A tunnel deformation parameter inversion method and device based on distributed optical fiber

Abstract

The present invention provides a tunnel deformation parameter inversion method and device based on distributed optical fiber, which relates to the technical field of tunnel deformation parameter inversion. The method specifically comprises: selecting monitoring points in a tunnel and arranging optical fiber sensors to obtain Brillouin scattering signals generated by the optical fiber sensors under the action of stress at the corresponding monitoring points; extracting the Brillouin frequency shift corresponding to the monitoring points, and determining an environmental effect frequency shift correction value using temperature change data, humidity change data, and the service life of the optical fiber sensors; determining the structural strain of the monitoring points based on the Brillouin frequency shift and the environmental effect frequency shift correction value, generating a three-dimensional strain field cloud map, spatially aligning the cloud map with the model geometric topology, and performing three-dimensional grid division; mapping the structural strain of the monitoring points to grid nodes at corresponding positions, determining the structural strains of other grid nodes based on the spatial distance attenuation law, completing the inversion of the deformation parameters of the entire tunnel, and improving the accuracy and efficiency of tunnel deformation monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel deformation parameter inversion, and in particular to a tunnel deformation parameter inversion method and device based on distributed optical fiber. Background Art

[0002] In modern infrastructure construction, tunnels serve as important transportation hubs and resource delivery channels, and their safety and stability are particularly critical. Traditional tunnel deformation monitoring methods rely primarily on point sensors, which have several shortcomings. First, point sensors can typically only monitor within a limited spatial range, resulting in sparse spatial distribution of data collection, making it difficult to fully reflect the overall deformation of the tunnel. Second, the placement and maintenance of point sensors are complex, often requiring regular manual inspections, which not only increases labor costs but also affects the timeliness and accuracy of the data.

[0003] With the development of science and technology, distributed fiber optic sensing technology has gradually emerged, and its application in tunnel monitoring has received increasing attention. Distributed fiber optic sensors can achieve continuous measurement of strain, temperature, and humidity along the length of the optical fiber, and compared with traditional point sensors, they have higher spatial resolution and sensitivity. However, despite the advantages of distributed fiber optic technology in terms of monitoring coverage, it still faces some technical challenges. First, the measurement results of fiber optic sensors are affected by environmental factors such as temperature, humidity, and fiber aging, which may lead to deviations in the measurement data and affect the accuracy of deformation analysis. Second, current signal processing and data analysis methods are unable to fully eliminate environmental noise, resulting in low-quality useful information extracted.

[0004] Furthermore, while distributed fiber optic sensing technology enables real-time monitoring, converting the acquired sensor data into tunnel deformation parameters remains a technical bottleneck due to the lack of effective inversion models and algorithms. Therefore, a new method is urgently needed to systematically address these technical issues, improve the accuracy and reliability of tunnel deformation monitoring, and ensure safe tunnel operations.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The object of the present invention is to provide a method and device for inverting tunnel deformation parameters based on distributed optical fiber to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A tunnel deformation parameter inversion method based on distributed optical fiber, comprising the following steps:

[0009] S1: Select monitoring points inside the tunnel and determine the coordinates of each monitoring point, arrange optical fiber sensors at each monitoring point, obtain Brillouin scattering signals generated by the optical fiber sensors under stress at the corresponding monitoring points, and amplify the Brillouin scattering signals;

[0010] S2: Perform spectrum analysis on the amplified Brillouin scattering signal at each monitoring point to extract the Brillouin frequency shift corresponding to each monitoring point. Simultaneously, collect temperature change data, humidity change data, and the service life of the optical fiber sensor at the monitoring point. The temperature change data, humidity change data, and the service life of the optical fiber sensor are used to determine the environmental effect frequency shift correction value.

[0011] S3: Determine the structural strain at each monitoring point based on the Brillouin frequency shift and the environmental effect frequency shift correction value. Generate a 3D strain field cloud map based on the coordinates of the monitoring points. Construct a beam-shell hybrid finite element model based on the tunnel structure. Spatial registration is performed between the 3D strain field cloud map and the model geometric topology, and 3D meshing is performed.

[0012] S4: Map the structural strain of each monitoring point to the grid node at the corresponding position. Based on the spatial distance attenuation law, determine the structural strain of other grid nodes to complete the inversion of the deformation parameters of the entire tunnel.

[0013] Furthermore, the fiber optic sensors are arranged in a specific manner as follows: optical fibers are laid along the main axis of the tunnel vault, vertical optical fibers are arranged on the tunnel sidewalls, optical fibers arranged on the bottom plate are distributed in a mesh pattern, and spirally wound optical fibers are arranged in joints and weak surrounding rock sections;

[0014] The three-dimensional absolute coordinates of each monitoring point are measured using a total station. A coordinate sequence is generated by interpolating the straight optical fiber segments arranged at equal intervals on the tunnel vault, tunnel sidewalls, and tunnel floor. Parametric curve fitting is used to calculate the continuous spatial coordinates of the spirally wound optical fiber segments arranged in joints and weak surrounding rock sections. The coordinate data is aligned with the tunnel axis coordinate system in the BIM model, and a measuring point-structure mapping relationship is established to generate the coordinates of the monitoring point. The monitoring point is the smallest spatial unit on the optical fiber sensor that can independently measure strain, temperature, and humidity.

[0015] The Brillouin scattering signal is amplified according to the following logic: an erbium-doped fiber amplifier is added to the fiber measurement system. The erbium-doped fiber amplifier is pumped by a fiber laser to amplify the Brillouin scattering signal. The intensity of the amplified signal is increased by the formula:

[0016] I out =G*I in

[0017] Where, I out is the intensity of the amplified optical signal in mW, G is the amplifier gain, which is calibrated by the experiment, and I in is the input optical signal intensity, in mW;

[0018] A narrowband optical filter is used to filter the Brillouin scattering signal to remove external noise interference, and the passband of the optical filter is ±0.01nm.

[0019] Furthermore, a spectrum analyzer is used to perform spectrum analysis on the amplified Brillouin scattering signal to extract the Brillouin frequency shift, which is based on the intensity of the amplified optical signal I out For analysis, the calculation formula is:

[0020]

[0021] Where Δf B is the Brillouin frequency shift, n is the fiber refractive index, V A is the speed of sound, λ is the laser wavelength, I out is the intensity of the amplified optical signal, I ref is the reference signal strength;

[0022] The temperature change data ΔT and humidity change data ΔH refer to real-time dynamic changes relative to the reference value. The specific logic for determining ΔT and ΔH is as follows: after the optical fiber sensor is installed, the average temperature and humidity values ​​of the stable environment within 24 hours at the monitoring point are used as the initial reference temperature and initial reference humidity, and the absolute value of the difference between the real-time temperature and the initial reference temperature is determined as the temperature change data ΔT, and the absolute value of the difference between the real-time humidity and the initial reference humidity is determined as the humidity change data ΔH. The reference value is updated every 30 days to eliminate seasonal environmental impact.

[0023] Furthermore, the environmental effect frequency shift correction value is determined using the temperature change data, the humidity change data, and the service time of the optical fiber sensor. The service time of the optical fiber sensor refers to the cumulative operating time from the time the optical fiber sensor is installed and officially put into monitoring to the current moment.

[0024] Determining environmental effects

[0025]

[0026] Where, EFE is the environmental effect frequency shift correction value, ΔT is the temperature change data, T x is the real-time temperature, T0 is the reference temperature, sgn is the sign function, when T x -T0>0, sgn(T x -T0)=1, when Tx -T0<0, sgn(T x -T0)=-1, when T x -T0=0,sgn(T x -T0)=0, ΔH is the humidity change data, H x is the real-time humidity, H0 is the reference humidity, when H x - When H0>0, sgn(H x -H0)=1, when H x -H0<0, sgn(H x -H0)=-1, when H x -H0=0,sgn(H x -H0)=0, t is the service time of the sensor, e is a natural constant, α, β and γ are preset weight values, and α>β>γ>0.

[0027] Furthermore, the structural strain of each monitoring point is determined based on the Brillouin frequency shift and the environmental effect frequency shift correction value. The formula for determining the structural strain is as follows:

[0028]

[0029] Where ∈ is the structural strain, Δf B is the Brillouin frequency shift, EFE is the environmental effect frequency shift correction value, k strain is the strain sensitivity coefficient of the tunnel material;

[0030] Determine k strain The specific logic is as follows: Under laboratory conditions, a uniaxial tensile test is performed on a tunnel structure specimen. By applying a known tensile force, the strain of the specimen under different loads is recorded:

[0031]

[0032] Where,∈ s is the strain, ΔL is the incremental length of the specimen, and L0 is the original length;

[0033] The stress-strain curve is drawn using the experimental data, and the average slope of the linear part of the curve is used as the strain sensitivity coefficient k. stra i n :

[0034] The structural strain of each monitoring point is plotted into a three-dimensional strain field cloud diagram according to its coordinates.

[0035] Furthermore, based on the tunnel's geometric features and material properties, a beam-shell hybrid finite element model was constructed to reflect the tunnel's actual conditions. The 3D strain field cloud map was spatially registered with the finite element model to ensure that the monitoring point data corresponded to the model node positions. The model was then meshed in 3D to generate mesh nodes.

[0036] The model is divided into three-dimensional grids, and the specific logic is as follows: based on the geometric topological structure of the beam-shell hybrid finite element model, the tunnel body is divided into non-uniform grids, and a grid density layer is set in the deformation-sensitive area. The grid size decays exponentially with the distance from the monitoring point. The minimum grid size in the density area is 1 / 5 of the average spacing between the monitoring points. The transition grid technology is used for joints and weak surrounding rock sections. Tetrahedral elements are generated through Delaunay triangulation to ensure that the ratio of adjacent grid sizes does not exceed 1:3; the finally generated grid nodes include three types: monitoring point mapping nodes, density area subdivision nodes, and transition area interpolation nodes. Each monitoring point mapping node stores its three-dimensional coordinates and the structural strain at the corresponding monitoring point, and the remaining nodes store three-dimensional coordinates.

[0037] Furthermore, based on the spatial distance attenuation law, the structural strains of other grid nodes are determined according to the following formula:

[0038]

[0039] Where,∈ j is the structural strain of the jth other grid node, j is the index of the other grid node, ∈ i is the structural strain of the i-th monitoring point mapping node, N is the total number of monitoring point mapping nodes, i is the index of the monitoring point mapping node, ω ij is the weight coefficient, which indicates the influence of the i-th monitoring point mapping node on the j-th other grid node, and is related to the spatial distance;

[0040] Among them, determine ω ij The formula is as follows:

[0041]

[0042] Where r i The three-dimensional coordinates (x i ,y i ,z i ), r j is the three-dimensional coordinate (x j ,y j ,z j ), e is a natural constant;

[0043] Based on the above steps, the structural strain of each grid node is determined, and the inversion of the deformation parameters of the entire tunnel is completed.

[0044] The present invention further provides a tunnel deformation parameter inversion device based on distributed optical fiber. The tunnel deformation parameter inversion device based on distributed optical fiber is used to perform the above-mentioned tunnel deformation parameter inversion method based on distributed optical fiber, comprising:

[0045] A monitoring point placement and signal acquisition module is used to select monitoring points inside the tunnel and determine the coordinates of each monitoring point, place a fiber optic sensor at each monitoring point, obtain the Brillouin scattering signal generated by the fiber optic sensor under the stress at the corresponding monitoring point, and amplify the Brillouin scattering signal;

[0046] a data processing module for performing spectrum analysis on the amplified Brillouin scattering signal at each monitoring point to extract the Brillouin frequency shift corresponding to each monitoring point, simultaneously collecting temperature change data, humidity change data, and the service life of the optical fiber sensor at the monitoring point, and using the temperature change data, humidity change data, and the service life of the optical fiber sensor to determine an environmental effect frequency shift correction value;

[0047] The strain calculation and modeling module is used to determine the structural strain at each monitoring point based on the Brillouin frequency shift and the environmental effect frequency shift correction value, generate a three-dimensional strain field cloud map based on the coordinates of the monitoring point, construct a beam-shell hybrid finite element model based on the tunnel structure, spatially align the three-dimensional strain field cloud map with the model geometric topology, and perform three-dimensional meshing;

[0048] The strain mapping and inversion module is used to map the structural strain of each monitoring point to the grid node at the corresponding position, determine the structural strain of other grid nodes based on the spatial distance attenuation law, and complete the inversion of the deformation parameters of the entire tunnel.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] By deploying fiber optic sensors within the tunnel, the present invention achieves comprehensive and continuous monitoring of tunnel deformation. This novel monitoring technology overcomes the limitations of traditional methods and can acquire strain, temperature, and humidity data in real time at each monitoring point, significantly improving the accuracy and real-time nature of data acquisition. By using Brillouin scattering signals, the Brillouin frequency shift can be precisely extracted and converted into structural strain through spectral analysis, ensuring timely feedback on the tunnel's deformation status. This highly sensitive monitoring method effectively captures minute deformation changes, providing reliable data support for tunnel safety maintenance. Furthermore, the environmental effect frequency shift correction mechanism introduced in the present invention significantly improves the reliability of monitoring results. By monitoring temperature and humidity changes in real time and combining them with the service life of the fiber optic sensors, environmental effect frequency shift correction values ​​can be scientifically calculated, eliminating external environmental interference with the monitoring data. This process ensures data accuracy and makes the inversion results more reliable. Furthermore, using the constructed three-dimensional beam-shell hybrid finite element model, the strain field data generated at the monitoring points can be spatially aligned with the model, visually displaying the overall deformation status of the tunnel. By visualizing the strain field, engineers can more quickly and accurately identify potential deformation risks, providing a scientific basis for tunnel safety assessments and maintenance decisions. Overall, this invention not only improves the accuracy and efficiency of tunnel deformation monitoring but also lays the foundation for intelligent, real-time tunnel safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Schematic diagram of the overall method flow of the present invention;

[0052] Figure 2 Schematic diagram of the overall device module of the present invention. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0054] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0055] Example:

[0056] See also Figure 1 , the present invention provides a technical solution:

[0057] A tunnel deformation parameter inversion method based on distributed optical fiber, comprising the following steps:

[0058] S1: Select monitoring points inside the tunnel and determine the coordinates of each monitoring point, arrange optical fiber sensors at each monitoring point, obtain Brillouin scattering signals generated by the optical fiber sensors under stress at the corresponding monitoring points, and amplify the Brillouin scattering signals;

[0059] In this embodiment, the optical fiber sensors are arranged as follows: optical fibers are laid along the main axis of the tunnel vault, vertical optical fibers are arranged on the tunnel sidewalls, optical fibers are arranged on the floor in a cross-net pattern, and spirally wound optical fibers are arranged in joints and weak surrounding rock sections.

[0060] The three-dimensional absolute coordinates of each monitoring point are measured using a total station. A coordinate sequence is generated by interpolating the straight optical fiber segments arranged at equal intervals on the tunnel vault, tunnel sidewalls, and tunnel floor. Parametric curve fitting is used to calculate the continuous spatial coordinates of the spirally wound optical fiber segments arranged in joints and weak surrounding rock sections. The coordinate data is aligned with the tunnel axis coordinate system in the BIM model, and a measuring point-structure mapping relationship is established to generate the coordinates of the monitoring point. The monitoring point is the smallest spatial unit on the optical fiber sensor that can independently measure strain, temperature, and humidity.

[0061] The Brillouin scattering signal is amplified according to the following logic: an erbium-doped fiber amplifier is added to the fiber measurement system. The erbium-doped fiber amplifier is pumped by a fiber laser to amplify the Brillouin scattering signal. The intensity of the amplified signal is increased by the formula:

[0062] Iout =G*I in

[0063] Where, I out is the intensity of the amplified optical signal in mW, G is the amplifier gain, G = 10, I in is the input optical signal intensity, in mW;

[0064] A narrowband optical filter is used to filter the Brillouin scattering signal to remove external noise interference, and the passband of the optical filter is ±0.01nm.

[0065] The advantage of the S1 lies in its ability to comprehensively monitor the entire tunnel structure by selecting multiple monitoring points and deploying fiber optic sensors within the tunnel. Compared to traditional localized sensor deployment, this approach significantly improves spatial coverage and data continuity, avoiding blind spots and information loss that can result from localized monitoring. Furthermore, the amplification of Brillouin scattering signals ensures effective capture of weak signals, thereby improving the sensitivity and accuracy of monitoring data.

[0066] By introducing a distributed fiber optic sensor layout in step S1, not only is real-time monitoring of tunnel deformation possible, but it also lays the foundation for subsequent data analysis. This comprehensive monitoring enables timely identification of potential tunnel risks, reduces the probability of engineering accidents, and provides a scientific basis for maintenance decisions. Furthermore, the effective implementation of this step significantly improves the efficiency of data acquisition and processing throughout the entire solution, facilitating subsequent spectral analysis and environmental effect correction, thereby ensuring the reliability and accuracy of the final inversion results.

[0067] S2: Perform spectrum analysis on the amplified Brillouin scattering signal at each monitoring point to extract the Brillouin frequency shift corresponding to each monitoring point. Simultaneously, collect temperature change data, humidity change data, and the service life of the optical fiber sensor at the monitoring point. The temperature change data, humidity change data, and the service life of the optical fiber sensor are used to determine the environmental effect frequency shift correction value.

[0068] In this embodiment, a spectrum analyzer is used to perform spectrum analysis on the amplified Brillouin scattering signal to extract the Brillouin frequency shift, which is based on the intensity of the amplified optical signal I out For analysis, the calculation formula is:

[0069]

[0070] Where Δf B is the Brillouin frequency shift, n is the fiber refractive index, n=1.468, V A is the speed of sound waves, V A=5000m / s, λ is the laser wavelength, I out is the intensity of the amplified optical signal, I ref is the reference signal strength;

[0071] Confirm I ref The specific logic is as follows: place a piece of unstrained bare fiber in a constant temperature environment of about 20°C, and the bare fiber length is ≥10m. Use the same laser wavelength λ and power to measure the Brillouin scattering signal intensity. Repeat the measurement multiple times and take the average value as I ref .

[0072] The temperature change data ΔT and humidity change data ΔH refer to real-time dynamic changes relative to the reference value. The specific logic for determining ΔT and ΔH is as follows: after the optical fiber sensor is installed, the average temperature and humidity values ​​of the stable environment within 24 hours at the monitoring point are used as the initial reference temperature and initial reference humidity, and the absolute value of the difference between the real-time temperature and the initial reference temperature is determined as the temperature change data ΔT, and the absolute value of the difference between the real-time humidity and the initial reference humidity is determined as the humidity change data ΔH. The reference value is updated every 30 days to eliminate seasonal environmental impact.

[0073] Temperature data and humidity data are collected through digital temperature sensors and digital humidity sensors respectively.

[0074] The environmental effect frequency shift correction value is determined using the temperature change data, the humidity change data, and the service time of the optical fiber sensor. The service time of the optical fiber sensor refers to the cumulative operating time from the time the optical fiber sensor is installed and officially put into monitoring to the current moment.

[0075] The formula for determining the frequency shift correction value for environmental effects is as follows:

[0076]

[0077] Where, EFE is the environmental effect frequency shift correction value, ΔT is the temperature change data, T x is the real-time temperature, T0 is the reference temperature, sgn is the sign function, when T x -T0>0, sgn(T x -T0)=1, when T x -T0<0, sgn(T x -T0)=-1, when T x -T0=0,sgn(T x -T0)=0. ΔH is the humidity change data, H x is the real-time humidity, H0 is the reference humidity, when H x - When H0>0, sgn(H x -H0)=1, when Hx -H0<0, sgn(H x -H0)=-1, when H x -H0=0,sgn(H x -H0) = 0. t is the sensor's service life, e is a natural constant, and α, β, and γ are preset weights, with α = 0.5, β = 0.3, and γ = 0.2. This is because temperature and humidity are real-time dynamic interference sources, their impact far greater than the slowly accumulated effects of aging. Changes in temperature and humidity directly alter the refractive index and acoustic velocity of the optical fiber, significantly affecting the Brillouin frequency shift. Experiments have shown that for every 1°C change in temperature, the Brillouin frequency shift can shift by approximately 1 MHz / °C. Relatively speaking, temperature has the greatest impact, so it is assigned the highest weight. Humidity changes indirectly affect frequency shift through material expansion. Fiber aging is a long-term cumulative effect, with a frequency shift typically ranging from 0.01 to 0.1 MHz / year, approaching saturation over time. Therefore, it is assigned the lowest weight.

[0078] For the temperature correction term α*ln(1+ΔT), the effect of temperature on Brillouin frequency shift is usually nonlinear, and the logarithmic function can better describe this relationship, avoiding excessive correction values ​​when the temperature changes greatly. +1 can ensure that when ΔT=0, the temperature correction term is zero, avoiding mathematical uncertainty. The temperature weight coefficient α is used to reflect the sensitivity of temperature to frequency shift. For the humidity correction term The effect of humidity on optical fiber is usually weaker than that of temperature, and the effect tends to be saturated under high humidity. The square root function can weaken the sudden change in the high humidity area and make the correction smoother. -t ), in exponential form, is used to characterize that the aging effect of optical fiber sensors usually accumulates gradually over time, but does not grow indefinitely, but tends to saturation, such as the fatigue limit of the material.

[0079] The environmental effect frequency shift correction (EFE) is used to correct for environmental and sensor aging interference on the Brillouin frequency shift. Temperature fluctuations cause changes in the fiber's refractive index and acoustic velocity, leading to a drift in the Brillouin frequency shift. This drift increases with increasing temperature, and generally increases with increasing temperature, while decreases with decreasing temperature. This indicates a positive correlation between ΔT and EFE. Humidity fluctuations can cause the fiber coating to expand or contract due to moisture absorption, or directly alter the acoustic properties of the surrounding medium, thereby affecting the Brillouin frequency shift. This effect increases with increasing humidity. When the fiber's polymer coating absorbs moisture, it expands, exerting lateral pressure on the fiber, causing an increase in the Brillouin frequency. Conversely, when humidity decreases, the coating loses water and contracts, reducing stress on the fiber and reducing the Brillouin frequency shift. This indicates a positive correlation between ΔH and EFE. As the usage time increases, the polymer material in the optical fiber may undergo chemical changes, resulting in changes in the material's physical properties, such as elastic modulus and density. These changes will affect the propagation speed of sound waves in the optical fiber, thereby causing changes in the Brillouin frequency shift. Therefore, when t increases, EFE will also increase accordingly, indicating that t and EFE are positively correlated.

[0080] The advantage of step S2 lies in its in-depth processing of the Brillouin scattering signal at each monitoring point through spectral analysis, accurately extracting the Brillouin frequency shift and, combined with real-time temperature and humidity data, correcting for environmental effects. This approach significantly improves the accuracy of monitoring results. Compared to traditional methods that typically rely solely on single signal analysis and static environmental parameters, the dynamic analysis in step S2 provides real-time feedback, ensuring that the monitoring data more accurately reflects the actual deformation of the tunnel.

[0081] By implementing step S2, the overall solution achieves high-precision inversion of tunnel deformation parameters. The combination of spectral analysis and environmental effect correction eliminates interference from external environmental factors in monitoring results, enhancing data reliability. This process provides accurate baseline data for subsequent structural strain calculations, thereby improving the effectiveness and scientific nature of the entire tunnel deformation monitoring system. Furthermore, this step facilitates automated and real-time data processing, enabling engineers to respond quickly and ensuring tunnel safety and stability.

[0082] S3: Determine the structural strain at each monitoring point based on the Brillouin frequency shift and the environmental effect frequency shift correction value. Generate a 3D strain field cloud map based on the coordinates of the monitoring points. Construct a beam-shell hybrid finite element model based on the tunnel structure. Spatial registration is performed between the 3D strain field cloud map and the model geometric topology, and 3D meshing is performed.

[0083] In this embodiment, the structural strain of each monitoring point is determined based on the Brillouin frequency shift and the environmental effect frequency shift correction value. The formula for determining the structural strain is as follows:

[0084]

[0085] Where, ∈ is the structural strain, which is used to characterize the degree of deformation of the structure under the action of external force; Δf B is the Brillouin frequency shift, EFE is the environmental effect frequency shift correction value, k strain is the strain sensitivity coefficient of the tunnel material; the core idea of ​​this formula is to use the difference between the Brillouin frequency shift and the environmental effect frequency shift correction value, combined with the strain sensitivity coefficient of the material, to determine the actual strain of the tunnel structure.

[0086] Determine k strain The specific logic is as follows: Under laboratory conditions, a uniaxial tensile test is performed on a tunnel structure specimen. By applying a known tensile force, the strain of the specimen under different loads is recorded:

[0087]

[0088] Where,∈ s is the strain, ΔL is the incremental length of the specimen, and L0 is the original length;

[0089] The stress-strain curve is drawn using the experimental data, and the average slope of the linear part of the curve is used as the strain sensitivity coefficient k. strain :

[0090] The structural strain of each monitoring point is plotted into a three-dimensional strain field cloud diagram according to its coordinates.

[0091] Based on the tunnel's geometric features and material properties, a beam-shell hybrid finite element model was constructed to reflect the tunnel's actual conditions. The 3D strain field cloud map was spatially registered with the finite element model to ensure that the monitoring point data corresponded to the model node locations. The model was then divided into 3D meshes to generate mesh nodes.

[0092] The model is divided into three-dimensional grids, and the specific logic is as follows: based on the geometric topological structure of the beam-shell hybrid finite element model, the tunnel body is divided into non-uniform grids, and a grid density layer is set in the deformation-sensitive area. The grid size decays exponentially with the distance from the monitoring point. The minimum grid size in the density area is 1 / 5 of the average spacing between the monitoring points. The transition grid technology is used for joints and weak surrounding rock sections. Tetrahedral elements are generated through Delaunay triangulation to ensure that the ratio of adjacent grid sizes does not exceed 1:3; the finally generated grid nodes include three types: monitoring point mapping nodes, density area subdivision nodes, and transition area interpolation nodes. Each monitoring point mapping node stores its three-dimensional coordinates and the structural strain at the corresponding monitoring point, and the remaining nodes store three-dimensional coordinates.

[0093] The advantage of step S3 lies in its ability to accurately calculate the structural strain at each monitoring point and generate a three-dimensional strain field contour map by combining the Brillouin frequency shift and the environmental effect frequency shift correction. This process not only provides a visual representation of the tunnel's overall deformation state but also ensures a high-precision match between the monitoring data and the model's geometric topology by constructing a beam-shell hybrid finite element model based on the tunnel structure. Compared to traditional two-dimensional monitoring methods, step S3 enables comprehensive analysis in three dimensions, providing a deeper and more comprehensive understanding of the tunnel's deformation state.

[0094] By implementing step S3, the overall solution enables a higher level of deformation parameter inversion. The generation of three-dimensional strain field cloud maps enables engineers to intuitively identify the tunnel's deformation patterns and potential weaknesses, providing a crucial basis for subsequent maintenance and reinforcement. Furthermore, the introduction of a beam-shell hybrid finite element model provides a more realistic physical representation for tunnel structural analysis, improving the accuracy and reliability of the inversion results. This process not only enhances data visualization but also facilitates intelligent decision-making, ensuring the safety and sustainability of the tunnel.

[0095] S4: Map the structural strain of each monitoring point to the grid node at the corresponding position, determine the structural strain of other grid nodes based on the spatial distance attenuation law, and complete the inversion of the deformation parameters of the entire tunnel;

[0096] In this embodiment, the structural strains of other grid nodes are determined based on the spatial distance attenuation law, and the formula used is:

[0097]

[0098] Where,∈ j is the structural strain of the jth other grid node, j is the index of the other grid node, ∈ i is the structural strain of the monitoring point mapping node, N is the total number of monitoring point mapping nodes, i is the index of the monitoring point mapping node, ω ijis the weight coefficient, representing the degree of influence of monitoring point mapping node i on the jth grid node, which is related to spatial distance. The main purpose of this formula is to transfer the strain information of a group of monitoring points to a specific grid node j through a weighted averaging method, thereby predicting the structural strain of grid node j. For each monitoring point mapping node, its strain is assigned a weight based on its distance from the grid node. Monitoring point mapping nodes with higher weights contribute more to the strain calculation of the grid node, while monitoring points with lower weights contribute less. The numerator of the formula is the sum of the strain values ​​of all monitoring points multiplied by their corresponding weights, reflecting the combined influence of all monitoring points in calculating the grid node strain. The denominator is the sum of all weights, which is used to normalize the result to a reasonable range and ensure that the final strain value is a weighted average. In tunnel deformation parameter monitoring, it is usually impossible to deploy monitoring points at every location. Therefore, this distance-attenuated weighted averaging method can effectively utilize existing monitoring data to infer strain in other areas. This method not only increases monitoring density but also better reflects the strain distribution of the entire structure.

[0099] Among them, determine ω ij The formula is as follows:

[0100]

[0101] Where, ω ij is the weight coefficient, and its value is related to the spatial distance between the two. The closer the distance, the greater the weight; r i The three-dimensional coordinates (x i ,y i ,z i ), r j is the three-dimensional coordinate (x j ,y j ,z j ), e is a natural constant; the coefficient of the square of the distance is set to -2, so that the weight decays at a faster rate, ensuring that the influence of distant monitoring points on the grid nodes is negligible.

[0102] Based on the above steps, the structural strain of each grid node is determined, and the inversion of the deformation parameters of the entire tunnel is completed.

[0103] The advantage of step S4 is that by mapping the structural strain at each monitoring point to the corresponding grid node and inferring the structural strain at other grid nodes using the spatial distance decay law, a comprehensive inversion of the deformation state of the entire tunnel is achieved. This method not only improves the spatial distribution accuracy of strain data but also enables a reasonable estimation of the tunnel's deformation characteristics over a wider range. Compared to traditional methods that rely solely on local monitoring data, step S4 can more comprehensively reflect the overall structural health of the tunnel.

[0104] By implementing step S4, the overall solution enables refined inversion of tunnel deformation parameters. This step extends deformation parameters beyond direct observation at monitoring points. Instead, it enhances understanding of the entire structure through spatial inference, improving the ability to identify potential areas of deformation and damage. This advantage enables engineers to more quickly develop maintenance measures and reinforcement plans, ensuring the safety and stability of the tunnel. Furthermore, the precise data mapping and extrapolation in step S4 provides a crucial data foundation for monitoring and analysis, enhancing the reliability and scientific nature of the entire tunnel monitoring system.

[0105] See also Figure 2 A tunnel deformation parameter inversion device based on distributed optical fiber, comprising:

[0106] A monitoring point placement and signal acquisition module is used to select monitoring points inside the tunnel and determine the coordinates of each monitoring point, place a fiber optic sensor at each monitoring point, obtain the Brillouin scattering signal generated by the fiber optic sensor under the stress at the corresponding monitoring point, and amplify the Brillouin scattering signal;

[0107] a data processing module for performing spectrum analysis on the amplified Brillouin scattering signal at each monitoring point to extract the Brillouin frequency shift corresponding to each monitoring point, simultaneously collecting temperature change data, humidity change data, and the service life of the optical fiber sensor at the monitoring point, and using the temperature change data, humidity change data, and the service life of the optical fiber sensor to determine an environmental effect frequency shift correction value;

[0108] The strain calculation and modeling module is used to determine the structural strain at each monitoring point based on the Brillouin frequency shift and the environmental effect frequency shift correction value, generate a three-dimensional strain field cloud map based on the coordinates of the monitoring point, construct a beam-shell hybrid finite element model based on the tunnel structure, spatially align the three-dimensional strain field cloud map with the model geometric topology, and perform three-dimensional meshing;

[0109] The strain mapping and inversion module is used to map the structural strain of each monitoring point to the grid node at the corresponding position, determine the structural strain of other grid nodes based on the spatial distance attenuation law, and complete the inversion of the deformation parameters of the entire tunnel.

[0110] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0111] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0112] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0113] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A tunnel deformation parameter inversion method based on distributed optical fiber, characterized in that: The specific steps include: S1: Select monitoring points inside the tunnel and determine the coordinates of each monitoring point, arrange optical fiber sensors at each monitoring point, obtain Brillouin scattering signals generated by the optical fiber sensors under stress at the corresponding monitoring points, and amplify the Brillouin scattering signals; S2: Perform spectrum analysis on the amplified Brillouin scattering signal at each monitoring point to extract the Brillouin frequency shift corresponding to each monitoring point. Simultaneously, collect temperature change data, humidity change data, and the service life of the optical fiber sensor at the monitoring point. The temperature change data, humidity change data, and the service life of the optical fiber sensor are used to determine the environmental effect frequency shift correction value. S3: Determine the structural strain at each monitoring point based on the Brillouin frequency shift and the environmental effect frequency shift correction value. Generate a 3D strain field cloud map based on the coordinates of the monitoring points. Construct a beam-shell hybrid finite element model based on the tunnel structure. Spatial registration is performed between the 3D strain field cloud map and the model geometric topology, and 3D meshing is performed. S4: Map the structural strain of each monitoring point to the grid node at the corresponding position, determine the structural strain of other grid nodes based on the spatial distance attenuation law, and complete the inversion of the deformation parameters of the entire tunnel; The fiber optic sensors are arranged in the following manner: optical fibers are laid along the main axis of the tunnel vault, vertical optical fibers are arranged on the tunnel sidewalls, optical fibers are arranged on the bottom plate in a cross-net pattern, and spirally wound optical fibers are arranged in joints and weak surrounding rock sections. The three-dimensional absolute coordinates of each monitoring point are measured using a total station. A coordinate sequence is generated by interpolating the straight optical fiber segments arranged at equal intervals on the tunnel vault, tunnel sidewalls, and tunnel floor. Parametric curve fitting is used to calculate the continuous spatial coordinates of the spirally wound optical fiber segments arranged in joints and weak surrounding rock sections. The coordinate data is aligned with the tunnel axis coordinate system in the BIM model, and a measuring point-structure mapping relationship is established to generate the coordinates of the monitoring point. The monitoring point is the smallest spatial unit on the optical fiber sensor that can independently measure strain, temperature, and humidity. The Brillouin scattering signal is amplified according to the following logic: an erbium-doped fiber amplifier is added to the fiber measurement system. The erbium-doped fiber amplifier is pumped by a fiber laser to amplify the Brillouin scattering signal. The intensity of the amplified signal is increased by the formula: I out =G*I in Where, I out is the intensity of the amplified optical signal in mW, G is the amplifier gain, which is calibrated by the experiment, and I in is the input optical signal intensity, in mW; A narrowband optical filter is used to filter the Brillouin scattering signal to remove external noise interference, wherein the passband of the optical filter is ±0.01 nm; The spectrum of the amplified Brillouin scattering signal is analyzed by a spectrum analyzer to extract the Brillouin frequency shift, which is based on the intensity of the amplified optical signal I out For analysis, the calculation formula is: Where Δf B is the Brillouin frequency shift, n is the fiber refractive index, V A is the speed of sound, λ is the laser wavelength, I out is the intensity of the amplified optical signal, I ref is the reference signal strength; The temperature change data ΔT and humidity change data ΔH refer to real-time dynamic changes relative to the reference value. The specific logic for determining ΔT and ΔH is as follows: after the optical fiber sensor is installed, the average temperature and humidity values ​​of the stable environment at the monitoring point within 24 hours are used as the initial reference temperature and initial reference humidity. The absolute value of the difference between the real-time temperature and the initial reference temperature is determined as the temperature change data ΔT, and the absolute value of the difference between the real-time humidity and the initial reference humidity is determined as the humidity change data ΔH. The reference value is updated every 30 days to eliminate seasonal environmental effects. The environmental effect frequency shift correction value is determined using the temperature change data, the humidity change data, and the service time of the optical fiber sensor. The service time of the optical fiber sensor refers to the cumulative operating time from the time the optical fiber sensor is installed and officially put into monitoring to the current moment. The formula for determining the frequency shift correction value for environmental effects is as follows: Where, EFE is the environmental effect frequency shift correction value, ΔT is the temperature change data, T x is the real-time temperature, T0 is the reference temperature, sgn is the sign function, when T x -T0>0, sgn(T x -T0)=1, when T x -T0<0, sgn(T x -T0)=-1, when T x -T0=0,sgn(T x -T0)=0, ΔH is the humidity change data, H x is the real-time humidity, H0 is the reference humidity, when H x -H0>0, sgn(H x -H0)=1, when H x -H0<0, sgn(H x -H0)=-1, when H x -H0=0,sgn(H x -H0)=0, t is the service time of the sensor, e is a natural constant, α, β and γ are preset weight values, and α>β>γ>0; The structural strain of each monitoring point is determined based on the Brillouin frequency shift and the environmental effect frequency shift correction value. The formula for determining the structural strain is as follows: Where ∈ is the structural strain, Δf B is the Brillouin frequency shift, EFE is the environmental effect frequency shift correction value, k strain is the strain sensitivity coefficient of the tunnel material; Determine k strain The specific logic is as follows: Under laboratory conditions, a uniaxial tensile test is performed on a tunnel structure specimen. By applying a known tensile force, the strain of the specimen under different loads is recorded: Where,∈ s is the strain, ΔL is the incremental length of the specimen, and L0 is the original length; The stress-strain curve is drawn using the experimental data, and the average slope of the linear part of the curve is used as the strain sensitivity coefficient k. strain : The structural strain of each monitoring point is plotted into a three-dimensional strain field cloud map according to its coordinates; Based on the spatial distance attenuation law, the structural strain of other grid nodes is determined according to the following formula: Where,∈ j is the structural strain of the jth other grid node, j is the index of the other grid node, ∈ i is the structural strain of the i-th monitoring point mapping node, N is the total number of monitoring point mapping nodes, i is the index of the monitoring point mapping node, ω ij is the weight coefficient, which indicates the influence of the i-th monitoring point mapping node on the j-th other grid node, and is related to the spatial distance; Among them, determine ω ij The formula is as follows: Where r i The three-dimensional coordinates (x i ,y i ,z i ), r j is the three-dimensional coordinate (x j ,y j ,z j ), e is a natural constant; Based on the above steps, the structural strain of each grid node is determined, and the inversion of the deformation parameters of the entire tunnel is completed.

2. The tunnel deformation parameter inversion method based on distributed optical fiber according to claim 1, characterized in that: Based on the tunnel's geometric features and material properties, a beam-shell hybrid finite element model was constructed to reflect the tunnel's actual conditions. The 3D strain field cloud map was spatially registered with the finite element model to ensure that the monitoring point data corresponded to the model node locations. The model was then divided into 3D meshes to generate mesh nodes. The model is divided into three-dimensional grids, and the specific logic is as follows: based on the geometric topological structure of the beam-shell hybrid finite element model, the tunnel body is divided into non-uniform grids, and a grid density layer is set in the deformation-sensitive area. The grid size decays exponentially with the distance from the monitoring point. The minimum grid size in the density area is 1 / 5 of the average spacing between the monitoring points. The transition grid technology is used for joints and weak surrounding rock sections. Tetrahedral elements are generated through Delaunay triangulation to ensure that the ratio of adjacent grid sizes does not exceed 1:3; the finally generated grid nodes include three types: monitoring point mapping nodes, density area subdivision nodes, and transition area interpolation nodes. Each monitoring point mapping node stores its three-dimensional coordinates and the structural strain at the corresponding monitoring point, and the remaining nodes store three-dimensional coordinates.

3. A tunnel deformation parameter inversion device based on distributed optical fiber, characterized by: The distributed optical fiber-based tunnel deformation parameter inversion device is used to execute the distributed optical fiber-based tunnel deformation parameter inversion method according to any one of claims 1 to 2, comprising: A monitoring point placement and signal acquisition module is used to select monitoring points inside the tunnel and determine the coordinates of each monitoring point, place a fiber optic sensor at each monitoring point, obtain the Brillouin scattering signal generated by the fiber optic sensor under the stress at the corresponding monitoring point, and amplify the Brillouin scattering signal; a data processing module for performing spectrum analysis on the amplified Brillouin scattering signal at each monitoring point to extract the Brillouin frequency shift corresponding to each monitoring point, simultaneously collecting temperature change data, humidity change data, and the service life of the optical fiber sensor at the monitoring point, and using the temperature change data, humidity change data, and the service life of the optical fiber sensor to determine an environmental effect frequency shift correction value; The strain calculation and modeling module is used to determine the structural strain at each monitoring point based on the Brillouin frequency shift and the environmental effect frequency shift correction value, generate a three-dimensional strain field cloud map based on the coordinates of the monitoring point, construct a beam-shell hybrid finite element model based on the tunnel structure, spatially align the three-dimensional strain field cloud map with the model geometric topology, and perform three-dimensional meshing; The strain mapping and inversion module is used to map the structural strain of each monitoring point to the grid node at the corresponding position, determine the structural strain of other grid nodes based on the spatial distance attenuation law, and complete the inversion of the deformation parameters of the entire tunnel.

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