Soft rock tunnel TBM construction suitability evaluation method based on big data model

Abstract

A soft rock tunnel TBM construction suitability evaluation method based on a big data model comprises the following steps that 1, soft rock tunnel TBM construction cases are counted, and a big model database is established; step 2, establishing a TBM tunneling suitability grading standard: step 21, carrying out large deformation grade division; 22, geological factors influencing TBM tunneling are analyzed; step 23, establishing a TBM tunneling suitability grading standard; step 3, target tunnel TBM tunneling suitability evaluation: step 31, TBM tunneling suitability grading; 32, assigning the suitability scores of different grades of the TBM, and defining a soft rock tunnel TBM construction suitability evaluation index s; step 33, calculating a TBM construction suitability evaluation index s, and evaluating the TBM construction suitability; and step 34, inputting big data model training. According to the method, the use effect of the TBM can be qualitatively and quantitatively evaluated, so that construction preparation and a construction plan can be made in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel construction, and in particular to a method for evaluating the suitability of soft rock tunnel TBM construction based on a big data model. Background Art

[0002] A large number of engineering practices have shown that TBM excavation has unique advantages in tunnel projects with long and extra-long tunnels, but the applicability of TBM excavation to long and large tunnels and the suitability of different types of surrounding rocks are not universal, especially for high-stress soft surrounding rocks. The treatment of large-scale deformation is still a difficult problem in tunnel construction. Large deformation of soft rock in tunnels is not only harmful, difficult to manage, and costly, but also causes casualties and equipment damage, causing great trouble to the design, construction and even operation of tunnel projects. For example, the surrounding rock compression deformation in the Gansu Yintao Project caused the TBM to get stuck; the Qinghai Yinda Jihuang Project had large deformation of the surrounding rock many times under large faults and deep burial conditions, causing more than 10 TBM jams and shutdowns for more than 2 years; Kunming Shanggongshan Tunnel had several jams due to the combined effects of faults and soft surrounding rocks, and even caused the TBM rear shield to be pulled apart and produce serious compression deformation.

[0003] Due to the complexity and diversity of the on-site rock mass environment, domestic and foreign scholars hold different views and opinions on the mechanism, classification and grading methods of large deformation, which makes it difficult to form a unified standard for the prevention and control measures of large deformation. Existing studies mainly classify tunnel large deformation from the perspectives of surrounding rock lithology, tunnel relative deformation, absolute deformation, strength-stress ratio, relative strain, etc. At present, the main purpose of soft rock large deformation classification is to measure rock mass stability, which is more inclined to serve tunnel design and stability evaluation. It is difficult to be directly used for TBM excavation evaluation, but it can provide a reference for it. Wang Shichun analyzed the influence of tunnel engineering geological conditions on TBM excavation efficiency and established a TBM working condition classification method based on rock compressive strength, wear resistance, hardness and other parameters. Shang Yanjun, Yin Juntao and others conducted an in-depth analysis of soft rock large deformation, water inrush, rock burst and other problems encountered during TBM excavation. Based on the characteristics of double shield TBM, Li Jianbin and others explored its feasibility in the construction of cross-sea tunnels, water diversion projects, especially long and large railways and passenger dedicated line tunnels. In summary, some scholars have discussed the factors affecting TBM adaptability and classified the tunnel surrounding rock under TBM construction conditions, but few scholars have analyzed the TBM construction suitability classification under different large deformation levels. Therefore, it is necessary to establish a soft rock tunnel TBM construction suitability evaluation method to quantitatively evaluate the TBM construction suitability under different surrounding rock deformation levels and avoid engineering losses caused by TBM incompatibility. Summary of the invention

[0004] The technical problem to be solved by the present invention is that: in view of the problem that there is currently a lack of a TBM construction suitability evaluation method under different large deformation levels, it is impossible to quantitatively evaluate whether a TBM is suitable for application in a certain soft rock tunnel. The present invention provides a soft rock tunnel TBM construction suitability evaluation method based on a big data model. The method takes into account the large deformation level and the main geological factors affecting TBM excavation, and can qualitatively and quantitatively evaluate the use effect of the TBM, so that construction preparations and construction plans can be made in advance, avoiding the loss of construction period, manpower and material resources caused by improper selection of construction methods.

[0005] In order to solve the above technical problems, the present invention proposes the following technical solutions: A method for evaluating the suitability of soft rock tunnel TBM construction based on a big data model, which comprises the following steps:

[0006] Step 1: Establish a large model database;

[0007] First, statistics on TBM construction cases in soft rock tunnels were collected to establish a large model database. This database was used for subsequent analysis of geological factors affecting TBMs and to optimize the suitability assignment of different levels of defined TBMs through engineering examples.

[0008] Step 2: Establishing TBM excavation suitability classification standards, further including the following steps 21 to 23:

[0009] Step 21: Classify the large deformation level into five levels: basically no deformation, slight, medium, severe, and extremely severe;

[0010] Step 22: Analysis of geological factors affecting TBM excavation;

[0011] Step 23: Establish TBM excavation suitability classification standards;

[0012] Use the soft rock large deformation classification in step 21 to characterize the influence of surrounding rock deformation on TBM, comprehensively consider the main geological factors affecting TBM excavation, and establish the TBM excavation suitability classification standard under different large deformation levels;

[0013] Step 3: TBM excavation suitability evaluation of the target tunnel, further comprising the following steps 31 to 34:

[0014] Step 31: TBM excavation suitability classification: According to the specific project geological survey report, TBM construction suitability is classified based on the classification standards established in step 2;

[0015] Step 32: Assigning suitability scores of different levels of TBM;

[0016] According to the proportion of the length of different suitability sections of TBM, the suitability evaluation index s for TBM construction of soft rock tunnels is defined, s = A-level tunnel length percentage * corresponding score + B-level tunnel length percentage * corresponding score + C-level tunnel length percentage * corresponding score + D-level tunnel length percentage * corresponding score + E-level tunnel length percentage * corresponding score;

[0017] Step 33: Calculate the TBM construction suitability evaluation index s, and evaluate the TBM construction suitability according to the TBM construction suitability evaluation zoning map;

[0018] Step 34: Input big data model training: Input the calculation results and actual applications into big data model training, continuously expand the case library, and optimize and correct the assignment of TBM suitability scores of different levels to improve the accuracy of the method of the present invention.

[0019] The above technical solution is further limited in that, step 21: the large deformation level is divided into five levels: basically no deformation, slight, medium, severe, and extremely severe. The specific characteristics and indicators are as follows:

[0020]

[0021] The above technical solution is further limited in that, in step 22, the main geological characteristics affecting the suitability of TBM excavation are: rock mass integrity Kv, rock uniaxial saturated compressive strength Rb, surrounding rock strength stress ratio S, weak structural surface angle, and groundwater.

[0022] The above technical solution is further limited in that, in step 23, rock mass integrity, strength-stress ratio, and uniaxial compressive strength are selected as indicators for large deformation classification and TBM construction suitability evaluation of soft rock tunnels, and TBM excavation suitability classification standards under different large deformation levels are established, as shown in the following table:

[0023] TBM construction suitability classification standard

[0024]

[0025]

[0026] Note: “-” in the table means none, A (very suitable), B (suitable), C (relatively suitable), D (poor suitability), E (unsuitable).

[0027] The above technical solution is further limited in that, in step 32, the initial assignment of suitability scores of different levels of TBM is shown in the following table:

[0028] TBM construction suitability classification A B C D E Score 100 75 50 25 0 .

[0029] The above technical solution is further limited in that, in step 33, the TBM construction suitability evaluation zoning map is as follows: Figure 1 shown.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The method of the present invention can quantitatively evaluate whether a TBM is suitable for application in a soft rock tunnel, filling the gap in this technology and being groundbreaking in this technical field.

[0032] 2. The method of the present invention takes into account the large deformation level and the main geological factors affecting TBM excavation, and can qualitatively and quantitatively evaluate the use effect of TBM, so that construction preparation and construction planning can be done in advance, avoiding the loss of construction period, manpower and material resources caused by improper selection of construction methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is the TBM construction suitability evaluation zoning map of the present invention.

[0034] Figure 2 It is a flow chart of the TBM construction suitability evaluation method of the present invention. DETAILED DESCRIPTION

[0035] See also Figure 1 to Figure 2 , a method for evaluating the suitability of soft rock tunnel TBM construction based on big data model, including the following steps:

[0036] Step 1: Establish a large model database;

[0037] Firstly, statistics on TBM construction cases in soft rock tunnels were collected to establish a large model database. This database will be used for subsequent analysis of geological factors affecting TBM and for optimizing the suitability assignment of different levels of defined TBMs through engineering examples.

[0038] Step 2: Establishing TBM excavation suitability classification standards, further including the following steps 21 to 23:

[0039] Step 21: Classify the large deformation levels;

[0040] According to the research of domestic and foreign scholars on the large deformation classification method, the large deformation level is divided into five levels: basically no deformation, slight, medium, severe, and extremely severe. The specific characteristics and indicators are as follows:

[0041]

[0042]

[0043] Step 22: Analysis of geological factors affecting TBM excavation;

[0044] Through a large number of TBM tunneling tests, it is found that the main geological characteristics affecting the suitability of TBM tunneling are as follows:

[0045] (1) Rock mass integrity Kv: The TBM tunneling speed first increases and then decreases with the increase of rock mass integrity. When Kv > 0.55, if the uniaxial compressive strength of the rock is between 30 - 60 MPa, the TBM can maintain safe and efficient tunneling; when Kv < 0.35, the rock mass is relatively fragmented, and collapses and instability are likely to occur after excavation, severely restricting the tunneling efficiency of the TBM.

[0046] (2) Uniaxial saturated compressive strength of rock Rb: Either too low or too high rock strength is not conducive to the rapid tunneling of the TBM. The former will cause difficulty in self - stability and large deformation after tunnel excavation, while the latter will result in problems such as easy wear of TBM cutters and difficulty in rock breaking. Generally, the TBM has a higher tunneling efficiency in medium - hard to hard rocks with Rb = 30 - 150 MPa.

[0047] (3) Surrounding rock strength - stress ratio S: When 60 MPa < Rb ≤ 100 MPa and S < 4 or 30 MPa < Rb ≤ 60 MPa and S < 2, it is not conducive to the rapid tunneling of the TBM.

[0048] (4) Angle of weak structural plane: When the angle between the dominant structural plane and the tunnel axis direction is less than 30°, and locally between 30° - 50°, collapses and rock falls are likely to occur at the arch crown position, affecting the operation of the TBM.

[0049] (5) Groundwater: When the groundwater is active near the tunnel fault, water inrush and mud gushing may occur, deteriorating the construction environment and affecting the normal tunneling of the TBM.

[0050] Step 23: Establish the grading standard for TBM tunneling suitability;

[0051] For tunnel construction, the deformation of the surrounding rock is the most important factor affecting TBM tunneling. Using the soft rock large - deformation grading in Step 21 to characterize the impact of surrounding rock deformation on the TBM, comprehensively considering the main geological factors affecting TBM tunneling, select rock mass integrity, strength - stress ratio, and uniaxial compressive strength as the evaluation indexes for soft rock tunnel large - deformation grading and TBM construction suitability, and establish the grading standard for TBM tunneling suitability under different large - deformation grades as shown in the following table.

[0052] Grading Standard for TBM Construction Suitability

[0053]

[0054]

[0055] Note: In the table, "-" means none, A (very suitable), B (suitable), C (relatively suitable), D (poor suitability), E (unsuitable).

[0056] Step 3: TBM excavation suitability evaluation of the target tunnel, further comprising the following steps 31 to 34:

[0057] Step 31: TBM excavation suitability classification: According to the specific engineering geological survey report, the TBM construction suitability is classified based on the classification standards established in step 2.

[0058] Taking a tunnel in Guangdong as an example, the large deformation level is first predicted according to the geological survey report. On this basis, the TBM construction suitability classification is carried out based on the TBM construction suitability classification method established in this paper (see the table below).

[0059]

[0060]

[0061] Step 32: Assigning TBM suitability scores at different levels

[0062] Based on the overall geological conditions of the tunnel and the proportion of the length of different suitability sections of TBM, the suitability evaluation index s for TBM construction in soft rock tunnels is defined as follows:

[0063] s = A-level cave length percentage * corresponding score + B-level cave length percentage * corresponding score + C-level cave length percentage * corresponding score + D-level cave length percentage * corresponding score + E-level cave length percentage * corresponding score

[0064] Based on engineering experience, the initial assignment of suitability scores for different levels of TBM is shown in the following table:

[0065] TBM construction suitability classification A B C D E Score 100 75 50 25 0

[0066] By weighting, the suitability evaluation index s for TBM construction of soft rock tunnels is calculated to quantitatively evaluate the suitability of TBM construction of the entire tunnel.

[0067] Step 33: Calculate the TBM construction suitability evaluation index s;

[0068] Using the data of a tunnel in Guangdong in step 31, we calculated that s = 55.35. Figure 1 According to the TBM construction suitability evaluation index s zone, the tunnel is suitable for TBM construction.

[0069] Step 34: Input big data model training: Input the calculation results and actual applications into big data model training, continuously expand the case library, and optimize and correct the assignment of TBM suitability scores of different levels to improve the accuracy of the method of the present invention.

Claims

1. A method for evaluating the suitability of soft rock tunnel TBM construction based on a big data model, characterized in that: It includes the following steps: Step 1: Establish a large model database; First, statistics on TBM construction cases in soft rock tunnels were collected to establish a large model database. This database was used for subsequent analysis of geological factors affecting TBMs and to optimize the suitability assignment of different levels of defined TBMs through engineering examples. Step 2: Establishing TBM excavation suitability classification standards, further including the following steps 21 to 23: Step 21: Classify the large deformation level into five levels: basically no deformation, slight, medium, severe, and extremely severe; Step 22: Analysis of geological factors affecting TBM excavation; Step 23: Establish TBM excavation suitability classification standards; Use the soft rock large deformation classification in step 21 to characterize the influence of surrounding rock deformation on TBM, comprehensively consider the main geological factors affecting TBM excavation, and establish the TBM excavation suitability classification standard under different large deformation levels; Step 3: TBM excavation suitability evaluation of the target tunnel, further comprising the following steps 31 to 34: Step 31: TBM excavation suitability classification: According to the specific project geological survey report, TBM construction suitability is classified based on the classification standards established in step 2; Step 32: Assigning suitability scores of different levels of TBM; According to the proportion of the length of different suitability sections of TBM, the suitability evaluation index s for TBM construction of soft rock tunnels is defined, s = A-level tunnel length percentage * corresponding score + B-level tunnel length percentage * corresponding score + C-level tunnel length percentage * corresponding score + D-level tunnel length percentage * corresponding score + E-level tunnel length percentage * corresponding score; Step 33: Calculate the TBM construction suitability evaluation index s, and evaluate the TBM construction suitability according to the TBM construction suitability evaluation zoning map; Step 34: Input big data model training: Input the calculation results and actual applications into big data model training, continuously expand the case library, and optimize and correct the assignment of TBM suitability scores of different levels to improve the accuracy of the method of the present invention.

2. The method for evaluating the suitability of soft rock tunnel TBM construction based on a big data model according to claim 1 is characterized in that: Step 21: Divide the large deformation level into five levels: basically no deformation, slight, medium, severe, and extremely severe. The specific characteristics and indicators are as follows:

3. The method for evaluating the suitability of soft rock tunnel TBM construction based on a big data model according to claim 1 is characterized in that: In step 22, the main geological characteristics that affect the suitability of TBM excavation are: rock mass integrity Kv, rock uniaxial saturated compressive strength Rb, surrounding rock strength stress ratio S, weak structural surface angle, and groundwater.

4. The method for evaluating the suitability of soft rock tunnel TBM construction based on a big data model according to claim 1 is characterized in that: In step 23, rock mass integrity, strength-stress ratio, and uniaxial compressive strength are selected as indicators for large deformation classification and TBM construction suitability evaluation of soft rock tunnels, and TBM excavation suitability classification standards under different large deformation levels are established, as shown in the following table: TBM construction suitability classification standard Note: "-" in the table means none, A (very suitable), B (suitable), C (relatively suitable), D (poor suitability), E (unsuitable).

5. The method for evaluating the suitability of soft rock tunnel TBM construction based on a big data model according to claim 1 is characterized in that: In step 32, the initial assignment of suitability scores for different levels of TBM is shown in the following table: 。 6. The method for evaluating the suitability of soft rock tunnel TBM construction based on a big data model according to claim 1 is characterized in that: In step 33, the TBM construction suitability evaluation zoning map is shown in Figure 1.

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