Dam seepage monitoring method and device based on fiber bragg grating

Abstract

The invention relates to the technical field of hydraulic engineering safety monitoring, and discloses a fiber bragg grating-based dam seepage monitoring method and device, which comprises a first fiber bragg grating in a stainless steel shell, a second fiber bragg grating in an isolation structure and a third fiber bragg grating coupled with a humidity-sensitive polymer material, the method comprises the steps of collecting initial wavelength information of a dam in a healthy state; collecting central wavelength data of each fiber bragg grating, calling the reference data and the calibration coefficient to perform temperature decoupling calculation, and generating a physical quantity value; and utilizing a spatial interpolation algorithm to generate a physical field cloud picture, extracting feature data and generating an alarm signal. The isolation structure is arranged in the stainless steel shell, and the second fiber bragg grating is suspended in the small foam ball to realize decoupling with external mechanical stress, so that the interference of the temperature cross sensitivity characteristic on a measurement result is eliminated, and the calculation precision of the pore water pressure and humidity in a variable-temperature environment is improved.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering safety monitoring technology, specifically to a method and device for monitoring seepage in dams based on fiber optic gratings. Background Technology

[0002] Dams and dikes are essential infrastructure for flood control and water resource utilization, and their internal seepage conditions directly affect the long-term stability of the dam body. Abnormal seepage can reduce the shear strength of the dam soil, leading to seepage failure phenomena such as piping or soil erosion. Monitoring the seepage field inside the dam and understanding its evolution is of great significance for assessing the safety status of the project.

[0003] Existing seepage monitoring technologies, such as point-based measurement methods like piezometers, struggle to cover long-distance dams, suffer from insufficient spatial resolution, and fail to capture localized seepage channels. While distributed fiber optic temperature sensing technology offers a distributed capability, its reliance on the temperature difference between seepage water and the soil limits its sensitivity in environments with small temperature differences. Although fiber Bragg grating sensors possess electromagnetic interference and corrosion resistance, their inherent temperature and strain-dependent sensitivity in complex dam environments leads to the accuracy of pore water pressure and humidity measurements being affected by changes in ambient temperature.

[0004] Furthermore, monitoring a single physical quantity is insufficient for the joint analysis of changes in unsaturated soil moisture and saturated seepage pressure, leading to inadequate identification of early seepage and reliability in event determination. Therefore, eliminating the interference of temperature cross-sensitivity on measurements and achieving continuous spatial monitoring and cross-validation of multiple physical quantities are problems that need to be solved in this field. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and device for monitoring seepage in dams based on fiber Bragg gratings. This solves the problems in existing fiber Bragg grating monitoring where the accuracy of pore water pressure and humidity measurements is limited due to the cross-sensitivity of temperature, and where the lack of spatial continuity analysis and cross-validation mechanisms for monitoring single physical quantities leads to insufficient ability to identify early seepage.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method and device for monitoring seepage in dams based on fiber Bragg gratings. This invention provides a method for monitoring seepage in dams based on fiber Bragg gratings, comprising the following steps: S10, System Initialization Phase: The porous sintered stainless steel shell is embedded in the interior of the dam according to a preset array, and the hardware communication connection is completed. The initial wavelength information of the dam in a healthy state is collected as reference data. S20. Data Acquisition and Calculation Stage: The fiber optic demodulator acquires the center wavelength data of the first fiber optic grating, the second fiber optic grating, and the third fiber optic grating. The data processing and early warning host calls the reference data and preset calibration coefficients to perform temperature decoupling calculation on the center wavelength data, generating physical quantity values ​​of temperature, pore water pressure, and humidity. S30, Seepage Field Reconstruction and Evaluation Stage: The data processing and early warning host uses a spatial interpolation algorithm to process the physical quantity values, generate temperature field, strain field and pore water pressure field cloud maps covering the cross section of the dam, and extract physical field feature data based on the temperature field, strain field and pore water pressure field cloud maps. S40, Intelligent Early Warning Stage: The physical field characteristic data is compared with the preset alarm threshold, and an alarm signal of the corresponding level is generated according to the preset hierarchical early warning rules.

[0007] By employing the above technical solution, this invention utilizes three fiber optic gratings encapsulated within a specific structure to simultaneously sense temperature, pore water pressure, and humidity inside a dam. During data processing, differential calculations are performed using baseline data under healthy conditions, reducing errors introduced by the initial sensor installation. This method compensates for pressure and humidity data using independent temperature sensing data, obtaining temperature-corrected physical quantity values. Spatial interpolation algorithms are used to reconstruct discrete measurement point data into a continuously distributed physical field cloud map, revealing the overall seepage state and stress distribution inside the dam, improving the spatial continuity of the monitoring data, and providing multi-dimensional characteristic data for assessing dam stability.

[0008] Furthermore, in step S10, the acquisition of the initial wavelength information of the dam in a healthy state as reference data includes: the fiber grating demodulator performs multiple wavelength scans on all online sensing units and takes the average value to lock the initial center wavelengths of the first fiber grating, the second fiber grating and the third fiber grating respectively. A benchmark database is established, and the initial center wavelength is associated with and stored with the strain sensitivity coefficient, temperature sensitivity coefficient and cross sensitivity coefficient of each fiber grating to generate the benchmark data.

[0009] By adopting the above technical solution, the zero-point reference of each sensing unit was determined by averaging multiple scans, and a reference database containing the initial wavelength and sensitivity coefficient was constructed. This method establishes a unified standard for subsequent calculation of relative changes, reduces reference deviations caused by equipment startup fluctuations or instantaneous environmental noise, and ensures data consistency during long-term monitoring.

[0010] Furthermore, in step S20, the specific process of performing temperature decoupling calculation on the center wavelength data to generate physical quantity values ​​of temperature, pore water pressure, and humidity is as follows: calculate the change in ambient temperature based on the wavelength drift of the second fiber grating and the temperature sensitivity coefficient of the second fiber grating. Using the ambient temperature change, the component caused by temperature is subtracted from the total wavelength shift of the first fiber grating to obtain the strain value caused by pressure and convert it into a pore water pressure value. Using the ambient temperature change, the component caused by temperature is subtracted from the total wavelength shift of the third fiber grating to obtain the strain value caused by humidity and convert it into a physical quantity value of humidity.

[0011] By employing the above technical solution, a second fiber grating in a mechanically isolated state is used to measure the simple temperature change, which is then used as a compensation parameter. Through calculation, the temperature-induced spectral drift component is separated from the mixed signal of the first and third fiber gratings. This processing method solves the problem of temperature interference with pressure and humidity readings in fiber optic sensing, improving the accuracy of pore water pressure and humidity data.

[0012] Furthermore, in step S30, the extraction of physical field feature data based on the temperature field, the strain field, and the pore water pressure field cloud map includes: extracting zero-pressure contour lines as wetting line position data based on the pore water pressure field cloud map, and determining whether the wetting line position data exceeds the design envelope. Based on the temperature field cloud map, extract the temperature gradient anomaly region and identify whether there is any local temperature difference anomaly data that does not conform to the seasonal ground temperature change pattern. Based on the strain field cloud map, strain concentration regions are extracted to identify whether there is continuously increasing compressive strain data.

[0013] By employing the above technical solution, the system extracts key feature indicators from the physical field cloud map. The dynamic location of the seepage line is determined using zero-pressure contour lines to assess the seepage stability of the earth-rock dam. Potential leakage channels can be identified by utilizing regions with abnormal temperature gradients, and the heat conduction effect caused by seepage can be used for auxiliary diagnosis. The deformation trend inside the dam body is monitored using strain concentration zones. The characteristic data in these three dimensions reflect the seepage path, intensity, and impact on the dam structure, enabling a comprehensive assessment of the dam's operational status.

[0014] Furthermore, step S30 also includes: incorporating the physical quantity value of humidity into the fusion analysis and generating a humidity field cloud map using spatial interpolation; Based on the humidity field cloud map, the position of the leading edge of the high humidity area is extracted to determine the location data of the moist front; The seepage event was cross-validated by comparing the location data of the wetting line determined by the pore water pressure field cloud map with the location data of the wetting front determined by the humidity field cloud map.

[0015] By employing the above technical solution, the location of the wetting front is determined by utilizing the response characteristics of humidity sensors to changes in unsaturated soil moisture. Since the humidity field changes in the early stages of soil moisture increase, while the pressure field only changes after hydrostatic pressure is established, comparing the consistency between the wetting front location and the phreatic line location verifies the authenticity of the seepage event and identifies the diffusion trend of unsaturated seepage, thus improving the monitoring system's ability to detect early seepage.

[0016] Furthermore, in step S40, the preset alarm threshold includes a warning line set for the location of the immersion line; The hierarchical early warning rules include: Level 1 early warning rule: when a local area of ​​any physical field shows a numerical anomaly but does not form a continuous growth trend, a Level 1 alarm signal is generated; Level 2 warning rule: When the physical field characteristic data shows that the infiltration line position data reaches the warning line, or when any physical field shows a continuous abnormal growth trend, a level 2 alarm signal is generated; Level 3 warning rule: When the physical field characteristic data shows that the wetting line position data exceeds the warning line, and the temperature field cloud map in the corresponding area shows abnormal temperature difference and the strain field cloud map shows abnormal deformation, a level 3 alarm signal is generated.

[0017] By adopting the above technical solution, a hierarchical early warning system based on multiphysics field fusion was constructed. The first-level early warning targets local fluctuations; Level II warning is for situations where the seepage trend continues and the infiltration line reaches the warning line, indicating the development of seepage risk; The three-level early warning system confirms the formation of structural deformation or concentrated leakage channels based on the spatial overlap of the wetting line exceeding the warning line, abnormal temperature, and abnormal strain. This tiered mechanism combines the mutual verification of multiple physical quantities, reducing the false alarm rate for single-parameter anomalies and providing managers with a tiered decision-making basis.

[0018] This invention also provides a dam seepage monitoring device based on fiber Bragg gratings, comprising a stainless steel shell, an elastic diaphragm installed inside the stainless steel shell, a first fiber Bragg grating fixed to the inner side of the elastic diaphragm, an isolation structure installed at the center of the interior of the stainless steel shell, a waterproof plastic tube installed inside the isolation structure, a humidity sensing module disposed outside the waterproof plastic tube, a second fiber Bragg grating encapsulated inside the waterproof plastic tube, a humidity sensing module filled with a moisture-sensitive polymer material, a third fiber Bragg grating coupled to the moisture-sensitive polymer material, and armored optical fibers connected to the signal output ends of the first, second, and third fiber Bragg gratings, respectively. A fiber Bragg grating demodulator is connected to the end of the armored optical fiber, and a data processing and early warning host is connected to the communication port of the fiber Bragg grating demodulator.

[0019] Preferably, the interior of the waterproof plastic tube is filled with multiple foam balls, and the second fiber optic grating is encased within the multiple foam balls; the humidity sensing module has a tubular structure.

[0020] Preferably, the isolation structure includes a middle plastic tube, and a plurality of connecting blocks are evenly distributed on the outer wall of the middle plastic tube. The connecting blocks abut against the inner wall of the stainless steel shell, and the waterproof plastic tube and the humidity sensing module are installed side by side in the internal cavity of the middle plastic tube.

[0021] Preferably, the first fiber grating, the second fiber grating, and the third fiber grating are connected in series on the same optical fiber to form a composite fiber grating sensing chain. Multiple composite fiber grating sensing chains are distributed horizontally in layers or vertically in a grid pattern along the interior of the dam to form a multi-physical quantity fiber grating sensing array.

[0022] This invention provides a method and device for monitoring seepage in dams based on fiber Bragg gratings. It has the following beneficial effects: 1. This invention achieves decoupling from external mechanical stress by setting an isolation structure inside the stainless steel shell and suspending the second fiber grating in foam spheres. The independent temperature data obtained thereby is used to perform decoupled calculations on the data of the first and third fiber gratings, eliminating the interference of temperature cross-sensitivity on the measurement results and improving the calculation accuracy of pore water pressure and humidity physical quantities under variable temperature conditions.

[0023] 2. This invention determines the location data of the wetting front by utilizing the response characteristics of moisture-sensitive polymer materials to changes in unsaturated soil moisture, and cross-compares it with the location data of the phreatic line determined by the pore water pressure field. By utilizing the characteristic that the wetting front forms before the hydrostatic pressure, the early unsaturated seepage diffusion trend can be identified, thereby improving the reliability of seepage event determination.

[0024] 3. This invention distributes multiple composite fiber optic grating sensor chains in an array along the interior of the dam, and uses a spatial interpolation algorithm to reconstruct discrete center wavelength data into a continuous cloud map covering the temperature field, strain field, and pore water pressure field of the dam cross section. This intuitively displays the physical field gradient distribution inside the dam, providing spatially continuous data support for locating seepage channels and structural deformation areas. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the structure of a dam seepage monitoring device based on a fiber Bragg grating according to the present invention; Figure 2 This is a cross-sectional view of a dam seepage monitoring device based on a fiber Bragg grating according to the present invention. Figure 3 This is a side view of a dam seepage monitoring device based on a fiber Bragg grating according to the present invention; Figure 4 This is an overall schematic diagram of a dam seepage monitoring device based on a fiber Bragg grating according to the present invention. Figure 5 This is a schematic diagram of the isolation structure of a dam seepage monitoring device based on a fiber Bragg grating according to the present invention. Figure 6 This is a flowchart illustrating the implementation of a dam seepage monitoring method based on fiber Bragg grating according to the present invention.

[0026] Among them, 1. Stainless steel shell; 201. First fiber grating; 202. Elastic diaphragm; 301. Waterproof plastic tube; 302. Foam ball; 303. Second fiber grating; 401. Humidity sensing module; 402. Third fiber grating; 403. Humidity-sensitive polymer material; 5. Isolation structure; 501. Middle layer plastic tube; 502. Connecting block; 6. Armored optical fiber; 7. Composite fiber grating sensing chain; 8. Multi-physical quantity fiber grating sensing array; 9. Dam. Detailed Implementation

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] See attached document Figure 1 -Appendix Figure 5This invention provides a dam seepage monitoring device based on fiber Bragg gratings, comprising a stainless steel shell 1, an elastic diaphragm 202 installed inside the stainless steel shell 1, a first fiber Bragg grating 201 fixed to the inner side of the elastic diaphragm 202, an isolation structure 5 installed at the center of the interior of the stainless steel shell 1, a waterproof plastic tube 301 installed inside the isolation structure 5, a humidity sensing module 401 disposed outside the waterproof plastic tube 301, a second fiber Bragg grating 303 encapsulated inside the waterproof plastic tube 301, a humidity-sensitive polymer material 403 filled inside the humidity-sensitive polymer material 403, a third fiber Bragg grating 402 coupled to the humidity-sensitive polymer material 403, and armored optical fibers 6 connected together at the signal output ends of the first fiber Bragg grating 201, the second fiber Bragg grating 303, and the third fiber Bragg grating 402, the end of which is connected to a fiber Bragg grating demodulator, and the communication port of the fiber Bragg grating demodulator connected to a data processing and early warning host.

[0029] Specifically, the stainless steel outer shell 1 is manufactured using a porous sintering process. Its porous structure allows pore water to freely permeate into the device and transmit fluid pressure, while simultaneously filtering external soil particles. The elastic diaphragm 202 acts as a pressure-sensing element, deforming when it senses external fluid pressure. The first fiber grating 201 is attached to the inner side of the elastic diaphragm 202, and its center wavelength drifts due to the combined effects of strain caused by pore water pressure and ambient temperature. The second fiber grating 303, through the isolation structure 5, decouples from external mechanical stress, providing independent temperature reference data. The third fiber grating 402, in conjunction with the moisture-sensitive polymer material 403, constitutes a humidity sensing module 401. The hygroscopic expansion property of the moisture-sensitive polymer material 403 drives the third fiber grating 402 to generate axial strain. The fiber grating demodulator is responsible for emitting broadband excitation light and demodulating the reflected wavelength. The data processing and early warning host, based on the calibration coefficients of each fiber grating, calculates the received wavelength data, separating the physical quantities of temperature, pore water pressure, and humidity.

[0030] See attached document Figure 1 -Appendix Figure 3 The interior of the waterproof plastic tube 301 is filled with multiple foam balls 302, and the second fiber optic grating 303 is wrapped in multiple foam balls 302; the humidity sensing module 401 has a tubular structure.

[0031] Specifically, the waterproof plastic tube 301 is a rigid tube, and the foam balls 302 inside it create a loose buffer environment. The second fiber grating 303 is suspended between the foam balls 302. This encapsulation method blocks the transmission of mechanical stress from the stainless steel shell 1 or the isolation structure 5, ensuring that the second fiber grating 303 is in a stress-free state, thus ensuring that the change in its reflected wavelength is only caused by the ambient temperature. The humidity sensing module 401 plays a role in containment and protection, and the moisture-sensitive polymer material 403 inside it is tightly coupled to the third fiber grating 402. When the ambient humidity increases, external water vapor permeates in through the shell, the moisture-sensitive polymer material 403 expands in volume and stretches the third fiber grating 402; when the humidity decreases, the material contracts, the grating strain decreases, thereby realizing the monitoring of the moisture content in unsaturated soil.

[0032] See attached document Figure 1 Appendix Figure 2 Appendix Figure 3 and attached Figure 5 The isolation structure 5 includes a middle plastic tube 501. Multiple connecting blocks 502 are evenly distributed on the outer wall of the middle plastic tube 501. The connecting blocks 502 abut against the inner wall of the stainless steel shell 1. The waterproof plastic tube 301 and the humidity sensing module 401 are installed side by side in the internal cavity of the middle plastic tube 501.

[0033] Specifically, the isolation structure 5 serves as the mounting bracket for the internal core components, suspending and fixing the middle-layer plastic tube 501 to the geometric center axis of the stainless steel outer shell 1 via connecting blocks 502. The connecting blocks 502 are evenly distributed along the circumference, providing both support and positioning to prevent contact between the internal components and the inner wall of the outer shell, while also providing fluid channels to ensure unimpeded permeation of pore water and pressure transmission within the device. The waterproof plastic tube 301 and the humidity sensing module 401 are installed in a parallel bundle within the middle-layer plastic tube 501. This compact layout ensures that the temperature sensing unit, humidity sensing unit, and pressure sensing unit are in the same thermal environment, reducing temperature gradient errors caused by measurement point position deviations and improving the accuracy of subsequent temperature compensation calculations.

[0034] See attached document Figure 1 -Appendix Figure 4 The first fiber grating 201, the second fiber grating 303 and the third fiber grating 402 are connected in series on the same optical fiber to form a composite fiber grating sensing chain 7. Multiple composite fiber grating sensing chains 7 are distributed horizontally in layers or vertically in a grid pattern along the interior of the dam 9 to form a multi-physical quantity fiber grating sensing array 8.

[0035] Specifically, this embodiment employs wavelength division multiplexing (WDM) technology to connect fiber gratings for sensing three physical quantities—temperature, pressure, and humidity—in series on a single optical fiber to form a basic sensing unit. Multiple sensing units are connected in series at preset intervals to form a composite fiber grating sensing chain 7. In practical applications, multiple composite fiber grating sensing chains 7 are horizontally deployed along different elevations of the dam 9 or vertically deployed along the cross-section, collectively constructing a multi-physical-quantity fiber grating sensing array 8. The fiber grating demodulator acquires the wavelength information of each sensing point in the array through armored optical fiber 6. The data processing and early warning host utilizes a spatial interpolation algorithm to reconstruct a continuous temperature field, strain field, and pore water pressure field model inside the dam 9 based on the discrete data acquired by the array. This model comprehensively determines the seepage path and the location of the seepage line, enabling real-time monitoring and graded early warning of the seepage state of the dam 9.

[0036] See attached document Figure 6 This invention provides a method for monitoring seepage in dams based on fiber Bragg gratings, comprising the following steps: Step S10 is the system initialization phase. During the construction period, the composite fiber optic grating sensor chain is installed and the equipment is connected. After the device is operational, a set of initial physical field data of the dam under healthy conditions is collected and stored as a benchmark for subsequent analysis.

[0037] Step S20 is the data acquisition and processing stage. The fiber optic demodulator acquires the center wavelength data of all sensing units at a set frequency. After receiving the data, the data processing and early warning host combines the strain sensitivity coefficient, temperature cross-sensitivity coefficient of each sensing unit, and the temperature change measured by nearby temperature sensing units to perform temperature compensation calculations on the center wavelength data, thereby calculating the accurate physical quantity value.

[0038] Step S30 is the seepage field reconstruction and evaluation stage. Using a spatial interpolation algorithm, temperature field, strain field, and pore water pressure field cloud maps of the dam cross section are generated based on the measurement point data. At the same time, the analysis model is started to check whether the wetting line calculated from the pore water pressure field exceeds the design envelope, whether there are local low temperature zones in the temperature field that do not conform to the seasonal variation pattern, and whether there is a continuous increase in compressive strain in certain areas of the strain field.

[0039] Step S40 is the intelligent early warning stage. Tiered early warnings are executed according to preset rules. A Level 1 warning is issued when a slight anomaly occurs in a local area of ​​any physical field but does not form a clear trend. A Level 2 warning is issued when the wetting line approaches the warning line or a physical field exhibits a persistent anomaly. A Level 3 warning is triggered when the wetting line exceeds the warning line and significant anomalies are observed in both the temperature and strain fields of the corresponding area, notifying relevant personnel and automatically recording the alarm time data.

[0040] The specific implementation process and principles of each of the above steps are explained in detail below.

[0041] Step S10 is the system initialization and baseline establishment phase. This step aims to construct the physical connection architecture of the monitoring system, establish the mapping relationship between the physical locations of sensors and logical channels, and establish the zero-point baseline data necessary for subsequent comparative analysis. This step specifically includes the following sub-steps: Step S101: Physical Construction and Connection of the Sensor Network. During the construction or reinforcement phase of the dam, a multi-physical-quantity fiber Bragg grating sensor array is buried inside the dam body according to the pre-designed monitoring grid. The sensor array consists of multiple composite fiber Bragg grating sensor chains, which are laid horizontally along different elevations of the dam or vertically along the cross-section. To ensure the accuracy and durability of the measurements, the monitoring device used in this embodiment is constructed with a sealed, corrosion-resistant porous sintered stainless steel shell. This special material shell protects the internal sensitive elements from direct soil compression while allowing free infiltration of external groundwater, ensuring that the probe can accurately sense the pore water pressure and humidity of the environment. The buried sensor array is led out to the monitoring station along the dam via armored optical cable and connected to the optical channel interface of the fiber Bragg grating demodulator. The fiber Bragg grating demodulator establishes a communication connection with the data processing and early warning host via an industrial Ethernet interface. During this process, the splicing and protection of armored optical cables are conventional construction techniques in the field of optical communication. Those skilled in the art can choose fusion splicing or cold splicing methods according to the site environment, which will not be elaborated here.

[0042] Step S102: Logical mapping and parameter configuration of the sensing units. The data processing and early warning host starts the monitoring software and reads the center wavelength information of each sensing unit scanned by the fiber Bragg grating demodulator. Since the fiber Bragg grating sensor network uses wavelength division multiplexing (WDM) or space division multiplexing (SDM) technology, the host needs to establish the correspondence between physical nodes and logical data. Specifically, the host maps the channel number and wavelength range of the demodulator to the actual spatial coordinates of the dam. The sensors are bound together, and each sensor unit is assigned a unique identifier. Simultaneously, the factory-calibrated characteristic parameters of each sensor unit are entered into the host database. These characteristic parameters include the strain sensitivity coefficient of the first fiber Bragg grating. and temperature sensitivity coefficient Temperature sensitivity coefficient of the second fiber grating The strain sensitivity coefficient of the third fiber grating and temperature sensitivity coefficient ; and temperature cross-sensitivity coefficient These calibration coefficients serve as constants for subsequent decoupling calculations of multiple physical quantities.

[0043] Step S103: Initial health state baseline data acquisition. After confirming that the dam structure is in a stable state, with no abnormal seepage and the ambient temperature is within non-extreme conditions, the system performs a baseline value locking operation. The fiber optic demodulator performs multiple wavelength scans on all online sensing units and takes the average value to obtain the initial center wavelength of the three fiber optic gratings in each sensing unit. Specifically, the recorded data is: the initial center wavelength of the first fiber optic grating. This serves as a reference for subsequent calculations of pressure and temperature-coupled wavelength drift; the initial center wavelength of the second fiber grating. This serves as the benchmark for subsequent calculations of the pure temperature wavelength shift; the initial center wavelength of the third fiber grating. This serves as a benchmark for subsequent calculations of humidity and temperature-coupled wavelength drift.

[0044] Step S104: Establish a benchmark database. The characteristic parameters configured in step S102 are compared with the initial center wavelength acquired in step S103. , , A baseline database for dam seepage monitoring is generated through associated storage. This database remains unchanged during subsequent long-term monitoring unless the monitoring device is recalibrated or undergoes irreversible physical displacement. Otherwise, all real-time monitoring data are relative changes calculated relative to the initial values ​​in this baseline database. This mechanism eliminates absolute errors caused by sensor manufacturing tolerances, ensuring that the monitoring results accurately reflect the relative evolution of the physical field within the dam.

[0045] Step S20 is the multi-parameter data acquisition and decoupling calculation stage. This step is the core processing step of the monitoring method, aiming to accurately separate three physical quantities—temperature, pore water pressure, and humidity—from the composite spectral signal using the physical characteristic equations of fiber optic gratings. The host computer immediately performs calculations upon receiving the data. As a general calculation logic, for example, for the first... Each strain sensing unit has a precise strain value. Through formula Calculation, where It is its strain sensitivity coefficient. It is the amount of change in its wavelength. It is its temperature cross-sensitivity coefficient, and The temperature is then measured by the nearest temperature sensing unit. Based on this general logic, and considering the specific sensor structure in this embodiment, this step specifically includes the following sub-steps: Step S201: Real-time wavelength scanning and drift calculation. The fiber Bragg grating demodulator continuously scans the sensor array at a preset sampling frequency to obtain the real-time center wavelength of the three fiber Bragg gratings in each sensor unit at the current moment. The data processing and early warning host receives the above data and calls the reference wavelength data stored in the database to perform differential calculation. The wavelength drift of the first fiber Bragg grating is calculated. Calculate the wavelength shift of the second fiber grating. Calculate the wavelength shift of the third fiber grating. .in, , , These are the center wavelengths of the first, second, and third fiber gratings at the current moment.

[0046] Step S202, Independent Calculation of Ambient Temperature. Temperature is calculated using a second fiber grating (FBG2). Because the FBG2 is placed within a tiny, stress-free PVC tube filled with foam balls, it achieves decoupling from external mechanical stress, thus reducing wavelength shift. Caused solely by changes in ambient temperature. The host utilizes the temperature sensitivity coefficient of the second fiber Bragg grating. The change in ambient temperature can be calculated using the following formula. : ; The calculation yielded This represents the precise change in ambient temperature at the location of the sensing unit, which will be used as a temperature compensation parameter for subsequent pressure and humidity calculations.

[0047] Step S203: Temperature compensation and calculation of pore water pressure. The first fiber grating (FBG1) is precisely attached to the inner center of a highly elastic metal diaphragm inside the monitoring device, and its wavelength shift... The membrane strain is caused by pore water pressure. and changes in ambient temperature The result of their combined action. Calculated in step S202 of the host invocation. and the strain sensitivity coefficient of the pre-set first fiber grating. and temperature sensitivity coefficient Decoupling is performed based on the fiber grating coupled-mode theory formula. The wavelength drift equation of the first fiber grating is: ; Will Substituting into the above equation and rearranging the terms, we can separate the simple strain value caused by fluid pressure. : ; Obtain simple strain values Then, the host machine converts the strain value into the current pore water pressure physical quantity based on the mechanical calibration curve of the elastic diaphragm. This process eliminates the cross-sensitivity error of temperature fluctuations in pressure measurement, enabling accurate measurement of pore water pressure.

[0048] Step S204: Humidity temperature compensation and calculation. Humidity is calculated using a third fiber grating (FBG3). The FBG3 is coupled to a humidity-sensitive polymer material (usually using a pre-stretched fixing method), and its wavelength shift is... Additional strain caused by the moisture absorption and expansion of moisture-sensitive materials and changes in ambient temperature The result of their combined action. The host also invokes the information obtained in step S202. and the strain sensitivity coefficient of the third fiber grating and temperature sensitivity coefficient First, the wavelength drift equation of the third fiber grating is established based on coupled-mode theory: ; Subsequently, by rearranging terms, the strain caused by humidity changes was separated. : ; Calculated Subsequently, the host computer uses the pre-determined swelling characteristic curve of the moisture-sensitive polymer material to convert the strain value into the relative moisture content or ambient humidity of the soil and rock. This step enables independent monitoring of the soil's moisture content, providing data support for subsequent analysis of wetting front movement.

[0049] Step S30 is the seepage field reconstruction and multi-physical quantity fusion evaluation stage. This step utilizes the discrete physical quantity data calculated in step S20 to perform spatial reconstruction and comprehensive analysis of the seepage state inside the dam. This step specifically includes the following sub-steps: Step S301: Visual Reconstruction of the Seepage Field. The data processing and early warning host calls the graphics processing module to spatially process the temperature, strain, and pore water pressure data calculated by each sensing unit. Since the multi-physical fiber grating sensor array is deployed at discrete spatial points within the dam body, a Kriging spatial interpolation algorithm is used for numerical reconstruction to obtain a continuous physical field distribution on the monitoring profile. Specifically, the host uses the actual spatial coordinates of each sensing unit... Given a sample point, the numerical value of the physical quantity calculated from that point. Using sample values, the spatial correlation between points is analyzed through variogram analysis, weighting coefficients are calculated, and then the values ​​in the areas without sensor deployment on the monitoring profile are estimated. Through the above calculations, continuous distribution cloud maps covering the monitoring cross-section of the dam are generated, namely temperature field cloud maps, strain field cloud maps, and pore water pressure field cloud maps. The cloud maps use color gradients to represent the numerical magnitude of physical quantities, which can intuitively display the gradient changes, extreme value distributions, and anomaly concentration areas of the physical field inside the dam. The specific mathematical implementation of the Kriging interpolation algorithm is existing technology in the field of data processing and will not be elaborated here.

[0050] Step S302, wetting line analysis of the pore water pressure field. Based on the reconstructed pore water pressure field contour map, the host extracts the contour lines where the pore water pressure is zero. These contour lines physically represent the location of the phreatic line within the dam body. The analysis model compares the extracted real-time phreatic line location with pre-stored dam design data in the database, specifically including the theoretical phreatic line and the safety envelope. If the real-time phreatic line is higher than the safety envelope in a certain area, or if the phreatic line rises abnormally at the outlet point on the back slope, then the area is considered to have a seepage stability risk. Furthermore, the model calculates the local seepage gradient based on the pore water pressure distribution below the phreatic line; if the gradient exceeds the critical hydraulic gradient of the soil, then a seepage failure risk is identified.

[0051] Step S303: Temperature field tracer analysis. The heat conduction and convection effects of the seepage flow are used for tracing. Since there is usually a significant temperature difference between the reservoir water temperature and the soil temperature inside the dam body in different seasons, the seepage flow carries heat and migrates rapidly within the soil, causing the temperature field near the seepage channel to change differently from the surrounding soil. The mainframe analyzes the distribution characteristics of the temperature field cloud map to identify whether there are local temperature anomaly areas that do not conform to the seasonal geothermal variation pattern. For example, in summer, if a significant low-temperature area appears deep within the dam body, and this low-temperature area is distributed in a band and extends towards the backwater side, it is determined that there is a high probability of concentrated seepage channels in this area. This method of using temperature as a tracer indicator can help verify the results of pore water pressure monitoring, especially when the flow velocity is high and the pore pressure dissipation is not obvious, the response of the temperature field is often more pronounced.

[0052] Step S304: Deformation analysis of the strain field. The main unit analyzes the strain field contour map to check for abnormal, continuous compressive or tensile strain growth regions within the dam body. Under conditions other than sudden drops in water level or drastic changes in external loads such as earthquakes, a sustained increase in local strain in a certain area may indicate undercutting, piping leading to material loss, or uneven settlement and consolidation deformation within the soil. The abnormal locations of the strain field are spatially superimposed with those of the pressure and temperature fields. If these three locations highly overlap spatially, it confirms the presence of structural damage or seepage failure pathways at that location.

[0053] Step S305: Wetting Front Capture and Multi-Field Cross-validation in Humidity Field. Humidity field data is incorporated into the fusion analysis system, utilizing the sensitivity of humidity sensors to changes in unsaturated soil moisture to capture the propagation process of the wetting front. Since pore water pressure sensors typically only register readings after the soil reaches saturation and generates hydrostatic pressure, while humidity sensors can respond in the early stages of increased soil moisture content, the host computer verifies the authenticity of seepage events by comparing the consistency between the phreatic line position (determined by the pressure field) and the leading edge position of the high-humidity zone (determined by the humidity field). If the humidity field shows a sudden increase in moisture content in a certain area, while the corresponding pressure field response lags behind, it indicates the possible presence of unsaturated seepage or an early leakage channel, providing a crucial time lead for early warning. Through cross-validation of the four physical fields—temperature, pressure, strain, and humidity—it is possible to effectively distinguish between the sensor's own zero-point drift and actual physical seepage, eliminating false alarms that may arise from monitoring a single physical quantity.

[0054] Step S40 is the intelligent hierarchical early warning stage. Based on the multi-physical quantity fusion evaluation results from step S30, this step executes hierarchical early warnings according to preset logical rules to achieve differentiated response strategies. This step specifically includes the following sub-steps: Step S401: Setting early warning thresholds and rules. Configure safety thresholds for each physical quantity in the data processing and early warning host. Set the warning water level for pore water pressure. and dangerous water level line ,in This corresponds to the upper limit of the allowable phreatic line envelope in the dam design. A temperature anomaly threshold is set. This threshold is determined based on the local seasonal background geothermal temperature. A strain amplitude threshold is set. This is used to determine the degree of soil deformation. A humidity saturation threshold is set. These threshold parameters are used to determine the arrival of a moist front. These parameters can be dynamically adjusted based on the dam's engineering grade, geological conditions, and historical monitoring data.

[0055] Step S402: Triggering and Response to Level 1 (Blue) Warning. The host monitors the data of each physical field in real time. When the value of any physical field (temperature, pore water pressure, or humidity) in a local area exceeds the baseline value but does not exceed the warning threshold set in step S401, and does not show a continuous monotonically increasing trend on the time axis, it is judged as a Level 1 risk. At this time, the system triggers a blue warning state. The host marks the display status of the corresponding measuring point as "attention" on the monitoring software interface and adds the abnormal point to the key observation list, increasing its data sampling weight, but does not send external alarm signals for the time being. This level is intended to alert maintenance personnel to slight fluctuations in data and eliminate random noise interference from the sensors themselves.

[0056] Step S403, Triggering and Response to Level II (Yellow) Warning. A Level II risk level is determined and a yellow warning is triggered when monitoring data meets any of the following conditions: Condition 1, the wetting line position calculated from the pore water pressure field reaches or exceeds the warning water level. However, it has not yet reached the danger level. Condition two: A physical field (such as a humidity field or a temperature field) exhibits a sustained abnormal growth trend, meaning the derivative of the data with respect to time remains positive and exceeds a preset rate for multiple consecutive sampling periods, indicating that seepage is developing. Upon triggering a yellow alert, the host computer displays a warning message on the interface and sends an alert log to the monitoring center via the background service. The system generates a brief report including the location of the abnormal area, the type of abnormal physical quantity, and its changing trend, recommending that management personnel increase the frequency of manual inspections of the area to verify whether there are signs of surface seepage or piping.

[0057] Step S404, Triggering and Response to Level 3 (Red) Warning. This is the highest level of alarm in this invention, designed to respond to impending or ongoing destructive seepage. A red warning is triggered when the multiphysics co-index condition is met; specifically, the wetting line position displayed by the pore water pressure field exceeds the danger level. Furthermore, within the corresponding spatial region, the temperature field exhibits significant seepage temperature differential anomalies. Meanwhile, the strain field shows that the soil exhibits significant deformation (i.e., the strain field value). This multi-parameter spatiotemporal coincidence confirmed the continuity of the internal seepage channels and the instability of the soil structure. Upon triggering a red alert, the main unit immediately executes an emergency response procedure: activating the on-site audible and visual alarms to warn surrounding personnel; sending danger alerts to all relevant safety personnel via SMS, app push notifications, and email through the built-in wireless communication module; and automatically locking and encrypting all monitoring data within a preset time period (e.g., 24 hours before and after) before and after the alarm. This mechanism ensures the integrity of critical accident data, providing detailed data support for subsequent disaster cause analysis, accountability, and the development of engineering emergency response plans.

Claims

1. A method for monitoring seepage in dams based on fiber Bragg gratings, characterized in that, Includes the following steps: S10, System initialization stage: The porous sintered stainless steel shell (1) is embedded in the interior of the dam (9) according to the preset array, and the hardware communication connection is completed. The initial wavelength information of the dam (9) in a healthy state is collected as the reference data. S20, Data Acquisition and Calculation Stage: The fiber optic demodulator acquires the center wavelength data of the first fiber optic grating (201), the second fiber optic grating (303), and the third fiber optic grating (402). The data processing and early warning host calls the reference data and the preset calibration coefficients to perform temperature decoupling calculation on the center wavelength data and generate physical quantity values ​​of temperature, pore water pressure, and humidity. S30, seepage field reconstruction and evaluation stage: The data processing and early warning host uses a spatial interpolation algorithm to process the physical quantity values, generate a cloud map of temperature field, strain field and pore water pressure field covering the cross section of the dam (9), and extract physical field feature data based on the temperature field, strain field and pore water pressure field cloud map; S40, Intelligent Early Warning Stage: The physical field characteristic data is compared with the preset alarm threshold, and an alarm signal of the corresponding level is generated according to the preset hierarchical early warning rules.

2. The method for monitoring dam seepage based on fiber Bragg gratings according to claim 1, characterized in that, In step S10, the initial wavelength information of the dam (9) in a healthy state is collected as reference data, including: The fiber grating demodulator performs multiple wavelength scans on all online sensing units and takes the average value to lock the initial center wavelengths of the first fiber grating (201), the second fiber grating (303), and the third fiber grating (402), respectively. A benchmark database is established, and the initial center wavelength is associated with and stored with the strain sensitivity coefficient, temperature sensitivity coefficient and cross sensitivity coefficient of each fiber grating to generate the benchmark data.

3. The method for monitoring dam seepage based on fiber Bragg gratings according to claim 1, characterized in that, In step S20, the specific process of performing temperature decoupling calculations on the center wavelength data to generate physical quantity values ​​of temperature, pore water pressure, and humidity is as follows: The change in ambient temperature is calculated based on the wavelength shift of the second fiber grating (303) and the temperature sensitivity coefficient of the second fiber grating (303). Using the ambient temperature change, the component caused by temperature is subtracted from the total wavelength shift of the first fiber grating (201) to obtain the strain value caused by pressure and convert it into a pore water pressure value; Using the change in ambient temperature, the component caused by temperature is subtracted from the total wavelength shift of the third fiber grating (402) to obtain the strain value caused by humidity and convert it into a relative moisture content value.

4. The method for monitoring dam seepage based on fiber Bragg gratings according to claim 1, characterized in that, In step S30, the extraction of physical field feature data based on the temperature field, strain field, and pore water pressure field cloud map includes: Based on the pore water pressure field cloud map, zero-pressure contour lines are extracted as wetting line location data, and it is determined whether the wetting line location data exceeds the design envelope. Based on the temperature field cloud map, extract the temperature gradient anomaly region and identify whether there is any local temperature difference anomaly data that does not conform to the seasonal ground temperature change pattern. Based on the strain field cloud map, strain concentration regions are extracted to identify whether there is continuously increasing compressive strain data.

5. The method for monitoring dam seepage based on fiber Bragg gratings according to claim 4, characterized in that, The S30 step further includes: The physical quantity of humidity is incorporated into the fusion analysis, and a humidity field cloud map is generated using spatial interpolation. Based on the humidity field cloud map, the position of the leading edge of the high humidity area is extracted, and the position data of the moist front is determined according to the position of the leading edge of the high humidity area. The seepage event is cross-validated by comparing the location data of the wetting line determined by the pore water pressure field cloud map with the location data of the wetting front determined by the humidity field cloud map.

6. The method for monitoring dam seepage based on fiber Bragg gratings according to claim 1, characterized in that, In step S40, the preset alarm threshold includes a warning line set for the location of the immersion line. The tiered early warning rules include: Level 1 warning rule: When a local area of ​​any physical field shows a numerical anomaly but does not form a continuous growth trend, a Level 1 warning signal is generated; Level 2 warning rule: When the physical field characteristic data shows that the infiltration line position data reaches the warning line, or when any physical field shows a continuous abnormal growth trend, a level 2 alarm signal is generated; Level 3 warning rule: When the physical field characteristic data shows that the wetting line position data exceeds the warning line, and the temperature field cloud map in the corresponding area shows abnormal temperature difference and the strain field cloud map shows abnormal deformation, a level 3 alarm signal is generated.

7. A dam seepage monitoring device based on fiber Bragg grating, comprising a stainless steel housing (1), characterized in that, An elastic diaphragm (202) is installed at the pressure position of the stainless steel shell (1). A first fiber optic grating (201) is fixed to the inner side of the elastic diaphragm (202). An isolation structure (5) is installed at the center of the interior of the stainless steel shell (1). A waterproof plastic tube (301) is installed inside the isolation structure (5). A humidity sensing module (401) is provided outside the waterproof plastic tube (301). A second fiber optic grating (303) is encapsulated inside the waterproof plastic tube (301). A humidity-sensitive polymer material (403) is filled inside the humidity-sensitive polymer material (403). A third fiber optic grating (402) is coupled to the humidity-sensitive polymer material (403). The signal output ends of the first fiber optic grating (201), the second fiber optic grating (303), and the third fiber optic grating (402) are all connected to an armored optical fiber (6). A fiber optic demodulator is connected to the end of the armored optical fiber (6). The communication port of the fiber optic demodulator is connected to a data processing and early warning host.

8. A dam seepage monitoring device based on fiber Bragg grating according to claim 7, characterized in that, The interior of the waterproof plastic tube (301) is filled with a plurality of foam balls (302), and the second fiber grating (303) is wrapped in the plurality of foam balls (302); the humidity sensing module (401) is a tubular structure.

9. A dam seepage monitoring device based on fiber Bragg grating according to claim 7, characterized in that, The isolation structure (5) includes a middle plastic tube (501), and a plurality of connecting blocks (502) are evenly distributed on the outer wall of the middle plastic tube (501). The connecting blocks (502) abut against the inner wall of the stainless steel shell (1). The waterproof plastic tube (301) and the humidity sensing module (401) are installed side by side in the internal cavity of the middle plastic tube (501).

10. A dam seepage monitoring device based on fiber Bragg grating according to claim 7, characterized in that, The first fiber grating (201), the second fiber grating (303) and the third fiber grating (402) are connected in series on the same optical fiber to form a composite fiber grating sensing chain (7). Multiple composite fiber grating sensing chains (7) are distributed horizontally or vertically in a grid pattern along the interior of the dam (9) to form a multi-physical quantity fiber grating sensing array (8).

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