Bridge load test crack increment change observation method and system

Abstract

The present application relates to the technical fields of bridge load test, and proposes a bridge load test crack increment change observation method and system, which comprises: collecting prestressed beam circumferential strain distribution data and double-wavelength strain response data; calculating interface bonding stiffness change parameters; compensating prestress modulation effect and converting polar coordinate system for strain response data and stiffness change parameters to obtain micro-crack characteristic signals; collecting load data and beam group arrangement parameters, combining with interface bonding stiffness change parameters to establish a micro-crack expansion prediction model; inputting the micro-crack characteristic signals into the micro-crack expansion prediction model to output micro-crack development trend prediction results; and adjusting the sampling parameters of the double-wavelength time division multiplexing algorithm and the characteristic extraction parameters of the prestress modulation effect compensation according to the micro-crack development trend prediction results. The present application improves the precision of micro-crack increment change observation in bridge load test, and realizes the automatic monitoring and prediction of prestressed concrete bridge micro-cracks.

Description

Technical Field

[0001] This invention relates to the field of bridge load testing technology, and in particular to a method and system for observing the incremental changes of cracks in bridge load testing. Background Technology

[0002] Prestressed concrete bridges are an important structural form widely used in modern bridge engineering. They significantly improve the load-bearing capacity and durability of the structure by using pre-applied compressive stress to counteract the tensile stress generated by the load. During long-term service, prestressed concrete bridges are subjected to a combination of factors, including load fatigue, temperature changes, material aging, and environmental erosion. This leads to complex damage evolution processes such as microcrack initiation at the prestressed tendon-concrete interface, bond degradation, and stress redistribution. Bridge load testing, as a crucial means of assessing the safety performance of bridge structures, requires precise monitoring and prediction of microcrack increments during the testing process to promptly identify potential structural safety hazards.

[0003] In existing technologies, crack observation in bridge load tests mainly employs surface strain measurement and basic signal processing methods to achieve basic crack detection functions. However, existing methods do not adequately consider the inherent mechanical correlation mechanism between the complex spatial arrangement of prestressed tendons and the signal characteristics of microcracks. They also struggle to organically integrate the objective physical constraints of interface bonding with the actual effects of prestress modulation, resulting in the inability to achieve high-precision and high-reliability automatic observation and prediction of incremental changes in bridge microcracks. Summary of the Invention

[0004] In view of this, the present invention proposes a method and system for observing the incremental changes of cracks in bridge load tests. This solves the problem that existing methods do not adequately consider the inherent mechanical correlation mechanism between the complex spatial arrangement of prestressed tendons and the characteristics of microcrack signals, and are unable to organically integrate the objective physical constraints of interface bonding with the actual effects of prestress modulation, thus making it impossible to achieve high-precision and high-reliability automatic observation and prediction of the incremental changes of bridge microcracks.

[0005] The technical solution of this invention is implemented as follows: On the one hand, this invention provides a method for observing the incremental change of cracks in bridge load tests, including the following steps: Distributed optical fiber sensors are arranged on the surface of the prestressed tendon duct in a bidirectional cross spiral winding. A dual-wavelength time-division multiplexing algorithm is used to collect circumferential strain distribution data and dual-wavelength strain response data of the prestressed tendon. Based on the circumferential strain distribution data of the prestressed tendons, calculate the interface bond stiffness variation parameters; By performing prestress modulation effect compensation and polar coordinate system transformation on the dual-wavelength strain response data and the interface bonding stiffness variation parameters, the microcrack characteristic signal is obtained. By collecting load data and bundle arrangement parameters, and combining them with the interface bonding stiffness variation parameters, a microcrack propagation prediction model is established. The microcrack characteristic signal is input into the microcrack propagation prediction model, and the microcrack development trend prediction result is output. Based on the predicted microcrack development trend, the sampling parameters of the dual-wavelength time-division multiplexing algorithm and the feature extraction parameters for prestress modulation effect compensation are adjusted.

[0006] Based on the above technical solutions, preferably, the step of compensating for the prestress modulation effect and transforming the polar coordinate system on the dual-wavelength strain response data and the interface bonding stiffness variation parameters to obtain the microcrack characteristic signal includes: A prestress loss compensation matrix is ​​constructed to separate the prestress attenuation component from the true strain component of the microcrack in the dual-wavelength strain response data. The coefficients of the prestress loss compensation matrix are dynamically adjusted based on the interface bonding stiffness change parameter to obtain the compensated pure microcrack strain data. The strain data of the pure microcracks is converted from the rectangular coordinate system of the prestressed tendon duct surface to the polar coordinate system with the duct axis as the origin. Polar coordinate descriptions of the radial strain components and circumferential strain components are established. Radial crack feature signals and circumferential crack feature signals in the polar coordinate system are extracted to obtain the microcrack feature signals.

[0007] Based on the above technical solutions, preferably, the construction of the prestress loss compensation matrix includes: Based on the initial tension force of the prestressed tendon and the prestress loss rate at the current moment, calculate the theoretical strain distribution caused by prestress attenuation; The strain vectors are established according to the first wavelength data and the second wavelength data, and a dual-wavelength strain difference matrix is ​​constructed. Based on the interface bonding stiffness variation parameters, a bonding stiffness weighting coefficient is established, and the dual-wavelength strain difference matrix is ​​weighted accordingly. The compensation matrix parameters are optimized using the minimum mean square error criterion to minimize the residual between the compensated pure microcrack strain data and the theoretical strain distribution.

[0008] Based on the above technical solutions, preferably, the acquisition of load data and bundle arrangement parameters, combined with the interface bonding stiffness variation parameters, establishes a microcrack propagation prediction model, including: Based on the load data and the bundle arrangement parameters, a stress field coupling influence matrix between adjacent prestressed bundles is established, and the bundle effect coefficient is calculated. Based on the time series characteristics of the load data, load change rate parameters are extracted, and a load rate-sensitive microcrack propagation dynamic equation is established. Using the interface bond stiffness variation parameter as a dynamic weighting factor, combined with the bundle group effect coefficient and the load change rate parameter, a multi-parameter coupled microcrack propagation prediction model is constructed. The microcrack propagation prediction model is used to predict the microcrack development rate and propagation direction at different prestressed bundle locations based on the current interface state and load conditions.

[0009] Based on the above technical solutions, preferably, the establishment of the stress field coupling influence matrix between adjacent prestressed tendons includes: Determine the spatial coordinates and relative positions of the prestressed tendons based on the bundle arrangement parameters, and calculate the distance attenuation factor between adjacent prestressed tendons. The stress influence domain of a single prestressed tendon is established based on the theory of elasticity, and the superposition effect of stress fields of multiple prestressed tendons is calculated by superposition principle. By combining the interface bonding stiffness variation parameters, the stress transfer efficiency under different bonding states is corrected, and a bundle group interaction coefficient matrix based on interface degradation is established. The equivalent stress distribution at each prestressed bundle position is obtained by iteratively solving the product of the bundle group interaction coefficient matrix and the load vector, which serves as the stress boundary condition for predicting microcrack propagation. The formula for calculating the beam group effect coefficient is: ; in, Prestressed tendons and The bundle grouping coefficient between them; This serves as the distance attenuation reference coefficient. Prestressed tendons and Spatial distance between them; This refers to the characteristic distance parameter of the stress-affected domain; The weighting coefficient for the influence of interfacial bonding stiffness; This represents the total number of sector regions. For the first Each sector region at time Interfacial bonding stiffness variation parameters; Prestressed tendons In the Spatial weighting factors for each sector region; Prestressed tendons In the Spatial weighting factors for each sector region; It is an exponential function.

[0010] Based on the above technical solutions, preferably, the step of arranging the distributed optical fiber sensors in a bidirectional cross-spiral winding on the surface of the prestressed tendon duct, and using a dual-wavelength time-division multiplexing algorithm to collect circumferential strain distribution data and dual-wavelength strain response data of the prestressed tendon includes: Along the axial direction of the prestressed tendon duct, the first fiber optic sensor is wound with a positive helix angle and the second fiber optic sensor is wound with a negative helix angle to form a bidirectional cross-helix monitoring grid on the surface of the prestressed tendon duct. The fiber optic sensors are then fixed to the surface of the prestressed tendon duct using epoxy resin adhesive. A first wavelength optical signal and a second wavelength optical signal are respectively transmitted to the first fiber optic sensor and the second fiber optic sensor. The backscattering spectral data of the two fiber optic sensors are alternately acquired through a dual-wavelength time-division multiplexing control algorithm. The circumferential strain distribution data of the prestressed bundle and the dual-wavelength strain response data are obtained by analysis.

[0011] Based on the above technical solutions, preferably, the step of calculating the interface bond stiffness variation parameter according to the circumferential strain distribution data of the prestressed tendon includes: The circumferential strain distribution data of the prestressed tendons is processed by sliding window filtering to identify abnormal abrupt changes and strain gradient change regions in the strain distribution, and to extract the effective strain characteristic parameters in the circumferential direction of the prestressed tendon duct. Based on the effective strain characteristic parameters, the interface shear stiffness inversion algorithm is used to calculate the local bond stiffness values ​​at different circumferential positions of the prestressed tendon and concrete interface. By comparing the changes in local bond stiffness values ​​at adjacent time points, the interface bond stiffness variation parameters are obtained.

[0012] Based on the above technical solutions, preferably, the step of inputting the microcrack characteristic signal into the microcrack propagation prediction model and outputting the microcrack development trend prediction result includes: The microcrack feature signals are subjected to time-series normalization and noise filtering. The amplitude change rate, frequency domain feature parameters and spatial distribution density of the microcrack feature signals are extracted and combined to obtain a standardized model input vector. The model input vector is input into the microcrack propagation prediction model for iterative calculation, and the crack propagation rate and direction angle data are output. Based on the crack propagation rate and direction angle data, combined with the time step parameter, the microcrack development trend prediction result is generated. The microcrack development trend prediction result includes microcrack length increment, propagation path, and development stage assessment.

[0013] Based on the above technical solutions, preferably, the step of adjusting the sampling parameters of the dual-wavelength time-division multiplexing algorithm and the feature extraction parameters for prestress modulation effect compensation according to the microcrack development trend prediction results includes: Based on the changes in crack propagation rate in the predicted microcrack development trend, the matching degree between the current sampling frequency and the crack propagation speed is analyzed, and the sampling time interval and data acquisition window length of the dual-wavelength time-division multiplexing algorithm are dynamically adjusted so that the fiber optic sensor can capture the rapidly changing microcrack signal. Based on the development stage assessment information in the microcrack development trend prediction results, the characteristics of the development stage of the microcrack are identified, and the filtering parameters, compensation coefficients, and feature extraction thresholds of the prestress modulation effect compensation are adjusted accordingly.

[0014] On the other hand, the present invention also provides a bridge load test crack increment change observation system, the system comprising: The data acquisition module is used to arrange distributed optical fiber sensors on the surface of the prestressed tendon duct in a bidirectional cross spiral winding, and to acquire circumferential strain distribution data and dual-wavelength strain response data of the prestressed tendon using a dual-wavelength time-division multiplexing algorithm. The stiffness variation parameter calculation module is used to calculate the interface bond stiffness variation parameter based on the circumferential strain distribution data of the prestressed tendon. The stiffness variation parameter compensation module is used to compensate for the prestress modulation effect and transform the polar coordinate system of the dual-wavelength strain response data and the interface bonding stiffness variation parameter to obtain the microcrack characteristic signal. The prediction model building module is used to collect load data and bundle arrangement parameters, and combine them with the interface bonding stiffness variation parameters to build a microcrack propagation prediction model. The crack development trend prediction module is used to input the microcrack characteristic signal into the microcrack propagation prediction model and output the microcrack development trend prediction result. The feedback adjustment module is used to adjust the sampling parameters of the dual-wavelength time-division multiplexing algorithm and the feature extraction parameters of the prestress modulation effect compensation based on the microcrack development trend prediction results.

[0015] The method and system for observing crack increment changes in bridge load tests according to the present invention have the following advantages over the prior art: (1) By using a distributed optical fiber sensor with bidirectional cross-spiral winding and a dual-wavelength time-division multiplexing algorithm, combined with the calculation of interface bonding stiffness change parameters and compensation of prestress modulation effect, the characteristic signal of microcracks is extracted by polar coordinate system transformation. Based on the bundle group effect coefficient and load change rate parameter, a multi-parameter coupled microcrack propagation prediction model is established. The sampling parameters and compensation parameters are dynamically adjusted according to the prediction results, which improves the accuracy and reliability of microcrack increment change observation in bridge load test and realizes high-precision automated monitoring and prediction of microcracks in prestressed concrete bridges. (2) By constructing a prestress loss compensation matrix, the prestress attenuation component and the true strain component of microcracks are separated. The coefficients of the compensation matrix are dynamically adjusted based on the interface bonding stiffness change parameter and the parameters are optimized by the minimum mean square error criterion. Combined with dual-wavelength strain difference matrix analysis and bonding stiffness weight coefficient weighting, and with polar coordinate system transformation, the radial and circumferential strain components are accurately described. This improves the extraction accuracy and identification reliability of microcrack characteristic signals under complex prestressing environment, and realizes high-fidelity feature extraction and multi-dimensional signal analysis of microcracks on the surface of prestressed tendon pipe. (3) By establishing the stress field coupling influence matrix and bundle group effect coefficient between adjacent prestressed tendons, the load change rate parameter is extracted based on the time series characteristics of load data, and a load rate sensitive microcrack propagation dynamic equation is established. The interface bond stiffness change parameter is used as a dynamic weighting factor and the bundle group interaction coefficient matrix based on interface degradation is corrected. The equivalent stress distribution of each prestressed tendon position is obtained by superposition principle of elasticity theory and iterative solution as stress boundary condition, which improves the accuracy of microcrack development trend prediction under complex bundle group arrangement environment and realizes the modeling and prediction of microcrack propagation of prestressed concrete bridge under multi-parameter coupling conditions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for observing the incremental change of cracks in a bridge load test according to the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 This invention provides a method for observing the incremental change of cracks in bridge load tests, comprising the following steps: Distributed optical fiber sensors are arranged on the surface of the prestressed tendon duct in a bidirectional cross spiral winding. A dual-wavelength time-division multiplexing algorithm is used to collect circumferential strain distribution data and dual-wavelength strain response data of the prestressed tendon. Based on the circumferential strain distribution data of the prestressed tendons, calculate the interface bond stiffness variation parameters; By performing prestress modulation effect compensation and polar coordinate system transformation on the dual-wavelength strain response data and the interface bonding stiffness variation parameters, the microcrack characteristic signal is obtained. By collecting load data and bundle arrangement parameters, and combining them with the interface bonding stiffness variation parameters, a microcrack propagation prediction model is established. The microcrack characteristic signal is input into the microcrack propagation prediction model, and the microcrack development trend prediction result is output. Based on the predicted microcrack development trend, the sampling parameters of the dual-wavelength time-division multiplexing algorithm and the feature extraction parameters for prestress modulation effect compensation are adjusted.

[0020] Specifically, this embodiment employs a distributed optical fiber sensor with a bidirectional cross-spiral winding arrangement and a dual-wavelength time-division multiplexing algorithm. Combined with the calculation of interface bond stiffness variation parameters and compensation for prestress modulation effects, it utilizes polar coordinate system transformation to extract microcrack characteristic signals. Based on the bundle group effect coefficient and load change rate parameters, a multi-parameter coupled microcrack propagation prediction model is established. The sampling parameters and compensation parameters are dynamically adjusted according to the prediction results. This solves the problem that traditional methods are difficult to effectively separate prestress modulation interference and accurately predict the development trend of microcracks under multi-bundle group coupling environments. It improves the accuracy and reliability of observing the incremental changes of microcracks in bridge load tests and realizes high-precision automated monitoring and prediction of microcracks in prestressed concrete bridges.

[0021] The process involves arranging distributed optical fiber sensors in a bidirectional, cross-spiral winding on the surface of the prestressed tendon duct, and employing a dual-wavelength time-division multiplexing algorithm to acquire circumferential strain distribution data and dual-wavelength strain response data of the prestressed tendon, including: Along the axial direction of the prestressed tendon duct, the first fiber optic sensor is wound with a positive helix angle and the second fiber optic sensor is wound with a negative helix angle to form a bidirectional cross-helical monitoring grid on the surface of the prestressed tendon duct. The fiber optic sensors are then fixed to the surface of the prestressed tendon duct using epoxy resin adhesive.

[0022] In one specific embodiment, the absolute values ​​of the positive helix angle and the negative helix angle are equal and both are preset helix angles. The bidirectional cross-helix monitoring grid forms multiple grid intersections in the circumferential direction of the prestressed tendon duct. By calculating the strain difference between adjacent grid intersections, local strain concentration areas on the surface of the prestressed tendon duct are identified, and the location coordinates of the local strain concentration areas are recorded as suspected microcrack initiation points.

[0023] A first wavelength optical signal and a second wavelength optical signal are respectively transmitted to the first fiber optic sensor and the second fiber optic sensor. The backscattering spectral data of the two fiber optic sensors are alternately acquired through a dual-wavelength time-division multiplexing control algorithm. The circumferential strain distribution data of the prestressed bundle and the dual-wavelength strain response data are obtained by analysis.

[0024] In one specific embodiment, the dual-wavelength time-division multiplexing control algorithm adopts a fixed time interval polling method. Within a preset time window, the first wavelength optical signal is first transmitted to the first fiber optic sensor and the first backscattered spectral data is collected. Then, the second wavelength optical signal is transmitted to the second fiber optic sensor and the second backscattered spectral data is collected. By comparing and analyzing the differences in spectral characteristics between the first backscattered spectral data and the second backscattered spectral data, the interface peeling strain change and the interface slip strain change can be distinguished.

[0025] Specifically, this embodiment uses a distributed optical fiber sensor array arranged in a bidirectional, cross-spiral winding pattern with positive and negative helical angles to form a full-coverage monitoring grid. A dual-wavelength time-division multiplexing control algorithm is used to emit different wavelength light signals and alternately collect backscattered spectral data. The strain difference at grid intersections is combined to calculate and identify localized strain concentration areas and suspected microcrack initiation points. This embodiment distinguishes between interface peeling and interface slip strain changes by comparing and analyzing the differences in dual-wavelength spectral characteristics. This solves the problem that traditional single-sensor arrays struggle to achieve comprehensive monitoring of the prestressed tendon duct surface and accurately differentiate between different types of interface damage. It improves the spatial resolution of prestressed tendon circumferential strain distribution measurement and the accuracy of interface state identification, achieving high-precision location detection of microcracks in prestressed concrete bridges and identification of multiple damage modes.

[0026] The step of calculating the interface bond stiffness variation parameter based on the circumferential strain distribution data of the prestressed tendon includes: The circumferential strain distribution data of the prestressed tendons is processed by sliding window filtering to identify abnormal abrupt changes and strain gradient change regions in the strain distribution, and to extract the effective strain characteristic parameters in the circumferential direction of the prestressed tendon duct.

[0027] In one specific embodiment, the sliding window filtering process adopts an adaptive window length adjustment mechanism. The initial window length is determined according to the diameter of the prestressed tendon duct. When the standard deviation of the strain data is detected to exceed a preset threshold, the window length is automatically reduced. When the strain data tends to be stable, the window length is automatically expanded. By calculating the difference sequence of the strain data before and after filtering, data points whose absolute difference exceeds the strain mutation threshold are marked as abnormal mutation points.

[0028] Based on the effective strain characteristic parameters, the interface shear stiffness inversion algorithm is used to calculate the local bond stiffness values ​​at different circumferential positions of the prestressed tendon and concrete interface. By comparing the changes in local bond stiffness values ​​at adjacent time points, the interface bond stiffness variation parameters are obtained.

[0029] In one specific embodiment, the interface shear stiffness inversion algorithm is based on the shear deformation relationship between the prestressed tendon and the concrete interface. The circumference of the prestressed tendon duct is divided into multiple equally divided sector regions. A strain-shear stiffness mapping relationship is established for each sector region. By solving the least squares fitting equation of strain distribution and shear stiffness distribution, the local bond stiffness value of each sector region is obtained. The rate of change of the local bond stiffness value of the same sector region at adjacent time points is used as the interface bond stiffness change parameter of that sector region.

[0030] Specifically, this embodiment identifies abrupt changes in strain distribution and regions of strain gradient variation through sliding window filtering with adaptive window length adjustment. Based on the prestressed tendon duct diameter, the window parameters are dynamically adjusted and strain abrupt change data points are marked. An interface shear stiffness inversion algorithm is used to divide the circumference of the prestressed tendon duct into multiple equally divided sector regions and establish a strain-shear stiffness mapping relationship. This embodiment calculates the local bond stiffness values ​​of each sector region by solving the least squares fitting equation and obtains the rate of change parameter. This solves the problem that traditional methods struggle to accurately extract the effective characteristics of the prestressed tendon's circumferential strain and precisely quantify the dynamic changes in the interface bond state. It improves the accuracy of interface bond stiffness parameter calculation and the reliability of time-varying characteristic analysis under complex strain environments, achieving high-precision quantitative evaluation and dynamic monitoring of the bond performance between the prestressed tendon and concrete.

[0031] The process of compensating for prestress modulation effect and transforming polar coordinates on the dual-wavelength strain response data and the interface bonding stiffness variation parameters to obtain microcrack characteristic signals includes: A prestress loss compensation matrix is ​​constructed to separate the prestress attenuation component from the true strain component of the microcrack in the dual-wavelength strain response data. The coefficients of the prestress loss compensation matrix are dynamically adjusted based on the interface bonding stiffness change parameter to obtain the compensated pure microcrack strain data. The strain data of the pure microcracks is converted from the rectangular coordinate system of the prestressed tendon duct surface to the polar coordinate system with the duct axis as the origin. Polar coordinate descriptions of the radial strain components and circumferential strain components are established. Radial crack feature signals and circumferential crack feature signals in the polar coordinate system are extracted to obtain the microcrack feature signals.

[0032] The construction of the prestress loss compensation matrix includes: Based on the initial tension force of the prestressed tendon and the prestress loss rate at the current moment, calculate the theoretical strain distribution caused by prestress attenuation; The strain vectors are established according to the first wavelength data and the second wavelength data, and a dual-wavelength strain difference matrix is ​​constructed. Based on the interface bonding stiffness variation parameters, a bonding stiffness weighting coefficient is established, and the dual-wavelength strain difference matrix is ​​weighted accordingly. The compensation matrix parameters are optimized using the minimum mean square error criterion to minimize the residual between the compensated pure microcrack strain data and the theoretical strain distribution.

[0033] In one specific embodiment, the optimization formula for the prestress loss compensation matrix is: ; ; in, This is the prestress loss compensation matrix; To minimize the function value Value-based solution function, These are the optimization variables for the prestress loss compensation matrix; This represents the total number of measurement time points; For the first A vector of pure microcrack strain data at each time point; For the first The theoretical strain distribution vector at each time point; For the first A dual-wavelength strain response data vector at each time point; For the first The vector of prestress attenuation components at each time point; These are the regularization weight parameters; The total number of sector areas monitored on the interface; For the first The bond stiffness weighting coefficient for each sector region For the first Parameters for the variation of interfacial bonding stiffness in a sector-shaped region; For the compensation matrix, the first OK; For reference compensation matrix number OK; It is an L2 norm; It is the Frobenius norm.

[0034] The formula for the microcrack characteristic signal is: ; ; in, This represents the characteristic signal vector of the microcrack; This is a characteristic signal of radial cracks; Characteristic signals of circumferential cracks; This is a polar coordinate transformation enhancement matrix; This is strain data for pure microcracks; The principal direction angle of strain is expressed in rad. This is the angle correction for polar coordinates, in rad. The radial gradient enhancement coefficient, The circumferential gradient enhancement coefficient; These are polar radial coordinates, in mm. These are polar angular coordinates, in rad. This is the reference angle in the polar coordinate system, in rad. For radial component nonlinearity correction coefficients; For nonlinear correction coefficients of the circumferential component; The radial-circular coupling coefficient is 1; The radial-circular coupling coefficient is 2; For radial position correlation function, ; This is an angular position-related function. ; This is the radial characteristic scale parameter, in mm; This represents the cyclic harmonic order.

[0035] Specifically, this embodiment separates the prestress attenuation component from the true strain component of microcracks by constructing a prestress loss compensation matrix. Based on the interface bond stiffness variation parameter, the compensation matrix coefficients are dynamically adjusted and the parameters are optimized using the minimum mean square error criterion. Combined with dual-wavelength strain difference matrix analysis and weighted processing of bond stiffness weight coefficients, and with polar coordinate system transformation, accurate descriptions of radial and circumferential strain components are established. This solves the problem that traditional methods are difficult to effectively eliminate prestress modulation interference and accurately extract multi-directional microcrack features. It improves the extraction accuracy and identification reliability of microcrack feature signals under complex prestressed environments, and realizes high-fidelity feature extraction and multi-dimensional signal analysis of microcracks on the surface of prestressed tendon pipes.

[0036] The collected load data and bundle arrangement parameters, combined with the interface bond stiffness variation parameters, are used to establish a microcrack propagation prediction model, including: Based on the load data and the bundle arrangement parameters, a stress field coupling influence matrix between adjacent prestressed bundles is established, and the bundle effect coefficient is calculated. Based on the time series characteristics of the load data, load change rate parameters are extracted, and a load rate-sensitive microcrack propagation dynamic equation is established. Using the interface bond stiffness variation parameter as a dynamic weighting factor, combined with the bundle group effect coefficient and the load change rate parameter, a multi-parameter coupled microcrack propagation prediction model is constructed. The microcrack propagation prediction model is used to predict the microcrack development rate and propagation direction at different prestressed bundle locations based on the current interface state and load conditions.

[0037] The establishment of the stress field coupling influence matrix between adjacent prestressed tendons includes: Determine the spatial coordinates and relative positions of the prestressed tendons based on the bundle arrangement parameters, and calculate the distance attenuation factor between adjacent prestressed tendons. The stress influence domain of a single prestressed tendon is established based on the theory of elasticity, and the superposition effect of stress fields of multiple prestressed tendons is calculated by superposition principle. By combining the interface bonding stiffness variation parameters, the stress transfer efficiency under different bonding states is corrected, and a bundle group interaction coefficient matrix based on interface degradation is established. By iteratively solving the product of the bundle group interaction coefficient matrix and the load vector, the equivalent stress distribution at each prestressed bundle position is obtained, which serves as the stress boundary condition for predicting microcrack propagation.

[0038] In one specific embodiment, the formula for calculating the beam group effect coefficient is: ; in, Prestressed tendons and The bundle grouping coefficient between them; This serves as the distance attenuation reference coefficient. Prestressed tendons and The spatial distance between them is expressed in mm. This is the characteristic distance parameter of the stress influence domain, in mm; The weighting coefficient for the influence of interfacial bonding stiffness; This represents the total number of sector regions. For the first Each sector region at time The interfacial bond stiffness variation parameter, in N / mm. 3 ; Prestressed tendons In the Spatial weighting factors for each sector region; Prestressed tendons In the Spatial weighting factors for each sector region; It is an exponential function.

[0039] The load rate-sensitive microcrack propagation dynamics equation is as follows: ; in, The value represents the microcrack propagation rate, expressed in mm / s. The reference spread rate constant is expressed in mm / s. The range of equivalent stress intensity factors, in units of ; This is the crack propagation threshold stress intensity factor, in units of ; The crack propagation index; This is the load rate sensitivity coefficient; The load change rate is expressed in N / s. The load rate influence index; This represents the total number of prestressed tendons. For the first Factors affecting the bond state of root prestressed tendons; For the first root prestressed tendon at time The interfacial bond stiffness variation parameter, in N / mm. 3 .

[0040] Specifically, this embodiment establishes a stress field coupling influence matrix and bundle group effect coefficient calculation between adjacent prestressed tendons, extracts load change rate parameters based on the time series characteristics of load data, and establishes a load rate-sensitive microcrack propagation dynamic equation. It combines the interface bond stiffness change parameter as a dynamic weighting factor and the bundle group interaction coefficient matrix based on interface degradation, and obtains the equivalent stress distribution at each prestressed tendon position as stress boundary conditions through the superposition principle of elasticity theory and iterative solution. This solves the problem that traditional methods are difficult to accurately consider the multi-bundle group coupling effect and the influence of dynamic loads in predicting microcrack propagation, improves the accuracy of microcrack development trend prediction in complex bundle group arrangement environments, and realizes the modeling and prediction of microcrack propagation in prestressed concrete bridges under multi-parameter coupling conditions.

[0041] The step of inputting the microcrack characteristic signal into the microcrack propagation prediction model and outputting the microcrack development trend prediction result includes: The microcrack feature signals are subjected to time-series normalization and noise filtering. The amplitude change rate, frequency domain feature parameters and spatial distribution density of the microcrack feature signals are extracted and combined to obtain a standardized model input vector.

[0042] In one specific embodiment, the time-series normalization processing adopts the moving average benchmark method, using the mean of the microcrack characteristic signals within a preset time window as the benchmark value, and calculating the normalized amplitude of the signal at the current moment relative to the benchmark value. The noise filtering employs an adaptive threshold filtering algorithm, which dynamically adjusts the filtering threshold based on the signal-to-noise ratio of the microcrack feature signal. Data points with signal amplitudes less than the dynamic filtering threshold are identified as noise and filtered out, while retaining effective microcrack feature change information.

[0043] The model input vector is input into the microcrack propagation prediction model for iterative calculation, and the crack propagation rate and direction angle data are output. Based on the crack propagation rate and direction angle data, combined with the time step parameter, the microcrack development trend prediction result is generated. The microcrack development trend prediction result includes microcrack length increment, propagation path, and development stage assessment.

[0044] In one specific embodiment, the iterative calculation process adopts a multi-step prediction strategy. First, a short-term prediction is performed to obtain the microcrack propagation parameters within a single future time step. Then, the short-term prediction results are used as the initial conditions for the next prediction to perform long-term prediction. The development stage assessment divides crack development into three stages—initiation stage, stable propagation stage, and rapid propagation stage—based on the changing trend of microcrack length increments. A corresponding risk assessment level is assigned to each stage to obtain the phased prediction results of microcrack development trends.

[0045] Specifically, this embodiment uses a moving average benchmark method for time-series normalization and an adaptive threshold filtering algorithm for noise filtering. It extracts the amplitude change rate, frequency domain characteristic parameters, and spatial distribution density of microcrack characteristic signals to construct a standardized model input vector. A multi-step prediction strategy, combining short-term and long-term predictions, iteratively calculates and outputs crack propagation rate and direction angle data. Based on the microcrack length increment trend, crack development is divided into initiation, stable propagation, and rapid propagation stages, and corresponding risk assessment levels are assigned. This embodiment solves the problem that traditional methods struggle to effectively handle complex noise environments and accurately predict multi-stage microcrack development and evolution. It improves the accuracy of microcrack propagation trend prediction and the reliability of staged risk assessment, achieving intelligent prediction and hierarchical risk management of the entire lifecycle of microcrack development in prestressed concrete bridges.

[0046] The step of adjusting the sampling parameters of the dual-wavelength time-division multiplexing algorithm and the feature extraction parameters for prestress modulation effect compensation based on the microcrack development trend prediction results includes: Based on the changes in crack propagation rate in the predicted microcrack development trend, the matching degree between the current sampling frequency and the crack propagation speed is analyzed, and the sampling time interval and data acquisition window length of the dual-wavelength time-division multiplexing algorithm are dynamically adjusted so that the fiber optic sensor can capture the rapidly changing microcrack signal.

[0047] In one specific embodiment, the dynamic adjustment process adopts an adaptive sampling strategy. When the crack propagation rate exceeds a preset rapid propagation threshold, the sampling time interval is shortened to less than 50% of the original, while the data acquisition window length is expanded to obtain more historical data. When the crack propagation rate is lower than a preset stability threshold, the sampling time interval is appropriately extended to save system resources. By establishing a dynamic mapping relationship between the propagation rate and the sampling parameters, the sampling parameters can be intelligently adjusted.

[0048] Based on the development stage assessment information in the microcrack development trend prediction results, the characteristics of the development stage of the microcrack are identified, and the filtering parameters, compensation coefficients, and feature extraction thresholds of the prestress modulation effect compensation are adjusted accordingly.

[0049] In one specific embodiment, when a microcrack is identified as being in its initiation stage, the cutoff frequency of the filter parameters is reduced to preserve high-frequency detail information and improve the sensitivity of the compensation coefficient. When microcracks are identified as being in a stable propagation phase, moderate filtering parameters and compensation coefficients are used. When a microcrack is identified as being in a rapid propagation phase, the cutoff frequency of the filter parameters is increased to highlight the main trend of change, the compensation coefficient is increased to enhance the signal amplitude, and the feature extraction threshold is decreased to capture minute abnormal changes.

[0050] Specifically, this embodiment analyzes the changes in crack propagation rate in the microcrack development trend prediction results, establishes a dynamic mapping relationship between propagation rate and sampling parameters, and adopts an adaptive sampling strategy. Based on rapid propagation thresholds and stable thresholds, it dynamically adjusts the sampling time interval and data acquisition window length of the dual-wavelength time-division multiplexing algorithm. Based on development stage assessment information, it identifies different characteristics of the initiation stage, stable propagation stage, and rapid propagation stage, and accordingly adjusts the filtering parameters, compensation coefficients, and feature extraction thresholds for prestress modulation effect compensation. This embodiment achieves intelligent parameter adjustment by lowering or increasing the cutoff frequency, adjusting the sensitivity of the compensation coefficients, and optimizing the feature extraction thresholds. This solves the problem that traditional fixed-parameter methods are difficult to adapt to the dynamic development process of microcracks and the differences in characteristics at different development stages. It improves the fiber optic sensing system's ability to capture rapidly changing microcrack signals and the monitoring accuracy at different development stages, realizing adaptive optimization and parameter control in the microcrack monitoring process of prestressed concrete bridges. The present invention also provides a system for observing the incremental change of cracks in bridge load tests, the system comprising: The data acquisition module is used to arrange distributed optical fiber sensors on the surface of the prestressed tendon duct in a bidirectional cross spiral winding, and to acquire circumferential strain distribution data and dual-wavelength strain response data of the prestressed tendon using a dual-wavelength time-division multiplexing algorithm. The stiffness variation parameter calculation module is used to calculate the interface bond stiffness variation parameter based on the circumferential strain distribution data of the prestressed tendon. The stiffness variation parameter compensation module is used to compensate for the prestress modulation effect and transform the polar coordinate system of the dual-wavelength strain response data and the interface bonding stiffness variation parameter to obtain the microcrack characteristic signal. The prediction model building module is used to collect load data and bundle arrangement parameters, and combine them with the interface bonding stiffness variation parameters to build a microcrack propagation prediction model. The crack development trend prediction module is used to input the microcrack characteristic signal into the microcrack propagation prediction model and output the microcrack development trend prediction result. The feedback adjustment module is used to adjust the sampling parameters of the dual-wavelength time-division multiplexing algorithm and the feature extraction parameters of the prestress modulation effect compensation based on the microcrack development trend prediction results.

[0051] Specifically, this embodiment of a bridge load test crack increment change observation system utilizes a data acquisition module to achieve bidirectional cross-spiral winding distributed optical fiber sensor arrangement and dual-wavelength time-division multiplexing data acquisition. Combined with a stiffness change parameter calculation module, it calculates interface bond stiffness change parameters. A stiffness change parameter compensation module compensates for prestress modulation effects and performs polar coordinate system transformation. A prediction model building module constructs a multi-parameter coupled microcrack propagation prediction model. A crack development trend prediction module outputs the development trend prediction results. A feedback adjustment module dynamically optimizes sampling parameters and feature extraction parameters based on the prediction results, forming a closed-loop control. This solves the problem of traditional bridge load test microcrack monitoring systems struggling to achieve comprehensive high-precision detection and adaptive adjustment, improving the system integration and monitoring reliability of microcrack increment change observation in prestressed concrete bridges.

[0052] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for observing the change in crack increment during bridge load tests, characterized in that, Includes the following steps: Distributed optical fiber sensors are arranged on the surface of the prestressed tendon duct in a bidirectional cross spiral winding. A dual-wavelength time-division multiplexing algorithm is used to collect circumferential strain distribution data and dual-wavelength strain response data of the prestressed tendon. Based on the circumferential strain distribution data of the prestressed tendons, calculate the interface bond stiffness variation parameters; By performing prestress modulation effect compensation and polar coordinate system transformation on the dual-wavelength strain response data and the interface bonding stiffness variation parameters, the microcrack characteristic signal is obtained. By collecting load data and bundle arrangement parameters, and combining them with the interface bonding stiffness variation parameters, a microcrack propagation prediction model is established. The microcrack characteristic signal is input into the microcrack propagation prediction model, and the microcrack development trend prediction result is output. Based on the predicted microcrack development trend, the sampling parameters of the dual-wavelength time-division multiplexing algorithm and the feature extraction parameters for prestress modulation effect compensation are adjusted. The collected load data and bundle arrangement parameters, combined with the interface bond stiffness variation parameters, are used to establish a microcrack propagation prediction model, including: Based on the load data and the bundle arrangement parameters, a stress field coupling influence matrix between adjacent prestressed bundles is established, and the bundle effect coefficient is calculated. Based on the time series characteristics of the load data, load change rate parameters are extracted, and a load rate-sensitive microcrack propagation dynamic equation is established. Using the interface bonding stiffness variation parameter as a dynamic weighting factor, combined with the bundle group effect coefficient and the load change rate parameter, a multi-parameter coupled microcrack propagation prediction model is constructed. The microcrack propagation prediction model is used to predict the microcrack development rate and propagation direction at different prestressed bundle positions based on the current interface state and load conditions. The establishment of the stress field coupling influence matrix between adjacent prestressed tendons includes: Determine the spatial coordinates and relative positions of the prestressed tendons based on the bundle arrangement parameters, and calculate the distance attenuation factor between adjacent prestressed tendons. The stress influence domain of a single prestressed tendon is established based on the theory of elasticity, and the superposition effect of stress fields of multiple prestressed tendons is calculated by superposition principle. By combining the interface bonding stiffness variation parameters, the stress transfer efficiency under different bonding states is corrected, and a bundle group interaction coefficient matrix based on interface degradation is established. The equivalent stress distribution at each prestressed bundle position is obtained by iteratively solving the product of the bundle group interaction coefficient matrix and the load vector, which serves as the stress boundary condition for predicting microcrack propagation. The formula for calculating the beam group effect coefficient is: ； in, Prestressed tendons and The bundle grouping coefficient between them; This serves as the distance attenuation reference coefficient. Prestressed tendons and Spatial distance between them; This refers to the characteristic distance parameter of the stress influence domain; The weighting coefficient for the influence of interfacial bonding stiffness; This represents the total number of sector regions. For the first Each sector region at time Interfacial bonding stiffness variation parameters; Prestressed tendons In the Spatial weighting factors for each sector region; Prestressed tendons In the Spatial weighting factors for each sector region; It is an exponential function; The load rate-sensitive microcrack propagation dynamics equation is as follows: ； in, This represents the microcrack propagation rate. The reference spread rate constant; This refers to the range of equivalent stress intensity factors; The crack propagation threshold stress intensity factor; The crack propagation index; This is the load rate sensitivity coefficient; The rate of change of load; The load rate influence index; This represents the total number of prestressed tendons. For the first Factors affecting the bond state of root prestressed tendons; For the first root prestressed tendon at time The parameters of interfacial bonding stiffness variation.

2. The method for observing crack increment changes in bridge load tests as described in claim 1, characterized in that, The process of compensating for prestress modulation effect and transforming polar coordinates on the dual-wavelength strain response data and the interface bonding stiffness variation parameters to obtain microcrack characteristic signals includes: A prestress loss compensation matrix is ​​constructed to separate the prestress attenuation component from the true strain component of the microcrack in the dual-wavelength strain response data. The coefficients of the prestress loss compensation matrix are dynamically adjusted based on the interface bonding stiffness change parameter to obtain the compensated pure microcrack strain data. The pure microcrack strain data is converted from the rectangular coordinate system of the prestressed tendon duct surface to the polar coordinate system with the duct axis as the origin. Polar coordinate descriptions of radial strain components and circumferential strain components are established. Radial crack feature signals and circumferential crack feature signals in the polar coordinate system are extracted to obtain the microcrack feature signals.

3. The method for observing the change in crack increment during bridge load tests as described in claim 2, characterized in that, The construction of the prestress loss compensation matrix includes: Based on the initial tension force of the prestressed tendon and the prestress loss rate at the current moment, calculate the theoretical strain distribution caused by prestress attenuation; The strain vectors are established according to the first wavelength data and the second wavelength data, and a dual-wavelength strain difference matrix is ​​constructed. Based on the interface bonding stiffness variation parameters, a bonding stiffness weighting coefficient is established, and the dual-wavelength strain difference matrix is ​​weighted accordingly. The compensation matrix parameters are optimized using the minimum mean square error criterion to minimize the residual between the compensated pure microcrack strain data and the theoretical strain distribution.

4. The method for observing the incremental change of cracks in a bridge load test as described in claim 1, characterized in that, The process involves arranging distributed optical fiber sensors in a bidirectional, cross-spiral winding on the surface of the prestressed tendon duct, and employing a dual-wavelength time-division multiplexing algorithm to acquire circumferential strain distribution data and dual-wavelength strain response data of the prestressed tendon, including: Along the axial direction of the prestressed tendon duct, the first fiber optic sensor is wound with a positive helix angle and the second fiber optic sensor is wound with a negative helix angle to form a bidirectional cross-helix monitoring grid on the surface of the prestressed tendon duct. The fiber optic sensors are then fixed to the surface of the prestressed tendon duct using epoxy resin adhesive. A first wavelength optical signal and a second wavelength optical signal are respectively transmitted to the first fiber optic sensor and the second fiber optic sensor. The backscattering spectral data of the two fiber optic sensors are alternately acquired through a dual-wavelength time-division multiplexing control algorithm. The circumferential strain distribution data of the prestressed bundle and the dual-wavelength strain response data are obtained by analysis.

5. The method for observing the change in crack increment during bridge load tests as described in claim 1, characterized in that, The step of calculating the interface bond stiffness variation parameters based on the circumferential strain distribution data of the prestressed tendons includes: The circumferential strain distribution data of the prestressed tendons is processed by sliding window filtering to identify abnormal abrupt changes and strain gradient change regions in the strain distribution, and to extract the effective strain characteristic parameters in the circumferential direction of the prestressed tendon duct. Based on the effective strain characteristic parameters, the interface shear stiffness inversion algorithm is used to calculate the local bond stiffness values ​​at different circumferential positions of the prestressed tendon and concrete interface. By comparing the changes in local bond stiffness values ​​at adjacent time points, the interface bond stiffness variation parameters are obtained.

6. The method for observing crack increment changes in bridge load tests as described in claim 1, characterized in that, The step of inputting the microcrack characteristic signal into the microcrack propagation prediction model and outputting the microcrack development trend prediction result includes: The microcrack feature signals are subjected to time-series normalization and noise filtering. The amplitude change rate, frequency domain feature parameters and spatial distribution density of the microcrack feature signals are extracted and combined to obtain a standardized model input vector. The model input vector is input into the microcrack propagation prediction model for iterative calculation, and the crack propagation rate and direction angle data are output. Based on the crack propagation rate and direction angle data, combined with the time step parameter, the microcrack development trend prediction result is generated. The microcrack development trend prediction result includes microcrack length increment, propagation path, and development stage assessment.

7. The method for observing the change in crack increment during bridge load tests as described in claim 1, characterized in that, The step of adjusting the sampling parameters of the dual-wavelength time-division multiplexing algorithm and the feature extraction parameters for prestress modulation effect compensation based on the microcrack development trend prediction results includes: Based on the changes in crack propagation rate in the predicted microcrack development trend, the matching degree between the current sampling frequency and the crack propagation speed is analyzed, and the sampling time interval and data acquisition window length of the dual-wavelength time-division multiplexing algorithm are dynamically adjusted so that the fiber optic sensor can capture the rapidly changing microcrack signal. Based on the development stage assessment information in the microcrack development trend prediction results, the characteristics of the development stage of the microcrack are identified, and the filtering parameters, compensation coefficients, and feature extraction thresholds of the prestress modulation effect compensation are adjusted accordingly.

8. A system for observing the incremental change of cracks in a bridge load test, used to perform a method for observing the incremental change of cracks in a bridge load test as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition module is used to arrange distributed optical fiber sensors on the surface of the prestressed tendon duct in a bidirectional cross spiral winding, and to acquire circumferential strain distribution data and dual-wavelength strain response data of the prestressed tendon using a dual-wavelength time-division multiplexing algorithm. The stiffness variation parameter calculation module is used to calculate the interface bond stiffness variation parameter based on the circumferential strain distribution data of the prestressed tendon. The stiffness variation parameter compensation module is used to compensate for the prestress modulation effect and transform the polar coordinate system of the dual-wavelength strain response data and the interface bonding stiffness variation parameter to obtain the microcrack characteristic signal. The prediction model building module is used to collect load data and bundle arrangement parameters, and combine them with the interface bonding stiffness variation parameters to build a microcrack propagation prediction model. The crack development trend prediction module is used to input the microcrack characteristic signal into the microcrack propagation prediction model and output the microcrack development trend prediction result. The feedback adjustment module is used to adjust the sampling parameters of the dual-wavelength time-division multiplexing algorithm and the feature extraction parameters of the prestress modulation effect compensation based on the microcrack development trend prediction results.

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