Rapid evaluation method for water penetration depth and water content distribution of cement-based material based on hyperspectral imaging technology

Abstract

The invention discloses a rapid evaluation method for the water penetration depth and the water content distribution of a cement-based material based on a hyperspectral imaging technology, and aims to solve the problems of low evaluation speed and low efficiency of the water penetration depth and the water content distribution of the existing cement-based material. The evaluation method comprises the following steps: 1, carrying out a water absorption test experiment on the cement-based material; 2, shooting through an infrared hyperspectral camera to obtain a hyperspectral image; 3, correcting the hyperspectral image; 4, acquiring a reflectivity gradient graph of the cement-based material sample along the height direction through the corrected image, and drawing a reflectivity time-varying characteristic graph along the height direction of the sample; 5, generating and mapping a corrected hyperspectral image of the sample by utilizing addition average analysis; 6, quantifying the moisture content and the spectral characteristics, and calibrating an equation; and 7, visually outputting the moisture penetration process and the moisture content. The near-infrared hyperspectral imaging technology is utilized to scan the cement-based material, the moisture penetration depth is evaluated, and the visualization effect of moisture distribution is enhanced based on the addition average method.

Description

Technical Field

[0001] The invention belongs to the technical field of cement-based material permeability testing, and in particular relates to a method for evaluating the water penetration depth and water content distribution of cement-based materials based on hyperspectral imaging technology. Background Art

[0002] The water penetration behavior of cement-based materials is closely related to the service durability of structures such as buildings, water conservancy, transportation and marine engineering. On the one hand, water infiltration will produce direct physical and chemical effects on cement-based materials, such as soft water dissolution, shrinkage and swelling, freeze-thaw damage, etc., causing the service performance of engineering materials and components to deteriorate; on the other hand, water is the main carrier for corrosive media to enter cement-based materials, such as Cl - 、SO4 2- Salt ions further accelerate the degradation of its performance and significantly reduce the service life and safety of engineering structures. Therefore, to accurately understand and evaluate the service status and durability of cement-based materials, it is necessary to first quantify the water penetration behavior of cement-based materials, and quickly and effectively evaluate the distribution and content of water penetration in cement-based materials is the most important prerequisite.

[0003] At present, the methods for evaluating the moisture penetration distribution and content of cement-based materials mainly adopt mass weighing method, nuclear magnetic resonance method, neutron imaging method and X-ray computed tomography method. However, the above methods still face huge challenges in terms of observation efficiency, test cost and application scenarios. The mass weighing method (such as invention patent ZL201310441396.9) can only evaluate the moisture penetration from a macroscopic perspective by the mass change before and after the moisture penetration. It cannot obtain the moisture distribution on the microscopic scale and the test efficiency is not high. The accuracy of the result is greatly affected by the experimental personnel's operating ability; the nuclear magnetic resonance method (such as invention patents ZL201610625326.2 and ZL202410195881.0) uses the nuclear magnetic resonance phenomenon of hydrogen atoms in water to measure moisture information, but this method is easily affected by paramagnetic ions, has high requirements on the internal composition of the cement-based material sample being tested, and has limited applicability; the neutron imaging method (such as invention patent ZL202111409416.5) realizes moisture visualization by measuring the signal attenuation of the neutron beam. Although The distribution state of moisture can be accurately captured, but this method still cannot effectively distinguish between the bound water of hydrated calcium silicate in cement-based materials and the free water infiltrated from the external environment. In addition, the experimental process is complicated and costly, and its promotion and application are limited. X-ray computed tomography (such as invention patents ZL201510617455.2, ZL201810244594.9 and CN202411190762.2) uses the grayscale value changes of X-ray scanning images to determine the moisture transmission behavior, thereby realizing quantitative analysis of the moisture content and distribution in cement-based materials. This method has acceptable efficiency and accuracy, but is limited by the bulky X-ray generator. It is only suitable for laboratory test scenarios and cannot perform in-situ observations of outdoor physical engineering structures, so its application scenarios are limited. Summary of the invention

[0004] The purpose of the present invention is to solve the problems that the existing evaluation methods of water penetration depth and water content distribution of cement-based materials are slow, inefficient, expensive, and the application scenarios are limited to laboratories. A method for quickly evaluating the water penetration depth and water content distribution of cement-based materials based on a near-infrared hyperspectral camera is proposed. Only the raw data (image) acquisition process can be shortened from the current minute level to the millisecond level, and the computer configuration and computing power cost required for subsequent data and image processing are also greatly reduced, fully satisfying the rapid and efficient evaluation of the water penetration process of cement-based materials. At the same time, the near-infrared hyperspectral camera is small in size and light in weight. It can be combined with drone technology to realize on-site in-situ evaluation of outdoor structures, greatly expanding the application scenarios, and providing an efficient and practical research method for quantitative evaluation of water penetration behavior of cement-based materials and durability of engineering structures.

[0005] The method for rapidly evaluating the moisture penetration depth and moisture content distribution of cement-based materials based on hyperspectral imaging technology of the present invention is implemented by the following steps:

[0006] Step 1: Water absorption test:

[0007] The bottom of the cement-based material sample is immersed in water to conduct a water absorption test experiment;

[0008] Step 2: Near-infrared hyperspectral camera scanning:

[0009] An infrared hyperspectral camera is installed above the cement-based material sample, and during the water absorption test experiment, the infrared hyperspectral camera is used to obtain hyperspectral images of the sample at different water absorption times;

[0010] Step 3: Hyperspectral image correction:

[0011] Performing dark correction and white correction processing on the sample hyperspectral image obtained in step 2 to obtain a corrected sample hyperspectral image;

[0012] Step 4: Quick assessment of reflectance spectral gradient and water penetration depth:

[0013] The reflectivity gradient map of the cement-based material sample along the height direction is obtained through the corrected sample hyperspectral image. The reflectivity gradient map takes wavelength as the horizontal coordinate and reflectivity as the vertical coordinate. The reflectivity gradient curve of the cement-based material sample at different heights is drawn, and the valley wavelength is determined by the reflectivity gradient curve. The cement-based material sample (corresponding to the water absorption time) is divided into a dry area, a transition area (including the wetting front) and a completely wetting area;

[0014] The reflectivity at the valley wavelength at different heights is obtained from the reflectivity gradient diagram of each cement-based material sample, and the sample height is used as the horizontal coordinate and the reflectivity at the valley wavelength is used as the vertical coordinate to obtain the sample height-reflectivity curve of the corresponding water absorption time, wherein the inflection point (mutation turning point) from the transition zone to the dry zone is the water penetration depth, and the sample height-reflectivity curve at different water absorption times during the water absorption test experiment is drawn to obtain the reflectivity time-varying characteristic diagram along the sample height direction;

[0015] During the water absorption test, the square root of the water absorption time is used as the horizontal coordinate, and the water penetration depth is used as the vertical coordinate. The water penetration evaluation model inside the cement-based material is fitted to complete the evaluation of the water penetration depth.

[0016] Step 5: Reflectance spectrum mapping and visualization:

[0017] The calibrated sample hyperspectral image was generated and mapped using the added average analysis, and the area of ​​the cement-based material sample was selected as the area-of-interest (ROI);

[0018] That is, the corrected sample hyperspectral image is imported into the Python function library in the form of a single-band grayscale image and converted into a two-dimensional array format, the maximum reflectance and the minimum reflectance are obtained respectively, and then the two-dimensional coordinates of the maximum reflectance and the minimum reflectance are located, and the extreme values ​​(maximum reflectance, minimum reflectance) are extracted as normalized baselines and mapped on the corrected sample image to obtain the mapped hyperspectral image;

[0019] Step 6: Quantification and calibration equation of moisture content and spectral characteristics:

[0020] During the water absorption test, the cement-based material sample is cut into two halves along the height direction, and one half of the sample is cut into thin slices along the height direction. The unit volume moisture content of the thin slice sample is calculated, and the data points are established based on the reflectivity value at the valley wavelength and the unit volume moisture content, so as to fit the relationship curve between the reflectivity value at the valley wavelength of the sample and the unit volume moisture content.

[0021] Step 7: Visual output of water penetration process and water content:

[0022] The mapped hyperspectral image of the cement-based material sample is output, and the moisture content of different areas in the mapped hyperspectral image is calibrated through the relationship curve between the valley wavelength reflectance value of the sample and the moisture content per unit volume, so as to complete the rapid evaluation of the moisture content distribution of the cement-based material.

[0023] The present invention aims to address the problems of slow speed, low efficiency, high cost and limited application scenarios of existing assessment methods for water penetration depth and moisture content distribution of cement-based materials. Based on the specific absorption characteristics of water molecules in the near-infrared band, near-infrared hyperspectral imaging technology is used to scan cement-based materials in the process of water penetration and absorption. The spectral image effect is improved by correcting the scanned image, and the reflectivity gradient of the cement-based material in the process of water penetration and absorption is obtained based on the difference in image surface reflectivity. Then, the water absorption process of the cement-based material is visualized based on the addition averaging method, and the spectral reflectivity of the image is calibrated in combination with the slice weighing method, thereby realizing real-time tracking of the evolution of moisture distribution and penetration depth in the process of water penetration and absorption of cement-based materials.

[0024] The innovation of the rapid assessment method of water penetration depth and water content distribution of cement-based materials based on hyperspectral imaging technology of the present invention lies in:

[0025] 1) A rapid assessment method for the water penetration depth of cement-based materials is proposed based on the specific absorption characteristics of water molecules in the near-infrared spectral band.

[0026] The specific absorption characteristics of water molecules in the near-infrared band, in which the absorption peak at 1400-1450nm is dominated by the absorption of the OH group of water. This absorption phenomenon is caused by the multiplication and combination effects of the vibration modes of functional groups and is highly sensitive to the distribution of water. Utilizing this specific absorption characteristic, the spectral data of cement-based materials is acquired through hyperspectral scanning, and a reflectivity gradient along the height direction is established. Taking the peak wavelength of the absorption peak as the key parameter, an efficient and rapid evaluation of the water penetration depth inside the cement-based material is achieved. The present invention proposes a method for rapidly and quantitatively evaluating the water penetration depth of cement-based materials under complex working conditions based on near-infrared absorption characteristics, which is particularly suitable for cement-based materials doped with mineral substitutes, and significantly reduces the evaluation error.

[0027] 2) By converting the hyperspectral image into a two-dimensional array, an automated and rapid retrieval method for the extreme values ​​of the hyperspectral image and their coordinates is proposed to facilitate rapid mapping and imaging of the reflectivity gradient.

[0028] The maximum and minimum reflectance values ​​of a hyperspectral image reflect the dynamic range of the pixel, and there may be slight differences in the extreme values ​​of the image reflectance at different time points. If a unified normalization process is not performed, these differences may lead to errors and inconsistencies in the quantification of moisture distribution. The present invention proposes to convert the hyperspectral image into a two-dimensional array format through the Python "Pillow" library, and use the np.max(), np.min() and np.argwhere() functions in the numpy library to quickly retrieve the maximum and minimum reflectance values ​​and their corresponding two-dimensional coordinates, thereby avoiding manual retrieval errors and improving processing efficiency and reliability. The present invention ultimately realizes a normalized hyperspectral data analysis method based on a unified extreme value benchmark, so that the moisture distribution at different time points can be quantitatively compared under the same standard, and the mapping imaging of the reflectance gradient is facilitated by the rapid positioning of extreme points.

[0029] 3) The spectral reflectance data were calibrated based on the slice weighing method to solve the quantitative relationship between moisture content and spectral characteristics. Combined with the addition averaging method, a real-time tracking and precise quantification method for moisture distribution and evolution during water absorption of cement-based materials was proposed.

[0030] Determining the unit volume moisture content during the water absorption process of cement-based materials is the key to quantifying the distribution and evolution of moisture. The present invention calibrates the hyperspectral reflectance data through the slice weighing method, slices the sample after water absorption along the height direction, calculates its unit volume moisture content, and fits the functional relationship between reflectance and moisture content in combination with the spectral characteristics of the key wavelength band. The moisture distribution visualization method proposed based on the addition average method reduces the random noise and systematic errors in a single measurement, thereby improving the signal-to-noise ratio of the spectral image and enhancing the visualization effect. The present invention establishes a real-time tracking and precise quantification method of moisture distribution based on spectral calibration, which can visualize the dynamic distribution of moisture in the water absorption process of cement-based materials in real time, and significantly improve the reliability and accuracy of the measurement.

[0031] The method for rapidly evaluating the moisture penetration depth and moisture content distribution of cement-based materials based on hyperspectral imaging technology of the present invention includes the following beneficial effects:

[0032] 1) The existing in-situ assessment method of water penetration depth of physical cement-based structures has low accuracy, especially for cement-based structures doped with mineral substitutes such as blast furnace slag. The present invention is based on the specific absorption characteristics of the -OH functional group in the water molecule in the near-infrared band to quickly and efficiently determine the wetting front position and assess the water penetration depth at a faster speed.

[0033] 2) Computer functions are used to assist in retrieving the maximum and minimum reflectances of hyperspectral images and their corresponding coordinates, which helps to quickly map the reflectance and ensure that the hyperspectral images at different time points are established on the same basis, thereby improving the reliability and comparability of the results.

[0034] 3) Based on the addition average method, the random noise and systematic error in the measurement are reduced, the signal-to-noise ratio of the spectral image is improved, and the visualization effect of the moisture distribution is enhanced. The slice weighing method calibration provides an accurate quality basis for the spectral reflectance data, establishes a quantitative relationship between the spectral information and the actual moisture content, improves the accuracy of the assessment, and reduces the cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a workflow diagram of the water absorption test experiment of cement-based material samples and the scanning with a hyperspectral camera in the embodiment;

[0036] Figure 2 This is a schematic diagram of the system architecture of an infrared hyperspectral camera scanning a cement-based material sample in an embodiment;

[0037] Figure 3 The images before and after the hyperspectral image correction in step 3 of the embodiment, wherein (a) the original mapping image, (b) the dark-corrected image, and (c) the white-corrected image;

[0038] Figure 4It is a flow chart for visualizing water penetration in the embodiment;

[0039] Figure 5 The reflectivity gradient map and partitions of the cement-based material sample during the water absorption process obtained in step 4 of the embodiment;

[0040] Figure 6 It is a time-varying characteristic diagram of reflectivity at a wavelength of 1410 nm along the sample height direction in step 4 of the embodiment;

[0041] Figure 7 The water penetration assessment model obtained in step 4 of the embodiment;

[0042] Figure 8 The maximum reflectivity (a) and minimum reflectivity (b) test diagram of the cement-based material sample in step 5 of the embodiment;

[0043] Fig. 9 is the calibration line between the water content and the reflectivity obtained in step 6 of the embodiment;

[0044] Fig.10 This is the visualization of the water penetration process and water content in step seven of Example 1. DETAILED DESCRIPTION

[0045] Specific implementation method 1: The rapid assessment method of water penetration depth and water content distribution of cement-based materials based on hyperspectral imaging technology in this implementation method is implemented according to the following steps:

[0046] Step 1: Water absorption test:

[0047] The bottom of the cement-based material sample is immersed in water to conduct a water absorption test experiment;

[0048] Step 2: Near-infrared hyperspectral camera scanning:

[0049] An infrared hyperspectral camera is installed above the cement-based material sample, and during the water absorption test experiment, the infrared hyperspectral camera is used to obtain hyperspectral images of the sample at different water absorption times;

[0050] Step 3: Hyperspectral image correction:

[0051] Performing dark correction and white correction processing on the sample hyperspectral image obtained in step 2 to obtain a corrected sample hyperspectral image;

[0052] Step 4: Quick assessment of reflectance spectral gradient and water penetration depth:

[0053] The reflectivity gradient map of the cement-based material sample along the height direction is obtained through the corrected sample hyperspectral image. The reflectivity gradient map takes wavelength as the horizontal coordinate and reflectivity as the vertical coordinate. The reflectivity gradient curve of the cement-based material sample at different heights is drawn, and the valley wavelength is determined by the reflectivity gradient curve. The cement-based material sample (corresponding to the water absorption time) is divided into a dry area, a transition area (including the wetting front) and a completely wetting area;

[0054] The reflectivity at the valley wavelength at different heights is obtained from the reflectivity gradient diagram of each cement-based material sample, and the sample height is used as the horizontal coordinate and the reflectivity at the valley wavelength is used as the vertical coordinate to obtain the sample height-reflectivity curve of the corresponding water absorption time, wherein the inflection point (mutation turning point) from the transition zone to the dry zone is the water penetration depth, and the sample height-reflectivity curve at different water absorption times during the water absorption test experiment is drawn to obtain the reflectivity time-varying characteristic diagram along the sample height direction;

[0055] During the water absorption test, the square root of the water absorption time is used as the horizontal coordinate, and the water penetration depth is used as the vertical coordinate. The water penetration evaluation model inside the cement-based material is fitted to complete the evaluation of the water penetration depth.

[0056] Step 5: Reflectance spectrum mapping and visualization:

[0057] The calibrated sample hyperspectral image was generated and mapped using the added average analysis, and the area of ​​the cement-based material sample was selected as the area-of-interest (ROI);

[0058] That is, the corrected sample hyperspectral image is imported into the Python function library in the form of a single-band grayscale image and converted into a two-dimensional array format, the maximum reflectance and the minimum reflectance are obtained respectively, and then the two-dimensional coordinates of the maximum reflectance and the minimum reflectance are located, and the extreme values ​​(maximum reflectance, minimum reflectance) are extracted as normalized baselines and mapped on the corrected sample image to obtain the mapped hyperspectral image;

[0059] Step 6: Quantification and calibration equation of moisture content and spectral characteristics:

[0060] During the water absorption test, the cement-based material sample is cut into two halves along the height direction, and one half of the sample is cut into thin slices along the height direction. The unit volume moisture content of the thin slice sample is calculated, and the data points are established based on the reflectivity value at the valley wavelength and the unit volume moisture content, so as to fit the relationship curve between the reflectivity value at the valley wavelength of the sample and the unit volume moisture content.

[0061] Step 7: Visual output of water penetration process and water content:

[0062] The mapped hyperspectral image of the cement-based material sample is output, and the moisture content of different areas in the mapped hyperspectral image is calibrated through the relationship curve between the valley wavelength reflectance value of the sample and the moisture content per unit volume, so as to complete the rapid evaluation of the moisture content distribution of the cement-based material.

[0063] Specific implementation method 2: This implementation method is different from specific implementation method 1 in that the cement-based material sample in step 1 is cylindrical.

[0064] Specific implementation method three: The difference between this implementation method and specific implementation method one or two is that in step two, the frame rate of the infrared hyperspectral camera is controlled to be 20 to 40 fps, and the exposure time is 6 to 8 ms.

[0065] Specific implementation method 4: The difference between this implementation method and any one of specific implementation methods 1 to 3 is that in step 2, during the water absorption test experiment, 8 to 12 hyperspectral images of the sample with different water absorption times are obtained by taking pictures with an infrared hyperspectral camera.

[0066] In this embodiment, 8 to 12 sample hyperspectral images are taken at intervals from the start of the water absorption test experiment until the sample completely absorbs water.

[0067] Specific implementation method five: The difference between this implementation method and any one of specific implementation methods one to four is that in step four, the lowest point in the wavelength range of 1400 to 1450 nm of the reflectivity gradient curve is determined as the valley wavelength.

[0068] Specific implementation method six: The difference between this implementation method and any one of specific implementation methods one to five is that in the reflectivity gradient curve of the cement-based material sample at different heights in step four, the reflectivity corresponding to the valley wavelength in the transition zone has a gradient change along the height direction.

[0069] Specific implementation method seven: The difference between this implementation method and any one of specific implementation methods one to six is ​​that in step five, the corrected sample hyperspectral image is imported into the Pillow module in the Python function library in the form of a single-band grayscale image and converted into a two-dimensional array format.

[0070] Specific implementation eight: This implementation differs from any one of specific implementations one to seven in that in step six, when the water absorption time is 50 to 300 minutes, the cement-based material sample is cut into two halves along the height direction, and one half of the sample is cut into thin slice samples along the height direction.

[0071] Specific implementation method 9: The difference between this implementation method and any one of specific implementation methods 1 to 8 is that the thickness of the thin slice sample in step 6 is 1 to 2 mm.

[0072] Specific embodiment 10: This embodiment differs from specific embodiments 1 to 9 in that the calculation formula of the unit volume moisture content of the thin slice sample in step 6 is as follows:

[0073]

[0074] Embodiment: The method for rapid assessment of water penetration depth and water content distribution of cement-based materials based on hyperspectral imaging technology in this embodiment is implemented by the following steps:

[0075] Step 1: Scanning sample preparation and water absorption test

[0076] Cement slurry specimens were prepared using ordinary Portland cement (OPC), and the designed W / C (water-cement ratio) of the cement slurry were 0.4, 0.5 and 0.6, respectively. The stirred slurry was poured into a cylindrical mold with a diameter of 5 cm and a height of 10 cm. The mold was demoulded 24 hours after pouring and placed in water for curing. After one year of curing, the specimen was evenly cut into two equal parts and placed in a drying room at 40°C until the mass change within 24 hours was less than 0.1%. In order to prevent water absorption on the side, the specimen was wrapped with aluminum foil tape, leaving only the top and bottom surfaces exposed. Before drying, the top and bottom of the specimen were trimmed by about 1 mm, and then the bottom surface of the specimen was partially immersed in water with an immersion height of 5 mm. Figure 1 shown.

[0077] Step 2: Determine the optimal parameters for near-infrared hyperspectral camera scanning

[0078] When scanning the cement slurry sample, the infrared hyperspectral camera lens is perpendicular to the sample, and the distance is adjusted so that the captured sample image occupies about 80% of the screen. The reflected light intensity of the sample image is a combination of the photon dose and dark current at a given exposure time during the scanning process. In order to minimize image blur and maintain image brightness, the maximum reflected light intensity is maintained at 80% of the maximum brightness, which is mainly achieved by adjusting the camera's frames per second (FPS) and the camera's exposure time. Among them, the exposure time determines the scanning speed. Under the premise of ensuring image quality, the exposure time should be minimized. The two parameter settings in this embodiment are shown in Table 1:

[0079] Table 1 Frames per second (FPS) and camera exposure time determination

[0080]

[0081] Step 3: Hyperspectral image correction

[0082] Spectral images usually need to be calibrated to ensure the accuracy of the spectral data. Calibration is mainly divided into dark calibration and white calibration. Calibration is performed by collecting and processing black and white reference data. The calibration principle is shown in formula (1). Dark calibration is achieved by covering the lens, and white calibration is achieved by scanning the reflectance spectrum of a high-reflectance standard white plate at the same position as the test sample. Figure 3As shown, green spots (spots of very low reflectivity) appear on the raw image. These are mainly caused by dark current noise, pixel response non-uniformity, system noise or baseline drift. After recording and subtracting noise and deviation from the dark reference data, the spots of very low reflectivity disappear. White calibration compensates for the changes in illumination and reflectivity across the scene, and the entire image presents a uniform reflectivity.

[0083]

[0084] Step 4: Rapid assessment of reflectance spectral gradient and water penetration depth

[0085] Based on the specific absorption characteristics of the -OH functional group in the water molecule in the near-infrared band of 1400-1450nm, the reflectivity gradient of the cement-based material along the height direction is obtained, such as Figure 5 As shown. According to the reflectivity value, three different areas are distinguished: dry area, transition area (including wetting front) and completely wet area. The transition area (including wetting front) gradually moves upward with the increase of water absorption time, which is also the key to determine the water penetration depth in cement paste. Taking the wavelength value of 1410nm corresponding to the peak point as the specific analysis target, the time-varying characteristics of the reflectivity along the height direction are obtained, and the linear model for water penetration evaluation inside the material is established based on the reflectivity gradient. The specific process is as follows:

[0086] The reflectance gradient map of the cement-based material sample along the height direction is obtained by using the corrected sample hyperspectral image ( Figure 5 ), the reflectivity gradient graph is based on wavelength as the horizontal coordinate and reflectivity as the vertical coordinate, and the reflectivity gradient curves of cement-based material samples at different heights are drawn. The valley wavelength is determined to be 1410nm from the reflectivity gradient curve, and the cement-based material sample (corresponding to the water absorption time) is divided into a dry area, a transition area (including the wetting front) and a completely wetting area;

[0087] The reflectivity at the valley wavelength at different heights is obtained from the reflectivity gradient map of each cement-based material sample. The sample height is taken as the horizontal coordinate and the reflectivity at the valley wavelength is taken as the vertical coordinate to obtain the sample height-reflectivity curve of the corresponding water absorption time. The inflection point (mutation turning point) from the transition zone to the dry zone is the water penetration depth. The sample height-reflectivity curve at different water absorption times during the water absorption test experiment is plotted to obtain the reflectivity time-varying characteristic diagram along the sample height direction, as shown in Figure 6 As shown;

[0088] During the water absorption test, the square root of the water absorption time was used as the horizontal axis and the water penetration depth was used as the vertical axis to fit the water penetration evaluation model inside the cement-based material, as shown in Figure 7 As shown;

[0089] Step 5: Reflectance Spectrum Mapping and Visualization

[0090] The calibrated sample hyperspectral image was generated and mapped using the added average analysis, and the area of ​​the cement-based material sample was selected as the area-of-interest (ROI);

[0091] The corrected sample hyperspectral image is imported into the Python function library as a single-band grayscale image and converted into a two-dimensional array format through the Python "Pillow" library. The maximum and minimum reflectance values ​​are obtained by using the np.max() and np.min() functions in the numpy library, respectively. Then, the two-dimensional coordinates of the maximum and minimum reflectance values ​​are located by using the np.argwhere() function. Figure 8 The reflectance spectrum data of the maximum and minimum extreme points are displayed. The extreme values ​​(maximum reflectance 6592, minimum reflectance 1437) are extracted as normalized baselines and mapped on the calibrated sample image to obtain the mapped hyperspectral image;

[0092] The visualization principle is as follows:

[0093]

[0094] In this embodiment, each unit pixel in the sample hyperspectral image is taken as a single ROI area, and the reflectivity addition average is equal to the reflectivity value of the unit pixel. N is the total number of gradients of the visualized color gradient. In this embodiment, the color gradient N is set to 100. Based on the above visualization principle, pseudo color mapping is used to display the color gradient corresponding to the reflectivity values ​​of different pixel points, thereby obtaining the mapped hyperspectral image;

[0095] Step 6: Quantification and calibration equations of moisture content and spectral characteristics

[0096] The cement slurry sample was evenly cut into two identical pieces along the height direction for the hyperspectral camera spectral scanning and moisture content calibration respectively. The water absorption process of the sample was kept consistent. After the same time (300 min), the sample used for moisture content calibration was cut into 2 mm thick slices along the height direction and the moisture content per unit volume was calculated according to formula (4). The relationship between the sample reflectance value and the moisture content per unit volume in the 1410 nm wavelength band was fitted by the functional relationship ( Fig.10 ) and use this functional relationship to calibrate the internal moisture distribution of the sample during the water absorption process.

[0097]

[0098] Step 7: Visual output of water penetration process and water content

[0099] Based on the mapped hyperspectral image, the moisture content in different areas is calibrated through the relationship curve between the valley wavelength reflectance value of the sample and the moisture content per unit volume, and the moisture penetration distribution and moisture content image of the cement-based material sample are output, such as Fig.10 As shown, a rapid assessment of the moisture content distribution of cement-based materials is completed.

[0100] This embodiment is Fig.10 It can be seen that the dry area and the completely wet area account for the vast majority of the samples, and the wet front maintains an irregular line and gradually moves forward over time. The moisture distribution in the transition zone is uneven, and changes greatly along the height. In particular, the moisture content at the end of the transition zone varies greatly, which is caused by the complexity and differences of the local pore structure. This method can quickly and effectively evaluate the moisture penetration distribution and content of cement-based materials, and provides an efficient and practical research method for the quantitative evaluation of the moisture penetration behavior of cement-based materials and the durability of engineering structures.

Claims

1. A rapid assessment method for moisture penetration depth and moisture content distribution of cement-based materials based on hyperspectral imaging technology, characterized by The rapid assessment method for moisture penetration depth and moisture content distribution of cement-based materials is implemented according to the following steps: Step 1: Water absorption test: The bottom of the cement-based material sample is immersed in water to conduct a water absorption test experiment; Step 2: Near-infrared hyperspectral camera scanning: An infrared hyperspectral camera is installed above the cement-based material sample, and during the water absorption test experiment, the infrared hyperspectral camera is used to obtain hyperspectral images of the sample at different water absorption times; Step 3: Hyperspectral image correction: Performing dark correction and white correction processing on the sample hyperspectral image obtained in step 2 to obtain a corrected sample hyperspectral image; Step 4: Quick assessment of reflectance spectral gradient and water penetration depth: The reflectivity gradient map of the cement-based material sample along the height direction is obtained through the corrected sample hyperspectral image. The reflectivity gradient map takes wavelength as the horizontal coordinate and reflectivity as the vertical coordinate. The reflectivity gradient curve of the cement-based material sample at different heights is drawn, and the valley wavelength is determined by the reflectivity gradient curve, and the cement-based material sample is divided into a dry area, a transition area and a completely wet area. The reflectivity at the valley wavelength at different heights is obtained from the reflectivity gradient map of each cement-based material sample. The sample height is taken as the horizontal coordinate and the reflectivity at the valley wavelength is taken as the vertical coordinate to obtain the sample height-reflectivity curve of the corresponding water absorption time. The inflection point from the transition zone to the dry zone is the water penetration depth. The sample height-reflectivity curve at different water absorption times during the water absorption test experiment is drawn to obtain the reflectivity time-varying characteristic map along the sample height direction. During the water absorption test, the square root of the water absorption time is used as the horizontal coordinate, and the water penetration depth is used as the vertical coordinate. The water penetration evaluation model inside the cement-based material is fitted to complete the evaluation of the water penetration depth. Step 5: Reflectance spectrum mapping and visualization: The corrected hyperspectral image of the sample was generated and mapped using the added average analysis, and the area of ​​the cement-based material sample was selected as the region of interest; That is, the corrected sample hyperspectral image is imported into the Python function library in the form of a single-band grayscale image and converted into a two-dimensional array format, the maximum reflectance and the minimum reflectance are obtained respectively, and then the two-dimensional coordinates of the maximum reflectance and the minimum reflectance are located, and the extreme values ​​(maximum reflectance, minimum reflectance) are extracted as normalized baselines and mapped on the corrected sample image to obtain the mapped hyperspectral image; Step 6: Quantification and calibration equation of moisture content and spectral characteristics: During the water absorption test, the cement-based material sample is cut into two halves along the height direction, and one half of the sample is cut into thin slices along the height direction. The unit volume moisture content of the thin slice sample is calculated, and the data points are established based on the reflectivity value at the valley wavelength and the unit volume moisture content, so as to fit the relationship curve between the reflectivity value at the valley wavelength of the sample and the unit volume moisture content. Step 7: Visual output of water penetration process and water content: For the hyperspectral image of cement-based material samples after mapping, the moisture content in different areas of the mapped hyperspectral image is calibrated through the relationship curve between the valley wavelength reflectance value of the sample and the moisture content per unit volume, so as to complete the rapid evaluation of the moisture content distribution of cement-based materials.

2. The method for rapid assessment of water penetration depth and water content distribution of cement-based materials based on hyperspectral imaging technology according to claim 1 is characterized in that In step 1, the cement-based material specimen is cylindrical.

3. The method for rapid assessment of water penetration depth and water content distribution of cement-based materials based on hyperspectral imaging technology according to claim 1 is characterized in that In step 2, the frame rate of the infrared hyperspectral camera is controlled to be 20-40fps, and the exposure time is 6-8ms.

4. The method for rapid assessment of water penetration depth and water content distribution of cement-based materials based on hyperspectral imaging technology according to claim 1 is characterized in that In step 2, during the water absorption test experiment, 8 to 12 hyperspectral images of the sample with different water absorption times are obtained by taking pictures with an infrared hyperspectral camera.

5. The method for rapid assessment of water penetration depth and water content distribution of cement-based materials based on hyperspectral imaging technology according to claim 1 is characterized in that In step 4, the lowest point in the 1400-1450 nm wavelength range of the reflectivity gradient curve is determined as the valley wavelength.

6. The method for rapid assessment of water penetration depth and water content distribution of cement-based materials based on hyperspectral imaging technology according to claim 1 is characterized in that In the reflectivity gradient curve of the cement-based material sample at different heights in step 4, the reflectivity corresponding to the valley wavelength in the transition zone has a gradient change along the height direction.

7. The method for rapid assessment of water penetration depth and water content distribution of cement-based materials based on hyperspectral imaging technology according to claim 1 is characterized in that In step 5, the corrected sample hyperspectral image is imported into the Pillow module in the Python function library as a single-band grayscale image and converted into a two-dimensional array format.

8. The method for rapid assessment of water penetration depth and water content distribution of cement-based materials based on hyperspectral imaging technology according to claim 1, characterized in that In step six, when the water absorption time is 50 to 300 minutes, the cement-based material sample is cut into two halves along the height direction, and one half of the sample is cut into thin slice samples along the height direction.

9. The method for rapid assessment of water penetration depth and water content distribution of cement-based materials based on hyperspectral imaging technology according to claim 1, characterized in that The thickness of the thin slice sample in step six is ​​1 to 2 mm.

10. The method for rapid assessment of water penetration depth and water content distribution of cement-based materials based on hyperspectral imaging technology according to claim 1, characterized in that The calculation formula for the moisture content per unit volume of the thin slice sample in step 6 is as follows:

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