A method, device and system for detecting the compaction degree of subgrade

Abstract

The present invention relates to the technical field of on-site basic soil survey, and specifically relates to a subgrade compaction degree detection method, device and system, including: obtaining the wet weight of the soil sample at the subgrade experiment site after repeated hammering and the dry weight after further drying to determine the current dry density of the soil sample; obtaining the density time series, humidity time series, pressure time series and applied pressure time series during the repeated hammering of the soil sample to determine the current fitting weight of the soil sample; adjusting the water content of the soil sample, and performing weighted curve fitting on the dry density of the soil sample at different water contents according to the fitting weights of the soil sample at different water contents, so as to obtain the maximum dry density and determine the compaction degree of the soil sample. The present invention generates a more accurate fitting curve to improve the accuracy of subgrade compaction degree detection.

Description

Technical Field

[0001] The present invention relates to the technical field of on-site basic soil survey, and particularly relates to a method, device and system for detecting the compaction degree of subgrade. Background Art

[0002] The detection of subgrade compaction degree can ensure that the actual technical indexes of subgrade fillers meet the design requirements, thus guaranteeing the stability and bearing capacity of the whole subgrade and prolonging the service life of the road surface. For the detection of subgrade compaction degree, in the existing methods, the subgrade compaction degree is often obtained by the core cutter method, which is a traditional detection method. By pressing the core cutter into the soil, then taking out the soil sample in the core cutter, weighing its wet weight and dry weight, calculating the dry density of the soil sample, and then judging whether the compaction degree meets the standard.

[0003] Existing problems: In the process of obtaining the subgrade compaction degree by the core cutter method, it is necessary to fit multiple groups of data to obtain a parabola, and then obtain the standard dry density of the soil sample. However, in the process of obtaining the highest point of the parabola as the standard dry density according to the parabola, it is generally obtained only based on a small number of data points, which may result in a low accuracy of the finally obtained standard dry density of the soil sample, thus leading to a low detection accuracy of the subgrade compaction degree. Summary of the Invention

[0004] The present invention provides a method, device and system for detecting the compaction degree of subgrade to solve the existing problems.

[0005] The method, device and system for detecting the compaction degree of subgrade of the present invention adopt the following technical solutions:

[0006] An embodiment of the present invention provides a method for detecting the compaction degree of subgrade, and the method includes the following steps:

[0007] Using the core cutter method, obtain the wet weight of the soil sample at the subgrade test site after repeated ramming, the dry weight after further drying, and the volume of the core cutter; determine the current dry density of the soil sample according to the wet weight and dry weight of the soil sample and the volume of the core cutter;

[0008] Obtain the density value of the soil sample after each ramming, and adaptively adjust the pressure applied during the repeated ramming of the soil sample according to the difference between the density values of the soil sample after adjacent rammings to complete the repeated ramming operation of the soil sample;

[0009] Obtain the density time series, humidity time series, pressure time series and applied pressure time series during the repeated ramming operation of the soil sample; determine the current fitting weight of the soil sample according to the differences among the density time series, humidity time series, pressure time series and applied pressure time series;

[0010] Adjust the water content of the soil sample. According to the method for obtaining the current dry density and the current fitting weight of the soil sample, obtain the dry density and fitting weight of the soil sample at different water contents; according to the fitting weights of the soil sample at different water contents, perform weighted curve fitting on the dry densities of the soil sample at different water contents to obtain the dry density fitting curve of the soil sample at different water contents; obtain the maximum dry density in the dry density fitting curves of the soil sample at different water contents, and determine the degree of compaction of the soil sample according to the current dry density and the maximum dry density of the soil sample.

[0011] Further, the step of determining the current dry density of the soil sample according to the wet weight and dry weight of the soil sample and the volume of the core cutter includes the following specific steps:

[0012] Calculate the difference between the wet weight and the dry weight of the soil sample as the first difference, and record the ratio of the first difference to the dry weight of the soil sample as the water content of the soil sample;

[0013] Calculate the ratio of the wet weight of the soil sample to the volume of the core cutter as the wet density of the soil sample;

[0014] Determine the current dry density of the soil sample according to the water content and the wet density of the soil sample.

[0015] Further, the step of determining the current dry density of the soil sample according to the water content and the wet density of the soil sample includes the following specific steps:

[0016] Calculate the sum value of 1 plus the water content of the soil sample as the first sum value, and record the ratio of the wet density of the soil sample to the first sum value as the current dry density of the soil sample.

[0017] Further, the step of obtaining the density value of the soil sample after each hammering, adaptively adjusting the applied pressure during the repeated compaction of the soil sample according to the difference between the density values of the soil sample after adjacent hammerings, and completing the repeated compaction operation of the soil sample includes the following specific steps:

[0018] Preset the cut-off number of hammerings , and use the preset initial applied pressure Perform repeated compaction on the soil sample to obtain the density value of the soil sample after each hammering. If the difference between the density values of the soil sample after adjacent hammerings is less than the preset judgment threshold , then adaptively adjust the applied pressure for the next hammering until the th hammering and then stop to complete the repeated compaction operation of the soil sample;

[0019] The method for adaptively adjusting the applied pressure for the next hammering is as follows: If the difference between the density values of the soil sample after the th and the Less than the preset judgment threshold , then subtract 1 from The difference value is used as the second difference value, and the product of the second difference value and the preset pressure growth parameter is used as the second product, and the sum value of the applied pressure of the th hammering and the second product is used as the applied pressure of the th hammering, where is the density value of the soil sample after the th hammering, is the density value of the soil sample after the th hammering, is an integer greater than 1.

[0020] Further, determining the current fitting weight of the soil sample according to the differences between the density time series, humidity time series, pressure time series, and applied pressure time series includes the following specific steps:

[0021] In the density time series, humidity time series, and pressure time series, a triple formed by density, humidity, and pressure data at the same moment is used as one piece of data, and one piece of data at each moment is obtained;

[0022] Using the PCA dimensionality reduction method, one piece of data at each moment is transformed into a single scalar data, and the scalar data at each moment is obtained. According to the scalar data at all moments, a scalar data time series is formed;

[0023] Using the APCA segmentation method, the scalar data time series is segmented to obtain several scalar segmentation points, and in chronological order, all scalar segmentation points form a scalar segmentation point time series;

[0024] According to the acquisition method of the scalar segmentation point time series, a density segmentation point time series, humidity segmentation point time series, pressure segmentation point time series, and applied pressure segmentation point time series are respectively obtained from the density time series, humidity time series, pressure time series, and applied pressure time series;

[0025] According to the matching errors between the scalar segmentation point time series and the density segmentation point time series, humidity segmentation point time series, and pressure segmentation point time series, the error value of each scalar segmentation point is determined;

[0026] According to the error value of each scalar segmentation point, and the Euclidean distance between each scalar segmentation point in the scalar segmentation point time series and each applied pressure segmentation point in the applied pressure segmentation point time series, the unbelievability of each quantity segmentation point is determined;

[0027] Calculate the mean of the untrustworthiness of all scalar segmentation points , take as the current fitting weight of the soil sample; where is the exponential function with the natural constant as the base.

[0028] Furthermore, determining the error value of each scalar segmentation point according to the matching errors between the scalar segmentation point time series and the density segmentation point time series, the humidity segmentation point time series, and the pressure segmentation point time series respectively includes the following specific steps:

[0029] Use the DTW algorithm to match the scalar segmentation point time series with the density segmentation point time series to obtain several density segmentation points matched by each scalar segmentation point;

[0030] Among all the density segmentation points matched by the th scalar segmentation point, calculate the time interval between the th scalar segmentation point and each density segmentation point matched by the th scalar segmentation point, and record the maximum time interval as the density error duration of the th scalar segmentation point;

[0031] According to the DTW matching results of the scalar segmentation point time series with the humidity segmentation point time series and the pressure segmentation point time series respectively, obtain the humidity error duration and the pressure error duration of each scalar segmentation point according to the density error duration of each scalar segmentation point;

[0032] Obtain the maximum value among the density error duration, the humidity error duration, and the pressure error duration of the th scalar segmentation point , and record the ratio of the density error duration of the th scalar segmentation point to as the first error, record the ratio of the humidity error duration of the th scalar segmentation point to as the second error, and record the ratio of the pressure error duration of the th scalar segmentation point to as the third error. Take the mean of the first error, the second error, and the third error as the error value of the th scalar segmentation point .

[0033] Furthermore, determining the untrustworthiness of each quantity segmentation point according to the error value of each scalar segmentation point and the Euclidean distance between each scalar segmentation point in the scalar segmentation point time series and each applied pressure segmentation point in the applied pressure segmentation point time series includes the following specific steps:

[0034] In the scalar segmentation point time series and the applied pressure segmentation point time series, according to the moment and scalar data value of each scalar segmentation point and the moment and applied pressure value of each applied pressure segmentation point, the Euclidean distance calculation method is used to obtain the Euclidean distance between each scalar segmentation point and each applied pressure segmentation point;

[0035] Among all the applied pressure segmentation points, calculate the Euclidean distance between the th scalar segmentation point and each applied pressure segmentation point, and statistically obtain the minimum Euclidean distance Take as the initial error of the th scalar segmentation point;

[0036] Take the mean value of the initial error of the th scalar segmentation point and the error value as the untrustworthiness of the th scalar segmentation point .

[0037] Furthermore, obtaining the maximum dry density in the dry density fitting curve of the soil sample at different water contents, and determining the degree of compaction of the soil sample according to the current dry density and the maximum dry density of the soil sample includes the following specific steps:

[0038] Obtain the maximum dry density in the dry density fitting curve of the soil sample at different water contents as the standard dry density, and take the ratio of the current dry density of the soil sample to the standard dry density as the degree of compaction of the soil sample.

[0039] A subgrade compaction degree detection device adopts the above-mentioned subgrade compaction degree detection method. This device includes the following modules:

[0040] Soil sample dry density acquisition module: used to use the cutting ring method to obtain the wet weight of the soil sample at the subgrade test site after repeated hammering, the dry weight after further drying, and the volume of the cutting ring; determine the current dry density of the soil sample according to the wet weight, dry weight of the soil sample and the volume of the cutting ring;

[0041] Soil sample hammering analysis module: used to obtain the density value of the soil sample after each hammering, and adaptively adjust the applied pressure during the repeated hammering of the soil sample according to the difference between the density values of the soil sample after adjacent hammerings, and complete the repeated hammering operation of the soil sample;

[0042] Fitting weight analysis module: used to obtain the density time series, humidity time series, pressure time series and applied pressure time series during the repeated hammering operation of the soil sample; determine the current fitting weight of the soil sample according to the differences between the density time series, humidity time series, pressure time series and applied pressure time series;

[0043] Soil sample compaction degree acquisition module: used to adjust the water content of the soil sample, and obtain the dry density and fitting weight of the soil sample at different water contents according to the acquisition method of the current dry density and the current fitting weight of the soil sample; perform weighted curve fitting on the dry density of the soil sample at different water contents according to the fitting weight of the soil sample at different water contents to obtain the dry density fitting curve of the soil sample at different water contents; obtain the maximum dry density in the dry density fitting curve of the soil sample at different water contents, and determine the compaction degree of the soil sample according to the current dry density and the maximum dry density of the soil sample.

[0044] A subgrade compaction degree detection system includes a computer program, and the computer program realizes the steps of the aforementioned subgrade compaction degree detection method when executed by a computer.

[0045] The beneficial effects of the technical solution of the present invention are:

[0046] In the embodiment of the present invention, the wet weight of the soil sample at the subgrade experimental site after repeated hammering and the dry weight after further drying are obtained to determine the current dry density of the soil sample, and the density time series, humidity time series, pressure time series, and applied pressure time series during the repeated hammering process of the soil sample are obtained to determine the current fitting weight of the soil sample. Thus, during the hammering process, by using real-time monitoring devices (high-precision density meters, hygrometers, etc.), according to the data change trend during the hammering process, the hammering parameters are automatically fed back and adjusted. Furthermore, the fitting weights of different fitting data points are obtained through the data fluctuations during the hammering process of different participating fitting data points, and then a higher fitting curve can be obtained based on a small number of data points. Adjust the water content of the soil sample, and perform weighted curve fitting on the dry density of the soil sample at different water contents according to the fitting weight of the soil sample at different water contents, so as to obtain the maximum dry density and determine the compaction degree of the soil sample. Therefore, a more accurate fitting curve can be generated based on a small number of key data points and their fluctuations. Due to the use of real-time feedback and dynamic adjustment, the collected data can more truly reflect the compaction process, thus significantly improving the accuracy of the maximum dry density. So far, the present invention generates a more accurate fitting curve to improve the accuracy of subgrade compaction degree detection. Description of the Drawings

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0048] Figure 1It is the flowchart of the steps of a method for detecting the compaction degree of a roadbed according to the present invention;

[0049] Figure 2 It is the module structure diagram of a device for detecting the compaction degree of a roadbed according to the present invention;

[0050] Figure 3 It is a schematic diagram of a core cutter. Specific embodiments

[0051] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, with reference to the accompanying drawings and preferred embodiments, a method, device, and system for detecting the compaction degree of a roadbed according to the present invention, including its specific embodiments, structures, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0053] The following specifically describes the specific solutions of a method, device, and system for detecting the compaction degree of a roadbed provided by the present invention with reference to the accompanying drawings.

[0054] Please refer to Figure 1 , which shows the flowchart of the steps of a method for detecting the compaction degree of a roadbed provided by an embodiment of the present invention. The method includes the following steps:

[0055] Step S001: Using the core cutter method, obtain the wet weight of the soil sample at the roadbed test site after repeated hammering, the dry weight after further drying, and the volume of the core cutter; determine the current dry density of the soil sample based on the wet weight and dry weight of the soil sample and the volume of the core cutter.

[0056] It should be noted that the equipment required in the process of measuring the compaction degree by the core cutter method includes: core cutter, core cutter handle, hammer, soil cutting knife, weighing equipment (electronic balance), oven, weighing dish, vernier caliper, straight ruler, record book and pen, alcohol, spirit level, shovel, brush, and protective oil. Among them, the schematic diagram of the core cutter is as Figure 3 shown.

[0057] It should be further explained that the knife ring is a common instrument used to take samples of original soil (undisturbed) for experiments. It is mainly used to measure soil density, compression, shear and permeability tests. The knife ring is usually a short tubular steel product with a blade on one end. When in use, the blade should be facing downward to avoid tilting so that it can cut vertically and evenly. The technical requirements for the knife ring include: the inner diameter is 70±0.16 millimeters (mm), the height is 52±0.16 (mm), the wall thickness is 2.1±0.10 (mm), the inner wall should be flat, smooth and the surface should be free of damage. The knife ring handle is used to fix the knife ring and provide a point of force for compaction. The knife ring handle can ensure that the knife ring is vertically and evenly stressed when it is driven into the soil. The method of use is to place the knife ring handle on the knife ring and hit the knife ring handle with a hammer to make the knife ring evenly drive into the compacted layer. The hammer is used to hit the handle of the ring cutter so that the ring cutter is evenly driven into the soil. The method of use is to hit the handle of the ring cutter evenly with the hammer to ensure that the ring cutter is driven vertically into the compacted layer until the soil sample is higher than the ring cutter. The soil cutter is used to trim the soil around the ring cutter to ensure that the soil sample in the ring cutter is complete and there is no excess soil. The method of use is to use the soil cutter to cut the soil around the ring cutter. After removing the ring cutter, use the soil trimming knife to cut off the excess soil at both ends from the edge to the middle, and use a ruler to check until it is leveled. The weighing device (electronic balance) is used to weigh the mass of the ring cutter and the soil sample. The mass accuracy should reach 0.1 gram (g). The method of use is to wipe the ring cutter, weigh the mass of the ring cutter, remove the ring cutter and the soil sample, wipe the outer wall of the ring cutter, and weigh the total weight of the ring cutter and the soil sample. The oven is used to dry soil samples to determine the moisture content of the soil samples. The technical requirement is that the temperature is controlled at 105 to 110 degrees Celsius (℃). The method of use is to put the soil sample in the oven and dry it at a temperature of 105 to 110 degrees Celsius (℃) to constant weight, and record the mass of the dry soil. The weighing dish is used to hold the soil sample for easy weighing. The method of use is to put the soil sample in the weighing dish and weigh the wet weight of the soil sample. The vernier caliper is used to measure the size of the ring knife to ensure that the ring knife meets the technical requirements. The technical requirements are a range of 200 millimeters (mm) and a graduation value of 0.02 millimeters (mm). The method of use is to use a vernier caliper to check the inner diameter, height and wall thickness of the ring knife to ensure that it meets the technical requirements. The ruler is used to detect the flatness of the soil sample in the ring knife to ensure that the soil sample is leveled. The method of use is to use a ruler to detect the flatness of the soil sample in the ring knife until it is leveled. The notebook and pen are used to record test data, including the mass of the ring knife, the wet weight and dry weight of the soil sample, etc. The method of use is to record the data of each test in detail in the notebook for subsequent calculation and analysis. Alcohol is used to quickly determine the moisture content of the soil sample (alcohol combustion method). The method of use is to pour the alcohol into the weighing dish, ignite the alcohol, let the soil sample burn in the alcohol flame until dry, and record the mass of the dry soil. The level ruler is used to ensure that the weighing instrument is placed on a level ground to improve the accuracy of weighing, and to ensure that the ring knife is driven vertically into the soil to improve the accuracy of sampling. The method of use is to place the level ruler under the weighing instrument to ensure that it is placed on a level ground, and to place the level ruler on the handle of the ring knife to ensure that the ring knife is driven vertically into the soil.The spade is used to shovel the surface floating soil and clean the test site. The usage method is to use the spade to shovel the surface floating soil at the test site and clean a ground area of about 30 centimeters (cm) × 30 centimeters (cm). The brush is used to clean the surface of the test site to ensure the surface is clean. The usage method is to use the brush to clean the surface of the test site to remove the floating soil and debris. The protective oil is used to protect the core cutter and prevent rust. The usage method is to wipe the core cutter clean and apply some protective oil after use to prevent rust.

[0058] Preferably, in an embodiment of the present invention, the method for obtaining the current dry density of the soil sample includes:

[0059] Collect the soil sample at the subgrade test site using the core cutter method and send it to the laboratory. Repeatedly hammer the soil sample in the laboratory to ensure the uniformity and representativeness of the soil sample. Then weigh the repeatedly hammered soil sample to obtain the wet weight of the soil sample. Then dry and weigh the soil sample to obtain the dry weight of the soil sample. And obtain the volume of the core cutter from the instruction manual of the core cutter.

[0060] It should be noted that: The specific process for obtaining the wet weight and dry weight of the soil sample is as follows: Wipe the core cutter clean, weigh the mass of the core cutter, and apply a thin layer of vaseline on the inner wall of the core cutter. At the sampling location at the test site, clean the ground area of about 30 centimeters (cm) × 30 centimeters (cm) with a brush, and use a small spade to shovel off the floating and uneven soil on the surface of the compacted layer. The shoveling depth is about one-third of the current thickness of the compacted layer, but do not disturb the underlying soil. Place the core cutter vertically with the blade down on the surface of the shoveled surface floating soil and press firmly to fix the core cutter on the surface of the compacted layer. Then place the core cutter handle on the core cutter, pay attention to whether the core cutter handle is vertical, and then use a hammer to strike the core cutter handle to evenly drive the core cutter into the compacted layer until the soil sample protrudes above the core cutter. Use a soil cutting knife to cut the soil around the core cutter. After taking out the core cutter, wipe the soil on the outer wall of the core cutter clean, remove the core cutter handle, and use a pickaxe to dig out the core cutter and the sample. Use a soil trimming knife to trim the excess soil at both ends of the taken-out sample from the edge to the middle, and use a ruler to detect until it is trimmed flat. Wipe the outer wall of the core cutter clean and weigh the total weight of the core cutter and the sample with a balance. Send the representative soil sample retrieved from the site to the laboratory, repeatedly hammer the soil sample until it is uniform, then put the soil sample into a weighing dish, weigh the wet weight of the soil sample, put the weighing dish into an oven, dry it to a constant weight at a temperature of 105 to 110 degrees Celsius (°C), weigh the dried soil sample to obtain the dry weight of the soil sample, and this is described as an example.

[0061] Calculate the difference between the wet weight of the soil sample and the dry weight of the soil sample as the first difference, and record the ratio of the first difference to the dry weight of the soil sample as the water content of the soil sample.

[0062] Calculate the ratio of the wet weight of the soil sample to the volume of the core cutter as the wet density of the soil sample.

[0063] Calculate the sum value of 1 plus the water content of the soil sample as the first sum value, and record the ratio of the wet density of the soil sample to the first sum value as the current dry density of the soil sample.

[0064] Step S002: Obtain the density value of the soil sample after each hammering, and adaptively adjust the applied pressure during the repeated compaction of the soil sample according to the difference between the density values of the soil sample after adjacent hammerings, and complete the repeated compaction operation of the soil sample.

[0065] It should be noted that: The degree of compaction of the soil sample is the ratio of the current dry density of the soil sample to the standard dry density of the soil sample. Among them, the existing method for obtaining the standard dry density of the soil sample is as follows: First, repeatedly hammer the soil sample retrieved from the site in the laboratory, and then dry it to obtain the dry density of the soil sample. After that, continuously change the water content of the soil sample and repeat the above process to measure the dry density of the soil sample at different water contents. Finally, based on the dry densities of the soil sample at multiple different water contents, a parabola is obtained, and the maximum dry density corresponding to the optimum water content at the highest point of the parabola is used as the standard dry density of the soil sample.

[0066] It should be further noted that: In the existing process of obtaining the highest point of the parabola based on the parabola, it is generally obtained only according to four to five data points, which results in a relatively low accuracy of the finally obtained standard dry density of the soil sample. Based on this, in this embodiment, first, the fitting weights of different fitting data points are obtained through the trend and fitting difference of a few data points, and then a fitting result with higher accuracy is obtained. Considering that only the final data of the compaction is considered and the real-time feedback during the compaction process is ignored when compacting the soil sample retrieved from the site in the laboratory. Based on this, in this embodiment, during the compaction process, real-time monitoring devices (such as high-precision density meters, hygrometers, etc.) are used to automatically feedback and adjust the compaction parameters according to the data change trend during the compaction process. Then, the fitting weights of different fitting data points are obtained through the data fluctuation during the compaction process of different participating fitting data points. Furthermore, a higher fitting curve can be obtained based on a few data points, thereby improving the accuracy of the obtained maximum dry density.

[0067] Preferably, in an embodiment of the present invention, the specific process of repeatedly compacting the soil sample in the laboratory is as follows:

[0068] Preset the initial applied pressure To be 25 kPa (kilopascals), preset the pressure growth parameter To be 3 kPa (kilopascals), preset the cut-off number of hammerings To be 50 times, preset the judgment threshold To be 0.3, and this is used as an example for description.

[0069] First, use the preset initial applied pressure The soil sample is repeatedly tamped, and the density value of the soil sample after each tamping is obtained. When first appears, the difference obtained by subtracting from 1 is used as the second difference. The product of the second difference and is used as the second product. The sum of and the second product is used as the applied pressure for the th tamping . Among them, is the density value of the soil sample after the th tamping, and is the density value of the soil sample after the th tamping.

[0070] It should be noted that: the above is an integer greater than 1. If is 4, the applied pressures for the first 4 tampings are , and the applied pressure for the 5th tamping is , and and are both greater than or equal to . , and are the density values of the soil samples after the 1st, 2nd, and 3rd tampings respectively. In this embodiment, a high-precision densitometer is used to monitor the density value of the soil sample after each tamping, and the applied pressure is changed by adjusting the drop height of the tamping. A pressure sensor is used to monitor the applied pressure of each tamping, and this is described as an example. When the change in the density value of the soil sample after tamping under is small, the applied pressure of the tamping is increased, so that the effect of repeated tamping is better, that is, the difference between the density values of the soil samples after the th and the th tampings is smaller, and the

[0071] more the applied pressure of the th tamping needs to be increased. Then starting from the th tamping, is used to repeatedly tamp the soil sample continuously, and the density value of the soil sample after each tamping is obtained. When first appears, the difference obtained by subtracting from 1 is used as the third difference. The product of the third difference and is used as the third product. The sum of and the third product is used as the applied pressure for the th tamping . Among them, is the density value of the soil sample after the The density value of the soil sample after the th pounding is an integer greater than 1. That is, from the th to the th pounding, the applied pressure is .

[0072] Then, starting from the th pounding, is used to continuously and repeatedly compact the soil sample. And so on, the applied pressure during the repeated compaction of the soil sample is adaptively adjusted until the th pounding, and then the repeated compaction operation of the soil sample is completed.

[0073] Step S003: Obtain the density time series, humidity time series, pressure time series, and applied pressure time series during the repeated compaction operation of the soil sample; determine the current fitting weight of the soil sample according to the differences among the density time series, humidity time series, pressure time series, and applied pressure time series.

[0074] Preferably, in an embodiment of the present invention, the method for obtaining the current fitting weight of the soil sample includes:

[0075] During the repeated compaction operation of the soil sample, collect the applied pressure for each pounding, the density value, humidity value, and pressure value of the soil sample after each pounding, and each pounding corresponds to a timestamp, so as to obtain the density time series, humidity time series, pressure time series, and applied pressure time series during the repeated compaction operation of the soil sample.

[0076] It should be noted that: in this embodiment, the humidity change of the soil sample is monitored in real time by a humidity meter, and the stress change of the soil sample during compaction is monitored by a pressure sensor, so as to understand the deformation and compaction degree of the soil sample. Among them, the applied pressure value refers to the applied pressure value when pounding the soil sample, and the pressure value refers to the pressure value received by the soil sample. In this embodiment, the min-max normalization method is used to standardize the data in these four dimensions respectively, unifying the dimension. This is a well-known technology, and the specific method will not be introduced here. Due to the influence of the pounding action on the sensor monitoring during the compaction process, it will cause sensor data errors, but the data change trend remains unchanged. Therefore, the continuity of the sensor data is used to assist in improving the calculation accuracy of the compaction degree by the traditional core cutter method.

[0077] In the density time series, humidity time series, and pressure time series, form a triple of density, humidity, and pressure data at the same moment as a piece of data, and obtain a piece of data at each moment.

[0078] It should be noted that: in this embodiment, it is necessary to obtain the stability and volatility of the data at each moment according to the changes in each piece of data. Data points with higher stability represent a more accurate state of the soil sample, so higher weights can be assigned. While data points with larger fluctuations may be caused by equipment errors or soil sample non-uniformity, and lower weights are assigned. At the same time, if the pressure changes frequently at the moments before and after a certain moment and is relatively close to the parameter change time, it may cause the data at that moment to be affected by the pressure change, resulting in inaccurate data values.

[0079] For a piece of data at each moment, using the method of PCA dimensionality reduction, the piece of data at each moment is transformed into a single scalar data, obtaining the scalar data at each moment. According to the scalar data at all moments, a scalar data time series is formed.

[0080] It should be noted that: PCA dimensionality reduction (Principal Component Analysis Dimensionality Reduction) is a well-known technology, and the specific method will not be introduced here. In this embodiment, the three-dimensional data is transformed into one-dimensional data through PCA dimensionality reduction. Among them, the scalar data time series represents the state change data of the soil sample.

[0081] Using the method of APCA segmentation, the scalar data time series is segmented to obtain several scalar segmentation points. In chronological order, all scalar segmentation points form a scalar segmentation point time series.

[0082] It should be noted that: APCA segmentation is a method based on Adaptive Piecewise Constant Approximation for segmenting time series. This method segments the time series into multiple constant segments, making the data within each segment as similar as possible, while there are obvious differences between segments. This is a well-known technology, and the specific method will not be introduced here.

[0083] Then, using the method of APCA segmentation, in the same way as obtaining the scalar segmentation point time series, the density segmentation point time series, humidity segmentation point time series, pressure segmentation point time series, and applied pressure segmentation point time series are respectively obtained from the density time series, humidity time series, pressure time series, and applied pressure time series.

[0084] Using the DTW algorithm, the scalar segmentation point time series is matched with the density segmentation point time series to obtain several density segmentation points matched with each scalar segmentation point.

[0085] It should be noted that the DTW algorithm (Dynamic Time Warping Algorithm) is a well-known technology, and the specific method will not be introduced here.

[0086] Among all the density segmentation points matching the th scalar segmentation point, calculate the time interval between each density segmentation point where the th scalar segmentation point matches the th scalar segmentation point, and record the maximum time interval as the density error duration of the th scalar segmentation point.

[0087] According to the DTW matching results of the scalar segmentation point time series with the humidity segmentation point time series and the pressure segmentation point time series respectively, obtain the humidity error duration and the pressure error duration of each scalar segmentation point according to the density error duration of each scalar segmentation point.

[0088] Obtain the maximum value among the density error duration, humidity error duration, and pressure error duration of the th scalar segmentation point , and record the ratio of the density error duration of the th scalar segmentation point to as the first error, record the ratio of the humidity error duration of the th scalar segmentation point to as the second error, and record the ratio of the pressure error duration of the th scalar segmentation point to as the third error. Take the average value of the first error, the second error, and the third error as the error value of the th scalar segmentation point .

[0089] In the scalar segmentation point time series and the applied pressure segmentation point time series, according to the time and scalar data value of each scalar segmentation point and the time and applied pressure value of each applied pressure segmentation point, use the Euclidean distance calculation method to obtain the Euclidean distance between each scalar segmentation point and each applied pressure segmentation point.

[0090] It should be noted that: the closer the distance between the scalar segmentation point and the applied pressure segmentation point, the greater the influence of the scalar segmentation point on the change of the applied pressure and the greater the error. The Euclidean distance calculation method is a well-known technology, and the calculation formula for the Euclidean distance between the th scalar segmentation point and the th applied pressure segmentation point is: , where and are respectively the th scalar segmentation point and the The moment of a pressure application segmentation point is the scalar data value of the th scalar segmentation point, and is the pressure application value of the

[0091] th pressure application segmentation point. Among all the pressure application segmentation points, calculate the Euclidean distance between the th scalar segmentation point and each pressure application segmentation point, and statistically obtain the minimum Euclidean distance . Take as the initial error of the th scalar segmentation point , where is the exponential function with the natural constant as the base. In this embodiment, is used to present the inverse proportional relationship and normalization processing. Implementers can set the inverse proportional function and normalization function according to actual situations.

[0092] Take the mean of the initial error of the th scalar segmentation point and the error value as the untrustworthiness of the th scalar segmentation point .

[0093] Calculate the mean of the untrustworthiness of all scalar segmentation points , and take as the current fitting weight of the soil sample.

[0094] It should be noted that: the greater the error of the scalar segmentation point, the less trustworthy it is, and the smaller the fitting weight should be.

[0095] Step S004: Adjust the water content of the soil sample. According to the method for obtaining the current dry density and the current fitting weight of the soil sample, obtain the dry density and fitting weight of the soil sample at different water contents; perform weighted curve fitting on the dry density of the soil sample at different water contents according to the fitting weight of the soil sample at different water contents to obtain the dry density fitting curve of the soil sample at different water contents; obtain the maximum dry density in the dry density fitting curve of the soil sample at different water contents, and determine the degree of compaction of the soil sample according to the current dry density and the maximum dry density of the soil sample.

[0096] Preferably, in an embodiment of the present invention, the method for obtaining the degree of compaction of the soil sample includes:

[0097] For the dried soil sample, use the water addition method to adjust the water content of the soil sample to obtain soil samples at different water contents.

[0098] It should be noted that the water addition method is a well-known technology. In this embodiment, the different water contents set are {5%, 6%, 7%, …, 19%, 20%}. Taking 6% as an example, that is, directly adding grams of water to the dried soil sample, and then fully mixing to make the water evenly distributed, obtaining a soil sample with a water content of 6%. Among them, is the dry weight of the soil sample, and this is used as an example for description.

[0099] According to the acquisition method of the current dry density and fitting weight of the soil sample, obtain the dry density and fitting weight of the soil sample at different water contents.

[0100] According to the fitting weights of the soil sample at different water contents, use the weighted least squares method to perform weighted curve fitting on the dry densities of the soil sample at different water contents, and obtain the dry density fitting curve of the soil sample at different water contents.

[0101] It should be noted that the weighted least squares method is a well-known technology, and the specific method will not be introduced here. The horizontal axis of the dry density fitting curve of the soil sample at different water contents is the water content, and the vertical axis is the dry density.

[0102] Obtain the maximum dry density in the dry density fitting curve of the soil sample at different water contents as the standard dry density, and take the ratio of the current dry density of the soil sample to the standard dry density as the compaction degree of the soil sample.

[0103] It should be noted that in this embodiment, during the calculation process of the ratio, if the denominator is 0, then make the denominator 1 to ensure the ratio holds, and this is used as an example for description. Thus, through the above process, a more accurate fitting curve can be generated based on a few key data points and their fluctuations. Due to the use of real-time feedback and dynamic adjustment, the collected data can more truly reflect the compaction process, thereby significantly improving the accuracy of the standard dry density.

[0104] In a second aspect, please refer to Figure 2 (Module structure diagram of a subgrade compaction degree detection device), which shows a subgrade compaction degree detection device provided by an embodiment of the present invention. The device includes the following modules:

[0105] Soil sample dry density acquisition module: used to use the cutting ring method to obtain the wet weight of the soil sample at the subgrade experiment site after repeated compaction, the dry weight after further drying, and the volume of the cutting ring; determine the current dry density of the soil sample according to the wet weight and dry weight of the soil sample and the volume of the cutting ring;

[0106] Soil sample compaction analysis module: used to obtain the density value of the soil sample after each hammering, and adaptively adjust the applied pressure during the repeated compaction process of the soil sample according to the difference between the density values of the soil sample after adjacent hammerings, and complete the repeated compaction operation of the soil sample;

[0107] Fitting weight analysis module: used to obtain the density time series, humidity time series, pressure time series, and applied pressure time series during the repeated compaction operation of the soil sample; determine the current fitting weight of the soil sample according to the differences among the density time series, humidity time series, pressure time series, and applied pressure time series.

[0108] Soil sample compaction degree acquisition module: used to adjust the water content of the soil sample, and obtain the dry density and fitting weight of the soil sample at different water contents according to the acquisition method of the current dry density and the current fitting weight of the soil sample; perform weighted curve fitting on the dry density of the soil sample at different water contents according to the fitting weights of the soil sample at different water contents, and obtain the dry density fitting curve of the soil sample at different water contents; obtain the maximum dry density in the dry density fitting curves of the soil sample at different water contents, and determine the compaction degree of the soil sample according to the current dry density and the maximum dry density of the soil sample.

[0109] In a third aspect, the present invention also provides a subgrade compaction degree detection system, including a computer program, and the computer program, when executed by a computer, implements the steps of the aforementioned subgrade compaction degree detection method.

[0110] So far, the present invention is completed. In summary, in the embodiments of the present invention, the wet weight of the soil sample at the subgrade experiment site after repeated compaction and the dry weight after further drying are obtained to determine the current dry density of the soil sample, and the density time series, humidity time series, pressure time series, and applied pressure time series during the repeated compaction process of the soil sample are obtained to determine the current fitting weight of the soil sample. The water content of the soil sample is adjusted, and weighted curve fitting is performed on the dry density of the soil sample at different water contents according to the fitting weights of the soil sample at different water contents, so as to obtain the maximum dry density to determine the compaction degree of the soil sample. The present invention improves the accuracy of subgrade compaction degree detection by generating a more accurate fitting curve.

[0111] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for detecting roadbed compaction, characterized in that: The method comprises the following steps: Use the knife ring method to obtain the wet weight of the soil sample at the roadbed test site after repeated compaction and the dry weight after further drying, as well as the volume of the knife ring; determine the current dry density of the soil sample based on the wet weight and dry weight of the soil sample and the volume of the knife ring; Obtain the density value of the soil sample after each hammering, and according to the difference between the density values ​​of the soil samples after adjacent hammerings, adaptively adjust the pressure applied during the repeated hammering of the soil sample to complete the repeated hammering operation of the soil sample; Obtaining a density time series, a humidity time series, a pressure time series, and an applied pressure time series during the repeated compaction operation of the soil sample; determining a current fitting weight of the soil sample according to differences among the density time series, the humidity time series, the pressure time series, and the applied pressure time series; Adjust the moisture content of the soil sample, and obtain the dry density and fitting weight of the soil sample at different moisture contents according to the method of obtaining the current dry density of the soil sample and the current fitting weight of the soil sample; perform weighted curve fitting on the dry density of the soil sample at different moisture contents according to the fitting weight of the soil sample at different moisture contents, and obtain the dry density fitting curve of the soil sample at different moisture contents; obtain the maximum dry density in the dry density fitting curve of the soil sample at different moisture contents, and determine the compaction degree of the soil sample according to the current dry density of the soil sample and the maximum dry density; The specific steps of determining the current fitting weight of the soil sample according to the difference between the density time series, the humidity time series, the pressure time series and the applied pressure time series are as follows: In the density time series, humidity time series and pressure time series, a triplet of density, humidity and pressure data at the same time is taken as a piece of data, and a piece of data at each time is obtained; Use the PCA dimensionality reduction method to convert a piece of data at each moment into a single scalar data, obtain the scalar data at each moment, and construct a scalar data time series sequence based on the scalar data at all moments; The APCA segmentation method is used to segment the scalar data time series to obtain a number of scalar segmentation points. In time order, all the scalar segmentation points form a scalar segmentation point time series sequence. According to the method for acquiring the scalar segmentation point time series sequence, respectively acquire the density segmentation point time series sequence, the humidity segmentation point time series sequence, the pressure segmentation point time series sequence and the applied pressure segmentation point time series sequence from the density time series sequence, the humidity time series sequence, the pressure time series sequence and the applied pressure time series sequence; According to the matching errors between the scalar segmentation point time series sequence and the density segmentation point time series sequence, the humidity segmentation point time series sequence and the pressure segmentation point time series sequence, the error value of each scalar segmentation point is determined; Determine the unreliability of each scalar segmentation point according to the error value of each scalar segmentation point and the Euclidean distance between each scalar segmentation point and each pressure applied segmentation point in the scalar segmentation point time series sequence and the pressure applied segmentation point time series sequence; Calculate the mean of the unreliability of all scalar segment points ,Will As the current fitting weight of the soil sample; where is an exponential function with a natural constant as its base.

2. A method for detecting roadbed compaction according to claim 1, characterized in that: The specific steps of determining the current dry density of the soil sample according to the wet weight and dry weight of the soil sample and the volume of the ring knife are as follows: Calculating the difference between the wet weight of the soil sample and the dry weight of the soil sample as a first difference, and recording the ratio of the first difference to the dry weight of the soil sample as the water content of the soil sample; Calculate the ratio of the wet weight of the soil sample to the volume of the ring cutter as the wet density of the soil sample; The current dry density of the soil sample is determined according to the water content of the soil sample and the wet density of the soil sample.

3. A method for detecting roadbed compaction according to claim 2, characterized in that: The step of determining the current dry density of the soil sample according to the water content of the soil sample and the wet density of the soil sample comprises the following specific steps: The sum of the water contents of the soil sample 1 and 1 is calculated as the first sum, and the ratio of the wet density of the soil sample to the first sum is recorded as the current dry density of the soil sample.

4. A method for detecting roadbed compaction according to claim 1, characterized in that: The density value of the soil sample after each hammering is obtained, and the pressure applied during the repeated hammering of the soil sample is adaptively adjusted according to the difference between the density values ​​of the soil samples after adjacent hammerings, so as to complete the repeated hammering operation of the soil sample, including the following specific steps: Preset hammering cut-off times , using the preset initial applied pressure The soil sample is hammered repeatedly to obtain the density value of the soil sample after each hammering. If the difference between the density values ​​of the soil samples after adjacent hammering is less than the preset judgment threshold , the pressure applied for the next hammering is adjusted adaptively until the Stop after the first hammering to complete the repeated hammering operation of the soil sample; The method for adaptively adjusting the pressure applied for the next beating is as follows: Second and The difference between the density values ​​of the soil samples after the first hammering Less than the preset judgment threshold , then subtract 1 from The difference between the second difference and the preset pressure growth parameter The product of The sum of the pressure applied by the first blow and the second product is taken as the The pressure applied by the blows is, For the The density of the soil sample after the first hammering is For the The density of the soil sample after the first hammering is is an integer greater than 1.

5. A method for detecting roadbed compaction according to claim 1, characterized in that: The error value of each scalar segmentation point is determined according to the matching errors between the scalar segmentation point time series sequence and the density segmentation point time series sequence, the humidity segmentation point time series sequence and the pressure segmentation point time series sequence, and the specific steps include the following: Use the DTW algorithm to match the scalar segmentation point time series with the density segmentation point time series to obtain a number of density segmentation points that match each scalar segmentation point; In the Among all density segmentation points that match the scalar segmentation points, calculate the scalar segmentation point and the The time interval of each density segmentation point matched by the scalar segmentation point, and the maximum time interval is recorded as Density error duration of scalar segment points; According to the DTW matching results of the scalar segmentation point time series sequence with the humidity segmentation point time series sequence and the pressure segmentation point time series sequence, the humidity error duration and the pressure error duration of each scalar segmentation point are obtained according to the density error duration of each scalar segmentation point; Get the The maximum value among the density error duration, humidity error duration and pressure error duration of the scalar segmentation point , will The density error duration of the scalar segment point is The ratio of is recorded as the first error, and the The humidity error duration of the scalar segment point is The ratio of is recorded as the second error, The pressure error duration of the scalar segment point is The ratio of the first error, the second error and the third error is recorded as the third error, and the average of the first error, the second error and the third error is recorded as the third error. The error value of the scalar segment point .

6. A method for detecting roadbed compaction according to claim 1, characterized in that: The unreliability of each scalar segmentation point is determined according to the error value of each scalar segmentation point and the Euclidean distance between each scalar segmentation point and each pressure applied segmentation point in the scalar segmentation point time series sequence and the pressure applied segmentation point time series sequence, and the specific steps include the following: In the scalar segmentation point time series sequence and the pressure applied segmentation point time series sequence, according to the time and scalar data value of each scalar segmentation point and the time and applied pressure value of each pressure applied segmentation point, the Euclidean distance between each scalar segmentation point and each pressure applied segmentation point is obtained by using the Euclidean distance calculation method; Among all the pressure applied segment points, calculate the The Euclidean distance between the scalar segmentation point and each pressure applied segmentation point, and the minimum Euclidean distance is calculated. ,Will As the Initial error of scalar segmentation points ; The first Initial error of scalar segmentation points and error value The mean value of The unreliability of scalar segmentation points .

7. A method for detecting roadbed compaction according to claim 1, characterized in that: The step of obtaining the maximum dry density in the dry density fitting curve of the soil sample at different water contents, and determining the compaction degree of the soil sample according to the current dry density of the soil sample and the maximum dry density, comprises the following specific steps: The maximum dry density in the dry density fitting curve of the soil sample at different water contents is obtained as the standard dry density, and the ratio of the current dry density of the soil sample to the standard dry density is taken as the compaction degree of the soil sample.

8. A roadbed compaction detection device, using a roadbed compaction detection method as claimed in any one of claims 1 to 7, characterized in that: The device includes the following modules: Soil sample dry density acquisition module: used to use the knife ring method to obtain the wet weight of the soil sample at the roadbed test site after repeated compaction and the dry weight after further drying, as well as the volume of the knife ring; according to the wet weight and dry weight of the soil sample and the volume of the knife ring, determine the current dry density of the soil sample; Soil sample compaction analysis module: used to obtain the density value of the soil sample after each hammering, and adaptively adjust the pressure applied during the repeated compaction of the soil sample according to the difference between the density values ​​of the soil sample after adjacent hammerings, so as to complete the repeated compaction operation of the soil sample; Fitting weight analysis module: used to obtain the density time series, humidity time series, pressure time series and applied pressure time series in the process of repeated compaction operation of the soil sample; according to the difference between the density time series, humidity time series, pressure time series and applied pressure time series, determine the current fitting weight of the soil sample; Soil sample compaction degree acquisition module: used to adjust the moisture content of the soil sample, and acquire the dry density and fitting weight of the soil sample at different moisture contents according to the acquisition method of the current dry density of the soil sample and the current fitting weight of the soil sample; perform weighted curve fitting on the dry density of the soil sample at different moisture contents according to the fitting weight of the soil sample at different moisture contents, and acquire the dry density fitting curve of the soil sample at different moisture contents; acquire the maximum dry density in the dry density fitting curve of the soil sample at different moisture contents, and determine the compaction degree of the soil sample according to the current dry density of the soil sample and the maximum dry density; The specific steps of determining the current fitting weight of the soil sample according to the difference between the density time series, the humidity time series, the pressure time series and the applied pressure time series are as follows: In the density time series, humidity time series and pressure time series, a triplet of density, humidity and pressure data at the same time is taken as a piece of data, and a piece of data at each time is obtained; Use the PCA dimensionality reduction method to convert a piece of data at each moment into a single scalar data, obtain the scalar data at each moment, and construct a scalar data time series sequence based on the scalar data at all moments; The APCA segmentation method is used to segment the scalar data time series to obtain a number of scalar segmentation points. In time order, all the scalar segmentation points form a scalar segmentation point time series sequence. According to the method for acquiring the scalar segmentation point time series sequence, respectively acquire the density segmentation point time series sequence, the humidity segmentation point time series sequence, the pressure segmentation point time series sequence and the applied pressure segmentation point time series sequence from the density time series sequence, the humidity time series sequence, the pressure time series sequence and the applied pressure time series sequence; According to the matching errors between the scalar segmentation point time series sequence and the density segmentation point time series sequence, the humidity segmentation point time series sequence and the pressure segmentation point time series sequence, the error value of each scalar segmentation point is determined; Determine the unreliability of each scalar segmentation point according to the error value of each scalar segmentation point and the Euclidean distance between each scalar segmentation point and each pressure applied segmentation point in the scalar segmentation point time series sequence and the pressure applied segmentation point time series sequence; Calculate the mean of the unreliability of all scalar segment points ,Will As the current fitting weight of the soil sample; where is an exponential function with a natural constant as its base.

9. A roadbed compaction detection system, comprising a computer program, characterized in that: When the computer program is executed by a computer, the computer program implements the steps of a method for detecting roadbed compaction as described in any one of claims 1 to 7.

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