CN116026785A - Carbonization depth detection method based on near infrared hyperspectral imaging - Google Patents
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Abstract
The invention provides a carbonization depth detection method based on near infrared hyperspectral imaging, which comprises the steps of preprocessing near infrared hyperspectral imaging data through concrete carbonization intrinsic and characterization detection to obtain carbonization depth data of a plurality of silicate concrete standard test blocks with set intensity levels under different carbonization periods, and establishing a silicate concrete carbonization-near infrared spectrum database on the surface of the silicate concrete standard test blocks corresponding to the preprocessed carbonization depth data; and then a mapping model between carbonization characterization and carbonization depth intrinsic of the surface of the silicate concrete standard test block is established, and the carbonization depth of the component can be calculated by utilizing the mapping model and near infrared hyperspectral imaging data acquired on site. The invention can quickly and comprehensively grasp the carbonization condition of the component and even the concrete of the structure through simple near infrared hyperspectral imaging shooting without contacting the component or carrying out damage detection on the component.
Description
Technical Field
The invention relates to the technical field of concrete carbonization depth detection, in particular to a carbonization depth detection method based on near infrared hyperspectral imaging.
Background
At present, the method for detecting the carbonization depth of the member concrete on site utilizes the principle that the alkalinity of the concrete is lost due to carbonization, the phenolphthalein or a litmus reagent is coated on the member, and the depth of a color-changing region of the concrete is measured and taken as the carbonization depth of the concrete. The method generally needs to drill holes or core samples on the surface of the member concrete, thus the method belongs to contact type damage detection, and the carbonization depth of the concrete of a single measuring point can be obtained only in each test, so that the carbonization condition of the member and even the structure can not be rapidly and comprehensively mastered.
Disclosure of Invention
The invention aims to provide a carbonization depth detection method based on near infrared hyperspectral imaging, which aims to solve at least one technical problem in the prior art.
In order to solve the technical problems, the invention provides a carbonization depth detection method based on near infrared hyperspectral imaging, which comprises the following steps:
s10, concrete carbonization intrinsic and characterization detection
Selecting a plurality of silicate concrete standard test blocks with set intensity levels for carbonization depth test and near infrared hyperspectral imaging; further obtaining carbonization depth data of the silicate concrete standard test block with set strength grade and near infrared hyperspectral imaging data of the surface of the silicate concrete standard test block corresponding to the carbonization depth data;
s20, preprocessing near infrared hyperspectral imaging data
Performing atmospheric correction, mixed noise correction and data normalization processing on the acquired near infrared hyperspectral imaging data;
s30, placing the silicate concrete standard test block into a rapid aging test box to accelerate the carbonization speed of the silicate concrete standard test block;
taking out the sample after setting the carbonization period, and repeating the steps S10-S20 to obtain carbonization depth data of the silicate concrete standard test block and near infrared hyperspectral imaging data of the surface of the silicate concrete standard test block corresponding to the preprocessed carbonization depth data;
s40, placing the silicate concrete standard test block into the rapid aging test box again to accelerate the carbonization speed of the silicate concrete standard test block; taking out the sample after setting the carbonization period, and repeating the steps S10-S20 to obtain carbonization depth data of the silicate concrete standard test block and near infrared hyperspectral imaging data of the surface of the silicate concrete standard test block corresponding to the preprocessed carbonization depth data;
s50, repeating the step S40 for a plurality of times, and further obtaining carbonization depth data of silicate concrete standard test blocks under a plurality of different carbonization periods, and near infrared hyperspectral imaging data of the surfaces of the silicate concrete standard test blocks corresponding to the preprocessed carbonization depth data;
s60, replacing silicate concrete standard test blocks with different set strength grades, and repeating the steps S10-S50 to obtain carbonization depth data of the silicate concrete standard test blocks with different set strength grades under a plurality of different carbonization periods, and near infrared hyperspectral imaging data of the surface of the silicate concrete standard test block corresponding to the preprocessed carbonization depth data;
s70, repeating the step S60, so as to obtain carbonization depth data of a plurality of silicate concrete standard test blocks with set strength grades under different carbonization periods, and the surface near infrared hyperspectral imaging data of the silicate concrete standard test blocks corresponding to the preprocessed carbonization depth data, so as to establish a silicate concrete carbonization-near infrared spectrum database;
s80, establishing a mapping model between carbonization characterization and carbonization depth eigenvalue of the surface of a silicate concrete standard test block, and further pushing out carbonization depth of the silicate concrete member through carbonization characteristic ion spectrum peak value reflected by near infrared hyperspectral imaging data of the surface of the silicate concrete member;
adopting a convolutional neural network based on an attention mechanism, and testing data according to 3: the 7 proportion is divided into a learning data set and a training data set for deep learning, and a cross validation test is carried out to ensure that the model precision is not lower than 95%.
S90, when the concrete structure engineering is subjected to field test, the near infrared hyperspectral imaging shooting is directly carried out on the concrete surface of the component, the spectrum data of the concrete surface is obtained, the spectrum data is brought into the mapping model, and the carbonization depth of the component is calculated.
The detection method does not need to contact with a component or damage the component, and can quickly and comprehensively grasp the carbonization condition of the component and even the concrete of the structure through simple near infrared hyperspectral imaging shooting. Through experimental comparison, the method is compared with the traditional carbonization depth test result, the error in the test of the method is not more than 0.2mm, and the precision is higher than that of the traditional detection method.
Further, the set intensity levels include at least four intensity levels of C25, C35, C45, C55; and at least four sub-libraries with intensity levels of C25, C35, C45 and C55 are arranged in the database.
Further, the set carbonization period is 5-10 days.
Preferably, the set carbonization period is 7 days. Thus, in step S50, by repeating step S40 a plurality of times, carbonization depth data and corresponding near infrared hyperspectral imaging data for a plurality of carbonization cycles of 0, 7, 14, 21, … … are obtained therefrom.
Further, in step S10, engineering index data and chemical component index data are detected for the silicate concrete standard test block; the database comprises engineering index data and chemical component index data, and further the mapping relation among the engineering index data, the chemical component index data, the carbonization depth and the near infrared hyperspectral imaging data is obtained.
Further, the engineering index data includes: age, water-cement ratio, water content, and type and mass ratio of concrete admixture.
Further, the chemical composition index includes: surface permeability and carbonate radical of different age concrete
) Hydroxide ion (OH) - ) Is included in the image obtained by scanning the image with an electron microscope.
And the near infrared spectrum data comprise near infrared spectrum imaging data of concrete surfaces in different ages and corresponding concrete carbonization depths.
Further, in step S10, a phenolphthalein reagent test is performed on the first surface of the silicate concrete standard test block, so as to obtain concrete carbonization depth data.
Further, in step S10, powder is collected on the second surface of the silicate concrete standard test block, and chemical component detection is performed.
Further, in step S10, near infrared hyperspectral imaging is performed on the third surface of the silicate concrete standard test block, so as to obtain near infrared hyperspectral imaging data.
Further, in step S50, the total test period is set to 10 to 50 of the set carbonization periods, that is, step S40 is repeated 9 to 49 times. Preferably, the total test period is set to 20 of said set carbonization periods.
Preferably, the method further comprises the step S01 of detecting the microstructure such as the initial concrete compressive strength, the initial permeability, the initial porosity and the like of the silicate concrete standard test block.
Namely, part of silicate concrete standard test blocks are used for initial data detection in the step S01, and the other part of silicate concrete standard test blocks are used for carbonization depth data detection and near infrared spectrum imaging data detection in the steps S10-80.
Preferably, the silicate concrete standard test block is an cube; the side length of the cube is 100-300mm. Preferably, the sides of the cube are 150mm long.
By adopting the technical scheme, the invention has the following beneficial effects:
according to the carbonization depth detection method based on near infrared hyperspectral imaging, which is provided by the invention, the carbonization condition of the component and even the structure can be rapidly and comprehensively mastered through simple near infrared hyperspectral imaging shooting without contacting the component or detecting the damage of the component. Through experimental comparison, the method is compared with the traditional carbonization depth test result, the error in the test of the method is not more than 0.2mm, and the precision is higher than that of the traditional detection method.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a carbonization depth detection method based on near infrared hyperspectral imaging according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The invention is further illustrated with reference to specific embodiments.
Example 1
As shown in fig. 1, the carbonization depth detection method based on near infrared hyperspectral imaging provided in this embodiment includes the following steps:
s10, concrete carbonization intrinsic and characterization detection
Selecting a plurality of silicate concrete standard test blocks with set intensity levels for carbonization depth test and near infrared hyperspectral imaging; further obtaining carbonization depth data of the silicate concrete standard test block with set strength grade and near infrared hyperspectral imaging data of the surface of the silicate concrete standard test block corresponding to the carbonization depth data;
s20, preprocessing near infrared hyperspectral imaging data
Performing atmospheric correction, mixed noise correction and data normalization processing on the acquired near infrared hyperspectral imaging data;
s30, placing the silicate concrete standard test block into a rapid aging test box to accelerate the carbonization speed of the silicate concrete standard test block;
taking out the sample after setting the carbonization period, and repeating the steps S10-S20 to obtain carbonization depth data of the silicate concrete standard test block and near infrared hyperspectral imaging data of the surface of the silicate concrete standard test block corresponding to the preprocessed carbonization depth data;
s40, placing the silicate concrete standard test block into the rapid aging test box again to accelerate the carbonization speed of the silicate concrete standard test block; taking out the sample after setting the carbonization period, and repeating the steps S10-S20 to obtain carbonization depth data of the silicate concrete standard test block and near infrared hyperspectral imaging data of the surface of the silicate concrete standard test block corresponding to the preprocessed carbonization depth data;
s50, repeating the step S40 for a plurality of times, and further obtaining carbonization depth data of silicate concrete standard test blocks under a plurality of different carbonization periods, and near infrared hyperspectral imaging data of the surfaces of the silicate concrete standard test blocks corresponding to the preprocessed carbonization depth data;
s60, replacing silicate concrete standard test blocks with different set strength grades, and repeating the steps S10-S50 to obtain carbonization depth data of the silicate concrete standard test blocks with different set strength grades under a plurality of different carbonization periods, and near infrared hyperspectral imaging data of the surface of the silicate concrete standard test block corresponding to the preprocessed carbonization depth data;
s70, repeating the step S60, so as to obtain carbonization depth data of a plurality of silicate concrete standard test blocks with set strength grades under different carbonization periods, and the surface near infrared hyperspectral imaging data of the silicate concrete standard test blocks corresponding to the preprocessed carbonization depth data, so as to establish a silicate concrete carbonization-near infrared spectrum database;
s80, establishing a mapping model between carbonization characterization and carbonization depth eigenvalue of the surface of a silicate concrete standard test block, and further pushing out carbonization depth of the silicate concrete member through carbonization characteristic ion spectrum peak value reflected by near infrared hyperspectral imaging data of the surface of the silicate concrete member;
adopting a convolutional neural network based on an attention mechanism, and testing data according to 3: the 7 proportion is divided into a learning data set and a training data set for deep learning, and a cross validation test is carried out to ensure that the model precision is not lower than 95%.
S90, when the concrete structure engineering is subjected to field test, the near infrared hyperspectral imaging shooting is directly carried out on the concrete surface of the component, the spectrum data of the concrete surface is obtained, the spectrum data is brought into the mapping model, and the carbonization depth of the component is calculated.
The detection method does not need to contact with a component or damage the component, and can quickly and comprehensively grasp the carbonization condition of the component and even the concrete of the structure through simple near infrared hyperspectral imaging shooting. Through experimental comparison, the method is compared with the traditional carbonization depth test result, the error in the test of the method is not more than 0.2mm, and the precision is higher than that of the traditional detection method.
Wherein the set intensity level comprises at least four intensity levels of C25, C35, C45 and C55; and at least four sub-libraries with intensity levels of C25, C35, C45 and C55 are arranged in the database.
Further, the set carbonization period is 5-10 days. Preferably, the set carbonization period is 7 days. Thus, in step S50, by repeating step S40 a plurality of times, carbonization depth data and corresponding near infrared hyperspectral imaging data for a plurality of carbonization cycles of 0, 7, 14, 21, … … are obtained therefrom.
In the step S10, engineering index data and chemical component index data detection are carried out on the silicate concrete standard test block; the database comprises engineering index data and chemical component index data, so as to obtainEngineering index data, chemical composition index data, carbonization depth and near infrared hyperspectral imaging data. The engineering index data includes: age, water-cement ratio, water content, and type and mass ratio of concrete admixture. The chemical composition index comprises: surface permeability and carbonate radical of different age concrete
) Hydroxide ion (OH) - ) Is included in the image obtained by scanning the image with an electron microscope.
And the near infrared spectrum data comprise near infrared spectrum imaging data of concrete surfaces in different ages and corresponding concrete carbonization depths.
On the basis of the technical scheme, in step S10, phenolphthalein reagent testing is carried out on the first surface of the silicate concrete standard test block, so that concrete carbonization depth data are obtained. And collecting powder on the second surface of the silicate concrete standard test block, and detecting chemical components. And carrying out near infrared hyperspectral imaging on a third surface of the silicate concrete standard test block, thereby obtaining near infrared hyperspectral imaging data.
Further, in step S50, the total test period is set to 10 to 50 of the set carbonization periods, that is, step S40 is repeated 9 to 49 times. Preferably, the total test period is set to 20 of said set carbonization periods.
Preferably, the method further comprises the step S01 of detecting the microstructure such as the initial concrete compressive strength, the initial permeability, the initial porosity and the like of the silicate concrete standard test block.
Namely, part of silicate concrete standard test blocks are used for initial data detection in the step S01, and the other part of silicate concrete standard test blocks are used for carbonization depth data detection and near infrared spectrum imaging data detection in the steps S10-80.
Preferably, the silicate concrete standard test block is an cube; the side length of the cube is 100-300mm. Preferably, the sides of the cube are 150mm long.
According to the carbonization depth detection method based on near infrared hyperspectral imaging, which is provided by the invention, the carbonization condition of the component and even the structure can be rapidly and comprehensively mastered through simple near infrared hyperspectral imaging shooting without contacting the component or detecting the damage of the component. Through experimental comparison, the method is compared with the traditional carbonization depth test result, the error in the test of the method is not more than 0.2mm, and the precision is higher than that of the traditional detection method.
Example 2
This embodiment is substantially the same as embodiment 1 except that:
in step S10, after the cement of the silicate concrete standard test block is fully hydrated, near infrared hyperspectral imaging is carried out on the silicate concrete standard test block by adopting near infrared spectrums of various wave bands, and carbonate ions and hydroxide ions (OH - ) And moisture (H) 2 O) spectral data of concentration.
More preferably, in step S90, after the cement of the member is sufficiently hydrated, near infrared hyperspectral imaging is performed on the concrete surface of the member using near infrared spectrums of various bands, and the information about carbonate and hydroxide ions (OH) in the member is separately identified - ) And moisture (H) 2 O) spectral data of concentration; by interaction with carbonate and hydroxide ions (OH) - ) And moisture (H) 2 And O) comparing the spectral data of the concentrations, and calculating the carbonization depth of the component.
The macro concrete carbonization is that the neutralization reaction between carbon dioxide in air and alkaline matters in concrete is carried out, and as the neutralization reaction is continuously carried out, the concentration of carbonate ions on the surface of the concrete is increased, the concentration of hydroxide ions is reduced, and the carbonization depth of the concrete is gradually increased. CO in an exposed environment 2 Ca (OH) in the hardened concrete 2 The relation between the molar concentration of (2) and the carbonization depth of the concrete is shown in the formula (1):
In the method, in the process of the invention,
is the carbonization depth of the concrete;kis the carbonization coefficient of the concrete;tis exposure time; />
Is CO in concrete 2 Is a diffusion coefficient equivalent to that of the metal oxide; />
To expose CO in the environment 2 Molar concentration of (2); />
、/>
、/>
And->
Respectively represent Ca (OH) in hardened concrete 2 、CSH、C 3 S and C 2 Molar concentration of S.
When the cement hydration is sufficient, the cement hydration agent in the formula (1)
And->
Can be ignored, thereby utilizing the carbonate radical which is a sensitive characteristic component of the carbonization reaction of the concrete>
) Hydroxide ion (OH) - ) And moisture (H) 2 And O) near infrared hyperspectral imaging is carried out on the concrete surface of the component, interference factors in the detection process are further reduced, and the detection precision is further improved.
More preferably, in step S40, near infrared hyperspectral imaging is performed on the test block using near infrared spectrums having wavelengths of 1875nm, 1454nm, 1380nm, 1135nm and 942nm, respectively, and the difference between the near infrared hyperspectral images is recognized with respect to moisture (H 2 O) spectral data of concentration; and, in step S90, near infrared light having wavelengths of 1875nm, 1454nm, 1380nm, 1135nm and 942nm, respectively, is usedNear infrared hyperspectral imaging of the spectrum on the concrete surface of the passing component, and identification of the moisture (H) 2 O) spectral data of concentration. That is, by comparing the peaks of the near infrared spectra having wavelengths of 1875nm, 1454nm, 1380nm, 1135nm and 942nm, it is possible to infer moisture (H 2 O) concentration. Experiments show that the spectrum of the above wavelength is specific to moisture (H 2 O) is more sensitive.
In the same way, in step S40, near infrared hyperspectral imaging is performed on the test block by using near infrared spectrums with wavelengths of 1400nm, 2200nm and 2300nm, respectively, and the hydroxyl ions (OH - ) Spectral data of concentration; and, in step S90, near infrared hyperspectral imaging is performed on the concrete surface of the member using near infrared spectrums having wavelengths of 1400nm, 2200nm, 2300nm, respectively, and the information about hydroxide ions (OH - ) Spectral data of concentration.
In step S40, near infrared hyperspectral imaging is carried out on the test block by adopting near infrared spectrums with wavelengths of 2550nm, 2350nm, 2160nm, 2000nm and 1900nm, and spectral data about carbonate concentration in the test block are identified; in step S90, near infrared hyperspectral imaging is carried out on the concrete surface of the component by adopting near infrared spectrums with wavelengths of 2550nm, 2350nm, 2160nm, 2000nm and 1900nm, and spectral data about carbonate concentration in the component are identified.
Through experiments and comparison, the more sensitive wave band is adopted selectively to react carbonate and hydroxyl ions (OH - ) And moisture (H) 2 And O) concentration spectrum data are detected, compared with the prior art and the embodiment 1, the detection result is more accurate, and the error in the test is not more than 0.1mm.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. The carbonization depth detection method based on near infrared hyperspectral imaging is characterized by comprising the following steps of:
s10, concrete carbonization intrinsic and characterization detection
Selecting a plurality of silicate concrete standard test blocks with set intensity levels for carbonization depth test and near infrared hyperspectral imaging; further obtaining carbonization depth data of the silicate concrete standard test block with set strength grade and near infrared hyperspectral imaging data of the surface of the silicate concrete standard test block corresponding to the carbonization depth data;
s20, preprocessing near infrared hyperspectral imaging data
Performing atmospheric correction, mixed noise correction and data normalization processing on the acquired near infrared hyperspectral imaging data;
s30, placing the silicate concrete standard test block into a rapid aging test box to accelerate the carbonization speed of the silicate concrete standard test block;
taking out the sample after setting the carbonization period, and repeating the steps S10-S20 to obtain carbonization depth data of the silicate concrete standard test block and near infrared hyperspectral imaging data of the surface of the silicate concrete standard test block corresponding to the preprocessed carbonization depth data;
s40, placing the silicate concrete standard test block into the rapid aging test box again to accelerate the carbonization speed of the silicate concrete standard test block; taking out the sample after setting the carbonization period, and repeating the steps S10-S20 to obtain carbonization depth data of the silicate concrete standard test block and near infrared hyperspectral imaging data of the surface of the silicate concrete standard test block corresponding to the preprocessed carbonization depth data;
s50, repeating the step S40 for a plurality of times, and further obtaining carbonization depth data of silicate concrete standard test blocks under a plurality of different carbonization periods, and near infrared hyperspectral imaging data of the surfaces of the silicate concrete standard test blocks corresponding to the preprocessed carbonization depth data;
s60, replacing silicate concrete standard test blocks with different set strength grades, and repeating the steps S10-S50 to obtain carbonization depth data of the silicate concrete standard test blocks with different set strength grades under a plurality of different carbonization periods, and near infrared hyperspectral imaging data of the surface of the silicate concrete standard test block corresponding to the preprocessed carbonization depth data;
s70, repeating the step S60, so as to obtain carbonization depth data of a plurality of silicate concrete standard test blocks with set strength grades under different carbonization periods, and the surface near infrared hyperspectral imaging data of the silicate concrete standard test blocks corresponding to the preprocessed carbonization depth data, so as to establish a silicate concrete carbonization-near infrared spectrum database;
s80, establishing a mapping model between carbonization characterization and carbonization depth eigenvalue of the surface of a silicate concrete standard test block, and further pushing out carbonization depth of the silicate concrete member through carbonization characteristic ion spectrum peak value reflected by near infrared hyperspectral imaging data of the surface of the silicate concrete member;
adopting a convolutional neural network based on an attention mechanism, and testing data according to 3: the 7 proportion is divided into a learning data set and a training data set for deep learning, and a cross verification test is carried out to ensure that the model precision is not lower than 95%;
s90, when the concrete structure engineering is subjected to field test, the near infrared hyperspectral imaging shooting is directly carried out on the concrete surface of the component, the spectrum data of the concrete surface is obtained, the spectrum data is brought into the mapping model, and the carbonization depth of the component is calculated.
2. The carbonization depth detection method according to claim 1, wherein the set intensity level comprises at least four intensity levels of C25, C35, C45, C55; and at least four sub-libraries with intensity levels of C25, C35, C45 and C55 are arranged in the database.
3. The carbonization depth detection method according to claim 1, wherein the set carbonization period is 5 to 10 days.
4. The carbonization depth detection method according to claim 1, further comprising detecting engineering index data and chemical composition index data of a silicate concrete standard test block in step S10; the database comprises engineering index data and chemical component index data, and further the mapping relation among the engineering index data, the chemical component index data, the carbonization depth and the near infrared hyperspectral imaging data is obtained.
5. The carbonization depth detection method according to claim 4, wherein the engineering index data comprises: age, water-cement ratio, water content, and type and mass ratio of concrete admixture.
6. The carbonization depth detection method according to claim 4, wherein the chemical composition index comprises: and (3) scanning images by using electron microscope, wherein the surface permeability, carbonate and hydroxide ion contents of the concrete at different ages are different.
7. The method according to claim 1, wherein in step S10, phenolphthalein reagent testing is performed on the first surface of the standard silicate concrete block, so as to obtain concrete carbonization depth data.
8. The method according to claim 1, wherein in step S10, powder is collected on the second surface of the standard silicate concrete block to perform chemical component detection.
9. The method according to claim 1, wherein in step S10, near infrared hyperspectral imaging is performed on a third surface of the silicate concrete standard block, so as to obtain near infrared hyperspectral imaging data.
10. The method for detecting the carbonization depth according to claim 1, further comprising step S01 of detecting the initial concrete compressive strength, the initial permeability and the initial porosity of the silicate concrete standard test block.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117169139A (en) * | 2023-11-02 | 2023-12-05 | 北京科技大学 | Glass curtain wall structural adhesive mechanical property identification method based on reflection hyperspectrum |
| CN117760951A (en) * | 2024-02-21 | 2024-03-26 | 中冶建筑研究总院（深圳）有限公司 | method, system and device for evaluating alkali-aggregate reaction durability of concrete structure |
| CN117760951B (en) * | 2024-02-21 | 2024-05-31 | 中冶建筑研究总院（深圳）有限公司 | Method, system and device for evaluating alkali-aggregate reaction durability of concrete structure |
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2023
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117169139A (en) * | 2023-11-02 | 2023-12-05 | 北京科技大学 | Glass curtain wall structural adhesive mechanical property identification method based on reflection hyperspectrum |
| CN117169139B (en) * | 2023-11-02 | 2024-01-26 | 北京科技大学 | Glass curtain wall structural adhesive mechanical property identification method based on reflection hyperspectrum |
| CN117760951A (en) * | 2024-02-21 | 2024-03-26 | 中冶建筑研究总院（深圳）有限公司 | method, system and device for evaluating alkali-aggregate reaction durability of concrete structure |
| CN117760951B (en) * | 2024-02-21 | 2024-05-31 | 中冶建筑研究总院（深圳）有限公司 | Method, system and device for evaluating alkali-aggregate reaction durability of concrete structure |
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