Simulation device and monitoring device for slope displacement monitoring by using strain sensing nerves

Abstract

The invention discloses a simulation device and a monitoring device for slope displacement monitoring by using strain sensing nerves, and relates to the technical field of slope displacement monitoring, the simulation device comprises a model box, an air bag, an air pump, a detection module, a fiber bragg grating strain sensing module, a control system, a demodulation module and an analysis module, the model box is used for accommodating a soil body and forming a slope model in the soil body; the air bag is embedded in the slope model; the air extracting pump is communicated with the air bag and can extract air in the air bag; the detection module is used for detecting and recording the settlement process of the slope model; the fiber bragg grating strain sensing module is embedded into the slope model; and the control system predicts the side slope settlement process by adopting an artificial neural network model according to strain information detected by the fiber grating strain sensing module. The fiber grating strain sensing technology is designed and applied to simulate settlement monitoring, and a new thought and method are provided for settlement monitoring in actual engineering.

Description

Technical Field

[0001] The invention relates to the technical field of slope displacement monitoring, and in particular to a slope displacement monitoring simulation device and a monitoring device using strain sensor nerves. Background Art

[0002] Landslides pose a serious threat to human life and infrastructure. Real-time monitoring and early warning of slope instability have become a critical and urgent issue for disaster prevention and mitigation. Landslides are one of the most destructive geological disasters, causing huge casualties and property losses every year. Slope stability evaluation and real-time monitoring of slope instability are of great significance. Globally, especially in mountainous terrain. According to the different monitoring locations, landslide monitoring can be divided into surface monitoring and internal monitoring. Sliding between soil-bedrock layers is an important internal cause of large-scale landslide disasters. It is necessary to understand the stability state and sliding mechanism of the interface. Therefore, real-time monitoring of the interface and early warning of landslides are essential for disaster prevention, mitigation and relief. In recent years, with the development of geotechnical engineering, various landslide monitoring technologies and instruments have been explored, especially internal monitoring of slopes. Currently, microgravity methods are widely used to detect large cavities, such as karst cavities, but high budgets limit their use in large areas. Recent developments have utilized geophysical surveys, such as seismic analysis and electromagnetic methods, which are highly manual and low-resolution. Global navigation satellite systems can achieve large-scale ground deformation measurements, but they do not allow the acquisition of subsurface deformation. Although the above technologies have been successfully applied to investigate geological processes related to slope formation, there is still a lack of high-sensitivity technologies that can provide large-scale continuous field monitoring data records. Fiber Bragg grating sensors may solve this problem. Therefore, how to use fiber Bragg grating sensors to detect and predict slope instability is a difficult problem that needs to be solved urgently. Summary of the invention

[0003] The purpose of the present invention is to provide a slope displacement monitoring simulation device and a monitoring device using strain sensing nerves to solve the problems existing in the above-mentioned prior art, design and apply fiber grating strain sensing technology for settlement monitoring, and provide new ideas and methods for settlement monitoring in actual engineering.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] The present invention provides a slope displacement monitoring simulation device using strain sensing nerves, comprising:

[0006] A model box, used for containing soil and forming a slope model therein;

[0007] an air bag, embedded in the slope model;

[0008] An air pump connected to the air bag and capable of extracting the gas in the air bag;

[0009] A detection module, used for detecting and recording the settlement process of the slope model;

[0010] A fiber Bragg grating strain sensing module is embedded in the slope model;

[0011] A demodulation module and an analysis module, wherein the demodulation module is connected to the strain sensing module, the analysis module is connected to the demodulation module, and the analysis module obtains strain information during the sedimentation process based on the demodulation module; and

[0012] A control system, wherein the control system uses an artificial neural network model to predict the slope settlement process according to the strain information detected by the fiber grating strain sensing module.

[0013] Preferably, the detection module includes a camera, and the bottom plate of the model box is transparent. The camera captures the image of the slope model through the bottom plate and transmits it to the control system. The control system uses particle image velocimetry technology to obtain the settlement process of the slope model based on the image.

[0014] Preferably, the strain sensing module comprises a sensing cable formed by a plurality of fiber grating strain sensors connected in series, and the sensing cable is arranged in a coiled or reciprocating manner on a horizontal plane in the slope model.

[0015] Preferably, the sensing cable is provided with a plurality of anchor plates, and the width of the anchor plates is greater than that of the sensing cable.

[0016] Preferably, the strain sensing module comprises a plurality of sensing cables, and the plurality of sensing cables are respectively arranged on different horizontal planes.

[0017] Preferably, the distance from the side wall of the model box to the boundary of the air bag is greater than the width of the air bag, thereby eliminating the boundary effect.

[0018] Preferably, the control system can utilize the strain information data during the settlement process and the slope model settlement data to train an artificial neural network model.

[0019] Preferably, the strain information data and the settlement data are divided into a training set and a validation set; the training set is used to train the artificial neural network model, and the validation set is used to test the artificial neural network model.

[0020] Preferably, the sedimentation data predicted by the artificial neural network model is compared with the actual sedimentation data obtained by the particle image velocimeter, using the root mean square error as a comparison indicator.

[0021] The present invention also provides a device for monitoring slope displacement using strain sensing nerves, including a fiber grating strain sensing module and a control system. The fiber grating strain sensing module is buried in the slope to be monitored, and the control system is communicatively connected to the fiber grating strain sensing module. The control system inputs the strain information data collected by the fiber grating strain sensing module into a trained artificial neural network model to obtain slope settlement information.

[0022] Compared with the prior art, the present invention has achieved the following technical effects:

[0023] The present invention provides a simulation device for monitoring slope displacement using strain sensing nerves, which uses experiments to simulate the slope collapse process and collects strain information and real settlement information to verify the feasibility of using fiber Bragg grating strain sensing nerves to monitor slope displacement. Therefore, the present invention designs and applies fiber Bragg grating strain sensing technology to monitor settlement, which provides a new idea and method for settlement monitoring in actual engineering.

[0024] The present invention realizes real-time deformation monitoring through a fiber grating strain sensor and predicts slope settlement or displacement using an artificial neural network model, thereby improving accuracy and sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A schematic diagram of the structure of a device for simulating slope displacement monitoring using strain sensing nerves provided in an embodiment of the present invention;

[0027] Figure 2 Schematic diagram of the sensor cable layout trajectory;

[0028] Figure 3 It is a schematic diagram of artificial neural network;

[0029] In the figure: 1-model box; 2-slope model; 3-hose; 4-air pump; 5-camera; 6-fiber Bragg grating strain sensing module; 7-demodulation module; 8-air bag. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] The present invention provides a slope displacement monitoring simulation device using strain sensor nerves, such as Figure 1 to Figure 3 As shown, it includes: a model box 1, an air bag 8, an air pump 4, a detection module, a fiber Bragg grating strain sensing module 6, a control system, a demodulation module 7 and an analysis module. The model box 1 is used to contain soil and form a slope model 2 therein; the air bag 8 is embedded in the slope model 2; the air pump 4 is connected to the air bag 8 and can extract the gas in the air bag 8; the detection module is used to detect and record the settlement process of the slope model 2; the fiber Bragg grating strain sensing module 6 is embedded in the slope model 2; the demodulation module 7 is connected to the strain sensing module, and the analysis module is connected to the demodulation module 7, and the analysis module obtains the strain information in the settlement process based on the demodulation module 7; the control system uses an artificial neural network model to predict the slope settlement process according to the strain information detected by the fiber Bragg grating strain sensing module 6.

[0033] The present invention provides a simulation device for monitoring slope displacement using strain sensing nerves, which uses experiments to simulate the slope collapse process and collects strain information and real settlement information to verify the feasibility of using fiber Bragg grating strain sensing nerves to monitor slope displacement. Therefore, the present invention designs and applies fiber Bragg grating strain sensing technology to monitor settlement, which provides a new idea and method for settlement monitoring in actual engineering.

[0034] Specifically, the detection module includes a camera 5. The bottom plate of the model box 1 is transparent. The camera 5 captures the image of the slope model 2 through the bottom plate and transmits it to the control system. The control system uses particle image velocimetry technology to obtain the settlement process of the slope model 2 based on the image.

[0035] In this embodiment, the control system can use the strain information data during the settlement process and the settlement data of the slope model 2 to train the artificial neural network model. The strain information data and the settlement data are divided into a training set and a validation set; the training set is used to train the artificial neural network model, and the validation set is used to test the artificial neural network model. The settlement data predicted by the artificial neural network model is compared with the actual settlement data obtained by the particle image velocimeter, and the root mean square error is used as a comparison indicator.

[0036] In some embodiments, the strain sensing module includes a sensing cable formed by a plurality of fiber grating strain sensors connected in series, and the sensing cable is arranged in a winding or reciprocating manner on a horizontal plane in the slope model 2 .

[0037] In some embodiments, the strain sensing module includes a plurality of sensing cables, and the plurality of sensing cables are respectively disposed on different horizontal planes.

[0038] In some embodiments, a plurality of anchor plates are disposed on the sensing cable, and the width of the anchor plates is greater than that of the sensing cable.

[0039] This embodiment can reduce the disturbance to the soil. During the experiment, anchor plates of various sizes can be set to study the influence of anchor plates of different sizes on the sensing cable-soil coupling.

[0040] In some embodiments, the distance from the side wall of the mold box 1 to the boundary of the air bag 8 is greater than the width of the air bag 8, thereby eliminating the boundary effect.

[0041] The experimental steps are as follows:

[0042] (1) In order to simulate the collapse process, a series of tests were conducted using two soil densities (30% and 60%) and multiple embedding ratios. The embedding ratio is the ratio of the buried depth of the air bag 8 to the width of the air bag 8. There are four embedding ratios to choose from, namely 1, 2, 3 and 4.

[0043] (2) At the beginning of the simulation monitoring, an air bag 8 filled with air is placed in the center of the model box 1, and then the soil is filled. The fiber Bragg grating strain sensor is set on the soil layer at a set height and fixed with an anchor plate. If necessary, tape can be used to connect the inner wall of the model box 1 and the fiber Bragg grating strain sensor to strengthen the fixation. Next, the soil is compacted layer by layer to a predetermined density (1.610-3kg / m 3 or 1.710-3kg / m 3 ), each layer is about 32 mm thick. After each layer is compacted, the soil surface is leveled to keep it uniform. After compaction is completed, air is accurately extracted from the air bag 8 to simulate collapse, and the amount of deflation is equivalent to the sliding distance of the resulting slope displacement. During the experiment, the fiber grating strain sensor detects the strain and obtains the strain curve during the test. In order to replicate the effect of covering layer pressure in an infinitely long optical cable, each layer of optical fiber cable is fixed with tape on both sides of the model box 1. The collapse rate is set to 9 ml / s until V reaches 300 ml, and then increased to 21 ml / s. The experiment ends when V reaches 1000 ml.

[0044] (3) In order to capture the soil deformation, a 48-megapixel digital camera 5 was placed 10.8 m away from the model box, and the camera 5 parameters were adjusted to obtain the best clarity. A photo was automatically taken every 1.5 s for PIV analysis. At the same time, the strain measurement data was acquired in real time using a grating interrogator (demodulation module 7). The strain measurement accuracy of the grating interrogator is 1 mε, which can achieve extremely high strain measurement spatial resolution. During the test, the spatial resolution of the grating interrogator was set to 10 mm.

[0045] (4) The anchor plate connects the sensor cable to the surrounding soil, which can enhance the mechanical coupling between the soil and the sensor cable. The present invention carried out a collapse test of a micro-anchored optical fiber cable, and the anchor plate fixed on the sensor cable was made of an acrylic plate. In order to minimize the disturbance to the soil. Two anchor plate sizes were set in the experiment to study the effects of anchor plates of different sizes on the sensor cable-soil coupling. In addition, unanchored sensor cables were tested for comparison. The sensor cables were laid at three heights. In addition, each layer of the sensor cable was divided into two sections: an unanchored section and a micro-anchored section. The test was terminated when the volume of gas extracted from the air bag 8 (i.e., the collapsed volume) was 1400 ml.

[0046] The fiber Bragg grating strain sensing module 6 and the analysis module are based on the demodulation module 7 to obtain the strain distribution during the collapse process. The light source emits highly coherent continuous light with a linear scanning frequency, and then the light is divided into two paths by a coupler. One part of the linear light is used as the reference light, and the other part is used as the detection light, which is reflected by the random refractive index of the optical fiber. The reference light and the signal light interfere with each other in the coupler, and the two lights in a mixed state are received by the photodetector. Since the proportion of the scattered signal to the total signal fluctuates at a specific frequency at each position of the optical fiber, the sensing area and the actual position can be automatically located according to the wave frequency. According to the spatial resolution of the interrogator, the scattered signals are separated into multiple signal windows. The spectral shift of each signal window is calculated by cross-correlation operation, and the shift is linearly related to the strain or temperature change. Since the duration of each test is short and the temperature change is 1.5 degrees Celsius, the error caused by temperature fluctuations in all tests is very small compared with the strain curve caused by soil deformation. Therefore, in this study, there is no need for temperature compensation of strain measurement.

[0047] The simulation device for slope displacement monitoring using strain sensing nerves provided in the embodiment of the present invention is to evaluate the feasibility of fiber Bragg grating strain sensing nerves in slope displacement monitoring. In combination with artificial neural network (ANN) and particle image velocimetry (PIV) technology, a series of model tests were carried out to explore the relationship between strain measurement and slope displacement, and a conversion model between strain data and slope displacement can be established. The results show that as the collapse volume increases, the failure mechanism of the soil above the slope develops from a triangular failure plane to a vertical failure plane. At the same time, the fiber Bragg grating strain sensing nerves buried in the soil realize real-time deformation monitoring of the ground soil. In addition, the characteristics of the measured strain profile indicate the location of the slope and the related shear zone. The ANN model based on strain data can better predict surface settlement. In addition, micro-anchored optical fiber cables have been proven to improve the soil-fiber strain transfer efficiency and are used for large deformation monitoring of ground collapse.

[0048] The present invention also provides a device for monitoring slope displacement using strain sensing nerves, including a fiber grating strain sensing module 6 and a control system. The fiber grating strain sensing module 6 is buried in the slope to be monitored, and the control system is communicatively connected with the fiber grating strain sensing module 6. The control system inputs the strain information data collected by the fiber grating strain sensing module 6 into a trained artificial neural network model to obtain slope settlement information.

[0049] The present invention realizes real-time deformation monitoring through a fiber grating strain sensor and predicts slope settlement or displacement using an artificial neural network model, thereby improving accuracy and sensitivity.

[0050] The present invention can well predict the slope displacement during strain measurement by establishing an ANN model (artificial neural network model). Increasing the number of sensor cable layers may improve the prediction accuracy of the model. Therefore, once the sensing optical cable (sensing cable) is used to monitor the slope displacement, the influence of the number of sensing optical cable layers on the neural network analysis should be fully considered during the data processing process.

[0051] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A slope displacement monitoring simulation device using strain sensing nerves, characterized in that: include: A model box, used for containing soil and forming a slope model therein; an air bag, embedded in the slope model; An air pump, connected to the air bag and capable of extracting the gas in the air bag; A detection module, used for detecting and recording the settlement process of the slope model; A fiber Bragg grating strain sensing module is embedded in the slope model; A demodulation module and an analysis module, wherein the demodulation module is connected to the strain sensing module, the analysis module is connected to the demodulation module, and the analysis module obtains strain information during the sedimentation process based on the demodulation module; as well as A control system, wherein the control system uses an artificial neural network model to predict the slope settlement process according to the strain information detected by the fiber grating strain sensing module.

2. The device for simulating slope displacement monitoring using strain sensing nerves according to claim 1 is characterized in that: The detection module includes a camera. The bottom plate of the model box is transparent. The camera takes an image of the slope model through the bottom plate and transmits the image to the control system. The control system uses particle image velocimetry technology to obtain the settlement process of the slope model based on the image.

3. The device for monitoring slope displacement using strain sensing nerves according to claim 1 is characterized in that: The strain sensing module comprises a sensing cable formed by a plurality of fiber grating strain sensors connected in series, and the sensing cable is arranged in a coiled or reciprocating manner on a horizontal plane in the slope model.

4. The device for simulating slope displacement monitoring using strain sensing nerves according to claim 3 is characterized in that: The sensing cable is provided with a plurality of anchor plates, and the width of the anchor plates is greater than that of the sensing cable.

5. The device for simulating slope displacement monitoring using strain sensing nerves according to claim 3 is characterized in that: The strain sensing module includes a plurality of sensing cables, and the plurality of sensing cables are respectively arranged on different horizontal planes.

6. The device for monitoring slope displacement using strain sensing nerves according to claim 1, characterized in that: The distance from the side wall of the model box to the boundary of the air bag is greater than the width of the air bag, thereby eliminating the boundary effect.

7. The device for simulating slope displacement monitoring using strain sensing nerves according to claim 1 is characterized in that: The control system can use the strain information data during the settlement process and the slope model settlement data to train the artificial neural network model.

8. The device for monitoring slope displacement using strain sensing nerves according to claim 7 is characterized in that: The strain information data and the settlement data are divided into a training set and a verification set; the training set is used to train the artificial neural network model, and the verification set is used to test the artificial neural network model.

9. The device for simulating slope displacement monitoring using strain sensing nerves according to claim 8 is characterized in that: The sedimentation data predicted by the artificial neural network model are compared with the actual sedimentation data obtained by the particle image velocimeter, and the root mean square error is used as a comparison index.

10. A device for monitoring slope displacement using strain sensing nerves, characterized in that: It includes a fiber grating strain sensing module and a control system. The fiber grating strain sensing module is buried in the slope to be monitored. The control system is communicatively connected with the fiber grating strain sensing module. The control system inputs the strain information data collected by the fiber grating strain sensing module into a trained artificial neural network model to obtain slope settlement information.

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