CN115979891B - Detection method for high-pressure liquid-gas mixed fluid jet crushing and solidified clay - Google Patents
Detection method for high-pressure liquid-gas mixed fluid jet crushing and solidified clay Download PDFInfo
- Publication number
- CN115979891B CN115979891B CN202310250317.XA CN202310250317A CN115979891B CN 115979891 B CN115979891 B CN 115979891B CN 202310250317 A CN202310250317 A CN 202310250317A CN 115979891 B CN115979891 B CN 115979891B
- Authority
- CN
- China
- Prior art keywords
- separation
- sampling
- stress
- density
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W30/00—Technologies for solid waste management
- Y02W30/50—Reuse, recycling or recovery technologies
- Y02W30/91—Use of waste materials as fillers for mortars or concrete
Landscapes
- Investigation Of Foundation Soil And Reinforcement Of Foundation Soil By Compacting Or Drainage (AREA)
Abstract
The invention belongs to the field of data processing and optimization, and provides a method for detecting high-pressure liquid-gas mixed fluid jet crushing and solidified cohesive soil.
Description
Technical Field
The invention belongs to the field of data processing and optimization, and particularly relates to a detection method for high-pressure liquid-gas mixed fluid jet crushing and solidified clay.
Background
In the prior art, three-dimensional modeling is often carried out on the solidified soil body based on detection data such as sonar and infrared scanning to obtain a soil body three-dimensional data model, an algorithm similar to image convolution is used for searching on the soil body three-dimensional data model, whether all parts of the solidified soil body are qualified or not is detected, or whether the pressure value of mixed fluid jet crushing is qualified or not is deduced reversely from the detection result, time is consumed, detection is not thorough, and hidden danger is left for subsequent construction safety. In addition, in the existing detection method of the solidified soil body, for example, a soil binding solidifying agent and a soil detection method provided in patent document with publication number of CN114034839A, although actual parameter indexes of a soil sample to be detected can be calculated according to a fitting formula of a working curve, the detection means is poor in accuracy and difficult to detect the soil body in a large range, and accuracy is still difficult to improve in a more efficient mode on detection of the soil body in a large range.
Disclosure of Invention
The invention aims to provide a detection method for high-pressure liquid-gas mixed fluid jet crushing and solidified clay, which aims to solve one or more technical problems in the prior art and at least provides a beneficial selection or creation condition.
The invention provides a detection method for high-pressure liquid-gas mixed fluid jet crushing and solidification cohesive soil, which comprises the steps of sampling on a mathematical model of a region to be detected to obtain a plurality of sampling points, obtaining stress values and density values of the sampling points, carrying out numerical separation treatment on the sampling points according to the stress values and the density values of the sampling points to obtain separation numbers, calculating separation function groups of the sampling points according to the separation numbers, detecting soil body by using the separation function groups of the sampling points, and judging whether the soil body is firm or not.
In order to achieve the above object, according to an aspect of the present invention, there is provided a method for detecting high-pressure liquid-gas mixed fluid jet breaking and solidification of clay, the method comprising the steps of:
s100, selecting an area of the solidified cohesive soil as an area to be detected, and sampling on the area to be detected to obtain a plurality of sampling points;
s200, sampling soil at each sampling point to obtain a stress value and a density value of the soil;
s300, carrying out numerical separation processing on each sampling point according to the stress value and the density value of each sampling point to obtain a separation number;
s400, calculating a separation function set of each sampling point according to the separation number;
s500, detecting soil bodies by using the separation function groups of the sampling points, and judging whether the soil bodies are firm or not.
Further, in S100, the method for sampling the cured clay region to obtain a plurality of sampling points includes: and sampling is carried out on the region to be detected by using a Monte Carlo algorithm or a random sampling algorithm to obtain a plurality of different sampling points, wherein the number of the sampling points is preferably more than 3.
Further, in S200, the method for sampling the soil body at each sampling point to obtain the stress value and the density value thereof includes: and respectively sampling the soil body at each sampling point, and respectively obtaining the stress value and the density value of the sampled soil body, wherein the numerical value of the horizontal stress value of the sampled soil body is obtained as the stress value by an in-situ soil body horizontal stress tester, and the numerical value of the dry density of the soil is calculated as the density value by obtaining the density and the water content of the soil.
Further, in S300, according to the stress value and the density value of each sampling point, the numerical separation processing is performed on each sampling point, and the method for obtaining the separation number is as follows:
the broken and resolidified clay is difficult to perceive by a conventional machine if an uneven part exists, however, through practical observation, the distribution of stress values and density values of the broken and resolidified clay is found to be very characteristic, in the case of finishing high-pressure smashing and resolidifying clay, the condition of the ground is to be detected to what extent, the stress values and the density values are important measurement indexes, the common probability distribution of the stress values and the density values can effectively reflect the real-time stabilizing condition of the ground and the change thereof, therefore, the sampling separation coefficient needs to be calculated, the coordinates of each sampling point on a two-dimensional plane are recorded, the set of all the sampling points is set Sampset, the number of elements in the set Sampset is n, the element with the number i in the Sampset is i, i epsilon 1, n, the element with the number i in the Sampset is the sampling point Samp (i), the stress value corresponding to the sampling point Samp (i) is marked as str (i), the density value corresponding to the sampling point Samp (i), the separation coefficient corresponding to the sampling point Sampset (i) is calculated, and the sampling coefficient is calculated as the sampling coefficient (afu) is calculated as a sampling coefficient:
calculating the sampling separation coefficient can be realized by calculating the common probability distribution of the stress value and the density value relative to each sampling point serving as an individual on the overall area to be detected, wherein the numerical distribution of the stress value and the density value of each sampling point on the overall area to be detected can be effectively extracted in an exponential ratio solving mode, so that the condition of the land is better detected to what degree;
selecting s sampling points with the largest sampling separation coefficient value as s separation fixed points on the two-dimensional plane, wherein s represents the number of the separation fixed points, the specific value of s can depend on the area size of the area to be detected, the larger the area is, the larger the value of s can be, the s should not be smaller than 3, and the value of s can be preferably equal to 3 or 4; respectively carrying out two-by-two straight lines among the s separation fixed points to obtain a region with the largest area formed by the straight lines between the separation fixed points as a separation region, wherein the two-by-two straight lines are respectively carried out among the separation fixed points to generate a plurality of different connecting lines, the connecting lines comprise the separation fixed points as points or points of corners inside, thus forming regions with different geometric shapes, and selecting the region with one geometric shape with the largest occupied area as the separation region; the elements falling into the separation area in the collection Sampset are acquired to form a collection capset, the number of the elements in the collection capset is recorded as m as a separation number (based on the principle of three-point coplanarity, under the condition that s separation points are not smaller than 3, the area with the most prominent characteristic of the sampling separation coefficient in the range of the area to be detected can be rapidly extracted, the separation number obtained from the area can be effectively used as the number of the characteristics of the dimension of the subsequent separation function group, and the sampling detection speed of the area to be detected is improved).
Further, in S400, the method for calculating the separation function set of each sampling point according to the separation number is as follows:
the serial numbers of the elements in the Lapset are j, j epsilon [1, m ] (here, according to the specific numerical value of the separation number m, it is determined how many dimensions are, namely, there is an upper limit value, then the obtained m inner part serial numbers j) are rearranged and allocated numbers, wherein the serial numbers j can be numbers different from the serial numbers i, the elements with the serial numbers j in the Lapset are Lap (j), the numerical value of the sampling separation coefficient corresponding to the Lap (j) is recorded as ifus (j), then the ifus (j) corresponding to each Lap (j) is formed into an m-dimension array as Lapvec, and the array Lapvec is the separation function array.
Further, in S500, the method for detecting the soil body by using the separation function set of each sampling point to determine whether the soil body is firm includes: sampling again on the area to be detected to obtain a plurality of different sampling points as sampling points, wherein the sampling points are different from the sampling points in the step S100, namely one sampling point cannot be adopted in the step S100 if the sampling points are already sampled in the step S100, and preferably, the number of the sampling points is not more than the number of elements in the set Sampset, for example, the number is preferably 12-15; the number of the resampling points is denoted as k, wherein the serial number of each resampling point is d, d E [1, k ], the resampling point with the serial number d is Rsamp (d), the stress value corresponding to the Rsamp (d) is denoted as rstr (d), and the density value corresponding to the Rsamp (d) is denoted as rdns (d);
in order to unify the numerical correlation between the different physical quantities, the numerical calculation is performed in a dimensionless manner;
and then calculating the stress density separation degree of the resampling points, wherein the stress density separation degree is required to calculate the stress value and the density value corresponding to each resampling point and each dimension of the separation function set respectively, and then counting the calculation results of the stress value and the density value corresponding to each resampling point and each dimension of the separation function set to be used as the numerical value of the stress density separation degree,
preferably, the stress density separation degree is recorded as β, and the calculation formula of the stress density separation degree is:
alternatively, the density value must not be zero in the absence of anomalies, and for better measurement and statistics of the numerical relationship between stress and density, the stress density separation can be calculated as:
because single disposable sampling is insufficient to thoroughly detect soil on the area to be detected, once detection is omitted, serious hidden danger is caused to the safety of subsequent building construction, so that the resampling point is needed, the accuracy and the safety are improved, the stress density separation degree is calculated and extracted by combining the stress value and the density value of the resampling point with the dimensional characteristic of a separation function group on the resampling basis, the stress density separation degree is calculated, the stress value and the density value are favorably fully utilized relative to the common probability distribution of each sampling point serving as an individual on the overall area to be detected, the stress density separation degree is compared with the subsequent standard stress density separation degree, the comprehensive and thorough calculation measurement can be performed on the broken and solidified cohesive soil, the starting progress of the engineering on the land is quickened, and the safety of the subsequent engineering is better ensured;
then, calculating the stress value and the density value corresponding to each sampling point in the set Sampset and each dimension of the separation function set, and then counting the calculation results of the stress value and the density value corresponding to each sampling point and each dimension of the separation function set to obtain a value of standard stress density separation degree, wherein the value of standard stress density separation degree is preferably recorded as a, and the calculation formula of the standard stress density separation degree can be:
or, the calculation formula corresponding to the stress density separation degree, under the condition of better measuring and counting the numerical relation between the stress and the density, can also be:
comparing the stress density separation degree with the standard stress density separation degree in a numerical value, if the numerical difference between the stress density separation degree and the standard stress density separation degree exceeds a threshold value, judging that the soil body is not firm, wherein the method for judging that the numerical difference between the stress density separation degree and the standard stress density separation degree exceeds the threshold value can be any one or more of calculating an absolute value of a numerical value difference between the stress density separation degree and the standard stress density separation degree, calculating a ratio or calculating a distance value of probability distribution to compare with a preset, input or calculated threshold value, and preferably judging that the numerical difference between the stress density separation degree and the standard stress density separation degree exceeds the threshold value, and specifically can be: and calculating the absolute value of the difference between a and beta as c, taking the value of one third of a as the threshold value, and judging that the soil body is not firm if the value of c is larger than or equal to the threshold value, so that the speed and the safety of carrying out construction on the soil body are improved.
The beneficial effects of the invention are as follows: the invention provides a method for detecting high-pressure liquid-gas mixed fluid jet crushing and solidified cohesive soil, which comprises the steps of sampling on a mathematical model of a region to be detected to obtain a plurality of sampling points, obtaining stress values and density values of the sampling points, carrying out numerical separation treatment on the sampling points according to the stress values and the density values of the sampling points to obtain separation numbers, calculating separation function groups of the sampling points according to the separation numbers, detecting soil body by using the separation function groups of the sampling points, judging whether the soil body is firm, and realizing the beneficial effects of rapidly checking the solidified cohesive soil and improving construction safety.
Drawings
The above and other features of the present invention will become more apparent from the detailed description of the embodiments thereof given in conjunction with the accompanying drawings, in which like reference characters designate like or similar elements, and it is apparent that the drawings in the following description are merely some examples of the present invention, and other drawings may be obtained from these drawings without inventive effort to those of ordinary skill in the art, in which:
FIG. 1 is a flow chart of a method for detecting high-pressure liquid-gas mixed fluid jet breaking and solidified clay;
fig. 2 is a system structure diagram of a detection system for high-pressure liquid-gas mixed fluid jet breaking and solidification of clay.
Detailed Description
The conception, specific structure, and technical effects produced by the present invention will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present invention. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
In the description of the present invention, a number means one or more, a number means two or more, and greater than, less than, exceeding, etc. are understood to not include the present number, and above, below, within, etc. are understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
Fig. 1 is a flowchart of a method for detecting high-pressure liquid-gas mixed fluid jet crushed and solidified clay according to the present invention, and a method for detecting high-pressure liquid-gas mixed fluid jet crushed and solidified clay according to an embodiment of the present invention will be described with reference to fig. 1.
The invention provides a detection method for high-pressure liquid-gas mixed fluid jet crushing and solidification of clay, which specifically comprises the following steps:
s100, selecting an area of the solidified cohesive soil as an area to be detected, and sampling on the area to be detected to obtain a plurality of sampling points;
s200, sampling soil at each sampling point to obtain a stress value and a density value of the soil;
s300, carrying out numerical separation processing on each sampling point according to the stress value and the density value of each sampling point to obtain a separation number;
s400, calculating a separation function set of each sampling point according to the separation number;
s500, detecting soil bodies by using the separation function groups of the sampling points, and judging whether the soil bodies are firm or not.
Further, in S100, the method for sampling the cured clay region to obtain a plurality of sampling points includes: and sampling is carried out on the region to be detected by using a Monte Carlo algorithm or a random sampling algorithm to obtain a plurality of different sampling points, wherein the number of the sampling points is preferably more than 3.
Further, in S200, the method for sampling the soil body at each sampling point to obtain the stress value and the density value thereof includes: and respectively sampling the soil body at each sampling point, and respectively obtaining the stress value and the density value of the sampled soil body, wherein the numerical value of the horizontal stress value of the sampled soil body is obtained as the stress value by an in-situ soil body horizontal stress tester, and the numerical value of the dry density of the soil is calculated as the density value by obtaining the density and the water content of the soil.
Further, in S300, according to the stress value and the density value of each sampling point, the numerical separation processing is performed on each sampling point, and the method for obtaining the separation number is as follows:
the coordinates of all sampling points on a two-dimensional plane are recorded, the set of all the sampling points is set Sampset, the number of elements in the set Sampset is n, the sequence number of the elements in the Sampset is i, i epsilon [1, n ], the element with the sequence number i in the Sampset is sampling point Samp (i), the stress value corresponding to the sampling point Samp (i) is recorded as str (i), the density value corresponding to the sampling point Samp (i) is recorded as dns (i), the sampling separation coefficient Ifus (i) corresponding to the sampling point Samp (i) is calculated, and the calculation formula of the sampling separation coefficient is as follows:
selecting s sampling points with the largest sampling separation coefficient value as s separation fixed points on the two-dimensional plane, wherein s represents the number of the separation fixed points, the specific value of s can depend on the area size of the area to be detected, the larger the area is, the larger the value of s can be, the s should not be smaller than 3, and the value of s can be preferably equal to 3 or 4; respectively carrying out two-by-two straight lines among the s separation fixed points to obtain a region with the largest area formed by the straight lines between the separation fixed points as a separation region, wherein the two-by-two straight lines are respectively carried out among the separation fixed points to generate a plurality of different connecting lines, the connecting lines comprise the separation fixed points as points or points of corners inside, thus forming regions with different geometric shapes, and selecting the region with one geometric shape with the largest occupied area as the separation region; and acquiring elements falling into the separation area in the set Sampset to form a set capset, and acquiring the number of the elements in the capset as a separation number and recording as m.
Further, in S400, the method for calculating the separation function set of each sampling point according to the separation number is as follows:
the serial numbers of elements in the Lapset are j, j epsilon [1, m ], the elements with the serial numbers of j in the Lapset are Lap (j), the numerical value of the sampling separation coefficient corresponding to the Lap (j) is marked as ifus (j), then the ifus (j) corresponding to each Lap (j) is formed into an m-dimension array as Lapvec, and the array Lapvec is the separation function array.
Further, in S500, the method for detecting the soil body by using the separation function set of each sampling point to determine whether the soil body is firm includes: sampling again on the area to be detected to obtain a plurality of different sampling points as sampling points, wherein the sampling points are different from the sampling points in the step S100, namely one sampling point cannot be adopted in the step S100 if the sampling points are already sampled in the step S100, and preferably, the number of the sampling points is not more than the number of elements in the set Sampset, for example, the number is preferably 12-15; the number of the resampling points is denoted as k, wherein the serial number of each resampling point is d, d E [1, k ], the resampling point with the serial number d is Rsamp (d), the stress value corresponding to the Rsamp (d) is denoted as rstr (d), and the density value corresponding to the Rsamp (d) is denoted as rdns (d);
the dimensionless treatment can be carried out on each numerical value, so as to calculate the stress density separation degree of the resampling points, the stress density separation degree needs to calculate the stress value and the density value corresponding to each resampling point and each dimension of the separation function set respectively, then the calculation results of the stress value and the density value corresponding to each resampling point and each dimension of the separation function set are counted, and the calculated results are taken as the numerical value of the stress density separation degree,
preferably, the stress density separation degree is recorded as β, and the calculation formula of the stress density separation degree is:
alternatively, the density value must not be zero in the absence of anomalies, and for better measurement and statistics of the numerical relationship between stress and density, the stress density separation can be calculated as:
then, calculating the stress value and the density value corresponding to each sampling point in the set Sampset and each dimension of the separation function set, and then counting the calculation results of the stress value and the density value corresponding to each sampling point and each dimension of the separation function set to obtain a value of standard stress density separation degree, wherein the value of standard stress density separation degree is preferably recorded as a, and the calculation formula of the standard stress density separation degree can be:
alternatively, it may also be:
comparing the stress density separation degree with the standard stress density separation degree in a numerical value, if the numerical difference between the stress density separation degree and the standard stress density separation degree exceeds a threshold value, judging that the soil body is not firm, wherein the method for judging that the numerical difference between the stress density separation degree and the standard stress density separation degree exceeds the threshold value can be any one or more of calculating an absolute value of a numerical value difference between the stress density separation degree and the standard stress density separation degree, calculating a ratio or calculating a distance value of probability distribution to compare with a preset, input or calculated threshold value, and preferably judging that the numerical difference between the stress density separation degree and the standard stress density separation degree exceeds the threshold value, and specifically can be: and calculating the absolute value of the difference between a and beta as c, taking the value of one third of a as the threshold value, and judging that the soil body is not firm if the value of c is larger than or equal to the threshold value.
The detection system for high-pressure liquid-gas mixed fluid jet crushing and curing clay is operated in any computing equipment of a desktop computer, a notebook computer, a palm computer or a cloud data center, and the computing equipment comprises: a processor, a memory, and a computer program stored in and running on the processor, wherein the processor implements the steps in the method for detecting the high-pressure liquid-gas mixture fluid jet breaking and solidification of clay when executing the computer program, and the operable system can comprise, but is not limited to, a processor, a memory, a server cluster, and the like.
The embodiment of the invention provides a detection system for high-pressure liquid-gas mixed fluid jet crushing and curing clay, as shown in fig. 2, which comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the above-mentioned detection method embodiment of high-pressure liquid-gas mixed fluid jet breaking and solidified clay when executing the computer program, and the processor executes the computer program can be executed in a unit of the following system:
the sampling unit is used for sampling on the mathematical model of the region to be detected to obtain a plurality of sampling points;
the numerical value acquisition unit is used for acquiring stress values and density values of all the sampling points;
the numerical value separation processing unit is used for carrying out numerical value separation processing on each sampling point according to the stress value and the density value of each sampling point to obtain a separation number;
a separation function set calculation unit for calculating a separation function set of each sampling point according to the separation number;
and the detection unit is used for detecting the soil body by using the separation function groups of the sampling points and judging whether the soil body is firm or not.
Preferably, all undefined variables in the present invention may be threshold set manually if not explicitly defined.
The detection system for the high-pressure liquid-gas mixed fluid jet crushing and curing clay can be operated in computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud data center and the like. The detection system for the high-pressure liquid-gas mixed fluid jet breaking and curing of the cohesive soil comprises, but is not limited to, a processor and a storage. It will be understood by those skilled in the art that the examples are merely examples of the method for detecting the high-pressure liquid-gas mixture fluid jet breaking and curing clay, and the method for detecting the high-pressure liquid-gas mixture fluid jet breaking and curing clay is not limited thereto, and may include more or less components than examples, or may combine some components, or different components, for example, the system for detecting the high-pressure liquid-gas mixture fluid jet breaking and curing clay may further include an input/output device, a network access device, a bus, and the like.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete component gate or transistor logic devices, discrete hardware components, or the like. The general processor may be a microprocessor or the processor may be any conventional processor, etc., and the processor is a control center of the detection system for high-pressure liquid-gas mixed fluid jet breaking and curing clay, and various interfaces and lines are used to connect the various sub-areas of the detection system for high-pressure liquid-gas mixed fluid jet breaking and curing clay.
The memory can be used for storing the computer program and/or the module, and the processor can realize various functions of the detection method for the high-pressure liquid-gas mixed fluid jet breaking and solidified clay by running or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The invention provides a method for detecting high-pressure liquid-gas mixed fluid jet crushing and solidified cohesive soil, which comprises the steps of sampling on a mathematical model of a region to be detected to obtain a plurality of sampling points, obtaining stress values and density values of the sampling points, carrying out numerical separation treatment on the sampling points according to the stress values and the density values of the sampling points to obtain separation numbers, calculating separation function groups of the sampling points according to the separation numbers, detecting soil body by using the separation function groups of the sampling points, judging whether the soil body is firm, and realizing the beneficial effects of rapidly checking the solidified cohesive soil and improving construction safety.
Although the present invention has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiment or any particular embodiment so as to effectively cover the intended scope of the invention. Furthermore, the foregoing description of the invention has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the invention that may not be presently contemplated, may represent an equivalent modification of the invention.
Claims (3)
1. The method for detecting the broken and solidified clay by high-pressure liquid-gas mixed fluid injection is characterized by comprising the following steps of:
s100, selecting an area of the solidified cohesive soil as an area to be detected, and sampling on the area to be detected to obtain a plurality of sampling points;
s200, sampling soil at each sampling point to obtain a stress value and a density value of the soil;
s300, carrying out numerical separation processing on each sampling point according to the stress value and the density value of each sampling point to obtain a separation number;
s400, calculating a separation function set of each sampling point according to the separation number;
s500, detecting soil bodies by using the separation function groups of the sampling points, and judging whether the soil bodies are firm or not;
in S300, according to the stress value and the density value of each sampling point, performing numerical separation processing on each sampling point to obtain a separation number, where the method includes:
the coordinates of all sampling points on a two-dimensional plane are recorded, the set of all the sampling points is set Sampset, the number of elements in the set Sampset is n, the sequence number of the elements in the Sampset is i, i epsilon [1, n ], the element with the sequence number i in the Sampset is sampling point Samp (i), the stress value corresponding to the sampling point Samp (i) is recorded as str (i), the density value corresponding to the sampling point Samp (i) is recorded as dns (i), the sampling separation coefficient Ifus (i) corresponding to the sampling point Samp (i) is calculated, and the calculation formula of the sampling separation coefficient is as follows:
selecting s sampling points with the largest sampling separation coefficient value as s separation points on the two-dimensional plane, respectively carrying out two-to-two straight lines between the separation points in the s separation points, obtaining a region with the largest area formed by the straight lines between the separation points as a separation region, obtaining elements falling into the separation region in a set Sampset to form a set Lapset, and obtaining the number of the elements in the Lapset as separation numbers to be recorded as m;
in S400, the method for calculating the separation function set of each sampling point according to the separation number is as follows:
the serial numbers of elements in the Lapset are j, j epsilon [1, m ], the elements with the serial numbers of j in the Lapset are Lap (j), the numerical value of a sampling separation coefficient corresponding to the Lap (j) is recorded as ifus (j), then the ifus (j) corresponding to each Lap (j) is formed into an m-dimension array as Lapvec, and the array Lapvec is a separation function array;
in S500, the method for detecting the soil body by using the separation function set of each sampling point and judging whether the soil body is firm includes: sampling again on the area to be detected to obtain a plurality of different sampling points as resampling points, and recording the number of the resampling points as k, wherein the serial number of each resampling point is d, d E [1, k ], the resampling point with the serial number d is Rsamp (d), the stress value corresponding to the Rsamp (d) is recorded as rstr (d), and the density value corresponding to the Rsamp (d) is recorded as rdns (d);
and further calculating the stress density separation degree of the resampling points, wherein the stress density separation degree is required to calculate the stress value and the density value corresponding to each resampling point and each dimension of the separation function set respectively, and then counting the calculation results of the stress value and the density value corresponding to each resampling point and each dimension of the separation function set, wherein the calculation results are used as the numerical value of the stress density separation degree, the stress density separation degree is recorded as beta, and the calculation formula of the stress density separation degree is as follows:
alternatively, the stress density separation is calculated as:
then, calculating the stress value and the density value corresponding to each sampling point in the set Sampset and each dimension of the separation function set respectively, and then counting the calculation results of the stress value and the density value corresponding to each sampling point and each dimension of the separation function set to be used as the numerical value of the standard stress density separation degree;
and comparing the stress density separation degree with the standard stress density separation degree in a numerical value manner, and judging that the soil body is not firm if the numerical value difference between the stress density separation degree and the standard stress density separation degree exceeds a threshold value.
2. The method for detecting the high-pressure liquid-gas mixed fluid jet breaking and curing cohesive soil according to claim 1, wherein in S100, selecting a region of the cured cohesive soil as a region to be detected, and sampling on the region to be detected to obtain a plurality of sampling points comprises: and sampling by using a Monte Carlo algorithm or a random sampling algorithm on the region to be detected to obtain a plurality of different sampling points.
3. The method for detecting high-pressure liquid-gas mixed fluid jet breaking and solidification clay according to claim 1, wherein in S200, the method for sampling the soil body at each sampling point to obtain the stress value and the density value thereof comprises the following steps: and respectively sampling the soil body at each sampling point, and respectively obtaining the stress value and the density value of the sampled soil body, wherein the numerical value of the horizontal stress value of the sampled soil body is obtained as the stress value by an in-situ soil body horizontal stress tester, and the numerical value of the dry density of the soil is calculated as the density value by obtaining the density and the water content of the soil.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310250317.XA CN115979891B (en) | 2023-03-16 | 2023-03-16 | Detection method for high-pressure liquid-gas mixed fluid jet crushing and solidified clay |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310250317.XA CN115979891B (en) | 2023-03-16 | 2023-03-16 | Detection method for high-pressure liquid-gas mixed fluid jet crushing and solidified clay |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115979891A CN115979891A (en) | 2023-04-18 |
| CN115979891B true CN115979891B (en) | 2023-06-23 |
Family
ID=85958190
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310250317.XA Active CN115979891B (en) | 2023-03-16 | 2023-03-16 | Detection method for high-pressure liquid-gas mixed fluid jet crushing and solidified clay |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115979891B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117371184B (en) * | 2023-09-20 | 2024-04-16 | 广东省水利水电第三工程局有限公司 | Hydration reaction structure strength change simulation method and system for large concrete |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2021130911A (en) * | 2020-02-18 | 2021-09-09 | 五洋建設株式会社 | Viscous soil characteristic estimation method, construction method using viscous soil, viscous soil characteristic estimation device, and program |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| MX349507B (en) * | 2013-08-06 | 2017-08-02 | Bp Corp North America Inc | Image-based direct numerical simulation of petrophysical properties under simulated stress and strain conditions. |
| WO2017032520A1 (en) * | 2015-08-27 | 2017-03-02 | Freie Universitaet Berlin | A method for determining the hydraulic permeability of rocks in a subsurface region |
| CN106845419A (en) * | 2017-01-22 | 2017-06-13 | 深圳市唯特视科技有限公司 | A kind of method that Gait Recognition performance is assessed based on capturing movement framework |
| CN108595585B (en) * | 2018-04-18 | 2019-11-12 | 平安科技（深圳）有限公司 | Sample data classification method, model training method, electronic equipment and storage medium |
| CN109470580B (en) * | 2018-09-26 | 2021-05-11 | 中国石油天然气集团公司 | Method for evaluating cohesive soil strength parameters of different sea areas in China |
| CN113408178A (en) * | 2021-07-06 | 2021-09-17 | 大连理工大学 | Function probability distribution-based soil and stone material stress deformation parameter random field simulation method |
| CN114297950A (en) * | 2021-11-24 | 2022-04-08 | 淮北矿业股份有限公司 | Method for calculating distribution of water loss compression amount of pressure-bearing water-containing loose layer region |
| CN115112865A (en) * | 2022-07-06 | 2022-09-27 | 中国热带农业科学院热带作物品种资源研究所 | Effective state analysis method for soil |
| CN115775601A (en) * | 2022-11-23 | 2023-03-10 | 西南科技大学 | Abnormal data monitoring method and system for material force-heat test |
-
2023
- 2023-03-16 CN CN202310250317.XA patent/CN115979891B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2021130911A (en) * | 2020-02-18 | 2021-09-09 | 五洋建設株式会社 | Viscous soil characteristic estimation method, construction method using viscous soil, viscous soil characteristic estimation device, and program |
Non-Patent Citations (2)
| Title |
|---|
| Improvement of calcareous expansive soils in semi-arid environments;Zalihe Nalbantoglu;《Journal of Arid Environments》;全文 * |
| 水泥土强度变异性统计分析;张沂;《 路基工程 》;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115979891A (en) | 2023-04-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115979891B (en) | Detection method for high-pressure liquid-gas mixed fluid jet crushing and solidified clay | |
| CN113077476B (en) | Height measurement method, terminal device and computer storage medium | |
| CN109712131A (en) | Quantization method, device, electronic equipment and the storage medium of Lung neoplasm feature | |
| CN108269274B (en) | Image registration method based on Fourier transform and Hough transform | |
| Liu | Additive white Gaussian noise level estimation based on block SVD | |
| CN111275821A (en) | Power line fitting method, system and terminal | |
| CN112435297B (en) | Target object pose determining method and device, storage medium and electronic equipment | |
| Chang et al. | Object volume estimation based on 3d point cloud | |
| CN113283082A (en) | Centroid trajectory generation method and device, computer readable storage medium and robot | |
| CN107481319B (en) | Hidden surface random point cloud generator | |
| Covre et al. | Monte Carlo-based 3D surface point cloud volume estimation by exploding local cubes faces | |
| CN116266365A (en) | Point cloud data extraction method and device, electronic equipment and storage medium | |
| CN114743075A (en) | Point cloud model detection method and device, electronic equipment and readable storage medium | |
| CN114152454A (en) | Mechanical equipment fault diagnosis method based on CEEMDAN-CSE model and establishment method of model | |
| CN108227750B (en) | Ground target real-time tracking performance evaluation method and system | |
| CN113156920B (en) | Method, device, equipment and medium for detecting noise interference of PD controller | |
| CN113176729B (en) | Method, device, equipment and medium for detecting noise interference of PID (proportion integration differentiation) controller | |
| CN114081396B (en) | Map data reporting method and device of sweeper, storage medium and electronic equipment | |
| CN113420101B (en) | Geographic information processing method and device, electronic equipment and storage medium | |
| CN113506600B (en) | Particle uniformity evaluation method, device and equipment | |
| CN114998560B (en) | Positive fault section three-dimensional attribute-based related wrinkle processing method and system | |
| CN113204876B (en) | Noise gain calculation method, device, equipment and medium of PD controller | |
| CN115546271B (en) | Visual analysis method, device, equipment and medium based on depth joint characterization | |
| CN114897923B (en) | Natural gas hydrate CT image threshold segmentation method, system, equipment and medium | |
| CN111177886B (en) | Marine ranging planning and soil thickness prediction method based on geophysical prospecting data analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |