Combined bridge interface damage time-varying analysis method and system

Abstract

The invention relates to the technical field of electric digital data processing, and discloses a combined bridge interface damage time-varying analysis method and system, and the method comprises the steps: obtaining a slip sequence and temperature gradient data of a joint interface; extracting a high-frequency dynamic response signal through dynamic residual filtering; calculating a displacement compensation amount based on the thermal stress characteristic matrix, and eliminating the compensation amount from the dynamic response signal to determine a net slip characteristic value; mapping the slip space distribution difference into a topological feature vector by using an interface stress distribution mapping operator; analyzing the unsteady oscillation characteristics of the dynamic response signal, determining the cementation failure probability, and outputting a damage analysis index in combination with a topological feature vector, so that accurate decoupling of environment thermally induced displacement interference and a structure damage signal is realized, the influence of non-damage factors is eliminated, and a rigidity degradation critical point is captured by utilizing an interface topological evolution mechanism; the early interface void recognition reliability is improved, and bridge safety evaluation is supported.

Description

Technical Field

[0001] This invention belongs to the field of electrical digital data processing technology, and in particular relates to a method and system for time-varying analysis of interface damage in composite bridges. Background Technology

[0002] Currently, the main focus is on the sensor displacement data analysis needs in the health monitoring of composite bridges. In current engineering practice, displacement sensors are deployed to collect slip characteristic parameters at the interface between the steel beam and the concrete slab, and a calculation model based on linear mapping rules is applied to evaluate the structural integrity of the interface connectors. This approach converts geometric displacement signals through a preset fixed stiffness matrix to establish a static damage assessment logic. The mechanical behavior of the steel-concrete interface follows a non-uniform shear force transmission mechanism, and the interface shear stress exhibits gradient characteristics in spatial distribution. This stress distribution topology and its dynamic variation over long time directly determine the effective shear stiffness and load transfer efficiency of the interface. However, during long-term service, the interface is driven by alternating loads to produce micro-slip, and the stress distribution characteristics exhibit nonlinear evolution and rheological properties. Because existing data processing operators are based on the idealized linear spring assumption, they treat interface slip as an isolated geometric displacement and ignore the reference offset caused by changes in interface friction conditions and concrete creep. This leads to stability defects in the monitoring system when dealing with spurious damage signals caused by thermal stress due to environmental temperature differences, resulting in error accumulation and decreased prediction accuracy in long-term analysis results.

[0003] Industry attempts have focused on introducing neural network algorithms to capture such nonlinear characteristics, which can lead to unexplainable judgments when faced with sudden impact loads or random sensor fluctuations. However, methods using high-order finite element simulation for data synchronization compensation are limited by computational overhead, making real-time feature extraction at the edge monitoring end difficult. While hardware aspects such as structural layout and sensor morphology can initially capture overall interface deformation, existing computational models still struggle to accurately reconstruct the physical evolution of interface forces at the software logic level of analytical algorithms. For example, Chinese invention patent CN120030651B discloses a steel-concrete composite material under vehicle dynamic load. The rapid calculation method for interface slip in composite beams establishes an analytical formula for dynamic response based on Timoshenko beam theory, which effectively improves the calculation speed of transient slip. However, such analytical schemes often treat the interface as an ideal continuum with a constant service state. In long-term monitoring scenarios, its calculation logic is difficult to be compatible with the nonlinear temperature field interference caused by environmental sunlight, and it cannot effectively isolate the systematic reference offset caused by material creep. Due to the lack of a physical stress distribution topology mapping mechanism, the existing fast calculation model often produces cumulative errors in the analysis results when dealing with aliased monitoring data under multi-source interference due to the lack of physical causal logic, and it is difficult to identify the critical inflection point of interface stiffness from quantitative to qualitative change.

[0004] Therefore, how to construct a nonlinear mapping operator with physical mechanism support and low computational cost to achieve accurate extraction and time-varying analysis of interface damage features in aliasing monitoring data has become the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention provides a time-varying analysis method for combined bridge interface damage, comprising the following steps: Step S1: Obtain the slip data sequence of each monitoring node in the steel-concrete interface of the composite bridge and the real-time temperature gradient data on both sides of the steel-concrete interface. Step S2: Perform dynamic residual filtering on the slip data sequence to extract the high-frequency dynamic response signal generated by the steel-concrete interface under load; Step S3: Construct a thermal stress feature matrix based on real-time temperature gradient data, calculate the displacement compensation amount characterizing the thermally induced displacement component of the interface, and remove the displacement compensation amount from the high-frequency dynamic response signal to extract the net slip feature value characterizing the physical damage of the interface. Step S4: Substitute the net slip characteristic value into the preset interface stress distribution mapping operator, use the preset nonlinear distribution function to describe the non-uniform distribution characteristics of the net slip characteristic value in the spatial dimension, and map the non-uniform distribution characteristics into a topological feature vector that characterizes the topological characteristics of the interface shear stiffness, so as to characterize the plastic evolution process of the steel-concrete joint interface during the service cycle. Step S5: Analyze the unsteady oscillation characteristics of the high-frequency dynamic response signal in the frequency domain, calculate the change in the system stability damping ratio based on the stability criterion, determine the probability of early cementation failure of the steel-concrete interface, and output a time-varying analysis index characterizing the degree of interface damage by combining the topological feature vector.

[0006] Preferably, in step S3, the displacement compensation amount is calculated. The process is as follows: ,in, This is the displacement compensation amount, in mm; This is the preset thermal expansion correction coefficient; For the first Heat conduction weighting factor for each monitoring node; For the thermal stress characteristic matrix corresponding to the first The equivalent temperature difference value of each monitoring node, in °C; This represents the total number of monitoring nodes.

[0007] Preferably, step S2 includes: extracting the quasi-static response component generated by the overall deformation of the structure in the slip data sequence using a low-frequency fitting algorithm; subtracting the quasi-static response component from the slip data sequence to obtain a high-frequency residual sequence; and performing noise reduction optimization on the high-frequency residual sequence to generate a high-frequency dynamic response signal.

[0008] Preferably, the process of analyzing the unsteady oscillation characteristics in step S5 includes: performing a fast Fourier transform on the high-frequency dynamic response signal to extract the energy distribution spectrum of the steel-concrete joint interface under the load pulse sequence; identifying the abnormal peak frequency in the energy distribution spectrum and comparing it with the preset interface self-excited vibration characteristic frequency to obtain the accompanying disturbance signal characterizing the microscopic delamination of the joint interface.

[0009] Preferably, the process of determining the early cementation failure probability in step S5 includes: calculating the dynamic stiffness attenuation coefficient of the interface based on the rate of change of the energy envelope of the accompanying disturbance signal; inputting the dynamic stiffness attenuation coefficient into a preset stability judgment matrix; and calculating and outputting the early cementation failure probability when the dynamic stiffness attenuation coefficient exceeds a preset critical threshold.

[0010] Preferably, after step S5, the method further includes: real-time monitoring of the response energy level of the load frequency, and switching the analysis mode to mean-completion mode when the response energy level is lower than a preset energy threshold; in mean-completion mode, using historical damage step size data to perform interpolation calculations on the missing time-varying response points to smooth the time-varying analysis indicators.

[0011] Preferably, the process of constructing the interface stress distribution mapping operator in step S4 includes: introducing critical state variables characterizing the sudden change in interface stress, establishing a nonlinear function mapping between slip displacement and interface shear stress transmission; using the nonlinear function mapping to perform spatial weight allocation on the net slip characteristic value, extracting micro-damage parameters characterizing the evolution of the interface shear zone, and encapsulating the micro-damage parameters into a topological feature vector.

[0012] Preferably, the process of outputting time-varying analysis indicators in step S5 includes: extracting slip data of the unloading segment of the load cycle and calculating the hysteretic characteristic parameters of the slip response; based on elastic-complex mechanics logic, identifying and removing residual deformation components caused by concrete creep from the hysteretic characteristic parameters to obtain a net damage indicator characterizing the damage to the interface structure.

[0013] Preferably, step S5 further includes: comparing the time-varying analysis index with the preset safety threshold, predicting the remaining service life of the steel-concrete interface based on the evolution rate of the time-varying analysis index, establishing a mapping relationship library between the topological feature vector and the micro-damage state of the interface, and retrieving and outputting the corresponding interface stiffness degradation level information from the mapping relationship library based on the topological feature vector calculated in real time.

[0014] A combined bridge interface damage time-varying analysis system includes a data acquisition module, a filtering module, a compensation extraction module, a topology mapping module, and a comprehensive analysis module. The data acquisition module is used to acquire the slip data sequence of each monitoring node in the steel-concrete interface of the composite bridge and the real-time temperature gradient data on both sides of the steel-concrete interface. The filtering module is used to perform dynamic residual filtering on the slip data sequence to extract the high-frequency dynamic response signal generated by the steel-concrete interface under load. The compensation extraction module is used to construct a thermal stress feature matrix based on real-time temperature gradient data, calculate the displacement compensation amount characterizing the thermally induced displacement component of the interface, and remove the displacement compensation amount from the high-frequency dynamic response signal to extract the net slip feature value characterizing the physical damage of the interface. The topology mapping module is used to substitute the net slip characteristic values ​​into a preset interface stress distribution mapping operator, and use a preset nonlinear distribution function to map the spatial distribution difference of the net slip characteristic values ​​into a topological feature vector that characterizes the topological features of the interface shear stiffness. The comprehensive analysis module is used to analyze the unsteady oscillation characteristics of high-frequency dynamic response signals in the frequency domain, calculate the change in the system stability damping ratio based on stability criteria to determine the probability of early cementation failure of the steel-concrete joint interface, and output time-varying analysis indicators characterizing the degree of interface damage by combining topological feature vectors.

[0015] Compared with existing technologies, the combined bridge interface damage time-varying analysis method of the present invention has the following advantages: 1. In bridge interface damage analysis, a stress distribution analysis mechanism based on a virtual rolling contact zone is constructed. This mechanism transforms interface slip characteristic parameters into a nonlinear stress distribution function with spatial topological features. This changes the traditional linearized treatment that treats interface slip as an isolated geometric displacement. The proportional distribution characteristics of the forward and backward slip zones are used to refine the transmission and evolution of interface shear force. This overcomes the problem of cumulative error divergence in prediction step size caused by traditional linear spring models in long-term iterative calculations. The system can reconstruct the microscopic damage state of interface stiffness through analytical operators without the need to establish a high-order finite element model, reducing the dependence on hardware computing power and enabling the stable operation of a high-precision damage identification algorithm in a low-power edge monitoring unit.

[0016] 2. By utilizing the interface neutral point spatial vector tracking and decoupling mechanism, the physical equilibrium position of the interface shear stress is located by identifying the reversal point of the gradient distribution of slip characteristic parameters along the interface length direction. Based on the offset trend of the spatial position vector of this reversal point under continuous dynamic load cycles, the interface friction reference drift caused by environmental temperature and humidity fluctuations and the resistance attenuation caused by damage to the shear connection section are essentially separated. This solves the problem of false early warnings caused by the high superposition of environmental disturbance signals and structural damage signals in existing monitoring technologies, enabling the damage evaluation index to exclude interference from non-damaging factors and ensuring the physical robustness of the analysis results during extreme climate change cycles.

[0017] 3. By integrating the rolling bite-in critical criterion and the elastic recovery characteristic mapping module, a multi-dimensional collaborative dynamic monitoring closed loop is constructed. When the interface response energy level is lower than the preset energy threshold, it automatically switches to the static mean compensation mode to avoid numerical oscillations and random jumps in the neutral point under low signal-to-noise ratio conditions. During the unloading segment of the load cycle, the hysteresis characteristics of the slip parameters are extracted and combined with the elastic recovery mechanics logic to calculate and remove the concrete creep component in the residual deformation. This multi-feature intertwined composite effect enables the system to not only capture the critical inflection point of the interface stiffness from quantitative to qualitative change, but also to accurately separate the material heterogeneity evolution and structural shear damage without adding additional strain monitoring equipment. This achieves nonlinear coupling between monitoring resources and effective load events, ensuring the smoothness and logical self-consistency of the time-varying analysis curve throughout the entire life cycle. Attached Figure Description

[0018] Figure 1 This is a flowchart of the signal extraction and topological feature mapping process of the combined bridge interface damage time-varying analysis method of the present invention; Figure 2 This is a schematic diagram of the multi-level hardware architecture and data flow of the combined bridge interface damage time-varying analysis system of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, bottom, transverse, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0021] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0023] This invention provides a time-varying analysis method and system for interface damage in composite bridges. By constructing an electro-digital data processing architecture, it achieves precise analysis of the damage state of the steel-concrete joint interface in composite bridges. It employs a data transformation process based on the equivalent logic of virtual rolling mechanics to convert the physical displacement signals collected by the underlying sensors into high-order interface stress topological feature vectors, thereby reconstructing the microscopic damage evolution path of the interface at the electro-digital processing level. The system architecture mainly consists of a data acquisition module, a filtering module, a compensation extraction module, a topology mapping module, and a comprehensive analysis module. A closed-loop interaction is established through a defined data flow direction. The data acquisition module collects the original slip sequence and real-time temperature gradient data of each monitoring node. The filtering and compensation module removes environmental interference factors, and the generated net slip feature value is input to the topology mapping module. A preset nonlinear distribution function is used to complete the mapping from the geometric displacement space to the stress topology space. Finally, the comprehensive analysis module analyzes the unsteady-state oscillation characteristics and outputs damage evaluation indicators.

[0024] In long-term monitoring of composite bridges, the raw signals acquired by sensors often contain a large number of quasi-static response components generated by the overall structural deformation. This masks the high-frequency dynamic response characteristics reflecting local interface damage, posing an obstacle to data processing applications. The system employs a filtering module to execute a dynamic residual filtering procedure and extracts the quasi-static response components from the slip data sequence using a low-frequency fitting algorithm. This low-frequency fitting algorithm uses a filtering model based on a moving average window, with the sliding window length set to... The sampling frequency is fixed at 1000.0 Hz in the underlying data processing, the sliding window length is 1024 sampling points, and the overlap rate of the window movement is precisely set to 50.0%. Before performing dynamic residual filtering, the data in each buffer is weighted using a Hanning window function to suppress spectral leakage during frequency domain analysis. Random abnormal pulses caused by sampling current fluctuations are automatically removed by comparing the root mean square energy values ​​of adjacent windows. The value is calibrated based on 10 times the first-order natural vibration period of the bridge; the system subtracts the quasi-static response component from the original slip data sequence to obtain the high-frequency residual sequence, and performs noise reduction and optimization processing on it to generate a high-frequency dynamic response signal; this procedure realizes the extraction of local slip features of the interface by deconstructing the signal energy in the time and frequency domain, providing a data basis for subsequent accurate damage identification.

[0025] Large-span composite beams are affected by solar radiation, resulting in a non-uniform temperature gradient between the steel beam and the concrete slab, causing thermally induced displacement components at the interface. Direct damage analysis would lead to systematic biases in the results. To address this challenge, the system introduces an equivalent thermal convexity compensation operator. It utilizes real-time temperature gradient data collected by the data acquisition module on both sides of the joint surface to construct a thermal stress characteristic matrix, and calculates the displacement compensation amount based on this matrix. The calculation process follows the formula below: ,in, This is the displacement compensation amount, in mm; The preset thermal expansion correction coefficient is experimentally calibrated based on the material's linear expansion coefficient. For the first The heat conduction weighting factor of each monitoring node is used to characterize the non-uniformity of heat transfer; For the thermal stress characteristic matrix corresponding to the first The equivalent temperature difference value of each monitoring node, in units of ; The total number of monitoring nodes; the system removes displacement compensation from the high-frequency dynamic response signal. The net slip characteristic value representing the physical damage at the interface is extracted; the thermal crown compensation logic in the rolling process is used to transform the complex nonlinear temperature field interference into a definite digital compensation amount, ensuring that the benchmark for damage judgment is not affected by the fluctuation of the ambient temperature difference.

[0026] To clarify the execution path of the interface stress distribution mapping operator, the system adopts a distribution function with explicit physical parameters. Construct a displacement-to-stress conversion model, distribution function Follow the formula as follows: ,in, Vertical axis The equivalent shear stress component at the point, in MPa; The interfacial bonding strength constant is preset based on material test data; The interface friction coefficient is dynamically corrected through the interface neutral point space vector tracking procedure. The specific correction step logic is as follows: the system compares the physical coordinate position of the sliding direction reversal point on the longitudinal span of the bridge in real time. Whenever the reversal point shifts by 1.0 mm in unidirectional cumulative displacement towards the bridge support, the processor automatically reduces the interface friction coefficient by 0.02 from the initial value of 0.35 until the reversal point returns to the calibration zero range, establishing a causal mapping between interface stress distribution and physical resistance, ensuring that the calculated interface shear force reflects the real-time state of the structure in the longitudinal coordinate position. The total length of the monitored section is expressed in mm. The equivalent thickness of the concrete flange is expressed in mm. This function maps the geometric spatial distribution of net slip eigenvalues ​​into a topological eigenvector characterizing the interface shear stiffness state by nonlinearly integrating the slip distribution gradient. It solves the problem of missing mechanical evolution paths in the electrical digital processing through physical prior constraints.

[0027] During a quiet period when the ambient temperature gradient variation amplitude is less than 2℃, a known mass calibration load is applied at the mid-span of the main beam, and the initial slip sequence output by the sensor array is simultaneously acquired. Using this initial slip sequence as the input variable, the least squares method is applied to the distribution function. Nonlinear fitting is performed until the goodness of fit reaches the convergence condition of 0.95 to determine the interfacial bonding strength constant. coefficient of friction with interface The baseline initial value, where The unit is megapascal (MPa), which characterizes the initial bond resistance between materials. It is a dimensionless constant that characterizes the frictional properties of the contact surface. The distribution function is locked using the reference initial value. The physical initial boundary ensures that the calculated interfacial shear force is within the longitudinal coordinate range. Discrete values ​​reflect the initial health state of the structure. Interfacial friction reference drift caused by fluctuations in environmental humidity or material oxidation often manifests as a monotonic shift in sensor signals similar to physical damage, which conventional filtering methods cannot isolate from the physical mechanism level. To address this technical obstacle, the system executes an interface neutral point spatial vector tracking procedure. Based on the distribution gradient of slip characteristic parameters along the interface length direction, it identifies the interface shear stress reversal point—the physical location where the interface shear stress direction reverses—and extracts the spatial position vector of this reversal point. The system compares the temporal deflection of this spatial position vector with a preset rolling slip model to determine the reference drift of the interface friction coefficient. When the system detects a unidirectional shift of the spatial position vector while the load peak remains unchanged, the discrimination unit identifies it as interface friction reference drift and uses this reference drift to perform reverse real-time compensation of the stiffness matrix parameters. By capturing the displacement of the neutral point—a core physical feature—and based on the system's ability to distinguish between interface aging and structural damage from a physical logic level, false warnings caused by changes in environmental factors are avoided.

[0028] Under conditions of low bridge traffic or light vehicle passage, where the amplitude of excitation is small, the sensor displacement signal is easily submerged by noise, causing random jumps in the analytical operator. This stability problem is addressed by integrating the rolling bite-in critical criterion, extracting the dynamic characteristic components of the slip characteristic parameters, and calculating the interface response energy level under the current operating conditions. The system determines the response level of the interface. Whether the preset energy threshold is met, which indicates that the interface slip is sufficient to drive the formation of the forward and backward slip distribution topology; when the response energy level is lower than the energy threshold, the system automatically switches to mean-complementation mode, maintaining the stiffness matrix parameters at the current static mean, and using historical data for interpolation calculation; when the response energy level meets the threshold requirements, the interface mechanical mapping operator is activated; this logic, by introducing a physical threshold, ensures the numerical stability of the system under low signal-to-noise ratio conditions, and realizes the nonlinear coupling of monitoring resources and effective load events; steel-concrete interface damage usually begins with a very small loss of chemical bonding force. Such early delamination responds slowly in the overall displacement signal, and traditional methods are difficult to implement. The system detects early signs of damage. To improve the timeliness of early warning, an interface-associated vibration analysis module is constructed to extract high-frequency residual components that the master operator fails to fit from the slip acquisition stream. The system performs a Fast Fourier Transform (FFT) to obtain the energy distribution spectrum and compares it with a preset mill vibration energy model. When the spectrum shows abnormal peaks in a specific frequency band, the system calculates the change in the system stability damping ratio based on stability criteria, determines the probability of early interface cementation failure, and outputs it as a risk correction term for the damage assessment index. This scheme utilizes signal residuals to detect the transition of interface damage from an adhesive state to a discrete frictional state, thus advancing the timing of maintenance intervention.

[0029] For the generation of damage analysis indices, the comprehensive analysis module executes a quantitative calculation procedure based on multi-source data fusion. The formula is as follows: ,in, For damage analysis indicators; This is the topology deflection weighting coefficient; it is fixed at 0.65 in the system software configuration. The bonding failure weighting coefficient is fixed at 0.35 in the system software configuration; and satisfies... Normalization constraints; The rate of change of the Euclidean distance between the topological feature vector generated at the current moment and the initial stress topological vector; The probability of early cementation failure of the interface is determined by the interface-accompanied flutter analysis module. This procedure linearly weights and fuses the topological deflection, which characterizes stiffness degradation, and the flutter energy characteristics, which characterize micro-cementation failure, to output a comprehensive evaluation value characterizing the interface damage state of the composite bridge, realizing the direct conversion of monitoring data into structural safety evaluation indicators. Irreversible creep of concrete during long-term service leads to the slow migration of the slip reference, exhibiting a trend similar to that of shear member damage, which traditional algorithms find difficult to accurately decouple. The system adopts a reuse procedure based on the elastic recovery characteristics of rolled pieces to extract the residual deformation characteristics of slip feature parameters during the unloading segment of the load cycle. The system calls the post-rolling elastic recovery mechanical model to calculate the concrete creep component in the residual deformation and corrects the damage weight of the stiffness matrix parameters based on the creep component. Through physical analysis of the hysteresis feature parameters of the slip data, the creep component is subtracted from the total slip offset to extract the net damage index characterizing the interface structural damage. This strategy solves the problem of long-cycle creep interference by utilizing the unloading process information in the dynamic load cycle without adding additional strain monitoring equipment.

[0030] Example 1: Under summer operating conditions of a long-span composite beam bridge, the ambient temperature gradient reaches... Furthermore, the bridge deck load was under heavy-load vehicle traffic at a slow speed. Due to the difference in thermophysical properties of the materials, the steel beams and concrete slabs experienced asymmetric thermal expansion displacement. In the original data sequence collected by the slip sensor, the thermally induced displacement component accounted for more than 85% of the total displacement amplitude. Environmental interference of this magnitude caused the high-frequency dynamic response characteristics reflecting local interface damage to be masked by the background thermal noise component. The system called the filtering processing module to perform dynamic residual filtering, extracting the high-frequency residual sequence from the slip sequence. The compensation extraction module then used the formula... Calculate the displacement compensation amount And remove them from the high-frequency residual sequence, where, This is the displacement compensation amount, in mm. The preset thermal expansion correction coefficient is calibrated based on the material's linear expansion coefficient. For the first The heat conduction weighting factor for each monitoring node For the corresponding to the first The equivalent temperature difference value of each monitoring node, in °C. The total number of monitoring nodes is used to obtain the net slip characteristic value after removing the interference of non-uniform temperature field.

[0031] The topology mapping module substitutes the net slip eigenvalues ​​into the interface stress distribution mapping operator, which utilizes a nonlinear distribution function constructed based on the mechanical equations of the metal rolling contact zone. The spatial distribution characteristics of displacement are converted into the topological feature vector of interface shear stiffness. At the same time, the interface accompanying flutter analysis module performs a fast Fourier transform on the extracted high-frequency residual components. By identifying the energy peak variation of the spectrum within a preset frequency band, the flutter signal of the interface transitioning from an adhesive state to a discrete frictional state is analyzed. The system determines the physical damage state of the interface under the current working condition through the composite calculation of the drift of the topological feature vector and the failure probability determined by the energy spectrum. The system executes the above data transformation procedure and decouples the temperature-induced displacement and interface damage flutter signal in the time and frequency domain while maintaining the number of sensor monitoring points.

[0032] Example 2: To verify the damage analysis capability of the composite bridge interface under solar interference, this experiment was conducted in a 1:1 scale steel-concrete composite beam test platform with a span of 6.0m, a concrete flange thickness of 150mm, and 36 cylindrical head weld studs arranged at the joint interface. Gaussian white noise with a signal-to-noise ratio of 20dB was actively superimposed on the signal source to simulate industrial environmental interference; sampling period... Set to 0.5ms, where, The sampling period, measured in milliseconds (ms), is determined by balancing the effective frequency upper limit of the monitored signal (50Hz) with the processor's computational load. Specifically, when the upper frequency limit of the monitored signal is between 10Hz and 50Hz, to satisfy the sampling theorem and preserve the interface dynamic response characteristics, [the following setting is used]. The time was set at 0.5 ms; during the test, a climate simulation system was used to apply 15 ms to the top surface of the composite beam. Up to 35 The non-uniform temperature gradient was simulated by applying alternating loads simulating vehicle loads using a hydraulic servo system. The experimental group processed data using the method of this invention; control group one did not perform equivalent thermal convexity compensation, control group two did not perform topology mapping analysis, and control group three set the temperature gradient to 45°C. It exceeds the preset compensation temperature range, see Table 1.

[0033] Table 1: Different groups in Comparison table of processed data under temperature gradient interference Analyzing the data in Table 1, the formula for the sample group of this invention is executed. Calculate the displacement compensation amount ,in, This is the displacement compensation amount, in mm. This is a thermal expansion correction factor, calibrated based on the material's linear expansion coefficient. The total number of monitoring nodes, For the first The heat conduction weighting factor for each monitoring node For the first The equivalent temperature difference values ​​of each monitoring node are expressed in °C. Control group one, due to the failure to remove the thermal displacement component, exhibited a damage assessment deviation of 562.1%, while the sample group of this invention suppressed the deviation to within 1.8% through compensation logic. Furthermore, data from control group three showed that when the environmental temperature gradient exceeded... After the performance inflection point, the thermal displacement caused by the nonlinear creep of concrete led to an increase in the judgment bias to 14.2%, which confirmed the effectiveness of the compensation operator in a specific temperature range. To verify the synergistic effect of the topology mapping module and the interface-accompanied flutter analysis module, three sample groups with interface void gradients were set up in the experiment. At the component level, the slip amplitude was in the damage initiation stage below 0.02 mm, and at the system level, the displacement topology characteristic deflection was in the measurement noise range. The interface-accompanied flutter analysis module performed a fast Fourier transform (FFT) on the slip sequence to obtain the energy distribution spectrum. By identifying the characteristic energy peak variation in the energy distribution spectrum in the 120 Hz to 180 Hz frequency band, the current interface failure probability was determined to be 0.88, thus completing the judgment of the early cementation failure state.

[0034] Example 3: This example combines Figures 1 to 2 A description of a time-varying analysis method and system for interface damage in composite bridges, such as... Figure 1 As shown, step S1 acquires the slip data sequence of each monitoring node in the steel-concrete interface of the composite bridge and the real-time temperature gradient data on both sides of the steel-concrete interface. Step S2 performs dynamic residual filtering on the slip data sequence to extract the high-frequency dynamic response signal generated by the steel-concrete interface under load. Step S3 constructs the thermal stress feature matrix and calculates the displacement compensation amount. This compensation amount is removed from the high-frequency dynamic response signal to extract the net slip feature value characterizing the physical damage of the interface. Based on this, step S4 uses the interface stress distribution mapping operator and nonlinear distribution function to map the net slip feature value into a topological feature vector characterizing the topological characteristics of the interface shear stiffness to characterize the plastic evolution process of the steel-concrete interface during the service cycle. Finally, step S5 analyzes the unsteady oscillation characteristics of the high-frequency dynamic response signal and calculates the probability of early cementation failure. Combined with the topological feature vector, a time-varying analysis index characterizing the degree of interface damage is output.

[0035] like Figure 2 As shown, the physical architecture is divided into a bridge on-site monitoring terminal, a secure encrypted transmission network, a monitoring center data processing terminal, and an engineering monitoring workstation. The bridge on-site monitoring terminal takes the interface of the steel-concrete composite beam as the monitoring object, deploys a slip monitoring node array and a multi-point temperature gradient sensor, and connects to the data acquisition module through an intelligent data acquisition and transmission instrument with integrated analog-to-digital conversion and preliminary caching functions. The generated encrypted data stream is transmitted to the monitoring center data processing terminal via the secure encrypted transmission network, along with the original signal and feature data. The monitoring center data processing terminal deploys a high-performance computing cluster as the core algorithm server, running the core software of the interface damage time-varying analysis system, which includes a filtering module, a compensation extraction module, a topology mapping module, and a comprehensive analysis module. The time-varying analysis indicators or early warning information processed by this core software are output to the engineering monitoring workstation, which includes a visualization screen or PC terminal, and stored in the historical feature database.

[0036] Example 4: In a monitoring scenario of a composite continuous beam bridge with a span of 120m, the steel beams are made of Q345qD grade steel, and the concrete flanges are made of C50 grade concrete. The system acquires real-time slip and temperature data sequences of each monitoring node and executes parameter calibration procedures to determine the core operation operators. The system selects a 4-hour period with no vehicle load and monotonically changing ambient temperature as the calibration window, and synchronously collects quasi-static slip components. Real-time temperature gradient data of the monitoring node array Fitting using linear regression method With real-time temperature gradient data The slope of the generated thermal displacement correlation curve is determined as the thermal expansion correction factor. During the unloaded operation phase before the bridge opens to traffic, the system collects the raw slip noise signal energy levels from the sensor array, calculates three times the standard deviation of the energy level, and sets this as the energy threshold. In this scenario, determine the thermal expansion correction coefficient. The energy threshold is set at 0.115 mm / ℃. It is 0.012 mm².

[0037] During system operation, the interface, along with the flutter analysis module, executes the following processing procedures for the acquired high-frequency residual sequences. Perform a 1024-point Fast Fourier Transform to generate the energy distribution spectrum. Identify the energy distribution spectrum The maximum energy peak and its corresponding characteristic frequency in the 150Hz to 300Hz frequency band The system calculates the stability damping ratio according to the following formula. : ,in, For stability damping ratio, This represents the frequency bandwidth corresponding to the maximum energy peak at -3dB, in Hz. The characteristic frequency is expressed in Hz; the system calculates the real-time stability damping ratio. Compared with the initial damping ratio The difference is used to determine the change in damping ratio. And according to the linear mapping function Early bonding failure probability at the output interface ,in, This represents the probability of early bonding failure at the interface. This is a proportionality coefficient, preset based on fatigue failure test data of shear members. This represents the change in damping ratio.

[0038] During the calculation cycle of this embodiment, the system identifies It is 12.4Hz. The frequency is 225.6 Hz, and the real-time stability damping ratio is calculated. It is 0.0275, compared to the initial damping ratio. The change in its damping ratio is 0.0122. The value is 0.0153, which is the scaling factor. Under the preset condition of 50, the probability of early bonding failure at the interface is calculated and determined. The value is 0.765. By introducing the above calibration procedure and quantitative calculation logic, the environmental correction coefficient and damage judgment criteria are transformed from qualitative descriptions into quantitative parameters based on physical monitoring data, and a mapping path from frequency domain energy distribution to physical failure probability is established.

[0039] Example 5: In the initial deployment condition of a newly built composite continuous beam bridge with a span of 150m, the system executes the interface mechanical benchmark initialization procedure to determine the initial boundary conditions of the interface stress distribution mapping operator. The data acquisition module operates at an ambient temperature of 18°C. Up to 22 The initial zero-point slip value of each monitoring node was collected during the quiet period when there was no traffic load on the bridge surface. Simultaneously, the initial displacement alignment data of the composite beam is acquired using leveling equipment and converted into initial interface curvature characteristics, which are then input into the topology mapping module. The system utilizes a distribution function based on rolling contact mechanics. Describe the spatial coupling relationship between the initial curvature characteristics of the interface and the shear member distribution density parameters, and determine the initial stress topology vector of the bridge structure in the initial stable state. ,in, This is the distribution function, used to calculate the interfacial shear force in the longitudinal coordinate system. discrete values ​​at that point This is the initial zero-position slip value, in mm. This is the initial stress topology vector.

[0040] When the system faces signal zero-point drift interference caused by aging sensor components or displacement of the composite beam support system, the comprehensive analysis module executes periodic zero-point reconstruction logic. During the annual neutral temperature period, the system automatically triggers environmental characteristic statistics tasks, collects slip data sequences under environmental excitation, and uses the elastic-complex mechanical model of the rolled piece to analyze the convergence characteristics of the residual displacement at the interface, dynamically updating the initial stress topology vector in the topology mapping module. This procedure separates the data offset of the hardware layer from the physical creep response of the structural layer during the data processing. It utilizes the self-calibration characteristics of the data transformation process to offset the interference of long-cycle time factors, thereby improving the comparability of monitoring data throughout the entire life cycle of the bridge while maintaining the sensor monitoring point deployment scheme.

[0041] Example 6: In the operation scenario of a composite beam bridge in a coastal high-humidity environment with asymmetrical traffic flow, the variation in the thickness of the oxide layer on the surface of the interface material causes the static baseline of the slip characteristic parameters to shift. At the same time, the irregular gust excitation signal and the micro-vehicle load response highly overlap in the time domain, resulting in the original signal input to the data acquisition module containing non-stationary noise that cannot be eliminated by a preset fixed threshold. After a 12-month monitoring period, the analytical accuracy of the mapping operator deviates due to the discretization of the initial physical parameters.

[0042] The processor extracts net slip feature values ​​from the past 30 complete load cycles from the storage unit to construct a discretized sample set. Statistical algorithms are used to calculate the probability distribution characteristics of the sample set within the current time window, and the adaptive correction coefficients of the analytical function are determined based on these characteristics. ,in, It is a dimensionless correction factor used to adjust the distribution function based on the real-time displacement alignment data of the bridge. The physical curvature response; simultaneously, the comprehensive analysis module executes feature space transformation logic based on wavelet packet decomposition to decompose the slip data sequence into 8 energy complementary frequency band components. The frequency band signal with a center frequency in the range of 120Hz to 240Hz is selected as the input data for the interface accompanying flutter analysis module. The system calculates the frequency band energy ratio and compares it with the preset mill operation stability criterion, outputting the early interface cementation failure probability after signal-to-noise ratio self-checking logic correction. The system outputs the dynamically reconstructed initial stress topology vector. The system separates the signal offset caused by environmental and meteorological factors from the physical evolution of the interface cementation state. By dynamically updating the operating parameters of the analytical operator online, it offsets the calculation deviation caused by the heterogeneous evolution of material properties. Under the premise of maintaining the sensor monitoring point layout scheme, the system obtains time-varying evaluation indicators characterizing the damage state of the steel-concrete interface.

[0043] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A time-varying analysis method for interface damage in combined bridges, characterized in that, Includes the following steps: Step S1: Obtain the slip data sequence of each monitoring node in the steel-concrete interface of the composite bridge and the real-time temperature gradient data on both sides of the steel-concrete interface. Step S2: Perform dynamic residual filtering on the slip data sequence to extract the high-frequency dynamic response signal generated by the steel-concrete interface under load; Step S3: Construct a thermal stress feature matrix based on real-time temperature gradient data, calculate the displacement compensation amount characterizing the thermally induced displacement component of the interface, and remove the displacement compensation amount from the high-frequency dynamic response signal to extract the net slip feature value characterizing the physical damage of the interface. Step S4: Substitute the net slip characteristic value into the preset interface stress distribution mapping operator, use the preset nonlinear distribution function to describe the non-uniform distribution characteristics of the net slip characteristic value in the spatial dimension, and map the non-uniform distribution characteristics into a topological feature vector that characterizes the topological characteristics of the interface shear stiffness, so as to characterize the plastic evolution process of the steel-concrete joint interface during the service cycle. Step S5: Analyze the unsteady oscillation characteristics of the high-frequency dynamic response signal in the frequency domain, calculate the change in the system stability damping ratio based on the stability criterion, determine the probability of early cementation failure of the steel-concrete interface, and output a time-varying analysis index characterizing the degree of interface damage by combining the topological feature vector.

2. The method for time-varying analysis of interface damage in composite bridges according to claim 1, characterized in that, In step S3, the displacement compensation amount is calculated. The process is as follows: ,in, This is the displacement compensation amount, in mm; This is the preset thermal expansion correction coefficient; For the first Heat conduction weighting factor for each monitoring node; For the thermal stress characteristic matrix corresponding to the first The equivalent temperature difference value of each monitoring node, in °C; This represents the total number of monitoring nodes.

3. The method for time-varying analysis of interface damage in a composite bridge according to claim 1, characterized in that, Step S2 includes: extracting the quasi-static response component generated by the overall deformation of the structure from the slip data sequence using a low-frequency fitting algorithm; subtracting the quasi-static response component from the slip data sequence to obtain a high-frequency residual sequence; and performing noise reduction optimization on the high-frequency residual sequence to generate a high-frequency dynamic response signal.

4. The time-varying analysis method for interface damage of a composite bridge according to claim 1, characterized in that, The process of analyzing the unsteady oscillation characteristics in step S5 includes: performing a fast Fourier transform on the high-frequency dynamic response signal to extract the energy distribution spectrum of the steel-concrete joint interface under the load pulse sequence; identifying the abnormal peak frequency in the energy distribution spectrum and comparing it with the preset interface self-excited vibration characteristic frequency to obtain the accompanying disturbance signal characterizing the microscopic delamination of the joint interface.

5. The time-varying analysis method for interface damage of a composite bridge according to claim 4, characterized in that, The process of determining the probability of early cementation failure in step S5 includes: calculating the dynamic stiffness attenuation coefficient of the interface based on the rate of change of the energy envelope of the accompanying disturbance signal; inputting the dynamic stiffness attenuation coefficient into a preset stability judgment matrix; and calculating and outputting the probability of early cementation failure when the dynamic stiffness attenuation coefficient exceeds a preset critical threshold.

6. The time-varying analysis method for interface damage of a composite bridge according to claim 1, characterized in that, Step S5 and beyond also includes: real-time monitoring of the response energy level of the load frequency, and switching the analysis mode to mean-filling mode when the response energy level is lower than the preset energy threshold; in mean-filling mode, using historical damage step size data to perform interpolation calculations on the missing time-varying response points to smooth the time-varying analysis indicators.

7. The time-varying analysis method for interface damage of a composite bridge according to claim 1, characterized in that, The process of constructing the interface stress distribution mapping operator in step S4 includes: introducing critical state variables that characterize the sudden change in interface stress, establishing a nonlinear function mapping between slip displacement and interface shear stress transmission; using the nonlinear function mapping to perform spatial weight allocation on the net slip eigenvalues, extracting micro-damage parameters that characterize the evolution of the interface shear zone, and encapsulating the micro-damage parameters into topological feature vectors.

8. The time-varying analysis method for interface damage of a composite bridge according to claim 1, characterized in that, The process of outputting time-varying analysis indicators in step S5 includes: extracting slip data of the unloading segment of the load cycle and calculating the hysteretic characteristic parameters of the slip response; based on the elastic-complex mechanics logic, identifying and removing residual deformation components caused by concrete creep from the hysteretic characteristic parameters to obtain the net damage index characterizing the interface structure damage.

9. The time-varying analysis method for interface damage of a composite bridge according to claim 1, characterized in that, Step S5 further includes: comparing the time-varying analysis index with the preset safety threshold, predicting the remaining service life of the steel-concrete interface based on the evolution rate of the time-varying analysis index, establishing a mapping relationship library between the topological feature vector and the micro-damage state of the interface, and retrieving and outputting the corresponding interface stiffness degradation level information from the mapping relationship library based on the real-time calculated topological feature vector.

10. A time-varying analysis system for composite bridge interface damage, used to implement the time-varying analysis method for composite bridge interface damage according to any one of claims 1 to 9, characterized in that, It includes a data acquisition module, a filtering module, a compensation extraction module, a topology mapping module, and a comprehensive analysis module: The data acquisition module is used to acquire the slip data sequence of each monitoring node in the steel-concrete interface of the composite bridge and the real-time temperature gradient data on both sides of the steel-concrete interface. The filtering module is used to perform dynamic residual filtering on the slip data sequence to extract the high-frequency dynamic response signal generated by the steel-concrete interface under load. The compensation extraction module is used to construct a thermal stress feature matrix based on real-time temperature gradient data, calculate the displacement compensation amount characterizing the thermally induced displacement component of the interface, and remove the displacement compensation amount from the high-frequency dynamic response signal to extract the net slip feature value characterizing the physical damage of the interface. The topology mapping module is used to substitute the net slip eigenvalues ​​into a preset interface stress distribution mapping operator, and use a preset nonlinear distribution function to map the spatial distribution difference of the net slip eigenvalues ​​into a topological feature vector that characterizes the topological features of the interface shear stiffness. The comprehensive analysis module is used to analyze the unsteady oscillation characteristics of high-frequency dynamic response signals in the frequency domain, calculate the change in the system stability damping ratio based on stability criteria to determine the probability of early cementation failure of the steel-concrete interface, and output time-varying analysis indicators characterizing the degree of interface damage by combining topological feature vectors.

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