Building pile foundation bearing capacity intelligent evaluation method and system

Abstract

This application provides an intelligent assessment method and system for the bearing capacity of building pile foundations. It collects water pressure response signals at multiple depths within a sonic logging tube under vertical excitation using intelligent sensors, obtaining a wave train diagram. Based on the wave train diagram, the stress waves are separated using dual wave velocity characteristics to identify the propagation differences of stress waves in different media, obtaining the longitudinal wave component and the pipe wave component of the fluid within the sonic logging tube when the stress wave generated by vertical excitation causes pile vibration. Multiple abnormal reflection waveforms caused by changes in the pile cross-sectional impedance are identified from the pipe wave component. Based on all abnormal reflection waveforms and the longitudinal wave component, the pile body is reconstructed to obtain the pile foundation's defect characteristics. Based on the pile defect characteristics and the bearing capacity assessment model, the integrity status of the pile foundation structure is intelligently identified, and the bearing capacity of the pile foundation is assessed. Using the scheme of this application, pile defects can be efficiently and intelligently identified and the bearing capacity of building pile foundations can be assessed using a single sonic logging tube.

Description

Technical Field

[0001] This application relates to intelligent assessment, and more specifically, to a method and system for intelligent assessment of the bearing capacity of building pile foundations. Background Technology

[0002] As a key supporting component of building structures, the accurate assessment of the bearing capacity of building pile foundations is directly related to the safety and stability of the overall project. Pile foundation testing technology, by analyzing the integrity and bearing capacity of the pile body, is an important link in ensuring project quality. Especially in deep and long piles or complex geological conditions, higher requirements are placed on the accuracy and reliability of testing methods.

[0003] Traditional pile foundation testing methods, such as the low-strain method, suffer from significant stress wave energy attenuation in deep and long piles, making it difficult to obtain clear signals from the pile bottom. The acoustic transmission method requires multiple sonic logging tubes, but is prone to clogging and has a limited evaluation range. The side-hole transmission method requires additional drilling, resulting in high cost and low efficiency. These methods all suffer from limitations in detection depth, weak anti-interference capabilities, and operational complexity, making it difficult to meet the precise assessment needs under complex working conditions. Therefore, how to efficiently and intelligently identify pile defects and assess the bearing capacity of building pile foundations using a single sonic logging tube has become a challenge for the industry. Summary of the Invention

[0004] This application provides a method and system for intelligent assessment of the bearing capacity of building pile foundations, which can efficiently and intelligently identify pile defects and assess the bearing capacity of building pile foundations through a single sonic logging tube.

[0005] In a first aspect, this application provides an intelligent assessment method for the bearing capacity of building pile foundations, wherein the pile foundation includes a pile top and a pile body, and a single sonic logging tube is pre-embedded in the pile body of the pile foundation. The method includes the following steps: Vertical vibration is applied to the top of the pile foundation, and the response signals of water pressure at multiple depths in the pre-embedded sonic logging tube in the pile body are collected by intelligent sensors under vertical vibration, thereby obtaining the wave train diagram of the water pressure response of the pile foundation. Based on the wave train diagram of the pile foundation water pressure response, the stress wave generated by vertical excitation is separated by dual wave velocity characteristics, thereby identifying the propagation differences of the stress wave in different media, and obtaining the longitudinal wave component when the stress wave generated by vertical excitation causes the pile body to vibrate and the pipe wave component of the fluid in the acoustic logging pipe during excitation oscillation. Multiple abnormal reflection waveforms generated by the change in the cross-sectional impedance of the pile body are identified from the pipe wave component. Then, the pile body is reconstructed based on all abnormal reflection waveforms and the longitudinal wave component to obtain the pile body defect characteristics of the pile foundation. Based on the characteristics of the pile defects and the preset bearing capacity assessment model, the integrity status of the pile structure of the pile foundation is intelligently identified, and the bearing capacity of the pile foundation is assessed.

[0006] In some embodiments, the response signals of water pressure at multiple depths within the sonic logging tubes pre-embedded in the pile body under vertical vibration are collected by intelligent sensors, thereby obtaining the wave train diagram of the pile foundation water pressure response. Specifically, this includes: Multiple hydrophone arrays are pre-installed inside the acoustic tube; The response signals of water pressure at different depths of the fluid were collected using all the hydrophone arrays; The response signals of all water pressures are arranged and spliced ​​to obtain the wave train diagram of the water pressure response of the pile foundation.

[0007] In some embodiments, the dual-wave velocity feature separation of the stress wave generated by vertical excitation based on the wave train diagram of the pile foundation water pressure response specifically includes: The arrival times of multiple first-arrival waves of the stress wave generated by vertical excitation are determined based on the wave train diagram of the pile foundation water pressure response. By performing dual-wave velocity analysis on the arrival times of all first-arrival waves, multiple dual-wave velocity characteristic parameters of the stress wave generated by vertical excitation are obtained. Based on all the dual-wave velocity characteristic parameters, the stress wave generated by vertical excitation is separated into longitudinal wave velocity and tube wave velocity.

[0008] In some embodiments, identifying the propagation differences of the stress wave in different media to obtain the longitudinal wave component of the pile body when the stress wave generated by vertical excitation causes vibration and the pipe wave component of the fluid in the acoustic logging pipe during excitation specifically includes: The propagation difference characteristics of stress waves in solid-liquid media are determined based on the longitudinal wave velocity and the tube wave velocity obtained by separating the dual wave velocity characteristics. The propagation difference characteristics are reconstructed in the time domain to obtain the longitudinal wave component of the pile body during vibration and the pipe wave component of the fluid in the acoustic logging pipe during excitation oscillation.

[0009] In some embodiments, identifying multiple abnormal reflection waveforms generated by changes in pile cross-sectional impedance from the wave components specifically includes: Determine the envelope curve of the signal amplitude of the tube wave component; Based on the envelope curve, multiple abnormal reflection waveforms generated by the change in the cross-sectional impedance of the pile body are determined.

[0010] In some embodiments, the pile body is reconstructed based on all abnormal reflection waveforms and the longitudinal wave component to obtain the pile body defect characteristics of the pile foundation, specifically including: Multiple potential defect locations were identified based on all abnormal reflection waveforms; Determine the attenuation coefficient of the longitudinal wave component at different depths; The pile body is reconstructed based on all potential defect locations and all attenuation coefficients to obtain the pile body defect characteristics of the pile foundation.

[0011] In some embodiments, intelligent identification of the integrity status of the pile foundation structure based on the characteristics of pile defects and a preset bearing capacity assessment model specifically includes: Obtain the preset bearing capacity assessment model; The probability distribution for determining the integrity level of the pile foundation structure based on the characteristics of the pile defects; The bearing capacity of the pile foundation is evaluated based on the probability distribution.

[0012] Secondly, this application provides an intelligent assessment system for the bearing capacity of building pile foundations, wherein the pile foundation includes a pile top and a pile body, and a single sonic logging tube is pre-embedded in the pile body. The system includes: The acquisition module is used to apply vertical excitation to the top of the pile foundation and acquire the water pressure response signals at multiple depths in the sonic logging tubes embedded in the pile body under vertical excitation through intelligent sensors, thereby obtaining the wave train diagram of the water pressure response of the pile foundation. The processing module is used to perform dual-wave velocity feature separation on the stress wave generated by vertical excitation based on the wave train diagram of the pile foundation water pressure response, thereby identifying the propagation differences of the stress wave in different media and obtaining the longitudinal wave component when the stress wave generated by vertical excitation causes the pile body to vibrate and the pipe wave component of the fluid in the acoustic logging pipe during excitation fluctuation. The processing module is also used to identify multiple abnormal reflection waveforms generated by the change in the cross-sectional impedance of the pile body from the pipe wave component, and then reconstruct the pile body based on all abnormal reflection waveforms and the longitudinal wave component to obtain the pile body defect characteristics of the pile foundation. The execution module is used to intelligently identify the integrity status of the pile structure based on the characteristics of the pile defects and a preset bearing capacity assessment model, and to assess the bearing capacity of the pile foundation.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described intelligent assessment method for the bearing capacity of building pile foundations.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described intelligent assessment method for the bearing capacity of building pile foundations.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The intelligent assessment method and system for the bearing capacity of building pile foundations provided in this application first applies vertical vibration to the top of the pile foundation, and then uses intelligent sensors to collect the water pressure response signals at multiple depths within the pre-embedded sonic logging pipe in the pile body under vertical vibration, thereby obtaining a wave train diagram of the pile foundation's water pressure response. Based on the wave train diagram of the pile foundation's water pressure response, the stress waves generated by vertical vibration are separated by dual wave velocity characteristics, thereby identifying the propagation differences of the stress waves in different media, obtaining the longitudinal wave component when the stress waves generated by vertical vibration cause the pile body to vibrate and the pipe wave component when the fluid in the sonic logging pipe fluctuates during vibration. Multiple abnormal reflection waveforms generated by changes in the cross-sectional impedance of the pile body are identified from the pipe wave component, and then the pile body is reconstructed based on all abnormal reflection waveforms and the longitudinal wave component to obtain the pile body defect characteristics of the pile foundation. Based on the pile body defect characteristics and a preset bearing capacity assessment model, the integrity state of the pile body structure of the pile foundation is intelligently identified, and the bearing capacity of the pile foundation is assessed.

[0016] Therefore, in the intelligent assessment method for the bearing capacity of building pile foundations, this application first applies vertical excitation to the top of the pile foundation and collects the water pressure response signals at multiple depths within the pre-embedded sonic logging pipe in the pile body under vertical excitation using intelligent sensors. This yields the wave train of the pile foundation's water pressure response. Based on the wave train diagram of the pile foundation's water pressure response, the stress wave generated by vertical excitation is separated by dual wave velocity characteristics. This allows for the identification of the propagation differences of the stress wave in different media, obtaining the longitudinal wave component when the stress wave causes the pile body to vibrate and the pipe wave component of the fluid within the sonic logging pipe during excitation fluctuations. The propagation difference characteristic refers to the fundamentally different propagation laws exhibited by the stress wave in the solid concrete medium of the pile body and the fluid medium within the sonic logging pipe. The pipe wave component refers to the component that propagates in the fluid within the sonic logging pipe. The system consists of components representing Stoneley wave signals at different depths. The longitudinal wave component refers to the stress wave component that vibrates along the pile's axial direction and propagates at different depths. Abrupt changes in the cross-sectional impedance at pile defects significantly alter the Stoneley wave propagation path, thus generating strong anomalous reflections within the sonic logging tube. The amplitude attenuation of the longitudinal wave component is influenced by both pile integrity and the constraint of the surrounding soil. Secondly, multiple anomalous reflection waveforms generated by changes in pile cross-sectional impedance are identified from the sonic wave components. Based on all anomalous reflection waveforms and the longitudinal wave component, the pile is reconstructed to obtain the pile defect characteristics. Based on these characteristics and a pre-defined bearing capacity assessment model, the integrity status of the pile structure is intelligently identified, and the bearing capacity of the pile foundation is assessed. This scheme can efficiently and intelligently identify pile defects and assess the bearing capacity of building pile foundations using a single sonic logging tube. Attached Figure Description

[0017] Figure 1This is an exemplary flowchart of an intelligent assessment method for the bearing capacity of building pile foundations according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating dual-wave velocity feature separation according to some embodiments of this application; Figure 3 This is an exemplary flowchart illustrating the determination of pile defect characteristics according to some embodiments of this application; Figure 4 This is a structural schematic diagram of an intelligent assessment system for the bearing capacity of building pile foundations, as shown in some embodiments of this application. Figure 5 This is a structural schematic diagram of a computer device for implementing an intelligent assessment method for the bearing capacity of building pile foundations, according to some embodiments of this application. Detailed Implementation

[0018] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 The figure is an exemplary flowchart of an intelligent assessment method for the bearing capacity of building pile foundations according to some embodiments of this application. The intelligent assessment method for the bearing capacity of building pile foundations mainly includes the following steps: In step 101, vertical vibration is applied to the top of the pile foundation, and the response signals of water pressure at multiple depths in the pre-embedded sonic logging tube of the pile body under vertical vibration are collected by intelligent sensors, thereby obtaining the wave train diagram of the water pressure response of the pile foundation.

[0020] In specific implementation, the vertical excitation applied to the top of the pile foundation can be achieved in the following way: fill a single sonic logging tube with water, and use a standard measuring hammer to apply a transient vertical excitation force to the center of the top of the pile foundation, thereby generating a stress wave in the pile body. This stress wave propagates along the pile body and excites the vibration of the fluid in the sonic logging tube through the pile-pipe coupling effect, that is, vertical excitation is generated. Other methods can also be used in other embodiments, which are not limited here.

[0021] In some embodiments, the response signals of water pressure at multiple depths within the sonic logging tubes pre-embedded in the pile body under vertical vibration are collected by intelligent sensors, and the wave train diagram of the pile foundation water pressure response is obtained by the following steps: Multiple hydrophone arrays are pre-installed inside the acoustic tube; The response signals of water pressure at different depths of the fluid were collected using all the hydrophone arrays; The response signals of all water pressures are arranged and spliced ​​to obtain the wave train diagram of the water pressure response of the pile foundation.

[0022] In specific implementation, the acquisition of water pressure response signals of fluids at different depths through all hydrophone arrays can be achieved in the following way: by synchronously acquiring dynamic water pressure time history signals of fluids at different depths through all deployed hydrophone arrays, all dynamic water pressure time history signals are used as the water pressure response signals of the fluids at the corresponding depths. Each hydrophone converts the received pressure fluctuation signal into an electrical signal, which is recorded by a data acquisition instrument; the sampling frequency is 50kHz; other methods can also be used in other embodiments, which are not limited here.

[0023] In specific implementation, the wave train diagram of the water pressure response of the pile foundation can be obtained by arranging and splicing all the water pressure response signals. Specifically, the water pressure response signals collected by all hydrophones are arranged and spliced ​​according to their corresponding detection depths. Specifically, with the detection depth as the vertical axis and time as the horizontal axis, the water pressure response signals at each depth are mapped onto a two-dimensional plane of depth-time in grayscale through data gridding processing, and finally a water pressure response wave train diagram is generated. Other methods can also be used in other embodiments, which are not limited here.

[0024] It should be noted that the water pressure time history signal in this application refers to the original signal reflecting the change of fluid pressure in a single-hole pipe wave over time, directly collected by a hydrophone in a pre-embedded sonic logging pipe under the background of legal testing; the water pressure response signal refers to the signal of dynamic wave response generated in the sonic logging pipe through the solid-liquid coupling interface after the excitation energy is transmitted through the pile body; the wave train diagram is a graph describing the change of the wave energy of stress generated by the excitation of a single sonic logging pipe in the depth-time domain; among them, the wave train diagram is used to intuitively show the dynamic propagation process of the wave generated by the excitation, which is convenient for subsequent judgment of pile foundation defects.

[0025] In step 102, the stress wave generated by vertical excitation is separated into two wave velocity characteristics according to the wave train diagram of the pile foundation water pressure response, thereby identifying the propagation differences of the stress wave in different media and obtaining the longitudinal wave component when the stress wave generated by vertical excitation causes the pile body to vibrate and the pipe wave component of the fluid in the acoustic logging pipe during excitation oscillation.

[0026] In some embodiments, reference Figure 2 The diagram is an exemplary flowchart of dual-wave velocity feature separation in some embodiments of this application. In this embodiment, the dual-wave velocity feature separation of the stress wave generated by vertical excitation based on the wave train diagram of the pile foundation water pressure response can be achieved by the following steps: First, in step 1021, the arrival times of multiple first-arrival waves of the stress wave generated by vertical excitation are determined according to the wave train diagram of the pile foundation water pressure response. Secondly, in step 1022, dual-wave velocity analysis is performed on all first-to-last wave arrival times to obtain multiple dual-wave velocity characteristic parameters of the stress wave generated by vertical excitation. Finally, in step 1023, the stress wave generated by vertical excitation is separated into longitudinal wave velocity and tube wave velocity based on all dual-wave velocity characteristic parameters.

[0027] In specific implementation, determining the arrival times of multiple first-arrival waves of the stress wave generated by vertical excitation based on the wave train diagram of the pile foundation water pressure response can be achieved in the following way: An automatic first-arrival wave picking algorithm combined with the wave train diagram of the pile foundation water pressure response is used to process the time history signal at each depth using a seismic pickup algorithm. Specifically, this algorithm calculates the local stability of each time history signal, and the time corresponding to the highest local stability of each time history signal is taken as the arrival time of the first-arrival wave of the corresponding time history signal. The set consisting of all depths and all first-arrival wave arrival times is taken as the depth-time dataset of the stress wave generated by vertical excitation. Other methods can also be used in other embodiments, which are not limited here.

[0028] In specific implementation, the dual-wave velocity analysis of all arrival times of the first and last waves can be performed to obtain multiple dual-wave velocity characteristic parameters of the stress wave generated by vertical excitation. This can be achieved in the following way: First, a clustering algorithm (such as the K-Means algorithm) is used to identify two sets of data points belonging to longitudinal waves and tube waves, respectively, based on all arrival times of the first and last waves. Then, weighted least squares is applied independently to each set of points for linear fitting, so that the slope of each line is taken as the slow speed of the corresponding set of points, and the reciprocal of the slope of each line is taken as the wave speed of the corresponding set of points, thereby obtaining the dual-wave velocity characteristic parameters of the stress wave generated by vertical excitation. Other methods can also be used in other embodiments, which are not limited here.

[0029] It should be noted that the dual wave velocity characteristic parameters in this application are parameters that characterize the propagation characteristics of stress waves in the solid medium of the pile body and the fluid medium of the acoustic logging tube, specifically including slowness and wave velocity; among them, slowness directly reflects the time delay required for stress fluctuations to propagate a unit distance in the medium, and is the direct input for wave field separation and filtering; wave velocity is directly related to the physical properties of the medium and is a key indicator for evaluating the strength and uniformity of the pile body material.

[0030] In specific implementation, the separation of the stress wave generated by vertical excitation into longitudinal wave velocity and tube wave velocity based on all dual-wave velocity characteristic parameters can be achieved in the following way: According to the range of the three-dimensional longitudinal wave velocity of the stress wave in the intact concrete pile, the set of data points with higher wave velocities is classified as the longitudinal wave data point set, and the wave velocity corresponding to the longitudinal wave data point set is taken as the longitudinal wave velocity. The remaining set of data points is classified as the Stoneley wave data point set, and the wave velocity corresponding to the Stoneley wave data point set is taken as the tube wave velocity. Here, the Stoneley wave is an interface wave that propagates at the interface of two different media (usually solid-fluid). The single acoustic tube contains a solid wall and a liquid interface, so the Stoneley wave can be taken as a tube wave. Other methods can also be used in other embodiments, which are not limited here.

[0031] It should be noted that the tube wave velocity in this application refers to the phase velocity of the Stoneley wave propagating in the solid-liquid interface coupling system of the sonic logging tube, which is used to characterize the propagation efficiency of stress wave energy at the solid-liquid interface of a single-hole sonic logging tube. The longitudinal wave velocity is a parameter value describing the compression wave velocity of stress wave propagating in the solid medium of the pile concrete, which is used to reflect the structural stiffness of the pile. Among them, the longitudinal wave reflects the overall mechanical properties of the solid medium of the pile, while the tube wave reflects the detailed information of the interaction between the stress wave and the local cross-sectional impedance when the stress wave propagates along the pile. By separating the two, the longitudinal wave velocity can be used to macroscopically judge the quality of the pile concrete, and the sensitivity of the tube wave to defects can be used for precise location and identification.

[0032] In some embodiments, identifying the propagation differences of the stress wave in different media to obtain the longitudinal wave component of the pile body when the stress wave generated by vertical excitation causes vibration and the pipe wave component of the fluid in the acoustic logging pipe during excitation fluctuation can be achieved by the following steps: The propagation difference characteristics of stress waves in solid-liquid media are determined based on the longitudinal wave velocity and the tube wave velocity obtained by separating the dual wave velocity characteristics. The propagation difference characteristics are reconstructed in the time domain to obtain the longitudinal wave component of the pile body during vibration and the pipe wave component of the fluid in the acoustic logging pipe during excitation oscillation.

[0033] In specific implementation, the propagation difference characteristics of stress waves in solid-liquid media can be determined based on the longitudinal wave velocity and pipe wave velocity obtained by separating the dual wave velocity characteristics. This can be achieved in the following way: obtain the frequency of vertical excitation, and then count the wave numbers of longitudinal waves and pipe waves at that frequency. Then, input the longitudinal wave velocity and pipe wave velocity as input parameters into the simplified vibration model of the pile-soil-pile-test pipe fluid coupling, and inversely derive the equivalent attenuation coefficient of the longitudinal wave in the pile solid and the equivalent attenuation coefficient of the pipe wave in the fluid-pipe wall. Finally, the set consisting of the two equivalent attenuation coefficients, the longitudinal wave velocity, the pipe wave velocity, the corresponding slow velocity of the longitudinal wave, the corresponding slow velocity of the pipe wave, and the wave numbers of the longitudinal wave and pipe wave is taken as the propagation difference characteristics of stress waves in solid-liquid media. Other methods can also be used in other embodiments, which are not limited here.

[0034] In specific implementation, the time-domain reconstruction of the propagation difference characteristics to obtain the longitudinal wave component during pile vibration and the pipe wave component during excitation oscillation of the fluid inside the acoustic logging pipe can be achieved in the following way: First, based on the separated longitudinal wave velocity and pipe wave velocity, theoretical propagation models of the two waves are constructed as matched filters: for the longitudinal wave, the numerical solution of the three-dimensional elastic wave equation with the longitudinal wave velocity as the propagation speed is used as the reference signal; for the pipe wave, a model based on Biot (Marc Simon) is used. Biot (Marc Simon Biot) uses fluid potential function theory and a pipe wave propagation model with pipe wave velocity as the characteristic wave velocity as the reference signal. Secondly, a bidirectional matched filtering process is applied to the wave train diagram of the water pressure response according to a matched filter: In the first filtering channel, using the longitudinal wave reference signal as a template, global cross-correlation calculation and adaptive filtering are used to suppress the Stoneley wave component and enhance the longitudinal wave component from the original wave train diagram, ultimately outputting a longitudinal wave component characterizing pile vibration with a two-dimensional structure of depth and time, i.e., the aforementioned longitudinal wave component includes vibration signals of longitudinal waves at different depths; In the second filtering channel, using the Stoneley wave reference signal as a template, global cross-correlation calculation and adaptive filtering are used to suppress the longitudinal wave component and enhance the Stoneley wave component from the wave train diagram, thereby outputting a pipe wave component characterizing fluid fluctuations within the sonic logging pipe with a two-dimensional structure of depth and time, i.e., the aforementioned pipe wave component includes vibration signals of Stoneley waves at different depths; Other methods can be used in other embodiments, which are not limited here.

[0035] It should be noted that the propagation difference characteristics in this application refer to the fundamentally different propagation laws exhibited by stress waves in the solid concrete medium of the pile body and the fluid medium inside the sonic logging tube. The tube wave component refers to the component composed of Stoneley wave signals propagating at different depths in the fluid of the sonic logging tube, which is the interface wave component excited by the pile vibration through the pile-tube coupling effect. The longitudinal wave component refers to the component composed of stress waves vibrating along the pile axis and propagating at different depths along the pile axis, which is used to reflect the elastic properties of the pile foundation. Among them, the abrupt change in cross-sectional impedance at the pile defect will significantly change the propagation path of the Stoneley wave, thereby exciting strong abnormal reflections in the sonic logging tube. The amplitude attenuation law of the longitudinal wave component is affected by both the integrity of the pile body and the constraint effect of the surrounding soil. Therefore, it can be used as an indicator to evaluate the strength and overall uniformity of the pile body material, which is convenient for subsequent extraction of defect characteristics.

[0036] In step 103, multiple abnormal reflection waveforms generated by the change in the cross-sectional impedance of the pile body are identified from the pipe wave component. Then, the pile body is reconstructed based on all abnormal reflection waveforms and the longitudinal wave component to obtain the pile body defect characteristics of the pile foundation.

[0037] In some embodiments, identifying multiple abnormal reflection waveforms generated by changes in pile cross-sectional impedance from the wave components can be achieved using the following steps: Determine the envelope curve of the signal amplitude of the tube wave component; Based on the envelope curve, multiple abnormal reflection waveforms generated by the change in the cross-sectional impedance of the pile body are determined.

[0038] In specific implementation, the envelope curve of the signal amplitude of the tube wave component can be determined in the following way: envelope analysis is performed on the separated tube wave component, and the instantaneous amplitude of the tube wave component signal is calculated using Hilbert transform, thereby obtaining the envelope curve of the tube wave energy distribution with depth; where the instantaneous amplitude is a sequence that changes with time, i.e., the envelope of the tube wave signal. The abnormal reflection waveform generated by the change in pile cross-sectional impedance based on the envelope curve can be determined in the following way: first, a dynamic threshold is set on the envelope curve to identify all local energy peak points exceeding the dynamic threshold; then, the depth position of each corresponding peak point of the tube wave component in the wave train diagram is traced back to analyze the waveform morphology characteristics of the tube wave at each depth position. If and only if a K-shaped waveform morphology characteristic is identified, it is judged as a unique waveform pattern formed by coupling at the defect during tube wave propagation, thus this waveform is taken as the abnormal reflection waveform generated by the change in pile cross-sectional impedance, and its corresponding depth position is recorded; where the dynamic threshold can be set to 3-5 times the background noise level; other methods can also be used in other embodiments, which are not limited here.

[0039] In some embodiments, reference Figure 3As shown, this figure is an exemplary flowchart for determining the characteristics of pile defects in some embodiments of this application. In this embodiment, the pile body is reconstructed based on all abnormal reflection waveforms and the longitudinal wave component to obtain the characteristics of the pile defect. This can be achieved by the following steps: First, in step 1031, multiple potential defect locations are determined based on all abnormal reflection waveforms; Secondly, in step 1032, the attenuation coefficient of the longitudinal wave component at different depths is determined; Finally, in step 1033, the pile body is reconstructed based on all potential defect locations and all attenuation coefficients to obtain the pile body defect characteristics of the pile foundation.

[0040] In specific implementation, determining multiple potential defect locations based on all abnormal reflection waveforms can be achieved in the following way: all depth locations corresponding to each abnormal reflection waveform are considered as potential defect locations; determining the attenuation coefficient of the P-wave component at different depths can be achieved in the following way: performing time-frequency analysis on the P-wave component along the depth direction, calculating the root mean square amplitude of the P-wave signal at each depth point, and using an exponential function to fit the attenuation curve of the P-wave amplitude along the depth, thereby calculating the attenuation coefficient at each depth point using a linear regression method combined with this attenuation curve; reconstructing the pile body based on all potential defect locations and all attenuation coefficients to obtain the pile body defect characteristics of the pile foundation can be achieved in the following way: for all potential defect locations... The location of a defect is determined. A potential defect location is considered a real defect if it simultaneously meets the following conditions: First, the attenuation coefficient of the longitudinal wave at the potential defect location exceeds twice the standard deviation of the average attenuation coefficient of the complete pile section; second, the phase of the longitudinal wave at the potential defect location experiences a sudden rise or fall; third, the dominant frequency of the longitudinal wave at the potential defect location shifts towards a lower frequency direction. Then, the longitudinal dimension of the defect is calculated by combining the broadening characteristics of the K-shaped reflected wave with the wave velocity of the pipe wave. The attenuation coefficient of the longitudinal wave is used as the defect level. Finally, a complete pile defect characteristic parameter table containing the defect location, size, and level is output, and this defect characteristic parameter table is used as the pile defect characteristic of the pile foundation. Other methods can be used in other embodiments, which are not limited here.

[0041] It should be noted that the pile defect characteristics in this application describe the characteristics of defects in the pile body, including the aforementioned defect location, size, and grade. Specifically, in a single-hole sonic logging tube, when a stress wave propagates to a pile defect, it will excite a K-shaped reflected wave with a specific shape in the tube wave component. This characteristic serves as a direct basis for defect identification. The broadening of the reflected wave and the corresponding changes and attenuation of the longitudinal wave velocity are used to quantify the longitudinal size and severity of the defect, achieving a multi-dimensional quantitative assessment of the defect and providing a more accurate basis for judging the safety of the pile foundation. Furthermore, the mutual verification of the longitudinal wave and Stoneley wave response characteristics reduces the risk of misjudgment and improves the reliability of defect identification and integrity assessment; facilitating subsequent intelligent identification of the integrity status of the pile structure.

[0042] In step 104, the integrity status of the pile structure is intelligently identified based on the characteristics of the pile defect and the preset bearing capacity assessment model, and the bearing capacity of the pile foundation is assessed.

[0043] In some embodiments, the intelligent identification of the integrity status of the pile foundation structure based on the characteristics of pile defects and a preset bearing capacity assessment model can be achieved through the following steps: Obtain the preset bearing capacity assessment model; The probability distribution for determining the integrity level of the pile foundation structure based on the characteristics of the pile defects; The bearing capacity of the pile foundation is evaluated based on the probability distribution.

[0044] In specific implementation, the probability distribution for determining the integrity level of the pile foundation structure based on the pile defect characteristics can be achieved in the following way: First, the defect location, size, and level in the pile defect characteristics are used as input feature vectors and input into a pre-trained pile integrity classification model. Then, the probability value of the pile belonging to each integrity level is calculated through multi-layer nonlinear transformations within the classification model. This classification model is based on a deep neural network architecture, and the integrity levels can be divided into Class I, Class II, Class III, and Class IV according to the "Technical Specification for Testing Building Foundation Piles". Based on the probability distribution... The bearing capacity of the pile foundation can be evaluated in the following way: the probability distribution, pile defect characteristics, longitudinal wave velocity, and energy amplitude of anomalous reflected waves are used as comprehensive input features and input into a pre-trained bearing capacity evaluation model. This model is constructed based on a particle swarm optimization-backpropagation neural network. The optimal initial weights and thresholds of the neural network are determined by the particle swarm optimization algorithm. Then, the hidden layers in the neural network are used to perform nonlinear mapping on the input features, and the estimated value of the pile foundation bearing capacity is obtained by processing with an activation function. Other methods can also be used in other embodiments, which are not limited here.

[0045] In another aspect, in some embodiments, this application provides an intelligent assessment system for the bearing capacity of building pile foundations, with reference to... Figure 4 The figure is a structural schematic diagram of an intelligent assessment system for the bearing capacity of building pile foundations according to some embodiments of this application. The intelligent assessment system for the bearing capacity of building pile foundations includes: a data acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to apply vertical vibration to the top of the pile foundation and to acquire the response signals of water pressure at multiple depths in the sonic logging tubes embedded in the pile body under vertical vibration through intelligent sensors, thereby obtaining the wave train diagram of the water pressure response of the pile foundation. Processing module 402, in this application, is used to perform dual wave velocity feature separation on the stress wave generated by vertical excitation based on the wave train diagram of the pile foundation water pressure response, thereby identifying the propagation differences of the stress wave in different media, and obtaining the longitudinal wave component when the stress wave generated by vertical excitation causes the pile body to vibrate and the pipe wave component of the fluid in the acoustic logging pipe during excitation fluctuation. It should be noted that the processing module 402 in this application is also used to identify multiple abnormal reflection waveforms generated by the change in the cross-sectional impedance of the pile body from the pipe wave component, and then reconstruct the pile body based on all abnormal reflection waveforms and the longitudinal wave component to obtain the pile body defect characteristics of the pile foundation. The execution module 403 in this application is mainly used to intelligently identify the integrity status of the pile structure based on the characteristics of the pile defect and the preset bearing capacity assessment model, and to assess the bearing capacity of the pile foundation.

[0046] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described intelligent assessment method for the bearing capacity of building pile foundations.

[0047] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device for implementing an intelligent assessment method for the bearing capacity of building pile foundations, according to some embodiments of this application. The intelligent assessment method for the bearing capacity of building pile foundations in the above embodiments can... Figure 5 The computer device shown is used to implement this, and the computer device includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0048] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0049] The communication bus 502 can be used to transmit information between the aforementioned components.

[0050] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0051] The memory 503 stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiments can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0052] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0053] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0054] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0055] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described intelligent assessment method for the bearing capacity of building pile foundations.

[0056] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0057] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for intelligent evaluation of bearing capacity of building pile foundation, wherein, The pile foundation includes a pile top and a pile body, and a single sonic logging tube is pre-embedded in the pile body. The method is characterized by the following steps: Vertical vibration is applied to the top of the pile foundation, and the response signals of water pressure at multiple depths in the pre-embedded sonic logging tube in the pile body are collected by intelligent sensors under vertical vibration, thereby obtaining the wave train diagram of the water pressure response of the pile foundation. Based on the wave train diagram of the pile foundation water pressure response, the stress wave generated by vertical excitation is separated by dual wave velocity characteristics, thereby identifying the propagation differences of the stress wave in different media, and obtaining the longitudinal wave component when the stress wave generated by vertical excitation causes the pile body to vibrate and the pipe wave component of the fluid in the acoustic logging pipe during excitation oscillation. Multiple abnormal reflection waveforms generated by the change in the cross-sectional impedance of the pile body are identified from the pipe wave component. Then, the pile body is reconstructed based on all abnormal reflection waveforms and the longitudinal wave component to obtain the pile body defect characteristics of the pile foundation. Based on the characteristics of the pile defects and the preset bearing capacity assessment model, the integrity status of the pile structure of the pile foundation is intelligently identified, and the bearing capacity of the pile foundation is assessed.

2. The method of claim 1, wherein, The response signals of water pressure at multiple depths within the sonic logging tubes embedded in the pile body under vertical vibration are collected by intelligent sensors, thereby obtaining the wave train diagram of the pile foundation's water pressure response. Specifically, this includes: Multiple hydrophone arrays are pre-installed inside the acoustic tube; The response signals of water pressure at different depths of the fluid were collected using all the hydrophone arrays; The response signals of all water pressures are arranged and spliced ​​to obtain the wave train diagram of the water pressure response of the pile foundation.

3. The method of claim 1, wherein, The separation of the stress wave generated by vertical excitation based on the wave train diagram of the pile foundation water pressure response specifically includes: The arrival times of multiple first-arrival waves of the stress wave generated by vertical excitation are determined based on the wave train diagram of the pile foundation water pressure response. By performing dual-wave velocity analysis on the arrival times of all first-arrival waves, multiple dual-wave velocity characteristic parameters of the stress wave generated by vertical excitation are obtained. Based on all the dual-wave velocity characteristic parameters, the stress wave generated by vertical excitation is separated into longitudinal wave velocity and tube wave velocity.

4. The method of claim 1, wherein, Identifying the propagation differences of the stress wave in different media, and obtaining the longitudinal wave component of the stress wave generated by vertical excitation causing the pile body to vibrate, and the pipe wave component of the fluid inside the acoustic logging pipe during excitation fluctuations, specifically includes: The propagation difference characteristics of stress waves in solid-liquid media are determined based on the longitudinal wave velocity and the tube wave velocity obtained by separating the dual wave velocity characteristics. The propagation difference characteristics are reconstructed in the time domain to obtain the longitudinal wave component of the pile body during vibration and the pipe wave component of the fluid in the acoustic logging pipe during excitation oscillation.

5. The method of claim 1, wherein, The multiple abnormal reflection waveforms generated by the change in pile cross-sectional impedance identified from the aforementioned wave components specifically include: Determine the envelope curve of the signal amplitude of the tube wave component; Based on the envelope curve, multiple abnormal reflection waveforms generated by the change in the cross-sectional impedance of the pile body are determined.

6. The method of claim 1, wherein, Based on all abnormal reflection waveforms and the longitudinal wave component, the pile body is reconstructed to obtain the specific characteristics of the pile body defects, including: Multiple potential defect locations were identified based on all abnormal reflection waveforms; Determine the attenuation coefficient of the longitudinal wave component at different depths; The pile body is reconstructed based on all potential defect locations and all attenuation coefficients to obtain the pile body defect characteristics of the pile foundation.

7. The method of claim 1, wherein, The intelligent identification of the integrity status of the pile foundation structure based on the pile defect characteristics and the preset bearing capacity assessment model specifically includes: Obtain the preset bearing capacity assessment model; The probability distribution for determining the integrity level of the pile foundation structure based on the characteristics of the pile defects; The bearing capacity of the pile foundation is evaluated based on the probability distribution.

8. A building pile foundation bearing capacity intelligent evaluation system, wherein, The pile foundation includes a pile top and a pile body, and a single sonic logging tube is pre-embedded in the pile body. The system is characterized by comprising: The acquisition module is used to apply vertical excitation to the top of the pile foundation and acquire the water pressure response signals at multiple depths in the sonic logging tubes embedded in the pile body under vertical excitation through intelligent sensors, thereby obtaining the wave train diagram of the water pressure response of the pile foundation. The processing module is used to perform dual-wave velocity feature separation on the stress wave generated by vertical excitation based on the wave train diagram of the pile foundation water pressure response, thereby identifying the propagation differences of the stress wave in different media and obtaining the longitudinal wave component when the stress wave generated by vertical excitation causes the pile body to vibrate and the pipe wave component of the fluid in the acoustic logging pipe during excitation fluctuation. The processing module is also used to identify multiple abnormal reflection waveforms generated by the change in the cross-sectional impedance of the pile body from the pipe wave component, and then reconstruct the pile body based on all abnormal reflection waveforms and the longitudinal wave component to obtain the pile body defect characteristics of the pile foundation. The execution module is used to intelligently identify the integrity status of the pile structure based on the characteristics of the pile defects and a preset bearing capacity assessment model, and to assess the bearing capacity of the pile foundation.

9. A computer device, comprising: The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the intelligent assessment method for the bearing capacity of building pile foundations as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. When the computer program is executed by the processor, it implements the intelligent assessment method for the bearing capacity of building pile foundations as described in any one of claims 1 to 7.

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