Bridge technical condition evaluation method and evaluation system thereof

Abstract

The invention discloses a bridge technical condition evaluation method and system, and the method comprises the steps: carrying out the real-time dynamic load data collection of a bridge, deploying a plurality of types of sensors at bridge structure parts, including a main beam, a bridge pier, a support, and a stay cable, and transmitting the data of the sensors to a cloud or an edge computing device in real time through a wireless Internet of Things; adopting a finite element analysis model and combining the collected dynamic load data to perform simulation calculation on stress conditions of the bridge under different working conditions; performing health assessment on sensor data and finite element analysis results by adopting a deep learning and multi-modal fusion technology, and setting a health index threshold to automatically trigger health early warning; according to bridge maintenance data tracking based on the block chain, related data of each detection, maintenance and reinforcement are recorded in the block chain, and a non-tampering maintenance resume is formed. The health index threshold value is set through the deep neural network and the multi-modal fusion technology, health early warning is automatically triggered, and the potential bridge structure failure risk is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge condition assessment, and in particular to a bridge technical condition assessment method and an assessment system thereof. Background Art

[0002] Bridges are a vital component of modern transportation infrastructure, and their technical condition is directly related to road safety and service life. For a long time, bridge safety monitoring and maintenance have relied primarily on regular manual inspections and traditional static assessment methods. These methods suffer from long inspection cycles, poor real-time performance, high labor costs, and limited assessment accuracy, making them unable to meet the efficient and intelligent bridge health management requirements of modern transportation networks.

[0003] At present, the technical condition assessment of bridges mainly adopts manual inspection and structural inspection:

[0004] On-site surveys are conducted by professionals and combined with instrument measurements (such as crack width and deformation). This method relies on manual experience, is highly subjective, and makes real-time monitoring difficult. In addition, sensors are deployed in key structural parts of the bridge to collect strain, acceleration, temperature and other data. However, the data is single and lacks global stress analysis, making it difficult to provide high-precision health assessments.

[0005] In order to overcome the above problems, the present invention proposes a bridge technical condition assessment method and an assessment system thereof to solve the above problems. Summary of the Invention

[0006] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0007] In view of the problems existing in the above-mentioned existing bridge technical condition assessment method and assessment system, the present invention is proposed.

[0008] Therefore, the purpose of the present invention is to provide a bridge technical condition assessment method and assessment system, which sets the health index threshold through deep neural network and multimodal fusion technology, automatically triggers health warning, and avoids potential bridge structure failure risks.

[0009] To solve the above technical problems, the present invention provides the following technical solutions: a method for evaluating the technical condition of a bridge, comprising the following steps:

[0010] Real-time dynamic load data collection for bridges: Multiple types of sensors are deployed on bridge structures, including main beams, piers, supports, and stay cables. Sensor data is transmitted in real time to the cloud or edge computing devices via wireless IoT.

[0011] Finite element analysis models are used in combination with the collected dynamic load data to simulate the stress conditions of the bridge under different working conditions;

[0012] Using deep learning and multimodal fusion technology, we conduct health assessments on sensor data and finite element analysis results, and set health index thresholds to automatically trigger health warnings.

[0013] Blockchain-based bridge maintenance data tracking records the relevant data of each inspection, repair, and reinforcement into the blockchain, forming an unalterable maintenance history.

[0014] As a preferred solution of the bridge technical condition assessment method of the present invention, the multiple types of sensors include:

[0015] Strain sensor: monitors the strain of the bridge under dynamic loads;

[0016] Accelerometer: captures the vibration response data of the bridge;

[0017] Displacement sensor: monitors the displacement changes of the bridge under different working conditions;

[0018] Temperature sensor: collects environmental parameters and analyzes the impact of temperature on bridge performance;

[0019] Traffic flow sensor: records the number, weight, and speed of vehicles traveling on the bridge.

[0020] As a preferred embodiment of the bridge technical condition assessment method of the present invention, the finite element analysis model simulates and calculates the stress conditions of the bridge under different working conditions, including static analysis, dynamic analysis and fatigue analysis:

[0021] The static analysis is used to calculate the stress, deformation and displacement of the bridge under normal use;

[0022] The dynamic analysis is used to simulate the vibration response of the bridge under external dynamic loads;

[0023] The fatigue analysis is used to predict fatigue damage of the bridge during long-term operation and to evaluate the remaining service life.

[0024] As a preferred embodiment of the bridge technical condition assessment method of the present invention, the health assessment based on sensor data and finite element analysis results includes:

[0025] 1) Stress and strain analysis: whether the stress / strain at key parts of the bridge exceeds the design safety threshold;

[0026] If the actual stress σ actual Exceeding the design stress limit σlimit , marked as structural anomaly:

[0027] σ actual >σ limit → Early warning;

[0028] 2) Vibration response analysis: whether there are any abnormal changes in the bridge vibration mode;

[0029] Analyze the vibration signal through fast Fourier transform to determine the change of the main frequency component:

[0030] Actual frequency f actual and baseline frequency f baseline The difference between the threshold Δf threshold Judgment:

[0031] f actual -f baseline >Δf threshold →Structural damage exists;

[0032] 3) Displacement and deformation assessment: whether the mid-span deflection and support displacement of the bridge exceed the standard;

[0033] Calculate the actual deformation D through the displacement sensor actual With threshold D threshold Judgment:

[0034] D actual >D threshold →Abnormal;

[0035] 4) The impact of temperature on bridge health;

[0036] Set temperature influence factor T factor , the adjusted stress σ adjusted Make a judgment:

[0037] σ adjusted =σ actual -T factor ×ΔT

[0038] ΔT temperature change. If the adjusted stress exceeds the standard, it is a real structural problem.

[0039] As a preferred solution of the bridge technical condition assessment method of the present invention, the health assessment is performed using deep learning and multimodal fusion technology:

[0040] The health index calculations are:

[0041] 1) Stress / strain health index calculation:

[0042]

[0043] 2) Calculation of vibration modal health index:

[0044]

[0045] 3) Displacement health index calculation:

[0046]

[0047] 4) Temperature affects health index calculation:

[0048]

[0049] As a preferred embodiment of the bridge technical condition assessment method of the present invention, the total score of the health index is calculated as:

[0050] H=αH s +βH v +γH D +δH t

[0051] Among them, α, β, γ, and δ are weight coefficients.

[0052] As a preferred embodiment of the bridge technical condition assessment method of the present invention, the following steps are performed to determine the warning level based on the total health index:

[0053] Red Level 1 warning: H<0.5, severe structural damage;

[0054] Orange Level 2 warning: 0.5≤H<0.65, abnormal vibration mode, stress exceeds limit;

[0055] Yellow Level 3 Warning: 0.65≤H<0.75, slight displacement abnormality, environmental impact;

[0056] Green: Normal: H ≥ 0.75, the bridge is in normal condition.

[0057] A bridge technical condition assessment system, comprising:

[0058] Data acquisition layer, including sensor data acquisition, dynamic load monitoring, and environmental data collection;

[0059] Data transmission layer, transmitted via 5G / fiber / wireless network;

[0060] Data processing and analysis layer, including multimodal deep learning analysis and finite element calculation;

[0061] Health assessment layer, including structural damage identification, health score monitoring and alarm.

[0062] Beneficial effects of the present invention:

[0063] 1. Real-time dynamic load data collection:

[0064] Multiple types of sensors are deployed at key locations such as main beams, piers, supports, and stay cables to acquire data on structural deformation, vibration, stress, temperature, and other aspects. These data are then transmitted to the cloud or edge computing devices via wireless IoT, enabling all-weather, real-time monitoring.

[0065] 2. Finite element analysis combined with real-time monitoring:

[0066] Finite element simulation models are used in combination with sensor data to simulate working conditions and accurately assess the stress conditions of bridge structures under different loads, thus overcoming the limitations of a single monitoring method.

[0067] 3. Set health index thresholds through deep neural networks and multimodal fusion technology to automatically trigger health warnings and avoid potential bridge structure failure risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0069] Figure 1 This is an evaluation flow chart of the bridge technical condition assessment method and assessment system of the present invention. DETAILED DESCRIPTION

[0070] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0071] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0072] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0073] Furthermore, the present invention is described in detail with reference to schematic diagrams. For ease of illustration, when describing the embodiments of the present invention, cross-sectional views illustrating device structures may be partially enlarged and not to scale. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of protection of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0074] Reference Figure 1 , provides a bridge technical condition assessment method, comprising the following steps:

[0075] Real-time dynamic load data collection for bridges: Multiple types of sensors are deployed on bridge structures, including main beams, piers, supports, and stay cables. Sensor data is transmitted in real time to the cloud or edge computing devices via wireless IoT.

[0076] Finite element analysis models are used in combination with the collected dynamic load data to simulate the stress conditions of the bridge under different working conditions;

[0077] Using deep learning and multimodal fusion technology, we conduct health assessments on sensor data and finite element analysis results, and set health index thresholds to automatically trigger health warnings.

[0078] Blockchain-based bridge maintenance data tracking records the relevant data of each inspection, repair, and reinforcement into the blockchain, forming an unalterable maintenance history.

[0079] Among them, the multi-type sensors include:

[0080] Strain sensor: monitors the strain of the bridge under dynamic loads;

[0081] Accelerometer: captures the vibration response data of the bridge;

[0082] Displacement sensor: monitors the displacement changes of the bridge under different working conditions;

[0083] Temperature sensor: collects environmental parameters and analyzes the impact of temperature on bridge performance;

[0084] Traffic flow sensor: records the number, weight, and speed of vehicles traveling on the bridge.

[0085] The finite element analysis model simulates the stress conditions of the bridge under different working conditions, including static analysis, dynamic analysis, and fatigue analysis:

[0086] Static analysis is used to calculate the stress, deformation, and displacement of a bridge under normal use;

[0087] Dynamic analysis is used to simulate the vibration response of a bridge under external dynamic loads;

[0088] Fatigue analysis is used to predict fatigue damage in long-term bridge operation and assess remaining life;

[0089] Specifically:

[0090]

[0091]

[0092] The algorithms mentioned above are all existing technologies and will not be described in detail here;

[0093] Furthermore, sensor data and finite element analysis results are used to conduct health assessments, including:

[0094] 1) Stress and strain analysis: whether the stress / strain of key parts of the bridge exceeds the design safety threshold; if the actual stress σ actual Exceeding the design stress limit σ limit , marked as structural anomaly:

[0095] σ actual >σ limit → Early warning;

[0096] 2) Vibration response analysis: whether there are any abnormal changes in the bridge vibration mode;

[0097] Analyze the vibration signal through fast Fourier transform to determine the change of the main frequency component:

[0098] Actual frequency f actual and baseline frequency f baseline The difference between the threshold Δf threshold Judgment:

[0099] f actual -f baseline >Δf threshold →Structural damage exists;

[0100] 3) Displacement and deformation assessment: whether the mid-span deflection and support displacement of the bridge exceed the standard;

[0101] Calculate the actual deformation D through the displacement sensor actual With threshold D threshold Judgment:

[0102] D actual >D threshold →Abnormal;

[0103] 4) The impact of temperature on bridge health;

[0104] Set temperature influence factor T factor , the adjusted stress σ adjusted Make a judgment:

[0105] σadjusted =σ actual -T factor ×ΔT

[0106] ΔT temperature change. If the adjusted stress exceeds the standard, it is a real structural problem.

[0107] Furthermore, deep learning and multimodal fusion technology are used for health assessment: the health index calculations are:

[0108] 1) Stress / strain health index calculation:

[0109]

[0110] 2) Calculation of vibration modal health index:

[0111]

[0112] 3) Displacement health index calculation:

[0113]

[0114] 4) Temperature affects health index calculation:

[0115]

[0116] Among them, the total score of the health index is calculated as:

[0117] H=αH s +βH v +γH D +δH t

[0118] Among them, α, β, γ, and δ are weight coefficients.

[0119] Specifically, the warning level is determined based on the total health index results, including:

[0120] Red Level 1 warning: H<0.5, severe structural damage;

[0121] Orange Level 2 warning: 0.5≤H<0.65, abnormal vibration mode, stress exceeds limit;

[0122] Yellow Level 3 Warning: 0.65≤H<0.75, slight displacement abnormality, environmental impact;

[0123] Green: Normal: H ≥ 0.75, the bridge is in normal condition.

[0124] Specific examples:

[0125]

[0126]

[0127] Among them, a bridge technical condition assessment system includes:

[0128] Data acquisition layer, including sensor data acquisition, dynamic load monitoring, and environmental data collection;

[0129] Data transmission layer, transmitted via 5G / fiber / wireless network;

[0130] Data processing and analysis layer, including multimodal deep learning analysis and finite element calculation;

[0131] Health assessment layer, including structural damage identification, health score monitoring and alarm.

[0132] This embodiment also provides a computer device suitable for a bridge technical condition assessment method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a bridge technical condition assessment method proposed in the above embodiment.

[0133] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0134] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements a bridge technical condition assessment method as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A bridge technical condition assessment method, characterized in that: The following steps are involved: Real-time dynamic load data collection for bridges: Multiple types of sensors are deployed on bridge structures, including main beams, piers, supports, and stay cables. Sensor data is transmitted in real time to the cloud or edge computing devices via wireless IoT. Finite element analysis models are used in combination with the collected dynamic load data to simulate the stress conditions of the bridge under different working conditions; Using deep learning and multimodal fusion technology, we conduct health assessments on sensor data and finite element analysis results, and set health index thresholds to automatically trigger health warnings. Blockchain-based bridge maintenance data tracking records the relevant data of each inspection, repair, and reinforcement into the blockchain, forming an unalterable maintenance history.

2. The bridge technical condition assessment method according to claim 1, characterized in that: Multiple sensor types include: Strain sensor: monitors the strain of the bridge under dynamic loads; Accelerometer: captures the vibration response data of the bridge; Displacement sensor: monitors the displacement changes of the bridge under different working conditions; Temperature sensor: collects environmental parameters and analyzes the impact of temperature on bridge performance; Traffic flow sensor: records the number, weight, and speed of vehicles traveling on the bridge.

3. The bridge technical condition assessment method according to claim 2, characterized in that: The finite element analysis model simulates the stress conditions of the bridge under different working conditions, including static analysis, dynamic analysis and fatigue analysis: The static analysis is used to calculate the stress, deformation and displacement of the bridge under normal use; The dynamic analysis is used to simulate the vibration response of the bridge under external dynamic loads; The fatigue analysis is used to predict fatigue damage of the bridge during long-term operation and to evaluate the remaining service life.

4. The bridge technical condition assessment method according to claim 3, characterized in that: Sensor data and finite element analysis results are used to perform health assessments, including: 1) Stress and strain analysis: whether the stress / strain at key parts of the bridge exceeds the design safety threshold; If the actual stress σ actual Exceeding the design stress limit σ limit , marked as structural anomaly: σ actual >σ limit → Early warning; 2) Vibration response analysis: whether there are any abnormal changes in the bridge vibration mode; Analyze the vibration signal through fast Fourier transform to determine the change of the main frequency component: Actual frequency f actual and baseline frequency f baseline The difference between the threshold Δf threshold Judgment: f actual -f baseline >Δf threshold →Structural damage exists; 3) Displacement and deformation assessment: whether the mid-span deflection and support displacement of the bridge exceed the standard; Calculate the actual deformation D through the displacement sensor actual With threshold D threshold Judgment: D actual >D threshold →Abnormal; 4) The impact of temperature on bridge health; Set temperature influence factor T factor , the adjusted stress σ adjusted Make a judgment: s adjusted =s actual -T factor ×ΔT ΔT temperature change. If the adjusted stress exceeds the standard, it is a real structural problem.

5. The bridge technical condition assessment method according to claim 4, characterized in that: Using deep learning and multimodal fusion technology for health assessment: The health index calculations are: 1) Stress / strain health index calculation: 2) Calculation of vibration modal health index: 3) Displacement health index calculation: 4) Temperature affects health index calculation:

6. The bridge technical condition assessment method according to claim 5, characterized in that: The total health index score is calculated as: H=αH s +βH v +γH D +δH t Among them, α, β, γ, and δ are weight coefficients.

7. The bridge technical condition assessment method according to claim 6, characterized in that: The warning level is determined based on the total health index results, including: Red Level 1 warning: H<0.5, severe structural damage; Orange Level 2 warning: 0.5≤H<0.65, abnormal vibration mode, stress exceeds limit; Yellow Level 3 Warning: 0.65≤H<0.75, slight displacement abnormality, environmental impact; Green: Normal: H ≥ 0.75, the bridge is in normal condition.

8. A bridge technical condition assessment system, characterized by: include: Data acquisition layer, including sensor data acquisition, dynamic load monitoring, and environmental data collection; Data transmission layer, transmitted via 5G / fiber / wireless network; Data processing and analysis layer, including multimodal deep learning analysis and finite element calculation; The health assessment layer includes structural damage identification, health score monitoring and alarm.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the bridge technical condition assessment method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a bridge technical condition assessment method according to any one of claims 1 to 7 are implemented.

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