CN115880257A - Method for rapidly predicting intensity of ocean porous reef limestone - Google Patents
Method for rapidly predicting intensity of ocean porous reef limestone Download PDFInfo
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Abstract
The invention discloses a method for rapidly predicting the intensity of ocean porous reef limestone. It comprises the following steps: selecting a porous reef limestone rock sample on a construction site, and acquiring pore geometric characteristic quantization parameters on the surface of the rock sample; calculating the density of the rock sample; substituting the pore geometric characteristic quantization parameter and density into a formula
Calculating the strength of the porous reef limestone, wherein sigma is the strength of the porous reef limestone, rho is the density of the rock sample,
quantifying a parameter, C, for the pore geometry of the surface of a rock sample 1 Is a constant number, C 2 Is a constant. The method for rapidly predicting the strength of the marine porous reef limestone can accurately and rapidly obtain the strength of the porous reef limestone under the condition of less required calculation parameters, is convenient for field construction personnel to rapidly judge whether the strength of a constructed stratum meets construction requirements, avoids conveying rock samples to land for detection, accelerates the engineering construction progress and reduces the construction cost of open sea engineering.
Description
Technical Field
The invention relates to the technical field of island engineering construction, in particular to a method for quickly predicting the intensity of ocean porous reef limestone.
Background
The ocean contains abundant natural resources, but the land area exposed out of the sea surface is extremely rare, the island engineering construction is efficiently and safely carried out, and the production and living activities of human beings in open sea are facilitated. The reef limestone stratum is used as a main body of the island geology, and the strength of the reef limestone stratum is an important index in engineering construction, so that the construction progress and the safety are influenced. The reef limestone stratum is mainly formed by the complicated deposition, deterioration and other actions of the remains of the biomass, the space variability is large, stratum properties ascertained during actual engineering construction often have great access to earlier-stage exploration data, accidents such as pile slipping and the like caused by wrong judgment of bearing capacity strength of the reef limestone stratum occur in domestic and foreign engineering construction, and the construction progress is seriously influenced. Therefore, the bearing capacity strength of the reef limestone stratum is judged quickly and accurately during actual engineering construction, and the method is of great importance.
In the prior art, the strength test of the porous reef limestone needs to use various professional equipment such as a pressure tester, but the strength test through the pressure tester has the following defects: firstly, if professional equipment used for strength testing is placed on a high-temperature, high-humidity and high-salt construction site, the service life of the equipment is easily shortened, so that a porous reef limestone sample needs to be sent to a professional laboratory on land for detection, the engineering construction progress is delayed, and the cost of open sea engineering construction is increased; secondly, the rock is processed into a standard sample, and the process is time-consuming and labor-consuming.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for quickly predicting the intensity of the ocean porous reef limestone, which is convenient for constructors to accurately and quickly judge whether the intensity of the porous reef limestone meets the construction requirements on a construction site.
In order to achieve the purpose, the invention designs a method for quickly predicting the intensity of marine porous reef limestone, which is characterized by comprising the following steps of:
selecting a porous reef limestone rock sample on a construction site, and acquiring pore geometric characteristic quantitative parameters of the surface of the rock sample
Step two, calculating the density rho of the rock sample;
step three, quantizing parameters of the geometric characteristics of the pores
Substituting the density rho into a formula (1) to calculate the strength of the porous reef limestone, wherein the formula (1) is as follows
In the formula (I), the compound is shown in the specification,
sigma is the strength of the porous reef limestone,
p is the density of the rock sample,
C 1 is a constant number of times, and is,
C 2 is a constant.
Further, in step one, the pore geometric feature quantization parameter
The quantization is performed by the following steps,
step 1), photographing the surface of a porous reef limestone rock sample, and importing the photograph into image processing software in a computer, wherein the image processing software needs to have a graying function, an image segmentation function and an area automatic counting function;
step 2), setting an image scale;
step 3), selecting an analysis area of the photo in the image processing software, and converting the analysis area into a gray image by utilizing a graying function in the image processing software;
step 4), utilizing an image segmentation function in image processing software to segment pores and a solid skeleton in the gray image so as to enable the gray image to become a binary image, wherein the gray value of a pore pixel point and the gray value of a solid skeleton pixel point in the binary image are two numerical values;
step 5), automatically counting the pore area in the binary image by utilizing the automatic area counting function in the image processing software, and deriving the pore area as an original pore area data set;
step 6), carrying out effective processing on the original pore area data set to obtain a high-precision effective pore area data set;
and 7) substituting the effective pore area data set into the formula (2) to calculate the quantitative parameters of the geometric characteristics of the pores
The formula (2) is as follows
In the formula (I), the compound is shown in the specification,
n is the number of pores in the effective pore area data set,
S i is the area of the i-th aperture,
Furthermore, in the step 1), when the surface of the porous reef limestone rock sample is photographed, the photographing angle is perpendicular to the photographing surface, and meanwhile, the flash lamp is turned on.
Further, in step 1), the Image processing software is MATLAB or Image J.
Further, in step 2), the method for setting the image scale includes placing a graduated scale on the surface of the shot area, measuring the length in the image with the graduated scale in the image, and obtaining the image scale according to the ratio = actual length/length in the image in combination with the actual length of the graduated scale.
Further, in step 2), the method for setting the image scale includes placing a line segment with a certain length on the surface of the shooting area, measuring the in-image length of the line segment in the image, and obtaining the image scale according to the scale = actual length/in-image length by combining the actual length of the line segment.
Further, in step 3), the analysis area refers to an area with clear pore structure and no obvious distortion in the image.
Further, in step 6), the step of efficiently processing the raw pore area data set is as follows,
step a, dividing all data in a pore area distribution interval into first-level statistical intervals according to order of magnitude difference;
b, dividing each primary statistical interval into a plurality of secondary statistical intervals in equal proportion;
and c, selecting a primary statistical interval with the largest proportion of the total pore area to the total pore area in the interval and the pore areas in the internal secondary statistical interval being not 0 as an effective interval, and taking a data set in the effective interval as an effective pore area data set.
Further, in the second step, the density ρ is obtained by weighing the dried rock sample, placing the rock sample in a bucket filled with water, wherein the volume of the overflow liquid is the volume of the rock sample, and finally calculating the density of the rock sample according to the formula (3), wherein the formula (3) is as follows
In the formula (I), the compound is shown in the specification,
p is the density of the rock sample,
m is the mass of the rock sample,
v is the rock sample volume.
Further, in step three, C in the formula (1) 1 、C 2 The determination method comprises the steps of determining a porous reef limestone rock sample into a standard size, measuring weight, volume and pore geometric characteristic quantitative parameters, performing a pressure test to obtain rock sample strength, guiding rock sample strength data into computer software for fitting, and further obtaining C 1 、C 2 And (4) taking values.
The invention has the advantages that:
1. the method can conveniently identify the pore geometric structure of the porous reef limestone, quantize the pore geometric structure characteristics of the porous reef limestone and apply the quantized pore geometric characteristic quantization parameters to the porous reef limestone strength prediction formula;
2. the invention has lower requirements on instrument equipment and operators, can quickly process data by using simple equipment on a construction site, avoids sending rock samples to land for detection, accelerates the project construction progress and reduces the construction cost of open sea projects;
3. the method can accurately and quickly obtain the strength of the porous reef limestone under the condition of less required calculation parameters, and is convenient for field construction personnel to quickly judge whether the strength of the constructed stratum meets the construction requirements;
the method for rapidly predicting the strength of the marine porous reef limestone can accurately and rapidly obtain the strength of the porous reef limestone under the condition of less required calculation parameters, is convenient for field construction personnel to rapidly judge whether the strength of a constructed stratum meets construction requirements, avoids conveying rock samples to land for detection, accelerates the engineering construction progress and reduces the construction cost of open sea engineering.
Drawings
FIG. 1 is a flow chart of the method for rapidly predicting the intensity of the marine porous reef limestone according to the invention;
FIG. 2 is a flow chart of a method for quantifying pore geometry characteristics of a porous reef limestone in accordance with the present invention;
FIG. 3a is an original image of a photograph;
FIG. 3b is a graph of image graying;
FIG. 3c is an image binarization;
FIG. 3d is an automatic statistics of pore data;
FIG. 4 is a graph illustrating data distribution curves of the image-recognized original pore area in FIG. 3d and the manually-recognized original pore area in the prior art.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
In the description of the present invention, it is to be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships illustrated in the drawings, and are used merely for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the invention.
As shown in figure 1, the method for rapidly predicting the intensity of the marine porous reef limestone comprises the following steps:
step one, selecting a porous reef limestone rock sample on a construction site, and acquiring pore geometric characteristic quantitative parameters of the surface of the rock sample
Step two, calculating the density rho of the rock sample;
step three, quantizing parameters of the geometric characteristics of the pores
Substituting the density rho into a formula (1) to calculate the strength of the porous reef limestone, wherein the formula (1) is as follows
In the formula (I), the compound is shown in the specification,
sigma is the strength of the porous reef limestone,
p is the density of the rock sample,
C 1 is a constant number of times, and is,
C 2 is a constant.
In the first step, the pore geometric characteristic quantization parameter
Quantified by the following steps, as shown in fig. 2:
step 1), photographing the surface of the porous reef limestone sample, and importing the photograph into image processing software in a computer, wherein the image processing software needs to have a graying function, an image segmentation function and an area automatic counting function.
Specifically, select comparatively smooth porous reef limestone rock sample surface as the shooting face, utilize high definition shooting equipment to shoot, require that the rock sample surface pore structure is clear in the image of shooing. In order to enable the image to meet the requirements, when the surface of the porous reef limestone rock sample is photographed, the photographing angle is required to be perpendicular to the photographing surface, and meanwhile, the flash lamp is started. The high-definition shooting device can be a digital camera with a flash lamp, and can also be a mobile phone with a high-definition camera and a flash lamp.
In this embodiment, a digital camera pair with a lens having a focal length of 28-200 mm is adopted
The standard cylinder sample was photographed. The camera stand and the flash lamp are used in the photographing process, and the picture is shown in fig. 3 a.
The Image processing software is software with an Image processing function, such as MATLAB or Image J, and the type and the model of the Image processing software are not taken as the limitation of the invention.
In this embodiment, the shot picture is imported into the open source Image processing software Image J in the computer.
And step 2), setting an image scale.
Specifically, the method for setting the image scale comprises the following steps: placing a graduated scale on the surface of a shooting area, measuring the length of an image with the graduated scale in the image, and obtaining an image scale according to the ratio of the scale = actual length/length in the image by combining the actual length of the graduated scale; or placing a line segment with a certain length on the surface of the shooting area, measuring the in-image length of the line segment in the image, and combining the actual length of the line segment to obtain the image scale according to the scale = actual length/in-image length. On the basis of the image scale, when subsequent length measurement, automatic area statistics and other operations are carried out, the obtained results are the actual length and the actual area.
In this embodiment, the diameter of the sample in the image is 3000Pixels by a straight line tool and a measurement function in software, and compared with the actual size (the diameter is 50 mm), the scale bar is 60.
And 3) selecting an analysis area of the photo in the image processing software, and converting the analysis area into a gray image by utilizing a gray function in the image processing software.
Specifically, the analysis area refers to an area with clear pore structure and no obvious distortion in an image. And selecting an analysis area with better shooting quality by using a frame selection tool in the image processing software, and deleting the rest image areas by using a reverse selection-deletion function.
In this embodiment, the "8-bit" function of the software is utilized to convert the image into a gray scale image, as shown in fig. 3 b.
And 4) utilizing an image segmentation function in the image processing software to segment the pores and the solid skeleton in the gray image so as to enable the gray image to become a binary image, wherein the gray value of the pore pixel points and the gray value of the solid skeleton pixel points in the binary image are two numerical values.
Specifically, each pixel point in the grayscale image is represented by a grayscale value (also called intensity value or brightness value), and the value range of the grayscale value is 0 to 255. Gray value 0 and gray value 255 can be selected as pixel points in the binary image, wherein the pixel points with the gray value 0 are black, the pixel points with the gray value 255 are white, the pore area in the binary image is generally white, the skeleton area is black, or vice versa.
In the step, the range of the gray value is selected by a manual adjustment mode, so that the pore area is closer to the reality, and meanwhile, adjacent pores are not communicated as far as possible.
In this embodiment, the pore separation effect under each interval value is manually compared, and [25,125] is selected as the gray value interval selected during the image binarization, so as to obtain a binarized image as shown in fig. 3c, where white is the pore and black is the solid skeleton.
And 5) automatically counting the pore area in the binary image by utilizing an area automatic counting function in the image processing software, and deriving the pore area as an original pore area data set.
In this embodiment, the "analysis partitions" function of the software is utilized to automatically count the pore area in the image, and an original pore area data set is obtained, as shown in fig. 3 d.
The data distribution curve of the image recognition original pore area in the present embodiment and the data distribution curve of the prior art in which the original pore area is manually recognized are shown in fig. 4. As can be seen from fig. 4, compared with the accurate manual recognition result, the original data curve obtained by the image recognition has a better matching degree in the middle section and a poorer matching effect at the two ends of the curve. This is because in the process of image recognition, noise in the image is also easily counted as pores, and the area of the noise is generally small, so that the noise is smaller than 0.1mm 2 Within the interval (2), the result value of the image recognition is greater than the result of the manual recognition.
In addition, during the image recognition process, if adjacent pores are closely spaced, a pore may be recognized, which causes the area data of some single pores in the image recognition result to be larger.
In the statistics of the original pore area data set, since noise and adjacent pore connections are included and distorted, it is necessary to effectively process the original pore area data set.
And 6), effectively processing the original pore area data set to obtain a high-precision effective pore area data set.
Specifically, the steps for efficiently processing the raw pore area data set are as follows:
step a, dividing all data in a pore area distribution interval into first-level statistical intervals according to order of magnitude difference;
b, dividing each primary statistical interval into a plurality of secondary statistical intervals in equal proportion;
and c, selecting a primary statistical interval with the largest proportion of the total pore area to the total pore area in the interval and the pore areas in the internal secondary statistical interval being not 0 as an effective interval, and taking a data set in the effective interval as an effective pore area data set.
The first-level statistical interval with the largest proportion of the total pore area to the total pore area in the interval is selected as an effective interval so as to eliminate the 'noise' data with large quantity and small area. The first-level statistical interval with the pore area not being 0 in the second-level statistical interval is selected as an effective interval to eliminate the data of adjacent pore communicating bodies, and because the occurrence frequency of the adjacent pore communicating bodies is not high, even if the area of the communicating bodies is higher than the real pore data by one order of magnitude, the communicating bodies cannot be distributed in the second-level interval under the first-level interval.
As can be seen from fig. 4, the pore areas of the samples identified this time are mostly distributed in [1 × 10 ] -1 And 1) within the interval. By adopting the method for effectively processing the original pore area data set, the original pore area data is divided into: [1*10 -4 ,1*10 -3 )、[1*10 -3 ,1*10 -2 )、[1*10 -2 ,1*10 -1 )、[1*10 -1 ,1)、[1,1*10 1 ) And 5 primary statistical intervals in total. Each primary statistical interval is divided into 10 secondary statistical intervals, such as primary statistical interval [1,1 × 10 ] 1 ) The secondary statistical intervals below the secondary statistical intervals are ten secondary statistical intervals of [1,2 ], [2,3 ], [3,4 ], [4, 5], [5,6 ], [6,7 ], [7,8 ], [8,9 ] and [9, 10). The results show a primary statistical interval [1 x 10% -1 And 1) the sum of the pore areas in the first-level statistical interval is the largest, and the pore areas in the next ten second-level statistical intervals are not 0, then the first-level statistical interval [1 x 10 ] is taken -1 And 1) the data in the table is an effective pore area data set.
And 7) substituting the effective pore area data set into the formula (2) to calculate the quantitative parameters of the geometric characteristics of the pores
Formula (2) is asLower part
In the formula (I), the compound is shown in the specification,
n is the number of pores in the effective pore area data set,
S i is the area of the i-th aperture,
The result of the pore geometry quantization parameter in this example is 0.2585.
In order to compare the effective processing effects, the original pore area data of the gray scale image is compared with the artificially identified pore area data and the effective pore area data, and the result is shown in table 1 below.
TABLE 1 comparison of data
As can be seen from Table 1, the software obtained by the method for effectively processing the original pore area data set according to the invention can automatically identify that the effective pore area data accords with the manual identification result.
Quantifying parameters using the pore geometry described above
The quantitative method obtains the quantitative parameters ^ and ^ of the geometric characteristics of the five pores of the porous reef ash rock samples>
Are respectively 0.424mm 2 、0.417mm 2 、0.394mm 2 、0.03mm 2 、0.033mm 2 。
In the second step, the density rho is obtained by firstly weighing the dried weight of the rock sample, then placing the rock sample into a bucket full of water, wherein the volume of the overflow liquid is the volume of the rock sample, and finally calculating by a formula (3) to obtain the density of the rock sample, wherein the formula (3) is as follows
In the formula (I), the compound is shown in the specification,
p is the density of the rock sample,
m is the mass of the rock sample,
v is the rock sample volume.
If the obtained porous reef limestone sample is dry, directly weighing the weight of the sample; if the obtained porous reef limestone rock sample is wet, the porous reef limestone rock sample needs to be dried before weighing, the rock sample can be exposed to the sun for drying by utilizing the characteristic of strong illumination of tropical oceans, and the rock sample can also be dehydrated and dried by utilizing simple equipment such as a blower, an oven and an oven.
The densities rho of the five porous reef limestone samples are 1.02g/cm respectively 3 、0.96g/cm 3 、1.35g/cm 3 、0.94g/cm 3 、0.94g/cm 3 。
In step three, C in the formula (1) 1 、C 2 The determination method comprises the following steps: in the geological exploration stage of the initial stage of engineering construction, after a porous reef limestone rock sample obtained by exploration drilling is made into a standard size in a laboratory, quantitative parameters of weight, volume and geometric characteristics of pores are measured, then a pressure test is carried out to obtain rock sample strength, then rock sample strength data are led into computer software for fitting, and further C is obtained 1 、C 2 And (4) taking values.
In this embodiment, according to the results of the early-stage indoor tests, the values of C1 and C2 are 2.88 to-9.32, respectively.
Quantifying parameters of the pore geometric characteristics of the five porous reef limestone samples
Substituting the density rho into formula (1) to calculate rock sample strength, and calculating the result and laboratory testThe strength results are compared as shown in table 2 below.
TABLE 2 comparison of formula predicted strength and test strength for porous reef limestone samples
The difference in table 2 above is the value of the formula predicted intensity minus the test intensity. Due to the fact that the original pore area data set is simplified to a certain extent, the result obtained by the method for rapidly predicting the marine porous reef limestone strength has a certain deviation from the strength value measured by an actual test, but the actual engineering geological condition is complex and variable, and the deviation can be accepted in engineering practice.
The method for rapidly predicting the ocean porous reef limestone strength can accurately and rapidly obtain the strength of the porous reef limestone under the condition of less required calculation parameters, is convenient for field construction personnel to rapidly judge whether the strength of a constructed stratum meets construction requirements, avoids conveying rock samples to land for detection, accelerates the engineering construction progress and reduces the construction cost of open-sea engineering.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (10)
1. A method for rapidly predicting the intensity of marine porous reef limestone is characterized by comprising the following steps:
selecting a porous reef limestone rock sample on a construction site, and acquiring pore geometric characteristic quantitative parameters of the surface of the rock sample
Step two, calculating the density rho of the rock sample;
step three, quantizing the parameters of the geometric characteristics of the pores
Substituting the sum density rho into a formula (1) to calculate the strength of the porous reef limestone, wherein the formula (1) is as follows
In the formula (I), the compound is shown in the specification,
sigma is the strength of the porous reef limestone,
p is the density of the rock sample,
C 1 is a constant number of times, and is,
C 2 is a constant.
2. The method for rapidly predicting the intensity of the marine porous reef limestone as claimed in claim 1, wherein the method comprises the steps of: in step one, the pore geometric feature quantization parameter
The quantization is performed by the following steps,
step 1), photographing the surface of a porous reef limestone rock sample, and importing the photograph into image processing software in a computer, wherein the image processing software needs to have a graying function, an image segmentation function and an area automatic counting function;
step 2), setting an image scale;
step 3), selecting an analysis area of the photo in the image processing software, and converting the analysis area into a gray image by utilizing a graying function in the image processing software;
step 4), utilizing an image segmentation function in image processing software to segment pores and a solid framework in the gray image so as to enable the gray image to become a binary image, wherein the gray value of a pore pixel point and the gray value of a solid framework pixel point in the binary image are two numerical values;
step 5), automatically counting the pore area in the binary image by utilizing the automatic area counting function in the image processing software, and deriving the pore area as an original pore area data set;
step 6), carrying out effective processing on the original pore area data set to obtain a high-precision effective pore area data set;
and 7) substituting the effective pore area data set into the formula (2) to calculate the pore geometric characteristic quantization parameter
The formula (2) is as follows
In the formula (I), the compound is shown in the specification,
n is the number of apertures in the effective aperture area data set,
S i is the area of the i-th aperture,
3. The method for rapidly predicting the intensity of the marine porous reef limestone as claimed in claim 2, wherein: in the step 1), when the surface of the porous reef limestone rock sample is photographed, the photographing angle is perpendicular to the photographing surface, and meanwhile, the flash lamp is started.
4. The method for rapidly predicting the intensity of the marine porous reef limestone as claimed in claim 3, wherein the method comprises the steps of: in the step 1), the Image processing software is MATLAB or Image J.
5. The method for rapidly predicting the strength of the marine porous reef limestone as claimed in claim 4, wherein the method comprises the steps of: in the step 2), the method for setting the image scale is to place a scale on the surface of the shooting area, measure the length of the image with the scale in the image, and obtain the image scale according to the scale = actual length/length in the image by combining the actual length of the scale.
6. The method for rapidly predicting the strength of the marine porous reef limestone as claimed in claim 4, wherein the method comprises the steps of: in step 2), the method for setting the image scale includes placing a line segment with a certain length on the surface of the shooting area, measuring the in-image length of the line segment in the image, and obtaining the image scale according to the scale = actual length/in-image length by combining the actual length of the line segment.
7. The method for rapidly predicting the intensity of the marine porous reef limestone as claimed in claim 5 or 6, wherein the method comprises the following steps: in the step 3), the analysis area refers to an area with no obvious distortion and clear pore structure in the image.
8. The method for rapidly predicting the strength of the marine porous reef limestone as claimed in claim 2, wherein: in step 6), the step of efficiently processing the raw pore area data set is as follows,
step a, dividing all data in a pore area distribution interval into first-level statistical intervals according to order of magnitude difference;
b, dividing each primary statistical interval into a plurality of secondary statistical intervals in equal proportion;
and c, selecting a primary statistical interval with the largest proportion of the total pore area to the total pore area in the interval and the pore areas in the internal secondary statistical interval being not 0 as an effective interval, and taking a data set in the effective interval as an effective pore area data set.
9. The method for rapidly predicting the intensity of the marine porous reef limestone as claimed in claim 1, wherein the method comprises the steps of: in the second step, the density rho is obtained by firstly weighing the dried weight of the rock sample, then placing the rock sample into a bucket full of water, wherein the volume of the overflow liquid is the volume of the rock sample, and finally calculating by a formula (3) to obtain the density of the rock sample, wherein the formula (3) is as follows
In the formula (I), the compound is shown in the specification,
p is the density of the rock sample,
m is the mass of the rock sample,
v is the rock sample volume.
10. The method for rapidly predicting the strength of the marine porous reef limestone as claimed in claim 1, wherein: in step three, C in the formula (1) 1 、C 2 The determination method comprises the steps of determining a porous reef limestone rock sample into a standard size, measuring weight, volume and pore geometric characteristic quantitative parameters, performing a pressure test to obtain rock sample strength, guiding rock sample strength data into computer software for fitting, and further obtaining C 1 、C 2 And (4) taking values.
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| CN117390934A (en) * | 2023-12-11 | 2024-01-12 | 武汉理工大学三亚科教创新园 | Finite element model construction method and terminal for porous medium reef limestone erosion process |
| CN117390934B (en) * | 2023-12-11 | 2024-03-29 | 武汉理工大学三亚科教创新园 | Finite element model construction method and terminal for porous medium reef limestone erosion process |
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