A method and system for detecting exposed rebar in tunnel lining in strong seismic zones.

Abstract

This invention provides a method and system for detecting exposed rebar in tunnel lining in strong earthquake zones, comprising: constructing a standard rebar structure for the tunnel in a strong earthquake zone according to tunnel construction drawings; determining several rebar sampling locations in the tunnel in a strong earthquake zone; performing image sampling on each of the rebar sampling locations to obtain several regional rebar features in the tunnel in a strong earthquake zone; reconstructing the constructed rebar structure in the tunnel in a strong earthquake zone based on the regional rebar features; identifying structural differences between the constructed rebar structure and the standard rebar structure; determining the exposed rebar defect features of the corresponding rebar sampling locations based on the difference attributes corresponding to each of the structural difference information; generating and displaying an exposed rebar defect report for the tunnel in a strong earthquake zone. This method utilizes the convenience and accuracy of image recognition to improve the quality of exposed rebar identification work and effectively improves worker efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel exposed reinforcement identification and detection, and particularly relates to a method and system for exposed reinforcement detection of tunnel lining in a strong earthquake area. BACKGROUND

[0002] A strong earthquake area refers to an area where earthquake activity is frequent or the intensity of earthquakes is strong. When building a tunnel in such an area, the impact of earthquakes on the tunnel structure must be considered. A strong earthquake area usually refers to an area that is prone to strong earthquakes in history or geological structure, which may experience high seismic intensity or frequent earthquake activity. Due to the particularity of the terrain in a strong earthquake area, special consideration must be given to the impact of earthquakes on the tunnel structure when building a tunnel in a strong earthquake area. Precise seismic design, appropriate excavation methods, and reinforced support structures are needed to ensure the safety of the tunnel.

[0003] Reinforcement is a commonly used load-bearing material in tunnel construction. Generally speaking, the quality of reinforcement affects the quality of the tunnel, so when building a tunnel, it is necessary to identify and detect whether the reinforcement has been installed correctly or deviated from the required position due to construction or design reasons, resulting in partial exposure, loss, or non-compliance with design specifications. Such problems can affect the strength and stability of the tunnel structure, and even endanger the safety of the tunnel.

[0004] Therefore, the present application provides a method and system for exposed reinforcement detection of tunnel lining in a strong earthquake area. SUMMARY

[0005] The present application provides a method and system for exposed reinforcement detection of tunnel lining in a strong earthquake area, which identifies and detects the reinforcement in the strong earthquake area, locates the position of the reinforcement that is installed correctly or deviates from the required position in a timely manner, and avoids the impact of improper installation of reinforcement on the quality of the tunnel.

[0006] The present application provides a method for exposed reinforcement detection of tunnel lining in a strong earthquake area, comprising:

[0007] Step 1: Construct a standard reinforcement structure for the strong earthquake area based on the tunnel construction drawings, and determine a plurality of reinforcement sampling positions in the strong earthquake area;

[0008] Step 2: Image sampling is performed on each of the reinforcement sampling positions to obtain a plurality of regional reinforcement characteristics of the strong earthquake area;

[0009] Step 3: Based on the regional reinforcement characteristics, the construction reinforcement structure of the strong earthquake area is restored, and the structural difference information between the construction reinforcement structure and the standard reinforcement structure is identified;

[0010] Step 4: determining the missing steel bar defect characteristics of the corresponding steel bar sampling position according to the difference attribute corresponding to each structural difference information, generating and displaying the missing steel bar defect report of the tunnel strong earthquake zone.

[0011] In an implementable manner,

[0012] The step 1 comprises:

[0013] Step 11: screening a plurality of tunnel structure characteristics contained in the tunnel construction drawing, restoring the overall tunnel structure of the tunnel by using the tunnel structure characteristics, performing tunnel topography analysis on the overall tunnel structure, and positioning the regional range of the tunnel strong earthquake zone;

[0014] Step 12: performing tunnel material analysis on the regional range in the overall tunnel structure, obtaining the tunnel steel bar characteristics of the tunnel strong earthquake zone in the overall tunnel structure, and constructing the standard steel bar structure of the tunnel strong earthquake zone;

[0015] Step 13: determining the steel bar orientation corresponding to each steel bar in the tunnel strong earthquake zone according to the standard steel bar structure, and regarding the steel bar intersection points between different steel bars as the steel bar sampling positions of the tunnel strong earthquake zone.

[0016] In an implementable manner,

[0017] The step 2 comprises:

[0018] Step 21: controlling the shooting equipment of the tunnel site to respectively perform image sampling on each steel bar sampling position, obtaining the sampling image corresponding to each steel bar sampling position, respectively identifying the steel bar appearance of the corresponding steel bar sampling position in each sampling image, and establishing the steel bar appearance spatial array of the tunnel strong earthquake zone according to the steel bar appearance;

[0019] Step 22: respectively performing shape identification on each steel bar appearance in the steel bar appearance spatial array, regarding the steel bar sampling positions with the same steel bar shape as the same sampling class, respectively identifying a plurality of shape inflection points corresponding to each steel bar shape, respectively performing pixel enhancement on the corresponding shape inflection points in each sampling image, and obtaining the inflection point details corresponding to each shape inflection point;

[0020] Step 23: respectively inputting each inflection point detail into the corresponding steel bar appearance in the steel bar appearance spatial array for detail enhancement, respectively obtaining the new details corresponding to each sampling class, generating a plurality of steel bar detail information corresponding to the sampling class, using the steel bar detail information to finely adjust the details of the corresponding steel bar shape, and drawing the steel bar shape image corresponding to the steel bar sampling position;

[0021] Step 24: Project each of the steel bar shape images to obtain a plurality of steel bar basic features, respectively adjust the steel bar basic features by using each of the steel bar detail information, cluster the generated steel bar detail features, and regard the steel bar features corresponding to each clustering result as the regional steel bar features of the tunnel strong earthquake zone.

[0022] In an implementable manner,

[0023] Further comprising:

[0024] Mark the regional steel bar features corresponding to each array bit in the steel bar appearance space array, determine the regional presentation features corresponding to each regional range in the tunnel strong earthquake zone, and display the regional presentation features.

[0025] In an implementable manner,

[0026] The step 3 comprises:

[0027] Step 31: Identify a plurality of steel bar construction modes contained in the tunnel strong earthquake zone based on the regional steel bar features, perform structural restoration simulation on the tunnel strong earthquake zone according to a plurality of regional steel bar features contained in a steel bar construction range corresponding to each of the steel bar construction modes, and obtain a construction steel bar structure of the tunnel strong earthquake zone.

[0028] Step 32: Perform a compression test on the construction steel bar structure to obtain a position compression value corresponding to each of the steel bar sampling positions in the tunnel strong earthquake zone, determine a standard compression value corresponding to each of the steel bar sampling positions according to the standard steel bar structure, and generate a first type of difference information between the construction steel bar structure and the standard steel bar structure.

[0029] Step 33: Superimpose the construction steel bar structure on the standard steel bar structure, screen a non-overlapping part of the construction steel bar structure and the standard steel bar structure, and determine a second type of difference information between the construction steel bar structure and the standard steel bar structure.

[0030] Step 34: Combine the first type of difference information and the second type of difference information corresponding to the same steel bar sampling position to generate a plurality of structural difference information between the construction steel bar structure and the standard steel bar structure.

[0031] In an implementable manner,

[0032] The step 4 comprises:

[0033] Step 41: Respectively perform attribute identification on each of the structural difference information to obtain a steel bar attribute corresponding to each of the steel bar sampling positions, and respectively perform specification identification on each of the difference structural information by using AI to obtain a steel bar specification corresponding to each of the steel bar sampling positions.

[0034] Step 42: identifying a missing steel bar attribute and a missing steel bar degree corresponding to each steel bar sampling position based on the steel bar attribute and the steel bar specification corresponding to each steel bar sampling position, generating a corresponding missing steel bar defect feature, positioning each missing steel bar defect feature respectively, generating a missing steel bar defect report of the tunnel strong earthquake zone and displaying.

[0035] In an implementable manner,

[0036] Further comprising:

[0037] Deriving a tunnel safety level of the tunnel strong earthquake zone according to the missing steel bar defect report;

[0038] When the safety level is lower than the specified level, a safety warning is given to the tunnel strong earthquake zone.

[0039] The present application provides a steel bar detection system for tunnel strong earthquake zone lining, comprising:

[0040] A positioning sampling module is configured to construct a standard steel bar structure of the tunnel strong earthquake zone according to a tunnel construction drawing, and determine a plurality of steel bar sampling positions of the tunnel strong earthquake zone;

[0041] A sampling execution module is configured to perform image sampling on each steel bar sampling position respectively, and obtain a plurality of regional steel bar features of the tunnel strong earthquake zone;

[0042] A difference analysis module is configured to restore a construction steel bar structure of the tunnel strong earthquake zone based on the regional steel bar features, and identify structural difference information between the construction steel bar structure and the standard steel bar structure;

[0043] A missing steel bar identification module is configured to determine a missing steel bar defect feature of a corresponding steel bar sampling position according to a difference attribute corresponding to each structural difference information, and generate a missing steel bar defect report of the tunnel strong earthquake zone and display.

[0044] In an implementable manner,

[0045] The positioning sampling module comprises:

[0046] A range positioning unit is configured to filter a plurality of tunnel structure features contained in the tunnel construction drawing, restore an overall tunnel structure of the tunnel by using the tunnel structure features, perform tunnel topography analysis on the overall tunnel structure, and position a regional range of the tunnel strong earthquake zone;

[0047] A standard analysis unit is configured to perform tunnel material analysis on the regional range in the overall tunnel structure, obtain tunnel steel bar features of the tunnel strong earthquake zone in the overall tunnel structure, and construct a standard steel bar structure of the tunnel strong earthquake zone.

[0048] a positioning execution unit, configured to determine a steel bar orientation corresponding to each steel bar in the tunnel strong earthquake zone according to the standard steel bar structure, and regard a steel bar intersection point between different steel bars as a steel bar sampling position of the tunnel strong earthquake zone.

[0049] In an implementable manner,

[0050] The sampling execution module comprises:

[0051] a spatial arrangement unit, configured to control a shooting device at a tunnel site to perform image sampling on each steel bar sampling position to obtain a sampling image corresponding to each steel bar sampling position, identify a steel bar appearance corresponding to the steel bar sampling position in each sampling image, and establish a steel bar appearance spatial array of the tunnel strong earthquake zone according to the steel bar appearance.

[0052] a detail enhancement unit, configured to perform shape identification on each steel bar appearance in the steel bar appearance spatial array, regard the steel bar sampling positions with the same steel bar shape as a same sampling class, identify a plurality of shape inflection points corresponding to each steel bar shape, perform pixel enhancement on the corresponding shape inflection points in each sampling image, and obtain an inflection point detail corresponding to each shape inflection point.

[0053] a shape adjustment unit, configured to input each inflection point detail into the corresponding steel bar appearance in the steel bar appearance spatial array for detail enhancement, obtain new details corresponding to each sampling class, generate a plurality of steel detail information corresponding to the sampling class, perform detail fine-tuning on the corresponding steel bar shape by using the steel detail information, and draw a steel bar shape image corresponding to the steel bar sampling position.

[0054] a feature generation unit, configured to project each steel bar shape image to obtain a plurality of steel bar basic features, adjust the steel bar basic features by using each steel detail information, cluster the generated steel detail features, and regard a steel feature corresponding to each clustering result as a regional steel feature of the tunnel strong earthquake zone.

[0055] The implementable beneficial effects of the technical scheme are as follows: the standard steel bar structure of the tunnel strong earthquake zone is constructed under the guidance of the tunnel construction drawing, the steel bar sampling positions of the strong earthquake zone are determined by using the structure, image sampling is further performed on each steel bar sampling position respectively, the corresponding area steel bar features are obtained, the construction steel bar structure of the tunnel strong earthquake zone is restored, the missing steel defect features of each steel bar sampling position are analyzed by identifying the structural difference information between the steel bar construction structure and the standard steel bar structure, and thus the missing steel report of the tunnel strong earthquake zone is constructed, in this way, all the steel bars in the strong earthquake zone can be identified and detected in a short time, the convenience and accuracy of image identification are utilized to improve the quality of missing steel identification work, the work efficiency of workers is effectively improved, the safety of the working environment of the workers is ensured, and deviation during subsequent concrete pouring is avoided.

[0056] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and attained by means of the instrumentalities particularly pointed out in the written description and claims hereof.

[0057] The technical scheme of the present application will be further described in detail below with the aid of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0058] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate embodiments of the present application and are used to explain the present application, and do not constitute a limitation on the present application. In the drawings:

[0059] Figure 1 It is a work flow diagram of a kind of exposed steel bar detection method for tunnel strong earthquake zone lining in the embodiment of the present application;

[0060] Figure 2 It is the composition schematic diagram of a kind of exposed steel bar detection system for tunnel strong earthquake zone lining in the embodiment of the present application. DETAILED DESCRIPTION

[0061] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described here are only used as illustration and explanation of the present application, and are not used as limitation on the present application.

[0062] Example 1

[0063] The present embodiment provides a kind of exposed steel bar detection method for tunnel strong earthquake zone lining, as shown in Figure 1 It includes:

[0064] Step 1: according to tunnel construction drawing, the standard steel bar structure of the tunnel strong earthquake zone is constructed, and the steel bar sampling positions of the tunnel strong earthquake zone are determined;

[0065] Step 2: image sampling is performed on each of the steel bar sampling positions to obtain regional steel bar features of the tunnel strong earthquake zone;

[0066] Step 3: based on the regional steel bar features, the construction steel bar structure of the tunnel strong earthquake zone is restored, and structural difference information between the construction steel bar structure and the standard steel bar structure is identified;

[0067] Step 4: according to the difference attribute corresponding to each of the structural difference information, the missing steel defect feature of the corresponding steel bar sampling position is determined, a missing steel defect report of the tunnel strong earthquake zone is generated and displayed.

[0068] In this example, the standard steel bar structure represents the standard structure that the steel bars in the tunnel strong earthquake zone need to present, which is derived according to the construction drawings;

[0069] In this example, the regional steel bar feature represents the feature presented by the tunnel region in the strong earthquake zone;

[0070] In this example, the construction steel bar structure represents the feature presented by the steel bars on site in the tunnel strong earthquake zone;

[0071] In this example, the structural difference information represents the difference between the construction steel bar structure and the standard steel bar structure;

[0072] In this example, the missing steel defect feature represents the feature presented when the steel bars in the tunnel strong earthquake zone are exposed, including: steel bar exposure, steel bar loss, steel bar virtual welding, steel bar missing welding, and steel bar installation position error.

[0073] The working principle and beneficial effects of the above technical solution are as follows: under the guidance of the tunnel construction drawings, the standard steel bar structure of the tunnel strong earthquake zone is constructed, and the steel bar sampling position of the strong earthquake zone is determined using the structure. Further, image sampling is performed on each of the steel bar sampling positions to obtain the corresponding regional steel bar features, and then the construction steel bar structure of the tunnel strong earthquake zone is restored. Then, the missing steel defect feature of each steel bar sampling position is analyzed by identifying the structural difference information between the steel bar construction structure and the standard steel bar structure, thereby constructing the missing steel report of the tunnel strong earthquake zone. In this way, all steel bars in the strong earthquake zone can be identified and detected in a short time. The convenience and accuracy of image recognition are used to improve the quality of missing steel identification work, effectively improve the work efficiency of workers, ensure the safety of the working environment of workers, and avoid deviation during subsequent concrete pouring.

[0074] Embodiment 2

[0075] On the basis of Embodiment 1, the missing steel detection method for the tunnel strong earthquake zone lining, the step 1, comprises:

[0076] Step 11: screen a plurality of tunnel structure features contained in the tunnel construction drawing, restore the overall tunnel structure of the tunnel by using the tunnel structure features, perform tunnel topography analysis on the overall tunnel structure, and locate the regional range of the tunnel strong earthquake zone;

[0077] Step 12: perform tunnel material analysis on the regional range in the overall tunnel structure, obtain the tunnel reinforcement features of the tunnel strong earthquake zone in the overall tunnel structure, and construct the standard reinforcement structure of the tunnel strong earthquake zone;

[0078] Step 13: determine the reinforcement trend corresponding to each reinforcement in the tunnel strong earthquake zone according to the standard reinforcement structure, and regard the reinforcement intersection points between different reinforcements as the reinforcement sampling positions of the tunnel strong earthquake zone.

[0079] In this example, the tunnel structure features represent the features presented after the tunnel is completed;

[0080] In this example, the reinforcement trend represents the installation trend of a complete reinforcement during the construction of the tunnel.

[0081] The working principle and beneficial effects of the above technical solution are as follows: the overall tunnel structure of the tunnel is restored by using the tunnel structure features contained in the tunnel construction drawing, then the strong earthquake zone in the tunnel is determined through topography analysis, further combined with tunnel material analysis to construct the standard reinforcement structure of the tunnel strong earthquake zone, and finally the reinforcement intersection points between different reinforcements are regarded as the reinforcement sampling positions of the tunnel strong earthquake zone. In this way, the positions that need to be sampled can be screened out, the number of unnecessary sampling is effectively reduced, and the sampling quality and efficiency are improved.

[0082] Embodiment 3

[0083] On the basis of Embodiment 1, the method for detecting exposed reinforcement of a tunnel strong earthquake zone, the step 2 comprises:

[0084] Step 21: control the shooting equipment at the tunnel site to perform image sampling on each of the reinforcement sampling positions to obtain a sampling image corresponding to each of the reinforcement sampling positions, identify the reinforcement appearance corresponding to the reinforcement sampling position in each of the sampling images, and establish a reinforcement appearance spatial array of the tunnel strong earthquake zone according to the reinforcement appearance.

[0085] Step 22: perform shape identification on each of the reinforcement appearances in the reinforcement appearance spatial array, regard the reinforcement sampling positions with the same reinforcement shape as the same sampling class, identify a plurality of shape inflection points corresponding to each of the reinforcement shapes, perform pixel enhancement on the corresponding shape inflection points in each of the sampling images, and obtain the inflection point details corresponding to each of the shape inflection points.

[0086] Step 23: input each of the inflection point details into the corresponding steel bar appearance in the steel bar appearance space array for detail enhancement, respectively obtain the added details corresponding to each of the sampling classes, generate a plurality of steel bar detail information corresponding to the sampling classes, use the steel bar detail information to fine-tune the steel bar shape corresponding to the steel bar sampling position, and draw a steel bar shape image corresponding to the steel bar sampling position;

[0087] Step 24: respectively project each of the steel bar shape images to obtain a plurality of steel bar basic features, respectively adjust the steel bar basic features using each of the steel bar detail information, cluster the generated steel bar detail features, and regard the steel bar features corresponding to each of the clustering results as the regional steel bar features of the tunnel strong earthquake area.

[0088] In this example, the steel bar appearance space array represents the result of spatially arranging steel bar appearances according to the positional relationship between steel bar sampling positions;

[0089] In this example, the shape inflection point represents a point at which the state of the steel bar changes as presented in the steel bar shape;

[0090] In this example, the inflection point detail represents the details presented by the shape inflection point;

[0091] In this example, the steel bar basic feature represents a feature that can only express the shape of the steel bar.

[0092] The working principle and beneficial effects of the above technical solution are as follows: by using a shooting device to obtain a sampling image of each steel bar sampling position from image materials of the steel bar sampling position, then recognizing the steel bar appearance contained in the sampling image to establish a steel bar appearance space array of the tunnel strong earthquake area, and then classifying the steel bar sampling positions through shape recognition, pixel enhancement is performed on a plurality of shape inflection points corresponding to one steel bar shape, the inflection point details of each shape inflection point are determined, the steel bar shape is further fine-tuned through analysis of the added details of each sampling class, the steel bar basic features are determined through projection, the steel bar basic features are then adjusted in detail, and finally the regional steel bar features of the tunnel strong earthquake area are constructed by using clustering. In this way, the steel bars in the tunnel strong earthquake area can be deeply analyzed to determine their shape and details, and the regional steel bar features of the tunnel strong earthquake area are established, which facilitates subsequent identification and detection work.

[0093] Embodiment 4

[0094] Based on embodiment 3, the method for detecting exposed steel bars in a tunnel strong earthquake area further comprises:

[0095] Mark the region steel feature corresponding to each array bit in the steel appearance space array, determine the region feature corresponding to each region range in the tunnel strong earthquake zone and display.

[0096] The working principle and beneficial effects of the above technical solution are: by presenting the features of each region range in the tunnel strong earthquake zone in the steel appearance space array, it is convenient for workers to analyze the safety of the region according to the features of each region range.

[0097] Embodiment 5

[0098] Based on the embodiment 1, the step 3 of the method for detecting exposed steel in tunnel strong earthquake zone, comprising:

[0099] Step 31: Based on the region steel feature, identify several steel building modes contained in the tunnel strong earthquake zone, according to the region steel feature contained in the steel building range corresponding to each steel building mode, perform structural reduction simulation on the tunnel strong earthquake zone, and obtain the construction steel structure of the tunnel strong earthquake zone.

[0100] Step 32: Perform compression test on the construction steel structure, obtain the position compression value corresponding to each steel sampling position in the tunnel strong earthquake zone, determine the standard compression value corresponding to each steel sampling position according to the standard steel structure, and generate a type of difference information between the construction steel structure and the standard steel structure.

[0101] Step 33: Superimpose the construction steel structure to the standard steel structure, filter the non-overlapping part of the construction steel structure and the standard steel structure, and determine the second type of difference information between the construction steel structure and the standard steel structure.

[0102] Step 34: Combine the first type of difference information and the second type of difference information corresponding to the same steel sampling position, and generate several structure difference information between the construction steel structure and the standard steel structure.

[0103] In this example, the process of structural reduction simulation is: model simulation in virtual space;

[0104] In this example, the position compression value represents the maximum pressure value that a steel sampling position can withstand when pressure is applied to it;

[0105] In this example, the first type of difference information represents the difference between the position compression value and the standard compression value of a steel sampling position;

[0106] In this example, the second type of difference information represents the difference between the actual structure and the standard structure of a steel sampling position.

[0107] The working principle and beneficial effects of the above technical solution are as follows: the construction steel structure of the tunnel strong earthquake area is restored by using the construction method of the steel bars in the tunnel strong earthquake area, then the position compression value of each steel bar sampling position is analyzed by applying a test, and the differences between the construction steel structure and the standard steel structure are determined by analyzing the differences between the construction steel structure and the standard steel structure, so that the differences between the construction steel structure and the standard steel structure can be determined, and the different values corresponding to each difference can be determined, the accuracy of the missing steel detection is effectively improved, and the effects of deep mining and deep identification are achieved.

[0108] Embodiment 6

[0109] Based on the embodiment 1, the step 4 of the missing steel detection method for the tunnel lining in the strong earthquake area comprises:

[0110] Step 41: respectively identifying the attribute of each structural difference information to obtain the steel attribute corresponding to each steel bar sampling position, and respectively identifying the specification of each structural difference information by using AI to obtain the steel specification corresponding to each steel bar sampling position;

[0111] Step 42: identifying the missing steel attribute and the missing steel degree of the corresponding steel bar sampling position based on the steel attribute and the steel specification corresponding to each steel bar sampling position, generating the corresponding missing steel defect feature, respectively positioning each missing steel defect feature, generating the missing steel defect report of the tunnel strong earthquake area and displaying.

[0112] In this example, the steel attribute includes: steel appearance, steel to non-steel ratio, steel type.

[0113] The working principle and beneficial effects of the above technical solution are as follows: the steel attribute and the steel specification of each steel bar sampling position are determined by identifying the attribute and the specification of the structural difference information, then the missing steel defect feature of the steel bar sampling position is analyzed, and finally each missing steel position is positioned to generate the corresponding missing steel defect report, so that each missing steel attribute can be identified and quickly positioned, and effective technical reference is provided for workers.

[0114] Embodiment 7

[0115] Based on the embodiment 1, the missing steel detection method for the tunnel lining in the strong earthquake area further comprises:

[0116] Deriving the tunnel safety level of the tunnel strong earthquake area according to the missing steel defect report;

[0117] When the safety level is lower than the specified level, a safety warning is given to the tunnel strong earthquake area.

[0118] In this example, the specified level is level 2, that is, the tunnel strong earthquake zone at level 2 and below does not have corresponding seismic capacity.

[0119] The working principle and beneficial effects of the above technical solution are as follows: when the safety level of the tunnel strong earthquake zone is low, it indicates that the quality of the tunnel at this position is unqualified, and the corresponding safety warning is given to remind the workers to reinforce the quality immediately.

[0120] Embodiment 8

[0121] This embodiment provides a exposed steel bar detection system for tunnel strong earthquake zone lining, as shown in Figure 2 , comprising:

[0122] The positioning sampling module is configured to construct a standard steel bar structure of the tunnel strong earthquake zone according to a tunnel construction drawing, and determine a plurality of steel bar sampling positions of the tunnel strong earthquake zone.

[0123] The sampling execution module is configured to perform image sampling on each of the steel bar sampling positions respectively, and obtain a plurality of regional steel bar features of the tunnel strong earthquake zone.

[0124] The difference analysis module is configured to restore a construction steel bar structure of the tunnel strong earthquake zone based on the regional steel bar features, and identify structural difference information between the construction steel bar structure and the standard steel bar structure.

[0125] The missing steel bar identification module is configured to determine a missing steel bar defect feature of a corresponding steel bar sampling position according to a difference attribute corresponding to each of the structural difference information, generate a missing steel bar defect report of the tunnel strong earthquake zone, and display the missing steel bar defect report.

[0126] In this example, the standard steel bar structure represents a standard structure that the steel bar of the tunnel strong earthquake zone needs to present, which is derived according to the construction drawing;

[0127] In this example, the regional steel bar feature represents a feature presented by a tunnel region in the strong earthquake zone;

[0128] In this example, the construction steel bar structure represents a feature presented by a steel bar on site of the tunnel strong earthquake zone;

[0129] In this example, the structural difference information represents a difference between the construction steel bar structure and the standard steel bar structure;

[0130] In this example, the missing steel bar defect feature represents a feature presented when a steel bar in the tunnel strong earthquake zone is externally leaked, including: steel bar exposure, steel bar loss, steel bar virtual welding, steel bar missing welding, and steel bar installation position error.

[0131] The working principle and beneficial effects of the technical solution are as follows: the standard steel bar structure of the tunnel strong earthquake zone is constructed under the guidance of the tunnel construction drawing, the steel bar sampling position of the strong earthquake zone is determined by using the structure, image sampling is further performed on each steel bar sampling position, the corresponding area steel bar features are obtained, the construction steel bar structure of the tunnel strong earthquake zone is restored, the missing steel defect features of each steel bar sampling position are analyzed by identifying the structural difference information between the steel bar construction structure and the standard steel bar structure, the missing steel report of the tunnel strong earthquake zone is constructed, in this way, all the steel bars in the strong earthquake zone can be identified and detected in a short time, the convenience and accuracy of image recognition are used to improve the quality of missing steel identification work, the work efficiency of workers is effectively improved, the safety of the working environment of workers is ensured, and deviation during subsequent concrete pouring is avoided.

[0132] Embodiment 9

[0133] Based on the embodiment 8, the steel bar detection system for tunnel strong earthquake zone lining, the positioning sampling module comprises:

[0134] The range positioning unit is configured to: screen a plurality of tunnel structure features contained in the tunnel construction drawing, restore the overall tunnel structure of the tunnel by using the tunnel structure features, perform tunnel topography analysis on the overall tunnel structure, and position the area range of the tunnel strong earthquake zone.

[0135] The standard analysis unit is configured to: perform tunnel material analysis on the area range in the overall tunnel structure, obtain the tunnel steel bar features of the tunnel strong earthquake zone in the overall tunnel structure, and construct the standard steel bar structure of the tunnel strong earthquake zone.

[0136] The positioning execution unit is configured to: determine the steel bar running direction corresponding to each steel bar in the tunnel strong earthquake zone according to the standard steel bar structure, and regard the steel bar intersection points between different steel bars as the steel bar sampling positions of the tunnel strong earthquake zone.

[0137] In this example, the tunnel structure features represent the features presented after the tunnel is completed.

[0138] In this example, the steel bar running direction represents the installation running direction of a complete steel bar during the construction of the tunnel.

[0139] The working principle and beneficial effects of the technical solution are as follows: the overall tunnel structure of the tunnel is restored by using the tunnel structure features contained in the tunnel construction drawings, the strong earthquake zone in the tunnel is determined through terrain analysis, the standard steel bar structure of the strong earthquake zone of the tunnel is further constructed in combination with tunnel material analysis, and finally the steel bar intersection points between different steel bars are regarded as the steel bar sampling positions of the strong earthquake zone of the tunnel. In this way, the positions that need to be sampled can be screened out, the number of unnecessary sampling can be effectively reduced, and the sampling quality and efficiency can be improved.

[0140] Embodiment 10

[0141] Based on the embodiment 8, the sampling execution module comprises:

[0142] The spatial arrangement unit is configured to control the shooting device of the tunnel site to perform image sampling on each steel bar sampling position to obtain a sampling image corresponding to each steel bar sampling position, identify the appearance of the steel bar corresponding to each steel bar sampling position in each sampling image, and establish a steel bar appearance spatial array of the strong earthquake zone of the tunnel according to the appearance of the steel bar.

[0143] The detail enhancement unit is configured to perform shape identification on each steel bar appearance in the steel bar appearance spatial array, regard the steel bar sampling positions with the same steel bar shape as the same sampling class, identify a plurality of shape inflection points corresponding to each steel bar shape, perform pixel enhancement on the corresponding shape inflection points in each sampling image to obtain an inflection point detail corresponding to each shape inflection point, and obtain new details corresponding to each sampling class.

[0144] The shape adjustment unit is configured to input each inflection point detail into the corresponding steel bar appearance in the steel bar appearance spatial array for detail enhancement, obtain new details corresponding to each sampling class, generate a plurality of steel detail information corresponding to the sampling class, perform detail fine-tuning on the corresponding steel bar shape by using the steel detail information, and draw a steel bar shape image corresponding to the steel bar sampling position.

[0145] The feature generation unit is configured to project each steel bar shape image to obtain a plurality of steel bar basic features, adjust the steel bar basic features by using each steel detail information, cluster the generated steel detail features, and regard the steel features corresponding to each clustering result as the regional steel features of the strong earthquake zone of the tunnel.

[0146] In this example, the steel bar appearance spatial array represents the result of spatial arrangement of the steel bar appearance according to the positional relationship between the steel bar sampling positions.

[0147] In this example, the morphological inflection point represents a point in the appearance of the reinforcement at which the state of the reinforcement changes;

[0148] In this example, the inflection point detail represents a detail in the appearance of the morphological inflection point;

[0149] In this example, the reinforcement base feature represents a feature that can only be expressed in the appearance of the reinforcement.

[0150] The working principle and beneficial effects of the above technical solutions are as follows: the sampling image of each reinforcement sampling position is obtained by using the image material of the reinforcement sampling position obtained by the shooting device, the appearance of the reinforcement contained in the sampling image is recognized to establish the reinforcement appearance space array of the tunnel strong earthquake area, then the reinforcement sampling position is classified through morphological recognition, the pixel enhancement is performed on the several morphological inflection points corresponding to one reinforcement appearance, the inflection point details of each morphological inflection point are determined, the details of the reinforcement appearance are further fine-tuned by analyzing the new details of each sampling class, the reinforcement base feature is determined through projection, the details of the reinforcement base feature are adjusted, finally, the regional reinforcement feature of the tunnel strong earthquake area is constructed by using the clustering method, in this way, the reinforcement in the tunnel strong earthquake area can be deeply analyzed, the appearance and details thereof are determined, the regional reinforcement feature of the tunnel strong earthquake area is established, and the subsequent recognition and detection work is facilitated.

[0151] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalents, the present application also intends to include these modifications and variations.

Claims

1. A method for exposed reinforcement detection of a lining in a tunnel in a strong earthquake region, characterized by, The application relates to a tunnel strong earthquake area steel bar defect detection method, which comprises the following steps: Step 1: constructing a standard steel bar structure of a tunnel strong earthquake area according to tunnel construction drawings, and determining a plurality of steel bar sampling positions of the tunnel strong earthquake area; Step 2: respectively sampling images of each steel bar sampling position to obtain a plurality of regional steel bar features of the tunnel strong earthquake area; Step 3: restoring a construction steel bar structure of the tunnel strong earthquake area based on the regional steel bar features, and identifying structural difference information between the construction steel bar structure and the standard steel bar structure; Step 4: determining a missing steel bar defect feature of a corresponding steel bar sampling position according to a difference attribute corresponding to each structural difference information, generating a missing steel bar defect report of the tunnel strong earthquake area and displaying the missing steel bar defect report; Wherein, the step 2 comprises: Step 21: controlling a shooting device at a tunnel site to respectively sample images of each steel bar sampling position to obtain a sampling image corresponding to each steel bar sampling position, respectively identifying a steel bar appearance of the corresponding steel bar sampling position in each sampling image, and establishing a steel bar appearance space array of the tunnel strong earthquake area according to the steel bar appearance; Step 22: respectively identifying the shape of each steel bar appearance in the steel bar appearance space array, regarding the steel bar sampling positions with the same steel bar shape as the same sampling class, respectively identifying a plurality of shape inflection points corresponding to each steel bar shape, respectively performing pixel enhancement on the corresponding shape inflection points in each sampling image to obtain inflection point details corresponding to each shape inflection point; Step 23: respectively inputting each inflection point detail into the corresponding steel bar appearance in the steel bar appearance space array to perform detail enhancement, respectively acquiring new details corresponding to each sampling class, generating a plurality of steel bar detail information of the corresponding sampling class, using the steel bar detail information to finely adjust the details of the corresponding steel bar shape, and drawing a steel bar shape image corresponding to the steel bar sampling position; Step 24: respectively projecting each steel bar shape image to obtain a plurality of steel bar basic features, respectively adjusting the steel bar basic features by using each steel bar detail information, clustering the generated steel bar detail features, and regarding the steel bar features corresponding to each clustering result as regional steel bar features of the tunnel strong earthquake area.

2. A method for exposed reinforcement detection of a lining in a tunnel in a strong earthquake region according to claim 1, wherein The step 1 comprises: Step 11: screening a plurality of tunnel structure features contained in the tunnel construction drawings, restoring the overall tunnel structure of the tunnel by using the tunnel structure features, performing tunnel topography analysis on the overall tunnel structure, and positioning the regional range of the tunnel strong earthquake area; Step 12: performing tunnel material analysis on the regional range in the overall tunnel structure, obtaining tunnel steel bar features of the tunnel strong earthquake area in the overall tunnel structure, and constructing a standard steel bar structure of the tunnel strong earthquake area; Step 13: determining a steel bar trend corresponding to each steel bar in the tunnel strong earthquake area according to the standard steel bar structure, and regarding the steel bar intersection points between different steel bars as the steel bar sampling positions of the tunnel strong earthquake area.

3. A method for exposed reinforcement detection of a lining in a tunnel in a strong earthquake region according to claim 1, wherein The application further comprises: Mark the regional steel features corresponding to each array bit in the steel appearance space array, determine the regional presentation features corresponding to each regional range in the tunnel strong earthquake zone, and display the regional presentation features.

4. The exposed reinforcement detection method for tunnel lining in a strong earthquake region according to claim 1, wherein The step 3 comprises: Step 31: Identify several steel construction modes contained in the tunnel strong earthquake zone based on the regional steel features, and perform structural reduction simulation on the tunnel strong earthquake zone according to several regional steel features contained in the steel construction range corresponding to each steel construction mode to obtain a construction steel structure of the tunnel strong earthquake zone; Step 32: Perform a pressure test on the construction steel structure to obtain a position compression resistance value corresponding to each steel sampling position in the tunnel strong earthquake zone, determine a standard compression resistance value corresponding to each steel sampling position according to the standard steel structure, and generate a first difference information between the construction steel structure and the standard steel structure; Step 33: Superimpose the construction steel structure into the standard steel structure, screen a non-overlapping part of the construction steel structure and the standard steel structure, and determine a second difference information between the construction steel structure and the standard steel structure; Step 34: Combine the first difference information and the second difference information corresponding to the same steel sampling position to generate several structural difference information between the construction steel structure and the standard steel structure.

5. A method for exposed reinforcement detection of a lining in a tunnel in a strong earthquake region according to claim 1, wherein The step 4 comprises: Step 41: Identify the steel attribute corresponding to each steel sampling position by respectively identifying the attribute of each structural difference information, and identify the steel specification corresponding to each steel sampling position by respectively identifying the specification of each structural difference information using AI; Step 42: Identify the missing steel attribute and the missing steel degree corresponding to the steel sampling position based on the steel attribute and the steel specification corresponding to each steel sampling position, generate a missing steel defect feature corresponding to the steel sampling position, respectively position each missing steel defect feature, generate a missing steel defect report of the tunnel strong earthquake zone, and display the missing steel defect report.

6. A method for exposed reinforcement detection of a lining in a tunnel in a strong earthquake region according to claim 1, wherein Further comprising: Deriving a tunnel safety level of the tunnel strong earthquake zone according to the missing steel defect report; When the safety level is lower than a specified level, performing a safety warning on the tunnel strong earthquake zone.

7. A system for exposed reinforcement detection of a lining in a tunnel in a strong seismic region, characterized by, Comprise: A positioning sampling module configured to construct a standard steel structure of a tunnel strong earthquake zone according to a tunnel construction drawing, and determine several steel sampling positions of the tunnel strong earthquake zone; A sampling execution module configured to respectively sample an image of each steel sampling position to obtain several regional steel features of the tunnel strong earthquake zone; A difference analysis module configured to restore a construction steel structure of the tunnel strong earthquake zone based on the regional steel features, and identify structural difference information between the construction steel structure and the standard steel structure; A missing steel identification module configured to determine a missing steel defect feature of a steel sampling position according to a difference attribute corresponding to each structural difference information, generate a missing steel defect report of the tunnel strong earthquake zone, and display the missing steel defect report; and The sampling execution module comprises a spatial arrangement unit configured to control the shooting device at the tunnel site to perform image sampling on each steel bar sampling position to obtain a sampling image corresponding to each steel bar sampling position, identify the appearance of the steel bar corresponding to each steel bar sampling position in each sampling image, and establish a spatial array of the appearances of the steel bars in the tunnel strong earthquake zone according to the appearances of the steel bars. The detail enhancement unit is configured to perform shape identification on each appearance of the steel bar in the spatial array of the appearances of the steel bars, regard the steel bar sampling positions with the same shape of the steel bar as the same sampling class, identify a plurality of shape inflection points corresponding to each shape of the steel bar, perform pixel enhancement on the corresponding shape inflection points in each sampling image, and obtain an inflection point detail corresponding to each shape inflection point. The shape adjustment unit is configured to input each inflection point detail into the corresponding appearance of the steel bar in the spatial array of the appearances of the steel bars to perform detail enhancement, obtain new details corresponding to each sampling class, generate a plurality of steel detail information corresponding to the sampling class, perform detail fine-tuning on the corresponding shape of the steel bar by using the steel detail information, and draw a steel shape image corresponding to the steel bar sampling position. The feature generation unit is configured to project each steel shape image to obtain a plurality of steel basic features, adjust the steel basic features by using each steel detail information, cluster the generated steel detail features, and regard the steel features corresponding to each cluster result as the regional steel features of the tunnel strong earthquake zone.

8. A system for exposed tendon detection of a lining in a tunnel in a seismically active region as defined in claim 7, wherein The positioning sampling module comprises: The range positioning unit is configured to screen a plurality of tunnel structure features contained in the tunnel construction drawing, restore the overall tunnel structure of the tunnel by using the tunnel structure features, perform tunnel topography analysis on the overall tunnel structure, and position the regional range of the tunnel strong earthquake zone. The standard analysis unit is configured to perform tunnel material analysis on the regional range in the overall tunnel structure, obtain the tunnel steel feature of the tunnel strong earthquake zone in the overall tunnel structure, and construct a standard steel structure of the tunnel strong earthquake zone. The positioning execution unit is configured to determine the steel bar orientation corresponding to each steel bar in the tunnel strong earthquake zone according to the standard steel structure, and regard the steel bar intersection points between different steel bars as the steel bar sampling positions of the tunnel strong earthquake zone.

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