Full-life-cycle multi-objective optimization design method and system for long-piled wharf structure

Abstract

The invention discloses a full-life-cycle multi-objective optimization design method and system for a long-pile wharf structure, and belongs to the technical field of information processing, and the method comprises the steps: S1, building an optimization analysis model; s2, carrying out single-target optimization analysis; s3, constructing a construction cost objective function; s4, setting structural design variables; s5, based on the stress performance and the effective bearing capacity of the component, establishing a single-target optimization model; s6, establishing an objective function among structural safety, material economy, construction feasibility and life cycle carbon emission through a multi-objective optimization method, and calculating an optimal solution set in combination with a genetic algorithm; s7, establishing a multi-objective optimization model containing life cycle cost and carbon emission control, and solving by adopting an intelligent optimization algorithm; s8, integrating the carbon emission function and the life cycle cost function, and constructing a multi-objective function system; and S9, on the basis of the established multi-objective optimization model, solving is carried out by adopting a non-dominated sorting genetic algorithm.

Description

Technical Field

[0001] The present invention belongs to the technical field of information processing, and particularly relates to a full-life-cycle multi-objective optimization design method and system for high-piled wharf structures. Background Art

[0002] In the current field of port construction, due to its unique design and functions, the high-piled beam-slab wharf structure can adapt to and meet various complex engineering requirements, and is thus widely used in the construction of modern ports. With its strong load-bearing capacity and flexible layout design, this structural form provides great convenience for port operation. However, despite its excellent performance in terms of functions, the high-piled beam-slab wharf structure also has some problems that cannot be ignored. The most prominent problems include high construction costs, uncertainties in maintenance costs throughout its entire life cycle, and large carbon emissions, which have a profound impact on the economic benefits and environmental impact of the wharf.

[0003] In traditional port wharf design methods, designers often focus on the initial construction cost of the structure or its load-bearing performance, without comprehensively considering the three key factors of the comprehensive cost, reliability, and carbon emissions of the wharf structure throughout its entire life cycle. The limitations of this design method have led to the neglect of the long-term performance and environmental impact of the wharf structure. Therefore, these traditional methods lack sufficient consideration of the concept of green and low-carbon design and fail to incorporate the concepts of environmental protection and sustainable development into the core considerations of the design. To address this challenge, modern port construction requires the adoption of more comprehensive and forward-looking design methods to ensure that the wharf structure is not only economically feasible but also environmentally sustainable, thus achieving a win-win situation between economic benefits and environmental protection. Summary of the Invention

[0004] To solve the technical problems existing in the known technology, the present invention provides a full-life-cycle multi-objective optimization design method and system for high-piled wharf structures. When designing the high-piled wharf structure, a technical solution based on time-varying reliability theory and carbon emission assessment is adopted. This technical solution comprehensively considers the economy, durability, and environmental protection performance of the structure. Through this comprehensive analysis, multi-objective optimization design throughout the life cycle can be achieved. Such a design not only guides us to make more reasonable choices in the design stage but also ensures low carbon emissions and long-term sustainability of the structure, thus providing scientific decision-making support for the future development of the wharf structure.

[0005] The first object of the present invention is to provide a full-life-cycle multi-objective optimization design method for high-piled wharf structures, including: S1. Take the full straight pile high-piled wharf structure as the research object and establish an optimization analysis model. The optimization analysis model quantifies the proportion of the bearing capacity of components used to resist external service loads in the actual project to their total bearing capacity through the effective bearing ratio. S2. Conduct single-objective optimization analysis: According to the effective bearing ratio, with the span and beam height as variables, determine the optimal structural responses under different combinations. S3. Construct a construction cost objective function. S4. Set structural design variables and adopt a discrete jump value-taking method to set the variable change interval. S5. Based on the mechanical properties and effective bearing capacity of components, establish a single-objective optimization model, and carry out optimization calculations in combination with the objective function formula to obtain the optimal structural parameter combination under the bearing capacity constraint, providing initial design parameters and reference boundaries for the multi-objective optimization model. S6. After completing the single-objective optimization and obtaining the preliminary structural parameters, conduct a systematic optimization design of the full straight pile high-piled wharf structure. Through the multi-objective optimization method, establish an objective function among structural safety, material economy, construction feasibility, and life cycle carbon emissions, and calculate the optimal solution set in combination with the genetic algorithm. S7. Establish a multi-objective optimization model including life cycle cost and carbon emission control, and use an intelligent optimization algorithm to solve it to obtain the optimal structural design parameter combination that takes into account both economy and environmental sustainability. S8. Integrate the carbon emission function and the life cycle cost function to construct a multi-objective function system. S9. Based on the established multi-objective optimization model, use the non-dominated sorting genetic algorithm to solve it.

[0006] Preferably, the full straight pile high-piled wharf structure includes cross beams, longitudinal beams, panels, pile caps, and foundation piles.

[0007] Preferably, S2 includes: Under the condition of known engineering external loads, obtain the effective bearing ratios corresponding to different beam heights and spans, and draw the corresponding relationship diagram among beam height, span, and effective bearing ratio.

[0008] Preferably, the construction cost objective function takes the minimum material consumption per unit area as the optimization goal. Based on the volume and distribution of typical components, the cost expression is obtained through the ratio of the total material volume to the design area:

[0009] In the formula: W min is the material required for the project per unit area, with the unit of m 3 ; is the number of components in a specific project section; is the volume of a single component, with the unit of m 3 ; S is the unit plane area of the project section, with the unit of m 2 ; it can be taken as 1; n is the total number of components in all project sections.

[0010] Preferably, S4 includes: giving the objective function of the high-piled wharf variables of span and beam height according to the cost expression, with the unit of material consumption per square meter.

[0011] Preferably, in S7, the improved life-cycle unit area cost function is as follows: ; where: C is the improved life-cycle unit area cost function, A is the area of the entire structure, is the sum of the management cost, maintenance cost, and special inspection cost of the structure during the operation stage, which is related to the building area of the wharf; i is the i-th building material; is the quantity of the i-th building material; is the cost required for constructing a unit of material; is the cost required for the construction unit of material; is the cost required for transporting a unit of material per unit distance; is the transportation distance of the i-th material from construction to the project; is the cost before the i-th maintenance of the wharf component; is the failure probability before the i-th repair of the wharf component; n is the duration of the entire life cycle, with the unit of year; is the repair cost for reaching the i-th repair index; is the increase in reliability performance after the i-th repair; is the change value of the maintenance cost of the component over time from the last repair of the component to the end of its service life; is the remaining time of the wharf component from the last repair to the end of its service life; is the cost that does not change with time when the wharf component is repaired; is the economic loss caused by the shutdown per unit time; is the total time required for the wharf failure repair; is the total cost required for the selected variables in the construction period from production to use; is the percentage of the demolition cost in the design stage and construction cost, generally taken as 3%; is the discount rate of the cost in the first year, characterized by the social discount rate and the price fluctuation level for calculation. The specific calculation model is as follows:

[0012] In the formula: is the social discount rate; is the cost discount rate in the i-th year; is the annual change rate of PPI.

[0013] Preferably, in S8, the carbon emission function per unit area of the wharf throughout its life cycle is as follows: ; where P is the carbon emission function per unit area of the wharf throughout its life cycle, is the emission factor of the i-th type of energy; is the consumption of the i-th type of material; is the number of kilometers of transportation of the i-th type of material; is the energy consumed annually during the normal operation stage, with the unit of kWh; is the service life, is the carbon emission during the maintenance stage, with the unit of .

[0014] The second object of the present invention is to provide a full-life cycle multi-objective optimization design system for a high-pile wharf structure, including: An optimization analysis model construction module, taking the full straight-pile high-pile wharf structure as the research object, to establish an optimization analysis model; the optimization analysis model quantifies the proportion of the bearing capacity of components used to resist external service loads in their total bearing capacity through the effective bearing ratio; A single-objective optimization analysis module, according to the effective bearing ratio, with the span and beam height as variables, to determine the optimal structural responses under different combinations; A cost function module, to construct a construction cost objective function; A setting module, to set structural design variables, and adopt a discrete jump value-taking method to set the variable change interval; A single-objective optimization module, based on the mechanical properties and effective bearing capacity of components, to establish a single-objective optimization model, and carry out optimization calculations in combination with the objective function formula to obtain the optimal structural parameter combination under the bearing capacity constraint; to provide initial design parameters and reference boundaries for the multi-objective optimization model; A genetic algorithm calculation module, after completing the single-objective optimization and obtaining the preliminary structural parameters, to carry out the systematic optimization design of the full straight-pile high-pile wharf structure; through a multi-objective optimization method, to establish an objective function among structural safety, material economy, construction feasibility and life cycle carbon emissions, and combine the genetic algorithm to calculate the optimal solution set; A parameter combination calculation module, to establish a multi-objective optimization model including life cycle cost and carbon emission control, and adopt an intelligent optimization algorithm to solve it, to obtain the optimal structural design parameter combination that takes into account economy and environmental sustainability; An integration module that integrates the carbon emission function and the life cycle cost function to construct a multi-objective function system; An optimization module that, based on the established multi-objective optimization model, uses the non-dominated sorting genetic algorithm for solution.

[0015] The third object of the present invention is to provide an information data processing terminal for implementing the above-mentioned full life cycle multi-objective optimization design method for high-piled wharf structures.

[0016] The fourth object of the present invention is to provide a computer-readable storage medium including instructions that, when run on a computer, cause the computer to execute the above-mentioned full life cycle multi-objective optimization design method for high-piled wharf structures.

[0017] The advantages and positive effects of the present invention are: By adopting the above technical solution, the present invention first establishes a single-objective optimization model, takes the effective bearing ratio of components as the core parameter, deeply analyzes the influence of variables such as different pile diameters, beam heights, spans, etc. on the structural bearing performance, and strives to minimize the construction cost on the premise of meeting the bearing requirements. On this basis, the present invention further introduces the full life cycle theory and the time-varying reliability model, constructs a structural maintenance time history function considering the material deterioration effect, derives the critical maintenance time nodes of various components, and then establishes a full life cycle cost function, incorporating the operation and maintenance costs into the optimization objectives, thereby enhancing the sustainability of the design.

[0018] In order to comprehensively evaluate the environmental impact within the full life cycle of the structure, the present invention proposes a full life cycle carbon emission assessment method based on the carbon emission factor method, calculates the carbon emissions item by item according to the structure construction, operation, and demolition stages, establishes a carbon emission function, and uses it as the second optimization objective to ensure that environmental factors are fully considered in the design process.

[0019] The present invention finally forms a multi-objective optimization model including the life cycle cost function and the carbon emission function, takes the pile foundation side length, transverse and longitudinal beam heights, beam spans, slab thickness, pile cap size, etc. as optimization variables, uses the non-dominated sorting genetic algorithm (NSGA-II) to solve the model, obtains a series of Pareto optimal solutions, provides multiple alternative solutions for different design requirements, and thus provides more flexible choices for designers.

[0020] The present invention also deeply studies the parameter sensitivity of the intelligent algorithm in the model, uses the orthogonal test method to screen and optimize the key control parameters of NSGA-II to improve the solution efficiency and the distribution quality of the solutions. Through the method of the present invention, a multi-objective design decision-making system for high-piled wharf structures is established, breaking through the limitations of traditional optimization means in terms of objective dimensions and evaluation scopes, having good engineering applicability, and providing a new perspective and tool for the design of high-piled wharf structures. Brief Description of the Drawings

[0021] Figure 1 Schematic diagram of the multi-objective optimization model provided by the embodiment of the present invention; Figure 2 Variation diagram of the effective bearing ratio under different combinations of span and beam height provided by the embodiment of the present invention; Figure 3 Relationship diagram of the construction and operation and maintenance costs of the structure provided by the embodiment of the present invention; Figure 4 Carbon emission evaluation logic diagram provided by the embodiment of the present invention; Figure 5 Flowchart for solving using the non-dominated sorting genetic algorithm (NSGA-II) provided by the embodiment of the present invention; Figure 6 Pareto optimal solution set diagram of the multi-objective optimization genetic algorithm provided by the embodiment of the present invention; Figure 7 Comparison diagram of different objectives of each group of optimization results in the embodiment of the present invention. Detailed Description of the Invention

[0022] In order to further understand the content, features and effects of the present invention, the following embodiments are cited and described in detail in conjunction with the accompanying drawings: The core objective of the present invention is to solve a significant problem existing in the design process of traditional high-piled wharves, that is, designers often only focus on minimizing the initial construction cost, while ignoring the possible performance degradation of the structure during the long-term service life and its potential impact on the environment. To overcome this limitation, the present invention proposes an innovative full-life-cycle multi-objective optimization design method for high-piled wharf structures. This method not only considers the initial construction cost of the structure, but also comprehensively takes into account the performance stability, maintenance cost, and environmental friendliness of the structure during service. Through this comprehensive optimization design, it aims to achieve the coordinated unity of the safety, economic benefits, and low-carbon environmental protection goals of the structure, so as to ensure the long-term stable operation of the structure while reducing the negative impact on the environment and maximizing economic benefits.

[0023] As Figures 1 to 7 shown, the technical solution of the present invention is as follows: The first preferred embodiment, a full-life-cycle multi-objective optimization design method for a high-piled wharf structure, includes: S1. Taking the full straight-pile high-pile wharf structure as the research object, an optimization analysis model is established; the optimization analysis model quantifies the proportion of the bearing capacity of components used to resist external service loads in the actual project to their total bearing capacity through the effective bearing ratio; the optimization analysis model aims to improve the effective bearing ratio on the premise of meeting the structural safety. By adjusting parameters such as pile diameter, beam height, and span, the finite element method is used to calculate the strength of the structure, so as to find the optimal parameter combination.

[0024] The present invention deeply studies the full straight-pile high-pile wharf structure and constructs an optimization analysis model aiming to improve the utilization efficiency of structural materials. The full straight-pile high-pile wharf structure mainly consists of key components such as cross beams, longitudinal beams, panels, pile caps, and foundation piles. Among them, the cross beam plays a crucial role in the bearing process. Considering that the wharf structure not only bears the influence of external service loads such as vehicles, equipment, and waves during actual use, but also bears the burden of its own weight, it is particularly crucial to distinguish the contribution of different loads to the bearing capacity of components when conducting design optimization. To achieve this goal, the present invention proposes a new concept called "effective bearing ratio", which is used to quantify the proportional relationship between the bearing capacity of components used to resist external service loads and their total bearing capacity in the real engineering environment. Through this ratio, we can clearly understand the effective bearing efficiency of components to service loads when parameters such as component size, pile diameter, cross beam height, and span remain unchanged, thus providing a scientific basis for the design and optimization of the wharf structure.

[0025] S2. Conduct single-objective optimization analysis: According to the effective bearing ratio, with the variables being span and beam height, determine the optimal structural response under different combinations; the meaning of the optimal structural response is that the structural response corresponding to the optimal effective bearing ratio achieves a balance between minimizing material usage and rationalizing stress distribution while meeting the bearing capacity requirements. That is, the most economical working condition that meets the structural strength requirements.

[0026] This step starts to conduct single-objective optimization analysis, which is based on the important concept of "effective bearing ratio" proposed in S1. In this analysis, the present invention selects two key variables in the design of high-pile wharves, namely span and beam height. By changing different combinations of these two parameters, the optimal response of the wharf structure under various combinations is determined. The results of this analysis provide solid basic data support for the subsequent multi-objective optimization work. The specific results can be clearly seen in Figure 2 . It can be clearly observed from Figure 2 that, on the premise of knowing the external load conditions of the project, the selection of beam height and span affects each other. Therefore, in the subsequent optimization work, based on the data provided by Figure 2 , query and determine the effective bearing ratio under different combinations of beam height and span.

[0027] S3. Construct the construction cost objective function; the construction target cost function aims to minimize the material consumption per unit area. On the premise of meeting the structural safety requirements, it realizes the minimization of the construction cost of the high-pile wharf structure, verifying the feasibility and practicality of the optimization model. However, as a single-objective optimization method, it has not fully considered the trade-off among structural safety, economy, and environmental adaptability, and it is difficult to comprehensively reflect the multi-dimensional requirements in complex projects. Therefore, subsequent research will introduce multi-objective optimization methods to explore better design strategies under multiple constraints and performance goals, providing a more scientific and balanced optimization plan for the wharf structure. This objective function aims to minimize the material consumption per unit area. Based on the volume and distribution of typical components such as cross and longitudinal beams, panels, pile caps, and foundation piles, the cost expression is obtained through the ratio of the total material volume to the area of the design area; (1) In the formula: W min is the material required for the project per unit area, with the unit of m 3 ; is the number of components in a specific project section; is the volume of a single component, with the unit of m 3 ; S is the planar area of the project section, with the unit of m 2 , and it can be taken as 1; n is the total number of components in all project sections.

[0028] S4. Set the structural design variables. It includes key parameters such as the height and span of cross and longitudinal beams, and the side length of prestressed concrete foundation piles. Considering their influence on structural performance and modeling efficiency, and combining with engineering construction modulus and construction convenience, a discrete jump value-taking method is used to set the variable change interval. According to formula (1), the objective function of the variables of the high-pile wharf can be given as the material consumption per square meter for the span and beam height.

[0029] S5. Based on the mechanical properties and effective bearing capacity of components, establish a single-objective optimization model of the structure, and carry out optimization calculations in combination with the objective function formula to obtain the optimal combination of structural parameters (such as the optimal span and beam height) under the bearing capacity constraint. This optimization process aims to reduce the initial cost of structure construction, improve the material utilization efficiency, and provide initial design parameters and reference boundaries for the subsequent multi-objective optimization model.

[0030] S6. After completing the single-objective optimization and obtaining the preliminary structural parameters, conduct a systematic optimization design of the full straight-pile high-pile wharf structure; establish an objective function among structural safety, material economy, construction feasibility, and life-cycle carbon emissions through a multi-objective optimization method, and calculate the optimal solution set in combination with the genetic algorithm; Based on the completion of the single-objective optimization and obtaining the preliminary structural parameters, further carry out the systematic optimization design of the wharf structure. To overcome the traditional empirical process of "design - check - finalize", a structural iterative optimization mode of "design - check - optimize and redesign" is proposed. By introducing modern calculation methods and intelligent optimization algorithms, under the premise of meeting the structural bearing requirements and functional needs, multiple rounds of automated optimization are carried out on parameters such as structural geometric dimensions and material configurations, so as to seek a better balance among various performance objectives. This optimization process no longer relies on manually setting multiple design schemes for manual comparison, but establishes an objective function among multiple indicators such as structural safety, material economy, construction feasibility, and life-cycle carbon emissions through a multi-objective optimization method, and calculates the optimal solution set (Pareto optimal solution set) in combination with the genetic algorithm.

[0031] S7. Establish a multi-objective optimization model including life-cycle cost and carbon emission control, solve it using an intelligent optimization algorithm, and obtain the optimal combination of structural design parameters that takes into account economy and environmental sustainability; On the basis of completing the preliminary structural design with the construction cost as the optimization objective in S5, further expand the optimization scope, construct a life-cycle cost function, and systematically evaluate the costs of the structure in each stage of construction, operation, maintenance, renovation, and scrapping. On this basis, introduce the carbon emission factor method, combine the carbon emission characteristics of structural materials, construction processes, and operation and maintenance processes, construct a carbon emission objective function, and comprehensively consider the reliability decay law during the service period of the structure to determine the key maintenance time nodes. By establishing a multi-objective optimization model including life-cycle cost and carbon emission control, solve it using an intelligent optimization algorithm, and obtain the optimal combination of structural design parameters that takes into account economy and environmental sustainability.

[0032] Specifically, the factors in the life-cycle cost function are as follows: The cost in the design stage is represented by ; The construction stage has a long cycle and heavy tasks, and the cost composition is relatively complex, mainly including four categories: ① Construction engineering costs, covering direct costs, indirect costs, price differences, and taxes; ② Installation engineering costs, covering equipment installation and commissioning costs, and the calculation method is the same as that of construction costs; ③ Mechanical equipment costs, including equipment purchase or lease and their transportation costs; ④ Other costs, such as site, management, technical services, and production preparation costs, etc. Estimate based on a simplified method of the consumption of main materials and equipment, and represent it by : (2) Wherein: is the total cost required for the selected variables from production to use during the construction period; i is the i-th building material; is the quantity of the i-th building material; is the cost required for building a unit of material; is the cost required for the construction unit's materials; is the cost required per unit distance for transporting a unit of material; is the transportation distance of the i-th material from the construction site to the project.

[0033] Generally, the construction cost and operation and maintenance cost of a building are treated separately. However, the operation and maintenance cost is often greater than the one-time investment in project construction and will directly affect the construction and operation costs during the design stage. Therefore, reducing the construction cost will mean an increase in the operation cost, while increasing the construction cost will reduce the operation and maintenance cost. As Figure 3 shown, it is necessary to find an optimal solution between the two.

[0034] The costs during the operation stage include common operation costs such as labor, equipment, and resource consumption. It is also necessary to focus on the structural safety of the wharf. Therefore, whenever the structure needs to be maintained, this will account for a large part of the cost. If the wharf structure is not repaired until it is damaged, the losses caused at that time may be very large. Therefore, it is necessary to prevent various potential hazards of the wharf in advance, use reliable indicators to illustrate the reliability degree of the wharf components, give the time nodes and costs for repair, and then combine with the geometric dimensions of the wharf components in the design stage to comprehensively obtain the minimum cost of the wharf during the entire cycle.

[0035] The tonnage of the wharf is determined at the initial stage of design. Therefore, for the structural optimization of the wharf, the present invention mainly focuses on geometric optimization. So, the present invention only considers the influence of geometric changes on the maintenance cost represented by the reliability index. This part of the cost can be represented by as follows: (3) Wherein: is the sum of the management cost, maintenance cost, and special inspection cost of the structure during the operation stage, which is related to the floor area of the wharf; is the cost for additional maintenance due to the non-compliance of the reliability index.

[0036] During the process of the reliability of the wharf structure decreasing over time, once it approaches or is lower than the target reliability When this happens, it is necessary to maintain the structure of the wharf to prevent future potential hazards. The decision-making on the maintenance and reinforcement plan should consider both the cost required for maintenance and the resulting benefits, and will additionally consider the carbon emissions generated by wharf maintenance. A maintenance plan decision-making based on time-varying reliability is given: (4) In the formula: \(E\) is each maintenance plan, is the plan with the greatest economic benefit among all plans; is the time-varying reliability during the service stage; is the target reliability; is the cost for additional maintenance due to non-compliance of the reliability index; is the allowable cost for the maintenance of wharf components.

[0037] Clarify the appearance grade of durability loss and the repair target of components, and then comprehensively consider to obtain the repair plan. Once the wharf components meet the maintenance conditions, scientific maintenance should be carried out on the wharf components. The maintenance cost can be divided into the following three parts: (1)Failure maintenance cost During the design stage of high-piled wharves, it is necessary to focus on the safety of the upper components of the wharf and ensure their safety and reliability throughout the service process according to the specifications. Although the components meet the requirements during design, they may still fail during the service process. In this process, continuous inspections are required to ensure timely maintenance of the wharf components before failure. The components only have a certain probability of failure in this process. Therefore, the calculation of the failure maintenance cost should be based on the failure probability: (5) In the formula: is the cost before the i th maintenance of the wharf components; is the failure probability before the i th repair of the wharf components; is the total failure maintenance cost.

[0038] (2)Cost during the repair process

[0039] During the entire life cycle of a wharf, the longer the time, the greater the probability of wharf component damage, the greater the degree of damage, the more complex the technology required to repair the damaged components, the further increase in the amount of repair materials used, and thus the rising cost of each repair. Therefore, the cost of the entire repair process is related to the number of repairs, repair time, and repair materials. In addition, there will still be a period of time between the last repair time of the wharf components and the end of the entire wharf service life. Although no repairs to the wharf will be considered during this period, in order to avoid damage to the wharf caused by accidental events, routine monitoring and maintenance during this period are essential and even more cautious. Therefore, the unit maintenance cost during this period will increase. The cost calculation of the repair process designed in this paper is as follows: (6) Where: is the total cost of the repair process; is the repair cost for reaching the repair index for the i-th time; is the increase in reliable performance after the i-th repair; is the change value of the maintenance cost of the component over time from the last repair time of the component to the expiration of the service life; is the remaining time from the last repair of the wharf component to the expiration of the service life; is the cost that does not change with time when the wharf component is repaired; (3)Cost of repair downtime

[0040] When considering the maintenance cost of wharf components, it not only includes the component failure maintenance cost and the repair process cost, but also the business loss caused by repair downtime should be considered. Since the damage types and degrees of components are different, different repair methods are adopted, and thus the downtime caused by repair is also different. The business loss caused by downtime must also be attributed to the total repair cost. The specific downtime cost can be calculated according to the following formula: (7) Where: is the repair downtime cost; is the economic loss caused per unit time of downtime; is the total time required for wharf failure repair; The maintenance cost considered in the present invention, the cost during repair, and the cost lost due to repair downtime. Combining the above, the repair cost function based on the time-varying reliability index can be obtained as follows: (8) In the whole life cycle of a wharf, the abandonment and demolition stage is the last stage. Since the design life of a wharf is generally relatively long, there are relatively few wharves that have reached the abandonment and demolition stage at present. Therefore, the research on the demolition costs of this part is not comprehensive, and the demolition costs of other construction industries can be used for reference. The cost in the demolition stage is represented by as shown in the following formula.

[0041] If the service life of a high-pile wharf is within 0 < N ≤ 50, then (9) If the service life of a high-pile wharf N ≥ 50, then (10) In the formula: is the percentage of the demolition cost in the design stage and the construction cost, generally taking 3%; is the cost discount rate, which can be characterized and calculated by the social discount rate and the price fluctuation level. The specific calculation model is as follows: (11) In the formula: is the social discount rate; is the cost discount rate in the i-th year; is the annual change rate of PPI; is the annual change rate of PPI.

[0042] Combining the above costs, the life cycle cost (LCC) of a high-pile wharf is obtained, which refers to all the expenses during the process from the emergence to the demise of the wharf. The life cycle cost of the wharf can be divided into four stages according to the life cycle, namely the design stage, the construction stage, the operation stage, and the demolition stage. This paper has analyzed the cost composition of each stage, especially conducted in-depth research on the cost composition of the operation and maintenance stage, and obtained the total life cycle cost function of the high-pile wharf as shown in Formula 12.

[0043] (12) Only qualitative analysis is carried out in the design stage, so the cost in the design stage is not included in the calculation. In addition, the operation and maintenance stage cost based on time-varying reliability is substituted into the above function and simplified to obtain the improved life cycle cost function per unit area as follows: (13) Among them: C is the improved life cycle cost function per unit area, A is the entire structural area, is the sum of the management cost, maintenance cost, and special inspection cost of the structure in the operation stage, which is related to the building area of the wharf; i is the i-th building material; is the quantity of the i-th building material; is the cost required to build a unit of material; is the cost required for the construction unit's materials; is the cost required per unit distance for transporting a unit of material; is the transportation distance of the i-th material from construction to the project; is the cost before the i-th maintenance of the wharf components; is the failure probability before the i-th repair of the wharf components; n is the duration of the entire life cycle, in years; is the repair cost for reaching the repair index for the i-th time; is the increase in reliability performance after the i-th repair; is the change value of the maintenance cost of the components over time from the last repair of the components until the end of service life; is the remaining time from the last repair of the wharf components until the end of service life; is the cost that does not change over time when the wharf components are repaired; is the economic loss caused per unit time of shutdown; is the total time required for the wharf failure repair; is the total cost required for the selected variables from production to use during the construction period; is the percentage of the demolition cost in the design stage and construction cost, generally taking 3%; is the discount rate of the cost in the first year, characterized by the social discount rate and the price fluctuation level for calculation, and the specific calculation model is as shown in formula (11).

[0044] S8. Integrate the carbon emission function and the life cycle cost function to construct a multi-objective function system; the above multi-objective optimization model including life cycle cost and carbon emission control takes into account the structural economy and environmental protection, and measures the comprehensive performance of the structure during the service life through mutually related objective functions; the carbon emission evaluation method is shown in Figure 4 .

[0045] Specific calculation steps for the carbon emission in the whole life cycle: (I) Planning and design stage The planning and design stage refers to the carbon emissions generated by the design unit during the period from the start of project planning, through drawing design, to before the start of construction. It mainly includes the carbon emissions generated by air conditioners, hot water, and other living equipment required for the office site. Although the proportion of this part of the carbon emissions in the entire project cycle is very small, the decisions made in the design and planning stage will have a great impact on the carbon emissions in other stages of the project's whole life cycle. Therefore, usually in actual calculations, this part of the carbon emissions is ignored.

[0046] The carbon emissions in the planning and design stage can be calculated by the following formula: (14) Where: is the carbon emissions in the planning and design stage; is the emission factor of the i-th energy source in the planning and design stage; is the office energy consumption, which is mainly electricity in the planning and design stage.

[0047] Although the carbon emissions in the planning and design stage are the least in the entire life cycle of the wharf, the function type and structure type of the wharf are determined in this stage. At the same time, the material usage and energy usage are also determined. The design scheme selected and the factors considered will directly determine whether the wharf is low-carbon in the entire life cycle. In addition, the throughput of the wharf design and the recycling rate of materials will directly affect the carbon emissions of the wharf. Therefore, although the emissions in this stage are small, the effect of a good design scheme is very significant.

[0048] (2) Construction stage The energy and material consumption in the construction stage of the wharf are relatively large. Therefore, the proportion of carbon emissions in this stage in the entire cycle is also relatively large. For the convenience of calculation, the construction stage is divided into three parts of carbon emissions: raw material production and processing, material transportation, and construction process. The consumption of materials and energy required for these three parts will be calculated separately according to the principle of sub-items, and the consumption of energy such as electricity, gasoline, and diesel consumed during material production will be considered. The specific calculation method is to multiply the carbon emission factors of each material and energy by the corresponding usage obtained by consulting the literature. The specific formula is shown in 15.

[0049] (15) Where: is the carbon emissions in the process of raw material construction; is the carbon emissions in the process of raw material production and processing; is the carbon emissions in the process of material transportation; is the carbon emissions in the construction process.

[0050] ① Raw material production and processing This paper mainly uses the carbon emission inventory method. Therefore, it is necessary to determine some of the main materials and their usage amounts. For high-piled wharves, these materials include steel bars, concrete, cement, rubber, glass, etc. Not only the usage amounts of these raw materials need to be considered, but also their transportation and processing should be taken into account. However, for wharves, there are a wide variety of materials, and reinforced concrete materials are mainly used. Therefore, this paper only makes a quantitative calculation for reinforced concrete as shown in Equation 16: (16) In the formula: is the total carbon emissions generated during the production process of raw materials; is the emission factor of the i-th type of energy for the production and processing of raw materials; is the consumption amount of the i-th type of material.

[0051] ② Material transportation The materials used in the project need to be transported to the project location for construction after production and processing. For example, the transportation of steel bars. The demand for steel bars is large and the transportation distance is usually not negligible. This part of the energy consumption is related to the transportation method and distance. In addition, the distances between the material production site, processing site, construction site, and waste material yard cannot be ignored. Therefore, the transportation distance is also an important content for calculation. The specific calculation formula is shown in Equation 17: (17) In the formula: is the carbon emission factor of the energy consumed by the i-th type of transportation tool for transporting the corresponding material, with the unit of ; is the mass of the i-th type of material that needs to be transported, with the unit of t; is the number of kilometers for the transportation of the i-th type of material, with the unit of km; When calculating carbon emissions in actual engineering, special personnel can conduct data statistics on the on-site energy consumption situation, transportation distance, and transportation times to obtain relatively accurate values. However, usually in most cases, only estimates can be made based on statistical data.

[0052] ③ Construction process The carbon emissions in this part are mainly due to different design schemes, resulting in different construction methods. Whether it is the construction period or construction equipment, they will all be different. For example, different side lengths of foundation piles require different barges and pile driving boats. The construction difficulty and duration will cause differences in carbon emissions. Therefore, the carbon emissions in this part will be attributed to the different energy consumption of different equipment to calculate the specific carbon emissions, as shown in Equation 18.

[0053] (18) In the formula: is the number of shifts of the i-th construction equipment, with the unit of shift; is the energy consumption per shift of the i-th construction equipment, with the unit of kg; is the carbon emission factor per shift of the i-th construction equipment, with the unit of .

[0054] (3) Operation and maintenance stage The operation stage is the main stage of the whole life cycle, from the completion of construction to before recycling and disposal. The carbon emissions during this period are mainly divided into two parts, namely, the carbon emissions caused by normal operation energy consumption and the additional carbon emissions released during the maintenance and repair of the wharf. The carbon neutralization effect brought by the surrounding green plants is not considered. The carbon emissions in the operation and maintenance stage can be calculated by the following formula: (19) (20) In the formula: is the carbon emission in the operation and maintenance stage, with the unit of ; is the carbon emission caused by normal operation energy consumption, with the unit of ; is the carbon emission in the repair stage, with the unit of ; is the annual energy consumption in the normal operation stage, with the unit of kWh; is the emission factor of the i-th energy in the operation and maintenance stage, with the unit of t / MWh; is the service life.

[0055] (4) Recycling and demolition stage There is little carbon emission data in the existing recycling and demolition stage. For the port industry, the design life is generally 50 years or more. Therefore, most wharves have not reached the retirement stage. According to the analysis of the existing literature, the recycling and demolition stage can refer to the building construction industry, mainly including the carbon emissions of the mechanical equipment used during demolition and the carbon emissions of the recycling part. The recycling part is further divided into two parts: acting as waste treatment and recyclable. However, it is very difficult to classify and recycle the building materials in detail for the recycling of high-piled wharves. In most cases, only the carbon emissions during transportation need to be considered.

[0056] Incorporate low carbon into the design of the wharf structure optimization. Taking each component of the wharf as a variable, calculate the carbon emissions of the entire life cycle of the wharf under each plan. With the lowest total carbon emissions as the objective function, the carbon emission objective function is as follows:

[0057]

[0058] (21) P is the carbon emission function per unit area of the wharf's entire life cycle, is the carbon emissions during the recycling and demolition stage, is the emission factor of the i-th type of energy during the planning and design stage, is the emission factor of the i-th type of energy for raw material production and processing; is the carbon emission factor of the energy consumed by the i-th type of transportation vehicle for transporting the corresponding materials, is the carbon emission factor per work shift of the i-th type of construction equipment, is the emission factor of the i-th type of energy during the operation and maintenance stage; is the office energy consumption, mainly electricity during the planning and design stage; is the consumption of the i-th type of material; is the number of kilometers for transporting the i-th type of material; is the number of work shifts of the i-th type of construction equipment; is the energy consumed per year during the normal operation stage, with the unit of kWh; is the carbon emissions during the maintenance stage, with the unit of kWh; is the service life, A is the area of the entire structure, and the carbon emission result per unit area is calculated.

[0059] Since it is difficult to quantify the carbon emissions in many stages of the above function, such as the energy consumption during the design stage, the bill of quantities and the number of work shifts during the construction process in the construction stage. In addition, there is a lack of measured data for carbon emissions during the demolition stage, so 9% of the materialization stage is taken. The simplified function is as follows: (22) Among them, P is the carbon emission function per unit area of the wharf's entire life cycle, is the emission factor of the i-th type of energy; is the consumption of the i-th type of material; is the number of kilometers for transporting the i-th type of material; is the energy consumed per year during the normal operation stage, with the unit of kWh; is the service life, is the carbon emissions during the maintenance stage, with the unit of 。

[0060] S9. Based on the established multi-objective optimization model, the non-dominated sorting genetic algorithm (NSGA-II) is used for solution. It is one of the most widely used multi-objective evolutionary algorithms (MOEAs), with powerful global search ability and good optimization performance. By combining non-dominated sorting, crowding degree calculation and elitist strategy, this algorithm can effectively generate a set of Pareto optimal solutions representing different trade-off relationships, providing multiple balance options for engineering decisions.

[0061] The present invention uses the non-dominated sorting genetic algorithm (NSGA-II) to realize the optimization solution of the structural parameters of high-piled wharves under the dual objectives of "total life cycle cost" and "total carbon emissions". The overall process of the algorithm is as Figure 5 shown, and the corresponding relationship between its steps and the structural design of high-piled wharves is as follows: Initialize the population to generate the first-generation population of design parameters. Each "individual" corresponds to a set of structural design schemes, specifically including geometric parameters such as pile diameter, pile spacing, crossbeam height, longitudinal beam size, and panel thickness. The parameter range is set according to structural specifications, empirical data or preliminary design results to form a reasonable design space.

[0062] Non-dominated sorting of the first-generation population: Calculate the values of two objective functions for all design schemes (individuals) in the initial generation. Objective 1: Total life cycle cost, including the costs of construction, operation and maintenance, repair and demolition phases; Objective 2: Total carbon emissions over the entire life cycle of the structure, including carbon emissions generated during material production, transportation, construction and use phases. Divide the levels according to the non-dominated sorting method, and preferentially retain the design schemes that are not inferior in both objectives.

[0063] The selection, crossover, and mutation operations adopt the genetic algorithm selection mechanism (such as tournament selection) to perform parameter crossover and small-scale mutation on the design individuals with higher fitness in the population. For example, combine an individual (scheme) of "larger pile diameter + moderate beam height" with another individual (scheme) of "wider pile spacing + thinner panel" to generate a new design scheme, so as to simulate the process of scheme combination and parameter exploration in structural design. Merging the parent and offspring populations Combine the current population with the newly generated offspring to form an enlarged set of design schemes, which is convenient for screening better solutions from a larger design space. Fast non-dominated sorting Perform non-dominated sorting on the merged population again to divide different levels. The sorting result represents which batch of structural design schemes have better trade-offs under the dual objectives of "life cycle cost - carbon emissions" in the current optimization generation. Crowding degree calculation and new population generation Calculate the "crowding degree" for the design individuals within the same level, that is, the distribution density of the solutions, which is used to maintain the diversity of the solution set and avoid the optimization falling into local convergence. Prioritize the selection of structural design schemes with uniform distribution and strong representativeness to form a new generation of population. Iterative evolution (Gen = Gen+1) Set the maximum number of evolutionary generations, such as 50 generations or 100 generations. Each time a round of parent-offspring alternation and new population generation is completed, perform an iteration of "Gen = Gen+1", repeat steps 3 - 6, and gradually evolve a better solution set. Termination determination and result output When the number of evolutionary generations reaches the set upper limit, the algorithm terminates and outputs the final Pareto front solution set. This solution set corresponds to multiple groups of structural design parameter combinations, each of which achieves different balances between the life cycle cost and carbon emission control, and can be preferentially adopted by designers according to the actual engineering requirements. Based on the above ideas, through the two objective functions established previously, that is, the life cycle cost calculation method considering reliability with the unit area cost as the evaluation index is the objective function 1, such as y1 in formula (23), and the life cycle carbon emission evaluation method with the unit area carbon emission as the evaluation index is the objective function 2, such as y2 in formula (23), respectively take the reduction of the unit area cost and the reduction of the unit area carbon emission within the life cycle as the optimization objectives.

[0064] (23) That is, the objective functions of multi-objective optimization are as follows: (24) In the formula, C is the improved life cycle unit area cost function, which emphasizes the cost of the wharf in the later operation and maintenance. Combining the concept of structural reliability, it gives the sum of the initial construction cost and subsequent maintenance cost of each component; P is the carbon emission function per unit area of the wharf life cycle.

[0065] Based on the established multi-objective model, the non-dominated sorting genetic algorithm (NSGA-II) is used for solving. This algorithm is implemented through the following steps: S9.1, Encoding: Adopt real number encoding method, directly use design parameter values (such as beam height, panel thickness, etc.) to represent individuals, enhancing the intuitiveness and accuracy of the model; S9.2, Initializing the population: Randomly generate multiple design schemes within the set parameter range to cover a wide solution space; S9.3, Non-dominated sorting: Classify individuals according to objective functions such as cost and carbon emissions, and retain the schemes that perform well in multiple objectives; S9.4, Crowding degree comparison: Evaluate the distribution density of individuals in the solution space, guide the search diversity, and avoid premature convergence; S9.5, Selection: Combine non-dominated rank and crowding degree indicators to select representative individuals as the basis for the next generation of reproduction; S9.6, Crossover and mutation: Use genetic operations to generate new solutions, and generate potential excellent designs through recombination and perturbation; S9.7, Generating a new generation of population: Integrate the parent and offspring individuals and reorder them, retain the excellent solutions and update the optimization direction to form a Pareto front solution set.

[0066] Apply the multi-objective optimization model, adopt the multi-objective optimization algorithm, and after multiple iterations, obtain the Pareto optimal solution set, Figure 6 which is the Pareto front for this optimization project. The abscissa is the objective function of the life cycle cost, and the ordinate is the objective function of the life cycle carbon emissions. One five-pointed star represents a Pareto optimal solution, and all Pareto optimal solutions constitute the Pareto front. That is, any point on the Pareto front can be used as a solution to the multi-objective optimization problem. This wharf project analyzes the optimal solutions by selecting five variables: the height of the transverse and longitudinal beams, the panel thickness, the spacing of the transverse beams, the height of the transverse and longitudinal beams, and the pile side length. Part of the Pareto front solution set after 1000 generations of iterative calculations by the NSGA-II algorithm, with the abscissa representing the objective function of the life cycle cost and the ordinate representing the objective function of the life cycle carbon emissions. It can be clearly seen from the figure that among these solutions, the two objective functions affect each other, and an increase in one objective value will cause a decrease in the other objective value. For the AB section, the rate of decrease of objective 2 is greater than the rate of increase of objective 1. For the BC section, the rate of decrease of objective 2 is less than the rate of increase of objective 1. Point B can be used as the inflection point of the Pareto front solution. Therefore, for decision-makers, if they want to sacrifice the value of objective 2 to obtain the increase of objective 1, taking solutions in the AB section will obtain higher value, and vice versa.

[0067] Table 1 is the randomly selected local optimal solution table

[0068] Five of these solutions are selected as shown in Table 1. These solutions show that the cost and low carbon of the optimized design fluctuate within a certain range with small changes in variables. The optimal values of the crossbeam and longitudinal beam heights are distributed rather dispersedly, while the values of the panel thickness are all close to 0.3 m. The optimal values of the crossbeam spacing and longitudinal beam spacing are both close to the upper limit values, and the value of the pile foundation is close to 0.7 m or 0.75 m. Among them, when the cost is high, the carbon emissions are low; when the cost is low, the carbon emissions are high. The maximum cost per unit area in the whole life cycle is 262,800 yuan, and the corresponding carbon emissions per unit area are 3361 。

[0069] The multi-objective optimization algorithm provides a way to solve multi-objective optimization and gives several solutions for reference. However, the final decision still needs to be made according to the specific engineering situation and the decision-maker's thinking. The comparison of the cost and carbon emissions of the optimization results is as Figure 7 shown in the figure. The green bar chart in the figure represents the carbon emissions, and the red represents the cost. The cost and carbon emissions in the whole life cycle affect each other within a certain range. In actual engineering design, one of the optimization schemes must be selected according to the different focuses of different investors. It is assumed in this paper that environmental constraints are the first influencing factor during engineering construction. Therefore, the lowest-carbon design scheme is selected, that is, the second group of optimization schemes is selected as the final optimization result.

[0070] The second preferred embodiment is a whole-life cycle multi-objective optimization design system for a high-pile wharf structure, which is used to implement the method of the first preferred embodiment. The system includes: An optimization analysis model construction module takes the full straight-pile high-pile wharf structure as the research object and establishes an optimization analysis model; the optimization analysis model quantifies the proportion of the bearing capacity of components used to resist external service loads in their total bearing capacity through the effective bearing ratio; A single-objective optimization analysis module determines the optimal structural responses under different combinations according to the effective bearing ratio, with the variables being the span and beam height; A cost function module constructs a construction cost objective function; A setting module sets the structural design variables and sets the variable change interval in a discrete jump value-taking manner; A single-objective optimization module establishes a single-objective optimization model based on the mechanical properties and effective bearing capacity of components, and conducts optimization calculations in combination with the objective function formula to obtain the optimal combination of structural parameters under the bearing capacity constraint; provides initial design parameters and reference boundaries for the multi-objective optimization model; The genetic algorithm calculation module conducts systematic optimization design of the full straight-pile high-pile wharf structure after completing single-objective optimization and obtaining the preliminary structural parameter basis; by means of the multi-objective optimization method, an objective function is established among structural safety, material economy, construction feasibility, and life-cycle carbon emissions, and the genetic algorithm is combined to calculate the optimal solution set; The parameter combination calculation module establishes a multi-objective optimization model including life-cycle cost and carbon emission control, and uses an intelligent optimization algorithm to solve it to obtain the optimal structural design parameter combination that takes into account both economy and environmental sustainability; The integration module integrates the carbon emission function and the life-cycle cost function to construct a multi-objective function system; The optimization module is based on the established multi-objective optimization model and uses the non-dominated sorting genetic algorithm for solution.

[0071] The third preferred embodiment is a computer program product, including a computer program, which when executed by a processor, implements the above-mentioned full life-cycle multi-objective optimization design method for the high-pile wharf structure.

[0072] The fourth preferred embodiment is an information data processing terminal for implementing the above-mentioned full life-cycle multi-objective optimization design method for the high-pile wharf structure.

[0073] The fifth preferred embodiment is a computer-readable storage medium, including instructions, which when running on a computer, cause the computer to execute the above-mentioned full life-cycle multi-objective optimization design method for the high-pile wharf structure.

[0074] The present invention comprehensively introduces the life-cycle concept, not only considering the durability of the structure, but also taking into account economy and environmental protection; When applying the present invention, a time-varying reliability model is adopted, which can accurately predict the best time point for maintenance, thereby optimizing the operation and maintenance strategy; The present invention also uses the NSGA-II multi-objective evolutionary algorithm, effectively avoiding the subjectivity of artificial weight assignment, providing a series of Pareto optimal design schemes, and greatly improving the scientific nature of engineering decision-making; In addition, the present invention is particularly applicable to the design optimization fields of port wharves and other large-scale structural projects that need to serve for a long time.

[0075] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in whole or in part in the form of a computer program product, the computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more integrated available media. The available medium may be a magnetic medium (such as a floppy disk, a hard disk, or a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state disk (SSD)).

[0076] The above description is only a preferred embodiment of the present invention, and does not impose any form of limitation on the present invention. Any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention are all within the scope of the technical solutions of the present invention.

Claims

1. A full-life cycle multi-objective optimization design method for a high-piled wharf structure, characterized in that, Including: S1. Taking the high-piled wharf structure with all vertical piles as the research object, an optimization analysis model is established; The optimization analysis model quantifies the proportion of the bearing capacity of components used to resist external service loads in actual projects to their total bearing capacity through the effective bearing ratio; S2. Conduct single-objective optimization analysis: According to the effective bearing ratio, with the span and beam height as variables, determine the optimal structural responses under different combinations; S3. Construct a construction cost objective function; S4. Set structural design variables, and adopt a discrete jump value-taking method to set the variable change interval; S5. Based on the mechanical properties and effective bearing capacity of components, establish a single-objective optimization model, and carry out optimization calculations in combination with the objective function formula to obtain the optimal structural parameter combination under the bearing capacity constraint; Provide initial design parameters and reference boundaries for the multi-objective optimization model; S6. After completing single-objective optimization and obtaining the preliminary structural parameters, conduct systematic optimization design of the high-piled wharf structure with all vertical piles; Through the multi-objective optimization method, establish an objective function among structural safety, material economy, construction feasibility and life cycle carbon emissions, and combine the genetic algorithm to calculate the optimal solution set; S7. Establish a multi-objective optimization model including life cycle cost and carbon emission control, and use an intelligent optimization algorithm to solve it to obtain the optimal structural design parameter combination that takes into account economy and environmental sustainability; S8. Integrate the carbon emission function and the life cycle cost function to construct a multi-objective function system; S9. Based on the established multi-objective optimization model, use the non-dominated sorting genetic algorithm to solve it.

2. The full-life multi-objective optimization design method for the high-piled wharf structure according to claim 1, characterized in that The high-piled wharf structure with all vertical piles includes cross beams, longitudinal beams, panels, pile caps and foundation piles.

3. The full-life cycle multi-objective optimization design method for the high-piled wharf structure according to claim 1, characterized in that S2 includes: Under the condition of known engineering external loads, obtain the effective bearing ratios corresponding to different beam heights and spans, and draw the corresponding relationship diagram among beam height, span and effective bearing ratio.

4. The full-life multi-objective optimization design method for the high-piled wharf structure according to claim 1, characterized in that, The construction cost objective function takes the minimum material consumption per unit area as the optimization goal, and based on the volume and distribution of typical components, obtains the cost expression through the ratio of the total material volume to the design area: Wherein: W min is the material required for the project per unit area, with the unit of m 3 ; is the number of components in a specific project section; is the volume of a single component, in m 3 ; S is the unit plane area of the engineering section, with the unit of m 2 ; n is the total number of components in all project sections.

5. The full-life-cycle multi-objective optimization design method for the high-piled wharf structure according to claim 4, characterized in that S4 includes: According to the cost expression, give the objective function of the high-piled wharf with the span and beam height as variables, and the unit is the material consumption per square meter.

6. The full-life multi-objective optimization design method for the high-piled wharf structure according to claim 4, characterized in that In S7, the improved life cycle cost function per unit area is as follows: ； Where: C is the improved unit area cost function of the whole life cycle, A is the area of the entire structure, which is the sum of the management cost, maintenance cost, and special inspection cost of the structure during the operation stage, related to the building area of the wharf; i is the i-th building material; is the quantity of the i-th building material; is the cost required for building a unit of material; is the cost required for the construction unit's materials; is the cost required per unit distance for transporting the i-th material; is the transportation distance of the i-th material from construction to the project; is the cost before the i-th maintenance of the wharf components; is the failure probability before the i-th repair of the wharf components; n is the duration of the entire life cycle, in years; is the repair cost for the i-th time when reaching the repair index; is the increase in reliability performance after the i-th repair; is the change value of the maintenance cost of the components over time from the last repair of the components until the end of the service life; is the remaining time of the wharf components from the last repair until the end of the service life; is the cost that does not change with time when the wharf components are repaired; is the economic loss caused per unit time of shutdown; is the total time required for the failure repair of the wharf; is the total cost required for the selected variables from production to use during the construction period; is the percentage of the demolition cost in the design stage and construction cost; is the cost discount rate in the first year, characterized by the social discount rate and the price fluctuation level, and the specific calculation model is as follows: In the formula: is the social discount rate; is the cost discount rate in the i-th year; is the annual change rate of PPI.

7. The full-life multi-objective optimization design method for the high-piled wharf structure according to claim 4, characterized in that In S8, the carbon emission function per unit area of the wharf life cycle is as follows: ； Among them, P is the carbon emission function per unit area of the whole life cycle of the wharf, is the emission factor of the i-th energy source; is the consumption of the i-th material; is the number of kilometers for transporting the i-th material; is the annual energy consumption during the normal operation stage, with the unit of kWh; is the service life, is the carbon emission during the maintenance stage, with the unit of .

8. A full-life multi-objective optimization design system for a high-piled wharf structure, characterized in that, Including: An optimization analysis model construction module, taking the high-piled wharf structure with all vertical piles as the research object, and establishing an optimization analysis model; The optimization analysis model quantifies the proportion of the bearing capacity of components used to resist external service loads in actual projects to their total bearing capacity through the effective bearing ratio; A single-objective optimization analysis module, according to the effective bearing ratio, with the span and beam height as variables, determines the optimal structural responses under different combinations; A cost function module, constructing a construction cost objective function; A setting module, setting structural design variables, and adopting a discrete jump value-taking method to set the variable change interval; Single-objective optimization module, based on the mechanical properties and effective load-bearing capacity of components, establishes a single-objective optimization model, and conducts optimization calculations in combination with the objective function formula to obtain the optimal combination of structural parameters under the constraint of load-bearing capacity; provides initial design parameters and reference boundaries for the multi-objective optimization model; Genetic algorithm calculation module, after completing single-objective optimization and obtaining the preliminary structural parameters, conducts systematic optimization design of the full straight-pile high-pile wharf structure; establishes an objective function among structural safety, material economy, construction feasibility and life-cycle carbon emissions through the multi-objective optimization method, and combines the genetic algorithm to calculate the optimal solution set; Parameter combination calculation module, establishes a multi-objective optimization model including life-cycle cost and carbon emission control, and uses intelligent optimization algorithms to solve it to obtain the optimal combination of structural design parameters that takes into account economy and environmental sustainability; Integration module, integrates the carbon emission function and the life-cycle cost function to construct a multi-objective function system; Optimization module, based on the established multi-objective optimization model, uses the non-dominated sorting genetic algorithm to solve it.

9. An information data processing terminal, characterized in that A full-life-cycle multi-objective optimization design method for the high-pile wharf structure described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Including instructions, when it runs on a computer, it causes the computer to execute the full-life-cycle multi-objective optimization design method for the high-pile wharf structure described in any one of claims 1 to 7.

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