Roadbed compactness detection method, device and system

Abstract

The invention relates to the technical field of field foundation soil exploration, in particular to a roadbed compactness detection method, device and system.The roadbed compactness detection method comprises the steps that the wet weight of a soil sample at a roadbed experiment site after repeated hammering and the dry weight of the soil sample after further drying are obtained and used for determining the current dry density of the soil sample; the method comprises the following steps: acquiring a density sequential sequence, a humidity sequential sequence, a pressure sequential sequence and a pressure sequential sequence in a repeated hammering process of a 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 dry densities of the soil sample under different water contents according to the fitting weights of the soil sample under different water contents to obtain a weighted curve of the dry densities of the soil sample under different water contents. Therefore, the maximum dry density is obtained and is used for determining the compaction degree of the soil sample. According to the method, the more accurate fitting curve is generated, so that the subgrade compactness detection precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of on-site foundation soil survey, and in particular to a roadbed compaction detection method, device and system. Background Art

[0002] The detection of roadbed compaction can ensure that the actual technical indicators of the roadbed filler meet the design requirements, thereby ensuring the stability and bearing capacity of the entire roadbed, and thus extending the service life of the road surface. For the detection of roadbed compaction, the existing method often uses the ring knife method to obtain the roadbed compaction. This is a traditional detection method that presses the ring knife into the soil, then takes out the soil sample in the ring knife, weighs its wet weight and dry weight, calculates the dry density of the soil sample, and then determines whether the compaction meets the standard.

[0003] Existing problem: In the process of obtaining the compaction degree of the roadbed by the ring knife method, it is necessary to fit multiple sets 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 based on the parabola, it is generally obtained based on only a small number of data points, which may make the final obtained standard dry density of the soil sample less accurate, thereby resulting in lower detection accuracy of the roadbed compaction degree. Summary of the invention

[0004] The invention provides a roadbed compaction detection method, device and system to solve the existing problems.

[0005] A roadbed compaction detection method, device and system of the present invention adopts the following technical solutions: An embodiment of the present invention provides a method for detecting roadbed compaction, the method comprising 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; The moisture content of the soil sample is adjusted, and the dry density and fitting weight of the soil sample at different moisture contents are obtained according to the method of obtaining the current dry density of the soil sample and the current fitting weight of the soil sample; according to the fitting weight of the soil sample at different moisture contents, a weighted curve fitting is performed on the dry density of the soil sample at different moisture contents to obtain a fitting curve of the dry density of the soil sample at different moisture contents; the maximum dry density in the fitting curve of the dry density of the soil sample at different moisture contents is obtained, and the compaction degree of the soil sample is determined according to the current dry density of the soil sample and the maximum dry density.

[0006] Furthermore, the current dry density of the soil sample is determined according to the wet weight and dry weight of the soil sample and the volume of the cutter ring, and the specific steps include the following: 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.

[0007] Furthermore, the method 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.

[0008] Furthermore, 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 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.

[0009] Furthermore, the current fitting weight of the soil sample is determined according to the difference between the density time series, the humidity time series, the pressure time series and the applied pressure time series, and the specific steps include the following: 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 application segmentation point in the scalar segmentation point time series sequence and the pressure application 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.

[0010] Furthermore, 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 .

[0011] Furthermore, 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 .

[0012] Furthermore, 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 includes 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.

[0013] A roadbed compaction detection device adopts the roadbed compaction detection method, and 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 acquisition module: used to 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 acquisition method of the current dry density of the soil sample and the current fitting weight of the soil sample; according to the fitting weight of the soil sample at different moisture contents, perform weighted curve fitting on the dry density of the soil sample at different moisture contents to 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.

[0014] A roadbed compaction detection system includes a computer program, and when the computer program is executed by a computer, the steps of the roadbed compaction detection method described above are implemented.

[0015] The beneficial effects of the technical solution of the present invention are: In the embodiment of the present invention, the wet weight of the soil sample at the roadbed test 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 of the soil sample during repeated hammering are obtained to determine the current fitting weight of the soil sample. Thus, during the hammering process, the real-time monitoring equipment (high-precision densitometer, hygrometer, etc.) is used to automatically feedback and adjust the hammering parameters according to the data change trend during the hammering process, and then the fitting weights of different fitting data points are obtained according to the data fluctuations during the hammering process of different fitting data points, and then a higher fitting curve can be obtained according to a few data points. The water content of the soil sample is adjusted, and the dry density of the soil sample at different water contents is weightedly fitted according to the fitting weights of the soil sample at different water contents, so as to obtain the maximum dry density, which is used to determine the compaction degree of the soil sample, so that a more accurate fitting curve can be generated according to a few key data points and their fluctuations. Due to the use of real-time feedback and dynamic adjustment, the collected data can more realistically reflect the compaction process, thereby significantly improving the accuracy of the maximum dry density. Thus, the present invention is used to improve the accuracy of roadbed compaction detection by generating a more accurate fitting curve. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flow chart of the steps of a method for detecting roadbed compaction degree of the present invention; Figure 2 It is a module structure diagram of a roadbed compaction detection device of the present invention; Figure 3 Schematic diagram of the ring knife. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a roadbed compaction detection method, device and system proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, 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 invention belongs.

[0020] The specific scheme of a roadbed compaction detection method, device and system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a flow chart of a method for detecting roadbed compaction provided by an embodiment of the present invention, the method comprising the following steps: Step S001: 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.

[0022] It should be noted that the equipment needed in the process of measuring compaction by the ring knife method includes: ring knife, ring knife handle, hammer, soil cutting knife, weighing device (electronic balance), oven, weighing dish, vernier caliper, ruler, notebook and pen, alcohol, level ruler, shovel, brush and protective oil. Figure 3 shown.

[0023] 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 shovel is used to remove the surface soil and clean the test site. The method of use is to use the shovel to remove the surface soil at the test site and clean up an area of ​​about 30 cm (cm) × 30 cm (cm). The brush is used to clean the surface of the test site to ensure that the surface is clean. The method of use is to use the brush to clean the surface of the test site to remove the soil and debris. The protective oil is used to protect the ring knife to prevent rust. The method of use is to scrub the ring knife clean and apply some protective oil after use to prevent rust.

[0024] Preferably, in one embodiment of the present invention, the method for obtaining the current dry density of the soil sample includes: The soil samples at the roadbed test site were collected using the ring knife method and sent to the laboratory. The soil samples were repeatedly hammered in the laboratory to ensure the uniformity and representativeness of the soil samples. The soil samples after repeated hammering were weighed to obtain the wet weight of the soil samples. The soil samples were then dried and weighed to obtain the dry weight of the soil samples. The volume of the ring knife was obtained from the instruction manual of the ring knife.

[0025] It should be noted that the specific process of obtaining the wet weight and dry weight of the soil sample is as follows: wipe the ring knife clean, weigh the ring knife, and apply a thin layer of vaseline on the inner wall of the ring knife. At the sampling site of the test site, clean the ground with an area of ​​about 30 cm (cm) × 30 cm (cm) with a brush, and shovel away the floating and uneven soil on the surface of the compacted layer with a small shovel. The shoveling depth is about one-third of the current thickness of the compacted layer, but the lower soil must not be disturbed. Place the blade of the ring knife vertically downward on the surface of the shoveled surface floating soil and press hard to fix the ring knife on the surface of the compacted layer, then place the ring knife handle on the ring knife, pay attention to whether the ring knife handle is vertical, and then hammer the ring knife handle with a hammer to evenly drive the ring knife into the compacted layer until the soil sample is higher than the ring knife. Use a soil cutter to cut the soil around the ring knife, wipe the outer wall of the ring knife after the ring knife is taken out, remove the ring knife handle, and use a pickaxe to dig out the ring knife and the sample. Use a soil trimmer to cut off the excess soil at both ends of the sample from the edge to the middle, and use a ruler to check until it is level. Wipe the outer wall of the ring cutter clean, and use a balance to weigh the total weight of the ring cutter and the sample. Take a representative soil sample from the site and send it to the laboratory. Hammer the soil sample repeatedly until it is uniform, then put the soil sample in a weighing dish, weigh the wet weight of the soil sample, put the weighing dish in an oven, dry it at a temperature of 105 to 110 degrees Celsius (℃) to a constant weight, weigh the dried soil sample, and get the dry weight of the soil sample. This is described as an example.

[0026] The difference between the wet weight of the soil sample and the dry weight of the soil sample is calculated as the first difference, and the ratio of the first difference to the dry weight of the soil sample is recorded as the moisture content of the soil sample.

[0027] 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.

[0028] Calculate the sum of the moisture contents of 1 plus the water content of the soil sample as the first sum, and record the ratio of the wet density of the soil sample to the first sum as the current dry density of the soil sample.

[0029] Step S002: 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.

[0030] It should be noted that the compaction degree of a soil sample is the ratio of the current dry density of the soil sample to the standard dry density of the soil sample. The existing method for obtaining the standard dry density of a soil sample is as follows: first, the soil sample retrieved from the site is repeatedly hammered in the laboratory, and then dried to obtain the dry density of the soil sample. Then, the moisture content of the soil sample is continuously changed, and the above process is repeated. The purpose is to measure the dry density of the soil sample at different moisture contents. Finally, a parabola is obtained based on the dry density of multiple groups of soil samples at different moisture contents. The highest point of the parabola is the maximum dry density corresponding to the optimal moisture content, which is used as the standard dry density of the soil sample.

[0031] It should be further explained that: in the existing process of obtaining the highest point of the parabola based on the parabola, it is generally obtained based on four to five data points, which makes the accuracy of the final standard dry density of the soil sample low. Based on this, this embodiment first obtains the fitting weights of different fitting data points through the trends and fitting differences of a few data points, and then obtains a more accurate fitting result. Considering that in the process of compacting the soil samples retrieved from the site in the laboratory, only the final data of the compaction is used to ignore the real-time feedback during the compaction process. Based on this, in this embodiment, during the compaction process, real-time monitoring equipment (high-precision density meter, hygrometer, etc.) is used to automatically feedback and adjust the compaction parameters according to the data change trend during the compaction process, and then the fitting weights of different fitting data points are obtained through the data fluctuations during the compaction process of different data points involved in the fitting, and then a higher fitting curve can be obtained based on a few data points, thereby improving the accuracy of the maximum dry density obtained.

[0032] Preferably, in one embodiment of the present invention, the specific process of repeatedly compacting the soil sample in the laboratory is as follows: Preset initial applied pressure The preset pressure increase parameter is 25 kPa. 3 kilopascals (kPa), preset hammering cut-off times 50 times, preset judgment threshold The value is 0.3, and this example is used for description.

[0033] First use the preset initial pressure The soil sample is hammered repeatedly to obtain the density value of the soil sample after each hammering. When 1 is subtracted The difference between The product of, as the second product, will The sum of the products with the second is taken as the first The pressure of the blow .in, For the The density of the soil sample after the first hammering is For the The density value of the soil sample after the first hammering.

[0034] What needs to be explained is: is an integer greater than 1, if is 4, then the pressure applied in the first 4 blows is , the pressure applied for the fifth blow is ,and and Are greater than or equal to . , as well as They are the density values ​​of the soil sample after the first, second and third beatings, respectively. In this embodiment, a high-precision densitometer is used to monitor the density value of the soil sample after each beating, and the applied pressure is changed by adjusting the drop distance of the beating, and a pressure sensor is used to monitor the applied pressure of each beating, which is described as an example. When the density value of the soil sample after hammering changes little, increase the hammering pressure to make the repeated hammering effect better. Second and The difference between the density values ​​of the soil samples after the first hammering The smaller, the The more you beat, the more pressure you need to apply.

[0035] Then from Start with the first hammering, use Continue to hammer the soil sample repeatedly to obtain the density value of the soil sample after each hammering. When 1 is subtracted The difference between The product of, as the third product, will The sum of the products with the third is taken as the The pressure of the blow .in, 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. From The pressure applied by each blow is .

[0036] Then from the Start with the first hammering, use The soil sample is repeatedly compacted, and the pressure applied during the repeated compaction of the soil sample is adaptively adjusted until the first Stop after the first hammering to complete the repeated compaction operation of the soil sample.

[0037] 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 between the density time series, humidity time series, pressure time series and applied pressure time series.

[0038] Preferably, in one embodiment of the present invention, the method for obtaining the current fitting weight of the soil sample includes: During the repeated compaction of the soil sample, the applied pressure of each hammering and the density, humidity and pressure values ​​of the soil sample after each hammering are collected, and each hammering corresponds to a timestamp, thereby obtaining the density time series sequence, humidity time series sequence, pressure time series sequence and applied pressure time series sequence during the repeated compaction of the soil sample.

[0039] It should be noted that: in this embodiment, the humidity change of the soil sample is monitored in real time by a hygrometer, and the stress change of the soil sample during the compaction process 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 the soil sample is hammered, and the pressure value refers to the pressure value of the soil sample. In this embodiment, the minimum and maximum standard method is used to standardize the data of these four dimensions respectively, and the dimensions are unified. This is a well-known technology, and the specific method is not introduced here. Since the sensor monitoring is affected by the hammering action during the compaction process, it will cause sensor data errors, but the data change trend is unchanged, and then the continuity of the sensor data is used to assist the traditional ring knife method to improve the calculation accuracy of the compaction degree.

[0040] 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.

[0041] It should be noted that: in this embodiment, the stability and volatility of the data at each moment need to be obtained according to the change of each piece of data. Data points with higher stability represent more accurate soil sample states, so they can be given higher weights, while data points with larger fluctuations may be caused by equipment errors or soil sample inhomogeneity, and are given lower weights. At the same time, if the pressure changes frequently before and after a certain moment, and is relatively close to the parameter change time, the data at that moment may be affected by the pressure change, resulting in inaccurate data values.

[0042] For a piece of data at each moment, the PCA dimensionality reduction method is used to convert the data at each moment into a single scalar data, so as to obtain the scalar data at each moment, and a scalar data time series sequence is constructed based on the scalar data at all moments.

[0043] It should be noted that PCA dimensionality reduction (Principal Component Analysis Dimensionality Reduction) is a well-known technology, and the specific method is not introduced here. In this embodiment, three-dimensional data is converted into single-dimensional data through PCA dimensionality reduction. Among them, the scalar data time series sequence represents the state change data of the soil sample.

[0044] The APCA segmentation method is used to segment the scalar data time series sequence to obtain a number of scalar segmentation points. All the scalar segmentation points constitute a scalar segmentation point time series sequence in chronological order.

[0045] It should be noted that APCA segmentation is a method based on Adaptive Piecewise Constant Approximation, which is used to segment time series. This method divides the time series into multiple constant segments, so that the data in each segment is as similar as possible, while the data between segments are obviously different. This is a well-known technology, and the specific method will not be introduced here.

[0046] Then, using the APCA segmentation method, according to the method of obtaining the scalar segmentation point time series sequence, 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 are obtained from the density time series sequence, the humidity time series sequence, the pressure time series sequence and the applied pressure time series sequence respectively.

[0047] The DTW algorithm is used to match the scalar segmentation point time series sequence with the density segmentation point time series sequence to obtain a number of density segmentation points that match each scalar segmentation point.

[0048] 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.

[0049] 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 The density error duration of a scalar segment point.

[0050] 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.

[0051] 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. The error value of the scalar segment point .

[0052] In the scalar segmentation point timing sequence and the pressure applied segmentation point timing sequence, the Euclidean distance between each scalar segmentation point and each pressure applied segmentation point is obtained using the Euclidean distance calculation method according to the moment and scalar data value of each scalar segmentation point and the moment and applied pressure value of each pressure applied segmentation point.

[0053] It should be noted that the closer the distance between the scalar segmentation point and the pressure applied segmentation point is, the greater the influence of the applied pressure change on the scalar segmentation point is, and the greater the error is. The Euclidean distance calculation method is a well-known technology. scalar segmentation point and the The calculation formula of the Euclidean distance between the pressure applied segment points is: ,in and Respectively scalar segmentation point and the The moment of applying pressure segmentation point, For the scalar data value of scalar segmentation points, For the The applied pressure value of each pressure segment point.

[0054] 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 ,in is an exponential function with a natural constant as the base. To present the inverse proportional relationship and normalization processing, the implementer can set the inverse proportional function and normalization function according to the actual situation.

[0055] The first Initial error of scalar segmentation points and error value The mean value of The unreliability of scalar segmentation points .

[0056] Calculate the mean of the unreliability of all scalar segment points ,Will As the current fitting weight of the soil sample.

[0057] What needs to be explained is that the larger the error of the scalar segmentation point, the less credible it is, and the smaller the fitting weight should be.

[0058] Step S004: adjusting the moisture content of the soil sample, and obtaining 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; performing 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 obtaining the dry density fitting curve of the soil sample at different moisture contents; obtaining the maximum dry density in the dry density fitting curve of the soil sample at different moisture 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.

[0059] Preferably, in one embodiment of the present invention, the method for obtaining the compaction degree of a soil sample includes: For the dried soil samples, the water content of the soil samples was adjusted using the water addition method to obtain soil samples with different water contents.

[0060] 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, the water content is directly added to the dried soil sample. grams of water, and then mix thoroughly to evenly distribute the water to obtain a soil sample with a water content of 6%, of which is the dry weight of the soil sample, and this example is used for description.

[0061] According to the method of obtaining the current dry density and fitting weight of the soil sample, the dry density and fitting weight of the soil sample at different water contents are obtained.

[0062] According to the fitting weights of the soil samples at different moisture contents, the weighted least squares method is used to perform weighted curve fitting on the dry density of the soil samples at different moisture contents to obtain the dry density fitting curve of the soil samples at different moisture contents.

[0063] It should be noted that the weighted least square method is a well-known technique, and the specific method is not 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.

[0064] 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.

[0065] It should be noted that: in this embodiment, during the calculation of the ratio, if the denominator is 0, the denominator is set to 1 to ensure that the ratio is established, and this is described as an example. 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 realistically reflect the compaction process, thereby significantly improving the accuracy of the standard dry density.

[0066] Second, see Figure 2 (Module structure diagram of a roadbed compaction detection device), which shows a roadbed compaction detection device provided by an embodiment of the present invention, 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 acquisition module: used to 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 acquisition method of the current dry density of the soil sample and the current fitting weight of the soil sample; according to the fitting weight of the soil sample at different moisture contents, perform weighted curve fitting on the dry density of the soil sample at different moisture contents to 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.

[0067] In a third aspect, the present invention further proposes a roadbed compaction detection system, comprising a computer program, which implements the steps of the aforementioned roadbed compaction detection method when executed by a computer.

[0068] At this point, the present invention is completed. In summary, in an embodiment of the present invention, the wet weight of the soil sample at the roadbed test 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 of the soil sample during repeated compaction are obtained to determine the current fitting weight of the soil sample, and the moisture content of the soil sample is adjusted. According to the fitting weight of the soil sample at different moisture contents, a weighted curve fitting is performed on the dry density of the soil sample at different moisture contents, so as to obtain the maximum dry density to determine the compaction degree of the soil sample. The present invention is used to improve the accuracy of roadbed compaction detection by generating a more accurate fitting curve.

[0069] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should 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; The moisture content of the soil sample is adjusted, and the dry density and fitting weight of the soil sample at different moisture contents are obtained according to the method of obtaining the current dry density of the soil sample and the current fitting weight of the soil sample; according to the fitting weight of the soil sample at different moisture contents, a weighted curve fitting is performed on the dry density of the soil sample at different moisture contents to obtain a fitting curve of the dry density of the soil sample at different moisture contents; the maximum dry density in the fitting curve of the dry density of the soil sample at different moisture contents is obtained, and the compaction degree of the soil sample is determined according to the current dry density of the soil sample and the maximum dry density.

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 cutter ring 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 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.

6. A method for detecting roadbed compaction according to claim 5, 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 .

7. A method for detecting roadbed compaction according to claim 5, 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 .

8. 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.

9. A roadbed compaction detection device, using a roadbed compaction detection method as claimed in any one of claims 1 to 8, 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 acquisition module: used to 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 acquisition method of the current dry density of the soil sample and the current fitting weight of the soil sample; according to the fitting weight of the soil sample at different moisture contents, perform weighted curve fitting on the dry density of the soil sample at different moisture contents to 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.

10. 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 8.

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