A highway bridge disease identification method and system

Abstract

The present invention relates to the technical field of highway bridge defect identification, and discloses a highway bridge defect identification method and system. The method comprises: obtaining a highway bridge defect image, analyzing it, and determining the type and severity of the highway bridge defect; obtaining post-commissioning data of the highway bridge, as well as historical environmental data of the location of the highway bridge and data on buildings affecting it within a specified range of the highway bridge; determining the highway bridge's own impact weight based on the commissioning data, determining the environmental impact weight of the environment on the highway bridge based on the historical environmental data, and determining the highway bridge's architectural impact weight based on the data affecting the buildings; determining a comprehensive impact weight of the highway bridge based on the comprehensive impact weight, environmental impact weight, and architectural impact weight, and adjusting the severity of the defect based on the comprehensive impact weight to obtain a final severity of the highway bridge defect. The present invention improves the accuracy and timeliness of defect identification, ensuring the safe operation of highway bridges.

Description

Technical Field

[0001] The present invention relates to the technical field of highway bridge defect identification, and in particular to a highway bridge defect identification method and system. Background Art

[0002] Highway bridges, as key components of the transportation network, carry crucial transportation responsibilities. The health of bridge structures is directly linked to traffic safety and economic operation. However, due to a variety of factors, such as environmental erosion, material degradation, design flaws, overload, and inadequate maintenance, bridges can develop various defects during operation. If these defects are not promptly detected and addressed, they can lead to degradation of bridge performance and even accidents.

[0003] Traditional bridge defect identification methods rely primarily on manual inspections, performed by bridge engineers or inspectors through regular on-site inspections, using empirical judgment and simple tools. This approach is time-consuming and labor-intensive, inefficient, highly subjective, and lacks comprehensive coverage. Therefore, a highway bridge defect identification method and system are urgently needed to achieve efficient and accurate identification of bridge defects. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for identifying highway bridge defects, so as to solve the problems that traditional bridge defect identification methods mainly rely on manual inspection, based on experience judgment and the assistance of simple tools. This method is time-consuming and labor-intensive, inefficient, highly subjective, and difficult to fully cover.

[0005] The present invention provides a method for identifying highway bridge defects, the method comprising:

[0006] Obtaining highway bridge damage images, analyzing the highway bridge damage images, and determining the damage type and damage severity of the highway bridge damage;

[0007] Obtain data on the use of highway bridges after they are built, as well as historical environmental data on the locations of highway bridges and data on buildings that affect the scope of highway bridge regulations;

[0008] Determine the self-impact weight of the highway bridge based on the commissioning data, determine the environmental impact weight of the environment on the highway bridge based on the historical environmental data, and determine the construction impact weight of the highway bridge based on the construction impact data;

[0009] The comprehensive impact weight of the highway bridge is determined based on the self-impact weight, the environmental impact weight and the building impact weight, and the degree of damage is adjusted according to the comprehensive impact weight to obtain the final degree of damage of the highway bridge.

[0010] In some embodiments of the present application, analyzing the highway bridge damage image to determine the damage type and damage severity of the highway bridge damage includes:

[0011] Obtaining original pictures of highway bridges, wherein the original pictures of highway bridges are pictures of highway bridges when they have passed acceptance inspection but have not yet been put into use;

[0012] Denoising the highway bridge disease image using Gaussian filtering to obtain a de-noised highway bridge disease image;

[0013] Compare the noise reduction highway bridge disease pictures with the original pictures of the highway bridge to identify the difference areas;

[0014] Segment the difference areas to obtain the difference images of highway bridges;

[0015] Compare the highway bridge difference pictures with the highway bridge disease standard pictures, and determine the type of highway bridge disease based on the comparison results;

[0016] and determining the degree of damage corresponding to the damage type in the highway bridge difference image according to the damage type, and encoding the damage type and the damage degree;

[0017] Among them, the types of diseases include crack disease, peeling disease, weathering disease and rust disease;

[0018] Disease severity includes mild disease, moderate disease, severe disease, and serious disease;

[0019] Among the damage types, cracks, spalling, weathering, exposed steel bars and rust are coded as A, B, C and D respectively;

[0020] The disease severity levels of mild disease, moderate disease, severe disease and serious disease are coded as L1, L2, L3 and L4 respectively.

[0021] In some embodiments of the present application, determining the damage degree of the corresponding damage type in the highway bridge difference image according to the damage type includes:

[0022] Set disease evaluation standards corresponding to disease types according to disease types;

[0023] Among them, the severity of crack damage is determined based on the crack width;

[0024] Determine the severity of spalling disease based on the spalling area;

[0025] Determine the severity of weathering diseases based on weathering time and weathering area;

[0026] The severity of the rust disease is determined based on the rust time and rust area.

[0027] In some embodiments of the present application, determining the severity of a crack damage according to the crack width includes:

[0028] Presetting a first crack width, a second crack width, and a third crack width, wherein the first crack width, the second crack width, and the third crack width increase in sequence;

[0029] Obtaining a crack width in the highway bridge difference image, and setting a severity of the crack damage according to a relationship between the crack width and the first crack width, the second crack width, and the third crack width;

[0030] If the crack width is smaller than the first crack width, the damage level of the crack damage is set to mild damage A-L1;

[0031] If the crack width is greater than or equal to the first crack width and less than the second crack width, the damage level of the crack damage is set to moderate damage A-L2;

[0032] If the crack width is greater than or equal to the second crack width and less than the third crack width, the damage level of the crack damage is set to severe damage A-L3;

[0033] If the crack width is greater than or equal to the third crack width, the damage level of the crack damage is set to severe damage A-L4;

[0034] The severity of the spalling disease is determined based on the spalling area, including:

[0035] Presetting a first peeling area, a second peeling area, and a third peeling area, wherein the first peeling area, the second peeling area, and the third peeling area increase in sequence;

[0036] Obtaining a spalling area in the highway bridge difference image, and setting a severity of the spalling disease according to a relationship between the spalling area and the first spalling area, the second spalling area, and the third spalling area;

[0037] If the spalling area is smaller than the first spalling area, the spalling disease severity is set to be a mild disease B-L1;

[0038] If the spalling area is greater than or equal to the first spalling area and smaller than the second spalling area, the spalling disease severity is set to moderate disease B-L2;

[0039] If the spalling area is greater than or equal to the second spalling area and the spalling area is less than the third spalling area, the spalling disease severity is set to severe disease B-L3;

[0040] If the spalling area is greater than or equal to the third spalling area, the spalling disease severity is set to severe disease B-L4;

[0041] The severity of weathering diseases is determined based on the weathering time and weathering area, including:

[0042] Presetting a first weathering ratio, a second weathering ratio, and a third weathering ratio, wherein the first weathering ratio, the second weathering ratio, and the third weathering ratio increase in sequence;

[0043] Obtaining the weathering time and weathering area corresponding to the weathering damage in the highway bridge difference image, determining a weathering ratio between the weathering area and the weathering time, and setting the damage degree of the weathering damage based on a relationship between the weathering ratio and the first weathering ratio, the second weathering ratio, and the third weathering ratio;

[0044] If the weathering ratio is less than the first weathering ratio, the weathering disease severity is set to be a mild disease C-L1;

[0045] If the weathering ratio is greater than or equal to the first weathering ratio, and the weathering ratio is less than the second weathering ratio, the damage level of the weathering disease is set to moderate damage C-L2;

[0046] If the weathering ratio is greater than or equal to the second weathering ratio, and the weathering ratio is less than the third weathering ratio, the disease level of the weathering disease is set to severe disease C-L3;

[0047] If the weathering ratio is greater than or equal to the third weathering ratio, the weathering disease severity is set to severe disease C-L4;

[0048] The severity of the rust disease is determined based on the rust time and rust area, including:

[0049] Presetting a first corrosion ratio, a second corrosion ratio, and a third corrosion ratio, wherein the first corrosion ratio, the second corrosion ratio, and the third corrosion ratio increase in sequence;

[0050] Obtaining the corrosion time and corrosion area corresponding to the corrosion disease in the highway bridge difference image, determining a corrosion ratio between the corrosion area and the corrosion time, and setting the severity of the corrosion disease based on a relationship between the corrosion ratio and the first corrosion ratio, the second corrosion ratio, and the third corrosion ratio;

[0051] If the rust ratio is less than the first rust ratio, the rust disease severity is set to be a mild disease D-L1;

[0052] If the rust ratio is greater than or equal to the first rust ratio and less than the second rust ratio, the rust disease severity is set to moderate disease D-L2;

[0053] If the rust ratio is greater than or equal to the second rust ratio, and the rust ratio is less than the third rust ratio, the rust disease severity is set to severe disease D-L3;

[0054] If the rust ratio is greater than or equal to the third rust ratio, the rust disease severity is set to severe disease D-L4.

[0055] In some embodiments of the present application, determining the self-influence weight of the highway bridge according to the commissioning data includes:

[0056] The said data on use include the number of days of use and the daily load-bearing weight;

[0057] Determining the self-influencing factor of the highway bridge according to the daily load-bearing capacity;

[0058] Determine the self-influence weight of the highway bridge based on the number of days in use and the self-influence factor;

[0059] The self-influence weight is calculated according to the following formula:

[0060]

[0061] Among them, Qz represents the self-influence weight, T represents the number of days of use, qi represents the self-influence factor, Lq represents the design load of the highway bridge, Wq represents the daily bearing capacity, q1 represents the first self-influence factor, q2 represents the second self-influence factor, and q3 represents the third self-influence factor.

[0062] In some embodiments of the present application, determining the environmental impact weight of the environment on the highway bridge based on the historical environmental data includes:

[0063] The historical environmental data include daily rainfall, daily humidity and daily wind speed;

[0064] Pre-set rainfall threshold, humidity threshold and wind speed threshold;

[0065] Comparing the daily rainfall with the rainfall threshold, and filtering rainfall data whose daily rainfall is higher than the rainfall threshold;

[0066] Comparing the daily humidity value with the humidity threshold, and screening humidity data having daily humidity values higher than the humidity threshold;

[0067] Comparing the daily wind speed value with the wind speed threshold, and screening wind speed data having daily wind speed values higher than the wind speed threshold;

[0068] determining the environmental impact weight of the environment on the highway bridge based on the rainfall data, humidity data and wind speed data;

[0069] The environmental impact weight is calculated according to the following formula:

[0070]

[0071] Among them, Qh represents the environmental impact weight, a represents the rainfall impact factor, m represents the number of rainy days with daily rainfall higher than the rainfall threshold, Rx represents the daily rainfall on the xth day among m days, b represents the humidity impact factor, n represents the number of humidity days with daily humidity values higher than the humidity threshold, Sy represents the daily humidity value on the yth day among n days, c represents the wind speed impact factor, k represents the number of wind speed days with daily wind speed values higher than the wind speed threshold, and Wz represents the daily wind speed value on the zth day among k days.

[0072] In some embodiments of the present application, determining the building impact weight of a highway bridge according to the impact building data includes:

[0073] The affected building data include the distance between the affected building and the highway bridge and the construction area of the affected building;

[0074] determining a ratio between the construction area and the distance, and determining a construction impact weight of the highway bridge according to the ratio;

[0075] The building impact weight is calculated according to the following formula:

[0076]

[0077] Among them, Qj represents the building impact weight, d represents the impact building factor, p represents the number of affected buildings, Sji represents the construction area of the i-th affected building, and Sdi represents the distance between the i-th affected building and the highway bridge.

[0078] In some embodiments of the present application, the comprehensive impact weight is calculated according to the following formula:

[0079] Q=Qz+Qh+Qj.

[0080] In some embodiments of the present application, the damage degree is adjusted according to the comprehensive impact weight to obtain the final damage degree of the highway bridge damage, including:

[0081] Presetting a first comprehensive influence weight, a second comprehensive influence weight, and a third comprehensive influence weight, wherein the first comprehensive influence weight, the second comprehensive influence weight, and the third comprehensive influence weight increase in sequence;

[0082] Adjusting the damage degree according to the relationship between the comprehensive impact weight and the first comprehensive impact weight, the second comprehensive impact weight, and the third comprehensive impact weight to obtain a final damage degree of the highway bridge damage;

[0083] If the comprehensive impact weight is less than the first comprehensive impact weight, no adjustment is made to the disease severity;

[0084] If the comprehensive impact weight is greater than or equal to the first comprehensive impact weight, and the comprehensive impact weight is less than the second comprehensive impact weight, the disease severity is increased by one level; if the disease severity is severe, the disease severity is not adjusted;

[0085] If the comprehensive impact weight is greater than or equal to the second comprehensive impact weight, and the comprehensive impact weight is less than the third comprehensive impact weight, the disease severity is increased by two levels; if the disease severity is severe, the disease severity is not adjusted;

[0086] If the comprehensive impact weight is greater than or equal to the third comprehensive impact weight, the disease level will be increased by three levels; if the disease level is a serious disease, the disease level will not be adjusted.

[0087] The present invention also discloses a highway bridge defect identification system for applying the above-mentioned highway bridge defect identification method, the system comprising:

[0088] An acquisition module is used to obtain images of road bridge defects. The acquisition module is also used to obtain data on the road bridge being put into use after completion, as well as historical environmental data on the location of the road bridge and data on buildings that affect the road bridge within the specified range;

[0089] An image analysis module is used to analyze the highway bridge damage image to determine the damage type and damage degree of the highway bridge damage;

[0090] a weight determination module, configured to determine the self-impact weight of the highway bridge based on the commissioning data, determine the environmental impact weight of the environment on the highway bridge based on the historical environmental data, and determine the construction impact weight of the highway bridge based on the construction impact data; the weight determination module is further configured to determine the comprehensive impact weight of the highway bridge based on the self-impact weight, the environmental impact weight, and the construction impact weight;

[0091] The damage determination module is used to adjust the damage degree according to the comprehensive impact weight to obtain the final damage degree of the highway bridge damage.

[0092] Compared with the existing technology, the beneficial effect of the present invention is that the present invention adjusts the degree of damage by comprehensively considering the self-influence weight, environmental impact weight and building impact weight of the highway bridge, and can more comprehensively and accurately evaluate the actual damage condition of the bridge. It no longer relies solely on a single damage image analysis, but combines various influencing factors after the bridge is put into use, such as vehicle traffic, environmental temperature and humidity changes, construction impact of surrounding buildings, etc., so that the damage assessment results are closer to the actual situation, greatly improving the efficiency of damage identification, and being able to detect problems in a timely manner. In addition, the present invention obtains an accurate final degree of damage, which helps to formulate more targeted, scientific and reasonable maintenance and repair plans. It can predict the development trend of the disease in advance, reasonably arrange maintenance resources, avoid aggravated damage to the bridge structure due to untimely maintenance, and extend the service life of the bridge. The present invention uses advanced technical means to realize an automated and intelligent highway bridge disease identification method, which has become an urgent need to improve the accuracy and timeliness of disease identification and ensure the safe operation of highway bridges. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0094] Figure 1 It is a flow chart of a method for identifying highway bridge defects according to the present invention;

[0095] Figure 2 This is a functional block diagram of a highway bridge defect identification system of the present invention. DETAILED DESCRIPTION

[0096] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0097] like Figure 1 As shown, the present invention provides a method for identifying highway bridge defects, the method comprising:

[0098] S1, obtaining a highway bridge disease picture, analyzing the highway bridge disease picture, and determining the disease type and disease degree of the highway bridge disease.

[0099] S2, obtain the data of highway bridges put into use after completion, and obtain the historical environmental data of the location of the highway bridges and the data of the buildings affected by the highway bridges within the prescribed scope.

[0100] S3, determining the self-impact weight of the highway bridge according to the commissioning data, determining the environmental impact weight of the environment on the highway bridge according to the historical environmental data, and determining the construction impact weight of the highway bridge according to the construction impact data.

[0101] S4, determining the comprehensive impact weight of the highway bridge based on the self-impact weight, the environmental impact weight, and the building impact weight, and adjusting the damage degree according to the comprehensive impact weight to obtain the final damage degree of the highway bridge.

[0102] In some embodiments of the present application, the highway bridge disease picture is analyzed to determine the disease type and disease degree of the highway bridge disease, including: obtaining an original picture of the highway bridge, the original picture of the highway bridge is a picture of the highway bridge when it has passed acceptance and has not been put into use; using Gaussian filtering to denoise the highway bridge disease picture to obtain a noise-reduced highway bridge disease picture; comparing the noise-reduced highway bridge disease picture with the original picture of the highway bridge to determine the difference area; segmenting the difference area to obtain a highway bridge difference picture; comparing the highway bridge difference picture with the standard picture of the highway bridge disease, and determining the disease type of the highway bridge disease according to the comparison result; and determining the disease degree of the corresponding disease type in the highway bridge difference picture according to the disease type; wherein the disease types include crack disease, spalling disease, weathering disease and rust disease; the disease degree includes mild disease, moderate disease, severe disease and serious disease.

[0103] In this embodiment, original images of highway bridges are collected. These original images were taken when the bridges passed the acceptance inspection but were not yet put into use. In this way, these images can ensure that the corresponding highway bridges are not subjected to any external forces and are in a standard state. Subsequently, in order to eliminate possible noise interference in the images, Gaussian filtering technology is used to denoise these diseased images, thereby obtaining a set of noise-reduced highway bridge diseased images. Gaussian filtering technology is an effective linear smoothing filtering method widely used for noise removal in image processing and computer vision. This technology is based on the Gaussian function and achieves a smoothing effect by weighted averaging the neighborhood of image pixels. Next, these denoised images were compared in detail with the original bridge images to identify areas of difference between the two. These areas of difference were then segmented to form highway bridge difference images. These highway bridge difference images were then compared and analyzed with known standard images of highway bridge defects. Through this comparison, the specific type of bridge defect could be accurately determined. After determining the defect type, the specific severity of the corresponding defect type in the highway bridge difference images was further analyzed. In this process, the corresponding defect types included cracks, spalling, weathering, and rust; and the severity of the defect was subdivided into mild, moderate, severe, and critical. Through this detailed analysis, a comprehensive and accurate assessment of the health of highway bridges can be made, providing a scientific basis for subsequent maintenance and repair work.

[0104] In some embodiments of the present application, analyzing the highway bridge disease pictures to determine the disease type and disease degree of the highway bridge disease also includes: encoding the disease type and disease degree; cracks, spalling, weathering, exposed steel bars and rust in the disease types are coded as A, B, C, D respectively; mild diseases, moderate diseases, severe diseases and serious diseases in the disease degrees are coded as L1, L2, L3, L4 respectively.

[0105] Specifically, different types of damage are coded as follows: cracks, spalling, weathering, exposed rebar, and rust are labeled A, B, C, D, and E, respectively. The severity of damage is coded as L1, L2, L3, and L4 for mild, moderate, severe, and serious damage, respectively. This coding system facilitates better understanding and addressing of various damage issues, enabling effective repair and preservation measures. It also facilitates record-keeping and review.

[0106] In some embodiments of the present application, the degree of damage of the corresponding damage type in the highway bridge difference image is determined according to the damage type, including: setting the damage evaluation standard of the corresponding damage type according to the damage type; wherein, the degree of damage of crack damage is determined according to the crack width; the degree of damage of spalling damage is determined according to the spalling area; the degree of damage of weathering damage is determined according to the weathering time and weathering area; and the degree of damage of rust damage is determined according to the rust time and rust area.

[0107] In this embodiment, a set of scientific evaluation criteria is established for each type of disease. These criteria can reflect the severity of the disease and can quantitatively assess the damage to the bridge. For crack diseases, the impact of the crack on the bridge structure is determined based on its width. The wider the crack, the more serious the damage to the bridge may be, and the more timely repair and reinforcement are needed. Similarly, the severity of spalling diseases is evaluated by its area. The larger the spalling area, the more serious the damage to the bridge surface, which may affect the service life of the bridge. For weathering diseases, the extent is assessed based on the duration of weathering and the affected area. The longer the weathering time and the larger the affected area, the more serious the damage to the bridge caused by the weathering disease. The assessment of rust diseases is similar, and the extent of the damage to the bridge structure is determined based on the duration and area of rust. Rust not only destroys the appearance of the bridge, but more importantly, it weakens the bridge's load-bearing capacity, posing a huge safety hazard. In summary, this method can accurately identify and evaluate various types of defects in highway bridges, so as to formulate scientific and reasonable maintenance and protection measures to ensure the safe operation of bridges and extend their service life.

[0108] In some embodiments of the present application, the degree of crack disease is determined based on the crack width, including: pre-setting a first crack width, a second crack width, and a third crack width, wherein the first crack width, the second crack width, and the third crack width increase in sequence; obtaining the crack width in a highway bridge difference image, and setting the degree of the crack disease based on the relationship between the crack width and the first crack width, the second crack width, and the third crack width; if the crack width is less than the first crack width, the degree of the crack disease is set to mild disease A-L1; if the crack width is greater than or equal to the first crack width, and the crack width is less than the second crack width, the degree of the crack disease is set to moderate disease A-L2; if the crack width is greater than or equal to the second crack width, and the crack width is less than the third crack width, the degree of the crack disease is set to severe disease A-L3; if the crack width is greater than or equal to the third crack width, the degree of the crack disease is set to serious disease A-L4.

[0109] Determining the severity of the spalling disease based on the spalling area includes: presetting a first spalling area, a second spalling area, and a third spalling area, wherein the first spalling area, the second spalling area, and the third spalling area increase in sequence; obtaining the spalling area in the highway bridge difference image, and setting the severity of the spalling disease based on the relationship between the spalling area and the first spalling area, the second spalling area, and the third spalling area; if the spalling area is smaller than the first spalling area, setting the severity of the spalling disease to a mild severity (B-L1); if the spalling area is greater than or equal to the first spalling area and smaller than the second spalling area, setting the severity of the spalling disease to a moderate severity (B-L2); if the spalling area is greater than or equal to the second spalling area and smaller than the third spalling area, setting the severity of the spalling disease to a severe severity (B-L3); and if the spalling area is greater than or equal to the third spalling area, setting the severity of the spalling disease to a serious severity (B-L4).

[0110] Determining the degree of weathering damage according to weathering time and weathering area includes: presetting a first weathering ratio, a second weathering ratio and a third weathering ratio, wherein the first weathering ratio, the second weathering ratio and the third weathering ratio increase in sequence; obtaining the weathering time and the weathering area corresponding to the weathering damage in the highway bridge difference picture, determining the weathering ratio between the weathering area and the weathering time, and setting the degree of weathering damage according to the relationship between the weathering ratio and the first weathering ratio, the second weathering ratio and the third weathering ratio; if the weathering ratio is less than the first weathering ratio, the second weathering ratio and the third weathering ratio are used as the reference value; If the weathering ratio is greater than or equal to the first weathering ratio, and the weathering ratio is less than the second weathering ratio, the damage degree of the weathering disease is set to be a mild disease C-L1; if the weathering ratio is greater than or equal to the first weathering ratio, and the weathering ratio is less than the second weathering ratio, the damage degree of the weathering disease is set to be a moderate disease C-L2; if the weathering ratio is greater than or equal to the second weathering ratio, and the weathering ratio is less than the third weathering ratio, the damage degree of the weathering disease is set to be a severe disease C-L3; if the weathering ratio is greater than or equal to the third weathering ratio, the damage degree of the weathering disease is set to be a serious disease C-L4.

[0111] Determining the severity of the rust disease according to the rust time and the rust area includes: presetting a first rust ratio, a second rust ratio, and a third rust ratio, wherein the first rust ratio, the second rust ratio, and the third rust ratio increase in sequence; obtaining the rust time and the rust area corresponding to the rust disease in the highway bridge difference picture, determining the rust ratio between the rust area and the rust time, and setting the severity of the rust disease according to the relationship between the rust ratio and the first rust ratio, the second rust ratio, and the third rust ratio; if the rust ratio is less than the first rust ratio, the second rust ratio, and the third rust ratio are used as the rust ratio. If the rust ratio is greater than or equal to the first rust ratio and the rust ratio is less than the second rust ratio, the rust disease severity is set to be mild disease D-L1; if the rust ratio is greater than or equal to the first rust ratio and the rust ratio is less than the second rust ratio, the rust disease severity is set to be moderate disease D-L2; if the rust ratio is greater than or equal to the second rust ratio and the rust ratio is less than the third rust ratio, the rust disease severity is set to be severe disease D-L3; if the rust ratio is greater than or equal to the third rust ratio, the rust disease severity is set to be serious disease D-L4.

[0112] In some embodiments of the present application, determining the self-influence weight of the highway bridge according to the commissioning data includes: the commissioning data includes the number of days in use and the daily load-bearing weight; determining the self-influence factor of the highway bridge according to the daily load-bearing weight; determining the self-influence weight of the highway bridge according to the number of days in use and the self-influence factor;

[0113] The self-influence weight is calculated according to the following formula:

[0114]

[0115] Among them, Qz represents the self-influence weight, T represents the number of days of use, qi represents the self-influence factor, Lq represents the design load of the highway bridge, Wq represents the daily bearing capacity, q1 represents the first self-influence factor, q2 represents the second self-influence factor, and q3 represents the third self-influence factor.

[0116] In this embodiment, in order to make a more accurate assessment of the use status of highway bridges, this method introduces a comprehensive self-influence weight mechanism. This mechanism is mainly based on two key parameters: the number of days in use and the daily load-bearing weight. First, the number of days in use refers to the cumulative number of operating days since the bridge was put into use, which can reflect the utilization rate and length of operation time of the bridge. Secondly, the daily load-bearing weight refers to the sum of the traffic loads borne by the bridge within one day. This parameter is directly related to the structural safety and durability of the bridge. Within this framework, the self-influence factor of the highway bridge is determined by the daily load-bearing weight. Specifically, by statistically analyzing the daily load-bearing weight of the bridge, a quantitative indicator reflecting its bearing capacity can be obtained. Further, the self-influence weight of the highway bridge can be calculated by combining the number of days in use and the self-influence factor determined above.

[0117] In some embodiments of the present application, determining the environmental impact weight of the environment on the highway bridge based on the historical environmental data includes: the historical environmental data includes daily rainfall, daily humidity value and daily wind speed value; presetting a rainfall threshold, a humidity threshold and a wind speed threshold; comparing the daily rainfall with the rainfall threshold, and screening rainfall data with a daily rainfall higher than the rainfall threshold; comparing the daily humidity value with the humidity threshold, and screening humidity data with a daily humidity value higher than the humidity threshold; comparing the daily wind speed value with the wind speed threshold, and screening wind speed data with a daily wind speed value higher than the wind speed threshold; determining the environmental impact weight of the environment on the highway bridge based on the rainfall data, humidity data and wind speed data;

[0118] The environmental impact weight is calculated according to the following formula:

[0119]

[0120] Among them, Qh represents the environmental impact weight, a represents the rainfall impact factor, m represents the number of rainy days with daily rainfall higher than the rainfall threshold, Rx represents the daily rainfall on the xth day among m days, b represents the humidity impact factor, n represents the number of humidity days with daily humidity values higher than the humidity threshold, Sy represents the daily humidity value on the yth day among n days, c represents the wind speed impact factor, k represents the number of wind speed days with daily wind speed values higher than the wind speed threshold, and Wz represents the daily wind speed value on the zth day among k days.

[0121] In this example, to gain a deeper understanding and quantify the extent to which environmental factors impact the long-term operation of highway bridges, this study employed an analytical approach based on historical environmental data. This approach first required the collection of detailed historical environmental data, including but not limited to daily rainfall, relative humidity, and wind speed. These environmental parameters are crucial for assessing bridge health and predicting future maintenance needs. After collecting this data, specific thresholds were established to distinguish weather conditions that could significantly impact the bridge. These thresholds were based on past weather records and the bridge's historical response. Specifically, a rainfall threshold was established to ensure that only rainfall events exceeding normal levels were considered. Similar thresholds were set for relative humidity and wind speed to identify days that could significantly impact the bridge's structure and function. Next, the collected data was carefully screened and analyzed. Each day's rainfall, humidity, and wind speed values were compared to the corresponding thresholds to identify data from extreme weather conditions. Specifically, daily rainfall was evaluated, and days exceeding pre-defined thresholds were identified. Similar processing was performed for relative humidity and wind speed, ensuring that only data exceeding thresholds were included in the analysis. Finally, the filtered rainfall, humidity, and wind speed data were used to calculate and determine the environmental impact weights on highway bridges. This weight is a quantitative indicator that reflects the relative importance of different environmental factors on bridges. This approach not only allows for a better understanding of how environmental factors affect bridge health but also provides valuable reference information for future bridge design and maintenance.

[0122] It is understood that the units of rainfall impact factor a, humidity impact factor b, and wind speed impact factor c can be set according to actual conditions. If the unit of rainfall is "mm," the unit of rainfall impact factor a is "day / mm"; if the humidity value is expressed as relative humidity, which is dimensionless, the unit of humidity impact factor b is "day"; if the unit of wind speed value is "m / s," the unit of wind speed impact factor c is "day·second / m." Regardless of the setting of the units of rainfall impact factor a, humidity impact factor b, and wind speed impact factor c, the environmental impact weight must be dimensionless.

[0123] In some embodiments of the present application, determining the building impact weight of the highway bridge based on the impact building data includes: the impact building data includes the distance between the impact building and the highway bridge and the construction area of the impact building; determining the ratio between the construction area and the distance, and determining the building impact weight of the highway bridge based on the ratio;

[0124] The building impact weight is calculated according to the following formula:

[0125]

[0126] Among them, Qj represents the building impact weight, d represents the impact building factor, p represents the number of affected buildings, Sji represents the construction area of the i-th affected building, and Sdi represents the distance between the i-th affected building and the highway bridge.

[0127] In this embodiment, the construction of surrounding buildings may cause ground vibration or partial collapse. The larger the construction area and the closer it is to the highway bridge, the greater the impact on the highway bridge. This application proposes a method for determining building impact weights based on data on impacted buildings. This method primarily considers two key factors: the distance between the impacted building and the highway bridge, and the construction area of the impacted building. First, a large amount of relevant impacted building data is collected, including information on the distance between various buildings and the highway bridge and the construction area of these buildings. This data can be obtained through field surveys, satellite remote sensing image analysis, and other means. This data is then carefully collated and analyzed to facilitate subsequent calculations and evaluations. Next, a ratio between construction area and distance is determined. This ratio reflects the relative relationship between the scale of the impacted building and its distance from the highway bridge. By calculating this ratio, the degree of impact of different buildings on the highway bridge can be more intuitively understood. Finally, based on this ratio, the impact weight of the highway bridge is determined. Specifically, if a building is closer to the highway bridge and has a larger construction area, its impact weight will be higher. On the contrary, if a building is far away from a highway bridge or has a smaller construction area, its building impact weight will be lower.

[0128] This method can help us better understand and assess the impact of highway bridges on surrounding buildings, providing a scientific basis for relevant decision-making. For example, in urban planning, architectural design, traffic management, and other areas, this building impact weight can be used to make more reasonable layouts and designs, achieving harmonious development between transportation and architecture.

[0129] It's understandable that the dimensions of the impact factor d are determined based on actual circumstances. For example, if the unit of the building area Sji is "square meters," the unit of the distance Sdi between the affected building and the highway bridge is "meters," and the unit of the number of affected buildings p is "buildings," then the unit of the impact factor d is "buildings / meter." Regardless of the unit of the impact factor d, the building impact weight must remain dimensionless.

[0130] In some embodiments of the present application, the comprehensive impact weight is calculated according to the following formula:

[0131] Q=Qz+Qh+Qj.

[0132] In some embodiments of the present application, the degree of damage is adjusted according to the comprehensive impact weight to obtain the final degree of damage to the highway bridge, including: presetting a first comprehensive impact weight, a second comprehensive impact weight and a third comprehensive impact weight, wherein the first comprehensive impact weight, the second comprehensive impact weight and the third comprehensive impact weight increase in sequence; adjusting the degree of damage according to the relationship between the comprehensive impact weight and the first comprehensive impact weight, the second comprehensive impact weight and the third comprehensive impact weight to obtain the final degree of damage to the highway bridge; if the comprehensive impact weight is less than the first comprehensive impact weight, then the degree of damage is not adjusted; if the comprehensive impact weight is less than the first comprehensive impact weight, then the degree of damage is not adjusted; if the comprehensive impact weight is less than the first comprehensive impact weight, then the degree of damage is not adjusted; if the comprehensive impact weight is less than the first comprehensive impact weight, then the degree of damage is not adjusted. If the comprehensive impact weight is greater than or equal to the first comprehensive impact weight, and the comprehensive impact weight is less than the second comprehensive impact weight, the disease degree will be increased by one level. If the disease degree is a serious disease, the disease degree will not be adjusted; if the comprehensive impact weight is greater than or equal to the second comprehensive impact weight, and the comprehensive impact weight is less than the third comprehensive impact weight, the disease degree will be increased by two levels. If the disease degree is a serious disease, the disease degree will not be adjusted; if the comprehensive impact weight is greater than or equal to the third comprehensive impact weight, the disease degree will be increased by three levels. If the disease degree is a serious disease, the disease degree will not be adjusted.

[0133] In this embodiment, the adjustment method for the severity of a disease is determined by determining the range of the comprehensive impact weight. First, three different levels of comprehensive impact weight are set: the first comprehensive impact weight, the second comprehensive impact weight, and the third comprehensive impact weight. These three weights are arranged in ascending order. That is, the second comprehensive impact weight is higher than the first comprehensive impact weight, and the third comprehensive impact weight is higher than the second comprehensive impact weight. During the specific operation, these comprehensive impact weights are evaluated based on the specific circumstances of each disease. If the comprehensive impact weight of a disease is lower than the first comprehensive impact weight, no adjustment is made to the severity of the disease; its original level is retained. If the comprehensive impact weight is between the first and second comprehensive impact weights, the disease level is raised by one level. However, if the disease's original level is already severe, no adjustment is made. If the comprehensive impact weight is between the second and third comprehensive impact weights, the disease level is raised by two levels. However, if the disease's level reaches severe after the increase, no adjustment is made. Finally, if the combined impact weight is equal to or greater than the third combined impact weight, the damage level is increased by three levels. During this process, if the damage level remains severe after the increase, the original level is retained. This meticulous weighting and level adjustment ensures the scientific and rationality of highway bridge damage assessments, providing an important basis for bridge maintenance, management, and decision-making. This approach not only considers the severity of the damage itself but also factors such as geographic location, traffic volume, and bridge importance, making the assessment results more comprehensive and practical.

[0134] In some embodiments of the present application, after the highway bridge disease is identified based on the actual data of the highway bridge, it includes: issuing an early warning based on the identification result of the highway bridge disease, setting an early warning level according to the degree of the disease, and recording the identification result of the highway bridge disease and the early warning level: wherein, if the degree of the disease is a mild disease, a low-level early warning is issued; if the degree of the disease is a moderate disease, an intermediate early warning is issued; if the degree of the disease is a severe disease or a serious disease, a high-level early warning is issued.

[0135] In this embodiment, the early warning and recording of highway bridge defects requires not only focusing on the immediate status of the defect but also predicting its development trends. For identified defects, a corresponding warning level is established based on the severity and potential risk. For example, if the defect is mild, a low-level warning is issued; if the defect is moderate, a medium-level warning is issued; and if the defect is severe or critical, a high-level warning is issued. Recommendations can also be included, such as routine maintenance and upkeep, emergency repairs, or reinforcement measures.

[0136] It is understandable that the release of early warning information should ensure that it is timely, accurate and comprehensive. Through modern information communication means, such as the Internet, mobile phone APP or SMS services, early warning information can be quickly conveyed to relevant responsible units, management personnel and maintenance personnel. At the same time, the early warning information should also include the specific location, type, severity and recommended response measures of the disease, so that relevant personnel can take prompt action. In terms of records, a complete highway bridge disease identification and early warning information recording system should be established. The system should be able to record in detail the results of each disease identification, the release of early warning information, the implementation effect of response measures and the subsequent development of the disease. Through data analysis, the trend of highway bridge diseases can be predicted, providing strong data support for future maintenance and repair work. Through scientific methods, advanced technologies and perfect systems, the safety and smooth operation of highway bridges can be effectively guaranteed.

[0137] like Figure 2 As shown, the present invention also discloses a highway bridge defect identification system for applying the above-mentioned highway bridge defect identification method, the system comprising:

[0138] The acquisition module is used to obtain pictures of highway bridge defects. The acquisition module is also used to obtain data on the use of highway bridges after they are built, and to obtain historical environmental data on the location of the highway bridges and data on buildings that affect the highway bridges within the specified range.

[0139] The image analysis module is used to analyze the highway bridge damage image to determine the damage type and damage degree of the highway bridge damage.

[0140] A weight determination module is used to determine the self-impact weight of the highway bridge based on the commissioning data, determine the environmental impact weight of the environment on the highway bridge based on the historical environmental data, and determine the construction impact weight of the highway bridge based on the influencing construction data; the weight determination module is also used to determine the comprehensive impact weight of the highway bridge based on the self-impact weight, environmental impact weight and construction impact weight.

[0141] The damage determination module is used to adjust the damage degree according to the comprehensive impact weight to obtain the final damage degree of the highway bridge damage.

[0142] In this embodiment, the present invention adjusts the degree of damage by comprehensively considering the self-influence weight, environmental impact weight, and building impact weight of the highway bridge, and can more comprehensively and accurately evaluate the actual damage condition of the bridge. It no longer relies solely on a single damage image analysis, but combines various influencing factors after the bridge is put into use, such as vehicle traffic, environmental temperature and humidity changes, and the construction impact of surrounding buildings. This makes the damage assessment results closer to the actual situation, greatly improves the efficiency of damage identification, and can detect problems in a timely manner. In addition, the present invention obtains an accurate final degree of damage, which helps to formulate more targeted, scientific and reasonable maintenance and repair plans. It can predict the development trend of the disease in advance, reasonably arrange maintenance resources, avoid aggravated damage to the bridge structure due to untimely maintenance, and extend the service life of the bridge.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

[0144] The system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiment can be combined into one module or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps and are not to be regarded as improper limitations of the present invention.

[0145] Those skilled in the art should be able to appreciate that, in conjunction with the modules and method steps of each example described in the embodiments disclosed herein, it is possible to implement them with electronic hardware, computer software, or a combination of the two, and the programs corresponding to the software modules and method steps can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. In order to clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

Claims

1. A method for identifying highway bridge defects, characterized in that: The method comprises: Obtaining highway bridge damage images, analyzing the highway bridge damage images, and determining the damage type and damage severity of the highway bridge damage; Obtain data on the use of highway bridges after they are built, as well as historical environmental data on the locations of highway bridges and data on buildings that affect the scope of highway bridge regulations; Determine the self-impact weight of the highway bridge based on the commissioning data, determine the environmental impact weight of the environment on the highway bridge based on the historical environmental data, and determine the construction impact weight of the highway bridge based on the construction impact data; Determining a comprehensive impact weight of the highway bridge based on the self-impact weight, the environmental impact weight, and the building impact weight, and adjusting the damage degree according to the comprehensive impact weight to obtain a final damage degree of the highway bridge; Issue early warnings based on the identification results of highway bridge defects, set early warning levels based on the severity of the defects, and record the identification results and early warning levels: if the severity of the defect is mild, issue a low-level early warning; if the severity of the defect is moderate, issue an intermediate-level early warning; if the severity of the defect is severe or serious, issue a high-level early warning; The impact weights of highway bridges are determined based on the commissioning data, including: The said data on use include the number of days of use and the daily load-bearing weight; Determining the self-influencing factor of the highway bridge according to the daily load-bearing capacity; Determine the self-influence weight of the highway bridge based on the number of days in use and the self-influence factor; The self-influence weight is calculated according to the following formula: Among them, Qz represents the self-influence weight, T represents the number of days of use, qi represents the self-influence factor, Lq represents the design load of the highway bridge, Wq represents the daily load capacity, q1 represents the first self-influence factor, q2 represents the second self-influence factor, and q3 represents the third self-influence factor; The construction impact weight of the highway bridge is determined based on the impact building data, including: The affected building data include the distance between the affected building and the highway bridge and the construction area of the affected building; determining a ratio between the construction area and the distance, and determining a construction impact weight of the highway bridge according to the ratio; The building impact weight is calculated according to the following formula: Among them, Qj represents the building impact weight, d represents the impact building factor, p represents the number of affected buildings, Sji represents the construction area of the i-th affected building, and Sdi represents the distance between the i-th affected building and the highway bridge.

2. The highway bridge disease identification method according to claim 1, characterized in that: Analyze the highway bridge damage pictures to determine the damage type and damage degree of the highway bridge damage, including: Obtaining original pictures of highway bridges, wherein the original pictures of highway bridges are pictures of highway bridges when they have passed acceptance inspection but have not yet been put into use; Denoising the highway bridge disease image using Gaussian filtering to obtain a de-noised highway bridge disease image; Compare the noise reduction highway bridge disease pictures with the original pictures of the highway bridge to identify the difference areas; Segment the difference areas to obtain the difference images of highway bridges; Compare the highway bridge difference pictures with the highway bridge disease standard pictures, and determine the type of highway bridge disease based on the comparison results; and determining the degree of damage corresponding to the damage type in the highway bridge difference image according to the damage type, and encoding the damage type and the damage degree; Among them, the types of diseases include crack disease, peeling disease, weathering disease and rust disease; Disease severity includes mild disease, moderate disease, severe disease, and serious disease; Among the damage types, cracks, spalling, weathering, exposed steel bars and rust are coded as A, B, C and D respectively; The disease severity levels of mild disease, moderate disease, severe disease and serious disease are coded as L1, L2, L3 and L4 respectively.

3. The highway bridge disease identification method according to claim 2, characterized in that: Determining the degree of damage corresponding to the damage type in the highway bridge difference image according to the damage type includes: Set disease evaluation standards corresponding to disease types according to disease types; Among them, the severity of crack damage is determined based on the crack width; Determine the severity of spalling disease based on the spalling area; Determine the severity of weathering diseases based on weathering time and weathering area; The severity of the rust disease is determined based on the rust time and rust area.

4. The highway bridge disease identification method according to claim 3 is characterized in that: The severity of crack damage is determined based on the crack width, including: Presetting a first crack width, a second crack width, and a third crack width, wherein the first crack width, the second crack width, and the third crack width increase in sequence; Obtaining a crack width in the highway bridge difference image, and setting a severity of the crack damage according to a relationship between the crack width and the first crack width, the second crack width, and the third crack width; If the crack width is smaller than the first crack width, the damage level of the crack damage is set to mild damage A-L1; If the crack width is greater than or equal to the first crack width and less than the second crack width, the damage level of the crack damage is set to moderate damage A-L2; If the crack width is greater than or equal to the second crack width and less than the third crack width, the damage level of the crack damage is set to severe damage A-L3; If the crack width is greater than or equal to the third crack width, the damage level of the crack damage is set to severe damage A-L4; The severity of the spalling disease is determined based on the spalling area, including: Presetting a first peeling area, a second peeling area, and a third peeling area, wherein the first peeling area, the second peeling area, and the third peeling area increase in sequence; Obtaining a spalling area in the highway bridge difference image, and setting a severity of the spalling disease according to a relationship between the spalling area and the first spalling area, the second spalling area, and the third spalling area; If the spalling area is smaller than the first spalling area, the spalling disease severity is set to be a mild disease B-L1; If the spalling area is greater than or equal to the first spalling area and smaller than the second spalling area, the spalling disease severity is set to moderate disease B-L2; If the spalling area is greater than or equal to the second spalling area and the spalling area is less than the third spalling area, the spalling disease severity is set to severe disease B-L3; If the spalling area is greater than or equal to the third spalling area, the spalling disease severity is set to severe disease B-L4; The severity of weathering diseases is determined based on the weathering time and weathering area, including: Presetting a first weathering ratio, a second weathering ratio, and a third weathering ratio, wherein the first weathering ratio, the second weathering ratio, and the third weathering ratio increase in sequence; Obtaining the weathering time and weathering area corresponding to the weathering damage in the highway bridge difference image, determining a weathering ratio between the weathering area and the weathering time, and setting the damage degree of the weathering damage based on a relationship between the weathering ratio and the first weathering ratio, the second weathering ratio, and the third weathering ratio; If the weathering ratio is less than the first weathering ratio, the weathering disease severity is set to be a mild disease C-L1; If the weathering ratio is greater than or equal to the first weathering ratio, and the weathering ratio is less than the second weathering ratio, the damage level of the weathering disease is set to moderate damage C-L2; If the weathering ratio is greater than or equal to the second weathering ratio, and the weathering ratio is less than the third weathering ratio, the disease level of the weathering disease is set to severe disease C-L3; If the weathering ratio is greater than or equal to the third weathering ratio, the weathering disease severity is set to severe disease C-L4; The severity of the rust disease is determined based on the rust time and rust area, including: Presetting a first corrosion ratio, a second corrosion ratio, and a third corrosion ratio, wherein the first corrosion ratio, the second corrosion ratio, and the third corrosion ratio increase in sequence; Obtaining the corrosion time and corrosion area corresponding to the corrosion disease in the highway bridge difference image, determining a corrosion ratio between the corrosion area and the corrosion time, and setting the severity of the corrosion disease based on a relationship between the corrosion ratio and the first corrosion ratio, the second corrosion ratio, and the third corrosion ratio; If the rust ratio is less than the first rust ratio, the rust disease severity is set to be a mild disease D-L1; If the rust ratio is greater than or equal to the first rust ratio and less than the second rust ratio, the rust disease severity is set to moderate disease D-L2; If the rust ratio is greater than or equal to the second rust ratio, and the rust ratio is less than the third rust ratio, the rust disease severity is set to severe disease D-L3; If the rust ratio is greater than or equal to the third rust ratio, the rust disease severity is set to severe disease D-L4.

5. The highway bridge disease identification method according to claim 1, characterized in that: The environmental impact weights of the environment on highway bridges are determined based on the historical environmental data, including: The historical environmental data include daily rainfall, daily humidity and daily wind speed; Pre-set rainfall threshold, humidity threshold and wind speed threshold; Comparing the daily rainfall with the rainfall threshold, and filtering rainfall data whose daily rainfall is higher than the rainfall threshold; Comparing the daily humidity value with the humidity threshold, and screening humidity data having daily humidity values higher than the humidity threshold; Comparing the daily wind speed value with the wind speed threshold, and screening wind speed data having daily wind speed values higher than the wind speed threshold; determining the environmental impact weight of the environment on the highway bridge based on the rainfall data, humidity data and wind speed data; The environmental impact weight is calculated according to the following formula: Among them, Qh represents the environmental impact weight, a represents the rainfall impact factor, m represents the number of rainy days with daily rainfall higher than the rainfall threshold, Rx represents the daily rainfall on the xth day among m days, b represents the humidity impact factor, n represents the number of humidity days with daily humidity values higher than the humidity threshold, Sy represents the daily humidity value on the yth day among n days, c represents the wind speed impact factor, k represents the number of wind speed days with daily wind speed values higher than the wind speed threshold, and Wz represents the daily wind speed value on the zth day among k days.

6. The highway bridge disease identification method according to claim 5, characterized in that: The comprehensive impact weight is calculated according to the following formula: Q=Qz+Qh+Qj.

7. The highway bridge disease identification method according to claim 6, characterized in that: The damage degree is adjusted according to the comprehensive impact weight to obtain the final damage degree of the highway bridge damage, including: Presetting a first comprehensive influence weight, a second comprehensive influence weight, and a third comprehensive influence weight, wherein the first comprehensive influence weight, the second comprehensive influence weight, and the third comprehensive influence weight increase in sequence; Adjusting the damage degree according to the relationship between the comprehensive impact weight and the first comprehensive impact weight, the second comprehensive impact weight, and the third comprehensive impact weight to obtain a final damage degree of the highway bridge damage; If the comprehensive impact weight is less than the first comprehensive impact weight, no adjustment is made to the disease severity; If the comprehensive impact weight is greater than or equal to the first comprehensive impact weight, and the comprehensive impact weight is less than the second comprehensive impact weight, the disease severity is increased by one level; if the disease severity is severe, the disease severity is not adjusted; If the comprehensive impact weight is greater than or equal to the second comprehensive impact weight, and the comprehensive impact weight is less than the third comprehensive impact weight, the disease severity is increased by two levels; if the disease severity is severe, the disease severity is not adjusted; If the comprehensive impact weight is greater than or equal to the third comprehensive impact weight, the disease level will be increased by three levels; if the disease level is a serious disease, the disease level will not be adjusted.

8. A highway bridge defect identification system, used for applying the highway bridge defect identification method according to any one of claims 1 to 7, characterized in that: The system comprises: An acquisition module is used to obtain images of road bridge defects. The acquisition module is also used to obtain data on the road bridge being put into use after completion, as well as historical environmental data on the location of the road bridge and data on buildings that affect the road bridge within the specified range; An image analysis module is used to analyze the highway bridge damage image to determine the damage type and damage degree of the highway bridge damage; a weight determination module, configured to determine the self-impact weight of the highway bridge based on the commissioning data, determine the environmental impact weight of the environment on the highway bridge based on the historical environmental data, and determine the construction impact weight of the highway bridge based on the construction impact data; the weight determination module is further configured to determine the comprehensive impact weight of the highway bridge based on the self-impact weight, the environmental impact weight, and the construction impact weight; The damage determination module is used to adjust the damage degree according to the comprehensive impact weight to obtain the final damage degree of the highway bridge damage.

Images