Flexural Strength Prediction Method Based on Fracture Surface Characteristics and Porosity

Abstract

The present application discloses a flexural tensile strength prediction method based on fracture surface characteristics and porosity, belonging to the technical field of road engineering. The method includes: fabricating a geopolymer stabilized desert sand specimen; scanning the geopolymer stabilized desert sand specimen through a laser confocal microscope to obtain a fracture surface roughness parameter index set of the geopolymer stabilized desert sand specimen, where the fracture surface roughness parameter index set includes root mean square height, fractal dimension, steepness, arithmetic mean height of the surface topography, and core height; evaluating the significance of the fracture surface roughness parameter index set for the flexural tensile strength based on the grey correlation degree method to obtain the target fracture surface roughness parameter index; constructing an equation for the flexural tensile strength based on the target fracture surface roughness parameter index and the structural porosity; and predicting the flexural tensile strength of the geopolymer stabilized desert sand based on the equation for the flexural tensile strength. This method improves the accuracy and reliability of the flexural tensile strength prediction.

Description

Technical Field

[0001] This application belongs to the technical field of road engineering, and particularly relates to a flexural tensile strength prediction method based on fracture surface characteristics and porosity. Background Art

[0002] In the face of the dual challenges of global climate change and ecological protection, the strategic value of geopolymer building materials as a substitute for cement is becoming increasingly prominent. Currently, traffic infrastructure in desert areas faces complex working conditions such as environmental erosion, material deterioration, and insufficient bearing capacity. The engineering community is working hard to achieve breakthroughs in structural performance through the innovation of cementitious material systems. Geopolymer, with its strong interfacial bonding characteristics endowed by its three-dimensional network structure, provides a new path to break through the mechanical property bottleneck caused by the smoothness of desert sand particles and poor gradation.

[0003] The existing flexural tensile strength prediction technology for geopolymer-stabilized desert sand is mainly based on traditional cementitious material theory, but still faces significant technical bottlenecks. First, it is difficult to quantitatively model the multi-scale coupling relationship between flexural tensile strength, fracture surface roughness parameters, and porosity, resulting in limited prediction accuracy of single-parameter regression methods. Second, two-dimensional morphology characterization technology cannot reconstruct the three-dimensional fracture surface topological characteristics, resulting in too high distortion rates in the extraction of key parameters such as steepness and core height. In addition, the empirical model based on standard specimens is difficult to adapt to in-situ heterogeneous sand bodies, and the destructive four-point flexural tensile test requires repeated preparation of specimens. Related technologies have problems such as low prediction accuracy, complex calculation, and high calculation cost for the flexural tensile strength of geopolymer-stabilized desert sand. Summary of the Invention

[0004] This application aims to solve at least one of the technical problems existing in the prior art. For this reason, this application proposes a flexural tensile strength prediction method based on fracture surface characteristics and porosity, which improves the accuracy and reliability of flexural tensile strength prediction.

[0005] In a first aspect, this application provides a flexural tensile strength prediction method based on fracture surface characteristics and porosity, and the method includes:

[0006] Fabricate geopolymer-stabilized desert sand specimens;

[0007] Scan the geopolymer-stabilized desert sand specimens with a laser confocal microscope to obtain a set of fracture surface roughness parameter indicators for the geopolymer-stabilized desert sand specimens, and the set of fracture surface roughness parameter indicators includes root mean square height, fractal dimension, steepness, arithmetic mean height of the surface topography, and core height;

[0008] Evaluate the significance of the set of fracture surface roughness parameter indicators for flexural tensile strength based on the grey relational analysis method to obtain target fracture surface roughness parameter indicators;

[0009] Construct an equation for flexural tensile strength based on the target fracture surface roughness parameter index and the structural porosity;

[0010] Predict the flexural tensile strength of geopolymer stabilized desert sand based on the equation for the flexural tensile strength.

[0011] According to an embodiment of the present application, the method for evaluating the significance of the fracture surface roughness parameter index set on the flexural tensile strength based on the grey relational analysis method to obtain the target fracture surface roughness parameter index includes:

[0012] Calculate the grey relational degree between each index in the fracture surface roughness parameter index set and the flexural tensile strength based on the grey relational degree formula to obtain a grey relational degree set;

[0013] Sort all the indexes in the fracture surface roughness parameter index set in descending order according to the grey relational degree;

[0014] Take the index ranked first in the fracture surface roughness parameter index set as the target fracture surface roughness parameter index.

[0015] According to an embodiment of the present application, the calculation formula for the grey relational degree is as follows:

[0016]

[0017] Wherein, is the grey relational degree, is the grey relational coefficient between the reference sequence and the comparison sequence, is the data volume of the reference sequence.

[0018] According to an embodiment of the present application, the method for obtaining the fracture surface roughness parameter index set of the geopolymer stabilized desert sand specimen by scanning with a laser confocal microscope includes:

[0019] Scan the geopolymer stabilized desert sand specimen with a laser confocal microscope to obtain the three-dimensional data points of the geopolymer stabilized desert sand specimen ;

[0020] Perform three-dimensional reconstruction on the three-dimensional data points to obtain the laser confocal image of the geopolymer stabilized desert sand;

[0021] Calculate the fracture surface roughness parameter index set of the geopolymer stabilized desert sand specimen based on the laser confocal image.

[0022] According to an embodiment of the present application, the calculation formula for the equation of the flexural tensile strength is as follows:

[0023]

[0024] Among them, F is the flexural tensile strength, S is the target fracture surface roughness parameter index, and P is the structural porosity.

[0025] According to an embodiment of the present application, the fractal dimension is calculated by the box dimension method, and the calculation formula is as follows:

[0026]

[0027] Among them, D is the fractal dimension, is the minimum number of boxes required to cover the set each time, is the side length of the box.

[0028] According to an embodiment of the present application, the geopolymer stabilized desert sand specimen includes a geopolymer and desert sand, and the geopolymer includes fly ash, slag and an activator.

[0029] In a second aspect, the present application provides a flexural tensile strength prediction device based on fracture surface characteristics and porosity, and the device includes:

[0030] A production module for producing a geopolymer stabilized desert sand specimen;

[0031] A first processing module for scanning the geopolymer stabilized desert sand specimen through a laser confocal microscope to obtain a set of fracture surface roughness parameter indexes of the geopolymer stabilized desert sand specimen, and the set of fracture surface roughness parameter indexes includes the root mean square height, fractal dimension, steepness, arithmetic mean height of the surface topography and core height;

[0032] A second processing module for evaluating the significance of the set of fracture surface roughness parameter indexes to the flexural tensile strength based on the grey correlation degree method to obtain the target fracture surface roughness parameter index;

[0033] A construction module for constructing an equation of the flexural tensile strength based on the target fracture surface roughness parameter index and the structural porosity;

[0034] A prediction module for predicting the flexural tensile strength of the geopolymer stabilized desert sand based on the equation of the flexural tensile strength.

[0035] In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, it implements the flexural tensile strength prediction method based on fracture surface characteristics and porosity as described in the first aspect above.

[0036] Fourthly, the present application provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for predicting flexural tensile strength based on fracture surface features and porosity as described in the first aspect above is implemented.

[0037] Fifthly, the present application provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor, and the processor is configured to run a program or an instruction to implement the method for predicting flexural tensile strength based on fracture surface features and porosity as described in the first aspect.

[0038] Sixthly, the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, the method for predicting flexural tensile strength based on fracture surface features and porosity as described in the first aspect above is implemented.

[0039] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application.

[0040] The method for predicting flexural tensile strength based on fracture surface features and porosity provided by the present invention has the following beneficial effects compared with the prior art:

[0041] (1) By scanning the geopolymer stabilized desert sand specimens based on a laser confocal microscope to obtain a set of fracture surface roughness parameter indexes, and combining the grey relational analysis method to evaluate the significance of the fracture surface roughness parameters to the flexural tensile strength, the present invention can more accurately predict the flexural tensile strength of the geopolymer stabilized desert sand, reduce the repeated tests and test costs, and improve the accuracy and efficiency of the flexural tensile strength prediction and reduce the calculation cost and complexity of the flexural tensile strength through the evaluation and modeling of key parameters.

[0042] (2) By calculating the grey relational degree between each index in the set of fracture surface roughness parameter indexes and the flexural tensile strength based on the grey relational degree formula, and sorting the indexes from large to small according to the grey relational degree, the present invention selects the index ranked first as the target fracture surface roughness parameter index. It can effectively evaluate the relationship between each fracture surface roughness parameter and the flexural tensile strength, improve the accuracy and efficiency of the flexural tensile strength prediction, and reduce the calculation cost and complexity of the flexural tensile strength.

[0043] (3) By constructing an equation with the target fracture surface roughness parameter index and the structural porosity, the present invention obtains the predicted value of the flexural tensile strength, reveals the internal mechanism of the geopolymer stabilized desert sand from a microscopic perspective, realizes the prediction of the flexural tensile strength, and improves the reliability of the mechanical property evaluation of the geopolymer stabilized desert sand. Description of the Drawings

[0044] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the description of the embodiments in conjunction with the following drawings, where:

[0045] Figure 1 is a schematic flowchart of a flexural tensile strength prediction method based on fracture surface characteristics and porosity provided by an embodiment of the present application;

[0046] Figure 2 is a laser confocal scanning image of stabilized desert sand with oligomers under different ratios provided by an embodiment of the present application;

[0047] Figure 3 is a schematic diagram of the arithmetic mean height of the fracture surface provided by an embodiment of the present application;

[0048] Figure 4 is a schematic diagram of the fractal dimension under different mix ratios provided by an embodiment of the present application;

[0049] Figure 5 is a schematic structural diagram of a flexural tensile strength prediction device based on fracture surface characteristics and porosity provided by an embodiment of the present application;

[0050] Figure 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0051] Next, the technical solutions in the embodiments of the present application will be clearly described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application belong to the scope of protection of the present application.

[0052] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such used data may be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order different from those illustrated or described herein, and the objects distinguished by "first", "second", etc. generally belong to the same category, and do not limit the number of objects. For example, the first object may be one or multiple. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / " generally represents an "or" relationship between the associated objects before and after.

[0053] Next, in conjunction with the drawings, through specific embodiments and their application scenarios, the flexural tensile strength prediction method based on fracture surface characteristics and porosity, the flexural tensile strength prediction device based on fracture surface characteristics and porosity, the electronic device, and the readable storage medium provided by the embodiments of the present application will be described in detail.

[0054] Among them, the flexural tensile strength prediction method based on fracture surface characteristics and porosity can be applied to the terminal, and can be specifically executed by hardware or software in the terminal.

[0055] The terminal includes, but is not limited to, portable communication devices such as mobile phones or tablet computers having a touch-sensitive surface (for example, a touch screen display and / or a touchpad). It should also be understood that in some embodiments, the terminal may not be a portable communication device, but a desktop computer having a touch-sensitive surface (for example, a touch screen display and / or a touchpad).

[0056] In each of the following embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, a mouse, and a joystick.

[0057] The flexural tensile strength prediction method based on fracture surface characteristics and porosity provided by the embodiments of the present application. The execution subject of the flexural tensile strength prediction method based on fracture surface characteristics and porosity can be an electronic device or a functional module or functional entity in the electronic device that can implement the flexural tensile strength prediction method based on fracture surface characteristics and porosity. The electronic devices mentioned in the embodiments of the present application include, but are not limited to, mobile phones, tablet computers, computers, cameras, and wearable devices, etc. Hereinafter, taking the electronic device as the execution subject as an example, the flexural tensile strength prediction method based on fracture surface characteristics and porosity provided by the embodiments of the present application will be described.

[0058] In traditional water-stabilized bases, river sand and cement are the main raw materials. Among them, the high energy consumption and high emissions in the production process of cement not only consume fossil fuels but also release a large amount of carbon dioxide, making it one of the main sources of greenhouse gas emissions. Geopolymer is a green cementitious material formed by alkali-activated industrial waste tailings such as metakaolin, fly ash, and slag, which has excellent properties such as high strength, fire resistance, and frost resistance. It has the potential to largely replace cement. Its wide application in the field of civil engineering can not only consume a large amount of silicate solid waste but also contribute to addressing global climate change and completing the task of greenhouse gas emission reduction. On the other hand, the large-scale exploitation of river sand has brought serious negative impacts on the environment, including the destruction of the ecosystem, water pollution, and unstable river channel structures. Therefore, seeking environmentally friendly alternatives to river sand for use in civil engineering has become a current research hotspot. At the same time, due to the characteristics of desert sand particles being smooth and having poor gradation, its adhesion to cement is poor, resulting in a low replacement rate of desert sand in ordinary Portland concrete. Geopolymer can provide a more powerful adhesive force due to its special three-dimensional network structure. Therefore, using geopolymer as an adhesive material to stabilize desert sand in pavement structures can not only significantly reduce the use of cement and lower carbon emissions but also effectively solve problems such as the low utilization rate of desert sand resources and the high dependence on river sand resources, achieving the comprehensive and high-value utilization of solid waste resources.

[0059] To better understand the mechanical property changes of geopolymer-stabilized desert sand pavement bases, it is first necessary to clarify the response of pavement bases under external loads, temperature changes, and environmental impacts. With the continuous increase in traffic volume in desert areas, traditional pavement materials are gradually unable to meet the requirements for bearing capacity, durability, and stability. Geopolymer materials have become a potential choice for improving the performance of pavement bases in desert areas due to their excellent strength, durability, and environmental adaptability. In the study of geopolymer-stabilized sandy soil, the mechanical properties are usually affected by factors such as the morphology of sandy soil particles, contact characteristics, and the microstructures formed after geopolymer condensation. In particular, the fracture surface characteristics and roughness of the material play an important role in the mechanical property performance. Therefore, how to accurately evaluate the mechanical property changes of geopolymer-stabilized desert sand through quantitative analysis of the roughness parameters of fracture surface characteristics has become one of the current research focuses.

[0060] Existing research mainly focuses on the macro-mechanical properties of materials, such as compressive strength and tensile strength. However, at the micro level, especially the influence of fracture surface characteristic roughness parameter indicators on mechanical properties has not been fully explored, resulting in a relatively low prediction accuracy of the flexural tensile strength of geopolymer-stabilized desert sand.

[0061] Figure 1 is a schematic flow chart of the flexural tensile strength prediction method based on fracture surface characteristics and porosity provided by the embodiments of the present application, as Figure 1As shown, the flexural tensile strength prediction method based on fracture surface characteristics and porosity includes: Step 110, Step 120, Step 130, Step 140, and Step 150.

[0062] Step 110: Fabricate geopolymer-stabilized desert sand specimens;

[0063] In some embodiments, the geopolymer-stabilized desert sand specimens are geopolymer desert sand mixtures, which are prepared by mixing desert sand as aggregate and geopolymer as binder.

[0064] Step 120: Scan the geopolymer-stabilized desert sand specimens with a laser confocal microscope to obtain a set of fracture surface roughness parameter indexes of the geopolymer-stabilized desert sand specimens. The set of fracture surface roughness parameter indexes includes root mean square height, fractal dimension, steepness, arithmetic mean height of surface topography, and core height;

[0065] It is easy to understand that in order to quantify the surface roughness of the geopolymer-stabilized desert sand specimens, calculating roughness parameters through fracture surface topography information can reflect the topography of the geopolymer-stabilized desert sand surface, and these roughness parameters will affect the global characteristics of the geopolymer-stabilized desert sand surface topography.

[0066] In some embodiments, the step of scanning the geopolymer-stabilized desert sand specimens with a laser confocal microscope to obtain a set of fracture surface roughness parameter indexes of the geopolymer-stabilized desert sand specimens includes:

[0067] Scan the geopolymer-stabilized desert sand specimens with a laser confocal microscope to obtain three-dimensional data points of the geopolymer-stabilized desert sand specimens ;

[0068] Perform three-dimensional reconstruction on the three-dimensional data points to obtain a laser confocal image of the geopolymer-stabilized desert sand;

[0069] Based on the laser confocal image, calculate a set of fracture surface roughness parameter indexes of the geopolymer-stabilized desert sand specimens.

[0070] Exemplarily, scan the fracture surface of the geopolymer-stabilized desert sand specimens with a laser confocal microscope to obtain three-dimensional data points of the fracture surface , perform reconstruction on the three-dimensional data points of the fracture surface to obtain a laser confocal image of the geopolymer-stabilized desert sand, Figure 2 which is a laser confocal scanning image of oligomer-stabilized desert sand under different ratios provided by an embodiment of the present application. As Figure 2 shown, calculate a set of fracture surface roughness parameter indexes through the three-dimensional data points of the fracture surface.

[0071] The steepness parameter is used to evaluate the sharpness of the height distribution and is related to the geometry of the tips of peaks and valleys. The formula for calculating the steepness is as follows:

[0072]

[0073] Where Sku is the steepness, A is the surface area of the fracture surface, Z(x,y) is the target area, and Sq is the root mean square height.

[0074] The core height parameter is the distance between the upper and lower horizontal levels of the core surface of the fracture surface.

[0075] The root mean square height parameter is the root mean square value of Z(x,y) within the target area. The formula for calculating the root mean square height is as follows:

[0076]

[0077] Figure 3 is a schematic diagram of the arithmetic mean height of the fracture surface provided by the embodiment of the present application, as Figure 3 shown, the arithmetic mean height parameter of the surface topography is the arithmetic mean of the absolute coordinates within the target area. The formula for calculating the arithmetic mean height of the surface topography is as follows;

[0078]

[0079] Where Sa is the arithmetic mean height of the surface topography.

[0080] In this embodiment, by using a laser confocal microscope to scan the geopolymer stabilized desert sand specimen, three-dimensional data points are obtained and three-dimensional reconstruction is performed to obtain the laser confocal image of the geopolymer stabilized desert sand. Based on this image, the fracture surface roughness parameter index set is calculated, which can characterize the microscopic fracture characteristics of the geopolymer stabilized desert sand. The accuracy of the fracture surface roughness analysis of the geopolymer stabilized desert sand specimen is improved.

[0081] Step 130: Evaluate the significance of the fracture surface roughness parameter index set for the flexural tensile strength based on the grey relational analysis method to obtain the target fracture surface roughness parameter index;

[0082] It should be noted that different roughness parameters may have different results in quantifying the surface roughness of the geopolymer stabilized desert sand. By evaluating the significance of the fracture surface roughness parameter index set for the flexural tensile strength based on the grey relational analysis method, the index with the strongest significance for the flexural tensile strength is used as the target fracture surface roughness parameter index.

[0083] In some embodiments, a three-point bending beam test is used to fracture the test surface to obtain the flexural tensile strength index. The formula for calculating the flexural tensile strength is as follows:

[0084]

[0085] Among them, F is the flexural-tensile strength, q is the failure ultimate load, L is the span, b is the specimen width, and h is the specimen height.

[0086] Step 140: Construct an equation for the flexural-tensile strength based on the target fracture surface roughness parameter index and the structural porosity.

[0087] In some embodiments, the target fracture surface roughness parameter index is the root mean square height. An equation for the flexural-tensile strength is constructed through the root mean square height and the structural porosity. The relationship among the root mean square height, porosity, and flexural-tensile strength obtained through experiments follows the equation .

[0088] Step 150: Predict the flexural-tensile strength of the geopolymer stabilized desert sand based on the equation for the flexural-tensile strength.

[0089] According to the flexural-tensile strength prediction method based on the fracture surface characteristics and porosity provided by the embodiments of the present application, by scanning the geopolymer stabilized desert sand specimen based on a laser confocal microscope to obtain a set of fracture surface roughness parameter indexes, and combining the grey relational analysis method to evaluate the significance of the fracture surface roughness parameters to the flexural-tensile strength, the flexural-tensile strength of the geopolymer stabilized desert sand can be predicted more accurately, reducing repeated testing and test costs. Through the evaluation and modeling of key parameters, the accuracy and efficiency of the flexural-tensile strength prediction are improved, and the calculation cost and complexity of the flexural-tensile strength are reduced.

[0090] In some embodiments, evaluating the significance of the set of fracture surface roughness parameter indexes to the flexural-tensile strength based on the grey relational analysis method to obtain the target fracture surface roughness parameter index includes:

[0091] Calculating the grey relational degree between each index in the set of fracture surface roughness parameter indexes and the flexural-tensile strength based on the grey relational degree formula to obtain a grey relational degree set;

[0092] Sorting all the indexes in the set of fracture surface roughness parameter indexes from largest to smallest according to the grey relational degree;

[0093] Taking the index ranked first in the set of fracture surface roughness parameter indexes as the target fracture surface roughness parameter index.

[0094] It is easy to understand that the grey relational analysis method screens out the key factors that have the greatest influence on the reference sequence by comparing the correlation tightness between the reference sequence and the comparison sequences and sorting.

[0095] In some embodiments, taking the flexural tensile strength of geopolymer-stabilized desert sand as the reference sequence, and taking the root mean square height, fractal dimension, steepness, arithmetic mean height of the surface topography, and core height as the comparison sequences respectively, the grey relational degrees between each comparison sequence and the reference sequence are calculated, and the calculation process is as follows:

[0096] (1)Define the sequences , determine the parameter sequence and the comparison sequence .

[0097] (2)Normalization processing. Since the original data dimensions (units, properties) of each sequence are different, the mean value method is used to perform dimensionless processing on the original data. The calculation formula for mean value processing is as follows:

[0098]

[0099] (3)Calculate the absolute value difference. Calculate the absolute value difference between the reference sequence and the comparison sequence in the same period. The calculation formula is as follows:

[0100]

[0101] (4)Determine the maximum value and the minimum value. Compare the numerical sizes of all absolute value differences to determine the maximum absolute value difference and the minimum absolute value difference. The calculation formula is as follows:

[0102]

[0103]

[0104] (5)Calculate the grey relational coefficients between the reference sequence and the comparison sequence. The calculation formula is as follows:

[0105]

[0106] (6)In some embodiments, the calculation formula for the grey relational degree is as follows:

[0107]

[0108] Wherein, is the grey relational degree, is the grey relational coefficient between the reference sequence and the comparison sequence, is the data volume of the reference sequence.

[0109] Exemplarily, the calculation results obtained by the grey relational degree method are shown in Table 1. The significance ranking is root mean square height > arithmetic mean height > core height > steepness > fractal dimension. It can be seen from Table 1 that the root mean square height is the most significant index affecting the flexural tensile strength.

[0110] Table 1

[0111]

[0112] In this embodiment, by using the grey relational degree calculation formula to obtain the grey relational degree and screening out the key factors that have the greatest influence on the reference sequence, it is possible to more effectively evaluate the influence of different factors on the flexural-tensile strength of geopolymer stabilized desert sand, improve the accuracy of flexural-tensile strength prediction, and reduce the number of tests and costs.

[0113] Exemplarily, a MesoMR23-060V-1 nuclear magnetic resonance analysis device can be used to test the porosity of the specimens.

[0114] In this embodiment, by calculating the grey relational degree between each index in the fracture surface roughness parameter index set and the flexural-tensile strength based on the grey relational degree formula, and sorting the indexes from largest to smallest according to the grey relational degree, the index ranked first is selected as the target fracture surface roughness parameter index. It can effectively evaluate the relationship between each fracture surface roughness parameter and the flexural-tensile strength, improve the accuracy and efficiency of flexural-tensile strength prediction, and reduce the calculation cost and complexity of flexural-tensile strength.

[0115] In some embodiments, the calculation formula of the equation for the flexural-tensile strength is as follows:

[0116]

[0117] Wherein, F is the flexural-tensile strength, S is the target fracture surface roughness parameter index, and P is the structural porosity.

[0118] In some embodiments, Table 2 shows the changes in the fracture surface roughness parameters and porosity of geopolymer-stabilized desert sand under different ratios provided in the embodiments of the present application. As shown in Table 2, as the mix ratio changes from GDS (Geopolymer stabilized Desert Sand) 3.0 to GDS 9.0, the root mean square height, arithmetic mean height, core height, steepness, fractal dimension, and flexural tensile strength show a positive correlation trend, while the porosity and flexural tensile strength show a negative correlation. Therefore, it can be obtained that the larger the roughness parameter index value and the smaller the porosity, the stronger the mechanical properties. This rough surface can increase the locking effect between particles inside the specimen and improve the overall strength of the material. At the same time, the rough fracture surface has more micro-toughness characteristics, such as crack bifurcation and competitive propagation of multiple crack paths. This complex fracture path may disperse the stress concentration, thereby delaying the crack penetration. In addition, more energy is required to maintain the crack propagation. This high-energy dissipation process means that the material can withstand a greater load and ultimately shows higher strength. While a higher porosity reduces the density of the structure, causing the clusters to gradually decrease and the structure to become gradually loose, resulting in a decrease in flexural tensile strength.

[0119] Table 2

[0120]

[0121] Exemplarily, on the basis of selecting the roughness parameter index that has the most significant influence on the flexural tensile strength mechanical properties, it is further combined with the mixture porosity P to jointly construct a relationship formula reflecting the flexural tensile strength through a linear regression equation. By calculation, the target fracture surface roughness parameter index, porosity, and flexural tensile strength satisfy the following equation: 。

[0122] In this embodiment, by constructing an equation through the target fracture surface roughness parameter index and the structural porosity, the predicted value of the flexural tensile strength is obtained, revealing the internal mechanism of geopolymer-stabilized desert sand from a microscopic perspective, achieving the prediction of the flexural tensile strength, and improving the reliability of the mechanical property evaluation of geopolymer-stabilized desert sand.

[0123] In some embodiments, the fractal dimension is calculated using the box dimension method, and the calculation formula is as follows:

[0124]

[0125] where D is the fractal dimension, is the minimum number of boxes required to cover the set each time, is the side length of the box. It should be noted that the fractal behavior exhibited by the fracture surface can be quantified by the box-counting method, and the box dimension is calculated based on the three-dimensional box covering method: Assume a cube with a side length of δ, and its side length can be defined as: , , , . Then, measure the fracture surface with cube boxes with a side length of δ, and there are N(δ) cube boxes on the fracture surface. The relationship between the total number N( ) of the cube boxes and the side length δ can be expressed by the following formula:

[0126]

[0127] where is the number of covering cubes, is the side length of the box, D is the fractal dimension, is the minimum number of boxes required to cover the set each time.

[0128] The calculation formula for the number of covering cubes is as follows:

[0129]

[0130] where is the integral function, is the relative height of the difference points.

[0131] Figure 4 is a schematic diagram of the fractal dimension under different mix ratios provided by the embodiments of the present application. As Figure 4 shown, the fractal dimensions of 5 groups of mix ratios are obtained through fitting calculation, and the calculation formula for the fractal dimension is as follows:

[0132]

[0133] In this embodiment, the fractal dimension is calculated by the box dimension method. By analyzing the fractal characteristics of the geopolymer stabilized desert sand, the influence of its microstructure on the flexural tensile strength can be more accurately revealed, and the accuracy of the flexural tensile strength prediction is improved.

[0134] In some embodiments, the geopolymer stabilized desert sand specimen includes a geopolymer and desert sand, and the geopolymer includes fly ash, slag, and an activator.

[0135] In some embodiments, low-calcium fly ash is used, and blast furnace slag is used to prepare geopolymers through alkali activation. Industrial sodium hydroxide, sodium silicate, and deionized water are used to prepare an alkaline activator. The Kubuqi Desert is used as fine aggregate, and test schemes are designed with mass ratios of fly ash-slag (1:1) to desert sand of 1:3.0, 1:4.5, 1:6.0, 1:7.5, and 1:9.0. The mix proportion design is shown in Table 3. The compaction test is used to determine the optimum moisture content of the raw materials of desert sand-fly ash-slag. On this basis, GDS base specimens are prepared. Sodium hydroxide is added to sodium silicate to adjust the modulus to 1.4, and deionized water is added to adjust the concentration to 70%. After the activator is mixed with desert sand and left to stand (for 2 h), according to the mix proportion design in Table 3, desert sand is mixed with fly ash and slag. After being stirred evenly with a mortar mixer, it is poured into a mold of 40 mm×40 mm×160 mm and compacted into shape. After standing (for 4 h) and demolding, it is cured (for 7 d). After the curing is completed, the specimen is saturated with water for 24 h using a vacuum water saturation instrument, and then the porosity of the specimen is tested using a MesoMR23-060V-1 nuclear magnetic resonance analysis system. After the test is completed, the sample is fractured in a three-point bending form so as to obtain two samples about 1 cm long from the fractured sample for microscopic analysis. Using an OLS5100 laser confocal microscope, an image slice is obtained from the bottom of the surface depression to the top of the surface protrusion of the sample in the selected research area L×M = 2560 μm×2571 μm. Then, the commercial software of the OLS5100 laser confocal microscope of the confocal microscope is used to perform profile and roughness surface analysis on this fractured surface and obtain a set of roughness parameter indicators.

[0136] The calculation formula for adjusting the modulus of the alkali activator solution is as follows:

[0137]

[0138] Where, is the original modulus of the sodium silicate solution when it leaves the factory, M is the target modulus for preparing the sodium silicate solution, is the mass percentage of sodium oxide contained in the initial sodium silicate solution, is the mass of sodium hydroxide required to adjust every 100 g of the initial sodium silicate solution to the target modulus.

[0139] Table 3

[0140]

[0141] In this embodiment, by combining fly ash, slag, and activator to prepare a geopolymer material, it has good mechanical properties and durability, can effectively improve the compressive strength and corrosion resistance of concrete, promotes the application of environmentally friendly building materials, helps reduce the dependence on traditional cement, and reduces carbon emissions.

[0142] In the bending tensile strength prediction method based on fracture surface characteristics and porosity provided by an embodiment of the present application, the execution entity can be a bending tensile strength prediction device based on fracture surface characteristics and porosity. In the embodiments of the present application, taking the bending tensile strength prediction device based on fracture surface characteristics and porosity as an example to execute the bending tensile strength prediction method based on fracture surface characteristics and porosity, the bending tensile strength prediction device based on fracture surface characteristics and porosity provided by the embodiments of the present application is described.

[0143] An embodiment of the present application also provides a bending tensile strength prediction device based on fracture surface characteristics and porosity, as Figure 5 shown. The bending tensile strength prediction device based on fracture surface characteristics and porosity includes: a production module 510, a first processing module 520, a second processing module 530, a construction module 540, and a prediction module 550.

[0144] The production module 510 is used to produce geopolymer stabilized desert sand specimens;

[0145] The first processing module 520 is used to scan the geopolymer stabilized desert sand specimens through a laser confocal microscope to obtain a fracture surface roughness parameter index set of the geopolymer stabilized desert sand specimens, and the fracture surface roughness parameter index set includes root mean square height, fractal dimension, steepness, arithmetic mean height of the surface topography, and core height;

[0146] The second processing module 530 is used to evaluate the significance of the fracture surface roughness parameter index set for the bending tensile strength based on the grey relational analysis method to obtain a target fracture surface roughness parameter index;

[0147] The construction module 540 is used to construct an equation for the bending tensile strength based on the target fracture surface roughness parameter index and the structural porosity; <s

[0148] The prediction module 550 is used to predict the bending tensile strength of the geopolymer stabilized desert sand based on the equation for the bending tensile strength.

[0149] According to the bending tensile strength prediction method based on fracture surface characteristics and porosity provided by the embodiments of the present application, by scanning the geopolymer stabilized desert sand specimens through a laser confocal microscope to obtain a fracture surface roughness parameter index set, and combining the grey relational analysis method to evaluate the significance of the fracture surface roughness parameters for the bending tensile strength, the bending tensile strength of the geopolymer stabilized desert sand can be predicted more accurately, reducing repeated testing and test costs. Through the evaluation and modeling of key parameters, the accuracy and efficiency of the bending tensile strength prediction are improved, and the calculation cost and complexity of the bending tensile strength are reduced.

[0150] The bending tensile strength prediction device based on fracture surface characteristics and porosity provided by the embodiments of the present application can achieve Figures 1 to 4For the sake of avoiding repetition, the processes implemented in the embodiments of the flexural tensile strength prediction method based on fracture surface characteristics and porosity are not described in detail herein.

[0151] In some embodiments, as Figure 6 shown, an embodiment of the present application also provides an electronic device 600, including a processor 601, a memory 602, and a computer program stored on the memory 602 and executable on the processor 601. When the program is executed by the processor 601, it implements the processes of the above-mentioned embodiments of the flexural tensile strength prediction method based on fracture surface characteristics and porosity, and can achieve the same technical effects. For the sake of avoiding repetition, they are not described in detail herein.

[0152] It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.

[0153] An embodiment of the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the processes of the above-mentioned embodiments of the flexural tensile strength prediction method based on fracture surface characteristics and porosity, and can achieve the same technical effects. For the sake of avoiding repetition, they are not described in detail herein.

[0154] Among them, the processor is the processor in the electronic device in the above-mentioned embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc.

[0155] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above-mentioned flexural tensile strength prediction method when executed by a processor.

[0156] Among them, the processor is the processor in the electronic device in the above-mentioned embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory ROM, random access memory RAM, magnetic disks, or optical discs, etc.

[0157] Another embodiment of the present application provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the processes of the above-mentioned embodiments of the flexural tensile strength prediction method based on fracture surface characteristics and porosity, and can achieve the same technical effects. For the sake of avoiding repetition, they are not described in detail herein.

[0158] It should be understood that the chip mentioned in the embodiments of the present application can also be called a device-level chip, a device chip, a chip device, or a chip-on-device, etc.

[0159] It should be noted that in this text, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, but may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, the features described with reference to certain examples may be combined in other examples.

[0160] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to enable a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the flexural strength prediction method based on fracture surface characteristics and porosity in each embodiment of the present application.

[0161] In the description of the present application, "the first feature", "the second feature" may include one or more of such features.

[0162] In the description of the present application, the meaning of "a plurality of" is two or more.

[0163] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Those of ordinary skill in the art, under the inspiration of the present application and without departing from the spirit and scope protected by the claims of the present application, can also make many forms, all of which fall within the protection scope of the present application.

[0164] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0165] Although the embodiments of the present application have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and purposes of the present application, and the scope of the present application is defined by the claims and their equivalents.

Claims

1. A flexural tensile strength prediction method based on fracture surface characteristics and porosity, characterized in that The method includes: Fabricating a geopolymer-stabilized desert sand specimen; Scanning the geopolymer-stabilized desert sand specimen by a laser confocal microscope to obtain a set of fracture surface roughness parameter indices of the geopolymer-stabilized desert sand specimen, where the set of fracture surface roughness parameter indices includes root mean square height, fractal dimension, steepness, arithmetic mean height of the surface topography, and core height; Evaluating the significance of the set of fracture surface roughness parameter indices on the flexural tensile strength based on the grey relational analysis method to obtain the target fracture surface roughness parameter index; The evaluating the significance of the set of fracture surface roughness parameter indices on the flexural tensile strength based on the grey relational analysis method to obtain the target fracture surface roughness parameter index includes: Calculating the grey relational degree between each index in the set of fracture surface roughness parameter indices and the flexural tensile strength based on the grey relational degree formula to obtain a grey relational degree set; Sorting all the indices in the set of fracture surface roughness parameter indices in descending order according to the grey relational degree; Taking the index ranked first in the set of fracture surface roughness parameter indices as the target fracture surface roughness parameter index; Constructing an equation for the flexural tensile strength based on the target fracture surface roughness parameter index and the structural porosity; Predicting the flexural tensile strength of the geopolymer-stabilized desert sand based on the equation for the flexural tensile strength.

2. The flexural tensile strength prediction method based on fracture surface characteristics and porosity according to claim 1, characterized in that The calculation formula for the grey relational degree is as follows: ； Among them, is the grey correlation degree, is the grey correlation coefficient of the reference sequence and the comparison sequence, is the data volume of the reference sequence.

3. The flexural tensile strength prediction method based on fracture surface characteristics and porosity according to claim 1, characterized in that The scanning the geopolymer-stabilized desert sand specimen by a laser confocal microscope to obtain a set of fracture surface roughness parameter indices of the geopolymer-stabilized desert sand specimen includes: The geopolmer-stabilized desert sand specimens were scanned by a laser confocal microscope to obtain the three-dimensional data points of the geopolmer-stabilized desert sand specimens ; For the three-dimensional data points perform three-dimensional reconstruction to obtain a laser confocal image of geopolymer-stabilized desert sand; Calculating and obtaining a set of fracture surface roughness parameter indices of the geopolymer-stabilized desert sand specimen based on the laser confocal image.

4. The flexural tensile strength prediction method based on fracture surface characteristics and porosity according to claim 1, wherein The calculation formula for the equation of the flexural tensile strength is as follows: ； Where F is the flexural tensile strength, S is the target fracture surface roughness parameter index, and P is the structural porosity.

5. The flexural tensile strength prediction method based on fracture surface characteristics and porosity according to claim 1, wherein The fractal dimension is calculated by the box dimension method, and the calculation formula is as follows: ； where D is the fractal dimension, is the minimum number of boxes required to cover the set each time, is the side length of the box.

6. The flexural tensile strength prediction method based on fracture surface characteristics and porosity as claimed in claim 1, wherein The geopolymer-stabilized desert sand specimen includes geopolymer and desert sand, and the geopolymer includes fly ash, slag, and activator.

7. A flexural tensile strength prediction device based on fracture surface characteristics and porosity, which is implemented by using the flexural tensile strength prediction method based on fracture surface characteristics and porosity according to any one of claims 1 to 6, and is characterized in that The device includes: A fabricating module for fabricating a geopolymer-stabilized desert sand specimen; A first processing module for scanning the geopolymer-stabilized desert sand specimen by a laser confocal microscope to obtain a set of fracture surface roughness parameter indices of the geopolymer-stabilized desert sand specimen, where the set of fracture surface roughness parameter indices includes root mean square height, fractal dimension, steepness, arithmetic mean height of the surface topography, and core height; A second processing module for evaluating the significance of the set of fracture surface roughness parameter indices on the flexural tensile strength based on the grey relational analysis method to obtain the target fracture surface roughness parameter index; A constructing module for constructing an equation for the flexural tensile strength based on the target fracture surface roughness parameter index and the structural porosity; A predicting module for predicting the flexural tensile strength of the geopolymer-stabilized desert sand based on the equation for the flexural tensile strength.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the flexural tensile strength prediction method based on the fracture surface characteristics and porosity as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the flexural tensile strength prediction method based on the fracture surface characteristics and porosity as described in any one of claims 1 to 6.

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