Karst cave real-time identification method based on energy consumption index per revolution of cutterhead

Abstract

The invention discloses a karst cave real-time identification method based on an energy consumption index per revolution of a cutterhead. The method comprises the steps that shield tunneling parameters automatically recorded by a shield tunneling machine sensor are collected; calculating an energy consumption index EPRI per revolution of the cutterhead, a corresponding energy consumption change rate delta EPRI and an energy fluctuation index VEPRI per revolution of the cutterhead; a sliding window method is adopted, three continuous rows of data are analyzed in real time, the mean value and the standard deviation of EPRI and delta EPRI in each window are calculated and serve as threshold values for karst cave judgment, and the threshold values are updated in real time along with increase of parameters recorded by shield tunneling; and judging the karst cave according to the relationship that the three indexes EPRI, delta EPRI and VEPRI meet the threshold value. By adopting the method, the complexity of technical operation is effectively reduced, the quantitative identification effect is improved, and the possibility of false alarm and missing alarm is reduced, so that the karst cave is effectively identified, constructors are helped to adjust construction parameters in advance, and the engineering quality and economic benefits are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of shield construction, and particularly relates to a karst cave real-time identification method based on energy consumption index per revolution of a cutter head. BACKGROUND

[0002] In modern urban underground engineering, shield tunneling technology is widely used due to its ability to reduce traffic interference and improve construction safety. However, under complex geological conditions, this technology faces severe challenges. In karst areas, there are complex geological conditions such as karst caves and karst development zones with uncertain integrity and mechanical properties of the stratum, making the shield construction process extremely difficult. Due to the complexity and diversity of karst geology and the limitations of existing surveying techniques, it is difficult to accurately detect the location and scale of karst development zones during the surveying phase. Karst strata (such as caves and faults) can cause cutter wear problems and increase construction costs. In addition, karst water and unstable rock conditions increase the challenges of ground subsidence, collapse, and long-term operation risks during construction. Therefore, real-time monitoring and identification of surrounding geological conditions during construction is a key task to ensure the safety and efficiency of shield construction.

[0003] Currently, geological identification in shield construction mainly relies on geological survey methods, real-time monitoring techniques, and existing geological identification indicators. Geological survey methods include traditional and advanced techniques such as drilling, excavation exploration, and geological radar, which are mainly used for pre-construction geological condition assessment. These methods can provide direct or indirect information about underground geotechnical conditions, helping to develop construction strategies. Real-time monitoring techniques involve placing vibration sensors on shield construction equipment to monitor vibration signals generated during construction in real time, which can identify geological anomalies and potential risks. However, these methods have the following shortcomings: 1. The relevant specifications for on-site surveying are relatively conservative, with sparse arrangement of drill holes and survey holes, making it difficult to accurately detect the location and scale of special geology; 2. Precise instruments and sensors have high costs and maintenance fees, with strict requirements for installation and operation, making it difficult to fully cover and accurately reflect geological features in complex karst areas; 3. Precise instruments and vibration signal interpretation are dependent on other external disturbances, and it is difficult to distinguish different geological types in complex geological environments, requiring high technical level of operators and complex control systems, which not only increases the technical difficulty of construction, but also increases the cost, with high equipment and maintenance costs of high-tech systems, requiring professional knowledge and continuous technical support; 4. Existing geological identification indicators need to be based on fixed volumes or post-data statistics, which cannot achieve online updates of real-time stratum changes during excavation, and single indicators are difficult to balance sensitivity and specificity, being easily affected by sample deficiency, sampling frequency, and construction parameter fluctuations, leading to misjudgment or failure to identify karst caves. SUMMARY

[0004] The technical problem to be solved by the embodiment of the present application is to provide a karst cave real-time identification method based on energy consumption index per revolution of a cutter head, which can effectively identify karst caves by using real-time parameters of the cutter head.

[0005] In order to solve the above technical problem, the embodiment of the present application provides a karst cave real-time identification method based on energy consumption index per revolution of a cutter head, characterized in that the method comprises the following steps: S1: collecting shield tunneling parameters recorded automatically by a shield sensor, a sampling frequency being one group per second, taking time for each revolution of the cutter head as a unit, and taking an average value; S2: calculating energy consumption index EPRI per revolution of the cutter head, corresponding energy consumption change rate ΔEPRI, and energy fluctuation index VEPRI per revolution of the cutter head; S3: using a sliding window method to perform real-time analysis on three continuous rows of data, calculating mean values and standard deviations of EPRI and ΔEPRI in each window, taking the mean value of EPRI and the standard deviation of ΔEPRI as threshold values for karst cave judgment, and updating the threshold values in real time as the recorded parameters of the shield tunneling increase; S4: judging karst caves according to the relationship between the threshold values of the three indexes EPRI, ΔEPRI and VEPRI.

[0006] The energy consumption index EPRI per revolution of the cutter head is calculated by the following formula:

[0007] In the formula, F is the thrust of the shield, AR is the advance speed, T is the torque of the cutter head, CRS is the rotation speed of the cutter head, and 16.7 and 1.05 are coefficients for converting the dimension of EPRI to J / s.

[0008] The energy consumption change rate ΔEPRI is calculated by the following formula:

[0009] In the formula, the parameters with subscript 1 are shield parameters of the last round, the parameters with subscript 2 are shield parameters of the current round, ΔE is the energy consumption difference value of two continuous revolutions of the cutter head, and Δt is the cumulative time for one revolution of the current cutter head total The energy fluctuation index VEPRI per revolution of the cutter head is calculated by the following formula:

[0010] In the formula, EPRI i is the energy consumption index EPRI per revolution of the cutter head corresponding to the i th round, is the mean value of EPRI in this period of time, and n is the number of revolutions of the cutter head.

[0011] ​The step of determining the karst cave includes: For the area marked as a karst cave in the geological exploration report, the following determination rule is used: When the EPRI corresponding to one rotation of the cutter head exceeds its adaptive threshold, the karst cave is determined:

[0012] In the formula, μEPRI represents the average value of the EPRI of one rotation of the cutter head, and σEPRI represents the standard deviation of the EPRI of one rotation of the cutter head. For the area marked as a non-karst cave in the geological exploration report, the following determination rule is used: When the EPRI, ΔEPRI, and VEPRI all exceed their adaptive thresholds, the karst cave is determined:

[0013] In the formula, μΔEPRI represents the average value of the ΔEPRI of one rotation of the cutter head, σΔEPRI represents the standard deviation of the ΔEPRI of one rotation of the cutter head, μVEPRI represents the average value of the VEPRI of one rotation of the cutter head, and σVEPRI represents the standard deviation of the VEPRI of one rotation of the cutter head. For the part that has not been surveyed or the part that has unclear exploration and abnormal karst exploration, the following determination rule is used: When any two indicators exceed their corresponding thresholds, the karst cave is determined: .

[0014] The embodiment of the present application has the following beneficial effects: The present application can solve the problem of real-time and quantitative identification of karst caves in the process of shield construction. The present application first proposes to use the real-time cumulative rotation angle of the cutter head speed and calculate the time interval, taking "per rotation" as the minimum analysis unit, so that the energy consumption and stress analysis scale are completely consistent with the actual movement of the cutter head, the identification precision is improved, the threshold is calibrated on site using the online sliding window statistical mean and standard deviation, manual presetting is not required, the generalization ability is enhanced. This method reduces the complexity of technical operation, improves the quantitative identification effect, reduces the possibility of false positives and false negatives, thereby effectively identifying karst caves, helping construction personnel to adjust construction parameters in advance, improving engineering quality and economic benefits. Three kinds of determination logic are designed, taking into account the detection sensitivity and false alarm control, improving the online identification precision and generalization ability. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is a schematic flowchart of the method of the present application; Figure 2 is the cutter head speed variation with ring number and the calculation result of the time consumed per rotation of the embodiment of the present application; Figure 3is a shield tunneling parameter time change relation diagram per revolution of a cutter head of an embodiment of the present application; Figure 4 is an EPRI and delta EPRI time and stratum change schematic diagram of an embodiment of the present application; Figure 5 is a solution cavity recognition effect diagram of an embodiment of the present application; Figure 6 is a geological survey longitudinal section diagram of an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings.

[0017] Referring to Figure 1 , a solution cavity real-time recognition method based on a cutter head per revolution energy consumption index of an embodiment of the present application is implemented by the following method.

[0018] S1: Collect shield tunneling parameters recorded automatically by a shield machine sensor. Collect shield tunneling parameters per second, take an average value in units of time per revolution of a cutter head rotation; Referring to Figure 2 , according to formulas (1)-(3), by accumulating a cutter head per second rotation angle, judge 360° as a circle, when the accumulated angle is greater than or equal to 360°, record the time Δt of the current circle, and subtract 360° from the accumulated angle to continue the next circle monitoring; Referring to Figure 3 , export a group of shield tunneling parameters per second from a shield machine, take an average value of the time Δt per revolution calculated in Figure 2 .

[0019] Among them, the main parameters used are thrust F, torque T, cutter head rotation speed CRS, and advance speed AR. According to the cutter head rotation speed CRS, calculate the angle of cutter head rotation per second. Whenever the angle accumulates to 360°, it is recorded as one revolution of the cutter head.

[0020] Cutter head per second rotation angle (θt): (1) Angle accumulation (θaccumulated): (2) When the angle accumulation exceeds 360°, the number of seconds used to accumulate to 360° is the real-time time of one revolution of the cutter head. After recording the time point of one revolution, subtract 360° from the accumulated angle, that is: (3) Then continue real-time monitoring of the next revolution.

[0021] S2: The energy consumption per revolution index EPRI, the corresponding energy consumption change rate ΔEPRI and the energy fluctuation index VEPRI of the cutterhead per revolution are calculated as shown in the following formula: Figure 4

[0022] The energy consumption per revolution index EPRI is calculated by the following formula:

[0023] In the formula, F is the thrust of the shield, AR is the advance speed, T is the cutterhead torque, CRS is the cutterhead rotation speed, and 16.7 and 1.05 are coefficients for converting the dimension of EPRI to J / s (i.e. watt, W).

[0024] The energy consumption change rate ΔEPRI is calculated by the following formula:

[0025] In the formula, the parameters with subscript 1 are the parameters of the shield in the previous round, the parameters with subscript 2 are the parameters of the shield in the current round, ΔEtotal is the difference in energy consumption of the cutterhead in two consecutive revolutions, i.e. the energy consumed in the current round of cutterhead rotation minus the energy consumed in the previous round of cutterhead rotation, and Δt is the cumulative time for the current round of cutterhead rotation, i.e. the time consumed when θaccumulated reaches 360°.

[0026] The energy fluctuation index VEPRI of the cutterhead per revolution is used to represent the dispersion degree of the energy consumption per revolution of the cutterhead in a certain time, and is used to reflect whether the structure of the stratum is uniform and stable. The calculation formula is as follows:

[0027] In the formula, EPRIi is the energy consumption index EPRI of the cutterhead in the ith round, is the average EPRI in this period of time, and n is the number of cutterhead revolutions.

[0028] S3: Sliding window adaptive threshold calculation. The sliding window method is used to analyze the continuous three rows of data in real time, calculate the mean and standard deviation of EPRI, ΔEPRI and VEPRI in each window, the mean reflects the energy consumption of the current cutterhead revolution, and the standard deviation reflects the fluctuation of the index in the current tunneling area. The mean of EPRI and the standard deviation of ΔEPRI are used as the threshold for judging the karst cave. The threshold is updated in real time as the parameters of the shield tunneling record increase.

[0029] S4: Real-time quantitative judgment logic setting.

[0030] According to the relationship between the three indexes EPRI, ΔEPRI and VEPRI meeting the threshold, the logic for judging the karst cave is set. If the calculation result is greater than the threshold, it is a karst cave, otherwise it is other stratum ​The three indexes are derived from the original construction parameters of the shield machine in the tunneling process, such as the real-time collected thrust, torque, advancing speed and cutterhead rotating speed, and are calculated by a data reconstruction method of taking each rotation of the cutterhead as the minimum analysis unit, and have a unified physical basis and time scale.

[0031] To realize adaptive identification in different construction sections and different working conditions of the shield machine, the application does not use fixed empirical thresholds, but calculates the statistical characteristics of the three indexes in real time in the continuous cutterhead rotation based on a sliding window method.

[0032] Specifically, the mean and standard deviation in the sliding window constitute a dynamic threshold, which is used to determine whether the current index abnormally increases or fluctuates sharply.

[0033] Therefore, the three indexes are not used independently, but are compared with their corresponding adaptive thresholds, and participate in the subsequent karst cave identification logic judgment through the unified criterion of "whether exceeding the threshold", so as to realize a quantitative, real-time and updateable karst cave judgment system.

[0034] The steps of determining the karst cave include: A: For the area marked as a karst cave in the geological exploration report, the following judgment rule is used: When the EPRI corresponding to one rotation of the cutterhead exceeds its adaptive threshold, it is determined to be a karst cave:

[0035] In the formula, μEPRI represents the mean of the EPRI of one rotation of the cutterhead, and σEPRI represents the standard deviation of the EPRI of one rotation of the cutterhead; B: For the area marked as a non-karst cave in the geological exploration report, the following judgment rule is used: When the EPRI, ΔEPRI and VEPRI all exceed their adaptive thresholds, it is determined to be a karst cave:

[0036] In the formula, μΔEPRI represents the mean of the ΔEPRI of one rotation of the cutterhead, σΔEPRI represents the standard deviation of the ΔEPRI of one rotation of the cutterhead, μVEPRI represents the mean of the VEPRI of one rotation of the cutterhead, and σVEPRI represents the standard deviation of the VEPRI of one rotation of the cutterhead; C: For the part that has not been surveyed or the exploration is unclear and the karst exploration is abnormal, the following judgment rule is used: When any two indexes exceed their corresponding thresholds, it is determined to be a karst cave: .

[0037] Reference Figure 6A is used for the area marked as karst cave in the geological exploration report, B is used for the area marked as non-karst cave in the geological exploration report, and C is used for the part without exploration or unclear exploration and abnormal karst exploration.

[0038] For example, logical B is used for judgment in the 337th ring-340th ring and 345th-346th ring; logical A is used for identification in the 340th ring-345th ring; if the whole area of the 337th-346th ring is an area with unproven geological conditions, logical C is used for judgment in the whole area; Referring to Figure 5 The accuracy of the method provided by the application for identifying karst caves in the area marked as karst development in the geological exploration report is 70.01%, the accuracy of the method for identifying strata in the area marked as non-karst development in the geological exploration report is 99.67%, and the accuracy of the method for identifying karst caves in the area with unproven geological types is 76.33%.

[0039] The above only discloses a preferred embodiment of the application, and of course cannot limit the scope of the application. Any equivalent changes made according to the claims of the application are still within the scope of the application.

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

2. The method for real-time identification of karst caves based on the energy consumption index per revolution of the cutterhead as described in claim 1, characterized in that, 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.

3. The method for real-time identification of karst caves based on the energy consumption index per revolution of the cutterhead as described in claim 2, characterized in that, The energy consumption change rate ΔEPRI is 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, ΔE total Δt represents the energy consumption difference between two consecutive rotations of the cutter head, and Δt represents the cumulative time for the cutter head to rotate once.

4. The method for real-time identification of karst caves based on the energy consumption index per revolution of the cutterhead as described in claim 3, characterized in that, The energy fluctuation index VEPRI per revolution of the cutter head is calculated by the following formula: 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.

5. The method for real-time identification of karst caves based on the energy consumption index per revolution of the cutterhead according to any one of claims 1-4, characterized in that, The steps for identifying sinkholes 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, 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. When the geological survey report identifies an area as not a karst cave, the following judgment rule applies: When EPRI, ΔEPRI, and VEPRI all exceed their adaptive thresholds, it is determined to be a cave: 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. 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: 。

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