Concrete discreteness simulation method

Abstract

The invention discloses a concrete discreteness simulation method, which mainly solves the technical problem that the concrete discreteness is difficult to accurately and truly describe or represent by the existing concrete discreteness test and simulation method. The concrete discreteness is simulated by adopting a random discrete element method, the heterogeneity of the concrete is simulated through random aggregate distribution in consideration of the fact that the concrete is an anisotropic heterogeneous material and the tensile softening effect of the concrete in the fracturing process, and a linear softening model is given to contact between particles; the spatial distribution change of the bonding strength between the matrix and the aggregate is introduced into a random field, so that the sensitivity analysis of random parameters COV and SOF is carried out, and the influence of the aggregate content on the strength discreteness is also carried out. The concrete aggregate distribution and the strength randomness of the interface transition area are considered at the same time, so that the uncertainty and the failure mechanism of the uniaxial compressive strength of the concrete can be excellently reproduced, and the discreteness of the concrete can be truly and reliably simulated.

Description

Technical Field

[0001] The present invention application relates to the technical field of concrete performance evaluation, and particularly relates to a method for simulating the discreteness of concrete. Background Art

[0002] Concrete is a typical anisotropic material, and its macroscopic mechanical parameters such as uniaxial compressive strength, elastic modulus, tensile strength, fracture toughness, fracture energy, etc. all exhibit significant discreteness.

[0003] In actual engineering, due to the non-uniformity of mass concrete and the non-uniformity of the pouring process, the strength parameters of concrete structures (such as concrete gravity dams) have obvious discreteness and variability characteristics; for example, during a strong earthquake, cracks usually occur in the weakest part of the concrete gravity dam. Therefore, the minimum value of the mechanical parameters directly determines the seismic performance of the gravity dam. However, due to the discreteness of its mechanical characteristics, it directly affects the safety of concrete structures. Therefore, studying the discreteness of concrete strength and the like helps to more accurately evaluate the performance and quality of concrete and ensure that it meets the engineering requirements.

[0004] The inventor has found through research that the reason for the discreteness of concrete strength lies in the inhomogeneous microstructural structure inside the concrete, including the randomness of the shape and position of concrete aggregates. Similarly, the strength of the interfacial transition zone will also cause the discreteness of concrete strength. However, the initial defects of concrete widely exist inside it, and it is difficult to completely and quantitatively describe the discreteness at the microscopic level through experiments.

[0005] The information disclosed in this background art section is only used to deepen the understanding of the background art of the present disclosure, and should not be regarded as an admission or any form of suggestion that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0006] The inventor knows a method for studying the discreteness of concrete by numerical analysis, which simulates the discreteness of concrete by introducing random field parameters and based on a finite element model. However, the inventor has found that this simulation method is difficult to accurately describe or characterize the discreteness of concrete because it does not consider the microstructural structure that causes the discreteness of concrete strength.

[0007] In view of at least one of the above technical problems, the present disclosure provides a method for simulating the discreteness of concrete, mainly solving the technical problem that the existing experimental and simulation methods for the discreteness of concrete are difficult to accurately and truly describe or characterize the discreteness of concrete.

[0008] According to one aspect of the present disclosure, a method for simulating the discreteness of concrete is provided, which includes the following steps: (1) Based on the random distribution of concrete aggregates, a uniaxial compression specimen model is correspondingly simulated and generated; (2) In the spatial variation of the bond strength in the interfacial transition zone of the uniaxial compression specimen model, a random field corresponding to the description by the intrinsic mean μ γ , coefficient of variation COV and autocorrelation function R ([[]] x, y ) is introduced accordingly; (3) The coefficient of variation COV and correlation distance SOF are adjusted accordingly to change the spatial distribution of the concrete bond strength corresponding to the random field, and the uncertainty and variability of the uniaxial compression specimen model under the action of load are simulated and obtained.

[0009] In some embodiments of the present disclosure, in the step (1), the random distribution of the concrete aggregate P c ([[]] d< d 0 ) satisfies: ; where P C is the probability that any point in the concrete sample is located on the plane corresponding to the cross-section and within the intersection circle with a diameter d < d 0 ; d 0 is the projected diameter of the aggregate; P k is the volume fraction of the aggregate.

[0010] In some embodiments of the present disclosure, in the step (1), after the uniaxial compression specimen model is established accordingly, the micro-mechanical parameters of the model are calibrated, and the micro-mechanical parameters include elastic modulus, ratio of normal to tangential stiffness, tensile strength, cohesion, softening factor, and friction coefficient of concrete contact.

[0011] In some embodiments of the present disclosure, in the step (2), a series of random fields are generated accordingly by KL expansion based on the set parameters of the random field; where ; ξ x =x 2 -x 1 represents x the distance in the ξ y =y 2 -y 1 represents y the distance in theΔx denote x the distance between any two positions in the Δy denote y the distance between any two positions in the

[0012] In some embodiments of the present disclosure, in the step (3), numerical simulations are performed on the uniaxial compression specimen model for not less than 200 times, and the coefficient of variation COV value that keeps the uniaxial compressive strength stable and the relevant distance SOF value that does not affect the uniaxial compressive strength and failure mechanism are correspondingly selected.

[0013] In some embodiments of the present disclosure, in the step (3), the coefficient of variation COV is correspondingly adjusted to be not less than 4, and the relevant distance SOF is not less than 800 mm.

[0014] One or more technical solutions provided in the embodiments of the present application have at least any one of the following technical effects or advantages: The random discrete element method is used to simulate the discreteness of concrete. Considering that concrete itself is an anisotropic inhomogeneous material and its tensile softening effect during the fracturing process, the inhomogeneity of concrete is simulated by random aggregate distribution, and a linear softening model is given to the contact between particles. A random field is introduced into the spatial distribution change of the bond strength between the matrix and the aggregate, and thus the sensitivity analysis of the random parameters COV and SOF and the influence of the aggregate content on the strength discreteness are carried out. Since the randomness of the concrete aggregate distribution and the interface transition zone strength are considered simultaneously, the uncertainty of the uniaxial compressive strength of concrete and the failure mechanism can be excellently reproduced, and thus the discreteness of concrete can be truly and reliably simulated.

[0015] The concrete discreteness simulation method of the present application is of great significance for ensuring the quality and performance of concrete, improving the construction quality, optimizing the concrete mix ratio, etc.; through the simulation study on the strength discreteness of concrete, the behavior of concrete can be better understood and controlled, providing more reliable technical support for construction engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of a uniaxial compressive strength model specimen in an embodiment of the present application.

[0017] Figure 2 It is a schematic diagram of the relationship between two particle contacts in a linear softening contact model in an embodiment of the present application.

[0018] Figure 3 It is a curve graph of the random aggregate simulation result and the actual test result in an embodiment of the present application.

[0019] Figure 4 It is a schematic diagram of the random aggregate distribution corresponding to different specimen models in an embodiment of the present application.

[0020] Figure 5 Schematic diagram of concrete failure mode in an embodiment of the present application.

[0021] Figure 6 Density distribution diagram of cracks under different random aggregate distributions in an embodiment of the present application.

[0022] Figure 7 Convergence curve of average uniaxial compressive strength in an embodiment of the present application.

[0023] Figure 8 Curve graph of concrete stress-strain simulation results in an embodiment of the present application.

[0024] Figure 9 Spatial distribution of bond strength under different COV values when SOF = 10mm in an embodiment of the present application.

[0025] Figure 10 Spatial distribution of bond strength under different SOF values when COV = 0.2 in an embodiment of the present application.

[0026] Figure 11 Influence of coefficient of variation COV on average UCS and COV in an embodiment of the present application ucs Influence curve graph.

[0027] Figure 12 Schematic diagram of typical failure modes under different coefficients of variation conditions in an embodiment of the present application.

[0028] Figure 13 Crack superposition under different COVs in 200 Monte Carlo simulations in an embodiment of the present application.

[0029] Figure 14 Analysis diagram of SOF in an embodiment of the present application.

[0030] Figure 15 Spatial distribution of different bond strengths of the same aggregate when SOF = 300mm in an embodiment of the present application.

[0031] Figure 16 Crack superposition under different SOFs in 200 Monte Carlo simulations in an embodiment of the present application.

[0032] Figure 17 Schematic diagram of different aggregate volume fractions in an embodiment of the present application.

[0033] Figure 18 Analysis diagram of model parameters corresponding to different aggregate volume fractions in an embodiment of the present application.

[0034] Figure 19Crack superposition of different aggregate volume fractions under 200 Monte Carlo simulations in an embodiment of the present application. Detailed implementation manners

[0035] The programs involved or relied on in the following embodiments are all conventional or simple programs in the technical field of the present invention, and those skilled in the art can make conventional selections or adaptive adjustments according to specific application scenarios.

[0036] To better understand the technical solution of the present application, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0037] To solve the problem that it is difficult to completely and quantitatively describe the discreteness of the mechanical parameters of concrete at the micro and meso levels through existing concrete discreteness tests, and the indirect method based on the finite element model cannot truly reflect the discreteness of concrete, this example discloses a method for simulating the discreteness of concrete. By establishing a discrete element numerical model that can reflect the concrete matrix, aggregate, and interfacial transition zone, and correspondingly selecting a random field to describe the discreteness of the strength of the interfacial transition zone, the discreteness of the macroscopic mechanical characteristics of concrete is truly characterized.

[0038] Specifically, the inventors found in their long-term experimental research that neither the random distribution of single concrete aggregates nor the random distribution of the strength of a single interfacial transition zone can truly reflect the discreteness of the macroscopic mechanical characteristics of concrete indoor tests. Based on the practical experience of the inventors, the inventors believe that to truly simulate the discreteness of the macroscopic mechanical parameters of concrete, the random distribution of concrete aggregates and the random distribution of the strength of the interfacial transition zone should be considered simultaneously.

[0039] (1) Based on the random distribution of concrete aggregates, a uniaxial compression specimen model is correspondingly simulated and generated.

[0040] In this embodiment, it is set that the random distribution of concrete aggregates follows the Fuller formula. In a two-dimensional space, the random distribution of concrete aggregates P c ( d < d 0 ) satisfies: (1).

[0041] Wherein, P C is the probability that any point in the concrete sample is located on the plane corresponding to the cross-section and within the intersection circle with a diameter d < d 0 ; d 0 is the projected diameter of the aggregate; P k is the volume fraction of the aggregate.

[0042] Specifically, in this embodiment, based on the discrete element software PFC and using Clump to simulate concrete aggregates, it is assumed that the concrete aggregates are rigid particles. Refer to Figure 1 , a numerical model of a uniaxial compression specimen with a width of 10 cm and a height of 30 cm is established. Its lower platen is fixedly set, and the side boundaries are free.

[0043] After the numerical model of the uniaxial compression specimen is established, to verify the discovery that the random distribution of single concrete aggregates cannot accurately and realistically reflect the discreteness of the macroscopic mechanical characteristics of concrete, in this embodiment, based on the discrete element software PFC, the uniaxial compression mechanical characteristics of the established concrete uniaxial compression specimen model are simulated. Among them, considering that quasi-brittle materials usually exhibit tensile softening during the loading and failure process, therefore, in order to simulate the failure process of the specimen in this simulation model and make the model consistent with the loading and failure of the actual specimen, in this example, the mechanical properties of concrete are simulated based on the linear softening bond model.

[0044] Specifically, in the linear softening bond model, cracks propagate along the contact points. The interaction between two particles is realized through contact. The state of the two particles at the contact point can be represented by microscopic mechanical constants. Among them, the microscopic mechanical constants on the contact surface centered on the contact point include the normal stiffness K n , the tangential stiffness K s , the friction coefficient u , the surface spacing g s . And in this model, the softening bond contact between two particles will not break immediately when the tensile force reaches the peak tensile force at the contact of the two particles. Only when the tensile force reaches the threshold value controlled by the softening parameter, the softening bond contact will break to generate cracks. In addition, in the linear softening bond model, only tensile softening is considered in the discrete element region. Refer to Figure 2 (a) and Equation (2), while shear softening is not considered at the contact position between two particles. The actual force-displacement relationship in the tangential direction between two particle contacts is shown in Figure 2 (b).

[0045] (2).

[0046] Among them, F is the contact force, K n is the normal stiffness, C n is the normal bonding strength, K np is the slope of the post-peak region of the normal force-normal displacement between two contacting particles, U en is the elastic limit displacement,U tn is the ultimate normal displacement.

[0047] To study the discreteness of concrete strength, 8 actual concrete specimens were also established in this example, and uniaxial monotonic and cyclic compression tests were respectively carried out on the 8 actual concrete specimens. Among them, the size of the actual concrete specimen is 100×100×300mm 3 , the cement used is P.II 42.5R Portland cement, and the aggregate particle size is 5 - 20mm. The test results are shown in Table 1 and Figure 3 (b). It can be seen that the uniaxial compressive strength (UCS) of the actual concrete specimens has significant discreteness (25MPa - 43MPa).

[0048] .

[0049] In addition, in this embodiment, to fully simulate and display the discreteness of concrete, 200 uniaxial compression specimen models were generated based on the random distribution of concrete aggregates. Among them, the random generation of aggregates is realized by using random seeds. See Figure 4 , Figure 4 shows several different random aggregate distribution diagrams. The change of random seeds makes the random distribution of aggregates also show different expressions, and the bond strength of the interface transition zone of each simulated specimen is set to be the same. Then, each random aggregate specimen is simulated according to the micro-mechanical parameters calibrated in Table 2. In this example, the micro-mechanical parameters include elastic modulus, ratio of normal to tangential stiffness, tensile strength, cohesion, softening factor, and friction coefficient of concrete contact. After calibration, the performance of each concrete simulated specimen under uniaxial compression load is tested. The test results are shown in Figure 3 .

[0050] .

[0051] Figure 3 (a) is the uniaxial compression convergence curve of the concrete simulated specimens under different random aggregate distributions. The horizontal axis is the number of random aggregate distributions. It can be seen from this convergence curve that when the number of random aggregate distributions reaches 200, the uniaxial compressive strength of concrete is stable in the region. Figure 3(b) is the stress-strain diagram of concrete under random aggregate distribution of 200. Combining with Table 1, the simulated values of random aggregates in Table 1 are the means of 200 simulation results. It can be seen that the range of uniaxial compressive strength of concrete obtained from the simulated specimens of random aggregates is 31.37 MPa to 39.15 Mpa, while the range of uniaxial compressive strength of the actual specimens is 25 MPa to 43 Mpa. The uniaxial compressive strength of concrete obtained from the simulated specimens of random aggregates cannot well cover the actual test results. Therefore, it can be verified that the discovery of the inventor is that the uncertainty of the uniaxial compressive strength of concrete caused only by the random aggregate distribution cannot truly reflect the discreteness of concrete.

[0052] In addition, the failure modes of concrete in the uniaxial compression test include shear, axial splitting or multiple fracturing modes. Therefore, in this example, the mechanical properties of concrete under uniaxial compression load were tested on actual specimens. The test results are shown in Figure 5 (a). All specimens showed inclined failure surfaces, with angles of 63° to 79° relative to the vertical load perpendicular line. The failure mode of concrete was shear failure.

[0053] Similarly, in this example, simulation tests were also carried out on simulated specimens under different random aggregate distributions to obtain the failure mode of concrete under 80% post-peak loading conditions. See Figure 5 (b). It can be seen that the failure mode of the simulated specimens was also shear failure, reproducing the failure mode of actual concrete under uniaxial compression. And the inclination angle of the shear plane of the random aggregate concrete specimens had a certain degree of uncertainty, which was consistent with the indoor test results.

[0054] In order to further statistically analyze the crack morphology, in this example, the crack position information in PFC was extracted correspondingly, and the crack information of multiple samples was superimposed. The normalized density of microcracks was represented by color. Among them, the normalized density was the number of microcracks normalized by the maximum value per unit area (1 mm 2 ). In this example, a total of 50, 100, 150, and 200 results of random aggregated distributions were simulated. See Figure 6 The post-peak crack superposition results of different random aggregate specimens under 80% load shown in the figure reflect the average position and average failure morphology of the failure plane. It can be found from the figure that as the total number of random aggregate samples increases, the crack density of concrete becomes more uniform. This shows that the numerical simulation specimens do not have a dominant failure plane, which is inconsistent with the indoor actual test, further confirming the discovery that the uncertainty of the uniaxial compressive strength of concrete caused only by the random aggregate distribution cannot truly reflect the discreteness of concrete.

[0055] (2) In the spatial variation of the bond strength in the interfacial transition zone of the uniaxial compression specimen model, the corresponding introduction is made from the intrinsic mean μ γ , coefficient of variation COVand the autocorrelation function R ( x, y ) describes the random field.

[0056] Concrete has a uniform microstructure, with less aggregation of water around the aggregates and a relatively low porosity in the interfacial transition zone. Considering the structural characteristics of the concrete itself, the variability of its matrix is smaller. Therefore, the inventor believes that the bonding strength in the interfacial transition zone determines the uniaxial compressive strength and failure mechanism of the concrete. Thus, in this embodiment, a random field is introduced to simulate the discreteness of the concrete properties by considering the bonding strength in the interfacial transition zone. In this example, the mean value μ γ , coefficient of variation COV and the autocorrelation function R ( x, y ) are used to describe the introduced random field, where the autocorrelation function R ( x, y ) satisfies: (3).

[0057] Among them, ξ x =x 2 -x 1 represents the distance in the x direction, ξ y =y 2 -y 1 represents the distance in the y direction, Δx represents the distance between any two positions in the x direction, Δy represents the distance between any two positions in the y direction. A series of random fields are generated using the KL expansion.

[0058] (3) corresponds to adjusting the coefficient of variation COV and the correlation distance SOF to correspondingly change the spatial distribution of the concrete bonding strength corresponding to the random field, and simulate and obtain the uncertainty and variability of the uniaxial compression specimen model under the action of the load.

[0059] The inventor found during his long-term research and experimental process that even under the same failure mechanism, the uniaxial compressive strength of concrete shows significant discreteness. Based on this, the inventor believes that there are also significant differences in the discreteness between different specimens. To effectively and reliably simulate the uniaxial compressive strength and failure mechanism, in this embodiment, the microscopic parameters of each simulated specimen are kept the same, and a random analysis of the bonding strength in the interfacial transition zone is carried out based on the Monte Carlo simulation method model.

[0060] Specifically, in this embodiment, at least 200 numerical simulations are performed on the uniaxial compression specimen model introducing a random field into the bond strength space of the interface transition zone, Figure 7 showing a convergence result curve of random aggregate distribution. It can be seen from Figure 7 that when the number of simulations approaches 200 times, the average uniaxial compressive strength tends to converge. After comparing the convergence curves of each random aggregate distribution, it can be found that the convergence of the numerical simulation times shown by different random aggregate distributions is similar.

[0061] After introducing a random field into the spatial variation of the bond strength in the concrete interface transition zone, the spatial distribution of the concrete bonding strength is adjusted by changing the mesoscopic strength variation coefficient COV and the correlation distance SOF value of the particles, and the bond strength is obtained based on equations (4) and (5). γ with the average μ γ and each COV value change rule.

[0062] (4).

[0063] (5).

[0064] In this example, first, the random aggregate distribution (M1) corresponding to the upper limit value of the stress-strain curve shown in Figure 3 (b) is selected to analyze the sensitivity of the COV and SOF parameters. By changing the values of COV and SOF, after the discreteness of the simulation results coincides with that of the laboratory test results, and setting ξ x the horizontal and ξ y the SOF values in the vertical direction are the same, and then the stress-strain curve of the concrete is simulated and obtained as shown in Figure 8 (a). And by comparing the simulated results of the concrete random analysis shown in Table 3 with the results of the self-compacting test, it can be known that the range of the uniaxial compressive strength corresponding to the single aggregate random distribution is 30.29 MPa to 42.77 MPa, which cannot effectively cover the actual test results. Therefore, it can be known that the uncertainty of the uniaxial compressive strength caused by the single aggregate under the influence of the random field cannot truly reflect the mechanical properties of the concrete under load.

[0065] .

[0066] Furthermore, in this example, the random aggregate distribution (M2) corresponding to the lower limit value of the stress-strain curve shown in Figure 3 (b) is selected again to simulate the stress-strain of the concrete under two random aggregate conditions (M1, M2). Among them, since the microscopic stiffness remains unchanged, the Young's modulus of the simulated specimen remains unchanged. The result curve is shown in Figure 8(b), during 200 numerical simulation runs, the uniaxial compressive strength of the concrete ranges from 27.17 MPa to 42.77 MPa. Combining with Table 3, it can be seen that although the analysis results of the two aggregates under the influence of the random field are basically consistent with the uniaxial compressive strength range of the laboratory test, they still cannot fully cover the uniaxial compressive strength range of the laboratory test. The reason is caused by the variability between the matrices.

[0067] Therefore, in this example, a corresponding numerical model is established based on the discrete coupling of the random distribution of aggregates and the interfacial transition zone to reflect the discreteness of the concrete.

[0068] In this example, by adjusting the coefficient of variation COV and the correlation distance SOF, the bond strength of the interfacial transition zone of the concrete can be changed. In this example, the range of COV is selected as 0.05 - 4.5, and the corresponding range of SOF is 1 - 3000 mm for simulation tests to analyze the effects of different COV values and SOF values on the uniaxial compressive strength and failure mode of the concrete. The results are shown in Figure 9 and Figure 10 .

[0069] Specifically, the coefficient of variation COV refers to the degree of change in the bond strength space of the interfacial transition zone of the concrete. The larger the COV, the greater the change in the bond strength. In this example, ξ x = ξ y = 10 mm when, within the range of 0.05 - 4.5, the COV value is changed, see Figure 9 , 8 COV values are selected to participate in the simulation. The average uniaxial compressive strength UCS and COV obtained respectively under the action of the random field of a random aggregate distribution of one type of concrete ucs The variation relationship with the COV value is shown in Figure 11 , where COV ucs represents the coefficient of variation of the uniaxial compressive strength. It can be seen from Figure 11 that the average uniaxial compressive strength UCS continuously decreases with the increase of COV and drops from 38.48 MPa to 33.10 MPa; when the COV value exceeds 4.0, the average UCS tends to be stable and the larger the COV UCS . Correspondingly, when COV increases from 0.05 to 4, COV UCS continuously increases with the increase of COV and increases from 0.0085 to 0.0239. When the COV value exceeds 1, COV UCS tends to be stable. Figure 12For (M1), the crack propagation patterns of concrete with different COV values. When the COV value is less than 1.0, the variability of concrete is relatively small. Concrete generates obvious main cracks (about 80% UCS after peak strength), and the crack patterns are consistent, all showing shear failure. As the COV increases, when the COV value is greater than 1.0, the crack patterns of concrete are more complex and diverse, and concrete is more brittle, which can better characterize the situation of more internal defects in concrete.

[0070] In addition, Figure 13 For the change of crack density distribution diagrams under the same load stage (about 80% UCS after peak strength). As the COV increases, the development of cracks is more obvious, and the crack propagation paths are consistent. By comparing the crack density diagrams of different random aggregate distributions, it can be found that under the condition of the same aggregate distribution, the crack propagation paths are less affected by the internal bond strength of concrete.

[0071] To determine the influence of different SOF values on the mechanical properties and failure modes of concrete, in this example, based on the random aggregate distribution (M1) corresponding to the upper limit of the stress-strain curve in the random aggregate analysis as Figure 3 (b), when the COV is equal to 0.2, the variability of the internal structure of concrete is small, and the concrete structure is in a homogeneous state. Considering the change of 10 SOF values, the change range is 1 mm to 3000 mm. The maximum UCS value is used to normalize the UCS obtained by simulating different SOF values to determine the sensitivity of UCS to SOF. Figure 14 (a) shows the variation law of the average UCS with SOF. The normalized UCS first decreases and then increases as SOF increases, as Figure 14 (a) shows. When the SOF value is equal to 50 mm (about half of the sample width), the normalized UCS is at the minimum value (equal to 0.9573). When the SOF exceeds 1000 mm, the magnitude of the normalized UCS is the same as when the SOF is equal to 1 mm.

[0072] Figure 14 (b) shows the variation law of COV UCS with SOF, and a fitting curve of COV UCS changing with SOF is established. When SOF increases from 1 mm to 3000 mm, COV UCS constantly increases with the increase of SOF and increases from 0.0082 to 0.0501. However, when the SOF value exceeds 800 mm, COV UCSTends to be stable. Since the SOF reflects the correlation of the properties of concrete materials, as the SOF value increases, the correlation becomes stronger, and the spatial distribution of equal internal bond strength in concrete is larger. Considering the size of the model itself, which is 100 mm wide and 300 mm high, when the SOF is less than 50 mm, the size of the spatial distribution of the same bond strength is smaller than the width of the specimen. The minimum value of the bond strength is relatively small. The larger the SOF, the smaller the UCS, and the more heterogeneous the spatial distribution of the bond strength (see Figure 10 (a-c)).

[0073] When the SOF increases from 50 mm to 3000 mm, the size of the spatial distribution of the same bond strength increases, and the influence of the size of the specimen itself on it gradually decreases. The minimum value of the bond strength increases, the range of the bond strength decreases, and the properties of the concrete itself become more homogeneous, so the UCS increases. Due to the introduction of the random field, through the MCS method, under the same aggregate distribution, the COV value and the SOF value remain constant. For the model instances generated by different random seeds, the bond strength is still randomly distributed, as shown in Figure 15 .

[0074] When the SOF is greater than half of the width of the specimen, then the size of the spatial distribution of the same bond strength will be larger than the size of the specimen. If the area with weak bond strength increases, its UCS is smaller; while when the area with strong bond strength increases, the UCS will be larger. Therefore, as the SOF increases, the COV UCS increases. Under the same loading stage, as shown in Figure 16 , the crack accumulation density map of a single instance is consistent with the crack development as the SOF increases. It is found that under the condition of the same aggregate distribution, the crack morphology is less affected by the SOF.

[0075] In addition, in this example, the random aggregate distribution of concrete specimens with different aggregate volume fractions is also simulated. See Figure 17 . The aggregate volume fraction of concrete has a certain influence on both the mechanical properties and fracture properties of concrete. Since the coefficient of variation and the correlation distance remain constant when they exceed a certain value, the coefficient of variation of the uniaxial compressive strength of concrete remains constant. Therefore, when considering the influence of the aggregate volume fraction on the discreteness of concrete, in this example, the COV and SOF are 4.5 and 800 mm respectively to simulate the uniaxial compression test of concrete under different aggregate volume fractions to determine the influence of different aggregate volume fractions on the mechanical properties and failure modes of concrete.

[0076] The relationship between the COV UCS obtained under the action of the random field and the aggregate volume fraction is as shown in Figure 18 . As can be seen from the fitting curve in Figure 18 (a), the COV UCSIt increases linearly with the increase of the aggregate volume fraction. This is because as the aggregate volume fraction increases, the randomness of the bond strength also continuously increases, resulting in the COV UCS It continuously increases with the increase of the aggregate volume fraction.

[0077] Figure 18 (b) shows the variation law of the minimum UCS (UCS min ) with the aggregate volume fraction. From this fitting curve, it can be seen that the UCS min decreases linearly with the increase of the aggregate volume fraction. The reason is that the larger the volume fraction of the concrete, the greater the spatial randomness of the corresponding bond strength, making the concrete structure more prone to failure.

[0078] Figure 19 It shows the crack development morphology of concrete with the same kind of aggregate distribution under the action of the same random parameters. During hundreds of MCS runs, by comparing the crack density distribution maps of different concrete aggregate volume fractions, it can be seen that the crack propagation paths are affected differently by the aggregate content.

[0079] In summary, considering both the concrete aggregate distribution and the strength randomness of the interfacial transition zone, through the simulation of the discrete properties of concrete, it can be known that the COV affects the variability of the internal bond strength of concrete. As the COV increases, the variation range of the bond strength becomes larger, which leads to a decrease in the UCS and a greater difference. When the COV exceeds 4, the UCS remains stable, and the crack propagation of concrete becomes more obvious. For the same random aggregate distribution during hundreds of MCS processes, the failure mode of concrete remains consistent.

[0080] The SOF affects the spatial variation region of the internal bond strength of concrete. Considering the influence of the size of the specimen itself, when the correlation distance is about half of the specimen width, the UCS of the concrete is the smallest. As the SOF increases, the size of the same bond strength spatial distribution increases, and the influence of the size of the specimen itself on it gradually decreases. The properties of the concrete itself become more homogeneous and the UCS increases. When the SOF value exceeds 800 mm, the increase of the SOF has little influence on the uniaxial compressive strength and the failure mechanism.

[0081] COV UCS It increases linearly with the increase of the aggregate volume fraction, and the UCS min decreases linearly with the increase of the aggregate volume fraction. Through 200 MCS operations, the crack density maps of different concrete aggregate volume fractions are different, and the crack propagation paths are affected differently by the aggregate content.

[0082] Although some preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn the basic creative concept. Therefore, the appended claims are intended to be interpreted to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0083] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of its inventive concept. Thus, if these modifications and variations to the present invention fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these modifications and variations.

Claims

1. A method for simulating discreteness of concrete, characterized in that: The steps include: (1) Generate a uniaxial compression specimen model based on the simulation of the random distribution of concrete aggregate; (2) The spatial variation of the bond strength in the interface transition zone of the uniaxial compression specimen model is correspondingly introduced by the inherent mean μ γ , coefficient of variation COV and the autocorrelation function R ( x,y ) corresponds to the random field described; (3) Corresponding adjustment coefficient of variation COV and related distance SOF The uncertainty and variability of the uniaxial compression specimen model under load are simulated and obtained by correspondingly changing the spatial distribution of the concrete bonding strength corresponding to the random field.

2. The concrete discreteness simulation method according to claim 1, characterized in that: In step (1), the random distribution of the concrete aggregate P c ( d <d 0 )satisfy: ； in, P C Any point in the concrete sample is located on the plane corresponding to the cross section and on the diameter d <d 0 The probability of being within the intersection circle of d 0 is the projected diameter of the aggregate; P k is the volume fraction of aggregate.

3. The method for simulating discreteness of concrete according to claim 1, characterized in that: In the step (1), after the uniaxial compression specimen model is established, the micromechanical parameters of the model are calibrated, and the micromechanical parameters include elastic modulus, ratio of normal to tangential stiffness, tensile strength, cohesion, softening factor, and friction coefficient of concrete contact.

4. The method for simulating discreteness of concrete according to claim 1, characterized in that: In the step (2), a series of random fields are generated correspondingly through KL expansion based on the set parameters of the random field; in, ; ξ x =x 2 -x 1 express x The distance in direction, ξ y =y 2 -y 1 express y The distance in direction, Δx express x The distance between any two positions in a direction, Δy express y The distance between any two positions in a direction.

5. The method for simulating discreteness of concrete according to claim 1, characterized in that: In the step (3), the uniaxial compression specimen model is numerically simulated for no less than 200 times, and a coefficient of variation is selected so that the uniaxial compressive strength remains stable. COV Values ​​and related distances that do not affect the uniaxial compressive strength and failure mechanism SOF value.

6. The method for simulating discreteness of concrete according to claim 5, characterized in that: In step (3), the coefficient of variation is adjusted accordingly. COV Not less than 4, related distance SOF Not less than 800mm.

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