Bridge crack evaluation system and method combined with image recognition technology

Abstract

The invention discloses a bridge crack evaluation system and method combined with an image recognition technology, and relates to the technical field of bridge structure health monitoring, and the method comprises the following steps: collecting bridge crack images and environmental data of an area where a bridge is located, recognizing the collected bridge crack images, extracting crack information, and establishing a crack database; the quantitative information of the cracks is described through the database; on the basis of the crack database and the environmental data, a correlation mechanism between the environmental data and crack changes is constructed, so that the change trend of the cracks is predicted, and the environmental sensitivity of the cracks is calculated through the mechanism; and identifying a bridge risk area based on a correlation mechanism between the environmental data and the crack change. By calculating the comprehensive risk index of the bridge, the method can objectively quantify the health state of the bridge and divide the bridge into three areas. The division mode can help a bridge management unit to reasonably allocate maintenance resources and preferentially process high-risk areas.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge structure health monitoring, and in particular to a bridge crack assessment system and method combined with image recognition technology. Background Art

[0002] As critical infrastructure, the structural safety of bridges directly impacts transportation and public safety. However, due to long-term loads, environmental impact (such as temperature and humidity fluctuations, freeze-thaw cycles, wind impact, and acid rain corrosion), and material aging, bridge structures are prone to cracking. Crack propagation can lead to a decrease in the structural bearing capacity and even serious bridge collapse. Therefore, early identification of cracks, prediction of crack development trends, and safety assessment are core aspects of bridge maintenance.

[0003] Currently, bridge crack detection and assessment mainly rely on the following methods:

[0004] Manual inspection: Professionals conduct on-site inspections of bridge surface cracks and record them. This method relies on manual experience, is inefficient, susceptible to human error, and has difficulty detecting hidden cracks.

[0005] Non-destructive testing (NDT): such as ultrasonic testing, infrared thermal imaging, and X-rays. Although they offer high accuracy, the testing process is complex, costly, and difficult to monitor over a large area in real time.

[0006] Sensor monitoring: The stress state of the bridge is monitored by deploying accelerometers, strain gauges, etc. However, this method requires the installation of a large number of sensors on the bridge, which is costly to deploy and maintain, and it is difficult to directly assess the geometric characteristics of the crack.

[0007] These methods suffer from low detection efficiency, limited monitoring range, high costs, and insufficient ability to predict crack growth trends, making them unable to meet the needs of long-term bridge safety monitoring. Therefore, a comprehensive assessment method combining image recognition technology, environmental impact analysis, and crack growth prediction models is urgently needed to achieve automated detection of bridge cracks, predict their growth trends, and delineate risk areas. Summary of the Invention

[0008] 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.

[0009] In view of the above problems in the prior art, the present invention is proposed.

[0010] To solve the above technical problems, the present invention provides the following technical solution: a bridge crack assessment method combined with image recognition technology, comprising the following steps:

[0011] Collect bridge crack images and environmental data of the bridge area, identify the collected bridge crack images, extract crack information, and establish a crack database to describe the quantitative information of the cracks;

[0012] Based on the crack database and environmental data, a correlation mechanism between environmental data and crack changes is constructed to predict the crack change trend, and the environmental sensitivity of the crack is calculated through this mechanism;

[0013] Based on the correlation mechanism between environmental data and crack changes, bridge risk areas are identified and divided into: red warning area, yellow warning area, and green safety area.

[0014] As a preferred solution of the bridge crack assessment method combined with image recognition technology described in the present invention, wherein: the crack information includes: crack length L, crack width W, crack depth information D; based on the above information, a crack database D is established. c , define D c ={L i ,W i ,D i |i=1,2,...N}, i represents the crack number, N is the total number of cracks; the environmental data includes ambient temperature T, ambient humidity H, and wind speed w.

[0015] As a preferred solution of the bridge crack assessment method combined with image recognition technology described in the present invention, the specific steps of the association mechanism between environmental data and crack changes are as follows:

[0016] S201: Calculation of crack growth rate v based on the Paris-Erdogan law c ;

[0017] S202: Combined crack growth rate v c Calculate the sensitivity index S e , the environmental sensitivity of the crack is evaluated by this index, and the calculation formula is:

[0018]

[0019] Among them, k1, k2, k3 are the environmental impact weight parameters corresponding to each environmental data, S e The larger the value, the more sensitive the crack is to the environment and the more easily it is affected by environmental factors;

[0020] S203: According to the combined crack growth rate v cConstruct the crack growth equation L t ; Where L0 is the initial crack length, L t is the predicted crack length.

[0021] As a preferred solution of the bridge crack assessment method combined with image recognition technology described in the present invention, wherein: the crack growth rate v c The calculation formula is:

[0022] v c =C·(ΔK) m ·f·t

[0023] Where C and m represent the material test fitting constants, f represents the loading frequency, that is, when a vehicle passes through the bridge, a periodic load will be generated, t is the loading frequency time period, and ΔK represents the stress intensity factor amplitude. The calculation formula is:

[0024]

[0025] Where Y represents the geometric correction factor, σ represents the applied stress, and L s Represents the comprehensive equivalent crack size.

[0026] As a preferred solution of the bridge crack assessment method combined with image recognition technology described in the present invention, the comprehensive equivalent crack size L s The overall size of the crack is reflected by combining the crack length L, crack width W, and crack depth information D; its expression is:

[0027]

[0028] Among them, α, β, and γ are weight coefficients that reflect the contribution of each size to the overall characteristics of the crack.

[0029] As a preferred solution of the bridge crack assessment method combined with image recognition technology described in the present invention, wherein: according to the length L of the crack predicted in the future period t and sensitivity index S e To define the comprehensive bridge risk index R, which is used to quantify the risk, the expression is: R = L t ·(1+S e );

[0030] The risk areas are divided based on the calculation results of the comprehensive bridge risk index R.

[0031] As a preferred solution of the bridge crack assessment method combined with image recognition technology described in the present invention, the high risk threshold R is set 危 With the warning threshold R 警 ;

[0032] The corresponding areas are:

[0033] When R>R 危 Indicates a red alert area, requiring maintenance personnel to take emergency repair measures;

[0034] When R 警 <R≤R 危 Indicates a yellow warning area, requiring maintenance personnel to strengthen monitoring;

[0035] When R≤R 警 If it is a green safety zone, normal inspection is maintained.

[0036] A system applied to the above-mentioned bridge crack assessment method combined with image recognition technology, the system comprising:

[0037] The data acquisition module is responsible for collecting images of bridge cracks and bridge environmental data; the image processing and crack recognition module is used to intelligently process the collected crack images, identify crack characteristics, and extract quantitative information about the cracks; the crack database and environmental database management module stores and manages crack data and environmental data, providing data support; the crack extension prediction and environmental sensitivity analysis module uses the crack database and environmental data to analyze crack extension trends, calculate the environmental sensitivity of cracks, and predict future crack development; the bridge risk assessment and early warning module conducts safety assessments on bridges based on crack extension prediction results, divides risk areas, and provides early warning information.

[0038] The present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of the above-mentioned bridge crack assessment system and method combined with image recognition technology.

[0039] The present invention also discloses a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the steps of the above-mentioned bridge crack assessment system and method combined with image recognition technology.

[0040] Beneficial effects of the present invention:

[0041] 1. Based on image recognition technology, the present invention extracts geometric features such as crack length, width, and depth, avoiding the inefficiency of manual inspections while combining environmental data collection to establish a crack database, providing high-quality data support for subsequent analysis.

[0042] 2. This method calculates crack growth rates based on the Paris-Erdogan law, comprehensively considering the impact of environmental variables such as temperature, humidity, and wind speed on crack growth. Combined with the crack growth rate, it calculates a sensitivity index and crack growth prediction value. By calculating a comprehensive bridge risk index, this method objectively quantifies the health status of bridges and categorizes them into three zones: red warning, yellow caution, and green safety. This categorization can help bridge management units rationally allocate maintenance resources, prioritize high-risk areas, reduce over-maintenance in low-risk areas, and lower maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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:

[0044] Figure 1 This is a schematic diagram of the overall framework of a bridge crack assessment method combined with image recognition technology proposed in the present invention. DETAILED DESCRIPTION

[0045] 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.

[0046] 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.

[0047] 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.

[0048] Reference Figure 1 , as one embodiment of the present invention, provides a bridge crack assessment system and method incorporating image recognition technology, the method comprising the following steps:

[0049] Step 1: Collect bridge crack images and environmental data of the bridge area. Generally speaking, high-definition cameras, drones, laser scanners and other equipment can be used to collect bridge crack images (high-definition photos, infrared thermal imaging).

[0050] The collected bridge crack images are identified and crack information is extracted to establish a crack database. The database describes the quantitative information of the crack. Specifically, the crack information includes: crack length L, crack width W, and crack depth information D. Based on the above information, a crack database D is established. c , define D c ={L i ,W i ,D i |i=1,2,...N}, i represents the crack number, N is the total number of cracks; the environmental data includes ambient temperature T, ambient humidity H, and wind speed w.

[0051] Based on image recognition technology, geometric features such as crack length, width, and depth are extracted to avoid the inefficiency of manual inspections. At the same time, combined with environmental data collection, a crack database is established to provide high-quality data support for subsequent analysis.

[0052] Step 2: Based on the crack database and environmental data, a correlation mechanism between environmental data and crack changes is constructed to predict the change trend of cracks, and the environmental sensitivity of cracks is calculated through this mechanism.

[0053] Specifically, the correlation mechanism between environmental data and crack changes is as follows:

[0054] S201: Calculation of crack growth rate v based on the Paris-Erdogan law (crack growth theory) c .

[0055] The crack growth rate v c The calculation formula is:

[0056] v c =C·(ΔK) m ·f·t

[0057] Where C and m represent the material test fitting constants, and f represents the loading frequency, which means that when vehicles pass through the bridge, a periodic load will be generated. For bridge crack growth, traffic load is usually the main factor, so the loading frequency f is mainly determined by the vehicle flow on the bridge.

[0058] f=N v ·n / t

[0059] t is the loading frequency time period, N v is the number of vehicles passing through the bridge, n is the average number of axles per vehicle (axles / vehicle), because each axle may cause a load impact, ΔK is the amplitude of the stress intensity factor, and the calculation formula is:

[0060]

[0061] Where Y represents the geometric correction factor and σ represents the applied stress, which is mainly affected by external loads and structural response. If the traffic flow of the bridge is known, the applied stress can be calculated using the load distribution: σ = k·P / A, where k represents the load distribution coefficient.

[0062] P represents the vehicle load per unit time.

[0063] A represents the effective load-bearing cross-sectional area at the crack.

[0064] L s Represents the comprehensive equivalent crack size.

[0065] Furthermore, the comprehensive equivalent crack size L s The overall size of the crack is reflected by combining the crack length L, crack width W, and crack depth information D; its expression is:

[0066]

[0067] Among them, α, β, and γ are weight coefficients that reflect the contribution of each dimension to the overall characteristics of the crack. Through this formula, even if the dimension in one direction is small, it will not completely dominate the final value, ensuring the reasonable representation of the overall crack size.

[0068] S202: Combined crack growth rate v c Calculate the sensitivity index S e The environmental sensitivity of the crack can be evaluated by this index, and the response degree of the crack to the environment can be evaluated. The calculation formula is:

[0069]

[0070] Among them, k1, k2, k3 are the environmental impact weight parameters corresponding to each environmental data, S e The larger the value, the more sensitive the crack is to the environment and the more easily it is affected by environmental factors;

[0071] S203: According to the combined crack growth rate v c Construct the crack growth equation L t ; Where L0 is the initial crack length, L t is the predicted crack length.

[0072] Step 3: Based on the correlation mechanism between environmental data and crack changes, identify the risk areas of the bridge and divide the risk areas into: red warning area, yellow warning area, and green safety area.

[0073] Specifically, according to the crack length L predicted in the future cycle t and sensitivity index Se To define the comprehensive bridge risk index R, which is used to quantify the risk, the expression is: R = L t ·(1+S e ); Through the multiplication relationship, the risk value in a harsh environment can be magnified.

[0074] The risk areas are divided based on the calculation results of the bridge comprehensive risk index R. The high risk threshold R is set. 危 With the warning threshold R 警 ;

[0075] The corresponding areas are:

[0076] When R>R 危 Indicates a red alert area, requiring maintenance personnel to take emergency repair measures;

[0077] When R 警 <R≤R 危 Indicates a yellow warning area, requiring maintenance personnel to strengthen monitoring;

[0078] When R≤R 警 If it is a green safety zone, normal inspection is maintained.

[0079] In summary, by calculating the crack growth rate based on the Paris-Erdogan law formula, comprehensively considering the effects of temperature, humidity, and wind speed on crack growth, and combining the crack growth rate with the sensitivity index and crack growth prediction value, this method can objectively quantify the health status of bridges by calculating a comprehensive bridge risk index, categorizing them into three zones: red alert, yellow warning, and green safety. This categorization can help bridge management units rationally allocate maintenance resources, prioritize high-risk areas, reduce over-maintenance in low-risk areas, and lower maintenance costs.

[0080] This embodiment further provides a bridge crack assessment system incorporating image recognition technology, which is applied to the above-mentioned bridge crack assessment method incorporating image recognition technology. The system includes:

[0081] The data acquisition module is responsible for collecting images of bridge cracks and bridge environmental data; the image processing and crack identification module is used to intelligently process the collected crack images, identify crack characteristics, and extract quantitative information about the cracks.

[0082] The crack database and environmental database management module stores and manages crack data and environmental data, providing data support. The crack growth prediction and environmental sensitivity analysis module uses the crack database and environmental data to analyze crack growth trends, calculate crack environmental sensitivity, and predict future crack development.

[0083] The bridge risk assessment and early warning module conducts safety assessments on bridges based on crack propagation prediction results, divides risk areas, and provides early warning information.

[0084] This embodiment also provides a computer device, which is suitable for a bridge crack assessment method combined with image recognition technology, 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 crack assessment method combined with image recognition technology as proposed in the above embodiment.

[0085] 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.

[0086] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements a bridge crack assessment method combined with image recognition technology 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, magnetic disk or optical disk.

[0087] 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 crack assessment method combined with image recognition technology, characterized in that: include: Collect bridge crack images and environmental data of the bridge area, identify the collected bridge crack images, extract crack information, and establish a crack database to describe the quantitative information of the cracks; Based on the crack database and environmental data, a correlation mechanism between environmental data and crack changes is constructed to predict the crack change trend, and the environmental sensitivity of the crack is calculated through this mechanism; Based on the correlation mechanism between environmental data and crack changes, bridge risk areas are identified and divided into: red warning area, yellow warning area, and green safety area.

2. The bridge crack assessment method incorporating image recognition technology according to claim 1 is characterized by: The crack information includes: crack length L, crack width W, crack depth information D; based on the above information, a crack database D is established. c , define D c ={L i ,W i ,D i |i=1,2,...N}, i represents the crack number, N is the total number of cracks; the environmental data includes ambient temperature T, ambient humidity H, and wind speed w.

3. The bridge crack assessment method incorporating image recognition technology according to claim 2 is characterized by: The specific steps of the correlation mechanism between environmental data and crack changes are: S201: Calculation of crack growth rate v based on the Paris-Erdogan law c ; S202: Combined crack growth rate v c Calculate the sensitivity index S e , the environmental sensitivity of the crack is evaluated by this index, and the calculation formula is: Among them, k1, k2, k3 are the environmental impact weight parameters corresponding to each environmental data, S e The larger the value, the more sensitive the crack is to the environment and the more easily it is affected by environmental factors; S203: According to the combined crack growth rate v c Construct the crack growth equation L t ; Where L0 is the initial crack length, L t is the predicted crack length.

4. The bridge crack assessment method incorporating image recognition technology according to claim 3 is characterized by: The crack growth rate v c The calculation formula is: v c =C·(ΔK) m ·f·t Where C and m represent the material test fitting constants, f represents the loading frequency, that is, when a vehicle passes through the bridge, a periodic load will be generated, t is the loading frequency time period, and ΔK represents the stress intensity factor amplitude. The calculation formula is: Where Y represents the geometric correction factor, σ represents the applied stress, and L s Represents the comprehensive equivalent crack size.

5. The bridge crack assessment method incorporating image recognition technology according to claim 4 is characterized in that: The comprehensive equivalent crack size L s The overall size of the crack is reflected by combining the crack length L, crack width W, and crack depth information D; its expression is: Among them, α, β, and γ are weight coefficients that reflect the contribution of each size to the overall characteristics of the crack.

6. The bridge crack assessment method incorporating image recognition technology according to claim 5, characterized in that: According to the crack length L predicted in the future cycle t and sensitivity index S e To define the comprehensive bridge risk index R, which is used to quantify the risk, the expression is: R = L t ·(1+S e ); The risk areas are divided based on the calculation results of the comprehensive bridge risk index R.

7. The bridge crack assessment method incorporating image recognition technology according to claim 6, characterized in that: Set a high risk threshold R 危 With the warning threshold R 警 ; The corresponding areas are: When R>R 危 Indicates a red alert area, requiring maintenance personnel to take emergency repair measures; When R 警 <R≤R 危 Indicates a yellow warning area, requiring maintenance personnel to strengthen monitoring; When R≤R 警 If it is a green safety zone, normal inspection is maintained.

8. A bridge crack assessment system incorporating image recognition technology, applied to the bridge crack assessment method incorporating image recognition technology as described in any one of claims 1 to 7, characterized in that: The system includes: Data acquisition module, responsible for collecting images of bridge cracks and bridge environment data; Image processing and crack identification module, used to intelligently process the collected crack images, identify crack features, and extract quantitative information about the cracks; Crack database and environment database management module, which stores and manages crack data and environment data and provides data support; The crack growth prediction and environmental sensitivity analysis module uses the crack database and environmental data to analyze crack growth trends, calculate the environmental sensitivity of cracks, and predict future crack development; The bridge risk assessment and early warning module conducts safety assessments on bridges based on crack propagation prediction results, divides risk areas, and provides early warning information.

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 crack assessment method combined with image recognition technology 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 crack assessment method combined with image recognition technology according to any one of claims 1 to 7 are implemented.

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