A method for real-time identification of karst caves based on the energy consumption index per revolution of the cutterhead

Abstract

This invention discloses a real-time method for identifying karst caves based on the energy consumption index per cutterhead revolution. The method includes the following steps: collecting tunneling parameters automatically recorded by the tunnel boring machine's sensors; calculating the energy consumption index per cutterhead revolution (EPRI), the corresponding energy consumption change rate (ΔEPRI), and the energy fluctuation index per cutterhead revolution (VEPRI); using a sliding window method, performing real-time analysis on three consecutive rows of data, calculating the mean and standard deviation of EPRI and ΔEPRI within each window, and using these as thresholds for karst cave identification; updating the thresholds in real-time as the parameters recorded during tunnel boring machine operation increase; and determining karst caves based on the relationship between the three indices (EPRI, ΔEPRI, and VEPRI) satisfying the thresholds. This invention effectively reduces the complexity of technical operations, improves quantitative identification results, and reduces the possibility of false alarms and missed alarms, thereby effectively identifying karst caves, helping construction personnel adjust construction parameters in advance, and improving project quality and economic benefits.

Description

Technical Field

[0001] This invention relates to the field of tunnel boring machine (TBM) construction technology, and in particular to a method for real-time identification of karst caves based on the energy consumption index per revolution of the cutterhead. Background Technology

[0002] In modern urban underground engineering, shield tunneling technology is widely used due to its ability to reduce traffic disruption and improve construction safety. However, this technology faces severe challenges under complex geological conditions. Particularly in karst regions, the presence of caves and karst development zones presents complex geological conditions with uncertain strata integrity and mechanical properties, making shield tunneling exceptionally difficult. Due to the complexity and diversity of karst geology and the limitations of existing exploration techniques, accurately detecting the location and scale of karst development zones during the exploration phase is challenging. Karst strata (such as caves and faults) can lead to tool wear problems, increasing construction costs. Furthermore, karst water and unstable rock conditions increase the risks of ground subsidence, collapse, and long-term tunnel operation during construction. Therefore, real-time monitoring and identification of surrounding geological conditions during construction becomes a critical task to ensure the safety and efficiency of shield tunneling.

[0003] Currently, geological identification in tunnel boring machine (TBM) construction primarily relies on geological exploration methods, real-time monitoring technologies, and existing geological identification indicators. Geological exploration methods include traditional and advanced technologies such as drilling, excavation exploration, and ground-penetrating radar, mainly used for pre-construction geological condition assessment. These methods can provide direct or indirect information about underground rock and soil, aiding in the development of construction strategies. Real-time monitoring technologies, by deploying vibration sensors on the TBM equipment, monitor vibration signals generated during construction in real time, enabling the identification of geological anomalies and potential risks. However, these methods have the following drawbacks:

[0004] 1. Current on-site survey standards are relatively conservative, resulting in sparse borehole and exploration hole layouts, making it difficult to accurately detect the location and scale of special geological features. 2. Precision instruments and sensors are expensive to operate and maintain, with strict requirements for installation and operation. In complex karst areas, they often fail to provide comprehensive coverage and accurately reflect geological characteristics. 3. The interpretation of precision instruments and vibration signals is highly dependent on external interference and difficult to distinguish different geological types in complex geological environments. Highly skilled operators and complex control systems are required, increasing both the technical difficulty and cost of construction. High-tech systems also have high equipment and maintenance costs, requiring specialized knowledge and continuous technical support. 4. Existing geological identification indicators rely on fixed volumes or post-construction data statistics, making it impossible to update real-time geological changes during tunneling. Single indicators struggle to balance sensitivity and specificity, and are susceptible to factors such as insufficient samples, sampling frequency, and fluctuations in construction parameters, leading to misjudgments or failure to identify karst caves. Summary of the Invention

[0005] The technical problem to be solved by the embodiments of the present invention is to provide a method for real-time identification of karst caves based on the energy consumption index per revolution of the cutterhead, which can effectively identify karst caves using the real-time parameters of the cutterhead.

[0006] To address the aforementioned technical problems, this invention provides a method for real-time identification of karst caves based on the energy consumption index per revolution of the cutterhead, characterized by the following steps:

[0007] S1: Collect shield tunneling parameters automatically recorded by the shield machine's sensors. The sampling frequency is one set per second, with the time of each cutterhead rotation as the unit, and take the average value.

[0008] S2: Calculate the energy consumption index EPRI per revolution of the cutter head, the corresponding energy consumption change rate ΔEPRI, and the energy fluctuation index VEPRI per revolution of the cutter head;

[0009] S3: Using the sliding window method, real-time analysis is performed on three consecutive rows of data. The mean and standard deviation of EPRI and ΔEPRI within each window are calculated. The mean of EPRI and the standard deviation of ΔEPRI are used as the threshold for judging karst caves. The threshold is updated in real time as the parameters of the shield tunneling record increase.

[0010] S4: Determine the karst cave based on the relationship between the three indices EPRI, ΔEPRI, and VEPRI meeting the threshold.

[0011] The energy consumption per revolution of the cutter head, EPRI, is calculated using the following formula:

[0012]

[0013] In the formula, F is the shield thrust, AR is the propulsion speed, T is the cutterhead torque, CRS is the cutterhead rotation speed, and 16.7 and 1.05 are coefficients for converting the EPRI dimensions to J / s.

[0014] The energy consumption change rate ΔEPRI is calculated using the following formula:

[0015]

[0016] In the formula, the parameters marked with subscript 1 are the shield tunneling parameters of the previous loop, and the parameters marked with subscript 2 are the shield tunneling parameters of the current loop, ΔE total Δt represents the energy consumption difference between two consecutive rotations of the cutter head, and Δt represents the cumulative time for one rotation of the cutter head.

[0017] The energy fluctuation index VEPRI per revolution of the cutter head is calculated by the following formula:

[0018]

[0019] In the formula, EPRI i Let EPRI be the energy per revolution of the i-th cutter head. is the average EPRI value during this period, and n is the number of cutter head revolutions.

[0020] The steps for determining the location of a sinkhole include:

[0021] When an area is marked as a karst cave in a geological survey report, the following judgment rules shall be used:

[0022] When the EPRI corresponding to one revolution of the cutterhead exceeds its adaptive threshold, it is determined to be a cavern.

[0023]

[0024] In the formula, μEPRI represents the mean EPRI of the cutter head rotating one revolution, and σEPRI represents the standard deviation of EPRI of the cutter head rotating one revolution.

[0025] When the geological survey report identifies an area as not a karst cave, the following judgment rule applies:

[0026] When EPRI, ΔEPRI, and VEPRI all exceed their adaptive thresholds, it is determined to be a cave:

[0027]

[0028] In the formula, μΔEPRI represents the mean value of ΔEPRI when the cutter head rotates one revolution, σΔEPRI represents the standard deviation of ΔEPRI when the cutter head rotates one revolution, μVEPRI represents the mean value of VEPRI when the cutter head rotates one revolution, and σVEPRI represents the standard deviation of VEPRI when the cutter head rotates one revolution.

[0029] When used for areas where no survey or exploration has been conducted, or where karst exploration anomalies are identified, the following judgment rules shall apply:

[0030] If any two indicators exceed their corresponding thresholds, it is determined to be a karst cave:

[0031] .

[0032] Implementing the embodiments of this invention has the following beneficial effects: This invention can solve the problem of difficulty in real-time and quantitative identification of karst caves during shield tunneling construction. This invention proposes for the first time to utilize the cutterhead rotation speed to accumulate the rotation angle in real time and calculate the time interval, using "per revolution" as the smallest analysis unit. This ensures that the energy consumption and stress analysis scale is completely consistent with the actual movement of the cutterhead, improving identification accuracy. By using an online sliding window to statistically analyze the mean and standard deviation, on-site self-calibration of the threshold is achieved without manual preset, enhancing generalization ability. This method reduces the complexity of technical operations, improves quantitative identification results, and reduces the possibility of false alarms and missed alarms, thereby effectively identifying karst caves and helping construction personnel adjust construction parameters in advance, improving project quality and economic benefits. Three judgment logics are designed to balance detection sensitivity and false alarm control, improving online identification accuracy and generalization ability. Attached Figure Description

[0033] Figure 1 This is a schematic flowchart of the method of the present invention;

[0034] Figure 2 These are the calculation results of the cutter head rotation speed as a function of the ring number and the time consumed per revolution in this embodiment of the invention;

[0035] Figure 3 This is a graph showing the relationship between shield tunneling parameters and cutterhead rotation time in an embodiment of the present invention;

[0036] Figure 4 This is a schematic diagram illustrating the changes of EPRI and ΔEPRI over time and formation according to an embodiment of the present invention;

[0037] Figure 5 This is a diagram illustrating the effect of cave identification in an embodiment of the present invention;

[0038] Figure 6 This is a longitudinal section of the geological survey according to an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

[0040] Reference Figure 1 As shown in the figure, a method for real-time identification of karst caves based on the energy consumption index per revolution of the cutterhead according to an embodiment of the present invention is implemented through the following method.

[0041] S1: Collect shield tunneling parameters automatically recorded by the shield machine's sensors. The shield tunneling parameters collected per second are averaged, with the time for each revolution of the cutterhead as the unit.

[0042] Reference Figure 2According to formulas (1) to (3), by accumulating the rotation angle of the cutter head per second, we determine that 360° is one revolution. When the accumulated angle is ≥360°, we record the time Δt of this revolution and reduce the accumulated angle by 360° to continue monitoring the next revolution.

[0043] Reference Figure 3 It will export one set of tunneling parameters per second from the tunnel boring machine, according to Figure 2 The average value of the time Δt per revolution calculated in the calculation is taken.

[0044] The main parameters used are thrust F, torque T, cutter head rotation speed CRS, and feed speed AR. The angle of the cutter head rotation per second is calculated based on the cutter head rotation speed CRS, and each time the cumulative angle reaches 360°, it is recorded as one rotation of the cutter head.

[0045] The angle of rotation of the cutter head per second (θt):

[0046] (1)

[0047] Angular accumulation (θaccumulated):

[0048] (2)

[0049] Once the accumulated angle exceeds 360°, the number of seconds it takes to reach 360° is taken as the real-time time for one revolution of the cutter head. After recording the time for one revolution, subtract 360° from the accumulated angle, i.e.:

[0050] (3)

[0051] Then, we continue with the next round of real-time monitoring.

[0052] S2: Calculation of the energy consumption index EPRI per cutter head revolution, the corresponding energy consumption change rate ΔEPRI, and the energy fluctuation index VEPRI per cutter head revolution, such as... Figure 4 As shown.

[0053] The energy consumption per revolution of the cutter head, EPRI, is calculated using the following formula:

[0054]

[0055] In the formula, F is the shield thrust, AR is the propulsion speed, T is the cutterhead torque, CRS is the cutterhead rotation speed, and 16.7 and 1.05 are coefficients for converting EPRI dimensions to J / s (i.e., watts, W).

[0056] The rate of change in energy consumption ΔEPRI is calculated using the following formula:

[0057]

[0058] Among them, the parameters marked with subscript 1 are the shield parameters of the previous circle, the parameters marked with subscript 2 are the shield parameters of the current circle, ΔEtotal is the energy consumption difference between two consecutive rotations of the cutterhead, that is, the energy consumed by the current rotation of the cutterhead minus the energy consumed by the previous rotation of the cutterhead, and Δt is the cumulative time for the current rotation of the cutterhead, that is, the time consumed when θaccumulated reaches 360°.

[0059] The Energy Fluctuation Index per Cutterhead Rotation (VEPRI) is used to characterize the dispersion of energy consumption per cutterhead rotation over a certain period of time, reflecting whether the formation structure is uniform and stable. The calculation formula is as follows:

[0060]

[0061] In the formula, EPRIi is the energy index EPRI per revolution corresponding to the i-th cutter head. is the average EPRI value during this period, and n is the number of cutter head revolutions.

[0062] S3: Sliding Window Adaptive Threshold Calculation. A sliding window method is used to analyze three consecutive rows of data in real time, calculating the mean and standard deviation of EPRI, ΔEPRI, and VEPRI within each window. The mean reflects the energy consumed per revolution of the cutterhead, and the standard deviation reflects the fluctuation of this indicator in the current tunneling area. The mean of EPRI and the standard deviation of ΔEPRI are used as the threshold for cavern detection. The threshold is updated in real time as the parameters in the shield tunneling record increase.

[0063] S4: Real-time quantitative judgment logic setting for karst caves.

[0064] Based on the relationship between the three indices EPRI, ΔEPRI, and VEPRI meeting thresholds, a logic is set to determine if a formation is a karst cave. If the calculated result is greater than the threshold, it is considered a karst cave; otherwise, it is considered another stratum.

[0065] The above three indicators are all derived from the original construction parameters such as thrust, torque, propulsion speed and cutterhead rotation speed collected in real time by sensors during the tunneling process. They are calculated using the data reconstruction method of "taking each rotation of the cutterhead as the smallest unit of analysis" and have a unified physical basis and time scale.

[0066] To achieve adaptive identification under different construction sections and different tunnel boring machine operating conditions, this invention does not use fixed empirical thresholds, but instead uses a sliding window method to calculate the statistical characteristics of the above three indicators in real time during the continuous rotation of the cutterhead.

[0067] Specifically, the mean and standard deviation within the sliding window form a dynamic threshold, which is used to determine whether the current indicator shows an abnormal increase or drastic fluctuation.

[0068] Therefore, the three indicators are not used independently, but are compared with their corresponding adaptive thresholds. The unified criterion of "whether the threshold is exceeded" is used to participate in the subsequent cave identification logic judgment, thereby realizing a quantitative, real-time and updatable cave identification system.

[0069] The steps for identifying a cave include:

[0070] A: When using geological survey reports to identify areas as karst caves, the following judgment rules shall be applied:

[0071] When the EPRI corresponding to one revolution of the cutterhead exceeds its adaptive threshold, it is determined to be a cavern.

[0072]

[0073] In the formula, μEPRI represents the mean EPRI of the cutter head rotating one revolution, and σEPRI represents the standard deviation of EPRI of the cutter head rotating one revolution.

[0074] B: When using geological survey reports to identify areas as non-karst caves, the following judgment rules shall be applied:

[0075] When EPRI, ΔEPRI, and VEPRI all exceed their adaptive thresholds, it is determined to be a cave:

[0076]

[0077] In the formula, μΔEPRI represents the mean value of ΔEPRI when the cutter head rotates one revolution, σΔEPRI represents the standard deviation of ΔEPRI when the cutter head rotates one revolution, μVEPRI represents the mean value of VEPRI when the cutter head rotates one revolution, and σVEPRI represents the standard deviation of VEPRI when the cutter head rotates one revolution.

[0078] C: When dealing with areas where no survey or exploration has been conducted, or where karst exploration anomalies are identified, the following judgment rules shall be applied:

[0079] If any two indicators exceed their corresponding thresholds, it is determined to be a karst cave:

[0080] .

[0081] Reference Figure 6 A is used for areas marked as karst caves in geological survey reports, B is used for areas marked as non-karst caves in geological survey reports, and C is used for areas that have not been surveyed or whose exploration is unclear, as well as areas with abnormal karst exploration.

[0082] For example, logic B is used to make judgments in the 337-340 ring and 345-346 ring; logic A is used to identify the 340-345 ring; if the entire 337-346 ring area is an area with unknown geological conditions, then logic C is used to make judgments for the entire area.

[0083] Reference Figure 5 The method for real-time identification of karst caves based on the energy consumption index per revolution of the cutterhead provided by this invention has an accuracy rate of 70.01% in identifying karst caves in areas marked as karst development in geological survey reports, and an accuracy rate of 99.67% in identifying strata in areas marked as non-karst development in geological survey reports. If the area is an area with an unknown geological type, the accuracy rate of the method for identifying karst caves is 76.33%.

[0084] The above description is merely a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. Therefore, any equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for real-time identification of karst caves based on the energy consumption index per revolution of the cutterhead, characterized in that, Includes the following steps: S1: Collect shield tunneling parameters automatically recorded by the shield machine's sensors. The sampling frequency is one set per second, with the time of each cutterhead rotation as the unit, and take the average value. S2: Calculate the energy consumption per revolution of the cutter head, EPRI, and the corresponding energy consumption change rate. Energy fluctuation index per revolution of the cutterhead (VEPRI); The energy consumption per revolution of the cutter head, EPRI, is calculated using the following formula: In the formula, F is the shield thrust, AR is the propulsion speed, T is the cutterhead torque, CRS is the cutterhead rotation speed, and 16.7 and 1.05 are coefficients for converting the EPRI dimensions to J / s; The energy consumption change rate Calculated using the following formula: In the formula, the parameters marked with subscript 1 are the shield tunneling parameters of the previous loop, and the parameters marked with subscript 2 are the shield tunneling parameters of the current loop. This represents the energy consumption difference between two consecutive rotations of the cutter head. This is the cumulative time for the current cutter head to rotate one revolution. The energy fluctuation index VEPRI per revolution of the cutter head is calculated using the following formula: In the formula, EPRI i Let EPRI be the energy per revolution of the i-th cutter head. Here, n represents the average EPRI value over this period, and n is the number of cutterhead revolutions. S3: Using the sliding window method, perform real-time analysis on three consecutive rows of data, and calculate the EPRI and EPRI within each window. The mean and standard deviation of EPRI, and The standard deviation is used as the threshold for judging karst caves, and the threshold is updated in real time as the parameters of the shield tunneling record increase; S4: According to EPRI, The determination of a cavern is based on the relationship between the three indicators (VEPRI, VEPRI, and VEPRI) and the threshold values. The steps for determining a cavern include: When an area is marked as a karst cave in a geological survey report, the following judgment rules shall be used: When the EPRI corresponding to one revolution of the cutterhead exceeds its adaptive threshold, it is determined to be a cavern. In the formula, This represents the average EPRI value after one revolution of the cutter head. The standard deviation of EPRI represents one revolution of the cutter head; When the geological survey report identifies an area as not a karst cave, the following judgment rule applies: When EPRI, When both VEPRI and VEPRI exceed their adaptive thresholds, it is determined to be a cave. In the formula, This indicates that the cutter head rotates one revolution. The mean, This indicates that the cutter head rotates one revolution. standard deviation This represents the average value of VEPRI after one revolution of the cutter head. The standard deviation of VEPRI represents one revolution of the cutter head; When used for areas where no survey or exploration has been conducted, or where karst exploration anomalies are identified, the following judgment rules shall apply: If any two indicators exceed their corresponding thresholds, it is determined to be a karst cave: 。

Images