin model and the accurate mapping of the full amount of data. At the same time, the virtual test model can accurately simulate the service status of the steel bridge deck system, ensuring that the steel bridge deck disease development model can predict the formation and development degree of steel bridge deck diseases with credibility and reference.

[0024] The digital twin model is the basis of digital twins. The digital twin model of bridge deck characteristics is a digital modeling of the properties, methods, behaviors and other characteristics of the steel bridge deck entity and its performance development process in digital space, including the construction and association of the steel bridge deck geometric-physical model, the steel bridge deck twin database and the steel bridge deck rule attribute model.

[0025] In this embodiment, in the Internet of Things sensing system, the temperature parameters of the steel bridge deck system in service are monitored by temperature sensors, and the structural response parameters of the steel bridge deck system in service are monitored by strain sensors and deflection sensors. The weight and number of passing vehicles are detected respectively by the bridge weighing system and the video monitoring system, and the axle load parameters of the steel bridge deck system in service are monitored in cooperation with the load sensors.

[0026] Multi-source perception is the prerequisite for the construction of digital twin models. By deploying various types of sensors at key locations on the bridge, a full-domain and full-time IoT perception system is established to accurately monitor the service status of the steel bridge deck system in multiple dimensions and levels. Massive data such as images, videos, and various operation monitoring indicators can be fully acquired to achieve all-round perception of the environment, traffic loads, stress status, maintenance history, etc., and real-time acquisition of full-domain and full-volume operation data of the steel bridge deck system can provide a data foundation for the digital twin of the steel bridge deck system.

[0027] The evolution law of the service status of steel bridge decks is very complex. In order to accurately understand the service status of steel bridge decks, it is necessary to always grasp the global development and subtle changes of key service parameters of steel bridge decks, and realize the synchronous operation of steel bridge deck models and steel bridge deck entities in a twin environment. Through IoT perception, digital mapping and multi-dimensional modeling technology, the digital twin model is accurately established and the full amount of data is accurately mapped. At the same time, the virtual test model accurately simulates the service status of steel bridge decks, ensuring that the steel bridge deck disease development model predicts the formation and development of steel bridge deck diseases with credibility and reference.

[0028] Furthermore, the steel bridge deck disease data is monitored manually and regularly, and the type, location and severity information of the detected diseases are input into the steel bridge deck twin database; the performance status data of different pavement layers of the steel bridge deck are corresponding to the structural coordinates of the digital twin model, and then the steel bridge deck disease data is integrated into the digital twin model to establish a mapping relationship.

[0029] Specifically, the steel bridge deck pavement structure layer includes the upper pavement, the lower pavement, the waterproof bonding layer, and the steel bridge deck. The performance status of the steel bridge deck pavement structure includes the road surface damage status, rutting, flatness, anti-skid performance, interlayer bonding failure, etc. Based on the performance status data of different layers of the pavement layer structure system, the data is coded to establish a one-to-one correspondence between the real bridge data and the structural coordinates of the steel bridge deck pavement twin model, and the type information, location information and degree information of the detected defects are input into the steel bridge deck system twin database, thereby realizing the accurate mapping of the performance data of the steel bridge deck pavement in the space-time dimension. At the same time, three-dimensional rendering software such as U3D and Unreal Engine are used to visualize the defect data and reproduce the service status of the real bridge in the digital twin model.

[0030] In the process of establishing the mapping relationship between the steel bridge deck entity and the steel bridge deck model, the static physical entity relationship mapping is first established. In the digital twin model, according to the spatial position distribution of the steel bridge deck entity, the model information of the Internet of Things detection equipment such as temperature and load is constructed at the corresponding scale, and the Internet of Things detection equipment is numbered accordingly, and the corresponding number is associated with the corresponding device ID of the Internet of Things detection equipment in the business library; secondly, the dynamic physical entity relationship mapping such as load is established, but the weight and number of axles of the target vehicle can be detected respectively through the data of the bridge weighing system and the video monitoring system, so that the vehicle model can be established in the digital twin model and the driving path can be preset to simulate and calculate the real-time position information of the vehicle and the corresponding load information of the bridge; it also includes establishing the entity relationship mapping of the manual entry data of the disease. When manually entering the disease data, the driving direction information, lane information, cable number information, disease type and other spatial location description information of the disease are entered into the system at the same time, which can be automatically matched to the corresponding position in the digital twin model to realize the association between the model and the disease data.

[0031] In this embodiment, the structural response of the steel bridge deck entity is directly acquired in real time by the sensor. In order to realize the data interaction between the digital twin models and the interaction of external real-time data, a flexible signal communication mechanism is established in the digital space, and real-time communication with external data such as API and HTTP interface can be carried out. In the process of establishing the mapping relationship between the steel bridge deck entity and the steel bridge deck model, the steel bridge deck twin database is used as a transit for mapping interaction; Set up an interactive interface between the temperature, axle load and structural response parameters and the steel bridge deck system model based on the Redis API database driver, and read the perception data, unified fusion data and structural response related data in the steel bridge deck system twin database in real time to drive the virtual test model; Considering that the steel bridge deck entity generates a large amount of real-time multi-source heterogeneous data during the implementation process, it is continuously input into the steel bridge deck twin database. According to the sensor type, the data type can be divided into temperature, load, structural response, etc. The data type, data format, and data sampling frequency generated by various sensors are different. In order to realize the real-time mapping of the steel bridge deck entity and the steel bridge deck model, support the joint processing and analysis of multi-source heterogeneous data, establish a steel bridge deck twin database based on a time series database, set up the temperature, axle load and structural response parameters based on the MQTT protocol and the data transmission interface of the steel bridge deck entity, update the digital twin model and correct the steel bridge deck disease development model.

[0032] In the above embodiment, the steel bridge deck system twin database is classified into real-time data, historical data and statistical data. The real-time data is obtained through the Internet of Things sensing system and regular manual inspection, and stored in Redis in the form of snapshots. The historical data is stored in time series, and the statistical data is obtained by regularly running the statistical program. Based on the digital twin model and in combination with real-time data, a virtual test model of the steel bridge deck system characteristics is constructed and driven, and the virtual test model is corrected according to the real-time data. Based on the corrected virtual test model and in combination with historical data and statistical data, a steel bridge deck system disease development model is constructed.

[0033] As a preferred embodiment of the above, considering that the measurement point arrangement of the service status perception system of the physical pavement of the steel bridge deck is limited, the digital twin model cannot fully reflect the structural response of the physical pavement of the steel bridge deck system using only the data of the measurement points. Based on the theoretical calculation method of the structural response of the steel bridge deck pavement, the intrinsic relationship between the structural response of the non-measurement point position and the structural response of the measurement point position is constructed, and the structural response correction coefficient of the non-measurement point position of different pavement structures is proposed. According to the measured structural response data of the measurement point position, the real-time numerical value of the structural response at the non-measurement point is obtained, and a structural response database at the non-measurement point is constructed. The interface of the structural response database at the non-measurement point is developed to realize the full-scale mapping of the digital twin of the steel bridge deck pavement to the structural response of the real bridge steel bridge deck pavement from point to surface.

[0034] Embodiment 2 This embodiment provides a steel bridge deck system defect detection system, including a steel bridge deck system digital module, a steel bridge deck system working condition update module, a steel bridge deck system defect prediction module, and a steel bridge deck system information visualization module; Among them, the steel bridge deck digital module is used to construct a digital twin model and virtual test model of the steel bridge deck characteristics; the steel bridge deck working condition update module uses the Internet of Things technology to automatically update the physical status information of the steel bridge deck system to the steel bridge deck system twin database; the steel bridge deck disease prediction module is used to correct and construct a steel bridge deck disease development model to predict the formation and development degree of steel bridge deck diseases; the steel bridge deck information visualization module provides a visualization twin service for real-time display of the steel bridge deck physical information and digital twin model.

[0035] The digital module of steel bridge deck system includes the geometry-physical model of steel bridge deck system, the twin database of steel bridge deck system and the regular attribute model of steel bridge deck system. Among them, the geometry of steel bridge deck system uses modeling software to build a lightweight model of the whole bridge structure according to the bridge design drawings, mainly including the simplified modeling of the main structure such as the main tower, cable, main beam, etc., and then adopts the layered modeling method to model the upper pavement layer, the lower pavement layer, the bonding layer, the bridge deck, the U rib, the diaphragm, etc., which is used to reflect the spatial condition of the bridge deck system; the physical characteristic model of the steel bridge deck system is composed of models describing the basic mechanical properties of the steel bridge deck pavement, including mechanical indicators such as the maximum tensile stress of the pavement layer, the inter-rib deflection of the steel bridge deck system, and the maximum shear stress of the layer bottom, which are used to reflect the physical and mechanical characteristics of the steel bridge deck pavement; the twin regular attribute model of the steel bridge deck system is established according to the relevant theories and national standards in the field of steel bridge deck pavement, including theoretical knowledge such as steel bridge deck pavement design theory, local standards related to steel bridge deck pavement, and steel bridge deck pavement maintenance theory. Its role is to provide theoretical support for the systematic analysis of possible future diseases of steel bridge deck pavement.

[0036] Furthermore, the steel bridge deck disease prediction module automatically updates the digital twin model and the steel bridge deck twin database with the physical disease status information of the steel bridge deck system based on the manually entered disease data, links it with the steel bridge deck system rule attribute model, uses environmental simulation technology to simulate the operating status of the bridge under different load conditions, and constructs a steel bridge deck disease development model to predict the formation and development degree of steel bridge deck diseases.

[0037] As an important part of the bridge, the performance status of the steel bridge deck system will change with the service time. Through manual inspection, the bridge disease data is continuously updated to reflect its actual performance status; regular inspection and evaluation of the steel bridge deck system can grasp the performance degradation of the steel bridge deck system in real time and discover potential safety hazards in time. Record the type, location, degree and other information of the disease and include it in the database. Use sensors installed on the bridge to monitor the key performance indicators of the bridge in real time, such as stress, deformation, etc., and detect and record abnormal conditions in time; collect vehicle and traffic information through the bridge weighing system and video monitoring system as a supplement to the disease data. Combined with the analysis of disease data over the years, the performance status and development trend of the steel bridge deck system can be better grasped, which is conducive to predicting the risks and hidden dangers that the steel bridge deck system may face.

[0038] Embodiment 3 This embodiment provides a method for constructing a steel bridge deck defect detection system, comprising the following steps: The first step is to establish a digital module for the steel bridge deck system: Based on Revit 3D modeling and rendering software, according to the bridge design drawings, a lightweight model of the entire bridge structure is constructed, mainly including simplified modeling of the main structure such as the main tower, cable, and main beam. For the steel bridge deck system structure, a refined modeling is carried out, and the layered modeling method is adopted to model the upper pavement layer, lower pavement layer, bonding layer, bridge deck, U rib, diaphragm, etc. of the steel bridge deck. After the model construction is completed, the model is exported to Obj format and imported into Unity3D in the form of a resource file. In Unity3D, the satellite image data of the model and the real physical world are loaded into the scene, and the terrain, waters and other geographical environments around the bridge are constructed based on satellite images. Texture mapping is performed on the bridge to complete the construction of the twin scene. The mechanical properties of the steel bridge deck system are dynamically simulated by mathematical approximation through modeling software to reflect the spatial conditions and physical and mechanical characteristics of the steel bridge deck system.

[0039] Secondly, based on the rules summarized from historical correlation data, including the standards and specifications in the field of steel bridge deck systems, a twin rule attribute model for steel bridge deck systems is established; The second step is to establish a steel bridge deck system condition update module: The Internet of Things technology is used to automatically update the digital twin model and the steel bridge deck system twin database with the physical monitoring data of the steel bridge deck system; the disease data is manually detected and the physical disease status information of the steel bridge deck system is automatically updated in the digital twin model and the steel bridge deck system twin database.

[0040] In the specific implementation process, firstly, a data online update module is established, and the Internet of Things technology is used to transmit the disease data in the form of messages to the steel bridge deck system twin database, and the system data is updated in real time; secondly, a digital twin model update model is established. Based on the detected disease data, the data of the digital twin model is updated in real time. At the same time, the uploaded real-time steel bridge deck pavement disease information is input into the rule attribute model in the digital module. The real-time steel bridge deck pavement disease information includes disease type, disease size, and disease location detection indicators.

[0041] Establish a steel bridge deck system disease prediction module: Manually input the disease data, automatically update the digital twin model and the twin database of the steel bridge deck system with the physical disease status information of the steel bridge deck system, connect the steel bridge deck system rule attribute model, use environmental simulation technology to simulate the operation status of the bridge under different load conditions, and build a steel bridge deck system disease development model to determine the location coordinates of the steel bridge deck system disease formation and the severity of the existing disease development; Establishing the steel bridge deck information visualization module: A correspondence is established between the performance status data of different pavement layers of the steel bridge deck system and the structural coordinates of the digital twin model, and the type, location and degree information of the defect are integrated into the digital twin model to achieve accurate mapping of the performance data of the steel bridge deck system in time and space dimensions. Three-dimensional rendering software such as U3D and Unreal Engine are used to visualize the defect data and reproduce the service status of the real bridge in the digital twin model.

[0042] Furthermore, when the physical disease information of the steel bridge deck system is different from the disease information of the steel bridge deck system disease development model, the rule attribute comparison mechanism is triggered; when there is a large difference between the manually detected bridge deck disease data and the disease information of the steel bridge deck system disease development model, prior knowledge is used for analysis, to evaluate the physical disease status of the steel bridge deck system and correct the virtual test model, thus forming a logical closed loop of the steel bridge deck disease detection system.

[0043] Specifically, the steel bridge deck rule attribute model compares the physical detection index of the steel bridge deck pavement condition with the corresponding virtual entity index obtained by the twin operation. If the physical detection index of the steel bridge deck pavement condition differs greatly from the corresponding virtual entity index, the rule attribute model is triggered to use the relevant local specifications and standards, steel bridge deck pavement maintenance theory and other prior knowledge for analysis, accurately evaluate the physical steel bridge deck pavement condition and correct the virtual entity operation program, delete the data in the digital twin model that was previously calculated incorrectly or has expired, and upload new physical detection indicators at the same time. Thus, the steel bridge deck pavement condition update module is established, forming a logical closed loop of the steel bridge deck pavement detection system.

[0044] Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for detecting steel bridge deck defects, characterized in that: The following steps are involved: Establish the mapping relationship between the steel bridge deck entity and the steel bridge deck model, and construct a digital twin model of the steel bridge deck characteristics; Based on the digital twin model and in combination with the real-time working parameters of the steel bridge deck entity, a virtual test model of the steel bridge deck characteristics is constructed and driven; Based on the virtual test model and the historical working condition parameters of the steel bridge deck entity, a steel bridge deck disease development model is constructed; The formation and development degree of steel bridge deck disease are predicted by the steel bridge deck disease development model; The physical information of the steel bridge deck system and the digital twin model are displayed in real time based on the visual twin service.

2. The steel bridge deck defect detection method according to claim 1, characterized in that: The digital twin model includes constructing and associating a geometric-physical model of a steel bridge deck system, a twin database of a steel bridge deck system, and a rule attribute model of a steel bridge deck system; The geometric-physical model of the steel bridge deck system is used to associate the geometric information of the steel bridge deck system with basic mechanical properties through modeling software; The steel bridge deck twin database includes IoT sensing system monitoring data recorded in time series, and bridge deck disease data manually detected in the corresponding time series; The steel bridge deck system rule attribute model is established according to the standards and specifications in the field of steel bridge deck systems, and reflects the rule attributes of steel bridge deck system defects.

3. The steel bridge deck defect detection method according to claim 2, characterized in that: In the IoT sensing system, the temperature parameters of the steel bridge deck system in service are monitored by temperature sensors, and the structural response parameters of the steel bridge deck system in service are monitored by strain sensors and deflection sensors; The weight and number of axles of passing vehicles are detected through the bridge weighing system and video monitoring system respectively, and the axle load parameters of the steel bridge deck system in service are monitored in conjunction with load sensors.

4. The steel bridge deck defect detection method according to claim 2, characterized in that: Manually and regularly monitor the steel bridge deck system disease data, and input the type information, location information and degree information of the detected disease into the steel bridge deck system twin database; A corresponding relationship is established between the performance status data of different pavement layers of the steel bridge deck system and the structural coordinates of the digital twin model, and then the disease data of the steel bridge deck system is integrated into the digital twin model to establish a mapping relationship.

5. The steel bridge deck defect detection method according to claim 2, characterized in that: Classify the twin database of the steel bridge deck system into real-time data, historical data and statistical data, the real-time data is obtained through the IoT sensing system and regular manual detection, the historical data is stored in time series, and the statistical data is obtained by regularly running a statistical program; Based on the digital twin model and in combination with real-time data, a virtual test model of the steel bridge deck characteristics is constructed and driven, and the virtual test model is corrected according to the real-time data. Based on the corrected virtual test model and in combination with historical data and statistical data, a steel bridge deck disease development model is constructed.

6. The steel bridge deck defect detection method according to claim 2, characterized in that: In the process of establishing the mapping relationship between the steel bridge deck system entity and the steel bridge deck system model, the steel bridge deck system twin database is used as a transfer to perform mapping interaction; An interactive interface between the temperature, axle load and structural response parameters and the steel bridge deck system model based on a Redis API database driver is set up to read the perception data, unified fusion data and structural response related data in the steel bridge deck system twin database in real time to drive the virtual test model; A data transmission interface between the temperature, axle load and structural response parameters and the steel bridge deck system entity based on the MQTT protocol is set to update the digital twin model and correct the steel bridge deck system disease development model.

7. A steel bridge deck disease detection system, characterized in that: The steel bridge deck defect detection method applied to any one of claims 1 to 6 above comprises a steel bridge deck digital module, a steel bridge deck working condition update module, a steel bridge deck defect prediction module, and a steel bridge deck information visualization module; The steel bridge deck system digital module is used to construct a digital twin model and a virtual test model of the steel bridge deck system characteristics; The steel bridge deck system working condition update module uses the Internet of Things technology to automatically update the physical condition information of the steel bridge deck system into the steel bridge deck system twin database; The steel bridge deck system disease prediction module is used to calibrate and construct a steel bridge deck system disease development model to predict the formation and development degree of steel bridge deck system diseases; The steel bridge deck information visualization module provides a visualization twin service for real-time display of the steel bridge deck entity information and the digital twin model.

8. The steel bridge deck defect detection system according to claim 7, characterized in that: The steel bridge deck system disease prediction module automatically updates the digital twin model and the steel bridge deck system twin database with the physical disease status information of the steel bridge deck system according to the manually entered disease data, links the steel bridge deck system rule attribute model, uses environmental simulation technology to simulate the operating status of the bridge under different load conditions, and constructs a steel bridge deck system disease development model to predict the formation and development degree of steel bridge deck system diseases.

9. A method for constructing a steel bridge deck disease detection system, characterized in that: The steel bridge deck defect detection system as described in claim 7 above comprises the following steps: Establish the digital module of steel bridge deck system: Based on Revit 3D modeling and rendering software, a lightweight model of the entire bridge structure was constructed according to the bridge design drawings. Then, a layered modeling method was used to model the upper pavement layer, lower pavement layer, bonding layer, bridge deck, U-rib, diaphragm, etc. of the steel bridge deck. The mechanical properties of the steel bridge deck were dynamically simulated by mathematical approximation through the modeling software to reflect the spatial conditions and physical and mechanical characteristics of the steel bridge deck system. Based on the rules summarized from historical correlation data, including the standards and specifications in the field of steel bridge deck systems, a twin rule attribute model for steel bridge deck systems is established; Establish the steel bridge deck system working condition update module: Use the Internet of Things technology to automatically update the digital twin model and the steel bridge deck twin database with the monitoring data of the steel bridge deck entity; manually detect the disease data and automatically update the digital twin model and the steel bridge deck twin database with the information on the disease status of the steel bridge deck entity; Establish a steel bridge deck system disease prediction module: Manually input the disease data, automatically update the digital twin model and the twin database of the steel bridge deck system with the physical disease status information of the steel bridge deck system, connect the steel bridge deck system rule attribute model, use environmental simulation technology to simulate the operation status of the bridge under different load conditions, and build a steel bridge deck system disease development model to determine the location coordinates of the steel bridge deck system disease formation and the severity of the existing disease development; Establishing the steel bridge deck information visualization module: A corresponding relationship is established between the performance status data of different pavement layers of the steel bridge deck system and the structural coordinates of the digital twin model, and the type information, location information and degree information of the defect are integrated into the digital twin model to achieve accurate mapping of the performance data of the steel bridge deck system in time and space dimensions. Three-dimensional rendering software such as U3D and Unreal Engine are used to visualize the defect data and reproduce the service status of the real bridge in the digital twin model.

10. The method for constructing a steel bridge deck defect detection system according to claim 9, characterized in that: When the physical disease information of the steel bridge deck system is different from the disease information of the steel bridge deck system disease development model, the rule attribute comparison mechanism is triggered; When there is a significant difference between the manually detected bridge deck disease data and the disease information of the steel bridge deck disease development model, prior knowledge is used for analysis to evaluate the physical disease status of the steel bridge deck and correct the virtual test model to form a logical closed loop of the steel bridge deck disease detection system.

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