Method for determining the threshold value of eliminating loess collapsibility by microwave irradiation

Abstract

The present invention discloses a method for determining the threshold value of loess collapsibility by microwave irradiation, which relates to the technical field of data prediction and updating, and is used to solve the problem of insufficient means for evaluating the effect of collapsibility control when the structural changes are not significant. By collecting collapsible loess samples and pre-processing them, their initial physical information data is obtained, microwave irradiation experimental conditions are set, and the trend of change in the collapsibility coefficient of the loess under these conditions is calculated. Based on the trend assessment, whether the temperature distribution analysis method is used to judge the changes in its internal structure is determined. If the changes are weak, the microstructural characteristics and mineral composition changes of the samples are further obtained to comprehensively evaluate the degree of reduction in collapsibility. If the degree of reduction is high, the porosity ratio parameter is introduced to quantitatively analyze the changes in collapsibility, and scanning electron microscopy or X-ray diffraction analysis is used to verify whether it has achieved the expected control target, so as to ensure the stability and safety of the loess.

Description

Technical Field

[0001] The present invention relates to the field of data prediction and updating, and more particularly to a method for determining a threshold value of loess collapsibility by microwave irradiation. Background Art

[0002] Loess is a loose soil with high porosity and weak cementation. While naturally strong, it is prone to structural collapse when exposed to water, creating a phenomenon known as "collapsibility," posing a significant threat to engineering safety. Traditional methods for treating loess collapsibility primarily include chemical modification (such as lime and cement curing) and physical modification (such as vibration compaction and heat treatment). However, these methods often suffer from long treatment cycles, unstable results, and poor adaptability.

[0003] In recent years, with the increasing research in soil modification, microwave heating technology has become a new approach for treating loess collapsibility, thanks to its advantages of non-contact, rapid heating, and controllable energy. Under microwave irradiation, water rapidly migrates and the structure of loess changes, potentially leading to the reorganization of pore structure and the enhancement of mineral cementation, potentially suppressing or eliminating collapsibility.

[0004] The existing technology has the following deficiencies:

[0005] Currently, existing research lacks quantitative analysis of the correlation between temperature field evolution and changes in collapsibility, making it impossible to scientifically determine the critical control parameters for microwave irradiation. Furthermore, when loess structural changes are insignificant, there is a lack of supplementary methods for assessing the degree of collapsibility reduction at the microscopic level (such as pore structure and mineral composition), limiting the applicability and reliability of the technology. Therefore, a method for determining the threshold for eliminating loess collapsibility using microwave irradiation was proposed.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for determining the threshold value of loess collapsibility by microwave irradiation. By using a comprehensive determination technology that combines dynamic monitoring of the microwave irradiation temperature field with analysis of the trend of changes in the collapsibility coefficient, supplemented by a multi-scale quantitative evaluation method of microstructural characteristics and mineral composition changes, the problems raised in the above-mentioned background technology are solved.

[0008] To achieve the above object, the present invention provides the following technical solution: a method for determining the threshold value of loess collapsibility by microwave irradiation, comprising the following steps:

[0009] Step S1: Collect collapsible loess samples and perform preprocessing to obtain their initial physical information data;

[0010] Step S2: setting microwave irradiation experimental conditions based on the initial physical information data of the pre-treated loess sample, calculating the collapsibility coefficient variation trend of the loess sample under the microwave irradiation experimental conditions, and evaluating whether to use the temperature distribution analysis method to judge the change of the loess internal structure based on the collapsibility coefficient variation trend of the loess;

[0011] Step S3: If the internal structure of the loess is judged to have changed slightly, the microstructural characteristics and mineral composition changes of the sample are further obtained to comprehensively evaluate the degree of reduction in the collapsibility of the loess. If the degree of reduction in collapsibility is high, the porosity ratio parameter is introduced to quantitatively analyze the change in collapsibility.

[0012] Step S4: Based on the quantitative analysis results of the collapsibility change, scanning electron microscopy testing or X-ray diffraction analysis is selected to verify whether the loess collapsibility reaches the expected control target.

[0013] In a preferred embodiment, in step S1, the ratio of the difference between the wet soil mass and the dry soil mass to the dry soil mass is defined as the natural moisture content;

[0014] The initial dry density is obtained by dividing the wet soil mass of the density measurement sample by the product of the volume of the density measurement sample and the natural moisture content plus one;

[0015] Based on the mass and temperature change of constant temperature water, the heat absorbed by the loess sample is calculated to obtain the natural specific heat capacity.

[0016] In a preferred embodiment, in step S2, the natural moisture content, initial dry density and natural specific heat capacity are constructed as input vectors of the support vector regression method;

[0017] The radial basis kernel function is used to map the input vector to a high-dimensional feature space, and a microwave power prediction model is constructed by solving the minimization problem.

[0018] The microwave power is obtained by solving the microwave power prediction model;

[0019] Calculate the total amount of heat required to completely evaporate the water in the loess sample as the evaporation energy;

[0020] The objective function of the Bayesian optimization method is to minimize the deviation between the evaporation energy and the theoretical heat energy provided by microwave irradiation.

[0021] The posterior probability model is constructed through Gaussian process and sample points of the objective function are selected based on the expected lifting function. The iteration converges to the optimal solution, which is the irradiation time.

[0022] In a preferred embodiment, in step S2, the collapsibility coefficient of the loess sample in the initial state is measured as the collapsibility coefficient before the experiment;

[0023] According to the microwave power and irradiation time, the loess samples were subjected to microwave irradiation experimental treatment;

[0024] After the microwave irradiation experiment is completed, the collapsibility coefficient of the loess sample is measured again as the post-experimental collapsibility coefficient;

[0025] Calculate the ratio of the collapsibility coefficient before the experiment to the collapsibility coefficient after the experiment;

[0026] When the ratio of the collapsibility coefficient before the experiment to the collapsibility coefficient after the experiment is less than one, it means that the collapsibility is weakened;

[0027] When the ratio of the collapsibility coefficient before the experiment to the collapsibility coefficient after the experiment is equal to one, it means there is no significant change;

[0028] When the ratio of the collapsibility coefficient before the experiment to the collapsibility coefficient after the experiment is greater than one, it indicates that the collapsibility is enhanced.

[0029] In a preferred embodiment, in step S2, when the collapsibility is weakened, the temperature data of the loess sample during the irradiation process is collected, a two-dimensional temperature spatial distribution matrix is ​​established, and the local temperature variance is calculated;

[0030] Set a variance threshold and compare the local temperature variance with the variance threshold;

[0031] If the local temperature variance is greater than the variance threshold, it is determined that the internal structure of the loess has changed drastically;

[0032] When the local temperature variance is less than the variance threshold, it is judged that the internal structure of the loess changes slightly.

[0033] In a preferred embodiment, in step S3, when it is determined that the internal structure of the loess has changed slightly, the microstructural characteristics of the loess are obtained, including the loess sample volume, pore volume, outer perimeter of the loess particles, and area of ​​the loess particles;

[0034] Porosity is the ratio of pore volume to loess sample volume;

[0035] The particle morphology index was calculated by the outer perimeter of the loess particles and the area of ​​the loess particles;

[0036] Analyze the mineral composition of loess samples, including the mass of expansive minerals, the total mineral content in the loess samples, and the mass of quartz;

[0037] The ratio of the mass of expansive minerals to the total mineral content in the loess sample was taken as the expansive mineral content value;

[0038] The ratio of quartz mass to the total mineral content in the loess sample was taken as the quartz content value.

[0039] In a preferred embodiment, in step S3, the degree of reduction in the collapsibility of loess is comprehensively evaluated by the TOPSIS method based on the microstructural characteristics and the changes in the mineral composition;

[0040] The porosity values, grain morphology index, expansion mineral content values, and quartz content values ​​were normalized and combined into a feature vector;

[0041] The TOPSIS method calculates the relative proximity of loess samples through eigenvectors;

[0042] Sort the relative proximity in descending order and select the median of the sorted values ​​as the proximity threshold;

[0043] If the relative proximity is greater than the proximity threshold, the degree of reduction in collapsibility is judged to be high; otherwise, the degree of reduction in collapsibility is judged to be low.

[0044] In a preferred embodiment, in step S3, when the degree of reduction in collapsibility is high, the porosity is further calculated to calculate the void ratio, and the change in collapsibility is quantitatively analyzed;

[0045] The sampling period is preset, and the porosity ratio at the start of sampling is subtracted from the porosity ratio at the end of sampling to obtain the porosity ratio change;

[0046] The ratio of the porosity change to the porosity at the start of sampling was taken as the degree of collapsibility change.

[0047] In a preferred embodiment, in step S4, the preset collapsibility threshold is compared with the collapsibility change degree. If the collapsibility change degree is greater than the preset collapsibility threshold, the collapsibility change degree is judged to be high; otherwise, the collapsibility change degree is judged to be low.

[0048] When the degree of collapsibility change is high, scanning electron microscopy is used to test and analyze whether the loess collapsibility reaches the expected control target;

[0049] When the degree of collapsibility change is low, X-ray diffraction analysis is used to verify whether the loess collapsibility reaches the expected control target.

[0050] Technical effects and advantages of the present invention:

[0051] The present invention collects collapsible loess samples and preprocesses them to obtain their initial physical information data, sets microwave irradiation experimental conditions, calculates the change trend of the loess collapsibility coefficient under the microwave irradiation experimental conditions, and evaluates whether to use the temperature distribution analysis method to judge the change of the internal structure of the loess based on the change trend of the loess collapsibility coefficient. When it is judged that the change of the internal structure of the loess is weak, the microstructure characteristics and mineral composition changes of the samples are further obtained to comprehensively evaluate the degree of reduction in the collapsibility of the loess. If the degree of reduction in the collapsibility is high, the porosity ratio parameter is introduced to quantitatively analyze the change in the collapsibility, and scanning electron microscopy testing or X-ray diffraction analysis is selected to verify whether the loess collapsibility reaches the expected control target, thereby ensuring the stability and safety of the loess. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart for implementing the method for determining the threshold value of loess collapsibility by microwave irradiation according to the present invention.

[0053] Figure 2 This is a schematic diagram of the steps of the method for determining the threshold value of loess collapsibility by microwave irradiation according to the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. Example

[0055] See also Figures 1 to 2 , the method for determining the threshold value of loess collapsibility by microwave irradiation is as follows:

[0056] Step S1: Collect collapsible loess samples and perform preprocessing to obtain their initial physical information data;

[0057] Step S2: setting microwave irradiation experimental conditions based on the initial physical information data of the pre-treated loess sample, calculating the collapsibility coefficient variation trend of the loess sample under the microwave irradiation experimental conditions, and evaluating whether to use the temperature distribution analysis method to judge the change of the loess internal structure based on the collapsibility coefficient variation trend of the loess;

[0058] Step S3: If the internal structure of the loess is judged to have changed slightly, the microstructural characteristics and mineral composition changes of the sample are further obtained to comprehensively evaluate the degree of reduction in the collapsibility of the loess. If the degree of reduction in collapsibility is high, the porosity ratio parameter is introduced to quantitatively analyze the change in collapsibility.

[0059] Step S4: Based on the quantitative analysis results of the collapsibility change, scanning electron microscopy testing or X-ray diffraction analysis is selected to verify whether the loess collapsibility reaches the expected control target.

[0060] The specific implementation is as follows:

[0061] In step S1, collapsible loess samples in the target area are collected on-site, and the undisturbed sampling method is used to ensure the integrity of the sample structure. After sampling, the loess samples are sealed to prevent water evaporation or structural disturbance from affecting the test results;

[0062] The collected loess samples were brought into the laboratory for pretreatment. Large-particle impurities were removed from the loess samples. The samples were placed under standard curing temperature conditions to achieve moisture balance. The quality of the loess samples was calibrated to ensure the stability of the loess samples. After the pretreatment of the loess samples was completed, the initial physical information data of the loess was obtained, including natural moisture content, initial dry density and natural specific heat capacity.

[0063] First, the natural moisture content was determined using the drying method. The drying method involves taking a loess sample and weighing the wet soil mass using a precision balance. The sample is then placed in an oven for constant temperature drying, removed, cooled to room temperature, and the dry soil mass is weighed again. The natural moisture content is defined as the ratio of the difference between the wet and dry soil masses to the dry soil mass. The natural moisture content reflects the degree of water saturation in the soil sample under its natural state. The higher the natural moisture content, the higher the degree of water saturation in the loess sample.

[0064] Secondly, the initial dry density is determined. The dry soil mass per unit volume of soil is determined by the ring knife method. The specific operation of the ring knife method is to use a ring knife to press into the loess sample in the vertical direction, scrape off the excess soil at the upper and lower edges, take the density measurement sample, weigh the wet soil mass of the density measurement sample under the water content state, and calculate the dry soil mass of the density measurement sample in combination with the natural moisture content. The specific calculation formula for calculating the initial dry density is: ,in, is the initial dry density, reflecting the compaction state of the soil, To measure the density of loess in the water-containing state, is the natural moisture content, The volume of the sample taken for density measurement by the knife ring;

[0065] Finally, the natural specific heat capacity is calculated using a mixed method calorimeter measurement and the principle of heat conservation. The specific operation is to place the loess sample in an oven for constant temperature drying and then cool it to room temperature. Constant temperature water is prepared as a heat exchange medium. The dry loess sample and constant temperature water are placed together in a calorimeter container with good thermal insulation performance. They are fully in contact and reach a thermal equilibrium state. Based on the principle of heat conservation, the specific calculation formula for the natural specific heat capacity is: ,in, is the natural specific heat capacity, is the specific heat capacity of water, For constant temperature water quality, is the mass of dry loess sample, is the initial temperature of the constant temperature water, is the final temperature of the mixture after the dry loess sample and constant temperature water are stabilized, is the initial temperature of the loess sample;

[0066] It should be noted that the in situ sampling method refers to a method in which a sampling tool with a protective structure is used to directly obtain undisturbed or undisturbed soil volume samples from the target soil layer during the geotechnical sampling process, which is used to maximize the preservation of the natural structure, moisture state, density and other physical properties of the soil; the ring knife is a special tool for cylindrical soil sampling and density measurement, with a fixed inner diameter, height and volume; the mixed method calorimeter is an experimental device used to measure the specific heat capacity of solid samples. It is constructed based on the principle of conservation of heat and operates in an adiabatic or quasi-adiabatic environment.

[0067] In step S2, based on the initial physical information of the pretreated loess sample, parameter indicators of the microwave irradiation experimental conditions are set, including microwave power and irradiation time;

[0068] Firstly, the support vector regression method is used to establish a microwave power prediction model. The natural moisture content, initial dry density and natural specific heat capacity are constructed as input vectors. Based on the historical data of microwave irradiation experiments of loess samples under different combinations of natural moisture content, initial dry density and natural specific heat capacity, a training sample set is constructed. The radial basis kernel function is used to map the input vector to a high-dimensional feature space. The microwave power prediction model is constructed by solving the minimization problem. The model output is ,in, is the microwave power, is the kernel function, and is the Lagrange multiplier, b is the bias term, is the training sample set, is the input vector, i is the index value of the training sample set, and n is the number of training sample sets. The microwave power that matches the initial physical information of the loess sample is obtained by solving the model;

[0069] Subsequently, the evaporation energy required to achieve evaporation was calculated based on the natural moisture content. The evaporation energy reflects the total amount of heat required to completely evaporate the water in the loess sample per unit mass. The evaporation energy was used as the target heat input, and the Bayesian optimization method was used to calculate the irradiation time that meets the evaporation heat energy requirement.

[0070] The Bayesian optimization method uses the minimum deviation between the evaporation energy and the theoretical thermal energy provided by microwave irradiation as the objective function. A posterior probability model is constructed through a Gaussian process, and sample points are selected based on the expected lift function. The method iteratively converges to the optimal solution, which is the irradiation time.

[0071] After completing the calculation of microwave power and irradiation time, the microwave irradiation experiment phase begins. By setting the microwave power and irradiation time, the degree of effect of microwave irradiation on the collapsibility of loess is verified, and the strength of its structural change is evaluated based on the change in the collapsibility coefficient and the temperature field distribution.

[0072] First, the collapsibility coefficient of the loess sample in its initial state was measured as the collapsibility coefficient before the experiment. The collapsibility coefficient measures the degree of volume compression of loess caused by water infiltration under loading conditions and is defined as the ratio of the vertical compression deformation of the loess sample under saturated loading conditions to the original height of the loess sample.

[0073] Subsequently, the loess samples were subjected to microwave irradiation experiments according to the microwave power and irradiation time determined by the above calculations. After the microwave irradiation experiment was completed, the loess samples were cooled to room temperature and the collapsibility coefficient of the loess samples was measured again using the same loading conditions and measurement method. This was used as the post-experimental collapsibility coefficient.

[0074] Calculate the ratio of the collapsibility coefficient before the experiment to the collapsibility coefficient after the experiment, and record it as , to quantify the changing trend of the collapsibility coefficient of loess samples before and after microwave irradiation experiments;

[0075] when When it is, it means that the collapsibility is weakened;

[0076] when : indicates no significant change;

[0077] when When , it indicates that the collapsibility increases;

[0078] When the collapsibility is weakened, in order to further identify the microscopic impact of microwave irradiation on the internal structure of loess, the temperature distribution analysis method is used to evaluate the changes in the internal structure of loess during the irradiation process.

[0079] The temperature distribution analysis method collects temperature data of loess samples during irradiation through an embedded thermocouple matrix, establishes a two-dimensional temperature spatial distribution matrix, calculates the local temperature variance, and evaluates the distribution consistency of heat energy within the loess.

[0080] A variance threshold is set, and the local temperature variance is compared with the variance threshold. The above variance threshold is obtained by professionals through experiments and will not be described in detail here;

[0081] If the local temperature variance is greater than the variance threshold, it is determined that the internal structure of the loess has changed significantly, and the heat energy transfer within the loess sample is significantly uneven, which usually corresponds to obvious pore structure changes or crack formation.

[0082] When the local temperature variance is less than the variance threshold, it is judged that the internal structure of the loess has changed slightly, indicating that the macroscopic heat conduction is relatively uniform and the degree of structural damage is relatively mild. Microscopic mineral composition changes and subtle pore structure adjustments may still exist within the loess sample. These changes do not show significant gradient heterogeneity in the temperature field, but they have a significant impact on collapsibility. Therefore, under the condition that the temperature distribution analysis determines that the structural changes are weak, further microstructural characteristics and mineral composition changes of the loess samples are collected to comprehensively evaluate the degree of reduction in the loess's collapsibility.

[0083] It should be noted that the support vector regression method is a regression analysis technology based on the support vector machine theory. By constructing a regression model, it realizes the fitting of the nonlinear mapping relationship between the input data and the target variable; the radial basis kernel function is a commonly used kernel function form, which is used to map the input data to a high-dimensional feature space, thereby realizing the linear separability of nonlinear data; the Bayesian optimization method is a global optimization strategy based on Bayesian statistical theory. By constructing a probability model of the objective function and combining the acquisition function, the optimal sampling point is selected within a finite number of evaluations; the Gaussian process is a non-parametric Bayesian statistical model that defines the correlation between any points in the input space through the mean function and the covariance function; the expected lift function is a commonly used acquisition function used to guide the selection of sampling points in Bayesian optimization; the embedded thermocouple matrix refers to an array structure composed of multiple thermocouple sensors embedded in the material or sample to be tested according to a certain spatial layout rule.

[0084] In step S3, when it is determined that the internal structure of the loess has changed slightly, a microstructure image of the loess sample is obtained by scanning electron microscopy testing, and the microstructure characteristics of the loess in the microstructure image are obtained, including the volume of the loess sample, the pore volume of the loess, the outer perimeter of the loess particles, and the area of ​​the loess particles;

[0085] Porosity is the ratio of the pore volume in loess to the volume of the loess sample. The ratio of the pore volume of loess to the volume of the loess sample is taken as the porosity value. The higher the porosity value, the stronger the collapsibility.

[0086] Irregular loess particles are prone to collapse. The degree of regularity can be measured by calculating the roundness of loess particles: , where P is the outer perimeter of the loess particle, A is the area of ​​the particle, and R is the particle morphology index;

[0087] The mineral composition of the loess samples was analyzed by X-ray diffraction, including the mass of expansive minerals, the total mineral content in the loess samples, and the mass of quartz;

[0088] The expansive mineral content is the content of expansive minerals in loess particles. The ratio of the mass of expansive minerals to the total mineral content in the loess sample is taken as the expansive mineral content value.

[0089] The quartz content is a non-expanding mineral in the loess sample. The higher the quartz content, the lower the collapsibility. The ratio of the quartz mass to the total mineral content in the loess sample is taken as the quartz content value.

[0090] The TOPSIS method was used to comprehensively evaluate the degree of reduction in loess collapsibility based on the microstructural characteristics and mineral composition changes:

[0091] The microstructural characteristics and mineral composition changes of multiple loess samples were analyzed, and the porosity values, particle morphology index, expansion mineral content values, and quartz content values ​​were normalized and combined into feature vectors.

[0092] In each eigenvector, the maximum and minimum values ​​of porosity, grain morphology index and expansion mineral content are taken as the ideal value and negative ideal value respectively, and the minimum and maximum values ​​of quartz content are taken as the ideal value and negative ideal value respectively;

[0093] The distance between each eigenvector and the ideal value and the negative ideal value is obtained by calculating the distance; the distance between the eigenvector and the ideal value is: ,in, is the jth evaluation index of the i-th sample, is the ideal value of the jth indicator, n is the number of evaluation indicators, is the distance between the i-th sample and the ideal value; the distance between the eigenvector and the negative ideal value is: ,in, is the negative ideal value of the j-th indicator, is the distance between the i-th sample and the negative ideal value;

[0094] Compute relative closeness using ideal and negative ideal values: ,in, is the relative proximity of the i-th loess sample; the greater the relative proximity, the greater the reduction in the collapsibility of the loess sample;

[0095] Sort the relative proximity in descending order, and select the median after sorting as the proximity threshold. If the relative proximity is greater than the proximity threshold, the degree of reduction in collapsibility is judged to be high; otherwise, the degree of reduction in collapsibility is judged to be low.

[0096] When the degree of collapsibility reduction is high, the change in collapsibility is quantitatively analyzed by calculating the void ratio, and the void ratio is calculated by the porosity: ,in, is the porosity, is the porosity ratio;

[0097] The sampling period is preset, and the porosity ratio at the start of sampling is subtracted from the porosity ratio at the end of sampling to obtain the porosity ratio change;

[0098] It should be explained that the starting time of the preset sampling period is set when the loess sample is not affected by the collapse, and the ending time of the sampling period is set when the porosity ratio of the loess sample remains unchanged;

[0099] If the porosity ratio of the loess sample changes more, it means that the loess sample has a greater collapsibility; if the porosity ratio changes less, it means that the loess sample has a smaller collapsibility.

[0100] The ratio of the porosity change to the porosity at the start of sampling was taken as the degree of collapsibility change.

[0101] The evaluation indicators are comprehensively ranked by the TOPSIS method, and the proximity threshold is determined by the median to quantify the degree of reduction in collapsibility. The degree of change in porosity is further calculated to achieve dynamic quantitative analysis of the change in collapsibility, which can provide more accurate prediction and evaluation of collapsibility and provide data support for subsequent control measures.

[0102] It should be noted that scanning electron microscopy testing is a high-resolution microscope that uses an electron beam to scan the surface of a sample and obtains images and material composition by analyzing the interaction between the sample and the electron beam; X-ray diffraction is a method that uses the principle of interaction between X-rays and matter to analyze the crystal structure, mineral composition and phase composition of the material. Based on information such as the diffraction angle and intensity, the crystal structure, mineral type, crystal size and other properties of the loess sample are inferred; the TOPSIS method is a multi-criteria decision analysis method that evaluates the quality of each sample by the distance from the ideal solution and the negative ideal solution, and ultimately determines the ranking of the degree of reduction in wetting resistance.

[0103] In step S4, the preset collapsibility threshold is compared with the collapsibility change degree. If the collapsibility change degree is greater than the preset collapsibility threshold, the collapsibility change degree is determined to be high; otherwise, the collapsibility change degree is determined to be low.

[0104] When the degree of collapsibility change is high, scanning electron microscopy is used to test and analyze whether the loess collapsibility reaches the expected control target;

[0105] When the degree of collapsibility change is low, X-ray diffraction analysis is used to verify whether the loess collapsibility reaches the expected control target.

[0106] By comparing the preset collapsibility threshold with the quantitative analysis results, a differentiated analysis verification mechanism is selected to further confirm whether the loess has achieved the expected collapsibility control target and verify the accuracy of the collapsibility assessment.

[0107] It should be noted that X-ray diffraction is used when the change in collapsibility is small. By analyzing the changes in the mineral composition of the loess samples, especially the content and type of swelling minerals, it is verified whether the collapsibility meets the expected target and whether the change in collapsibility is related to the change in mineral composition; scanning electron microscopy testing is used when the change in collapsibility is large. It can provide high-resolution microstructural images, analyze the micromorphology of the loess, the arrangement of soil particles, the pore structure, the particle morphology, etc., and evaluate whether the collapsibility reaches the expected control target by observing the impact of the change in collapsibility on the microstructural characteristics; the preset collapsibility threshold is used to judge the degree of change in collapsibility, and is specifically set by professionals. For example, the normal range of collapsibility changes in loess samples is statistically analyzed, and a maximum and minimum change value is set as the standard for collapsibility changes. The method is not unique and will not be elaborated here.

[0108] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0109] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0110] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0111] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0112] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0114] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0115] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0116] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0117] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for determining the threshold value of loess collapsibility by microwave irradiation, characterized by: The following steps are involved: Step S1: Collect collapsible loess samples and perform preprocessing to obtain their initial physical information data; Step S2: setting microwave irradiation experimental conditions based on the initial physical information data of the pre-treated loess sample, calculating the collapsibility coefficient variation trend of the loess sample under the microwave irradiation experimental conditions, and evaluating whether to use the temperature distribution analysis method to judge the change of the loess internal structure based on the collapsibility coefficient variation trend of the loess; In step S2, the natural moisture content, initial dry density and natural specific heat capacity are constructed as input vectors for the support vector regression method; The radial basis kernel function is used to map the input vector to a high-dimensional feature space, and a microwave power prediction model is constructed by solving the minimization problem. The microwave power is obtained by solving the microwave power prediction model; Calculate the total amount of heat required to completely evaporate the water in the loess sample as the evaporation energy; The objective function of the Bayesian optimization method is to minimize the deviation between the evaporation energy and the theoretical heat energy provided by microwave irradiation. The posterior probability model is constructed through Gaussian process and sample points of the objective function are selected based on the expected lifting function. The optimal solution is iteratively converged to the optimal solution, which is the irradiation time. In step S2, the collapsibility coefficient of the loess sample in the initial state is measured as the collapsibility coefficient before the experiment; According to the microwave power and irradiation time, the loess samples were subjected to microwave irradiation experimental treatment; After the microwave irradiation experiment is completed, the collapsibility coefficient of the loess sample is measured again as the post-experimental collapsibility coefficient; Calculate the ratio of the collapsibility coefficient before the experiment to the collapsibility coefficient after the experiment; When the ratio of the collapsibility coefficient before the experiment to the collapsibility coefficient after the experiment is less than one, it means that the collapsibility is weakened; When the ratio of the collapsibility coefficient before the experiment to the collapsibility coefficient after the experiment is equal to one, it means there is no significant change; When the ratio of the collapsibility coefficient before the experiment to the collapsibility coefficient after the experiment is greater than one, it indicates that the collapsibility is enhanced; Step S3: If the internal structure of the loess is judged to have changed slightly, the microstructural characteristics and mineral composition changes of the sample are further obtained to comprehensively evaluate the degree of reduction in the collapsibility of the loess. If the degree of reduction in collapsibility is high, the porosity ratio parameter is introduced to quantitatively analyze the change in collapsibility. Step S4: Based on the quantitative analysis results of the collapsibility change, scanning electron microscopy testing or X-ray diffraction analysis is selected to verify whether the loess collapsibility reaches the expected control target.

2. The method for determining the threshold value of loess collapsibility by microwave irradiation according to claim 1 is characterized in that: In step S1, the ratio of the difference between the wet soil mass and the dry soil mass to the dry soil mass is defined as the natural moisture content; The initial dry density is obtained by dividing the wet soil mass of the density measurement sample by the product of the volume of the density measurement sample and the natural moisture content plus one; Based on the mass and temperature change of constant temperature water, the heat absorbed by the loess sample is calculated to obtain the natural specific heat capacity.

3. The method for determining the threshold value of eliminating loess collapsibility by microwave irradiation according to claim 1 is characterized in that: In step S2, when the collapsibility is weakened, the temperature data of the loess sample during the irradiation process is collected, a two-dimensional temperature spatial distribution matrix is ​​established, and the local temperature variance is calculated; Set a variance threshold and compare the local temperature variance with the variance threshold; If the local temperature variance is greater than the variance threshold, it is determined that the internal structure of the loess has changed drastically; When the local temperature variance is less than the variance threshold, it is judged that the internal structure of the loess changes slightly.

4. The method for determining the threshold value of loess collapsibility by microwave irradiation according to claim 3 is characterized in that: In step S3, when it is determined that the internal structure of the loess has changed slightly, the microstructural characteristics of the loess are obtained, including the loess sample volume, pore volume, outer perimeter of the loess particles, and area of ​​the loess particles; Porosity is the ratio of pore volume to loess sample volume; The particle morphology index was calculated by the outer perimeter of the loess particles and the area of ​​the loess particles; Analyze the mineral composition of loess samples, including the mass of expansive minerals, the total mineral content in the loess samples, and the mass of quartz; The ratio of the mass of expansive minerals to the total mineral content in the loess sample was taken as the expansive mineral content value; The ratio of quartz mass to the total mineral content in the loess sample was taken as the quartz content value.

5. The method for determining the threshold value of loess collapsibility by microwave irradiation according to claim 4 is characterized in that: In step S3, the collapsibility reduction degree of loess is comprehensively evaluated by the TOPSIS method based on the microstructural characteristics and mineral composition changes; The porosity values, grain morphology index, expansion mineral content values, and quartz content values ​​were normalized and combined into a feature vector; The TOPSIS method calculates the relative proximity of loess samples through eigenvectors; Sort the relative proximity in descending order and select the median of the sorted values ​​as the proximity threshold; If the relative proximity is greater than the proximity threshold, the degree of reduction in collapsibility is judged to be high; otherwise, the degree of reduction in collapsibility is judged to be low.

6. The method for determining the threshold value of loess collapsibility by microwave irradiation according to claim 5, characterized in that: In step S3, when the degree of collapsibility reduction is high, the porosity is further calculated by the void ratio to quantitatively analyze the change in collapsibility; The sampling period is preset, and the porosity ratio at the start of sampling is subtracted from the porosity ratio at the end of sampling to obtain the porosity ratio change; The ratio of the porosity change to the porosity at the start of sampling was taken as the degree of collapsibility change.

7. The method for determining the threshold value of loess collapsibility by microwave irradiation according to claim 6, characterized in that: In step S4, the preset collapsibility threshold is compared with the collapsibility change degree. If the collapsibility change degree is greater than the preset collapsibility threshold, the collapsibility change degree is determined to be high; otherwise, the collapsibility change degree is determined to be low. When the degree of collapsibility change is high, scanning electron microscopy is used to test and analyze whether the loess collapsibility reaches the expected control target; When the degree of collapsibility change is low, X-ray diffraction analysis is used to verify whether the loess collapsibility reaches the expected control target.

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