Parameter optimization method and system for large diameter pipe piles

Abstract

The invention discloses a parameter optimization method and system for large-diameter pipe piles, and the invention relates to the technical field of building parameter optimization. The method comprises the following steps: determining the optimization parameters of large-diameter pipe piles and the value ranges corresponding to the parameters, and randomly generating several groups of optimization parameter combinations based on the value ranges of the optimization parameters; establishing a finite element pipe pile model, obtaining mechanical property data of pipe piles with different parameters, and estimating the settlement data of the pipe piles under different optimization parameter combinations based on the mechanical property data; calculating the soil risk coefficient based on the physical and mechanical characteristic data of the soil in the construction site, and calculating the maximum bearing capacity of the pipe column based on the side resistance load and end resistance load of the pipe column; generating a pipe pile parameter optimization equation based on the settlement data, maximum bearing capacity and soil risk coefficient obtained under the corresponding optimization parameter combination, using the pipe pile parameter optimization equation as a fitness function, and obtaining the optimal optimization parameter combination through a genetic algorithm.

Description

Technical Field

[0001] The invention relates to the technical field of building parameter optimization, and in particular to a parameter optimization method and system for large-diameter pipe piles. Background Art

[0002] With the acceleration of urbanization and the increasing complexity of infrastructure construction, pile foundations, as an important foundation treatment method, have been widely used in high-rise buildings, bridges, docks and other projects. Especially in areas with soft soil or insufficient bearing capacity, large-diameter pipe piles have gradually become an important choice in engineering design due to their excellent bearing capacity and adaptability. However, there are many technical difficulties in the design and construction of pipe piles, especially in optimizing design parameters to improve pile foundation performance. Traditional design methods often rely on experience and simple theoretical calculations, and fail to fully consider the complex interactions between factors such as soil characteristics and pile body shape, resulting in problems such as insufficient bearing capacity and excessive settlement in practical applications. These problems not only affect the safety and stability of the project, but may also lead to additional economic losses and construction delays.

[0003] In the design of large-diameter pipe piles, the selection of parameters such as pile diameter, pile length and wall thickness directly affects the mechanical properties and adaptability of the pipe piles. Different parameter combinations will lead to significant differences in the bearing capacity, settlement, lateral resistance and other aspects of the pipe piles. Therefore, how to scientifically and reasonably select these parameter combinations has become a core task in the current pile foundation design. At the same time, the physical and mechanical properties of the soil, such as cohesion, friction angle and density, also have an important influence on the bearing capacity and settlement performance of the pile foundation. When considering these influencing factors, traditional methods are usually limited to empirical judgment, lack of systematicity and scientificity, resulting in unsatisfactory optimization results.

[0004] In the prior art, the publication number CN115525939A discloses a parameter optimization method and device for steel trestle steel pipe piles, the method comprising: determining multiple parameters affecting the stability of the steel trestle structure and the value range corresponding to each parameter according to the steel trestle design case; uniformly taking values ​​of the corresponding parameters according to the value range of each parameter; determining the coefficient of the preset first-order buckling stability safety factor equation according to the value of each parameter; obtaining the optimal value of each parameter according to the value range of each parameter and the first-order buckling stability safety factor equation after the coefficient is determined; but this scheme relies on the parameters determined by the design case, which may lead to insufficient applicability for steel trestles under other different conditions or environments, and if the parameter range is wide, uniform value taking may lead to increased calculation amount, and the lack of intelligent optimization algorithm may make the parameter adjustment process cumbersome and time-consuming. The above information disclosed in the background technology section is only used to enhance the understanding of the background of the present disclosure, so it may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The object of the present invention is to provide a parameter optimization method and system for large-diameter pipe piles to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A parameter optimization method for large diameter pipe piles, comprising the following specific steps:

[0008] S1, determining the optimization parameters of the large diameter pipe pile and the value ranges corresponding to the parameters, and randomly generating a plurality of groups of optimization parameter combinations based on the value ranges of the optimization parameters, wherein the optimization parameters include the pile diameter, pile length and wall thickness of the pipe pile, and the optimization parameter combination is a group of pile diameter, pile length and wall thickness data;

[0009] S2, based on the data in the optimization parameter combination, a finite element pipe pile model is established, mechanical property data of the pipe pile is obtained through the finite element pipe pile model with different parameters, and settlement data of the pipe pile under different optimization parameter combinations are estimated based on the mechanical property data, wherein the mechanical property data includes the deformation modulus of the pile body and the effective bearing area of ​​the pipe pile;

[0010] S3, calculating the soil risk coefficient based on the physical and mechanical characteristic data of the soil in the construction site, and calculating the maximum bearing capacity of the pipe string based on the side resistance load and end resistance load of the pipe string, wherein the physical and mechanical characteristic data of the soil include the cohesion, friction angle and density of the soil;

[0011] S4, based on the settlement data, maximum bearing capacity and soil risk coefficient obtained under the corresponding optimization parameter combination, generate a pipe pile parameter optimization equation, use the pipe pile parameter optimization equation as a fitness function, and obtain the optimal optimization parameter combination through a genetic algorithm.

[0012] Furthermore, the maximum and minimum values ​​of the optimization parameters are determined according to the large-diameter pipe pile design case, and the value range of the optimization parameters is determined based on the maximum and minimum values ​​of each optimization parameter. Within the value range of each optimization parameter, random values ​​are taken to generate several groups of optimization parameter combinations until the optimization parameter combination data covers the entire value range of the optimization parameter. represents the set of all optimization parameters, where ,in , represents the u-th optimization parameter combination, are the pile diameter, pile length and wall thickness data in the u-th optimization parameter combination, respectively, where u is the index of the optimization parameter combination and n represents the number of optimization parameter combinations.

[0013] Furthermore, the strain and stress relationship of the pile body under load was analyzed by the finite element pile model, and the deformation modulus of the pile body was obtained. , the unit is , the effective bearing area of ​​the pile is obtained by simulating the bearing behavior of the pile foundation , the unit is ; Based on the deformation modulus of the pile and effective bearing area of ​​the pile The formula for estimating the settlement of pipe piles is:

[0014]

[0015] In the formula, Indicates the settlement of the pile, in units of , is the load borne by the pile foundation, in units of .

[0016] Furthermore, step S3 includes the following steps:

[0017] S31, based on the side resistance load and end resistance load of the pipe string, calculate the maximum bearing capacity of the pipe string, where the formula for calculating the maximum bearing capacity of the pipe string is:

[0018]

[0019] In the formula, Indicates the maximum bearing capacity of the pipe string. is the side resistance load, represents the end resistance load;

[0020] S32, calculating the soil risk coefficient based on the physical and mechanical characteristic data of the soil in the construction site, wherein the formula for calculating the soil risk coefficient is:

[0021]

[0022] In the formula, is the soil risk factor, is the benchmark risk value, , and They represent the average values ​​of the cohesion, friction angle and density of the soil at the current construction site, , and They represent the maximum values ​​of cohesion, friction angle and density of the soil at the current construction site, respectively. , and Respectively represent the weight coefficients of the ratio of the average and maximum values ​​of the cohesion, friction angle and density of the soil at the current construction site, where and , and Both are greater than 0.

[0023] Furthermore, the step S31 includes the following steps:

[0024] S311, based on the lateral resistance per unit length of the pipe pile and the lateral area of ​​the pipe pile, the formula for calculating the lateral resistance load of the pipe pile is:

[0025]

[0026] In the formula, is the effective buried depth of the pile, For the depth The soil friction resistance at ; For the depth The lateral area at

[0027] Among them, in depth The soil friction at is calculated based on the formula:

[0028]

[0029] In the formula, For Depth The effective stress at For Depth The friction angle at The unit is depth the cohesion of the soil at the site;

[0030] S312, based on the bearing capacity of the pile end and the bottom area of ​​the pile, the formula for calculating the end resistance load of the pipe column is:

[0031]

[0032] In the formula, is the bearing capacity of the pile end, is the bottom area of ​​the pile,

[0033] The bearing capacity of the pile end The calculation is based on the formula:

[0034]

[0035] In the formula, represents the effective vertical stress, Indicates the effective cohesion of the land. The bearing capacity coefficient that represents the effective cohesion.

[0036] Furthermore, based on the settlement data, maximum bearing capacity and soil risk coefficient obtained under the corresponding optimization parameter combination, the pile parameter optimization equation is generated, and the pile parameter optimization equation is used as the fitness function, where the specific expression of the pile parameter optimization equation is:

[0037]

[0038] In the formula, represents the fitness value corresponding to the jth optimization parameter combination, , and The maximum load capacity , settlement data and soil risk factor The weight coefficient of and , and Both are greater than 0.

[0039] Furthermore, the optimal optimization parameter combination is obtained by genetic method. The specific steps include: establishing an initial population, selecting the optimal parameter set Randomly select several optimization parameter combinations to form an initial population, select and mutate the optimization parameter combinations in the initial population to form a new population, select and mutate the new population, and iterate in sequence until an optimization parameter combination with a fitness value greater than or equal to the fitness threshold appears. Stop the iterative operation and output the optimization parameter combination with the highest fitness value in the current population as the optimal optimization parameter combination.

[0040] The present invention also provides a parameter optimization system for a large diameter pipe pile, and the parameter optimization system for a large diameter pipe pile is used to execute the parameter optimization method for a large diameter pipe pile, comprising:

[0041] An optimization parameter and range determination module is used to determine the optimization parameters of large-diameter pipe piles and the value ranges corresponding to each parameter, and randomly generate a number of groups of optimization parameter combinations based on the value ranges of the optimization parameters. The optimization parameters include the pile diameter, pile length and wall thickness of the pipe pile, and the optimization parameter combination is a group of pile diameter, pile length and wall thickness data;

[0042] A pipe pile settlement analysis module is used to establish a finite element pipe pile model based on the data in the optimization parameter combination, obtain mechanical property data of the pipe pile through the finite element pipe pile model with different parameters, and estimate the settlement data of the pipe pile under different optimization parameter combinations based on the mechanical property data, wherein the mechanical property data includes the deformation modulus of the pile body and the effective bearing area of ​​the pipe pile;

[0043] The maximum bearing capacity and risk factor estimation module is used to calculate the soil risk factor based on the physical and mechanical characteristic data of the soil in the construction site, and calculate the maximum bearing capacity of the pipe string based on the side resistance load and end resistance load of the pipe string. The physical and mechanical characteristic data of the soil include the cohesion, friction angle and density of the soil;

[0044] The optimal solution determination output module is used to generate a pipe pile parameter optimization equation based on the settlement data, maximum bearing capacity and soil risk coefficient obtained under the corresponding optimization parameter combination, and the pipe pile parameter optimization equation is used as the fitness function to obtain the optimal optimization parameter combination through a genetic algorithm.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] Determine the optimization parameters and their value ranges, generate optimization parameter combinations covering the entire value range based on the value range of the optimization parameters, and establish finite element models based on these combinations to obtain the mechanical property data of the pipe piles, which can more comprehensively and accurately reflect the performance of the pipe piles under actual working conditions. It improves the scientific nature of the pipe pile design and provides a solid data foundation for subsequent settlement prediction and bearing capacity calculation; secondly, by combining the physical and mechanical characteristics of the soil at the construction site, calculating the soil risk coefficient, and further optimizing the design parameters of the pipe piles, it can ensure the good adaptability of the pipe piles under different soil conditions and improve the safety and stability of the project; finally, using genetic algorithms as optimization tools, based on the generated pipe pile parameter optimization equations, it can effectively obtain the optimal parameter combination, making the design process more intelligent and flexible. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0048] Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0050] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0051] Example:

[0052] See also Figure 1 , the present invention provides a technical solution:

[0053] A parameter optimization method for large diameter pipe piles, comprising the following specific steps:

[0054] S1, determining the optimization parameters of the large diameter pipe pile and the value ranges corresponding to the parameters, and randomly generating a plurality of groups of optimization parameter combinations based on the value ranges of the optimization parameters, wherein the optimization parameters include the pile diameter, pile length and wall thickness of the pipe pile, and the optimization parameter combination is a group of pile diameter, pile length and wall thickness data;

[0055] According to the large diameter pipe pile design case, the maximum and minimum values ​​of the optimization parameters are determined. Based on the maximum and minimum values ​​of each optimization parameter, the value range of the optimization parameter is determined. Within the value range of each optimization parameter, random values ​​are taken to generate several groups of optimization parameter combinations until the optimization parameter combination data covers the entire value range of the optimization parameter. represents the set of all optimization parameters, where ,in , represents the u-th optimization parameter combination, are the pile diameter, pile length and wall thickness data in the u-th optimization parameter combination, respectively, where u is the index of the optimization parameter combination and n represents the number of optimization parameter combinations.

[0056] The method for obtaining the value range corresponding to each parameter is as follows: by collecting empirical data of historical large-diameter pipe pile design cases and publicly available design parameters, the maximum and minimum values ​​of each optimization parameter are obtained, and the value range of the optimization parameter is determined by the maximum and minimum values.

[0057] S2, based on the data in the optimization parameter combination, a finite element pipe pile model is established, mechanical property data of the pipe pile is obtained through the finite element pipe pile model with different parameters, and settlement data of the pipe pile under different optimization parameter combinations are estimated based on the mechanical property data, wherein the mechanical property data includes the deformation modulus of the pile body and the effective bearing area of ​​the pipe pile;

[0058] Use finite element software (such as ANSYS, ABAQUS, COMSOL, etc.) to establish the geometric model of the pipe pile to ensure that the size and shape of the model conform to the actual engineering design.

[0059] Through the post-processing function of the software, the stress and strain data of the pile body are extracted. The stress-strain relationship curve is generated based on the extracted data. Within the linear range of the stress-strain curve (usually the small strain area), the deformation modulus can be calculated by the slope; the effective bearing area refers to the surface area of ​​the pipe pile that can effectively transfer the load when loaded, which is usually related to the contact soil and friction of the pile. The load distribution information obtained through finite element analysis can identify the area around the pile foundation that can effectively bear the load.

[0060] The strain and stress relationship of the pile body under load is analyzed by the finite element pipe pile model, and the deformation modulus of the pile body is obtained. , the unit is , the effective bearing area of ​​the pile is obtained by simulating the bearing behavior of the pile foundation , the unit is ; Based on the deformation modulus of the pile and effective bearing area of ​​the pile The formula for estimating the settlement of pipe piles is:

[0061]

[0062] In the formula, Indicates the settlement of the pile, in units of , is the load borne by the pile foundation, in units of .

[0063] The load borne by the pile foundation It can be set by yourself when testing the finite element pile model, and can be set according to the actual required load.

[0064] S3, calculating the soil risk coefficient based on the physical and mechanical characteristic data of the soil in the construction site, and calculating the maximum bearing capacity of the pipe string based on the side resistance load and end resistance load of the pipe string, wherein the physical and mechanical characteristic data of the soil include the cohesion, friction angle and density of the soil;

[0065] The step S3 comprises the following steps:

[0066] S31, based on the side resistance load and end resistance load of the pipe string, calculate the maximum bearing capacity of the pipe string, where the formula for calculating the maximum bearing capacity of the pipe string is:

[0067]

[0068] In the formula, Indicates the maximum bearing capacity of the pipe string. is the side resistance load, represents the end resistance load;

[0069] S32, calculating the soil risk coefficient based on the physical and mechanical characteristic data of the soil in the construction site, wherein the formula for calculating the soil risk coefficient is:

[0070]

[0071] In the formula, is the soil risk factor, is the benchmark risk value, , and They represent the average values ​​of the cohesion, friction angle and density of the soil at the current construction site, , and They represent the maximum values ​​of cohesion, friction angle and density of the soil at the current construction site, respectively. , and Respectively represent the weight coefficients of the average and maximum ratios of the cohesion, friction angle and density of the soil at the current construction site; the benchmark risk value It is usually set through expert experience.

[0072] The cohesion of the soil has a greater influence on the stability of the soil when it is subjected to load or external force, while the influence of the soil friction angle and density is relatively weak. and , and Both are greater than 0.

[0073] The maximum values ​​of the cohesion, friction angle and density of the soil at the current construction site are obtained by drilling holes at the construction site and extracting soil samples. The original state of the soil should be kept as much as possible during sampling to reduce disturbance; triaxial shear test and undrained shear test are used to determine the cohesion and friction angle of the soil; the soil samples are dried in the laboratory, and their mass and volume are measured to calculate the dry density. Multi-point sampling and testing are carried out at the construction site, and soil parameters at different locations are recorded. All test results are statistically analyzed to determine , and The value of .

[0074] The step S31 comprises the following steps:

[0075] S311, based on the lateral resistance per unit length of the pipe pile and the lateral area of ​​the pipe pile, the formula for calculating the lateral resistance load of the pipe pile is:

[0076]

[0077] In the formula, is the effective buried depth of the pile, For the depth The soil friction resistance at ; For the depth The lateral area at

[0078] Among them, in depth The soil friction at is calculated based on the formula:

[0079]

[0080] In the formula, For Depth The effective stress at For Depth The friction angle at The unit is depth The cohesion of the soil at the depth The effective stress at can be calculated from the difference between the total soil stress and the pore water pressure;

[0081] S312, based on the bearing capacity of the pile end and the bottom area of ​​the pile, the formula for calculating the end resistance load of the pipe column is:

[0082]

[0083] In the formula, is the bearing capacity of the pile end, is the bottom area of ​​the pile,

[0084] The bearing capacity of the pile end The calculation is based on the formula:

[0085]

[0086] In the formula, represents the effective vertical stress, Indicates the effective cohesion of the land. The bearing capacity coefficient that represents the effective cohesion.

[0087] The effective vertical stress is the effective pressure in the soil due to its own weight and can be calculated using the following formula:

[0088]

[0089] In the formula, is the unit weight of soil, is the depth, i.e. the depth of the pile end inserted into the ground;

[0090] Effective cohesion of land Obtained through laboratory tests (e.g. triaxial tests), or reference literature values;

[0091] Bearing capacity factor of effective cohesion You can calculate the bearing capacity using theoretical formulas (such as Coulomb's theory or Bellman's formula) based on the friction angle of the soil and the design conditions, or consult local building codes or soil mechanics manuals, which usually provide some information on different soil conditions. Experience values ​​or tables.

[0092] S4, based on the settlement data, maximum bearing capacity and soil risk coefficient obtained under the corresponding optimization parameter combination, generate a pipe pile parameter optimization equation, use the pipe pile parameter optimization equation as a fitness function, and obtain the optimal optimization parameter combination through a genetic algorithm.

[0093] Based on the settlement data, maximum bearing capacity and soil risk coefficient obtained under the corresponding optimization parameter combination, the pile parameter optimization equation is generated and used as the fitness function. The specific expression of the pile parameter optimization equation is:

[0094]

[0095] In the formula, represents the fitness value corresponding to the jth optimization parameter combination, , and The maximum load capacity , settlement data and soil risk factor The weight coefficient of the load factor is the maximum load capacity, which determines whether the expected load requirements can be met. and soil risk factor Compared with the maximum bearing capacity, the influence on the performance of the pile is relatively small, so the and , and Both are greater than 0.

[0096] The optimal optimization parameter combination is obtained by genetic method. The specific steps include: establishing the initial population, selecting all the optimization parameter sets Randomly select several optimization parameter combinations to form an initial population, select and mutate the optimization parameter combinations in the initial population to form a new population, select and mutate the new population, and iterate in sequence until an optimization parameter combination with a fitness value greater than or equal to the fitness threshold appears, stop the iteration, output the optimization parameter combination with the highest fitness value in the current population as the optimal optimization parameter combination, set the mutation probability, generally 0.5; set the fitness threshold according to the actual needs of the string. Seeking the optimal solution through genetic algorithms is a mature technical means, which will not be elaborated here.

[0097] See also Figure 2 The present invention also provides a parameter optimization system for large diameter pipe piles, and the parameter optimization system for large diameter pipe piles is used to execute the parameter optimization method for large diameter pipe piles, comprising:

[0098] An optimization parameter and range determination module is used to determine the optimization parameters of large-diameter pipe piles and the value ranges corresponding to each parameter, and randomly generate a number of groups of optimization parameter combinations based on the value ranges of the optimization parameters. The optimization parameters include the pile diameter, pile length and wall thickness of the pipe pile, and the optimization parameter combination is a group of pile diameter, pile length and wall thickness data;

[0099] A pipe pile settlement analysis module is used to establish a finite element pipe pile model based on the data in the optimization parameter combination, obtain mechanical property data of the pipe pile through the finite element pipe pile model with different parameters, and estimate the settlement data of the pipe pile under different optimization parameter combinations based on the mechanical property data, wherein the mechanical property data includes the deformation modulus of the pile body and the effective bearing area of ​​the pipe pile;

[0100] The maximum bearing capacity and risk factor estimation module is used to calculate the soil risk factor based on the physical and mechanical characteristic data of the soil in the construction site, and calculate the maximum bearing capacity of the pipe string based on the side resistance load and end resistance load of the pipe string. The physical and mechanical characteristic data of the soil include the cohesion, friction angle and density of the soil;

[0101] The optimal solution determination output module is used to generate a pipe pile parameter optimization equation based on the settlement data, maximum bearing capacity and soil risk coefficient obtained under the corresponding optimization parameter combination, and the pipe pile parameter optimization equation is used as the fitness function to obtain the optimal optimization parameter combination through a genetic algorithm.

[0102] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0103] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A parameter optimization method for large diameter pipe piles, characterized in that: The specific steps include: S1, determining the optimization parameters of the large diameter pipe pile and the value ranges corresponding to the parameters, and randomly generating a plurality of groups of optimization parameter combinations based on the value ranges of the optimization parameters, wherein the optimization parameters include the pile diameter, pile length and wall thickness of the pipe pile, and the optimization parameter combination is a group of pile diameter, pile length and wall thickness data; S2, based on the data in the optimization parameter combination, a finite element pipe pile model is established, mechanical property data of the pipe pile is obtained through the finite element pipe pile model with different parameters, and settlement data of the pipe pile under different optimization parameter combinations are estimated based on the mechanical property data, wherein the mechanical property data includes the deformation modulus of the pile body and the effective bearing area of ​​the pipe pile; S3, calculating the soil risk coefficient based on the physical and mechanical characteristic data of the soil in the construction site, and calculating the maximum bearing capacity of the pipe string based on the side resistance load and end resistance load of the pipe string, wherein the physical and mechanical characteristic data of the soil include the cohesion, friction angle and density of the soil; The step S3 comprises the following steps: S31, based on the side resistance load and end resistance load of the pipe string, calculate the maximum bearing capacity of the pipe string, where the formula for calculating the maximum bearing capacity of the pipe string is: Q max =Q s +Q p In the formula, Q max Indicates the maximum bearing capacity of the pipe string, Q s is the side resistance load, Q p represents the end resistance load; S32, calculating the soil risk coefficient based on the physical and mechanical characteristic data of the soil in the construction site, wherein the formula for calculating the soil risk coefficient is: In the formula, R is the soil risk coefficient, k0 is the benchmark risk value, c, and ρ represent the average values ​​of the cohesion, friction angle and density of the soil at the current construction site, respectively. max , and ρ max They represent the maximum values ​​of the cohesion, friction angle and density of the soil at the current construction site, respectively; k1, k2 and k3 represent the weight coefficients of the ratio of the average value to the maximum value of the cohesion, friction angle and density of the soil at the current construction site, respectively; k1>k2>k3 and k1, k2 and k3 are all greater than 0; The step S31 comprises the following steps: S311, based on the lateral resistance per unit length of the pipe pile and the lateral area of ​​the pipe pile, the formula for calculating the lateral resistance load of the pipe pile is: Where, L is the effective buried depth of the pile, τ s (z) is the soil friction at depth z, in KN / m 2 ; A s (z) is the lateral area at depth z; The soil friction at depth z is calculated based on the formula: In the formula, σ z is the effective stress at depth z, is the friction angle at depth z, c z is the cohesion of the soil at depth z; S312, based on the bearing capacity of the pile end and the bottom area of ​​the pile, the formula for calculating the end resistance load of the pipe column is: Q p =q b *A b In the formula, q b is the bearing capacity of the pile end, A b is the bottom area of ​​the pile, The bearing capacity of the pile end is q b The calculation is based on the formula: q b =s v +b*N b In the formula, σ v represents the effective vertical stress, b represents the effective cohesion of the soil, N b The bearing capacity factor expressing the effective cohesion is: S4, based on the settlement data, maximum bearing capacity and soil risk coefficient obtained under the corresponding optimization parameter combination, generate a pipe pile parameter optimization equation, use the pipe pile parameter optimization equation as a fitness function, and obtain the optimal optimization parameter combination through a genetic algorithm; The specific expression of the pile parameter optimization equation is: In the formula, f(P u ) represents the fitness value corresponding to the u-th optimization parameter combination, ω1, ω2 and ω3 are the maximum carrying capacity Q max , weight coefficient of settlement data S and soil risk coefficient R, where ω1>ω2>ω3 and ω1, ω2 and ω3 are all greater than 0.

2. The parameter optimization method for large diameter pipe piles according to claim 1, characterized in that: According to the large diameter pipe pile design case, the maximum and minimum values ​​of the optimization parameters are determined. Based on the maximum and minimum values ​​of each optimization parameter, the value range of the optimization parameter is determined. Within the value range of each optimization parameter, a random value is taken to generate several groups of optimization parameter combinations until the optimization parameter combination data covers the entire value range of the optimization parameter. T is calibrated to represent all optimization parameter sets, where T = {P1, P2, ..., P u ,…,P n }, where P u ={p u1 , p u2 , p u3 }, P u represents the uth optimization parameter combination, p u1 , p u2 , p u3 are the pile diameter, pile length and wall thickness data in the u-th optimization parameter combination, respectively, where u is the index of the optimization parameter combination and n represents the number of optimization parameter combinations.

3. The parameter optimization method for large diameter pipe piles according to claim 1, characterized in that: The strain and stress relationship of the pile body under load is analyzed by the finite element pipe pile model, and the deformation modulus E of the pile body is obtained in kpa. The effective bearing area A of the pipe pile is obtained by simulating the bearing behavior of the pile foundation in m 2 The formula for estimating the settlement of the pipe pile based on the deformation modulus E of the pile body and the effective bearing area A of the pipe pile is: Where S represents the settlement of the pile, in mm, and Q is the load borne by the pile foundation, in KN.

4. The parameter optimization method for large diameter pipe piles according to claim 1, characterized in that: The optimal optimization parameter combination is obtained by genetic method. The specific steps include: establishing an initial population, randomly selecting several optimization parameter combinations from the optimization parameter set T to form an initial population, selecting and mutating the optimization parameter combinations in the initial population to form a new population, selecting and mutating the new population, iterating in sequence until an optimization parameter combination with a fitness value greater than or equal to a fitness threshold appears, stopping the iterative operation, and outputting the optimization parameter combination with the highest fitness value in the current population as the optimal optimization parameter combination.

5. A parameter optimization system for large diameter pipe piles, characterized in that: The parameter optimization system for large-diameter pipe piles is used to execute the parameter optimization method for large-diameter pipe piles according to any one of claims 1 to 4, comprising: An optimization parameter and range determination module is used to determine the optimization parameters of large-diameter pipe piles and the value ranges corresponding to each parameter, and randomly generate a number of groups of optimization parameter combinations based on the value ranges of the optimization parameters. The optimization parameters include the pile diameter, pile length and wall thickness of the pipe pile, and the optimization parameter combination is a group of pile diameter, pile length and wall thickness data; A pipe pile settlement analysis module is used to establish a finite element pipe pile model based on the data in the optimization parameter combination, obtain mechanical property data of the pipe pile through the finite element pipe pile model with different parameters, and estimate the settlement data of the pipe pile under different optimization parameter combinations based on the mechanical property data, wherein the mechanical property data includes the deformation modulus of the pile body and the effective bearing area of ​​the pipe pile; The maximum bearing capacity and risk factor estimation module is used to calculate the soil risk factor based on the physical and mechanical characteristic data of the soil in the construction site, and calculate the maximum bearing capacity of the pipe string based on the side resistance load and end resistance load of the pipe string. The physical and mechanical characteristic data of the soil include the cohesion, friction angle and density of the soil; The optimal solution determination output module is used to generate a pipe pile parameter optimization equation based on the settlement data, maximum bearing capacity and soil risk coefficient obtained under the corresponding optimization parameter combination, and the pipe pile parameter optimization equation is used as the fitness function to obtain the optimal optimization parameter combination through a genetic algorithm.

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