An AI optimization-based soil compaction pile foundation treatment method and system, and a medium
By using an AI-optimized soil compaction pile foundation treatment method, multiple design schemes are generated and the optimal scheme is selected using an AI optimization algorithm. This solves the problems of low design efficiency and poor flexibility in existing technologies, and enables rapid iteration and cost optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA MCC5 GROUP CORP LTD
- Filing Date
- 2026-04-07
- Publication Date
- 2026-07-03
AI Technical Summary
Existing soil compaction pile foundation treatment methods are inefficient, difficult to cope with complex geological conditions, and cannot be quickly iterated and verified, resulting in long design cycles, high costs, and poor flexibility.
An AI-based optimization approach is adopted. By acquiring soil physical parameters and design variables, multiple schemes are generated using a derivational design program. The optimal scheme is then selected using AI optimization algorithms such as genetic algorithms and particle swarm optimization. The scheme is then visualized using 3D modeling software, enabling dynamic adjustment and rapid iteration.
It significantly improves design efficiency, shortens the cycle, reduces errors from manual calculations, flexibly adapts to complex geological conditions, reduces construction costs, and ensures that the foundation treatment effect meets engineering safety standards.
Smart Images
Figure CN122333764A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of foundation treatment technology, specifically to an AI-optimized method, system, and medium for soil compaction pile foundation treatment. Background Technology
[0002] Soil compaction pile foundation treatment is a widely used foundation reinforcement technology applied to soft soil layers and uneven settlement environments. This technology involves driving piles into the foundation, utilizing the compaction effect of the piles to alter the soil's physical structure, thereby increasing soil density, enhancing foundation bearing capacity, reducing settlement, and effectively improving the overall stability of the foundation. The design of soil compaction pile foundation treatment schemes involves several key parameters, including pile diameter, pile spacing, number of piles, pile length, and whether pre-drilling is used. A well-designed scheme can significantly improve the foundation's bearing capacity, ensuring the safety and long-term stability of engineering structures, and has significant application value in building construction, road and bridge construction, and industrial plants.
[0003] Currently, soil compaction pile foundation treatment mainly employs two methods: The first method is the experience-based design method. Designers, based on soil compaction conditions and expected bearing capacity, select appropriate pile parameters based on engineering experience. Through extensive manual calculations, they gradually determine the number, size, and spacing of piles, and then drive the piles into the foundation according to these parameters. The second method is the standard design method. Designers conduct preliminary design according to the "Code for Design of Foundations and Substructures," calculate the required number of piles and pile parameters using standard formulas, and then drive the piles into the foundation according to the determined design parameters.
[0004] However, the above-mentioned methods have the following drawbacks: 1. The experience-based design method relies on experience-based selection and manual calculation, resulting in a large workload and long design cycle; moreover, as the complexity of the foundation treatment scheme increases, the design time and labor costs rise significantly, leading to low design efficiency. 2. The standard design method is often too simplistic, failing to consider complex factors such as soil heterogeneity, groundwater variations, and soil anisotropy, resulting in deviations between the design scheme and actual engineering needs, making it difficult to adapt to complex geological conditions. 3. Existing design methods struggle to optimize design schemes through calculation or simulation, failing to respond promptly to changes in design requirements under different working conditions, resulting in poor design flexibility and hindering rapid iteration and verification. Summary of the Invention
[0005] The purpose of this application is to provide an AI-optimized method, system, and medium for soil compaction pile foundation treatment, which solves the problems of low design efficiency, difficulty in dealing with complex geological conditions, and inability to quickly iterate and verify existing soil compaction pile foundation treatment methods.
[0006] The technical solution adopted by this application to solve its technical problem is: Firstly, an AI-optimized method for soil compaction pile foundation treatment is provided, including: S1. Obtain the soil physical parameters before foundation treatment, including soil void ratio, soil particle specific gravity and maximum dry density; S2. Input the design variables for soil compaction piles, including the pre-drilled hole diameter, pile diameter, and pile spacing; S3. Based on the soil physical parameters and the design variables, multiple soil compaction pile foundation treatment schemes are automatically generated through a derivational design program. The technical parameters of each scheme after foundation treatment are calculated, including the number of piles, void ratio after treatment, dry density, and compaction coefficient. S4. Using AI optimization algorithms, multiple soil compaction pile foundation treatment schemes are optimized and calculated. The optimization objective is to minimize the number of piles, and the constraint is that the compaction coefficient is ≥0.93. The optimized soil compaction pile foundation treatment scheme is then selected. S5. Output the optimal soil compaction pile foundation treatment scheme, which includes the number of piles, pile diameter, pile spacing and corresponding foundation treatment effect parameters.
[0007] Furthermore, in step S3, the derivative design program is implemented based on the Dynamo visual programming platform, and a mathematical calculation model for soil compaction pile foundation treatment is constructed through node-based programming.
[0008] Furthermore, in step S4, the AI optimization algorithm includes a genetic algorithm, a particle swarm optimization algorithm, and a simulated annealing algorithm.
[0009] Furthermore, in step S2, the design variables are set through an adjustable parameter input component, which includes a numerical slider or a numerical input box.
[0010] Furthermore, it also includes: S6. Based on the actual changes in soil conditions at the construction site, dynamically adjust the design variables of step S2, and re-execute steps S3 to S5.
[0011] Furthermore, it also includes: S7. Import the soil compaction pile foundation treatment scheme into the three-dimensional modeling software to generate a three-dimensional visualization model of the soil compaction pile foundation treatment.
[0012] Secondly, an AI-optimized soil compaction pile foundation treatment system is provided, including: The parameter acquisition module is used to acquire soil physical parameters before foundation treatment, including soil void ratio, soil particle specific gravity and maximum dry density. The design variable input module is used to input the design variables of the soil compaction pile, including the pre-drilled hole diameter, pile diameter, and pile spacing. The derivation generation module is used to automatically generate multiple soil compaction pile foundation treatment schemes based on the soil physical parameters and the design variables, and calculate the technical parameters of each scheme after foundation treatment, including the number of piles, void ratio after treatment, dry density and compaction coefficient. The AI optimization module is used to perform optimization calculations on multiple soil compaction pile foundation treatment schemes using AI optimization algorithms. The optimization objective is to minimize the number of piles, and the constraint is a compaction coefficient ≥ 0.93. The optimized soil compaction pile foundation treatment scheme is then selected. The scheme output module is used to output the optimal soil compaction pile foundation treatment scheme, which includes the number of piles, pile diameter, pile spacing and corresponding foundation treatment effect parameters.
[0013] Furthermore, it also includes: The dynamic adjustment module is used to dynamically adjust the design variables according to the actual soil conditions at the construction site and trigger the derivation generation module, the AI optimization module and the scheme output module to re-output the optimal soil compaction pile foundation treatment scheme. The visualization module is used to import the soil compaction pile foundation treatment scheme into 3D modeling software to generate a 3D visualization model of the soil compaction pile foundation treatment.
[0014] Thirdly, a system is provided, including a memory and a processor; The memory stores instructions that the processor can execute; When the processor is configured to execute the instructions, the system implements the method described in the first aspect.
[0015] Fourthly, a storage medium is provided, including computer instructions that, when executed on a computer, cause the computer to perform the method described in the first aspect.
[0016] The beneficial effects of this application are: The AI-optimized soil compaction pile foundation treatment method, system, and medium provided in this application automatically generate multiple soil compaction pile foundation treatment schemes through a derivational design program. An AI optimization algorithm is used to intelligently select schemes with the minimum number of piles as the optimization objective and a compaction coefficient ≥0.93 as the constraint. This significantly improves design efficiency, shortens the design cycle, and reduces human calculation errors. Furthermore, it flexibly adapts to complex geological conditions based on soil physical parameters such as soil porosity, soil particle specific gravity, and maximum dry density, enabling rapid iterative verification and dynamic adjustment of design schemes. Ultimately, while ensuring the foundation treatment effect meets engineering safety standards, it effectively optimizes the pile configuration and reduces construction costs. Attached Figure Description
[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a flowchart of the AI-optimized soil compaction pile foundation treatment method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the composition of the AI-optimized soil compaction pile foundation treatment system provided in the embodiments of this application; Figure 3 This is a schematic diagram of the hardware structure of the AI-optimized soil compaction pile foundation treatment system provided in the embodiments of this application.
[0019] Figure label: 100-system; 101 - Parameter Acquisition Module; 102 - Design Variable Input Module; 103 - Derivative Generation Module; 104 - AI Optimization Module; 105 - Solution Output Module; 106 - Dynamic Adjustment Module; 107 - Visualization Module; 200-system; 201-Memory; 202-Processor; 203-Communication interface; 204-Bus. Detailed Implementation
[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.
[0021] In the description of this application, the terms "upper," "lower," "left," "right," "front," "rear," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Unless otherwise specified, the above-mentioned orientational descriptions can be flexibly set in actual application, provided that the relative positional relationships shown in the accompanying drawings are satisfied.
[0022] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "set up," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.
[0023] See Figure 1 This application provides an AI-optimized method for soil compaction pile foundation treatment, comprising the following steps: S1. Obtain the soil physical parameters before foundation treatment, including soil void ratio, soil particle specific gravity and maximum dry density.
[0024] Specifically, before designing the soil compaction pile foundation treatment, soil physical parameters are obtained through on-site geological surveys and indoor geotechnical tests. These parameters include soil void ratio, soil particle specific gravity, and maximum dry density. The soil void ratio is the ratio of pore volume to solid particle volume in the soil, and can be determined using the ring cutter method or sand cone method. Soil particle specific gravity is the ratio of the weight of soil particles dried to constant weight at 105℃-110℃ to the weight of water at 4℃, and can be determined using the hydrometer bottle method. Maximum dry density is the dry density of the soil in its densest state, and can be determined through a standard compaction test.
[0025] The soil physical parameters in this step are input as known conditions into the subsequent design program, providing basic data support for the generation of soil compaction pile foundation treatment schemes. In this embodiment, soil physical parameters can be input through numerical input boxes.
[0026] S2. Input the design variables for the soil compaction piles, including the pre-drilled hole diameter, pile diameter, and pile spacing.
[0027] Specifically, based on engineering requirements and site conditions, design variables for soil compaction piles are determined. These design variables include the pre-drilled hole diameter, pile diameter, and pile spacing. In this embodiment, the design variables are set through an adjustable parameter input component. For example, the adjustable parameter input component includes a numerical slider or a numerical input box. Designers can adjust the values of the pre-drilled hole diameter, pile diameter, and pile spacing in real time by dragging the slider, or they can directly input specific values using the numerical input box, thus achieving flexible configuration of design variables to dynamically change the design scheme.
[0028] If there are special requirements for inputting design variables in certain situations, other input methods can be used, such as importing data from an Excel spreadsheet, to improve input efficiency.
[0029] S3. Based on the soil physical parameters and the design variables, multiple soil compaction pile foundation treatment schemes are automatically generated through a derivational design program. The technical parameters of each scheme after foundation treatment are calculated, including the number of piles, void ratio after treatment, dry density, and compaction coefficient.
[0030] Specifically, the derivative design program can be implemented based on the Dynamo visual programming platform, constructing a mathematical calculation model for soil compaction pile foundation treatment through node-based programming. Dynamo can seamlessly integrate with BIM software such as Revit, building data processing logic through node-based programming. This mathematical calculation model includes modules for calculating the number of piles, void ratio, dry density, and compaction coefficient. The pile number calculation module calculates the required number of piles based on the treatment area and pile spacing parameters; the void ratio calculation module calculates the void ratio of the soil between piles after treatment based on the pre-drilled hole diameter, pile diameter, and initial void ratio; the dry density calculation module calculates the dry density after treatment based on the soil particle specific gravity and the void ratio after treatment; and the compaction coefficient calculation module calculates the compaction coefficient based on the ratio of the dry density after treatment to the maximum dry density.
[0031] The program uses a derivational design approach to randomly generate multiple processing schemes within a set range of design variable values. For example, if the number of generated schemes is set to 80, each scheme corresponds to a different combination of pre-drilled borehole diameter, pile diameter, and pile spacing, the program automatically calculates the technical parameters for each scheme, such as the number of piles, the void ratio after processing, the dry density, and the compaction coefficient. The generated schemes and their corresponding technical parameters are then output in a list format, providing a data foundation for subsequent optimization calculations.
[0032] If the design requirements are complex, other programming tools (such as Python or a combination of Revit and Dynamo) can be used to further extend the calculation capabilities, or external geological data can be imported for more refined calculations.
[0033] S4. Using AI optimization algorithms, multiple soil compaction pile foundation treatment schemes are optimized and calculated. The optimization objective is to minimize the number of piles, and the constraint is that the compaction coefficient is ≥0.93. The optimized soil compaction pile foundation treatment scheme is then selected.
[0034] Specifically, based on the generation of multiple alternative solutions, an AI optimization algorithm is used for intelligent optimization calculation. In this embodiment, the AI optimization algorithm can employ heuristic optimization algorithms such as genetic algorithms, particle swarm optimization algorithms, or simulated annealing algorithms. The optimization objective is to minimize the number of piles, that is, to minimize the number of soil compaction piles while meeting engineering requirements, thereby reducing construction costs and time. A compaction coefficient ≥ 0.93 is used as a constraint to ensure that the compaction degree of the treated foundation meets the qualified standard required by specifications.
[0035] The optimization calculation process includes: using multiple soil compaction pile foundation treatment schemes as the initial population; calculating the fitness value of each scheme according to the optimization objective and constraints; evaluating schemes that meet the compaction coefficient ≥ 0.93 with a fitness value of 1 / n; assigning lower fitness values or eliminating schemes that do not meet the constraints; iteratively searching for the optimal solution through genetic operations such as selection, crossover, and mutation (taking genetic algorithm as an example) or particle position updates (taking particle swarm optimization algorithm as an example); terminating the optimization calculation when the number of iterations reaches a set value or the fitness value converges; and outputting several optimized schemes that meet the constraints and have the fewest piles (such as the first 10) for designers to further compare and select.
[0036] S5. Output the optimal soil compaction pile foundation treatment scheme, which includes the number of piles, pile diameter, pile spacing and corresponding foundation treatment effect parameters.
[0037] Specifically, based on project requirements, the optimal solution is selected from the multiple optimized solutions filtered in step S4 and output. This process can be automated; designers only need to confirm the optimal solution. Designers can directly guide construction based on the optimal soil compaction pile foundation treatment solution, or implement it after fine-tuning it based on engineering experience.
[0038] S6. Based on the actual changes in soil conditions at the construction site, dynamically adjust the design variables of step S2, and re-execute steps S3 to S5.
[0039] Specifically, by conducting on-site geological surveys or real-time monitoring, the soil physical parameters after changes at the construction site can be obtained; based on the actual changes, the range or initial value of the design variables in step S2 can be adjusted in the Dynamo platform; steps S3 to S5 can be re-executed, that is, a new set of schemes can be generated based on the derivational design program, the optimized schemes can be re-selected through AI optimization algorithms, and the optimal scheme can be output from the optimized schemes, so as to realize the rapid iterative update of the design scheme.
[0040] The dynamic adjustment mechanism in this step enables the design to respond flexibly to changes on site, avoids construction deviations caused by static design, and improves the reliability of foundation treatment.
[0041] S7. Import the soil compaction pile foundation treatment scheme into the three-dimensional modeling software to generate a three-dimensional visualization model of the soil compaction pile foundation treatment.
[0042] Specifically, the optimized soil compaction pile foundation treatment scheme selected in step S4 or the optimal soil compaction pile foundation treatment scheme output in step S5 can be imported into 3D modeling software to generate a 3D visualization model of the soil compaction pile foundation treatment, which intuitively displays the effect of each scheme, allowing designers to visually evaluate and modify the scheme from different angles and levels.
[0043] Through 3D visualization, designers, construction workers, and project managers can intuitively understand the spatial layout and effects of the design scheme, reduce communication errors, and improve decision-making efficiency and construction accuracy.
[0044] The AI-optimized soil compaction pile foundation treatment method provided in this application automatically generates multiple soil compaction pile foundation treatment schemes through a derivational design program. It uses an AI optimization algorithm to intelligently screen the schemes with the minimum number of piles as the optimization objective and a compaction coefficient ≥0.93 as the constraint. This not only significantly improves design efficiency, shortens the design cycle, and reduces errors from manual calculations, but also flexibly adapts to complex geological conditions based on soil physical parameters such as soil porosity, soil particle specific gravity, and maximum dry density. It enables rapid iterative verification and dynamic adjustment of design schemes, and ultimately effectively optimizes the pile configuration and reduces construction costs while ensuring that the foundation treatment effect meets engineering safety standards.
[0045] This application embodiment can divide the system and server into functional modules according to the above method examples. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.
[0046] When dividing each function into modules according to its corresponding function. Figure 2 A schematic diagram of a possible system configuration involved in the above embodiments is shown. See also Figure 2 A soil compaction pile foundation treatment system 100 based on AI optimization may include: a parameter acquisition module 101, a design variable input module 102, a derivation generation module 103, an AI optimization module 104, a scheme output module 105, a dynamic adjustment module 106, and a visualization module 107.
[0047] The system includes several modules: a parameter acquisition module 101, which acquires soil physical parameters before foundation treatment, including soil void ratio, soil particle specific gravity, and maximum dry density; a design variable input module 102, which inputs design variables for the soil compaction piles, including pre-drilled hole diameter, pile diameter, and pile spacing; a derivation generation module 103, which automatically generates multiple soil compaction pile foundation treatment schemes based on the soil physical parameters and design variables, and calculates the technical parameters of each scheme after foundation treatment, including the number of piles, void ratio after treatment, dry density, and compaction coefficient; an AI optimization module 104, which uses an AI optimization algorithm to optimize multiple soil compaction pile foundation treatment schemes, with the minimum number of piles as the optimization objective and a compaction coefficient ≥0.93 as a constraint, to select the optimized soil compaction pile foundation treatment scheme; and a scheme output module 105, which outputs the optimal soil compaction pile foundation treatment scheme, including the number of piles, pile diameter, pile spacing, and corresponding foundation treatment effect parameters. The dynamic adjustment module 106 is used to dynamically adjust the design variables according to changes in actual soil conditions at the construction site and trigger the derivation generation module, the AI optimization module, and the scheme output module to re-output the optimal soil compaction pile foundation treatment scheme. The visualization module 107 is used to import the soil compaction pile foundation treatment scheme into 3D modeling software to generate a 3D visualization model of the soil compaction pile foundation treatment.
[0048] Figure 2 The modules in the module can also be called units; for example, the parameter acquisition module can be called a Poisson ratio sequence unit. Figure 2 If the various modules in the system are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium.
[0049] See Figure 3 This application also provides a hardware structure for a system 200, including a memory 201 and a processor 202; optionally, it also includes a communication interface 203 connected to the processor 202. The memory 201, processor 202, and communication interface 203 are connected via a bus 204.
[0050] The memory 201 may be a read-only memory or other type of static storage device that can store static information and instructions, random access memory or other type of dynamic storage device that can store information and instructions, or it may be an electrically erasable programmable read-only memory, a read-only optical disc or other optical disc storage, optical disk storage, magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The embodiments of this application do not impose any limitations on this.
[0051] Processor 202 can be a central processing unit, a general-purpose processor, a network processor, a digital signal processor, a microprocessor, a microcontroller, a programmable logic device, or any combination thereof. Processor 202 can also be any other device with processing capabilities, such as a circuit, device, or software module. Processor 202 can also include multiple CPUs, and processor 202 can be a single-core processor or a multi-core processor. Here, processor 202 can refer to one or more devices, circuits, or processing cores for processing data.
[0052] The memory 201 can exist independently or be integrated with the processor 202. The memory 201 stores computer program code, and the processor 202 uses the computer program code stored in the memory 201 to implement the AI-optimized soil compaction pile foundation treatment method provided in this embodiment.
[0053] The communication interface 203 can be used to communicate with other devices or communication networks, such as Ethernet, wireless access networks, and wireless local area networks. The communication interface 203 can be a module, circuit, transceiver, or any device capable of enabling communication.
[0054] Bus 204 can be a standard bus for interconnecting peripheral components or an extended industry standard structure bus, etc. Bus 204 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by only one line, but this does not mean that there is only one bus or one type of bus.
[0055] This application also provides a storage medium including computer instructions. When the computer instructions are executed on a computer, the computer performs the AI-optimized soil compaction pile foundation treatment method provided in the above embodiments. The storage medium can be any available medium accessible to a computer, or it can include one or more data storage devices such as servers or data centers that can be integrated with media. For example, the available medium can be a magnetic medium, an optical medium, or a semiconductor medium.
[0056] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.
Claims
1. An AI optimization-based soil compaction pile foundation treatment method, characterized by, include: S1. Obtain the soil physical parameters before foundation treatment, including soil void ratio, soil particle specific gravity and maximum dry density; S2. Input the design variables for soil compaction piles, including the pre-drilled hole diameter, pile diameter, and pile spacing; S3. Based on the soil physical parameters and the design variables, multiple soil compaction pile foundation treatment schemes are automatically generated through a derivational design program. The technical parameters of each scheme after foundation treatment are calculated, including the number of piles, void ratio after treatment, dry density, and compaction coefficient. S4. Using AI optimization algorithms, multiple soil compaction pile foundation treatment schemes are optimized and calculated. The optimization objective is to minimize the number of piles, and the constraint is that the compaction coefficient is ≥0.
93. The optimized soil compaction pile foundation treatment scheme is then selected. S5. Output the optimal soil compaction pile foundation treatment scheme, which includes the number of piles, pile diameter, pile spacing and corresponding foundation treatment effect parameters. 2.The AI optimization-based soil compaction pile foundation treatment method according to claim 1, characterized in that, In step S3, the derivational design program is implemented based on the Dynamo visual programming platform, and a mathematical calculation model for soil compaction pile foundation treatment is constructed through node-based programming. 3.The AI optimization-based soil compaction pile foundation treatment method according to claim 1, characterized in that, In step S4, the AI optimization algorithm includes genetic algorithm, particle swarm optimization algorithm and simulated annealing algorithm. 4.The AI optimization-based soil compaction pile foundation treatment method according to claim 1, characterized in that, In step S2, the design variables are set through an adjustable parameter input component, which includes a numerical slider or a numerical input box. 5.The AI optimization-based soil compaction pile foundation treatment method according to claim 1, characterized in that, Also includes: S6. Based on the actual changes in soil conditions at the construction site, dynamically adjust the design variables of step S2, and re-execute steps S3 to S5. 6.The AI optimization-based soil compaction pile foundation treatment method according to claim 5, characterized in that, Also includes: S7. Import the soil compaction pile foundation treatment scheme into the three-dimensional modeling software to generate a three-dimensional visualization model of the soil compaction pile foundation treatment.
7. An AI optimization-based soil compaction pile foundation treatment system, characterized by, include: The parameter acquisition module is used to acquire soil physical parameters before foundation treatment, including soil void ratio, soil particle specific gravity and maximum dry density. The design variable input module is used to input the design variables of the soil compaction pile, including the pre-drilled hole diameter, pile diameter, and pile spacing. The derivation generation module is used to automatically generate multiple soil compaction pile foundation treatment schemes based on the soil physical parameters and the design variables, and calculate the technical parameters of each scheme after foundation treatment, including the number of piles, void ratio after treatment, dry density and compaction coefficient. The AI optimization module is used to perform optimization calculations on multiple soil compaction pile foundation treatment schemes using AI optimization algorithms. The optimization objective is to minimize the number of piles, and the constraint is a compaction coefficient ≥ 0.
93. The optimized soil compaction pile foundation treatment scheme is then selected. The scheme output module is used to output the optimal soil compaction pile foundation treatment scheme, which includes the number of piles, pile diameter, pile spacing and corresponding foundation treatment effect parameters. 8.The AI optimization-based soil compaction pile foundation treatment system according to claim 7, characterized in that, Also includes: The dynamic adjustment module is used to dynamically adjust the design variables according to the actual soil conditions at the construction site and trigger the derivation generation module, the AI optimization module and the scheme output module to re-output the optimal soil compaction pile foundation treatment scheme. The visualization module is used to import the soil compaction pile foundation treatment scheme into 3D modeling software and generate a 3D visualization model of the soil compaction pile foundation treatment.
9. A system, characterized by Including memory and processor; The memory stores instructions that the processor can execute; When the processor is configured to execute the instructions, the system implements the method of any one of claims 1 to 6.
10. A storage medium, characterized by Includes computer instructions that, when executed on a computer, cause the computer to perform the method of any one of claims 1 to 6.