Concrete structure health monitoring system and method based on self-sensing carbon fiber material

Abstract

The invention belongs to the technical field of civil engineering structure monitoring, and particularly relates to a concrete structure health monitoring system based on a self-sensing carbon fiber material, and the system comprises a self-sensing carbon fiber composite sensing network which is embedded in a concrete structure internal monitoring point and is used for obtaining a resistance change signal; the signal acquisition module is used for acquiring a resistance change signal; the data transmission module is connected with the signal acquisition module and is used for receiving the resistance change signal transmitted by the signal acquisition module; and the data processing and analyzing module is used for receiving the resistance change data signal transmitted by the data transmission module. The invention also provides a concrete structure health monitoring method based on the self-sensing carbon fiber material. The system takes a self-sensing carbon fiber composite sensing network as a core, has the functions of structure enhancement and health sensing, and realizes real-time monitoring, accurate identification, quantitative evaluation and intelligent early warning of concrete structure damage through cooperative work of multiple modules.

Description

Technical Field

[0001] This invention belongs to the field of civil engineering structural monitoring technology, specifically relating to a health monitoring system and method for concrete structures based on self-sensing carbon fiber materials. Background Technology

[0002] Concrete structures are widely used in major civil engineering projects such as bridges, buildings, tunnels, and dams due to their advantages of low cost, convenient construction, and high load-bearing capacity. However, during long-term service, concrete structures are susceptible to damage from loads, environmental erosion, material aging, and unexpected disasters, gradually developing cracks, deformation, and stiffness reduction. If this damage is not detected and addressed in a timely manner, it may lead to structural performance degradation or even cause major safety accidents such as collapses, resulting in serious casualties and economic losses.

[0003] Traditional methods for monitoring the health of concrete structures mainly include manual inspection and non-destructive testing. Manual inspection relies on the experience of the inspectors, which is inefficient, subjective, and difficult to effectively monitor damage inside the structure and in hidden parts. Although existing non-destructive testing technologies can detect structural damage to some extent, most of them are offline methods, which cannot achieve real-time continuous monitoring, and the test results are easily affected by environmental factors, resulting in limited accuracy.

[0004] To address these issues, intelligent sensing technology is increasingly being applied to the field of concrete structure health monitoring. Currently, research has employed fiber optic sensors and piezoelectric sensors embedded within concrete structures for monitoring. However, these sensors generally suffer from drawbacks such as poor compatibility with the concrete matrix, susceptibility to construction damage, high cost, and difficulty in forming large-scale sensing networks. For example, fiber optic sensors are brittle and prone to breakage during concrete pouring and vibration; piezoelectric sensors have limited sensing range, requiring extensive deployment to achieve full structural coverage, increasing system complexity and cost.

[0005] Carbon fiber composites have been widely used in structural reinforcement due to their superior properties such as high strength, high modulus, corrosion resistance, and lightweight. In recent years, research has revealed that carbon fiber composites exhibit a piezoresistive effect, where their resistance changes with stress and damage levels. This gives them the potential to integrate structural reinforcement with health monitoring. Based on this, the development of an intelligent concrete structure health monitoring system using self-sensing carbon fiber composites as the core sensing unit is crucial for achieving real-time, accurate, and efficient monitoring of the entire lifecycle of concrete structures, ensuring the safe service life of civil engineering structures. Summary of the Invention

[0006] To address the problems of low monitoring accuracy, poor real-time performance, poor sensor-matrix compatibility, and high system complexity in existing concrete structure health monitoring technologies, this invention provides a concrete structure health monitoring system and method based on self-sensing carbon fiber materials. The system uses a self-sensing carbon fiber composite material sensing network as its core, combining structural reinforcement and health sensing functions. Through the collaborative work of multiple modules, it achieves real-time monitoring, accurate identification, quantitative assessment, and intelligent early warning of concrete structure damage, providing a scientific basis for structural operation and maintenance.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A health monitoring system for concrete structures based on self-sensing carbon fiber materials includes: A self-sensing carbon fiber composite sensing network is embedded in monitoring points inside a concrete structure to acquire resistance change signals. The signal acquisition module is used to acquire the resistance change signal output by the self-sensing carbon fiber composite material sensing network; The data transmission module is connected to the signal acquisition module and is used to receive the resistance change signal transmitted by the signal acquisition module. The data processing and analysis module is used to receive the resistance change data signal from the data transmission module and perform signal noise reduction, feature extraction, damage identification and quantitative damage assessment. The early warning and decision-making module, connected to the data processing and analysis module, is used for data storage, status display, multi-level early warning, and output of operation and maintenance decision suggestions.

[0008] In a preferred embodiment, the self-sensing carbon fiber composite sensing network comprises a plurality of self-sensing carbon fiber composite elements, wherein the self-sensing carbon fiber composite elements comprise the following raw materials by mass fraction: 15%-25% carbon fiber cloth or carbon fiber bundle, 73%-84% epoxy resin matrix, 0.5%-2% graphene, and 0.3%-0.8% silane coupling agent KH-550; The self-sensing carbon fiber composite element is ribbed or plate-shaped. The preparation process adopts a vacuum-assisted resin transfer molding process. The specific steps are as follows: according to the above mass fraction, graphene and silane coupling agent KH-550 are added to the epoxy resin matrix, and a conductive resin matrix is ​​prepared by ultrasonic dispersion and mechanical stirring; carbon fiber cloth or carbon fiber bundles are laid flat in the mold, and the conductive resin matrix is ​​vacuum introduced, cured at 70-90℃ for 1.5-2.5h, and then cured at 110-130℃ for 0.5-1.5h to obtain the self-sensing carbon fiber composite element. The surface of the self-sensing carbon fiber composite material element is provided with metal electrodes. The electrodes are prepared by coating with silver paste. The size of the electrodes matches the size of the electrode contact part of the composite material element. After coating, they are naturally dried and led out through copper wires. The monitoring points of the self-sensing carbon fiber composite material sensing network are arranged at the mid-span, support, and 1 / 4 span of the concrete beam, the root and middle of the column, and the corner and mid-span of the slab.

[0009] In a preferred embodiment, the signal acquisition module includes a constant current source unit, a signal conditioning unit, an A / D conversion unit, and a microcontroller unit; The constant current source unit uses a programmable constant current source chip GP8102 to provide a DC constant current signal of 10μA-1mA for the self-sensing carbon fiber composite material sensing network, with a current adjustment accuracy of 0.1μA. The signal conditioning unit consists of an instrumentation amplifier, a low-pass filter, and a temperature compensation unit. The instrumentation amplifier is an INA128 with an adjustable gain range of 1-1000 times. The low-pass filter uses an RC active filter circuit with a cutoff frequency set to 5-20Hz. The temperature compensation unit uses a PT100 temperature sensor to collect ambient temperature data and performs temperature compensation on the measured signal using a temperature-resistance correction model. The correction model is as follows: R=R0[1+α·(T-T0)+β·(T-T0) 2 ]; Where R is the corrected resistance value at temperature T, R0 is the resistance value at the reference temperature T0 = 25℃ with no structural damage, and α and β are temperature coefficients, α = 0.00393 / ℃, β = -5.775 × 1 / ℃; The A / D conversion unit uses a 16-bit high-precision analog-to-digital converter ADS1115 with a conversion accuracy of 0.015% and a sampling frequency set to 1Hz-100Hz to convert the analog voltage signal conditioned by the signal conditioning unit into a digital signal. The microcontroller unit uses an STM32H743 microprocessor, which integrates an ARM Cortex-M7 core with a main frequency of 480MHz. It controls the output current of the constant current source unit and the sampling frequency of the A / D conversion unit, performs preliminary buffering of the acquired digital signals, and communicates with the data transmission module through the UART interface. In a preferred embodiment, the data transmission module includes a wireless communication unit, a wired communication unit, a data encryption unit, and a backup communication unit; The wireless communication unit adopts a LoRa wireless communication module with a communication frequency of 433MHz or 868MHz, a transmission distance of 1-5km, and a communication rate of 1.2-38.4kbps. It supports multi-node networking and is used for data transmission over long distances and in complex environments. The wired communication unit uses an Ethernet module, supports the TCP / IP protocol, and has a communication rate of 10 / 100Mbps; The data encryption unit uses the AES-128 encryption algorithm to encrypt the transmitted data. The encryption process is as follows: the data is grouped into 128-bit blocks, and 10 rounds of iteration are performed through initial round key addition, byte substitution, row shifting, column mixing, and round key addition to generate encrypted data. The backup communication unit adopts a 4G / 5G communication module, which supports full network connectivity. When the LoRa wireless communication module or Ethernet module communication is interrupted, it automatically switches to the 4G / 5G communication module channel. The data transmission module also integrates a signal strength detection unit to monitor the communication signal strength in real time. When the signal strength is below -90dBm, it issues a communication fault warning.

[0010] In a preferred embodiment, the data processing and analysis module includes a data preprocessing unit, a feature extraction unit, a damage identification unit, and a damage assessment unit connected sequentially via a data interface. The data preprocessing unit uses the Kalman filter algorithm to denoise the acquired digital signals, eliminating the influence of random noise. The calculation formula of the Kalman filter algorithm includes state prediction, covariance prediction, Kalman gain, state update, and covariance update. State prediction: ; Covariance prediction: ; Kalman gain: ; Status Update: ; Covariance update: ; in, Let be the posterior state estimate at time k; Let be the posterior state estimate at time k-1; A be the state transition matrix, with a value of 1; B be the control matrix, with a value of 0; u(k) be the control input, with a value of 0; P(k|k-1) be the prior covariance matrix at time k; P(k-1|k-1) be the posterior covariance matrix at time k-1; and Q be the process noise covariance, with a value of 1×1. K(k) is the Kalman gain at time k; H is the observation matrix, with a value of 1; M is the observation noise covariance, with a value of 1×1. Z(k) represents the observation at time k; I is the identity matrix; H T This is the transpose of matrix H; Let k be the prior state estimate at time k. Let be the posterior covariance matrix at time k; The feature extraction unit extracts three types of core feature parameters from the signals preprocessed by the data preprocessing unit: one is the resistance change rate ΔR / R0, where R0 is the resistance value at the reference temperature T0 = 25°C and when the structure is undamaged, and ΔR is the difference between the real-time resistance value and the initial resistance value R0; the second is the resistance change gradient d(ΔR / R0) / dt, which reflects the rate of resistance change; the third is the signal variance σ², which reflects the stability of the signal; the extraction frequency of the feature parameters is consistent with the A / D conversion sampling frequency; The damage identification unit constructs a damage identification model based on the support vector machine (SVM) algorithm to identify the presence and type of damage in the concrete structure; the objective function of the SVM algorithm is: min(1 / 2)·||w||² + C·Σξ; s.t.y(w· (x) + b) ≥ 1 - ξ, ξ ≥ 0; where, w is the weight vector; C is the penalty parameter, with a value range of 5 - 20; ξ is the slack variable; y is the sample label, 1 for no damage, 2 for cracks, 3 for deformation, and 4 for stiffness degradation; x is the sample feature vector, composed of the resistance change rate, resistance change gradient, and signal variance; (·) is the kernel function mapping, and b is the bias term; The training samples of the damage identification model are collected through a structural loading test, including feature parameter samples in the state of no damage and various damage states. The number of samples is ≥ 1000 groups, and the recognition accuracy of the trained model is ≥ 95%; The damage assessment unit constructs a quantitative damage assessment model based on the damage mechanics theory to evaluate the damage location, damage degree, and damage development trend; among them, the damage location is determined by the distribution of the resistance change gradient at each monitoring point, and the monitoring point with the largest resistance change gradient corresponds to the damage center position; The damage degree is quantitatively evaluated through the damage factor D. The relationship model between the damage factor D and the resistance change rate ΔR / R0 is: D = δ·(ΔR / R0) + ε·(ΔR / R0)² + γ·(ΔR / R0)³; where, δ, ε, and γ are model parameters, calibrated through a structural loading test. The value range of δ is 0.7 - 0.9, the value range of ε is 0.1 - 0.15, and the value range of γ is -0.01 - 0; the value range of the damage factor D is 0 - 1. D = 0 indicates no damage, 0 < D < 0.1 indicates extremely slight damage, 0.1 ≤ D < 0.3 indicates slight damage, 0.3 ≤ D < 0.5 indicates moderate damage, 0.5 ≤ D < 0.8 indicates severe damage, and D ≥ 0.8 indicates extremely severe damage; The trend of damage development is predicted by the time series change curve of damage factors. A linear regression model is used: D(t)=j·t+D0, where t is time, j is the rate of damage development, and D0 is the current damage factor. When j>0.01 / month, it indicates that the damage is developing rapidly and urgent intervention is required.

[0011] In a preferred embodiment, the early warning and decision-making module includes a data storage unit, a status display unit, an early warning unit, and a decision suggestion unit. The data storage unit adopts a dual-storage architecture of "local + cloud". Local storage uses an SD card to store the raw data and processing results of the most recent month. Cloud storage uses a MySQL cloud database to store all historical data, supporting long-term data traceability and multi-terminal access. The data storage period is consistent with the design life of the concrete structure. The status display unit develops a visual monitoring interface based on LabVIEW or Qt. The early warning unit adopts a multi-level early warning mechanism, setting four levels of early warning according to the magnitude of the damage factor D. The decision suggestion unit constructs a decision tree model based on the damage assessment results, the service life of the structure, design parameters, and environmental conditions, and outputs targeted operation and maintenance suggestions.

[0012] In a preferred embodiment, the system further includes a self-testing and fault diagnosis module, integrated into the microcontroller unit, to realize real-time monitoring and fault location of the working status of each component of the system. The self-testing and fault diagnosis module includes a hardware self-testing unit, a communication link diagnosis unit, and a sensing node fault diagnosis unit. The sensing node fault diagnosis unit realizes fault identification based on the feature analysis of sensing signals.

[0013] In a preferred embodiment, the data processing and analysis module further includes an adaptive learning unit that dynamically optimizes the parameters of the damage identification model and the damage assessment model based on the service data and environmental changes of the structure, thereby improving monitoring accuracy. The adaptive learning unit employs an incremental learning algorithm to periodically add newly collected monitoring data to the training set.

[0014] This invention also provides a method for health monitoring of concrete structures based on self-sensing carbon fiber materials, comprising the following steps: S1. System Initialization: Initial resistance values ​​are acquired for the self-sensing carbon fiber composite sensing network. The initial resistance value R0 of each monitoring node is recorded at a reference temperature T0=25℃ and the structure is undamaged, and stored in the early warning and decision-making module. Parameters are configured for the signal acquisition module, including constant current source output current, A / D conversion sampling frequency, and low-pass filter cutoff frequency. Communication parameters are configured for the data transmission module, including LoRa communication frequency, Ethernet IP address, and AES-128 encryption key. Early warning thresholds and adaptive learning periods are set. S2. Signal Acquisition and Preprocessing: The constant current source unit in the signal acquisition module outputs a stable DC constant current signal to the self-sensing carbon fiber composite sensing network. The signal conditioning unit amplifies, filters, and performs temperature compensation on the weak voltage signal output by the self-sensing carbon fiber composite sensing network. The A / D conversion unit converts the conditioned analog signal into a digital signal. The microcontroller unit performs preliminary buffering of the digital signal and then uploads it to the data processing and analysis module through the data transmission module. The preprocessing unit of the data processing and analysis module uses the Kalman filter algorithm to perform noise reduction processing on the digital signal to eliminate random noise. S3. Feature Extraction and Damage Recognition: The feature extraction unit of the data processing and analysis module extracts the resistance change rate ΔR / R0, the resistance change gradient d(ΔR / R0) / dt, and the signal variance σ² from the noise-reduced signal. The feature parameters are then input into the SVM damage recognition model trained by the damage recognition unit, and the damage recognition result is output. S4. Quantitative Damage Assessment: Based on the damage identification results, the damage assessment unit assesses the damage location, damage degree, and development trend. The damage location is determined by the resistance change gradient distribution of each monitoring point, with the monitoring point with the largest resistance change gradient corresponding to the damage center. The damage degree is quantitatively assessed by the damage factor D, and the damage development trend is predicted by the time series change curve of the damage factor. S5. Early Warning and Decision Output: The early warning unit of the early warning and decision module compares the damage factor D with the preset early warning threshold and activates the corresponding level of early warning; the status display unit updates and displays the status parameters and damage assessment results of each monitoring node in real time; the decision suggestion unit outputs targeted operation and maintenance suggestions based on the damage assessment results. S6. System Self-Check and Adaptive Learning: The self-check and fault diagnosis module periodically starts hardware self-check, communication link diagnosis and sensing node fault diagnosis to promptly detect and handle system faults; the adaptive learning unit incrementally trains the damage identification model and damage assessment model according to a set period to optimize model parameters.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The sensing unit has both enhancement and sensing functions. The present invention uses self-sensing carbon fiber composite material to construct a sensing network. This material not only has excellent mechanical properties and can enhance the concrete structure, improving the load-bearing capacity and durability of the structure, but also uses its piezoresistive effect to realize the self-sensing of the health status of the structure, solving the problems of poor compatibility between traditional sensors and concrete matrix and easy damage during construction.

[0016] (2) High monitoring accuracy and high reliability. The signal acquisition module adopts a high-precision constant current source, instrumentation amplifier and 16-bit A / D converter, and combined with the temperature compensation unit to effectively eliminate the influence of temperature on the measurement results and ensure the accuracy of signal acquisition; the data processing and analysis module uses Kalman filtering algorithm for noise reduction, constructs a damage identification model based on SVM algorithm, and realizes quantitative damage assessment by combining damage mechanics theory. The fusion of multiple algorithms improves the accuracy of damage identification and assessment. The accuracy of the trained damage identification model is ≥95%.

[0017] (3) Stable and secure data transmission. The data transmission module adopts a dual-mode redundant transmission method of "wired + wireless + backup". LoRa wireless communication is suitable for long distances and complex environments, Ethernet ensures high-speed transmission over short distances, and 4G / 5G serves as a backup channel to ensure uninterrupted communication. At the same time, the AES-128 encryption algorithm is used to encrypt the transmitted data, ensuring the security and privacy of data transmission.

[0018] (4) The system has a high degree of intelligence. The system integrates self-inspection and fault diagnosis modules, which can realize real-time monitoring of the working status of each component, fault location and self-recovery of minor faults; the adaptive learning unit in the data processing and analysis module dynamically optimizes the model parameters through incremental learning algorithm to improve the long-term monitoring accuracy of the system; the early warning and decision-making module outputs targeted operation and maintenance suggestions based on damage assessment results, providing a scientific basis for structural operation and maintenance.

[0019] (5) Full life cycle monitoring capability. The system adopts a dual storage architecture of "local + cloud". The data storage cycle is consistent with the design life of the concrete structure. It supports real-time monitoring, data traceability and trend prediction of the health status of the structure throughout its entire life cycle. It can detect early damage to the structure in a timely manner, provide decision support for the repair, reinforcement and replacement of the structure, extend the service life of the structure and reduce operation and maintenance costs. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the health monitoring system for concrete structures based on self-sensing carbon fiber materials according to the present invention; Figure 2 This is a schematic diagram of the concrete structure health monitoring method of the concrete structure health monitoring system based on self-sensing carbon fiber material of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] Example 1: Please see Figure 1As shown, the present invention provides a health monitoring system for concrete structures based on self-sensing carbon fiber materials, comprising: A self-sensing carbon fiber composite sensing network is embedded in monitoring points inside a concrete structure to acquire resistance change signals. The signal acquisition module is used to acquire the resistance change signal output by the self-sensing carbon fiber composite material sensing network; The data transmission module is connected to the signal acquisition module and is used to receive the resistance change signal transmitted by the signal acquisition module. The data processing and analysis module is used to receive the resistance change data signal from the data transmission module and perform signal noise reduction, feature extraction, damage identification and quantitative damage assessment. The early warning and decision-making module, connected to the data processing and analysis module, is used for data storage, status display, multi-level early warning, and output of operation and maintenance decision suggestions.

[0023] The self-sensing carbon fiber composite sensing network comprises several self-sensing carbon fiber composite elements. Each self-sensing carbon fiber composite element comprises the following raw materials by mass fraction: 15%-25% carbon fiber cloth or carbon fiber bundle, 73%-84% epoxy resin matrix, 0.5%-2% graphene, and 0.3%-0.8% silane coupling agent KH-550. The self-sensing carbon fiber composite element is in the form of a rib or plate. The preparation process adopts a vacuum-assisted resin transfer molding process. The specific steps are as follows: according to the above mass fraction, graphene and silane coupling agent KH-550 are added to the epoxy resin matrix, and a conductive resin matrix is ​​prepared by ultrasonic dispersion and mechanical stirring; carbon fiber cloth or carbon fiber bundles are laid flat in a mold, and the conductive resin matrix is ​​vacuum introduced, cured at 70-90℃ for 1.5-2.5h, and then post-cured at 110-130℃ for 0.5-1.5h to obtain the self-sensing carbon fiber composite element. The surface of the self-sensing carbon fiber composite element is provided with metal electrodes. The electrodes are prepared by coating with silver paste. The size of the electrodes matches the size of the electrode contact part of the composite element. After coating, they are naturally dried and led out through copper wires. The monitoring points of the sensing network are arranged at the mid-span, support, and 1 / 4 span of the concrete beam, the root and middle of the column, and the corner and mid-span of the slab.

[0024] The signal acquisition module includes a constant current source unit, a signal conditioning unit, an A / D conversion unit, and a microcontroller unit; The constant current source unit uses the programmable constant current source chip GP8102 to provide a DC constant current signal of 10μA-1mA for the self-sensing carbon fiber composite material sensing network, with a current adjustment accuracy of 0.1μA. The signal conditioning unit consists of an instrumentation amplifier, a low-pass filter, and a temperature compensation unit. The instrumentation amplifier is an INA128 with an adjustable gain range of 1-1000 times. The low-pass filter uses an RC active filter circuit with a cutoff frequency set to 5-20Hz. The temperature compensation unit uses a PT100 temperature sensor to collect ambient temperature data and performs temperature compensation on the measured signal using a temperature-resistance correction model. The correction model is as follows: R=R0[1+α·(T-T0)+β·(T-T0) 2 ] Where R is the corrected resistance value at temperature T, R0 is the resistance value at the reference temperature T0 = 25℃, and α β is the temperature coefficient, α = 0.00393 / ℃, β = -5.775 × 1 / ℃; The A / D conversion unit uses a 16-bit high-precision analog-to-digital converter ADS1115 with a conversion accuracy of 0.015% and a sampling frequency set to 1Hz-100Hz to convert the analog voltage signal conditioned by the signal conditioning unit into a digital signal. The microcontroller unit uses an STM32H743 microprocessor, which integrates an ARM Cortex-M7 core with a main frequency of 480MHz. It controls the output current of the constant current source unit and the sampling frequency of the A / D conversion unit, performs preliminary buffering of the acquired digital signals, and communicates with the data transmission module through the UART interface. The data transmission module includes a wireless communication unit, a wired communication unit, a data encryption unit, and a backup communication unit; The wireless communication unit adopts a LoRa wireless communication module with a communication frequency of 433MHz or 868MHz, a transmission distance of 1-5km, and a communication rate of 1.2-38.4kbps. It supports multi-node networking and is used for data transmission over long distances and in complex environments. The wired communication unit uses an Ethernet module, supports the TCP / IP protocol, and has a communication rate of 10 / 100Mbps; The data encryption unit uses the AES-128 encryption algorithm to encrypt the transmitted data. The encryption process is as follows: the data is grouped into 128-bit blocks, and 10 rounds of iteration are performed through initial round key addition, byte substitution, row shifting, column mixing, and round key addition to generate encrypted data. The backup communication unit uses a 4G / 5G communication module, which supports full network compatibility. When the LoRa wireless communication module or Ethernet module communication is interrupted, it automatically switches to the 4G / 5G communication module channel. The data transmission module also integrates a signal strength detection unit to monitor the communication signal strength in real time. When the signal strength is below -90dBm, it issues a communication fault warning.

[0025] The data processing and analysis module includes a data preprocessing unit, a feature extraction unit, a damage identification unit, and a damage assessment unit, which are connected sequentially through a data interface. The data preprocessing unit uses the Kalman filter algorithm to denoise the acquired digital signals, eliminating the influence of random noise. The calculation formula of the Kalman filter algorithm includes state prediction, covariance prediction, Kalman gain, state update, and covariance update: State prediction: ; Covariance prediction: ; Kalman gain: ; Status Update: ; Covariance update: ; in, Let be the posterior state estimate at time k; Let be the posterior state estimate at time k-1; A be the state transition matrix, with a value of 1; B be the control matrix, with a value of 0; u(k) be the control input, with a value of 0; P(k|k-1) be the prior covariance matrix at time k; P(k-1|k-1) be the posterior covariance matrix at time k-1; and Q be the process noise covariance, with a value of 1×1. K(k) is the Kalman gain at time k; H is the observation matrix, with a value of 1; M is the observation noise covariance, with a value of 1×1. Z(k) represents the observation at time k; I is the identity matrix; H T This is the transpose of matrix H; Let k be the prior state estimate at time k. Let be the posterior covariance matrix at time k; The feature extraction unit extracts three core feature parameters from the signal preprocessed by the data preprocessing unit: first, the resistance change rate ΔR / R0, where R0 is the initial resistance value at a reference temperature T0=25℃ and the structure is undamaged, and ΔR is the difference between the real-time resistance value and the initial resistance value R0; second, the resistance change gradient d(ΔR / R0) / dt, reflecting the rate of resistance change; and third, the signal variance σ², reflecting the stability of the signal. The feature parameter extraction frequency is consistent with the A / D conversion sampling frequency. The damage identification unit constructs a damage identification model based on the Support Vector Machine (SVM) algorithm to identify the presence and type of damage in concrete structures. The objective function of the SVM algorithm is: min(1 / 2)·||w||²+C·Σξ; sty(w· (x)+b)≥1-ξ,ξ≥0; Among them, \(w\) is the weight vector; \(C\) is the penalty parameter, with a value range of 5 - 20; \(\xi\) is the slack variable; \(y\) is the sample label, where no damage is 1, crack is 2, deformation is 3, and stiffness attenuation is 4; \(x\) is the sample feature vector, which consists of the resistance change rate, resistance change gradient, and signal variance; (·) is the kernel function mapping, and \(b\) is the bias term; The training samples of the damage identification model are collected through structural loading tests, including the feature parameter samples in the states of no damage and various damage conditions. The number of samples is ≥ 1000 groups, and the recognition accuracy of the trained model is ≥ 95%; The damage assessment unit constructs a quantitative damage assessment model based on damage mechanics theory to achieve the assessment of the damage location, damage degree, and damage development trend; among them, the damage location is determined by the distribution of the resistance change gradient at each monitoring point, and the monitoring point with the largest resistance change gradient corresponds to the damage center location; The damage degree is quantitatively evaluated through the damage factor \(D\). The relationship model between the damage factor \(D\) and the resistance change rate \(\Delta R / \) is: \(D = \delta\cdot(\Delta R / R_0)+\varepsilon\cdot(\Delta R / R_0)^2+\gamma\cdot(\Delta R / R_0)^3\); Among them, \(\delta\), \(\varepsilon\), and \(\gamma\) are model parameters, which are calibrated through structural loading tests. The value range of \(\delta\) is 0.7 - 0.9, the value range of \(\varepsilon\) is 0.1 - 0.15, and the value range of \(\gamma\) is -0.01 - 0; the value range of the damage factor \(D\) is 0 - 1. \(D = 0\) indicates no damage, \(0 < D < 0.1\) indicates extremely minor damage, \(0.1\leq D < 0.3\) indicates minor damage, \(0.3\leq D < 0.5\) indicates moderate damage, \(0.5\leq D < 0.8\) indicates severe damage, and \(D\geq0.8\) indicates extremely severe damage; The damage development trend is predicted through the time series change curve of the damage factor, using a linear regression model: \(D(t)=j\cdot t + D_0\), where \(t\) is the time, \(j\) is the damage development rate, and \(D_0\) is the current damage factor. When \(j>0.01 / month\), it indicates that the damage develops rapidly and urgent intervention is required.

[0026] The early warning and decision-making module includes a data storage unit, a status display unit, an early warning unit, and a decision suggestion unit. The data storage unit adopts a dual storage architecture of "local + cloud". Local storage uses an SD card to store the raw data and processing results of the most recent month. Cloud storage uses a MySQL cloud database to store all historical data, supporting long-term data traceability and multi-terminal access. The data storage period is consistent with the design life of the concrete structure. The status display unit develops a visual monitoring interface based on LabVIEW or Qt. The early warning unit adopts a multi-level early warning mechanism, setting four levels of early warning according to the magnitude of the damage factor D. The decision suggestion unit constructs a decision tree model based on the damage assessment results, the service life of the structure, design parameters, and environmental conditions, and outputs targeted operation and maintenance suggestions.

[0027] The system also includes a self-test and fault diagnosis module, which is integrated into the microcontroller unit to realize real-time monitoring of the working status of each component of the system and fault location. The self-test and fault diagnosis module includes a hardware self-test unit, a communication link diagnosis unit, and a sensing node fault diagnosis unit. The sensing node fault diagnosis unit realizes fault identification based on the feature analysis of sensing signals.

[0028] The data processing and analysis module also includes an adaptive learning unit, which dynamically optimizes the parameters of the damage identification model and the damage assessment model based on the structure of service data and environmental changes, thereby improving monitoring accuracy. The adaptive learning unit uses an incremental learning algorithm to periodically add newly collected monitoring data to the training set.

[0029] Example 2: See Figure 1 and Figure 2 The method for monitoring the health of concrete structures based on self-sensing carbon fiber materials, implemented using the self-sensing carbon fiber material-based concrete structure health monitoring system in Example 1, includes the following steps: S1. System Initialization: Initial resistance values ​​are acquired for the self-sensing carbon fiber composite sensing network, and the initial resistance values ​​of each monitoring node are recorded under conditions of no structural damage and 25°C. The data is stored in the early warning and decision-making module; the signal acquisition module is configured with parameters, including the constant current source output current, A / D conversion sampling frequency, and low-pass filter cutoff frequency; the data transmission module is configured with communication parameters, including LoRa communication frequency, Ethernet IP address, and AES-128 encryption key; and the early warning threshold and adaptive learning period are set. S2. Signal Acquisition and Preprocessing: The constant current source unit in the signal acquisition module outputs a stable DC constant current signal to the self-sensing carbon fiber composite sensing network. The signal conditioning unit amplifies, filters, and performs temperature compensation on the weak voltage signal output by the self-sensing carbon fiber composite sensing network. The A / D conversion unit converts the conditioned analog signal into a digital signal. The microcontroller unit performs preliminary buffering of the digital signal and then uploads it to the data processing and analysis module through the data transmission module. The preprocessing unit of the data processing and analysis module uses the Kalman filter algorithm to perform noise reduction processing on the digital signal to eliminate random noise. S3. Feature Extraction and Damage Recognition: The feature extraction unit of the data processing and analysis module extracts the resistance change rate ΔR / R0, the resistance change gradient d(ΔR / R0) / dt, and the signal variance σ² from the noise-reduced signal. The feature parameters are then input into the SVM damage recognition model trained by the damage recognition unit, and the damage recognition result is output. S4. Quantitative Damage Assessment: The damage assessment unit of the data processing and analysis module assesses the damage location, damage degree, and development trend based on the damage identification results. The damage location is determined by the resistance change gradient distribution of each monitoring point, and the monitoring point with the largest resistance change gradient corresponds to the damage center. The damage degree is quantitatively assessed by the damage factor D, and the damage development trend is predicted by the time series change curve of the damage factor. S5. Early Warning and Decision Output: The early warning unit of the early warning and decision module compares the damage factor D with the preset early warning threshold and activates the corresponding level of early warning; the status display unit updates and displays the status parameters and damage assessment results of each monitoring node in real time; the decision suggestion unit outputs targeted operation and maintenance suggestions based on the damage assessment results. S6. System Self-Check and Adaptive Learning: The self-check and fault diagnosis module periodically starts hardware self-check, communication link diagnosis and sensing node fault diagnosis to promptly detect and handle system faults; the adaptive learning unit incrementally trains the damage identification model and damage assessment model according to a set period to optimize model parameters.

[0030] The self-test and fault diagnosis module also has a fault self-recovery function. For minor faults, it can achieve self-recovery by restarting the corresponding module or re-establishing the communication connection.

[0031] In this invention, a self-sensing carbon fiber composite material is used to construct a sensing network. This material not only possesses excellent mechanical properties, reinforcing concrete structures and improving their load-bearing capacity and durability, but also utilizes its piezoresistive effect to achieve self-sensing of the structural health status. This solves the problems of poor compatibility between traditional sensors and the concrete matrix, and susceptibility to construction damage, realizing the concept of "structure as sensor." The signal acquisition module employs a high-precision constant current source, instrumentation amplifier, and 16-bit A / D converter, combined with a temperature compensation unit to effectively eliminate the influence of temperature on the measurement results, ensuring the accuracy of signal acquisition. The data processing and analysis module uses a Kalman filter algorithm for noise reduction, constructs a damage identification model based on the SVM algorithm, and achieves quantitative damage assessment by combining damage mechanics theory. The fusion of multiple algorithms improves the accuracy of damage identification and assessment. The accuracy of the trained damage identification model is ≥95%. The data transmission module adopts a dual-mode redundant transmission method of "wired + wireless + backup," and LoRa wireless communication is applicable. In long-distance and complex environments, Ethernet ensures high-speed transmission over short distances, while 4G / 5G serves as a backup channel to ensure uninterrupted communication. Simultaneously, the AES-128 encryption algorithm is used to encrypt transmitted data, guaranteeing data security and privacy. The system integrates self-testing and fault diagnosis modules, enabling real-time monitoring of the operational status of each component, fault location, and self-recovery from minor faults. The adaptive learning unit in the data processing and analysis module dynamically optimizes model parameters through incremental learning algorithms, improving the system's long-term monitoring accuracy. The early warning and decision-making module outputs targeted maintenance recommendations based on damage assessment results, providing a scientific basis for structural maintenance. The system adopts a dual-storage architecture of "local + cloud," with data storage cycles consistent with the design life of the concrete structure. It supports real-time monitoring, data traceability, and trend prediction of the structural health status throughout its entire lifecycle, enabling timely detection of early structural damage. This provides decision support for structural repair, reinforcement, and replacement, extending the structural service life and reducing maintenance costs.

[0032] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A health monitoring system for concrete structures based on self-sensing carbon fiber materials, characterized in that, include: A self-sensing carbon fiber composite sensing network is embedded in monitoring points inside a concrete structure to acquire resistance change signals. The signal acquisition module is used to acquire the resistance change signal output by the self-sensing carbon fiber composite material sensing network; The data transmission module is connected to the signal acquisition module and is used to receive the resistance change signal transmitted by the signal acquisition module. The data processing and analysis module is used to receive the resistance change data signal from the data transmission module and perform signal noise reduction, feature extraction, damage identification and quantitative damage assessment. The early warning and decision-making module, connected to the data processing and analysis module, is used for data storage, status display, multi-level early warning, and output of operation and maintenance decision suggestions.

2. The concrete structure health monitoring system based on self-sensing carbon fiber material according to claim 1, characterized in that: The self-sensing carbon fiber composite sensing network includes several self-sensing carbon fiber composite elements, each comprising the following raw materials by mass fraction: 15%-25% carbon fiber cloth or carbon fiber bundle, 73%-84% epoxy resin matrix, 0.5%-2% graphene, and 0.3%-0.8% silane coupling agent KH-550; The self-sensing carbon fiber composite element is ribbed or plate-shaped. The preparation process adopts a vacuum-assisted resin transfer molding process. The specific steps are as follows: according to the above mass fraction, graphene and silane coupling agent KH-550 are added to the epoxy resin matrix, and a conductive resin matrix is ​​prepared by ultrasonic dispersion and mechanical stirring; carbon fiber cloth or carbon fiber bundles are laid flat in the mold, and the conductive resin matrix is ​​vacuum introduced, cured at 70-90℃ for 1.5-2.5h, and then cured at 110-130℃ for 0.5-1.5h to obtain the self-sensing carbon fiber composite element. The surface of the self-sensing carbon fiber composite material element is provided with metal electrodes. The electrodes are prepared by coating with silver paste. The size of the electrodes matches the size of the electrode contact part of the composite material element. After coating, they are naturally dried and led out through copper wires. The monitoring points of the self-sensing carbon fiber composite material sensing network are arranged at the mid-span, support, and 1 / 4 span of the concrete beam, the root and middle of the column, and the corner and mid-span of the slab.

3. The concrete structure health monitoring system based on self-sensing carbon fiber material according to claim 2, characterized in that: The signal acquisition module includes a constant current source unit, a signal conditioning unit, an A / D conversion unit, and a microcontroller unit; The constant current source unit uses a programmable constant current source chip GP8102 to provide a DC constant current signal of 10μA-1mA for the self-sensing carbon fiber composite material sensing network, with a current adjustment accuracy of 0.1μA. The signal conditioning unit consists of an instrumentation amplifier, a low-pass filter, and a temperature compensation unit. The instrumentation amplifier is an INA128 with an adjustable gain range of 1-1000 times. The low-pass filter uses an RC active filter circuit with a cutoff frequency set to 5-20Hz. The temperature compensation unit uses a PT100 temperature sensor to collect ambient temperature data and performs temperature compensation on the measured signal using a temperature-resistance correction model. The correction model is as follows: R=R0[1+α·(T-T0)+β·(T-T0) 2 ]; Where R is the corrected resistance value at temperature T, R0 is the initial resistance value at the reference temperature T0 = 25℃ with no structural damage, and α and β are temperature coefficients, α = 0.00393 / ℃ and β = -5.775 × 1 / ℃; The A / D conversion unit uses a 16-bit high-precision analog-to-digital converter ADS1115 with a conversion accuracy of 0.015% and a sampling frequency set to 1Hz-100Hz to convert the analog voltage signal conditioned by the signal conditioning unit into a digital signal. The microcontroller unit uses an STM32H743 microprocessor with an integrated ARM Cortex-M7 core and a main frequency of 480MHz. It controls the output current of the constant current source unit and the sampling frequency of the A / D conversion, performs preliminary buffering of the acquired digital signals, and communicates with the data transmission module through the UART interface.

4. The concrete structure health monitoring system based on self-sensing carbon fiber material according to claim 3, characterized in that: The data transmission module includes a wireless communication unit, a wired communication unit, a data encryption unit, and a backup communication unit; The wireless communication unit adopts a LoRa wireless communication module with a communication frequency of 433MHz or 868MHz, a transmission distance of 1-5km, and a communication rate of 1.2-38.4kbps. It supports multi-node networking and is used for data transmission over long distances and in complex environments. The wired communication unit uses an Ethernet module, supports the TCP / IP protocol, and has a communication rate of 10 / 100Mbps; The data encryption unit uses the AES-128 encryption algorithm to encrypt the transmitted data. The encryption process is as follows: the data is grouped into 128-bit blocks, and 10 rounds of iteration are performed through initial round key addition, byte substitution, row shifting, column mixing, and round key addition to generate encrypted data. The backup communication unit adopts a 4G / 5G communication module, which supports full network connectivity. When the LoRa wireless communication module or Ethernet module communication is interrupted, it automatically switches to the 4G / 5G communication module channel. The data transmission module also integrates a signal strength detection unit to monitor the communication signal strength in real time. When the signal strength is below -90dBm, it issues a communication fault warning.

5. The concrete structure health monitoring system based on self-sensing carbon fiber material according to claim 4, characterized in that: The data processing and analysis module includes a data preprocessing unit, a feature extraction unit, a damage identification unit, and a damage assessment unit, which are connected sequentially through a data interface. The data preprocessing unit uses the Kalman filter algorithm to denoise the acquired digital signals, eliminating the influence of random noise. The calculation formula of the Kalman filter algorithm includes state prediction, covariance prediction, Kalman gain, state update, and covariance update. State prediction: ; Covariance prediction: ; Kalman gain: ; Status Update: ; Covariance update: ; in, Let be the posterior state estimate at time k; Let be the posterior state estimate at time k-1; A be the state transition matrix, with a value of 1; B be the control matrix, with a value of 0; u(k) be the control input, with a value of 0; P(k|k-1) be the prior covariance matrix at time k; P(k-1|k-1) be the posterior covariance matrix at time k-1; and Q be the process noise covariance, with a value of 1×1. K(k) is the Kalman gain at time k; H is the observation matrix, with a value of 1; M is the observation noise covariance, with a value of 1×1. Z(k) represents the observation at time k; I is the identity matrix; H T This is the transpose of matrix H. Let k be the prior state estimate at time k. Let be the posterior covariance matrix at time k; The feature extraction unit extracts three types of core feature parameters from the signal preprocessed by the data preprocessing unit: first, the resistance change rate ΔR / R0, where R0 is the initial resistance value at a reference temperature T0=25℃ and the structure is undamaged, and ΔR is the difference between the real-time resistance value and the initial resistance value R0; second, the resistance change gradient d(ΔR / R0) / dt, which reflects the rate of resistance change; and third, the signal variance σ², which reflects the stability of the signal. The feature parameter extraction frequency is consistent with the A / D conversion sampling frequency. The damage identification unit constructs a damage identification model based on the Support Vector Machine (SVM) algorithm to identify the presence and type of damage in the concrete structure. The objective function of the SVM algorithm is: min(1 / 2)·||w||²+C·Σξ; s.t.y(w· (x)+b)≥1-ξ,ξ≥0; Among them, w is the weight vector; C is the penalty parameter, with a value range of 5 - 20; ξ is the slack variable; y is the sample label, where no damage is 1, crack is 2, deformation is 3, and stiffness attenuation is 4; x is the sample feature vector, composed of the resistance change rate, resistance change gradient, and signal variance; (·) represents the kernel function mapping, and b is the bias term; The training samples of the damage identification model are collected through structural loading tests, including feature parameter samples in the states of no damage and various damage states. The number of samples is ≥ 1000 groups, and the recognition accuracy of the trained model is ≥ 95%; The damage assessment unit constructs a quantitative damage assessment model based on damage mechanics theory to achieve the assessment of the damage location, damage degree, and damage development trend; among them, the damage location is determined by the distribution of the resistance change gradient at each monitoring point, and the monitoring point with the largest resistance change gradient corresponds to the damage center position; The degree of damage is quantitatively assessed using the damage factor D, which is related to the rate of change in resistance ΔR / The relational model is as follows: D = δ·(ΔR / R0) + ε·(ΔR / R0)² + γ·(ΔR / R0)³; Among them, δ, ε, and γ are model parameters, calibrated through structural loading tests. The value range of δ is 0.7 - 0.9, the value range of ε is 0.1 - 0.15, and the value range of γ is -0.01 - 0; the value range of the damage factor D is 0 - 1. D = 0 indicates no damage, 0 < D < 0.1 indicates extremely slight damage, 0.1 ≤ D < 0.3 indicates slight damage, 0.3 ≤ D < 0.5 indicates moderate damage, 0.5 ≤ D < 0.8 indicates severe damage, and D ≥ 0.8 indicates extremely severe damage; The damage development trend is predicted through the time series change curve of the damage factor, using a linear regression model: D(t) = j·t + D0, where t is the time, j is the damage development rate, and D0 is the current damage factor. When j > 0.01 / month, it indicates that the damage develops rapidly and urgent intervention is required.

6. The health monitoring system for concrete structures based on self-sensing carbon fiber materials according to claim 5, characterized in that: The warning and decision-making module includes a data storage unit, a status display unit, a warning unit, and a decision-making suggestion unit. The data storage unit adopts a "local + cloud" dual storage architecture. The local storage uses an SD card to store the original data and processing results of the most recent 1 month; The cloud storage uses a MySQL cloud database to store all historical data, supporting long-term traceability and multi-terminal access of data. The data storage period is consistent with the design life of the concrete structure. The status display unit develops a visual monitoring interface based on LabVIEW or Qt. The warning unit adopts a multi-level warning mechanism and sets 4 levels of warnings according to the size of the damage factor D. The decision-making suggestion unit constructs a decision tree model based on the damage assessment results, the service life of the structure, design parameters, and environmental conditions, and outputs targeted operation and maintenance suggestions.

7. The concrete structure health monitoring system based on self-sensing carbon fiber material according to claim 6, characterized in that: The system also includes a self-check and fault diagnosis module, integrated in the microcontroller unit, to achieve real-time monitoring of the working states of each component of the system and fault location. The self-check and fault diagnosis module includes a hardware self-check unit, a communication link diagnosis unit, and a sensing node fault diagnosis unit. The sensing node fault diagnosis unit realizes fault identification based on the feature analysis of sensing signals.

8. The concrete structure health monitoring system based on self-sensing carbon fiber material according to claim 7, characterized in that: The data processing and analysis module also includes an adaptive learning unit, which dynamically optimizes the parameters of the damage identification model and the damage assessment model based on the service data and environmental changes of the structure, thereby improving the monitoring accuracy. The adaptive learning unit adopts an incremental learning algorithm to periodically add newly collected monitoring data to the training set.

9. The method for health monitoring of concrete structures based on self-sensing carbon fiber materials according to any one of claims 1-8, characterized in that, Includes the following steps: S1. System Initialization: Initial resistance values ​​are acquired for the self-sensing carbon fiber composite sensing network. The initial resistance value R0 of each monitoring node is recorded at a reference temperature T0=25℃ and the structure is undamaged, and stored in the early warning and decision-making module. Parameters are configured for the signal acquisition module, including constant current source output current, A / D conversion sampling frequency, and low-pass filter cutoff frequency. Communication parameters are configured for the data transmission module, including LoRa communication frequency, Ethernet IP address, and AES-128 encryption key. Early warning thresholds and adaptive learning periods are set. S2. Signal Acquisition and Preprocessing: The constant current source unit in the signal acquisition module outputs a stable DC constant current signal to the self-sensing carbon fiber composite sensing network. The signal conditioning unit amplifies, filters, and performs temperature compensation on the weak voltage signal output by the self-sensing carbon fiber composite sensing network. The A / D conversion unit converts the conditioned analog signal into a digital signal. The microcontroller unit performs preliminary buffering of the digital signal and then uploads it to the data processing and analysis module through the data transmission module. The preprocessing unit of the data processing and analysis module uses the Kalman filter algorithm to perform noise reduction processing on the digital signal to eliminate random noise. S3. Feature Extraction and Damage Recognition: The feature extraction unit of the data processing and analysis module extracts the resistance change rate ΔR / R0, the resistance change gradient d(ΔR / R0) / dt, and the signal variance σ² from the noise-reduced signal. The feature parameters are then input into the SVM damage recognition model trained by the damage recognition unit, and the damage recognition result is output. S4. Quantitative Damage Assessment: The damage assessment unit of the data processing and analysis module assesses the damage location, damage degree, and development trend based on the damage identification results. The damage location is determined by the resistance change gradient distribution of each monitoring point, and the monitoring point with the largest resistance change gradient corresponds to the damage center. The damage degree is quantitatively assessed by the damage factor D, and the damage development trend is predicted by the time series change curve of the damage factor. S5. Early Warning and Decision Output: The early warning unit of the early warning and decision module compares the damage factor D with the preset early warning threshold and activates the corresponding level of early warning; the status display unit updates and displays the status parameters and damage assessment results of each monitoring node in real time; the decision suggestion unit outputs targeted operation and maintenance suggestions based on the damage assessment results. S6. System Self-Check and Adaptive Learning: The self-check and fault diagnosis module periodically starts hardware self-check, communication link diagnosis and sensing node fault diagnosis to promptly detect and handle system faults; the adaptive learning unit incrementally trains the damage identification model and damage assessment model according to a set period to optimize model parameters.

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