MGPCC fracture damage monitoring method based on AE and DIC

Abstract

The invention discloses an MGPCC fracture damage monitoring method based on AE and DIC, and relates to the technical field of building material fracture damage monitoring, the method comprises the following steps: preparing an MGPCC fracture test piece, arranging an acoustic emission probe, installing the test piece on a 3-P-B fracture test device, carrying out displacement control loading, collecting AE signals and shooting images in real time during loading, and determining the fracture damage of the MGPCC. Then analyzing characteristic parameters, images and the like to determine FPZ related conditions, and finally establishing a fracture damage evolution model in combination with a monitoring result. Spatial distribution and characteristic parameters of a damage source are monitored in real time through the AE technology, a strain cloud picture and a displacement field are accurately obtained in combination with the DIC technology, microcrack propagation and FPZ formation and evolution laws in the MGPCC fracture process are revealed, multi-dimensional quantitative characterization of the fracture damage process is achieved, and the method is suitable for mass production. A more comprehensive and accurate technical means is provided for building material fracture damage monitoring, and the blank of MGPCC multi-scale crack resistance and damage evolution research is filled.

Description

Technical Field

[0001] This invention relates to the field of fracture damage monitoring technology for building materials, and more specifically to a method for monitoring fracture damage in MGPCC based on AE and DIC. Background Technology

[0002] Currently, researchers have explored the variation patterns and influencing factors of fracture parameters in MGPCC under different mix proportions. However, in-depth research and experimental explanations are still lacking regarding the specific multi-scale fracture inhibition and damage evolution during the MGPCC fracture process, and the variation pattern of FPZ at the initial crack tip remains unclear. Research on material damage based on AE and DIC technologies is relatively mature. For example, Rouchier et al. studied the progressive damage development of fiber-reinforced mortar using both AE and DIC technologies simultaneously. Zhou et al. combined AE and DIC technologies to simultaneously monitor the compressive-bending failure of multilayer composite materials and found that AE parameters are related to the damage process of the composite material, while DIC results clearly reflect the critical damage deformation of the layered regions.

[0003] Therefore, how to determine the mechanism of microcrack generation and propagation evolution during the MGPCC fracture process based on the sound source localization of AE and the displacement cloud map of DIC technology is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a method for monitoring fracture damage of MGPCC based on AE and DIC, so as to solve the problems existing in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for monitoring fracture damage in MGPCC based on AE and DIC, comprising:

[0007] MGPCC fracture specimens were prepared according to a predetermined mix ratio, and acoustic emission probes were arranged on the surface of the MGPCC fracture specimens.

[0008] The MGPCC fracture specimen was mounted on the 3-PB fracture testing apparatus, and displacement-controlled loading was used.

[0009] During the loading process, the acoustic emission probe acquires AE signals in real time, obtains characteristic parameters based on the AE signals, and determines the spatial distribution of damage points by AE sound source localization;

[0010] Multiple images were captured at different stages of specimen deformation. Based on DIC technology, displacement information at various points on the specimen surface was calculated, strain cloud maps were obtained, and the length of FPZ and crack propagation law were analyzed.

[0011] The variation of characteristic parameters with loading time, fracture types, and the propagation process from microcracks to macrocracks were analyzed. The location and range of the FPZ were determined using the AE sound source localization results, and the length and width of the FPZ were statistically analyzed.

[0012] The captured images are processed to obtain the displacement field and strain field on the surface of the specimen. The changes in the strain concentration region under different loading stages are analyzed to determine the formation and expansion process of the strain concentration zone.

[0013] By combining the monitoring results of AE and DIC technologies, a fracture damage evolution model for MGPCC is established. The fracture energy is linked to the cumulative AE characteristic parameters, and the Gamma distribution function is used to characterize the cumulative AE fracture damage evolution, thereby achieving a quantitative characterization of the fracture damage process of MGPCC.

[0014] Optionally, the preparation of the MGPCC fracture specimen according to the predetermined mix ratio specifically involves:

[0015] Specimen preparation: MGPCC fracture specimens were prepared according to the predetermined mix ratio. The specimen size was 40mm×40mm×200mm, the initial crack length was 16mm, the crack height ratio was 0.4, and the span-to-height ratio was 4.0.

[0016] Surface treatment: On the ligament part above the crack tip of the specimen, matte white paint and matte black paint are sprayed alternately to form a high-contrast artificial speckle field for DIC technology image processing;

[0017] Sensor arrangement: Four R6α probe sensors were arranged on the surface of the specimen as AE probes. The distances between the center point of the probe and the top and bottom edges of the specimen were 15mm, 25mm, 15mm, and 25mm, respectively, and the distance from the center of the probe to the crack in the middle of the specimen was 60mm. Vaseline was used as the coupling agent and the probes were fixed with electrical tape to ensure good elastic wave transmission.

[0018] Optionally, mounting the MGPCC fracture specimen on the 3-PB fracture testing apparatus specifically involves:

[0019] The specimen was mounted on the 3-PB fracture testing apparatus and loaded with displacement control at a rate of 0.1 mm / min. Simultaneously, the AE probe was connected to the digital AE system to acquire AE signals. A high-speed camera was aimed at the speckle field on the surface of the MGPCC fracture specimen and connected to the DIC acquisition system to capture images of the specimen during deformation.

[0020] Optionally, the characteristic parameters include amplitude, rise time, duration, ring count, AE energy, RA-AF, and Ib value.

[0021] Optionally, the average frequency AF is defined as the ratio of the ring count to the duration of the elastic wave in a single impact, and the RA value is the ratio of the rise time to the amplitude. The unit of AF measurement is kHz, and the unit of RA measurement is ms / V. Based on the established relationship between AF and RA values, the cracks generated during the fracture process are divided into tensile cracks and shear cracks. The calculation formulas for AF and RA values ​​are as follows:

[0022]

[0023] Optionally, the analysis formula for the Ib value is as follows:

[0024]

[0025] In the formula, σ and μ are the standard deviation and mean of the amplitude distribution in the AE event, respectively; N(μ-α1σ) is the cumulative count of AE events with amplitude greater than μ-α1σ; N(μ+α2σ) is the cumulative count of AE events with amplitude greater than μ+α2σ; α1 and α2 are empirical constants with values ​​of 0 and 1, respectively.

[0026] Optionally, during the 3-PB fracture process of MGPCC, since the internal structural failure cannot be directly observed, in order to characterize the evolution and location of internal damage, the spatial distribution of AE events in the specimen is studied through AE damage localization. The location of the AE damage source is inferred based on the time difference and positional difference of the longitudinal waves generated by the AE source reaching different AE probes. Based on the propagation characteristics of the AE signal, the distance from the AE source to the AE probe (d) is calculated. i The formula for calculating ) is:

[0027] d i =v p (t i -t)

[0028] Among them, v p The wave velocity is the longitudinal wave velocity, and t is the time of appearance of the AE sound source. i It is the transmission time of the AE signal from the AE sound source to the AE probe;

[0029] Based on the time difference and position difference, the location of the AE sound source is determined as follows:

[0030]

[0031] Where (x, y, z) are the coordinates of the AE sound source, (x i y i , z i () represents the coordinates of the AE probe.

[0032] Optionally, the principle of the DIC technology is as follows:

[0033] Multiple images were captured at different stages of specimen deformation. Based on the assumption that the gray-level distribution functions of the reference image and the current image are f(x,y) and g(x,y) respectively, a reference subset was selected at any coordinate position within the analysis region of the reference image. Let the center point of the subset be Q. After the specimen is subjected to load and deforms, the reference subset moves to the location of the target subset, i.e., point Q. 1 The DIC method employs a normalized least squares cross-correlation matching function C. LS By continuously tracking the movement trajectory of point Q, the relationship between point Q and point Q' can be obtained. 1 The difference in pixel coordinates is used to obtain the displacement information of the subset's center point.

[0034] After obtaining the displacement information of the subset center point Q, the displacement information of any point T within the analysis area of ​​the reference image can be calculated using the following formula:

[0035]

[0036] Among them, u rc v rc The displacement result with integer pixel precision is obtained through initial guessing, where △x and △y are the pixel coordinate differences between points Q and T.

[0037] Optionally, the normalized least squares cross-correlation matching function C LS Specifically:

[0038]

[0039] Where f(x,y) and g(x,y) are the gray values ​​of the same observation point in the image sub-regions before and after sample deformation, respectively; f m and g m The first value represents the average gray value of all observation points in the image sub-region before and after the sample deformation.

[0040] As can be seen from the above technical solution, compared with the prior art, this invention discloses a method for monitoring MGPCC fracture damage based on AE and DIC. First, an MGPCC fracture specimen is prepared and an acoustic emission probe is arranged. Then, the specimen is installed on a 3-PB fracture testing device for displacement-controlled loading. During loading, AE signals are acquired and images are captured in real time. Then, characteristic parameters and images are analyzed to determine the FPZ correlation. Finally, a fracture damage evolution model is established based on the monitoring results. This invention uses AE technology to monitor the spatial distribution and characteristic parameters of the damage source in real time, and combines it with DIC technology to accurately obtain strain cloud maps and displacement fields. The synergy of these two technologies can deeply reveal the microcrack propagation, FPZ formation and evolution laws during the MGPCC fracture process, achieving multi-dimensional quantitative characterization of the fracture damage process. This provides a more comprehensive and accurate technical means for monitoring fracture damage in building materials, filling the gap in research on multi-scale crack inhibition and damage evolution of MGPCC. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the method flow provided by the present invention;

[0043] Figure 2 The schematic diagram of the AE technology provided by this invention;

[0044] Figure 3 Typical AE waveform diagram provided by this invention;

[0045] Figure 4 This is a schematic diagram illustrating the relationship between AF and RA values ​​provided by the present invention;

[0046] Figure 5 A schematic diagram of the DIC provided for this invention;

[0047] Figure 6 A schematic diagram of the AE and DIC monitoring and testing device based on the 3-PB fracture test provided by the present invention;

[0048] Figure 7 The AE probe sensor arrangement dimension diagram provided by the present invention;

[0049] Figure 8 The present invention provides a diagram showing the amplitude variation during the fracture process of an N-0 mix ratio.

[0050] Figure 9The present invention provides a (cumulative) ringing count and (cumulative) AE energy distribution diagram of the peak load during the fracture process of MGPCC using nano-SiO2, PVA fiber and steel fiber;

[0051] Figure 10 The effect of N-0 and N-1.5 ratios on AF-RA during the fracture process of MGPCC provided by this invention;

[0052] Figure 11 The effect of N-0 and N-1.5 ratios provided by this invention on the change of MGPCC Ib value;

[0053] Figure 12 A schematic diagram showing the location of the AE source of the damage point generated during the fracture process of the MGPCC specimen with N-1.5 ratio provided by the present invention;

[0054] Figure 13 The present invention provides a bar chart showing the number of AE events at different positions along the X and Z coordinate axes of MGPCC under N-0;

[0055] Figure 14 A schematic diagram illustrating the strain comparison analysis at four load points provided by this invention;

[0056] Figure 15 Strain field contour maps of specimens T1-T4 in group P-0.6 provided for this invention;

[0057] Figure 16 The evolution curves of fracture energy and cumulative AE characteristic parameters (cumulative ringing count) for the optimal MGPCC ratio (N-1.5) provided by the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] This invention discloses a method for monitoring MGPCC fracture damage based on AE and DIC, such as... Figure 1 As shown, it includes:

[0060] MGPCC fracture specimens were prepared according to a predetermined mix ratio, and acoustic emission probes were arranged on the surface of the MGPCC fracture specimens.

[0061] The MGPCC fracture specimen was mounted on the 3-PB fracture testing apparatus, and displacement-controlled loading was used.

[0062] During the loading process, the acoustic emission probe acquires AE signals in real time, obtains characteristic parameters based on the AE signals, and determines the spatial distribution of damage points by AE sound source localization;

[0063] Multiple images were captured at different stages of specimen deformation. Based on DIC technology, displacement information at various points on the specimen surface was calculated, strain cloud maps were obtained, and the length of FPZ and crack propagation law were analyzed.

[0064] The variation of characteristic parameters with loading time, fracture types, and the propagation process from microcracks to macrocracks were analyzed. The location and range of the FPZ were determined using the AE sound source localization results, and the length and width of the FPZ were statistically analyzed.

[0065] The captured images are processed to obtain the displacement field and strain field on the surface of the specimen. The changes in the strain concentration region under different loading stages are analyzed to determine the formation and expansion process of the strain concentration zone.

[0066] By combining the monitoring results of AE and DIC technologies, a fracture damage evolution model for MGPCC is established. The fracture energy is linked to the cumulative AE characteristic parameters, and the Gamma distribution function is used to characterize the cumulative AE fracture damage evolution, thereby achieving a quantitative characterization of the fracture damage process of MGPCC.

[0067] The following explanations are provided: AE stands for Acoustic Emission Technology, DIC stands for Digital Image Correlation Technology, MGPCC stands for Multi-scale Geopolymer Cement Composite (MGPCC), and FPZ stands for Fracture Process Zone.

[0068] In a specific embodiment, the principle of AE technology is as follows: Figure 2 As shown, in specimens subjected to external loads, due to microstructural misalignment or the propagation of microcracks, stored energy is suddenly released, generating elastic waves that propagate through the material and are captured by a probe sensor on the specimen surface, converting them into electrical signals. The probe sensor used in this experiment is an R6α, operating at a frequency of 35–100 kHz, suitable for monitoring cracks in cement materials. Since most AE signals are too weak, they need to be amplified by a preamplifier; generally, the AE signal is amplified to 40 dB. Simultaneously, the acquisition threshold is set to 40 dB to filter out background noise from the experimental environment, ensuring a high signal-to-noise ratio. Finally, these elastic waves are transmitted to an AE data acquisition system for recording, storage, and analysis. This acquisition system is the Micro-11Express digital AE system manufactured by MISTRAS Corporation. Figure 3As shown, this is a typical AE waveform, from which several important parameters can be obtained, such as amplitude, rise time, duration, (cumulative) ring count, and (cumulative) AE energy.

[0069] According to RILEM, the average frequency (AF) is defined as the ratio of the ring count to the duration of the elastic wave in a single impact, and the RA value is the ratio of the rise time to the amplitude. AF is measured in kHz, and RA is measured in ms / V. Based on the established relationship between AF and RA values, cracks generated during the fracture process can be classified into tensile cracks and shear cracks, such as... Figure 4 As shown. Shear cracks occur when the AE signal has a high RA value and a low AF value; tensile cracks occur when the AE signal has a low RA value and a high AF value. The formulas for calculating AF and RA values ​​are shown below:

[0070]

[0071] In the fracture process of quasi-brittle materials, the damage inside and outside the specimen differs. Therefore, to study the formation and propagation of internal microcracks during the early fracture process, a large number of elastic wave events with different amplitudes released during the formation and propagation of microcracks can be detected using elastic wave (AE) technology. Since AE events are in the form of elastic waves, such as… Figure 3 As shown, AE amplitude distribution analysis, also known as "b-value analysis," can be used. The "b-value analysis" method was first used to study seismic waves generated by earthquakes and has been widely accepted by seismologists for quantitative analysis of seismic activity. Since the elastic waves of AE events are similar to seismic waves, "b-value analysis" can also be applied to AE analysis. Furthermore, the Gutenberg-Richter law (GR) of seismic activity can also be used for AE analysis, with the modified formula shown below:

[0072]

[0073] Where N is the number of AE events with an amplitude greater than AdB, and A dB is the peak amplitude in dB, and 'a' is the peak amplitude in logarithm. 10 The intercept on the N-axis, b, is the slope of the regression line, also known as the b-value for these AE events. A larger b-value is obtained when microcracks begin to form. In contrast, a smaller b-value is obtained when macrocracks develop. The "b-value analysis" is linked to and improved with statistical analysis, as shown in the following formula:

[0074]

[0075] In the formula, σ and μ are the standard deviation and mean of the amplitude distribution in the AE event, respectively; N(μ-α1σ) is the cumulative count of AE events with amplitude greater than μ-α1σ; N(μ+α2σ) is the cumulative count of AE events with amplitude greater than μ+α2σ; α1 and α2 are empirical constants with values ​​of 0 and 1, respectively.

[0076] During the 3-PB fracture process of MGPCC, since the internal structural failure cannot be directly observed, the spatial distribution of AE events (internal damage or failure) in the specimen can be studied through AE damage localization to characterize the evolution and location of internal damage. The location of the AE damage source is inferred based on the time difference and positional difference of the longitudinal waves generated by the AE source arriving at different AE probes. Based on the propagation characteristics of the AE signal, the distance from the AE source to the AE probe (d...) is... i The formula for calculating ) is:

[0077] d i =v p (t i -t)

[0078] Among them, v p The wave velocity is the longitudinal wave velocity, and t is the time of appearance of the AE sound source. i It is the transmission time of the AE signal from the AE sound source to the AE probe;

[0079] Based on the time difference and position difference, the location of the AE sound source is determined as follows:

[0080]

[0081] Where (x, y, z) are the coordinates of the AE sound source, (x i y i , z i () represents the coordinates of the AE probe.

[0082] Digital Image Correlation (DIC)

[0083] Digital image correlation (DIC) is a non-destructive optical technique that does not affect the testing process of the specimen. DIC can provide the deformation of the specimen surface during the test and can obtain the complete fracture development process without being limited by the number or size of cracks. This technique can perform full-field measurements of local displacements on the specimen surface. DIC has been used to measure stress intensity factors near crack tips, identify elastic properties, and damage patterns. The principle of DIC can be simply summarized as follows: multiple images are captured at different stages of specimen deformation, and the gray-level distribution functions of the assumed reference image (image before deformation) and the current image (image after deformation) are f(x,y) and g(x,y), respectively. Figure 5As shown, a reference subset is selected at any coordinate position within the analysis area of ​​the reference image. Let the center point of the subset be Q. After the specimen is subjected to a load and deforms, the reference subset moves to the location of the target subset, i.e., point Q. 1 The DIC method uses the normalized least squares cross-correlation matching function CLS to continuously track the trajectory of point Q, thereby obtaining the relationship between point Q and point Q'. 1 The difference in pixel coordinates is used to calculate the displacement information of the subset's center point. The formula is as follows:

[0084]

[0085] After obtaining the displacement information of the subset center point Q, the displacement information of any point T within the analysis area of ​​the reference image can be calculated using the following two formulas.

[0086]

[0087] Among them, u rc v rc The displacement result with integer pixel precision is obtained through initial guessing, where △x and △y are the pixel coordinate differences between points Q and T.

[0088] To clearly capture the development of microcracks during the fracture process at the crack tip, the ligamentous portion above the crack tip of the specimen was set as the image acquisition surface. A high-contrast artificial speckle field was created by alternately spraying matte white and matte black paint within the image observation area. The speckle pattern, acting as a carrier of deformation information, deforms synchronously with the specimen, facilitating subsequent image processing to obtain full-field displacement information.

[0089] 3-PB fracture test setup based on AE and DIC technologies

[0090] AE and DIC monitoring and testing devices based on 3-PB fracture tests, such as Figure 6 As shown. The fracture specimens of MGPCC with different ratios were 40mm×40mm×200mm in size, with a distance of 160mm between the two supports, a crack height ratio of 0.4, and a span-to-height ratio of 4.0. The initial crack length was 16mm, and the loading was controlled by displacement at a rate of 0.1mm / min.

[0091] In the AE acquisition experiment, four R6α probe sensors were used. The distances between the center point of the AE probe sensor and the top and bottom edges of the specimen were 15mm, 25mm, 15mm, and 25mm, respectively, and the distances between the center point of the AE probe sensor and the crack in the middle of the specimen were 60mm. Figure 7As shown. Vaseline was used as a coupling agent to fix four AE probe sensors. To ensure stability during loading and to expel air between the contact surfaces, thus ensuring good elastic wave transmission, electrical tape was used to fix the AE probe sensors to the surface of the specimen.

[0092] Study on fracture damage evolution of MGPCC based on AE parameters

[0093] By combining 3-PB fracture tests of MGPCC with different amounts of nano-SiO2 and steel-PVA hybrid fibers, the changes in parameters such as amplitude, (cumulative) ring count, (cumulative) AE energy, RA-AF and Ib value were analyzed to determine the influence of different mix proportions on the damage evolution during the fracture process of MGPCC. The damage points during the fracture process can be located by arranging four AE probes. The crack propagation paths obtained by the fracture test were compared and found to be very consistent.

[0094] Amplitude

[0095] According to the definition of AE technology, amplitude can be used to characterize the distribution, density, intensity, and attenuation of the AE signal generated during damage in MGPCC fracture. By combining the AE signal with the P-CMOD curve of the fracture process in MGPCC 3-PB fracture tests under different mix proportions, the trend of amplitude variation with the fracture process under different mix proportions can be obtained. Figure 8 The figure shows the amplitude variation during the fracture process of the N-0 mix ratio. High-density AE amplitudes represent the occurrence of numerous AE signals, indicating the generation and propagation of a large number of microcracks. The influence of nano-SiO2, steel fibers, and PVA fibers on the fracture process of MGPCC was analyzed using the AE signal amplitude distribution during the fracture process. It can be found that the distribution of AE signal amplitude is significantly related to the changes in the fracture process, and shows different distribution patterns in the pre-peak and post-peak loading stages. Figure 8 As shown, in the pre-peak stage, the AE signal amplitude data mostly remained stable between 50 and 60 dB, indicating that debonding was caused by matrix cracking and weak fiber-matrix region cracking, at which point microcrack activity reached its peak level. During the peak load loading stage, a large number of AE signal amplitude data appeared between 75 and 100 dB, indicating that a large number of fibers were pulled out and fractured at the fracture site. However, in the post-peak stage, a large number of AE signal amplitudes between 75 and 100 dB were still recorded, as the macroscopic through-cracks continued to crack upwards after the peak load, resulting in a large number of fiber pull-outs and fractures.

[0096] The addition of nano-SiO2 leads to a gradual increase in the intensity of the aberration-effect (AE) signal after peak load during MGPCC fracture, with a greater increase in AE signals between 50 and 75 dB. This is due to the filling effect and chemical reaction of nano-SiO2, which improves the microstructure of MGPCC, such as microcracks and pores, making the material matrix denser. It also inhibits the initiation and propagation of internal cracks during MGPCC fracture, optimizing its peak-to-zero (FPZ) and improving fracture performance. Therefore, overcoming significant resistance is required to generate more and lower AE signals during microcrack formation or crack propagation during fracture. Furthermore, the addition of nano-SiO2 enhances the bridging effect between the fiber and the matrix, reducing the signal generated by fiber pull-out. However, excessive nano-SiO2 cannot disperse properly in the matrix and causes agglomeration, resulting in numerous initial defects. This makes crack initiation and propagation easier during fracture, thus reducing the fracture performance of MGPCC.

[0097] Ring count and AE energy

[0098] Ring count refers to the number of echogenic AE (AE) signals emitted during crack initiation and propagation during the fracture process. The number of signals exceeding a given threshold is closely related to crack initiation and propagation, and ring count is a parameter reflecting the activity of AE signals. The trend of ring count changes indicates the severity and real-time changes in internal damage development within the material. Cumulative ring count reflects the total amount and frequency of AE signals during fracture; the faster the increase, the faster the propagation of microcracks within the material. AE energy is calculated based on the integral of the AE signal envelope and can be used as an indicator of the degree of cracking during fracture, as AE energy is related to crack initiation and propagation. The distribution of (cumulative) ring count and (cumulative) AE energy generated during the fracture process of MGPCC with an N-0 mix ratio is shown in the figure. Figure 9 As shown.

[0099] The effects of nano-SiO2, PVA fibers, and steel fibers on the (cumulative) ring count and (cumulative) AE energy of peak load during MGPCC fracture are shown in Table 1 and 2. Figure 9 As shown in the figure. Comparison revealed that with increasing nano-SiO2 doping, the ring count and AE energy of MGPCC initially decreased and then increased, while the cumulative ring count and cumulative AE energy initially increased and then decreased. Furthermore, the peak load and time to peak load of the specimen increased with increasing nano-SiO2 doping. The addition of nano-SiO2 improved the toughness of MGPCC, leading to increased microcrack propagation within the FPZ at the peak load, while reducing the number of through cracks. This resulted in a more ductile failure tendency. The AE signal generated by microcrack propagation was weaker than that generated by through cracks, leading to a decrease in ring count and AE energy. However, the increased micro-damage generated a large number of AE signals, resulting in an increase in cumulative ring count and cumulative AE energy. According to... Figure 9 It can be seen that as the amount of nano-SiO2 doping increases, the AE signal density also increases, leading to an increase in the cumulative ringing count and the cumulative AE energy.

[0100] Table 1 Summary of (cumulative) ring counts and (cumulative) AE energy of MGPCC under different mix proportions

[0101]

[0102] With increasing PVA fiber content, the ring count and AE energy of MGPCC mainly showed a decreasing trend, as did the cumulative ring count and cumulative AE energy. Furthermore, the peak load of the specimen initially increased and then decreased with increasing PVA fiber content. The decrease in (cumulative) ring count and (cumulative) AE energy at the peak load is because PVA fibers effectively inhibit the generation of microcracks and through-cracks at the peak load during fracture, resulting in fewer through-cracks at final failure and thus a weakened AE signal.

[0103] With increasing steel fiber content, the ring count and AE energy of MGPCC showed an increasing trend. The cumulative ring count and cumulative AE energy generally showed a trend of first decreasing and then increasing. Furthermore, the peak load and the time to reach the peak load of the specimen showed a trend of first increasing and then decreasing with increasing steel fiber content. The increase in ring count and AE energy is due to the delay and increase in peak load, which makes it necessary for the microcracks inside the FPZ to overcome more steel fiber resistance to propagate into through cracks, such as steel fiber debonding, pull-out, and breakage. This process generates more and stronger AE signals, thus increasing the (cumulative) ring count and (cumulative) AE energy.

[0104] Analysis of crack types during MGPCC fracture process based on AF-RA

[0105] Although the 3-PB fracture test is a Type I fracture test, the random distribution of aggregates and fibers can lead to crack deflection and fracture surface tilting. The ratio of tensile cracks to shear cracks varies with the crack bending angle, and the crack type in the MGPCC fracture process involves the coexistence of tensile and shear cracks. The relationship between AF and RA can be used to distinguish between the generation of tensile and shear cracks during the fracture process. Generally, bending failure is accompanied by the generation of tensile cracks, converting the released energy into compressive / expansion waves (P-waves), thus tensile cracks generate P-waves. On the other hand, shear crack generation is accompanied by shear waves (S-waves). However, P-waves propagate faster than S-waves, which have a longer rise time. Therefore, the RA value for shear failure is higher than that for bending failure, while the AF value for bending failure is higher.

[0106] The proportion of shear cracks increases with increasing fiber content, and the flexural toughness of MGPCC is positively correlated with the proportion of shear cracks. The following section uses N-0 and N-1.5 as examples to illustrate the effect of AF-RA on the fracture process of MGPCC. Figure 10 As shown.

[0107] Analysis of crack propagation in MGPCC based on Ib value

[0108] The Ib value of impact AEs is closely related to the development and propagation of cracks within the material. Many studies have shown that structures with higher Ib values ​​during fracture generate more impact AEs with smaller amplitudes, which then develop into microcracks. In contrast, lower Ib values ​​generate fewer impact AEs with larger amplitudes, resulting in predominantly macrocracks. In the failure process of MGPCCs, microcracks are the origin of macrocracks, while macrocracks are the result of microcracks. Throughout the loading process, microcracks continuously appear within the MGPCC, gradually expanding and converging to form large cracks, and the corresponding Ib value changes continuously. Therefore, the degree of cracking within the MGPCC can be inferred from the changes in its Ib value. Figure 11 The figure shows the effect of N-0 and N-1.5 ratios on the change of MGPCC Ib value.

[0109] The Ib values ​​of all specimens showed a trend of gradually decreasing after the load reached its peak. This can be attributed to the fact that during the crack initiation stage, the internal damage of the MGPCC mainly consists of the propagation of numerous microcracks in the FPZ, while in the instability stage, these microcracks gradually form through cracks, thus reducing the Ib value. Changes in the Ib value can serve as an early warning indicator for fracture failure of the specimens.

[0110] FPZ Study of MGPCC Based on AE Signal Localization

[0111] Visual observation, electronic extensometers, and strain gauges can only reveal the displacement and strain occurring on the surface of MGPCC fracture specimens. However, acoustic imaging (AE) technology can reveal the internal damage development during the fracture process of MGPCC specimens, such as the specific location of internal microcracks. In AE technology, the three-dimensional AE source location coordinates can be obtained by utilizing the time difference of the sound waves generated at the damage point reaching four sensor probes. According to AE-win software, the AE source locations of the damage points generated during the fracture process of MGPCC specimens with an N-1.5 mix ratio are as follows: Figure 12 As shown, the damage evolution law of MGPCC in the crack tip FPZ under peak load reflects the improving effect of nano-SiO2 and steel-PVA hybrid fibers on the fracture performance of MGPCC.

[0112] Observations reveal that, unlike the fracture damage of ordinary brittle materials, which have a smaller fracture zone (FPZ) and exhibit continuous upward development of fracture damage within the FPZ, most of the damage in MGPCC under peak load is concentrated at the initial crack tip and a location above it, consistent with the fracture process development. This verifies the accuracy of AE (Augmentation Effect) technology in determining damage location. Based on the concentration and distribution of damage locations determined by AE technology, it can be observed that at peak load, the damage within MGPCC is mainly distributed at the crack tip and a location above the crack tip, forming a distinct "two-row distribution." This is primarily because during the multi-scale fracture process of MGPCC, the crack tip initially bears the load. As the load reaches the initiation load, microcracks are generated in the FPZ, resulting in numerous damage points at the crack tip, which are then converted into AE signals and received. With further increases and transmission of the load, stress redistribution occurs within the FPZ above the crack tip. However, the bridging effect of the steel-PVA hybrid fiber prevents the propagation of many microcracks, resulting in fewer AE damage signals in this area. When the load reaches its peak load, some fibers in the FPZ are broken or pulled out. Microcracks within the FPZ rapidly expand and converge into through-cracks, causing damage. Due to the effect of the hybrid fibers, the MGPCC does not directly undergo brittle fracture; the fracture path is relatively fine and tortuous. As the load increases, the cracks continue to extend upwards, but due to the continued effect of the hybrid fibers in the upper part, the upward extension of the cracks encounters new resistance, resulting in a large amount of damage occurring at the ends of the FPZ, causing a large number of AE signals to be distributed at this location.

[0113] To study the length and width (L) of the FPZ FPZ and W FPZ Set X and Z coordinates along the length and height of the specimen, and count the number of AE events at each position on the X and Z axes. For example... Figure 13 The figure shows a bar chart of the number of AE events at different positions along the X and Z axes of the MGPCC under N-0 conditions. It can be observed that the AE signal weakens with increasing distance from the precast crack. To obtain the characteristics of FPZ, the following assumptions need to be made:

[0114] (1) Length and width of FPZ (L FPZ and W FPZ The number of AE events for each X-axis and Z-axis in the data must be greater than or equal to 20% of the number of AE events at peak load.

[0115] (2) When the number of AE events along the X and Z axes is less than 20% of the number of AE events at peak load, the corresponding damage is relatively low.

[0116] Table 2 L of MGPCC with different ratios under AE technologyFPZ and W FPZ

[0117]

[0118]

[0119] According to the L of FPZ in Table 2 FPZ and W FPZ The ratio of the sizes of W can reveal that FPZ / L FPZ The ratio is around 3. For cement-based materials, which are heterogeneous solids, at the peak load P... max W exists at this location FPZ ≈2×L FPZ The relation, and W FPZ The passivation effect at the crack tip is the main factor contributing to the quasi-brittle fracture mechanism.

[0120] Research on FPZ based on MGPCC surface deformation

[0121] By combining 3-PB fracture tests of MGPCC with different contents of nano-SiO2 and steel-PVA hybrid fibers, the instability fracture process of MGPCC was observed using DIC technology. The strain cloud map at the initial crack tip was digitally analyzed to explore the evolution law of FPZ and crack propagation of MGPCC under different mix proportions.

[0122] Currently, research on FPZ (Fractional Force Zone) is mainly achieved through local strain analysis at the crack tip of the specimen. Images of the MGPCC (Mechanical, Mechanical, and Containment) fracture process are obtained using DIC (Distributed Intensity Conversion) technology for analysis. To obtain the FPZ evolution of MGPCC under different loading processes, the T1-0.2P (Fractional Force Zone) was analyzed. max (Before the peak), T2-0.8P max (Before the peak), T3-P max and T4-0.8P max The strain at the four load points (after the peak) was compared and analyzed, such as... Figure 14 As shown. To study the L of FPZ FPZ Multiple marking lines are equidistantly set above the initial crack tip of the specimen. The length L of FPZ can be obtained by observing the change in horizontal displacement at different marking lines. FPZ ,

[0123] This paper uses the optimal doping group as an example to describe the method of analyzing the fracture process of observed specimens using DIC technology. Figure 15 The figure shows the strain field contour maps of specimens in group P-0.6 under different loading steps. As the fracture process progresses, strain concentration gradually occurs at the tip of the initial crack, and the FPZ (fiber foci zone) gradually forms. Eventually, the microcracks in the FPZ expand into through cracks, leading to fracture failure. At T1 = 0.2P... maxAt the loading point, the specimen is in the elastic stage of the fracture process. As the load increases, there is no excessive strain concentration inside the specimen; instead, strain occurs in multiple locations, including small strain at the crack tip. At this stage, the specimen is still in the microcrack formation phase. As the fracture process further develops, at T2 = 0.8P... max At the loading point, the specimen is in the quasi-brittle fracture stage. A significant strain concentration region forms at the crack tip, and the FPZ (fiber foci) has already exhibited a "flame-like" shape. However, a small amount of strain concentration still exists around the FPZ. Microcracks can be observed on the specimen surface, and these microcracks gradually expand and elongate. At T3 = P max At the loading point, which is the peak load, obvious cracks have appeared on the specimen surface, and the boundaries of the "flame-shaped" FPZs have become clear and are further extending upwards. After the load exceeds the peak load, at T4 = 0.8P... max At the loading point, a through crack has already formed at the upper end of the initial crack in the specimen, and the contour map of FPZ has been destroyed. As the through crack is generated, the length of FPZ gradually decreases, and the fracture process is basically over.

[0124] Damage characteristics study during MGPCC fracture process

[0125] In summary, it can be determined that AE (Augmentation Effect) technology can more accurately reflect the damage evolution during the fracture process of MGPCC. By linking the fracture energy with the cumulative AE characteristic parameters, a fracture damage evolution model for MGPCC can be established. Figure 16 The figure shows the evolution curves of fracture energy and cumulative AE characteristic parameters (cumulative ringing count) for the optimal MGPCC mix ratio (N-1.5), after normalization. As can be seen from the figure, fracture energy and cumulative ringing count exhibit similar evolution patterns, but there are certain differences between them. Fracture energy is often higher than cumulative ringing count. This is mainly because fracture energy is calculated based on the equivalent elastic model, i.e., the area between the P-CMOD curve and the origin, while cumulative ringing count is based on the actual damage changes during the MGPCC fracture process. Therefore, there are some differences between the evolution pattern of cumulative ringing count and equivalent fracture energy.

[0126] Furthermore, the fracture damage evolution of concrete was defined based on the cumulative acoustic emission parameters, as shown in the following formula:

[0127]

[0128] Where, N m The peak load P during the fracture process of MGPCC max The cumulative AE parameters at a certain point in time before, N is the MGPCC at the peak load P maxThe cumulative AE parameter at the fracture site. Meanwhile, for quasi-brittle materials like MGPCC, the fracture damage evolution can usually be characterized by the deformation generated during fracture. Therefore, the initial crack opening displacement (CMOD) value is used to characterize the fracture damage evolution of MGPCC, i.e.:

[0129] dD=f(CMOD)dCMOD

[0130] Where f(CMOD) is the MGPCC fracture damage evolution function. For monotonic fracture loading, the initial state D = CMOD = 0 can be assumed, and D can be obtained through combined calculation formulas. n :

[0131]

[0132] Among them, CMOD s This represents the CMOD value at a certain time point during the MGPCC fracture process, and its relationship with N. m Correspondingly.

[0133] The fracture damage evolution function f(CMOD) of MGPCC can be selected from different forms of concrete damage functions. Among them, the Weibull distribution function can be used, then D... n It can be represented as:

[0134]

[0135] Where k and λ represent parameters related to the geometry and size of the specimen, respectively.

[0136] If the logarithmic distribution function is used, then formula D n It can be represented as:

[0137]

[0138] Where σ and μ represent parameters related to the geometry and size of the specimen, respectively.

[0139] In addition, the Gamma distribution function is also a common distribution function. If the Gamma distribution function is used, then formula D... n It can be represented as:

[0140]

[0141] Where, υ and These represent parameters related to the specimen's geometry and dimensions, respectively. The function is shown below:

[0142]

[0143] To determine the applicability of different cumulative AE damage functions, a fitting comparison was performed based on the three functions mentioned above. Table 3 shows the calculation results of the Weibull distribution function, logarithmic distribution function, and Gamma distribution function used to fit the cumulative AE damage. The comparison revealed that the Gamma distribution function has the best fitting effect on the cumulative AE fracture damage. Therefore, the Gamma distribution function can be used to characterize the evolution of cumulative AE fracture damage in MGPCC, and the relationship between AE parameters and CMOD strain during fracture was established.

[0144] Table 3. Fitting of cumulative fracture damage for different distribution functions of AE

[0145]

[0146] Based on 3-PB fracture tests, the damage evolution of MGPCC during fracture was studied using AE and DIC techniques, and the size and variation law of the FPZ at the initial crack tip of the MGPCC specimen were investigated. The main conclusions are as follows:

[0147] (1) The damage evolution during the fracture process of MGPCC was studied by the variation of AE parameters. Different AE signal amplitudes correspond to different damages during the fracture process. The curves of (cumulative) ring count and (cumulative) AE energy change with time have obvious inflection points and accurately reflect the critical moments of mid-span cracking and peak load of MGPCC specimens. The addition of nano-SiO2 and steel-PVA hybrid fibers can significantly weaken the generation of inflection points in the fracture failure process and slow down the propagation of internal cracks.

[0148] (2) The fluctuations in AF-RA and Ib values ​​reflect the changes in crack types (tensile and shear cracks) and the transformation from microcracks to through cracks during the fracture process of MGPCC. The addition of nano-SiO2 and steel-PVA hybrid fibers increases the number of shear cracks during fracture, but tensile cracks still dominate. Due to the bridging effect of the hybrid fibers, the Ib value of MGPCC shows a slow decreasing trend after cracking, and there are still large upward fluctuations, indicating that more and more dispersed microcracks are forming, while reducing the generation of through cracks.

[0149] (3) Based on the damage localization analysis of AE, it was found that the damage localization under the peak load of MGPCC exhibits a clear "two-row distribution". Combined with the multi-scale fracturing mechanism of MGPCC, the length and width of FPZ (L) can be determined. FPZ and W FPZ With the increase of nano-SiO2 and steel-PVA hybrid fiber content, the L of MGPCC increases. FPZ and W FPZ It gradually increases. Comparison revealed that W FPZ / LFPZ A ratio of 2 to 3 indicates that W FPZ The passivation effect at the crack tip is the main factor contributing to the quasi-brittle fracture mechanism.

[0150] (4) Due to the multi-scale fracturing mechanism within MGPCCs, the FPZ (Fracturing Process Zone) of MGPCCs cannot be judged solely by the deformation of the slurry on the specimen surface, resulting in significant FPZ dimensional errors obtained by DIC (Distributed Injection) and AE (Advanced Imaging) techniques. Studies have found that the FPZ error decreases with decreasing steel-PVA hybrid fiber content, reaching a minimum at P-0 = 2.07 mm. This indicates that the difference in microcrack propagation between the specimen surface and interior is small at this point, indirectly proving the influence mechanism of steel-PVA hybrid fibers on FPZ. For monitoring the multi-scale fracturing process of MGPCCs, AE is more applicable than DIC.

[0151] (5) Based on the principle of energy evolution during fracture, the relationship between fracture energy and AE characteristic parameter evolution of MGPCC was verified. Based on the Gamma distribution model, the cumulative characteristic parameter of AE was linked with the strain during fracture, and the fracture damage function of MGPCC was established. This realized the quantitative characterization of fracture damage process based on AE characteristic parameter, and provided a theoretical basis for evaluating the fracture damage characteristics of MGPCC.

[0152] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0153] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring fracture damage in MGPCC based on AE and DIC, characterized in that, include: MGPCC fracture specimens were prepared according to a predetermined mix ratio, and acoustic emission probes were arranged on the surface of the MGPCC fracture specimens. The MGPCC fracture specimen was mounted on the 3-PB fracture testing apparatus, and displacement-controlled loading was used. During the loading process, the acoustic emission probe acquires AE signals in real time, obtains characteristic parameters based on the AE signals, and determines the spatial distribution of damage points by AE sound source localization; Multiple images were captured at different stages of specimen deformation. Based on DIC technology, displacement information at various points on the specimen surface was calculated, strain cloud maps were obtained, and the length of FPZ and crack propagation law were analyzed. The variation of characteristic parameters with loading time, fracture types, and the propagation process from microcracks to macrocracks were analyzed. The location and range of the FPZ were determined using the AE sound source localization results, and the length and width of the FPZ were statistically analyzed. The captured images are processed to obtain the displacement field and strain field on the surface of the specimen. The changes in the strain concentration region under different loading stages are analyzed to determine the formation and expansion process of the strain concentration zone. By combining the monitoring results of AE and DIC technologies, a fracture damage evolution model for MGPCC is established. The fracture energy is linked to the cumulative AE characteristic parameters, and the Gamma distribution function is used to characterize the cumulative AE fracture damage evolution, thereby achieving a quantitative characterization of the fracture damage process of MGPCC.

2. The method for monitoring MGPCC fracture damage based on AE and DIC according to claim 1, characterized in that, The preparation of MGPCC fracture specimens according to the predetermined mix ratio specifically involves: Specimen preparation: MGPCC fracture specimens were prepared according to the predetermined mix ratio. The specimen size was 40mm×40mm×200mm, the initial crack length was 16mm, the crack height ratio was 0.4, and the span-to-height ratio was 4.

0. Surface treatment: On the ligament part above the crack tip of the specimen, matte white paint and matte black paint are sprayed alternately to form a high-contrast artificial speckle field for DIC technology image processing; Sensor arrangement: Four R6α probe sensors were arranged on the surface of the specimen as AE probes. The distances between the center point of the probe and the top and bottom edges of the specimen were 15mm, 25mm, 15mm, and 25mm, respectively, and the distance from the center of the probe to the crack in the middle of the specimen was 60mm. Vaseline was used as the coupling agent and the probes were fixed with electrical tape to ensure good elastic wave transmission.

3. The method for monitoring MGPCC fracture damage based on AE and DIC according to claim 1, characterized in that, The specific steps of mounting the MGPCC fracture specimen on the 3-PB fracture testing device are as follows: The specimen was mounted on the 3-PB fracture testing apparatus and loaded with displacement control at a rate of 0.1 mm / min. Simultaneously, the AE probe was connected to the digital AE system to acquire AE signals. A high-speed camera was aimed at the speckle field on the surface of the MGPCC fracture specimen and connected to the DIC acquisition system to capture images of the specimen during deformation.

4. The method for monitoring MGPCC fracture damage based on AE and DIC according to claim 1, characterized in that, The characteristic parameters include amplitude, rise time, duration, ring count, AE energy, RA-AF, and Ib value.

5. The method for monitoring MGPCC fracture damage based on AE and DIC according to claim 4, characterized in that, It also includes the average frequency AF, defined as the ratio of the ring count to the duration of the elastic wave in a single impact, and the RA value, which is the ratio of the rise time to the amplitude. The unit of AF measurement is kHz, and the unit of RA measurement is ms / V. Based on the established relationship between AF and RA values, the cracks generated during the fracture process are divided into tensile cracks and shear cracks. The calculation formulas for AF and RA values ​​are as follows:

6. The method for monitoring MGPCC fracture damage based on AE and DIC according to claim 4, characterized in that, The analytical formula for the Ib value is as follows: In the formula, σ and μ are the standard deviation and mean of the amplitude distribution in the AE event, respectively; N(μ-α1σ) is the cumulative count of AE events with amplitude greater than μ-α1σ; N(μ+α2σ) is the cumulative count of AE events with amplitude greater than μ+α2σ; α1 and α2 are empirical constants with values ​​of 0 and 1, respectively.

7. The method for monitoring MGPCC fracture damage based on AE and DIC according to claim 1, characterized in that, During the 3-PB fracture process of MGPCC, since the internal structural failure cannot be directly observed, the spatial distribution of AE events in the specimen is studied through AE damage localization to characterize the evolution and location of internal damage. The location of the AE damage source is inferred based on the time difference and positional difference of the longitudinal waves generated by the AE source reaching different AE probes. Based on the propagation characteristics of the AE signal, the distance from the AE source to the AE probe (d) is calculated. i The formula for calculating ) is: d i =v p (t i -t) Among them, v p The wave velocity is the longitudinal wave velocity, and t is the time of appearance of the AE sound source. i It is the transmission time of the AE signal from the AE sound source to the AE probe; Based on the time difference and position difference, the location of the AE sound source is determined as follows: Where (x, y, z) are the coordinates of the AE sound source, (x i y i , z i () represents the coordinates of the AE probe.

8. The method for monitoring MGPCC fracture damage based on AE and DIC according to claim 1, characterized in that, The principle of the DIC technology is as follows: Multiple images were captured at different stages of specimen deformation. Based on the assumption that the gray-level distribution functions of the reference image and the current image are f(x,y) and g(x,y) respectively, a reference subset was selected at any coordinate position within the analysis region of the reference image. Let the center point of the subset be Q. After the specimen is subjected to load and deforms, the reference subset moves to the location of the target subset, i.e., point Q. 1 The DIC method employs a normalized least squares cross-correlation matching function C. LS By continuously tracking the movement trajectory of point Q, the relationship between point Q and point Q' can be obtained. 1 The difference in pixel coordinates is used to obtain the displacement information of the subset's center point. After obtaining the displacement information of the subset center point Q, the displacement information of any point T within the analysis area of ​​the reference image is calculated using the following formula: Among them, u rc v rc The displacement result with integer pixel precision is obtained through initial guessing, where △x and △y are the pixel coordinate differences between points Q and T.

9. The method for monitoring MGPCC fracture damage based on AE and DIC according to claim 8, characterized in that, The normalized least squares cross-correlation matching function C LS Specifically: Where f(x,y) and g(x,y) are the gray values ​​of the same observation point in the image sub-regions before and after sample deformation, respectively; f m and g m These represent the average gray values ​​of all observation points within the image sub-regions before and after sample deformation.

Images