Splitting design method of fabricated slab foundation based on optimal economic size of prefabricated component

Abstract

The application provides a prefabricated plate foundation splitting design method based on optimal economic size of prefabricated components, relates to the technical field of power transmission line tower plate foundations, and specifically comprises the following steps: under a set constraint condition, a plurality of splicing combinations of a predetermined plate foundation are randomly generated, a performance simulation test is performed and a cost is calculated by using a finite element analysis model, a comprehensive performance index and a comprehensive cost are generated through data processing, a cost-performance coupling coefficient is calculated, double-optimal parameter combinations are screened, a prediction model is constructed and trained, and the optimal combination of the splicing combination is obtained through iterative optimization combined with a genetic algorithm, so as to serve as the optimal economic size of the splicing combination. The application realizes multidimensional collaborative optimization, breaks through the limitation of traditional design, accurately balances performance and cost through intelligent optimization, fully considers engineering practice, enhances design landing performance, effectively improves construction efficiency, reduces cost, and guarantees foundation safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power transmission line tower slab foundation, in particular to a split design method of fabricated slab foundation based on optimal economic size of prefabricated components. BACKGROUND

[0002] In the technical field of power transmission line tower slab foundation, the traditional design method has significant limitations. Due to the complex external forces such as wind load, conductor tension, and ice load that the power transmission line tower needs to withstand, and the long-term exposure to different climate environments, the traditional design often separates the performance analysis of the foundation from the cost control, making it difficult to balance the relationship between the two at the early stage of design. On the one hand, to meet the performance requirements of the tower foundation in extreme working conditions such as bearing capacity, anti-overturning and anti-sliding stability, simply improving the design standard can easily lead to excessive use of materials and significant increase in cost. On the other hand, excessive compression of cost may result in insufficient foundation settlement control and reduced structural safety redundancy, posing safety risks in extreme weather such as strong winds and ice disasters.

[0003] In addition, with the application and promotion of fabricated construction technology in power transmission line tower foundations, the size design of splicing and combination becomes critical. Unreasonable splicing and combination size not only increases the complexity of production molds and the requirements on roads and vehicles during transportation, but also prolongs the construction period and increases the construction cost due to the difficulty of on-site hoisting and low assembly precision. At the same time, improper component size may affect the overall stress performance of the foundation and weaken its support effect on the power transmission line tower. Therefore, there is an urgent need for a scientific and reasonable design method that can systematically consider the special performance requirements and cost control of power transmission line tower slab foundations to determine the optimal economic size of splicing and combination and achieve efficient split design of fabricated slab foundations.

[0004] The prior art discloses a prefabricated structure design method, device, equipment and storage medium provided by CN119475541A, which comprises the following steps: obtaining design parameters of a prefabricated structure of a project; selecting cross-sectional size, reinforcement ratio and cavity ratio in the design parameters as variable parameters; obtaining constraint conditions of the prefabricated structure on mechanical properties and construction industry chain, obtaining initial population size, maximum iteration number, crossover rate and mutation rate; writing the initial population size, the maximum iteration number, the crossover rate, the mutation rate and the constraint conditions into configuration information; according to the configuration information, a preset genetic algorithm is adopted to process the variable parameters, and a design scheme meeting the mechanical properties of the prefabricated structure is obtained; the standard value of construction carbon emission corresponding to each design scheme and the standard value of bending strength of each design scheme are obtained, and the standard value of construction carbon emission corresponding to each design scheme and the standard value of bending strength of each design scheme are processed through an objective function, so that the comprehensive index corresponding to each design scheme is obtained, and the design scheme with the minimum comprehensive index is selected as the optimal design scheme meeting the mechanical properties of the prefabricated structure, so that the optimal design scheme with high strength and low carbon is quickly obtained.

[0005] However, there are still the following deficiencies: only the mechanical properties and construction carbon emission are concerned, the transportation and construction costs and their correlation are ignored, the basic performance indicators are not fully considered, and it is difficult to meet the actual demand; the practical constraints are ignored, the production mold, transportation equipment and lifting capacity limitations are not considered, which leads to the problems of difficult demolding, overloading and lifting difficulty in the production, transportation and construction links of the component, and affects the construction efficiency and quality; the optimization depth is insufficient, the genetic algorithm is not systematic in analyzing the combination of geometric parameters, lacks a scientific screening mechanism, is easy to fall into local optimization, and cannot realize the balance of performance and cost and the maximization of comprehensive benefits.

[0006] The above information disclosed in the background section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0007] The present application aims to provide a prefabricated component optimal economic size-based prefabricated plate foundation splitting design method to solve the problems in the background art.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0009] A prefabricated component optimal economic size-based prefabricated plate foundation splitting design method, comprising the following specific steps:

[0010] Under the set constraints, a plurality of splicing combinations of the predetermined slab foundation is randomly generated as individuals of an initial population based on a splicing model, the individuals of the initial population are input into a finite element analysis model for performance simulation test, a bearing capacity index, a settlement performance index, an anti-overturning stability index and an anti-sliding stability index corresponding to different individuals are obtained, and material cost, transportation cost and construction cost corresponding to different individuals are obtained;

[0011] The bearing capacity index, the settlement performance index, the anti-overturning stability index and the anti-sliding stability index corresponding to the same individual are processed, and a comprehensive performance index of the individual is generated, and the material cost, the transportation cost and the construction cost corresponding to the same individual are processed, and a comprehensive cost of the individual is generated;

[0012] The cost-performance coupling coefficient corresponding to the same individual is calculated by combining the ratio of the transportation cost and the construction cost corresponding to the same individual, all individuals are arranged in descending order according to the cost-performance coupling coefficient, and a threshold value is set in advance, and individuals with cost-performance coupling coefficients greater than the threshold value are retained, so as to complete the construction of the individual set with double optimal cost and performance;

[0013] The performance and cost prediction model is constructed, the individuals of the initial population are input into the performance and cost prediction model based on the individual set with double optimal cost and performance, and the comprehensive performance index and the comprehensive cost corresponding to different individuals are obtained;

[0014] The comprehensive cost and the comprehensive performance index corresponding to the same individual are weighted and summed to generate a comprehensive evaluation index corresponding to the individual, and the maximum of the comprehensive evaluation index corresponding to the individual is taken as an objective function, and the individuals of the initial population are iteratively optimized by a genetic algorithm to obtain an optimal individual, and the optimal combination of the corresponding splicing combination is extracted as an optimal economic size.

[0015] Further, the geometric parameters of the prefabricated component include length, width and height.

[0016] Further, the individuals of the initial population are constructed, and the specific steps are as follows:

[0017] The initial population is marked as U, and the initial population U={U1, U2,…, U i ,…,U μ}, U i is the i-th individual in the initial population, i is the index of the individual in the initial population, and i∈[1,μ], μ is the number of individuals in the initial population, and U i =(l i,j ,b i,j ,h i,j |j∈[1,λ i ]), wherein, l i,j ,b i,j ,hi,j respectively the length, width and height of the jth precast component of the ith individual, j is the index of the precast component, λ i is the number of precast components in the ith individual.

[0018] Further, the carrying capacity index, the settlement performance index, the anti-overturning stability index and the anti-sliding stability index corresponding to the same individual are processed to generate the comprehensive performance index of the individual, and the formula is as follows:

[0019] P i = ω1×P 1,i + ω2×P 2,i + ω3×P 3,i + ω4×P 4,i

[0020] Wherein, P i is the comprehensive performance index corresponding to the ith individual, i is the index of the individual in the initial population;

[0021] In the formula, P 1,i is the carrying capacity index corresponding to the ith individual, P 2,i is the settlement performance index corresponding to the ith individual, P 3,i is the anti-overturning stability index corresponding to the ith individual, P 4,i is the anti-sliding stability index corresponding to the ith individual;

[0022] In the formula, ω1 is the weight coefficient of the carrying capacity index, ω2 is the weight coefficient of the settlement performance index, ω3 is the weight coefficient of the anti-overturning stability index, and ω4 is the weight coefficient of the anti-sliding stability index. On the basis of w1+w2+w3+w4=1, let 0<w4<w3<w2<w1<1.

[0023] Further, the material cost, transportation cost and construction cost corresponding to the same individual are processed to generate the comprehensive cost of the individual, and the formula is as follows:

[0024] C i = C α,i + C β,i + C δ,i

[0025] Wherein, C i is the comprehensive cost corresponding to the ith individual, C α,i is the material cost corresponding to the ith individual, C β,i is the transportation cost corresponding to the ith individual, and C δ,i is the construction cost corresponding to the ith individual.

[0026] Further, the cost-performance coupling coefficient corresponding to the individual is calculated by combining the ratio of the transportation cost and the construction cost corresponding to the individual, and the formula is as follows:

[0027]

[0028] ΔP i =P i -P JZ

[0029] ΔC i =C i -C JZ

[0030]

[0031] Wherein, C PC,i is the cost-performance coupling coefficient of the ith individual, ΔP i is the change amount of the comprehensive performance index corresponding to the ith individual, ΔC i is the change amount of the comprehensive cost corresponding to the ith individual, P JZ is the reference value of the comprehensive performance index, C JZ is the reference value of the comprehensive cost, R i is the ratio of the transportation cost and the construction cost corresponding to the ith individual.

[0032] Further, the comprehensive evaluation index corresponding to the individual is generated by weighted sum of the comprehensive cost and the comprehensive performance index corresponding to the individual, and the formula is as follows:

[0033]

[0034] Wherein, ZPzs i is the comprehensive evaluation index corresponding to the ith individual;

[0035] In the formula, is the weight coefficient of the comprehensive performance index, is the weight coefficient of the comprehensive cost, and and The specific values are determined by the analytic hierarchy process.

[0036] Further, the optimal individual is obtained by iterative optimization of the individuals in the initial population through the genetic algorithm, and the optimal combination corresponding to the splicing combination is extracted as the optimal economic size, and the specific process is as follows:

[0037] The individuals in the initial population of the splicing combination are iteratively optimized. During the iterative optimization process, constraints are set on the geometric parameters of the splicing combination, namely, the maximum and minimum values ​​of length, width, and height are set respectively. Within the constraints of length, width, and height, the geometric parameters of the splicing combination are iteratively optimized. Specifically, the comprehensive evaluation index is sorted from largest to smallest, and individuals with the highest comprehensive evaluation index are selected as parents. Through crossover and mutation operations, the genes of the parent individuals are exchanged and mutated to generate new individuals. The comprehensive cost and comprehensive performance index of the newly generated individuals are obtained using a performance and cost prediction model, and their comprehensive evaluation index is calculated. The new individuals and their parents are used as a new population, and the selection, crossover, and mutation operations are repeated until a predetermined number of iterations are reached. The individual corresponding to the maximum comprehensive evaluation index is taken as the optimal combination of splicing combination geometric parameters, and the individual corresponding to the maximum comprehensive evaluation index is labeled as U. i1 =(l i1,j ,b i1,j ,h i1,j |j∈[1,λ i If the optimal economic dimension of the precast component is length l, then the optimal economic dimension of the precast component is length l. i1,j Width b i1,j and height h i1,j .

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] This invention is based on a splicing model, which randomly generates multiple splicing combinations of a predetermined plate foundation as individuals in the initial population. The individuals in the initial population are input into a finite element analysis model for performance simulation testing, and further processed to generate a comprehensive performance index and comprehensive cost. This changes the problem of the separation between performance and cost in traditional design, enabling designers to fully and intuitively understand the performance and cost of the foundation under different combinations of geometric parameters, thereby enabling targeted optimization in subsequent designs.

[0040] By introducing the ratio of transportation cost to construction cost to calculate the cost-performance coupling coefficient and setting a threshold for screening, a set of cost-performance dual-advantage splicing combinations is constructed. This process can quickly screen out the solution that takes into account both performance and cost advantages from a large number of parameter combinations. Compared with the existing technology that only optimizes around a single indicator, this method greatly improves the comprehensive benefits of the design solution, reduces the invalid attempts in subsequent design, and improves design efficiency.

[0041] By constructing a performance and cost prediction model, maximizing the comprehensive evaluation index as the goal, combining genetic algorithm to iteratively optimize the initial population, the design can predict the performance and cost of different splicing combinations based on historical data, and through the algorithm constantly searching for the optimal solution, the optimal economic size of the splicing combination is obtained, compared with the problem that the existing technology does not deeply analyze the geometric parameter combination, the method can more accurately find the optimal geometric parameter combination, and ensure that the foundation meets the performance requirements while realizing cost optimization. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 It is the whole method flow diagram of the application;

[0043] Figure 2 It is the fitting curve graph of the carrying capacity index and the comprehensive performance index of the application;

[0044] Figure 3 It is the fitting curve graph of the settlement performance index and the comprehensive performance index of the application;

[0045] Figure 4 It is the fitting curve graph of the anti-overturning stability index and the comprehensive performance index of the application;

[0046] Figure 5 It is the fitting curve graph of the anti-sliding stability index and the comprehensive performance index of the application;

[0047] Figure 6 It is the fitting curve graph of the change amount of the comprehensive performance index and the cost-performance coupling coefficient of the application;

[0048] Figure 7 It is the fitting curve graph of the change amount of the comprehensive cost index and the cost-performance coupling coefficient of the application;

[0049] Figure 8 It is the fitting curve graph of the ratio of transportation cost and construction cost and the cost-performance coupling coefficient of the application;

[0050] Figure 9 It is the fitting curve graph of the comprehensive performance index and the comprehensive evaluation index of the application;

[0051] Figure 10 It is the fitting curve graph of the comprehensive cost and the comprehensive evaluation index of the application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical scheme and advantages of the application more clear and obvious, the application is further described in detail below in combination with specific embodiments.

[0053] It should be noted that the technical terms or scientific terms used in the present application should be understood as the general meaning understood by those skilled in the art to which the present application belongs, unless otherwise defined. The "first", "second" and similar words used in the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Include" or "contain" and similar words mean that the elements or objects before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connected" or "connected" and similar words are not limited to physical or mechanical connection, but can include electrical connection, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to represent the relative positional relationship, when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0054] Embodiment 1:

[0055] Please refer to Figures 1 to 10 The present application provides a technical solution:

[0056] A prefabricated component optimal economic size-based split design method of fabricated slab foundation, the specific steps comprising:

[0057] S1. Under the constraint conditions of production process, transportation equipment and on-site hoisting capacity, based on the splicing model, randomly generate multiple splicing combinations of the predetermined slab foundation as individuals of the initial population, the splicing combination is the component combination of the fabricated predetermined slab foundation, input the individuals of the initial population into the finite element analysis model for performance simulation test, obtain the bearing capacity index, settlement performance index, anti-overturning stability index and anti-sliding stability index corresponding to different individuals, and obtain the material cost, transportation cost and construction cost corresponding to different individuals;

[0058] On the basis of the above embodiment, the splicing model is an optimization model, which is oriented to minimize the cost and maximize the structural performance, by adjusting the parameters of the splicing combination, the optimal splicing scheme is found under the constraint conditions of production process, transportation equipment and on-site hoisting capacity, the optimization model uses one of genetic algorithm, particle swarm algorithm and simulated annealing algorithm to realize the multiple splicing combinations of the predetermined slab foundation.

[0059] On the basis of the above embodiment, under the constraint conditions of production process, transportation equipment and on-site hoisting capacity, based on the splicing model, randomly generate multiple splicing combinations of the predetermined slab foundation, the specific process is as follows:

[0060] According to the production process, transportation equipment and on-site hoisting capacity of the splicing combination, the prefabricated slab foundation is split, the length, width and height of each prefabricated component need to meet the following conditions:

[0061] Production process constraint: l≤L max , b≤B max , h≤H max ;

[0062] Transportation constraint: l≤L tr , b≤b tr , h≤h tr ;

[0063] Hoisting constraint: p x V≤Gg max ;

[0064] wherein, l, b, h are the length, width and height of the prefabricated component respectively, L max , B max , H max are the maximum length, maximum width, maximum height of the mold respectively, L tr , b tr , h tr are the length limit, width limit, height limit of the vehicle respectively, V is the volume of the prefabricated component, and Gg max is the maximum lifting capacity of the hoisting equipment.

[0065] wherein, the maximum length L max , the maximum width B max , the maximum height H max of the mold are generally determined by the manufacturer according to the specifications of the production equipment, production process requirements and common market demand, which can be directly obtained from the technical parameter data of the production equipment or consulted from the manufacturer;

[0066] The length limit L tr , the width limit b tr , the height limit h tr of the vehicle are determined according to traffic regulations and the specifications of common transport vehicles, which can generally refer to the relevant standards and specifications of road transport vehicles, or directly consult the technical parameter manual of commonly used vehicles of the transport company; the volume V of the spliced combination can be calculated according to the design size of the predetermined slab foundation and the size of the prefabricated component after splitting, through the corresponding geometric volume formula;

[0067] The maximum lifting capacity Gg max of the hoisting equipment can be obtained from the product manual, technical specification table of the hoisting equipment, or consulted from the equipment supplier and relevant technical personnel. On the basis of the above embodiments, the geometric parameters of the prefabricated component include length, width and height.

[0068] On the basis of the above embodiments, the individuals of the initial population of the spliced combination are input into the finite element analysis model for performance simulation test, to obtain the corresponding bearing capacity index, settlement performance index, anti-overturning stability index and anti-sliding stability index of different spliced combinations, the method being as follows:

[0069] Finite element model establishment

[0070] According to the actual use of materials, the elastic modulus, Poisson's ratio, density, strength and other properties are accurately input in the finite element analysis software;

[0071] According to the geometric parameters of the initial population of splicing combinations, a three-dimensional geometric model is accurately established in the finite element software. For complex splicing combinations, appropriate simplification processing may be required, but the mechanical property simulation of key parts should be ensured;

[0072] Considering the interaction of the fabricated slab foundation with the superstructure, foundation, etc., the boundary conditions are reasonably set, for example, the contact between the bottom of the foundation and the foundation is set with appropriate constraints (such as fixed constraints, elastic constraints, etc.), and the connection between the foundation and the superstructure is set according to the actual connection mode (such as hinged connection, rigid connection, etc.);

[0073] The vertical load generated by the self-weight of the tower body, the weight of the conductor and the insulator string, the horizontal load caused by strong wind and unbalanced conductor tension, the eccentric load caused by conductor tension and eccentrically arranged ground wire, the additional vertical and horizontal load caused by icing, and the sudden load generated by conductor breakage are applied to simulate the actual load conditions;

[0074] Through finite element analysis, the stress distribution of the splicing combination and its splicing part under different load combinations is calculated, and the bearing capacity of the foundation is judged according to the strength criterion of the material. The bearing capacity index can be determined according to the ratio of the actual bearing capacity to the design bearing capacity;

[0075] The settlement of the foundation under vertical load is analyzed, the settlement of different parts of the foundation is obtained by finite element calculation, and indexes such as settlement difference are calculated. The settlement performance index can be determined according to the ratio of the settlement to the allowable settlement;

[0076] The overturning moment and anti-overturning moment of the foundation under horizontal load and eccentric load are calculated, and the anti-overturning stability index can be represented as the ratio of the anti-overturning moment to the overturning moment of the foundation. This index reflects the ability of the foundation to resist overturning.

[0077] The anti-sliding force and horizontal load of the foundation under horizontal load are analyzed. The anti-sliding force is generally provided by the friction between the foundation bottom and the foundation soil, and the anti-sliding stability index can be represented as the ratio of the anti-sliding force to the horizontal load. This index reflects the ability of the foundation to resist sliding.

[0078] On the basis of the above embodiments, the individuals of the initial population are constructed, and the specific steps are as follows:

[0079] The initial population is labeled as U, and the initial population U = {U1, U2, …, U i..., U μ}, U i is the i-th individual in the initial population, i is the index of the individual in the initial population, and i ∈ [1, μ], μ is the number of individuals in the initial population, U i = (l i,j , b i,j , h i,j |j ∈ [1, λ i ]), where l i,j , b i,j , h i,j are the length, width and height of the j-th prefabricated component of the i-th individual, j is the index of the prefabricated component, and λ i is the number of prefabricated components in the i-th individual.

[0080] On the basis of the above embodiment, the material cost, transportation cost and construction cost corresponding to different individuals are obtained, and the specific process is as follows:

[0081] The calculation formula of the material cost is:

[0082] C α,i = C 1,i + C 2,i

[0083] C 1,i = V i × P c

[0084] C 2,i = V i × ρ × P s

[0085] Wherein, C α,i is the material cost corresponding to the i-th individual, C 1,i is the concrete cost corresponding to the i-th individual, C 2,i is the steel bar cost corresponding to the i-th individual, V i is the volume corresponding to the i-th individual, P c is the unit price of concrete, P s is the unit price of steel bar, and ρ is the density of steel bar;

[0086] The transportation cost is related to the volume of the foundation and the transportation distance, and the calculation formula of the transportation cost is:

[0087] C β,i = V i × P t × S

[0088] Wherein, C β,i is the transportation cost corresponding to the i-th individual, P t is the transportation unit price, and S is the transportation distance;

[0089] The calculation formula of construction cost is:

[0090] C δ,i = V i * P v

[0091] Wherein, C δ,i is the construction cost corresponding to the ith individual, P v is the average cost required for unit volume construction.

[0092] Wherein, the overall concrete cost, steel cost, transportation cost and construction cost are calculated by first calculating the cost of each prefabricated component in the corresponding aspect, and then adding up the cost.

[0093] Wherein, the unit price of concrete P c , the unit price of steel P s are obtained through market research, contacting local concrete suppliers, steel markets or building material dealers, consulting the current prices of different grades and types of concrete and steel;

[0094] Generally, the density of steel ρ is about 7850 kg / m 3 ;

[0095] Transportation unit price P t Consult the transportation company, according to the transportation mode (such as highway transportation, railway transportation, etc.), transportation distance range, cargo type and other factors, to obtain the transportation price of unit volume goods;

[0096] Transportation distance S is determined according to the actual distance from the prefabricated component production site to the construction site, which can be measured by map software between two points, or the accurate transportation distance can be obtained by referring to the actual traffic route planning;

[0097] The average cost required for unit volume construction P v Refers to the industry standard quota or relevant engineering cost index manual to obtain the approximate value.

[0098] On the basis of the above embodiment, the geometric parameters, bearing capacity index, settlement performance index, anti-overturning stability index, anti-sliding stability index, material cost, transportation cost and construction cost of the spliced combination are standardized, and the standardized data are used for analysis and processing in the following text, so that various data are analyzed and processed under the same dimension, avoiding the problem that some data are ignored due to different dimensions.

[0099] S2. Process the data of the bearing capacity index, settlement performance index, overturning stability index and sliding stability index corresponding to the same entity to generate the comprehensive performance index of the entity. Process the data of the material cost, transportation cost and construction cost corresponding to the same entity to generate the comprehensive cost of the entity.

[0100] Table 1 shows the changes in the overall performance index as a function of each performance index.

[0101]

[0102] Based on the above embodiments, according to the data in Table 1, it can be seen that the four individual performance indices—bearing capacity index, settlement performance index, overturning stability index, and sliding stability index—all show a significant positive correlation with the comprehensive performance index. As the bearing capacity index increases from 20 to 115, the settlement performance index from 15 to 54, the overturning stability index from 12 to 32, and the sliding stability index from 10 to 29, the comprehensive performance index also gradually increases from 13.3 to 66.6. That is, the larger the value of the individual performance index, the larger the value of the comprehensive performance index. For details, please refer to Table 1 below.

[0103] like Figures 2-5 As shown, each individual performance index (bearing capacity index, settlement performance index, overturning stability index, and sliding stability index) exhibits a clear linear positive correlation with the comprehensive performance index. That is, as the value of each individual performance index increases, the comprehensive performance index also increases linearly, as indicated by the black comprehensive performance index data points closely distributed near the red fitted curve. This suggests that the fitted curve effectively reflects the relationship between the two. An increase in an individual performance index leads to an approximately linear increase in the comprehensive performance index, indicating a linear relationship between the comprehensive performance index and the bearing capacity index, settlement performance index, overturning stability index, and sliding stability index, respectively.

[0104] Based on the above embodiments, the bearing capacity index, settlement performance index, overturning stability index, and sliding stability index corresponding to the same individual are processed to generate the comprehensive performance index of that individual, based on the following formula:

[0105] P i =ω1×P 1,i +ω2×P 2,i +ω3×P 3,i +ω4×P 4,i

[0106] Among them, P iA comprehensive performance index corresponding to the i-th individual, the comprehensive performance index is used to combine the bearing capacity index, the settlement performance index, the anti-overturning stability index and the anti-sliding stability index to evaluate the comprehensive performance of the slab foundation, and the larger the comprehensive performance index P is, the better the comprehensive performance of the slab foundation is;

[0107] In the formula, P 1,i The bearing capacity index corresponding to the i-th individual, P 2,i The settlement performance index corresponding to the i-th individual, P 3,i The anti-overturning stability index corresponding to the i-th individual, P 4,i The anti-sliding stability index corresponding to the i-th individual;

[0108] On this basis, it should be noted that:

[0109] When the bearing capacity index P 1,i is larger, it indicates that the slab foundation can safely and stably bear more external load under the action of vertical, horizontal and eccentric load, the possibility of foundation structure damage is lower, the structural reliability and safety are higher, and thus the comprehensive performance of the slab foundation is significantly improved, and the comprehensive performance index P i is larger;

[0110] When the settlement performance index P 2,i of the slab foundation is larger, it means that the settlement amount of the foundation under the action of load is smaller, the stability of the upper structure can be better maintained, the problems such as structural cracks and deformation caused by foundation settlement are reduced, the normal use function of the building is ensured, thereby effectively improving the comprehensive performance of the slab foundation, and the comprehensive performance index P i is larger;

[0111] When the anti-overturning stability index P 3,i of the slab foundation is larger, it indicates that the ability of the foundation to resist overturning caused by horizontal load, eccentric load and the like is stronger, the foundation is not easy to be damaged by overturning under complex stress working conditions, the overall stability of the foundation and the upper structure is ensured, the contribution degree to the comprehensive performance is higher, and then the comprehensive performance index P i is larger;

[0112] When the anti-sliding stability index P 4,i of the slab foundation is larger, it represents that the friction between the foundation bottom surface and the foundation soil can better resist the horizontal thrust, the risk of foundation sliding is greatly reduced, the foundation can maintain stability under the action of horizontal load, and the overall performance of the foundation is effectively enhanced, so the comprehensive performance index P i is larger.

[0113] Therefore, the comprehensive performance index is positively correlated with the bearing capacity index, the settlement performance index, the anti-overturning stability index and the anti-sliding stability index.

[0114] Furthermore, the influence of each of the bearing capacity index, the settlement performance index, the anti-overturning stability index and the anti-sliding stability index on the performance of the slab foundation is independent and additive. That is, the influence of the change of each performance index on the performance of the slab foundation is independent of other parameters, and the combined influence can be embodied by simple addition.

[0115] In summary, the above-mentioned weighted summation formula is used to express the functional relationship between the comprehensive performance index and the bearing capacity index, the settlement performance index, the anti-overturning stability index and the anti-sliding stability index.

[0116] In the formula, ω1 is the weight coefficient of the bearing capacity index, ω2 is the weight coefficient of the settlement performance index, ω3 is the weight coefficient of the anti-overturning stability index, and ω4 is the weight coefficient of the anti-sliding stability index.

[0117] The bearing capacity is the core function of the slab foundation, and is directly related to whether the foundation can bear the weight of the upper structure and various additional loads. If the bearing capacity is insufficient, the foundation may be deformed or even damaged, leading to failure of the entire structure, which is the most critical factor to ensure the safety of the structure, and therefore is given the largest weight ω1.

[0118] Although the settlement performance is also very important, it is slightly less important than the bearing capacity. Excessive settlement can cause cracks, tilting and other problems in the upper structure, affecting the use function and appearance of the building, and even may cause safety hazards. However, as long as the settlement is within a certain range, it can be compensated or controlled through some technical measures, so the weight ω2 is less than the weight ω1 of the bearing capacity index.

[0119] The anti-overturning stability is mainly aimed at external forces that may cause the foundation to overturn, such as wind force, earthquake force and other horizontal loads, as well as eccentric loads. In most conventional buildings, these loads are not the main loads that exist continuously, and in the design, reasonable structural arrangement and foundation selection can be used to avoid excessive overturning moment. Therefore, its importance is relatively lower than the bearing capacity and the settlement performance, and therefore the weight ω3 is less than ω2.

[0120] The anti-sliding stability mainly involves the frictional force between the foundation bottom and the soil to resist the horizontal thrust. In general, through reasonable design of the foundation bottom size and foundation treatment, the anti-sliding stability of the foundation can be well ensured. Moreover, compared with the bearing capacity, the settlement performance and the anti-overturning stability, the possibility of sliding failure is relatively low, and the degree of influence on the overall performance of the foundation is relatively small, and therefore the weight ω4 is the smallest.

[0121] Therefore, on the basis of w1+w2+w3+w4=1, let 0<w4<w3<w2<w1<1.

[0122] As an embodiment, the value range of w1 is 0.4-1, the value range of w2 is 0.3-0.4, the value range of w3 is 0.2-0.3, and the value range of w4 is 0-0.2. The specific value is set by the technical personnel according to the actual situation, which is not limited here.

[0123] On the basis of the above embodiment, the material cost, transportation cost and construction cost corresponding to the same individual are data processed to generate the comprehensive cost of the individual, and the formula is as follows:

[0124] C i =C α,i +C β,i +C δ,i

[0125] Wherein, C i is the comprehensive cost corresponding to the i-th individual, C α,i is the material cost corresponding to the i-th individual, C β,i is the transportation cost corresponding to the i-th individual, and C δ,i is the construction cost corresponding to the i-th individual.

[0126] S3. According to the ratio of transportation cost and construction cost corresponding to different splicing combinations, the cost-performance coupling coefficient corresponding to different splicing combinations is calculated, all splicing combinations are arranged in descending order according to the cost-performance coupling coefficient, and a threshold value is set in advance. The splicing combination with a cost-performance coupling coefficient greater than the threshold value is reserved to complete the construction of the splicing combination set with double optimal cost-performance.

[0127] Table 2 is the change of cost-performance coupling coefficient with the change of comprehensive performance index, comprehensive cost, the ratio of transportation cost and construction cost and cost-performance coupling coefficient

[0128]

[0129] On the basis of the above embodiment, according to the data in Table 2, during the process that the change of comprehensive performance index increases from 10 to 80, the cost-performance coupling coefficient continuously increases from 0.0581 to 1.2, and both of them change in the same direction, so the change of comprehensive performance index is positively correlated with the cost-performance coupling coefficient.

[0130] The comprehensive cost changes from 76 to 20, the cost-performance coupling coefficient increases from 0.0581 to 1.2, and presents reverse change, so the change of comprehensive cost is negatively correlated with the cost-performance coupling coefficient.

[0131] The ratio of transportation cost to construction cost decreases from 0.9 to 0.2, and the cost-performance coupling coefficient increases from 0.0581 to 1.2, which presents reverse variation, so the ratio of transportation cost to construction cost is negatively correlated with the cost-performance coupling coefficient, and specific conditions refer to Table 2.

[0132] As shown in Figure 6 , the black data points represent the cost-performance coupling coefficient, which increases with the increase of the comprehensive performance index variation from left to right, the red fitting curve is not a straight line, indicating that the two present a nonlinear relationship, and the cost-performance coupling coefficient grows gradually faster with the increase of the comprehensive performance index variation, which shows that the influence of the comprehensive performance index variation on the cost-performance coupling coefficient is increasing;

[0133] As shown in Figure 7 , the black data points represent the cost-performance coupling coefficient, which increases with the increase of the comprehensive performance index variation from left to right, the red fitting curve is not a straight line, indicating that the two present a nonlinear relationship, and the cost-performance coupling coefficient grows gradually faster with the increase of the comprehensive performance index variation, which shows that the influence of the comprehensive performance index variation on the cost-performance coupling coefficient is increasing;

[0134] As shown in Figure 8 , the black data points are the cost-performance coupling coefficient, which gradually increases with the decrease of the ratio of transportation cost to construction cost, and the red fitting curve is not a straight line, indicating that the two present a nonlinear relationship, and the cost-performance coupling coefficient grows gradually faster with the decrease of the ratio of transportation cost to construction cost, which reflects the nonlinear functional relationship between the two.

[0135] On the basis of the above embodiment, the cost-performance coupling coefficient corresponding to an individual is calculated according to the ratio of transportation cost to construction cost corresponding to the individual, and the formula is as follows:

[0136]

[0137] ΔP i =P i -P JZ

[0138] ΔC i =C i -C JZ

[0139]

[0140] Wherein, C PC,iThe cost-performance coupling coefficient of the ith individual is used to combine the comprehensive performance index change amount, the comprehensive cost change amount, and the ratio of the transportation cost to the construction cost to comprehensively evaluate the advantages and disadvantages of the splicing combination, and the greater the cost-performance coupling coefficient, the better the performance of the splicing combination.

[0141] On this basis, it should be noted that:

[0142] When the comprehensive performance index change amount is greater, it indicates that the splicing combination has more significant improvement in comprehensive performance compared to the benchmark scheme. When the comprehensive performance is significantly improved, under the condition that the cost change is relatively stable, it means that more performance improvement is obtained per unit cost, that is, the splicing combination can obtain greater performance improvement with relatively less cost input, so its performance is better, thereby making the cost-performance coupling coefficient greater.

[0143] When the comprehensive cost change amount is greater, it indicates that the splicing combination increases more in cost compared to the benchmark scheme. Under the condition that the comprehensive performance improvement amplitude is certain, the greater the cost increase, the greater the cost paid for obtaining unit performance improvement, which will lead to a decrease in the overall performance-cost ratio, and the performance of the splicing combination is relatively worse, thereby making the cost-performance coupling coefficient smaller.

[0144] When the ratio of the transportation cost to the construction cost is greater, it indicates that the transportation cost accounts for a relatively higher proportion in the total cost than the construction cost. When the transportation cost is too high, various limitations in the transportation process may lead to an increase in construction difficulty, a prolongation of the construction period, or the need for additional transportation measures to ensure the safe transportation of the components, which will have a negative impact on the overall performance of the splicing combination. Even if the comprehensive performance index has a certain improvement, due to the many adverse factors caused by the high transportation cost, the actual effective performance improvement obtained under the same comprehensive cost change is weakened, so the performance of the splicing combination is worse, and the cost-performance coupling coefficient is smaller.

[0145] Therefore, the cost-performance coupling coefficient and the comprehensive performance index change amount are positively correlated, and the cost-performance coupling coefficient and the comprehensive cost change amount and the ratio of the transportation cost to the construction cost are negatively correlated.

[0146] Therefore, the above formula is used to express the functional relationship between the cost-performance coupling coefficient and the comprehensive performance index change amount, the comprehensive cost change amount, and the ratio of the transportation cost to the construction cost.

[0147] In the formula, ΔP i is the comprehensive performance index change amount corresponding to the ith individual, ΔC i is the comprehensive cost change amount corresponding to the ith individual, P JZ is the benchmark value of the comprehensive performance index, and C JZR is a reference value of the comprehensive performance index i Ri is a ratio of the transportation cost and the construction cost corresponding to the ith individual;

[0148] The reference value of the comprehensive performance index and the reference value of the comprehensive cost are obtained by calculation under ideal conditions according to the design specification of the slab foundation, the mechanical principle and the related engineering theory, and are taken as the reference values.

[0149] The cost-performance coupling coefficient integrates the three core indexes of the comprehensive performance index change amount, the comprehensive cost change amount and the ratio of the transportation cost and the construction cost into a single coefficient, and realizes the balanced optimization of multiple targets by scoring the splicing scheme; the cost bottleneck (such as high transportation cost) of the scheme is automatically identified through the ratio of the transportation cost and the construction cost, and targeted optimization is guided; and the optimal scheme can also be quickly screened.

[0150] S4. Constructing a performance and cost prediction model, taking the individual set with cost-performance double optimization as the basis, inputting the individuals of the initial population into the performance and cost prediction model to obtain the comprehensive performance index and the comprehensive cost corresponding to different individuals;

[0151] On the basis of the above embodiment, the performance and cost prediction model is composed of a deep learning network based on a multilayer perceptron, the deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, the first hidden layer, the second hidden layer and the third hidden layer each have at least two neurons, and each uses ReLU (Rectified Linear Unit) as an activation function;

[0152] In the performance and cost prediction model, the input features of the deep learning network of the multilayer perceptron include the individuals of the initial population.

[0153] The structure of the deep learning network of the multilayer perceptron is as follows:

[0154] Input layer: used for receiving a set of features;

[0155] First hidden layer: having 128 neurons, using ReLU as an activation function;

[0156] Second hidden layer: having 64 neurons, also using ReLU activation function;

[0157] Third hidden layer: having 32 neurons, using ReLU activation function;

[0158] Output layer: having 7 neurons, used for outputting the comprehensive performance index and the comprehensive cost corresponding to different individuals.

[0159] The process of training the performance and cost prediction model is as follows:

[0160] The individuals of the initial population are taken as input, and the comprehensive performance index and comprehensive cost corresponding to different individuals are taken as labels for training, and the mean square error is taken as the loss function. When the mean square error is in the range of [0, 0.01], the training of the performance and cost prediction model is completed.

[0161] S5. The comprehensive cost and the comprehensive performance index corresponding to the same individual are weighted and summed to generate a comprehensive evaluation index corresponding to the individual. The maximum of the comprehensive evaluation index corresponding to the individual is taken as the objective function, and the individuals of the initial population are iteratively optimized under the constraint condition of the geometric parameters of the splicing combination by a genetic algorithm to obtain an optimal individual. Based on the optimal individual, the optimal combination of the splicing combination is extracted and taken as the optimal economic size of the prefabricated component.

[0162] Table 3 shows the changes of the comprehensive evaluation index with the comprehensive cost and the comprehensive performance index

[0163] comprehensive Exponential ]] Overall cost Overall assessment index 20 115 0 25 110 1 30 105 2 35 100 3 40 95 4 45 90 5 50 85 6 55 80 7 60 75 8 65 70 9 70 65 10 75 60 11 80 55 12 85 50 13 90 45 14 95 40 15 100 35 16 105 30 17

[0164] On the basis of the above embodiment, as the comprehensive performance index gradually increases from 20 to 105, the comprehensive evaluation index increases from 0 to 17 accordingly, and both of them change in the same direction. Therefore, the comprehensive performance index and the comprehensive evaluation index are positively correlated. When the comprehensive cost gradually decreases from 115 to 30, the comprehensive evaluation index increases from 0 to 17, showing an opposite trend, and therefore, the comprehensive performance index and the comprehensive cost are negatively correlated. For specific conditions, refer to Table 3.

[0165] As shown in Figure 9 , the black comprehensive evaluation index data points are closely distributed on the red fitting curve, showing a straight line shape, which indicates that there is a linear relationship between the comprehensive evaluation index and the comprehensive performance index, and they are positively linearly correlated. That is, as the comprehensive performance index increases, the comprehensive evaluation index increases linearly at a fixed ratio, which is reflected in the graph as a straight upward trend;

[0166] As shown in Figure 10 , the black data points are linearly distributed along the red fitting curve, reflecting the linear relationship between the comprehensive evaluation index and the comprehensive cost, and they are negatively linearly correlated. As the comprehensive cost value increases, the comprehensive evaluation index decreases linearly, which is represented in the graph as a straight downward trend.

[0167] On the basis of the above embodiment, the comprehensive cost and the comprehensive performance index corresponding to the same individual are weighted and summed to generate a comprehensive evaluation index corresponding to the individual. The formula is as follows:

[0168]

[0169] Wherein, ZPzs iThe comprehensive evaluation index corresponding to the i th individual, the comprehensive evaluation index is used to combine the comprehensive cost and the comprehensive performance index 2 index parameters, and the performance and economic benefit of the plate foundation are comprehensively evaluated, and the larger the comprehensive evaluation index is, the better the performance and economic benefit of the plate foundation are;

[0170] In the formula, The weight coefficient of the comprehensive performance index is The weight coefficient of the comprehensive cost is And The specific value of is determined by the analytic hierarchy process, and the specific logic is as follows:

[0171] The comprehensive performance index and the comprehensive cost 2 indexes are marked, the relative importance values between each other are determined by the nine scale method, and the judgment matrix is constructed, wherein the index of the comprehensive performance index is marked as 1, the index of the comprehensive cost is marked as 2, and the constructed judgment matrix is constructed. uτ ] 2×2 is:

[0172]

[0173] Wherein, u represents the index of the comprehensive performance index, v represents the index of the comprehensive cost, and u∈[1,2], τ∈[1,2], represents the importance of the comprehensive evaluation index of the comprehensive performance index with index u relative to the comprehensive cost with index τ, q uτ The specific value of q uv =9 indicates that the comprehensive performance index with index u is very important to the comprehensive evaluation index compared with the comprehensive cost with index τ, q uτ =1 indicates that the comprehensive performance index with index u is very unimportant to the comprehensive evaluation index compared with the comprehensive cost with index τ.

[0174] Divide each element value in the judgment matrix by the sum of its column to obtain the normalized judgment matrix, calculate the mean value of each row element value in the normalized judgment matrix, and take the mean value of the first row element value as the proportion coefficient of the comprehensive performance index, and take the mean value of the second row element value as the proportion coefficient of the comprehensive cost. With the constraint condition that the sum of the scaled values is equal to 1, the comprehensive performance index and the comprehensive cost are scaled in proportion, and the scaled values are taken as the weights of the corresponding comprehensive performance index and comprehensive cost.

[0175] On this basis, it should be noted that:

[0176] When the comprehensive performance index P iThe greater, the better performance of the slab foundation in bearing capacity, settlement control, anti-overturning stability and anti-sliding stability, which means that the foundation can better withstand the upper structure load, reduce the structural safety hazards and maintenance costs caused by excessive settlement, overturning or sliding, and prolong the service life of the building and reduce the life cycle cost. From the economic benefit point of view, good performance can avoid secondary construction, reinforcement cost and use function loss caused by structural damage due to insufficient foundation performance, thereby improving the comprehensive evaluation index ZPzs i ;

[0177] The smaller the comprehensive cost C i , the less resources are invested in the design, material procurement, transportation and construction of the slab foundation. Under the premise of ensuring that the comprehensive performance index of the foundation does not decrease, lower cost directly improves the economic benefit of the project, reflecting the efficient use of resources. For example, by optimizing material selection and reasonably planning construction process to reduce cost, while maintaining high performance of the foundation, the performance value per unit cost is higher, thereby increasing the comprehensive evaluation index ZPzs i .

[0178] Therefore, the comprehensive evaluation index and the comprehensive performance index are positively correlated, and the comprehensive evaluation index and the comprehensive cost are negatively correlated.

[0179] Furthermore, the comprehensive performance index and the comprehensive cost are core indicators for comprehensively evaluating the performance and economic benefit of the slab foundation from the performance and cost perspectives. In actual engineering, performance improvement and cost control often restrict each other: higher bearing capacity and stronger stability usually require the use of higher quality materials and more complex processes, resulting in rising costs; and excessive cost compression may sacrifice performance, affecting the safety and durability of the foundation.

[0180] Simply adding the two cannot reflect this contradictory relationship, while the weighted sum formula can effectively balance the performance and cost through the weight coefficients and . For example, for important buildings with extremely high safety requirements, the weight of performance can be increased to highlight the importance of performance and allow appropriate cost increase to obtain higher performance guarantee; for cost-sensitive general projects, the weight of cost can be increased to strengthen cost control, and maximize economic benefit under the premise of meeting basic performance requirements.

[0181] Therefore, using the weighted sum formula to express the functional relationship between the comprehensive evaluation index and the comprehensive performance index and the comprehensive cost can more scientifically and comprehensively reflect the balance between performance and economic benefit of the slab foundation.

[0182] Based on the above embodiments, the individuals in the initial population are iteratively optimized using a genetic algorithm to obtain the optimal individuals. The optimal combination of the corresponding splicing combinations is then extracted as the optimal economic size. The specific process is as follows:

[0183] The individuals in the initial population of the spliced ​​combination are iteratively optimized. During the iterative optimization process, constraints are set on the geometric parameters of the spliced ​​combination, namely, the maximum and minimum values ​​of length, width, and height are set respectively. Within the constraints of length, width, and height, the geometric parameters of the spliced ​​combination are iteratively optimized. Specifically, the comprehensive evaluation index is sorted from largest to smallest, and individuals with the highest comprehensive evaluation index are selected as parents. The top-ranked individuals are those with a comprehensive evaluation index ZPzs. i The first 50% of individuals are crossovered and mutated, exchanging and combining genes from their parents to generate new individuals. A performance and cost prediction model is used to obtain the comprehensive cost and performance index of these new individuals, and their comprehensive evaluation index is calculated. These new individuals and their parents are then used as a new population, and the selection, crossover, and mutation operations are repeated until a predetermined number of iterations are reached. The individual corresponding to the highest comprehensive evaluation index is then selected as the optimal combination of splicing and combining geometric parameters, and labeled as U. i1 =(l i1,j ,b i1,j ,h i1,j |j∈[1,λ i If the optimal economic dimension of the precast component is length l, then the optimal economic dimension of the precast component is length l. i1,j Width b i1,j and height h i1,j .

[0184] 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.

[0185] 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 software, 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.

[0186] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, and may be located in one place, or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiment of the present application according to actual needs.

[0187] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for modular design of prefabricated slab foundations based on the optimal economic dimensions of prefabricated components, characterized in that: The specific steps include: Under the set constraints, based on the splicing model, multiple splicing combinations of a predetermined slab foundation are randomly generated as individuals in the initial population. The individuals in the initial population are input into the finite element analysis model for performance simulation testing. The bearing capacity index, settlement performance index, overturning stability index and sliding stability index corresponding to different individuals are obtained, as well as the material cost, transportation cost and construction cost corresponding to different individuals. The data of the bearing capacity index, settlement performance index, overturning stability index and sliding stability index corresponding to the same entity are processed to generate the comprehensive performance index of the entity. The data of the material cost, transportation cost and construction cost corresponding to the same entity are processed to generate the comprehensive cost of the entity. By combining the ratio of transportation cost to construction cost for the same individual, the cost-performance coupling coefficient for that individual is calculated. All individuals are sorted in descending order based on the cost-performance coupling coefficient, and a threshold is pre-set to retain individuals with a cost-performance coupling coefficient greater than the threshold, so as to complete the construction of a set of individuals with both cost and performance advantages. A performance and cost prediction model is constructed. Based on a set of individuals with both cost and performance advantages, the individuals of the initial population are input into the performance and cost prediction model to obtain the comprehensive performance index and comprehensive cost corresponding to different individuals. The comprehensive cost and comprehensive performance index corresponding to the same individual are weighted and summed to generate the comprehensive evaluation index corresponding to that individual. Taking the maximization of the comprehensive evaluation index corresponding to that individual as the objective function, the individuals in the initial population are iteratively optimized through a genetic algorithm to obtain the optimal individual. The optimal combination of the corresponding splicing combination is extracted as the optimal economic size. The cost-performance coupling coefficient for an individual is calculated by combining the ratio of transportation cost to construction cost, using the following formula: in, For the first The cost-performance coupling coefficient of an individual entity For the first The change in the comprehensive performance index corresponding to each individual. for The change in overall cost for each individual This serves as the benchmark value for the overall performance index. This serves as the benchmark for comprehensive cost. For the first The ratio of transportation cost to construction cost for each individual.

2. The prefabricated slab foundation modular design method based on the optimal economic dimensions of prefabricated components according to claim 1, characterized in that: The geometric parameters of prefabricated components include length, width, and height.

3. The prefabricated slab foundation modular design method based on the optimal economic dimensions of prefabricated components according to claim 2, characterized in that: The specific steps for constructing the initial population are as follows: The initial population was labeled as And the initial population , The first in the initial population Individual, This is the index of an individual in the initial population, and , The initial population size. ,in, The first The individual The length, width, and height of each prefabricated component For indexing prefabricated components, For the first The number of prefabricated components in an individual.

4. The prefabricated slab foundation modular design method based on the optimal economic dimensions of prefabricated components according to claim 1, characterized in that: The bearing capacity index, settlement performance index, overturning stability index, and sliding stability index corresponding to the same individual are processed to generate the comprehensive performance index of that individual, based on the following formula: in, For the first The comprehensive performance index corresponding to each individual. This is an index of individuals in the initial population; In the formula, For the first The carrying capacity index corresponding to each individual For the first The settlement performance index corresponding to each individual. For the first The overturning stability index corresponding to each individual. For the first The anti-slip stability index corresponding to each individual; In the formula, The weighting coefficients for the carrying capacity index, The weighting coefficients for the settlement performance index are... The weighting coefficients for the overturning stability index are... The weighting coefficients for the anti-slip stability index are... On this basis, let .

5. The prefabricated slab foundation modular design method based on the optimal economic dimensions of prefabricated components according to claim 4, characterized in that: The material costs, transportation costs, and construction costs corresponding to the same entity are processed to generate the comprehensive cost of that entity, based on the following formula: in, For the first The overall cost for each individual For the first The material cost for each individual unit For the first The transportation cost for each individual For the first The construction cost corresponding to each individual.

6. The prefabricated slab foundation modular design method based on the optimal economic dimensions of prefabricated components according to claim 5, characterized in that: The comprehensive evaluation index for an individual is generated by weighted summing of its comprehensive cost and comprehensive performance index, based on the following formula: in, For the first The comprehensive evaluation index corresponding to each individual; In the formula, These are the weighting coefficients for the overall performance index. This is the weighting coefficient for the overall cost, and and The specific value is determined by the analytic hierarchy process (AHP).

7. The prefabricated slab foundation modular design method based on the optimal economic dimensions of prefabricated components according to claim 2, characterized in that: The initial population is iteratively optimized using a genetic algorithm to obtain the optimal individuals. The optimal combination of the corresponding splicing combinations is then extracted as the optimal economic size. The specific process is as follows: The individuals in the initial population of the spliced ​​combination are iteratively optimized. During the iterative optimization process, constraints are set on the geometric parameters of the spliced ​​combination, namely, the maximum and minimum values ​​of length, width, and height. Within the constraints of length, width, and height, the geometric parameters of the spliced ​​combination are iteratively optimized. Specifically, the comprehensive evaluation index is sorted from largest to smallest, and individuals with the highest comprehensive evaluation index are selected as parents. Through crossover and mutation operations, the genes of the parent individuals are exchanged and mutated to generate new individuals. The comprehensive cost and comprehensive performance index of the newly generated individuals are obtained using a performance and cost prediction model, and their comprehensive evaluation index is calculated. The new individuals and their parents are used as a new population, and the selection, crossover, and mutation operations are repeated until a predetermined number of iterations are reached. The individual corresponding to the maximum comprehensive evaluation index is taken as the optimal combination of spliced ​​combination geometric parameters, and the individual corresponding to the maximum comprehensive evaluation index is labeled as... Then the optimal economic dimension of the precast component is the length. ,width and height .

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