Multi-modular assembly type pavement prefabricated slab detection method and equipment

Abstract

The invention discloses a multi-modular assembly type pavement prefabricated slab detection method and equipment, relates to the technical field of pavement measurement, and aims to overcome the defects of single-dimensional detection, low deployment efficiency and the like in the prior art. Constructing a multi-module detection platform, wherein a laser ranging module, a visual acquisition module and a fiber bragg grating strain module are mounted on a detachable bracket with scale marks; the FBG sensors are pre-embedded in the corners of the prefabricated slab, and strain of a stress concentration area is accurately captured. A unified space coordinate system is established, and space alignment and synchronous collection of multi-module data are achieved. After the laser data is preprocessed, the dimensional deviation is calculated, the distance, the surface image and the strain information are fused to construct a quality evaluation model, a two-dimensional deviation thermodynamic diagram is generated, quality areas are automatically classified according to threshold values, the detection method breaks through the limitation of single parameter detection, and the geometric dimension, the surface quality and the structural strain are covered in multiple dimensions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pavement detection, and in particular to a multi-module assembled pavement precast slab detection method. BACKGROUND

[0002] Under the background of the current extensive promotion of road construction and assembled construction, the size consistency and surface flatness of the assembled pavement precast slab become key indicators for guaranteeing the structural precision, construction efficiency and road performance. Although the current mainstream detection methods have their own advantages, they all have certain limitations in the factory quality detection of the assembled pavement slab. The traditional manual measurement method relies on manual tools such as tape measure, steel ruler and level for local sampling inspection, which is simple to operate but low in efficiency and strong in subjectivity, and it is difficult to realize full coverage and high-precision control of large-size slab. Although the contact three-coordinate measurement system (such as CMM) has high precision, it is not suitable for rapid detection requirements on the construction site due to its large size, high cost and complex deployment. The non-contact optical measurement method (such as laser scanning and structured light reconstruction) can obtain a three-dimensional profile with high resolution, but it is highly sensitive to environmental light and surface reflection characteristics, and the system cost is also high, which is not conducive to on-site rapid deployment and large-scale promotion. In recent years, embedded strain monitoring methods (such as fiber Bragg grating and patch strain gauge) are mainly applied to structural health monitoring during the service period, and have not formed a standardized process in the factory quality evaluation. In addition, the existing detection systems mainly focus on a single parameter (such as size or strain), and lack the fusion analysis capability of multi-source data such as size, surface quality and structural response, which makes it difficult to realize automatic, modular and expandable detection and comprehensive judgment of the assembled pavement precast slab. SUMMARY

[0003] The present application aims to overcome the shortcomings of the prior art and provides a multi-module assembled pavement precast slab detection method. Compared with the prior art, the disclosed method and device in the present application cooperatively collect and analyze three types of data of the collected precast slab size, surface and strain, and have high efficiency, high precision, low cost and on-site adaptability.

[0004] The present application is implemented by the following technical solutions: the present application discloses a multi-module assembled pavement precast slab detection method, comprising:

[0005] S100: obtaining the basic information of the precast slab to be detected, the detection platform comprising a plurality of different detection modules configured to collect quality data of the precast slab;

[0006] S200: the data collected by the detection platform at least includes point distance , surface image , and embedded strain value . All data are indexed by a unified timestamp and uploaded to the control unit synchronously;

[0007] S300: Geometric calibration and outlier rejection preprocessing of point distance raw data to form component surface height field , and compared with the theoretical model Calculate the size deviation:

[0008]

[0009] The size deviation is calculated as:

[0010]

[0011] wherein, is the edge feature point position;

[0012] S400: Spatial fusion of data from different modules to construct a deviation field and strain map in a unified coordinate system,

[0013] A weighted fusion model is used:

[0014]

[0015] wherein, is the comprehensive quality index function, is the distance deviation, is the image gray scale change, is the unit micro-strain , is the fusion weight, satisfying .

[0016] The flatness is described by the standard deviation index:

[0017]

[0018] The maximum strain is used to evaluate the potential structural internal stress concentration:

[0019] ;

[0020] S500: Generate a two-dimensional deviation heat map according to F(x,y), label high-risk areas, and classify according to threshold: F(x,y)≤0.3 is normal area, 0.3<F(x,y)≤0.6 is slight deviation area, F(x,y)>0.6 is risk area.

[0021] Preferably, it further comprises S600: generating a detection report, the detection report comprising at least one of a three-dimensional component model and a strain distribution map.

[0022] Preferably, the detection module comprises one or more of a laser ranging module, a visual acquisition module, and an embedded strain module.

[0023] Preferably, the detection modules are detachably mounted on the first support, and the first support is provided with a scale area.

[0024] Preferably, the embedded strain module is configured as a fiber Bragg grating sensor embedded in the prefabricated slab.

[0025] Preferably, the fiber Bragg grating sensor is arranged at a corner position of the prefabricated slab.

[0026] Preferably, the method for constructing a unified coordinate system comprises:

[0027] Taking the top left corner vertex of the prefabricated slab as the origin, the length direction as the X axis, the width direction as the Y axis, and the height direction as the Z axis, the spatial alignment of the multi-module data is completed through a coordinate transformation matrix.

[0028] Preferably, the laser ranging modules are uniformly arranged at the edges of the prefabricated slab, and the number of single-sided arrangement is two.

[0029] Preferably, the visual acquisition module is an industrial camera with a resolution of ≥2048*1536 pixels and is configured to acquire the surface image of the prefabricated slab.

[0030] The application also discloses a multi-module prefabricated slab detection device.

[0031] The application discloses a multi-module prefabricated slab detection method and device.

[0032] The multi-module prefabricated slab quality detection method in the application aims at the defects of single-dimensional detection and low deployment efficiency of the prior art, and constructs a multi-module detection platform: laser ranging, visual acquisition, and fiber Bragg (FBG) strain modules are installed on detachable supports with scale marks through quick release interfaces; FBG sensors are pre-buried in the corners of the prefabricated slab to accurately capture the strain in the stress concentration area. A unified spatial coordinate system is established to realize spatial alignment and synchronous acquisition of multi-module data. After pre-processing of laser data, size deviation is calculated, distance, surface image, and strain information are fused to construct a quality evaluation model, a two-dimensional deviation heat map is generated, and quality areas are automatically classified according to thresholds. The detection method breaks through the limitation of single parameter detection and covers geometric size, surface quality, and structural strain in multiple dimensions. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 It is an embodiment of the flowchart of adjusting parameters. DETAILED DESCRIPTION

[0034] The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various exemplary embodiments of the present disclosure as defined by the claims and their equivalents. The description includes various specific details to assist in that understanding but these are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the various embodiments described herein can be made without departing from the scope and spirit of the present disclosure. In addition, descriptions of well-known functions and constructions can be omitted for clarity and conciseness.

[0035] It will be understood that when an element or layer is referred to as being "on" or "connected to" or "coupled to" another element or layer, it can be directly on, connected or coupled to the other element or layer, or one or more intervening elements or layers can also be present. When an element is referred to as being "directly on" or "directly connected to" or "directly coupled to" another element or layer, no intervening elements or layers are present.

[0036] It will be understood that, although the terms first, second, third, etc. can be used herein to describe various elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms are used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. As such, a first element, a first component, a first display region, a first layer or a first section discussed below could be termed a second element, a second component, a second display region, a second layer or a second section without departing from the teachings of the exemplary embodiments. In the drawings, the size of various elements, layers, etc. can be exaggerated for clarity.

[0037] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0038] One embodiment discloses a multi-module assembly type pavement prefabricated slab detection method, comprising:

[0039] Step one: obtain the basic information of the prefabricated slab to be detected, the basic information of the prefabricated slab includes but is not limited to the size, shape, material mechanics and other parameters of the prefabricated slab, according to the basic information of the prefabricated slab to be detected, build a detection platform, the detection platform includes a plurality of different detection modules, configured to collect quality data of the prefabricated slab, the quality data can be geometric size, surface quality, structural strain, etc.

[0040] Step two: the data collected by the detection platform at least includes point distance , surface image , embedded strain value , all data are indexed with a unified timestamp , and are synchronously uploaded to a control unit;

[0041] Specifically, in step two, each module synchronously collects data according to a set frequency, and all data is timestamped. For indexing, data is synchronously uploaded to the control unit to ensure high consistency between spatial and temporal data. For example, the laser ranging module collects the distance between edge feature points at a frequency of 1Hz. The vision acquisition module is triggered synchronously with the laser module to acquire surface images at a frequency ≥1Hz, ensuring that the geometric data corresponds to the surface image at the same moment. The strain monitoring module, due to the dynamic characteristics of strain (such as instantaneous changes caused by loading and vibration), has its acquisition frequency set to 100Hz to capture the detailed features of stress fluctuations. The main control unit sends synchronous trigger signals to each module, with the trigger time serving as the timestamp reference t. 0 ， Upon receiving a trigger signal, each module synchronously acquires data and marks each frame of data with the current clock's timestamp. After the data is uploaded to the main control unit, the alignment of multi-source data is achieved through timestamp indexing to ensure that the point distance, surface image, and strain value under the same timestamp correspond to the same physical state of the precast slab.

[0042] Step 3: Perform geometric calibration and outlier removal preprocessing on the raw data of point distances to form the surface height field of the component. and with theoretical models Compare and calculate the dimensional deviations:

[0043]

[0044] Dimensional deviations are calculated as follows:

[0045]

[0046] in, The location of edge feature points is used to reflect the maximum deviation between the overall dimensions of the precast slab, such as length, width, and diagonal, and the design values, through the data in step three.

[0047] Step 4: Spatial fusion of data from different modules to construct a deviation field and strain map in a unified coordinate system.

[0048] A weighted fusion model is used to fuse information from three dimensions: distance deviation, surface grayscale change, and strain.

[0049]

[0050] in, For comprehensive quality index functions, For distance deviation, For image grayscale changes, unit microstrain , To integrate weights, satisfy .

[0051] Flatness is described by standard deviation index:

[0052]

[0053] Strain maximum is used to evaluate potential structural stress concentration:

[0054] ;

[0055] Further, the stress concentration risk of the prefabricated plate in the detection process (such as simulation loading and transportation vibration) is evaluated.

[0056] Step five: generate a two-dimensional deviation heat map according to F(x, y), map the comprehensive quality index F(x, y) to a color gradient heat map, intuitively present the spatial distribution of quality defects, based on industry standards and experimental data, the classification threshold is set to (0.3, 0.6) in this application file, label the high-risk area, and classify according to the threshold: F(x, y)≤0.3 is a normal area, 0.3<F(x, y)≤0.6 is a slight deviation area, and F(x, y)>0.6 is a risk area. For example, in the state of F(x, y)≤0.3, the geometric deviation is ≤1mm, the surface defect aperture is ≤1mm, and the strain is ≤200 .

[0057] Further, it further includes step six of generating a detection report, the detection report including at least one of a three-dimensional component model and a strain distribution map.

[0058] In an embodiment, the detection module includes one or more of a laser ranging module, a visual acquisition module, and an embedded strain module. The laser ranging module accurately acquires point distance The visual acquisition module captures surface images in real time The embedded strain module dynamically monitors strain values .

[0059] In an embodiment, the detection module is detachably mounted on the first support, and the first support is provided with a scale area.

[0060] Specifically, the detection module is detachably mounted on the first support through a quick release interface, and the first support is provided with a millimeter-level scale area. The detection module can be moved to different areas of the first support along the scale area.

[0061] In one embodiment, the embedded strain module is configured as a fiber Bragg grating sensor, which is embedded in the prefabricated plate. The fiber Bragg grating sensor works on the principle of optical wavelength modulation: when the prefabricated plate deforms, the grating period changes, causing the reflected light wavelength to drift. Wavelength data is collected by a demodulator and converted into strain values.

[0062] Furthermore, the fiber optic grating sensor is positioned at the corner of the precast slab, where stress concentration areas are more pronounced and strain changes are more significant, thus enabling the capture of early signals of structural damage.

[0063] In one embodiment, the method for constructing a unified coordinate system includes:

[0064] With the top left corner of the precast slab as the origin, the length direction is the X-axis, the width direction is the Y-axis, and the height direction is the Z-axis. The spatial alignment of the multi-module data is completed through a coordinate transformation matrix. The laser ranging module is evenly distributed on the edge of the precast slab, with two modules on each side. The vision acquisition module uses an industrial camera with a resolution of ≥2048×1536 pixels and is configured to acquire images of the precast slab surface.

[0065] The present invention also discloses a multi-modular prefabricated pavement slab testing device, which is used to perform the above-described testing method.

[0066] Please refer to Figure 1 To facilitate understanding, this application document explains the scheme using a specific testing scenario.

[0067] Construction of the testing platform: Taking a prefabricated road panel with dimensions of 3.5m×2.5m×0.2m as the testing object, the testing platform is set up as follows: 8 laser ranging modules (2 on each side) are evenly distributed along the edge of the component to accurately measure the edge data. 2 industrial camera modules are installed on the top-view frame for panel image acquisition. 4 resistance strain gauges are placed along the diagonal area of ​​the lower surface of the component to monitor the strain. A control host with integrated acquisition, fusion and visualization functions is configured to realize data processing. All modules are installed using scale-adjustable brackets. After the layout is completed, the component outline and panel surface coverage is ≥95%.

[0068] Data Acquisition: After the platform starts, each module synchronously acquires data at a frequency of 1Hz. The laser ranging module acquires laser ranging data for 280 points. The industrial camera module acquires images with a resolution of 2048×1536, and each image covers an area of ​​approximately [missing information]. Surface image Under simulated loading conditions, the resistance strain gauge records dynamic strain changes at a frequency of 100Hz; all data are synchronized with the main control unit system clock to achieve unified timestamps.

[0069] Data pre-processing: the measured edge feature point component long side is , the design value is . The short side is , the design value is . Substitute the size deviation calculation formula:

[0070]

[0071] Construct the laser point cloud height field , the measured plate surface height difference is , , .

[0072] According to the standard deviation formula:

[0073]

[0074] Calculation:

[0075]

[0076] Image gradient is obtained by Sobel operator processing:

[0077]

[0078] Automatic identification of edge corners exists 2 pinhole defects, the maximum aperture is 3mm, the system is automatically labeled as non-structural defects.

[0079] Data fusion processing: perform spatial fusion on data from different modules to construct a deviation field and strain map in a unified coordinate system. Adopt a weighted fusion model:

[0080]

[0081] Wherein, , , ; is the distance deviation (unit: mm), is the image gray change, is the unit micro-strain .

[0082] The fusion result is visually processed and then directly presented in the form of a two-dimensional heat map F(x, y), wherein the high-risk area is accurately marked, and such area usually appears as a point with a large size, a surface mutation and an overlap with high strain. The system automatically classifies according to the heat map value: when F(x, y) <= 0.3, it is determined as a normal area; if 0.3 < F(x, y) <= 0.6, it is a slightly deviated area, and re-inspection is suggested; and when F(x, y) > 0.6, it is identified as a risk area, and it is strongly suggested to take repair or remanufacturing measures. After detection, the fusion map clearly shows that there is an abnormal area with F(x, y) = 0.72 in the lower left corner, and the system automatically determines that it is a “key processing area”, and the defect map and accurate position coordinates are attached to the generated detection report.

[0083] Among them, four strain gauges are installed at the four corners of the bottom surface of the plate, and after loading the static pressure to simulate the transportation load (dynamic load + vibration), the maximum strain value of the measured strain response is , the strain change frequency peak value is 2.1 Hz, the system automatically derives the result items including the size deviation value (mm), the plate surface flatness, the defect image marked with the position and size, the strain dynamic response graph and the maximum value table, and the overall judgment of display qualified, repairable or unqualified; at the same time, the detection report is generated and can be uploaded to the cloud system.

[0084] In summary, the present application discloses a multi-module assembly type pavement precast slab quality detection method, which aims at the defects of single dimension detection and inefficient deployment of the prior art, and constructs a multi-module detection platform: laser ranging, visual acquisition and fiber bragg grating (FBG) strain module are installed on the detachable support with scale marks through quick release interface; the FBG sensor is pre-buried in the corner of the precast slab to accurately capture the strain of the stress concentration area. A unified spatial coordinate system is established to realize spatial alignment and synchronous acquisition of multi-module data. After pre-processing the laser data, the size deviation is calculated, the distance, surface image and strain information are fused to construct a quality evaluation model, a two-dimensional deviation heat map is generated, and the quality area is automatically classified according to the threshold. The detection method of the present application breaks through the limitation of single parameter detection and covers geometric size, surface quality and structural strain in multiple dimensions.

[0085] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can make equivalent replacement or change according to the technical solution and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

[0086] It is to be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a component" can include a combination of two or more components. Additionally, the terms "comprise," "comprises," and "comprising," or any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Furthermore, unless otherwise indicated herein, the terms "first," "second," "third," etc., are used herein merely as labels, and are not intended to impose ordinal import.

Claims

1. A multi-module assembled pavement prefabricated slab detection method, characterized in that, Comprising: S100: Obtain the basic information of the prefabricated slab to be detected, the detection platform includes multiple different detection modules configured to collect quality data of the prefabricated slab; S200: the data collected by the detection platform at least includes point distance , surface image , embedded strain value , all data is indexed with a unified timestamp , and is uploaded to the control unit synchronously; S300: Geometric calibration and outlier rejection preprocessing of the original data of point distance, forming the component surface height field , and compared with the theoretical model Calculate the size deviation: ； The size deviation calculation is: ； wherein, is the edge feature point position; S400: Spatially fuse data from different modules to construct a deviation field and a strain map under a unified coordinate system; A weighted fusion model is used: ； wherein, is a comprehensive quality index function, is a distance deviation, is an image gray variation, is a unit micro-strain, is a fusion weight, satisfying ; The flatness is described by the standard deviation index: ； The maximum strain is used to evaluate potential structural internal stress concentration: ； S500: Generate a two-dimensional deviation heat map according to F(x, y), label high-risk areas, and classify according to thresholds: F(x, y)≤0.3 is normal, 0.3<F(x, y)≤0.6 is a slight deviation area, and F(x, y)>0.6 is a risk area.

2. The method of claim 1, wherein the method further comprises: Also including S600: Generate a detection report, the detection report including at least one of a three-dimensional component model and a strain distribution map.

3. The method of claim 1, wherein the method further comprises: The detection modules include one or more of a laser ranging module, a visual acquisition module, and an embedded strain module.

4. The method of claim 3, wherein the method further comprises: The detection modules are all detachably installed on the first support, and the first support is arranged with a scale area.

5. The method of claim 3, wherein the method further comprises: The embedded strain module is configured as an optical fiber grating sensor embedded in the prefabricated slab.

6. The method of claim 5, wherein the method further comprises: The optical fiber grating sensor is arranged at the corner position of the prefabricated slab.

7. The method of claim 6, wherein the method further comprises: The construction method of the unified coordinate system includes: Taking the top left corner vertex of the prefabricated slab as the origin, the length direction as the X-axis, the width direction as the Y-axis, and the height direction as the Z-axis, the spatial alignment of multi-module data is completed through a coordinate transformation matrix.

8. The method of claim 3, wherein the method further comprises: determining the number of modules in the multi-module precast pavement slab based on the detected image. The laser ranging modules are uniformly arranged on the edge of the prefabricated slab, and the number of single-sided arrangement is two. ​ 9. The method of claim 6, wherein the method further comprises: determining the number of modules in the multi-module precast pavement slab based on the detected image. The visual acquisition module is an industrial camera with a resolution of ≥2048×1536 pixels, configured to acquire images of the surface of the prefabricated slab. ​ 10. A multi-modular prefabricated pavement slab testing device, characterized in that, The detection device is used to perform the detection method of any one of claims 1 to 9.

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