Pile foundation deformation distributed test method and system

Abstract

The invention relates to the technical field of pile foundation deformation measurement, in particular to a pile foundation deformation distributed test method and system, and the method comprises the steps: obtaining a time sequence strain value sequence of sampling points of a tower pile foundation, calculating a dynamic disturbance index, extracting a quasi-static signal, and determining a long-term strain value increment through time domain trend analysis; through space-time decoupling operation, correlation between time evolution behaviors of a sampling point and a physically adjacent sampling point is compared, and a space localization index is determined; taking the spatial localization index as a confidence coefficient weight, and performing weighted distribution on the long-term strain value increment to respectively determine an adaptive global trend component and a local abnormal component; and finally, performing spatial integral operation on the global trend component and the local abnormal component to obtain two decoupled deformation curves so as to determine the deformation state. The method improves the accuracy of the deformation test.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pile foundation deformation measurement. Specifically, it relates to a pile foundation deformation distributed testing method and system. BACKGROUND

[0002] The pile foundation of high-rise structures such as power transmission towers and communication towers in coastal areas is the basis for ensuring the safe and stable use of the towers, and the monitoring of its deformation state is of great significance. Distributed optical fiber sensing technology, especially BOTDR (optical time domain reflectometry) based on Brillouin scattering, uses a sensing optical fiber pre-buried in the pile foundation or attached to the surface to test deformation by means of a strain distribution profile composed of strain values at each sampling point along the length of the pile.

[0003] The deformation test based on BOTDR mainly relies on two core steps: first, by measuring the Brillouin frequency shift and performing temperature compensation, the strain value is calculated according to the strain sensing coefficient; second, by spatially integrating the strain profile along the length of the pile foundation, the total deformation or displacement is obtained.

[0004] However, the tower pile foundation in coastal areas is subjected to high wind loads, high salt mist, and high humidity for a long time. Not only will the tower pile foundation produce high-frequency strain due to high-frequency wind loads, but also local damage will occur due to the corrosive environment. The volume expansion of the corrosion products produced by corrosion will exert pressure on the surrounding concrete, thereby causing local strain in the tower pile foundation.

[0005] Therefore, the strain value obtained by calculating the strain value according to the strain sensing coefficient is actually a complex signal that mixes extremely weak corrosion strain with wind load dynamic strain of a large magnitude and global strain produced by real foundation settlement. The deformation test result is easily dominated and overwhelmed by strong interference signals, reducing the sensitivity of the test system and leading to inaccurate deformation test results. SUMMARY

[0006] To solve the problem that the strain value obtained by the existing deformation test based on BOTDR is a complex signal that mixes extremely weak corrosion strain with wind load dynamic strain of a large magnitude and global strain produced by real foundation settlement, leading to strong interference signals dominating and overwhelming the real corrosion signal and reducing the accuracy of the deformation test, the present application proposes a pile foundation deformation distributed testing method and system.

[0007] In a first aspect, the present application provides a pile foundation deformation distributed testing method, comprising: obtaining strain values at multiple sampling points along the axial direction of the pile foundation at multiple time instants through optical fiber sensors arranged on the surface of the tower pile foundation to form a time series of strain values for each sampling point; For each sampling point, a dynamic disturbance index of the sampling point is calculated based on a time sequence strain value sequence of the sampling point, a quasi-static signal of the sampling point is extracted from the time sequence strain value sequence by using the dynamic disturbance index, a long-term change rate is determined by performing time domain trend analysis on the quasi-static signal, and a long-term strain value increment of the sampling point is determined by combining a time length corresponding to the time sequence strain value sequence; A space-time decoupling operation is performed on the quasi-static signals of all sampling points, and a correlation of strain values of each sampling point with strain values of physically adjacent sampling points thereof changing over time is compared to determine a spatial localization index of the sampling point; The spatial localization index is taken as a confidence weight, and a weighted distribution operation is performed on the long-term strain value increment to respectively determine an adaptive global trend component and a local abnormal component of the sampling point; A deformation curve is determined according to the adaptive global trend components and the local abnormal components of all sampling points to realize deformation distributed testing of the tower pile foundation.

[0008] The technical solution firstly filters out wind load interference in the time domain by calculating a dynamic disturbance index that integrates local spatial correlation, then determines a mixed long-term strain value increment through two parallel analysis paths: one is through time domain trend analysis, and the other is through space-time decoupling analysis, finally, the adaptive global trend component and the local abnormal component are completely separated by using the spatial localization index to perform weighted distribution on the long-term strain value increment, thereby solving the problem of contaminated strain parameters and improving the deformation testing accuracy.

[0009] Preferably, the dynamic disturbance index of the sampling point is determined based on the following manner: a time sequence strain value sequence of each sampling point is obtained, a normalized standard deviation of the time sequence strain value sequence is taken as a time fluctuation index of the sampling point; a first time sequence strain value sequence of a previous adjacent sampling point of the sampling point and a second time sequence strain value sequence of a next adjacent sampling point of the sampling point are obtained; a first Pearson correlation coefficient and a second Pearson correlation coefficient between the time sequence strain value sequence of the sampling point and the first time sequence strain value sequence and the second time sequence strain value sequence are respectively calculated; an average value of the first Pearson correlation coefficient and the second Pearson correlation coefficient is determined as a local spatial correlation index of the sampling point; and the time fluctuation index and the local spatial correlation index are nonlinearly fused to obtain the dynamic disturbance index of the sampling point.

[0010] The technical solution realizes an AND operation through nonlinear fusion, and the final dynamic disturbance index is only larger when a signal simultaneously satisfies high time fluctuation and high local spatial correlation, so that the dynamic disturbance index can accurately identify strain characteristics caused by wind load and can eliminate sensor noise with the same high fluctuation but lacking spatial correlation.

[0011] Preferably, the method for extracting the quasi-static signal of the sampling point from the time series of strain values using the dynamic disturbance index comprises: performing a negative correlation nonlinear mapping of the product of the normalized root mean square of the time series of strain values of each sampling point and the dynamic disturbance index of the sampling point to determine a suppression factor; and performing element-by-element multiplication of the suppression factor and the time series of strain values of the sampling point to obtain a new time series of strain values as the quasi-static signal of the sampling point.

[0012] This technical solution physically constructs an adaptive time domain filter, which introduces the root mean square as an indicator of the total energy size of the signal, multiplies the indicator with the dynamic disturbance index to construct a dynamic disturbance energy physical quantity, maps the energy to a suppression factor through a negative correlation nonlinear mapping, can accurately suppress the signal components identified as strong dynamic interference, and at the same time retains all non-dynamic quasi-static signals.

[0013] Preferably, the method for performing time domain trend analysis on the quasi-static signal to determine a long-term change rate comprises: in the quasi-static signal of each sampling point, determining the gradient direction and gradient amplitude of each strain value according to the strain value and the adjacent strain values before and after the strain value to obtain a gradient direction set and a gradient amplitude set; performing statistical analysis on the gradient amplitude set to determine a gradient amplitude threshold, filtering the gradient amplitude set by the gradient amplitude threshold to obtain a gradient amplitude subset; determining a gradient direction subset corresponding to the gradient amplitude subset in the gradient direction set, and performing weighted average operation on the gradient amplitude subset with the gradient direction subset as the weight to obtain a long-term change rate.

[0014] This technical solution first identifies and eliminates all contaminated data points that are not likely to be real trends by performing statistical analysis on the gradient amplitude set to determine a gradient amplitude threshold and filtering, and then performs weighted average operation only on the remaining clean gradient subset, so that the final obtained long-term change rate is a highly robust estimate value that is not affected by any instantaneous abnormal value or sensor jump point, and accurately reflects the real physical change trend.

[0015] Preferably, determining the gradient direction and gradient amplitude of each strain value according to the strain value and the adjacent strain values before and after the strain value comprises: for each strain value, calculating the difference between the next adjacent strain value and the previous adjacent strain value, determining the sign of the difference as the gradient direction of the strain value, and determining the absolute value of the difference as the gradient amplitude of the strain value.

[0016] Preferably, the long-term strain value increment of the sampling point is determined based on the following manner: the product of the long-term change rate and the time length corresponding to the time series of strain values is determined as the long-term strain value increment of the sampling point.

[0017] Preferably, the quasi-static signal of all sampling points is subjected to a spatio-temporal decoupling operation to determine the spatial localization index of the sampling point, including: for the first sampling point, constructing a local spatio-temporal observation vector of the first sampling point : , , and are the quasi-static signal of the previous sampling point of the first sampling point, the quasi-static signal of the first sampling point and the quasi-static signal of the next sampling point of the first sampling point; calculating a local covariance matrix varying with time, the length of time being the length of time corresponding to the quasi-static signal of the sampling point, performing eigenvalue decomposition on the local covariance matrix to obtain three eigenvalues , and , and , , , , , the spatial localization index of the first sampling point , is a parameter to prevent the denominator from being 0.

[0018] This technical solution takes into account the essential difference between the settlement characteristics and the local corrosion characteristics of the tower pile foundation, constructs a local spatio-temporal observation vector, calculates a local covariance matrix generated by the evolution of the vector with time, performs eigenvalue decomposition, and reflects the difference between the settlement and local corrosion of the tower pile foundation through the eigenvalues. The final spatial localization index can accurately distinguish whether the strain value collected by a sampling point is caused by the settlement of the tower pile foundation itself or by physical corrosion.

[0019] Preferably, the long-term strain value increment is subjected to a weighted distribution operation to determine the adaptive global trend component and the local anomaly component of the sampling point, respectively, including: The long-term strain value increment is weighted by the confidence weight to calculate the local anomaly component; the long-term strain value increment is weighted by the value obtained by subtracting the confidence weight from 1 to calculate the adaptive global trend component.

[0020] Preferably, the adaptive global trend component and the local anomaly component of all sampling points are used to determine the deformation curve to realize distributed testing of the deformation of the tower pile foundation, including: ​​An adaptive global trend component of all sampling points is acquired to form an adaptive global trend component sequence, and a first spatial integral operation is performed on the adaptive global trend component sequence to obtain a global deformation curve representing the overall deformation state of the tower pile foundation; a local abnormal component of all sampling points is acquired to form a local abnormal component sequence, and a second spatial integral operation is performed on the local abnormal component sequence to obtain a local deformation curve representing deformation caused by local damage of the tower state; and decoupling diagnosis is performed on the global deformation curve and the local deformation curve to jointly determine the deformation state of the tower pile foundation.

[0021] In a second aspect, the present application also provides a pile deformation distributed testing system, comprising a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps of any one of the pile deformation distributed testing methods.

[0022] The present application has the following effects: In the time domain, the present application uses a dynamic disturbance index that integrates local spatial correlation, which can accurately filter out dynamic strain interference caused by wind load, and in the spatial domain, the present application separates the deformation caused by foundation settlement and real corrosion, and solves the problem of strong interference dominating the test results by using the strategy of separating interference layer by layer and decoupling and distributing using time-space evolution characteristics, thereby improving the accuracy of deformation testing. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 The present application is a flowchart. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0025] Referring to Figure 1 A pile deformation distributed testing method, specifically comprising the following steps: S1: acquiring a time sequence strain value sequence of each sampling point along the axial direction of the pile foundation.

[0026] Using Brillouin scattering-based optical time domain reflectometry (BOTDR), the sensing optical fiber is arranged along the axial direction of the tower pile foundation from the top of the pile to the bottom of the pile, and the BOTDR demodulator is used to continuously collect data at a preset time sampling rate of once per hour and one sampling point per 0.5 meters, to acquire the strain value of each sampling point at each time along the axial direction of the pile foundation, and to form a time sequence strain value sequence of each sampling point in the past month until the current testing time.

[0027] S2: Determine the dynamic disturbance index for each sampling point, and use the dynamic disturbance index to extract the quasi-static signal of that sampling point from the time-series strain value sequence.

[0028] For pole and pile foundations in coastal areas, the strain value is a complex composite signal due to the influence of sea winds and corrosive environment, mainly consisting of four components: wind load interference, sensor noise, global foundation settlement, and local physical corrosion. Among them, wind load and sensor noise manifest as high-frequency interference, while settlement and corrosion manifest as low-frequency quasi-static signals.

[0029] In one embodiment, the dynamic perturbation index for each sampling point is determined based on the following method: First, obtain the number Time-series strain value sequence of sampling points And the time-series strain value sequence of the previous adjacent sampling point. and the time-series strain value sequence of the next adjacent sampling point These are denoted as the first time-series strain value sequence and the second time-series strain value sequence, respectively.

[0030] Then, calculate The standard deviation, and obtain all The maximum and minimum values ​​of the standard deviation of the time-series strain value sequence for each sampling point are used to normalize the values ​​using the maximum and minimum values. Normalize the standard deviation to obtain Normalized standard deviation ,Will As an indicator of the time volatility of this sampling point, the following parameters were obtained according to standard Pearson correlation analysis: and Pearson correlation coefficient between ,as well as and Pearson correlation coefficient between Let these be denoted as the first Pearson correlation coefficient and the second Pearson correlation coefficient, respectively. The average of the first and second Pearson correlation coefficients is determined as the local spatial correlation index for that sampling point. .

[0031] Specifically: Calculate the first Pearson correlation coefficient The process is as follows: Let the first The time-series strain value sequence of each sampling point is as follows: , For the first The first strain value in the time-series strain value sequence of sampling points. For the first The total number of strain values ​​contained in the time-series strain value sequence of each sampling point. For the first The time-series strain value sequence of the sampling point is the first Each strain value.

[0032] Meanwhile, the first strain value sequence is The second strain value sequence is as follows: ; calculate and The degree of linear correlation between them, i.e., the first Pearson correlation coefficient :

[0033] in, yes , The sequence number of each included strain value, because and If the sequence is one-to-one, then the same index can be used for traversal. yes and The total number of strain values ​​contained in each. for The Each strain value, for The Each strain value, for The average value, for The average value.

[0034] Similarly, using the above formula, to Replace with The second Pearson correlation coefficient can then be calculated, which is used to quantify the synchronicity of two physically adjacent sampling points in terms of their temporal evolution trends.

[0035] Finally, the time volatility indicator Indicators related to local space Perform nonlinear fusion to obtain the first... Dynamic disturbance index of each sampling point Specifically, in a concrete implementation, nonlinear fusion can be achieved using multiplication and the hyperbolic tangent function:

[0036] in, For the first Dynamic disturbance index of each sampling point It is the hyperbolic tangent function. For the first Time volatility index of each sampling point For the first Local spatial correlation indicators for each sampling point.

[0037] At the sampling points at the top and bottom of the pile, when obtaining local spatial correlation indicators, the time-series strain value sequence of the sampling point and the time-series strain value sequence of the unique adjacent sampling point of the sampling point are obtained. Then, based on the time-series strain value sequences of the two, a Pearson correlation coefficient is calculated as the local spatial correlation indicator of the sampling point.

[0038] The dynamic disturbance index for each sampling point is calculated in this way to accurately eliminate the influence of wind load interference. In the formula for calculating the dynamic disturbance index, This represents the total fluctuation of all signal components (including high-frequency wind load, high-frequency noise, and low-frequency settlement / corrosion) in a physically represented manner. Wind load interference exhibits high-frequency fluctuation characteristics. The values ​​are very high, and since wind load is a physical excitation, it inevitably causes spatially correlated vibrations in the pile foundation as a continuous body. Therefore, its local spatial correlation index is also very high. The value will be very high, approaching 1, and the dynamic disturbance index will be even larger. Sensor noise also exhibits high-frequency fluctuation characteristics. The noise level is high, but the sensor noise is a random jump due to spatial decoupling, with almost no spatial correlation characteristics. The values ​​will be very low, resulting in a significant suppression of the final dynamic disturbance index. Regarding the physical settlement or corrosion of the tower pile foundation itself, this physical settlement or corrosion is usually caused by the pile foundation as a whole, with local spatial related indicators... The value will be high, but because the process of physical sedimentation or physical corrosion is usually an extremely slow quasi-static change, it has low-frequency characteristics. The dynamic disturbance index will be very small, resulting in a significant suppression of the final dynamic disturbance index. Therefore, the larger the dynamic disturbance index of a sampling point, the more likely the strain value at the sampling point is to include the strain value generated by the interference of wind load. Conversely, the smaller the dynamic disturbance index of a sampling point, the more likely the strain value at the sampling point is to include the strain value generated by sensor noise or physical settlement or physical corrosion of the tower pile foundation. This provides a reliable basis for accurately extracting the quasi-static signal.

[0039] In one embodiment, the method for extracting the quasi-static signal of sampling points from a time-series strain value sequence using a dynamic perturbation index is as follows: First, identify a repressor: Calculate the first The root mean square (RMS) of the time-series strain values ​​at each sampling point is obtained. The maximum and minimum values ​​among the RMS of the time-series strain values ​​at all sampling points are then normalized using the maximum-minimum normalization method. Perform a normalization operation to obtain the first... The normalized root mean square of each sampling point is used to perform a negatively correlated nonlinear mapping on the product of the dynamic perturbation index and the normalized root mean square of that sampling point to determine a suppression factor. Specifically, this nonlinear mapping can be implemented using the natural exponential function:

[0040] in, For the first The inhibition factor at each sampling point It is a natural exponential function. For the first The inhibition factor at each sampling point For the first The normalized root mean square of the time-series strain value sequence of each sampling point.

[0041] In this formula, The larger the value, the more likely it is to be the first. The larger the strain value at each sampling point, the more likely it is to include strain values ​​caused by wind load disturbance; conversely, the smaller the value, the more likely it is to include sensor noise or a true quasi-static signal. The root mean square (RMS) is a standard method in signal processing for measuring the total energy or average power of a time series. It reflects the magnitude of the total energy intensity of the signal; a severe vibration will have a high RMS value. A stable signal will have a low point. , It is a negatively correlated nonlinear mapping, which will The input value in the range is mapped to a... Output value for the interval. Part of it involves calculating a dynamic perturbation energy. Characterizing the total energy of the signal, It is a number in the range of 0 to 1. This is equivalent to the portion of the total energy contributed by the wind load.

[0042] In this formula, the stronger the wind load, the better. The larger, The larger the inhibitory factor, the stronger the inhibition factor. The smaller the value, the more likely the strain value at that sampling point is to include strain values ​​generated by wind load, thus requiring a smaller value. The strain value at this sampling point is suppressed. The weaker the wind load, The smaller, The smaller the value, the stronger the inhibitory factor. The larger the sampling point, the less likely the strain value at the sampling point contains strain values generated by wind load, the more likely it contains strain values generated by sensor noise, real settlement or physical corrosion of the tower pile foundation, and the larger the The strain value at the sampling point is retained.

[0043] Then, the The time series strain value sequence of the first sampling point is multiplied element by element to obtain a new time series strain value sequence, which is used as the quasi-static signal of the sampling point. When the dynamic disturbance energy is high, the suppression factor tends to 0, achieving strong suppression; when the dynamic disturbance energy is low, the signal is stable, the suppression factor tends to 1, and the signal is retained to the greatest extent, thereby constructing an adaptive time domain filter that can accurately suppress signal components identified as strong dynamic disturbances while retaining all non-dynamic quasi-static signals.

[0044] S3: Perform time domain trend analysis on the quasi-static signal of each sampling point to determine a long-term change rate, and determine the long-term strain value increment of the sampling point in combination with the time length.

[0045] Since the quasi-static signal of each sampling point may still physically contain two components: one is the real, slow long-term trend, for example, the settlement of the tower pile foundation and physical corrosion; the other is sensor noise, which needs to be further excluded to obtain the real strain value increment.

[0046] This step takes into account that the noise of the sensor is usually an instantaneous jump with high amplitude, while the settlement or physical corrosion of the tower pile foundation is usually a long-term trend with low amplitude.

[0047] Therefore, by analyzing the quasi-static signal of the sampling point, the gradient amplitude and gradient direction change are calculated, all noise interference that is not likely to be a real trend is physically removed, and the change process of the strain value is analyzed based on the remaining clean and slow data, thereby obtaining a long-term strain value increment that is not disturbed by noise.

[0048] In one embodiment, the method of performing time domain trend analysis on the quasi-static signal of each sampling point to determine a long-term change rate is as follows: First, in the quasi-static signal of each sampling point, the gradient direction and gradient amplitude of each strain value and its adjacent strain values are determined, including: For each strain value, the difference between the next adjacent strain value and the previous adjacent strain value of the strain value is calculated, as well as the time interval between the next adjacent strain value and the previous adjacent strain value, the sign (positive or negative) of the ratio of the difference and the time interval is taken as the gradient direction of the strain value, and the absolute value of the ratio of the difference and the time interval is taken as the gradient amplitude of the strain value; For example, for the first strain value of the quasi-static signal of the first sampling point , the previous adjacent strain value is , and the next adjacent strain value is , Since the sampling frequency is fixed, it is collected once an hour, i.e. , The gradient direction of , is: , is a sign function, when the number in the parentheses is positive, the value of the sign function is 1, when the number in the parentheses is negative, the value of the sign function is -1, and when the number in the parentheses is 0, the value of the sign function is 0, is an absolute value sign, is the time interval between and .

[0049] This is a central difference method, for the strain values at the endpoints of the quasi-static signal, forward or backward difference method can be used for adaptation. The reason for using the central difference method is that it cleverly averages out the local noise fluctuations by analyzing the adjacent strain values symmetrically on both sides of each strain value, providing second-order accuracy, resulting in more accurate results, obtaining a more stable and accurate reflection of the change direction and change intensity at each strain value.

[0050] Then, the gradient direction and the gradient amplitude of all strain values of the quasi-static signal of each sampling point form a gradient direction set and a gradient amplitude set, and the gradient amplitude set is statistically analyzed to determine a gradient amplitude threshold. Specifically, the standard deviation and the average value of the gradient amplitude set are calculated, and by using the three-sigma rule of statistics, the average value plus three times the standard deviation is set as the gradient amplitude threshold, and the gradient amplitudes greater than the gradient amplitude threshold are filtered to obtain a gradient amplitude subset consisting of the remaining gradient amplitudes.

[0051] The gradient direction subset corresponding to the gradient amplitude subset is obtained in the gradient direction set, and the gradient amplitude subset is weighted and averaged with the gradient direction subset as the weight to obtain a long-term change rate.

[0052] The weighted average operation is specifically as follows:

[0053] in, It is the first The long-term rate of change of strain values ​​at each sampling point is the total number of gradient magnitudes contained in the gradient magnitude subset (without deduplication), and 'o' is the index of the gradient magnitude in the gradient magnitude subset. It is the 0th gradient magnitude in the subset of gradient magnitudes. It is the gradient direction corresponding to the 0th gradient magnitude. First, the gradient magnitudes are weighted and summed using the gradient direction, and then the average is calculated to achieve the weighted average operation. Physically, it represents the average long-term rate of change of the sampling point, undisturbed by any noise. For example, the strain value at the sampling point changes daily at a rate of -0.005 microstrain values.

[0054] In one embodiment, the long-term strain increment at the sampling point is determined based on the following method: The product of the long-term rate of change and the corresponding time length of the static signal is used to determine the long-term strain value increment at the sampling point. For example, if the long-term rate of change is -0.01 strain value / day and the time length is 365 days, then the long-term strain value increment is -3.65 strain value.

[0055] In this way, the total deformation of each sampling point during the entire monitoring period, contributed solely by the long-term trend, is obtained, which is the long-term strain value increment. This long-term strain value increment is the total amount of strain value generated by the settlement of the tower pile foundation and physical corrosion, and is the basis for subsequent spatial analysis.

[0056] S4: By performing a spatiotemporal decoupling operation on the quasi-static signal of all sampling points, the correlation between the temporal evolution behavior of the sampling point and its physically adjacent sampling points is compared to determine the spatial localization index of the sampling point.

[0057] After obtaining a long-term strain value increment that combines the strain values ​​generated by the settlement and physical corrosion of the tower and pile foundation, the signal is decoupled based on the evolution characteristics of the signal throughout the time domain to distinguish between the settlement and physical corrosion of the tower and pile foundation.

[0058] Specifically, the settlement of tower pile foundation is a relatively holistic and global change. If settlement occurs, the sampling points at different locations on the pile foundation are spatially related and usually move together. Therefore, the strain values ​​at the sampling points also have a certain spatial correlation and produce coordinated changes.

[0059] The correlation of the time evolution behavior of each sampling point and its physically adjacent sampling points is based on the coherence principle of deformation mode. The global settlement of the tower pile foundation is a low degree of freedom common mode movement, which causes multiple sampling points in a local area to present a highly synchronized fluctuation trend on the time axis, i.e., their time evolution behaviors have a strong linear correlation. On the contrary, local corrosion damage is a high degree of freedom differential mode movement, which only causes a specific sampling point to abnormally expand, and has a lagging or decaying effect on the adjacent points, resulting in a non-correlation characteristic of the signals in the local area on the time axis.

[0060] Therefore, by constructing a local space-time observation vector and analyzing its covariance matrix, the distribution direction of the signal energy in the high-dimensional space is essentially evaluated as being concentrated on the main direction representing the common mode movement or diverging to the secondary direction representing the differential movement, so as to realize space-time decoupling.

[0061] In one embodiment, the method for determining the spatial localization index of each sampling point through the space-time decoupling operation comprises: For each sampling point, a local space-time observation vector is constructed, which is composed of the quasi-static signal of the sampling point and the quasi-static signals of its physically adjacent sampling points. Specifically, the local space-time observation vector of the i th sampling point is constructed as follows: wherein, is the quasi-static signal of the i th sampling point, is the quasi-static signal of the next sampling point of the i th sampling point, is the quasi-static signal of the previous sampling point of the i th sampling point.

[0062] The local covariance matrix changing over time is calculated, and the corresponding time length changing over time is the time length corresponding to the quasi-static signal. Since the collection time length of each sampling point is consistent, the length of the quasi-static signal is also consistent.

[0063] Eigenvalue decomposition is performed on the local covariance matrix to obtain at least three eigenvalues. The first three eigenvalues are taken, and the first three eigenvalues and their size relationship are , and the spatial localization index of the i th sampling point is calculated as follows:

[0064] wherein, is a parameter for preventing the denominator from being 0, which is usually set as a very small positive number​​​​​​​​ Eigenvalue decomposition is essentially principal component analysis of the local covariance matrix. These are the eigenvalues ​​of the covariance matrix. Represents the first The sampling point, the first The sampling point and the first The total energy of the quasi-static signal in a local area composed of sampling points is used to decouple the signal based on its evolution characteristics (covariance) over the entire time domain, in order to distinguish between global settlement and local physical corrosion of tower pile foundations. Since settlement is spatially dependent, the first... The sampling point, the first The sampling point and the first The energy of the quasi-static signal at each sampling point will be concentrated in the first principal component. Above, while localized physical corrosion is spatially uncorrelated, energy will leak into the second principal component. and the third principal component .

[0065] In the above eigenvalue decomposition, It is the largest eigenvalue, corresponding to the first principal component of the local covariance matrix. Physically, the first principal component captures the direction of maximum variance in the local observation vector, i.e., the common and strongly correlated trend of change among the three sampling points. Therefore, It characterizes the energy intensity of global settling (common mode component). and These are secondary eigenvalues, corresponding to directions orthogonal to the first principal component. Physically, they capture the inconsistent, asynchronous differences between the three sampling points; therefore, It characterizes the energy intensity of local anomalies (differential mode components).

[0066] The spatial localization index proposed in this invention is essentially a differential energy proportion coefficient. When pure global settlement occurs, the first... Each sampling point is highly synchronized with its neighboring sampling points, and the local covariance matrix tends to be a rank-1 matrix, with almost all energy concentrated in the area between the sampling points and the neighboring sampling points. ,at this time The index approaches 0, causing the spatial localization index to approach 0. When localized corrosion occurs, the first... The behavior of each sampling point is decoupled from its neighboring sampling points, disrupting the correlation and causing energy to leak from the first principal component to the secondary principal components. The increase leads to an increase in the spatial localization index, which then tends towards 1.

[0067] This spatiotemporal decoupling operation based on characteristic spectrum analysis not only qualitatively identifies damage types but also quantitatively provides a continuous physical confidence level (ranging from 0 to 1). It can adaptively separate mixed strain increments into settlement and corrosion components without prior knowledge, thus improving the accuracy of test results under complex working conditions.

[0068] Therefore, the spatial localization index has become a highly reliable physical confidence level. The smaller the value, the closer it is to 0, the more likely the strain value at the sampling point is to include the strain value generated by global settlement. The larger the value, the more likely the strain value at the sampling point is to include the strain value generated by local corrosion.

[0069] S5: Using the spatial localization index as a confidence weight, perform a weighted allocation operation on the long-term strain value increment to determine the adaptive global trend component and local anomaly component of the sampling point respectively.

[0070] In one embodiment, the weighted allocation operation includes: For the Each sampling point will be used to determine the spatial localization index. As the confidence weight and the first The long-term strain increments at each sampling point are multiplied to achieve a weighted calculation, and the product is used as the local anomaly component of the strain value at that sampling point; With the The long-term strain value increments of each sampling point are multiplied to achieve a weighted calculation, and the product is used as the first... The adaptive global trend component of the strain value at each sampling point.

[0071] The reason for adopting this approach is that the long-term strain increment at each sampling point is a mixed value, containing strain values ​​caused by tower and pile foundation settlement and strain values ​​caused by physical corrosion. The spatial localization index, which reflects the likelihood of actual physical corrosion, is a value in the range of 0 to 1. Using it as a confidence weight, a reasonable weighting operation is performed on the long-term strain increment. If the strain value at a sampling point is more likely to contain strain values ​​caused by actual physical corrosion, then the spatial localization index of that sampling point is larger, the confidence weight is larger, and it plays a dominant role in the long-term strain increment at that sampling point. The local anomaly component of the strain value at that sampling point will be larger because the local anomaly component reflects the characteristic of physical corrosion. At the same time, the adaptive global trend component of the strain value at that sampling point will be smaller because the adaptive global trend component reflects the global strain influence of tower and pile foundation settlement, and vice versa.

[0072] This method enables signal separation with clear physical meaning.

[0073] S6: Integrate the adaptive global trend component and local anomaly component of all sampling points to obtain two deformation curves. Analyze the deformation state of the tower pile foundation based on the deformation curves to complete the deformation test.

[0074] In one embodiment, adaptive global trend components of all sampling points are obtained to form an adaptive global trend component sequence. Spatial integration is performed on the adaptive global trend component sequence to obtain a global deformation curve that can represent the overall deformation state of the tower and pile foundation. At the same time, a local anomaly component sequence composed of local anomaly components of all sampling points is obtained. Spatial integration is performed on the local anomaly component sequence to obtain a local deformation curve that can represent the deformation of the tower and pile foundation caused by local damage (corrosion). Decoupled diagnosis is performed based on these two curves to jointly assess the deformation state of the tower and pile foundation.

[0075] Specifically, it includes: Obtain the distribution along the pile foundation axis The adaptive global trend components at each sampling point constitute an adaptive global trend component sequence. , The total number of sampling points. For the adaptive global trend component of the first sampling point, For the first An adaptive global trend component for each sampling point.

[0076] Since the sampling points are discretely distributed, the first spatial integration operation is calculated using the discrete cumulative summation formula. For the... sampling points ( The corresponding global cumulative deformation is:

[0077] in, For the first The global cumulative deformation at each sampling point, i.e., from the starting point to the nth sampling point. The cumulative displacement at each sampling point location, For the front The sequence number of each sampling point, for example... hour, For the front The index of each sampling point is any integer within the interval [1, 5]. For the first The adaptive global trend component (dimensionless strain value) of each sampling point. For the first The physical distance (in meters) between each sampling point and the previous sampling point.

[0078] Using the index of each sampling point as the x-axis, and the index of each sampling point as the y-axis... for the ordinate, the local abnormal component of all sampling points is connected in turn, to obtain a local deformation curve, which represents the differential motion mode of the pile foundation, and is used to reflect the deformation state caused by the local damage of the pile foundation. for the ordinate, the local abnormal component of all sampling points is connected in turn, to obtain a local deformation curve, which represents the differential motion mode of the pile foundation, and is used to reflect the deformation state caused by the local damage of the pile foundation.

[0079] At the same time, the local abnormal component of the sampling points distributed along the axial direction of the pile foundation is obtained, and a local abnormal component sequence is constructed. , is the total number of sampling points, is the local abnormal component of the first sampling point, is the local abnormal component of the th sampling point.

[0080] For the th sampling point ( ), the corresponding local cumulative deformation is:

[0081] wherein, is the local cumulative deformation of the th sampling point, is the serial number of the previous th sampling point, is the local abnormal component (dimensionless strain value) of the th sampling point, is the physical distance between the th sampling point and the previous sampling point (unit: meters).

[0082] for the ordinate, the local abnormal component of all sampling points is connected in turn, to obtain a local deformation curve, which represents the differential motion mode of the pile foundation, and is used to reflect the deformation state caused by the local damage of the pile foundation.

[0083] Since the global settlement component of huge magnitude has been separated into the global deformation curve, the value of the local deformation curve in most healthy areas is close to zero. If the local deformation curve presents an isolated and sharp displacement mutation at a certain position, it is determined that there is a local damage caused by reinforcement corrosion expansion or crack at this position, and the amplitude of the mutation represents the deformation degree caused by the local damage.

[0084] ​​In summary, the settlement of the tower pile foundation is evaluated by analyzing the global deformation curve, and the local damage degree of the tower pile foundation is determined with high sensitivity by analyzing the local deformation curve. This decoupling diagnosis solves the problems of signal flooding and low sensitivity, and improves the accuracy of deformation testing.

[0085] The second aspect of the embodiment of the present application also provides a pile deformation distributed testing system, which comprises a processor and a memory, and the memory stores a computer program, for example, instructions for time domain filtering, spatial decoupling and state evaluation. When the processor executes the computer program stored in the memory, the steps of the pile deformation distributed testing method are implemented, and the pile deformation distributed testing system further comprises a data interface for connecting a fiber sensor demodulator such as BOTDR to obtain the time sequence strain value sequence in S1.

[0086] The processor can be a central processor, a digital signal processor or a field programmable gate array, and the memory can be a volatile memory or a non-volatile memory, for example, a read-only memory (ROM), a hard disk drive or a solid state drive (SSD).

[0087] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of distributed testing of pile deformation, characterized in that, The method comprises the following steps: obtaining strain values of multiple sampling points along the axial direction of the pile foundation at multiple time points through the optical fiber sensor arranged on the surface of the tower pile foundation, so as to form a time sequence strain value sequence of each sampling point; for each sampling point, calculating a dynamic disturbance index of the sampling point based on the time sequence strain value sequence of the sampling point, extracting a quasi-static signal of the sampling point from the time sequence strain value sequence by using the dynamic disturbance index, performing time domain trend analysis on the quasi-static signal to determine a long-term change rate, and combining the time length corresponding to the time sequence strain value sequence to determine a long-term strain value increment of the sampling point; performing space-time decoupling operation on the quasi-static signals of all sampling points, comparing the strain values of each sampling point with the strain values of its physically adjacent sampling points to determine a spatial localization index of the sampling point; using the spatial localization index as a confidence weight to perform weighted distribution operation on the long-term strain value increment to determine an adaptive global trend component and a local abnormal component of the sampling point, respectively; determining a deformation curve according to the adaptive global trend components and the local abnormal components of all sampling points to realize distributed testing of the deformation of the tower pile foundation.

2. The pile shaft deformation distribution test method according to claim 1, wherein The dynamic disturbance index of the sampling point is determined in the following manner: obtaining the time sequence strain value sequence of each sampling point, and using the normalized standard deviation of the time sequence strain value sequence as a time fluctuation index of the sampling point; obtaining a first time sequence strain value sequence of a previous adjacent sampling point and a second time sequence strain value sequence of a subsequent adjacent sampling point of the sampling point; calculating a first Pearson correlation coefficient and a second Pearson correlation coefficient between the time sequence strain value sequence of the sampling point and the first time sequence strain value sequence and the second time sequence strain value sequence, respectively; determining an average value of the first Pearson correlation coefficient and the second Pearson correlation coefficient as a local spatial correlation index of the sampling point; and performing nonlinear fusion on the time fluctuation index and the local spatial correlation index to obtain the dynamic disturbance index of the sampling point.

3. The pile shaft deformation distribution test method according to claim 1, wherein The method for extracting the quasi-static signal of the sampling point from the time sequence strain value sequence by using the dynamic disturbance index comprises the following steps: performing negative nonlinear mapping on the product of the normalized root mean square of the time sequence strain value sequence of each sampling point and the dynamic disturbance index of the sampling point to determine an inhibition factor; performing element-by-element multiplication on the inhibition factor and the time sequence strain value sequence of the sampling point to obtain a new time sequence strain value sequence as the quasi-static signal of the sampling point.

4. The pile shaft deformation distribution test method according to claim 1, wherein The method for performing time domain trend analysis on the quasi-static signal to determine a long-term change rate comprises the following steps: in the quasi-static signal of each sampling point, determining a gradient direction and a gradient amplitude of each strain value according to the strain value and its adjacent strain values to obtain a gradient direction set and a gradient amplitude set; performing statistical analysis on the gradient amplitude set to determine a gradient amplitude threshold, filtering the gradient amplitude set by the gradient amplitude threshold to obtain a gradient amplitude subset; determining a gradient direction subset corresponding to the gradient amplitude subset in the gradient direction set, and performing weighted average operation on the gradient amplitude subset by using the gradient direction subset as a weight to obtain a long-term change rate.

5. The pile shaft deformation distribution test method according to claim 4, wherein The gradient direction and the gradient amplitude of each strain value are determined according to the strain value and its adjacent strain values before and after the strain value, including: For each strain value, the difference between the next adjacent strain value and the previous adjacent strain value of the strain value is calculated, the sign of the difference is determined as the gradient direction of the strain value, and the absolute value of the difference is determined as the gradient amplitude of the strain value.

6. The pile shaft deformation distribution test method according to claim 4, wherein The long-term strain value increment of the sampling point is determined based on the following manner: The product of the long-term change rate and the time length corresponding to the time series of strain values is determined as the long-term strain value increment of the sampling point.

7. The pile shaft deformation distribution test method according to claim 1, wherein The space-time decoupling operation is performed on the quasi-static signals of all sampling points, and the correlation of the strain value of each sampling point with the strain value of its physically adjacent sampling points changing over time is compared to determine the spatial localization index of the sampling point, including: for the first sampling point, constructing a local space-time observation vector of the first sampling point , and are the quasi-static signals of the previous sampling point of the first sampling point, the quasi-static signal of the first sampling point and the quasi-static signal of the next sampling point of the first sampling point; calculating a local covariance matrix changing over time, the length of time being the time length corresponding to the quasi-static signal of the sampling point; performing eigenvalue decomposition on the local covariance matrix to obtain three eigenvalues , and , and , the spatial localization index of the first sampling point , wherein is a parameter to prevent the denominator from being 0.

8. The pile shaft deformation distribution test method according to claim 1, wherein The long-term strain value increment is subjected to a weighted distribution operation to determine the adaptive global trend component and the local anomaly component of the sampling point, including: The long-term strain value increment is weighted by the confidence weight to calculate the local anomaly component of the sampling point, and the value obtained by subtracting the confidence weight from 1 is weighted to the long-term strain value increment to calculate the adaptive global trend component of the sampling point.

9. The pile shaft deformation distribution test method according to claim 1, wherein The deformation curve is determined according to the adaptive global trend component and the local anomaly component of all sampling points to realize the distributed deformation test of the tower pile foundation, including: The adaptive global trend components of all sampling points are obtained to form an adaptive global trend component sequence, and a first spatial integral operation is performed on the adaptive global trend component sequence to obtain a global deformation curve representing the overall deformation state of the tower pile foundation; the local anomaly components of all sampling points are obtained to form a local anomaly component sequence, and a second spatial integral operation is performed on the local anomaly component sequence to obtain a local deformation curve representing the deformation caused by the local damage of the tower; and the global deformation curve and the local deformation curve are decoupled and diagnosed to determine the deformation state of the tower pile foundation together.

10. A pile deformation distributed testing system, characterized in that, The distributed deformation test system of the pile foundation includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps of the distributed deformation test method of the pile foundation as claimed in any one of claims 1-9.

Images