Bridge pile foundation stress nondestructive testing method and system

Abstract

The present application relates to the technical field of bridge pile foundation detection, in particular to a bridge pile foundation stress nondestructive detection method and system, comprising: arranging multiple transducers circumferentially at a predetermined height of the pile foundation to form a transmitting and receiving array, transmitting excitation sound waves and collecting response signals, extracting a signal feature set containing wave arrival time, amplitude attenuation rate and frequency drift after preprocessing, inputting the feature set into an improved stress field inversion algorithm constructed according to the mapping relationship between stress and acoustic anisotropy parameters, calculating a three-dimensional stress distribution cloud map, identifying a stress concentration area and calculating its maximum principal stress value and direction, and outputting a structure integrity evaluation result after comparing the relevant parameters with the pile foundation design parameters. The method can realize the spatial distribution analysis and integrity quantitative evaluation of the internal stress of the pile foundation, and is suitable for bridge pile foundation stress nondestructive detection operation.

Description

Technical Field

[0001] This invention relates to the field of bridge pile foundation testing technology, and in particular to a method and system for non-destructive testing of stress in bridge pile foundations. Background Technology

[0002] Conventional non-destructive testing of bridge pile foundation stress often employs a single acoustic transducer. The testing process only collects a single acoustic parameter, and stress field inversion analysis relies on traditional algorithms, which can only obtain stress values ​​at local locations within the pile foundation. This type of testing method does not perform multi-point synchronous signal acquisition along the circumference of the pile, and signal analysis depends on only a few acoustic indicators, failing to fully characterize the true state of sound wave propagation within the pile foundation.

[0003] Traditional detection methods acquire signal features with limited dimensions, failing to simultaneously reflect the temporal, energy, and frequency variations of sound wave propagation. Traditional stress field inversion algorithms do not establish a corresponding relationship between stress and acoustic anisotropy parameters, making it impossible to achieve spatial visualization of internal stress in pile foundations, accurately determine the specific morphology of stress concentration areas, or quantify the value and direction of the maximum principal stress.

[0004] The test results cannot be directly quantitatively compared with the design parameters of the pile foundation structure, and the determination of the integrity of the pile foundation structure lacks comprehensive data support. It is necessary to construct a multi-dimensional acoustic signal feature extraction method, establish an acoustic parameter inversion model adapted to stress correlation, and realize the analysis of three-dimensional stress distribution of the pile foundation and targeted structural integrity assessment. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and to propose a non-destructive testing method and system for bridge pile foundation stress.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a non-destructive testing method for bridge pile foundation stress, comprising:

[0007] At a predetermined height position of the bridge pile foundation under test, multiple acoustic signal transducers are evenly arranged along the circumference of the pile body to form an acoustic signal transmitting array and a receiving array.

[0008] The acoustic wave signal transmitting array is controlled to transmit excitation acoustic wave signals of a predetermined frequency into the pile foundation, and the acoustic wave signal receiving array is simultaneously controlled to collect the response acoustic wave signals after propagation through the pile foundation.

[0009] The acquired response acoustic signal is preprocessed to extract a signal feature set, which includes wave arrival time, amplitude attenuation rate, and frequency drift.

[0010] The signal feature set is input into the improved stress field inversion algorithm to calculate the three-dimensional stress distribution cloud map inside the pile foundation. The improved stress field inversion algorithm is constructed based on the mapping relationship between stress and acoustic anisotropy parameters.

[0011] Based on the three-dimensional stress distribution cloud map, the location and shape of the stress concentration region are identified, and the maximum principal stress value and its direction in the stress concentration region are calculated.

[0012] The location, shape, maximum principal stress value and direction of the stress concentration area are compared with the pile foundation structure design parameters to output the pile foundation structure integrity assessment results.

[0013] As a further aspect of the present invention, the preprocessing of the acquired response acoustic signal to extract a signal feature set includes:

[0014] The response acoustic signal is digitally filtered to remove environmental noise and instrument-specific noise, resulting in a clean acoustic time-domain signal.

[0015] The pure acoustic wave time-domain signal is subjected to Hilbert transform processing to calculate the signal envelope and determine the wave arrival time;

[0016] The energy integral of the pure acoustic wave time-domain signal within a preset time window is calculated and compared with the energy integral of the excitation acoustic wave signal within the same time window to calculate the amplitude attenuation rate.

[0017] Spectral analysis is performed on the pure acoustic wave time-domain signal to obtain the signal's dominant frequency, and the offset of the dominant frequency relative to the center frequency of the excitation acoustic wave signal is calculated to obtain the frequency drift.

[0018] The arrival time, amplitude attenuation rate, and frequency drift of each acoustic signal receiving channel are combined to form the signal feature set.

[0019] As a further aspect of the present invention, the improved stress field inversion algorithm is constructed based on the mapping relationship between stress and acoustic anisotropy parameters. The working principle of the improved stress field inversion algorithm includes:

[0020] A constitutive acoustoelastic model of concrete material for bridge pile foundation is established. The constitutive acoustoelastic model describes the nonlinear relationship between longitudinal wave velocity, transverse wave velocity and stress tensor components of concrete under uniaxial and multiaxial stress states.

[0021] The constitutive acoustoelastic model is discretized, and the entire pile foundation structure is discretized into a finite number of voxel elements. An initial stress state and initial acoustic parameters are defined for each voxel element.

[0022] Using the wave arrival time in the signal feature set, the theoretical propagation path and theoretical propagation time of the acoustic signal in each voxel unit are calculated by acoustic ray tracing technology.

[0023] The theoretical propagation time is compared with the measured wave arrival time, and the time residual is calculated.

[0024] The amplitude attenuation rate and frequency drift in the signal feature set are compared with the amplitude attenuation rate and frequency drift predicted based on the current voxel element stress state and material model, and the amplitude residual and frequency residual are calculated.

[0025] Construct an optimization problem with the weighted sum of squares of the time residual, amplitude residual, and frequency residual as the objective function;

[0026] The optimization problem is solved by an iterative optimization algorithm. In each iteration, the stress state of all voxel elements is updated until the value of the objective function converges to within a preset threshold.

[0027] The stress state of all voxel elements after iterative convergence is reconstructed in three-dimensional space, and a three-dimensional stress distribution cloud map inside the pile foundation is output.

[0028] As a further aspect of the present invention, utilizing the arrival time of the signal feature set, the theoretical propagation path and theoretical propagation time of the acoustic signal in each voxel unit are calculated using acoustic ray tracing technology, including:

[0029] Based on the updated stress state of each voxel element in the current iteration step, the longitudinal wave velocity distribution of each voxel element is calculated based on the constitutive acoustoelastic model.

[0030] Starting from the position of each transmitter in the acoustic signal transmitting array and ending from the position of the corresponding receiver in the acoustic signal receiving array, the acoustic wave propagation medium is modeled as a non-uniform medium with continuously varying wave speed.

[0031] The fast-progression algorithm is applied to solve the process function equations in the non-uniform medium and calculate the wavefront arrival time field from each transmitter position to the entire computational region.

[0032] Based on the wavefront arrival time field, the gradient descent method is used to trace back from each receiver position to the transmitter position to obtain each sound wave ray path, which is the theoretical propagation path.

[0033] Along each of the theoretical propagation paths, the wave slack of each voxel unit along the path is integrated, where the wave slack is the reciprocal of the longitudinal wave velocity, and the integration result is the theoretical propagation time.

[0034] As a further aspect of the present invention, based on the three-dimensional stress distribution cloud map, the location and shape of the stress concentration region are identified, including:

[0035] Spatial gradient calculation is performed on the three-dimensional stress distribution cloud map to obtain the stress gradient tensor of each spatial point;

[0036] Calculate the magnitude of the stress gradient tensor and generate a stress gradient magnitude distribution map;

[0037] In the stress gradient modulus distribution map, continuous spatial regions where the gradient modulus value exceeds a preset threshold are marked as candidate stress concentration regions.

[0038] For each candidate stress concentration region, extract its three-dimensional geometric contour of stress distribution, and calculate the volume, triaxial length of the equivalent ellipsoid, and principal axis direction of the candidate stress concentration region;

[0039] Based on the uniformity of stress distribution within the candidate stress concentration region and the discontinuity of stress gradient at its boundary, the final stress concentration region is selected from the candidate stress concentration regions.

[0040] Record the geometric center coordinates, volume, morphological parameters, and principal axis direction of each stress concentration region as a description of its position and shape.

[0041] As a further aspect of the present invention, the calculation of the maximum principal stress value and its direction in the stress concentration region includes:

[0042] Extract the stress tensor of each voxel element from all voxel elements contained in the stress concentration region;

[0043] The stress tensor of each voxel element is decomposed into eigenvalues ​​to solve for the three principal stress values ​​and their corresponding principal directions of the voxel element.

[0044] From all voxel elements in the stress concentration region, find the largest principal stress value and take the largest principal stress value as the maximum principal stress value of the stress concentration region.

[0045] The principal direction corresponding to the voxel element with the maximum principal stress value is taken as the maximum principal stress direction of the stress concentration region.

[0046] Within the stress concentration region, the spatial distribution consistency of the principal stress directions is statistically analyzed. If the consistency is higher than a set standard, the direction of the maximum principal stress is taken as the representative direction of the entire region; otherwise, the vector average value of the principal directions within the region is calculated as the representative direction.

[0047] As a further aspect of the present invention, the location, shape, maximum principal stress value, and direction of the stress concentration region are compared with the pile foundation structure design parameters to output the pile foundation structure integrity assessment results, including:

[0048] Read the structural design parameters of the bridge pile foundation, including the pile foundation's geometric dimensions, concrete grade, design bearing capacity, and allowable stress threshold;

[0049] The location of the stress concentration area is compared with the key structural parts of the pile foundation to determine whether the stress concentration area appears at the pile top, pile bottom, abrupt change in cross section, or an area with historical damage.

[0050] The maximum principal stress value is compared with the allowable stress threshold to determine whether stress exceeds the limit.

[0051] The morphological parameters of the stress concentration region are matched with a typical damage mode database to identify potential damage types. The typical damage mode database contains stress distribution morphological features corresponding to cracks, voids, spalling, and rebar yielding.

[0052] Based on the location comparison results, stress over-limit judgment results, and potential damage type identification results, a qualitative structural integrity level and a quantitative risk assessment index are generated according to the preset assessment rules, which together constitute the pile foundation structural integrity assessment result.

[0053] As a further aspect of the present invention, the constitutive acoustoelastic model is constructed and calibrated in the following manner:

[0054] Prepare standard concrete specimens with the same materials, mix proportions, and curing conditions as bridge pile foundations;

[0055] Known uniaxial, biaxial, and triaxial stress states were applied to the standard concrete specimens in the laboratory, and the stress states covered the expected range of working stresses in the pile foundation.

[0056] Under each known stress state, the longitudinal and transverse wave velocities of the standard concrete specimens were measured in multiple directions using the same acoustic transducer as used in field testing.

[0057] A polynomial fitting function is established with stress tensor components as independent variables and longitudinal wave velocity change rate and transverse wave velocity change rate as dependent variables. The order of the polynomial fitting function is determined based on the goodness of fit of the experimental data.

[0058] Using wave velocity measurement data under all known stress states, the coefficients of each term in the polynomial fitting function are determined through multiple regression analysis;

[0059] The polynomial fitting function with determined coefficients is defined as the constitutive acoustoelastic model describing the concrete material of bridge pile foundations.

[0060] As a further aspect of the present invention, the method further includes a verification step of the calculation results of the improved stress field inversion algorithm:

[0061] During the construction of bridge pile foundations, a small number of fiber optic strain sensors are pre-embedded inside the pile body to form discrete strain monitoring points.

[0062] During the same time period of acoustic nondestructive testing, the strain measurement values ​​of all pre-embedded fiber optic strain sensors are read simultaneously.

[0063] Based on the theory of mechanics of materials, the strain measurement value is converted into the stress measurement value at the sensor location;

[0064] From the three-dimensional stress distribution cloud map, extract the calculated stress value corresponding to the position of the fiber grating strain sensor;

[0065] Compare the measured stress value with the calculated stress value, and calculate the average relative error and the maximum relative error between the measured stress value and the calculated stress value;

[0066] If both the average relative error and the maximum relative error are within acceptable ranges, the calculation results of the improved stress field inversion algorithm are verified to be reliable; otherwise, the parameters in the constitutive acoustoelastic model are calibrated and the inversion calculation is performed again.

[0067] As a further aspect of the present invention, the present invention also includes a bridge pile foundation stress non-destructive testing system, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, it implements the steps of the bridge pile foundation stress non-destructive testing method described above.

[0068] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0069] By extracting wave arrival time, amplitude attenuation rate, and frequency drift to form a signal feature set, it is possible to simultaneously capture the time-domain arrival characteristics, energy attenuation characteristics, and spectral shift characteristics of sound waves propagating within the pile foundation. The acquisition of multi-dimensional signal features can comprehensively reflect the acoustic response changes in different medium regions within the pile foundation, closely aligning with the changes in sound wave propagation parameters caused by differences in stress distribution within the pile foundation. The comprehensiveness of the signal features avoids the information gaps caused by single-parameter analysis, accurately reconstructing the true physical state of sound wave propagation within the pile foundation, and providing complete and accurate raw data for subsequent stress field inversion calculations. The dimensional coverage of the signal features can adapt to the acoustic signal analysis needs at different depths and circumferential positions of the pile foundation, ensuring a high degree of consistency between the signal analysis results and the actual stress state of the pile foundation, reducing the deviation between signal analysis and actual stress conditions, and ensuring that the stress-related information carried by the acoustic signals is fully preserved and utilized.

[0070] An improved stress field inversion algorithm is constructed based on the mapping relationship between stress and acoustic anisotropy parameters. This algorithm can transform multi-dimensional signal characteristics into spatial stress distribution data within the pile foundation, generating a three-dimensional stress distribution cloud map. This map visually presents the spatial location and geometric shape of stress concentration areas and accurately calculates the maximum principal stress value and distribution direction within these areas. A quantitative comparison of the location, shape, maximum principal stress value, and direction parameters of the stress concentration areas with the pile foundation design parameters directly reflects the degree of conformity between the actual stress state of the pile foundation and the design standards, resulting in a structural integrity assessment result that closely reflects the actual working conditions of the pile foundation. Stress analysis is expanded from single-point numerical calculation to three-dimensional spatial full-domain analysis. The assessment process relies on direct comparison between measured signal characteristics and design parameters, improving the accuracy and objectivity of the assessment results. This fully reflects the true stress distribution within the pile foundation, enabling a comprehensive quantitative judgment of the stress state of the pile foundation structure and providing sufficient measured data support for the determination of the pile foundation structural integrity. Attached Figure Description

[0071] Figure 1 This is a flowchart of a non-destructive testing method for bridge pile foundation stress according to the present invention;

[0072] Figure 2 This is a flowchart of preprocessing and signal feature extraction;

[0073] Figure 3 This is a flowchart for calculating the theoretical propagation path and time of acoustic ray tracing. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0075] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0076] See Figure 1 This invention provides a non-destructive testing method for stress in bridge pile foundations, the specific method including:

[0077] At a predetermined height of the bridge pile foundation under test, multiple acoustic signal transducers are uniformly arranged along the circumference of the pile, forming an acoustic signal transmitting array and a receiving array. The acoustic signal transmitting array is controlled to transmit excitation acoustic signals of a predetermined frequency into the pile foundation, and the acoustic signal receiving array is simultaneously controlled to collect the response acoustic signals after propagation through the pile foundation. The collected response acoustic signals are preprocessed to extract signal feature sets, including wave arrival time, amplitude attenuation rate, and frequency drift. The signal feature sets are input into an improved stress field inversion algorithm to calculate a three-dimensional stress distribution cloud map inside the pile foundation. The improved stress field inversion algorithm is constructed based on the mapping relationship between stress and acoustic anisotropy parameters. Based on the three-dimensional stress distribution cloud map, the location and shape of stress concentration areas are identified, and the maximum principal stress value and direction of the stress concentration areas are calculated. The location, shape, maximum principal stress value, and direction of the stress concentration areas are compared with the pile foundation structural design parameters to output the pile foundation structural integrity assessment results.

[0078] In one embodiment of the present invention, filtering is performed to remove environmental noise and inherent instrument noise, resulting in a pure acoustic time-domain signal. A Hilbert transform is then applied to the pure acoustic time-domain signal to calculate the signal envelope, determine the arrival time, calculate the energy integral of the pure acoustic time-domain signal within a preset time window, and compare it with the energy integral of the excitation acoustic signal within the same time window to calculate the amplitude attenuation rate. Spectral analysis is then performed on the pure acoustic time-domain signal to obtain the signal's dominant frequency, and the offset of the dominant frequency relative to the center frequency of the excitation acoustic signal is calculated to obtain the frequency drift. Finally, the arrival time, amplitude attenuation rate, and frequency drift of each acoustic signal receiving channel are combined to form a signal feature set.

[0079] In specific implementation, please refer to Figure 2The process of preprocessing the acquired acoustic response signal and extracting signal feature sets includes digital filtering, wave arrival time determination, amplitude attenuation rate calculation, frequency drift acquisition, and feature combination operations. In specific implementation, the acoustic response signal first enters a digital filter for processing. The digital filter adopts a bandpass filter design, with the passband range set to 0.5 to 2 times the center frequency of the excitation acoustic signal, and the stopband cutoff frequency set at a position offset by 50 Hz on both sides of the passband. The filter order is selected as an 8th-order Butterworth filter. This digital filtering process removes environmental noise and instrument-specific noise, resulting in a clean acoustic time-domain signal.

[0080] In some embodiments, a Hilbert transform is performed on the clean acoustic time-domain signal to calculate the signal envelope, which is used to determine the wave arrival time. The Hilbert transform constructs the imaginary part of the real signal into the Hilbert transform result of the real signal, forming an analytic signal. The magnitude of the analytic signal is the signal envelope. The first time point on the envelope that exceeds a set threshold is found, and this time point is the wave arrival time. It can be understood that the threshold is set to 20% to 30% of the peak value of the envelope to avoid misjudgment caused by noise interference.

[0081] In practice, the energy integral of the pure acoustic wave time-domain signal within a preset time window is calculated and compared with the energy integral of the excitation acoustic wave signal within the same time window to obtain the amplitude attenuation rate. The length of the preset time window is 10 times the period of the excitation acoustic wave signal, and the time window begins at the wave arrival time. The energy integral is obtained by numerically integrating the square of the signal amplitude within the time window. The formula for calculating the amplitude attenuation rate is:

[0082]

[0083] in: This represents the amplitude attenuation rate, in dB. This represents the energy integral of the excitation acoustic signal within a preset time window; This represents the energy integral of a pure sound wave time-domain signal within a preset time window. This formula quantifies the degree of energy loss during sound wave propagation in the pile foundation.

[0084] In some embodiments, spectral analysis is performed on the pure acoustic wave time-domain signal to obtain the dominant frequency of the signal, and the offset of the dominant frequency relative to the center frequency of the excitation acoustic wave signal is calculated to obtain the frequency drift. The spectral analysis is implemented using a Fast Fourier Transform (FFT), with the transform length being the square of the signal length. The dominant frequency is determined as the frequency corresponding to the maximum spectral amplitude, and the frequency drift is the difference between the dominant frequency of the received signal and the center frequency of the excitation signal. Optionally, when multiple local maxima exist, the peak frequency closest to the center frequency of the excitation signal is selected as the dominant frequency to improve the accuracy of the frequency drift calculation. It can be understood that the frequency drift reflects the influence of stress on the frequency characteristics of the acoustic wave and is one of the important input features for stress field inversion.

[0085] In practice, the arrival time, amplitude attenuation rate, and frequency drift of each acoustic signal receiving channel are combined to form a signal feature set. The signal feature set is stored in the form of a two-dimensional array, with the row dimension corresponding to different receiving channels and the column dimension sequentially arranging the three features: arrival time, amplitude attenuation rate, and frequency drift. This structured data can be directly input into the improved stress field inversion algorithm for subsequent calculations.

[0086] In one embodiment of the present invention, a constitutive acoustoelastic model of the bridge pile foundation concrete material is established. This model describes the nonlinear relationship between the longitudinal wave velocity, transverse wave velocity, and stress tensor components of the concrete under uniaxial and multiaxial stress states. The constitutive acoustoelastic model is discretized, dividing the entire pile foundation structure into a finite number of voxel elements. An initial stress state and initial acoustic parameters are defined for each voxel element. (See reference...) Figure 3Based on the updated stress state of each voxel element in the current iteration step, the longitudinal wave velocity distribution of each voxel element is calculated using the constitutive acoustoelastic model. Taking the position of each transmitter in the acoustic signal transmitting array as the starting point and the position of the corresponding receiver in the acoustic signal receiving array as the ending point, the acoustic wave propagation medium is modeled as a non-uniform medium with continuously varying wave velocity. A fast propagation algorithm is applied to solve the equations in the non-uniform medium, calculating the wavefront arrival time field from each transmitter position to the entire computational region. Based on the wavefront arrival time field, the gradient descent method is used to trace back from each receiver position to the transmitter position, obtaining each acoustic ray path. The acoustic ray path is the theoretical propagation path. Along each theoretical propagation path, the wave velocity of each voxel element on the path is calculated. The integral, where wave slowness is the reciprocal of the longitudinal wave velocity, yields the theoretical propagation time. This theoretical propagation time is compared with the measured wave arrival time to calculate the time residual. The amplitude attenuation rate and frequency drift in the signal feature set are then compared with the amplitude attenuation rate and frequency drift predicted based on the current voxel element stress state and material model to calculate the amplitude and frequency residuals. An optimization problem is constructed with the weighted sum of squares of the time, amplitude, and frequency residuals as the objective function. An iterative optimization algorithm is used to solve the optimization problem, updating the stress state of all voxel elements in each iteration until the objective function converges within a preset threshold. The stress state of all voxel elements after convergence is then reconstructed in three-dimensional space, outputting a three-dimensional stress distribution cloud map inside the pile foundation.

[0087] In practical implementation, the improved stress field inversion algorithm is constructed based on the mapping relationship between stress and acoustic anisotropy parameters. The three-dimensional stress field is reconstructed through a process of establishing a constitutive acoustoelastic model, discretization, acoustic ray tracing, residual calculation, and iterative optimization. Specifically, a constitutive acoustoelastic model of the bridge pile foundation concrete material is established. This model mathematically describes the nonlinear coupling relationship between the longitudinal wave velocity, transverse wave velocity, and stress tensor components of concrete under uniaxial and multiaxial stress states, serving as the physical basis for subsequent stress field calculations. The constitutive acoustoelastic model is discretized, dividing the entire pile foundation structure into several cubic voxel elements. The side length of each voxel element is set to 50 mm, and each voxel element is assigned initial stress state assumptions and initial longitudinal and transverse wave velocity acoustic parameter values.

[0088] In some embodiments, based on the updated stress state of each voxel element in the current iteration step, the longitudinal wave velocity distribution is calculated element by element using the constitutive acoustoelastic model, and acoustic ray tracing is performed. Taking the position of a single transmitter in the acoustic signal transmitting array as the starting point and the corresponding receiver position as the ending point, the pile foundation medium is considered as a non-uniform medium with continuously varying longitudinal wave velocities. The fast propagation algorithm is applied to solve the equation of motion to obtain the wavefront arrival time field from the transmitter to the entire domain. Based on the wavefront arrival time field, the gradient descent method is used to trace backward from the receiver position to the transmitter position, obtaining a curved theoretical propagation path. The theoretical propagation path reflects the acoustic ray deflection effect caused by the non-uniformity of wave velocity. The wave slowness of each voxel element is linearly integrated along each theoretical propagation path, with the wave slowness being the reciprocal of the longitudinal wave velocity. The integration result is the theoretical propagation time of the acoustic wave along that path.

[0089] In practical implementation, the time residual is obtained by subtracting the theoretical propagation time from the measured wave arrival time. The amplitude residual and frequency residual are obtained by subtracting the amplitude attenuation rate and frequency drift from the signal feature set and the amplitude attenuation rate and frequency drift predicted based on the current stress state, respectively. An optimization problem is constructed with the weighted sum of squares of the three types of residuals as the objective function. The expression of the objective function is as follows:

[0090]

[0091] in: Represents the objective function value. , , These are the weighting coefficients for the time residual, amplitude residual, and frequency residual, respectively. Indicates the theoretical propagation time. Indicates the measured wave arrival time. This indicates the predicted amplitude decay rate. This represents the measured amplitude attenuation rate. This indicates the predicted frequency shift. This represents the measured frequency drift. Optionally, the weighting coefficients are assigned based on the uncertainty level of each feature, with features of higher uncertainty receiving lower weights. The Levenberg-Marquardt iterative optimization algorithm is used to solve the objective function. In each iteration, the stress tensor components of all voxel elements are adjusted to gradually decrease the objective function value. Iteration stops when the change in the objective function value is less than a preset threshold or the maximum number of iterations is reached; at this point, the stress state of each voxel element is considered the optimal solution. The stress state after convergence is reconstructed using three-dimensional interpolation according to voxel space coordinates to generate a three-dimensional stress distribution cloud map inside the pile foundation. The cloud map visually reflects the stress distribution using color depth.

[0092] In one embodiment of the present invention, spatial gradient calculation is performed on the three-dimensional stress distribution cloud map to obtain the stress gradient tensor of each spatial point. The magnitude of the stress gradient tensor is calculated to generate a stress gradient modulus distribution map. In the stress gradient modulus distribution map, continuous spatial regions with gradient modulus values ​​exceeding a preset threshold are marked as candidate stress concentration regions. For each candidate stress concentration region, the three-dimensional geometric contour of its stress distribution is extracted, and the volume, triaxial length of the equivalent ellipsoid, and principal axis direction of the candidate stress concentration region are calculated. Based on the uniformity of stress distribution within the candidate stress concentration region and the discontinuity of stress gradient at its boundary, the final stress concentration region is selected from the candidate stress concentration regions. The geometric center coordinates, volume, morphological parameters, and principal axis direction of each stress concentration region are recorded. As a description of its location and shape, the stress tensor of each voxel element in the stress concentration region is extracted. Eigenvalue decomposition is performed on the stress tensor of each voxel element to solve for the three principal stress values ​​and their corresponding principal directions. The largest principal stress value is found among all voxel elements in the stress concentration region and is taken as the maximum principal stress value of the stress concentration region. The principal direction corresponding to the voxel element with the maximum principal stress value is taken as the maximum principal stress direction of the stress concentration region. Within the stress concentration region, the spatial distribution consistency of the principal stress directions is statistically analyzed. If the consistency is higher than the set standard, the maximum principal stress direction is taken as the representative direction of the entire region. Otherwise, the vector average value of the principal directions within the region is calculated as the representative direction.

[0093] In practical implementation, the process of identifying the location and shape of stress concentration regions based on a 3D stress distribution cloud map and calculating the maximum principal stress value and its direction encompasses spatial gradient calculation, candidate region marking, morphological analysis, and principal stress solution steps. Specifically, spatial gradient calculation is performed on the 3D stress distribution cloud map to obtain the stress gradient tensor at each spatial point. The stress gradient tensor is composed of the first-order partial derivatives of the stress field in each coordinate direction, characterizing the rate and direction of stress change. The magnitude of the stress gradient tensor is calculated to generate a stress gradient modulus distribution map; the gradient modulus value reflects the severity of local stress changes.

[0094] In some embodiments, in the stress gradient modulus distribution map, continuous spatial regions with gradient modulus values ​​exceeding a preset threshold are marked as candidate stress concentration regions. The preset threshold is set based on the statistical characteristics of the overall gradient modulus distribution, for example, taking the mean plus twice the standard deviation. For each candidate stress concentration region, its three-dimensional geometric contour of stress distribution is extracted. Based on the contour points, the volume of the candidate stress concentration region, the three-axis lengths and principal axis directions of the fitted equivalent ellipsoid are calculated. The equivalent ellipsoid is determined by least squares fitting. Based on the uniformity of stress distribution within the candidate stress concentration region and the discontinuity of stress gradient at the boundary, the final stress concentration region is selected from the candidate stress concentration regions. Regions with internal stress variation coefficients greater than a set value or abrupt changes in boundary gradient are eliminated. The geometric center coordinates, volume, morphological parameters, and principal axis directions of each stress concentration region are recorded as descriptive information of its location and shape, as shown in Table 1.

[0095] Table 1: Morphological parameters of a stress concentration region

[0096] Parameter name numerical values volume 125cm³ Equivalent major axis length 65mm Equivalent minor axis length 42mm Main spindle orientation angle 23°

[0097] In practical implementation, the stress tensor of each voxel element within the stress concentration region is extracted. Eigenvalue decomposition is performed on each stress tensor to solve for the three principal stress values ​​and their corresponding principal directions. The largest principal stress value is identified from all voxel elements within the region and taken as the maximum principal stress value for the stress concentration region. The principal direction of the corresponding voxel element is then taken as the maximum principal stress direction. Within the stress concentration region, the spatial distribution consistency of the principal stress directions is statistically analyzed. If the consistency exceeds a set standard, the maximum principal stress direction is taken as the representative direction for the entire region; otherwise, the vector average of the principal directions within the region is calculated as the representative direction. The formula for determining the consistency of principal directions is as follows:

[0098]

[0099] in: This represents the consistency coefficient in the main direction. Indicates the first The unit vector of the principal direction of an individual element. Denotes the vector norm. When If the principal directions are consistent, the direction of maximum principal stress is directly adopted; otherwise, the average vector value is calculated as the representative direction.

[0100] In one embodiment of the present invention, the structural design parameters of the bridge pile foundation are read. These parameters include the pile foundation's geometric dimensions, concrete grade, design bearing capacity, and allowable stress threshold. The location of the stress concentration area is compared with the key structural parts of the pile foundation to determine whether the stress concentration area appears at the pile top, pile bottom, abrupt changes in cross-section, or in areas with historical damage. The maximum principal stress value is compared with the allowable stress threshold to determine whether stress exceedance has occurred. The morphological parameters of the stress concentration area are matched with a typical damage pattern database to identify potential damage types. The typical damage pattern database contains stress distribution morphological characteristics corresponding to cracks, voids, spalling, and steel bar yielding. Based on the location comparison results, stress exceedance judgment results, and potential damage type identification results, a qualitative structural integrity level and a quantitative risk assessment index are generated according to preset evaluation rules, which together constitute the pile foundation structural integrity assessment result.

[0101] In practice, the process of comparing the location, shape, maximum principal stress value, and direction of the stress concentration area with the pile foundation structure design parameters to output the pile foundation structure integrity assessment results covers parameter reading, multi-dimensional comparison, damage matching, and comprehensive assessment steps. Specifically, the structural design parameters of the bridge pile foundation are read, including the total length of the pile foundation, cross-sectional diameter, concrete strength grade, design bearing capacity limit, and allowable tensile and compressive stress thresholds of the material. These parameters are derived from bridge engineering design drawings and construction acceptance data, serving as the benchmark for assessment. The geometric center coordinates of the stress concentration area are compared with the coordinate range of key structural parts of the pile foundation to determine whether the stress concentration area appears in the pile top loading zone, the pile bottom bearing layer contact surface, abrupt changes in cross-sectional dimensions, or an area with existing damage recorded in historical inspection reports. The location comparison uses three-dimensional Euclidean distance calculation; if the distance is less than the set tolerance, it is considered a coincidence.

[0102] In some embodiments, the maximum principal stress value of the stress concentration area is compared with the allowable stress threshold to determine whether stress exceedance has occurred. If the maximum principal stress value exceeds the design value of concrete compressive strength or the standard value of tensile strength, it is marked as a stress exceedance event. The morphological parameters of the stress concentration area are matched with a typical damage mode database, which contains stress distribution morphological features corresponding to typical damage types such as concrete cracks, internal voids, interface peeling, and steel bar yielding. Morphological parameters include volume, equivalent ellipsoid axis-to-length ratio, principal axis inclination angle, and internal stress distribution entropy. Potential damage types are identified by calculating the similarity index between the morphological parameters and database features. Based on the location comparison results, stress exceedance judgment results, and potential damage type identification results, a qualitative structural integrity level and a quantitative risk assessment index are generated according to preset assessment rules, which together constitute the pile foundation structural integrity assessment results (see Table 2).

[0103] Table 2: Schematic diagram of a pre-set evaluation rule

[0104] Location matching status Stress over-limit state Potential damage types Structural Integrity Level Risk assessment indicator R key parts yes crack Level III 0.75 Non-critical parts no none Level I 0.15 key parts no Hollow Level II 0.45

[0105] In practice, the formula for calculating quantitative risk assessment indicators is as follows:

[0106]

[0107] in: This represents a risk assessment indicator, with a value range of [0,1]. The larger the value, the higher the risk. The location importance coefficient is set to 1.0 for critical parts and 0.3 for non-critical parts. This is the stress exceedance factor, with 1.0 for exceeding the limit and 0.2 for not exceeding the limit; The damage severity coefficient is set at 0.8 for cracks, 0.6 for cavities, and 0.1 for no damage. , , The weighting factors for the corresponding items are set to 0.4, 0.4, and 0.2, respectively. Optionally, the weighting factors can be adjusted according to the actual safety requirements of the project to accommodate the differences in safety levels of different bridges. It can be understood that the qualitative structural integrity level is divided into Level I (intact), Level II (minor damage), Level III (moderate damage), and Level IV (severe damage), and the level classification is determined based on the combination logic of multiple criteria in the assessment rule table.

[0108] In one embodiment of the present invention, standard concrete specimens with the same materials, mix proportions, and curing conditions as bridge pile foundations are prepared. Known uniaxial, biaxial, and triaxial stress states are applied to the standard concrete specimens in the laboratory, covering the expected range of working stresses in the pile foundation. Under each known stress state, using the same acoustic transducer as in field testing, the longitudinal and transverse wave velocities of the standard concrete specimens in multiple directions are measured. A polynomial fitting function is established with stress tensor components as independent variables and the rates of change of longitudinal and transverse wave velocities as dependent variables. The order of the polynomial fitting function is determined based on the goodness of fit of the experimental data. Using wave velocity measurement data under all known stress states, the coefficients of each term in the polynomial fitting function are determined through multiple regression analysis. The polynomial fitting function with determined coefficients is defined as the function describing the bridge pile foundation. In the constitutive acoustoelastic model of concrete, a small number of fiber optic strain sensors are pre-embedded inside the pile body during the construction of bridge pile foundations, forming discrete strain monitoring points. During the same time period of acoustic nondestructive testing, the strain measurements of all pre-embedded fiber optic strain sensors are read synchronously. According to the theory of mechanics of materials, the strain measurements are converted into stress measurements at the sensor locations. From the three-dimensional stress distribution cloud map, the calculated stress values ​​corresponding to the fiber optic strain sensor locations are extracted. The measured stress values ​​and calculated stress values ​​are compared. The average relative error and the maximum relative error between the measured stress values ​​and the calculated stress values ​​are compared. If the average relative error and the maximum relative error are both within acceptable ranges, the calculation results of the improved stress field inversion algorithm are verified to be reliable. Otherwise, the parameters in the constitutive acoustoelastic model are calibrated, and the inversion calculation is performed again.

[0109] In practice, the construction and calibration of the constitutive acoustoelastic model, as well as the verification of the calculation results of the improved stress field inversion algorithm, were completed through a combination of laboratory experiments and field measurements. In the specific implementation, standard concrete specimens with the same materials, mix proportions, and curing conditions as the bridge pile foundation were prepared. The standard concrete specimens were 150mm × 150mm × 300mm prisms, and the tests were conducted after a curing period of 28 days. Known uniaxial, biaxial, and triaxial stress states were applied to the standard concrete specimens in the laboratory. The stress states covered the expected range of working stresses in the pile foundation. The uniaxial pressure increased from 0MPa to 40MPa, and the biaxial and triaxial stress ratios were set at 1:1 and 1:1:1, respectively. Each stress state was maintained stably for 10 minutes before measurement.

[0110] In some embodiments, under each known stress state, the same acoustic transducer as used in field testing is used to measure the longitudinal and transverse wave velocities of standard concrete specimens in multiple directions. Measurement directions include parallel to the stress direction, perpendicular to the stress direction, and at a 45° angle. Measurements in each direction are repeated three times, and the average value is taken to reduce random errors. A polynomial fitting function is established with the stress tensor components as independent variables and the rates of change of longitudinal and transverse wave velocities as dependent variables. The order of the polynomial fitting function is determined based on the goodness of fit of the experimental data. The goodness of fit is evaluated using the coefficient of determination R², and an order is considered appropriate when R² ≥ 0.98. Using wave velocity measurement data under all known stress states, the coefficients of each term in the polynomial fitting function are determined through multiple regression analysis. The multiple regression analysis uses the least squares method to solve the overdetermined equations to obtain stable coefficient estimates. The polynomial fitting function with determined coefficients is defined as a constitutive acoustoelastic model describing the concrete material of bridge pile foundations. This model is directly embedded in the improved stress field inversion algorithm.

[0111] In practical implementation, during the construction of bridge pile foundations, a small number of fiber optic strain sensors are pre-embedded inside the pile body to form discrete strain monitoring points. These pre-embedded points are distributed 1m below the pile top, in the middle of the pile body, and 1m above the pile bottom, with four sensors arranged in a cross shape at each cross section, totaling 12 monitoring points. During the same time period of acoustic non-destructive testing, the strain measurements of all pre-embedded fiber optic strain sensors are simultaneously read, with the time synchronization error controlled within 1 second to ensure consistent load conditions. Based on the theory of mechanics of materials, the strain measurements are converted into stress measurements at the sensor locations using the formula σ=Eε, where E is the elastic modulus of concrete (design value 36 GPa), and ε is the strain measurement. From the three-dimensional stress distribution cloud map, the calculated stress values ​​corresponding to the fiber optic strain sensor locations are extracted using three-dimensional spatial nearest neighbor interpolation, taking the stress value of the voxel element closest to the sensor coordinates as the calculated value.

[0112] Optionally, the measured stress values ​​are compared with the calculated stress values, and the average relative error and maximum relative error between them are calculated. The average relative error reflects the overall deviation level, while the maximum relative error reflects the local maximum deviation. It can be understood that if the average relative error is ≤10% and the maximum relative error is ≤20%, the calculation results of the improved stress field inversion algorithm are reliable, and no adjustment of the model parameters is required. Otherwise, the parameters in the constitutive acoustoelastic model are calibrated, and the inversion calculation is performed again. The calibration process involves fine-tuning the polynomial coefficients to make the model's predicted wave velocity closer to the measured wave velocity, thereby improving the inversion accuracy.

[0113] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A non-destructive testing method for stress in bridge pile foundations, characterized in that, The method includes: At a predetermined height position of the bridge pile foundation under test, multiple acoustic signal transducers are evenly arranged along the circumference of the pile body to form an acoustic signal transmitting array and receiving array. The acoustic wave signal transmitting array is controlled to transmit excitation acoustic wave signals of a predetermined frequency into the pile foundation, and the acoustic wave signal receiving array is simultaneously controlled to collect the response acoustic wave signals after propagation through the pile foundation. The acquired response acoustic signal is preprocessed to extract a signal feature set, which includes wave arrival time, amplitude attenuation rate, and frequency drift. The signal feature set is input into the improved stress field inversion algorithm to calculate the three-dimensional stress distribution cloud map inside the pile foundation. The improved stress field inversion algorithm is constructed based on the mapping relationship between stress and acoustic anisotropy parameters. Based on the three-dimensional stress distribution cloud map, the location and shape of the stress concentration region are identified, and the maximum principal stress value and its direction in the stress concentration region are calculated. The location, shape, maximum principal stress value and direction of the stress concentration area are compared with the pile foundation structure design parameters to output the pile foundation structure integrity assessment results. The preprocessing of the acquired response acoustic signal to extract a signal feature set includes: The response acoustic signal is digitally filtered to remove environmental noise and instrument-specific noise, resulting in a clean acoustic time-domain signal. The pure acoustic wave time-domain signal is subjected to Hilbert transform processing to calculate the signal envelope and determine the wave arrival time; The energy integral of the pure acoustic wave time-domain signal within a preset time window is calculated and compared with the energy integral of the excitation acoustic wave signal within the same time window to calculate the amplitude attenuation rate. Spectral analysis is performed on the pure acoustic wave time-domain signal to obtain the signal's dominant frequency, and the offset of the dominant frequency relative to the center frequency of the excitation acoustic wave signal is calculated to obtain the frequency drift. The arrival time, amplitude attenuation rate, and frequency drift of each acoustic signal receiving channel are combined to form the signal feature set.

2. The method for non-destructive testing of bridge pile foundation stress according to claim 1, characterized in that, The improved stress field inversion algorithm is constructed based on the mapping relationship between stress and acoustic anisotropy parameters. The working principle of the improved stress field inversion algorithm includes: A constitutive acoustoelastic model of concrete material for bridge pile foundation is established. The constitutive acoustoelastic model describes the nonlinear relationship between longitudinal wave velocity, transverse wave velocity and stress tensor components of concrete under uniaxial and multiaxial stress states. The constitutive acoustoelastic model is discretized, and the entire pile foundation structure is discretized into a finite number of voxel elements. An initial stress state and initial acoustic parameters are defined for each voxel element. Using the wave arrival time in the signal feature set, the theoretical propagation path and theoretical propagation time of the acoustic signal in each voxel unit are calculated by acoustic ray tracing technology. The theoretical propagation time is compared with the measured wave arrival time, and the time residual is calculated. The amplitude attenuation rate and frequency drift in the signal feature set are compared with the amplitude attenuation rate and frequency drift predicted based on the current voxel element stress state and material model, and the amplitude residual and frequency residual are calculated. Construct an optimization problem with the weighted sum of squares of the time residual, amplitude residual, and frequency residual as the objective function; The optimization problem is solved by an iterative optimization algorithm. In each iteration, the stress state of all voxel elements is updated until the value of the objective function converges to within a preset threshold. The stress state of all voxel elements after iterative convergence is reconstructed in three-dimensional space, and a three-dimensional stress distribution cloud map inside the pile foundation is output.

3. The method for non-destructive testing of bridge pile foundation stress according to claim 2, characterized in that, Using the arrival time of the signal in the signal feature set, the theoretical propagation path and theoretical propagation time of the acoustic signal in each voxel unit are calculated using acoustic ray tracing technology, including: Based on the updated stress state of each voxel element in the current iteration step, the longitudinal wave velocity distribution of each voxel element is calculated based on the constitutive acoustoelastic model. Starting from the position of each transmitter in the acoustic signal transmitting array and ending from the position of the corresponding receiver in the acoustic signal receiving array, the acoustic wave propagation medium is modeled as a non-uniform medium with continuously varying wave speed. The fast-progression algorithm is applied to solve the process function equations in the non-uniform medium and calculate the wavefront arrival time field from each transmitter position to the entire computational region. Based on the wavefront arrival time field, the gradient descent method is used to trace back from each receiver position to the transmitter position to obtain each sound wave ray path, which is the theoretical propagation path. Along each of the theoretical propagation paths, the wave slack of each voxel unit along the path is integrated, where the wave slack is the reciprocal of the longitudinal wave velocity, and the integration result is the theoretical propagation time.

4. The method for non-destructive testing of bridge pile foundation stress according to claim 1, characterized in that, Based on the aforementioned three-dimensional stress distribution cloud map, the location and shape of stress concentration regions are identified, including: Spatial gradient calculation is performed on the three-dimensional stress distribution cloud map to obtain the stress gradient tensor of each spatial point; Calculate the magnitude of the stress gradient tensor and generate a stress gradient magnitude distribution map; In the stress gradient modulus distribution map, continuous spatial regions where the gradient modulus value exceeds a preset threshold are marked as candidate stress concentration regions. For each candidate stress concentration region, extract its three-dimensional geometric contour of stress distribution, and calculate the volume, triaxial length of the equivalent ellipsoid, and principal axis direction of the candidate stress concentration region; Based on the uniformity of stress distribution within the candidate stress concentration region and the discontinuity of stress gradient at its boundary, the final stress concentration region is selected from the candidate stress concentration regions. Record the geometric center coordinates, volume, morphological parameters, and principal axis direction of each stress concentration region as a description of its position and shape.

5. The method for non-destructive testing of bridge pile foundation stress according to claim 4, characterized in that, The calculation of the maximum principal stress value and its direction in the stress concentration region includes: Extract the stress tensor of each voxel element from all voxel elements contained in the stress concentration region; The stress tensor of each voxel element is decomposed into eigenvalues ​​to solve for the three principal stress values ​​and their corresponding principal directions of the voxel element. From all voxel elements in the stress concentration region, find the largest principal stress value and take the largest principal stress value as the maximum principal stress value of the stress concentration region. The principal direction corresponding to the voxel element with the maximum principal stress value is taken as the maximum principal stress direction of the stress concentration region. Within the stress concentration region, the spatial distribution consistency of the principal stress directions is statistically analyzed. If the consistency is higher than a set standard, the direction of the maximum principal stress is taken as the representative direction of the entire region; otherwise, the vector average value of the principal directions within the region is calculated as the representative direction.

6. The method for non-destructive testing of bridge pile foundation stress according to claim 1, characterized in that, The location, shape, maximum principal stress value, and direction of the stress concentration region are compared with the pile foundation structure design parameters to output the pile foundation structure integrity assessment results, including: Read the structural design parameters of the bridge pile foundation, including the pile foundation's geometric dimensions, concrete grade, design bearing capacity, and allowable stress threshold; The location of the stress concentration area is compared with the key structural parts of the pile foundation to determine whether the stress concentration area appears at the pile top, pile bottom, abrupt change in cross section, or an area with historical damage. The maximum principal stress value is compared with the allowable stress threshold to determine whether stress exceeds the limit. The morphological parameters of the stress concentration region are matched with a typical damage mode database to identify potential damage types. The typical damage mode database contains stress distribution morphological features corresponding to cracks, voids, spalling, and rebar yielding. Based on the location comparison results, stress over-limit judgment results, and potential damage type identification results, a qualitative structural integrity level and a quantitative risk assessment index are generated according to the preset assessment rules, which together constitute the pile foundation structural integrity assessment result.

7. The method for non-destructive testing of bridge pile foundation stress according to claim 2, characterized in that, The constitutive acoustoelastic model is constructed and calibrated in the following manner: Prepare standard concrete specimens with the same materials, mix proportions, and curing conditions as bridge pile foundations; Known uniaxial, biaxial, and triaxial stress states were applied to the standard concrete specimens in the laboratory, and the stress states covered the expected range of working stresses in the pile foundation. Under each known stress state, the longitudinal and transverse wave velocities of the standard concrete specimens were measured in multiple directions using the same acoustic transducer as used in field testing. A polynomial fitting function is established with stress tensor components as independent variables and longitudinal wave velocity change rate and transverse wave velocity change rate as dependent variables. The order of the polynomial fitting function is determined based on the goodness of fit of the experimental data. Using wave velocity measurement data under all known stress states, the coefficients of each term in the polynomial fitting function are determined through multiple regression analysis; The polynomial fitting function with determined coefficients is defined as the constitutive acoustoelastic model describing the concrete material of bridge pile foundations.

8. The method for non-destructive testing of bridge pile foundation stress according to claim 2, characterized in that, The method further includes a verification step for the calculation results of the improved stress field inversion algorithm: During the construction of bridge pile foundations, a small number of fiber optic strain sensors are pre-embedded inside the pile body to form discrete strain monitoring points. During the same time period of acoustic nondestructive testing, the strain measurement values ​​of all pre-embedded fiber optic strain sensors are read simultaneously. Based on the theory of mechanics of materials, the strain measurement value is converted into the stress measurement value at the sensor location; From the three-dimensional stress distribution cloud map, extract the calculated stress value corresponding to the position of the fiber grating strain sensor; Compare the measured stress value with the calculated stress value, and calculate the average relative error and the maximum relative error between the measured stress value and the calculated stress value; If both the average relative error and the maximum relative error are within an acceptable range, then the calculation results of the improved stress field inversion algorithm are verified to be reliable. Otherwise, calibrate the parameters in the constitutive acoustoelastic model and recalculate the inversion.

9. A non-destructive testing system for bridge pile foundation stress, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the non-destructive testing method for bridge pile foundation stress as described in any one of claims 1 to 8.

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