Railway engineering survey complexity grading and decision-making method under double drive condition

Abstract

This invention provides a method for classifying and deciding on the complexity of railway engineering surveys under dual-drive conditions. The method includes steps such as: S1: establishing survey stages and classification standards for survey levels; obtaining the survey level of the railway project to be decided; S2: establishing standards for typical structures and importance levels of the railway project; determining the importance level of the railway project to be decided; S3: determining the survey demand level; and so on. The beneficial effects of this invention are: it comprehensively considers the impact of different needs and conditions on the complexity of surveys in railway engineering, improving and perfecting the original survey classification standards from multiple angles and perspectives, making the classification method more hierarchical, intuitive, and clear, and the classification results more detailed, accurate, and valuable for reference. Simultaneously, this method can quantitatively analyze the survey costs and efficiency of different schemes, ultimately achieving a scientific and rational decision-making process for survey technology schemes.

Description

Technical Field

[0001] This invention belongs to the field of engineering survey technology, specifically relating to a method for classifying and deciding on the complexity of railway engineering survey under dual-drive conditions. Background Technology

[0002] Classification of exploration levels is the first step in carrying out geotechnical exploration work. Its main purpose is to highlight the exploration focus, define the depth of work, allocate resources rationally, and facilitate process management.

[0003] The existing classification of geotechnical investigation levels is mainly based on the "Code for Geotechnical Investigation (GB 50021-2001)", which comprehensively evaluates the level of geotechnical investigation from three aspects: the importance level of the project, the complexity of the site, and the complexity level of the foundation. Based on this, the geotechnical investigation level is divided into three levels: A, B, and C. However, no level classification has been made for railway engineering investigation work.

[0004] With the development of infrastructure construction in my country, the requirements for refined management of exploration are becoming increasingly stringent. Considering the varying levels of exploration complexity at different stages, for different project types (demand-driven), and for different levels of geological complexity (condition-driven), it is necessary to construct a comprehensive, logically rigorous, and widely applicable method for classifying and deciding on the complexity of railway engineering exploration under dual-driven conditions. This method would intuitively reflect the difficulty and urgency of exploration tasks, effectively enhance the guidance for exploration work, and provide a basis for fuzzy hierarchical analysis (AHP) in making exploration technology decisions. Summary of the Invention

[0005] In view of this, the present invention provides a method for classifying and deciding on the complexity of railway engineering survey under dual-drive conditions, comprising the following steps:

[0006] S1: Establish standards for the classification of exploration stages and exploration levels; and based on these standards, determine the exploration level of the railway project to be decided.

[0007] S2: Establish standards for typical structures and importance levels in railway engineering projects; determine the importance level based on the typical structures included in the railway engineering project to be decided;

[0008] S3: Determine the exploration requirement level; Obtain the exploration requirement level based on the exploration level in step S1 and step S2;

[0009] S4: Establish the geological environment characteristics and geological complexity level to obtain the geological complexity level of the railway project to be decided;

[0010] S5: Determine the exploration condition level; Based on the geological complexity level in step S4, obtain the exploration condition level of the railway project to be decided.

[0011] S6: Determine the complexity level of railway engineering survey; Based on the survey requirement level in step S3 and the survey condition level in S5, determine the complexity level of the railway engineering project to be decided.

[0012] S7: Establish a decision-making model for railway engineering survey technology based on the analytic hierarchy process and determine the weight matrix A of the criterion layer;

[0013] A = [a k b k ], where k = 1, 2, 3, 4...

[0014] Where a k b k The weights are determined by two factors: exploration cost and exploration efficiency.

[0015] S8: Determine the scale y of survey costs for multiple alternative options in a railway project under consideration. i ;

[0016] S9: Determine the exploration efficiency scale z for multiple alternative options i ;

[0017] S10: Construct pairwise judgment matrices P for exploration cost and exploration efficiency 费用 Q 效率 Combined with the criterion layer weight matrix A, the final result matrix F is obtained, and the optimal solution among multiple schemes is obtained.

[0018] The above process includes the following steps:

[0019] Construct pairwise judgment matrices P for exploration cost and exploration efficiency 费用 Q 效率 ;

[0020] Where p ij =y i / y j ;

[0021] Where q ij =z i / z j

[0022] Calculate the product M of the factor scores in each row of the judgment matrix. i N i ;

[0023]

[0024] Calculate M i and N i The nth root:

[0025]

[0026] right and Normalize:

[0027]

[0028] Obtain the factor evaluation matrix

[0029] Calculate the final result matrix F, F = A*R = {f1, f2, f2, f2...}; select the maximum value f. max f max The corresponding solution is the optimal solution.

[0030] Furthermore, the exploration cost scale y is obtained in step S8 above. i The method includes the following steps: The reciprocal of the ratio of the cost of multiple alternative surveying techniques to the total cost in the railway project to be decided is used as the surveying cost scale y. i .

[0031] Furthermore, the exploration efficiency scale z is obtained in step S9 above. i The method includes the following steps: a) Determine the factor set, U = {exploration efficiency u1};

[0032] b. Determine the set of comments, taking V = {high v1, relatively high v2, medium v3, low v4};

[0033] c. Determine the fuzzy comprehensive evaluation matrix, and determine its membership degree using expert experience method, for element u i An evaluation is made; thus, the evaluation matrices W1, W2, W3, and W4 for each alternative are obtained.

[0034] d. Determine the fuzzy comprehensive evaluation scale to obtain the scoring matrix as the corresponding subdivision matrix S = [90 70 50 20] for the four elements of the language set. Calculate the efficiency scale z for multiple alternative exploration technology schemes. i =W i *S T .

[0035] Furthermore, the decision-making model for geostationary engineering survey technology in step S7 includes a target layer, a criterion layer, and a scheme layer connected sequentially from top to bottom; the target layer is the optimal survey technology, the criterion layer is the two influencing factors of survey cost and survey efficiency, and the scheme layer is a number of alternative survey technology schemes to be decided.

[0036] The beneficial effects of this invention's method for classifying and deciding on the complexity of railway engineering surveys under dual-drive conditions are as follows: This method comprehensively considers the impact of different needs and conditions on the complexity of surveys in railway engineering, improving and perfecting the original survey classification standards from multiple angles and perspectives. This makes the classification method more hierarchical, intuitive, and clear, and the classification results more detailed, accurate, and valuable for reference, conforming to the actual situation and general requirements of engineering surveys. Simultaneously, this method can quantitatively analyze the survey costs and efficiency of different schemes, weighing and comparing multiple alternative survey technology schemes under the influence of both survey costs and efficiency factors, ultimately achieving a scientific and rational decision on the survey technology scheme. Attached Figure Description

[0037] Figure 1 A schematic diagram illustrating the classification of the complexity levels of railway engineering surveys under dual-drive conditions.

[0038] Figure 2 Hierarchical Analysis Structure Diagram for Exploration Technology Decision-Making

[0039] Figure 3 A technical roadmap for decision-making methods in railway engineering surveying based on fuzzy hierarchical analysis under dual-drive conditions. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0041] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in the present application can be combined with each other.

[0042] This example illustrates the preliminary survey of a 600-meter-long tunnel in a certain project. Geological investigation revealed that the tunnel's location spans multiple geomorphic units with a relative elevation difference exceeding 500 meters. Four possible combinations of geological plans are available for reference. Please refer to these plans. Figures 1-3 The optimal result is determined by combining the railway engineering survey technology decision-making method based on fuzzy hierarchical analysis under dual-drive conditions, as described in this invention, and includes the following steps:

[0043] Step 1: Establish standards for the classification of exploration stages and exploration levels.

[0044] Based on the different requirements for technical and economic input and workload in each stage of railway engineering survey (reconnaissance, preliminary survey, detailed survey, supplementary detailed survey, and construction), and referring to Table 1, the survey stages are divided into three levels: I, II, and III.

[0045] Table 1. Comparison of Exploration Stage Levels

[0046]

[0047] Based on the assessment, this project belongs to the exploration stage, level II.

[0048] Step 2: Establish standards for typical structures and importance levels in railway engineering.

[0049] The importance level is related not only to the function, scale, and role and status of the project in the national transportation network, but also closely related to the consequences of a disaster. Based on the different importance levels of typical structures such as bridges, tunnels, and roads, and referring to Table 2, the importance of typical structures is divided into three levels: important, general, and minor.

[0050] Table 2. Comparison of Importance Levels of Typical Structures in Railway Engineering

[0051]

[0052] Note: 1. Extra-large bridges: total length L of multiple spans > 1000m, single span L K >150m;

[0053] Bridge: Total length of multiple spans 100m ≤ L ≤ 1000m, single span diameter 40m ≤ L K ≤150m;

[0054] Medium-sized bridge: Total length of multiple spans 30m < L < 100m, single span diameter 20m ≤ L K <40m;

[0055] Small bridge: Total length of multiple spans 8m ≤ L ≤ 30m, single span diameter 5m ≤ L K <20m.

[0056] 2. Extra-long tunnels: tunnel length L > 3000m; Long tunnels: tunnel length 1000m < L ≤ 3000m;

[0057] Medium-length tunnels: tunnel length 500m < L ≤ 1000m; Short tunnels: tunnel length L ≤ 500m.

[0058] 3. Long-span tunnels: excavation width B ≥ 18m; Medium-span tunnels: excavation width 14m ≤ B <

[0059] 18m;

[0060] For general span tunnels: excavation width 9m ≤ B < 14m; for small span tunnels: excavation width B < 9m.

[0061] 4. Deep-buried tunnels: burial depth h ≥ 2.5h p Shallow tunnels: burial depth h p ≤h<2.5h p ;

[0062] Ultra-shallow tunnel: h < h p (h) p (Equivalent height under load)

[0063] Based on the assessment, this project is classified as of importance level: general.

[0064] Step 3: Determine the level of exploration requirements.

[0065] Based on the classification of the overall exploration stage and the classification of the importance of typical structures, and referring to Table 3, the exploration requirements are divided into three levels: high, medium, and low.

[0066] Table 3. Comparison of Exploration Demand Levels

[0067]

[0068] Based on the assessment, this project falls under the intermediate level of exploration requirements.

[0069] Step 4: Establish standards for classifying geological environment characteristics and geological complexity levels.

[0070] Based on the characteristics of the railway engineering geological environment, eight factors were selected: topography, regional geological background, earthquakes, hydrogeological conditions, engineering geological characteristics of soil and rock masses, adverse geological phenomena, construction geological conditions and their relationship with road construction materials and important projects. Referring to Table 4, the engineering geological complexity levels of the influence of these factors were classified into three levels: simple, medium, and complex.

[0071] Table 4. Comparison of Geological Complexity Levels

[0072]

[0073]

[0074] Based on the assessment, the topographical and geomorphological factors of this project are complex, while other influencing factors are moderate or simple.

[0075] Step 5: Determine the exploration condition level.

[0076] Based on the statistics of geological complexity levels under different influencing factors, and referring to Table 5, the exploration conditions are divided into five levels: A, B, C, D, and E.

[0077] Table 5 Comparison of Exploration Condition Levels

[0078]

[0079] Based on the assessment, this project falls under the exploration requirement level: Level D.

[0080] Step 6: Determine the complexity level of railway engineering survey.

[0081] Based on the results of the comprehensive survey demand level and the survey condition level, and referring to Table 6, the complexity level of railway engineering survey is divided into five levels: complex, relatively complex, moderately complex, relatively simple, and simple.

[0082] Table 6 Comparison of Complexity Levels in Railway Engineering Surveying

[0083]

[0084] Based on the assessment, this project falls under the category of railway engineering survey complexity: relatively simple.

[0085] Step 7: Establish a decision-making model for railway engineering survey technology based on the analytic hierarchy process and determine the weight matrix of the criterion layer.

[0086] The decision-making model uses the optimal exploration technology as the objective layer, followed by a criterion layer that determines the two influencing factors: exploration cost and exploration efficiency, and a scheme layer corresponding to each alternative exploration technology. Referring to Table 7, the weight matrix A for the two factors, exploration cost and exploration efficiency, in the criterion layer is determined based on the level of exploration complexity.

[0087] A = [a k b k ], where k = 1, 2, 3, 4, 5.

[0088] Table 7. Comparison of Criterion Layer Weight Matrix

[0089]

[0090] Therefore, the weight matrix of the criteria layer in this project is A = [0.7 0.3].

[0091] S8: Determine the scale of exploration costs for alternative options.

[0092] The cost of exploration can be determined by the cost of alternative exploration technologies for existing or similar projects. Since it is a quantitative indicator and the lower the cost, the better, it is measured by the reciprocal of the ratio of the cost of each alternative exploration technology to the total cost. The lower the cost and the larger the reciprocal, the greater its priority and the larger the scale value.

[0093] The cost information for each option is known:

[0094] cost 130000 228000 101250 452500

[0095] It can be concluded that:

[0096]

[0097]

[0098] Step 9: Determine the exploration efficiency scale for the alternative plans.

[0099] Survey efficiency is generally derived from the accumulated experience of practitioners over a long period of practice. It is a qualitative indicator and can be evaluated using fuzzy comprehensive evaluation methods. Based on expert experience, its membership degree can be determined, and its priority can be compared and judged.

[0100] a. Determine the factor set,

[0101] The factor set is a general set composed of various factors that affect the evaluation object, and we take U = {exploration efficiency u1}.

[0102] b. Determine the set of comments.

[0103] The comment set is a collection of various possible outcomes that evaluators may make for the evaluated object, where V = {high v1, relatively high v2, medium v3, low v4}.

[0104] c. Determine the fuzzy comprehensive evaluation matrix.

[0105] The membership degree of element u is determined using expert experience. i Make an evaluation.

[0106] Option 1:

[0107] <![CDATA[Survey efficiency u1]]> 0.2 0.5 0.3 0

[0108] Option 2:

[0109] <![CDATA[Prospecting efficiency u1]]> 0 0.3 0.5 0.2

[0110] Option 3:

[0111] <![CDATA[Prospecting efficiency u1]]> 0 0.4 0.5 0.1

[0112] Option 4:

[0113] <![CDATA[Survey efficiency u1]]> 0.5 0.5 0 0

[0114] This yields the evaluation matrix for each alternative.

[0115] W1 = [0.2 0.5 0.3 0],

[0116] W2 = [0 0.3 0.5 0.2],

[0117] W3 = [0 0.4 0.5 0.1],

[0118] W4 = [0.5 0.5 0 0].

[0119] d. Determine the fuzzy comprehensive evaluation scale.

[0120] Different comments correspond to different scores. A certain scoring gradient is set, with the maximum score set at 100 points. 80-100 is considered "high", 60-80 is considered "relatively high", 40-60 is considered "medium", and 0-40 is considered "low". To simplify the calculation, the median value is used as the score, resulting in a scoring matrix S = [90 70 50 20], which is the corresponding subdivision matrix for the four elements of the comment set.

[0121] Then the efficiency scale z of each alternative exploration technology scheme i =W i *S T ,Right now:

[0122] z1=(0.2, 0.5, 0.3, 0)*(90, 70, 50, 20) T =68

[0123] z2=(0, 0.3, 0.5, 0.2)*(90, 70, 50, 20) T =50

[0124] z3 = (0, 0.4, 0.5, 0.1) * (90, 70, 50, 20) T =55

[0125] z4=(0.5, 0.5, 0, 0)*(90, 70, 50, 20) T =80

[0126] Step 10: Construct pairwise judgment matrices for the scheme-level indicators and select the optimal scheme.

[0127] a. Construct pairwise judgment matrices for exploration cost and exploration efficiency respectively:

[0128] Where p ij =y i / y j ;

[0129] Where q ij =z i / z j .

[0130] Calculate the product of the factor scores in each row of the judgment matrix:

[0131]

[0132] Calculate M i and N i The nth root:

[0133]

[0134] right and Normalize:

[0135] Right now:

[0136]

[0137]

[0138] b. Determine the factor evaluation matrix R

[0139]

[0140] c. Conduct a comprehensive evaluation of each alternative plan.

[0141] The final result matrix F is the product of the factor weight matrix and the factor evaluation matrix R:

[0142]

[0143] f max =0.353,

[0144] Based on the analysis, Option 3 is the optimal option.

[0145] The present invention has been described in detail above through embodiments, but the content described is only an exemplary embodiment of the present invention and should not be considered as limiting the scope of the present invention. The scope of protection of the present invention is defined by the claims. Any technical solutions designed by those skilled in the art using the technical solutions described in the present invention, or designed by those skilled in the art under the inspiration of the technical solutions of the present invention, within the substance and protection scope of the present invention, to achieve the above-mentioned technical effects, or any equivalent changes and improvements made to the scope of the application, should still fall within the patent protection scope of the present invention.

Claims

1. A method for classifying and deciding on the complexity of railway engineering surveys under dual-drive conditions, characterized in that: Includes the following steps: S1: Establish standards for the classification of exploration stages and exploration levels; and based on these standards, determine the exploration level of the railway project to be decided. S2: Establish standards for classifying the importance of typical structures in railway engineering projects; determine the importance level based on the typical structures included in the railway engineering project to be decided; S3: Determine the survey requirement level; Based on the survey level in step S1 and the survey level in step S2, obtain the survey requirement level of the railway project to be decided. S4: Establish geological environment characteristics and geological complexity level classification standards to obtain the geological complexity level of the railway project to be decided; S5: Determine the exploration condition level; Based on the geological complexity level in step S4, obtain the exploration condition level of the railway project to be decided. S6: Determine the complexity level of railway engineering survey; Based on the survey requirement level in step S3 and the survey condition level in S5, determine the complexity level of the railway engineering project to be decided. S7: Establish a decision-making model for railway engineering survey technology based on the analytic hierarchy process and determine the weight matrix A of the criterion layer; A= Where k = 1, 2, 3, 4... in , The weights are determined by two factors: exploration cost and exploration efficiency. S8: Determine the scale of survey costs for multiple alternative options in a railway project under consideration. ; S9: Determine the exploration efficiency scale for multiple alternative options ; S10: Construct pairwise judgment matrices P for exploration cost and exploration efficiency 费用 Q 效率 Combined with the criterion layer weight matrix A, the final result matrix F is obtained, and the optimal solution among multiple schemes is obtained. The above process includes the following steps: Construct pairwise judgment matrices P for exploration cost and exploration efficiency 费用 Q 效率 ; P 费用 = ,in / ; Q 效率 = ,in / . Calculate the product of the factor scores in each row of the judgment matrix. , ; ， calculate and The nth root: ， right and Normalize: ， Obtain the factor evaluation matrix Calculate the final result matrix F. Select the maximum value among them. , The corresponding solution is the optimal solution; The decision-making model for the geostationary engineering survey technology in step S7 includes a target layer, a criterion layer, and a scheme layer connected from top to bottom. The target layer is the optimal survey technology, the criterion layer is the two influencing factors of survey cost and survey efficiency, and the scheme layer is a number of alternative survey technology schemes to be decided.

2. The method for classifying and deciding on the complexity of railway engineering survey under dual-drive conditions according to claim 1, characterized in that: The exploration cost scale is obtained in step S8 above. The method includes the following steps: using the reciprocal of the ratio of the cost of multiple alternative surveying techniques to the total cost in the railway project to be decided as the surveying cost scale. .

3. The method for classifying and deciding on the complexity of railway engineering survey under dual-drive conditions according to claim 1, characterized in that: The exploration efficiency scale is obtained in step S9 above. The method includes the following steps: a) Determine the factor set; ; b. Determine the set of comments, and select... ; c. Determine the fuzzy comprehensive evaluation matrix, and determine its membership degree using expert experience method, for each element. Make an evaluation; This yields the evaluation matrix for each alternative. , , , d. Determine the fuzzy comprehensive evaluation scale to obtain the scoring matrix, which is the corresponding hierarchical matrix of the four elements of the language set. ; Calculate the efficiency scale of multiple alternative exploration technologies .

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