Seepage monitoring method and system based on optical fiber sensor

Abstract

The invention discloses a seepage monitoring method and system based on an optical fiber sensor, and relates to the technical field of slope seepage monitoring, and the method comprises the steps: distributing distributed optical fiber sensors along an inclinometer pipe in a slope; laying an optical fiber heating system; a distributed temperature sensor DTS is used for collecting scattered light signals of the distributed optical fiber sensor; the scattered light signals collected by the DTS are demodulated; acquiring strain data in the slope; performing temperature compensation on the obtained displacement data; according to the obtained distributed temperature data and the displacement data after temperature compensation, the position and range of seepage in the slope are determined; calculating the seepage rate and the seepage flow of the corresponding position through the seepage model and the heat conduction equation; and judging the seepage degree of the side slope. According to the invention, aiming at the low precision of slope seepage monitoring in the prior art, the distributed temperature sensor DTS is utilized to collect the temperature distribution data of the optical fiber along the way, and temperature compensation and the RBF neural network model are combined, so that the precision of judging the slope seepage is improved.

Description

Technical Field

[0001] This invention relates to the field of slope seepage monitoring technology, and in particular to a seepage monitoring method and system based on fiber optic sensors. Background Technology

[0002] Traditional methods for monitoring slope seepage mainly include the piezometer method, resistivity method, and acoustic attenuation method. These methods suffer from drawbacks such as low monitoring accuracy, limited monitoring range, and insufficient real-time performance, making them unsuitable for meeting the increasingly demanding requirements of slope seepage monitoring. The piezometer method involves placing piezometers inside the slope and using pressure changes caused by seepage to determine the seepage situation; however, piezometers are susceptible to external interference, resulting in unstable measurement accuracy. The resistivity method infers the seepage state based on resistivity changes caused by seepage, but it is easily affected by soil heterogeneity and temperature variations. The acoustic attenuation method uses the attenuation of sound wave energy caused by seepage to determine seepage, but it has high requirements for the location and frequency of the sound source, making it less applicable in the field.

[0003] In recent years, fiber optic sensing technology has been widely used in slope monitoring due to its advantages such as distributed measurement, resistance to electromagnetic interference, and corrosion resistance. Fiber optic sensors can achieve long-distance, high-density monitoring of parameters such as strain and temperature, providing important monitoring data for slope stability analysis. However, how to fully utilize the advantages of fiber optic sensors to achieve accurate monitoring and quantitative analysis of slope seepage remains a pressing technical challenge.

[0004] In related technologies, such as Chinese patent document CN111764368A, a horizontal testing system and method based on OFDR fiber optic sensing is provided. The testing system includes a horizontal testing component, a heated sensing fiber optic cable, a clinometer data acquisition instrument, a photoelectric conversion module, a DTS demodulator, an OFDR fiber optic data acquisition instrument, a fiber optic data processing and analysis system, and a monitoring result display and early warning system. In this invention, during testing, a clinometer equipped with distributed fiber optic strain sensors, temperature-compensated fibers, and acceleration-sensitive elements is connected in series via an optical cable. The heated sensing fiber optic cable is laid on the clinometer tube, and the clinometer is lowered along the tube, thereby acquiring information on the horizontal displacement, strain, and seepage field changes at the monitoring points. After processing and analysis by the data processing system, the test data is displayed graphically as changes in soil horizontal displacement with depth and seepage flow. However, while temperature-compensated fibers are used to compensate for the temperature of the clinometer, there is a certain spatial difference between the temperature-compensated fibers and the clinometer; therefore, the monitoring accuracy of this scheme needs further improvement. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the problem to be solved by this invention is: how to solve the problems of low monitoring accuracy, limited monitoring range, and insufficient real-time performance of existing methods.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a seepage monitoring method based on fiber optic sensors, comprising: deploying distributed fiber optic sensors along inclinometer tubes inside the slope; deploying fiber optic heating systems on the distributed fiber optic sensors; using a distributed temperature sensor (DTS) to collect the scattered light signals from the distributed fiber optic sensors; demodulating the scattered light signals collected by the DTS to obtain distributed temperature data along the fiber optic path; simultaneously, the distributed temperature sensor (DTS) establishes a positioning relationship between temperature and fiber length based on the fiber length at each measuring point; and, based on the established positioning relationship, demodulating the... The distributed temperature data is correlated with the fiber optic length to determine the location of the wetting measurement points and calculate the fiber optic length at each water outlet line. Strain data from distributed fiber optic sensors deployed on the inclinometer tube is collected and temperature compensation is performed to obtain temperature-compensated displacement data. Based on the obtained distributed temperature data and temperature-compensated displacement data, the location and range of seepage inside the slope are determined. Based on the determined location and range of seepage inside the slope, the seepage rate and seepage volume at the corresponding locations are calculated using a seepage model and heat conduction equations. Based on the calculated seepage rate and seepage volume, the degree of seepage on the slope is determined.

[0008] As a preferred embodiment of the seepage monitoring method based on fiber optic sensors described in this invention, the distributed fiber optic sensors are deployed along the inclinometer tube inside the slope using a winding or serpentine deployment method; the relationship between temperature and fiber length established based on the fiber length at each measuring point is expressed as follows:

[0009]

[0010] Where L is the length of the optical fiber at the intersection of the fiber and the immersion line at length L, i.e., at each water surface line, k is the spatial resolution of DTS, l is the distance from the measuring point to point O, O is the intersection of the immersion line and the optical fiber, and T1, T2, and T3 are the temperatures at each measuring point.

[0011] As a preferred embodiment of the seepage monitoring method based on fiber optic sensors described in this invention, the temperature-compensated displacement data includes two pairs of fiber optic sensors arranged on the inclinometer tube, each pair of sensors being respectively set on two opposite sides of the inclinometer tube; one pair of fibers AA' and CC' is used for temperature compensation, and the Brillouin frequency shift change of fibers AA' and CC' is expressed as:

[0012] Δv B1 =C vε Δε1+C vt ΔT1

[0013] Δv B2 =C vε Δε2+C vt ΔT2

[0014] Where, Δv B1 Let Δv be the Brillouin frequency shift of optical fiber AA'. B2 C represents the Brillouin frequency shift of optical fiber CC'. vε C is the Brillouin strain coefficient. vt Let Δε be the Brillouin temperature coefficient, Δε represent strain, and ΔT be the temperature change. Considering the inclinometer tube as a cantilever beam, the strain measured by optical fibers on both sides of any cross-section of the cantilever beam is expressed as:

[0015]

[0016] Where, ε i1 and ε i2 These are the strain values ​​measured by optical fibers arranged on both sides of the cantilever beam. The normal strain caused by force F, ε is the negative strain caused by force F. iN ε is the axial strain caused by the axial force. iΔt The strain of the optical fiber due to temperature change is considered. A temperature compensation formula is constructed, using the optical fiber strain data at each measuring point of the inclinometer tube as input to obtain the temperature-compensated pure bending strain, which is then used as the temperature-compensated displacement data. The temperature compensation formula is expressed as:

[0017]

[0018] Where, ε i Let ε be the strain value at point i on the cantilever beam. iN ε is the axial strain caused by the axial force. iΔt The strain of the optical fiber due to temperature change is equal on both sides of any cross section i. The axial strain at each point on the cross section is also equal.

[0019] As a preferred embodiment of the seepage monitoring method based on fiber optic sensors described in this invention, the determination of the location and range of seepage within the slope includes: using the locations of each inclinometer as constraints and the maximum sum of displacement gradients as the objective function, determining the coordinates of the n intersection points between the potential sliding surface and the inclinometers through a search algorithm, which are then used as n discrete points on the sliding surface; based on the obtained n discrete points on the sliding surface, using a pre-trained RBF neural network model, obtaining a slope sliding surface distribution map; and determining the location and range of seepage within the slope based on the obtained temperature distribution data and the obtained slope sliding surface distribution map.

[0020] As a preferred embodiment of the seepage monitoring method based on fiber optic sensors described in this invention, the slope sliding surface distribution map includes: normalizing and preprocessing the coordinate data of n discrete points on the sliding surface; generating grid coordinate points within the slope range according to a preset interval; establishing an RBF neural network model, using the planar coordinates (x, y) of the discrete points on the sliding surface as input and the elevation z as output, training the network model to obtain the nonlinear mapping relationship between the sliding surface elevation and the planar coordinates; using the trained RBF neural network model to simulate the coordinates of the n discrete points on the sliding surface and predict the sliding surface elevation coordinates corresponding to each point; and generating a two-dimensional or three-dimensional distribution map of the sliding surface coordinates within the slope range based on the predicted sliding surface elevation coordinates.

[0021] As a preferred embodiment of the seepage monitoring method based on fiber optic sensors described in this invention, the location and range of seepage within the slope include: using a two-dimensional or three-dimensional distribution map of the sliding surface coordinates as a spatial constraint; under the spatial constraint, extracting temperature values ​​within a preset range of the sliding surface from the temperature distribution data as temperature distribution features within the preset range of the sliding surface, wherein the preset range is the range of upper and lower threshold distances of the sliding surface; obtaining the relationship between local groundwater temperature and rock mass temperature, setting a temperature threshold based on the obtained relationship, and using a temperature threshold segmentation method to segment the temperature distribution features within the preset range of the sliding surface to obtain potential seepage areas within the preset range of the sliding surface, wherein the temperature threshold is determined based on the temperature difference between groundwater temperature and rock mass temperature; performing spatial cluster analysis on the obtained potential seepage areas, and determining the location and range of seepage within the slope based on the clustering results.

[0022] As a preferred embodiment of the seepage monitoring method based on fiber optic sensors described in this invention, the prediction of the sliding surface elevation coordinates corresponding to each point includes determining the input vector X = (x1, x2, ..., x...) of the RBF neural network model. n ) T And the output vector Y = (y1, y2, ..., y m ) T The nonlinear mapping function using the Gaussian function as the hidden layer is expressed as:

[0023]

[0024] Among them, h j c is the output of the j-th neuron in the hidden layer. j Let E be the center vector of the j-th neuron in the hidden layer, and let E be an n×1 unit vector. j Let ||xc| be the Gaussian function spread constant of the j-th hidden layer neuron, ||xc| be the norm, and ||xc| be the constant. j E||=(Xc j E) T (Xcj E), where X is the input vector.

[0025] Another objective of this invention is to provide a system for a seepage monitoring method based on fiber optic sensors, which solves a seepage monitoring problem based on fiber optic sensors by constructing a seepage monitoring system.

[0026] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a seepage monitoring system based on fiber optic sensors, comprising a sensor module, a positioning relationship establishment module, an immersion measuring point determination module, a temperature compensation module, a slope internal seepage determination module, a seepage rate and seepage flow calculation module, and a slope seepage degree judgment module; the sensor module is used to deploy distributed fiber optic sensors along inclinometer tubes inside the slope, and to deploy fiber optic heating systems on the distributed fiber optic sensors; the positioning relationship establishment module is used to use a distributed temperature sensor (DTS) to collect the scattered light signal of the distributed fiber optic sensors, demodulate the scattered light signal collected by the DTS to obtain distributed temperature data along the fiber optic path, and simultaneously, the distributed temperature sensor (DTS) establishes a positioning relationship between temperature and fiber optic length based on the fiber optic length at each measuring point; the immersion... The wetting point determination module is used to determine the location of the wetting points by matching the demodulated distributed temperature data with the fiber optic length according to the established positioning relationship, and to calculate the fiber optic length at each water outlet line; the temperature compensation module is used to collect the strain data of the distributed fiber optic sensors deployed on the inclinometer tube and perform temperature compensation to obtain temperature-compensated displacement data; the slope internal seepage determination module is used to determine the location and range of internal seepage in the slope according to the obtained distributed temperature data and temperature-compensated displacement data; the seepage rate and seepage flow calculation module is used to calculate the seepage rate and seepage flow at the corresponding location according to the determined location and range of internal seepage in the slope, using the seepage model and heat conduction equation; the slope seepage degree judgment module is used to judge the degree of slope seepage according to the calculated seepage rate and seepage flow.

[0027] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the seepage monitoring method based on a fiber optic sensor as described above.

[0028] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a seepage monitoring method based on a fiber optic sensor as described above.

[0029] The beneficial effects of this invention are as follows: This invention provides a seepage monitoring method based on fiber optic sensors. By deploying distributed fiber optic sensors along the inclinometer tube inside the slope using a winding or serpentine deployment method, and incorporating a heating system with constant heat input on the optical fibers, it achieves large-scale, high-density monitoring of temperature and strain inside the slope, overcoming the limitations of traditional single-point sensors with limited monitoring range. Distributed temperature sensors (DTS) are used to collect scattered light signals from the optical fibers, obtaining detailed temperature distribution data along the fiber. Demodulation processing of the scattered light signals yields high spatial resolution distributed temperature monitoring results, providing reliable temperature field information for subsequent seepage analysis. The strain data collected by the distributed fiber optic sensors is converted into displacement data, which is used as virtual inclinometer data. Temperature compensation eliminates the influence of temperature changes and axial strain on strain measurement, improving the accuracy of displacement calculation and laying the foundation for slope slip surface identification. Based on the displacement data from the virtual inclinometer, the spatial gradient distribution curve of the displacement on the inclinometer tube is calculated. Combined with the top elevation of the virtual inclinometer and the position constraints of the inclinometer tube, and using the maximization of the sum of displacement gradients as the objective function, a search algorithm intelligently determines the coordinates of the intersection point between the potential sliding surface and the inclinometer tube, achieving rapid and accurate identification of the slope sliding surface. Using an RBF neural network model, with the discrete point set of the potential sliding surface as training samples, a nonlinear mapping relationship between the sliding surface elevation and plane coordinates is constructed to fit the circular arc sliding surface of the slope, obtaining a high-precision and highly applicable spatial distribution model of the sliding surface, providing boundary conditions for subsequent seepage analysis. Innovatively, the monitoring information of the temperature and displacement fields inside the slope is comprehensively utilized. Using temperature distribution data and the spatial distribution of the slope sliding surface as constraints, spatial clustering and other methods are used to accurately determine the location and range of seepage inside the slope, achieving a fine characterization of the seepage area. Based on the determination of the seepage location and range, a seepage model and heat conduction equation were introduced to quantitatively calculate the seepage rate and seepage volume in the seepage area, realizing the quantitative characterization and degree evaluation of slope seepage, and providing an important basis for slope stability analysis and early warning. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 The flowchart illustrates a seepage monitoring method based on an optical fiber sensor, as provided in the first embodiment of the present invention.

[0032] Figure 2 The diagram shows a seepage monitoring system based on an optical fiber sensor, which is provided as a second embodiment of the present invention.

[0033] Figure 3 This is a schematic diagram of a heating power monitoring circuit for a seepage monitoring method based on an optical fiber sensor, provided in the third embodiment of the present invention.

[0034] Figure 4 This is a schematic diagram of a new fiber optic layout design for a seepage monitoring method based on a fiber optic sensor, provided in the third embodiment of the present invention.

[0035] Figure 5 This is a schematic diagram of a new fiber optic cable layout for a seepage monitoring method based on a fiber optic sensor, provided in the third embodiment of the present invention.

[0036] Figure 6 This is a schematic diagram of a fiber optic monitoring method for slope seepage lines based on a new fiber optic layout, provided as a third embodiment of the present invention.

[0037] Figure 7 This is a schematic diagram of the fiber temperature stabilization time distribution curve along the fiber, which is provided as a seepage monitoring method based on a fiber optic sensor in the third embodiment of the present invention.

[0038] Figure 8 This is a schematic diagram illustrating the calculation of the fiber length L at the location of the seepage line under ideal conditions, which is provided for the seepage monitoring method based on fiber optic sensors in the third embodiment of the present invention.

[0039] Figure 9 This is a schematic diagram of the slope seepage line location H, provided for a seepage monitoring method based on an optical fiber sensor, according to the third embodiment of the present invention.

[0040] Figure 10 The curve showing the relationship between heating power per meter and stable temperature rise of optical fiber in a seepage monitoring method based on optical fiber sensor provided in the third embodiment of the present invention.

[0041] Figure 11 The third embodiment of the present invention provides a seepage monitoring method based on an optical fiber sensor, showing the temperature rise process curve of the optical fiber under different heating powers.

[0042] Figure 12 This is a schematic diagram of the wetting line positioning relationship determination model for a seepage monitoring method based on an optical fiber sensor, provided in the third embodiment of the present invention.

[0043] Figure 13 This is a schematic diagram of a water depth measurement model for a seepage monitoring method based on an optical fiber sensor, provided as a third embodiment of the present invention.

[0044] Figure 14The third embodiment of the present invention provides a seepage monitoring method based on an optical fiber sensor, with a heating power of 9W / m and a water depth of 10cm. The curve of the optical fiber temperature stabilization moment along the flow path is shown.

[0045] Figure 15 The heating power is 9W / m and the water depth is 20cm. The curve of the fiber optic temperature distribution along the flow path at the stable moment is provided in the third embodiment of the present invention.

[0046] Figure 16 The heating power is 9W / m and the water depth is 30cm. The curve of the fiber optic temperature distribution along the flow path at the stable moment is provided in the third embodiment of the present invention.

[0047] Figure 17 The heating power is 9W / m and the water depth is 40cm. The curve of the fiber optic temperature distribution along the flow path at the stable moment is provided in the third embodiment of the present invention.

[0048] Figure 18 The heating power is 9W / m and the water depth is 50cm. The curve of the fiber optic temperature distribution along the flow path at the stable moment is provided in the third embodiment of the present invention.

[0049] Figure 19 The heating power is 9W / m and the water depth is 60cm. The curve of the fiber optic temperature distribution along the flow path at the stable moment is provided in the third embodiment of the present invention.

[0050] Figure 20 The third embodiment of the present invention provides a curve showing the relationship between the relative length ΔL of the optical fiber at the water surface and the water depth H in a seepage monitoring method based on an optical fiber sensor.

[0051] Figure 21 The third embodiment of the present invention provides three sets of verification curves showing the temperature distribution along the fiber optic cable at stable water depth, which is a seepage monitoring method based on an optical fiber sensor.

[0052] Figure 22 The third embodiment of the present invention provides a plan view of the 25cm water level immersion line and the arrangement of three optical fiber layers in a seepage monitoring method based on an optical fiber sensor.

[0053] Figure 23 The third embodiment of the present invention provides a flow monitoring method based on fiber optic sensors, which is a flow monitoring method for seepage at a water level of 25cm and a heating power of 15W / m. The flow rate is shown in the graph of the temperature drop process at each measuring point of the bottom fiber optic layer.

[0054] Figure 24The third embodiment of the present invention provides a flow monitoring method based on fiber optic sensors, which is a flow monitoring method for seepage at a water level of 25cm and a heating power of 13W / m. The flow rate is shown in the graph of the temperature drop process at each measuring point of the bottom fiber optic layer.

[0055] Figure 25 The third embodiment of the present invention provides a flow monitoring method based on an optical fiber sensor, which is a flow monitoring method for seepage at a water level of 25cm and a heating power of 11W / m. The flow rate is shown in the graph.

[0056] Figure 26 This is a temperature difference diagram at typical moments at various measuring points in the bottom fiber optic layer under different heating powers at a water level of 25cm, provided as a third embodiment of the present invention, for a seepage monitoring method based on a fiber optic sensor.

[0057] Figure 27 The third embodiment of the present invention provides a flow monitoring method based on fiber optic sensors for seepage control, showing the temperature drop process at various measuring points in the intermediate fiber optic layer from a water level of 25cm to a heating power of 15W / m.

[0058] Figure 28 The third embodiment of the present invention provides a flow monitoring method based on fiber optic sensors for seepage control, showing the temperature drop process at various measuring points in the intermediate fiber optic layer from a water level of 25cm to a heating power of 13W / m.

[0059] Figure 29 The third embodiment of the present invention provides a flow monitoring method based on an optical fiber sensor, which is a flow monitoring method for seepage from a water level of 25cm to a heating power of 11W / m. The flow monitoring method is shown in the graph.

[0060] Figure 30 The third embodiment of the present invention provides a seepage monitoring method based on an optical fiber sensor, showing the temperature drop at typical times at various measuring points in the middle optical fiber layer under different heating powers at a water level of 25cm.

[0061] Figure 31 A comparison diagram of the 15cm water level optical fiber and the piezometer positioning immersion line in a seepage monitoring method based on an optical fiber sensor provided in the third embodiment of the present invention.

[0062] Figure 32 A comparison diagram of the 25cm water level optical fiber and the piezometer positioning immersion line in a seepage monitoring method based on an optical fiber sensor provided in the third embodiment of the present invention.

[0063] Figure 33 A comparison diagram of the 35cm water level optical fiber and the piezometer positioning immersion line in a seepage monitoring method based on an optical fiber sensor provided in the third embodiment of the present invention.

[0064] Figure 34This is a plan view of the water level infiltration lines and the arrangement of the three optical fiber layers in a seepage monitoring method based on an optical fiber sensor, provided in the third embodiment of the present invention. Detailed Implementation

[0065] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0066] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0067] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a seepage monitoring method based on fiber optic sensors, including: deploying distributed fiber optic sensors along a clinometer tube inside the slope, and deploying a fiber optic heating system on the distributed fiber optic sensors; using a distributed temperature sensor (DTS) to collect the scattered light signal from the distributed fiber optic sensors, demodulating the scattered light signal collected by the DTS to obtain distributed temperature data along the fiber optic path, and simultaneously establishing a positioning relationship between temperature and fiber length based on the fiber length at each measuring point; according to the established positioning relationship, matching the demodulated distributed temperature data with the fiber length to determine the location of the wetting measuring point, and calculating the fiber length at each water surface line; collecting strain data from the distributed fiber optic sensors deployed on the clinometer tube, and performing temperature compensation to obtain temperature-compensated displacement data; determining the location and range of seepage inside the slope based on the obtained distributed temperature data and temperature-compensated displacement data; calculating the seepage rate and seepage volume at the corresponding location based on the determined location and range of seepage inside the slope using a seepage model and heat conduction equation; and judging the degree of seepage on the slope based on the calculated seepage rate and seepage volume.

[0068] By using distributed temperature sensors (DTS) to collect temperature distribution data along the optical fiber, and combining temperature compensation and RBF neural network models, the location, range, and corresponding seepage rate and flow rate of seepage inside the slope can be determined, thereby accurately judging the degree of slope seepage.

[0069] Step 1: The distributed optical fiber sensor is laid out along the inclinometer tube inside the slope, using a winding or serpentine layout. An optical fiber heating system is then installed on the distributed optical fiber sensor to provide a constant heat input, causing the distributed optical fiber sensor to generate temperature changes in the seepage area.

[0070] Among them, inclinometer tubes are commonly used slope displacement monitoring devices. By drilling holes inside the slope and installing a series of inclinometer tube sections, an inclinometer measures the tilt angle at different depths, thereby calculating the slope displacement change. In this application, inclinometer tubes are used to deploy distributed fiber optic sensors, providing a convenient channel for monitoring. Spiral winding is a fiber optic deployment method where the fiber is spirally wound around the outer wall of the inclinometer tube, increasing the fiber length and coverage, and improving the sensitivity and spatial resolution of strain and temperature monitoring. Serpentine routing is another fiber optic deployment method, where the fiber is laid in a serpentine path on the outer wall of the inclinometer tube, also increasing fiber length and coverage. Compared to spiral winding, serpentine routing is easier to implement, but the monitoring density is relatively low. A fiber optic heating system is a heating device deployed on the distributed fiber optic sensors, forming a stable temperature gradient by providing a constant heat input to the fiber. In seepage monitoring, the location and extent of seepage can be determined by analyzing the temperature anomalies caused by seepage. Scattered light signals refer to the scattering phenomenon produced by the interaction of light with the medium during propagation. In distributed fiber optic sensing, signals such as Rayleigh scattering and Raman scattering are commonly used for temperature and strain measurements. By analyzing the characteristics of the scattered light signals, information on the temperature and strain distribution along the fiber can be obtained. Seepage models are mathematical models describing the seepage flow of groundwater within slopes; commonly used models include Darcy's law and Richards' equation. Seepage models allow for the analysis of seepage rate, seepage direction, and other characteristics, providing a basis for slope stability evaluation. In this application, the seepage model is combined with the heat conduction equation to quantitatively calculate the seepage rate and seepage volume.

[0071] Furthermore, the distributed optical fiber sensor employs a Brillouin scattering optical fiber sensor; the inclinometer tube is a flexible inclinometer tube. Specifically, in this application, the distributed optical fiber sensor uses a Brillouin scattering optical fiber sensor, utilizing the Brillouin scattering effect in the optical fiber to achieve distributed strain and temperature measurement. Brillouin scattering is an inelastic scattering phenomenon in optical fibers. When the optical fiber is subjected to strain or temperature changes, the frequency of the scattered light shifts. By measuring this frequency shift, information on the strain and temperature distribution along the optical fiber can be obtained. The choice of inclinometer tube is also an important factor when deploying distributed optical fiber sensors inside a slope. This application uses a flexible inclinometer tube as the carrier for optical fiber deployment. Compared to traditional rigid inclinometer tubes, flexible inclinometer tubes have better adaptability and flexibility. Flexible inclinometer tubes are typically made of highly flexible materials, such as PVC and PE polymers. These materials have good bending properties and can adapt to slope deformation, reducing the mutual constraint between the inclinometer tube and the surrounding soil, and improving the reliability of displacement monitoring. At the same time, the lightweight nature of the flexible inclinometer tube facilitates on-site installation and deployment, reducing construction difficulty.

[0072] Step 2: The distributed temperature sensor (DTS) is used to collect the scattered light signal from the distributed optical fiber sensor. The scattered light signal collected by the DTS is demodulated to obtain distributed temperature data along the optical fiber. At the same time, the distributed temperature sensor (DTS) establishes a positioning relationship between temperature and optical fiber length based on the optical fiber length at each measuring point.

[0073] Furthermore, based on the fiber length at each measuring point, a positioning relationship between temperature and fiber length is established, as follows:

[0074]

[0075] Wherein, the optical fiber intersects the wetting line at length L, point O is the intersection of the wetting line and the optical fiber, k is the spatial resolution of DTS, l is the distance of the measuring point from point O, and T1, T2 and T3 are the temperatures of each measuring point.

[0076] Step 3: Based on the established positioning relationship, the demodulated distributed temperature data is correlated with the fiber length to determine the location of the immersion measurement point and calculate the fiber length at each water outlet line.

[0077] Step four: Collect strain data from the distributed fiber optic sensors deployed on the inclinometer tube and perform temperature compensation to obtain the compensated displacement data; wherein, temperature compensation is used to eliminate the influence of temperature changes and axial strain on strain measurement.

[0078] Furthermore, strain data from distributed fiber optic sensors are collected and temperature compensation is performed to obtain compensated displacement data. This includes: two pairs of fiber optic sensors are arranged on the inclinometer tube, with each pair positioned on opposite sides of the tube; one pair of fibers AA' and CC' is used for temperature compensation, and the Brillouin frequency shift change of fibers AA' and CC' is:

[0079] Δv B1 =C vε Δε1+C vt ΔT1

[0080] Δv B2 =C vε Δε2+C vt ΔT2

[0081] Where, Δv B1 Let Δv be the Brillouin frequency shift of optical fiber AA'. B2 C represents the Brillouin frequency shift of optical fiber CC'. vε C is the Brillouin strain coefficient. vt Let Δε1 be the Brillouin temperature coefficient, Δε1 be the strain, and ΔT1 be the temperature change. Using the inclinometer tube as a cantilever beam, the strain measured by optical fibers on both sides of any cross-section of the cantilever beam is expressed as:

[0082]

[0083] in, The normal strain caused by force F, ε is the negative strain caused by force F. iN ε is the axial strain caused by the axial force. iΔt To compensate for the strain of the optical fiber caused by temperature changes, a temperature compensation formula is constructed. The optical fiber strain data at each measuring point of the inclinometer tube is used as input to obtain the pure bending strain after temperature compensation, which is then used as the displacement data after temperature compensation.

[0084] Furthermore, the temperature compensation formula is as follows:

[0085]

[0086] Where, ε i Let ε be the strain value at point i on the cantilever beam. i1 and ε i2 These are the strain values ​​measured by optical fibers arranged on both sides of the cantilever beam. and ε represents the strain generated on both sides of section i by the applied load. iΔt The strain of the optical fiber due to temperature change is equal on both sides of any cross section i. The axial strain at all points on the cross section is also equal, where: Where, ε iN This refers to the axial strain caused by the axial force.

[0087] In practical slope monitoring environments, temperature variations and axial forces often cause discrepancies between the strain values ​​measured by fiber optic sensors and the actual displacement. Therefore, a temperature compensation mechanism is needed to ensure the reliability of the monitoring data. By arranging fiber optic sensors on both sides of the cantilever beam, the strain values ​​ε on both sides of the cross-section can be measured. i1 and ε i2 Since the strain caused by temperature change is equal at all points on the cross section, it can be determined through ε. i1 and ε i2 The average value is used to represent the temperature strain ε. i Simultaneously, the strain ε caused by the axial force iN It can also be done through ε i1 and ε i2 The difference is calculated. The temperature strain ε is then used. i and axial strain ε iNBy separating the measured strain values, the true strain value caused by the external load can be obtained. The technical significance of this temperature compensation method lies in its effective elimination of the interference of ambient temperature changes and axial force on strain measurement, thereby improving the accuracy and reliability of strain data.

[0088] Step 5: Based on the distributed temperature data obtained in Step 3 and the displacement data after temperature compensation in Step 4, determine the location and extent of seepage inside the slope.

[0089] Further, in step five, based on the distributed temperature data obtained in step three and the displacement data after temperature compensation in step four, the location and extent of seepage inside the slope are determined. This includes: using the locations of each inclinometer as constraints and the maximum sum of displacement gradients as the objective function, a search algorithm is used to determine the coordinates of the potential sliding surface and the n intersection points of the inclinometers, which are then used as n discrete points on the sliding surface; based on the n discrete points on the sliding surface, a pre-trained RBF neural network model is used to obtain a slope sliding surface distribution map; and based on the obtained temperature distribution data and the obtained slope sliding surface distribution map, the location and extent of seepage inside the slope are determined.

[0090] The objective function is expressed as:

[0091] Z(x) = max∑Δf i

[0092] Where Z(x) is the objective function, Δf i Let be the gradient distribution function of the displacement of the i-th virtual inclinometer along the inclinometer tube, with the following constraints:

[0093] 0 < y i <h i

[0094]

[0095] 0≤x i ≤x t

[0096] f i =f(y i i = 1, 2, ..., n

[0097] Among them, f i (k) represents the displacement measurement value of the Kth monitoring point on the i-th inclinometer tube, h i Let y be the top elevation of the i-th inclinometer tube. i Let x be the elevation of the intersection point of the potential sliding surface and the i-th inclinometer tube. i x is the distance of the i-th inclinometer tube from the origin. t is the x-coordinate value at the toe of the slope, and n is the number of inclinometer tubes installed inside the slope.

[0098] Furthermore, the equation of the slope sliding surface is obtained, including: normalizing the coordinate data of n discrete points on the sliding surface; generating grid coordinate points within the slope range according to a preset interval; establishing an RBF neural network model, using the plane coordinates (x, y) of the discrete points on the sliding surface as input and the elevation z as output, training the network model to obtain the nonlinear mapping relationship between the sliding surface elevation and the plane coordinates; using the trained RBF neural network model to simulate the coordinates of the n discrete points on the sliding surface and predict the sliding surface elevation coordinates corresponding to each point; and generating a two-dimensional or three-dimensional distribution map of the sliding surface coordinates within the slope range based on the predicted sliding surface elevation coordinates.

[0099] Furthermore, based on the obtained temperature distribution data and the obtained slope sliding surface distribution map, the location and range of seepage within the slope are determined, including: using the obtained two-dimensional or three-dimensional distribution map of the sliding surface coordinates as a spatial constraint; under the spatial constraint, extracting temperature values ​​within a preset range of the sliding surface from the temperature distribution data as temperature distribution features within the preset range of the sliding surface; wherein, the preset range is the range of upper and lower threshold distances of the sliding surface; obtaining the relationship between local groundwater temperature and rock mass temperature, and setting a temperature threshold based on the obtained relationship; using a temperature threshold segmentation method to segment the temperature distribution features within the preset range of the sliding surface to obtain potential seepage areas within the preset range of the sliding surface; wherein, the temperature threshold is determined based on the temperature difference between groundwater temperature and rock mass temperature; performing spatial cluster analysis on the obtained potential seepage areas, and determining the location and range of seepage within the slope based on the clustering results.

[0100] The temperature threshold segmentation method is used to segment the temperature distribution characteristics within a preset range of the sliding surface to obtain the potential seepage area within the preset range of the sliding surface. This includes: calculating the mean and standard deviation of the temperature distribution characteristics within the preset range of the sliding surface; based on the temperature threshold, identifying points with temperature values ​​less than the mean minus the standard deviation as low-temperature anomalies, and identifying points with temperature values ​​greater than the mean plus the standard deviation as high-temperature anomalies; and using the spatial distribution area of ​​the low-temperature anomalies and high-temperature anomalies as the potential seepage area.

[0101] Spatial clustering employs the density-based DBSCAN algorithm to group temperature anomalies into clusters and remove discrete noise points, ultimately yielding the clustered seepage areas. Spatial clustering analysis is then performed on the obtained potential seepage areas to determine the location and extent of seepage within the slope. This includes: performing DBSCAN clustering on all anomalies within the potential seepage area, setting a cluster radius Eps and a minimum number of points MinPts; defining the neighborhood of each anomaly with Eps as the core, and marking it as a core point if the number of anomalies in the neighborhood exceeds MinPts; grouping the core point and its neighborhood anomalies into a single cluster; iterating until all anomalies are classified or marked as noise points; and extracting the contour boundaries of each cluster based on the clustering results, defining the area within the contour boundaries as the location and extent of seepage within the slope.

[0102] Furthermore, the trained RBF neural network model is used to simulate the coordinates of n discrete points on the sliding surface, predicting the corresponding elevation coordinates of the sliding surface for each point. This includes determining the input vector X = (x1, x2, ..., x...) of the RBF neural network model. n ) T And the output vector Y = (y1, y2, ..., y m ) T The nonlinear mapping function using the Gaussian function as the hidden layer is expressed as:

[0103]

[0104] Among them, h j c is the output of the j-th neuron in the hidden layer. j The center vector of the j-th neuron in the hidden layer, E is an n×1 unit vector, e j Let ||xc| be the Gaussian function spread constant of the j-th hidden layer neuron, ||xc| be the norm, and ||xc| be the constant. j E||=(Xc j E) T (Xc j E).

[0105] The process includes simulating the coordinates of n discrete points on the sliding surface using a trained RBF neural network model to predict the elevation coordinates of each point on the sliding surface. It also includes calculating the output of the RBF neural network model according to the following formula:

[0106]

[0107] Where X = (x1, x2, ..., x n ) T The input vector is Y = (y1, y2, ..., y3). m ) TThe output vector is W = (w1, w2, ..., w m ) T This is the weight matrix from the hidden layer to the output layer.

[0108] Among them, the RBF neural network possesses powerful nonlinear fitting capabilities, effectively handling the complex nonlinear relationship between the sliding surface elevation and spatial coordinates. The introduction of the Gaussian function, through local responses in the input space, enables adaptive capture and approximation of nonlinear features. This nonlinear fitting capability allows the RBF neural network to better describe the spatial morphology of the sliding surface, improving prediction accuracy.

[0109] Step 6: Based on the determined location and extent of seepage within the slope, calculate the seepage rate and flow rate at the corresponding location using the seepage model and heat conduction equation.

[0110] Step 7: Determine the degree of seepage on the slope based on the calculated seepage rate and seepage volume.

[0111] Example 2, refer to Figure 2 The second embodiment of the present invention differs from the previous embodiment in that it provides a seepage monitoring system based on a fiber optic sensor, including: a sensor module, a positioning relationship establishment module, an infiltration measurement point determination module, a temperature compensation module, a slope internal seepage determination module, a seepage rate and seepage flow calculation module, and a slope seepage degree judgment module.

[0112] The sensor module is used to deploy distributed optical fiber sensors along the inclinometer tube inside the slope, and to deploy an optical fiber heating system on the distributed optical fiber sensors.

[0113] The positioning relationship establishment module is used to collect the scattered light signal of the distributed optical fiber sensor using the distributed temperature sensor (DTS), demodulate the scattered light signal collected by the DTS to obtain distributed temperature data along the optical fiber. At the same time, the distributed temperature sensor (DTS) establishes a positioning relationship between temperature and optical fiber length based on the optical fiber length at each measuring point.

[0114] The immersion measurement point determination module is used to determine the location of the immersion measurement point by matching the demodulated distributed temperature data with the fiber length according to the established positioning relationship, and to calculate the fiber length at each water surface line.

[0115] The temperature compensation module is used to collect strain data from the distributed fiber optic sensors deployed on the inclinometer tube and perform temperature compensation to obtain temperature-compensated displacement data.

[0116] The slope internal seepage determination module is used to determine the location and extent of seepage inside the slope based on the obtained distributed temperature data and temperature-compensated displacement data.

[0117] The seepage rate and seepage flow calculation module is used to calculate the seepage rate and seepage flow at the corresponding location based on the determined location and range of seepage within the slope, using the seepage model and heat conduction equation.

[0118] The slope seepage degree assessment module is used to determine the degree of slope seepage based on the calculated seepage rate and seepage volume.

[0119] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0121] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0122] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0123] Example 3, referring to Figures 3-34 This is the third embodiment of the present invention, which differs from the previous two embodiments in that it is used to verify and explain the technical effects adopted in the present invention, so as to verify the real effect of the method.

[0124] The main instruments and equipment used include a DTS temperature measurement system, a data acquisition and processing system, and a heating system. The DTS temperature measurement system mainly consists of two parts: a distributed fiber optic temperature measurement host and a multimode temperature-sensing fiber. The distributed fiber optic temperature measurement host internally encapsulates a laser, optical devices, and a data storage module. This application uses the Sentinel DTS-LR distributed fiber optic temperature measurement host manufactured by Sensornet in the UK. It can achieve distributed temperature measurement along the fiber length, with a maximum measurement distance of 10km and a maximum temperature measurement accuracy of 0.01℃. The spatial resolution is 1.02m, meaning that the fiber optic temperature measurement value is the average temperature value along a 1.02m long fiber. Therefore, the temperature at each measurement point is the average temperature of the fiber within a 0.51m range before and after it.

[0125] The Sentinel DTS is equipped with a pulsed laser device capable of continuously emitting 10-nanosecond light pulses, which connect to 50 / 125 multimode fiber via an E2000 connector. The E2000 connector is a push-button type; simply push it into the socket to connect to the DTS system host, and push the button to disconnect the connector. The fiber at the end of the E2000 connector can be fused with an external temperature-sensing fiber using a fiber optic fusion splicer to form a complete DTS temperature measurement system. There are two commonly used types of 50 / 125 multimode fiber: two-core armored fiber with an internal stainless steel flexible tube, and a four-core armored fiber (model ZTT-GYXTW-4A1a) with built-in steel wire reinforcement. This paper uses the former for slope seepage fiber monitoring experiments. The DTS system obtains the corresponding location and temperature information for each point by collecting and analyzing the time and intensity information of the Raman back-reflected light generated when the incident light pulse propagates within the fiber, thus obtaining the distributed temperature curve along the entire fiber. The experiment used a single-channel, single-end measurement method, and the MM mode was used when splicing the optical fibers.

[0126] The DTS data acquisition and processing system mainly consists of two parts: data acquisition and data output. The data acquisition section allows setting the time interval and spatial resolution for each monitoring session. The data output section includes data storage and plotting functions. Data storage uses DDF and TDF formats to store data files. The DDF file contains basic information such as the location points and temperature at a fixed time point on the fiber optic cable; the TDF file contains the temperature values ​​of the fixed observation points over time. The plotting function can generate three types of curves: distributed temperature curves, raw data curves, and point-based data curves. The distributed temperature curve uses temperature as the ordinate and fiber length as the abscissa; the raw data curve uses the Stokes and anti-Stokes beams of the Sentinel DTS as the ordinate and fiber length as the abscissa; and the point-based data curve represents the temperature change over time at a fixed observation point on the fiber optic cable.

[0127] The experiment uses a heating method to monitor seepage, therefore a stable voltage needs to be applied to the steel wire or metal tubing within the optical fiber for heating. The heating system includes an adjustable AC power supply, a multi-function multimeter, and a metal resistor inside the optical fiber to generate heat; a voltage regulator can be used to adjust the voltage. It is particularly important to note that the buried length of the optical fiber in the experiment is generally short. Because the resistance of the metal tubing in the cable is relatively low (0.75 W / m), the total resistance in the heating circuit is also low. Figure 3 As shown, if a relatively large heating power is required in the experiment, the heating system will generate a large current. Therefore, when selecting a voltage regulator, a voltage regulator with a larger rated current should be chosen. This application selects a TDGC2-5 type single-phase voltage regulator. Figure 4 , Figure 7 and Figure 8 As shown, its maximum range is 20A×250V, that is, the rated current is 20A, and the output voltage can be adjusted to any value within 0V~250V to meet the requirements of different heating powers in the test.

[0128] There are two methods to monitor heating power: one is to use a multimeter to display the circuit voltage or current. The circuit diagrams for both methods are shown below. Figure 1 As shown. Suppose a section of armored optical fiber of length L is heated by an electric current. Before heating, the total resistance R of the fiber is measured using the voltmeter-ammeter method. One method for monitoring the heating power (Circuit 1) is to measure the current I in real time using a multimeter connected in series in the heating circuit, and then adjust the current in the heating circuit using a voltage regulator to change the heating power P = I. 2 R. Heating power monitoring method two (circuit two) involves using a multimeter connected in parallel with the heating circuit to measure the voltage U in real time, and then using a voltage regulator to adjust the voltage of the heating circuit, thereby changing the heating power P = U. 2Both monitoring circuits can theoretically monitor heating power. Monitoring circuit one offers higher accuracy because the multimeter in this circuit displays the current in the heating circuit (i.e., the current passing through the metal armor or steel wire inside the optical fiber). If the fiber resistance is known, this calculated power represents the actual heating power of the fiber. However, this method connects the multimeter in series in the circuit, and prolonged exposure to a large current can easily damage the multimeter, resulting in significant reading fluctuations. Therefore, accurate monitoring of heating power is difficult in practice. Monitoring circuit two uses P=U... 2 The heating power is calculated using the / R method. This power actually includes the heating from the external energized conductor and the metal armor inside the optical fiber. However, since the resistance and length of the energized copper conductor are much smaller than those of the conductor inside the optical fiber, the heating from the energized conductor can be ignored. Furthermore, this method provides a more stable voltage reading on the multimeter. Considering all factors, this experiment uses the voltage monitoring method, i.e., circuit two, to monitor the heating power. Because a large excitation current is generated when the voltage regulator is switched on, which may cause the air circuit breaker in the test chamber to trip, to avoid sudden power outages during DTS monitoring that could interrupt the test or damage the DTS monitor, the voltage regulator should be switched on before the DTS monitor is turned on. If the voltage regulator is successfully energized without causing a power outage, the DTS monitor can then be turned on for the test. Other necessary instruments for the test include an electronic multimeter and a PT100 calibrated temperature sensor.

[0129] With the rapid development of distributed fiber optic temperature measurement technology, fiber optic temperature measurement systems have been widely applied in many fields. However, due to limitations such as sampling interval and spatial resolution (1.0m), the data collected by straight fiber optic layouts largely cannot meet the measurement requirements of seepage lines in slopes for hydraulic engineering. Therefore, this section designs a new fiber optic layout that improves positioning accuracy through serpentine winding, such as... Figure 4 As shown, the manufacturing method is as follows: Fabrication of the fiber optic fixing bracket. Assuming the width of the fiber optic laying layer slope is l meters and the length of the monitored embankment section is d, then the fixing bracket is l meters long and d meters wide. Fabrication of the new fiber optic layout. To prevent the internal stainless steel flexible tubing from breaking during bending when the armored fiber is wound, the two long sides of the fixing bracket are first marked alternately with l1 and l2, as shown... Figure 4 As shown in Figure a, the values ​​of l1 and l2 must ensure that the bending radius of the optical fiber is greater than 12D (D is the diameter of the optical cable); then, take a certain length of optical fiber, and... Figure 4 The optical fibers are wound in the order marked 1, 2, ..., 6 in section b and then fixed to the support, ultimately forming... Figure 4 c and Figure 5 A complete new fiber optic cable layout.

[0130] Figure 6This diagram illustrates a new fiber optic monitoring method for slope seepage lines. Four fiber optic monitoring layers are laid according to this new pattern. The seepage line and the boundary of the capillary water rise zone within the slope intersect with the fiber optic layers. Therefore, by using the fiber optic heating method and monitoring temperature changes along the fiber optic line, the development process of seepage within the slope can be perceived. Furthermore, the fiber length at the intersection with the fiber optic layer can be determined based on the temperature difference between the fibers on both sides of the seepage line. Then, using the measurement point positioning formula, this distance is converted into a horizontal distance from the upstream embankment, thereby determining the location of the seepage line at that layer.

[0131] In order to derive the positioning relationship of the immersion line by utilizing the fiber length position of the point where there is a significant temperature difference between the optical fibers on both sides of the immersion line, we will only consider the heat exchange between the optical fiber and the seepage water, and ignore the influence of factors such as capillary water. Figure 7 The figure shows the temperature distribution curve along the fiber when it stabilizes. The stable temperature of the fiber above the immersion line is T1, the stable temperature of the fiber below the immersion line is T2, and the stable temperature of measuring point B in the middle of the inclined transition section is T3. According to the principle of fiber temperature measurement, it should satisfy T2 < T3 < T1. Obviously, measuring point B is closer to the immersion line.

[0132] Suppose that the optical fiber intersects the impregnation line at length L, point O is the intersection of the impregnation line and the optical fiber, and point B is the optical fiber measuring point closest to the impregnation line (i.e., Figure 7 (e.g., measurement point B in the diagram) Figure 8 As shown. Let the spatial resolution of the DTS system be k, the position point on the optical fiber at a distance k / 2 from point B and below the immersion line be E, the length of OE be x, and point D be the position point on the other side at a distance k / 2 from point B (e.g., ...). Figure 8 (As shown), the average temperature of the fiber optic segment DE is the temperature measured at point B. If the temperature at each point on the fiber optic segment DO is T1, the temperature on segment OE is T2, and the temperature measured at point B is T3, then the following relationship exists:

[0133]

[0134] Let the fiber length at point O be L, and the fiber length at point B be l. From the diagram, the relationship between L, l, and x can be derived as follows:

[0135]

[0136] L is:

[0137]

[0138] like Figure 9To determine the location H of the immersion line, the fiber length L at n water depths H and the water surface can be measured using a new fiber optic layout to simulate the immersion line measurement. This will yield n sets of (H, L) observations. By analyzing the relationship between H and L, the formula H for locating the immersion line using the new fiber optic layout can be derived.

[0139] (L).

[0140] Before using the fiber optic heating method to measure water depth and determine the relationship between the new fiber optic layout and the immersion line, it is necessary to determine the heating power of the fiber optic cable. While excessive heating power is beneficial for signal amplification and easier observation, it increases costs; conversely, insufficient heating power leads to inadequate signal amplification and potential errors. Therefore, selecting a heating power that meets the observation requirements without increasing costs is crucial during the experiment.

[0141] The optical fiber used in the water depth measurement experiment was 17m long, the total length of the fiber in the fiber optic cabling section (fiber on the support) was 10.8m, and the length of the heating fiber was 13.8m. The total resistance of the heating fiber was measured to be 10.3Ω using the voltmeter-ammeter method. To obtain a suitable heating power for the fiber in air and amplify the temperature signal to an appropriate level, the heating power was selected from 3W / m to 19W / m, gradually increasing in increments of 2W / m. The output voltage was calculated at each heating power, and the voltage regulator was set to the corresponding voltage for heating. Table 1 shows the output voltage and temperature rise of the fiber at the corresponding heating power in air.

[0142] Table 1 Output voltage and temperature rise of optical fiber under different heating powers in air

[0143] Heating power P 3 5 7 9 11 13 15 17 19 Output voltage U 20.66 26.68 31.57 35.79 39.57 43.02 46.21 49.19 52.01 Initial temperature T1 11.34 11.17 10.45 11.05 9.45 11.04 10.21 9.32 10.84 Stable temperature T2 22.34 29.03 34.81 42.06 46.11 53.26 57.76 61.60 69.99 Temperature rise ΔT 11.00 17.86 24.35 31.01 36.66 42.22 47.55 52.28 59.15

[0144] As shown in Table 1, the temperature rise of the optical fiber varies under different heating powers in air. The temperature rise of the optical fiber increases with increasing heating power. To further investigate the relationship between heating power and optical fiber temperature rise, a curve is plotted with heating power P as the x-axis and temperature rise ΔT as the y-axis, as shown in Table 1. Figure 10 As shown. From Figure 10 It can be seen that the heating power per meter is approximately linearly related to the stable temperature rise of the optical fiber, and the fit between the two is good. Therefore, the relationship between the heating power per meter of the optical fiber and the stable temperature rise in air is: ΔT = 3.1977●P. This indicates that the stable temperature rise of the optical fiber is positively correlated with the heating power per meter.

[0145] To better select the heating power, it is necessary to further analyze the fiber heating process under different powers. Figure 11The heating process curve of the optical fiber is shown. It can be seen from the heating process curve that the higher the heating power, the greater the stable temperature rise of the optical fiber, the longer the time to reach stability, and the more obvious the heating effect. In order to control the heating power within a reasonable range, this paper selects three power levels—5W / m, 9W / m, and 13W / m—as the heating power of the optical fiber for experiments to analyze and compare the impact of the three heating powers on water depth measurement.

[0146] Figure 12 This is a schematic diagram of the model for determining the positioning relationship of the immersion line. In the experiment, the fixed support dimensions of the fiber optic layout were l = 2.0 meters and d = 0.35 meters; the fiber winding dimensions were l1 = 0.15 meters and l2 = 0.05 meters. To determine the positioning relationship of the immersion line for the new fiber optic layout, the fiber length at the corresponding water surface line was obtained by changing the water depth, given a fixed heating power P per meter. Heating powers of 5 W / m, 9 W / m, and 13 W / m were selected for three experiments. Six water depths were measured in each experiment: 10 cm, 20 cm, 30 cm, 40 cm, 50 cm, and 60 cm.

[0147] The specific experimental steps are as follows: The bracket with the optical fiber wound around it is vertically fixed in the center of the water tank. The optical fiber is connected to the DTS (Digital Transmission System), the power is turned on, and the DTS is activated. The initial temperature is monitored for 5 minutes. A heating power of 5W / m is selected. Referring to Table 1, the corresponding heating voltage is 26.68V. The voltage regulator is quickly switched to 26.68V to begin heating the optical fiber. After the optical fiber reaches a stable temperature, the current DTS monitoring time and measurement value are recorded. Simultaneously, water is rapidly added to the tank to a depth of 10cm. By observing the temperature drop curve at the underwater optical fiber measuring point in the DTS, once the temperature drop curve stabilizes, the current DTS monitoring time and measurement value are recorded. Simultaneously, water is rapidly added to the tank to a depth of 20cm. This process is repeated continuously to complete the monitoring of the remaining water depths under the heating power condition of 5W / m. The heating power per meter is varied, and the above process is repeated to complete the water depth measurement experiments under the remaining two heating power conditions. In this model test, although six water depth measurement tests were conducted at three heating powers of 5W / m, 9W / m, and 13W / m, at depths of 10cm, 20cm, 30cm, 40cm, 50cm, and 60cm, it was found in the data processing that the patterns shown in the water depth measurement results were basically the same for each heating power. Therefore, the water depth measurement test at a heating power of 9W / m was used as an example to analyze the results of the determination of the wetting line positioning relationship.

[0148] In this experiment, the spatial resolution was set to 1.02m. The DTS temperature measurement system automatically generated 11 measurement points in the fiber optic layout monitoring section. The fiber length at each measurement point is shown in Table 2. The first temperature monitoring point in the fiber optic layout section was defined as the data acquisition start point for the fiber optic layout section. In this experiment, the fiber length at the data acquisition start point of the new fiber optic layout was 6.09m, and the fiber optic layout section was... Figure 13The optical fiber is shown by thick lines. During the analysis of the results, only the measurement points of this part of the optical fiber are used for drawing and analysis.

[0149] Table 2. Statistical table of fiber length at temperature points in the fiber optic layout section during this test.

[0150]

[0151] See the diagram of the test results. Figures 14-19 These are the temperature distribution curves along the fiber optic cable at stable temperatures in six water depths of 10cm, 20cm, 30cm, 40cm, 50cm, and 60cm, respectively, under a heating power of 9W / m. Based on the method for determining the measurement point near the wetting line under ideal conditions, [the following is a continuation of the previous sentence]. Figures 14-19 Table 2 shows the fiber optic length *l* at measuring point B near the water surface in the six sets of water depth measurement experiments (second column of Table 3). In this experiment, the spatial resolution of the DTS system was 1.02 m, so the fiber optic length *L* at each water surface can be calculated (last column of Table 3). In Table 3, T1 represents the temperature of any measuring point in the air after point C, to avoid the influence of water temperature on its temperature value; T2 represents the temperature of any measuring point in the water before point A, to avoid the influence of air temperature on its temperature value. Since fiber optic temperature measurement has a certain degree of fluctuation, to eliminate its error, the average value of five consecutive monitoring data collected by the DTS system after the temperatures at the corresponding measuring points T1, T2, and T3 have stabilized is taken as their calculated temperature.

[0152] Table 39W / m Operating Condition: Calculation Table for Fiber Optic Length at Corresponding Waterline Location

[0153]

[0154]

[0155] Similarly, the values ​​of L under heating power of 5W / m and 13W / m can be calculated, as shown in Table 4. (Table...) This is the average value of L under three power levels. The actual value L′ refers to the length of the optical fiber at the corresponding waterline, measured with a tape measure. ΔL is the length of the optical fiber in the underwater fiber optic section. l0 is the length of the connecting fiber between the fiber optic cable section and the DTS host. In this experiment, l0 is 6.20m.

[0156] Table 4 Comparison of Calculated and Actual Fiber Lengths at the Waterline

[0157]

[0158]

[0159] The table shows that the calculated values ​​of the fiber length L at the corresponding water surface line are basically the same under the three heating powers, indicating that the experimental results are independent of the heating power. (Average values ​​of L under the three powers) The errors between the actual values ​​and the actual values ​​are all within 2%, therefore determining the fiber length at the location of the immersion line is feasible. To further investigate the relationship between water depth H and ΔL, a curve is plotted with ΔL as the x-axis and H as the y-axis (see...). Figure 20 The fitting equation is: H = 0.1927●ΔL - 0.0058; then the relationship between H and L is: H = 0.1927●(L - l0) - 0.0058. To verify the accuracy of the positioning relationship, three water depths of 13cm, 37cm and 63cm were randomly selected as verification depths under a heating power of 9W / m. Figure 21 The image shows the fiber temperature distribution at the moment when the fiber temperature reaches a stable state at three different water depths. The fiber lengths near the measuring point B at the water surface line for the three water depths are obtained (second column of Table 5). The fiber lengths L at the water surface line for the three water depths are calculated (last column of Table 5), and H is determined. The relevant calculation results are shown in Tables 5 and 6.

[0160] Table 59W / m Operating Condition Corresponding Verification Waterline Fiber Length Calculation Table

[0161]

[0162]

[0163] Table 6 Comparison and Analysis of Calculated and Actual Water Depths

[0164] Actual water depth (m) L(m) ΔL(m) Calculate the water depth H (m) error(%) 0.13 6.899 0.699 0.129 0.903 0.37 8.153 1.953 0.370 0.000 0.63 9.414 3.214 0.614 2.613

[0165] Table 6 shows that the error between the calculated water depth and the actual water depth is within 3%, indicating that the new fiber optic layout has high positioning accuracy. Using the new fiber optic layout from the previous section, a fiber optic monitoring test model for the slope seepage line was designed, consisting of a DTS system, a slope seepage test model, a heating system, and a data processing system. First, using H = 0.1927·ΔL - 0.0058, the horizontal distance from the temperature measuring point of each fiber optic layout section to the inlet of the water tank test section was calculated. The calculation results are shown in Table 7. In subsequent analyses, the horizontal distance values ​​in the table are used to represent the corresponding fiber optic measuring points.

[0166] Table 7 Horizontal distances of each fiber optic layer measuring point from the upstream side of the test section of the water tank.

[0167]

[0168]

[0169] Nine tests were conducted under this operating condition, with heating powers of 11W / m, 13W / m, and 15W / m corresponding to a water level of 15cm; 11W / m, 13W / m, and 15W / m corresponding to a water level of 25cm; and 11W / m, 13W / m, and 15W / m corresponding to a water level of 35cm. The results will now be analyzed using three tests under the 25cm water level condition as examples. Figure 22 Diagram showing the 25cm water level immersion line and the arrangement of three layers of optical fibers; Figures 23-26 These are temperature drop curves at various measuring points in the bottom fiber optic layer under the following conditions: water level of 25cm, heating power of 15W / m, 13W / m, and 11W / m. Figure 26 This is a typical temperature difference distribution along the fiber optic layer at various measuring points at the bottom at corresponding power. Figures 27-29 These are temperature drop curves at various measuring points in the intermediate fiber optic layer under the following conditions: water level of 25cm, heating power of 15W / m, 13W / m, and 11W / m. Figure 30 This is a distribution diagram of the temperature difference along the fiber path at typical moments at various measuring points in the intermediate fiber layer under corresponding power. Among them, Figures 23-26 and Figures 27-29 In the diagram, solid red lines represent measuring points below the wetting line, blue interlaced lines represent measuring points close to the wetting line, and black dotted lines represent measuring points above the wetting line.

[0170] As shown in the figure, within the same time frame, the closer the fiber optic measuring point is to the upstream side of the model test section, the greater the temperature drop; conversely, the further away, the smaller the temperature drop. From the initial cooling moment, the temperature drop process line of the bottom fiber layer is gradually divided into two parts by the temperature drop process line at the 1.26m measuring point: the part above 1.26m consists of measuring points with temperature drops less than their respective values, namely measuring points at 1.45m, 1.65m, 1.84m, and 2.00m, whose temperature drops are relatively similar; the part below 1.26m consists of measuring points with temperature drops greater than their respective values, namely measuring points at 0.08m, 0.28m, 0.47m, 0.67m, 0.87m, and 1.06m, whose temperature drops show significant differences. The lower the heating power, the less distinct the boundary in the fiber layer temperature drop process diagram. The temperature drops of the bottom and middle fiber layers begin to show significant inflection points at the 1.26m and 0.45m measuring points, respectively, and thereafter, the temperature drops at all measuring points are basically the same. It can be concluded that in the bottom fiber layer, the measuring points at 0.08m, 0.28m, 0.47m, 0.67m, 0.87m, and 1.06m should be below the wetting line, the 1.26m measuring point should be close to the wetting line, and the 1.45m, 1.65m, 1.84m, and 2.00m measuring points should be above the wetting line. Similarly, in the middle fiber layer, the 0.45m measuring point should be close to the wetting line. This is because below the wetting line, the heat transfer of the optical fiber can be described in two ways: heat conduction between the optical fiber and the saturated sand, and heat convection from the water flow to the optical fiber. Above the wetting line, the heat transfer of the optical fiber is only one way: heat conduction between the optical fiber and the unsaturated sand. Therefore, within the same cooling time, the temperature drop of the optical fiber below the wetting line is greater than that above the wetting line, and the temperature difference between them also differs due to the difference in seepage velocity; while the temperature drop of the optical fiber above the wetting line is basically the same and relatively small.

[0171] In summary, the method for determining the location of measuring points near the phreatic line under stable seepage conditions can be summarized as follows: the measuring points where all temperature drop processes of the fiber optic layer are divided into two parts, or the measuring point before the temperature difference value begins to remain basically constant in the fiber optic temperature difference distribution curve. The new fiber optic layout combined with the relationship can accurately determine the position of the water surface line. However, compared to the water surface line, the medium near the phreatic line on the slope is more complex; the interface is no longer simply air and water. To locate the phreatic line, the horizontal distance corresponding to the fiber optic measuring point closest to the phreatic line is now taken as the horizontal distance of the phreatic line of that layer from the upstream embankment. Table 8 lists the positions of the phreatic lines of each layer located by the fiber optic cable and the piezometer observations when the water level is 15cm, 25cm, and 35cm.

[0172] From Table 8 and Figures 31-33It can be seen that the fiber optic positioning of the phreatic line is basically close to that observed by the piezometer, but there is a certain difference. This may be due to the allowable error caused by human measurement readings and the error in the fiber optic positioning calculation. The data in the table shows that the error between the observed value and the fiber optic positioning value is within 10%, and the error decreases with longer positioning distances. This indicates that the method of determining the proximity of the measuring point to the phreatic line under stable seepage conditions and using its corresponding horizontal distance as the horizontal distance between the phreatic line of that layer and the upstream embankment is feasible.

[0173] Table 8 Comparison of Immersion Line Fiber Location Values ​​and Observed Values

[0174]

[0175] As can be seen from the above, the greater the fiber heating power, the better the fiber monitoring effect. Therefore, the results of the unsteady seepage test are analyzed using the 15W / m heating power test as an example.

[0176] Figure 34The diagrams depict the phreatic lines at 15cm, 25cm, and 35cm water levels, drawn using piezometric tube observations. The upstream and downstream sides of the test section and the arrangement of the three fiber optic layers are marked on the diagrams. The diagrams show that after the fiber optic cables reach a relatively stable temperature, when seepage occurs in the sand, the temperature of the fiber optic measuring points within the phreatic line and the area affected by capillary water decreases. However, the temperature difference at measuring points unaffected by capillary water remains near zero, indicating a relatively stable temperature. Furthermore, as the seepage progresses, the temperature at the fiber optic measuring points affected by seepage and capillary water gradually decreases over time. This indicates that the seepage can cross the phreatic line from the saturated zone into the unsaturated zone and continue its journey within the unsaturated zone. When the water level rises to 15cm, the temperature difference of the bottom fiber optic layer changes significantly around 0.47m, and begins to approach 0℃ around 1.06m. This indicates that the intersection of the wetting line at 15cm water level and the bottom fiber optic layer is around 0.47m, while the capillary water rise zone is located around 1.06m at this moment. Similarly, it can be deduced that the wetting line at 25cm water level intersects the bottom fiber optic layer around 1.26m and the middle fiber optic layer around 0.45m; the wetting line at 35cm water level intersects the bottom fiber optic layer around 1.65m, the middle fiber optic layer around 0.85m, and the upper fiber optic layer around 0.22m. In summary, under unsteady seepage conditions, the measuring points near the wetting line are those where the temperature difference changes by more than 1.5℃ along the temperature difference distribution curve, while the capillary water rise zone is located at measuring points where the temperature difference is close to 0℃. Therefore, the new fiber optic layout can locate both the wetting line and the capillary water rise zone. When the water level rises, the downstream advancement of the wetting line is mainly reflected in the temperature drop change of the same layer of optical fiber, while the upward rise of the wetting line is mainly reflected in the temperature drop change of different optical fiber layers. For example, for the bottom optical fiber layer, when the water level is 15cm, the wetting line is near the 0.47m measuring point; when the water level rises to 25cm, the wetting line advances to the 1.26m measuring point; and when the water level rises to 35cm, the wetting line travels to the 1.65m measuring point. For different layers of optical fibers, at a water level of 15cm, the wetting line only intersects with the bottom fiber layer, while the middle and upper fiber layers are only affected by capillary water. When the water level rises to 25cm, the wetting line intersects with both the bottom and middle fiber layers, while the upper fiber layer is only affected by capillary water. When the water level reaches 35cm, the wetting line intersects with all three fiber layers. During the gradual rise of the water level, at the same time interval, measuring points previously below the wetting line remain below it, with minimal temperature drop; while measuring points previously affected by capillary water become below the wetting line, exhibiting significant temperature drop. Under the same water level conditions, the bottom fiber layer experiences the largest temperature drop, followed by the middle fiber layer, with the upper fiber layer experiencing the smallest. Therefore, under the same conditions, the farther the optical fiber layer is from the embankment foundation, the smaller its temperature drop.

[0177] To analyze the rationality of the new fiber optic monitoring results, this study investigates a finite element method for slope seepage calculation that considers the influence of seepage in the unsaturated zone, based on an analysis of slope seepage characteristics. The differences between the calculated results and the experimental results from fiber optic monitoring are also analyzed. When seepage occurs in a dam, the portion below the phreatic line is saturated soil, while the portion above the phreatic line is unsaturated soil, resulting in the simultaneous existence of saturated and unsaturated seepage. Traditional dam seepage analysis techniques primarily use the free water surface as the boundary for seepage calculations in the saturated zone, neglecting seepage in the unsaturated zone. In actual engineering, due to factors such as soil suction and hydraulic gradients, continuous water flow exists between the saturated and unsaturated zones, and in many cases, this seepage cannot be ignored. This makes traditional seepage analysis methods unable to truly reflect the actual situation. In recent years, with the development of theories on saturated and unsaturated seepage in porous media, numerical simulation methods are mainly used in studying saturated-unsaturated seepage problems in dams. The analytical principles of saturated-unsaturated seepage are briefly introduced below.

[0178] In porous media, there exists a continuity equation:

[0179]

[0180] Where ρ is the density of water; S is the source-sink term; v i For Darcy velocity, S w Saturation (saturation region S) w =1, unsaturated region 0 < S w <1), where n is the porosity. According to Darcy's Law:

[0181]

[0182] in, Let k be the saturated permeability tensor. r (θ) represents relative permeability, which is the ratio between the permeability tensor element of unsaturated soil and that of saturated soil, 0 ≤ k. r ≤1, k in saturation state r =1, is the permeability coefficient.

[0183] Substituting Darcy's law, we get:

[0184]

[0185] Since the moisture content θ = nS w Therefore, we have:

[0186]

[0187] If we disregard the compressibility of water (i.e. Then we have:

[0188]

[0189] That is, it is transformed into:

[0190]

[0191] make:

[0192] λ=S w

[0193]

[0194] Then we have:

[0195]

[0196] Among them, C(h) c ) represents water holding capacity, h = h c +z, h c For pressure head, S s S represents the elastic water storage ratio when the soil is saturated. s It is a constant, S when the soil is unsaturated. s =0, and S also exists in the saturated zone when the soil skeleton and water compressibility are not considered. s =0. If the change in soil porosity is ignored (i.e., S... s =0), which transforms into the differential equation of motion for saturated and unsaturated flow velocities expressed in terms of total head:

[0197]

[0198] Express it as pressure head h c The equations for unknown functions yield the differential equations for saturated and unsaturated seepage flow:

[0199]

[0200] Initial conditions:

[0201] h(x i ,0)=h(x i ,t0)i=1,2,3

[0202] Boundary conditions:

[0203]

[0204] and

[0205] Where, q n The normal flux is n, where the positive direction is the outward normal direction. iLet Γ1 be the cosine of the outward normal direction, Γ2 be the initial time, Γ3 be the known head boundary, Γ4 be the known flow boundary, and Γ5 be the saturated overflow surface boundary.

[0206] Two-dimensional unsaturated steady-state seepage can be directly simplified to:

[0207]

[0208] Where h is the total head, and h is the pressure head. c The sum of the positional head y, k x and k y Let be the permeability coefficients of the unsaturated soil in the x and y directions, respectively, varying with matrix suction. For solving a steady-state seepage field problem, the boundary conditions are changed to: Type I boundary conditions, i.e., known hydraulic head boundary Γ1:

[0209]

[0210] The second type of boundary condition, namely the known flow boundary condition Γ2

[0211]

[0212] Here, the positive direction of n is the outward normal direction of the boundary Γ2.

[0213] Based on the finite element method, the equations for two-dimensional steady-state seepage can be derived using the weighted residual method:

[0214]

[0215] Where {L} represents the element coordinate moments (i.e., L1, L2, L3), and L1, L2, L3 are the surface coordinates of each point within the element, which are related to the rectangular coordinates of the nodes: L1 = {(x2y3-x3y2)+(y2-y3)x+(x3-x2)y} / 2A, L2 = {(x3y1-x1y3)+(y3-y1)x+(x1-x3)y} / 2A, L3 = {(x1y2-x2y1)+(y1-y2)x+(x2-x1)y} / 2A, where x i y i Let x and y be the rectangular coordinates of the element nodes. Let A be the rectangular coordinates of a point inside the element. Let A be the area of ​​the element.

[0216] This is the permeability coefficient matrix.

[0217] {hn} is the head matrix at the node, i.e., {h1 h2 h3}. T , Let S be the external velocity perpendicular to the element boundary; S is the perimeter of the element. Rearranging, we obtain the simplified form of the flow governing equations:

[0218]

[0219] Where [B] is the matrix of the derivatives of the surface coordinates, that is:

[0220]

[0221] in, If the external velocity is perpendicular to the unit boundary, then... This refers to the flow rate along the normal direction of the boundary. When the flow velocity through the boundary is zero, meaning the boundary condition is an impermeable boundary, You can then obtain: ∫ A [B] T [k][B]dA{h n} = 0.

[0222] The above analysis shows that a velocity or head must be set at the boundary nodes to solve the seepage field. If the wetting line is considered as an impermeable upper boundary, then the nodal flow rates at each node on the wetting line are ignored, that is, the nodal flow rates from the saturated zone to the unsaturated zone are ignored, and thus the water flow from the saturated zone to the unsaturated zone is ignored. Therefore, only by using the finite element method that considers the seepage in the unsaturated zone can the actual engineering situation be truly reflected.

[0223] Finite element analysis of seepage in a slope model was performed using the finite element software ABAQUS.

[0224] In ABAQUS / Standard, based on the principles of unsaturated soil mechanics, the entire cross-section is considered as the analysis region and solved using a fixed mesh. The wetting line is taken as the point where the pore water pressure is zero, which offers greater convenience and better accuracy in the solution. Based on the slope physical model, a two-dimensional finite element model of the slope is established, 2.0m long and 0.45m high. The model's computational elements are quadrilaterals, and the slope model is divided into 1440 elements with a total of 4517 nodes. According to the experimental process, the upstream and downstream water level difference is constant, and the water level boundary conditions are set to 0.15m, 0.25m, and 0.35m. The permeability coefficient is taken as 1.0×10⁻⁵ m / s. Among the three calculated water levels, the 35cm water level has the most intersections between the wetting line and the fiber optic layer, making it more representative for comparison with fiber optic monitoring results. Therefore, the calculation results for the 35cm water level are used as an example for analysis. Analysis of the calculation results shows that ABAQUS takes the phreatic line as the point where the pore water pressure is zero when solving unsaturated seepage problems. To compare with the piezometric observations, the finite element method was used to calculate the phreatic line height at the corresponding location, as shown in Table 9. The errors of both methods are within 10%, indicating that the finite element solution results for unsaturated seepage on slopes basically meet the error requirements of actual engineering. This method for solving slope seepage is reasonable and feasible.

[0225] Table 9 Comparison of Calculation and Observation of Immersion Line Height at a Specific Horizontal Distance from the Upstream Side under Three Water Levels

[0226]

[0227]

[0228] The coordinates of nodes where the pore water pressure is zero were extracted using the appropriate program, which are the coordinates of the wetting line, as shown in Table 10. It can be seen that the wetting line calculated by the finite element method is higher than the measured wetting line, while the wetting line measured by the optical fiber is basically close to the measured wetting line.

[0229] Table 1. Coordinates of the location points of the 1035cm water level infiltration line.

[0230]

[0231] Water flows across the wetting line from the saturated zone into the unsaturated zone, where negative pore pressure (capillary suction) is generated (matrix suction). Therefore, seepage exists in both the saturated and unsaturated zones. According to Chapter 2, when the water level reaches 35 cm, the optical fibers above the wetting line experience a cooling effect, and this temperature decreases continuously as seepage progresses. This indicates that under the influence of soil capillary suction, water continuously crosses the wetting line into the unsaturated zone, leading to a sustained cooling of the optical fibers. Therefore, the finite element calculation results for unsaturated seepage are essentially consistent with the actual situation.

[0232] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An optical fiber sensor based seepage monitoring method characterized by: The application relates to a method for determining the position and range of seepage in a slope. The method comprises the following steps: arranging a distributed optical fiber sensor along a inclinometer tube inside a slope, arranging an optical fiber heating system on the distributed optical fiber sensor; Collecting scattered light signals of the distributed optical fiber sensor by using a distributed temperature sensor DTS, demodulating the scattered light signals collected by the DTS, and obtaining distributed temperature data of the optical fiber along the path, meanwhile, the distributed temperature sensor DTS establishes a positioning relationship between temperature and optical fiber length according to the optical fiber length at each measuring point; According to the established positioning relationship, the distributed temperature data obtained by demodulation is corresponded to the optical fiber length, the position of the wetted measuring point is determined, and the optical fiber length at each water outflow line is calculated; Collecting strain data of the distributed optical fiber sensor arranged on the inclinometer tube, and performing temperature compensation to obtain displacement data after temperature compensation; According to the obtained distributed temperature data and the displacement data after temperature compensation, the position and range of seepage in the slope are determined; According to the determined position and range of seepage in the slope, the seepage rate and seepage flow at the corresponding position are calculated through a seepage model and a heat conduction equation; According to the calculated seepage rate and seepage flow, the degree of seepage in the slope is judged.

2. A fibre optic sensor based seepage monitoring method as claimed in claim 1, wherein: The distributed optical fiber sensor is arranged along the inclinometer tube inside the slope in a winding or serpentine manner; The positioning relationship between temperature and optical fiber length established according to the optical fiber length at each measuring point is expressed as, Wherein, L is the optical fiber length at the intersection of the length L and the wetted line, that is, the optical fiber length at each water outflow line, k is the spatial resolution of the DTS, l is the length of the measuring point from point O, O is the intersection of the wetted line and the optical fiber, T1, T2 and T3 are the temperatures of the measuring points.

3. A fibre optic sensor based seepage monitoring method as claimed in claim 2, wherein: The displacement data after temperature compensation includes arranging two pairs of optical fiber sensors on the inclinometer tube, and each pair of optical fiber sensors is arranged on the two opposite sides of the inclinometer tube; Temperature compensation is performed on one pair of optical fibers AA' and CC', and the change amount of the Brillouin frequency shift of the optical fibers AA' and CC' is expressed as, Δv B1 = C vε Δε1+C vt ΔT1 Δv B2 = C vε Δε2+C vt ΔT2 where Δv B1 is the change in Brillouin frequency shift of the optical fiber AA', Δv B2 is the change in Brillouin frequency shift of the optical fiber CC', C vε is the Brillouin strain coefficient, C vt is the Brillouin temperature coefficient, Δε represents the strain, and ΔT is the temperature change; The inclinometer tube is regarded as a cantilever beam, and the strain of the optical fiber on both sides of any cross section of the cantilever beam is expressed as, where ε i1 and ε i2 are the strain values measured by the optical fibers arranged on both sides of the cantilever beam, is the positive strain caused by the force F, is the negative strain caused by the force F, ε iN is the axial strain due to the axial force, ε iΔt is the strain of the optical fiber due to the temperature change; A temperature compensation formula is constructed, the optical fiber strain data of each measuring point of the inclinometer tube are taken as input, the pure bending strain after temperature compensation is obtained, and the displacement data after temperature compensation is obtained; The temperature compensation formula is expressed as, where ε i is the strain value at point i on the cantilever beam, ε iN is the axial strain due to the axial force, ε iΔt is the strain of the optical fiber due to temperature change, the strain values of the optical fiber on both sides of the arbitrary section i due to temperature change are equal The axial strains of the points of the section are also equal.

4. A fibre optic sensor based seepage monitoring method as claimed in claim 3, wherein: The determination of the position and range of seepage in the slope includes taking the positions of the inclinometer tubes as constraint conditions, taking the maximum sum of displacement gradients as a target function, determining the position coordinates of n discrete points on the potential sliding surface and the inclinometer tubes through a search algorithm, and taking the n discrete points on the sliding surface as n discrete points on the sliding surface; According to the obtained n discrete points on the sliding surface, a pre-trained RBF neural network model is used to obtain a slope sliding surface distribution map; According to the obtained temperature distribution data and the obtained slope sliding surface distribution map, the position and range of seepage in the slope are determined.

5. A fibre optic sensor based seepage monitoring method as claimed in claim 4, wherein: The slope sliding surface distribution map includes normalizing and preprocessing the coordinate data of the n discrete points on the sliding surface; Grid coordinate points in the slope range are generated according to a preset interval; An RBF neural network model is established, the plane coordinates (x, y) of the discrete points on the sliding surface are taken as input, the elevation z is taken as output, the network model is trained, and a nonlinear mapping relationship between the elevation of the sliding surface and the plane coordinates is obtained. The RBF neural network model is used to simulate the coordinates of n discrete points on the sliding surface, and to predict the elevation coordinates of the points on the sliding surface; According to the predicted elevation coordinates of the sliding surface, a two-dimensional or three-dimensional distribution map of the sliding surface coordinates in the slope range is generated.

6. A fibre optic sensor based seepage monitoring method as claimed in claim 5, wherein: The position and range of the internal seepage of the slope include the two-dimensional or three-dimensional distribution map of the sliding surface coordinates as a spatial constraint condition; Under the spatial constraint condition, the temperature values in the temperature distribution data located in the preset range of the sliding surface are extracted as the temperature distribution characteristics in the preset range of the sliding surface, wherein the preset range is a threshold distance range above and below the sliding surface; The relationship between the local groundwater temperature and the rock mass temperature is obtained, the temperature threshold is set according to the obtained relationship, and the temperature threshold segmentation method is used to segment the temperature distribution characteristics in the preset range of the sliding surface to obtain the potential seepage area in the preset range of the sliding surface, wherein the temperature threshold is determined according to the temperature difference between the groundwater temperature and the rock mass temperature; The obtained potential seepage area is subjected to spatial clustering analysis, and the position and range of the internal seepage of the slope are determined according to the clustering results.

7. A fibre optic sensor based seepage monitoring method as claimed in claim 6, wherein: The predicted elevation coordinate of the sliding surface corresponding to each point comprises determining an input vector X = (x1, x2, …, x n ) T and an output vector Y = (y1, y2, …, y m ) T ; The Gaussian function is used as the nonlinear mapping function of the hidden layer, which is expressed as, where h j is the output of the jth neuron of the hidden layer, c j is the center vector of the jth neuron of the hidden layer, E is an n x 1 unit vector, e j is the Gaussian function spread constant of the jth neuron of the hidden layer, ||. || is a norm, taken || X - c j E|| = (X - c j E) T (X - c j E), X is an input vector.

8. A system for monitoring seepage using a fiber optic sensor based seepage monitoring method as claimed in any one of claims 1 to 7, characterized in that: The system comprises a sensor module, a positioning relationship establishing module, a wetting measuring point determining module, a temperature compensation module, a slope internal seepage determining module, a seepage rate and seepage flow calculation module, and a slope seepage degree judgment module. The sensor module is used to arrange the distributed optical fiber sensor along the inclinometer tube in the slope, and to arrange the optical fiber heating system on the distributed optical fiber sensor. The positioning relationship establishing module is used to collect the scattered light signals of the distributed optical fiber sensor by using the distributed temperature sensor DTS, to demodulate the scattered light signals collected by the DTS to obtain the distributed temperature data of the optical fiber along the path, and at the same time, the distributed temperature sensor DTS establishes the positioning relationship between the temperature and the optical fiber length according to the optical fiber length at each measuring point. The wetting measuring point determining module is used to correspond the demodulated distributed temperature data with the optical fiber length according to the established positioning relationship, to determine the position of the wetting measuring point, and to calculate the optical fiber length at each water outlet line. The temperature compensation module is used to collect the strain data of the distributed optical fiber sensor arranged on the inclinometer tube, and to perform temperature compensation to obtain the displacement data after temperature compensation. The slope internal seepage determining module is used to determine the position and range of the internal seepage of the slope according to the obtained distributed temperature data and the displacement data after temperature compensation. The seepage rate and seepage flow calculation module is used to calculate the seepage rate and seepage flow of the corresponding position by using the seepage model and the heat conduction equation according to the determined position and range of the internal seepage of the slope. The slope seepage degree judgment module is used to judge the degree of slope seepage according to the calculated seepage rate and seepage flow. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the seepage monitoring method based on the optical fiber sensor in any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the seepage monitoring method based on the optical fiber sensor in any one of claims 1-7.

Images