Intelligent detection method for fine-grained soil compaction degree

Abstract

The application relates to the technical field of test analysis, in particular to an intelligent detection method for the compaction degree of fine-grained soil. The method comprises the following steps: collecting the liquid limit, plastic limit, optimal moisture content and maximum dry density of the soil body; collecting the vibration signal at each time of rolling, determining the soil type mutation point and the measurement point according to the characteristics of the vibration signal, and collecting the soil wet density and moisture content of the measurement point; determining the soil stiffness index based on the vibration signal characteristics between the soil type mutation points and the moisture content of the measurement point, and completing the soil division of the measurement point; obtaining the compaction degree; correcting the initial compaction degree correction factor based on different rolling times to obtain the compaction degree correction factor; correcting the compaction degree of the soil with different stiffnesses based on the liquid limit, the plastic limit, the optimal moisture content, the moisture content of the measurement point and the compaction degree correction factor, and completing the compaction degree detection. The application improves the accuracy and efficiency of the compaction degree detection.

Description

Technical Field

[0001] This application relates to the field of testing and analysis technology, specifically to an intelligent detection method for the compaction degree of fine-grained soil. Background Technology

[0002] Fine-grained soil refers to soil with a particle size of less than 0.075 mm, mainly composed of silt and clay particles, including silty clay and clay. Its fine particles and large specific surface area make its physical and mechanical properties sensitive to factors such as moisture content and compaction work. Insufficient compaction can easily lead to compression deformation or seepage hazards. Compaction degree refers to the ratio of the dry density of compacted soil to its maximum dry density. Compaction degree directly determines the mechanical properties and engineering safety of the soil. In highway subgrades, dam seepage prevention systems, and building foundations, if the compaction degree of fine-grained soil does not meet the standards, it can lead to insufficient foundation bearing capacity, excessive post-construction settlement, and increased permeability coefficient, affecting the long-term stability of the structure and even causing disasters such as pavement cracking and dam leakage. Therefore, it is necessary to test the compaction degree to ensure that the soil density meets the design standards and to guarantee project quality and safety.

[0003] Traditional methods for testing the compaction degree of fine-grained soils mainly include the ring sampler method and the sand cone method. The ring sampler method requires excavating a test pit in the compacted soil layer, removing the soil sample from the ring, weighing it, and drying it. The compaction degree is determined by calculating the ratio of the dry density to the maximum dry density obtained from a standard indoor compaction test. The sand cone method requires excavating a hole at the test point and filling it with sand, then converting the sand volume into soil density. Both methods are time-consuming and only provide single-point data. Furthermore, traditional methods do not consider the impact of changes in soil moisture content on the compaction degree calculation, leading to significant deviations in the test results and making it difficult to accurately assess compaction quality and long-term stability. Summary of the Invention

[0004] To address the technical problem of low detection accuracy due to the influence of characteristics such as moisture content on compaction degree, this application provides an intelligent detection method for the compaction degree of fine-grained soil. The specific technical solution adopted is as follows:

[0005] This application proposes an intelligent method for detecting the compaction degree of fine-grained soil, which includes the following steps:

[0006] Collect the liquid limit, plastic limit, optimum moisture content, and maximum dry density of the soil.

[0007] For each compaction, vibration signals are collected. A window is set for the vibration signals, and the root mean square value, dominant frequency, and harmonic ratio are obtained based on the vibration signals within the window. These three values ​​are then used to construct the feature vector of the window. The average distance between each window and a preset number of previous windows is calculated. The average distance between each window and the previous windows is used to construct a distance sequence. The average distance is compared with the standard deviation of the average distance in the distance sequence to obtain soil type abrupt change points and measurement points. The soil wet density and moisture content at the measurement points are collected.

[0008] Calculate the root mean square value, dominant frequency, and harmonic ratio of the vibration signal at all times between the two soil type abrupt change points, and obtain the soil stiffness index by combining the water content ratio of the intermediate measurement points; compare the soil stiffness index with the preset threshold to classify the measurement points into high-stiffness soil, medium-stiffness soil, and low-stiffness soil.

[0009] The compaction degree is obtained based on the soil wet density, water content, and maximum dry density at the measurement points; the initial compaction degree correction factor is obtained by correcting the initial compaction degree correction factor based on different rolling times;

[0010] The compaction degree of soils with different stiffnesses is corrected by using liquid limit, plastic limit, optimum moisture content, moisture content at the measurement point, and compaction degree correction factor to complete the compaction degree test.

[0011] In the above-mentioned scheme, this application first collects soil parameters of the area to be compacted before compaction, and then collects vibration data during compaction. Next, based on the vibration data, it calculates the soil stiffness index to distinguish soil types, classifying soils into high, medium, and low stiffness categories. Finally, based on the soil type, it compensates for compaction by considering factors such as moisture content deviation and the number of compaction passes, using different correction formulas to calculate the final compaction degree. By combining vibration data, soil parameters, and ambient temperature information during compaction, intelligent detection of fine-grained soil compaction degree is achieved. This allows for rapid differentiation of soil types and targeted compaction degree compensation, effectively solving the problems of traditional detection methods being time-consuming, only able to acquire single-point data, and not considering the influence of moisture content changes. This improves the accuracy and efficiency of compaction degree detection, providing more reliable technical support for ensuring project quality and safety.

[0012] In one embodiment, the root mean square value is the root mean square of all vibration signals within the window, and the dominant frequency is extracted by performing an FFT transform on the vibration signals within the window.

[0013] In one embodiment, the distance between each window is the Euclidean distance between the feature vectors corresponding to the windows.

[0014] In one embodiment, the method for obtaining soil type abrupt change points and measurement points by comparing the average value with the standard deviation of the average value in the distance sequence is as follows:

[0015] When the average value of two consecutive windows is greater than a preset multiple of the standard deviation, the position corresponding to the middle value of the overlapping part of the two windows is recorded as the soil type mutation point, and the middle position of the two soil type mutation points is recorded as the measurement point.

[0016] In one embodiment, the soil stiffness index is positively correlated with the root mean square value, the dominant frequency, and the water content ratio at the measurement point, and negatively correlated with the harmonic ratio.

[0017] In one embodiment, the method for dividing the measurement points into high-stiffness soil, medium-stiffness soil, and low-stiffness soil by comparing the soil stiffness index with a preset threshold is as follows:

[0018] when When the soil at the measurement point is classified as high-stiffness soil, when... When the soil at the measurement point is classified as medium-stiffness soil; when At that time, the soil at the measurement point was classified as low-stiffness soil; SI represents the soil stiffness index.

[0019] In one embodiment, the expression for the compaction degree of high-stiffness soil is:

[0020] , Indicates the moisture content at the measurement point. This indicates the optimum moisture content. Indicates the plastic limit. This represents the first compaction correction factor. Indicates the degree of compaction. This indicates the compaction degree of high-stiffness soil after correction.

[0021] In one embodiment, the expression for the compaction degree of medium-stiffness soil is:

[0022] , Indicates the moisture content at the measurement point. This indicates the optimum moisture content. Indicates the plastic limit. Indicates liquid limit. This represents the second compaction correction factor. Indicates the degree of compaction. This indicates the compaction degree of medium-stiffness soil after correction.

[0023] In one embodiment, the expression for the compaction degree of low-stiffness soil is:

[0024] , Indicates the moisture content at the measurement point. This indicates the optimum moisture content. Indicates liquid limit. This represents the third compaction correction factor. Indicates the degree of compaction. This indicates the compaction degree of low-stiffness soil after correction.

[0025] In one embodiment, the expression for the compaction correction factor is:

[0026] , Let be the initial value of the i-th compaction correction factor. This represents an exponential function with the natural constant as its base. The number of times the material is rolled. As the attenuation factor, is the i-th compaction correction factor.

[0027] The beneficial effects of this application are as follows:

[0028] This application first collects soil parameters in the area to be compacted before compaction, and then collects vibration data during compaction. Next, based on the vibration data, it calculates the soil stiffness index to distinguish soil types, classifying them into high, medium, and low stiffness categories. Finally, based on the soil type, it compensates for compaction by considering factors such as moisture content deviation and the number of compaction passes, using different correction formulas to calculate the final compaction degree. By combining vibration data, soil parameters, and ambient temperature information during compaction, this application achieves intelligent detection of fine-grained soil compaction degree. It can quickly distinguish soil types and perform targeted compaction degree compensation, effectively solving the problems of traditional detection methods being time-consuming, only able to acquire single-point data, and not considering the influence of moisture content changes. This improves the accuracy and efficiency of compaction degree detection, providing more reliable technical support for ensuring project quality and safety. Attached Figure Description

[0029] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating an intelligent detection method for the compaction degree of fine-grained soil, provided as an embodiment of this application. Detailed Implementation

[0031] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an intelligent detection method for the compaction degree of fine-grained soil proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0033] An example of an intelligent detection method for the compaction degree of fine-grained soil:

[0034] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent detection method for the compaction degree of fine-grained soil provided in this application.

[0035] Please see Figure 1 The diagram illustrates a flowchart of an intelligent detection method for the compaction degree of fine-grained soil according to an embodiment of this application. The method includes the following steps:

[0036] Step S001: Collect the liquid limit, plastic limit, optimum moisture content, and maximum dry density of the soil.

[0037] Before compaction, soil-related data were collected in the test area, including the liquid limit, plastic limit, optimum moisture content, and maximum dry density of all blocks. The average value of all blocks was then calculated to obtain the liquid limit of the soil. Plastic Limit Optimal moisture content and maximum dry density In this embodiment, soil-related data is collected every 5m, that is, every 5m is a block.

[0038] The liquid limit is the critical moisture content at which soil transitions from a plastic to a liquid state, defining the upper limit of soil plasticity. In compaction scenarios, it is a key parameter for determining the clay content and plasticity of soil. When the measured moisture content exceeds the liquid limit, the soil is in a fluid plastic state, and compaction is prone to springy soil or mud pumping, making effective compaction impossible. Conversely, when the moisture content is below the liquid limit, the soil's construction suitability must be assessed in conjunction with the plastic limit.

[0039] The plastic limit is the critical moisture content at which soil transitions from a semi-solid to a plastic state, marking the lower limit at which soil begins to possess plasticity. In compaction scenarios, it, along with the liquid limit, constitutes the basis for judging the soil consistency state: when the moisture content is below the plastic limit, the soil is semi-solid, with high interparticle friction, and compaction easily leads to cracks; when it is above the plastic limit, the soil enters a plastic state, which is conducive to particle rearrangement, but the moisture content must be controlled to not exceed the liquid limit.

[0040] The optimum moisture content is the moisture content at which soil reaches its maximum dry density under standard compaction work. When the moisture content is close to the optimum moisture content, the thickness of the water film between soil particles is moderate, which reduces the frictional resistance between particles and allows the particles to be closely arranged through rolling, achieving the best density. When the moisture content is lower than the optimum moisture content, the cohesion between soil particles is weak, and loose pores are easily formed during compaction. When the moisture content is higher than the optimum moisture content, the pore water pressure increases, particles are easy to slip and water is difficult to drain, resulting in insufficient compaction.

[0041] Maximum dry density is the ultimate density value obtained by standard compaction of soil at its optimum moisture content.

[0042] At this point, the liquid limit, plastic limit, optimum moisture content, and maximum dry density were collected.

[0043] Step S002: Collect vibration signals during each compaction, determine soil type change points and measurement points based on the characteristics of the vibration signals, and collect the soil wet density and moisture content at the measurement points.

[0044] A MEMS triaxial accelerometer was installed directly above the central axis of the vibratory roller's drum, near the rigid connection area of ​​the exciter, to collect vibration signals from the drum during the rolling process. After each pass of compaction, the vibration signals were preprocessed. Then, windows were set, and the root mean square (RMS) value was calculated for each window. The dominant frequency was extracted after performing a 2048-point FFT transform on the signals from each window. The harmonic ratio (the energy ratio of the second harmonic to the fundamental frequency) H is used. The root mean square value, dominant frequency, and harmonic ratio of each window are used to construct a feature vector. In this embodiment, a 500ms sliding window is set with a sliding step size of 250ms.

[0045] The average distance between the current window and the previous three windows is calculated using the Exponentially Weighted Moving Average (EWMA) algorithm. The first three windows in the initial data collection phase were discarded and not included in the calculation due to noise interference from the road roller starting up. The distance between the windows was calculated using the Euclidean distance of the feature vectors.

[0046] The average distance of each window to all windows before it. Construct a distance sequence and calculate the standard deviation of the distance sequence. If the average distance between each window and the previous three windows is... When two consecutive windows meet this condition, the roller position at the time corresponding to the midpoint of the overlapping part of these two windows is recorded as the soil type abrupt change point, and the midpoint between the two soil type abrupt change points is recorded as the measurement point. After one pass of compaction, the wet density of the soil at the measurement point is collected using a nuclear density meter. and moisture content The soil temperature T is obtained through a temperature sensor.

[0047] Thus, the wet density and moisture content of the soil at the measurement point were obtained.

[0048] Step S003: Determine the soil stiffness index based on the vibration signal characteristics between abrupt soil type change points and the water content of the measurement points, and complete the soil division of the measurement points.

[0049] Fluctuations in soil moisture content directly alter the lubrication effect and capillary force between soil particles, causing a nonlinear response in dry density under the same compaction effort. When the moisture content deviates from the optimum moisture content, excessive moisture forms a water film between soil particles, leading to the "rubber soil" phenomenon. Conversely, excessively low moisture content causes a surge in friction between soil particles, resulting in increased compaction energy loss and making it difficult to reach the theoretical peak dry density. Without dynamic correction of compaction calculations based on moisture content, discrepancies will arise between the calculated dry density and the actual soil structural strength. This is especially problematic during rainy season construction or under conditions of sudden changes in moisture content. Uncorrected compaction data may mask the potential for shear strength degradation caused by excessive moisture or misjudge insufficient compaction due to drought.

[0050] Soil type affects moisture content and causes it to fluctuate because different types of soil have different water retention capacities. If a uniform model is used for correction without distinguishing soil types, the moisture content correction factor will become disconnected from the actual characteristics of the soil.

[0051] Different soil types exhibit varying dynamic response characteristics when subjected to vibration due to differences in their physical properties: fine-grained expansive soil dissipates vibration energy slowly and has a higher dominant frequency; porous loess dissipates energy moderately; and ordinary fine-grained soil, due to its moderate plasticity, displays different harmonic characteristics. The dominant frequency, energy decay rate, and harmonic component parameters contained in vibration data can reflect the differences in the soil's internal structure and mechanical properties. Therefore, soil types can be effectively distinguished by analyzing the characteristic parameters of the vibration signal. Thus, the first step is to calculate the soil stiffness index based on the vibration data collected during road roller operation to differentiate soil types.

[0052] For any two soil type abrupt change points, obtain the root mean square value, dominant frequency, and harmonic ratio of all vibration signals. Combine the above vibration data with the water content ratio of intermediate measurement points to obtain the soil stiffness index.

[0053] The soil stiffness index is positively correlated with the root mean square value, the dominant frequency, and the water content ratio at the measurement point, and negatively correlated with the harmonic ratio.

[0054] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the two variables change in the same direction. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large. The specific relationship is determined by the actual application, and this application does not impose any special restrictions.

[0055] It should be noted that negative correlation means that when one variable increases, the other variable decreases accordingly, and the two variables change in opposite directions. When one variable changes from large to small or from small to large, the other variable also changes from small to large or from large to small. The specific relationship is determined by practical application, and this application does not impose any special restrictions.

[0056] Preferably, in this embodiment, the expression for the soil stiffness index is:

[0057] , This represents the dominant frequency of all vibration signals between two points of abrupt change in soil type. This represents the root mean square value of all vibration signals between two points of abrupt change in soil type. This represents the harmonic ratio of all vibration signals between two points of abrupt change in soil type. This indicates the moisture content at the measurement point between two abrupt changes in soil type. Indicates liquid limit. Indicates the correction factor; Indicates the temperature correction factor. Represents the normalization function. This represents the soil stiffness index.

[0058] The correction coefficient quantifies the nonlinear reduction effect of moisture content on soil stiffness, with a value ranging from 0.3 to 0.7. The temperature correction factor corrects for vibration signal attenuation under high-temperature conditions; the higher the temperature, the larger the temperature correction factor, and the lower the temperature, the smaller the correction factor. The ideal temperature of 20 degrees Celsius is used as the dividing line. Temperature Correction Factor , Indicates the temperature at the measurement point at that time. This is an empirical coefficient. In this embodiment, the correction coefficient is set to 0.5, and the empirical coefficient is set to 0.02.

[0059] The dominant frequency reflects the vibrational coupling strength of the soil particle skeleton; the finer the particles and the denser the structure, the higher the dominant frequency. The root mean square (RMS) value of acceleration characterizes the energy decay rate; soils with high stiffness dissipate energy slowly, resulting in a lag in RMS decay. The harmonic ratio reflects the degree of nonlinear response; harmonic components are more pronounced in plastic soils, leading to a larger harmonic ratio. Furthermore, the soil's plasticity is corrected using the ratio of water content to liquid limit; higher water content brings the soil closer to a soft plastic state, resulting in a more significant stiffness reduction. Finally, a temperature correction factor eliminates interference from environmental thermal effects.

[0060] The higher the soil stiffness index, the denser the soil particle skeleton, the higher the stiffness, the more stable its mechanical properties and the stronger its bearing capacity, and the slower the vibration energy decays. Conversely, the lower the value, the looser or softer the soil is, the more significant the plastic deformation and the wider the vibration response frequency band. It is necessary to increase the compaction energy to improve the density and avoid the risk of excessive settlement due to insufficient compaction.

[0061] The soil stiffness is classified according to the obtained soil stiffness index.

[0062] In this embodiment, when When the soil is nearly semi-solid or solid, and has weak plastic deformation capacity, it is classified as high-stiffness soil; when At this point, the soil is in a plastic state, and the water film between particles provides both lubrication and maintains the strength of the skeletal structure, classifying it as medium-stiff soil; when At this point, the soil approaches a soft plastic to fluid plastic state, and the water film thickness increases significantly, leading to a decrease in the effective stress of the particle skeleton, thus classifying it as low-stiffness soil.

[0063] This completes the stiffness classification of the soil.

[0064] Step S004: Obtain compaction degree by modifying the initial compaction degree correction factor based on different rolling times.

[0065] First, the compaction degree is obtained based on the soil wet density, moisture content, and maximum dry density at the measurement points.

[0066] The existing expression for soil compaction is:

[0067] , This indicates the wet density of the soil at the measurement point. Indicates the moisture content at the measurement point. This indicates the maximum dry density at the measurement point. Indicates the degree of compaction.

[0068] In actual compaction, the number of compaction passes (n) directly affects the cumulative compaction degree of the soil, while the influence of moisture content deviation on compaction degree varies with the number of compaction passes. When the number of compaction passes is insufficient, the soil particles are not fully rearranged, and the influence of moisture content deviation on compaction degree is more significant. As the number of compaction passes increases, compaction energy accumulates, and the soil gradually approaches its optimal density state, at which point the influence of moisture content deviation is weakened due to energy replenishment. Therefore, a compaction degree correction factor needs to be established. The correlation model with the number of compaction cycles n can dynamically reflect the regulatory effect of energy accumulation on moisture content correction during the compaction process, making the correction results more consistent with the actual compaction characteristics.

[0069] The relationship between the number of compaction passes (n) and the degree of compaction exhibits a non-linear growth trend. The degree of compaction increases significantly during the initial compaction phase, but the rate of increase slows down and approaches stability in the later stages. (Compaction degree correction factor) The compaction energy should decrease with increasing compaction cycles (n) to reflect the compensation mechanism for the adverse effects of compaction energy on moisture content. Specifically, in the initial stage of compaction, A larger value highlights the significant impact of moisture content deviation; as n increases, The reduction according to a certain pattern reflects the gradual overcoming of the influence of moisture content deviation by the soil through continuous compaction, so that the modified model matches the dynamic characteristics of the compaction process.

[0070] Therefore, the initial compaction correction factor is adjusted based on the number of compaction cycles to obtain the compaction correction factor. The expression for the compaction correction factor is:

[0071] , Let be the initial value of the i-th compaction correction factor. This represents an exponential function with the natural constant as its base. The number of times the material is rolled. As the attenuation factor, Let be the i-th compaction correction factor, where i takes the value 1, 2, or 3. The attenuation factor is related to the soil type and compaction mechanical parameters, and in this embodiment, it is set to 0.3.

[0072] Thus, the compaction correction factor for each rolling operation was obtained.

[0073] Step S005: The compaction degree of soil with different stiffnesses is corrected by liquid limit, plastic limit, optimum moisture content, moisture content at the measurement point, and compaction degree correction factor to complete the compaction degree test.

[0074] Since soils with different stiffnesses have different compaction characteristics, the existing compaction degree expression cannot reflect these characteristics. Therefore, for soils with different stiffnesses, it is necessary to correct the compaction degree by using the liquid limit, plastic limit, optimum moisture content, and moisture content at the measurement point.

[0075] High-stiffness soils have large interparticle pores and low clay content. When the moisture content is lower than the optimum moisture content, the frictional resistance between soil particles is large, and the water film lubrication effect is insufficient. During the compaction process, the particles are difficult to rearrange and fill the pores, resulting in the actual dry density being lower than the maximum dry density and the compaction degree calculation result being higher. Conversely, excessive water fills the pores, forming a water wedge effect, reducing the effective stress between particles, and the rubber soil phenomenon is prone to occur during the compaction process. The actual dry density decreases significantly, and the compaction degree is also higher.

[0076] High-stiffness soils have low plastic limits and easily enter a plastic state when their moisture content exceeds the optimum moisture content, leading to swelling deformation. Using the plastic limit as a benchmark, the moisture content deviation is made dimensionless to enhance adaptability to soils with low plastic limits. Moisture content fluctuations in these soils are more sensitive to triggering the plastic state. After dimensionless processing, the rate of change of the correction factor with the moisture content deviation is proportional to the actual deviation of the compaction degree.

[0077] Therefore, the expression for the compaction degree of high-stiffness soil is:

[0078] , Indicates the moisture content at the measurement point. This indicates the optimum moisture content. Indicates the plastic limit. This represents the first compaction correction factor. Indicates the degree of compaction. This represents the compaction degree of the high-stiffness soil after correction. The empirical value of the first compaction degree correction factor ranges from 0.015 to 0.025; in this embodiment, the initial value of the first compaction degree correction factor is 0.02.

[0079] Medium-stiff soils have a particle size distribution between coarse and fine. When the moisture content is below the optimum moisture content, the cohesion between soil particles is weak, and insufficient moisture limits the lubrication effect of the particles. This leads to particle bridging during compaction, making it difficult for the actual dry density to approach the maximum dry density, resulting in an overestimation of the compaction degree. Conversely, when clay particles absorb water and expand, pore water pressure increases, increasing the resistance to particle rearrangement during compaction. Simultaneously, excess moisture occupies pore space, causing the actual dry density to decrease, again leading to an overestimation of the compaction degree. Compared to high-stiff soils, the compaction degree decreases faster and is more sensitive to fluctuations in moisture content.

[0080] The difference between the liquid limit and the plastic limit is the plasticity index, which reflects the clay content and viscosity characteristics. The compaction characteristics of medium-stiff soils are determined by the ratio of clay to silt, and the plasticity index is directly related to the interparticle cohesion and the water film lubrication effect. The higher the value, the more clay particles there are, the greater the resistance to particle rearrangement when the moisture content deviates, and the more significant the compaction deviation. Using the plasticity index as a benchmark, the correction factor is correlated with the inherent viscosity characteristics of the soil.

[0081] Therefore, the expression for the compaction degree of medium-stiff soil is:

[0082] , Indicates the moisture content at the measurement point. This indicates the optimum moisture content. Indicates the plastic limit. Indicates liquid limit. This represents the second compaction correction factor. Indicates the degree of compaction. This represents the compaction degree of medium-stiffness soil after correction. The empirical value of the second compaction degree correction factor ranges from 0.03 to 0.05; in this embodiment, the initial value of the second compaction degree correction factor is 0.04.

[0083] Low-stiffness soils are predominantly cohesive with high clay content. The bound water film between clay particles is thin, resulting in strong interparticle cohesion and high hardness. Compaction energy is insufficient to overcome interparticle resistance, making the soil difficult to compress. The actual dry density is far lower than the maximum dry density, leading to a significantly inflated compaction degree calculation. The soil is prone to brittle fracture, as the pores between particles cannot be effectively filled. After the clay particles fully absorb water, they enter a plastic flow state, reducing the soil's shear strength. Shear failure easily occurs during compaction, significantly lowering the actual dry density and resulting in a severely inflated compaction degree calculation.

[0084] The compaction characteristics of low-stiffness soils are determined by high clay content. The liquid limit is a direct reflection of the hydrophilicity of clay particles; the higher the value, the stronger the water absorption capacity of the clay particles. When the moisture content deviates, the change in water film thickness has a more significant impact on the interparticle cohesion. After the clay particles have fully absorbed water, they are in a plastic flow state, and pore water pressure dominates the interparticle stress. Using the liquid limit as a benchmark, the correction factor is correlated with the inherent hydrophilicity of the soil.

[0085] Therefore, the expression for the compaction degree of low-stiffness soil is:

[0086] , Indicates the moisture content at the measurement point. This indicates the optimum moisture content. Indicates liquid limit. This represents the third compaction correction factor. Indicates the degree of compaction. This represents the compaction degree of the low-stiffness soil after correction. The empirical value of the third compaction degree correction factor ranges from 0.05 to 0.07; in this embodiment, the initial value of the third compaction degree correction factor is 0.06.

[0087] For soils subjected to different compaction passes, a modified compaction correction factor is used in the compaction calculation formula to calculate the compaction degree for each compaction pass. This allows for intelligent detection of the compaction degree of fine-grained soils.

[0088] This completes the intelligent detection of compaction degree.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

[0090] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. An intelligent method for detecting the compaction degree of fine-grained soil, characterized in that, The method includes the following steps: Collect the liquid limit, plastic limit, optimum moisture content, and maximum dry density of the soil. For each compaction, vibration signals are collected. A window is set for the vibration signals, and the root mean square value, dominant frequency, and harmonic ratio are obtained based on the vibration signals within the window. These three values ​​are then used to construct the feature vector of the window. The average distance between each window and a preset number of previous windows is calculated. The average distance between each window and the previous windows is used to construct a distance sequence. The average distance is compared with the standard deviation of the average distance in the distance sequence to obtain soil type abrupt change points and measurement points. The soil wet density and moisture content at the measurement points are collected. Calculate the root mean square value, dominant frequency, and harmonic ratio of the vibration signal at all times between the two soil type abrupt change points, and obtain the soil stiffness index by combining the water content ratio of the intermediate measurement points; compare the soil stiffness index with the preset threshold to classify the measurement points into high-stiffness soil, medium-stiffness soil, and low-stiffness soil. The compaction degree is obtained based on the soil wet density, water content, and maximum dry density at the measurement points; the initial compaction degree correction factor is obtained by correcting the initial compaction degree correction factor based on different rolling times; The compaction degree of soils with different stiffnesses is corrected by liquid limit, plastic limit, optimum moisture content, moisture content at the measurement point, and compaction degree correction factor, thus completing the compaction degree test. The expression for the corrected compaction degree of high-stiffness soil is: , Indicates the moisture content at the measurement point. This indicates the optimum moisture content. Indicates the plastic limit. This represents the first compaction correction factor. Indicates the degree of compaction. This indicates the compaction degree of high-stiffness soil after correction. The expression for the corrected compaction degree of medium-stiff soil is: , Indicates the moisture content at the measurement point. This indicates the optimum moisture content. Indicates the plastic limit. Indicates liquid limit. This represents the second compaction correction factor. Indicates the degree of compaction. Indicates the compaction degree of medium-stiff soil after correction; The expression for the corrected compaction degree of low-stiffness soil is: , Indicates the moisture content at the measurement point. This indicates the optimum moisture content. Indicates liquid limit. This represents the third compaction correction factor. Indicates the degree of compaction. This indicates the compaction degree of low-stiffness soil after correction.

2. The intelligent detection method for the compaction degree of fine-grained soil as described in claim 1, characterized in that, The root mean square value is the root mean square of all vibration signals within the window, and the dominant frequency is extracted by performing an FFT transform on the vibration signals within the window.

3. The intelligent detection method for the compaction degree of fine-grained soil as described in claim 1, characterized in that, The distance between each window is the Euclidean distance between the feature vectors corresponding to those windows.

4. The intelligent detection method for the compaction degree of fine-grained soil as described in claim 1, characterized in that, The method for obtaining soil type abrupt change points and measurement points by comparing the average value with the standard deviation of the average value in the distance sequence is as follows: When the average value of two consecutive windows is greater than a preset multiple of the standard deviation, the position corresponding to the middle value of the overlapping part of the two windows is recorded as the soil type mutation point, and the middle position of the two soil type mutation points is recorded as the measurement point.

5. The intelligent detection method for the compaction degree of fine-grained soil as described in claim 1, characterized in that, The soil stiffness index is positively correlated with the root mean square value, the dominant frequency, and the water content ratio at the measurement point, and negatively correlated with the harmonic ratio.

6. The intelligent detection method for the compaction degree of fine-grained soil as described in claim 1, characterized in that, The method for dividing the measurement points into high-stiffness soil, medium-stiffness soil, and low-stiffness soil by comparing the soil stiffness index with a preset threshold is as follows: when When the soil at the measurement point is classified as high-stiffness soil, when... When the soil at the measurement point is classified as medium-stiffness soil; when At that time, the soil at the measurement point was classified as low-stiffness soil; SI represents the soil stiffness index.

7. The intelligent detection method for the compaction degree of fine-grained soil as described in claim 1, characterized in that, The expression for the compaction correction factor is: , Let be the initial value of the i-th compaction correction factor. This represents an exponential function with the natural constant as its base. The number of times the material is rolled. As the attenuation factor, is the i-th compaction correction factor.

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