A detection method and device for predicting railway mud overflow

Abstract

A detection method and device for predicting railway mud swells and mud leaks, comprising: step 1, obtaining a railway subgrade filling coefficient, a cyclic loading coefficient, and a radar detection coefficient; step 2, calculating a railway mud swell and mud leak risk value based on the subgrade filling coefficient, the cyclic loading coefficient, and the radar detection coefficient; and step 3, setting a risk value threshold, comparing the mud swell and mud leak risk value with the risk value threshold, and predicting the occurrence of mud swell and mud leaks when the mud swell and mud leak risk value is greater than or equal to the threshold. The present invention not only improves the accuracy of subgrade geological radar detection but also comprehensively considers subgrade filling and cyclic loading. Compared to existing technologies, the present invention comprehensively considers the three necessary conditions for mud swell and mud leaks, quantitatively and comprehensively predicting railway subgrade mud swell and mud leaks, meeting the requirements of resource conservation and quality and safety engineering construction.

Description

Technical Field

[0001] The present invention relates to the field of geotechnical engineering technology, and in particular to a detection method and device for predicting mud bubbling on railways. Background Art

[0002] Mud and mud are common railway subgrade diseases. The causes and mechanisms of this disease are complex, resulting from the combined effects of multiple factors. Currently, domestic and international scholars believe that the necessary conditions for its occurrence include poor subgrade filler material, the presence of free water, and cyclic loading. Soil quality is both a prerequisite and an internal factor for mud and mud. Free water affects the moisture content of the soil and is a necessary factor for the occurrence of mud and mud. Loading is an external factor.

[0003] Common methods for detecting mud and mud are sensors and geological radar. However, they have the following drawbacks:

[0004] 1. The sensors need to be installed inside the roadbed, and the number is large, which results in low efficiency.

[0005] 2. Geological radar can detect free water and mud in the roadbed. However, when interpreting roadbed defects using geological radar, the interpretation results are qualitative, placing high technical demands on the interpreter. Furthermore, factors such as roadbed filler and cyclic loading are not considered during the interpretation process. Summary of the Invention

[0006] In view of the technical defects and technical drawbacks in the prior art, the embodiments of the present invention provide a detection method and device for predicting railway mud bubbling that overcomes or at least partially solves the above problems. The specific solution is as follows:

[0007] As a first aspect of the present invention, a detection method for predicting railway mud bubbling is provided, the method comprising:

[0008] Step 1: Obtain the railway subgrade filling coefficient, cyclic load coefficient, and radar detection coefficient;

[0009] Step 2: Calculate the risk value of railway mud overflow based on the roadbed filling coefficient, cyclic load coefficient, and radar detection coefficient;

[0010] Step 3: Set a risk value threshold, compare the risk value of mud overflow with the risk value threshold, and predict the occurrence of mud overflow when the risk value of mud overflow is greater than or equal to the threshold.

[0011] Furthermore, in step 1, the roadbed filling coefficient is determined by actual roadbed filling, and the cyclic load coefficient is determined by the train speed design plan.

[0012] Furthermore, the radar detection coefficient is obtained by measuring with geological radar detection equipment.

[0013] Furthermore, the geological radar detection equipment includes a rail cart and a geological radar. The geological radar is installed on the rail cart. The rail cart is located below the geological radar and has a hole. The geological radar transmits and receives signals through the hole.

[0014] Furthermore, the radar detection coefficient measured by geological radar detection equipment specifically includes:

[0015] The geological radar of the geological radar detection equipment transmits high-frequency short-pulse electromagnetic waves to the target point road base and receives the radar reflected wave signal;

[0016] The correlation between the free water content of the roadbed and the geological radar reflection wave signal is obtained, and the free water content of the roadbed is analyzed based on the radar reflection wave signal, and the roadbed radar detection coefficient is calculated based on the free water content of the roadbed.

[0017] Furthermore, the radar reflected wave signal includes time-frequency characteristics, instantaneous amplitude, instantaneous frequency and instantaneous phase.

[0018] Furthermore, the method also includes: collecting radar reflection wave signals of roadbeds with different free water contents through geological radar, using the collected radar reflection wave signals as training sets to train a neural network to obtain a trained neural network; inputting the radar reflection wave signal of the current roadbed into the neural network, and outputting the current roadbed free water content through the neural network.

[0019] Furthermore, the roadbed radar detection coefficient is calculated based on the free water content of the roadbed. The calculation formula is as follows:

[0020] r3=β*f;

[0021] Where r3 is the radar detection coefficient, β is the conversion factor, and f is the free water content of the roadbed.

[0022] Furthermore, in step 2, the railway mud risk value is calculated based on the roadbed filling coefficient, cyclic load coefficient, and radar detection coefficient. The calculation formula is as follows:

[0023] A=r1+r2+r3;

[0024] Where A is the risk value of mud overflow, r1 is the roadbed filling coefficient, r2 is the cyclic load coefficient, and r3 is the radar detection coefficient.

[0025] As a second aspect of the present invention, a detection device for predicting railway mud bubbling is provided, the device comprising: a parameter acquisition module, a calculation module and a judgment module;

[0026] The parameter acquisition module is used to obtain the railway roadbed filling coefficient, cyclic load coefficient and radar detection coefficient;

[0027] The calculation module is used to calculate the railway mud risk value based on the roadbed filling coefficient, the cyclic load coefficient and the radar detection coefficient;

[0028] The judgment module is used to set a risk value threshold, compare the mud overflow risk value with the risk value threshold, and predict the occurrence of mud overflow when the mud overflow risk value is greater than or equal to the threshold.

[0029] The present invention has the following beneficial effects:

[0030] 1. The present invention not only improves the accuracy of roadbed geological radar detection, but also comprehensively considers roadbed filling materials and cyclic loads. Compared with the existing technology, the present invention comprehensively considers the three necessary conditions for mud and mud to occur, quantitatively and comprehensively predicts railway roadbed mud and mud, and meets the requirements of resource conservation and quality and safety engineering construction.

[0031] 2. The present invention opens a rectangular hole at the bottom of the geological radar in the cart, which improves the accuracy of the geological radar detection results and is easy to operate. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flow chart of a detection method for predicting railway mud and mud bubbling provided by an embodiment of the present invention.

[0033] Figure 2 A schematic longitudinal section diagram of a detection device provided in an embodiment of the present invention;

[0034] Figure 3 A schematic plan view of a detection device provided in an embodiment of the present invention;

[0035] Figure 4 A schematic cross-sectional view of a detection device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] As an embodiment of the present invention, a detection method for predicting railway mud bubbling is provided, the method comprising:

[0038] Step 1: Obtain the railway subgrade filling coefficient, cyclic load coefficient, and radar detection coefficient;

[0039] Step 2: Calculate the risk value of railway mud overflow based on the roadbed filling coefficient, cyclic load coefficient, and radar detection coefficient;

[0040] Step 3: Set a risk value threshold, compare the risk value of mud overflow with the risk value threshold, and predict the occurrence of mud overflow when the risk value of mud overflow is greater than or equal to the threshold.

[0041] The present invention not only improves the accuracy of roadbed geological radar detection, but also comprehensively considers roadbed filling materials and cyclic loads, quantitatively and comprehensively predicts railway roadbed mud and mud, and meets the requirements of resource conservation and quality safety engineering construction.

[0042] See also Figure 1 As shown, in the embodiment of the present invention, the risk value threshold is 0.6. When the risk value of mud overflow is greater than or equal to 0.6, there is a risk of mud overflow; otherwise, there is no risk of mud overflow.

[0043] In step 2, the risk value of railway mud and mud is calculated based on the roadbed filling coefficient, cyclic load coefficient, and radar detection coefficient. The calculation formula is as follows:

[0044] A=r1+r2+r3;

[0045] Where A is the risk value of mud overflow, r1 is the roadbed filling coefficient, r2 is the cyclic load coefficient, and r3 is the radar detection coefficient.

[0046] Optionally, the present invention determines the roadbed filling coefficient through actual roadbed filling, and determines the cyclic load coefficient through the train speed design scheme. See Tables 1 and 2 below. For groups A, B, and C of roadbed fillings, the corresponding roadbed filling coefficients are 0.05, 0.2, and 0.25, respectively. For the train speed design schemes of 350 km / h, 250 km / h, and 120 km / h, the corresponding cyclic load coefficients are 0.2, 0.1, and 0.05, respectively.

[0047] Roadbed filler Group A Group B Group C <![CDATA[r1]]> 0.05 0.2 0.25

[0048] Table 1 Reference value of roadbed filling coefficient (r1)

[0049] Load type 350km / h 250km / h 120km / h <![CDATA[r2]]> 0.2 0.1 0.05

[0050] Table 2 Reference values ​​of cyclic load coefficient (r2)

[0051] Optionally, the radar detection coefficient is obtained by measuring with geological radar detection equipment.

[0052] Analysis of radar reflection signals and practical railway subgrade testing demonstrates a significant correlation between the time-frequency characteristics, instantaneous amplitude, instantaneous frequency, and instantaneous phase of the ground-penetrating radar reflection signal and free water in the subgrade. The test results show that when the subgrade is wet, the high-frequency end of the radar waveform is significantly attenuated and more chaotic than that of a dry subgrade, while the radar wave reflection amplitude increases. Therefore, frequency and amplitude attributes can be used as a basis for identifying abnormal subgrade water content.

[0053] Based on this, the present invention designs a method for calculating the roadbed radar detection coefficient based on the radar reflection wave signal, which specifically includes:

[0054] The geological radar of the geological radar detection equipment transmits high-frequency short-pulse electromagnetic waves to the target point road base and receives the radar reflected wave signal;

[0055] The correlation between the free water content of the roadbed and the geological radar reflection wave signal is obtained, and the free water content of the roadbed is analyzed based on the radar reflection wave signal, and the roadbed radar detection coefficient is calculated based on the free water content of the roadbed.

[0056] The radar reflected wave signal includes time-frequency characteristics, instantaneous amplitude, instantaneous frequency and instantaneous phase.

[0057] Alternatively, see Figures 2 to 4 The geological radar detection equipment includes a rail trolley 1 and a geological radar 2. The rail trolley travels on the railway track 3. The geological radar is installed on the rail trolley 1. The rail trolley 1 opens a rectangular opening 4 at the bottom of the geological radar. The area of ​​the opening 4 is slightly smaller than the geological radar. The geological radar can transmit and receive reflected signals through the opening 4 to detect roadbed diseases and predict whether the roadbed will experience mud and slurry. The rectangular opening 4 can improve the problem of inaccurate collected data caused by the poor and discontinuous reception signals of the geological radar antenna due to the bottom plate structure of the rail trolley 1 during railway roadbed geological radar detection, thereby improving the accuracy of the detection results.

[0058] The rail cart includes: a traveling wheel group, including a front wheel group and a rear wheel group, each of the front wheel group and the rear wheel group includes a wheel axle and a pair of traveling wheels arranged at both ends of the wheel axle and used for traveling on the rails; a frame, including a bracket arranged above the traveling wheel group, and the bottom of the bracket is connected to the wheel axle; the geological radar is placed above the rectangular hole 4 at the bottom of the bracket.

[0059] Optionally, after the geological radar collects the original data along the roadbed, data preprocessing is required, including energy balancing, noise suppression, and resolution improvement. The radar detection coefficient is determined comprehensively through the geological radar's instantaneous amplitude, instantaneous frequency, instantaneous phase, time-frequency characteristics, etc.

[0060] Optionally, the method further includes: collecting radar reflection wave signals of roadbeds with different free water contents through geological radar, using the collected radar reflection wave signals as a training set to train a neural network to obtain a trained neural network; inputting the radar reflection wave signal of the current roadbed into the neural network, and outputting the current roadbed free water content through the neural network.

[0061] The roadbed radar detection coefficient is calculated based on the free water content of the roadbed. The calculation formula is as follows:

[0062] r3=β*f;

[0063] Where r3 is the radar detection coefficient, β is the conversion factor, and f is the free water content of the roadbed.

[0064] In the above embodiment, based on the trained neural network, when the radar reflection wave signal is received, the free water content of the roadbed can be obtained in real time through the neural network, and the radar detection coefficient can be further calculated.

[0065] As another embodiment of the present invention, a detection device for predicting mud bubbling on railways is also provided, the device comprising: a parameter acquisition module, a calculation module and a judgment module;

[0066] The parameter acquisition module is used to obtain the railway roadbed filling coefficient, cyclic load coefficient and radar detection coefficient;

[0067] The calculation module is used to calculate the railway mud risk value based on the roadbed filling coefficient, the cyclic load coefficient and the radar detection coefficient;

[0068] The judgment module is used to set a risk value threshold, compare the mud overflow risk value with the risk value threshold, and predict the occurrence of mud overflow when the mud overflow risk value is greater than or equal to the threshold.

[0069] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A detection method for predicting railway mud bubbling, characterized in that: The method comprises: Step 1: Obtain the railway subgrade filling coefficient, cyclic load coefficient, and radar detection coefficient; Step 2: Calculate the risk value of railway mud overflow based on the roadbed filling coefficient, cyclic load coefficient, and radar detection coefficient; Step 3: setting a risk value threshold, comparing the risk value of mud overflow with the risk value threshold, and predicting the occurrence of mud overflow when the risk value of mud overflow is greater than or equal to the threshold; In step 1, the radar detection coefficient is obtained by measuring with geological radar detection equipment, specifically including: The geological radar of the geological radar detection equipment transmits high-frequency short-pulse electromagnetic waves to the target point road base and receives the radar reflected wave signal; Obtain the correlation between the free water content of the roadbed and the geological radar reflection wave signal, analyze the free water content of the roadbed based on the radar reflection wave signal, and calculate the roadbed radar detection coefficient based on the free water content of the roadbed; The radar reflected wave signal includes time-frequency characteristics, instantaneous amplitude, instantaneous frequency and instantaneous phase; Among them, the radar reflection wave signals of roadbeds with different free water contents are collected by geological radar and used as training sets to train the neural network to obtain a trained neural network; the radar reflection wave signal of the current roadbed is input into the neural network, and the current roadbed free water content is output through the neural network.

2. The detection method for predicting railway mud bubbling according to claim 1, characterized in that: In step 1, the roadbed filling coefficient is determined by actual roadbed filling, and the cyclic load coefficient is determined by the train speed design plan.

3. The detection method for predicting railway mud bubbling according to claim 1, characterized in that: The geological radar detection equipment includes a rail cart and a geological radar. The geological radar is installed on the rail cart. The rail cart is located below the geological radar and has a hole. The geological radar transmits and receives signals through the hole.

4. The detection method for predicting railway mud bubbling according to claim 1, characterized in that: The roadbed radar detection coefficient is calculated based on the free water content of the roadbed. The calculation formula is as follows: r 3 = β*f ； Where, r 3 is the radar detection coefficient, β is the conversion factor, f is the free water content of the roadbed.

5. The detection method for predicting railway mud bubbling according to claim 1, characterized in that: In step 2, the railway mud risk value is calculated based on the roadbed filling coefficient, cyclic load coefficient, and radar detection coefficient. The calculation formula is as follows: A = r 1 + r 2 + r 3 ； Where, A is the risk value of mud overflow, r 1 is the roadbed filling coefficient, r 2 is the cyclic load factor, r 3 is the radar detection coefficient.

6. A detection device for predicting mud bubbling on railways, characterized in that: The device includes: a parameter acquisition module, a calculation module and a judgment module; The parameter acquisition module is used to obtain the railway roadbed filling coefficient, cyclic load coefficient and radar detection coefficient; The calculation module is used to calculate the risk value of railway mud overflow based on the roadbed filling coefficient, the cyclic load coefficient and the radar detection coefficient; The judgment module is used to set a risk value threshold, compare the risk value of mud overflow with the risk value threshold, and predict the occurrence of mud overflow when the risk value of mud overflow is greater than or equal to the threshold; The radar detection coefficient is obtained by measuring with geological radar detection equipment, specifically including: The geological radar of the geological radar detection equipment transmits high-frequency short-pulse electromagnetic waves to the target point road base and receives the radar reflected wave signal; Obtain the correlation between the free water content of the roadbed and the geological radar reflection wave signal, analyze the free water content of the roadbed based on the radar reflection wave signal, and calculate the roadbed radar detection coefficient based on the free water content of the roadbed; The radar reflected wave signal includes time-frequency characteristics, instantaneous amplitude, instantaneous frequency and instantaneous phase; Among them, the radar reflection wave signals of roadbeds with different free water contents are collected by geological radar and used as training sets to train the neural network to obtain a trained neural network; the radar reflection wave signal of the current roadbed is input into the neural network, and the current roadbed free water content is output through the neural network.

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