Bridge informatization management system based on digital twinning

Abstract

The invention relates to the technical field of bridge management, in particular to a bridge informatization management system based on digital twinning. Comprising a stress acquisition device and a traffic flow acquisition device, the stress acquisition device is arranged at a key stress point of a bridge, and the server comprises a bridge modeling module, a data receiving module, a model prediction module and a state analysis module; the bridge modeling module is used for establishing a bridge high-precision three-dimensional model through BIM and constructing a digital twinborn body of the bridge; the data receiving module is used for receiving the sensor measured value uploaded by the stress acquisition device and the traffic flow information uploaded by the traffic flow acquisition device; the model prediction module is used for converting the traffic flow data into a dynamic load and inputting the dynamic load into a digital twinborn body of the bridge for transient analysis to obtain a model prediction value of each bridge key stress point; and the analysis module is used for judging the bridge state according to an error value of the model prediction value and the sensor actual measurement value, and generating bridge state early warning when the error value exceeds an error threshold value.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge management, and specifically relates to a bridge informatization management system based on digital twin. Background Art

[0002] A bridge is a structure erected over rivers, lakes and seas to enable vehicles and pedestrians to pass smoothly. To adapt to the modern rapidly developing transportation industry, a bridge is also extended to a building that is erected across mountain streams, poor geological conditions or to meet other traffic needs to make passage more convenient.

[0003] For the informatization management of bridge safety monitoring, it is an important guarantee for the safe operation of bridges. In the field of bridge health monitoring, traditional technologies mainly rely on fixed sensor networks and regular manual inspections to achieve structural state assessment. However, the existing methods have the following significant defects. Traditional monitoring systems usually rely on fixed sensors to collect parameters such as local stress and vibration of bridges, and are only designed for static or periodic loads, and cannot reflect the dynamic load effects caused by sudden heavy-load vehicles or dense traffic flows in real time, resulting in generally large stress prediction errors, and the response delay during peak traffic hours can reach several hours, making it difficult to detect instantaneous overloading risks in a timely manner.

[0004] At the same time, traditional methods usually adopt the "post-maintenance" mode, relying on manual inspections to detect visible damages, but it is difficult to identify hidden fatigue damages and material property degradation in a timely manner. Some studies have shown that about 60% of bridge failure cases are due to undetected cumulative damages, and traditional technologies are difficult to provide a basis for preventive maintenance due to the lack of data-driven life prediction models. In addition, maintenance decisions highly rely on engineers' experience, with high labor costs and low efficiency, making it difficult to meet the management requirements of large-scale road networks. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a bridge informatization management system based on digital twin, which can timely detect bridge damages and give early warnings.

[0006] The basic solution provided by the present invention: A bridge informatization management system based on digital twin includes a server, a stress acquisition device and a traffic flow acquisition device. The stress acquisition device is arranged at key stress points of the bridge, and the traffic flow acquisition device is used to collect traffic flow information on the bridge. The server includes a bridge modeling module, a data receiving module, a model prediction module and a state analysis module;

[0007] The bridge modeling module is used to establish a high-precision three-dimensional model of the bridge through BIM and construct a digital twin body of the bridge;

[0008] The data receiving module is used to receive the measured values of sensors uploaded by the stress acquisition device and the traffic flow information uploaded by the traffic flow acquisition device;

[0009] A model prediction module, which is used to convert traffic flow data into dynamic loads, input them into the digital twin of the bridge for transient analysis, and obtain the model prediction values of the key stress points of each bridge;

[0010] A state analysis module, which is used to judge the bridge state according to the error value between the model prediction value and the measured value of the sensor. When the error value exceeds the error threshold, a bridge state warning is generated.

[0011] The principle and advantages of the present invention are as follows: By using BIM technology to construct a virtual twin of the bridge, through physical characteristics such as the geometric structure, material properties, and connection methods of the bridge, the digital mapping of the entire life cycle of the bridge is realized. Combining traffic flow information such as vehicle types, weights, and speeds, a real-time data closed-loop of "physical entity - virtual model" is formed. And through a stress acquisition device, the stress data of the key stress points of the bridge are collected in real time. At the same time, traffic flow information such as vehicle axle weight, vehicle speed, and position is converted into a dynamic load sequence, which is input into the digital twin for finite element transient simulation analysis to predict the stress response of the key points of the bridge. By comparing the error between the stress value predicted by the digital twin and the measured value of the sensor, the abnormal bridge structure, such as local damage and material degradation, is judged. When the error exceeds the preset error threshold, a warning is issued.

[0012] Compared with the prior art, it has the following advantages:

[0013] 1. In the traditional single use of sensor data analysis, in this solution, the real-time linkage of traffic flow load and sensor data is combined to comprehensively evaluate the bridge state, avoiding the limitations of single data source analysis.

[0014] 2. By comparing and analyzing the digital twin model data with the real-time collected data, it is judged whether the bridge is damaged according to the magnitude of the error, and it is discovered in advance before the hidden danger breaks out to prevent bridge accidents.

[0015] Furthermore, it further includes an environment detection module. The environment detection device is used to detect environment data, and the environment data includes temperature, humidity, and wind speed. The state analysis module includes a threshold adjustment module;

[0016] A data receiving module, which is also used to receive the environment data uploaded by the environment detection device;

[0017] The threshold adjustment module is used to obtain historical load data according to historical traffic flow data, calculate the cumulative damage degree of the key stress points of the bridge according to the historical load data, obtain the fatigue attenuation factor according to the cumulative damage degree, obtain the environment compensation factor according to the environment data, and dynamically adjust the error threshold of each key stress point of the bridge by combining the fatigue attenuation factor and the environment compensation factor.

[0018] The error threshold is dynamically adjusted through the fatigue attenuation factor and the environmental compensation factor. During long-term service, the performance of bridge materials will degrade due to fatigue load cycles and environmental erosion. For example, carbonation of concrete reduces its tensile strength, and corrosion of steel bars weakens the section bearing capacity. If a fixed threshold is still used, potential hidden damage may be ignored due to a too-high threshold, or false alarms may be triggered due to a too-low threshold. In this solution, the dynamic adjustment mechanism calculates the cumulative damage degree of the bridge in real time through the fatigue cumulative damage model, and attenuates the threshold according to an exponential law, so that the warning value always degrades synchronously with the actual bearing capacity of the bridge. For example, for a certain large bridge, the error threshold between the measured value and the predicted value in its design is 5 MPa. After 10 years of operation, due to fatigue damage, the dynamic threshold is automatically reduced to 4 MPa, while the traditional system fails to trigger an alarm because the threshold is fixed at 4 MPa.

[0019] At the same time, environmental factors will significantly affect the mechanical properties of bridges. For example, the elastic modulus of concrete decreases in a high-temperature environment, and the deformation increases under the same load. High humidity accelerates the corrosion of steel components and reduces the fatigue life. Strong winds cause bridge vibration and exacerbate the dynamic load effect. The dynamic threshold introduces an environmental compensation factor to quantify the influence of environmental parameters. For example, when the temperature rises to 40 °C and the humidity reaches 85%, the system automatically reduces the threshold from 5 MPa to 4.5 MPa. In contrast, in this scenario, the traditional fixed-threshold system misjudges the actually dangerous stress of 4.5 MPa as "safe" due to not considering the environmental coupling effect, resulting in a risk of structural damage.

[0020] Furthermore, the threshold adjustment module includes a fatigue attenuation factor module and an environmental compensation factor module;

[0021] The fatigue attenuation factor module is used to calculate the cumulative damage degree D of each key stress point of the bridge based on the Miner linear cumulative damage method:

[0022]

[0023] where N i is the fatigue life of the material at the i-th stress level, calibrated by the bridge material aging test, and n i is the actual number of cycles at the i-th stress level;

[0024] The fatigue attenuation factor e -λD is obtained according to the cumulative damage degree D, λ is the attenuation coefficient, calibrated by the bridge material aging test; The environmental compensation factor module obtains the environmental compensation factor according to temperature, humidity and wind speed:

[0025] 1 - α·ΔT·β H ·γ W

[0026] where ΔT is the difference between the current temperature and the reference temperature, H is the humidity, W is the wind speed, and α, β, and γ are the material sensitivity coefficients of the homogeneous material, which are calibrated by experiments;

[0027] A threshold adjustment module that obtains a dynamic threshold X based on the fatigue attenuation factor and the environmental compensation factor safe :

[0028] X safe = X0 × fatigue attenuation factor × environmental compensation factor

[0029] where X0 is the initial design threshold.

[0030] When calculating the fatigue attenuation factor, the Miner linear cumulative damage method is adopted. The actual number of cycles at the i-th stress level, when applied to the bridge, represents the stress amplitude range generated at the key stress points of the bridge under the action of vehicle loads, which is divided in the form of an interval. For example, from 10 MPa to 15 MPa, if a certain bridge experiences 100 cycles with a stress amplitude between 10 - 15 MPa in a year, n i = 100, N i is the fatigue life of the bridge under the stress amplitude cycle of 10 - 15 MPa. When D increases, the error threshold decays exponentially, reflecting the decrease in bridge durability. In the environmental compensation factor, as the temperature increases, the threshold decreases, while the humidity and wind speed amplify the temperature effect exponentially. α, β, and γ are the material sensitivity coefficients of the homogeneous material, which can be calibrated through material tests to adapt to different types of bridges such as steel bridges and concrete bridges.

[0031] Furthermore, the server further includes an early warning response module, and the early warning response module includes an early warning level module;

[0032] The early warning level module is used to obtain the early warning level according to the range of the interval where the error value is located when the error value between the model prediction value and the sensor measured value exceeds the error threshold;

[0033] The early warning strategy module is used to generate an early warning strategy according to the early warning level..

[0034] Furthermore, the early warning level module is provided with a first-level threshold;

[0035] The early warning strategy module is used to generate a first-level early warning strategy when the error value is greater than the error threshold and less than the first-level threshold. The first-level early warning strategy is to locate the abnormal area according to the position of the sensor where the error value of the sensor measured value exceeds the error threshold, and mark the abnormal point in the BIM high-precision three-dimensional model of the bridge and send it to the handheld terminal of the operation and maintenance personnel..

[0036] Furthermore, the early warning level module is also provided with a second-level threshold;

[0037] The early warning strategy module is used to generate a first-level early warning strategy and a second-level early warning strategy when the error value is greater than the first-level threshold and less than the second-level threshold. The second-level early warning strategy is to control the variable message sign at the bridge entrance to display traffic control information. Description of the Drawings

[0038] Figure 1 It is a logic block diagram of an embodiment of the bridge information management system based on digital twin of the present invention. Detailed Description of the Preferred Embodiments

[0039] The following is a further detailed description through specific embodiments:

[0040] The embodiment is basically as shown in the attached Figure 1 figures:

[0041] The bridge information management system based on digital twin includes a server, a stress acquisition device, and a traffic flow acquisition device. The stress acquisition device is arranged at the key stress points of the bridge. The traffic flow acquisition device is used to collect the traffic flow information on the bridge. The server includes a bridge modeling module, a data receiving module, a model prediction module, and a state analysis module;

[0042] The bridge modeling module is used to establish a high-precision three-dimensional model of the bridge through BIM and construct a digital twin of the bridge;

[0043] The data receiving module is used to receive the measured values of the sensors uploaded by the stress acquisition device and the traffic flow information uploaded by the traffic flow acquisition device;

[0044] The model prediction module is used to convert the traffic flow data into dynamic loads, input them into the digital twin of the bridge for transient analysis, and obtain the model prediction values of the key stress points of the bridge;

[0045] The state analysis module is used to judge the bridge state according to the error value between the model prediction value and the measured value of the sensor. When the error value exceeds the error threshold, a bridge state early warning is generated.

[0046] It further includes an environment detection module. The environment detection device is used to detect environment data, and the environment data includes temperature, humidity, and wind speed. The state analysis module includes a threshold adjustment module;

[0047] The data receiving module is also used to receive the environment data uploaded by the environment detection device;

[0048] The threshold adjustment module is used to obtain historical load data according to historical traffic flow data, calculate the cumulative damage degree of the key stress points of the bridge according to the historical load data, obtain the fatigue attenuation factor according to the cumulative damage degree, obtain the environment compensation factor according to the environment data, and dynamically adjust the error threshold of each key stress point of the bridge by combining the fatigue attenuation factor and the environment compensation factor.

[0049] The error threshold is dynamically adjusted through the fatigue attenuation factor and the environmental compensation factor. During long-term service, the performance of bridge materials will degrade due to fatigue load cycles and environmental erosion. For example, carbonation of concrete will reduce its tensile strength, and corrosion of steel bars will weaken the sectional bearing capacity. If a fixed threshold is still used, potential hidden damages may be ignored due to a too high threshold, or false alarms may be caused due to a too low threshold. In this solution, the dynamic adjustment mechanism calculates the cumulative damage degree of the bridge in real time through the fatigue cumulative damage model, and attenuates the threshold according to an exponential law, so that the warning value always degrades synchronously with the actual bearing capacity of the bridge. For example, for a certain large bridge, the error threshold between the measured value and the predicted value in its design is 5 MPa. After 10 years of operation, due to fatigue damage, the dynamic threshold is automatically lowered to 4 MPa, while the traditional system fails to trigger an alarm because the threshold is fixed at 4 MPa.

[0050] At the same time, environmental factors will significantly affect the mechanical properties of bridges. For example, the elastic modulus of concrete decreases under high-temperature environments, and the deformation increases under the same load. High humidity accelerates the corrosion of steel components and reduces the fatigue life. Strong winds cause bridge vibrations and exacerbate the dynamic load effect. The dynamic threshold introduces an environmental compensation factor to quantify the influence of environmental parameters. For example, when the temperature rises to 40 °C and the humidity reaches 85%, the system automatically lowers the threshold from 5 MPa to 4.5 MPa. In contrast, the traditional fixed-threshold system misjudges the actually dangerous stress of 4.5 MPa as "safe" in this scenario due to not considering the environmental coupling effect, resulting in a risk of structural damage.

[0051] The threshold adjustment module includes a fatigue attenuation factor module and an environmental compensation factor module;

[0052] The fatigue attenuation factor module is used to calculate the cumulative damage degree D of each key stress point of the bridge based on the Miner linear cumulative damage method:

[0053]

[0054] where N i is the fatigue life of the material under the i-th stress level, calibrated by the bridge material aging test, and n i is the actual number of cycles under the i-th stress level;

[0055] The fatigue attenuation factor e -λD is obtained according to the cumulative damage degree D, λ is the attenuation coefficient, calibrated by the bridge material aging test; the environmental compensation factor module obtains the environmental compensation factor according to temperature, humidity and wind speed:

[0056] 1 - α·ΔT·β H ·γ W

[0057] Where ΔT is the difference between the current temperature and the reference temperature, H is the humidity, W is the wind speed, and α, β, and γ are the material sensitivity coefficients of the homogeneous material, which are calibrated by experiments;

[0058] The threshold adjustment module obtains the dynamic threshold X according to the fatigue attenuation factor and the environmental compensation factor safe :

[0059] X safe = X0 × fatigue attenuation factor × environmental compensation factor

[0060] Where X0 is the initial design threshold.

[0061] When calculating the fatigue attenuation factor, the Miner linear cumulative damage method is adopted. The actual number of cycles at the i-th stress level, when applied to the bridge, represents the stress amplitude range generated at the key stress points of the bridge under the action of vehicle loads, which is divided in the form of intervals. For example, from 10 MPa to 15 MPa, if a certain bridge has experienced 100 cycles with a stress amplitude of 10 - 15 MPa in one year, n i = 100, N i Then it is the fatigue life of the bridge under the stress amplitude of 10 - 15 MPa. When D increases, the error threshold decays exponentially, reflecting the decline in bridge durability. In the environmental compensation factor, as the temperature rises, the threshold decreases, and at the same time, the humidity and wind speed amplify the temperature effect exponentially. α, β, and γ are the material sensitivity coefficients of the homogeneous material, which can be calibrated through material tests to adapt to different types of bridges such as steel bridges and concrete bridges.

[0062] For example, for a certain concrete bridge, the initial threshold X0 = 10 MPa, and the current parameters:

[0063] Cumulative damage degree D = 0.8, current temperature 35°, reference temperature 20°, ΔT = 15°, H = 75%, w = 5 m / s.

[0064] Fatigue attenuation factor e -λD = e -0.05×0.8 ≈ 0.961.

[0065] The environmental compensation factor is 1 - 0.003 × 15 × 1.02 75 × 1.01 5 ≈ 0.792

[0066] X safe = 10 × 0.961 × 0.792 ≈ 7.61 Mpa.

[0067] The server further includes an early warning response module, and the early warning response module includes an early warning level module;

[0068] An early warning level module, which is used to obtain the early warning level according to the range of the error value when the error value between the model prediction value and the actual measured value of the sensor exceeds the error threshold;

[0069] An early warning strategy module, which is used to generate an early warning strategy according to the early warning level..

[0070] The early warning level module is provided with a first-level threshold;

[0071] An early warning strategy module, which is used to generate a first-level early warning strategy when the error value is greater than the error threshold and less than the first-level threshold. The first-level early warning strategy is to locate the abnormal area according to the position of the sensor where the error value of the actual measured value of the sensor exceeds the error threshold, and mark the abnormal points on the BIM high-precision three-dimensional model of the bridge and send them to the handheld terminal of the operation and maintenance personnel..

[0072] The early warning level module is also provided with a second-level threshold;

[0073] An early warning strategy module, which is used to generate a first-level early warning strategy and a second-level early warning strategy when the error value is greater than the first-level threshold and less than the second-level threshold. The second-level early warning strategy is to control the variable message sign at the bridge entrance to display traffic control information.

[0074] This solution constructs a virtual twin of the bridge through BIM technology, realizes the digital mapping of the entire life cycle of the bridge through the physical characteristics such as the geometric structure, material properties, and connection methods of the bridge, combines traffic flow information such as vehicle type, weight, and speed, and forms a real-time data closed-loop of "physical entity - virtual model". And through the stress acquisition device, the stress data of the key stress points of the bridge are collected in real time. At the same time, the traffic flow information such as vehicle axle weight, vehicle speed, and position is converted into a dynamic load sequence and input into the digital twin for finite element transient simulation analysis to predict the stress response of the key points of the bridge. By comparing the error between the stress value predicted by the digital twin and the actual measured value of the sensor, the abnormal condition of the bridge structure, such as local damage and material degradation, is judged. When the error exceeds the preset error threshold, an early warning is triggered.

[0075] The above are only embodiments of the present invention. Specific structures and common knowledge such as characteristics that are well-known in the art are not described in detail herein. Those of ordinary skill in the art know all the common general technical knowledge in the technical field to which the invention pertains before the filing date or the priority date, are able to obtain all the prior art in this field, and have the ability to apply the conventional experimental means before this date. Those of ordinary skill in the art can, under the inspiration given in this application, combine their own abilities to complete and implement this solution. Some typical well-known structures or well-known methods should not become obstacles for those of ordinary skill in the art to implement this application. It should be noted that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can still be made, and these should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope claimed in this application should be based on the content of its claims, and the specific implementation manners and the like described in the specification can be used to interpret the content of the claims.

Claims

1. A digital twin-based bridge informatization management system, characterized in that: It includes a server, a stress acquisition device, and a traffic flow acquisition device. The stress acquisition device is installed at the key stress points of the bridge. The traffic flow acquisition device is used to collect traffic flow information on the bridge. The server includes a bridge modeling module, a data receiving module, a model prediction module, and a status analysis module; The bridge modeling module is used to establish a high-precision three-dimensional model of the bridge through BIM and construct a digital twin of the bridge; The data receiving module is used to receive the measured values of sensors uploaded by the stress acquisition device and the traffic flow information uploaded by the traffic flow acquisition device; The model prediction module is used to convert traffic flow data into dynamic loads, input them into the digital twin of the bridge for transient analysis, and obtain the model prediction values of each key stress point of the bridge; The status analysis module is used to judge the bridge status according to the error value between the model prediction value and the measured value of the sensor. When the error value exceeds the error threshold, a bridge status warning is generated.

2. The digital-twin-based bridge informatization management system according to claim 1, wherein: It further includes an environment detection module. The environment detection device is used to detect environment data, and the environment data includes temperature, humidity, and wind speed. The status analysis module includes a threshold adjustment module; The data receiving module is also used to receive the environment data uploaded by the environment detection device; The threshold adjustment module is used to obtain historical load data according to historical traffic flow data, calculate the cumulative damage degree of each key stress point of the bridge according to the historical load data, obtain the fatigue attenuation factor according to the cumulative damage degree, obtain the environment compensation factor according to the environment data, and dynamically adjust the error threshold of each key stress point of the bridge by combining the fatigue attenuation factor and the environment compensation factor.

3. The digital twin-based bridge informatization management system according to claim 2, characterized in that: The threshold adjustment module includes a fatigue attenuation factor module and an environment compensation factor module; The fatigue attenuation factor module is used to calculate the cumulative damage degree D of each key stress point of the bridge based on the Miner linear cumulative damage method: where N i is the fatigue life of the material under the i-th stress level, calibrated by the bridge material aging test, and n i is the actual number of cycles under the i-th stress level; Obtain the fatigue attenuation factor \(e\) according to the cumulative damage degree \(D\). -λD , where \(\lambda\) is the attenuation coefficient, which is calibrated through the bridge material aging test; The environment compensation factor module obtains the environment compensation factor according to temperature, humidity, and wind speed: 1-α·ΔT·β H ·γ W where ΔT is the difference between the current temperature and the reference temperature, H is the humidity, W is the wind speed, and α, β, γ are material sensitivity coefficients, which are calibrated by experiments; Threshold adjustment module, obtaining the dynamic threshold X according to the fatigue attenuation factor and the environmental compensation factor safe : X safe = X0 × fatigue attenuation factor × environmental compensation factor where X0 is the initial design threshold.

4. The digital twin-based bridge informatization management system according to claim 3, characterized in that: The server further includes a warning response module, and the warning response module includes a warning level module; The warning level module is used to obtain the warning level according to the range of the interval where the error value is located when the error value between the model prediction value and the measured value of the sensor exceeds the error threshold; The warning strategy module is used to generate a warning strategy according to the warning level.

5. The digital twin-based bridge informatization management system according to claim 4, wherein: The warning level module is provided with a first-level threshold; The warning strategy module is used to generate a first-level warning strategy when the error value is greater than the error threshold and less than the first-level threshold. The first-level warning strategy is to locate the abnormal area according to the position of the sensor where the error value of the measured value of the sensor exceeds the error threshold, and mark the abnormal point on the BIM high-precision three-dimensional model of the bridge and send it to the handheld terminal of the operation and maintenance personnel.

6. The digital twin-based bridge informatization management system according to claim 5, characterized in that: The warning level module is further provided with a second-level threshold; The warning strategy module is used to generate a first-level warning strategy and a second-level warning strategy when the error value is greater than the first-level threshold and less than the second-level threshold. The second-level warning strategy is to control the variable message sign at the bridge entrance to display traffic control information.

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