Method and system for evaluating bearing performance of dredged mud solidification energy pile

Abstract

The invention provides a method and system for evaluating the bearing performance of a dredged mud solidification energy pile, and the method comprises the steps: constructing a finite element model according to a to-be-evaluated configuration scheme and a material composition detection result of the dredged mud solidification energy pile; carrying out bearing performance analysis on the finite element model to obtain first analysis data; analyzing the bearing test data of the to-be-evaluated dredged mud solidification energy pile to obtain second analysis data; and synthesizing the first analysis data and the second analysis data to generate evaluation data. According to the method and the system for evaluating the bearing performance of the dredged mud solidification energy pile, the performance of the energy pile is clear, the energy pile can be conveniently used in various applicable scenes, and possible consequences caused after the energy pile is used in inapplicable scenes are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of bearing performance detection, and in particular to a bearing performance evaluation method and system for dredged mud solidification energy piles. Background Art

[0002] Dredged mud, also known as dredged silt, is a mixture of mud and water produced by dredging sediment from rivers, lakes, and other water bodies. Silt is formed when industrial wastewater, domestic sewage, urban surface runoff, and atmospheric precipitation enter the water body. Particulate matter, colloids, and water-soluble salts contained therein, through physical and chemical processes such as adsorption, complexation, and chemical reactions, are deposited to the bottom of the water body under certain hydraulic conditions, forming sediment. Depending on the dredging process, the resulting silt has a solids content ranging from 10% to 40%. Some dredged silt is contaminated with toxic and hazardous substances such as heavy metals and polychlorinated biphenyls (PCBs), requiring appropriate treatment and disposal. The treatment of dredged mud remains a pressing technical challenge.

[0003] Dredged mud is solidified and then made into energy piles. This pile is then buried in the ground for use. This not only solves the problem of dredged mud disposal, but also directly utilizes geothermal energy to heat or cool the superstructure. This reduces the fossil energy required for construction and contributes to energy conservation and carbon reduction. However, since energy piles bear the weight of the building above and other factors, a performance evaluation method is urgently needed to clarify their performance. Summary of the Invention

[0004] One of the purposes of the present invention is to provide a method and system for evaluating the bearing performance of dredged mud solidification energy piles, clarifying the performance of the energy piles, facilitating their use in various applicable scenarios, and avoiding the consequences that may be caused by their use in inappropriate scenarios.

[0005] An embodiment of the present invention provides a method for evaluating the bearing performance of a dredged mud solidification energy pile, comprising:

[0006] Construct a finite element model based on the configuration plan and material composition test results of the dredged mud solidification energy pile to be evaluated;

[0007] Performing load-bearing performance analysis on the finite element model to obtain first analysis data;

[0008] Analyze the bearing test data of the dredged mud solidification energy pile to be evaluated to obtain second analysis data;

[0009] The first analysis data and the second analysis data are integrated to generate evaluation data.

[0010] Preferably, the load-bearing test data is obtained through communication with the load-bearing test equipment.

[0011] Preferably, the steps for constructing the finite element model are as follows:

[0012] Using the structural data in the configuration plan, a three-dimensional model of the dredged mud solidification energy pile is constructed;

[0013] Based on the composition data in the configuration scheme and the material composition test results, the corresponding finite element unit components are retrieved from the pre-configured unit library;

[0014] The three-dimensional model is segmented by finite elements, and the finite element elements obtained by segmentation are replaced with finite element element components retrieved from the element library to obtain a finite element model.

[0015] Preferably, performing load-bearing performance analysis on the finite element model to obtain first analysis data includes:

[0016] Place the finite element model into multiple pre-configured simulation analysis environments to obtain simulation data for each simulation analysis environment;

[0017] The various simulation data are integrated to obtain the first analysis data.

[0018] Preferably, the first analysis data and the second analysis data are integrated to generate evaluation data, including:

[0019] extracting corresponding first evaluation data and second evaluation data from the first analysis data and the second analysis data for each evaluation item in the pre-configured evaluation item table;

[0020] Performing quantitative evaluation on the first evaluation data and the second evaluation data respectively to obtain a first evaluation value and a second evaluation value;

[0021] The weighted sum of the first evaluation value and the second evaluation value is used as the evaluation value of the corresponding evaluation item.

[0022] The present invention also provides a bearing performance evaluation system for dredged mud solidification energy piles, comprising: a construction module, a first analysis module, a second analysis module and a comprehensive generation module;

[0023] Among them, the construction module constructs a finite element model based on the configuration plan and material composition test results of the dredged mud solidification energy pile to be evaluated; the first analysis module performs a bearing performance analysis on the finite element model to obtain first analysis data; the second analysis module analyzes the bearing test data of the dredged mud solidification energy pile to be evaluated to obtain second analysis data; the comprehensive generation module combines the first analysis data and the second analysis data to generate evaluation data.

[0024] Preferably, the load-bearing test data is obtained through communication with the load-bearing test equipment.

[0025] Preferably, the steps for constructing the finite element model are as follows:

[0026] Using the structural data in the configuration plan, a three-dimensional model of the dredged mud solidification energy pile is constructed;

[0027] Based on the composition data in the configuration scheme and the material composition test results, the corresponding finite element unit components are retrieved from the pre-configured unit library;

[0028] The three-dimensional model is segmented by finite elements, and the finite element elements obtained by segmentation are replaced with finite element element components retrieved from the element library to obtain a finite element model.

[0029] Preferably, the first analysis module performs the following operations:

[0030] Place the finite element model into multiple pre-configured simulation analysis environments to obtain simulation data for each simulation analysis environment;

[0031] The various simulation data are integrated to obtain the first analysis data.

[0032] Preferably, the comprehensive generation module performs the following operations:

[0033] extracting corresponding first evaluation data and second evaluation data from the first analysis data and the second analysis data for each evaluation item in the pre-configured evaluation item table;

[0034] Performing quantitative evaluation on the first evaluation data and the second evaluation data respectively to obtain a first evaluation value and a second evaluation value;

[0035] The weighted sum of the first evaluation value and the second evaluation value is used as the evaluation value of the corresponding evaluation item.

[0036] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0037] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 Schematic diagram of a method for evaluating the bearing performance of a dredged mud solidification energy pile according to an embodiment of the present invention;

[0040] Figure 2 Schematic diagram of a bearing performance evaluation system for dredged mud solidification energy piles according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0042] The embodiment of the present invention provides a method for evaluating the bearing performance of dredged mud solidification energy piles, such as Figure 1 Shown, including:

[0043] Step 1: Construct a finite element model based on the configuration plan and material composition test results of the dredged mud solidification energy pile to be evaluated;

[0044] The manufacturing steps of the dredged mud solidification energy pile are as follows: adding high thermal conductivity materials such as graphite, polypropylene fiber, iron filings, etc. to the dredged mud, and materials that increase strength such as crushed stone, recycled stone, mineral admixtures, etc. may also be added. Basic curing agents such as quicklime, fly ash, bentonite, and deacidification may also be added to improve the curing effect; the objects and quantities added in the manufacturing steps, key parameters such as geogrid type, stiffness, length, pile diameter, circulating medium flow rate, and heat exchange tube arrangement type are statistically analyzed to obtain a configuration plan; the material composition test results are the analysis results obtained by conducting metallographic image analysis, material testing, and other tests on the materials intercepted after the load-bearing performance test of the dredged mud solidification energy pile to be evaluated.

[0045] The steps for constructing the finite element model are as follows: using the structural data in the configuration scheme, construct a three-dimensional model corresponding to the dredged mud solidification energy pile; using the composition data in the configuration scheme and the material composition test results, retrieve the corresponding finite element unit components from the pre-configured unit library; perform finite element segmentation on the three-dimensional model, and replace the segmented finite element units with the finite element unit components retrieved from the unit library to obtain the finite element model.

[0046] Step 2: Perform load-bearing performance analysis on the finite element model to obtain first analysis data;

[0047] Performing load-bearing performance analysis on the finite element model to obtain first analysis data includes: placing the finite element model into multiple pre-configured simulation analysis environments to obtain simulation data of each simulation analysis environment; and integrating each simulation data to obtain the first analysis data.

[0048] The simulation analysis environment is pre-configured and can be a simulation of the load-bearing test equipment or a simulation of the actual use scenario. Through a variety of simulation methods, the obtained simulation test data is used as the first analysis data. The simulation method can enrich the data used for evaluation and have overall representativeness;

[0049] Step 3: Analyze the bearing test data of the dredged mud solidification energy pile to be evaluated to obtain second analysis data;

[0050] The load-bearing test data is obtained through communication with the load-bearing test equipment. The load-bearing test equipment includes: pressure testing machines, tension testing machines, thermal shock chambers and other test equipment; the energy piles are actually tested by the load-bearing test equipment, and the data obtained can serve as an objective evaluation basis;

[0051] Step 4: Integrate the first analysis data and the second analysis data to generate evaluation data.

[0052] The first analysis data and the second analysis data are integrated to generate evaluation data, including: extracting corresponding first evaluation data and second evaluation data from the first analysis data and the second analysis data for each evaluation item in the pre-configured evaluation item table; quantitatively evaluating the first evaluation data and the second evaluation data to obtain a first evaluation value and a second evaluation value; and taking a weighted sum of the first evaluation value and the second evaluation value as the evaluation value of the corresponding evaluation item.

[0053] In one embodiment, a method for evaluating the bearing performance of dredged mud solidification energy piles includes:

[0054] Construct a finite element model based on the configuration plan and material composition test results of the dredged mud solidification energy pile to be evaluated;

[0055] Performing load-bearing performance analysis on the finite element model to obtain first analysis data;

[0056] Analyze the bearing test data of the dredged mud solidification energy pile to be evaluated to obtain second analysis data;

[0057] integrating the first analysis data and the second analysis data to generate evaluation data;

[0058] Among them, in addition to being obtained through communication with the load-bearing test equipment, the load-bearing test data can also be obtained through the big data platform to obtain a large amount of data to ensure the accuracy and effectiveness of the evaluation; the steps for obtaining data from the big data platform are as follows: based on the configuration plan and the material composition test results, construct an identification parameter set; match the identification parameter set with the identification parameter sets associated with each data on the big data platform one by one, and extract the matching data.

[0059] After obtaining a large amount of data from big data, how to accurately evaluate and analyze it requires considering both the authenticity and evaluability of the data. Therefore, data screening is necessary. Screening can be performed based on the credibility of the data source and the security of the link to the data source. Screening is performed through user-configured credibility thresholds and security thresholds. The credibility of the data source can be an inherent parameter configured by the corresponding user. This parameter is obtained by analyzing the user's behavior on the big data platform. The specific analysis can use a pre-trained neural network model, that is, a sampling neural network model to analyze the behavioral data. Security is an assessment configuration of the terminal where the data is generated, the number of nodes the data passes through after transmission, and the security of each node. It can also be achieved through another pre-trained neural network model.

[0060] When there are multiple data sets used to analyze the second evaluation value, the analysis method is as follows: first, the data obtained from the communication of the load-bearing test equipment is used as an analysis process, and the data obtained from the big data platform is used as a parallel analysis process to obtain multiple second analysis values; each second analysis value generates corresponding evaluation data; each evaluation data is labeled separately; in addition, a comprehensive weighted calculation can be performed to obtain a representative second analysis value, and the evaluation data generated by this is used as a representative evaluation; wherein, the calculation formula of the representative second analysis value is as follows: Where, F2 represents the second analytical value; F 12 A second analysis value for analyzing data acquired from communication with the load-bearing test equipment; F 2i is the second analysis value of the i-th data obtained from the big data platform; α i is the second-order weight coefficient corresponding to the second analysis value of the i-th data analysis obtained from the big data platform; μ1 and μ2 are pre-configured first-order weight coefficients respectively; n is the total amount of data obtained from the big data platform; the second-order weight coefficient can be determined by arranging the credibility and security of each data to form a data set, and then retrieving the weight distribution set from the pre-configured weight distribution library with the data set; the second-order weight coefficient corresponding to each data is sorted in sequence in the weight distribution set.

[0061] In one embodiment, a method for evaluating the bearing performance of dredged mud solidification energy piles includes:

[0062] Construct a finite element model based on the configuration plan and material composition test results of the dredged mud solidification energy pile to be evaluated;

[0063] Performing load-bearing performance analysis on the finite element model to obtain first analysis data;

[0064] Analyze the bearing test data of the dredged mud solidification energy pile to be evaluated to obtain second analysis data;

[0065] integrating the first analysis data and the second analysis data to generate evaluation data;

[0066] The load-bearing performance analysis of the finite element model is performed to obtain first analysis data, including:

[0067] Performing load-bearing performance analysis on the finite element model to obtain first analysis data includes: placing the finite element model into multiple pre-configured simulation analysis environments to obtain simulation data of each simulation analysis environment; and integrating each simulation data to obtain the first analysis data.

[0068] Additionally, it includes:

[0069] Publish each simulation data set and receive pre-registered expert feedback on each simulation data set; feedback includes: conclusions agreeing or disagreeing with the simulation data, supporting data for agreement, supporting data for disagreement, and conclusions agreeing or disagreeing with other experts' feedback; supporting data includes but is not limited to text, experimental records, and experimental videos;

[0070] Monitor the demonstration feedback of each simulation data to determine whether the simulation re-verification conditions are met;

[0071] When the simulation re-verification conditions are met, the simulation data is regenerated based on the demonstration feedback.

[0072] wherein, the argument feedback of the simulation data is evaluated, and when the reverse evaluation score is greater than or equal to a preset threshold, and / or the positive evaluation score is less than the reverse evaluation score, the simulation re-verification condition is met; wherein, the steps for generating the positive evaluation score and the negative evaluation score are as follows: identifying the relationship of the argument feedback and constructing a hierarchical topological structure based on the identified relationship; filling the argument feedback of the upper layer with entries based on the argument feedback at the lower layer; taking the argument feedback at the top layer filled with entries as representative feedback; grouping the representative feedback into a positive group and a negative group; and using a pre-configured quantitative scoring library to determine the weight coefficient of each feedback entry; determining the reference value based on the support value of the expert end supporting each feedback entry (configured by the expert end based on the total support value held in the argument feedback account); and taking the sum of the product of the reference value and the weight coefficient as the positive evaluation score or the negative evaluation score;

[0073] The hierarchical topology is a layered structure. The first layer consists of argument feedback that is not an agreement or disagreement with other experts' argument feedback; the second layer consists of argument feedback that indicates agreement or disagreement with the experts in the first layer; the third layer consists of argument feedback that indicates agreement or disagreement with the experts in the second layer; and so on. Feedback terms include: a first part indicating the conclusion and a second part indicating the type of evidence supporting the conclusion;

[0074] Among them, the simulation data is regenerated according to the demonstration feedback, including: extracting result data from the demonstration feedback data according to each item in the project table of the pre-configured simulation results, generating pseudo-simulation data based on the extracted result data and marking it to distinguish it from the simulation data.

[0075] The present invention also provides a bearing performance evaluation system for dredged mud solidification energy piles, such as Figure 2 As shown, it includes: a construction module 1, a first analysis module 2, a second analysis module 3 and a comprehensive generation module 4;

[0076] Among them, the construction module 1 constructs a finite element model based on the configuration plan and material composition test results of the dredged mud solidification energy pile to be evaluated; the first analysis module 2 performs a bearing performance analysis on the finite element model to obtain first analysis data; the second analysis module 3 analyzes the bearing test data of the dredged mud solidification energy pile to be evaluated to obtain second analysis data; the comprehensive generation module 4 combines the first analysis data and the second analysis data to generate evaluation data.

[0077] The load-bearing test data is obtained through communication with the load-bearing test equipment.

[0078] The steps for constructing the finite element model are as follows:

[0079] Using the structural data in the configuration plan, a three-dimensional model of the dredged mud solidification energy pile is constructed;

[0080] Based on the composition data in the configuration scheme and the material composition test results, the corresponding finite element unit components are retrieved from the pre-configured unit library;

[0081] The three-dimensional model is segmented by finite elements, and the finite element elements obtained by segmentation are replaced with finite element element components retrieved from the element library to obtain a finite element model.

[0082] The first analysis module performs the following operations:

[0083] Place the finite element model into multiple pre-configured simulation analysis environments to obtain simulation data for each simulation analysis environment;

[0084] The various simulation data are integrated to obtain the first analysis data.

[0085] Among them, the comprehensive generation module performs the following operations:

[0086] extracting corresponding first evaluation data and second evaluation data from the first analysis data and the second analysis data for each evaluation item in the pre-configured evaluation item table;

[0087] Performing quantitative evaluation on the first evaluation data and the second evaluation data respectively to obtain a first evaluation value and a second evaluation value;

[0088] The weighted sum of the first evaluation value and the second evaluation value is used as the evaluation value of the corresponding evaluation item

[0089] In one embodiment, a bearing performance evaluation system for dredged mud solidification energy piles includes:

[0090] Construction module 1, first analysis module 2, second analysis module 3 and comprehensive generation module 4;

[0091] Among them, the construction module 1 constructs a finite element model based on the configuration scheme and material composition test results of the dredged mud solidification energy pile to be evaluated; the first analysis module 2 performs a bearing performance analysis on the finite element model to obtain first analysis data; the second analysis module 3 analyzes the bearing test data of the dredged mud solidification energy pile to be evaluated to obtain second analysis data; the comprehensive generation module 4 combines the first analysis data and the second analysis data to generate evaluation data;

[0092] Among them, in addition to being obtained through communication with the load-bearing test equipment, the load-bearing test data can also be obtained through the big data platform to obtain a large amount of data to ensure the accuracy and effectiveness of the evaluation; the steps for obtaining data from the big data platform are as follows: based on the configuration plan and the material composition test results, construct an identification parameter set; match the identification parameter set with the identification parameter sets associated with each data on the big data platform one by one, and extract the matching data.

[0093] After obtaining a large amount of data from big data, how to accurately evaluate and analyze it requires considering both the authenticity and evaluability of the data. Therefore, data screening is necessary. Screening can be performed based on the credibility of the data source and the security of the link to the data source. Screening is performed through user-configured credibility thresholds and security thresholds. The credibility of the data source can be an inherent parameter configured by the corresponding user. This parameter is obtained by analyzing the user's behavior on the big data platform. The specific analysis can use a pre-trained neural network model, that is, a sampling neural network model to analyze the behavioral data. Security is an assessment configuration of the terminal where the data is generated, the number of nodes the data passes through after transmission, and the security of each node. It can also be achieved through another pre-trained neural network model.

[0094] When there are multiple data sets used to analyze the second evaluation value, the analysis method is as follows: first, the data obtained from the communication of the load-bearing test equipment is used as an analysis process, and the data obtained from the big data platform is used as a parallel analysis process to obtain multiple second analysis values; each second analysis value generates corresponding evaluation data; each evaluation data is labeled separately; in addition, a comprehensive weighted calculation can be performed to obtain a representative second analysis value, and the evaluation data generated by this is used as a representative evaluation; wherein, the calculation formula of the representative second analysis value is as follows: Where, F2 represents the second analytical value; F 12 A second analysis value for analyzing data acquired from communication with the load-bearing test equipment; F 2i is the second analysis value of the i-th data obtained from the big data platform; α i is the second-order weight coefficient corresponding to the second analysis value of the i-th data analysis obtained from the big data platform; μ1 and μ2 are pre-configured first-order weight coefficients respectively; n is the total amount of data obtained from the big data platform; the second-order weight coefficient can be determined by arranging the credibility and security of each data to form a data set, and then retrieving the weight distribution set from the pre-configured weight distribution library with the data set; the second-order weight coefficient corresponding to each data is sorted in sequence in the weight distribution set.

[0095] In one embodiment, a method for evaluating the bearing performance of dredged mud solidification energy piles includes:

[0096] Construction module 1, first analysis module 2, second analysis module 3 and comprehensive generation module 4;

[0097] Among them, the construction module 1 constructs a finite element model based on the configuration scheme and material composition test results of the dredged mud solidification energy pile to be evaluated; the first analysis module 2 performs a bearing performance analysis on the finite element model to obtain first analysis data; the second analysis module 3 analyzes the bearing test data of the dredged mud solidification energy pile to be evaluated to obtain second analysis data; the comprehensive generation module 4 combines the first analysis data and the second analysis data to generate evaluation data;

[0098] The load-bearing performance analysis of the finite element model is performed to obtain first analysis data, including:

[0099] Performing load-bearing performance analysis on the finite element model to obtain first analysis data includes: placing the finite element model into multiple pre-configured simulation analysis environments to obtain simulation data of each simulation analysis environment; and integrating each simulation data to obtain the first analysis data.

[0100] In addition, it also includes: simulation re-verification module;

[0101] The simulation re-verification module performs the following operations:

[0102] Publish each simulation data set and receive pre-registered expert feedback on each simulation data set; feedback includes: conclusions agreeing or disagreeing with the simulation data, supporting data for agreement, supporting data for disagreement, and conclusions agreeing or disagreeing with other experts' feedback; supporting data includes but is not limited to text, experimental records, and experimental videos;

[0103] Monitor the demonstration feedback of each simulation data to determine whether the simulation re-verification conditions are met;

[0104] When the simulation re-verification conditions are met, the simulation data is regenerated based on the demonstration feedback.

[0105] wherein, the argument feedback of the simulation data is evaluated, and when the reverse evaluation score is greater than or equal to a preset threshold, and / or the positive evaluation score is less than the reverse evaluation score, the simulation re-verification condition is met; wherein, the steps for generating the positive evaluation score and the negative evaluation score are as follows: identifying the relationship of the argument feedback and constructing a hierarchical topological structure based on the identified relationship; filling the argument feedback of the upper layer with entries based on the argument feedback at the lower layer; taking the argument feedback at the top layer filled with entries as representative feedback; grouping the representative feedback into a positive group and a negative group; and using a pre-configured quantitative scoring library to determine the weight coefficient of each feedback entry; determining the reference value based on the support value of the expert end supporting each feedback entry (configured by the expert end based on the total support value held in the argument feedback account); and taking the sum of the product of the reference value and the weight coefficient as the positive evaluation score or the negative evaluation score;

[0106] The hierarchical topology is a layered structure. The first layer consists of argument feedback that is not an agreement or disagreement with other experts' argument feedback; the second layer consists of argument feedback that indicates agreement or disagreement with the experts in the first layer; the third layer consists of argument feedback that indicates agreement or disagreement with the experts in the second layer; and so on. Feedback terms include: a first part indicating the conclusion and a second part indicating the type of evidence supporting the conclusion;

[0107] Among them, the simulation data is regenerated according to the demonstration feedback, including: extracting result data from the demonstration feedback data according to each item in the project table of the pre-configured simulation results, generating pseudo-simulation data based on the extracted result data and marking it to distinguish it from the simulation data.

[0108] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for evaluating the bearing performance of dredged mud solidification energy piles, characterized in that: include: Construct a finite element model based on the configuration plan and material composition test results of the dredged mud solidification energy pile to be evaluated; Performing load-bearing performance analysis on the finite element model to obtain first analysis data; Analyze the bearing test data of the dredged mud solidification energy pile to be evaluated to obtain second analysis data; The first analysis data and the second analysis data are integrated to generate evaluation data.

2. The method for evaluating the bearing performance of dredged mud solidification energy piles according to claim 1, characterized in that: The load test data is obtained through communication with the load test equipment.

3. The method for evaluating the bearing performance of dredged mud solidification energy piles according to claim 1, characterized in that: The steps for building the finite element model are as follows: Using the structural data in the configuration plan, a three-dimensional model of the dredged mud solidification energy pile is constructed; Based on the composition data in the configuration scheme and the material composition test results, the corresponding finite element unit components are retrieved from the pre-configured unit library; The three-dimensional model is segmented by finite elements, and the finite element elements obtained by segmentation are replaced with finite element element components retrieved from the element library to obtain a finite element model.

4. The method for evaluating the bearing performance of dredged mud solidification energy piles according to claim 1, wherein: Perform load-bearing performance analysis on the finite element model to obtain first analysis data, including: Place the finite element model into multiple pre-configured simulation analysis environments to obtain simulation data for each simulation analysis environment; The various simulation data are integrated to obtain the first analysis data.

5. The method for evaluating the bearing performance of dredged mud solidification energy piles according to claim 1, wherein: The first analysis data and the second analysis data are combined to generate evaluation data, including: extracting corresponding first evaluation data and second evaluation data from the first analysis data and the second analysis data for each evaluation item in the pre-configured evaluation item table; Performing quantitative evaluation on the first evaluation data and the second evaluation data respectively to obtain a first evaluation value and a second evaluation value; The weighted sum of the first evaluation value and the second evaluation value is used as the evaluation value of the corresponding evaluation item.

6. A bearing performance evaluation system for dredged mud solidification energy piles, characterized in that: include: A construction module, a first analysis module, a second analysis module and a comprehensive generation module; Among them, the construction module constructs a finite element model based on the configuration plan and material composition test results of the dredged mud solidification energy pile to be evaluated; the first analysis module performs a bearing performance analysis on the finite element model to obtain first analysis data; the second analysis module analyzes the bearing test data of the dredged mud solidification energy pile to be evaluated to obtain second analysis data; the comprehensive generation module combines the first analysis data and the second analysis data to generate evaluation data.

7. The bearing performance evaluation system for dredged mud solidification energy piles according to claim 6, characterized in that: The load test data is obtained through communication with the load test equipment.

8. The bearing performance evaluation system for dredged mud solidification energy piles according to claim 6, characterized in that: The steps for building the finite element model are as follows: Using the structural data in the configuration plan, a three-dimensional model of the dredged mud solidification energy pile is constructed; Based on the composition data in the configuration scheme and the material composition test results, the corresponding finite element unit components are retrieved from the pre-configured unit library; The three-dimensional model is segmented by finite elements, and the finite element elements obtained by segmentation are replaced with finite element element components retrieved from the element library to obtain a finite element model.

9. The bearing performance evaluation system for dredged mud solidification energy piles according to claim 6, characterized in that: The first analysis module performs the following operations: Place the finite element model into multiple pre-configured simulation analysis environments to obtain simulation data for each simulation analysis environment; The various simulation data are integrated to obtain the first analysis data.

10. The bearing performance evaluation system for dredged mud solidification energy piles according to claim 6, characterized in that: The synthesis generation module performs the following operations: extracting corresponding first evaluation data and second evaluation data from the first analysis data and the second analysis data for each evaluation item in the pre-configured evaluation item table; Performing quantitative evaluation on the first evaluation data and the second evaluation data respectively to obtain a first evaluation value and a second evaluation value; The weighted sum of the first evaluation value and the second evaluation value is used as the evaluation value of the corresponding evaluation item.

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