A pile foundation damage identification method based on low strain characteristic curve matching

Abstract

The application discloses a pile foundation damage identification method based on characteristic curve matching low strain, and belongs to the technical field of pile foundation detection, and specifically comprises the following steps: applying a standardized impact excitation to a target area pile foundation, collecting a time-domain velocity response signal and generating a full-band phase change curve; identifying a directional turning point on the curve, recording a turning direction symbol, a reflection depth and a phase angle change rate absolute value; constructing a three-dimensional coordinate model to cluster the turning points to obtain an aggregated cluster; generating a virtual geological interface in combination with geological stratification data, calculating a spatial overlap degree of the aggregated cluster and the interface to screen a to-be-determined common aggregated cluster; and verifying energy sensitivity, and distinguishing rock layer reflection and damage aggregated clusters according to linear or nonlinear response characteristics. The application distinguishes geological interface reflection and real pile body damage signals in each pile foundation, and realizes accurate evaluation of the damage condition of the pile foundation.

Description

Technical Field

[0001] This invention relates to the field of pile foundation testing technology, and specifically to a pile foundation damage identification method based on characteristic curve matching with low strain. Background Technology

[0002] As a primary form of deep foundation, the construction quality of pile foundations, particularly their concealed works, directly impacts the safety of the superstructure. The low-strain reflected wave method is a widely adopted international technique for rapid assessment of pile foundation integrity. This technology uses a hand hammer to excite the pile head, collects velocity response signals, and analyzes the arrival time, amplitude, and polarity of the reflected signals in the waveform based on one-dimensional stress wave theory. This allows for the determination of whether defects such as necking, widening, segregation, and fracture exist in the pile body, along with their approximate locations.

[0003] However, long-term engineering practice has revealed several inherent technical bottlenecks in this traditional method, severely restricting its accuracy and reliability. When stress waves propagate in the pile, they are reflected not only at the cross-section where the pile impedance changes, but also at the interface where the pile-soil interaction is strong or where the pile tip enters different strata (such as from soft rock to hard rock). These reflection signals caused by geological conditions ("false signals") and the reflection signals generated by defects in the pile itself ("true signals") have similar morphological characteristics in the time domain waveform, making them difficult to distinguish.

[0004] The testing personnel rely heavily on personal experience for identification, resulting in a high rate of misjudgment and missed judgment. This often leads to unnecessary core sampling verification, which greatly increases the engineering cost and time cycle. Therefore, there is an urgent need in this field for a pile foundation integrity diagnosis method that can effectively distinguish between geological interface reflections and actual pile damage. Summary of the Invention

[0005] The purpose of this invention is to provide a pile foundation damage identification method based on characteristic curve matching with low strain, thereby solving the following technical problems:

[0006] Therefore, there is an urgent need in this field for a method for diagnosing pile foundation integrity that can effectively distinguish between geological interface reflections and actual pile damage.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A method for identifying pile foundation damage under low strain based on characteristic curve matching includes the following steps:

[0009] S1, standardize the impact excitation of all pile foundations in the target area in sequence, synchronously collect the time domain velocity response signal of each pile foundation and perform complex signal analysis processing, generate the phase change curve of the full frequency band phase angle of each pile foundation as a function of time, and establish a phase curve database indexed by the pile foundation number;

[0010] S2, identify all directional inflection points on each phase change curve, and record the inflection direction sign, reflection depth, and absolute value of the phase angle change rate for each directional inflection point;

[0011] S3. Using the reflection depth as the horizontal axis, the absolute value of the phase angle change rate as the vertical axis, and the sign of the turning direction as the vertical axis, a three-dimensional coordinate model is constructed. The set of all direction turning points extracted from the pile foundation is mapped to this three-dimensional coordinate model to form a global feature point cloud and perform cluster analysis to obtain several aggregated clusters.

[0012] S4. Obtain geological stratification data of the target area and project it into the three-dimensional coordinate model to form a virtual geological stratification interface. Calculate the spatial overlap between each cluster and the virtual geological stratification interface. If the spatial overlap of any cluster is greater than or equal to a preset threshold, then mark the cluster as a cluster to be determined.

[0013] S5, perform energy sensitivity verification on the undetermined common clusters. Based on the verification results, the undetermined common clusters with linear response characteristics are identified as rock reflection clusters, and the undetermined common clusters with nonlinear response characteristics are identified as damage clusters.

[0014] As a further aspect of the present invention: the specific process for generating the phase change curve in S1 is as follows:

[0015] The acquired time-domain velocity response signal is subjected to Hilbert transform to obtain the orthogonal component corresponding to the time-domain velocity response signal. The time-domain velocity response signal is taken as the real part, and its orthogonal component is taken as the imaginary part, and they are synthesized into a complex analytic signal. The arctangent value of the ratio of the real part to the imaginary part of the complex analytic signal is calculated point by point to obtain the instantaneous phase angle of the full frequency band corresponding to the time-domain velocity response signal. The continuous relationship of the instantaneous phase angle changing with time is output to obtain the phase change curve.

[0016] As a further aspect of the present invention: the specific process of recording the sign of the turning direction, the reflection depth, and the absolute value of the rate of change of the phase angle at each turning point in S2 is as follows:

[0017] The locations where the phase angle change trends on the phase change curve reverse are identified as directional inflection points. For each directional inflection point, the characteristic of its phase angle change trend changing from decreasing to increasing or from increasing to decreasing is determined, and a positive directional sign is assigned to the former and a negative directional sign to the latter. Based on the propagation speed of the stress wave in the pile medium and the signal propagation time corresponding to the directional inflection point, the corresponding reflection interface depth is calculated. The phase angle change rate is obtained by calculating the ratio of the phase angle change to the time change in the vicinity of the directional inflection point and taking the absolute value.

[0018] As a further aspect of the present invention: S3 further includes pre-assigning values ​​to the turning direction symbols, uniformly mapping the positive direction symbols to a fixed positive value, and uniformly mapping the negative direction symbols to a fixed negative value.

[0019] As a further aspect of the present invention: the specific process of forming the virtual geological stratification interface in step S4 is as follows:

[0020] Geological stratification data of the target area is acquired, and the depth values ​​in the stratification data are used as the core positioning basis. The interface depth values ​​of each geological layer are mapped to the three-dimensional coordinate model, and precise alignment is performed on the same dimension with the reflection depth as the horizontal axis. At the same time, it extends infinitely along the vertical axis in three-dimensional space and extends infinitely along both the positive and negative directions of the vertical axis to generate a vertical plane that is integrated with the three-dimensional coordinate model and is perpendicular to the reflection depth axis. Each vertical plane represents a virtual geological stratification interface.

[0021] As a further aspect of the present invention: the specific calculation process for spatial overlap in S4 is as follows:

[0022] The distribution of all feature points in the cluster on the reflection depth axis is obtained, and the upper and lower depth limits are determined to define the distribution span of the cluster. Simultaneously, the nominal depth value of the target virtual geological layer interface is obtained, and a positive and negative depth deviation is assigned based on the prior geological data, thereby forming a geological interface characterization interval with the nominal depth as the center and upper and lower boundaries.

[0023] The distribution of all feature points in the cluster on the reflection depth axis is obtained, and the upper and lower depth limits are determined to define the distribution span of the cluster. The nominal depth value of the target virtual geological layer interface is obtained, and a positive and negative depth deviation is assigned based on the prior geological data, thereby forming a geological interface characterization interval with the nominal depth as the center and upper and lower boundaries.

[0024] The ratio of the length of the overlap between the distribution span of the aggregate cluster and the geological interface characterization interval to the distribution span of the aggregate cluster itself is defined as the spatial overlap between the aggregate cluster and the virtual geological stratification interface.

[0025] As a further aspect of the present invention: if there are two or more virtual geological stratification interfaces, the spatial overlap between the cluster and each virtual geological stratification interface is calculated respectively to obtain a numerical set containing all calculation results. Then, the numerical set is traversed and the maximum spatial overlap value is selected, and the maximum value is used as the criterion for determining whether the cluster is a cluster to be determined as a common cluster.

[0026] As a further aspect of the present invention: S4 further includes, if the spatial overlap of any aggregate cluster is less than a preset threshold, then the aggregate cluster is directly labeled as a damaged aggregate cluster.

[0027] Extract the information recorded at any directional inflection point contained in the damage cluster, and back-map this information to the original phase change curve database indexed by the pile foundation number. Accurately locate the specific pile foundation that generated the directional inflection point and its corresponding damage depth location and time window on the pile foundation phase curve. Based on this, generate a damage report containing the pile foundation number, damage depth location and corresponding time window information.

[0028] As a further aspect of the present invention: the specific process of energy sensitivity verification of the undetermined common cluster in step S5 is as follows:

[0029] Several pile foundations containing a certain number of directional turning points are selected from the undetermined common clusters as verification samples; different energy levels of standardized impact excitation are applied to any verification sample pile foundation in sequence, and the time-domain velocity response signal of the verification sample pile foundation at each energy level is collected and recorded simultaneously.

[0030] Extract the reflected wave signal corresponding to the depth position of the cluster feature point from the time-domain response signal and obtain its amplitude data; perform linear regression analysis on the amplitude data of the reflected wave signal of the same verification sample pile foundation under different impact energy levels and the impact energy level.

[0031] If the linear correlation is greater than or equal to the preset correlation threshold, the verification sample is determined to exhibit linear energy response characteristics; if it is less than the preset correlation threshold, the verification sample is determined to exhibit nonlinear energy response characteristics. Based on the combined results of all verification samples, the dominant response characteristics of the undetermined common cluster are determined according to the principle of statistical significance.

[0032] As a further aspect of the present invention, when the energy sensitivity verification result of the undetermined common cluster shows that there is no dominant response characteristic, the undetermined common cluster is automatically labeled as a damage-reflection mixed category cluster, and all pile foundation numbers contained in the damage-reflection mixed category cluster are extracted. Based on this, a core drilling verification instruction containing all corresponding pile foundation numbers is generated, and core drilling verification is performed on all pile foundations according to the core drilling verification instruction.

[0033] The beneficial effects of this invention are:

[0034] 1) It is understood that pile damage will cause a sudden change in cross-sectional impedance. When the stress wave propagates to the damage location, reflection and transmission will occur. This impedance change will cause a sharp change in the phase angle, forming a phase inflection point. Therefore, this invention applies standardized impact excitation to the pile foundation, collects the time-domain velocity response signal and performs complex signal analysis processing to generate a full-band phase angle curve that changes with time. It accurately extracts the dynamic change of the instantaneous phase angle and clearly establishes a direct correspondence between the phase inflection point and the damage. This provides a highly identifiable phase feature basis for damage identification and improves the accuracy of damage location.

[0035] 2) By constructing a three-dimensional coordinate model and performing intelligent clustering analysis on multi-source feature points, this invention can effectively realize the automatic identification and separation of geological interface reflection signals and real damage signals, breaking through the limitations of traditional waveform interpretation based on manual experience. The algorithm automatically identifies reflection signal clusters with spatial aggregation characteristics and performs precise screening based on their spatial correlation with known geological stratification data. Thus, interference signals not caused by pile-soil interaction or rock stratification changes can be eliminated during the engineering inspection stage, significantly improving the specificity of defect diagnosis and avoiding unnecessary verification work caused by misjudgment from the source.

[0036] 3) This invention constructs a virtual geological interface by combining geological stratification data of the target area. First, it filters out undetermined clusters by calculating the spatial overlap between the clusters and the virtual interface, and then verifies their energy sensitivity. Based on the direct correspondence between phase inflection points and damage, spatial overlap analysis can initially exclude reflection signals associated with the geological interface. In energy verification, the damage exhibits a nonlinear response due to the nonlinear characteristics of impedance abrupt change, which further accurately locks the real damage. It can not only accurately identify the damage location, but also effectively distinguish between geological interface reflections and real damage, reducing unnecessary engineering verification and improving engineering efficiency while ensuring diagnostic reliability. Attached Figure Description

[0037] The invention will now be further described with reference to the accompanying drawings.

[0038] Figure 1 This is a schematic diagram of the process for identifying pile foundation damage based on characteristic curve matching under low strain according to the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Please see Figure 1 As shown, this invention is a pile foundation damage identification method based on characteristic curve matching with low strain, comprising the following steps:

[0041] S1, standardize the impact excitation of all pile foundations in the target area in sequence, synchronously collect the time domain velocity response signal of each pile foundation and perform complex signal analysis processing, generate the phase change curve of the full frequency band phase angle of each pile foundation as a function of time, and establish a phase curve database indexed by the pile foundation number;

[0042] S2, identify all directional inflection points on each phase change curve, and record the inflection direction sign, reflection depth, and absolute value of the phase angle change rate for each directional inflection point;

[0043] S3. Using the reflection depth as the horizontal axis, the absolute value of the phase angle change rate as the vertical axis, and the sign of the turning direction as the vertical axis, a three-dimensional coordinate model is constructed. The set of all direction turning points extracted from the pile foundation is mapped to this three-dimensional coordinate model to form a global feature point cloud and perform cluster analysis to obtain several aggregated clusters.

[0044] S4. Obtain geological stratification data of the target area and project it into the three-dimensional coordinate model to form a virtual geological stratification interface. Calculate the spatial overlap between each cluster and the virtual geological stratification interface. If the spatial overlap of any cluster is greater than or equal to a preset threshold, then mark the cluster as a cluster to be determined.

[0045] S5, perform energy sensitivity verification on the undetermined common clusters. Based on the verification results, the undetermined common clusters with linear response characteristics are identified as rock reflection clusters, and the undetermined common clusters with nonlinear response characteristics are identified as damage clusters.

[0046] 1) It is understood that pile damage will cause a sudden change in cross-sectional impedance. When the stress wave propagates to the damage location, reflection and transmission will occur. This impedance change will cause a sharp change in the phase angle, forming a phase inflection point. Therefore, this invention applies standardized impact excitation to the pile foundation, collects the time-domain velocity response signal and performs complex signal analysis processing to generate a full-band phase angle curve that changes with time. It accurately extracts the dynamic change of the instantaneous phase angle and clearly establishes a direct correspondence between the phase inflection point and the damage. This provides a highly identifiable phase feature basis for damage identification and improves the accuracy of damage location.

[0047] 2) By constructing a three-dimensional coordinate model and performing intelligent clustering analysis on multi-source feature points, this invention can effectively realize the automatic identification and separation of geological interface reflection signals and real damage signals, breaking through the limitations of traditional waveform interpretation based on manual experience. The algorithm automatically identifies reflection signal clusters with spatial aggregation characteristics and performs precise screening based on their spatial correlation with known geological stratification data. Thus, interference signals not caused by pile-soil interaction or rock stratification changes can be eliminated during the engineering inspection stage, significantly improving the specificity of defect diagnosis and avoiding unnecessary verification work caused by misjudgment from the source.

[0048] 3) This invention constructs a virtual geological interface by combining geological stratification data of the target area. First, it filters out undetermined clusters by calculating the spatial overlap between the clusters and the virtual interface, and then verifies their energy sensitivity. Based on the direct correspondence between phase inflection points and damage, spatial overlap analysis can initially exclude reflection signals associated with the geological interface. In energy verification, the damage exhibits a nonlinear response due to the nonlinear characteristics of impedance abrupt change, which further accurately locks the real damage. It can not only accurately identify the damage location, but also effectively distinguish between geological interface reflections and real damage, reducing unnecessary engineering verification and improving engineering efficiency while ensuring diagnostic reliability.

[0049] In a preferred embodiment of the present invention, the specific process for generating the phase change curve in step S1 is as follows:

[0050] The acquired time-domain velocity response signal is subjected to Hilbert transform to obtain the orthogonal component corresponding to the time-domain velocity response signal. The time-domain velocity response signal is taken as the real part, and its orthogonal component is taken as the imaginary part, and they are synthesized into a complex analytic signal. The arctangent value of the ratio of the real part to the imaginary part of the complex analytic signal is calculated point by point to obtain the instantaneous phase angle of the full frequency band corresponding to the time-domain velocity response signal. The continuous relationship of the instantaneous phase angle changing with time is output to obtain the phase change curve.

[0051] By obtaining orthogonal components through Hilbert transform and synthesizing complex analytic signals, the phase information in the original time-domain velocity response signal can be fully preserved. This is because a real signal alone cannot fully reflect its phase characteristics, while the addition of orthogonal imaginary parts allows the complex analytic signal to more completely describe the details of the signal's phase changes. Calculating the instantaneous phase angle across the entire frequency band and generating phase change curves allows for a visual representation of the signal's phase state at different times. Phase changes are closely related to the propagation of stress waves within the pile foundation. When stress waves encounter reflecting interfaces (such as damage or geological stratification), significant phase changes occur, which are clearly reflected on the phase change curve. This provides reliable basic data for subsequent identification of directional inflection points on the phase change curve. These inflection points allow for further analysis of the characteristics of reflecting interfaces, thus providing a crucial basis for distinguishing between pile foundation damage and geological stratification reflections, achieving intelligent identification of pile foundation wave velocities, and ultimately realizing the goal of accurately identifying the internal condition of the pile foundation.

[0052] In another preferred embodiment of the present invention, the specific process of recording the sign of the turning direction, the reflection depth, and the absolute value of the rate of change of the phase angle at each turning point in step S2 is as follows:

[0053] The locations where the phase angle change trends on the phase change curve reverse are identified as directional inflection points. For each directional inflection point, the characteristic of its phase angle change trend changing from decreasing to increasing or from increasing to decreasing is determined, and a positive directional sign is assigned to the former and a negative directional sign to the latter. Based on the propagation speed of the stress wave in the pile medium and the signal propagation time corresponding to the directional inflection point, the corresponding reflection interface depth is calculated. The phase angle change rate is obtained by calculating the ratio of the phase angle change to the time change in the vicinity of the directional inflection point and taking the absolute value.

[0054] Because the turning points of the direction correspond to the reflection interfaces in the pile body, identifying these points can locate potential damage or geological stratification interfaces. Assigning symbols to the turning directions can distinguish the differences in phase change trends caused by different types of reflection interfaces, providing a basis for subsequent differentiation of interface properties. Calculating the reflection depth can determine the specific location of the reflection interface in the pile body and clarify the depth of possible anomalies. Calculating the absolute value of the phase angle change rate can reflect the severity of phase changes caused by the reflection interface, further characterizing the interface properties. Its advantage is that it can comprehensively capture the characteristics of each reflection interface from multiple dimensions, avoiding judgment bias caused by a single feature. It provides detailed and comprehensive basic data for subsequent cluster analysis of these feature points and differentiation between damage and geological stratification reflections. Ultimately, achieving the goal of accurately identifying pile foundation wave velocities and differentiating pile damage from geological interface reflections is crucial. By extracting these features, subsequent clustering and verification can be more targeted, thereby improving the accuracy and reliability of identification.

[0055] In another preferred embodiment of the present invention, step S3 further includes pre-assigning values ​​to the turning direction symbols, uniformly mapping the positive direction symbols to a fixed positive value, and uniformly mapping the negative direction symbols to a fixed negative value.

[0056] Because the direction inflection symbol itself is a qualitative classification identifier, it cannot be directly used for spatial positioning and clustering calculations in a 3D coordinate model. However, mapping it to fixed positive and negative values ​​transforms the directional features into quantifiable parameters. These parameters, along with reflection depth (horizontal axis) and the absolute value of the phase angle change rate (vertical axis), constitute the three dimensions of the 3D coordinate model, satisfying the spatial mapping requirements for numerical data. The advantage is that all features of the direction inflection points become calculable values, facilitating the subsequent mapping of all pile foundation direction inflection points to a 3D space to form a feature point cloud. Furthermore, during clustering analysis, the correlation between feature points can be determined by the differences or similarities between values, accurately aggregating points with the same or similar features. The aim is to provide unified numerical feature parameters for the construction of the 3D coordinate model and clustering analysis, ensuring that the direction inflection points of different pile foundations can be effectively analyzed and aggregated in the same spatial coordinate system. This is crucial for achieving the goal of accurately identifying pile foundation wave velocities, because only through numerical processing can feature points be reasonably aggregated in 3D space, allowing for subsequent differentiation of rock layer reflection and damage based on geological stratification data, thereby achieving intelligent identification.

[0057] In another preferred embodiment of the present invention, the specific process of forming the virtual geological stratification interface in step S4 is as follows:

[0058] Geological stratification data of the target area is acquired, and the depth values ​​in the stratification data are used as the core positioning basis. The interface depth values ​​of each geological layer are mapped to the three-dimensional coordinate model, and precise alignment is performed on the same dimension with the reflection depth as the horizontal axis. At the same time, it extends infinitely along the vertical axis in three-dimensional space and extends infinitely along both the positive and negative directions of the vertical axis to generate a vertical plane that is integrated with the three-dimensional coordinate model and is perpendicular to the reflection depth axis. Each vertical plane represents a virtual geological stratification interface.

[0059] Geological stratification data for the target area is obtained. This data typically comes from geological survey reports for the region and includes information on the depth of interfaces between different geological strata. For example, the report might record a certain depth for the interface between topsoil and clay layers, and another value for the interface between clay and rock layers. These depth values ​​from the stratification data are used as the core positioning basis because depth is a key parameter describing the vertical position of geological strata, consistent with the vertical position represented by the reflection depth dimension in a 3D coordinate model. Next, the depth value of each geological stratum's interface is mapped to the 3D coordinate model. Specifically, the position on the horizontal axis (reflection depth) of the 3D coordinate model that matches the geological interface depth value is found, achieving precise alignment. For example, if a geological interface depth is 10 meters, its horizontal position in the model is determined at 10 meters on the horizontal axis. Simultaneously, the model extends infinitely along the vertical axis (absolute value of the phase angle change rate) in three-dimensional space. This is because the geological interface is a continuous plane, and its existence is independent of the specific value of the phase angle change rate; regardless of whether the phase angle change rate is large or small, the interface always exists at that depth. It also extends infinitely along both the positive and negative directions of the vertical axis (the mapping value of the turning direction sign) because the reflection caused by the geological interface may lead to positive or negative turning points in the phase angle change trend. The interface itself is not limited to a single turning direction, therefore it needs to cover all possible values ​​of the vertical axis. Through this extension, a vertical plane perpendicular to the reflection depth axis is generated and integrated with the three-dimensional coordinate model. Each such vertical plane represents a virtual geological stratification interface; for example, the vertical plane corresponding to a depth of 10 meters represents the geological interface at that depth.

[0060] Because the interface depth in geological stratification data objectively reflects actual stratigraphic changes, mapping it to a 3D coordinate model to form a virtual geological stratification interface allows abstract geological information to be correlated with the feature point cloud (set of directional turning points) in the model in a visualized spatial planar form. This provides an intuitive reference standard for subsequently determining whether the aggregated clusters of feature points are related to the geological interface, as the feature points formed by reflections from the real geological interface should have a high degree of spatial consistency with the corresponding virtual geological stratification interface. By constructing a virtual geological stratification interface, a spatial reference is provided to distinguish between geological interface reflections and pile damage reflections. This provides a clear comparison object for subsequently calculating the spatial overlap between aggregated clusters and the virtual interface, clarifying which reflections originate from the geological interface. This allows for more accurate identification of reflections truly caused by pile damage, improving the reliability of the identification.

[0061] In another preferred embodiment of the present invention, the specific calculation process of spatial overlap in step S4 is as follows:

[0062] The distribution of all feature points in the cluster on the reflection depth axis is obtained, and the upper and lower depth limits are determined to define the distribution span of the cluster. Simultaneously, the nominal depth value of the target virtual geological layer interface is obtained, and a positive and negative depth deviation is assigned based on the prior geological data, thereby forming a geological interface characterization interval with the nominal depth as the center and upper and lower boundaries.

[0063] The distribution of all feature points in the cluster on the reflection depth axis is obtained, and the upper and lower depth limits are determined to define the distribution span of the cluster. The nominal depth value of the target virtual geological layer interface is obtained, and a positive and negative depth deviation is assigned based on the prior geological data, thereby forming a geological interface characterization interval with the nominal depth as the center and upper and lower boundaries.

[0064] The ratio of the length of the overlap between the distribution span of the aggregate cluster and the geological interface characterization interval to the distribution span of the aggregate cluster itself is defined as the spatial overlap between the aggregate cluster and the virtual geological stratification interface.

[0065] When calculating spatial overlap, first examine the specific values ​​of all feature points in the cluster on the reflection depth axis (horizontal coordinate). Find the minimum depth value as the lower limit and the maximum depth value as the upper limit. The range between these two values ​​represents the distribution span of the cluster. This is because the distribution span completely covers the depth positions of all feature points in the cluster, reflecting the overall distribution range of these points in the depth direction. Simultaneously, acquire the nominal depth value of the target virtual geological stratification interface. For example, if the standard depth recorded in the geological data is 10 meters, then based on existing geological exploration experience in the area (prior geological data), and considering the slight fluctuations that may exist in the actual stratigraphic interface, assign a positive deviation (e.g., +1 meter) and a negative deviation (e.g., -1 meter). This forms a geological interface representation range centered at 10 meters, extending from 11 meters upwards to 9 meters downwards. This is because geological interfaces are not perfectly flat planes; their actual positions may vary slightly near the nominal depth. Setting deviations better reflects the actual stratigraphic conditions and accurately represents the possible range of the geological interface. Finally, the common length is accurately obtained by calculating the intersection of the upper and lower limits. Finally, dividing the length of this overlapping portion by the distribution span of the cluster itself yields the spatial overlap between the cluster and the virtual geological stratification interface. This is because the ratio directly reflects the distribution proportion of the cluster's feature points within the geological interface representation range; a higher ratio indicates a stronger spatial correlation between the two.

[0066] Since the spatial overlap degree can quantitatively describe the association degree between the aggregation cluster and the virtual geological stratification interface in the depth direction, it can avoid the subjectivity of judgment only through intuitive observation. Its advantage is that through clear numerical calculation, it can objectively reflect the spatial coincidence of the characteristic point cluster and the geological interface, making the association degrees of different aggregation clusters with the geological interface comparable. Screen out the clusters that may be related to the geological stratification interface from numerous aggregation clusters, and provide a quantitative basis for distinguishing whether these clusters are from geological interface reflections or pile shaft damages in the subsequent process. Only through the quantified spatial overlap degree can we scientifically judge which aggregation clusters may be related to geological factors, and then provide a clear object for the subsequent energy sensitivity verification, improving the accuracy and reliability of the entire identification process.

[0067] In another preferred embodiment of the present invention, if there are more than two virtual geological stratification interfaces, calculate the spatial overlap degrees between the aggregation cluster and each virtual geological stratification interface respectively, obtain a numerical set containing all calculation results, then traverse this numerical set and select the maximum value of the spatial overlap degree therein, and use this maximum value as the discrimination criterion for determining whether this aggregation cluster is a pending common aggregation cluster.

[0068] When there are more than two virtual geological stratification interfaces, first clarify the specific information of each virtual geological stratification interface. For example, assume that there are three virtual geological stratification interfaces in the target area, corresponding to the geological interfaces at depths of X meters, Y meters, and Z meters respectively. For a certain aggregation cluster to be analyzed, calculate the spatial overlap degrees between this aggregation cluster and the virtual geological stratification interface at X meters, the virtual geological stratification interface at Y meters, and the virtual geological stratification interface at Z meters respectively according to the previous method of calculating the spatial overlap degree, and obtain three specific values, such as m, n, and p respectively. These values together form a numerical set containing all calculation results, that is, {m, n, p}. This is because each virtual geological stratification interface is an independent geological interface, and the aggregation cluster may have a stronger association with one of the interfaces. Calculating separately can fully present the spatial association of the aggregation cluster with each interface. Subsequently, traverse this numerical set, that is, view each value in the set in turn, compare their magnitudes, and select the largest value therefrom. For example, if m < n < p, then select p as the maximum spatial overlap degree corresponding to this aggregation cluster. This is because the maximum spatial overlap degree reflects the association degree between this aggregation cluster and the interface with the strongest association among all virtual geological stratification interfaces. Compared with other smaller overlap degrees, it can more accurately reflect whether the aggregation cluster has a significant association with the geological stratification interface. Finally, use this maximum value p as the discrimination criterion for determining whether this aggregation cluster is a pending common aggregation cluster, that is, determine whether this aggregation cluster belongs to the pending common aggregation cluster by comparing the size of p with a preset threshold.

[0069] When multiple virtual geological stratification interfaces exist, a cluster may only have a strong spatial association with one of these interfaces, while its association with other interfaces is weak. If the maximum value is not selected and all overlap values ​​are used directly for judgment, the association between the cluster and the geological interface may be misjudged due to some low overlap values. The advantage of this approach is that it can focus on the most likely association between the cluster and the geological interface, avoiding interference caused by the existence of multiple interfaces, and making the judgment results more consistent with reality. The goal is to accurately screen out those clusters that have a significant spatial association with a certain geological stratification interface in multi-geological-interface scenarios, providing accurate objects for subsequent energy sensitivity verification. This is crucial for the scheme to ultimately achieve the goal of distinguishing geological interface reflection from pile damage reflection. This method ensures more accurate determination of undetermined common clusters, thereby improving the reliability and accuracy of the entire intelligent pile foundation wave velocity identification method.

[0070] In another preferred embodiment of the present invention, step S4 further includes, if the spatial overlap of any aggregate cluster is less than a preset threshold, then the aggregate cluster is directly labeled as a damaged aggregate cluster.

[0071] Extract the information recorded at any directional inflection point contained in the damage cluster, and back-map this information to the original phase change curve database indexed by the pile foundation number. Accurately locate the specific pile foundation that generated the directional inflection point and its corresponding damage depth location and time window on the pile foundation phase curve. Based on this, generate a damage report containing the pile foundation number, damage depth location and corresponding time window information.

[0072] Because clusters with spatial overlap less than a preset threshold have weak correlations between their feature points and geological stratification interfaces, they are more likely to correspond to damage within the pile itself. Directly identifying these clusters as damage clusters can quickly filter out potential damage signal clusters. Generating a damage report with detailed information provides engineers with clear damage locations and signal evidence. This reduces the possibility of misjudging damage as geological interface reflections, improving the efficiency and accuracy of damage identification. The goal is to quickly and accurately identify and locate pile damage, providing specific and reliable information for pile foundation quality assessment. This is crucial for the scheme to ultimately achieve the goal of accurately identifying pile wave velocities and distinguishing pile damage from geological interface reflections, providing direct evidence for engineering decisions and ensuring the safety and reliability of pile foundation engineering.

[0073] In another preferred embodiment of the present invention, the specific process of performing energy sensitivity verification on the undetermined common cluster in step S5 is as follows:

[0074] Several pile foundations containing a certain number of directional turning points are selected from the undetermined common clusters as verification samples; different energy levels of standardized impact excitation are applied to any verification sample pile foundation in sequence, and the time-domain velocity response signal of the verification sample pile foundation at each energy level is collected and recorded simultaneously.

[0075] Extract the reflected wave signal corresponding to the depth position of the cluster feature point from the time-domain response signal and obtain its amplitude data; perform linear regression analysis on the amplitude data of the reflected wave signal of the same verification sample pile foundation under different impact energy levels and the impact energy level.

[0076] If the linear correlation is greater than or equal to the preset correlation threshold, the verification sample is determined to exhibit linear energy response characteristics; if it is less than the preset correlation threshold, the verification sample is determined to exhibit nonlinear energy response characteristics. Based on the combined results of all verification samples, the dominant response characteristics of the undetermined common cluster are determined according to the principle of statistical significance.

[0077] When selecting verification samples from undetermined common clusters, it is necessary to select pile foundations containing a certain number of directional inflection points. For example, if an undetermined common cluster involves 10 pile foundations, and 6 of them each contain more than 8 directional inflection points, then 3 of these 6 should be selected as verification samples. This is because these pile foundations have abundant characteristic points in the cluster and can better represent the overall characteristics of the cluster. When testing any verification sample pile foundation, standardized impact excitations of different energy levels are applied sequentially. For example, the pile head is first struck with a small force, then with a medium force, and then with a large force. Standardized impacts ensure that the manner of each impact (such as the striking position and angle) is consistent, only the energy level is different. At the same time, the time-domain velocity response signal of the pile foundation at each energy level is collected synchronously. This is because impacts of different energies will cause differences in the propagation characteristics of stress waves in the pile body, and the corresponding response signals will also be different. These signals can reflect the energy response characteristics of the reflection interface. Next, the reflected wave signal corresponding to the depth of the cluster's feature points is extracted from the acquired time-domain response signal. For example, if the cluster's feature points are concentrated at a depth of 12 meters, the waveform of the reflected wave at 12 meters is found from the time-domain signal of each energy level, and then the amplitude data (i.e., the peak value) of this waveform is read. This is because this depth is the location of the reflection interface corresponding to the undetermined common cluster, and the change in its reflected wave amplitude directly reflects the interface's response to different energy impacts. Then, linear regression analysis is performed on the reflected wave amplitude data of the same verification sample at different impact energy levels. For example, the amplitudes corresponding to low energy, medium energy, and high energy are correlated with the values ​​of low, medium, and high energy levels, respectively, to see if they show an approximately linear relationship. This is because linear regression quantifies the degree of correlation between the two; the more obvious the linear relationship, the more synchronous the changes in energy and amplitude. If the linear correlation obtained from the analysis is greater than or equal to a preset correlation threshold, the validation sample is determined to exhibit an energy-linear response characteristic; if it is less than the threshold, it is determined to exhibit an energy-nonlinear response characteristic. For example, after setting the preset threshold, the linear correlation of one sample meets the linearity criterion, while another meets the nonlinearity criterion. Finally, by combining the judgment results of all validation samples, the dominant response characteristic of the cluster is determined based on the principle of statistical significance. For example, if two out of three samples exhibit a linear response and this majority case is statistically significant, the cluster is determined to have a dominant linear response characteristic.

[0078] Although the undetermined common aggregate clusters have a high degree of spatial overlap with the geological stratification interface, they may contain real damage. The energy response characteristics of rock layer reflection and damage are different: as a stable interface, the impact energy and the reflected wave amplitude of the rock layer are usually linear; damage, due to the instability of the interface, will have a nonlinear relationship. Energy sensitivity verification can use this difference to accurately distinguish between the two, avoiding the limitations of relying solely on spatial overlap. Objective analysis of energy response characteristics reduces misjudgment and ultimately clarifies whether the undetermined common aggregate clusters are rock layer reflections or damage. This provides a key basis for accurately identifying the condition of the pile foundation, thus providing an accurate reference for pile foundation quality assessment and engineering decision-making.

[0079] In another preferred embodiment of the present invention, when the energy sensitivity verification result of the undetermined common cluster shows that there is no dominant response characteristic, the undetermined common cluster is automatically labeled as a damage-reflection mixed category cluster, and all pile foundation numbers contained in the damage-reflection mixed category cluster are extracted. Based on this, a core drilling verification instruction containing all corresponding pile foundation numbers is generated, and core drilling verification is performed on all pile foundations according to the core drilling verification instruction.

[0080] It is understandable that when a cluster of undetermined common features lacks a dominant energy response characteristic, it indicates that the feature points it contains may simultaneously involve geological interface reflection and pile damage. Preliminary signal analysis alone cannot accurately distinguish between them. In such cases, more direct physical verification methods are needed to avoid overlooking actual damage or misjudging geological interfaces due to ambiguous judgments, ensuring that the assessment of pile foundation conditions is not biased. Core drilling verification, a direct physical detection method, clarifies which pile foundations in the mixed clusters have damage and which only involve geological interface reflection, providing a solid basis for the final pile foundation quality assessment. When there is uncertainty in signal analysis, physical verification ensures the reliability of the results, providing strong support for engineering decisions.

[0081] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for identifying pile foundation damage under low strain based on characteristic curve matching, characterized in that, Includes the following steps: S1, standardize the impact excitation of all pile foundations in the target area in sequence, synchronously collect the time domain velocity response signal of each pile foundation and perform complex signal analysis processing, generate the phase change curve of the full frequency band phase angle of each pile foundation as a function of time, and establish a phase curve database indexed by the pile foundation number; S2, identify all directional inflection points on each phase change curve, and record the inflection direction sign, reflection depth, and absolute value of the phase angle change rate for each directional inflection point; S3. Using the reflection depth as the horizontal axis, the absolute value of the phase angle change rate as the vertical axis, and the sign of the turning direction as the vertical axis, a three-dimensional coordinate model is constructed. The set of all direction turning points extracted from the pile foundation is mapped to this three-dimensional coordinate model to form a global feature point cloud and perform cluster analysis to obtain several aggregated clusters. S4. Obtain geological stratification data of the target area and project it into the three-dimensional coordinate model to form a virtual geological stratification interface. Calculate the spatial overlap between each cluster and the virtual geological stratification interface. If the spatial overlap of any cluster is greater than or equal to a preset threshold, then mark the cluster as a cluster to be determined. The specific calculation process for spatial overlap is as follows: The distribution of all feature points in the cluster on the reflection depth axis is obtained, and the upper and lower depth limits are determined to define the distribution span of the cluster. The nominal depth value of the target virtual geological layer interface is obtained, and a positive and negative depth deviation is assigned based on the prior geological data, thereby forming a geological interface characterization interval with the nominal depth as the center and upper and lower boundaries. The ratio of the length of the overlap between the distribution span of the aggregate cluster and the geological interface characterization interval to the distribution span of the aggregate cluster is defined as the spatial overlap between the aggregate cluster and the virtual geological stratification interface. S5, perform energy sensitivity verification on the undetermined common clusters. Based on the verification results, determine the undetermined common clusters with linear response characteristics as rock reflection clusters, and determine the undetermined common clusters with nonlinear response characteristics as damage clusters. The specific process for energy sensitivity verification is as follows: Several pile foundations containing a certain number of directional turning points are selected from the undetermined common clusters as verification samples; different energy levels of standardized impact excitation are applied to any verification sample pile foundation in sequence, and the time-domain velocity response signal of the verification sample pile foundation at each energy level is collected and recorded simultaneously. Extract the reflected wave signal corresponding to the depth position of the feature point of the undetermined common cluster from the time-domain velocity response signal and obtain its amplitude data; perform linear regression analysis on the amplitude data of the reflected wave signal of the same verification sample pile foundation under different impact energy levels and the impact energy level. If the linear correlation is greater than or equal to the preset correlation threshold, the verification sample is determined to exhibit linear energy response characteristics; if it is less than the preset correlation threshold, the verification sample is determined to exhibit nonlinear energy response characteristics. Based on the combined results of all validation samples, the dominant response characteristics of the undetermined common cluster are determined according to the principle of statistical significance.

2. The pile foundation damage identification method based on characteristic curve matching with low strain according to claim 1, characterized in that, In S1, the specific process of generating the phase change curve is as follows: The acquired time-domain velocity response signal is subjected to Hilbert transform to obtain the orthogonal component corresponding to the time-domain velocity response signal. The time-domain velocity response signal is taken as the real part, and its orthogonal component is taken as the imaginary part, and they are synthesized into a complex analytic signal. The arctangent value of the ratio of the real part to the imaginary part of the complex analytic signal is calculated point by point to obtain the instantaneous phase angle of the full frequency band corresponding to the time-domain velocity response signal. The continuous relationship of the instantaneous phase angle changing with time is output to obtain the phase change curve.

3. The pile foundation damage identification method based on characteristic curve matching with low strain according to claim 1, characterized in that, In S2, the specific process of recording the sign of the turning direction, the reflection depth, and the absolute value of the rate of change of the phase angle at each turning point is as follows: The locations where the phase angle change trends on the phase change curve reverse are identified as directional inflection points. For each directional inflection point, the characteristic of its phase angle change trend changing from decreasing to increasing or from increasing to decreasing is determined, and a positive directional sign is assigned to the former and a negative directional sign to the latter. Based on the propagation speed of the stress wave in the pile medium and the signal propagation time corresponding to the directional inflection point, the corresponding reflection interface depth is calculated. By calculating the ratio of the phase angle change to the time change in the vicinity of the directional inflection point and taking the absolute value, the absolute value of the phase angle change rate is obtained.

4. The pile foundation damage identification method based on characteristic curve matching with low strain according to claim 3, characterized in that, S3 further includes pre-assigning values ​​to the turning direction symbols, uniformly mapping the positive direction symbols to a fixed positive value, and uniformly mapping the negative direction symbols to a fixed negative value.

5. The pile foundation damage identification method based on characteristic curve matching with low strain according to claim 1, characterized in that, In S4, the specific process of forming the virtual geological stratification interface is as follows: Geological stratification data of the target area is acquired, and the depth values ​​in the stratification data are used as the core positioning basis. The interface depth values ​​of each geological layer are mapped to the three-dimensional coordinate model, and precise alignment is performed on the same dimension with the reflection depth as the horizontal axis. At the same time, it extends infinitely along the vertical axis in three-dimensional space and extends infinitely along both the positive and negative directions of the vertical axis to generate a vertical plane that is integrated with the three-dimensional coordinate model and is perpendicular to the reflection depth axis. Each vertical plane represents a virtual geological stratification interface.

6. The pile foundation damage identification method based on characteristic curve matching with low strain according to claim 1, characterized in that, If there are more than two virtual geological stratification interfaces, the spatial overlap between the cluster and each virtual geological stratification interface is calculated to obtain a numerical set containing all the calculation results. Then, the numerical set is traversed and the maximum spatial overlap value is selected. This maximum value is used as the criterion for determining whether the cluster is a cluster to be determined as a common cluster.

7. The pile foundation damage identification method based on characteristic curve matching with low strain according to claim 1, characterized in that, S4 further includes that if the spatial overlap of any aggregate cluster is less than a preset threshold, the aggregate cluster is directly labeled as a damaged aggregate cluster. Extract the information recorded at any directional turning point contained in the damage cluster, and back-map this information to a phase curve database indexed by the pile foundation number. Accurately locate the specific pile foundation that generated the directional turning point and its corresponding damage depth location and time window on the pile foundation phase curve. Based on this, generate a damage report containing the pile foundation number, damage depth location, and corresponding time window information.

8. The pile foundation damage identification method based on characteristic curve matching with low strain according to claim 1, characterized in that, The method also includes automatically labeling the undetermined common cluster as a damage-reflection mixed category cluster when the energy sensitivity verification result of the undetermined common cluster shows that there is no dominant response characteristic, extracting all the pile foundation numbers contained in the damage-reflection mixed category cluster, generating a core drilling verification instruction containing all the corresponding pile foundation numbers, and performing core sampling verification on all pile foundations according to the core drilling verification instruction.

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