CN116842416A - Calculation method suitable for coral reef sand foundation pipe pile side friction resistance - Google Patents
Calculation method suitable for coral reef sand foundation pipe pile side friction resistance Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, in particular to a calculation method suitable for the side friction of a coral reef sand foundation tubular pile. According to the method, clustering categories under different clustering scales are obtained, the node structure characteristics of each clustering category are used as basic data, matched pixel points of the node structure characteristics among the clustering categories where interpolation positions are located under different clustering scales are analyzed, and then an associated clustering category set corresponding to each interpolation position is obtained. Giving weight to each cluster category in the associated cluster category set, analyzing interpolation data under the corresponding scale, and obtaining final difference data of the corresponding interpolation position through integration of the weight and the interpolation data. Accurate side friction calculation is performed by enhancing the geological data. According to the method, the quality of geological data is improved by analyzing the autocorrelation among the classes corresponding to the multiple scales, and the accuracy of calculating the side friction resistance is further improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a calculation method suitable for the side friction of a coral reef sand foundation tubular pile.
Background
The coral reef geological survey process requires acquisition and evaluation of geological data through basic information represented by a foundation pipe pile inserted into coral reef sand. Because the coral reef geological environment is complex, geological data acquisition difficulty is high, and geological data acquired according to the tubular pile has more missing data or abnormal data, so that the subsequent calculation of the friction resistance measured by the tubular pile is influenced. Therefore, in order to ensure the accuracy of the subsequent calculation result, interpolation processing needs to be performed on the geological data acquired by the tubular pile, and interpolation data at the position needing interpolation in the geological data is obtained through interpolation methods such as inverse distance weight interpolation and the like.
When interpolation is carried out on data in the prior art, the result and the precision of interpolation data depend on the density and the distribution of samples, if the distribution of the samples is uneven or the quantity is less, the error of the interpolation data is larger, the accuracy is lower, and for the data collected by a basic tubular pile deployed in coral reef sand, in order to ensure the efficiency of data collection, the samples of the collected geological data are fewer and the density is lower, the interpolation processing in the prior art can lead to the inaccuracy of the interpolation data, and further the accuracy of the calculation of the subsequent side friction resistance is affected.
Disclosure of Invention
In order to solve the technical problems that the interpolation result of coral reef sand geological data is inaccurate and the subsequent side friction resistance calculation is affected in the prior art, the invention aims to provide a calculation method applicable to the coral reef sand foundation tubular pile side friction resistance, and the adopted technical scheme is as follows:
the invention provides a calculation method of a side friction resistance of a tubular pile suitable for a coral reef sand foundation, which comprises the following steps:
obtaining geological data of each sampling position on a foundation pipe pile deployed in coral reef sand; the geological data at least comprise sand grain diameter and tubular pile contact area; obtaining interpolation positions in the geological data;
constructing an initial graph structure by taking each sampling position as a graph structure node, wherein the node value of each graph structure node in the initial graph structure is geological data corresponding to the sampling position; clustering graph structure nodes in the initial graph structure under a preset number of different clustering scales to obtain a clustering category under each clustering scale;
obtaining node structural characteristics of each cluster category; obtaining the matching similarity of the node structural features between the clustering categories of each interpolation position under different clustering scales; acquiring an associated cluster category set of each interpolation position between different cluster scales according to the matching similarity; in the associated cluster category set, taking the matching similarity between the center category and each cluster category as the weight of each cluster category under the corresponding cluster scale;
obtaining interpolation data of the interpolation position under the corresponding cluster scale of each cluster category in the associated cluster category set, and integrating the interpolation data according to the weight under the corresponding cluster scale to obtain final interpolation data corresponding to the interpolation position; obtaining the final interpolation data of all the interpolation positions, and obtaining enhanced geological data;
and obtaining the side friction resistance of the coral reef sand foundation pipe pile according to the data in the enhanced geological data.
Further, the constructing an initial graph structure with each sampling position as a node includes:
and establishing a topological graph structure by adopting a triangular subdivision method according to the coordinates of the sampling positions, and obtaining the initial graph structure.
Further, iterative clustering is carried out by adopting a clustering method of Laplace graph clustering, and the clustering categories under different clustering scales are obtained.
Further, the obtaining the node structure feature of each cluster category includes:
performing graph factorization on the cluster categories to obtain a decomposition subgraph; and obtaining the binary codes of each decomposition sub-graph through a minimum hash method, and taking the set of the binary codes of all the decomposition sub-graphs as the node structural characteristics.
Further, the obtaining the matching similarity of the node structural features between the clustering categories of each interpolation location under different clustering scales includes:
performing KM matching on the node structural features of the interpolation positions among the clustering categories under different clustering scales; in the KM matching process, each decomposition sub-graph between two clustering categories is used as a matching node, cosine similarity of binary codes between the decomposition sub-graphs is used as a matching value, and a matching node group with the matching value larger than a preset matching threshold is used as a reserved node group; and taking the ratio of the number of the reserved node groups to the maximum decomposition subgraphs in the two clustering categories as the matching similarity.
Further, the obtaining, according to the matching similarity, the set of associated cluster categories of each interpolation location between different cluster scales includes:
if the matching similarity is larger than a preset similarity threshold, the corresponding two clustering categories have an association relationship; and obtaining the association relation between all the clustering categories corresponding to the interpolation positions under different clustering scales, and taking the clustering category with the association relation as an association clustering category to obtain an association clustering category set.
Further, the obtaining interpolation data of the interpolation position under the corresponding cluster scale of each cluster category in the associated cluster category set includes:
and obtaining the interpolation data of the interpolation position under the corresponding clustering scale by using an inverse distance interpolation method according to the graph structure nodes in the clustering category where the interpolation position is located.
Further, the method for obtaining the final interpolation data comprises the following steps:
multiplying each interpolation data corresponding to the interpolation position by the corresponding weight to obtain adjusted interpolation data under the corresponding clustering scale; and averaging the adjusted interpolation data to obtain the final interpolation data.
The invention has the following beneficial effects:
according to the method, the problem that coral reef geological data has few samples distributed and scattered on a single scale is considered, the clustering result under each scale is obtained through the multi-scale clustering process, and the accuracy and stability of the interpolation result are improved through analyzing the correlation of the clustering categories corresponding to interpolation positions under each clustering scale. According to the method, the geological data at each sampling position is subjected to parameter space conversion by a method of constructing a graph structure, so that subsequent multi-scale clustering is facilitated. And further characterizing the autocorrelation among different scales by each matching similarity analyzing the node structural features among the cluster categories under different cluster scales, and obtaining an associated cluster category set corresponding to each interpolation position. The interpolation data under each scale can be weighted and summed through the weight, so that accurate final interpolation data is obtained, and the enhancement of geological data is realized. Accurate calculation of the side friction resistance can be realized by enhancing geological data.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for calculating the side friction of a pile suitable for a coral reef sand foundation according to one embodiment of the present invention.
Detailed Description
In order to further explain the technical means and effects adopted by the invention to achieve the preset aim, the following is a specific implementation, structure, characteristics and effects of the method for calculating the side friction resistance of the coral reef sand foundation pipe pile according to the invention, which are provided by the invention, with reference to the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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.
The invention provides a concrete scheme of a calculation method suitable for the side friction of the coral reef sand foundation pipe pile, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for calculating a side friction force of a pile suitable for a coral reef sand foundation according to one embodiment of the present invention is shown, where the method includes:
step S1: obtaining geological data of each sampling position on a foundation pipe pile deployed in coral reef sand; the geological data at least comprises sand diameter and tubular pile contact area; interpolation locations in the geological data are obtained.
The foundation pipe pile deployed in the coral reef sand comprises probes with various sensors, various geological data can be acquired through the probes, and geological data of stratum with different depths on the coral reef can be acquired through the deep penetration of the pipe pile. The data collected by the sensor in the probe can be transmitted and quantized to complete the collection of geological data, and the specific data collection mode and transmission quantization mode are technical means well known to those skilled in the art and are not described in detail herein.
Because the coral reef sand can show various characteristics such as the compaction degree of the current geology, the morphological characteristics of the gravel and the like to the side friction of the tubular pile, and the calculation of the side friction is mainly performed through the diameter of the sand grain and the contact area of the tubular pile, the acquired geological data at least comprise the diameter of the gravel and the contact area of the tubular pile. In some embodiments of the invention, the soil layer thickness, the stacking density, the friction angle and other parameters can be further included, and the soil layer thickness, the stacking density, the friction angle and other parameters can be acquired through specific implementation scenes. It should be noted that, various geological data are available at each sampling position, and each geological data is processed in the same way, so that in the following description, one geological data is exemplified and is collectively referred to as geological data.
The acquired geological data can cause missing data in the data due to the acquisition environment and the acquisition operation, and the position of the missing data is the interpolation position needing interpolation processing. It should be noted that, the obtaining of the interpolation position may be directly reflected by the time stamp of the data or the continuity of the coordinate information during the data collection, or may be obtained by the existing abnormal data detection algorithm, and the specific method is a technical means well known to those skilled in the art, which is not described herein.
Step S2: constructing an initial graph structure by taking each sampling position as a graph structure node, wherein the node value of each graph structure node in the initial graph structure is geological data of the corresponding sampling position; clustering is carried out on graph structure nodes in the initial graph structure under a preset number of different clustering scales, and clustering categories under each clustering scale are obtained.
The coral reef geological data has the data characteristics of few samples and low density, and the inaccuracy of interpolation data is easily caused by analysis on a single scale. Considering that clustering results of data at one position in coral reef geological data may have autocorrelation, namely a data structure under a small scale is similar to a data structure under a large scale, reference data in an interpolation process can be increased by acquiring the clustering category of the autocorrelation, and accuracy and stability of the interpolation result are enhanced. Therefore, cluster analysis under multiple scales needs to be performed on the collected geological data, and the more the cluster scale is, the more the number of samples contained in the corresponding cluster result is. Considering that the detection of coral reef geological data needs to pay attention to the sampling positions of the data, namely, the data sampling positions and the corresponding geological data are considered simultaneously in the clustering process, each sampling position is used as a graph structure node, an initial graph structure is constructed, and the node value of each graph structure node in the initial graph structure is the geological data of the corresponding sampling position. In the subsequent clustering process, clustering operation is carried out on the initial graph structure, and specific data values and sampling positions of geological data can be considered at the same time, so that the referential property among sample points in the obtained clustering result is stronger, and the referential property of the obtained interpolation result is stronger.
Preferably, in one embodiment of the present invention, constructing an initial graph structure with each of the sampling locations as a node includes: and establishing a topological graph structure by adopting a triangular subdivision method according to the coordinates of the sampling positions to obtain an initial graph structure.
It should be noted that the triangle splitting method is a technical means well known to those skilled in the art, and the specific process is not described again. In other embodiments of the present invention, the initial graph structure may be obtained by other graph structure construction algorithms, such as the Thiessen polygon construction algorithm.
Clustering under different clustering scales is carried out for a plurality of times on graph structure nodes in the initial graph structure, so that the clustering category under each clustering scale can be obtained. It should be noted that, the specific number of the clustering scales may be selected according to the specific data state of the geological data, and in one embodiment of the present invention, the number of the clustering scales is set to 5, that is, the initial graph structure is clustered by 5 clustering scales with different sizes, and each clustering scale corresponds to a group of clustering categories.
Preferably, in one embodiment of the present invention, iterative clustering is performed by using a clustering method of laplace graph clustering, so as to obtain the cluster category under different cluster scales. Laplace graph clustering is a technical means well known to those skilled in the art, and is not described and illustrated herein.
Step S3: obtaining node structural characteristics of each cluster type; obtaining the matching similarity of the node structural features among the clustering categories of each interpolation position under different clustering scales; acquiring an associated cluster class set of each interpolation position between different cluster scales according to the matching similarity; and in the associated cluster category set, the matching similarity between the center category and each cluster category is used as the weight of each cluster category under the corresponding cluster scale.
Because the clustering process is based on the graph structure, the obtained clustering category under each clustering scale can be regarded as a piece of node area in the graph structure, and analyzing the autocorrelation of the clustering categories among different scales can be regarded as analyzing the structural similarity among the node areas, so that in order to more accurately analyze the autocorrelation of the clustering categories among different scales, the node structural feature of each clustering category needs to be obtained first, and the node structural feature is used as basic data to perform subsequent similarity calculation.
Preferably, in one embodiment of the present invention, obtaining the node structure feature of each cluster category includes:
and carrying out graph factorization on the cluster category to obtain a decomposition subgraph. Each decomposition sub-graph contains all nodes, but the connection modes of the nodes between different decomposition sub-graphs are different, so that the decomposition sub-graph set of each cluster type can represent node structure distribution information in the corresponding cluster type. Binary codes of each decomposition sub-graph are obtained through a minimum hash method, and node structures in each decomposition sub-graph can be quantized through binary code conversion, so that subsequent similarity calculation is facilitated. The binary code of all the decomposition subgraphs is used as the node structural feature of the corresponding cluster category. It should be noted that, the factorization and the minimum hash method are all technical means well known to those skilled in the art, and specific processes are not repeated.
In other embodiments of the present invention, the node structure features in the cluster category may also be obtained by using methods such as chain code analysis or fourier transform, and specific algorithms are technical means well known to those skilled in the art, which are not described and illustrated herein.
After clustering in the step S2, the same interpolation position corresponds to different clustering categories under different clustering scales, so that the matching similarity of the node structural features between the clustering categories of the interpolation position under different clustering scales is obtained, and the larger the matching similarity is, the more similar the node structural features between the two clustering categories are, and the larger the correlation is. And obtaining a related cluster class set of each interpolation position among different cluster scales through matching the similarity, namely, each related cluster class in the related cluster class set corresponds to one cluster scale.
Preferably, because the node structure characteristics of each cluster category are obtained by constructing a method for decomposing the subgraph set in one embodiment of the invention, the node structure characteristics of the cluster categories where the interpolation positions are located under different cluster scales can be subjected to KM matching. In the KM matching process, each decomposition sub-graph between two clustering categories is used as a matching node, cosine similarity of binary codes between the decomposition sub-graphs is used as a matching value, and a matching node group with the matching value larger than a preset matching threshold is used as a reserved node group. The two matching nodes in the reserved node group are respectively subordinate to the clustering categories under different clustering scales, and the reserved nodes can be regarded as nodes with similar characteristics between the two different clustering categories, so that the ratio of the number of the reserved node groups to the maximum decomposition sub-graph number in the two clustering categories is used as matching similarity, namely the maximum decomposition sub-graph number represents the maximum value of the matching node group, and the larger the number of the reserved node groups is, the larger the matching node ratio with similar characteristics is, and the larger the matching similarity of the corresponding two clustering categories is. The matching similarity is formulated as:
wherein P is (a,b) Representing the matching similarity between the clustering category a and the clustering category b, N' representing the number of the reserved node groups after being matched between the clustering category a and the clustering category b, max () being a maximum value selection function, N a For the number of decomposition subgraphs of cluster class a, N b The number of decomposition subgraphs for cluster category b.
In the embodiment of the invention, the matching threshold is set to 0.7.
It should be noted that, in some embodiments of the present invention, the node structure features are not in a collection form, so that similarity calculation can be directly performed on the node structure features to obtain matching similarity.
Through the calculation process of the matching similarity, the matching similarity between all the cluster categories corresponding to the interpolation position can be obtained, the larger the matching similarity is, the larger the correlation between the two cluster categories is, and then the associated cluster category sets between different cluster scales corresponding to the interpolation position are screened out, namely, each associated cluster category in the associated cluster category sets is of different cluster scales.
Preferably, in one embodiment of the present invention, obtaining a set of associated cluster categories between different cluster scales for each interpolation location according to the matching similarity includes: if the matching similarity is greater than a preset similarity threshold, the corresponding two clustering categories have an association relationship; and obtaining the association relations among all the clustering categories corresponding to the interpolation positions under different clustering scales, and taking the clustering category with the association relation as the association clustering category to obtain an association clustering category set. For example, after the interpolation position A is calculated by matching the similarity, it is determined that the cluster categories corresponding to the scale 1 and the scale 2 have an association relationship, the cluster categories corresponding to the scale 1 and the scale 3 do not have an association relationship, and the cluster categories corresponding to the scale 2 and the scale 3 have an association relationship, so that after the association relationship is integrated, the association cluster category set corresponding to the interpolation position A is the cluster category set corresponding to the scale 1, the scale 2 and the scale 3.
In one embodiment of the present invention, the similarity threshold is set to 0.7 considering that the matching pixels are each a value between 0 and 1.
Each interpolation position corresponds to an associated cluster class set, and the associated cluster class set is the basis for obtaining final interpolation data of the interpolation position, and because correlations exist among cluster classes in the associated cluster class set, accurate final difference data can be obtained through subsequent weighted summation by giving weight to each associated cluster class in the associated cluster class set. Because the center class in the associated cluster class set is the class with the largest matching similarity in the matching process, the matching similarity between each cluster class in the set and the center class can be obtained by taking the center class as the basic class, and the corresponding matching similarity is taken as the weight of the cluster class under the corresponding cluster scale. It should be noted that, in the weight obtaining process, the matching similarity between the center category and each cluster category in the set needs to be calculated, and the matching similarity between the center category and itself is 1, so the weight under the cluster scale corresponding to the center category is also 1.
Step S4: obtaining interpolation data of interpolation positions under the corresponding clustering scale of each clustering class in the associated clustering class set, and integrating the interpolation data according to the weight under the corresponding clustering scale to obtain final interpolation data of the corresponding interpolation positions; and obtaining final interpolation data of all interpolation positions, and obtaining enhanced geological data.
Because interpolation data analysis is needed for the interpolation position according to the associated cluster category set, interpolation data of the interpolation position under the corresponding cluster scale of each cluster category in the associated cluster category set can be obtained first. And integrating the interpolation data according to the weight under the corresponding clustering scale, so as to obtain the final difference data under the corresponding interpolation position. Because interpolation data and weights under one clustering scale are in one-to-one correspondence, and relevance exists between scales corresponding to clustering categories in the associated clustering category set, final interpolation data obtained through integration of the weights and the interpolation data is more accurate and has stronger stability. And (3) analyzing all interpolation positions by the same method to obtain the enhanced geological data without missing data.
Preferably, obtaining interpolation data of interpolation locations under a cluster scale corresponding to each cluster category in the set of associated cluster categories in one embodiment of the present invention includes: and obtaining interpolation data of the interpolation position under the corresponding clustering scale by using an inverse distance interpolation method according to the graph structure nodes in the clustering class where the interpolation position is located. It should be noted that, the inverse distance interpolation method is a technical means well known to those skilled in the art, and will not be described herein.
Preferably, in one embodiment of the present invention, the method for obtaining final interpolation data includes:
multiplying each interpolation data corresponding to the interpolation position by the corresponding weight to obtain adjustment interpolation data under the corresponding clustering scale; and averaging the adjusted interpolation data to obtain final interpolation data. The interpolation data under different scales can be accurately integrated by a weighted averaging method, and final interpolation data with strong referential property is obtained.
Step S5: and obtaining the side friction resistance of the coral reef sand foundation pipe pile according to the data in the enhanced geological data.
The data in the final interpolation data has good integrity, namely, each sampling position has corresponding geological data, so that accurate calculation of the side friction resistance can be performed, the side friction resistance can be calculated through a formula in the prior art, and the specific formula comprises:
Fs＝t×A
wherein Fs is side friction force, t is static friction force between coral reef sand and the pipe pile, and A is side surface area of the pipe pile.
For coral reef sand, the static friction force is mainly determined by two parameters of sand grain diameter and contact area, and can be obtained by calculating the friction coefficient recorded by the existing data and combining the two parameters. The specific method for obtaining the side friction resistance is a technical means well known to those skilled in the art, and is not described and limited herein.
In summary, the embodiment of the invention obtains the cluster types under different cluster scales, uses the node structure characteristics of each cluster type as basic data, analyzes the matched pixel points of the node structure characteristics among the cluster types where the interpolation positions are located under different cluster scales, and further obtains the associated cluster type set corresponding to each interpolation position. Giving weight to each cluster category in the associated cluster category set, analyzing interpolation data under the corresponding scale, and obtaining final difference data of the corresponding interpolation position through integration of the weight and the interpolation data. Accurate side friction calculation is performed by enhancing the geological data. According to the method, the quality of geological data is improved by analyzing the autocorrelation among the classes corresponding to the multiple scales, and the accuracy of calculating the side friction resistance is further improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (8)
1. The method for calculating the side friction resistance of the coral reef sand foundation pipe pile is characterized by comprising the following steps of:
obtaining geological data of each sampling position on a foundation pipe pile deployed in coral reef sand; the geological data at least comprise sand grain diameter and tubular pile contact area; obtaining interpolation positions in the geological data;
constructing an initial graph structure by taking each sampling position as a graph structure node, wherein the node value of each graph structure node in the initial graph structure is geological data corresponding to the sampling position; clustering graph structure nodes in the initial graph structure under a preset number of different clustering scales to obtain a clustering category under each clustering scale;
obtaining node structural characteristics of each cluster category; obtaining the matching similarity of the node structural features between the clustering categories of each interpolation position under different clustering scales; acquiring an associated cluster category set of each interpolation position between different cluster scales according to the matching similarity; in the associated cluster category set, taking the matching similarity between the center category and each cluster category as the weight of each cluster category under the corresponding cluster scale;
obtaining interpolation data of the interpolation position under the corresponding cluster scale of each cluster category in the associated cluster category set, and integrating the interpolation data according to the weight under the corresponding cluster scale to obtain final interpolation data corresponding to the interpolation position; obtaining the final interpolation data of all the interpolation positions, and obtaining enhanced geological data;
and obtaining the side friction resistance of the coral reef sand foundation pipe pile according to the data in the enhanced geological data.
2. A method for calculating a pile side friction force applicable to a coral reef sand foundation according to claim 1, wherein the constructing an initial graph structure with each of the sampling positions as a node includes:
and establishing a topological graph structure by adopting a triangular subdivision method according to the coordinates of the sampling positions, and obtaining the initial graph structure.
3. The method for calculating the side friction of the coral reef sand foundation pipe pile, which is suitable for the method for calculating the side friction of the coral reef sand foundation pipe pile, is characterized by adopting a clustering method of Laplace graph clustering to perform iterative clustering, and obtaining the clustering types under different clustering scales.
4. A method for calculating a pile side friction force applicable to a coral reef sand foundation according to claim 1, wherein the obtaining the node structure characteristics of each of the cluster categories includes:
performing graph factorization on the cluster categories to obtain a decomposition subgraph; and obtaining the binary codes of each decomposition sub-graph through a minimum hash method, and taking the set of the binary codes of all the decomposition sub-graphs as the node structural characteristics.
5. A method for calculating a suitable coral reef sand foundation tube stake side friction force according to claim 4, wherein the obtaining the matching similarity of the node structural features between the clustering categories at different clustering scales for each of the interpolation locations comprises:
performing KM matching on the node structural features of the interpolation positions among the clustering categories under different clustering scales; in the KM matching process, each decomposition sub-graph between two clustering categories is used as a matching node, cosine similarity of binary codes between the decomposition sub-graphs is used as a matching value, and a matching node group with the matching value larger than a preset matching threshold is used as a reserved node group; and taking the ratio of the number of the reserved node groups to the maximum decomposition subgraphs in the two clustering categories as the matching similarity.
6. The method for calculating the side friction of the coral reef sand foundation pipe pile according to claim 1, wherein the obtaining the set of associated clustering categories of each interpolation position between different clustering scales according to the matching similarity comprises:
if the matching similarity is larger than a preset similarity threshold, the corresponding two clustering categories have an association relationship; and obtaining the association relation between all the clustering categories corresponding to the interpolation positions under different clustering scales, and taking the clustering category with the association relation as an association clustering category to obtain an association clustering category set.
7. The method for calculating the side friction of the coral reef sand foundation pipe pile according to claim 1, wherein the obtaining interpolation data of the interpolation position under the corresponding cluster scale of each cluster type in the associated cluster type set comprises:
and obtaining the interpolation data of the interpolation position under the corresponding clustering scale by using an inverse distance interpolation method according to the graph structure nodes in the clustering category where the interpolation position is located.
8. The method for calculating the side friction of the coral reef sand base tubular pile according to claim 1, wherein the method for obtaining the final interpolation data comprises the following steps:
multiplying each interpolation data corresponding to the interpolation position by the corresponding weight to obtain adjusted interpolation data under the corresponding clustering scale; and averaging the adjusted interpolation data to obtain the final interpolation data.
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| CN117095743A (en) * | 2023-10-17 | 2023-11-21 | 山东鲁润阿胶药业有限公司 | Polypeptide spectrum matching data analysis method and system for small molecular peptide donkey-hide gelatin |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113503156A (en) * | 2021-07-12 | 2021-10-15 | 山东科技大学 | Reservoir vertical heterogeneity quantitative determination method, system, medium, equipment and terminal |
| JP2021167533A (en) * | 2020-04-10 | 2021-10-21 | 株式会社オムテック | Foundation pile bearing power calculation device considering liquefaction, and method for the same |
| CN113550288A (en) * | 2021-06-17 | 2021-10-26 | 中交二航局第三工程有限公司 | Clay stratum side friction resistance measuring device and measuring method |
| KR102452877B1 (en) * | 2022-06-02 | 2022-10-11 | 채수근 | Calculation method of surface friction of SDA pile constructed in weathered rock and bedrock |
| CN115935477A (en) * | 2022-12-14 | 2023-04-07 | 大连理工大学 | Machine learning-based energy pile green low-carbon building design method |
| CN116245013A (en) * | 2022-12-29 | 2023-06-09 | 中国地质大学（武汉） | Geological prediction model construction method, modeling method, equipment and storage medium |
-
2023
- 2023-07-18 CN CN202310881301.9A patent/CN116842416B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2021167533A (en) * | 2020-04-10 | 2021-10-21 | 株式会社オムテック | Foundation pile bearing power calculation device considering liquefaction, and method for the same |
| CN113550288A (en) * | 2021-06-17 | 2021-10-26 | 中交二航局第三工程有限公司 | Clay stratum side friction resistance measuring device and measuring method |
| CN113503156A (en) * | 2021-07-12 | 2021-10-15 | 山东科技大学 | Reservoir vertical heterogeneity quantitative determination method, system, medium, equipment and terminal |
| KR102452877B1 (en) * | 2022-06-02 | 2022-10-11 | 채수근 | Calculation method of surface friction of SDA pile constructed in weathered rock and bedrock |
| CN115935477A (en) * | 2022-12-14 | 2023-04-07 | 大连理工大学 | Machine learning-based energy pile green low-carbon building design method |
| CN116245013A (en) * | 2022-12-29 | 2023-06-09 | 中国地质大学（武汉） | Geological prediction model construction method, modeling method, equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 张永涛 等: "珊瑚礁灰岩地层灌注桩侧摩阻力特征及发挥系数取值研究与应用", 岩石力学与工程学报, vol. 42, no. 3, 31 March 2023 (2023-03-31), pages 748 - 759 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117095743A (en) * | 2023-10-17 | 2023-11-21 | 山东鲁润阿胶药业有限公司 | Polypeptide spectrum matching data analysis method and system for small molecular peptide donkey-hide gelatin |
| CN117095743B (en) * | 2023-10-17 | 2024-01-05 | 山东鲁润阿胶药业有限公司 | Polypeptide spectrum matching data analysis method and system for small molecular peptide donkey-hide gelatin |
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