Quality detection acceptance management method for building decoration construction based on visual large model

Abstract

The invention relates to the technical field of detection and acceptance, and discloses a quality detection and acceptance management method for building decoration construction based on a visual large model, which comprises the following steps: before laying tiles, pre-burying a fiber bragg grating (FBG) net in a bonding layer according to M * N grids, and arranging grid points on the FBG net according to a fixed interval; 0.2% of conductive carbon black is doped into the pointing slurry to form a grouting layer; acquiring a first characteristic parameter of a fiber bragg grating (FBG) network; executing bidirectional pulse temperature difference excitation of preset times on a fiber bragg grating (FBG) network; after the execution of the bidirectional pulse temperature difference excitation is completed, collecting a second characteristic parameter: constructing a risk thermodynamic diagram based on the first characteristic parameter and the second characteristic parameter; marking a high-risk grid point based on the risk thermodynamic diagram, selecting and drilling a core sample of the high-risk grid point, and carrying out a drawing and shearing combined test to obtain a bonding retention rate; and judging whether the adhesion retention rate exceeds the limit or not, and generating a quality detection acceptance result.

Description

Technical Field

[0001] The present invention relates to the field of inspection and acceptance technology, and more specifically, to a quality inspection and acceptance management method for building decoration construction based on a visual large model. Background Art

[0002] Publication number CN114511199A discloses a project delivery and acceptance management method, equipment, and computer storage medium. By detecting the color, joint spacing, and edge height difference of each floor tile in the room to be decorated, it ensures that quality issues in the tile acceptance in the room can be resolved in a timely manner.

[0003] However, while low-water-absorption, high-modulus ceramic tiles are widely used due to their superior performance, the thermal expansion mismatch between the tiles and the bonding layer can lead to significant thermal stresses in outdoor facades, subject to large, high-frequency day-night temperature swings. Conventional testing focuses only on instantaneous bond strength, while actual failure occurs after thousands of thermal fatigue cycles, often resulting in qualified completions only to fall off months later.

[0004] Existing technologies have obvious defects: the accelerated aging test cycle is long, ultrasonic and infrared tests are difficult to detect micro-damage on the tile bonding surface, and large-area pull-out tests are highly destructive. Summary of the Invention

[0005] The present invention provides a quality inspection and acceptance management method for building decoration construction based on a visual large model, which solves the technical problems raised in the background technology.

[0006] The present invention provides a quality inspection and acceptance management method for building decoration construction based on a visual large model, comprising:

[0007] Step 1: Before laying tiles, do the following:

[0008] Step 11, pre-buried a fiber Bragg grid (FBG) in an M×N grid in the bonding layer, wherein grid points of the fiber Bragg grid are arranged at a fixed interval;

[0009] Step 12, adding 0.2% conductive carbon black into the grouting slurry to form a grouting layer;

[0010] Step 2, collecting the first characteristic parameters of the fiber Bragg FBG network;

[0011] Step 3, performing a preset number of bidirectional pulse temperature difference excitations on the fiber Bragg FBG network;

[0012] Step 4: After the bidirectional pulse temperature difference excitation is completed, collect the second characteristic parameter:

[0013] Step 5: construct a risk heat map based on the first characteristic parameter and the second characteristic parameter;

[0014] Step 6: Mark high-risk points based on the risk heat map, select and drill core samples from high-risk points for combined pull-out and shear tests to obtain the bond retention rate; determine whether the bond retention rate exceeds the limit and generate quality inspection and acceptance results.

[0015] Furthermore, the fiber Bragg FBG network is pre-buried in random areas before laying the tiles.

[0016] Furthermore, the first characteristic parameter includes:

[0017] The reference wavelength of each grid point in the fiber Bragg FBG network;

[0018] Obtain the reference resistance at each grid point in the grouting layer by electrical resistivity tomography;

[0019] The baseline background noise at each grid point is obtained by acoustic emission.

[0020] Furthermore, bidirectional pulse temperature difference excitation includes:

[0021] Step 31: Focus the light spot with a diameter of 40 mm and a power density of 6 kW / m 2 Irradiate with a carbon dioxide laser beam for 8 seconds, and stop when the temperature of any area on the tile surface rises to 80°C;

[0022] Step 32: 30 seconds after the end of the carbon dioxide laser beam irradiation, switch to -30°C liquid nitrogen mist cooling for 8 seconds;

[0023] Step 33: naturally return to room temperature for 16 seconds.

[0024] Furthermore, the second characteristic parameter includes:

[0025] Obtain the actual wavelength, actual resistance, and actual background noise at each grid point in the fiber Bragg FBG network.

[0026] Furthermore, based on the first characteristic parameter and the second characteristic parameter, a risk heat map is constructed, including:

[0027] The drift wavelength is obtained by subtracting the reference wavelength and the actual wavelength of the i-th grid point, and the strain value of the tile bonding surface is obtained by decoupling the drift wavelength.

[0028] The resistance drift value is obtained by subtracting the reference resistance and the actual resistance at the i-th grid point;

[0029] The actual background noise at the i-th grid point is converted into a time noise curve, and the reference background noise at the i-th grid point is used as a dividing line to extract the number of peaks of the time noise curve above the dividing line to obtain the crack activity value;

[0030] The strain value, resistance drift value and crack activity value of the tile bonding surface at each grid point are determined to form a risk heat map.

[0031] Furthermore, high-risk points are marked, including:

[0032] The tile bonding surface strain value, resistance drift value and crack activity value of each grid point are normalized and then multiplied to obtain the risk index of the corresponding grid point;

[0033] Sort the grid points from large to small based on the risk index to form a feature sequence;

[0034] A preset number of grid points in the feature sequence are extracted from front to back and marked as high-risk grid points.

[0035] Furthermore, core samples from high-risk grid points were selected and drilled for combined pull-out and shear tests to obtain the bond retention rate, including:

[0036] Connect the high-risk grid points sequentially into closed geometric figures, and extract the centroid of the geometric figures based on the centroid method;

[0037] The core sample was drilled from the center of mass and subjected to a pull-out shear test. The pull-out loading rate and shear loading rate of the pull-out shear test were 0.05 MPa·s -1 and 0.03 MPa·s -1 ;

[0038] Collect pull-out load and core sample cross-sectional area;

[0039] When the core sample breaks, it is considered to be damaged and the pull-out shear test is stopped;

[0040] The pull-out load and cross-sectional area of ​​the core sample at the sampling moment before the core sample failure are taken as the ultimate load and ultimate cross-sectional area respectively;

[0041] The pull-out load and core sample cross-sectional area at the initial sampling moment of the pull-out shear combined test are taken as the reference load and reference cross-sectional area;

[0042] The ratio of the ultimate load to the ultimate cross-sectional area is taken as the pull-out bond strength;

[0043] The ratio of the reference load and the reference cross-sectional area is taken as the reference bond strength;

[0044] The bond retention rate is the ratio of the baseline bond strength to the pull-out bond strength.

[0045] Furthermore, it is determined whether the bonding retention rate exceeds the limit and a quality inspection and acceptance result is generated, including:

[0046] Compare the bond retention to a preset bond retention threshold:

[0047] If the bonding retention rate is ≥ the preset bonding retention rate threshold, the quality inspection and acceptance passes; otherwise, the quality inspection and acceptance fails.

[0048] The beneficial effects of the present invention are as follows: before the exterior wall tiles are pasted, a fiber Bragg FBG network is pre-embedded in the bonding layer and intelligent conductive grouting is added, bidirectional pulse temperature difference excitation is used to drive the tile-base system to produce a thermal fatigue effect, and then a risk thermodynamic map is generated through multimodal data fusion guided by a large visual model. Finally, the centroid core is drilled and a combined tension-shear test is carried out to calculate the bonding retention rate. This can accurately identify and quantify the potential failure areas under thermal cycles in a minimally destructive manner, thereby realizing visualization and efficiency of the quality acceptance of exterior wall tiles. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0050] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. In addition, features described with respect to some examples may also be combined in other examples.

[0051] like Figure 1 As shown in FIG, the quality inspection and acceptance management method for building decoration construction based on the visual big model includes:

[0052] Step 1: Before laying tiles, do the following:

[0053] Step 11, pre-buried a fiber Bragg grid (FBG) in an M×N grid in the bonding layer, wherein grid points of the fiber Bragg grid are arranged at a fixed interval;

[0054] Step 12, adding 0.2% conductive carbon black into the grouting slurry to form a grouting layer;

[0055] Step 2, collecting the first characteristic parameters of the fiber Bragg FBG network;

[0056] Step 3, performing a preset number of bidirectional pulse temperature difference excitations on the fiber Bragg FBG network;

[0057] Step 4: After the bidirectional pulse temperature difference excitation is completed, collect the second characteristic parameter:

[0058] Step 5: construct a risk heat map based on the first characteristic parameter and the second characteristic parameter;

[0059] Step 6: Mark high-risk points based on the risk heat map, select and drill core samples from high-risk points for combined pull-out and shear tests to obtain the bond retention rate; determine whether the bond retention rate exceeds the limit and generate quality inspection and acceptance results.

[0060] In one embodiment of the present invention, the fiber Bragg grid (FBG) is pre-buried in random areas before laying the tiles.

[0061] Specifically, by pre-embedding the fiber optic Bragg mesh in random areas, the construction team can avoid the high-quality laying of the fiber optic Bragg mesh when laying tiles on the facade, which will affect the results of quality inspection and acceptance.

[0062] Preferably, the pre-buried area of ​​the fiber Bragg grid FBG network is at least 4 square meters, and the grid points are arranged at intervals of 5 cm.

[0063] In one embodiment of the present invention, the first characteristic parameter includes:

[0064] The reference wavelength of each grid point in the fiber Bragg FBG network;

[0065] Obtain the reference resistance at each grid point in the grouting layer by electrical resistivity tomography;

[0066] The baseline background noise at each grid point is obtained by acoustic emission.

[0067] Specifically, the grid points are extremely sensitive to temperature and strain, and the paving process, micro-movement of the base layer, or the ambient temperature itself can cause wavelength drift. By recording the reference wavelength λ0 of each grid point in its initial state.

[0068] The distribution of conductive carbon black in the grouting layer, humidity status and construction quality will affect the initial resistance. The baseline resistance ρ0 of each grid point in the initial state is recorded.

[0069] The acoustic emission sensor is extremely sensitive to environmental noise. The baseline background noise E0 of each grid point in the initial state is recorded.

[0070] In one embodiment of the present invention, bidirectional pulse temperature difference excitation includes:

[0071] Step 31: Focus the light spot with a diameter of 40 mm and a power density of 6 kW / m 2 Irradiate with a carbon dioxide laser beam for 8 seconds, and stop when the temperature of any area on the tile surface rises to 80°C;

[0072] Specifically, the 40mm light spot simulates the actual area exposed to direct sunlight; 80℃ achieves significant thermal expansion without causing thermal degradation of the material; 8s can quickly penetrate and heat the bonding surface of tiles with a thickness of several millimeters, forming a thermal gradient that penetrates the bonding layer and effectively induces interface strain.

[0073] Step 32: 30 seconds after the end of the carbon dioxide laser beam irradiation, switch to -30°C liquid nitrogen mist cooling for 8 seconds;

[0074] Specifically, a 30-second interval ensures that heat is fully transferred to the tile bonding surface and forms a stable high-temperature field before sudden cooling. A -30°C cold shock introduces severe thermal contraction, creating a transient temperature difference of up to 110°C. An 8-second cold shock causes local microcracks to initiate on the tile bonding surface, simulating a natural freeze-thaw cycle.

[0075] Step 33: naturally return to room temperature for 16 seconds.

[0076] Specifically, the temperature naturally returns to room temperature within 16 seconds to ensure a closed hot and cold cycle.

[0077] It should be noted that bidirectional pulsed temperature differential excitation completes an extreme heating and cooling cycle within 40 seconds, significantly shortening the natural aging time. By alternating forced heating, sudden cooling, and natural reheating, multiaxial strain and microcrack evolution are rapidly induced on the tile bonding surface, providing a controllable and efficient fatigue damage pre-embedded scenario for subsequent nondestructive detection or destructive testing, thereby accelerating the assessment and acceptance of exterior wall tile bonding quality.

[0078] In one embodiment of the present invention, the second characteristic parameter includes:

[0079] Obtain the actual wavelength, actual resistance, and actual background noise at each grid point in the fiber Bragg FBG network.

[0080] In one embodiment of the present invention, constructing a risk heat map based on the first characteristic parameter and the second characteristic parameter includes:

[0081] The drift wavelength is obtained by subtracting the reference wavelength and the actual wavelength of the i-th grid point, and the strain value of the tile bonding surface is obtained by decoupling the drift wavelength.

[0082] The resistance drift value is obtained by subtracting the reference resistance and the actual resistance at the i-th grid point;

[0083] The actual background noise at the i-th grid point is converted into a time noise curve, and the reference background noise at the i-th grid point is used as a dividing line to extract the number of peaks of the time noise curve above the dividing line to obtain the crack activity value;

[0084] The strain value, resistance drift value and crack activity value of the tile bonding surface at each grid point are determined to form a risk heat map.

[0085] Specifically, the actual wavelength of the ith grating point of the fiber Bragg FBG network, compared with the baseline wavelength of the ith grating point, can quantify the optical length change caused by thermal expansion or mechanical strain on the tile bonding surface.

[0086] The actual resistance of the i-th grid point, compared with the baseline, can reflect the change in conductivity caused by hollowing or water content in the grouting layer.

[0087] The actual background noise collected at the i-th grid point is used as a benchmark to extract the peaks in the noise curve that are higher than the baseline background noise to quantify the micro-acoustic emission events of crack initiation and slippage on the tile bonding surface.

[0088] It should be noted that the calculation of the strain value of the tile bonding surface is as follows:

[0089] Δλ i =λ i -λ 0,i

[0090] Among them, λ i represents the actual wavelength of the i-th grid point, λ 0，i represents the baseline wavelength of the i-th grid point, Δλ i represents the drift wavelength of the i-th grid point;

[0091] Δλ T，i =K T (T i -T 0，i )

[0092] Among them, K T Indicates the temperature sensitivity coefficient of the optical fiber, which is the calibration value corresponding to the grid point, T i Indicates the ambient temperature corresponding to the actual wavelength of the i-th grid point, T 0，i Indicates the ambient temperature corresponding to the baseline wavelength of the i-th grid point, Δλ T，i Indicates drift compensation;

[0093] Δλ ε,i =Δλ i -Δλ T,i

[0094] Where Δλ ε，i represents the strain component;

[0095]

[0096] Among them, K ε is the strain sensitivity coefficient of the fiber Bragg FBG network, which is equal to the reference wavelength Δλ 0，i Multiply by the fiber photoelastic coefficient (1-p e ), p e is the photoelastic constant of the fiber Bragg FBG network, ε i Indicates the strain value of the tile bonding surface.

[0097] In one embodiment of the present invention, marking high-risk grid points includes:

[0098] The tile bonding surface strain value, resistance drift value and crack activity value of each grid point are normalized and then multiplied to obtain the risk index of the corresponding grid point;

[0099] Sort the grid points from large to small based on the risk index to form a feature sequence;

[0100] A preset number of grid points in the feature sequence are extracted from front to back and marked as high-risk grid points.

[0101] Specifically, the tile bonding surface strain value, resistance drift value, and crack activity value of each grid point are normalized, including:

[0102] The maximum tile bonding surface strain value, the maximum resistance drift value, and the maximum crack activity value among all grid points are obtained, so as to normalize the tile bonding surface strain value, the resistance drift value, and the crack activity value based on the corresponding maximum values.

[0103] In one embodiment of the present invention, core samples of high-risk grid points are selected and drilled to conduct a pull-out shear test to obtain the bond retention rate, including:

[0104] Connect the high-risk grid points sequentially into closed geometric figures, and extract the centroid of the geometric figures based on the centroid method;

[0105] Specifically, by locating the centroid of the closed geometric body, cores are drilled at the risk centroid to ensure that the core sample can represent the high-risk area while reducing the damage to the appearance and structure of the finished wall caused by multiple drilling points.

[0106] The core sample was drilled from the center of mass and subjected to a pull-out shear test. The pull-out loading rate and shear loading rate of the pull-out shear test were 0.05 MPa·s -1 and 0.03 MPa·s -1 ;

[0107] Specifically, a cylindrical core specimen is mounted in a biaxially loaded testing machine (vertical pull-off and horizontal shear), with the upper and lower fixtures coaxial. The pull-off and shear loads are set and simultaneously applied at the pull-off and shear loading rates. This synchronized biaxial loading triggers the ultimate bearing capacity of the tile bonding surface under a true multiaxial stress state, preventing underestimation of bond performance from single pull-off or shear testing.

[0108] Collect pull-out load and core sample cross-sectional area;

[0109] When the core sample breaks, it is considered to be damaged and the pull-out shear test is stopped;

[0110] The pull-out load and cross-sectional area of ​​the core sample at the sampling moment before the core sample failure are taken as the ultimate load and ultimate cross-sectional area respectively;

[0111] The pull-out load and core sample cross-sectional area at the initial sampling moment of the pull-out shear combined test are taken as the reference load and reference cross-sectional area;

[0112] The ratio of the ultimate load to the ultimate cross-sectional area is taken as the pull-out bond strength;

[0113] The ratio of the reference load and the reference cross-sectional area is taken as the reference bond strength;

[0114] The bond retention rate is the ratio of the baseline bond strength to the pull-out bond strength.

[0115] In one embodiment of the present invention, determining whether the bonding retention rate exceeds a limit and generating a quality inspection and acceptance result include:

[0116] Compare the bond retention to a preset bond retention threshold:

[0117] If the bonding retention rate is ≥ the preset bonding retention rate threshold, the quality inspection and acceptance passes; otherwise, the quality inspection and acceptance fails.

[0118] Specifically, the preset adhesion retention rate threshold is usually in the range of 80% to 90% to ensure that the exterior wall tiles will not fall off during long-term service.

[0119] The above describes the embodiments of this embodiment, but this embodiment is not limited to the above specific implementation methods. The above specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make many forms based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A quality inspection and acceptance management method for building decoration construction based on a visual large model, characterized in that: include: Step 1: Before laying tiles, do the following: Step 11, pre-buried a fiber Bragg grid (FBG) in an M×N grid in the bonding layer, wherein grid points of the fiber Bragg grid are arranged at a fixed interval; Step 12, adding 0.2% conductive carbon black into the grouting slurry to form a grouting layer; Step 2, collecting the first characteristic parameters of the fiber Bragg FBG network; Step 3, performing a preset number of bidirectional pulse temperature difference excitations on the fiber Bragg FBG network; Step 4: After the bidirectional pulse temperature difference excitation is completed, collect the second characteristic parameter: Step 5: construct a risk heat map based on the first characteristic parameter and the second characteristic parameter; Step 6: Mark high-risk grid points based on the risk heat map, select and drill core samples from high-risk grid points for combined pull-out and shear tests to obtain the bond retention rate; Determine whether the bonding retention rate exceeds the limit and generate quality inspection and acceptance results.

2. The quality inspection and acceptance management method for building decoration construction based on visual large model according to claim 1 is characterized in that: Fiber Bragg FBG networks are pre-buried in random areas before laying tiles.

3. The quality inspection and acceptance management method for building decoration construction based on visual large model according to claim 1 is characterized in that: The first characteristic parameters include: The reference wavelength of each grid point in the fiber Bragg FBG network; Obtain the reference resistance at each grid point in the grouting layer by electrical resistivity tomography; The baseline background noise at each grid point is obtained by acoustic emission.

4. The quality inspection and acceptance management method for building decoration construction based on visual large model according to claim 3 is characterized in that: Bidirectional pulse temperature difference excitation, including: Step 31: Focus the light spot with a diameter of 40 mm and a power density of 6 kW / m 2 The carbon dioxide laser beam is irradiated for 8 seconds. When the temperature of any area on the tile surface rises to 80°C ° stop; Step 32: 30 seconds after the end of the CO2 laser beam irradiation, switch to -30C ° Liquid nitrogen mist cold shock for 8s; Step 33: naturally return to room temperature for 16 seconds.

5. The quality inspection and acceptance management method for building decoration construction based on visual large model according to claim 4 is characterized in that: The second characteristic parameters include: Obtain the actual wavelength, actual resistance, and actual background noise at each grid point in the fiber Bragg FBG network.

6. The quality inspection and acceptance management method for building decoration construction based on visual large model according to claim 5 is characterized in that: Based on the first characteristic parameter and the second characteristic parameter, a risk heat map is constructed, including: The drift wavelength is obtained by subtracting the reference wavelength and the actual wavelength of the i-th grid point, and the strain value of the tile bonding surface is obtained by decoupling the drift wavelength. The resistance drift value is obtained by subtracting the reference resistance and the actual resistance at the i-th grid point; The actual background noise at the i-th grid point is converted into a time noise curve, and the reference background noise at the i-th grid point is used as a dividing line to extract the number of peaks of the time noise curve above the dividing line to obtain the crack activity value; The strain value, resistance drift value and crack activity value of the tile bonding surface at each grid point are determined to form a risk heat map.

7. The quality inspection and acceptance management method for building decoration construction based on visual large model according to claim 6 is characterized in that: Mark high-risk points, including: The tile bonding surface strain value, resistance drift value and crack activity value of each grid point are normalized and then multiplied to obtain the risk index of the corresponding grid point; Sort the grid points from large to small based on the risk index to form a feature sequence; A preset number of grid points in the feature sequence are extracted from front to back and marked as high-risk grid points.

8. The quality inspection and acceptance management method for building decoration construction based on visual large model according to claim 7 is characterized in that: Select and drill core samples from high-risk grid points for combined pull-out and shear tests to obtain bond retention, including: Connect the high-risk grid points sequentially into closed geometric figures, and extract the centroid of the geometric figures based on the centroid method; The core sample was drilled from the center of mass and subjected to a pull-out shear test. The pull-out loading rate and shear loading rate of the pull-out shear test were 0.05 MPa·s -1 and 0.03 MPa·s -1 ; Collect pull-out load and core sample cross-sectional area; When the core sample breaks, it is considered to be damaged and the pull-out shear test is stopped; The pull-out load and cross-sectional area of ​​the core sample at the sampling moment before the core sample failure are taken as the ultimate load and ultimate cross-sectional area respectively; The pull-out load and core sample cross-sectional area at the initial sampling moment of the pull-out shear combined test are taken as the reference load and reference cross-sectional area; The ratio of the ultimate load to the ultimate cross-sectional area is taken as the pull-out bond strength; The ratio of the reference load and the reference cross-sectional area is taken as the reference bond strength; The bond retention rate is the ratio of the baseline bond strength to the pull-out bond strength.

9. The quality inspection and acceptance management method for building decoration construction based on visual large model according to claim 8 is characterized in that: Determine whether the bonding retention rate exceeds the limit and generate quality inspection and acceptance results, including: Compare the bond retention to a preset bond retention threshold: If the bonding retention rate is ≥ the preset bonding retention rate threshold, the quality inspection and acceptance passes; otherwise, the quality inspection and acceptance fails.

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