Design and optimization method of large-diameter long pipe segment pipe pile hoist based on finite element analysis

Abstract

The application provides a large-diameter long-tube-section pipe pile sling design and optimization method based on finite element analysis, relates to the technical field of mechanical engineering and structure optimization, and establishes a whole finite element model containing a pipe pile, a sling and hoisting equipment, simulates dynamic response in the whole hoisting process, determines a reasonable range of a sling girder span height ratio, a number of hoisting points and a spacing, constructs multiple sling control schemes, carries out finite element analysis, collects pipe pile stress and vertical deflection data, calculates variation coefficients and structural complexity, generates effect function values reflecting scheme excellence, carries out optimization by using a particle swarm algorithm, and finally selects an optimal sling control scheme based on the effect function values, so that comprehensive optimization of the sling in terms of safety, stability and economy is realized.

Description

Technical Field

[0001] This invention relates to the field of mechanical engineering and structural optimization technology, specifically to a design and optimization method for lifting large-diameter long pipe piles based on finite element analysis. Background Technology

[0002] In large-scale foundation construction, the hoisting of large-diameter, long-section pipe piles is a critical step. Traditional lifting equipment design relies heavily on empirical formulas and static mechanical analysis, which fails to accurately reflect the dynamic response and stress distribution of the pipe pile during hoisting. This leads to redundant or insufficient strength in the lifting equipment structure, potentially causing safety hazards such as pipe pile deformation, stress concentration, and even fracture. Furthermore, existing methods lack coordinated optimization of lifting point layout, lifting equipment materials, and cross-sectional parameters, making it difficult to achieve structural lightweighting and cost control while ensuring safety. Therefore, a scientific and systematic approach is urgently needed to comprehensively consider the interaction between the lifting equipment and the pipe pile, enabling precise and efficient optimization of the lifting equipment design.

[0003] In the prior art, CN114722686B discloses a method for designing and optimizing lifting lugs of large equipment based on finite element analysis. This method includes: establishing a finite element model for overall lifting analysis and a refined model for lug strength and local stress; adjusting the mass of the finite element model by introducing a gravitational acceleration amplification factor; and determining appropriate lifting point positions based on the overall stress and deformation of the equipment. This method, based on boundary data mapping between the two models, reduces the number of computational meshes and improves computational efficiency and accuracy. It calculates the lug strength based on strength theory, calculates the membrane stress and surface bending stress of the cylindrical section in the refined model based on path stress linearization theory, and conducts stress assessment to optimize the lug dimensions.

[0004] The main problems with the above schemes are: they do not take into account the dynamic response of the pipe piles during the hoisting process, the evaluation indicators focus on strength evaluation and overall deformation, which are all based on static analysis results and cannot reflect the stress fluctuation of the pipe piles during the entire hoisting process, and the optimization objective is singular; during optimization, some schemes may not meet the actual requirements, resulting in the optimal results that cannot be applied to practice, and the above schemes cannot accurately distinguish this type of optimization scheme.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a design and optimization method for lifting large-diameter long pipe piles based on finite element analysis, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A design and optimization method for lifting equipment of large-diameter long pipe piles based on finite element analysis, the specific steps of which include:

[0009] Step 1: Determine the range of span-to-height ratio, number of lifting points, and spacing between lifting points of the main beam of the lifting device. Within the range of span-to-height ratio, number of lifting points, and spacing between lifting points of the main beam of the lifting device, generate multiple span-to-height ratios, number of lifting points, and spacing between lifting points, and arrange and combine them to construct multiple sets of lifting device control schemes.

[0010] Step 2: For each lifting control scheme, establish a corresponding finite element model based on the pipe pile parameters and lifting equipment parameters;

[0011] Step 3: Perform finite element analysis on the finite element models under different lifting control schemes to determine whether the lifting control scheme can complete the lifting work. Collect the pipe pile stress and vertical deflection of the pipe pile during the lifting process for each lifting control scheme, calculate the pipe pile stress variation coefficient and vertical deflection variation coefficient, and calculate the structural complexity of different lifting control schemes.

[0012] Step 4: Evaluate the superiority of the lifting control scheme based on the coefficient of variation of pipe pile stress, coefficient of variation of pipe pile vertical deflection and structural complexity. With the optimization objective of maximizing superiority, iteratively optimize the lifting control scheme using particle swarm optimization algorithm, and select the lifting control scheme with the highest superiority as the optimal lifting control scheme.

[0013] Furthermore, the principle of constructing multiple sets of spreader control schemes is as follows:

[0014] Set the span-to-depth ratio of the main beam to a range of 100%. The number of lifting points is within the range of ;

[0015] The range of lifting point spacing is determined based on the number of lifting points and the span of the main beam of the lifting equipment, using the following formula:

[0016] ;

[0017] ;

[0018] in, This indicates the lower limit of the spacing between suspension points. Indicates the span of the main beam of the lifting device. Indicates the number of lifting points. Indicates the upper limit of the distance between the suspension points;

[0019] The range of the suspension point spacing is: .

[0020] Furthermore, when establishing the finite element model, the pipe pile is simplified as a hollow cylinder and modeled using shell elements. The pipe pile parameters include geometric dimensions, pipe pile weight, elastic modulus, and Poisson's ratio. The geometric dimensions include the inner diameter, outer diameter, and length of the pipe pile. The hoisting equipment parameters include hoisting equipment parameters, lifting device connection method, and hoisting speed. The hoisting equipment parameters include the rated lifting capacity, maximum lifting height, and working radius of the hoisting equipment. The lifting device connection method includes welding and bolting connections. The hoisting speed includes lifting speed, descent speed, and horizontal movement speed.

[0021] Furthermore, the principle underlying the calculation of the coefficient of variation of pipe pile stress and the coefficient of variation of pipe pile vertical deflection is as follows:

[0022] In the finite element model, multiple stress monitoring points are set at equal intervals along the length of the pipe pile, and the average value of all stress monitoring points is taken as the pipe pile stress. Multiple deflection monitoring points are set at equal intervals in a direction passing through the center point of the pipe pile and perpendicularly downwards, and the maximum deflection is taken as the vertical deflection of the pipe pile. During the entire hoisting process, several data acquisition times are divided at equal time intervals. The coefficient of variation is calculated based on the pipe pile stress and vertical deflection at each acquisition time, using the following formula:

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] ;

[0029] in, This represents the average stress in the pipe pile. Indicates the index of the acquisition time, and , Indicates the number of data collection moments. Indicates the first The stress of the pipe pile at each acquisition time, The standard deviation of the stress in the pipe pile is represented by... This represents the coefficient of variation of stress in the pipe pile. This represents the average vertical deflection of the pipe pile. Indicates the first Vertical deflection of the pipe pile at each data acquisition moment. This represents the standard deviation of the vertical deflection of the pipe pile. This represents the coefficient of variation of the vertical deflection of the pipe pile.

[0030] Furthermore, the formula for calculating structural complexity is:

[0031] ;

[0032] in, Indicates structural complexity. Indicates the span-to-height ratio of the main beam of the lifting device. Indicates the number of lifting points. These represent the weighting coefficients for the span-to-height ratio of the main beam and the number of lifting points, respectively. and .

[0033] Furthermore, the principle of iteratively optimizing the spreader control scheme using the particle swarm optimization algorithm is as follows:

[0034] Each iteration of each particle corresponds to a hoist control scheme, denoted as: ,in, Indicates the first The particle in the first The corresponding spreader control scheme for the next iteration. Indicates particle index, Index representing the number of iterations, Indicates the first The particle in the first The span-to-height ratio of the main beam of the next iteration. Indicates the first The particle in the first Number of suspension points in the next iteration Indicates the first The particle in the first The spacing between the suspension points in the next iteration;

[0035] Input the lifting control scheme into the finite element model and perform simulation. Determine whether each lifting control scheme can complete the lifting. Assign a valid label to the lifting control scheme that can complete the lifting and an invalid label to the lifting control scheme that cannot complete the lifting.

[0036] The coefficient of variation of pipe pile stress, coefficient of variation of pipe pile vertical deflection, and structural complexity under each lifting device control scheme are subjected to maximum-minimum normalization. The performance of the lifting device control scheme is characterized by calculating the effect function value. Based on the normalization results, the effect function value of the lifting device control scheme with a valid label is calculated for each particle after each iteration, and the effect function value of the lifting device control scheme with an invalid label is set to 0.001. The specific calculation formula is as follows:

[0037] ;

[0038] in, Indicates the first The particle in the first The effect function value of the corresponding spreader control scheme after the next iteration. Indicates the first The particle in the first The normalized coefficient of variation of pipe pile stress in the next iteration. Indicates the first The particle in the first The normalized vertical deflection variation coefficient of the pipe pile in the next iteration. Indicates the first The particle in the first Normalized structural complexity of the next iteration Indicates the first The particle in the first After the next iteration, the corresponding spreader control scheme is assigned a valid label. Indicates the first The particle in the first After the next iteration, the corresponding spreader control scheme was assigned an invalid label. These represent the weighting coefficients for the normalized coefficient of variation of pipe pile stress, the normalized coefficient of variation of pipe pile vertical deflection, and the normalized structural complexity, respectively. and ;

[0039] The initial position and initial velocity of each particle are randomly initialized, and a maximum number of iterations is set. The particle velocity is updated in each iteration using the following formula:

[0040] ;

[0041] in, Indicates the first The particle in the first Speed ​​at the next iteration Represents velocity inertia weight. Indicates the first The particle in the first The speed of each iteration They represent individual learning factors and social learning factors, respectively. , This indicates the random numbers used to avoid the algorithm getting trapped in local optima, and , Indicates the first The historical best position of the nth particle, i.e., the nth The position where a particle maximizes the effect function value during historical iterations. This represents the globally optimal position, which is the position where all particles maximize the effect function value throughout the historical iterations. This represents the number of times the j-th particle is consecutively assigned an invalid label, with the t-th iteration as the endpoint.

[0042] Set an iteration limit. When the iteration limit is reached, output the lifting device control scheme corresponding to the globally optimal position, which is the lifting device control scheme with the largest effect function value, and take it as the optimal lifting device control scheme.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] This invention establishes a finite element model of the pipe pile, lifting device, and hoisting equipment as a complete "lifting system." This accurately simulates the dynamic loads and responses throughout the entire lifting, moving, and lowering process, improving the reliability of the analysis and ensuring that the optimized lifting device can not only support the pipe pile but also match the actual lifting equipment. Furthermore, this invention ensures the accuracy of optimization by deriving the theoretically feasible range of key parameters through finite element analysis, starting from the core performance requirements of the lifting device. It achieves collaborative design of materials and structure, finding the optimal balance between cost and performance.

[0045] This invention also simulates the entire hoisting process using finite element analysis, overcoming the shortcomings of existing technologies in reflecting dynamic fluctuations. This makes the hoisting scheme more closely resemble reality. Simultaneously, optimizations were made in three aspects: "stable force," "small deformation," and "low cost," finding a control scheme that satisfies performance, stability, and economy. Furthermore, when optimizing using the particle swarm optimization algorithm, the particle velocity update rule was segmented. For particles that consistently perform well, a standard particle swarm optimization algorithm is used for local fine-grained search; while for particles that have been trapped in invalid regions, their flight speed is significantly increased through an amplification factor, enabling them to quickly escape invalid regions and explore new possible regions. This approach allows for in-depth development of excellent solution regions and proactively and efficiently escapes local traps and invalid regions, greatly accelerating the convergence speed and increasing the probability of finding the global optimum or satisfactory solution, ensuring the practicality of the optimization results. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the method flow of an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of the fitting curve of the effect function value of the effective label in an embodiment of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0049] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0050] Example:

[0051] Please see Figures 1 to 2 The present invention provides a technical solution:

[0052] A design and optimization method for lifting equipment of large-diameter long pipe piles based on finite element analysis, the specific steps of which include:

[0053] Step 1: Determine the range of span-to-height ratio, number of lifting points, and spacing between lifting points of the main beam of the lifting device. Within the range of span-to-height ratio, number of lifting points, and spacing between lifting points of the main beam of the lifting device, generate multiple span-to-height ratios, number of lifting points, and spacing between lifting points, and arrange and combine them to construct multiple sets of lifting device control schemes.

[0054] In this embodiment, the principle of constructing multiple sets of spreader control schemes is as follows:

[0055] Set the span-to-depth ratio of the main beam to a range of 100%. The number of lifting points is within the range of ;

[0056] The formula for calculating the span-to-depth ratio of the main beam of the lifting device is: ,in, Indicates the span-to-height ratio of the main beam of the lifting device. Indicates the span of the main beam of the lifting device. Indicates the height of the main beam of the lifting device, and the stiffness of the main beam is related to... The height is directly proportional to the stiffness and bending resistance; the greater the height, the greater the stiffness and bending resistance. The self-weight of the main beam is directly proportional to... The greater the height, the heavier the lifting gear structure and the higher the material cost; by changing... To change the span-to-height ratio of the main beam of the lifting device, if the span-to-height ratio is too large, the height will be affected. A smaller span-to-height ratio results in lower material costs and less structural complexity, but insufficient main beam stiffness can lead to excessive deflection or instability. Conversely, a smaller span-to-height ratio results in a higher height. If the span is too large, the main beam will have greater stiffness and stronger bending resistance, but this will result in a bulky lifting device structure and higher costs. Based on the actual main beam stiffness requirements and the actual main beam self-weight limitations, the specific span-to-height ratio value is determined within the range of the main beam span-to-height ratio of the lifting device.

[0057] The number of lifting points affects the stress distribution. More lifting points result in less stress concentration, but increase structural complexity, manufacturing costs, and control difficulty. Fewer lifting points result in a simpler structure, but may lead to excessive local stress and deflection exceeding the limit. For large-diameter long pipe piles, the common number of lifting points is 2 to 6. 2 to 3 lifting points are suitable for pipe piles with shorter spans and higher stiffness, while 4 to 6 lifting points are suitable for pipe piles with longer spans and greater flexibility, in order to control the deformation and vibration of the pipe pile.

[0058] The range of lifting point spacing is determined based on the number of lifting points and the span of the main beam of the lifting equipment, using the following formula:

[0059] ;

[0060] ;

[0061] in, This indicates the lower limit of the spacing between suspension points. Indicates the span of the main beam of the lifting device. Indicates the number of lifting points. Indicates the upper limit of the distance between the suspension points;

[0062] The range of the suspension point spacing is: .

[0063] The spacing between lifting points should be set to ensure that the pipe pile is subjected to stress as evenly as possible during hoisting, avoiding local stress concentration. The number of lifting points, n, determines the number of support points of the lifting equipment for the pipe pile. As the number of lifting points increases, the load borne by each lifting point decreases, but the structural complexity increases. The empirical coefficients are set to 0.7 and 0.8 respectively. 0.7 means that the distance between the two lifting points and their nearest endpoint is 0.15S, and 0.8 means that the expected distance between the two lifting points is 0.2S. This ensures that the lifting points are not too close to each other, avoiding structural redundancy and local stiffness overlap, while also preventing the lifting point spacing from being too large, which would lead to excessive mid-span deflection or stress concentration. The number of regions between the two lifting points is n-1 segments. The distance between the two lifting points divided by the number of regions between the two lifting points is the length of the spacing between each region.

[0064] Step 2: For each lifting control scheme, establish a corresponding finite element model based on the pipe pile parameters and lifting equipment parameters;

[0065] In this embodiment, when establishing the finite element model, the pipe pile is simplified as a hollow cylinder and modeled using shell elements. The pipe pile parameters include geometric dimensions, pipe pile weight, elastic modulus, and Poisson's ratio. The geometric dimensions include the inner diameter, outer diameter, and length of the pipe pile. The hoisting equipment parameters include lifting equipment parameters, lifting device connection method, and hoisting speed. The lifting equipment parameters include the rated lifting capacity, maximum lifting height, and working radius of the lifting equipment. The lifting device connection method includes welding and bolting connections. The hoisting speed includes lifting speed, lowering speed, and horizontal movement speed.

[0066] Modeling is performed using ANSYS. A hollow cylinder is created based on the outer diameter, inner diameter, and length of the pipe pile model. A mesh is generated along the circumference and length of the pipe pile, with the mesh size being three times the wall thickness of the pipe pile. The elastic modulus and Poisson's ratio of the pipe pile are then input into the hollow cylinder.

[0067] In the parameters of the hoisting equipment, the rated lifting capacity represents the maximum static load force applied at the lifting point, which is used to verify whether the strength of the lifting device and the pipe pile meets the safety requirements. The maximum lifting height is used to set the lifting stroke of the hoisting equipment. The working radius is used to set the hoisting path. The connection method of the lifting device includes welding and bolting. The hoisting speed is used to define the dynamic load conditions during the hoisting process, reflecting the inertial force and impact effect during the hoisting process. The hoisting equipment transmits the force to the lifting device through the lifting point, and then to the pipe pile.

[0068] Step 3: Perform finite element analysis on the finite element models under different lifting control schemes to determine whether the lifting control scheme can complete the lifting work. Collect the pipe pile stress and vertical deflection of the pipe pile during the lifting process for each lifting control scheme, calculate the pipe pile stress variation coefficient and vertical deflection variation coefficient, and calculate the structural complexity of different lifting control schemes.

[0069] In this embodiment, the principle for determining whether a lifting control scheme can complete the lifting is as follows: the parameters of the control pile are consistent with the parameters of the lifting equipment. Different lifting control schemes are output into the finite element model to simulate the lifting environment, and the following judgment conditions are given: the maximum equivalent stress of the pile must not exceed the yield strength of the pile material; the maximum vertical deflection of the pile during the lifting process is not higher than the upper limit of deflection; the stress of the main beam and lifting point of the lifting equipment must not exceed the allowable stress of its material and buckling instability must not occur; the rated lifting capacity, maximum lifting height and working radius of the lifting equipment meet the requirements of the lifting path. A lifting control scheme that meets all the above conditions is considered to be able to complete the lifting work; otherwise, it is considered to be unable to complete the lifting work.

[0070] The principle underlying the calculation of the coefficient of variation of stress and the coefficient of variation of vertical deflection of pipe piles is as follows:

[0071] In the finite element model, multiple stress monitoring points are set at equal intervals along the length of the pipe pile, and the average value of all stress monitoring points is taken as the pipe pile stress. Multiple deflection monitoring points are set at equal intervals in a direction passing through the center point of the pipe pile and perpendicularly downwards, and the maximum deflection is taken as the vertical deflection of the pipe pile. During the entire hoisting process, several data acquisition times are divided at equal time intervals. The coefficient of variation is calculated based on the pipe pile stress and vertical deflection at each acquisition time, using the following formula:

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] in, This represents the average stress in the pipe pile. Indicates the index of the acquisition time, and , Indicates the number of data collection moments. Indicates the first The stress of the pipe pile at each acquisition time, The standard deviation of the stress in the pipe pile is represented by... This represents the coefficient of variation of stress in the pipe pile. This represents the average vertical deflection of the pipe pile. Indicates the first Vertical deflection of the pipe pile at each data acquisition moment. This represents the standard deviation of the vertical deflection of the pipe pile. This represents the coefficient of variation of the vertical deflection of the pipe pile.

[0079] During hoisting, the stress and deflection of the pipe pile are dynamically changing. Stress is used to determine whether the pipe pile has undergone plastic deformation or failure. Large-diameter, long-section pipe piles bear dynamic loads during hoisting. If the local stress exceeds the material's yield strength, it may lead to permanent deformation or fracture of the pipe pile. The vertical deflection of the pipe pile reflects the degree of bending deformation under load. This scheme not only ensures that the pipe pile is not pulled apart, but also ensures that the hoisting process is as smooth and controllable as possible, without violent shaking or impact. Therefore, the coefficient of variation, calculated based on the ratio of the standard deviation to the mean, is used to measure the relative dispersion of the data around its mean. The coefficient of variation of pipe pile stress reflects the degree of fluctuation of the pipe pile stress during hoisting. A smaller stress level indicates more stable stress changes during hoisting, a more uniform support of the lifting equipment for the pipe pile, and a safer hoisting process. A large value indicates drastic stress changes during the hoisting process, which may be due to impact, vibration, or localized stress concentration. It takes into account the dynamic changes over time and assesses the degree of stress dispersion during hoisting, thus more comprehensively reflecting whether the support of the hoisting equipment on the pipe pile is uniform and whether the movement is smooth.

[0080] The vertical deflection of a pipe pile reflects the degree of bending deformation during hoisting. The greater the deflection, the less rigid the pipe pile is or the more unreasonable the arrangement of the hoisting points, which may lead to excessive local bending of the pipe pile and vibration or swaying during hoisting. This reflects the stability of deformation during hoisting. The smaller the value, the smoother the deflection change, and the more stable the hoisting process. The larger the value, the more severe the deflection fluctuation, which may indicate impact, swaying, or uneven stress at the suspension points. There is an upper limit to the vertical deflection of pipe piles. This is to ensure that the deflection is not too large, which could lead to yielding or permanent deformation of the pipe pile material, and to prevent instability, local buckling, or cracking of the pipe pile due to excessive bending. The upper limit of deflection is usually taken as [value missing]. .

[0081] The smaller the coefficient of variation, the smaller the change in stress and deflection of the pipe pile over time, and the better the control effect of the lifting device control scheme.

[0082] The formula for calculating structural complexity is:

[0083] ;

[0084] in, Indicates structural complexity. Indicates the span-to-height ratio of the main beam of the lifting device. Indicates the number of lifting points. These represent the weighting coefficients for the span-to-height ratio of the main beam and the number of lifting points, respectively. and .

[0085] Structural complexity reflects the manufacturing difficulty and cost of the lifting device, as well as its structural stiffness and material usage. Higher structural complexity generally means a more complex manufacturing process, greater material usage, and higher precision requirements, leading to increased manufacturing costs. A greater number of lifting points results in a more complex lifting device structure, with more welding or bolt connections, consequently increasing manufacturing cycle and cost. The span-to-height ratio of the main beam indicates that a higher beam height results in greater stiffness, but also greater material usage and a heavier structure. Structural complexity is inversely proportional to the span-to-height ratio and directly proportional to the number of lifting points. Changing the span-to-height ratio directly affects the main beam height, having a greater impact on the overall manufacturing and material costs of the lifting device, and this impact is global. The impact of lifting points is incremental; more lifting points mean more bolt connections, and the impact on the overall structure is less than the impact of changing the span-to-height ratio. .

[0086] Step 4: Evaluate the superiority of the lifting control scheme based on the coefficient of variation of pipe pile stress, coefficient of variation of pipe pile vertical deflection and structural complexity. With the optimization objective of maximizing superiority, iteratively optimize the lifting control scheme using particle swarm optimization algorithm, and select the lifting control scheme with the highest superiority as the optimal lifting control scheme.

[0087] In this embodiment, the principle of iteratively optimizing the spreader control scheme using the particle swarm optimization algorithm is as follows:

[0088] Each iteration of each particle corresponds to a hoist control scheme, denoted as: ,in, Indicates the first The particle in the first The corresponding spreader control scheme for the next iteration. Indicates particle index, Index representing the number of iterations, Indicates the first The particle in the first The span-to-height ratio of the main beam of the next iteration. Indicates the first The particle in the first Number of suspension points in the next iteration Indicates the first The particle in the first The spacing between the suspension points in the next iteration;

[0089] Input the lifting control scheme into the finite element model and perform simulation. Determine whether each lifting control scheme can complete the lifting. Assign a valid label to the lifting control scheme that can complete the lifting and an invalid label to the lifting control scheme that cannot complete the lifting.

[0090] The coefficient of variation of pipe pile stress, coefficient of variation of pipe pile vertical deflection, and structural complexity under each lifting device control scheme are subjected to maximum-minimum normalization. The performance of the lifting device control scheme is characterized by calculating the effect function value. Based on the normalization results, the effect function value of the lifting device control scheme with a valid label is calculated for each particle after each iteration, and the effect function value of the lifting device control scheme with an invalid label is set to 0.001. The specific calculation formula is as follows:

[0091] ;

[0092] in, Indicates the first The particle in the first The effect function value of the corresponding spreader control scheme after the next iteration. Indicates the first The particle in the first The normalized coefficient of variation of pipe pile stress in the next iteration. Indicates the first The particle in the first The normalized vertical deflection variation coefficient of the pipe pile in the next iteration. Indicates the first The particle in the first Normalized structural complexity of the next iteration Indicates the first The particle in the first After the next iteration, the corresponding spreader control scheme is assigned a valid label. Indicates the first The particle in the first After the next iteration, the corresponding spreader control scheme was assigned an invalid label. These represent the weighting coefficients for the normalized coefficient of variation of pipe pile stress, the normalized coefficient of variation of pipe pile vertical deflection, and the normalized structural complexity, respectively. and ;

[0093] The stress variation coefficient reflects the degree of stress fluctuation in the pipe pile during hoisting and is directly related to structural safety. Excessive stress concentration or fluctuation can lead to plastic deformation or even fracture of the pipe pile, posing the most serious risk during hoisting. The value is the largest; the vertical deflection coefficient of variation reflects the stability of the pipe pile's bending deformation during hoisting. Excessive deflection fluctuations may cause the pipe pile to become unstable and sway, affecting the stability of hoisting. However, its impact on hoisting is slightly weaker than the change in pipe pile stress. Therefore... Structural complexity affects economic efficiency and manufacturing feasibility, and is a secondary optimization objective. Cost reduction is only considered after ensuring safety and stability. Minimum; the specific values ​​of the three are: , , .

[0094] The minimum coefficient of variation for pipe pile stress and the minimum coefficient of variation for pipe pile vertical deflection are both 0. The maximum coefficient of variation for pipe pile stress is set to 0.3, and the maximum coefficient of variation for pipe pile vertical deflection is set to 0.25. Based on the formula for calculating structural complexity, the minimum structural complexity corresponds to the case where the span-to-height ratio is at its maximum and the number of lifting points is at its minimum. Substituting the maximum span-to-height ratio and the minimum number of lifting points into the calculation, the minimum structural complexity is 0.25. The maximum structural complexity is set to the case where the span-to-height ratio is at its minimum and the number of lifting points is at its maximum. Substituting the maximum span-to-height ratio and the minimum number of lifting points into the calculation, the maximum structural complexity is 2.25. Taking the minimum coefficient of variation for pipe pile stress as an example, given the minimum and maximum coefficients of variation for pipe pile stress, the coefficients of variation for pipe pile stress after particle iteration are normalized to their minimum and maximum values, and the results are calculated. Similarly, the calculations yielded the following results: and .

[0095] As shown in Table 1, take , ,along with As the coefficient of variation of the pipe pile stress increases, the value of the effect function decreases, reflecting that the higher the coefficient of variation of the pipe pile stress, the lower the value of the effect function and the worse the performance of the lifting control scheme.

[0096] Table 1. Variation of Effect Function Values ​​with Normalized Coefficient of Variation of Pipe Pile Stress

[0097]

[0098] The higher the superiority of the lifting control scheme, the better the scheme's effect. The superiority of the lifting control scheme is reflected by the effect function value. The larger the effect function value, the higher the superiority of the scheme. The smaller the coefficient of variation of the pipe pile stress, the smaller the stress fluctuation and the smoother the lifting. The smaller the coefficient of variation of the pipe pile vertical deflection, the more stable the deformation and the safer the lifting process. The smaller the structural complexity, the simpler the lifting structure, and the lower the cost and manufacturing complexity. The effect function value is inversely proportional to the coefficient of variation of the pipe pile stress, the coefficient of variation of the pipe pile vertical deflection, and the structural complexity. This means that the smaller the values ​​of these parameters, the better the overall performance of the scheme in terms of safety, stability, and economy, and the higher its superiority.

[0099] The effect function value is calculated using a piecewise function. Spreader control schemes assigned a valid label are considered practically feasible and are given a positive evaluation score based on their performance. Spreader control schemes assigned an invalid label are considered practically infeasible. However, if calculated using the same method, invalid schemes might still obtain higher effect function values ​​due to better performance in certain indicators. To avoid optimizing structures that do not conform to reality, all invalid-labeled spreader control schemes are assigned an extremely low score, allowing them to be naturally eliminated during the optimization process.

[0100] The initial position and initial velocity of each particle are randomly initialized, and a maximum number of iterations is set. The particle velocity is updated in each iteration using the following formula:

[0101] ;

[0102] in, Indicates the first The particle in the first Speed ​​at the next iteration Represents velocity inertia weight. Indicates the first The particle in the first The speed of each iteration They represent individual learning factors and social learning factors, respectively. , This indicates the random numbers used to avoid the algorithm getting trapped in local optima, and , Indicates the first The historical best position of the nth particle, i.e., the nth The position where a particle maximizes the effect function value during historical iterations. This represents the globally optimal position, which is the position where all particles maximize the effect function value throughout the historical iterations. This represents the number of times the j-th particle is consecutively assigned an invalid label, with the t-th iteration as the endpoint.

[0103] When all solutions of a particle from the initial moment to the current iteration have been assigned valid labels, the particle velocity for the next iteration is calculated using the standard PSO velocity update formula. If the same velocity update strategy is applied to all particles, invalid particles may search near infeasible solutions, wasting computational resources. Invalid particles refer to particles that have been assigned invalid labels during the iteration process. Therefore, for invalid particles, they are allowed to quickly leave the infeasible solution region with a large velocity and turn to other possible feasible regions. A magnification factor greater than 1 indicates that the velocity in the (t+1)th iteration is amplified compared to the velocity in the tth iteration, causing the particle to produce a larger displacement in the next iteration, thus quickly escaping the infeasible solution region; magnification factor Among the components, Indicates the first The particle in the first The number of consecutive invalid iterations in the iterations preceding and adjacent to t, when At that time, it means that only the first The next iteration is assigned an invalid label, when When, explain the first and the Each iteration assigns an invalid label, and so on. The larger the value, the more likely the particle is to originate from the [missing information]. As the iteration progresses, the more times a particle is continuously assigned an invalid label, the more it searches in the infeasible solution region, requiring a greater speed to escape. This indicates the current iteration number. As iterations proceed, As the amplification factor gradually increases, the overall amplification factor gradually decreases. In the early stages of optimization, the algorithm encourages bold exploration, allowing for a large speed to explore the vast solution space even if particles fail. In the later stages of optimization, the algorithm gradually converges. At this point, perturbations should be reduced, and a more refined search should be performed. Providing a large speed in the later stages may disrupt the already found optimal solution region, causing the algorithm to fail to converge.

[0104] Set an iteration limit. When the iteration limit is reached, output the lifting device control scheme corresponding to the globally optimal position, which is the lifting device control scheme with the largest effect function value, and take it as the optimal lifting device control scheme.

[0105] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0106] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0107] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0108] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes 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. A method for designing and optimizing lifting devices for large-diameter long pipe piles based on finite element analysis, characterized in that, The specific steps include: Step 1: Determine the range of span-to-height ratio, number of lifting points, and spacing between lifting points of the main beam of the lifting device. Within the range of span-to-height ratio, number of lifting points, and spacing between lifting points of the main beam of the lifting device, generate multiple span-to-height ratios, number of lifting points, and spacing between lifting points, and arrange and combine them to construct multiple sets of lifting device control schemes. Step 2: For each lifting control scheme, establish a corresponding finite element model based on the pipe pile parameters and lifting equipment parameters; Step 3: Perform finite element analysis on the finite element models under different lifting control schemes to determine whether the lifting control scheme can complete the lifting work. Collect the pipe pile stress and vertical deflection of the pipe pile during the lifting process for each lifting control scheme, calculate the pipe pile stress variation coefficient and vertical deflection variation coefficient, and calculate the structural complexity of different lifting control schemes. Step 4: Evaluate the superiority of the lifting control scheme based on the coefficient of variation of pipe pile stress, coefficient of variation of pipe pile vertical deflection and structural complexity. With the optimization objective of maximizing superiority, iteratively optimize the lifting control scheme using particle swarm optimization algorithm, and select the lifting control scheme with the highest superiority as the optimal lifting control scheme. The formula for calculating structural complexity is: in, Indicates structural complexity. Indicates the span-to-height ratio of the main beam of the lifting device. Indicates the number of lifting points. These represent the weighting coefficients for the span-to-height ratio of the main beam and the number of lifting points, respectively. and .

2. The method for designing and optimizing a lifting device for large-diameter long pipe piles based on finite element analysis according to claim 1, characterized in that: The principle behind constructing multiple spreader control schemes in step 1 is as follows: Set the span-to-depth ratio of the main beam to a range of 100%. The number of lifting points is within the range of ; The range of lifting point spacing is determined based on the number of lifting points and the span of the main beam of the lifting equipment, using the following formula: in, This indicates the lower limit of the spacing between suspension points. Indicates the span of the main beam of the lifting device. Indicates the number of lifting points. Indicates the upper limit of the distance between the suspension points; The range of the suspension point spacing is: .

3. The method for designing and optimizing a lifting device for large-diameter long pipe piles based on finite element analysis according to claim 1, characterized in that: In step 2, when establishing the finite element model, the pipe pile is simplified as a hollow cylinder and modeled using shell elements. The pipe pile parameters include geometric dimensions, pipe pile weight, elastic modulus, and Poisson's ratio. The geometric dimensions include the inner diameter, outer diameter, and length of the pipe pile. The hoisting equipment parameters include the hoisting equipment parameters, the lifting attachment connection method, and the hoisting speed. The hoisting equipment parameters include the rated lifting capacity, maximum lifting height, and working radius of the hoisting equipment. The lifting attachment connection method includes welding and bolting connections. The hoisting speed includes the lifting speed, descent speed, and horizontal movement speed.

4. The method for designing and optimizing a lifting device for large-diameter long pipe piles based on finite element analysis according to claim 1, characterized in that: The principle upon which step 3 calculates the coefficient of variation of pipe pile stress and the coefficient of variation of pipe pile vertical deflection is based: In the finite element model, multiple stress monitoring points are set at equal intervals along the length of the pipe pile, and the average value of all stress monitoring points is taken as the pipe pile stress. Multiple deflection monitoring points are set at equal intervals in a direction passing through the center point of the pipe pile and perpendicularly downwards, and the maximum deflection is taken as the vertical deflection of the pipe pile. During the entire hoisting process, several data acquisition times are divided at equal time intervals. The coefficient of variation is calculated based on the pipe pile stress and vertical deflection at each acquisition time, using the following formula: in, This represents the average stress in the pipe pile. Indicates the index of the acquisition time, and , Indicates the number of data collection moments. Indicates the first The stress of the pipe pile at each acquisition time, The standard deviation of the stress in the pipe pile is represented by... This represents the coefficient of variation of stress in the pipe pile. This represents the average vertical deflection of the pipe pile. Indicates the first Vertical deflection of the pipe pile at each data acquisition moment. This represents the standard deviation of the vertical deflection of the pipe pile. This represents the coefficient of variation of the vertical deflection of the pipe pile.

5. The design and optimization method for lifting large-diameter long pipe piles based on finite element analysis according to claim 1, characterized in that: The principle behind iteratively optimizing the spreader control scheme using the particle swarm optimization algorithm in step 4 is as follows: Each iteration of each particle corresponds to a hoist control scheme, denoted as: ,in, Indicates the first The particle in the first The corresponding spreader control scheme for the next iteration. Indicates particle index, Index representing the number of iterations, Indicates the first The particle in the first The span-to-height ratio of the main beam of the next iteration. Indicates the first The particle in the first Number of suspension points in the next iteration Indicates the first The particle in the first The spacing between the suspension points in the next iteration; Input the lifting control scheme into the finite element model and perform simulation. Determine whether each lifting control scheme can complete the lifting. Assign a valid label to the lifting control scheme that can complete the lifting and an invalid label to the lifting control scheme that cannot complete the lifting. The coefficient of variation of pipe pile stress, coefficient of variation of pipe pile vertical deflection, and structural complexity under each lifting device control scheme are subjected to maximum-minimum normalization. The performance of the lifting device control scheme is characterized by calculating the effect function value. Based on the normalization results, the effect function value of the lifting device control scheme with a valid label is calculated for each particle after each iteration, and the effect function value of the lifting device control scheme with an invalid label is set to 0.

001. The specific calculation formula is as follows: in, Indicates the first The particle in the first The effect function value of the corresponding spreader control scheme after the next iteration. Indicates the first The particle in the first The normalized coefficient of variation of pipe pile stress in the next iteration Indicates the first The particle in the first The normalized vertical deflection variation coefficient of the pipe pile in the next iteration. Indicates the first The particle in the first Normalized structural complexity of the next iteration Indicates the first The particle in the first After the next iteration, the corresponding spreader control scheme is assigned a valid label. Indicates the first The particle in the first After the next iteration, the corresponding spreader control scheme was assigned an invalid label. These represent the weighting coefficients for the normalized coefficient of variation of pipe pile stress, the normalized coefficient of variation of pipe pile vertical deflection, and the normalized structural complexity, respectively. and ; The initial position and initial velocity of each particle are randomly initialized, and a maximum number of iterations is set. The particle velocity is updated in each iteration using the following formula: in, Indicates the first The particle in the first Speed ​​at the next iteration Represents velocity inertia weight. Indicates the first The particle in the first The speed of the next iteration They represent individual learning factors and social learning factors, respectively. , This indicates the random numbers used to avoid the algorithm getting trapped in local optima, and , Indicates the first The historical best position of the nth particle, i.e., the nth The position where a particle maximizes the effect function value during historical iterations. This represents the globally optimal position, which is the position where all particles maximize the effect function value throughout the historical iterations. This represents the number of times the j-th particle is consecutively assigned an invalid label, with the t-th iteration as the endpoint. Set an iteration limit. When the iteration limit is reached, output the lifting device control scheme corresponding to the globally optimal position, which is the lifting device control scheme with the largest effect function value, and take it as the optimal lifting device control scheme.

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