Pile foundation integrity detection method, device, equipment and storage medium

Abstract

The present invention discloses a pile foundation integrity detection method, comprising: applying a variable amplitude acoustic wave excitation sequence to the pile foundation structure, collecting acoustic response signals under different excitation amplitudes, and obtaining nonlinear dynamic response data reflecting the rheological properties of the material; performing dispersion analysis and hysteresis characteristic analysis on the nonlinear dynamic response data, extracting the stress memory dissipation coefficient and the nonlinear memory capacity index, and establishing a time-varying characteristic curve; determining the location and type of the defect based on the abnormal change interval and memory capacity decay rate of the time-varying characteristic curve, and obtaining the evolution state parameters of the defect; calculating the material degradation rate and the structural damage development rate based on the historical change trend of the evolution state parameters, predicting the pile foundation structure health status index, and forming a full life cycle performance evolution curve. The present invention establishes a detection method that takes into account the rheological properties of the material through stress memory activation and nonlinear dynamic response analysis, which can accurately evaluate the performance evolution law of the pile foundation throughout its life cycle.

Description

Technical Field

[0001] The present invention relates to the technical field of pile foundation quality detection, and in particular to a pile foundation integrity detection method, device, equipment and storage medium. Background Art

[0002] Pile foundations, as an important form of deep foundation, play a critical load-bearing role in large-scale projects such as buildings, bridges, and offshore platforms by transferring superstructure loads to deep, solid soil or rock layers. Because pile foundation components are buried underground, their construction is susceptible to geological conditions, construction techniques, and environmental factors, potentially leading to quality defects such as broken piles, necking, and mud inclusions. These defects are often invisible to the naked eye, and once they occur, they can pose structural safety risks and cause serious engineering accidents. Therefore, pile foundation integrity testing is crucial for ensuring project quality and structural safety.

[0003] Currently, pile foundation integrity testing primarily utilizes techniques such as low-strain reflection wave testing, acoustic transmission testing, and core drilling. Acoustic testing has become the mainstream method due to its non-destructive and efficient nature. However, these testing methods are generally based on static testing principles, simplifying the pile body into an ideal elastic body model for analysis. In practical engineering, concrete, as a complex rheological material, undergoes microstructural evolution under long-term loads, exhibiting significant time-varying mechanical properties. In particular, for aged pile foundations with many years of service, the internal microcrack network, porosity distribution, and interfacial properties undergo progressive changes over time. These rheological effects directly influence the propagation characteristics of acoustic waves within the pile, including wave velocity, attenuation coefficient, and dispersion. Because traditional testing methods ignore these time-varying effects, systematic errors exist in the assessment of aged pile foundations, failing to accurately reflect the true performance evolution of the pile foundation throughout its lifecycle. This presents an urgent technical challenge in the field of pile foundation integrity testing. Summary of the Invention

[0004] The main purpose of the present invention is to solve the technical problems that the existing pile foundation integrity detection methods ignore the rheological properties of the material and cannot accurately evaluate the performance evolution of the pile foundation throughout its life cycle.

[0005] A first aspect of the present invention provides a pile foundation integrity detection method, the pile foundation integrity detection method comprising:

[0006] Applying a variable amplitude acoustic wave excitation sequence to the pile foundation structure, the variable amplitude acoustic wave excitation sequence including an initial low amplitude excitation and an incremental amplitude excitation, collecting acoustic response signals of the pile foundation structure at different excitation amplitudes, and obtaining nonlinear dynamic response data reflecting the rheological properties of the material;

[0007] Performing dispersion analysis and hysteresis analysis on the nonlinear dynamic response data, extracting the stress memory dissipation coefficient and nonlinear memory capacity index in the acoustic response signal, and establishing a time-varying characteristic curve characterizing the evolution of the microstructure of the pile foundation material;

[0008] According to the abnormal change interval and memory capacity decay rate of the time-varying characteristic curve, combined with the spatial distribution law of the memory characteristics, the defect location and type in the pile foundation structure are determined, and the evolution state parameters of the defect are obtained;

[0009] Based on the historical change trend of the evolution state parameters, the material degradation rate and the structural damage development rate are calculated, the health status index of the pile foundation structure at different time nodes is predicted, and a full life cycle performance evolution curve is formed.

[0010] Preferably, applying a variable amplitude acoustic wave excitation sequence to the pile foundation structure, the variable amplitude acoustic wave excitation sequence including an initial low amplitude excitation and an incremental amplitude excitation, collecting acoustic response signals of the pile foundation structure at different excitation amplitudes, and obtaining nonlinear dynamic response data reflecting the rheological properties of the material, includes:

[0011] The pile foundation structure is divided into the pile top connection section, the pile body friction section, and the pile end support section along the depth direction of the pile foundation. Initial low-amplitude longitudinal compression wave excitation and transverse shear wave excitation are applied to each depth section to obtain the pre-analysis acoustic response signal of each depth section.

[0012] Performing a resonant frequency analysis on the pre-analyzed acoustic response signal of each depth segment, determining a first longitudinal compression wave excitation frequency range based on the vertical stress state of the pile top connection segment, determining a first transverse shear wave excitation frequency range based on the transverse stress state of the pile top connection segment, determining a second longitudinal compression wave excitation frequency range based on the vertical friction resistance distribution of the pile body friction segment, determining a second transverse shear wave excitation frequency range based on the lateral earth pressure distribution of the pile body friction segment, and determining a third longitudinal compression wave excitation frequency range and a third transverse shear wave excitation frequency range based on the end soil reaction force of the pile end support segment;

[0013] Applying longitudinal compression wave excitation and transverse shear wave excitation in a spiral progressive manner to each depth segment according to the determined excitation frequency range, wherein the spiral progressive manner includes increasing the amplitude by a preset step size and rotating the phase by a preset angle, and collecting the dynamic acoustic response signal at each progressive point;

[0014] The complex transfer function and inner-loop dissipation coefficient of the dynamic acoustic response signal of each depth segment at different progressive points are calculated respectively. According to the nonlinear distortion degree of the complex transfer function and the cumulative change rate of the inner-loop dissipation coefficient, the nonlinear dynamic response data characterizing the rheological properties of the material are formed.

[0015] Preferably, according to the determined excitation frequency range, longitudinal compression wave excitation and transverse shear wave excitation are applied to each depth segment in a spiral progressive manner, wherein the spiral progressive manner includes increasing the amplitude by a preset step size and rotating the phase by a preset angle, and collecting the dynamic acoustic response signal of each progressive point, including:

[0016] According to the stiffness characteristics and stress transfer mode of the pile top connection section, a first set of spiral progressive parameters is set, the longitudinal compression wave amplitude increment is set to a first preset step length, the transverse shear wave amplitude increment is set to a second preset step length, and the phase rotation angle increment is set to a first preset angle, and a first set of dynamic acoustic response signals is collected at the pile top connection section according to the first set of spiral progressive parameters;

[0017] According to the pile-soil interaction characteristics of the pile body friction section, the first set of spiral progressive parameters are adjusted, the longitudinal compression wave amplitude increment is set to correspond to the soil lateral resistance distribution to a third preset step length, the transverse shear wave amplitude increment is set to correspond to the soil deformation characteristics to a fourth preset step length, and the phase rotation angle increment is set to correspond to the stress wave diffraction effect to a second preset angle, and a second set of dynamic acoustic response signals are collected;

[0018] Based on the end constraint condition of the pile end support section, the longitudinal components of the first set of spiral progressive parameters are decomposed radially to form an annular excitation parameter set, the annular excitation parameter set including radial compression wave amplitude increment and radial shear wave amplitude increment, and a third set of dynamic acoustic response signals is collected;

[0019] The first group of dynamic acoustic response signals, the second group of dynamic acoustic response signals, and the third group of dynamic acoustic response signals are combined and processed to obtain a dynamic acoustic response signal for each progressive point.

[0020] Preferably, the dispersion analysis and hysteresis characteristic analysis are performed on the nonlinear dynamic response data, the stress memory dissipation coefficient and the nonlinear memory capacity index in the acoustic response signal are extracted, and a time-varying characteristic curve characterizing the evolution of the microstructure of the pile foundation material is established, including:

[0021] According to the vertical stress distribution law of the pile foundation depth section, the nonlinear dynamic response data is divided into a compression main control area, a shear main control area and a composite control area according to depth, and the nonlinear dynamic response data of each area is subjected to frequency band decomposition to obtain waveform propagation mode data of each area;

[0022] Based on the waveform propagation mode data of the compression main control area, the reflection coefficient and transmission coefficient of the longitudinal compression wave at the interface between concrete and steel bars are calculated, and the hysteresis characteristics of the reflection coefficient and transmission coefficient are analyzed to obtain the first stress memory dissipation coefficient of the interface;

[0023] Based on the waveform propagation mode data of the shear main control area, the energy attenuation characteristics of the transverse shear wave at the pile-soil interface are analyzed, and the influence coefficient of soil damping on shear wave propagation is calculated. The influence coefficient is correlated with the nonlinear dynamic response data to obtain the first nonlinear memory capacity index;

[0024] The coupling effect between the longitudinal compression wave and the transverse shear wave is calculated for the waveform propagation mode data of the composite control area. The coupling effect is combined with the first stress memory dissipation coefficient and the first nonlinear memory capacity index of the interface to obtain the second stress memory dissipation coefficient and the second nonlinear memory capacity index of the interface.

[0025] According to the first stress memory dissipation coefficient of the interface, the second stress memory dissipation coefficient of the interface, the first nonlinear memory capacity index and the second nonlinear memory capacity index, a memory characteristic function is constructed according to the depth distribution to generate a time-varying characteristic curve characterizing the evolution of the microstructure of the pile foundation material.

[0026] Preferably, determining the defect location and type in the pile foundation structure based on the abnormal change interval and memory capacity decay rate of the time-varying characteristic curve and combining the spatial distribution law of the memory characteristics to obtain the evolution state parameters of the defect includes:

[0027] Calculating the first-order derivative and second-order derivative of the time-varying characteristic curve in the depth direction, performing run analysis on the first-order derivative to obtain a trend mutation point, performing singular value analysis on the second-order derivative to obtain a curvature mutation point, determining the combined distribution of the trend mutation point and the curvature mutation point as an abnormal change interval, extracting the memory capacity decay rate and memory feature spatial distribution data within each abnormal change interval, and obtaining memory feature abnormal segment data;

[0028] Performing multi-scale decomposition on the memory feature abnormal section data to extract high-frequency oscillation features, medium-frequency attenuation features, and low-frequency displacement features, matching the combined pattern of the high-frequency oscillation features, medium-frequency attenuation features, and low-frequency displacement features with the memory feature spectrum of the defect type to obtain defect type identification parameters;

[0029] Classifying the defect type identification parameters according to the defect mechanics evolution law, calculating the displacement, cross-sectional change rate, wave impedance change rate, energy dissipation rate, and stress concentration factor at each depth position, determining an area where the parameter value is greater than a first threshold and the displacement is greater than a first displacement reference value as a fracture defect, determining an area where the parameter value is greater than a second threshold and the cross-sectional change rate is greater than a first cross-sectional change reference value as a necking defect, determining an area where the parameter value is greater than a third threshold and the wave impedance change rate is greater than a first impedance change reference value as a mud inclusion defect, determining an area where the parameter value is greater than a fourth threshold and the energy dissipation rate is greater than a first energy dissipation reference value as a loose defect, and determining an area where the parameter value is greater than a fifth threshold and the stress concentration factor is greater than the first stress concentration reference value as a crack defect, thereby obtaining defect position coordinates and defect type identification;

[0030] Calculating the time series characteristics of the memory capacity decay rate at the defect location coordinates, extracting the first-order difference sequence and the second-order difference sequence of the decay rate, calculating the mean of the first-order difference sequence as the defect expansion rate parameter, and calculating the mean of the second-order difference sequence as the defect expansion acceleration parameter, to obtain the defect expansion state vector;

[0031] According to the defect position coordinates and defect type identification, a stress field analysis is performed on the defect expansion state vector, the stress concentration coefficient and stress intensity factor around the defect are calculated, and the stress field analysis results are combined with the defect expansion state vector to form the defect evolution state parameters.

[0032] Preferably, the time series characteristics of the memory capacity decay rate at the defect position coordinates are calculated, the first-order difference sequence and the second-order difference sequence of the decay rate are extracted, the mean of the first-order difference sequence is calculated as the defect expansion rate parameter, and the mean of the second-order difference sequence is calculated as the defect expansion acceleration parameter to obtain the defect expansion state vector, including:

[0033] According to the soil permeability and groundwater level variation at the depth of the pile foundation, the memory capacity attenuation rate is corrected for environmental impacts to obtain an initial attenuation rate sequence, and the first-order difference value and the second-order difference value of the initial attenuation rate sequence are calculated;

[0034] Performing a depth-stratified cumulative effect analysis on the initial attenuation rate sequence, calculating a cumulative influence coefficient based on the overlying soil pressure, and performing cumulative effect correction on the first-order difference value and the second-order difference value to obtain a corrected first-order difference sequence and a corrected second-order difference sequence;

[0035] According to the vertical load transfer law of the pile foundation, the mean value of the modified first-order difference sequence is calculated as the defect growth rate parameter, and the mean value of the modified second-order difference sequence is calculated as the defect growth acceleration parameter;

[0036] The defect growth rate parameter and the defect growth acceleration parameter are combined according to the depth distribution rule to obtain a defect growth state vector.

[0037] Preferably, the calculation of the material degradation rate and the structural damage development rate based on the historical change trend of the evolution state parameters, the prediction of the health status index of the pile foundation structure at different time nodes, and the formation of the life cycle performance evolution curve include:

[0038] Calculating the stress concentration factor change rate and the stress intensity factor change rate based on historical data of the defect expansion state vector and stress field analysis results in the evolution state parameters to form a stress parameter change rate sequence, performing wavelet decomposition on the stress parameter change rate sequence to extract long-term trend components and periodic fluctuation components to obtain material performance evolution characteristic data;

[0039] Performing piecewise linear regression on the long-term trend component in the material performance evolution characteristic data, calculating the regression slope and performing jump point detection, comparing the regression slope value with the standard nonlinear parameter of the pile foundation material to obtain the material degradation rate, performing spectral analysis on the periodic fluctuation component in the material performance evolution characteristic data, and extracting the main frequency component as the degradation fluctuation correction value;

[0040] Performing spatial correlation analysis on the material degradation rate and the defect position coordinates in the evolution state parameters, calculating the defect distribution density and defect extension impact range of each depth segment, and calculating the damage accumulation rate in the depth direction in combination with the degradation fluctuation correction value to obtain the structural damage development rate;

[0041] Calculating the elastic modulus degradation coefficient, stress memory attenuation coefficient, and bearing capacity degradation coefficient of the pile foundation structure based on the material degradation rate and the structural damage development rate, performing principal component analysis on the elastic modulus degradation coefficient, stress memory attenuation coefficient, and bearing capacity degradation coefficient, extracting the principal component coefficient and principal component contribution rate, and obtaining a health status index;

[0042] The historical evolution data of the health status index are decomposed into time series, and the trend component, periodic component and fluctuation component are extracted. The extended prediction value of the trend component is calculated, the periodic extrapolation value of the periodic component is calculated, and the fluctuation range value of the fluctuation component is calculated. The extended prediction value of the trend component, the periodic extrapolation value of the periodic component and the fluctuation range value of the fluctuation component are combined to obtain the health status prediction value of the future time node, and form a full life cycle performance evolution curve.

[0043] A second aspect of the present invention provides a pile foundation integrity detection device, the pile foundation integrity detection device comprising:

[0044] An excitation acquisition module is used to apply a variable amplitude acoustic wave excitation sequence to the pile foundation structure, wherein the variable amplitude acoustic wave excitation sequence includes an initial low amplitude excitation and an incremental amplitude excitation, collects the acoustic response signals of the pile foundation structure under different excitation amplitudes, and obtains nonlinear dynamic response data reflecting the rheological properties of the material;

[0045] a memory feature extraction module for performing dispersion analysis and hysteresis characteristic analysis on the nonlinear dynamic response data, extracting the stress memory dissipation coefficient and nonlinear memory capacity index in the acoustic response signal, and establishing a time-varying characteristic curve characterizing the evolution of the microstructure of the pile foundation material;

[0046] a defect identification module for determining the location and type of defects in the pile foundation structure based on the abnormal change interval and memory capacity decay rate of the time-varying characteristic curve, combined with the spatial distribution law of the memory characteristics, and obtaining the evolution state parameters of the defects;

[0047] The health assessment module is used to calculate the material degradation rate and the structural damage development rate based on the historical change trend of the evolution state parameters, predict the health status index of the pile foundation structure at different time nodes, and form a full life cycle performance evolution curve.

[0048] A third aspect of the present invention provides a pile foundation integrity detection device, comprising: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a circuit; the at least one processor calls the instructions in the memory so that the pile foundation integrity detection device executes the steps of the above-mentioned pile foundation integrity detection method.

[0049] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the above-mentioned pile foundation integrity detection method.

[0050] The technical solution provided in the embodiments of this application excites the pile foundation structure through a variable-amplitude acoustic excitation sequence. This excitation method includes two stages: initial low-amplitude excitation and incremental amplitude excitation. Low-amplitude excitation can activate the stress memory information stored within the material without causing new damage, while incremental amplitude excitation can guide the material's response characteristics to different stress levels. This combined excitation method enables the detection process to fully capture the nonlinear dynamic response characteristics of the material.

[0051] Dispersion analysis and hysteresis analysis of the acquired nonlinear dynamic response data can be used to extract the stress memory dissipation coefficient and nonlinear memory capacity indicators. Dispersion analysis reflects the differences in wave propagation speed at different frequencies, which are closely related to the material's microstructural state. Hysteresis analysis reveals the energy loss mechanism of the material during cyclic loading, which directly reflects the material's memory effect. By combining these two indicators, a time-varying characteristic curve is established to characterize the evolution of the material's microstructure.

[0052] Based on the abnormal variation range and memory capacity decay rate of the time-varying characteristic curve, combined with the spatial distribution of the memory characteristics, the location and type of defects in the pile foundation structure can be determined. Abnormal changes in the time-varying characteristic curve often correspond to sudden changes in material properties, the memory capacity decay rate reflects the degradation trend of material properties over time, and the spatial distribution pattern reveals the development pattern of the defect. This multi-dimensional analysis method makes defect identification more accurate and reliable.

[0053] Based on the historical trends of the acquired evolutionary state parameters, the material degradation rate and structural damage development rate are calculated, enabling prediction of the health index of the pile foundation structure at different time points. This prediction is no longer limited to static test results, but fully considers the time-varying characteristics of material properties, enabling a more accurate assessment of the performance evolution of the pile foundation structure throughout its lifecycle. This approach overcomes the systematic errors inherent in traditional testing methods when evaluating the performance of aged pile foundations. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0055] Figure 1 A schematic diagram of an embodiment of a pile foundation integrity detection method according to an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of an embodiment of a pile foundation integrity detection device in an embodiment of the present invention;

[0057] Figure 3 Schematic diagram of an embodiment of a pile foundation integrity detection device in an embodiment of the present invention.

[0058] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), such directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0061] In addition, the descriptions of "first", "second", etc. in the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, and must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0062] An embodiment of the present application provides a pile foundation integrity detection method. Figure 1 A flowchart of a pile foundation integrity detection method provided in one embodiment of the present application. In this embodiment, the method includes:

[0063] See also Figure 1 , applying a variable amplitude acoustic wave excitation sequence to the pile foundation structure, wherein the variable amplitude acoustic wave excitation sequence includes an initial low amplitude excitation and an incremental amplitude excitation, collecting acoustic response signals of the pile foundation structure under different excitation amplitudes, and obtaining nonlinear dynamic response data reflecting the rheological properties of the material;

[0064] In one embodiment of the present invention, applying a variable amplitude acoustic wave excitation sequence to the pile foundation structure, the variable amplitude acoustic wave excitation sequence including an initial low amplitude excitation and an incremental amplitude excitation, collecting acoustic response signals of the pile foundation structure at different excitation amplitudes, and obtaining nonlinear dynamic response data reflecting the rheological properties of the material, includes:

[0065] The pile foundation structure is divided into the pile top connection section, the pile body friction section, and the pile end support section along the depth direction of the pile foundation. Initial low-amplitude longitudinal compression wave excitation and transverse shear wave excitation are applied to each depth section to obtain the pre-analysis acoustic response signal of each depth section.

[0066] Performing a resonant frequency analysis on the pre-analyzed acoustic response signal of each depth segment, determining a first longitudinal compression wave excitation frequency range based on the vertical stress state of the pile top connection segment, determining a first transverse shear wave excitation frequency range based on the transverse stress state of the pile top connection segment, determining a second longitudinal compression wave excitation frequency range based on the vertical friction resistance distribution of the pile body friction segment, determining a second transverse shear wave excitation frequency range based on the lateral earth pressure distribution of the pile body friction segment, and determining a third longitudinal compression wave excitation frequency range and a third transverse shear wave excitation frequency range based on the end soil reaction force of the pile end support segment;

[0067] Applying longitudinal compression wave excitation and transverse shear wave excitation in a spiral progressive manner to each depth segment according to the determined excitation frequency range, wherein the spiral progressive manner includes increasing the amplitude by a preset step size and rotating the phase by a preset angle, and collecting the dynamic acoustic response signal at each progressive point;

[0068] The complex transfer function and inner-loop dissipation coefficient of the dynamic acoustic response signal of each depth segment at different progressive points are calculated respectively. According to the nonlinear distortion degree of the complex transfer function and the cumulative change rate of the inner-loop dissipation coefficient, the nonlinear dynamic response data characterizing the rheological properties of the material are formed.

[0069] The following is a detailed description of the steps involved in the above embodiment:

[0070] The process of dividing the pile foundation structure into the pile top connection section, pile friction section, and pile end support section along the depth of the pile foundation is achieved by first determining the pile foundation's geometric dimensions and soil distribution through coring and lateral stress testing. The pile top connection section refers to the top area of ​​the pile foundation that connects to the building foundation or cap, typically accounting for 10%-15% of the total length of the pile foundation. The pile friction section refers to the middle area that transmits load through friction with the surrounding soil, accounting for 60%-75% of the total length of the pile foundation. The pile end support section refers to the bottom area that directly contacts the bearing stratum and transmits vertical pressure, accounting for 15%-20% of the total length of the pile foundation. After the division is completed, an ultrasonic testing instrument is used to deploy a sensor array at each depth section. The sensor array includes at least three sensors perpendicular to the pile axis and at least three sensors parallel to the pile axis. An initial low-amplitude (usually 0.1MPa-0.5MPa) longitudinal compression wave excitation and transverse shear wave excitation are applied to each depth segment. The longitudinal compression wave excitation is generated by a piezoelectric vibrator perpendicular to the axis of the pile body, and the transverse shear wave excitation is generated by a piezoelectric vibrator parallel to the axis of the pile body. The receiver collects the acoustic response signal of each depth segment and records it through a high-speed data acquisition card to form a pre-analysis acoustic response signal database. In practical applications, for example, when testing a 25-meter-long prestressed concrete pile, 0-3.5 meters can be divided into the pile top connection section, 3.5-18 meters can be divided into the pile body friction section, and 18-25 meters can be divided into the pile end support section. The purpose of this division is to use differentiated detection parameters based on the stress characteristics and environmental conditions of different depth segments of the pile foundation to improve detection accuracy.

[0071] The resonant frequency analysis of the pre-analyzed acoustic response signal for each depth segment is implemented by first performing a spectral analysis of the pre-analyzed acoustic response signal using a fast Fourier transform (FFT) to identify the primary resonant frequency components for each depth segment. For the pile top connection, strain gauges are used to measure the axial compressive stress (caused by vertical loads transmitted from the superstructure, typically 1 MPa-5 MPa) and bending stress (caused by wind loads or eccentric loads, typically 0.5 MPa-2 MPa) at the pile top. The stress-frequency response curves are used to determine the first longitudinal compression wave excitation frequency range (typically 5 kHz-15 kHz). Transverse shear stress (caused by horizontal loads, typically 0.3 MPa-1 MPa) is measured at the pile top to determine the first transverse shear wave excitation frequency range (typically 3 kHz-8 kHz). For the pile friction section, the vertical friction force distribution (the friction force generated between the pile surface and the surrounding soil, typically 0.02 MPa-0.1 MPa) is measured using a pile-soil interface stress sensor to determine the second longitudinal compression wave excitation frequency range (typically 3 kHz-10 kHz). The lateral soil pressure distribution (the horizontal compressive force exerted by the surrounding soil on the pile, typically 0.01 MPa-0.08 MPa) is measured to determine the second transverse shear wave excitation frequency range (typically 2 kHz-6 kHz). For the pile tip support section, a pressure sensor is used to measure the end soil reaction force (the support force between the pile tip and the bearing layer, typically 1.5 MPa-8 MPa) to determine the third longitudinal compression wave excitation frequency range (typically 8 kHz-20 kHz) and the third transverse shear wave excitation frequency range (typically 4 kHz-12 kHz). For example, for a pile foundation in medium-dense sand, the longitudinal compression wave excitation frequency range for the pile top connection section can be determined to be 8 kHz-12 kHz. This step takes into account the correlation between the stress state at different depths of the pile foundation and the acoustic wave propagation characteristics, which helps to improve the signal-to-noise ratio and sensitivity of subsequent detection.

[0072] The implementation process of applying spiral progressive acoustic wave excitation according to a determined excitation frequency range is to use a programmable digital waveform generator and a power amplifier to apply acoustic wave excitation to each depth segment according to a preset spiral progressive scheme. The spiral progressive method refers to the simultaneous increase of the acoustic wave amplitude and the rotation phase angle to form a three-dimensional spiral parameter trajectory. Specifically, the longitudinal compression wave amplitude starts from the initial value (usually 0.2MPa) and increases according to a preset step size (usually 0.1MPa-0.3MPa); the transverse shear wave amplitude starts from the initial value (usually 0.1MPa) and increases according to a preset step size (usually 0.05MPa-0.15MPa); at the same time, the phase angle starts from 0° and rotates according to a preset angle (usually 15°-45°). At each progressive point, the exciter emits an acoustic wave signal with a specific amplitude and phase, and the receiver collects the corresponding dynamic acoustic response signal. For example, for the pile friction section, the longitudinal compression amplitude increment can be set to 0.15 MPa, the transverse shear amplitude increment to 0.08 MPa, and the phase rotation angle increment to 30°, forming a spiral parameter trajectory with 20 progressive points. This spiral progressive method can fully capture the nonlinear dynamic response characteristics of the material and provide richer information than the traditional single parameter incremental method.

[0073] The calculation of the characteristic parameters of the dynamic acoustic response signal is achieved by processing the acquired dynamic acoustic response signal using a digital signal processor. First, the complex transfer function H(ω) is calculated for each depth segment at different progressive points. The complex transfer function represents the amplitude ratio and phase difference between the output signal and the input signal and is obtained by dividing the output signal spectrum by the input signal spectrum. Next, the inner-loop dissipation coefficient ε is calculated. The inner-loop dissipation coefficient represents the proportion of energy consumed by the microstructural movement within the concrete material during the acoustic loading-unloading cycle and is calculated as the ratio of the area of ​​the feedback loop enclosed by the stress-strain curve to the area under the loading curve. Next, the nonlinear distortion δ of the complex transfer function is analyzed. The nonlinear distortion δ represents the ratio of the higher-order harmonic components to the fundamental frequency component in the response signal caused by material nonlinearity and is calculated as the ratio of the sum of the higher-order harmonic amplitudes to the fundamental frequency amplitude. Finally, the cumulative change rate λ of the inner-loop dissipation coefficient is analyzed. The cumulative change rate represents the trend of change of the inner-loop dissipation coefficient with increasing excitation amplitude and is calculated by dividing the difference in the inner-loop dissipation coefficient between consecutive progressive points by the amplitude increment. For example, in healthy concrete piles, the nonlinear distortion is typically less than 5%, and the cumulative rate of change of the inner ring dissipation coefficient is typically less than 0.02 / MPa. However, in areas with microcracks, these two parameters can reach 15% and 0.08 / MPa, respectively. By analyzing these parameters, nonlinear dynamic response data representing the material's rheological properties is generated, providing a scientific basis for subsequent defect identification. This data processing method surpasses traditional linear acoustic analysis and can sensitively capture the evolution of concrete microstructure.

[0074] In one embodiment of the present invention, the method of applying longitudinal compression wave excitation and transverse shear wave excitation in a spiral progressive manner to each depth segment according to the determined excitation frequency range, wherein the spiral progressive manner includes increasing the amplitude by a preset step size and rotating the phase by a preset angle, and collecting the dynamic acoustic response signal at each progressive point, includes:

[0075] According to the stiffness characteristics and stress transfer mode of the pile top connection section, a first set of spiral progressive parameters is set, the longitudinal compression wave amplitude increment is set to a first preset step length, the transverse shear wave amplitude increment is set to a second preset step length, and the phase rotation angle increment is set to a first preset angle, and a first set of dynamic acoustic response signals is collected at the pile top connection section according to the first set of spiral progressive parameters;

[0076] According to the pile-soil interaction characteristics of the pile body friction section, the first set of spiral progressive parameters are adjusted, the longitudinal compression wave amplitude increment is set to correspond to the soil lateral resistance distribution to a third preset step length, the transverse shear wave amplitude increment is set to correspond to the soil deformation characteristics to a fourth preset step length, and the phase rotation angle increment is set to correspond to the stress wave diffraction effect to a second preset angle, and a second set of dynamic acoustic response signals are collected;

[0077] Based on the end constraint condition of the pile end support section, the longitudinal components of the first set of spiral progressive parameters are decomposed radially to form an annular excitation parameter set, the annular excitation parameter set including radial compression wave amplitude increment and radial shear wave amplitude increment, and a third set of dynamic acoustic response signals is collected;

[0078] The first group of dynamic acoustic response signals, the second group of dynamic acoustic response signals, and the third group of dynamic acoustic response signals are combined and processed to obtain a dynamic acoustic response signal for each progressive point.

[0079] The following is a detailed description of the steps involved in the above embodiment:

[0080] Please continue reading Figure 1 , performing dispersion analysis and hysteresis characteristic analysis on the nonlinear dynamic response data, extracting the stress memory dissipation coefficient and nonlinear memory capacity index in the acoustic response signal, and establishing a time-varying characteristic curve characterizing the evolution of the microstructure of the pile foundation material;

[0081] In one embodiment of the present invention, the dispersion analysis and hysteresis characteristic analysis of the nonlinear dynamic response data are performed to extract the stress memory dissipation coefficient and the nonlinear memory capacity index in the acoustic response signal, and to establish a time-varying characteristic curve characterizing the evolution of the microstructure of the pile foundation material, including:

[0082] According to the vertical stress distribution law of the pile foundation depth section, the nonlinear dynamic response data is divided into a compression main control area, a shear main control area and a composite control area according to depth, and the nonlinear dynamic response data of each area is subjected to frequency band decomposition to obtain waveform propagation mode data of each area;

[0083] Based on the waveform propagation mode data of the compression main control area, the reflection coefficient and transmission coefficient of the longitudinal compression wave at the interface between concrete and steel bars are calculated, and the hysteresis characteristics of the reflection coefficient and transmission coefficient are analyzed to obtain the first stress memory dissipation coefficient of the interface;

[0084] Based on the waveform propagation mode data of the shear main control area, the energy attenuation characteristics of the transverse shear wave at the pile-soil interface are analyzed, and the influence coefficient of soil damping on shear wave propagation is calculated. The influence coefficient is correlated with the nonlinear dynamic response data to obtain the first nonlinear memory capacity index;

[0085] The coupling effect between the longitudinal compression wave and the transverse shear wave is calculated for the waveform propagation mode data of the composite control area. The coupling effect is combined with the first stress memory dissipation coefficient and the first nonlinear memory capacity index of the interface to obtain the second stress memory dissipation coefficient and the second nonlinear memory capacity index of the interface.

[0086] According to the first stress memory dissipation coefficient of the interface, the second stress memory dissipation coefficient of the interface, the first nonlinear memory capacity index and the second nonlinear memory capacity index, a memory characteristic function is constructed according to the depth distribution to generate a time-varying characteristic curve characterizing the evolution of the microstructure of the pile foundation material.

[0087] The following is a detailed description of the steps involved in the above embodiment:

[0088] The process of dividing the nonlinear dynamic response data into regions based on the vertical stress distribution within the pile foundation depth is achieved by measuring the vertical stress distribution along the depth of the pile foundation using a strain gauge array and analyzing the dominant stress directions through a data processing system. The compression-dominant region refers to the area in the pile foundation where the vertical compressive stress is significantly greater than the transverse stress, typically located in the pile top connection section and the upper region. The shear-dominant region refers to the area where transverse shear stress dominates, typically located at the friction interface between the pile shaft and the soil. The combined-dominant region refers to the area where compressive and shear stresses act together and couple with each other, typically located in the pile end support section. The nonlinear dynamic response data from each region is then subjected to multi-scale frequency band decomposition using wavelet transform, decomposing the data into three sub-bands: high-frequency band (above 10kHz), mid-frequency band (3kHz-10kHz), and low-frequency band (below 3kHz). The high-frequency band primarily reflects microcracks and interface scattering characteristics, the mid-frequency band primarily reflects the overall elastic deformation characteristics of the material, and the low-frequency band primarily reflects the macroscopic deformation characteristics of the structure. By combining the characteristic information of each frequency band, waveform propagation mode data for each region is obtained. For example, in a 30-meter-long pile foundation, the 0-5-meter pile top section is primarily a compression-controlled area, the 5-25-meter pile body section is primarily a shear-controlled area, and the 25-30-meter pile end section is primarily a combined-controlled area. This zoning method considers the stress distribution characteristics of the pile foundation during load-bearing, allowing for targeted analysis of the material response characteristics of different areas and improving detection accuracy.

[0089] The analysis of the waveform propagation pattern data in the compression main control area begins by calculating the reflection and transmission coefficients of the longitudinal compression wave at the concrete-rebar interface using frequency-domain reflectometry. The reflection coefficient represents the proportion of sound wave energy reflected at the interface, while the transmission coefficient represents the proportion of sound wave energy that continues to propagate through the interface. The specific calculation process involves transmitting a longitudinal compression wave signal, receiving the reflected and transmitted waves, and calculating the amplitude ratio of the reflected wave to the incident wave to obtain the reflection coefficient, while calculating the amplitude ratio of the transmitted wave to the incident wave to obtain the transmission coefficient. These two coefficients are then subjected to a hysteresis analysis. Specifically, during the increasing and decreasing cycles of the acoustic excitation amplitude, the changes in the reflection and transmission coefficients are recorded. The ratio of the hysteresis loop area to the total area under the closed curve is calculated to obtain the interface first stress memory dissipation coefficient. The interface first stress memory dissipation coefficient characterizes the "memory effect" formed at the concrete-rebar interface under the action of historical stresses. A larger value of this coefficient indicates more severe microscopic damage to the interface. For example, the stress memory dissipation coefficient of a healthy concrete-rebar interface is typically in the range of 0.02-0.05, while in areas with interfacial debonding, it can reach 0.15-0.25. This analytical method transcends the limitations of traditional testing, which focuses solely on transient impedance changes, and can identify early signs of interface degradation, providing critical data for analyzing material performance degradation trends.

[0090] The analysis of waveform propagation pattern data in the shear-dominant region utilizes a bidirectional shear wave measurement system to analyze the energy attenuation characteristics of transverse shear waves at the pile-soil interface. First, a transverse shear wave of a specific frequency is transmitted into the pile. Receivers placed at different depths record the waveform amplitude attenuation and calculate the energy attenuation rate per unit propagation distance. The influence of soil damping on shear wave propagation is then considered. The dynamic viscous damping ratio of the surrounding soil is measured using a soil dynamic parameter tester. Combined with the shear wave attenuation data at different depths, the influence coefficient of soil damping on shear wave propagation is calculated. This influence coefficient is then correlated with the nonlinear dynamic response data, and a regression equation is used to calculate the first nonlinear memory capacity index. This first nonlinear memory capacity index characterizes the strain "memory" capacity formed at the pile-soil interface under long-term stress. A higher index indicates more difficult interfacial strain release and more severe microstructural degradation. For example, for pile sections in sandy soils, the first nonlinear memory capacity index typically ranges from 0.10 to 0.30; for pile sections in clayey soils, it typically ranges from 0.25 to 0.45. This analysis method fully considers the complexity of pile-soil interaction and can reflect the differences in the service status of pile foundations under different soil conditions.

[0091] The analysis of waveform propagation mode data in the composite control area is implemented using a longitudinal-short wave coupling measurement system, which simultaneously transmits longitudinal compression waves and transverse shear waves. The composite wavefield data is recorded using a multi-channel data acquisition system. First, the coupling effect between the longitudinal compression wave and the transverse shear wave is calculated, i.e., the mutual conversion and energy exchange between the two waves during propagation. The specific calculation process involves analyzing the shear wave response components under longitudinal wave excitation and the longitudinal wave response components under shear wave excitation, and calculating the energy exchange rate between the two. This coupling effect is then combined with the previously obtained interface first stress memory dissipation coefficient and first nonlinear memory capacity index to obtain the interface second stress memory dissipation coefficient and second nonlinear memory capacity index using a multi-parameter fitting algorithm. The second stress memory dissipation coefficient characterizes the energy dissipation characteristics under composite stress states, while the second nonlinear memory capacity index characterizes the deformation recovery capacity under composite stress states. For example, in areas with intact pile tips, the second stress memory dissipation coefficient is typically in the range of 0.03-0.08, and the second nonlinear memory capacity index is typically in the range of 0.15-0.35. This analysis method can fully reflect the material response characteristics under complex stress states at the pile end and is of great significance for identifying the bearing capacity of the pile end.

[0092] The process for constructing a memory characteristic function based on the multiple indicators obtained above is to first arrange the interface first stress memory dissipation coefficient, the interface second stress memory dissipation coefficient, the first nonlinear memory capacity index, and the second nonlinear memory capacity index by depth, forming four depth distribution sequences. Principal component analysis is then used to extract the key features of these four sequences, and a weighted fusion algorithm is used to construct a memory characteristic function. This function reflects the distribution pattern of the material memory properties at each depth of the pile foundation. By analyzing the evolution of this function along the time axis, a time-varying characteristic curve is generated, characterizing the evolution of the pile foundation material's microstructure. The ordinate of the time-varying characteristic curve represents the normalized comprehensive memory characteristic value, and the abscissa represents depth. Comparing the curves obtained at different time points clearly demonstrates the temporal evolution of material properties. For example, in the early service life of a concrete pile, the time-varying characteristic curve is smooth and low. After five years of service, the curve fluctuates in localized areas, indicating that the material microstructure has begun to evolve. After 10 years of service, the curve exhibits peaks at multiple locations, indicating widespread material degradation. This time-varying characteristic curve provides an intuitive graphical representation for evaluating the performance of pile foundations throughout their lifecycle, making the test results easier to understand and apply.

[0093] Please continue reading Figure 1 , according to the abnormal change interval and memory capacity decay rate of the time-varying characteristic curve, combined with the spatial distribution law of the memory characteristics, determine the location and type of the defect in the pile foundation structure, and obtain the evolution state parameters of the defect;

[0094] In one embodiment of the present invention, determining the defect location and type in the pile foundation structure based on the abnormal change interval and memory capacity decay rate of the time-varying characteristic curve, combined with the spatial distribution law of the memory characteristics, and obtaining the evolution state parameters of the defect include:

[0095] Calculating the first-order derivative and second-order derivative of the time-varying characteristic curve in the depth direction, performing run analysis on the first-order derivative to obtain a trend mutation point, performing singular value analysis on the second-order derivative to obtain a curvature mutation point, determining the combined distribution of the trend mutation point and the curvature mutation point as an abnormal change interval, extracting the memory capacity decay rate and memory feature spatial distribution data within each abnormal change interval, and obtaining memory feature abnormal segment data;

[0096] Performing multi-scale decomposition on the memory feature abnormal section data to extract high-frequency oscillation features, medium-frequency attenuation features, and low-frequency displacement features, matching the combined pattern of the high-frequency oscillation features, medium-frequency attenuation features, and low-frequency displacement features with the memory feature spectrum of the defect type to obtain defect type identification parameters;

[0097] Classifying the defect type identification parameters according to the defect mechanics evolution law, calculating the displacement, cross-sectional change rate, wave impedance change rate, energy dissipation rate, and stress concentration factor at each depth position, determining an area where the parameter value is greater than a first threshold and the displacement is greater than a first displacement reference value as a fracture defect, determining an area where the parameter value is greater than a second threshold and the cross-sectional change rate is greater than a first cross-sectional change reference value as a necking defect, determining an area where the parameter value is greater than a third threshold and the wave impedance change rate is greater than a first impedance change reference value as a mud inclusion defect, determining an area where the parameter value is greater than a fourth threshold and the energy dissipation rate is greater than a first energy dissipation reference value as a loose defect, and determining an area where the parameter value is greater than a fifth threshold and the stress concentration factor is greater than the first stress concentration reference value as a crack defect, thereby obtaining defect position coordinates and defect type identification;

[0098] Calculating the time series characteristics of the memory capacity decay rate at the defect location coordinates, extracting the first-order difference sequence and the second-order difference sequence of the decay rate, calculating the mean of the first-order difference sequence as the defect expansion rate parameter, and calculating the mean of the second-order difference sequence as the defect expansion acceleration parameter, to obtain the defect expansion state vector;

[0099] According to the defect position coordinates and defect type identification, a stress field analysis is performed on the defect expansion state vector, the stress concentration coefficient and stress intensity factor around the defect are calculated, and the stress field analysis results are combined with the defect expansion state vector to form the defect evolution state parameters.

[0100] The following is a detailed description of the steps involved in the above embodiment:

[0101] The process of calculating the derivatives of the time-varying characteristic curve and identifying abnormal change intervals is implemented by numerically differentiating the time-varying characteristic curve using a numerical differentiation processor. First, the central difference method is used to calculate the first-order derivative of the time-varying characteristic curve in the depth direction. This is the rate of change of the time-varying characteristic value between two adjacent points, which characterizes the curve's trend. The second-order derivative, the rate of change of the first-order derivative, is then calculated to characterize the curve's curvature. Run analysis is performed on the calculated first-order derivative sequence. Run analysis counts the number of consecutively rising or falling points in the sequence. Points with a sudden decrease or increase in the run length are marked as trend abrupt changes. Singular value analysis is performed on the second-order derivative sequence. Second-order derivative values ​​exceeding this threshold are identified by setting a threshold, and the corresponding points are marked as curvature abrupt changes. Regions where trend abrupt changes or curvature abrupt changes are spatially overlapped or close to each other are defined as abnormal change intervals. For each abnormal change interval, the memory capacity decay rate within the interval is calculated (the rate of decrease in memory capacity per unit time). The spatial distribution of memory features, including the distribution density and distribution gradient of the eigenvalues, is then extracted. For example, at a depth of 15-18 meters in a pile foundation, run analysis of the first-order derivative revealed a sudden decrease in run length from six points to two. Simultaneously, the second-order derivative peaked at 2.5 at this location (compared to a normal range of 0.3-0.8), thus identifying this depth segment as an abnormal variation interval. This method for identifying abnormal intervals comprehensively considers both the trend and curvature of the time-varying characteristic curve, accurately capturing abnormal points on the curve and avoiding potential misjudgments caused by a single indicator.

[0102] The multi-scale decomposition of the memory signature anomaly segment data is achieved by using wavelet transform to perform frequency band decomposition. An appropriate wavelet basis function (such as the Daubechies wavelet) is selected to decompose the memory signature anomaly segment data into three sub-bands: high-frequency oscillation features (8-20 Hz), medium-frequency attenuation features (2-8 Hz), and low-frequency displacement features (0-2 Hz). High-frequency oscillation features represent the material response changes on short timescales, primarily reflecting microcracks and interface scattering effects; medium-frequency attenuation features represent energy decay behavior on medium timescales, primarily reflecting the material's damping properties and energy dissipation mechanisms; and low-frequency displacement features represent the overall deformation trend on long timescales, primarily reflecting changes in the overall structural stiffness. Characteristic parameters are extracted from each sub-band, including amplitude, frequency, phase, and attenuation rate. These characteristic parameters are combined to form a feature vector, which is then pattern-matched against a pre-established memory signature library of defect types. This library, constructed using test data from a large number of standard specimens, contains typical characteristic patterns for different defect types. By calculating similarity metrics, the defect type that best matches the current abnormal section is identified and the defect type identification parameters are output. For example, a fracture defect typically manifests as a more than 30% increase in the amplitude of the high-frequency oscillation feature, a more than 2-fold increase in the attenuation rate of the mid-frequency attenuation feature, and a significant step-like phenomenon in the low-frequency displacement feature. This multi-scale decomposition method comprehensively captures the multidimensional characteristics of the defect, improving the accuracy and reliability of defect type identification.

[0103] The process of parameter classification and defect determination based on the mechanical evolution of defects begins with the use of acoustic wave parameter inversion technology to calculate specific mechanical parameters at each depth based on the defect type identification parameters. Displacement represents the maximum displacement amplitude of the material under the action of acoustic waves and is calculated through vibration displacement. The cross-sectional change rate represents the percentage change in the cross-sectional area of ​​the pile and is calculated based on the acoustic wave reflection time and amplitude change. The wave impedance change rate represents the percentage change in the characteristic impedance of the acoustic wave propagation medium and is calculated based on the ratio of the incident wave to the reflected wave. The energy dissipation rate represents the proportion of energy lost during acoustic wave propagation and is calculated based on the energy difference before and after propagation. The stress concentration factor represents the ratio of local stress to nominal stress and is calculated through finite element stress analysis. The defect type is then determined by comparing it with standard thresholds: when the parameter value is greater than the first threshold (1.8) and the displacement is greater than the first displacement benchmark value (0.15 mm), it is determined to be a fracture defect; when the parameter value is greater than the second threshold (1.5) and the cross-sectional change rate is greater than the first cross-sectional change benchmark value (15%), it is determined to be a necking defect; when the parameter value is greater than the third threshold (1.3) and the wave impedance change rate is greater than the first impedance change benchmark value (25%), it is determined to be a mud inclusion defect; when the parameter value is greater than the fourth threshold (1.2) and the energy dissipation rate is greater than the first energy dissipation benchmark value (35%), it is determined to be a loose defect; and when the parameter value is greater than the fifth threshold (1.4) and the stress concentration factor is greater than the first stress concentration benchmark value (2.5), it is determined to be a crack defect. These thresholds and benchmark values ​​were determined through statistical analysis of a large number of engineering cases, taking into account the defect characteristics under different pile foundation types and soil conditions. For example, during a pile foundation inspection for a water conservancy project, a parameter value of 1.9 and a displacement of 0.18 mm at the 12th meter were detected, indicating a fracture defect. A parameter value of 1.6 and a cross-sectional change rate of 18% at the 20th meter were identified as a necking defect. This multi-parameter comprehensive determination method overcomes the limitations of single-parameter determination and improves the accuracy of defect identification.

[0104] The implementation process of calculating the time series characteristics of the memory capacity decay rate at the defect location coordinates is to obtain historical data of the memory capacity decay rate at the defect location through regular detection to form a time series. The time series is smoothed to eliminate the influence of random fluctuations. Then, the first-order difference sequence of the decay rate is calculated, that is, the difference in the decay rate between two adjacent time points, which represents the rate of change of the decay rate. The second-order difference sequence is then calculated, that is, the difference of the first-order difference sequence, which represents the acceleration of the change of the decay rate. The arithmetic mean of the first-order difference sequence is taken to obtain the defect expansion rate parameter, which represents the average rate of change of the memory capacity of the defect area. The arithmetic mean of the second-order difference sequence is taken to obtain the defect expansion acceleration parameter, which represents the changing trend of the defect expansion rate. The defect expansion rate parameter and the defect expansion acceleration parameter are combined to form a defect expansion state vector, which reflects the evolution characteristics of the defect in the time dimension. For example, a pile foundation of a certain project was tested 8 times in two years. By analyzing the time series of the memory capacity decay rate of the fracture defect at the 15th meter, the mean of the first-order difference series was calculated to be 0.05 / month, indicating that the decay rate increased by 0.05 per month; the mean of the second-order difference series was 0.002 / month. 2 , indicating that the decay rate is increasing at an accelerating rate each month. This time series analysis method can reveal the dynamic trend of defect evolution and provide a basis for predicting future defect development.

[0105] The process for implementing stress field analysis based on defect location and type begins by establishing a finite element model of the pile foundation containing defects. The model's geometric and material parameters are then set based on the defect location coordinates and defect type identifier. For fracture defects, a region of cross-sectional discontinuity is set; for necking defects, a region of reduced cross-sectional area is set; for mud inclusion defects, a region of material parameter anomaly is set; for loose defects, a region of reduced elastic modulus is set; and for crack defects, the crack geometry is set. The actual loading conditions of the pile foundation are input into the finite element software, including vertical and horizontal loads transmitted from the superstructure, as well as lateral pressure from the soil surrounding the pile. A finite element analysis is then run to calculate the stress distribution around the defect. The stress concentration factor at the defect tip or edge is extracted, which is the ratio of the local maximum stress to the nominal far-field stress. For crack-type defects, a stress intensity factor is also calculated, representing the strength of the stress field near the crack tip. The stress field analysis results are combined with the previously obtained defect extension state vector to form defect evolution state parameters that contain both temporal and spatial information. For example, a crack defect was found at the 18th meter of a pile foundation of a high-rise building. Finite element analysis showed that the stress concentration coefficient at this location was 3.2 and the stress intensity factor was 1.5 MPa·m 0.5 , combined with the defect expansion state vector (expansion rate 0.03 / month, expansion acceleration 0.001 / month 2) to form complete evolutionary state parameters. This stress field analysis method provides a mechanical basis for evaluating the impact of defects on the safety of pile foundation structures and helps to formulate scientific maintenance and reinforcement strategies.

[0106] In one embodiment of the present invention, the time series characteristics of the memory capacity decay rate at the defect location coordinates are calculated, the first-order difference sequence and the second-order difference sequence of the decay rate are extracted, the mean of the first-order difference sequence is calculated as the defect expansion rate parameter, and the mean of the second-order difference sequence is calculated as the defect expansion acceleration parameter, to obtain the defect expansion state vector, including:

[0107] According to the soil permeability and groundwater level variation at the depth of the pile foundation, the memory capacity attenuation rate is corrected for environmental impacts to obtain an initial attenuation rate sequence, and the first-order difference value and the second-order difference value of the initial attenuation rate sequence are calculated;

[0108] Performing a depth-stratified cumulative effect analysis on the initial attenuation rate sequence, calculating a cumulative influence coefficient based on the overlying soil pressure, and performing cumulative effect correction on the first-order difference value and the second-order difference value to obtain a corrected first-order difference sequence and a corrected second-order difference sequence;

[0109] According to the vertical load transfer law of the pile foundation, the mean value of the modified first-order difference sequence is calculated as the defect growth rate parameter, and the mean value of the modified second-order difference sequence is calculated as the defect growth acceleration parameter;

[0110] The defect growth rate parameter and the defect growth acceleration parameter are combined according to the depth distribution rule to obtain a defect growth state vector.

[0111] The following is a detailed description of the steps involved in the above embodiment:

[0112] The environmental impact correction of the memory capacity attenuation rate is achieved by first measuring the permeability coefficient of the soil around the pile foundation using an in-situ permeability test and monitoring the seasonal changes in the groundwater level using an automatic water level gauge. The permeability coefficient is a parameter that characterizes the ease with which soil allows water to flow through. The permeability coefficient of clay is approximately 10 -9 -10 -8 cm / s, the permeability coefficient of sand is about 10 -5 -10 -3cm / s. Groundwater level fluctuation patterns include both annual amplitude and rate of change, typically represented by a water level curve. After obtaining these environmental parameters, an environmental impact correction model was developed that accounts for the influence of moisture on the rheological properties of concrete. The correction process involves multiplying the original memory capacity decay rate by the permeability and water level coefficients. The permeability coefficient increases with increasing soil permeability, while the water level coefficient increases with increasing water level fluctuation. This correction yields an initial decay rate sequence that eliminates environmental influences. This sequence is then numerically differentiated, with the first-order difference calculated as the difference in decay rate between adjacent time points. The second-order difference is then calculated by the difference of the first-order differences. For example, a bridge pile foundation located near a seasonal river experiences an annual groundwater level fluctuation of up to 2 meters. Environmental impact correction can correct the original memory capacity decay rate from 0.08 / month to 0.06 / month, eliminating the 40% additional attenuation caused by water level fluctuations. This environmental impact correction method addresses the difficulty of traditional testing methods in distinguishing between environmental factors and intrinsic material degradation, improving the accuracy of degradation assessments.

[0113] The depth-stratified cumulative effect analysis of the initial attenuation rate series begins by measuring the soil pressure distribution along the depth of the pile foundation using a static cone penetration instrument and a soil pressure gauge, obtaining overburden pressure data at each depth. Overburden pressure refers to the pressure exerted by the weight of the overburden on a specific depth. It generally increases with depth, and the rate of increase is related to the soil density. A cumulative effect analysis model is then developed based on the stress accumulation theory in material mechanics, accounting for differences in stress histories at different depths. The calculation involves calculating the cumulative influence coefficient based on the ratio of the overburden pressure to the standard pressure (usually 100 kPa). The cumulative influence coefficient represents the degree of influence of the overburden pressure on the cumulative deformation of the material and generally increases with increasing overburden pressure. The initial first- and second-order difference values ​​are corrected by multiplying them by the cumulative influence coefficient for the corresponding depth to obtain the corrected first- and second-order difference series. For example, in a 30-meter-long friction pile, the overburden pressure at a depth of 5 meters is approximately 90 kPa, with a cumulative impact coefficient of 0.9; the overburden pressure at a depth of 20 meters is approximately 360 kPa, with a cumulative impact coefficient of 1.8. Using this cumulative effect correction, the first-order difference value at a depth of 5 meters is corrected from 0.03 / month to 0.027 / month, and the first-order difference value at a depth of 20 meters is corrected from 0.04 / month to 0.072 / month. This depth-based cumulative effect analysis method overcomes the limitation of traditional detection methods that ignore depth effects and reflects the true differences in material degradation rates at different depths.

[0114] The process for calculating defect growth parameters based on the vertical load transfer law of pile foundations involves measuring the axial force distribution of the pile foundation under actual load using a pile strain gauge and analyzing the load transfer law along depth. The vertical load transfer law of a pile foundation refers to the process by which the superstructure load is transferred to the foundation through the pile body, involving two transmission mechanisms: end resistance and lateral friction. For end-bearing piles, end resistance predominates, with load transfer primarily through the pile end. For friction piles, lateral friction predominates, with load transfer primarily through the pile side surface. For friction end-bearing piles, both mechanisms act together. Based on the axial force distribution characteristics, a weighted average is calculated for the modified first-order difference series, with the weight coefficient proportional to the load transfer contribution at that depth, to obtain the defect growth rate parameter. Similarly, a weighted average is calculated for the modified second-order difference series to obtain the defect growth acceleration parameter. The defect growth rate parameter represents the average growth rate of the defect area size, while the defect growth acceleration parameter represents the changing trend of this growth rate. For example, the test results of a concrete friction pile show that the weighted average value of the corrected first-order difference series is 0.042 / month, indicating that the defect area expands by 4.2% per month; the weighted average value of the second-order difference series is 0.003 / month. 2 , indicating an expansion rate of 0.3% per month. This calculation method, which takes into account load transfer laws, reflects the development characteristics of defects in pile foundations under actual load conditions and is more consistent with engineering practice than traditional methods.

[0115] The process of combining defect growth rate parameters and defect growth acceleration parameters based on depth distribution patterns is to first divide the pile foundation depth into several discrete points, each corresponding to a depth position. The spacing between points is typically 1 / 5 to 1 / 3 of the pile diameter. For each depth position, the defect growth rate parameter and defect growth acceleration parameter at that position are combined into a two-dimensional vector. The two-dimensional vectors for all depth positions are then arranged in depth order to form a vector sequence describing the defect growth status of the entire pile, known as the defect growth state vector. The defect growth state vector is a multidimensional array that contains complete information on the defect development trends at each depth position in the pile foundation. For example, a 25-meter-long pile foundation is divided into 50 depth points at 0.5-meter intervals. A set of defect growth rate parameters and growth acceleration parameters are calculated for each point, resulting in a defect growth state vector containing 100 elements. By plotting the depth distribution curves of the defect growth rate parameters and growth acceleration parameters, key defect development areas can be intuitively identified. For example, in the inspection of foundation piles of a high-rise building, it was found that the defect expansion rate parameter in the 12-15 meter depth section was significantly higher than that in other areas, reaching more than 0.06 / month, and the expansion acceleration parameter also reached 0.004 / month. 2 The above shows that this area is the key area for defect development. This state vector representation fully reflects the development characteristics of defects in the spatial and temporal dimensions, providing a scientific basis for formulating accurate maintenance strategies.

[0116] Please continue reading Figure 1 Based on the historical change trend of the evolution state parameters, the material degradation rate and the structural damage development rate are calculated, the health status index of the pile foundation structure at different time nodes is predicted, and the performance evolution curve of the entire life cycle is formed.

[0117] In one embodiment of the present invention, the calculation of the material degradation rate and the structural damage development rate based on the historical change trend of the evolution state parameters, the prediction of the health status index of the pile foundation structure at different time nodes, and the formation of the life cycle performance evolution curve include:

[0118] Calculating the stress concentration factor change rate and the stress intensity factor change rate based on historical data of the defect expansion state vector and stress field analysis results in the evolution state parameters to form a stress parameter change rate sequence, performing wavelet decomposition on the stress parameter change rate sequence to extract long-term trend components and periodic fluctuation components to obtain material performance evolution characteristic data;

[0119] Performing piecewise linear regression on the long-term trend component in the material performance evolution characteristic data, calculating the regression slope and performing jump point detection, comparing the regression slope value with the standard nonlinear parameter of the pile foundation material to obtain the material degradation rate, performing spectral analysis on the periodic fluctuation component in the material performance evolution characteristic data, and extracting the main frequency component as the degradation fluctuation correction value;

[0120] Performing spatial correlation analysis on the material degradation rate and the defect position coordinates in the evolution state parameters, calculating the defect distribution density and defect extension impact range of each depth segment, and calculating the damage accumulation rate in the depth direction in combination with the degradation fluctuation correction value to obtain the structural damage development rate;

[0121] Calculating the elastic modulus degradation coefficient, stress memory attenuation coefficient, and bearing capacity degradation coefficient of the pile foundation structure based on the material degradation rate and the structural damage development rate, performing principal component analysis on the elastic modulus degradation coefficient, stress memory attenuation coefficient, and bearing capacity degradation coefficient, extracting the principal component coefficient and principal component contribution rate, and obtaining a health status index;

[0122] The historical evolution data of the health status index are decomposed into time series, and the trend component, periodic component and fluctuation component are extracted. The extended prediction value of the trend component is calculated, the periodic extrapolation value of the periodic component is calculated, and the fluctuation range value of the fluctuation component is calculated. The extended prediction value of the trend component, the periodic extrapolation value of the periodic component and the fluctuation range value of the fluctuation component are combined to obtain the health status prediction value of the future time node, and form a full life cycle performance evolution curve.

[0123] The following is a detailed description of the steps involved in the above embodiment:

[0124] The process of calculating stress parameter change rate sequences and performing wavelet decomposition involves first collecting historical data on defect growth state vectors and stress field analysis results. Data sequences are then acquired at different time points through regular monitoring. Data analysis software is then used to calculate the change rates of two key stress parameters: the stress concentration factor (SCF) and the stress intensity factor (SIF). The SCF rate of change is the percentage change in the SCF per unit time, representing the rate of change in the stress field intensity around the defect. The SCF rate of change is the percentage change in the SCF per unit time, representing the rate of change in the driving force for crack growth. These two rates of change are arranged in chronological order to form a stress parameter change rate sequence. This sequence is then subjected to time-frequency analysis using a wavelet transform. An appropriate wavelet basis function (such as the Morlet wavelet) is selected, and the decomposition scale (typically 5-8 levels) is set to perform a multi-scale decomposition of the sequence. From the decomposition results, a long-term trend component (lowest frequency band) and a periodic fluctuation component (mid-frequency band) are extracted. The long-term trend component reflects the continuous evolution of material properties, while the periodic fluctuation component reflects the periodic variation of material properties. For example, during a five-year monitoring period, the long-term trend component of the rate of change of the stress concentration factor for a high-rise building's pile foundation showed an average annual increase of 3.5%, while the cyclical fluctuation component exhibited significant seasonal variation, with the highest rate of change during the high temperatures of summer and the lowest rate of change during the low temperatures of winter. This wavelet decomposition method can effectively separate the long-term trend and cyclical fluctuations in the evolution of material properties, avoiding the limitations of traditional single-time-domain analysis.

[0125] The implementation process of piecewise linear regression and spectral analysis of material property evolution characteristic data begins with piecewise linear regression analysis of the long-term trend component. Using a sliding window method, the entire data series is divided into several time periods, typically 3-6 months each. Linear regression is performed on the data within each time period to obtain the slope of the regression line. The regression slope represents the rate of change in material properties within that time period. A jump point detection algorithm is then used to identify time points where the regression slope changes significantly. These jump points represent key turning points in the material degradation process. The resulting regression slope is compared with the standard nonlinear parameters of pile foundation materials. The standard nonlinear parameters are benchmark values ​​for material degradation obtained through standard testing, typically 0.5%-1.5% / year. This comparison results in the material degradation rate, which indicates how much faster or slower the actual material degradation is than the standard value. Simultaneously, a fast Fourier transform (FFT) is performed on the periodic fluctuation component to analyze its spectral characteristics. The frequency component with the highest energy is extracted as the main frequency component, and the amplitude corresponding to this frequency is calculated as the degradation fluctuation correction value. For example, continuous monitoring of a bridge pile foundation revealed that the regression slope of the long-term trend component of material properties was 2.1% / year, 1.75 times the standard nonlinear parameter (1.2% / year), indicating a material degradation rate of 1.75. The main frequency of the periodic fluctuation component was 0.083 times / month (a period of approximately 12 months), with an amplitude of 0.4%, indicating a degradation fluctuation correction of ±0.4%. This combined analysis method comprehensively captures both the rate and fluctuation characteristics of material degradation, providing a scientific basis for evaluating the evolution of material properties.

[0126] The process for calculating the structural damage growth rate through spatial correlation analysis is to first import the material degradation rate and defect location coordinate data into spatial analysis software to construct a three-dimensional spatial distribution model. The defect distribution density is then calculated for each depth segment, representing the number of defects or the proportion of defective area per unit length. The defect distribution density reflects the spatial concentration of defects, with higher density indicating more severe local damage. The extended impact range of each defect is then calculated, representing the area where the stress field around the defect is significantly affected, typically expressed as 2-5 times the defect size. For all defects, their impact ranges are spatially superimposed to produce a global impact range distribution map. Combined with the degradation fluctuation correction value, the depth-wise damage accumulation rate—the actual damage growth rate after accounting for fluctuation effects—is calculated. The calculation involves multiplying the material degradation rate by the defect distribution density and impact range distribution, then adding the degradation fluctuation correction value. The resulting structural damage growth rate reflects the overall damage development trend of the pile foundation structure. For example, the material degradation rate at a depth of 15-18 meters in a water conservancy project pile foundation was 1.8, the defect density was 0.25 defects per meter, and the average impact range was 3.2 times the defect size. The calculated structural damage growth rate in this section was 0.3% per month, higher than at other depths. This spatial correlation analysis method considers the spatial characteristics and interaction effects of defect distribution, enabling damage assessment to move from the material scale to the structural scale.

[0127] The process for calculating the health index of a pile foundation structure begins by applying a material constitutive model based on the material degradation rate and the structural damage development rate to calculate three key performance indicators of the pile foundation structure: the elastic modulus degradation coefficient, the stress memory decay coefficient, and the bearing capacity degradation coefficient. The elastic modulus degradation coefficient indicates the degree of structural stiffness loss and is typically obtained through dynamic and static load tests; the stress memory decay coefficient indicates the degree of material memory effect reduction and is obtained through acoustic testing; and the bearing capacity degradation coefficient indicates the degree of structural bearing capacity loss and is obtained through static load tests or numerical simulations. These three coefficients are then imported into statistical analysis software for principal component analysis. Principal component analysis is a dimensionality reduction technique that transforms multiple correlated variables into a small number of uncorrelated principal components. By calculating eigenvalues ​​and eigenvectors, principal component coefficients and principal component contribution ratios are extracted. The principal component coefficients represent the weights of the original variables in the principal components, while the principal component contribution ratios indicate the degree to which each principal component explains the total variance. Finally, the principal component coefficients are multiplied by the original coefficients and the weighted sum is calculated to obtain a comprehensive evaluation indicator—the health index. The health index is a dimensionless value ranging from 0 to 100, with 100 indicating perfect health and 0 indicating complete failure. For example, the elastic modulus degradation coefficient of a highway bridge pile foundation is 0.82, the stress memory decay coefficient is 0.78, and the bearing capacity degradation coefficient is 0.85. Principal component analysis yields a health index of 83.6, indicating that the structure is in good condition but has slightly deteriorated. This principal component analysis method transforms multidimensional performance indicators into a single evaluation metric, simplifying the representation of structural health and facilitating intuitive understanding and decision-making for engineers.

[0128] The process of time series decomposition and forecasting of the health status index begins with collecting historical data for the health status index. Data from at least 8-10 time points is typically required to ensure reliable analysis. Time series analysis software is then used to perform seasonal decomposition of the data into three components: trend, cycle, and fluctuation. The trend component reflects long-term trends and is typically fitted using a polynomial or exponential function. The cycle component reflects cyclical variations and is typically fitted using a Fourier series or seasonal autoregressive model. The fluctuation component reflects random fluctuations and is typically described using a statistical distribution function. A time series extrapolation model (such as the ARIMA model) is applied to the trend component for extended forecasting, calculating trend values ​​for future time points. A periodic function is applied to the cycle component for periodic extrapolation, calculating cyclical variations for future time points. Statistical analysis is performed on the fluctuation component, calculating confidence intervals for the fluctuation range, typically at a 95% confidence level. The predicted values ​​of the three components are then recombined using the original decomposition method to obtain predicted health status values ​​for future time points, forming a continuous curve from past to future, known as the lifecycle performance evolution curve. For example, analysis of historical data on the health index of an industrial plant's pile foundation revealed a linear downward trend, with an average annual decrease of 2.1 points. The cyclical component exhibited significant seasonal variation, with an amplitude of ±1.5 points. The 95% confidence interval for the fluctuation component was ±0.8 points. Combined predictions revealed that the health index would decrease from its current level of 85.2 to 74.7 ± 2.3 over the next five years, indicating that the structure will slowly deteriorate while remaining within a safe range. This time series decomposition prediction method leverages the evolutionary patterns in historical data to scientifically predict pile foundation performance, providing a basis for developing long-term maintenance strategies.

[0129] The above describes the pile foundation integrity detection method according to the embodiment of the present invention. The following describes the pile foundation integrity detection device according to the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a pile foundation integrity detection device includes:

[0130] The excitation acquisition module 101 is used to apply a variable amplitude acoustic wave excitation sequence to the pile foundation structure, wherein the variable amplitude acoustic wave excitation sequence includes an initial low amplitude excitation and an incremental amplitude excitation, collect acoustic response signals of the pile foundation structure under different excitation amplitudes, and obtain nonlinear dynamic response data reflecting the rheological properties of the material;

[0131] A memory feature extraction module 102 is configured to perform dispersion analysis and hysteresis analysis on the nonlinear dynamic response data, extract the stress memory dissipation coefficient and nonlinear memory capacity index from the acoustic response signal, and establish a time-varying characteristic curve representing the evolution of the microstructure of the pile foundation material;

[0132] The defect identification module 103 is used to determine the location and type of defects in the pile foundation structure based on the abnormal change interval and memory capacity decay rate of the time-varying characteristic curve, combined with the spatial distribution law of the memory characteristics, and obtain the evolution state parameters of the defects;

[0133] The health assessment module 104 is used to calculate the material degradation rate and the structural damage development rate based on the historical change trend of the evolution state parameters, predict the health status index of the pile foundation structure at different time nodes, and form a full life cycle performance evolution curve.

[0134] above Figure 2 The pile foundation integrity detection device in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The pile foundation integrity detection device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0135] Figure 3 The diagram is a schematic diagram of the structure of a pile foundation integrity testing device provided by an embodiment of the present invention. The pile foundation integrity testing device 200 may vary significantly depending on configuration or performance. It may include one or more central processing units (CPUs) 210 (e.g., one or more processors), a memory 220, and one or more storage media 230 (e.g., one or more mass storage devices) storing application programs 233 or data 232. The memory 220 and storage media 230 may be either transient or persistent storage. The program stored in the storage medium 230 may include one or more modules (not shown), each of which may include a series of instructions and operations within the pile foundation integrity testing device 200. Furthermore, the processor 210 may be configured to communicate with the storage medium 230, executing the series of instructions and operations stored in the storage medium 230 on the pile foundation integrity testing device 200 to implement the steps of the aforementioned pile foundation integrity testing method.

[0136] The pile foundation integrity detection device 200 may further include one or more power supplies 240, one or more wired or wireless network interfaces 250, one or more input and output interfaces 260, and / or one or more operating systems 231, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 3 The structure of the pile foundation integrity detection device shown does not constitute a limitation on the pile foundation integrity detection device provided by the present invention, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0137] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, cause the computer to execute the steps of the pile foundation integrity detection method.

[0138] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0139] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0140] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A pile foundation integrity detection method, characterized in that: include: Applying a variable amplitude acoustic wave excitation sequence to the pile foundation structure, the variable amplitude acoustic wave excitation sequence including an initial low amplitude excitation and an incremental amplitude excitation, collecting acoustic response signals of the pile foundation structure at different excitation amplitudes, and obtaining nonlinear dynamic response data reflecting the rheological properties of the material; The nonlinear dynamic response data is subjected to frequency dispersion analysis and hysteresis characteristic analysis, the stress memory dissipation coefficient and nonlinear memory capacity index in the acoustic response signal are extracted, and a time-varying characteristic curve characterizing the evolution of the microstructure of the pile foundation material is established, specifically comprising: dividing the nonlinear dynamic response data into a compression main control area, a shear main control area and a composite control area according to the vertical stress distribution law of the pile foundation depth section, performing frequency band decomposition on the nonlinear dynamic response data of each area, and obtaining waveform propagation mode data of each area; for the waveform propagation mode data of the compression main control area, calculating the reflection coefficient and transmission coefficient of the longitudinal compression wave at the interface between concrete and steel bars, performing hysteresis characteristic analysis on the reflection coefficient and transmission coefficient, and obtaining the first stress memory dissipation coefficient of the interface; for the waveform propagation mode data of the shear ... data, analyze the energy attenuation characteristics of the transverse shear wave at the pile-soil interface, calculate the influence coefficient of soil damping on the propagation of the shear wave, and correlate the influence coefficient with the nonlinear dynamic response data to obtain the first nonlinear memory capacity index; for the waveform propagation mode data of the composite control area, calculate the coupling effect of the longitudinal compression wave and the transverse shear wave, and combine the coupling effect with the first stress memory dissipation coefficient of the interface and the first nonlinear memory capacity index to obtain the second stress memory dissipation coefficient and the second nonlinear memory capacity index of the interface; based on the first stress memory dissipation coefficient of the interface, the second stress memory dissipation coefficient of the interface, the first nonlinear memory capacity index and the second nonlinear memory capacity index, construct a memory characteristic function according to the depth distribution, and generate a time-varying characteristic curve that characterizes the evolution of the microstructure of the pile foundation material; According to the abnormal change interval and memory capacity decay rate of the time-varying characteristic curve, combined with the spatial distribution law of the memory characteristics, the defect location and type in the pile foundation structure are determined, and the evolution state parameters of the defect are obtained; Based on the historical change trend of the evolution state parameters, the material degradation rate and the structural damage development rate are calculated, the health status index of the pile foundation structure at different time nodes is predicted, and a full life cycle performance evolution curve is formed.

2. The pile foundation integrity detection method according to claim 1, characterized in that: The method includes applying a variable amplitude acoustic wave excitation sequence to the pile foundation structure, wherein the variable amplitude acoustic wave excitation sequence includes an initial low amplitude excitation and an incremental amplitude excitation, collecting acoustic response signals of the pile foundation structure under different excitation amplitudes, and obtaining nonlinear dynamic response data reflecting the rheological characteristics of the material, including: The pile foundation structure is divided into the pile top connection section, the pile body friction section, and the pile end support section along the depth direction of the pile foundation. Initial low-amplitude longitudinal compression wave excitation and transverse shear wave excitation are applied to each depth section to obtain the pre-analysis acoustic response signal of each depth section. Performing a resonant frequency analysis on the pre-analyzed acoustic response signal of each depth segment, determining a first longitudinal compression wave excitation frequency range based on the vertical stress state of the pile top connection segment, determining a first transverse shear wave excitation frequency range based on the transverse stress state of the pile top connection segment, determining a second longitudinal compression wave excitation frequency range based on the vertical friction resistance distribution of the pile body friction segment, determining a second transverse shear wave excitation frequency range based on the lateral earth pressure distribution of the pile body friction segment, and determining a third longitudinal compression wave excitation frequency range and a third transverse shear wave excitation frequency range based on the end soil reaction force of the pile end support segment; Applying longitudinal compression wave excitation and transverse shear wave excitation in a spiral progressive manner to each depth segment according to the determined excitation frequency range, wherein the spiral progressive manner includes increasing the amplitude by a preset step size and rotating the phase by a preset angle, and collecting the dynamic acoustic response signal at each progressive point; The complex transfer function and inner-loop dissipation coefficient of the dynamic acoustic response signal of each depth segment at different progressive points are calculated respectively. According to the nonlinear distortion degree of the complex transfer function and the cumulative change rate of the inner-loop dissipation coefficient, the nonlinear dynamic response data characterizing the rheological properties of the material are formed.

3. The pile foundation integrity detection method according to claim 2, characterized in that: According to the determined excitation frequency range, longitudinal compression wave excitation and transverse shear wave excitation are applied to each depth segment in a spiral progressive manner, wherein the spiral progressive manner includes increasing the amplitude by a preset step size and rotating the phase by a preset angle, and collecting the dynamic acoustic response signal of each progressive point, including: According to the stiffness characteristics and stress transfer mode of the pile top connection section, a first set of spiral progressive parameters is set, the longitudinal compression wave amplitude increment is set to a first preset step length, the transverse shear wave amplitude increment is set to a second preset step length, and the phase rotation angle increment is set to a first preset angle, and a first set of dynamic acoustic response signals is collected at the pile top connection section according to the first set of spiral progressive parameters; According to the pile-soil interaction characteristics of the pile body friction section, the first set of spiral progressive parameters are adjusted, the longitudinal compression wave amplitude increment is set to correspond to the soil lateral resistance distribution to a third preset step length, the transverse shear wave amplitude increment is set to correspond to the soil deformation characteristics to a fourth preset step length, and the phase rotation angle increment is set to correspond to the stress wave diffraction effect to a second preset angle, and a second set of dynamic acoustic response signals are collected; Based on the end constraint condition of the pile end support section, the longitudinal components of the first set of spiral progressive parameters are decomposed radially to form an annular excitation parameter set, the annular excitation parameter set including radial compression wave amplitude increment and radial shear wave amplitude increment, and a third set of dynamic acoustic response signals is collected; The first group of dynamic acoustic response signals, the second group of dynamic acoustic response signals, and the third group of dynamic acoustic response signals are combined and processed to obtain a dynamic acoustic response signal for each progressive point.

4. The pile foundation integrity detection method according to claim 1, characterized in that: The method of determining the location and type of defects in the pile foundation structure based on the abnormal change interval and memory capacity attenuation rate of the time-varying characteristic curve and the spatial distribution law of the memory characteristics, and obtaining the evolution state parameters of the defects, includes: Calculating the first-order derivative and second-order derivative of the time-varying characteristic curve in the depth direction, performing run analysis on the first-order derivative to obtain a trend mutation point, performing singular value analysis on the second-order derivative to obtain a curvature mutation point, determining the combined distribution of the trend mutation point and the curvature mutation point as an abnormal change interval, extracting the memory capacity decay rate and memory feature spatial distribution data within each abnormal change interval, and obtaining memory feature abnormal segment data; Performing multi-scale decomposition on the memory feature abnormal section data to extract high-frequency oscillation features, medium-frequency attenuation features, and low-frequency displacement features, matching the combined pattern of the high-frequency oscillation features, medium-frequency attenuation features, and low-frequency displacement features with the memory feature spectrum of the defect type to obtain defect type identification parameters; Classifying the defect type identification parameters according to the defect mechanics evolution law, calculating the displacement, cross-sectional change rate, wave impedance change rate, energy dissipation rate, and stress concentration factor at each depth position, determining an area where the parameter value is greater than a first threshold and the displacement is greater than a first displacement reference value as a fracture defect, determining an area where the parameter value is greater than a second threshold and the cross-sectional change rate is greater than a first cross-sectional change reference value as a necking defect, determining an area where the parameter value is greater than a third threshold and the wave impedance change rate is greater than a first impedance change reference value as a mud inclusion defect, determining an area where the parameter value is greater than a fourth threshold and the energy dissipation rate is greater than a first energy dissipation reference value as a loose defect, and determining an area where the parameter value is greater than a fifth threshold and the stress concentration factor is greater than the first stress concentration reference value as a crack defect, thereby obtaining defect position coordinates and defect type identification; Calculating the time series characteristics of the memory capacity decay rate at the defect location coordinates, extracting the first-order difference sequence and the second-order difference sequence of the decay rate, calculating the mean of the first-order difference sequence as the defect expansion rate parameter, and calculating the mean of the second-order difference sequence as the defect expansion acceleration parameter, to obtain the defect expansion state vector; According to the defect position coordinates and defect type identification, a stress field analysis is performed on the defect expansion state vector, the stress concentration coefficient and stress intensity factor around the defect are calculated, and the stress field analysis results are combined with the defect expansion state vector to form the defect evolution state parameters.

5. The pile foundation integrity detection method according to claim 4, characterized in that: The time series characteristics of the memory capacity decay rate at the defect position coordinates are calculated, the first-order difference sequence and the second-order difference sequence of the decay rate are extracted, the mean of the first-order difference sequence is calculated as the defect expansion rate parameter, and the mean of the second-order difference sequence is calculated as the defect expansion acceleration parameter, to obtain the defect expansion state vector, including: According to the soil permeability and groundwater level variation at the depth of the pile foundation, the memory capacity attenuation rate is corrected for environmental impacts to obtain an initial attenuation rate sequence, and the first-order difference value and the second-order difference value of the initial attenuation rate sequence are calculated; Performing a depth-stratified cumulative effect analysis on the initial attenuation rate sequence, calculating a cumulative influence coefficient based on the overlying soil pressure, and performing cumulative effect correction on the first-order difference value and the second-order difference value to obtain a corrected first-order difference sequence and a corrected second-order difference sequence; According to the vertical load transfer law of the pile foundation, the mean value of the modified first-order difference sequence is calculated as the defect growth rate parameter, and the mean value of the modified second-order difference sequence is calculated as the defect growth acceleration parameter; The defect growth rate parameter and the defect growth acceleration parameter are combined according to the depth distribution rule to obtain a defect growth state vector.

6. The pile foundation integrity detection method according to claim 1, characterized in that: Based on the historical change trend of the evolution state parameters, the material degradation rate and the structural damage development rate are calculated, the health status index of the pile foundation structure at different time nodes is predicted, and a full life cycle performance evolution curve is formed, including: Calculating the stress concentration factor change rate and the stress intensity factor change rate based on historical data of the defect expansion state vector and stress field analysis results in the evolution state parameters to form a stress parameter change rate sequence, performing wavelet decomposition on the stress parameter change rate sequence to extract long-term trend components and periodic fluctuation components to obtain material performance evolution characteristic data; Performing piecewise linear regression on the long-term trend component in the material performance evolution characteristic data, calculating the regression slope and performing jump point detection, comparing the regression slope value with the standard nonlinear parameter of the pile foundation material to obtain the material degradation rate, performing spectral analysis on the periodic fluctuation component in the material performance evolution characteristic data, and extracting the main frequency component as the degradation fluctuation correction value; Performing spatial correlation analysis on the material degradation rate and the defect position coordinates in the evolution state parameters, calculating the defect distribution density and defect extension impact range of each depth segment, and calculating the damage accumulation rate in the depth direction in combination with the degradation fluctuation correction value to obtain the structural damage development rate; Calculating the elastic modulus degradation coefficient, stress memory attenuation coefficient, and bearing capacity degradation coefficient of the pile foundation structure based on the material degradation rate and the structural damage development rate, performing principal component analysis on the elastic modulus degradation coefficient, stress memory attenuation coefficient, and bearing capacity degradation coefficient, extracting the principal component coefficient and principal component contribution rate, and obtaining a health status index; The historical evolution data of the health status index are decomposed into time series, and the trend component, periodic component and fluctuation component are extracted. The extended prediction value of the trend component is calculated, the periodic extrapolation value of the periodic component is calculated, and the fluctuation range value of the fluctuation component is calculated. The extended prediction value of the trend component, the periodic extrapolation value of the periodic component and the fluctuation range value of the fluctuation component are combined to obtain the health status prediction value of the future time node, and form a full life cycle performance evolution curve.

7. A pile foundation integrity detection device, characterized in that: The pile foundation integrity detection device adopts the pile foundation integrity detection method according to any one of claims 1 to 6, and the pile foundation integrity detection device comprises: An excitation acquisition module is used to apply a variable amplitude acoustic wave excitation sequence to the pile foundation structure, wherein the variable amplitude acoustic wave excitation sequence includes an initial low amplitude excitation and an incremental amplitude excitation, collects the acoustic response signals of the pile foundation structure under different excitation amplitudes, and obtains nonlinear dynamic response data reflecting the rheological properties of the material; The memory feature extraction module is used to perform dispersion analysis and hysteresis characteristic analysis on the nonlinear dynamic response data, extract the stress memory dissipation coefficient and nonlinear memory capacity index in the acoustic response signal, and establish a time-varying characteristic curve that characterizes the evolution of the microstructure of the pile foundation material. Specifically, the module includes: dividing the nonlinear dynamic response data into a compression main control area, a shear main control area, and a composite control area according to the vertical stress distribution law of the pile foundation depth section, performing frequency band decomposition on the nonlinear dynamic response data of each area, and obtaining the waveform propagation mode data of each area; for the waveform propagation mode data of the compression main control area, calculating the reflection coefficient and transmission coefficient of the longitudinal compression wave at the interface between concrete and steel bars, performing hysteresis characteristic analysis on the reflection coefficient and transmission coefficient, and obtaining the first stress memory dissipation coefficient of the interface; for the waveform propagation mode data of the shear ... and performing hysteresis characteristic analysis on the reflection coefficient and transmission coefficient, and obtaining the first stress memory dissipation coefficient of the interface. The waveform propagation mode data of the composite control area is used to analyze the energy attenuation characteristics of the transverse shear wave at the pile-soil interface, and the influence coefficient of the soil damping on the shear wave propagation is calculated. The influence coefficient is correlated with the nonlinear dynamic response data to obtain the first nonlinear memory capacity index; for the waveform propagation mode data of the composite control area, the coupling effect of the longitudinal compression wave and the transverse shear wave is calculated, and the coupling effect is combined with the first stress memory dissipation coefficient of the interface and the first nonlinear memory capacity index to obtain the second stress memory dissipation coefficient of the interface and the second nonlinear memory capacity index; based on the first stress memory dissipation coefficient of the interface, the second stress memory dissipation coefficient of the interface, the first nonlinear memory capacity index and the second nonlinear memory capacity index, a memory characteristic function is constructed according to the depth distribution to generate a time-varying characteristic curve characterizing the evolution of the microstructure of the pile foundation material; a defect identification module for determining the location and type of defects in the pile foundation structure based on the abnormal change interval and memory capacity decay rate of the time-varying characteristic curve, combined with the spatial distribution law of the memory characteristics, and obtaining the evolution state parameters of the defects; The health assessment module is used to calculate the material degradation rate and the structural damage development rate based on the historical change trend of the evolution state parameters, predict the health status index of the pile foundation structure at different time nodes, and form a full life cycle performance evolution curve.

8. A pile foundation integrity detection device, characterized in that: The pile foundation integrity detection device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the pile foundation integrity detection device to perform the steps of the pile foundation integrity detection method according to any one of claims 1 to 6.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the pile foundation integrity detection method according to any one of claims 1 to 6 are implemented.

Images