A micro pipe jacking construction process optimization method, system, device and medium

Abstract

The application provides a micro pipe jacking construction process optimization method, system, device and storage medium, the method comprises the following steps: obtaining construction parameters based on construction design files and historical construction records; obtaining geological data based on geological exploration of the construction area; calculating efficiency risk, cost risk and safety risk based on the construction parameters and the geological data; applying a preset comprehensive risk assessment model to calculate the comprehensive risk value R before construction; initializing the parameter population; evaluating the fitness of each individual based on the efficiency risk, cost risk and safety risk; performing selection, crossover and mutation operations by genetic algorithm to generate a new population, and the individual with the highest fitness score is used as the recommended value of the construction parameters. The application can effectively improve construction efficiency, reduce cost, ensure construction safety, and realize fine management and risk control of the construction process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of civil engineering, and particularly relates to a micro pipe jacking construction process optimization method, system, device and medium. BACKGROUND

[0002] Micro pipe jacking construction is a trenchless underground pipeline construction technology that has been widely used due to its minimal impact on ground transportation and the environment. However, existing micro pipe jacking construction techniques face many challenges during implementation, including uncertainty in construction parameter selection, complexity of geological conditions, and inaccuracy in risk assessment. These factors often lead to low construction efficiency, cost overruns, and increased safety risks.

[0003] In the process of implementing the embodiments of the present application, the inventors have found that at least the following problems or defects exist in the prior art: The determination of construction parameters is usually based on experience rather than systematic analysis, lacking comprehensive consideration of geological conditions and construction risks; the existing risk assessment method is too simplified and fails to fully reflect various risk factors that may occur during the construction process; in addition, the optimization and adjustment of construction parameters usually require a large number of trials and errors, which is inefficient and costly. SUMMARY

[0004] The present application provides a micro pipe jacking construction process optimization method, system, medium and computing device.

[0005] In a first aspect of the present application, a micro pipe jacking construction process optimization method is provided, comprising:

[0006] Step 101, obtaining construction parameters based on a construction design file and historical construction records, the construction parameters including pipe diameter, pushing speed and soil pressure;

[0007] Step 102, obtaining geological data based on geological exploration of the construction area, the geological data including soil type, groundwater level and soil type distribution;

[0008] Step 103, based on the construction parameters and geological data, respectively calculating efficiency risk R eff , cost risk R cost and safety risk R safety ;

[0009] Step 104, applying a preset comprehensive risk assessment model to calculate the comprehensive risk value R before construction;

[0010] Step 105, initializing a parameter population, each individual in the parameter population representing a set of construction parameters, using a simulation engine to simulate the construction process, including soil deformation, pipe jacking machine pushing and underground water flow, to preliminarily verify the construction parameters;

[0011] Step 106, evaluating the fitness of each individual based on the efficiency risk R eff , cost risk R cost and safety risk R safety ;

[0012] Step 107, performing a genetic algorithm to generate a new population by selection, crossover and mutation operations, and the individual with the highest fitness score is taken as the recommended value of the construction parameter.

[0013] Further, in step 104, the preset comprehensive risk assessment model is shown in the following formula (1);

[0014]

[0015] In the formula, m, n, p are respectively the number of efficiency risk R eff , cost risk R cost and safety risk R safety ; w eff , w cost and w safety are respectively the weight of efficiency risk R eff , cost risk R cost and safety risk R safety ; j, k, l are respectively the ordinal number of efficiency risk R eff , cost risk R cost and safety risk R safety .

[0016] Further, in step 103, the calculation of efficiency risk Reff is shown in the following formula (2);

[0017]

[0018] In the formula, α is the efficiency risk coefficient, Vactual is the actual propulsion speed, and Vplan is the planned propulsion speed.

[0019] The calculation of cost risk R cost is shown in the following formula (3);

[0020]

[0021] In the formula, β is the cost risk coefficient, Cactual is the actual cost, and Cplan is the planned cost.

[0022] Further, in step 103, the calculation of safety risk R safety is shown in the following formula (4);

[0023]

[0024] In the formula, γ is the safety risk coefficient, Oi S is the occurrence frequency of the i-th safety event i S is the corresponding severity score; wherein,

[0025] The safety events include equipment failure, environmental factors, and emergency situations.

[0026] Further, the efficiency risk coefficient α is calculated according to the following formula (5):

[0027]

[0028] In the formula, Tplan is the planned total construction time, m is the total number of construction stages, V plan，j V is the planned advancing speed of the j-th stage actual，j V is the actual advancing speed of the j-th stage

[0029] The cost risk coefficient β is calculated according to the following formula (6):

[0030]

[0031] In the formula, σ cost is the historical standard deviation of the cost, indicating the degree of cost fluctuation; μ cost is the average value of the cost.

[0032] Further, the safety risk coefficient γ is calculated according to the following formula (7):

[0033]

[0034] In the formula, P i is the occurrence probability of the i-th safety event; C i is the severity score of the i-th safety event; and n is the total number of safety events.

[0035] Further, the step 107 further includes:

[0036] A genetic algorithm is executed, and a selection operation is performed according to the fitness score, and individuals with high fitness scores are selected for crossover and mutation; wherein, the crossover operation generates new construction parameter combinations, and the mutation operation introduces new genetic diversity;

[0037] The selection, crossover, and mutation steps are repeated to generate a new population until the optimal construction parameters are found.

[0038] The optimal construction parameters are input into the simulation engine for verification testing, and the verification testing includes simulating soil deformation, pipe jacking machine advancing, and groundwater flow.

[0039] In a second aspect of the present application, a micro pipe jacking construction process optimization system is provided, comprising:

[0040] a construction parameter module configured to obtain construction parameters based on a construction design file and historical construction records, the construction parameters comprising a pipe diameter, a pushing speed, and a soil pressure;

[0041] a geological data module configured to obtain geological data based on geological exploration of a construction area, the geological data comprising a soil type, a groundwater level, and a soil type distribution;

[0042] a risk calculation module configured to calculate an efficiency risk R eff , a cost risk R cost , and a safety risk R safety based on the construction parameters and the geological data, respectively;

[0043] a risk assessment module configured to calculate a comprehensive risk value R based on a preset comprehensive risk assessment model;

[0044] a parameter population module configured to initialize a parameter population, each individual in the parameter population representing a set of construction parameters, and simulate a construction process using a simulation engine, including soil deformation, pipe jacking, and groundwater flow, to preliminarily verify the construction parameters;

[0045] a fitness module configured to evaluate a fitness of each individual based on the efficiency risk R eff , the cost risk R cost , and the safety risk R safety ;

[0046] a genetic algorithm module configured to perform a genetic algorithm to generate a new population through selection, crossover, and mutation operations, and the individual with the highest fitness score being used as a recommended value of the construction parameters.

[0047] In a third aspect of the present application, an electronic device is provided, comprising at least one processor, a memory, and an input-output unit; wherein the memory is configured to store a computer program, and the processor is configured to invoke the computer program stored in the memory to execute the method of any one of the first aspect.

[0048] In a fourth aspect of the present application, a computer-readable storage medium is provided, comprising instructions which, when executed on a computer, cause the computer to execute the method of any one of the first aspect.

[0049] The above-mentioned embodiments of the present application have at least the following beneficial effects: The present application provides a scientific and systematic micro-tube pipe construction process optimization method by comprehensively considering construction parameters, geological data and risk assessment. The method can more accurately assess the risks that may be encountered during construction, including efficiency risk, cost risk and safety risk, by obtaining construction parameters based on construction design documents and historical construction records, and combining geological data obtained from geological exploration. This not only improves the safety and reliability of construction, but also helps to reduce uncertainty and potential economic losses during construction.

[0050] In addition, the present application uses genetic algorithm to optimize construction parameters, and can quickly find the optimal combination of construction parameters by simulating the construction process and evaluating individual fitness. This method can improve construction efficiency, shorten construction period, and reduce construction cost. The effectiveness and feasibility of the optimized construction parameters in actual construction are ensured through the verification test of the simulation engine. Overall, the implementation of the present application can provide a more efficient, economic and safe technical solution for micro-tube pipe construction. BRIEF DESCRIPTION OF DRAWINGS

[0051] One or more embodiments are exemplarily illustrated by the figures in the drawings corresponding thereto, which do not constitute a limitation on the embodiments, and elements with the same reference numerals in the drawings represent similar elements, unless otherwise specified, and the figures in the drawings do not constitute a proportional limit.

[0052] Figure 1 is a flowchart of the micro-tube pipe construction process optimization method provided by the present application;

[0053] Figure 2 is a structural schematic diagram of the micro-tube pipe construction process optimization system provided by the present application;

[0054] Figure 3 The structural schematic diagram of an electronic device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0055] For the convenience of understanding the present application, the present application will be described in more detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that when an element is described as being "connected" to another element, it can be directly on the other element or one or more intervening elements can be present therebetween. The terms "upper", "lower", "left", "right", "top", "bottom", "top", "bottom", and the like as used in this specification indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", and the like are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0056] Unless otherwise defined, all technical and scientific terms used in the specification have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0057] The present application will be described in detail below in conjunction with the accompanying drawings and specific embodiments. Figure 1 A micro-tube construction process optimization method 100 provided by the embodiments of the present application will be described in detail below in conjunction with specific embodiments.

[0058] Figure 1 is a flowchart of a micro-tube construction process optimization method provided by the present application. The micro-tube construction process optimization method provided by one embodiment of the present application comprises:

[0059] Step 101, obtaining construction parameters based on construction design files and historical construction records, the construction parameters including pipe diameter, pushing speed and soil pressure;

[0060] It should be noted that micro-tube construction is a trenchless underground construction technology that allows the pipe or other underground structure to be pushed into the ground using a small-scale excavation on the ground without the need for large-scale excavation of the ground. This technology is widely used in urban infrastructure construction, such as the laying of sewage, rainwater, water, gas and power pipelines, as well as the construction of underground facilities such as transportation and communication.

[0061] Step 101 obtains construction parameters based on construction design files and historical construction records. These parameters are key inputs in the construction process and directly affect the efficiency and quality of construction. Construction parameters include pipe diameter, pushing speed and soil pressure. The pipe diameter determines the size of the pipe machine, the pushing speed affects the construction progress, and the soil pressure is a key factor to ensure the stability of the pipe.

[0062] Pipe diameter refers to the inner diameter of the pipe used in pipe jacking construction. It is a key parameter determined by engineering design requirements and pipe conveying capacity. Pipe diameter directly affects the flow rate, structural stability, and construction difficulty of the pipe. For example, a larger diameter can provide greater conveying capacity, but it also requires more jacking force and more complex construction techniques.

[0063] Jacking speed refers to the speed at which the pipe jacking machine advances during construction, usually measured in terms of jacking distance per day or per minute. This speed depends on various factors, including geological conditions, pipe material, pipe jacking machine performance, and construction environment. Reasonable jacking speed can ensure construction efficiency while avoiding ground heave or pipe damage caused by excessive jacking speed.

[0064] Soil pressure, also known as ground pressure, refers to the pressure exerted by the surrounding soil on the outer wall of the pipe during underground construction of the pipe jacking machine. It is calculated based on the properties of the soil (such as soil type, density, moisture, etc.) and the jacking force of the pipe jacking machine. Soil pressure is crucial to the design and construction safety of the pipe, as it relates to whether the pipe can withstand the pressure of the surrounding soil without deforming or being damaged.

[0065] In some embodiments, step 101 can be implemented by establishing a database that stores all relevant parameters of historical construction projects. By analyzing these data, the best construction parameters suitable for specific geological conditions and construction requirements can be determined. For example, the pipe diameter can be selected according to the soil type and groundwater level in the construction area, the jacking speed can be set according to previous construction experience, and the soil pressure can be determined through soil mechanics calculations.

[0066] Step 102, based on the geological data obtained from the geological exploration of the construction area, the geological data includes soil type, groundwater level and soil type distribution;

[0067] Soil type determines the physical and chemical properties of the soil, such as soil density, internal friction angle, cohesion, permeability, etc., which directly affect the jacking efficiency of the pipe jacking machine and the required jacking force. Different soil types have different adaptability to pipe jacking construction, for example, cohesive soil may require more jacking force, while sandy soil may face the risk of hole collapse. Soil type also affects the stability of the pipe and the support capacity of the surrounding soil on the pipe.

[0068] The level of groundwater level determines the hydrogeological conditions of the construction area, which has an important influence on drainage, water reduction and pipe sealing during pipe jacking construction. High groundwater level may increase the difficulty of construction, as it is necessary to control groundwater to prevent instability caused by the difference in water pressure between the inside and outside of the pipe.

[0069] Soil type distribution provides information about the variation of soil along the construction path, which is crucial for predicting and planning for different geological conditions that may be encountered during construction. Understanding the vertical and horizontal distribution of soil types helps in designing appropriate pipe jacking construction plans and preparing for construction challenges under different geological conditions. Soil type distribution data can also help the construction team identify areas that require special treatment, such as weak soil layers or obstacles.

[0070] In some embodiments, the purpose of step 102 is to acquire geological data based on geological exploration of the construction area. Geological data is crucial for assessing risks that may be encountered during construction. These data can include soil types, groundwater levels, and soil type distribution.

[0071] Specifically, step 102 can be accomplished through geological exploration work, which can include soil sampling, laboratory testing, and measurement of groundwater levels. Soil types will be classified according to their physical and chemical properties, measurement of groundwater levels will help determine water pressure problems that may be encountered during construction, and soil type distribution maps provide a visual representation of soil variation in the construction area.

[0072] Preferably, the geological data acquired in step 102 can be input into a comprehensive geological information system (GIS) that can analyze and display the spatial distribution of soil properties and groundwater levels. These information will provide valuable insights for the construction team, helping to predict and prepare for geological problems that may be encountered during construction.

[0073] Step 103, based on the construction parameters and geological data, respectively calculate the efficiency risk R eff , cost risk R cost and safety risk R safety ;

[0074] In some embodiments, the efficiency risk R eff , cost risk R cost and safety risk R safety are calculated based on construction parameters and geological data.

[0075] Specifically, risk calculation can be achieved by establishing mathematical models that take into account various risk factors and their impact on the construction process.

[0076] Preferably, risk calculation can use probability theory and mathematical statistics methods to assess the likelihood and consequences of different risk factors.

[0077] Step 104, apply a pre-set comprehensive risk assessment model to calculate the comprehensive risk value R before construction.

[0078] The pre-construction integrated risk value R is calculated using a pre-set integrated risk assessment model, which can integrate efficiency, cost, and safety risks.

[0079] Specifically, the integrated risk assessment model can use a weighted summation method to assign different weights to different risk factors according to their importance.

[0080] First, the model is constructed by identifying key risk factors in the construction project, including efficiency risk, cost risk, and safety risk. Each type of risk is defined by a series of parameters, for example, efficiency risk may be related to actual and planned progress speeds, while cost risk is related to the comparison of actual and budgeted costs.

[0081] After identifying the parameters, the model integrates these risk factors into a unified formula through a weighted summation method. The allocation of weights reflects the importance of different risk factors in the overall risk, and these weights are determined based on expert knowledge and historical data analysis. For example, if a project has higher safety requirements, the weight of safety risk may be increased accordingly.

[0082] In the assessment of safety risk, the principles of probability theory are used to assess the likelihood of different safety events. In the assessment of efficiency and cost risk, statistical analysis may be used to determine the distribution and expected value of risk.

[0083] In addition, the model also uses the concept of multi-objective optimization to consider multiple risk objectives in the construction process to minimize risk. Through sensitivity analysis, the model can show the impact of weight changes on the integrated risk value, providing support for project decision-making.

[0084] In specific implementation, the integrated risk assessment model can be realized through risk assessment software, which allows users to input relevant parameters and automatically calculate the integrated risk value. At the same time, combined with expert systems and real-time data integration, the model can dynamically update construction parameters and geological data to ensure the accuracy and real-time nature of risk assessment.

[0085] The results of risk assessment can be displayed through visualization tools, including charts and graphs, to help the project team intuitively understand the risk assessment results and make wise decisions accordingly. The application of this integrated risk assessment model not only provides a quantitative risk assessment method, but also significantly improves the risk management capabilities of construction projects by integrating real-time data and expert knowledge.

[0086] Preferably, the integrated risk assessment model can be adjusted according to the changes in project characteristics and construction environment to achieve dynamic risk management.

[0087] Step 105, initialize a parameter population, in which each individual represents a set of construction parameters, simulate the construction process including soil deformation, pipe jacking machine propulsion and groundwater flow using a simulation engine to preliminarily verify the construction parameters;

[0088] Specifically, the parameter population can be generated by a computer program, covering all possible combinations of construction parameters.

[0089] Preferably, the initialization of the parameter population can use a design experiment method such as orthogonal experiment design to ensure the diversity and representativeness of the population.

[0090] Step 106, evaluate the fitness of each individual based on the efficiency risk R eff , cost risk R cost and safety risk R safety ;

[0091] Specifically, the fitness of each individual is evaluated based on the efficiency risk, cost risk and safety risk, which reflects the degree to which the individual meets the construction requirements.

[0092] Specifically, the fitness evaluation can use a multi-objective optimization algorithm to convert different risks into a single fitness index.

[0093] Preferably, the fitness evaluation can combine the experience and knowledge of construction experts to balance the importance of different risk factors through weighting and adjustment.

[0094] Step 107, execute a genetic algorithm for selection, crossover and mutation operations to generate a new population, and the individual with the highest fitness score is recommended as the construction parameter value.

[0095] Step 107 is to execute a genetic algorithm for selection, crossover and mutation operations to generate a new population and optimize the construction parameters.

[0096] Specifically, the execution of the genetic algorithm can be realized through a special genetic algorithm software or a self-programmed program, and the algorithm selects high fitness individuals from the parameter population for iteration.

[0097] Preferably, the iteration process of the genetic algorithm can combine parallel computing and cloud computing technology to improve the calculation speed and the ability to handle large-scale populations.

[0098] In some embodiments, the preset comprehensive risk assessment model is shown in formula (1);

[0099]

[0100] In the formula, m, n, p are respectively the efficiency risk R eff , cost risk R cost and safety risk Rsafety the number of w eff , w cost , and w safety are the weights of efficiency risk R eff , cost risk R cost , and safety risk R safety , respectively; j, k, l are the ordinal numbers of efficiency risk R eff , cost risk R cost , and safety risk R safety , respectively.

[0101] The calculation of efficiency risk Reff is shown in calculation formula (2);

[0102]

[0103] In the formula, a is the efficiency risk coefficient, Vactual is the actual pushing speed, and Vplan is the planned pushing speed.

[0104] In some embodiments, the efficiency risk coefficient a can be a quantitative indicator to measure the impact of construction progress delay on the overall efficiency of the project. It reflects the relative importance of the deviation between the actual construction speed and the planned construction speed on the project efficiency. The efficiency risk coefficient is usually derived based on historical construction project data analysis, which may involve expert opinions and statistical methods to determine.

[0105] The actual pushing speed Vactual refers to the rate at which the pipe jacking machine actually advances during the construction process, usually measured in meters / hour or meters / day. The actual pushing speed Vactual can be obtained by field measurement, reflecting the current construction progress.

[0106] The planned pushing speed Vplan is the pushing speed predetermined according to the construction plan and design documents before construction, which is the expected target for the timely completion of the construction project. The planned pushing speed Vplan is usually determined during the construction planning stage and serves as a benchmark for evaluating whether the construction progress meets expectations.

[0107] The calculation of cost risk R cost is shown in calculation formula (3);

[0108]

[0109] In the formula, β is the cost risk coefficient, Cactual is the actual cost, and Cplan is the planned cost.

[0110] The cost risk coefficient β is used to quantify the impact of cost overrun on the financial health of the project, reflecting the risk level of actual cost exceeding the budget. The cost risk coefficient β can be determined based on the uncertainty of cost estimation and the cost overrun data of historical projects.

[0111] Actual cost Cactual refers to the sum of all costs that have occurred and are expected to occur during the construction process, including material, labor, equipment, and other expenses. Actual cost Cactual data can be obtained through financial records and cost accounting.

[0112] Planned cost Cplan is the total project cost estimated before construction based on the budget, which is the target of project cost control. Planned cost Cplan can be determined during the project budgeting phase and serves as a benchmark for monitoring and controlling project costs.

[0113] In some embodiments, the calculation of safety risk R safety As shown in equation (4);

[0114]

[0115] In the formula, γ is the safety risk coefficient, O i is the frequency of occurrence of the i-th safety event, S i is the corresponding severity score; wherein,

[0116] The safety event includes equipment failure, environmental factors and emergency.

[0117] The safety risk coefficient γ measures the impact of potential safety incidents during construction on the project safety goal.

[0118] This coefficient is determined by analyzing historical safety incident data and potential risk factors in the construction environment.

[0119] Safety events refer to any incident that may occur during construction that may cause personal injury or property damage. Including but not limited to equipment failure, environmental factors and emergency, etc. The frequency of safety events refers to the number of times or probability of safety events occurring within a certain period of time. It can be obtained through historical safety records and on-site safety monitoring data. Severity score is a quantitative assessment of the severity of the consequences of a safety event. According to the degree of impact on personnel safety, equipment damage and environmental impact.

[0120] In some embodiments, the efficiency risk coefficient α is calculated as shown in equation (5);

[0121]

[0122] In the formula, Tplan is the planned total construction time, m is the total number of construction stages, V plan，j is the planned speed of the jth stage, V actual，j is the actual speed of the jth stage;

[0123] The total planned construction time Tplan is the scheduled length of time required to complete the entire construction project. It can be determined during the project planning phase and serves as a baseline for project time management. The total number of construction stages m refers to the number of all major construction stages divided for completing the construction project, reflecting the complexity of the project construction and the level of detail of the construction plan.

[0124] The planned advancement speed V plan，j of the jth stage is the scheduled advancement speed of the specific construction stage j according to the construction plan, which can be used to assess whether the construction progress of the stage conforms to the plan. The actual advancement speed V actual，j of the jth stage refers to the actual speed of construction progress achieved in the construction stage j, which can be obtained through on-site monitoring and measurement.

[0125] The calculation formula of the cost risk coefficient β is shown in equation (6);

[0126]

[0127] In the formula, σ cost is the historical standard deviation of the cost, indicating the degree of cost fluctuation; μ cost is the average value of the cost.

[0128] The historical standard deviation σ cost of the cost is a statistical indicator measuring the degree of dispersion of the distribution of cost data, which can be calculated by analyzing the historical cost data of similar projects. The average value μ cost of the cost is the arithmetic mean of all cost data, reflecting the general level of the cost.

[0129] In some embodiments, the calculation formula of the safety risk coefficient γ is shown in equation (7);

[0130]

[0131] In the formula, P i is the occurrence probability of the ith safety event; C i is the severity score of the ith safety event; and n is the total number of safety events.

[0132] Further, a genetic algorithm is executed to perform selection operation according to the fitness scores, selecting individuals with high fitness scores for crossover and mutation; wherein the crossover operation generates new construction parameter combinations, and the mutation operation introduces new genetic diversity;

[0133] The selection, crossover and mutation steps are repeated to generate a new population until the optimal construction parameters are found;

[0134] The optimal construction parameters are input into the simulation engine for verification testing, which includes simulating soil deformation, pipe jacking machine advancement and groundwater flow.

[0135] In some embodiments, the execution of the genetic algorithm begins with the selection operation, which is based on the fitness scores of the individuals. Individuals with high fitness scores mean that they represent superior combinations of construction parameters in the current population. The purpose of selecting these high-scoring individuals is to pass them on as parents to the next generation, with the expectation that they will gradually approach the optimal solution during the iterative process of the genetic algorithm.

[0136] It should be noted that the selection of individuals is based on the selection of individuals. This is a process that simulates the genetic recombination in biological genetics. In the crossover operation, parts of the construction parameters of two parent individuals may be exchanged or combined to produce new offspring individuals. Such operations help to explore a wider parameter space, increasing the possibility of finding better solutions. Mutation operations are also introduced into the genetic algorithm. Mutation operations introduce randomness into the construction parameters of offspring individuals, simulating the phenomenon of gene mutation in nature. This step helps to avoid the algorithm falling into local optimal solutions, maintains the genetic diversity of the population, and thus improves the global search ability of the algorithm.

[0137] After the selection, crossover and mutation operations are performed, we get a new population. This process will be repeated, each generation based on the previous generation, optimizing the fitness score step by step until the optimal construction parameters are found or the predetermined number of iterations is reached.

[0138] When the genetic algorithm iteration is complete, the individual with the highest fitness score is selected as the recommended value. This means that the construction parameter combination represented by this individual is considered the best solution in the current algorithm iteration.

[0139] This optimal construction parameter is input into the simulation engine for verification testing. This step is crucial because it will simulate key aspects of the construction process, such as soil deformation, pipe jacking machine propulsion and groundwater flow, to ensure that the selected parameters are also effective and safe in actual operation.

[0140] Exemplarily, specific numerical values are combined to illustrate.

[0141] Step 1, initialize the population: randomly generate an initial construction parameter population, assuming the population size is 50, and each individual contains the following parameters: pipe diameter, propulsion speed, soil pressure.

[0142] Assume the pipe diameter range is 1.0 meters to 2.5 meters, the propulsion speed range is 5 meters / hour to 15 meters / hour, and the soil pressure range is 100 kPa to 500 kPa.

[0143] Step 2, calculate fitness: for each individual, calculate the efficiency risk R eff , cost risk R cost and safety risk Rsafety The fitness function is shown in equation (8);

[0144] F = weff Reff + wcost Rcost + wsafety Rsafety (8)

[0145] where weff is the weight of the efficiency risk R eff , wcost is the weight of the cost risk R safety , and wsafety is the weight of the safety risk R safety .

[0146] The specific values can be weff = 0.3, wcost = 0.4, and wsafety = 0.3.

[0147] Step 3: Perform selection operation: Use roulette wheel selection method to select parent individuals according to fitness scores, high fitness individuals have a higher probability of being selected.

[0148] Step 4: Perform crossover operation: Selected parent individuals are crossed, assuming single-point crossover method is used, half of the parameter genes of two parent individuals are exchanged to generate new offspring individuals.

[0149] Step 5: Perform mutation operation: New offspring individuals are mutated with a certain probability (e.g. 1%), for example, the pushing speed parameter is mutated, randomly increased or decreased by 5%.

[0150] Step 6: Determine the optimal individual: Repeat the selection, crossover, and mutation operations, generate a new population every generation, and set the iteration number to 100 generations. After 100 generations of iteration, select the individual with the highest fitness score as the recommended value of the optimal construction parameter, assuming the optimal individual's parameters are: pipe diameter 2.2 meters, pushing speed 12 meters / hour, soil pressure 300 kPa.

[0151] Step 7: Simulation verification: Input the optimal construction parameters into the simulation engine to simulate the soil deformation, pipe jacking, and groundwater flow during construction.

[0152] Assuming the simulation results show that the pipe jacking machine advances smoothly, and the soil deformation and groundwater flow are within the safety range. Analyze the simulation results to confirm the effectiveness and safety of the optimal construction parameters, if satisfied, these parameters can be used for actual construction.

[0153] Through the example of genetic algorithm steps, the complete process from the generation of the initial population to the determination of the optimal parameters, and the final simulation verification can be seen.

[0154] The above embodiments of the present application have at least the following beneficial effects: the present application can accurately evaluate various risks that may be encountered during construction by obtaining key construction parameters such as pipe diameter, pushing speed and soil pressure based on construction design documents and historical construction records, and combining geological data such as soil type, groundwater level and distribution obtained through geological exploration. This comprehensive evaluation not only improves the safety of construction, but also improves construction efficiency and economic benefits by optimizing construction parameters. Further, the present application can realize intelligent optimization of construction parameters through the application of genetic algorithm. Through the simulation engine to simulate the construction process, the fitness of each parameter population individual is evaluated, and selection, crossover and mutation operations are performed, which can efficiently generate new populations until the optimal construction parameters are found. This process not only greatly reduces the trial-and-error cost in traditional construction parameter adjustment, but also improves the scientificity and accuracy of construction parameter selection through the introduction of the iterative optimization mechanism of genetic algorithm, thereby further reducing construction cost, shortening construction period and improving construction quality while ensuring construction safety.

[0155] As shown in Figure 2 , a micro pipe jacking construction process optimization system 200 of some embodiments, the system 200 comprises:

[0156] a construction parameter module 201 for obtaining construction parameters based on construction design documents and historical construction records, the construction parameters including pipe diameter, pushing speed and soil pressure;

[0157] a geological data module 202 for obtaining geological data based on geological exploration of the construction area, the geological data including soil type, groundwater level and soil type distribution;

[0158] a risk calculation module 203 for calculating efficiency risk R eff , cost risk R cost and safety risk R safety based on the construction parameters and geological data, respectively;

[0159] a risk assessment module 204 for calculating the comprehensive risk value R before construction by applying a preset comprehensive risk assessment model;

[0160] a parameter population module 205 for initializing a parameter population, each individual in the parameter population representing a set of construction parameters, using a simulation engine to simulate the construction process, including soil deformation, pipe jacking machine pushing and groundwater flow, to preliminarily verify the construction parameters;

[0161] a fitness module 206 for evaluating the fitness of each individual based on the efficiency risk R eff , cost risk R cost and safety risk R safety ;

[0162] a genetic algorithm module 207, configured to perform genetic algorithm to select, crossover and mutate, to generate a new population, and the individual with the highest fitness score as the recommended value of the construction parameter.

[0163] It can be understood that the modules described in the micro-tube construction process optimization system 200 correspond to the steps described in the micro-tube construction process optimization method with reference to Figure 1 The operations, features and advantages described above for the micro-tube construction process optimization method also apply to the micro-tube construction process optimization system 200 and the modules contained therein, and will not be repeated here.

[0164] Reference is made below to Figure 3 , which shows a structural diagram of a structure 300 of an electronic device suitable for implementing some embodiments of the present application. The electronic device in some embodiments of the present application can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet PCs), PMPs (Portable Multimedia Players), car terminals (such as car navigation terminals), and the like, as well as fixed terminals such as digital TVs, desktop computers, and the like. Figure 3 The terminal device shown is merely an example and should not impose any limitation on the functions and use range of the embodiments of the present application.

[0165] As shown in Figure 3 , the electronic device 300 can include a processing device (such as a central processor, a graphics processor, etc.) 301, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 302 or loaded into a random access memory (RAM) 303 from a storage device 308. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0166] Generally, the following devices can be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, and the like; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, and the like; storage devices 308 including, for example, a magnetic tape, a hard disk, and the like; and communication devices 309. The communication devices 309 can allow the electronic device 300 to communicate with other devices wirelessly or through wires to exchange data. Although Figure 3The electronic device 300 is shown with various means, but it is to be understood that not all of the means illustrated in FIG. 3 need be present in every embodiment. A greater or lesser number of means can be implemented. Figure 3 Each block denoted in the flowchart can represent one means, or a number of means depending on the needs.

[0167] Further, the storage medium of the embodiments of the present application stores program instructions capable of implementing all the methods described above, wherein the program instructions can be stored in the storage medium in the form of a software product, including a number of instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media capable of storing program codes, or a terminal device such as a computer, a server, a mobile phone, a tablet, etc.

[0168] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, another division manner can be adopted, for example, a plurality of units or components can be combined or integrated into another system, or some features can be omitted or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0169] In addition, each functional unit in the various embodiments of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit. The above is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process conversion using the contents of the specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

[0170] The specific implementation of the application is described in detail above, but it is only as an example, and the application is not limited to the specific implementation described above. Any equivalent modification or alternative to the application made by those skilled in the art is also within the scope of the application, and therefore, equivalent transformations, modifications, improvements, etc. made without departing from the spirit and principle range of the application should be covered within the scope of the application.

[0171] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and are not limited thereto; under the idea of the present application, the technical features in the above examples or different examples can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of the present application as described above, which are not provided in detail for the sake of simplicity; although the application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: the technical solutions recorded in the foregoing examples can still be modified, or some technical features can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solution deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. An optimized method for micro-pipe jacking construction, characterized in that, The method includes: Step 101: Obtain construction parameters based on construction design documents and historical construction records. The construction parameters include pipe jacking diameter, advancing speed, and earth pressure. Step 102: Obtain geological data based on geological exploration of the construction area, including soil type, groundwater level, and soil type distribution; Step 103: Calculate the efficiency risk based on the construction parameters and geological data. Cost risks and security risks ; Step 104: Calculate the comprehensive risk value R before construction using a pre-set comprehensive risk assessment model; Step 105: Initialize the parameter population. In the parameter population, each individual represents a set of construction parameters. Use a simulation engine to simulate the construction process, including soil deformation, pipe jacking machine advancement, and groundwater flow, in order to initially verify the construction parameters. Step 106, based on the aforementioned efficiency risk Cost risks and security risks Assess the fitness of each individual; Step 107: Perform selection, crossover, and mutation operations using a genetic algorithm to generate a new population. The individual with the highest fitness score is used as the recommended value for the construction parameters. In step 104, the preset comprehensive risk assessment model is shown in calculation formula (1); （1）； In the formula, m, n, and p represent efficiency risk, respectively. Cost risks and security risks Quantity; , and Efficiency risk Cost risks and security risks The weights; j, k, and l represent efficiency risk, respectively. Cost risks and security risks ordinal number; In step 103, the computational efficiency risk As shown in calculation formula (2); （2） In the formula, For efficiency risk coefficient, For the actual speed of advancement, To accelerate the progress of the plan; The computational cost risk As shown in calculation formula (3); (3) In the formula, Cost risk coefficient, For actual cost, For planned costs; In step 103, the calculation of security risks As shown in calculation formula (4); （4） In the formula, For safety risk factor, Let be the frequency of occurrence of the i-th security event. The severity is assigned accordingly; the safety events include: equipment failure, environmental factors, and emergency situations.

2. The method for optimizing micro-pipe jacking construction technology according to claim 1, characterized in that, The efficiency risk coefficient The calculation formula is shown in calculation formula (5); （5） In the formula, Let m be the total planned construction time, and m be the total number of construction stages. For the speed of progress of the plan in the j-th stage, This represents the actual propulsion speed in the j-th stage; The cost risk coefficient The calculation formula is shown in equation (6); （6） In the formula, The historical standard deviation of costs indicates the degree of cost fluctuation. This represents the average cost.

3. The method for optimizing micro-pipe jacking construction technology according to claim 2, characterized in that, The security risk coefficient The calculation formula is shown in equation (7); （7） In the formula, Let be the probability of the i-th security event occurring; The severity score is given for the i-th security incident; n is the total number of security incidents.

4. The method for optimizing micro-pipe jacking construction technology according to claim 1, characterized in that, Step 107 further includes: The genetic algorithm is executed to perform selection operations based on fitness scores, selecting individuals with high fitness scores for crossover and mutation; among them, the crossover operation generates new combinations of construction parameters, and the mutation operation introduces new genetic diversity; Repeat the selection, crossover, and mutation steps to generate a new population until the optimal construction parameters are found; The optimal construction parameters are input into the simulation engine for verification testing, which includes simulating soil deformation, pipe jacking machine advancement, and groundwater flow.

5. A micro-pipe jacking construction process optimization system, characterized in that, The system includes: The construction parameter module is used to obtain construction parameters based on construction design documents and historical construction records. These construction parameters include pipe jacking diameter, advancing speed, and earth pressure. The geological data module is used to obtain geological data based on geological exploration of the construction area. The geological data includes soil type, groundwater level and soil type distribution. The risk calculation module is used to calculate the efficiency risk based on the construction parameters and geological data. Cost risks and security risks ; The risk assessment module is used to calculate the comprehensive risk value R before construction by applying a preset comprehensive risk assessment model; The parameter population module is used to initialize the parameter population. In the parameter population, each individual represents a set of construction parameters. The simulation engine is used to simulate the construction process, including soil deformation, pipe jacking machine advancement and groundwater flow, in order to initially verify the construction parameters. The fitness module is used to assess the efficiency risk. Cost risks and security risks Assess the fitness of each individual; The genetic algorithm module is used to perform selection, crossover, and mutation operations to generate a new population. The individual with the highest fitness score is used as the recommended value for the construction parameters.

6. An electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the method described in any one of claims 1-4 when it is run.

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