Multi-objective optimization method for immersed tube tunnel construction scheme

Abstract

The application relates to a multi-objective optimization method for a construction scheme of a immersed tube tunnel, and belongs to the technical field of immersed tube tunnel construction. The method comprises the following steps: based on the construction site conditions of the immersed tube tunnel, a plurality of feasible construction schemes of the immersed tube tunnel are determined; a multi-objective optimization model is constructed based on the construction period, construction carbon emission and construction cost as optimization objectives, and the construction period is taken as a constraint condition; a multi-objective optimization algorithm is used to solve the multi-objective optimization model, and a Pareto solution set is obtained; a multi-objective decision method is used to screen an optimal solution from the Pareto optimal solution set, and an optimal solution set is formed; and an optimal solution with the lowest sum of carbon transaction amount and total investment in the optimal solution set is calculated as the final scheme of the immersed tube tunnel construction, which can guarantee the timeliness and economy of the construction scheme, effectively control the carbon emission in the whole construction process, and meet the requirements of the 'green construction' concept and global ecological environment protection.

Description

Technical Field

[0001] This invention belongs to the field of immersed tunnel construction technology, and in particular relates to a multi-objective optimization method for immersed tunnel construction schemes. Background Technology

[0002] Immersed tunnels are underground transportation facilities that span rivers, bays, and other bodies of water. In actual engineering construction, multiple construction schemes are often designed for the same immersed tunnel project. These different schemes differ in construction technology, equipment selection, and other aspects, resulting in variations in carbon emissions, construction periods, and construction costs. Currently, the selection of immersed tunnel construction schemes typically relies solely on two indicators: construction period and construction cost—prioritizing the scheme with the shorter construction period and lower cost. While this traditional evaluation method helps ensure the project's timeliness and economic efficiency, its considerations are relatively singular, failing to incorporate carbon emissions generated throughout the entire construction process into a comprehensive evaluation, thus failing to reflect the scheme's environmental sustainability performance.

[0003] With increasing global awareness of ecological and environmental protection, the concept of "green construction" continues to deepen in the field of infrastructure construction. Immersed tunnel projects are large-scale, consume significant resources and energy, and generate substantial carbon emissions during construction, with an environmental impact that cannot be ignored. Against this backdrop, relying solely on construction period and cost as the basis for decision-making is no longer sufficient to meet the requirements of current sustainable engineering development. Summary of the Invention

[0004] To address the shortcomings of related technologies, this invention provides a multi-objective optimization method for immersed tunnel construction schemes. By constructing a multi-objective optimization model encompassing construction period, carbon emissions, and construction costs, and setting a construction period constraint, a Pareto solution set is obtained using a multi-objective optimization algorithm. The optimal solution set is then selected through a multi-objective decision-making method, ultimately choosing the scheme with the lowest sum of carbon trading amount and total investment as the final construction scheme. This method ensures both the timeliness and economy of the construction scheme while effectively controlling carbon emissions throughout the construction process, aligning with the "green construction" concept and global ecological environmental protection requirements. It achieves synergistic optimization of construction period, cost, and environmental benefits in immersed tunnel engineering, enhancing the scientific, rational, and sustainable nature of construction scheme selection. This method is applicable to the optimization decision-making of construction schemes for various types of immersed tunnel projects.

[0005] This invention provides a multi-objective optimization method for the construction scheme of immersed tunnels, comprising the following steps: S1. Based on the construction site conditions of the immersed tunnel, several feasible construction schemes for the immersed tunnel were determined. S2. Based on the optimization objectives of construction period, construction carbon emissions, and construction cost, a multi-objective optimization model is constructed, with construction period as the constraint condition. S3. Solve the multi-objective optimization model using a multi-objective optimization algorithm to obtain the Pareto solution set; S4. Use a multi-objective decision-making method to select the optimal solution from the Pareto optimal solution set to form the optimal solution set; S5. Calculate the optimal solution with the lowest sum of carbon trading amount and total investment in the optimal solution set and take it as the final solution for the construction of the immersed tunnel.

[0006] The technical solution involves a series of steps: determining feasible construction schemes, constructing a multi-objective optimization model with objectives of construction period, carbon emissions, and construction cost, obtaining Pareto solutions using multi-objective optimization algorithms, selecting the optimal solution using multi-objective decision-making methods, and determining the final scheme based on the minimum sum of carbon trading amount and total investment. This forms a complete and engineerable decision-making process for immersed tunnel construction schemes. Compared to traditional decision-making methods that only consider construction period and cost, this approach incorporates carbon emissions throughout the entire construction process into a quantitative optimization system, enabling collaborative decision-making based on multi-dimensional indicators. This methodological process ensures that the construction scheme achieves comprehensive optimization in terms of economy, construction period efficiency, and green and low-carbon characteristics.

[0007] In some embodiments of this application, in step S2, the multi-objective optimization model F is: F = min(DU, CE, COST). Where DU represents the construction period; CE represents the carbon emissions during construction; and COST represents the construction cost.

[0008] In the technical solution, the multi-objective optimization model is defined as simultaneously minimizing the construction period, carbon emissions during construction, and construction costs. Environmental indicators and traditional engineering indicators are modeled and optimized in a unified manner, so that the three objectives can be solved in a balanced way under the same mathematical framework. This avoids the deterioration of other indicators caused by prioritizing a single indicator, and provides a quantifiable, solvable, and comparable multi-objective evaluation basis for immersed tunnel construction schemes.

[0009] In some embodiments of this application, the construction of the immersed tunnel includes multiple procedures, each of which includes multiple execution modes; the construction period DU is: DU= ; Where P is the set of all process paths in the node network, and L... n It is the nth path in P, P L It is path L n The set of processes in d ij It is the time corresponding to the j-th execution mode of the i-th process, m i It is the number of execution mode groups, y ij This is the i-process execution mode.

[0010] In the technical solution, by constructing a construction period calculation formula based on the process network path set, the construction time corresponding to different execution modes of each process, and the process execution status variables, it can truly reflect the total construction period composition of the immersed tunnel under multiple processes, multiple paths, and multiple construction modes, realize the accurate quantification of the construction period of different construction schemes, and improve the accuracy of the construction period target calculation and the engineering adaptability in the multi-objective optimization model.

[0011] In some embodiments of this application, the construction carbon emissions (CE) are: CE= ; Where P is the set of all process paths in the node network, and L... n It is the nth path in P, P L It is path L n The set of processes in d ij It is the time corresponding to the j-th execution mode of the i-th process, m i It is the number of execution mode groups, y ij It is the i-process execution mode, c ij It is the carbon emissions of process i.

[0012] In the technical solution, by combining the process path, process execution mode, construction time and unit process carbon emission to construct a construction carbon emission calculation formula, the overall construction carbon emission can be decomposed into each process and corresponding execution mode, realizing the refined accounting of carbon emission of different construction schemes, and providing real and reliable environmental benefit quantitative indicators for multi-objective optimization.

[0013] In some embodiments of this application, the carbon emissions of step i are c ij =E pu +E eu +E fu +E mu ; Among them, E pu E represents the carbon emissions from manual processes in the i-th step. eu E represents the carbon emissions from purchased electricity in the i-th process. fu It is the carbon emissions from fossil fuel consumption in the i-th process, E mu It is the carbon emission of building materials in the i-th process; E pu =EF p ×AD p ; Among them, EF p It is an artificial carbon emission factor, AD p It represents the number of man-days required for a specific process. E eu =EF e ×AD e ; Among them, EF e It is the local carbon emission factor for electricity, AD e It represents the electricity consumption of a certain process. E fu =EF f ×AD f ; Among them, EF f It is a carbon emission factor of fossil fuels, AD f It represents the amount of fossil fuels consumed in a certain process. E mu = ; Among them, EF mk It is the carbon emission factor of the kth building material, AD mk It represents the consumption of the kth type of building material.

[0014] The technical solution further breaks down the carbon emissions of the process into labor, purchased electricity, fossil fuels, and building materials, and provides corresponding calculation formulas for the product of carbon emission factors and consumption. This comprehensively covers the main sources of carbon emissions in immersed tunnel construction, making carbon emission sources traceable, decomposable, and calculable, and significantly improving the comprehensiveness and accuracy of carbon emission calculation.

[0015] In some embodiments of this application, the construction cost (COST) is: COST= ; in, It is the direct cost of the j-th execution mode of the i-th process; y ij It is the i-process execution mode; ; in, It is the material consumption of the kth type in the jth execution mode of the i-th process; It is the unit cost of the k-th material in the j-th execution mode of the i-th process; It represents the number of workers in the j-th execution mode of the i-th process; It is the worker's working time in the j-th execution mode of the i-th process; It is the unit labor cost; It is the number of the h-th type of machine in the j-th execution mode of the i-th process; It is the working time of the h-th machine in the j-th execution mode of the i-th process; It is the unit cost of machinery.

[0016] In the technical solution, by breaking down construction costs into material costs, labor costs, and machinery costs, and directly linking them with different execution modes of each process to construct cost calculation formulas, it is possible to accurately reflect the differences in construction costs under different processes, equipment, and resource configurations, thereby achieving structured and refined calculation of construction costs and ensuring the authenticity and comparability of cost indicators in multi-objective optimization.

[0017] In some embodiments of this application, in step S3, the multi-objective optimization algorithm is any one of the following: multi-objective genetic algorithm, mosquito swarm algorithm, multi-objective evolutionary algorithm, and multi-objective particle swarm algorithm.

[0018] In some embodiments of this application, the method for selecting the optimal solution in step S4 includes: S41. Based on the Pareto solution set, construct a data matrix X containing m solutions to be evaluated and n evaluation indicators, X = (x ij ) mn x ij This represents the value of the j-th evaluation index for the i-th scheme; S42. Normalize the data matrix X to obtain the standardized matrix Z, Z = z ij , z ij This represents the normalized value of the j-th evaluation index of the i-th scheme; S43. The score Si of each solution to be evaluated is sorted, and the solution with the highest score is the optimal solution.

[0019] In the technical solution, by constructing an evaluation data matrix, normalizing the indicator data, and calculating and ranking the solution scores through standardized decision-making steps, multiple feasible solutions in the Pareto solution set can be objectively and quantitatively ranked, avoiding the subjectivity and arbitrariness of manual decision-making, and realizing standardized decision-making for scientifically selecting the solution with the best comprehensive performance from multiple non-dominated solutions.

[0020] In some embodiments of this application, in step S43, the formula for calculating the score Si is: ; The distance between the evaluated object and the maximum value; The distance between the evaluated object and the minimum value.

[0021] In the technical solution, by using a scoring formula based on the ratio of the distance between the evaluation object and the maximum and minimum values ​​to rank the schemes, it is possible to transform indicators of different dimensions such as construction period, carbon emissions, and cost into a unified comprehensive evaluation value, so as to realize the comparability and selection of multiple objective indicators and improve the intuitiveness and scientific nature of the decision-making results.

[0022] In some embodiments of this application, the distance between the evaluation object and the maximum value is... for: ; Distance between the evaluation object and the minimum value for: ; in, The maximum value of the i-th evaluation scheme after normalization; The minimum value of the i-th evaluation scheme after normalization; w j This represents the weight coefficient of the j-th evaluation indicator; z ij This represents the normalized value of the j-th evaluation index of the i-th scheme to be evaluated; m represents the number of schemes to be evaluated.

[0023] In the technical solution, by clearly defining the specific calculation method for the distance between the evaluation object and the optimal and worst values, and introducing the weight coefficient of the evaluation index, the importance of each index can be reasonably allocated in multi-objective decision-making, so that the final score and ranking results are more in line with the actual needs of the project, and further improve the rationality and pertinence of the optimal result of the immersed tunnel construction scheme.

[0024] Based on the above technical solutions, the multi-objective optimization method for immersed tunnel construction schemes in this embodiment of the invention constructs a multi-objective optimization model covering construction period, carbon emissions, and construction costs, sets a construction period constraint, uses a multi-objective optimization algorithm to obtain a Pareto solution set, and uses a multi-objective decision-making method to screen the optimal solution set. Finally, the scheme with the lowest sum of carbon trading amount and total investment is selected as the final construction scheme. This method can ensure the timeliness and economy of the construction scheme, effectively control the carbon emissions throughout the construction process, conform to the concept of "green construction" and global ecological environmental protection requirements, achieve synergistic optimization of construction period, cost and environmental benefits in immersed tunnel engineering construction, improve the scientificity, rationality and sustainability of construction scheme selection, and is applicable to the optimization decision-making of construction schemes for various types of immersed tunnel projects. Attached Figure Description

[0025] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a flowchart of an embodiment of the multi-objective optimization method for the construction scheme of immersed tunnel according to the present invention. Detailed Implementation

[0026] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0027] In the description of this invention, it should be understood that the terms "center", "lateral", "longitudinal", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0028] The terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include one or more of that feature.

[0029] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0030] like Figure 1 As shown, this invention provides a multi-objective optimization method for the construction scheme of immersed tunnels, including the following steps: S1. Based on the construction site conditions of the immersed tunnel, several feasible construction schemes for the immersed tunnel were determined. S2. Based on the optimization objectives of construction period, construction carbon emissions, and construction cost, a multi-objective optimization model is constructed, with construction period as the constraint condition. S3. Solve the multi-objective optimization model using a multi-objective optimization algorithm to obtain the Pareto solution set; S4. Use a multi-objective decision-making method to select the optimal solution from the Pareto optimal solution set to form the optimal solution set; S5. Calculate the optimal solution with the lowest sum of carbon trading amount and total investment in the optimal solution set and take it as the final solution for the construction of the immersed tunnel.

[0031] The aforementioned multi-objective optimization method for immersed tunnel construction schemes involves a series of steps: determining feasible construction schemes, constructing a multi-objective optimization model with objectives of construction period, carbon emissions, and construction cost, obtaining Pareto solutions using multi-objective optimization algorithms, selecting the optimal solution using multi-objective decision-making methods, and determining the final scheme based on the minimum sum of carbon trading amount and total investment. This forms a complete and engineerable decision-making process for immersed tunnel construction schemes. Compared to traditional decision-making methods that only consider construction period and cost, this method incorporates carbon emissions throughout the entire construction process into a quantitative optimization system, enabling collaborative decision-making based on multi-dimensional indicators. From a methodological perspective, this method ensures that the construction scheme achieves comprehensive optimization in terms of economy, construction period efficiency, and green and low-carbon characteristics.

[0032] In step S2, the multi-objective optimization model F is: F = min(DU, CE, COST); Where DU represents the construction period; CE represents the carbon emissions during construction; and COST represents the construction cost.

[0033] By defining the multi-objective optimization model as simultaneously minimizing the construction period, carbon emissions during construction, and construction costs, environmental indicators and traditional engineering indicators are modeled and optimized in a unified manner. This allows the three objectives to be solved in a balanced way within the same mathematical framework, avoiding the deterioration of other indicators caused by prioritizing a single indicator. This provides a quantifiable, solvable, and comparable multi-objective evaluation basis for immersed tunnel construction schemes.

[0034] Immersed tunnel construction involves multiple procedures, which are typically carried out sequentially. However, in practice, some procedures can be performed simultaneously to save time. In this embodiment, the construction period is the sum of the number of days for each procedure, the carbon emissions are the sum of the carbon emissions from each procedure during construction, and the construction cost is the sum of the construction costs for each procedure.

[0035] In this application, a construction period calculation formula is constructed based on the process network path set, the construction time corresponding to different execution modes of each process, and the process execution status variables. This formula can accurately reflect the total construction period composition of immersed tunnels under multiple processes, multiple paths, and multiple construction modes, and can achieve accurate quantification of the construction period of different construction schemes, thereby improving the accuracy of the construction period target calculation and the engineering adaptability in the multi-objective optimization model.

[0036] Specifically, the construction period is: DU= ; Where P is the set of all process paths in the node network, and L... n It is the nth path in P, P L It is path L n The set of processes in d ij It is the time corresponding to the j-th execution mode of the i-th process, m i It is the number of execution mode groups, y ij This is the i-process execution mode.

[0037] In this application, a formula for calculating construction carbon emissions is constructed by combining process path, process execution mode, construction time and unit process carbon emissions. This formula can decompose the overall construction carbon emissions into each process and corresponding execution mode, realize the refined accounting of carbon emissions for different construction schemes, and provide real and reliable quantitative indicators of environmental benefits for multi-objective optimization.

[0038] Specifically, the carbon emissions (CE) from construction are: CE= ; Where P is the set of all process paths in the node network, and L... n It is the nth path in P, P L It is path L n The set of processes in d ij It is the time corresponding to the j-th execution mode of the i-th process, m i It is the number of execution mode groups, y ij It is the i-process execution mode, c ij It is the carbon emissions of process i.

[0039] In this application, the carbon emissions of the process are further broken down into labor, purchased electricity, fossil fuels, and building materials, and corresponding formulas for calculating the product of carbon emission factors and consumption are given. This can comprehensively cover the main sources of carbon emissions in immersed tunnel construction, making the sources of carbon emissions traceable, decomposable, and calculable, and greatly improving the comprehensiveness and accuracy of carbon emission calculation.

[0040] Specifically, the carbon emissions of process i are c ij =E pu +E eu +E fu +E mu ; Among them, E pu E represents the carbon emissions from manual processes in the i-th step. eu E represents the carbon emissions from purchased electricity in the i-th process. fu It is the carbon emissions from fossil fuel consumption in the i-th process, Emu It is the carbon emission of building materials in the i-th process; E pu =EF p ×AD p ; Among them, EF p It is an artificial carbon emission factor, AD p It represents the number of man-days required for a specific process. E eu =EF e ×AD e ; Among them, EF e It is the local carbon emission factor for electricity, AD e It represents the electricity consumption of a certain process. E fu =EF f ×AD f ; Among them, EF f It is a carbon emission factor of fossil fuels, AD f It represents the amount of fossil fuels consumed in a certain process. E mu = ; Among them, EF mk It is the carbon emission factor of the kth building material, AD mk It represents the consumption of the kth type of building material.

[0041] In this application, construction costs are broken down into material costs, labor costs, and machinery costs, and cost calculation formulas are constructed by directly linking them to different execution modes of each process. This can accurately reflect the differences in construction costs under different processes, equipment, and resource configurations, realize structured and refined calculation of construction costs, and ensure the authenticity and comparability of cost indicators in multi-objective optimization.

[0042] Specifically, the construction cost COST is: COST= ; in, It is the direct cost of the j-th execution mode of the i-th process; y ij It is the i-process execution mode; ; in, It is the material consumption of the kth type in the jth execution mode of the i-th process; It is the unit cost of the k-th material in the j-th execution mode of the i-th process; It represents the number of workers in the j-th execution mode of the i-th process; It is the worker's working time in the j-th execution mode of the i-th process; It is the unit labor cost; It is the number of the h-th type of machine in the j-th execution mode of the i-th process; It is the working time of the h-th machine in the j-th execution mode of the i-th process; It is the unit cost of machinery.

[0043] In some embodiments of this application, in step S3, the multi-objective optimization algorithm is any one of the following: multi-objective genetic algorithm, mosquito swarm algorithm, multi-objective evolutionary algorithm, and multi-objective particle swarm algorithm.

[0044] In some embodiments of this application, by constructing an evaluation data matrix, normalizing the index data, calculating and ranking the scheme scores, a standardized decision-making process can be implemented to objectively quantify and rank multiple feasible schemes in the Pareto solution set, avoiding the subjectivity and arbitrariness of manual decision-making, and achieving standardized decision-making for scientifically selecting the solution with the best comprehensive performance from multiple non-dominated solutions.

[0045] In step S4, the method for selecting the optimal solution includes: S41. Based on the Pareto solution set, construct a data matrix X containing m solutions to be evaluated and n evaluation indicators, X = (x ij ) mn x ij This represents the value of the j-th evaluation index for the i-th scheme; S42. Normalize the data matrix X to obtain the standardized matrix Z, Z = z ij , z ij This represents the normalized value of the j-th evaluation index of the i-th scheme; S43. The score Si of each solution to be evaluated is sorted, and the solution with the highest score is the optimal solution.

[0046] In some embodiments, by using a scoring formula based on the ratio of the distance between the evaluation object and the maximum and minimum values ​​to rank the schemes, indicators of different dimensions such as construction period, carbon emissions, and cost can be transformed into a unified comprehensive evaluation value, enabling the comparison and selection of multiple objective indicators and improving the intuitiveness and scientific nature of the decision-making results.

[0047] Specifically, in step S43, the formula for calculating the score Si is: ; The distance between the evaluated object and the maximum value; The distance between the evaluated object and the minimum value.

[0048] In some embodiments, by explicitly providing the specific calculation method for the distance between the evaluation object and the optimal and worst values, and introducing the weight coefficient of the evaluation index, the importance of each index can be reasonably allocated in multi-objective decision-making, so that the final score and ranking results are more in line with the actual needs of the project, and further improve the rationality and pertinence of the optimal results of the immersed tunnel construction scheme.

[0049] Distance between the evaluation object and the maximum value for: ; Distance between the evaluation object and the minimum value for: ; in, The maximum value of the i-th evaluation scheme after normalization; The minimum value of the i-th evaluation scheme after normalization; w j This represents the weight coefficient of the j-th evaluation indicator; z ij This represents the normalized value of the j-th evaluation index of the i-th scheme to be evaluated; m represents the number of schemes to be evaluated.

[0050] In actual construction, different weights can be assigned to the construction period, carbon emissions, and construction cost according to the construction requirements in order to find the optimal construction plan corresponding to the construction requirements.

[0051] In this embodiment of the application, an ordered weighted average operator is used to calculate the weights of construction period, construction carbon emissions and construction cost. First, a set of decision data is rearranged in descending order, and then the weight values ​​corresponding to different positions are integrated in an ordered weighted manner. The weight is only related to the corresponding position.

[0052] The specific implementation steps are as follows: 1) Sort the importance of the indicators given by the n experts (a1, a2, ..., ai, ... an) in descending order to obtain (b1, b2, ..., bi, ... bn). Where ai∈[1, 9], the evaluators score the importance of the indicators within this range, and the score is proportional to the importance.

[0053] 2) By using combinations to weight the numerical value bj, the weighted vector is obtained as follows: ; 3) Weight the decision value bj to obtain the importance level of each indicator: ; 4) Calculate the relative weight value w of each indicator. The calculation formula is as follows: .

[0054] After obtaining the weights of construction period, carbon emissions during construction, and construction cost, calculate the carbon trading amount and total cost under the corresponding weight system in the optimal solution set, and select the optimal solution from the optimal solution set with the lowest sum of the two.

[0055] In actual construction, the construction of immersed tunnels involves multiple procedures, which can be regarded as independent of each other. Each procedure includes multiple construction execution modes. By selecting and combining the appropriate modes from the construction modes corresponding to each procedure, the overall construction plan of the immersed tunnel can be formed.

[0056] The following section describes the multi-objective optimization process of the construction scheme of this invention by using the ant colony algorithm to solve the multi-objective optimization model and taking the mid-terminal sealing and installation process of immersed tunnel construction as an example.

[0057] In immersed tunnels, the installation sequence of end sealing doors is as follows: steel beam bracket → steel beam → outer bracket → steel sealing door → sealing steel plate → manhole door closure → water tightness test; among them, the steel beam bracket and steel shell are manufactured together.

[0058] It should be noted that spacers can be placed between the steel beams and the steel beam brackets to ensure that the side of the steel beam near the pipe section is in the same plane as the side of the outer bracket near the pipe section, thus ensuring the installation of the steel sealing door.

[0059] (1) Steel beam installation Steel beams are installed using truck cranes or crawler cranes, equipped with hand-operated hoists and aerial work platforms. The installation is achieved by tightening the hand-operated hoists (≤5T) and adjusting the 5T slings. Before hoisting, the adjusting bolts at the bottom of the steel beam are screwed in. A 5T hand-operated hoist is then installed at the lifting points on the beam's brackets. During hoisting, the lifting points welded to the steel beam itself are used.

[0060] (2) Installation of outer corbel The outer corbels of the end-sealing gate are distributed around the central pipe gallery and the driveway. The construction method of segmented processing and on-site installation and docking is adopted. The parts that are difficult to install at the top are processed and assembled on-site, and aerial work platforms are used for assisted installation. The outer corbels are installed in a construction sequence from bottom to top.

[0061] When constructing the outer corbel, first mark the outer edge line of the corbel's working surface. Lay fireproof cloth in the welding area and secure it with strong magnets or supports. Perform pre-weld protection work on the surrounding area. Welding can only begin after adjustments are completed and deemed satisfactory. During welding, control the deformation of the outer corbel and adjust it promptly to ensure the working surface of the outer corbel is on the same plane. After welding, perform localized anti-corrosion treatment according to design requirements.

[0062] After welding is completed, all welds must be visually inspected and must be free of defects such as cracks, slag inclusions, and incomplete filling of the arc crater. The welds on the water-facing side of the outer corbel must meet the watertightness requirements, and all fillet welds should be subjected to 100% magnetic particle testing.

[0063] (3) Installation of steel sealing door The installation sequence for steel-sealed doors is as follows: install the steel-sealed door → initial pre-tightening of the tie rod → fine adjustment of the door panel and addition of shims → final pre-tightening of the tie rod.

[0064] It should be noted that the pouring holes and vents at the steel sealing door have a significant impact on the installation. The concrete in the pouring holes and vents needs to be removed after initial setting. The protruding parts of the pouring holes and vents should be cut off with a cutting machine (300A plasma cutter). After cutting, steel plates should be used for sealing.

[0065] 1) Install door sealing and tie rods, and pre-tighten them. Before installing the steel sealing door, first mark the control edge line according to the construction drawings. Then, install two hand-operated hoists at each end of the steel beam and connect them to the steel sealing door. With the help of a truck crane or crawler crane, position the steel sealing door and fix it to the steel beam by tie rods.

[0066] 2) After the door panel is installed, carefully inspect it, add shims and tighten the pull strip. If necessary, make fine adjustments or add shims. After the adjustment is completed, tighten the pull strip again.

[0067] (4) Installation of steel sealing steel plate for steel door The end sealing gate relies on the welded seams of the sealing steel plate to ensure a watertight effect. The sealing steel plate is set between the steel sealing gates and between the steel sealing gates and the steel shell. The sealing steel plate is fully welded with a 6mm weld around its perimeter, and the weld should ensure watertightness.

[0068] The installation should proceed from both sides towards the center. Each sealing steel plate should be welded from top to bottom. Immediately after welding, a 100% magnetic particle inspection and a 100% airtightness inspection should be performed. Any defects should be repaired immediately.

[0069] (5) Manhole door sealing After the end sealing door is installed and the internal pipe construction is completed, the manhole door is sealed. The manhole door is sealed and water-stopped by using a rubber sealing strip and a tie rod for pre-tightening.

[0070] When using the ant colony optimization algorithm to perform multi-objective optimization of the end-sealing door installation scheme, all parameters must first be initialized, and the current cycle number NC=1 must be recorded. At the beginning of the first cycle, all ants are placed at the starting point, called step 1. Then, each ant is assigned a randomly selected path from the starting point to the ending point, representing the randomly selected execution mode for each step experienced by each ant. This process is equivalent to simulating the project once and generating a set of initial feasible solutions and pheromones.

[0071] Table 1 shows the relevant construction data for the installation of the end-sealing door.

[0072]

[0073] The objective function value for the installation of the sealing gate at the end of the immersed tunnel segment is to calculate the total construction period, total cost, and total carbon emissions of the path traversed by each ant.

[0074] Table 2 shows the relevant data on the construction period, construction cost, and carbon emissions for each process under different construction modes.

[0075]

[0076] According to formula (1), random dynamic weights are assigned to the construction period target, construction cost target and carbon emission target to expand the search range, reduce the probability of getting trapped in local optima and enhance the randomness of the algorithm.

[0077] (1) In equation (1), r i It is a random number between 0 and 1.

[0078] The multi-objective optimization problem is transformed into a single-objective optimization problem by using dynamic weights. The multi-objective optimization model f(x) is: (2) In equation (2), , , These represent the total time, total cost, and total carbon emissions of the k-th ant in the colony after completing its path. , , These represent the values ​​with the highest time, cost, and carbon emission values ​​among all ants. , These represent the minimum time, cost, and carbon emission values ​​among all ants, respectively. r is a random number between 0 and 1; it is introduced to prevent invalid values ​​from appearing in the overall objective function.

[0079] In the formula, x represents the index of the solution generated in this iteration. The smaller the overall objective value, the better the corresponding solution.

[0080] Once all the ants have completed one cycle, the pheromone concentration on each path can be calculated using the following formula: (3) In equation (3), (i, j) indicates that the current process adopts the i-th execution mode and the next process adopts the j-th execution mode; and Let represent the pheromone concentrations of each edge after the nc-th and nc-1-th iterations, respectively; The pheromone increment along the path after one cycle can be calculated using formula (4): (4) In equation (4), The pheromone left on the route (i, j) after the k-th ant completes one cycle is represented by the following formula: If the k-th ant travels along the route (i, j) during this journey (5) In equation (5), f(k) represents the comprehensive value of the construction period, cost, and carbon emission target for the k-th ant. Ants choose their paths based on two main factors: the pheromone concentration along each edge and the path visibility. The formula for calculating path visibility is as follows: (6) In equation (6), , , These represent the maximum values ​​of construction period, cost, and carbon emissions for the next process under different construction plans; , , These represent the minimum values ​​for construction period, cost, and carbon emissions for the next process under different construction plans.

[0081] Based on the pheromone concentration and path visibility of each edge, the selection probability of each edge is calculated using the following formula: (7) (8) In Equation (8), q is a random number uniformly distributed between (0, 1), and q0 is a given parameter between (0, 1). J is selected according to Formula (7). When making the selection, a random number q uniformly distributed between (0, 1) is generated and compared with the previously given parameter. If q < q0, the ant selects the path in the first way. If q ≥ q0, the selection is made according to the probability determined by Formula (7).

[0082] The parameter α represents the importance of pheromone accumulation during the ant's activity. The parameter β reflects the importance of the heuristic factor during the operation, that is, the strength of the deterministic factor. A larger β value will increase the possibility of the search falling into the local optimum, resulting in a faster convergence rate, but also reducing the overall randomness of the search. On the contrary, a smaller β value may cause the algorithm to fall into a simple random search during operation, thus losing the ability to find the optimal solution.

[0083] If the number of runs NC reaches the predetermined maximum number of loops NCmax, the algorithm will stop looping. Otherwise, the program will continue to run, enter the next cycle, and repeat the steps mentioned above until the maximum number of loops is reached. After the loop is completed, the Pareto solution set is screened out from all solutions.

[0084] Table 3 shows part of the solution set.

[0085]

[0086] Construct the standardized matrix

[0087] Data normalization:

[0088] Obtain the normalized matrix zij

[0089] , , , The weights of the construction period, construction carbon emissions, and construction cost can be calculated respectively, and the carbon trading volume and total cost under the corresponding weight system in the optimal solution set can be calculated respectively. Select the plan in the optimal solution set with the lowest sum of the two as the optimal plan.

[0090] The aforementioned multi-objective optimization method for immersed tunnel construction schemes constructs a multi-objective optimization model encompassing construction period, carbon emissions, and construction costs, and sets a construction period constraint. A Pareto solution set is obtained using a multi-objective optimization algorithm. The optimal solution set is then selected through a multi-objective decision-making method, ultimately choosing the scheme with the lowest sum of carbon trading amount and total investment as the final construction scheme. This method ensures both the timeliness and economy of the construction scheme while effectively controlling carbon emissions throughout the construction process. It aligns with the concept of "green construction" and global ecological environmental protection requirements, achieving synergistic optimization of construction period, cost, and environmental benefits in immersed tunnel engineering. It enhances the scientific, rational, and sustainable nature of construction scheme selection and is applicable to the optimization decision-making of construction schemes for various types of immersed tunnel projects.

[0091] Finally, it should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0092] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A multi-objective optimization method for the construction scheme of immersed tunnels, characterized in that, Includes the following steps: S1. Based on the construction site conditions of the immersed tunnel, several feasible construction schemes for the immersed tunnel were determined. S2. Based on the optimization objectives of construction period, construction carbon emissions, and construction cost, a multi-objective optimization model is constructed, with construction period as the constraint condition. S3. Solve the multi-objective optimization model using a multi-objective optimization algorithm to obtain the Pareto solution set; S4. Use a multi-objective decision-making method to select the optimal solution from the Pareto optimal solution set to form an optimal solution set; S5. Calculate the optimal solution with the lowest sum of carbon trading amount and total investment in the optimal solution set and take it as the final solution for the construction of the immersed tunnel.

2. The multi-objective optimization method for the construction scheme of immersed tunnels according to claim 1, characterized in that, In step S2, the multi-objective optimization model F is: F = min(DU, CE, COST). Where DU represents the construction period; CE represents the carbon emissions during construction; and COST represents the construction cost.

3. The multi-objective optimization method for the construction scheme of immersed tunnels according to claim 2, characterized in that, The construction of immersed tunnels involves multiple procedures, each with various execution modes; the construction period (DU) is: DU= ; Where P is the set of all process paths in the node network, and L... n It is the nth path in P, P L It is path L n The set of processes in d ij It is the time corresponding to the j-th execution mode of the i-th process, m i It is the number of execution mode groups, y ij This is the i-process execution mode.

4. The multi-objective optimization method for the construction scheme of immersed tunnels according to claim 2, characterized in that, The carbon emissions (CE) from the construction are: CE= ; Where P is the set of all process paths in the node network, and L... n It is the nth path in P, P L It is path L n The set of processes in d ij It is the time corresponding to the j-th execution mode of the i-th process, m i It is the number of execution mode groups, y ij It is the i-process execution mode, c ij It is the carbon emissions of process i.

5. The multi-objective optimization method for the construction scheme of immersed tunnels according to claim 4, characterized in that, The carbon emissions of process i are c ij =E pu +E eu +E fu +E mu ; Among them, E pu E represents the carbon emissions from manual processes in the i-th step. eu E represents the carbon emissions from purchased electricity in the i-th process. fu It is the carbon emissions from fossil fuel consumption in the i-th process, E mu It is the carbon emission of building materials in the i-th process; E pu =EF p ×AD p ; Among them, EF p It is an artificial carbon emission factor, AD p It represents the number of man-days required for a specific process. E eu =EF e ×AD e ; Among them, EF e It is the local carbon emission factor for electricity, AD e It represents the electricity consumption of a certain process. E fu =EF f ×AD f ; Among them, EF f It is a carbon emission factor of fossil fuels, AD f It represents the amount of fossil fuels consumed in a certain process. E mu = ； Among them, EF mk It is the carbon emission factor of the kth building material, AD mk It represents the consumption of the kth type of building material.

6. The multi-objective optimization method for the construction scheme of immersed tunnels according to claim 2, characterized in that, The construction cost COST is: COST= ; in, It is the direct cost of the j-th execution mode of the i-th process; y ij It is the i-process execution mode; ； in, It is the material consumption of the kth type in the jth execution mode of the i-th process; It is the unit cost of the k-th material in the j-th execution mode of the i-th process; It represents the number of workers in the j-th execution mode of the i-th process; It is the worker's working time in the j-th execution mode of the i-th process; It is the unit labor cost; It is the number of the h-th type of machine in the j-th execution mode of the i-th process; It is the working time of the h-th machine in the j-th execution mode of the i-th process; It is the unit cost of machinery.

7. The multi-objective optimization method for the construction scheme of immersed tunnels according to claim 1, characterized in that, In step S3, the multi-objective optimization algorithm is any one of the following: multi-objective genetic algorithm, mosquito swarm algorithm, multi-objective evolutionary algorithm, and multi-objective particle swarm algorithm.

8. The multi-objective optimization method for the construction scheme of immersed tunnels according to claim 1, characterized in that, In step S4, the method for selecting the optimal solution includes: S41. Based on the Pareto solution set, construct a data matrix X containing m solutions to be evaluated and n evaluation indicators, X = (x ij ) mn x ij This represents the value of the j-th evaluation index for the i-th scheme; S42. Normalize the data matrix X to obtain the standardized matrix Z, Z = z ij , z ij This represents the normalized value of the j-th evaluation index of the i-th scheme; S43. The score Si of each solution to be evaluated is sorted, and the solution with the highest score is the optimal solution.

9. The multi-objective optimization method for the construction scheme of immersed tunnels according to claim 8, characterized in that, In step S43, the formula for calculating the score Si is: ； The distance between the evaluated object and the maximum value; The distance between the evaluated object and the minimum value.

10. The multi-objective optimization method for the construction scheme of immersed tunnels according to claim 9, characterized in that, Distance between the evaluation object and the maximum value for: ； Distance between the evaluation object and the minimum value for: ； in, The maximum value of the i-th evaluation scheme after normalization; The minimum value of the i-th evaluation scheme after normalization; w j This represents the weight coefficient of the j-th evaluation indicator; z ij This represents the normalized value of the j-th evaluation index of the i-th scheme to be evaluated; m represents the number of schemes to be evaluated.

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