Pipe-jacking tunneling machine model selection evaluation system and model selection method based on fuzzy mathematics

Abstract

The invention discloses a pipe-jacking tunneling machine type selection evaluation system and method based on fuzzy mathematics. The method comprises the steps that firstly, a multi-dimensional key evaluation index system covering planning and design requirements, hydrogeological conditions, surrounding environment factors and cutterhead configuration characteristics is constructed; secondly, establishing each index membership function aiming at different jacking pipe types and cutterhead configuration combinations by applying a fuzzy mathematics theory, and generating a membership matrix of jacking pipe adaptive type selection; and thirdly, determining a comprehensive index weight by adopting a subjective and objective weighting method fusion strategy, and finally constructing a fuzzy comprehensive evaluation model based on the membership matrix and the comprehensive weight, thereby realizing the adaptability quantitative evaluation of the pipe-jacking tunneling machines with different types and cutterheads. According to the method, the defect that traditional model selection depends on subjective experience is overcome, quantitative evaluation of pipe-jacking model selection under the complex stratum condition is achieved, the model selection accuracy and efficiency are improved, a reliable solution is provided for scientific model selection of the pipe-jacking tunneling machine, and the method has outstanding practicability and wide application prospects.

Description

Technical Field

[0001] This invention relates to the field of trenchless construction technology for underground pipelines, specifically to a selection and evaluation system and method for tunnel boring machines based on fuzzy mathematics. Background Technology

[0002] Pipe jacking, leveraging its trenchless technology, can efficiently traverse complex geological conditions and underground obstacles, significantly reducing disturbance to the surface environment, shortening construction time, and lowering costs. It is particularly suitable for engineering scenarios with stringent construction restrictions, such as urban core areas and major transportation routes. Its technical principle involves using a main jacking cylinder in conjunction with the thrust systems between pipeline sections and intermediate sections to advance prefabricated pipe segments sequentially along the designed axis. A rotating cutterhead at the front end cuts the soil in real time, and a mud sleeve forms a lubrication and drag-reducing layer, ensuring the pipeline accurately traverses underground spaces and reaches the receiving shaft. As the core equipment, the pipe jacking machine plays a crucial role in fractured strata, excavated soil removal, and face stability control. Facing complex conditions such as soft soil, composite strata, and soft-over-hard surfaces, the pipe jacking machine must simultaneously address the combined challenges of controlling stratum deformation, matching tunneling resistance, and ensuring equipment durability. Its selection directly determines the success or failure of the construction.

[0003] In the field of trenchless underground pipeline construction, the selection decision of tunnel boring machines (TBMs) directly affects project efficiency, cost control, and the safety of the surrounding environment. With accelerated urbanization, the demand for underground pipeline laying is surging, and the construction environment is becoming increasingly complex. Traditional selection methods have significant limitations: First, they rely heavily on subjective experience. Current selection processes largely depend on the personal experience of engineers, making decisions based on qualitative analysis of geological reports and equipment parameters, lacking quantitative evaluation standards. This approach easily leads to differences in selection for different projects under similar geological conditions, and may even cause construction accidents due to equipment incompatibility with the strata. Second, geological adaptability assessment is fragmented. TBM construction often traverses complex strata, requiring comprehensive consideration of the interactive effects of multiple parameters such as soil permeability coefficient, groundwater pressure, and particle size distribution. However, existing technologies only evaluate single indicators (such as soil strength) or local parameter combinations, failing to establish a systematic evaluation index system. Third, the diversity of cutterhead configurations is not fully reflected. In practical engineering, slurry balance and earth pressure balance pipe jacking machines can be equipped with various cutterheads (such as spoke type, face plate type, hybrid type, etc.) to adapt to different soil and rock conditions. Especially when dealing with rock strata and complex strata with uneven hardness, the selection of cutterhead type is crucial to tunneling efficiency and safety. Existing methods do not consider cutterhead type as an independent evaluation dimension, leading to deviations between the selection results and actual working conditions.

[0004] Given the aforementioned shortcomings of existing methods for selecting tunnel jacking machines, there is an urgent need to establish a selection and evaluation system based on multi-dimensional quantitative analysis. Summary of the Invention

[0005] The purpose of this invention is to address the problems existing in the prior art by providing a selection and evaluation system and method for tunnel jacking machines based on fuzzy mathematics. This invention aims to solve the technical problems of traditional tunnel jacking selection methods, such as strong reliance on subjective experience, fragmented geological adaptability assessment, and lack of multi-objective collaborative optimization. By constructing a multi-dimensional quantitative evaluation system and combining it with fuzzy mathematics theory, the invention achieves scientific and precise selection of tunnel jacking machines under complex geological conditions.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A selection and evaluation system for tunnel boring machines based on fuzzy mathematics, comprising: The indicator system construction module is used to screen and establish a core evaluation indicator system covering three dimensions: planning and design requirements, hydrogeological conditions, and surrounding environmental factors. The membership calculation module, based on fuzzy mathematics theory, constructs membership functions for each core evaluation index for different combinations of different types of tunnel jacking machines and cutterhead configurations, and generates a membership matrix for the adaptive selection of tunnel jacking machines. The weight determination module adopts a fusion strategy of subjective and objective weighting, and combines an improved genetic algorithm and the analytic hierarchy process to calculate the comprehensive weight of each core evaluation indicator. The fuzzy comprehensive evaluation module, based on the membership matrix and the comprehensive weight, constructs a fuzzy comprehensive evaluation model through fuzzy matrix multiplication, which is used to calculate the fitness values ​​of different types of tunnel boring machines and output the fitness evaluation results of different types of tunnel boring machines; The results output module is used to visualize the evaluation results in a multi-dimensional format; The cutterhead structure parameter adaptation module establishes a rule library for adapting cutterhead layout and structural parameters for different formation types. It also includes a data storage module for storing indicator data, membership functions, evaluation results, and historical evaluation cases, providing data support for the iterative optimization of the fuzzy comprehensive evaluation model.

[0007] Furthermore, the core evaluation indicators included in the planning and design requirements are pipe jacking diameter, jacking length and pipe jacking depth; the core evaluation indicators included in the hydrogeological conditions are formation permeability coefficient, formation particle size distribution, groundwater pressure, rock strength and formation homogeneity; and the core evaluation indicators included in the surrounding environmental factors are surface subsidence control requirements.

[0008] Furthermore, the subjective and objective weighting method integration strategy includes: integrating the subjective evaluation results of experts at different levels through the analytic hierarchy process (AHP), assigning them evaluation weights of 50%, 30%, and 20% respectively, and constructing a judgment matrix through the AHP. A correction coefficient β for the working condition of the tunnel boring machine is introduced, and a comprehensive weight vector W = β × W1 + (1-β) × W2 is constructed, where W1 refers to the initial subjective weight vector and W2 refers to the objective weight vector. At the same time, the improved genetic algorithm is used to optimize the comprehensive weight. The population size is set to 5000 and the maximum number of iterations is 1000 generations. The fitness function is the weighted sum of the consistency index of the judgment matrix and the entropy weight information loss rate. Through adaptive selection, crossover and mutation operations, the consistency ratio CR of the judgment matrix is ​​ensured to be <0.1.

[0009] Furthermore, the evaluation results include fitness level classification, index weight ranking, and selection recommendations. The fitness level classification protects fully adapted, highly adapted, moderately adapted, low adapted, weakly adapted, and unadapted individuals. The index weight ranking clarifies the degree of influence of each evaluation parameter on the selection decision. The selection recommendations propose optimal solutions for tunnel boring machines or geological improvement measures for different fitness levels.

[0010] A selection method for a tunnel boring machine (TBM) selection and evaluation system based on fuzzy mathematics, the method comprising the following steps: The core evaluation indicator system is constructed using the indicator system construction module. Based on the membership degree calculation module, a membership function is established, the membership degree matrix is ​​generated, and the correlation between the core evaluation indicators and the adaptability of different types of tunnel boring machines is quantified. The comprehensive weight is determined by the weight determination module, and the comprehensive weight of each core evaluation indicator is output. The fuzzy comprehensive evaluation model is used to perform fuzzy comprehensive evaluation on different types of tunnel jacking machines, and the adaptive evaluation results are output. Based on the adaptive evaluation results, fitness value levels are divided, selection suggestions are generated and visualized to assist decision-makers in selecting the optimal type of tunnel boring machine.

[0011] This selection method, which integrates fuzzy mathematics theory and subjective and objective weighting strategies, achieves intelligent selection decision-making for tunnel boring machines under complex geological conditions by constructing a scientific indicator system, accurately quantifying adaptive relationships, and optimizing weight calculation models.

[0012] Furthermore, the membership function is constructed using engineering practice data and follows the following mapping relationship: when the stratum permeability coefficient > 10 -4When the rock density is m / s, the membership degree of the slurry balance pipe jacking machine is 1; when the fine particle content of the stratum is >40%, the membership degree of the earth pressure balance pipe jacking machine is 1; when the uniaxial compressive strength of the rock is >60MPa, the membership degree of the pipe jacking machine equipped with a roller cutterhead is 1; when the stratum homogeneity is poor, the membership degree of the pipe jacking machine with a hybrid and variable cutterhead configuration is higher; when the uniaxial compressive strength of the rock is >60MPa, the membership degree of the pipe jacking machine equipped with a roller cutterhead is 1; when the stratum homogeneity is poor, the membership degree of the pipe jacking machine with a hybrid cutterhead configuration is 1.

[0013] Furthermore, the calculation formula of the fuzzy comprehensive evaluation model is: B=A×R, where B is the adaptive evaluation result of the comprehensive evaluation, A is the comprehensive weight vector of the core evaluation index, and R is the membership matrix.

[0014] Furthermore, the selection recommendations combine adaptability level and sensitivity analysis of core evaluation indicators to select tunnel boring machine types with a high adaptability level or above, and propose ground improvement or equipment parameter adjustment schemes for weakly adaptable or unadaptable working conditions.

[0015] Furthermore, when the fitness value obtained from the fuzzy comprehensive evaluation shows that a certain type of tunnel boring machine is highly or moderately adapted to the overall project, but its membership degree for one or more local key indicators in the core evaluation index system is lower than the preset weak adaptation threshold, the result output module will generate a local section maladaptation warning. The maladaptation warning clearly identifies the specific maladaptation indicators and the corresponding project section, and outputs special construction control measures or tunnel boring machine configuration optimization schemes for the local section.

[0016] Furthermore, the membership function constructed for at least one core evaluation index has a membership degree that exhibits a nonlinear change within the domain of the input parameters. This is used to characterize the non-uniform mapping relationship in which the core evaluation index is particularly sensitive to the adaptability of the tunnel boring machine within a specific critical range.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: 1. This pipe jacking machine selection and evaluation system includes modules such as index system construction, membership degree calculation, weight determination, fuzzy comprehensive evaluation, and result output. It overcomes the shortcomings of traditional selection relying on subjective experience, realizes quantitative evaluation of pipe jacking machine selection under complex geological conditions, improves selection accuracy and efficiency, provides a reliable solution for the scientific selection of pipe jacking machines, and has outstanding practicality and broad application prospects; 2. Through the quantitative evaluation method of multi-index coupling, the pipe jacking machine can be evaluated in all aspects, and a more reasonable mapping relationship between quantitative index parameters and equipment adaptability can be obtained, realizing scientific selection decision-making for pipe jacking machines under complex geological conditions, and providing more comprehensive and accurate data support for subsequent selection. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the selection and evaluation system and method for tunnel boring machines based on fuzzy mathematics according to the present invention. Figure 2 This is a hierarchical structure diagram of the evaluation index system for a fuzzy mathematics-based selection and evaluation method for tunnel boring machines according to the present invention. Figure 3 This is a comparison chart of membership function curves of different types of tunnel boring machines under the evaluation method for selecting tunnel boring machines based on fuzzy mathematics according to the present invention; Figure 4 This is a visualization interface diagram of the selection and evaluation results of different types of tunnel boring machines according to the present invention. Detailed Implementation

[0019] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the description of this invention, it should be noted that the terms "middle", "upper", "lower", "left", "right", "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.

[0021] Example 1: A selection and evaluation system for tunnel boring machines based on fuzzy mathematics is provided, comprising: The indicator system construction module is used to screen and establish a core evaluation indicator system covering three dimensions: planning and design requirements, hydrogeological conditions, and surrounding environmental factors. The membership calculation module, based on fuzzy mathematics theory, constructs membership functions for each core evaluation index for different combinations of different types of tunnel jacking machines and cutterhead configurations, and generates a membership matrix for the adaptive selection of tunnel jacking machines. The weight determination module adopts a fusion strategy of subjective and objective weighting, and combines an improved genetic algorithm and the analytic hierarchy process to calculate the comprehensive weight of each core evaluation indicator. The fuzzy comprehensive evaluation module, based on the membership matrix and the comprehensive weight, constructs a fuzzy comprehensive evaluation model through fuzzy matrix multiplication, which is used to calculate the fitness values ​​of different types of tunnel boring machines and output the fitness evaluation results of different types of tunnel boring machines; The results output module is used to visualize the evaluation results in a multi-dimensional format; The cutterhead structure parameter adaptation module establishes an adaptation rule library for cutterhead arrangement and structural parameters for different formation types. For example, in a composite formation with soft upper layer and hard lower layer (hard rock accounting for 30%–60%), a hybrid cutterhead is adapted, limiting the cutterhead opening ratio to 30%–40%, cutter spacing to 80–120 mm, cutterhead rotation speed to 5–8 r / min, and the ratio of roller cutters to scrapers to 1:2; for high-permeability sand layers (k > 10), a hybrid cutterhead is adapted, limiting the cutterhead aperture ratio to 30%–40%, cutter spacing to 80–120 mm, cutterhead rotation speed to 5–8 r / min, and the ratio of roller cutters to scrapers to 1:2. -4 m / s): Compatible with panel-type cutter heads, limiting the cutter head opening ratio to 20%~30% and the cutter spacing to 60~100mm, equipped with a cutter head sealing system with a sealing performance level ≥IP68; It also includes a data storage module for storing indicator data, membership functions, evaluation results, and historical evaluation cases, providing data support for the iterative optimization of the fuzzy comprehensive evaluation model.

[0022] This tunnel boring machine (TBM) selection and evaluation system includes modules for index system construction, membership degree calculation, weight determination, fuzzy comprehensive evaluation, and result output. It overcomes the shortcomings of traditional selection that relies on subjective experience, realizes quantitative evaluation of TBM selection under complex geological conditions, improves selection accuracy and efficiency, provides a reliable solution for the scientific selection of TBMs, and has outstanding practicality and broad application prospects.

[0023] The types of tunnel boring machines include slurry balance tunnel boring machines and earth pressure balance tunnel boring machines. The cutterhead configurations include spoke cutterheads, face plate cutterheads, and hybrid cutterheads, forming six selection modes for tunnel boring machines.

[0024] The membership calculation module constructs membership functions for different types of tunnel boring machines (SPB slurry balance tunnel boring machines and EPB earth pressure balance tunnel boring machines) and cutterhead configurations. These functions include membership functions for tunnel jacking length, tunnel cross-sectional dimensions, groundwater pressure, soil particle size distribution, soil permeability, surface settlement and deformation, cutterhead opening ratio α, rock strength σ, and formation homogeneity U. Details are as follows: (1) Membership function of pipe jacking length, let x be the pipe jacking length in meters; The membership function for the jacking length of a slurry-balanced pipe jacking machine is: , The membership function for the jacking length of an earth pressure balance pipe jacking machine is: .

[0025] (2) Membership function of the jacking pipe cross-section size, let x1 be the jacking pipe cross-section size, in meters; The membership function for the cross-sectional dimensions of the jacking pipe of a slurry-balanced pipe jacking machine is as follows: , The membership function for the cross-sectional dimensions of the pipe jacking machine for earth pressure balance pipe jacking is: .

[0026] (3) Groundwater pressure membership function, let x2 be the groundwater pressure, in MPa; The groundwater pressure membership function for a slurry-balanced pipe jacking machine is: , The groundwater pressure membership function for earth pressure balance pipe jacking machines is: .

[0027] (4) Membership function of soil particle size distribution, let x3 be the soil particle size distribution, which has no unit; The membership function for soil particle size distribution for a slurry balance pipe jacking machine is: , The membership function for soil particle size distribution for earth pressure balance pipe jacking machines is: .

[0028] (5) Soil permeability membership function, let x4 be the soil permeability, in m / s; The soil permeability membership function for a slurry balance pipe jacking machine is: , The soil permeability membership function for earth pressure balance pipe jacking machines is: .

[0029] (6) Membership function of surface settlement deformation, where x5 is the soil permeability in mm; The membership function for surface settlement deformation of a slurry-balanced pipe jacking machine is: , The membership function for surface settlement deformation of earth pressure balance pipe jacking machines is: .

[0030] (7) Membership function f1 of cutterhead aperture ratio α (for hybrid cutterheads, adapted to composite formations, x6 is the cutterhead aperture ratio): .

[0031] (8) Rock strength membership function (subdivided according to cutterhead type to adapt to different rock strata), x7 is the rock strength, unit MPa; The rock strength membership function f2 for the cutterhead (compatible with earth pressure balance / slurry balance pipe jacking machines): ; Rock strength membership function f3 for spoke / panel cutterhead jacking machines (compatible with earth pressure balance / slurry balance pipe jacking machines): ; Rock strength membership function f4 for hybrid cutterhead (compatible with earth pressure balance / slurry balance pipe jacking machines): .

[0032] (9) Membership function of stratigraphic homogeneity U (subdivided according to stratigraphic type, U is the quantification level: level 1 = extremely homogeneous, level 2 = relatively homogeneous, level 3 = generally homogeneous, level 4 = relatively heterogeneous, level 5 = extremely heterogeneous), x8 is the stratigraphic homogeneity, without unit; For spoke / panel cutterheads, the rock strength membership function f5 is: ; The rock strength membership function f6 for the hybrid cutterhead: .

[0033] Furthermore, the core evaluation indicators included in the planning and design requirements are pipe jacking diameter, jacking length and pipe jacking depth; the core evaluation indicators included in the hydrogeological conditions are formation permeability coefficient, formation particle size distribution, groundwater pressure, rock strength and formation homogeneity; and the core evaluation indicators included in the surrounding environmental factors are surface subsidence control requirements.

[0034] The pipe jacking diameter reflects the equipment's specification compatibility; the jacking length affects the equipment's power and endurance requirements; the pipe jacking depth reflects earth pressure; the stratum permeability coefficient is a key parameter for controlling the stability of the tunnel face; the stratum particle size distribution is an indicator of the adaptability of spoil improvement and transportation; the groundwater pressure reflects the sealing and pressure control requirements; the rock strength can evaluate cutter wear and tunneling efficiency; the stratum uniformity affects the cutterhead selection and construction stability; and the surface settlement control requirements are an indicator of construction accuracy in environmentally sensitive areas. By constructing these indicators, a comprehensive evaluation of the pipe jacking machine can be achieved, resulting in a more reasonable mapping relationship between quantitative indicator parameters and equipment adaptability, providing more comprehensive and accurate data support for subsequent selection.

[0035] Furthermore, the subjective and objective weighting method integration strategy includes: integrating the subjective evaluation results of experts at different levels through the analytic hierarchy process (AHP), assigning them evaluation weights of 50%, 30%, and 20% respectively, and constructing a judgment matrix through the AHP. A correction coefficient β for the working conditions of a tunnel boring machine (TBM) is introduced, and a comprehensive weight vector W = β × W1 + (1-β) × W2 is constructed, where W1 refers to the initial subjective weight vector and W2 refers to the objective weight vector. Simultaneously, the improved genetic algorithm is used to optimize the comprehensive weights, with a population size of 5000 and a maximum iteration count of 1000 generations. The fitness function is the weighted sum of the consistency index of the judgment matrix and the entropy weight information loss rate. Through adaptive selection, crossover, and mutation operations, the consistency ratio CR of the judgment matrix is ​​ensured to be <0.1. In the weight determination module, subjective weighting, objective optimization, and coupling optimization are integrated, enabling further optimization of the judgment matrix.

[0036] Furthermore, the evaluation results include fitness level classification, index weight ranking, and selection recommendations. The fitness level classification protects fully adapted (≥0.9), highly adapted (0.8~0.9), moderately adapted (0.7~0.8), poorly adapted (0.6~0.7), weakly adapted (0.4~0.6), and unadapted (<0.4). The index weight ranking clarifies the degree of influence of each evaluation parameter on the selection decision. The selection recommendations propose optimal pipe jacking machine schemes or geological improvement measures for different fitness levels.

[0037] Example 2: A selection method for a fuzzy mathematics-based tunneling machine selection and evaluation system is provided, combined with... Figures 1-3 As shown, the selection method includes the following steps: The core evaluation indicator system is constructed using the indicator system construction module. Based on the membership degree calculation module, a membership function is established, the membership degree matrix is ​​generated, and the correlation between the core evaluation indicators and the adaptability of different types of tunnel boring machines is quantified. The comprehensive weight is determined by the weight determination module, and the comprehensive weight of each core evaluation indicator is output. The fuzzy comprehensive evaluation model is used to perform fuzzy comprehensive evaluation on different types of tunnel jacking machines, and the adaptive evaluation results are output. Based on the adaptive evaluation results, fitness value levels are divided, selection suggestions are generated and visualized to assist decision-makers in selecting the optimal type of tunnel boring machine.

[0038] Furthermore, the membership function is constructed using engineering practice data and follows the following mapping relationship: when the stratum permeability coefficient > 10 -4When the rock density is m / s, the membership degree of the slurry balance pipe jacking machine is 1; when the fine particle content of the stratum is >40%, the membership degree of the earth pressure balance pipe jacking machine is 1; when the uniaxial compressive strength of the rock is >60MPa, the membership degree of the pipe jacking machine equipped with a roller cutterhead is 1; when the stratum homogeneity is poor, the membership degree of the pipe jacking machine with a hybrid and variable cutterhead configuration is higher; when the uniaxial compressive strength of the rock is >60MPa, the membership degree of the pipe jacking machine equipped with a roller cutterhead is 1; when the stratum homogeneity is poor, the membership degree of the pipe jacking machine with a hybrid cutterhead configuration is 1.

[0039] Specifically, for the combination of target pipe jacking type (such as slurry balance, earth pressure balance) and cutterhead configuration (such as spoke type, panel type, hybrid type), membership functions for each indicator are constructed based on engineering practice data: Pipe jacking diameter: Slurry balance pipe jacking machines have higher membership in the large diameter (0.4~3m) range, while earth pressure balance pipe jacking machines are more adaptable in the medium diameter (1.5~5m) range; Formation permeability coefficient: Permeability coefficient > 10 -4 At a speed of m / s, the membership degree of the slurry balance pipe jacking machine is 1; 10 -7 ~10 -4 For m / s ranges, the membership degree needs to be adjusted in conjunction with soil improvement measures; for stratum particle size distribution: when the fine particle content is >40%, the membership degree of the earth pressure balance tunnel boring machine (EPB) is 1; when it is <30%, it needs to be improved through additives to enhance adaptability; for groundwater pressure: when the pressure is >0.3MPa, the slurry balance tunnel boring machine is preferred, and when it is <0.3MPa, the membership degree of the EPB tunnel boring machine is higher; for rock strength: for high-strength rock strata (>60MPa), the EPB tunnel boring machine or slurry tunnel boring machine equipped with a roller cutterhead is preferred, with a membership degree of 1; for medium- and low-strength rock strata (20-60MPa), a panel cutterhead or a hybrid cutterhead can be selected according to the degree of fracture development, and the corresponding membership degree can be assigned. For stratum homogeneity: for composite strata (soft upper layer, hard lower layer, interbedded layers, etc.), a hybrid or variable cutterhead needs to be selected, and the membership degree should be adjusted according to the soft-hard ratio, rock dip angle, etc.; in homogeneous strata, the spoke or panel cutterhead has a higher membership degree. Surface settlement control: In environmentally sensitive areas (settlement limit < 5mm), slurry balance pipe jacking machines are preferred; in general areas (limit < 10mm), earth pressure balance pipe jacking machines can be used. Substitute the actual engineering parameters into the membership function to generate an m×n membership matrix R (m is the number of indicators, n is the number of pipe jacking types).

[0040] Experts at different levels were invited to compare the importance of the indicators pairwise to construct a judgment matrix, and the initial weights were calculated using the analytic hierarchy process. Objective optimization: The initial weights were optimized using an improved genetic algorithm, with a population size of 5000 and a maximum number of iterations of 1000 generations. The consistency index was minimized through adaptive crossover and mutation operations, and the comprehensive weight vector A that satisfies CR < 0.1 was output.

[0041] Furthermore, the calculation formula of the fuzzy comprehensive evaluation model is: B=A×R, where B is the adaptive evaluation result of the n×1 order comprehensive evaluation, A is the 1×m order core evaluation index comprehensive weight vector (fitness value of each pipe jacking type), and R is the m×n order membership matrix.

[0042] Furthermore, the selection recommendations combine adaptability level and sensitivity analysis of core evaluation indicators to select pipe jacking machine types with a high adaptability level or above, and propose ground improvement or equipment parameter adjustment schemes for weakly adaptable or unsuitable working conditions, such as proposing optimization suggestions like soil improvement (e.g., adding bentonite) and equipment parameter adjustment (e.g., lengthening the screw conveyor); for unsuitable types, they are clearly excluded and it is recommended to replace the pipe jacking machine model or adjust the construction plan.

[0043] like Figure 4 The image shows the evaluation results interface output by the simulated terminal, which intuitively presents the selection decision support information. The interface consists of three main areas: the upper left is the basic project information bar (displaying key parameters such as basic information of the pipe jacking project and geological type); the upper right is a heat map of index weights and membership degrees (using color depth to represent the weight and membership degree); and the bottom is the selection results area, with a bar chart on the left comparing the fitness values ​​of different pipe jacking types, and a dashboard on the right displaying the fitness level along with textual selection suggestions.

[0044] Furthermore, when the fitness value obtained from the fuzzy comprehensive evaluation shows that a certain type of tunnel boring machine is highly or moderately adapted to the overall project, but its membership degree for one or more local key indicators in the core evaluation index system is lower than the preset weak adaptation threshold, the result output module will generate a local section maladaptation warning. The maladaptation warning clearly identifies the specific maladaptation indicators and the corresponding project section, and outputs special construction control measures or tunnel boring machine configuration optimization schemes for the local section.

[0045] Furthermore, the membership function constructed for at least one core evaluation index has a membership degree that exhibits a nonlinear change within the domain of the input parameters. This is used to characterize the non-uniform mapping relationship in which the core evaluation index is particularly sensitive to the adaptability of the tunnel boring machine within a specific critical range.

[0046] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A selection and evaluation system for tunnel boring machines based on fuzzy mathematics, characterized in that, include: The indicator system construction module is used to screen and establish a core evaluation indicator system covering three dimensions: planning and design requirements, hydrogeological conditions, and surrounding environmental factors. The membership calculation module, based on fuzzy mathematics theory, constructs membership functions for each core evaluation index for different combinations of different types of tunnel jacking machines and cutterhead configurations, and generates a membership matrix for the adaptive selection of tunnel jacking machines. The weight determination module adopts a fusion strategy of subjective and objective weighting, and combines an improved genetic algorithm and the analytic hierarchy process to calculate the comprehensive weight of each core evaluation indicator. The fuzzy comprehensive evaluation module, based on the membership matrix and the comprehensive weight, constructs a fuzzy comprehensive evaluation model through fuzzy matrix multiplication, which is used to calculate the fitness values ​​of different types of tunnel boring machines and output the fitness evaluation results of different types of tunnel boring machines; The results output module is used to visualize the evaluation results in a multi-dimensional format; The cutterhead structure parameter adaptation module establishes a rule library for adapting cutterhead layout and structural parameters for different formation types. It also includes a data storage module for storing indicator data, membership functions, evaluation results, and historical evaluation cases, providing data support for the iterative optimization of the fuzzy comprehensive evaluation model.

2. The fuzzy mathematics-based selection and evaluation system for tunnel boring machines according to claim 1, characterized in that, The core evaluation indicators included in the planning and design requirements are pipe diameter, jacking length and pipe burial depth. The core evaluation indicators included in the hydrogeological conditions are formation permeability coefficient, formation particle size distribution, groundwater pressure, rock strength and formation homogeneity. The core evaluation indicators included in the surrounding environmental factors are surface subsidence control requirements.

3. The fuzzy mathematics-based selection and evaluation system for tunnel boring machines according to claim 1, characterized in that, The subjective and objective weighting method integration strategy includes: integrating the subjective evaluation results of experts at different levels through the analytic hierarchy process (AHP), assigning them evaluation weights of 50%, 30%, and 20% respectively, and constructing a judgment matrix through the AHP. A correction coefficient β for the working condition of the tunnel boring machine is introduced, and a comprehensive weight vector W = β × W1 + (1-β) × W2 is constructed, where W1 refers to the initial subjective weight vector and W2 refers to the objective weight vector. At the same time, the improved genetic algorithm is used to optimize the comprehensive weight. The population size is set to 5000 and the maximum number of iterations is 1000 generations. The fitness function is the weighted sum of the consistency index of the judgment matrix and the entropy weight information loss rate. Through adaptive selection, crossover and mutation operations, the consistency ratio CR of the judgment matrix is ​​ensured to be <0.

1.

4. The fuzzy mathematics-based selection and evaluation system for tunnel boring machines according to claim 1, characterized in that, The evaluation results include fitness level classification, index weight ranking, and selection recommendations. The fitness level classification protects fully adapted, highly adapted, moderately adapted, low adapted, weakly adapted, and unadapted. The index weight ranking clarifies the degree of influence of each evaluation parameter on the selection decision. The selection recommendations propose optimal solutions for tunnel boring machines or geological improvement measures for different fitness levels.

5. The selection method of the fuzzy mathematics-based tunneling machine selection and evaluation system according to any one of claims 1 to 4, characterized in that, The selection method includes the following steps: The core evaluation indicator system is constructed using the indicator system construction module. Based on the membership degree calculation module, a membership function is established, the membership degree matrix is ​​generated, and the correlation between the core evaluation indicators and the adaptability of different types of tunnel boring machines is quantified. The comprehensive weight is determined by the weight determination module, and the comprehensive weight of each core evaluation indicator is output. The fuzzy comprehensive evaluation model is used to perform fuzzy comprehensive evaluation on different types of tunnel jacking machines, and the adaptive evaluation results are output. Based on the adaptive evaluation results, fitness value levels are divided, selection suggestions are generated and visualized to assist decision-makers in selecting the optimal type of tunnel boring machine.

6. The selection method of the fuzzy mathematics-based tunneling machine selection and evaluation system according to claim 5, characterized in that, The membership function is constructed using engineering practice data and follows the mapping relationship as follows: when the stratum permeability coefficient > 10 -4 When the rock density is m / s, the membership degree of the slurry balance pipe jacking machine is 1; when the fine particle content of the stratum is >40%, the membership degree of the earth pressure balance pipe jacking machine is 1; when the uniaxial compressive strength of the rock is >60MPa, the membership degree of the pipe jacking machine equipped with a roller cutterhead is 1; when the stratum homogeneity is poor, the membership degree of the pipe jacking machine with a hybrid and variable cutterhead configuration is higher; when the uniaxial compressive strength of the rock is >60MPa, the membership degree of the pipe jacking machine equipped with a roller cutterhead is 1; when the stratum homogeneity is poor, the membership degree of the pipe jacking machine with a hybrid cutterhead configuration is 1.

7. The selection method of the fuzzy mathematics-based tunneling machine selection and evaluation system according to claim 5, characterized in that, The calculation formula of the fuzzy comprehensive evaluation model is: B=A×R, where B is the adaptive evaluation result of the comprehensive evaluation, A is the comprehensive weight vector of the core evaluation index, and R is the membership matrix.

8. The selection method of the fuzzy mathematics-based tunneling machine selection and evaluation system according to claim 5, characterized in that, The selection recommendations combine adaptability level and sensitivity analysis of core evaluation indicators to select tunnel boring machine types with a high adaptability level or above, and propose ground improvement or equipment parameter adjustment schemes for weakly adaptable or unadaptable working conditions.

9. The selection method of the fuzzy mathematics-based tunneling machine selection and evaluation system according to claim 5, characterized in that, When the fitness value obtained from the fuzzy comprehensive evaluation shows that a certain type of tunnel boring machine is highly or moderately adapted to the overall project, but its membership degree for one or more local key indicators in the core evaluation index system is lower than the preset weak adaptation threshold, the result output module will generate a local section maladaptation warning. The maladaptation warning clearly identifies the specific maladaptation indicator and the corresponding project section, and outputs special construction control measures or tunnel boring machine configuration optimization schemes for the local section.

10. The selection method of the fuzzy mathematics-based tunneling machine selection and evaluation system according to claim 5, characterized in that, The membership function constructed for at least one core evaluation index has a membership degree that changes nonlinearly within the domain of the input parameters. This is used to characterize the non-uniform mapping relationship in which the core evaluation index is particularly sensitive to the adaptability of the tunnel boring machine within a specific critical range.

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