TBM front jamming risk evaluation method and computer device

Abstract

The invention belongs to the technical field of tunneling machine construction, and particularly relates to a TBM front jamming risk evaluation method and a computer device. The method comprises the steps that S1, current tunnel face surrounding rock quality evaluation is conducted according to TBM tunneling parameters; performing current equipment card machine risk evaluation; evaluating the surrounding rock quality of the front tunnel face according to the advanced geological forecast data; and S2, according to the current tunnel face surrounding rock quality evaluation, the current equipment jamming risk evaluation and the front tunnel face surrounding rock quality evaluation, performing front jamming risk comprehensive evaluation. According to the method, when TBM jamming risk evaluation is carried out, comprehensive evaluation is carried out on the equipment jamming risk of a front construction section according to parameters of the TBM in the tunneling process in combination with current and front geological conditions; by comprehensively considering the digging jamming and advanced geological conditions, the jamming risk evaluation result is more comprehensive and more accurate. According to the method, the technical problems of incomplete evaluation and inaccurate evaluation result of the TBM front jamming risk evaluation method in the prior art are solved.

Description

Technical Field

[0001] The invention belongs to the technical field of tunnel boring machine construction, and in particular relates to a TBM front-end stuck risk assessment method and a computer device. Background Art

[0002] When TBM is constructed in complex geological conditions such as fault fracture zones and landslides, the poor stability and deformation of the surrounding rock will generate loads on the cutterhead and the tunnel face, increasing the resistance to cutterhead rotation and the resistance to shield propulsion. When the two resistances exceed the equipment ratings of the TBM, the cutterhead and shield will get stuck. The key to preventing the machine from getting stuck is to evaluate the current TBM stuck condition and the geological conditions ahead. If the current risk of the machine getting stuck is high and the geological conditions ahead are poor, there will be a greater risk of the machine getting stuck ahead. For a greater risk of the machine getting stuck, necessary anti-stuck treatment measures should be taken to reduce the engineering problems caused by the machine getting stuck. Therefore, evaluating the risk of the machine getting stuck is of great significance to improving the excavation safety and efficiency of the TBM.

[0003] The key factor affecting the machine jam is the load of the surrounding rock on the TBM shield and cutterhead, and the influence of rock geological parameters such as plastic zone, collapse volume, surrounding rock strength, and crushing conditions on the machine jam is crucial. Due to the large randomness of rock geological parameters, when evaluating the risk of TBM jam in the fault fracture zone, it is necessary to consider the influence of multiple scales: it is necessary to accurately evaluate the machine jam risk of the TBM in the current formation, reasonably judge the machine jam situation, and provide a decision-making basis for the adjustment of excavation parameters, and it is also necessary to understand the machine jam risk of the formation in front of the face, and provide a decision-making basis for the selection of advanced support measures and the implementation of anti-jamming and escape measures.

[0004] The Chinese invention patent application with application publication number CN116753033A and application publication date September 15, 2023, discloses a high-risk early warning method for TBM jams, which realizes advanced early warning of TBM jam risks based on advanced geological forecasts, and realizes real-time early warning of TBM jam risks based on the ratio of thrust to the designed maximum thrust and the ratio of torque to the rated torque. However, the above technical solution does not conduct a comprehensive evaluation of TBM jam risks based on the above advanced early warning and real-time early warning, resulting in an incomplete evaluation of TBM jam risks and inaccurate evaluation results. Summary of the invention

[0005] The purpose of the present invention is to provide a TBM front-end jammer risk assessment method and a computer device to solve the technical problems of incomplete assessment and inaccurate assessment results in the prior art TBM front-end jammer risk assessment method.

[0006] In order to solve the above technical problems, the present invention provides a technical solution for a TBM front-end stuck machine risk assessment method: a TBM front-end stuck machine risk assessment method, the method comprising: S1. Evaluate the quality of the surrounding rock of the current tunnel face according to TBM excavation parameters; evaluate the risk of current equipment jam; and evaluate the quality of the surrounding rock of the tunnel face ahead according to advanced geological forecast data; S2. Perform a comprehensive assessment of the risk of a machine jam ahead based on the current tunnel face surrounding rock quality assessment, the current equipment jam risk assessment, and the front tunnel face surrounding rock quality assessment.

[0007] The beneficial effect of the above technical solution is that the technical solution of the TBM front-end stuck risk assessment method of the present invention belongs to an improved invention. When conducting TBM stuck risk assessment, the present invention conducts a comprehensive assessment of the equipment stuck risk in the front construction section based on multi-source data in the tunnel construction process, the TBM's own parameters in the excavation process, and the current and front geological conditions; comprehensively considers the excavation machine stuck and the advanced geological conditions, so that the stuck risk assessment results are more comprehensive and the results are more accurate. The present invention solves the technical problems of incomplete evaluation and inaccurate evaluation results of TBM front-end stuck risk assessment methods in the prior art.

[0008] Further, the surrounding rock quality evaluation of the front tunnel face is the surrounding rock quality grade of the front tunnel face; the surrounding rock quality evaluation of the current tunnel face is the surrounding rock quality grade of the current tunnel face; the current equipment jam risk evaluation is the current equipment jam risk evaluation grade; the front jam risk comprehensive evaluation is the front jam risk comprehensive evaluation grade; The comprehensive assessment level of the risk of machine jam ahead is a weighted average of the current tunnel face surrounding rock quality assessment level, the current equipment machine jam risk assessment level, and the front tunnel face surrounding rock quality assessment level.

[0009] Furthermore, the TBM excavation parameters include cutterhead penetration and cutterhead torque; the quality of surrounding rock of the current tunnel face is evaluated according to the torque characteristic index; the torque characteristic index is obtained according to the cutterhead penetration and cutterhead torque.

[0010] Furthermore, the torque characteristic index is obtained according to the following formula: TPI=F r / p Where TPI is the torque characteristic index; p is the penetration of the cutter head; T is the cutter head torque; R i is the i-th level of the current surrounding rock quality evaluation level; N is the total level of the current surrounding rock quality evaluation level; T f It is the resistance torque of the cutterhead when it is idling without breaking the rock.

[0011] Furthermore, the method of evaluating the surrounding rock quality of the front tunnel face according to the advanced geological prediction data is: obtaining the distribution of the fault and fracture zone in the front stratum along the tunnel axis according to the advanced geological prediction data, and evaluating the surrounding rock quality of the front tunnel face according to the distribution range of the fault and fracture zone.

[0012] Further, the current equipment stuck risk assessment includes the current equipment stuck cutter disc risk assessment, and the front equipment stuck risk comprehensive assessment includes the front cutter disc stuck risk comprehensive assessment; And / or the current device stuck risk assessment includes the current device stuck shield risk assessment, and the front device stuck risk comprehensive assessment includes the front device stuck shield risk comprehensive assessment.

[0013] Furthermore, the current equipment shield stuck risk assessment is obtained according to shield parameters during TBM shutdown; the shield parameters include at least one of shield pressure, shield stress and shield strain.

[0014] Furthermore, the current equipment cutter head stuck risk assessment is obtained based on the cutter head parameters during the recovery of excavation after TBM shutdown; the cutter head parameters include the cutter head idling torque; the greater the cutter head idling torque, the greater the current equipment cutter head stuck risk.

[0015] Furthermore, the method further comprises: S3. As the TBM advances, steps S1 to S2 are continuously repeated to dynamically update the comprehensive risk assessment of the machine stuck ahead.

[0016] The present invention also provides a technical solution for a computer device: a computer device, comprising a processor, wherein the processor is used to execute a computer program to implement the steps of the TBM front card machine risk assessment method as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a method flow chart of an embodiment of a TBM front-end card machine risk assessment method of the present invention; Figure 2 It is a schematic diagram of the TBM front-end jamming risk assessment process in an embodiment of the TBM front-end jamming risk assessment method of the present invention. DETAILED DESCRIPTION

[0018] When conducting TBM stuck risk assessment, the present invention comprehensively evaluates the risk of equipment stuck in the front construction section based on multi-source data in the tunnel construction process, TBM parameters in the excavation process, and current and forward geological conditions; comprehensively considers the excavation stuck machine and the forward geological conditions, so that the stuck machine risk assessment results are more comprehensive and the results are more accurate. The present invention solves the technical problems of incomplete evaluation and inaccurate evaluation results of TBM stuck risk assessment methods in the prior art.

[0019] Example of TBM front-end jammer risk assessment method: A method for assessing the risk of a TBM stuck ahead of the tunnel face, the method firstly establishes a TBM monitoring parameter database to monitor the parameters reflecting the TBM stuck state, such as shield pressure and shield displacement; secondly, establishes advanced geological prediction data, mainly to monitor the fault fracture zone ahead of the tunnel face; then, the risk level of the digging machine is evaluated through analysis and mining of the stuck state parameters, and the risk level of the ahead machine is evaluated through the advanced geological prediction data; finally, a comprehensive grading method for the risk of the digging machine and the ahead machine is formed through the evaluation results of the risk level of the digging machine and the ahead machine, so as to achieve a comprehensive evaluation of the risk of the stuck machine; during the implementation process, the risk of the stuck machine can be dynamically adjusted according to the evaluation results of the digging machine and the ahead machine state.

[0020] Specifically, the method comprises the following steps: Figure 1 and Figure 2 As shown: (1) Collect multi-source data of TBM construction, including excavation parameters such as cutterhead penetration p, cutterhead torque T, shield pressure P, and advanced geological prediction data. Through data processing, extract effective data, including penetration, torque, thrust, etc. that reflect the surrounding rock conditions of the tunnel face, shield pressure, shield displacement, etc. that reflect the stress state of the shield, and advanced geological prediction crushing zone range data that reflect the crushing characteristics in front of the tunnel face.

[0021] (2) Current face rock quality evaluation R. Through the penetration, torque, thrust and other data in the tunneling parameters, characteristic indicators are established through data extraction to reflect the comprehensive rock quality of the face.

[0022] The torque characteristic index TPI is established using the cutter penetration p and cutter torque T, where F r It is the tangential force evaluated by a single knife. The calculation method is shown in the following formula, which reflects the comprehensive quality of the tunnel face in excavating the surrounding rock.

[0023] Where TPI is the torque characteristic index; p is the penetration of the cutter head; T is the cutter head torque; R i is the i-th level of the current surrounding rock quality evaluation level; N is the total level of the current surrounding rock quality evaluation level; T f It is the resistance torque of the cutterhead when it is idling without breaking the rock, and can be determined through an idling test.

[0024] Generally speaking, the TPI value in the fault fracture zone is small. The TPI distribution range of the fault can be determined by counting the TPI value of the fault fracture zone. Define the current face surrounding rock quality evaluation level: Level I: TPI ≥ 6; Level II, TPI 4-6; Level III, TPI 3-4, Level IV, TPI 2-3; Level V, TPI < 2.

[0025] In other implementations, the torque characteristic index TPI may not be used to evaluate the quality of the surrounding rock of the current tunnel face, and other tunneling parameters related to the quality of the surrounding rock of the tunnel face may be used, such as the thrust characteristic index FPI: FPI=(FF f ) / Np Where, F is the total thrust, F f is the dry push friction resistance, which can be determined through dry push test, N is the number of hobs, and p is the penetration rate.

[0026] (3) Evaluation of the current equipment jamming situation J. When the quality of the surrounding rock of the tunnel face is poor, there is a risk of fault fracture zone. At this time, the jamming situation of the surrounding rock squeezing the shield during the shutdown process is determined according to the shield pressure value during the shutdown process; the jamming situation of the cutter disc is determined by the torque value during the cutter disc startup process. The jamming situation is determined based on the comprehensive judgment of the cutter disc and the shield. In this embodiment, the jamming situation is divided into two situations: the shield jamming situation and the cutter disc jamming situation.

[0027] For the shield stuck situation, the top shield pressure statistics of the unstuck machine are used as the discrimination threshold Ps, and the shield stuck risk is judged in combination with the top shield pressure change during the TBM shutdown process. When the top shield pressure is constant and lower than the discrimination threshold during shutdown, the current device has a low risk of shield stuck; when the top shield pressure increases and exceeds the discrimination threshold during shutdown, the current shield stuck risk is high.

[0028] Define the current device card shield risk rating level: Level I, shield pressure <1.5Ps, Level II, shield pressure between 1.5 and 2Ps, Level III, shield pressure between 2 and 3Ps, Level IV, shield pressure between 3 and 5Ps, Level V, shield pressure >5Ps.

[0029] In other implementations, shield pressure may not be used to evaluate the risk of shield jamming of the current device, but other indicators may be used, such as shield deformation or shield stress.

[0030] For the case of cutterhead stuck, the torque statistics when the cutterhead is not stuck remain basically constant, which is used as the torque discrimination threshold Ts. When the idling torque when the TBM stops and resumes excavation greatly exceeds the discrimination threshold, the cutterhead is at risk of getting stuck. When the torque value is small, the risk of the cutterhead getting stuck is small. The above method is used to comprehensively obtain the risk of the cutterhead and shield getting stuck.

[0031] Define the risk assessment level of the current equipment cutter disc stuck: Level I, cutter disc idling torque <1.5Ts, Level II, cutter disc idling torque between 1.5 and 2Ts, Level III, cutter disc idling torque between 2 and 3Ts, Level IV, cutter disc idling torque between 3 and 5Ts, Level V, cutter disc idling torque >5Ts.

[0032] (4) Evaluation of the quality of the surrounding rock at the tunnel face ahead. Using the advanced geological prediction data, the range of the broken surrounding rock ahead is obtained, and a comprehensive evaluation of the quality of the surrounding rock ahead is performed based on the length and crushing conditions of the broken surrounding rock.

[0033] Based on the advanced geological prediction data obtained by the seismic wave method, the distribution of the fault fracture zone of the front stratum along the tunnel axis is obtained according to the interpretation of the prediction results. When the front is a fault fracture zone and the distribution range is large, the evaluation result of the surrounding rock quality in front is poor.

[0034] The quality evaluation levels of the surrounding rock of the front face are defined as follows: Level I, the range of the broken zone (i.e., the length of the broken zone) is ≤5m; Level II, the range of the fault broken zone is 5 to 10m; Level III, the range of the broken zone is 10 to 20m; Level IV, the range of the broken zone is 20 to 30m; Level V, the range of the broken zone is >30m.

[0035] (5) Comprehensive evaluation of the risk of machine jam ahead A. Based on the current tunnel face surrounding rock quality evaluation, the current equipment jam risk evaluation, and the front tunnel face surrounding rock quality evaluation, a comprehensive evaluation of the risk of machine jam ahead is conducted.

[0036] Define the comprehensive risk assessment level of the front jam machine and related anti-jamming measures: Level I: low risk, low possibility of machine jam, no action is required to pass; Level II, medium risk, low possibility of machine jam, can be passed by using steel bar rows and steel mesh support measures; Level III: higher risk, with a high possibility of machine jam. Pay attention to the slag grading and particle size, and reduce the risk of machine jam by adjusting the excavation parameters; Level IV: high risk, high risk of machine jam, measures such as cutter head expansion and shield shrinkage are taken to pass; Level V, extremely high risk, the risk of machine jam is very high, and it is easy to get stuck. It can be passed by adopting methods such as advanced pipe roofing and advanced grouting reinforcement.

[0037] Specifically, in this embodiment, the comprehensive risk assessment level A of the front jamming machine adopts the weighted average of the current tunnel face surrounding rock quality assessment level R, the current equipment jamming machine risk assessment level J and the front tunnel face surrounding rock quality assessment level, rounded down. And according to the current jamming of the cutter disc and the jamming shield, the jamming of the cutter disc and the jamming shield in front are determined. As shown in the following formula: A = (ω1×R+ω2×J+ω3×G) / (ω1+ω2+ω3) Among them, A is the comprehensive evaluation level of the risk of machine jam ahead, R is the quality evaluation level of the surrounding rock of the current face, J is the risk level of the current equipment jam, G is the quality evaluation level of the surrounding rock of the front face, and ω1, ω2, and ω3 are their corresponding weights respectively.

[0038] In other implementations, the comprehensive evaluation of the risk of the machine stuck ahead can also use a machine learning algorithm such as a neural network method. For example, according to the data of the historical excavation process of the TBM, the surrounding rock quality evaluation of the current face, the risk evaluation of the current equipment stuck, the surrounding rock quality evaluation of the face ahead and the comprehensive evaluation of the risk of the machine stuck ahead are respectively performed to obtain R, J, G and A; then the current face surrounding rock quality evaluation level R, the current equipment stuck risk evaluation level J and the surrounding rock quality evaluation level G of the face ahead are used as the input of the neural network, and the comprehensive evaluation level A of the risk of the machine stuck ahead is used as the output to train the pre-established machine learning model, and the comprehensive evaluation of the risk of the machine stuck ahead is performed through the trained and learning model. The machine learning model can be a BP neural network, an RBF neural network, a support vector regression, etc.

[0039] (6) Dynamic update of comprehensive evaluation of machine jam risk ahead. As the TBM advances, different levels are continuously updated according to (2) to (4) based on the excavation monitoring situation and advanced geological forecast data, and then (5) is used to dynamically evaluate the risk of machine jam ahead.

[0040] (7) Fault fracture zone stuck machine risk assessment system. Real-time acquisition of excavation monitoring data and advanced geological forecasts, use of computer systems to process data, obtain TPI, shield pressure, cutter head torque, geological forecast results, etc., and use computer systems to calculate the current and front TBM stuck machine risk levels in real time, and push the results to different users to achieve dynamic evaluation of stuck machine risks.

[0041] Computer device embodiment: A computer device includes a processor, wherein the processor is used to execute a computer program to implement the steps of the TBM front-end card machine risk assessment method described above. The specific TBM front-end card machine risk assessment method has been introduced in sufficient detail in the TBM front-end card machine risk assessment method embodiment described above, and will not be repeated here.

[0042] The present invention has the following characteristics: The present invention evaluates the risk of TBM jamming during construction in fault fracture zones. Based on the multi-source data of tunnel construction, on the basis of excavation parameters, shield monitoring parameters, and advanced geological forecast data analysis and mining, the present invention realizes comprehensive quality evaluation of the surrounding rock of the face, evaluation of the current equipment jamming situation, and evaluation of the quality of the surrounding rock in front of the face, and then comprehensively evaluates the risk of TBM jamming in the front stratum. And as the TBM excavation proceeds, the jamming risk is dynamically updated, and a comprehensive evaluation of the jamming risk can be effectively achieved. After obtaining the risk of TBM jamming, targeted anti-jamming and escape measures can be taken, such as expansion excavation, advanced support, etc., to ensure the safety of tunnel construction by the tunnel boring machine and effectively reduce the risk of equipment downtime caused by tunnel boring machine jamming.

[0043] The present invention establishes an equipment stuck state evaluation method based on TBM excavation parameters, and evaluates the front geology in combination with advanced geological forecast data, and evaluates the risk of TBM stuck ahead of excavation by combining the excavation + advanced results, thus forming a stuck risk early warning method. This method is based on multi-source data, can more accurately identify TBM stuck risks at different scales, and can dynamically update the stuck risk ahead according to the current stuck state identification result, and has the advantages of high real-time performance and accurate evaluation. The evaluation result of the stuck risk is of great significance for the selection of TBM anti-stuck and escape measures, and can ensure the safety of TBM in disaster geological construction.

[0044] According to the present invention, tunneling parameters, shield monitoring parameters, and advanced geological prediction data can be used to sequentially identify broken surrounding rock, identify current equipment jams, and evaluate the risk of jams ahead, and jointly identify the risk of TBM jams. As tunneling progresses, the risk of TBM jams can be dynamically updated, and the evaluation of jam risks can be effectively achieved.

[0045] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention is described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments without creative work, or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A TBM front-end jam risk assessment method, characterized in that: The method includes: S1. Evaluate the quality of the surrounding rock of the current tunnel face according to TBM excavation parameters; evaluate the risk of current equipment jam; and evaluate the quality of the surrounding rock of the tunnel face ahead according to advanced geological forecast data; S2. Perform a comprehensive assessment of the risk of a machine jam ahead based on the current tunnel face surrounding rock quality assessment, the current equipment jam risk assessment, and the front tunnel face surrounding rock quality assessment.

2. The TBM front jam risk assessment method according to claim 1 is characterized in that: The surrounding rock quality evaluation of the front tunnel face is the surrounding rock quality grade of the front tunnel face; the surrounding rock quality evaluation of the current tunnel face is the surrounding rock quality grade of the current tunnel face; the current equipment jam risk evaluation is the current equipment jam risk evaluation grade; the front jam risk comprehensive evaluation is the front jam risk comprehensive evaluation grade; The comprehensive assessment level of the risk of machine jam ahead is a weighted average of the current tunnel face surrounding rock quality assessment level, the current equipment machine jam risk assessment level, and the front tunnel face surrounding rock quality assessment level.

3. The TBM front jam risk assessment method according to claim 2 is characterized in that: The TBM excavation parameters include cutter head penetration and cutter head torque; the quality of surrounding rock of the current tunnel face is evaluated according to the torque characteristic index; the torque characteristic index is obtained according to the cutter head penetration and cutter head torque.

4. The TBM front jam risk assessment method according to claim 3 is characterized in that: The torque characteristic index is obtained according to the following formula: TPI=F r / p Where TPI is the torque characteristic index; p is the penetration of the cutter head; T is the cutter head torque; R i is the i-th level of the current surrounding rock quality evaluation level; N is the total level of the current surrounding rock quality evaluation level; T f It is the resistance torque of the cutterhead when it is idling without breaking the rock.

5. The TBM front jam risk assessment method according to claim 1 or 2, characterized in that: The method of evaluating the quality of the surrounding rock of the front tunnel face according to the advanced geological prediction data is as follows: the distribution of the rock-breaking zone in the front stratum along the tunnel axis is obtained according to the advanced geological prediction data, and the quality of the surrounding rock of the front tunnel face is evaluated according to the distribution range of the rock-breaking zone.

6. The TBM front jam risk assessment method according to claim 1 or 2, characterized in that: The current equipment stuck risk assessment includes the current equipment stuck cutter disc risk assessment, and the front equipment stuck risk comprehensive assessment includes the front equipment stuck cutter disc risk comprehensive assessment; And / or the current device stuck risk assessment includes the current device stuck shield risk assessment, and the front device stuck risk comprehensive assessment includes the front device stuck shield risk comprehensive assessment.

7. The TBM front jam risk assessment method according to claim 6, characterized in that: The current equipment shield stuck risk assessment is obtained according to shield parameters during TBM shutdown; the shield parameters include at least one of shield pressure, shield stress and shield strain.

8. The TBM front jam risk assessment method according to claim 6, characterized in that: The current equipment cutter head stuck risk assessment is obtained based on the cutter head parameters during the recovery process of the TBM after shutdown; the cutter head parameters include the cutter head idling torque; the greater the cutter head idling torque, the greater the current equipment cutter head stuck risk.

9. The TBM front jam risk assessment method according to claim 1 or 2, characterized in that: The method further includes: S3, as the TBM advances, continuously repeating steps S1 to S2, so that the comprehensive risk assessment of the front jamming machine is continuously and dynamically updated.

10. A computer device comprising a processor, characterized in that: The processor is used to execute a computer program to implement the steps of the TBM front card machine risk assessment method as described in any one of claims 1 to 9.

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