Optical fiber-structure body coupling coordination degree evaluation method

Abstract

The invention relates to the technical field of hydraulic structure monitoring, and discloses an optical fiber-structural body coupling coordination degree evaluation method, which specifically comprises the following steps: step 1, hydraulic structure physical model building and data acquisition: building a hydraulic structure physical model, and burying distributed optical fibers in a key area of the hydraulic structure physical model; the optical fiber strain sensor is connected with the BOTDA demodulator and is used for acquiring optical fiber strain data; and structural body strain and displacement monitoring is carried out by using a DIC. The method not only considers the strain data of the optical fiber, but also introduces the strain data of the structural body, and enables the evaluation of the coupling coordination degree to be more comprehensive and scientific through the time sequence analysis of the data. According to the coupling coordination degree evaluation result, the arrangement position of the optical fiber and the model selection of the sensor are guided, so that the performance of the whole optical fiber monitoring system is optimized, the adaptability and the reliability of the system in a complex environment are improved, and parts which are not involved in the device are the same as those in the prior art or can be realized by adopting the prior art.

Description

Technical Field

[0001] This invention relates to the field of hydraulic structure monitoring technology, and in particular to a method for evaluating the coordination degree of fiber-optic-structure coupling. Background Technology

[0002] Fiber optic monitoring technology has been widely used in monitoring the deformation of hydraulic structures due to its high sensitivity and resistance to electromagnetic interference. However, because hydraulic structures exhibit large deformation characteristics during movement, traditional fiber-to-structure coupling evaluation methods cannot meet monitoring requirements, necessitating a new coupling coordination evaluation method to improve the accuracy and effectiveness of monitoring.

[0003] The coupling between optical fiber and the structure under test is a common key issue in optical fiber monitoring technology, especially in the field of optical fiber monitoring of hydraulic structures. Because hydraulic structures have large deformation characteristics when they are damaged, not only is coupling important, but also survival rate is also important.

[0004] Currently, theoretical models of fiber-structure deformation coupling can be divided into two main categories: interface progressive failure mechanics models and strain transfer models. The progressive failure mechanics models can be further divided into strain softening models and ideal elastic-plastic models, while the strain transfer models are mainly used to quantify the strain transfer coefficient between layers of sensing optical cables.

[0005] The progressive failure mechanics model of the interface is mainly based on pull-out tests of optical sensing cables in soil, represented by the international standard ASTM F3079-14 (2014). It primarily uses the interfacial shear strength as an indicator to evaluate fiber-soil deformation coupling, assuming that if the interfacial shear stress is less than the shear strength, the fiber strain measurement is valid. This evaluation method neglects the strain transfer from the soil to the optical sensing cable and cannot correct the strain data.

[0006] Strain transfer models were initially proposed by scholars in the field of structural health monitoring, aiming to quantitatively describe the strain transfer from civil structures to sensing optical fibers. These models are typically based on shear hysteresis theory (with the intermediate layer in a pure shear state), and there are also fiber strain-shear displacement calculation methods or models based on beam theory or geometric principles, which will not be elaborated upon here. Currently, the quantitative relationship between fiber strain measurements and external parameters such as load, displacement, and deflection is mainly established through calibration tests. However, research on the definition, theoretical models, and evaluation methods of fiber-structure deformation coupling under complex deformation conditions is still relatively limited.

[0007] Due to differences in monitoring objects and purposes, the strategies for data interpretation and analysis also differ. Specifically, most studies focus on improving the coupling between the optical fiber and the measured structure to enhance measurement accuracy, which plays a crucial role in many applications. Structural fracture almost inevitably leads to weakened or completely decoupled coupling between the optical fiber and the structure, a fact confirmed in similar physical model experiments. The mechanical behavior of rigid structures before and after fracture is a key research focus, exhibiting characteristics such as layered, blocky, rotational, slipping, and large deformation. Deep structures possess high ground stress and strong disturbance characteristics in their strain fields, while current sensing optical cables have shear strengths lower than the shear stress of the structure, exhibiting large deformation and high stress characteristics. Structural fracture easily leads to optical cable breakage. A review of optical fiber monitoring examples reveals that in field measurements, as structural movement intensifies, optical cables within boreholes are prone to breakage, resulting in monitoring failure.

[0008] In the field application of fiber optic monitoring for hydraulic structures, simply increasing the coupling between the fiber and the structure often leads to excessive fiber loss or breakage when the structure undergoes large deformations (especially in areas of floor bulging or fault activation in tunnels), resulting in frequent failures of the fiber optic monitoring system. Alternatively, some systems arbitrarily assume that monitoring data with low coupling is unreliable, arbitrarily assuming that fiber-structure decoupling occurs at crack locations due to critical layer breakage or delamination, thus abandoning the acquisition or analysis of fiber optic data. Methods for evaluating the effectiveness of fiber optic monitoring data based on fiber coupling are not well-suited for hydraulic engineering applications. It is necessary to combine field engineering experience to develop targeted methods for evaluating the effectiveness of fiber optic monitoring data in scenarios involving large deformations in hydraulic structures, and to continuously improve fiber optic monitoring theories, methods, and systems by linking theory with field practice. In hydraulic structure monitoring, the fiber optic deployment location and sensor selection have a significant impact on monitoring effectiveness. Traditional decision-making methods rely on expert experience and trial and error, which are time-consuming and inefficient. With the development of deep learning technology, more accurate decision analysis can be performed using model prediction results.

[0009] The interaction between optical fibers and structures should be fully considered, a new method for evaluating the coupling coordination degree of optical fibers and structures should be developed, and the monitoring effect of optical fibers should be classified and evaluated, so as to expand the application scope and fields of optical fibers and provide theoretical guidance for optical fiber monitoring of large deformation of hydraulic structures.

[0010] To address this, we propose a method for evaluating the coordination degree of fiber-structure coupling. Summary of the Invention

[0011] The main objective of this invention is to provide a method for evaluating the coupling coordination degree of optical fiber and structure. This method can effectively solve the problem that in the field application of optical fiber monitoring of hydraulic structures, simply increasing the coupling between the optical fiber and the structure can lead to excessive optical fiber loss or breakage when the structure undergoes large deformation, resulting in frequent failures of the optical fiber monitoring system. Alternatively, it can lead to the arbitrary conclusion that monitoring data with low coupling is unreliable, and that the optical fiber-structure decoupling at the crack location is caused by the failure of the key layer or the development of delamination, thus abandoning the acquisition or analysis of optical fiber data.

[0012] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0013] A method for evaluating the compatibility of fiber-structure coupling includes the following steps:

[0014] Step 1: Construction of physical model of hydraulic structure, data acquisition and processing: Distributed optical fiber is buried in the constructed physical model of hydraulic structure; and connected to BOTDA demodulator for acquiring optical fiber strain data; DIC is used for strain monitoring to acquire structural strain data; and IQR-based method is used to obtain processed optical fiber strain data and structural strain data.

[0015] Step 2: Coupling analysis: Select the time series data of fiber strain data and DIC monitoring of structural strain data in the fiber-optic-structure system of the hydraulic structure physical model test as the data source, input them into SPSS statistical software, and calculate the comprehensive evaluation index S1(t) and S2(t).

[0016] Step 3: Establishment of evaluation index system: Based on the coupling theory in physics, the evaluation index system of fiber-structure system is determined, including processed fiber strain data, processed structure strain data, and comprehensive evaluation indices S1(t) and S2(t).

[0017] Step 4: Coupling coordination degree calculation: Using the comprehensive evaluation indices S1(t) and S2(t), calculate the coupling coordination degree of the fiber-structure system and analyze the degree of interrelation between fiber deformation and structural deformation elements in the fiber-structure system.

[0018] Step 5: Coupling Coordination Prediction: Extract time series features, statistical features, and frequency domain features, and combine them with the comprehensive evaluation indices S1(t) and S2(t). Select a Long Short-Term Memory (LSTM) network as a deep learning model, train the LSTM model using the extracted features, and learn the relationship between fiber strain and structural strain data. Adjust the model parameters through cross-validation to achieve the best performance and perform coupling coordination prediction.

[0019] Step 6: Intelligent classification and evaluation of coupling coordination degree: Based on the coupling coordination degree calculation in Step 4, the fiber-structure coupling relationship is divided into four types, and the classification and evaluation are carried out based on the coupling coordination degree prediction results in Step 5.

[0020] Step 7: Intelligent Adaptation of Fiber Optic Monitoring System: Based on the coupling coordination degree classification evaluation results in Step 6, adapt the fiber optic deployment location and sensor selection to improve the application effect of the fiber optic monitoring system in hydraulic structure monitoring.

[0021] Furthermore, in step 1, the hydraulic structure physical model is constructed using water, plaster, and white powder at a scale of 1:200 to simulate the real hydraulic structure and its working environment.

[0022] Furthermore, the method based on IQR (interquartile range) described in step 1 includes the following steps:

[0023] S1. Identify and remove abnormal values ​​in fiber optic strain data and structural strain data that are outside the normal range;

[0024] S2. Use the moving average filtering method to remove high-frequency noise, and apply the isolated forest algorithm to identify and process outliers.

[0025] S3. Perform Z-score standardization on the cleaned data to give it zero mean and unit variance.

[0026] S4. For data with different dimensions, use the min-max normalization method to scale the data to the range of [0,1].

[0027] S5. Convert the standardized data into a serialization format suitable for input to deep learning models;

[0028] S6. Based on the requirements of time step and sequence length, the data is divided into multiple time windows to obtain the processed fiber strain data and structural strain data.

[0029] Furthermore, in step 4, when calculating the coupling coordination degree, where,

[0030] The coupling function CS(t) between the fiber and the structure is:

[0031]

[0032] In the formula, S1(t) represents the comprehensive evaluation index of the optical fiber system under a certain working face advance distance;

[0033] S2(t) represents the comprehensive evaluation index of the structural system under a certain working face advance distance;

[0034] Coupling degree CS(t)∈[0,1];

[0035] Let αS and βS be undetermined coefficients, and for the fiber-structure system, they can be obtained using the following formula:

[0036]

[0037] The fiber-structure coupling compatibility function can be expressed as:

[0038]

[0039] In the formula, D S (t) represents the coupling coordination degree of the fiber-to-structure system; T S (t) is the overall coordination index of the fiber-optic structure system, which reflects the complementary relationship between the subsystems.

[0040] Furthermore, in step 4, as the working face advances further, the deformation of the structure increases, the comprehensive evaluation index of the optical fiber remains basically unchanged, while the comprehensive evaluation index of the structure shows a downward trend; the greater the deformation of the structure, the lower its comprehensive evaluation index.

[0041] Furthermore, in step 5, the time series features are time delay and time span, the statistical features are mean, variance, skewness, kurtosis and correlation coefficient, the frequency domain features are power spectral density and frequency distribution, and the cross-validation adjusts the model parameters including the number of hidden layers, learning rate and batch size.

[0042] Furthermore, in step 6, the fiber-structure coupling coordination degree classification evaluation can effectively distinguish between positive and negative indices in fiber deformation and structure deformation elements. By determining the evaluation index system and calculating the coupling degree and coupling coordination degree, the overall state of the fiber-structure system can be comprehensively reflected.

[0043] Furthermore, in step 6, the closer the coupling coordination degree value is to 1, the better the coupling coordination degree. By analyzing the structural mechanics of the fiber-structure, four classification models of fiber coupling coordination degree applicable to large deformation of hydraulic structures have been redefined, which can provide theoretical guidance for fiber monitoring of large deformation of hydraulic structures.

[0044] During the elastic deformation stage, the coupling coordination degree is in the range of [0.8,1), which shows coordinated development and belongs to the high coupling-high coordination type.

[0045] Upon entering the elastoplastic deformation stage, the coupling coordination degree drops to the range of [0.4, 0.6), exhibiting a transitional harmony, but still belonging to the high coupling-high coordination type;

[0046] When the structure enters the failure and collapse stage, and the coupling coordination degree is in the range of [0.2, 0.6), it still exhibits a transitional harmony, but transforms into a low coupling-high coordination type;

[0047] If the coupling coordination drops to the range of [0, 0.2], it exhibits disordered decay, belonging to the low coupling-low coordination type.

[0048] Furthermore, in step 7, the intelligent adaptation selection of the fiber optic monitoring system employs the following steps:

[0049] S11. Define a multi-objective optimization problem, including the primary objective: coupling coordination degree DS(t), intelligent classification of coupling coordination degree, and secondary objectives: sensor cost, construction and installation cost, and maintenance difficulty index.

[0050] S21. Find a set of fiber optic deployment locations and sensor selections that maximize the coupling coordination degree DS(t) while minimizing sensor cost, construction and installation cost, and maintenance difficulty.

[0051] S31. Apply particle swarm optimization algorithm to find the optimal fiber optic deployment location and sensor selection.

[0052] S41. Randomly generate a set of possible fiber optic deployment locations and sensor selections as the initial locations of the particle swarm.

[0053] S51. Use the weighted summation method to evaluate the fitness of each particle. Based on the particle's current position, velocity, and individual and global optimal position, repeat the steps of evaluating fitness and updating position until the stopping condition is met. If the maximum number of iterations is reached or the quality of the solution no longer improves significantly, select the particle with the highest fitness from the particle swarm as the optimal solution, which is the optimal fiber optic deployment location and sensor selection.

[0054] S61. Using the Q-learning reinforcement learning algorithm, the optimal strategy is learned by simulating the long-term effects of different deployment locations and selections.

[0055] S71. Combining the prediction results of deep learning models and the optimization results of multi-objective optimization algorithms helps decision-makers select the most suitable fiber optic deployment locations and sensor types, thereby improving the application effect of fiber optic monitoring systems in hydraulic structure monitoring.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] This invention overcomes the limitations of existing technologies that rely solely on fiber optic strain data analysis. By comprehensively considering the interaction between the fiber optic cable and the structure, it provides a more accurate method for evaluating coupling relationships. This invention not only considers fiber optic strain data but also incorporates structural strain data. Through time-series analysis of these data, the evaluation of coupling compatibility becomes more comprehensive and scientific. Based on the coupling compatibility evaluation results, this invention guides the placement of fiber optic cables and the selection of sensors, thereby optimizing the performance of the entire fiber optic monitoring system and improving its adaptability and reliability in complex environments. Parts not covered in this invention are identical to or can be implemented using existing technologies.

[0058] Innovation in the evaluation model: This invention employs advanced data processing technology, combined with coupling coordination degree calculation, to establish a scientific and effective evaluation model for dynamically evaluating the coupling relationship of fiber-optic-structure systems.

[0059] Combination of algorithms and models: This invention designs specialized judgment algorithms and training methods. These algorithms and models can process multi-channel time series data, improving the ability to identify and predict deformation of hydraulic structures.

[0060] Application-oriented system optimization: The design concept of this invention focuses on practical application. The evaluation results guide the optimization of the fiber optic monitoring system, ensuring the practicality and operability of the invention. Attached Figure Description

[0061] Figure 1 This is a comprehensive evaluation index diagram of the fiber-structure coupling coordination degree of the fiber-structure coupling coordination degree evaluation method of the present invention.

[0062] Figure 2 This is a diagram illustrating the fiber-structure coupling coordination degree analysis method of the present invention. Detailed Implementation

[0063] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0064] A method for evaluating the compatibility of fiber-structure coupling includes the following steps:

[0065] Step 1: Construction and Data Acquisition of Hydraulic Structure Physical Model: A 1:200 scale physical model of the hydraulic structure was constructed using water, plaster, and white powder to simulate the real hydraulic structure and its working environment. Distributed optical fibers were buried in key areas of the physical model and connected to a BOTDA demodulator to acquire fiber strain data. A DIC was used for strain monitoring to acquire structural strain data. An IQR-based method was used to obtain processed fiber strain data and structural strain data.

[0066] Specifically, the IQR (interquartile range) based method includes the following steps:

[0067] S1. Identify and remove abnormal values ​​in fiber optic strain data and structural strain data that are outside the normal range;

[0068] S2. Use the moving average filtering method to remove high-frequency noise, and apply the isolated forest algorithm to identify and process outliers.

[0069] S3. Perform Z-score standardization on the cleaned data to give it zero mean and unit variance.

[0070] S4. For data with different dimensions, use the min-max normalization method to scale the data to the range of [0,1].

[0071] S5. Convert the standardized data into a serialization format suitable for input to deep learning models;

[0072] S6. According to the requirements of time step and sequence length, the data is divided into multiple time windows to obtain the processed fiber strain data and structure strain data.

[0073] Step 2: Coupling analysis: Select the time series data of fiber strain data and DIC monitoring of structural strain data in the fiber-optic-structure system of the hydraulic structure physical model test as the data source, input them into SPSS statistical software, and calculate the comprehensive evaluation index S1(t) and S2(t).

[0074] Step 3: Establishment of evaluation index system: Based on the coupling theory in physics, the evaluation index system of fiber-structure system is determined, including fiber strain data, structure strain data, and comprehensive evaluation indexes S1(t) and S2(t).

[0075] Step 4: Coupling coordination degree calculation: Using the comprehensive evaluation indices S1(t) and S2(t), calculate the coupling coordination degree of the fiber-structure system and analyze the degree of interrelation between fiber deformation and structural deformation elements in the fiber-structure system.

[0076] The coupling function CS(t) between the fiber and the structure is:

[0077]

[0078] In the formula, S1(t) represents the comprehensive evaluation index of the optical fiber system under a certain working face advance distance;

[0079] S2(t) represents the comprehensive evaluation index of the structural system under a certain working face advance distance;

[0080] Coupling degree CS(t)∈[0,1];

[0081] Let αS and βS be undetermined coefficients, and for the fiber-structure system, they can be obtained using the following formula:

[0082]

[0083] The fiber-structure coupling compatibility function can be expressed as:

[0084]

[0085] In the formula, DS(t) is the coupling coordination degree of the fiber-to-structure system; TS(t) is the comprehensive coordination degree index of the fiber-to-structure system, which reflects the complementary relationship between the subsystems.

[0086] like Figure 1 As shown, with the increase of the working face advancement distance, the structural deformation increases, the comprehensive evaluation index of the optical fiber remains basically unchanged, while the comprehensive evaluation index of the structure shows a downward trend. The greater the structural deformation, the lower its comprehensive evaluation index.

[0087] Step 5: Coupling Coordination Prediction: Extract time series features (time delay, time span), statistical features (mean, variance, skewness, kurtosis, correlation coefficient), and frequency domain features (power spectral density, frequency distribution). Combine these with the comprehensive evaluation indices S1(t) and S2(t), and select a Long Short-Term Memory (LSTM) network as the deep learning model. Train the LSTM model using the extracted features to learn the relationship between fiber strain and structural strain data. Adjust model parameters (number of hidden layers, learning rate, batch size) through cross-validation to achieve optimal performance. Provide the running program code:

[0088]

[0089]

[0090] Step 6: Intelligent classification and evaluation of coupling coordination degree: Based on the coupling coordination degree calculation in Step 4, the fiber-structure coupling relationship is divided into four types: high coupling-high coordination, low coupling-high coordination, high coupling-low coordination, and low coupling-low coordination. The classification and evaluation are then carried out based on the coupling coordination degree prediction results in Step 5.

[0091] Specifically, the fiber-structure coupling coordination degree classification evaluation can effectively distinguish between positive and negative indices in fiber deformation and structure deformation elements. By determining the evaluation index system and calculating the coupling degree and coupling coordination degree, it can comprehensively reflect the overall state of the fiber-structure system.

[0092] Step 7: Intelligent Adaptation of Fiber Optic Monitoring System

[0093] The following steps are used when selecting an intelligent adapter for a fiber optic monitoring system:

[0094] S11. Define a multi-objective optimization problem, including the primary objective: coupling coordination degree DS(t), intelligent classification of coupling coordination degree, and secondary objectives: sensor cost, construction and installation cost, and maintenance difficulty index.

[0095] S21. Find a set of fiber optic deployment locations and sensor selections that maximize the coupling coordination degree DS(t) while minimizing sensor cost, construction and installation cost, and maintenance difficulty.

[0096] S31. Apply particle swarm optimization algorithm to find the optimal fiber optic deployment location and sensor selection.

[0097] S41. Randomly generate a set of possible fiber optic deployment locations and sensor selections as the initial locations of the particle swarm.

[0098] S51. Use the weighted summation method to evaluate the fitness of each particle. Based on the particle's current position, velocity, and individual and global optimal position, repeat the steps of evaluating fitness and updating position until the stopping condition is met. If the maximum number of iterations is reached or the quality of the solution no longer improves significantly, select the particle with the highest fitness from the particle swarm as the optimal solution, which is the optimal fiber optic deployment location and sensor selection.

[0099] S61. Using the Q-learning reinforcement learning algorithm, the optimal strategy is learned by simulating the long-term effects of different deployment locations and selections.

[0100] S71. Combining the prediction results of deep learning models and the optimization results of multi-objective optimization algorithms helps decision-makers select the most suitable fiber optic deployment location and sensor type, thereby improving the application effect of fiber optic monitoring systems in hydraulic structure monitoring.

[0101] The fiber-structure coupling relationship is classified into four types: high coupling-high coordination, low coupling-high coordination, high coupling-low coordination, and low coupling-low coordination. The classification and evaluation are then performed based on the structural deformation stage. The running program code is provided.

[0102]

[0103]

[0104]

[0105]

[0106] It should be noted that as the working face advances further, the structural deformation increases, while the comprehensive evaluation index of the optical fiber remains basically unchanged, although the comprehensive evaluation index of the structure shows a downward trend; the greater the structural deformation, the lower its comprehensive evaluation index. The fiber-structure coupling coordination degree classification evaluation can effectively distinguish between positive and negative indicators in the fiber deformation and structural deformation elements. By determining the evaluation index system and calculating the coupling degree and coupling coordination degree, the overall state of the fiber-structure system can be comprehensively reflected.

[0107] Specifically, based on the three stages of structural deformation, the corresponding fiber-structure coupling coordination degree is divided into three types: high coupling-high coordination, low coupling-high coordination, and low coupling-low coordination, as shown in Table 1. The closer the coupling coordination degree value is to 1, the better the coupling coordination degree. Through analysis of fiber-structure structural mechanics, four classification models of fiber coupling coordination degree applicable to large deformation of hydraulic structures have been redefined, which can provide theoretical guidance for fiber optic monitoring of large deformation of hydraulic structures.

[0108] The fiber-structure coupling coordination evaluation method of this invention, in the elastic deformation stage, the coupling coordination degree is in the range of [0.8, 1), showing coordinated development and belonging to the high coupling-high coordination type; entering the elastoplastic deformation stage, the coupling coordination degree drops to the range of [0.4, 0.6), showing transitional harmony, but still belonging to the high coupling-high coordination type; when the structure enters the fracture and collapse stage, when the coupling coordination degree is in the range of [0.2, 0.6), it still shows transitional harmony, but transforms into the low coupling-high coordination type; if the coupling coordination degree drops to the range of [0, 0.2], it shows imbalance and decay, belonging to the low coupling-low coordination type.

[0109]

[0110]

[0111] Table 1. Fiber-to-structure coupling compatibility

[0112] The curves showing the variation of fiber-structure coupling compatibility under different loads, such as... Figure 2 As shown, structural deformation arises from the development and expansion of microcracks into macrocracks, exhibiting a phased development process of elastic deformation, elastoplastic deformation, and fracture collapse. The entire process can be divided into three stages. The coupling coordination degree decreases to around 0.8 during the elastic deformation stage, then remains constant, slightly recovering during the structural fracture collapse stage. However, the fiber-to-structure coupling degree continues to decrease as structural deformation intensifies. Unlike the fiber-to-structure coupling degree, the fiber-to-structure coupling coordination degree maintains a relatively high level even during the structural fracture collapse stage.

[0113] In some embodiments, for hydraulic structure applications, the coupling coordination of the fiber-optic-structure system generally falls into four categories: high coupling-high coordination, low coupling-high coordination, high coupling-low coordination, and low coupling-low coordination. In fiber-optic structural health monitoring, only "high coupling-high coordination" and "low coupling-low coordination" are typically emphasized, while the other two are neglected. In particular, the "high coupling-low coordination" case is often overlooked. When the coupling between the fiber and the structure is high, and the structure undergoes large deformation—that is, when a local area of ​​the fiber-optic-structure system (especially when the tunnel floor bulges) experiences large deformation or excessive bending—this leads to excessive fiber loss or fiber breakage, ultimately causing the fiber-optic-structure system to fail.

[0114] "Low coupling and high coordination" is often mistaken for fiber optic monitoring failure, leading to a neglect of interpreting and analyzing fiber optic data and missing opportunities to sense large-scale structural deformation. However, for localized areas of a fiber-to-structure system with "low coupling," such as decoupling of the fiber-structure at the crack location due to a critical layer failure, the entire fiber-to-structure system may still be in a "high coordination" stage. In such cases, quantitative or qualitative information about structural deformation can still be obtained from the fiber optic monitoring data. The degree of fiber-to-structure coupling coordination can guide fiber selection and optimized deployment, thereby better guiding the application of fiber optics in the safety monitoring of hydraulic engineering projects.

[0115] Fiber-structure coupling compatibility analysis:

[0116] The initial design purpose of anchored optical cables was to enhance fiber-soil coupling to adapt to large deformation monitoring in sandy terrain. Until now, discussions on anchored optical cables have focused on how to improve fiber-soil coupling for large-range measurements of structural deformation. In geotechnical engineering, most current research on fiber coupling focuses on the fiber-soil interface. However, while research on fiber-optic monitoring of concrete structures is effective during the elasto-plastic deformation stage, it often leads to extreme fiber breakage under large deformation conditions, resulting in monitoring failures. Therefore, based on the concept of fiber coupling coordination, this paper analyzes the fiber-soil coupling coordination, and the research results can provide theoretical guidance for fiber-optic monitoring of large deformation in sedimentary rocks within concrete structures.

[0117] Therefore, through fiber-structure coupling coordination analysis, it was found that in fiber optic monitoring of water conservancy projects, it is necessary to optimize the fiber optic deployment location and, in terms of sensor selection, prioritize anchored optical cables.

[0118] Theoretical calculation of fiber-structure coupling compatibility:

[0119] Research on the coupling degree between optical fibers and structures is becoming increasingly mature. By subdividing the entire length of the optical fiber and obtaining the coupling degree per unit length, the strain transfer coefficient between the fiber and the structure can be clarified, thus enabling a better equivalent conversion between fiber strain and structural strain. However, the results of optical fiber coupling degree research have poor adaptability to the monitoring of hydraulic structures. Therefore, it is necessary to take a systems science perspective and propose analyzing the coupling coordination degree of the entire optical fiber within the fiber-structure system, focusing on the entire fiber segment. This aims to ensure the robustness and monitoring effectiveness of the fiber-structure system, thereby meeting the monitoring needs under large deformations in hydraulic structures.

[0120] Coupling coordination degree analysis is a quantitative research method based on coupling theory in physics to explore the degree of interrelation between two or more systems or modes of motion. It reflects the phenomena of interdependence, restriction, promotion, influence and connection between system elements.

[0121] In some embodiments, the fiber-to-structure coupling coordination evaluation method, by determining the evaluation index system and calculating the coupling degree and coupling coordination degree, can comprehensively reflect the overall state of the fiber-to-structure system. The closer the coupling coordination degree value is to 1, the better the coupling coordination degree. Through the analysis of the structural mechanics of fiber-to-structure systems, four classification models of fiber coupling coordination degree applicable to large deformations of hydraulic structures have been redefined, which can provide theoretical guidance for fiber optic monitoring of large deformations in hydraulic structures.

[0122] In the similar physical model test, the time-series data of fiber strain and DIC monitoring of structural strain in the fiber-optic-structure system are used as the data source; Equations 1-3 are calculated sequentially. According to the logical relationship of sequential and parallel calculation, the parameters from the physical world are given to Equations 1 and 2, and the results obtained from the first calculated formula are given to the later calculated formula 3.

[0123] For example, the final calculated value of a certain parameter, DS(t), represents the coupling coordination degree of the fiber-structure system. The significance (the suitable range is [0.8, 1); values ​​below this range indicate no coupling, and values ​​above this range will not exceed it) and applications of this value are explained. The fiber-structure coupling coordination degree evaluation method of this invention can effectively distinguish between positive and negative indicators in fiber deformation and structural deformation elements, comprehensively reflecting the overall state of the fiber-structure system. This method can better guide the application of optical fibers in the safety monitoring of water conservancy projects, improving the accuracy of monitoring data and the survival rate of optical fiber monitoring systems.

[0124] In the field application of fiber optic monitoring for hydraulic structures, those skilled in the art often focus solely on increasing the coupling between the fiber and the structure. This leads to excessive fiber loss or breakage when the structure undergoes large deformations (especially in areas of floor bulging or fault activation in tunnels), resulting in frequent failures of the fiber optic monitoring system. Alternatively, some may arbitrarily conclude that monitoring data with low coupling is unreliable, and that fiber-structure decoupling at crack locations due to critical layer breakage or delamination leads to abandoning the acquisition or analysis of fiber optic data. This invention proposes a novel method for evaluating the coupling coordination degree of the fiber-structure. This method overcomes the limitations of existing technologies that rely solely on fiber strain data analysis. By comprehensively considering the interaction between the fiber and the structure, it provides a more accurate means of evaluating coupling relationships. This invention not only considers fiber strain data but also incorporates structural strain data. Through time-series analysis of these data, the evaluation of coupling coordination degree becomes more comprehensive and scientific. This invention utilizes fiber coupling coordination degree prediction and intelligent classification, and comprehensively evaluates fiber optic deployment and sensor selection schemes through deep learning and multi-objective optimization algorithms. This achieves optimal decision-making for both monitoring effectiveness and cost-effectiveness, improving its adaptability and reliability in complex environments.

[0125] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the coordination degree of fiber-optic-structure coupling, characterized in that: Includes the following steps: Step 1: Construction of physical model of hydraulic structure, data acquisition and processing: Distributed optical fibers are buried in the constructed physical model of hydraulic structure; It is connected to a BOTDA demodulator for acquiring fiber optic strain data. DIC was used for strain monitoring to collect structural strain data; an IQR-based method was used to obtain processed fiber optic strain data and structural strain data. Step 2: Coupling analysis: Select the time series data of fiber strain data and DIC monitoring of structural strain data in the fiber-optic-structure system of the hydraulic structure physical model test as the data source, input them into SPSS statistical software, and calculate the comprehensive evaluation index S1(t) and S2(t). Step 3: Establishment of evaluation index system: Based on the coupling theory in physics, the evaluation index system of fiber-structure system is determined, including processed fiber strain data, processed structure strain data, and comprehensive evaluation indices S1(t) and S2(t). Step 4: Coupling coordination degree calculation: Using the comprehensive evaluation indices S1(t) and S2(t), calculate the coupling coordination degree of the fiber-structure system and analyze the degree of interrelation between fiber deformation and structural deformation elements in the fiber-structure system. Step 5: Coupling Coordination Prediction: Extract time series features, statistical features, and frequency domain features, and combine them with the comprehensive evaluation indices S1(t) and S2(t). Select a Long Short-Term Memory (LSTM) network as a deep learning model, train the LSTM model using the extracted features, and learn the relationship between fiber strain and structural strain data. Adjust the model parameters through cross-validation to achieve the best performance and perform coupling coordination prediction. Step 6: Intelligent classification and evaluation of coupling coordination degree: Based on the coupling coordination degree calculation in Step 4, the fiber-structure coupling relationship is divided into four types, and the classification and evaluation are carried out based on the coupling coordination degree prediction results in Step 5. Step 7: Intelligent adaptation of fiber optic monitoring system: Based on the coupling coordination degree classification evaluation results in Step 6, adapt the fiber optic deployment location and sensor selection.

2. The fiber-structure coupling coordination evaluation method according to claim 1, characterized in that: In step 1, the physical model of the hydraulic structure is built using water, plaster, and white powder at a scale of 1:200 to simulate the real hydraulic structure and its working environment.

3. The fiber-structure coupling coordination evaluation method according to claim 1, characterized in that: The method based on IQR (interquartile range) described in step 1 includes the following steps: S1. Identify and remove abnormal values ​​in fiber optic strain data and structural strain data that are outside the normal range; S2. Use the moving average filtering method to remove high-frequency noise, and apply the isolated forest algorithm to identify and process outliers. S3. Perform Z-score standardization on the cleaned data to give it zero mean and unit variance. S4. For data with different dimensions, use the min-max normalization method to scale the data to the range of [0,1]. S5. Convert the standardized data into a serialization format suitable for input to deep learning models; S6. Based on the requirements of time step and sequence length, the data is divided into multiple time windows to obtain the processed fiber strain data and structural strain data.

4. The method for evaluating the coordination degree of fiber-structure coupling according to claim 1, characterized in that: In step 4, when calculating the coupling coordination degree, where, The coupling function CS(t) between the fiber and the structure is: In the formula, S1(t) represents the comprehensive evaluation index of the optical fiber system under a certain working face advance distance; S2(t) represents the comprehensive evaluation index of the structural system under a certain working face advance distance; Coupling degree CS(t)∈[0,1]; Let αS and βS be undetermined coefficients, and for the fiber-structure system, they can be obtained using the following formula: The fiber-structure coupling compatibility function can be expressed as: In the formula, D S (t) represents the coupling coordination degree of the fiber-to-structure system; T S (t) is the overall coordination index of the fiber-optic structure system, which reflects the complementary relationship between the subsystems.

5. The fiber-structure coupling coordination evaluation method according to claim 1, characterized in that: In step 4, as the working face advances further, the deformation of the structure increases. The comprehensive evaluation index of the optical fiber remains basically unchanged, while the comprehensive evaluation index of the structure shows a downward trend. The greater the deformation of the structure, the lower its comprehensive evaluation index.

6. The method for evaluating the coordination degree of fiber-structure coupling according to claim 1, characterized in that: In step 5, the time series features are time delay and time span, the statistical features are mean, variance, skewness, kurtosis and correlation coefficient, the frequency domain features are power spectral density and frequency distribution, and the cross-validation adjusts the model parameters including the number of hidden layers, learning rate and batch size.

7. The method for evaluating the coordination degree of fiber-structure coupling according to claim 1, characterized in that: In step 6, the fiber-structure coupling coordination degree classification evaluation can effectively distinguish between positive and negative indices in fiber deformation and structure deformation elements. By determining the evaluation index system and calculating the coupling degree and coupling coordination degree, the overall state of the fiber-structure system can be comprehensively reflected.

8. The method for evaluating the coordination degree of fiber-structure coupling according to claim 1, characterized in that: In step 6, the four types of fiber-structure coupling relationships are high coupling-high coordination, low coupling-high coordination, high coupling-low coordination, and low coupling-low coordination. The closer the coupling coordination degree value is to 1, the better the coupling coordination degree. Through the analysis of fiber-structure structure mechanics, four classification models of fiber coupling coordination degree applicable to large deformation of hydraulic structures have been redefined, which can provide theoretical guidance for fiber monitoring of large deformation of hydraulic structures. During the elastic deformation stage, the coupling coordination degree is in the range of [0.8,1), which shows coordinated development and belongs to the high coupling-high coordination type. Upon entering the elastoplastic deformation stage, the coupling coordination degree drops to the range of [0.4, 0.6), exhibiting a transitional harmony, but still belonging to the high coupling-high coordination type; When the structure enters the failure and collapse stage, and the coupling coordination degree is in the range of [0.2, 0.6), it still exhibits a transitional harmony, but transforms into a low coupling-high coordination type; If the coupling coordination drops to the range of [0, 0.2], it exhibits disordered decay, belonging to the low coupling-low coordination type.

9. The method for evaluating the coordination degree of fiber-structure coupling according to claim 1, characterized in that: In step 7, the following steps are taken when the fiber optic monitoring system intelligently adapts and selects its system: S11. Define a multi-objective optimization problem, including the primary objective: coupling coordination degree DS(t), intelligent classification of coupling coordination degree, and secondary objectives: sensor cost, construction and installation cost, and maintenance difficulty index. S21. Find a set of fiber optic deployment locations and sensor selections that maximize the coupling coordination degree DS(t) while minimizing sensor cost, construction and installation cost, and maintenance difficulty. S31. Apply particle swarm optimization algorithm to find the optimal fiber optic deployment location and sensor selection. S41. Randomly generate a set of possible fiber optic deployment locations and sensor selections as the initial locations of the particle swarm. S51. Use the weighted summation method to evaluate the fitness of each particle. Based on the particle's current position, velocity, and individual and global optimal position, repeat the steps of evaluating fitness and updating position until the stopping condition is met. If the maximum number of iterations is reached or the quality of the solution no longer improves significantly, select the particle with the highest fitness from the particle swarm as the optimal solution, which is the optimal fiber optic deployment location and sensor selection. S61. Using the Q-learning reinforcement learning algorithm, the optimal strategy is learned by simulating the long-term effects of different deployment locations and selections. S71. Combining the prediction results of deep learning models and the optimization results of multi-objective optimization algorithms helps decision-makers select the most suitable fiber optic deployment locations and sensor types, thereby improving the application effect of fiber optic monitoring systems in hydraulic structure monitoring.

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