A rigid base self-adaptive sediment thickness measuring method and system based on a virtual simulation system

Abstract

The application relates to the technical field of sediment measurement, and particularly discloses a rigid base self-adaptive sediment thickness measuring method and system based on a virtual simulation system, which comprises the following steps: controlling a detection device to be lowered, and obtaining the depth of the top surface of a sediment layer of a target pile hole; under the assistance of dynamic high-frequency vibration, controlling a probe in the detection device to uniformly penetrate into the sediment layer, and obtaining resistance-depth time series data corresponding to the target pile hole; according to the resistance-depth time series data, obtaining the key feature depth corresponding to the target pile hole; according to the key feature depth and the corresponding soil type, determining the virtual rigid base depth corresponding to the target pile hole; and according to the virtual rigid base depth and the top surface depth, determining the sediment thickness corresponding to the target pile hole, so that the accuracy and reliability of the sediment thickness can be effectively improved.

Description

Technical Field

[0001] This application relates to the field of sediment measurement technology, specifically to an adaptive sediment thickness measurement method and system based on a virtual simulation system for rigid substrates. Background Technology

[0002] In the construction of drilled cast-in-place piles with mud-wall protection, the presence of sediment at the bottom of the hole directly affects the bearing capacity of the pile tip. The sediment layer is essentially a loose-dense transitional medium composed of drill cuttings, mud cake, rock powder, etc., exhibiting significant heterogeneity and nonlinearity in its physical and mechanical properties. Excessive sediment thickness reduces the pile's side friction and end resistance, making it a crucial factor affecting the quality of drilled cast-in-place pile foundations. Therefore, accurate detection of sediment thickness is a vital step in cast-in-place pile construction. However, there is no clear geometric boundary between the sediment and the bearing layer; rather, it is a continuous transition zone from loose sediment to dense undisturbed soil. Within this transition zone, the material's density, particle interlocking degree, and shear strength exhibit gradual changes. Current technologies typically rely on the operator's touch to determine the moment of bottom contact, a method that severely impacts the accuracy of sediment thickness measurement. Furthermore, some studies have proposed detection devices based on one-dimensional wave theory, identifying the sediment interface by the abrupt change in the velocity-time history curve caused by the change in wave impedance at the interface between the test rod and the sediment. However, traditional manual methods and their derivatives have many shortcomings. There is no clear geometric interface between the sediment and the bearing layer. Traditional methods attempt to find a point of abrupt change as the interface location, which inherently leads to cognitive biases. The measurement results heavily depend on the operator's tactile judgment, and the judgments of different people vary significantly. Furthermore, factors such as rope wear and mud buoyancy can also introduce large measurement errors.

[0003] In summary, existing sediment thickness detection technologies have many unresolved technical problems, mainly the lack of effective solutions to the interface ambiguity between sediment and the bearing layer; difficulty in distinguishing local hard points from the actual bearing layer; and consequently, a lack of ability to adaptively adjust detection strategies according to soil type, ultimately affecting the consistency and traceability of measurement results.

[0004] To address the aforementioned issues, this application proposes a rigid substrate adaptive sediment thickness measurement method and system based on a virtual simulation system. Through dynamic high-frequency vibration-assisted penetration, vibration drag reduction analysis, automatic identification of key feature depths, and dynamic optimization of soil compensation coefficients, the fully automated and accurate measurement of sediment thickness is achieved. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a rigid substrate adaptive sediment thickness measurement method and system based on a virtual simulation system, which can effectively improve the accuracy and reliability of sediment thickness measurement.

[0006] In a first aspect, embodiments of this application provide an adaptive sediment thickness measurement method for a rigid substrate based on a virtual simulation system, comprising: The detection equipment was lowered and the top depth of the sediment layer in the target pile hole was obtained. With the assistance of dynamic high-frequency vibration, the probe in the detection device is controlled to penetrate the sediment layer at a uniform speed and acquire the resistance-depth time series data corresponding to the target pile hole. Based on the resistance-depth time series data, the key feature depth corresponding to the target pile hole is obtained; The virtual rigid base depth corresponding to the target pile hole is determined based on the key feature depth and its corresponding soil type. The sediment thickness corresponding to the target pile hole is determined based on the depth of the virtual rigid base and the depth of the top surface.

[0007] In some embodiments, controlling the probe in the detection device to penetrate the sediment layer at a uniform speed under dynamic high-frequency vibration assistance includes: For each detection depth, obtain the penetration resistance corresponding to that detection depth; Based on the penetration resistance, determine the adaptive vibration parameters corresponding to the detection depth; and When the penetration resistance is greater than or equal to the resistance threshold, the probe is controlled to run for a preset duration according to the adaptive vibration parameters.

[0008] In some embodiments, obtaining the key feature depth corresponding to the target pile hole based on the resistance-depth time series data includes: When the penetration resistance is greater than or equal to the resistance threshold, the instantaneous resistance of the probe after it has been running with the adaptive vibration parameters is obtained. The vibration drag reduction rate is determined based on the instantaneous resistance and the penetration resistance. Based on the vibration drag reduction rate, determine the formation label corresponding to the detection depth; When the formation label corresponding to the detection depth is a preset label, the detection depth is determined to be the key feature depth based on the resistance-depth time series data.

[0009] In some embodiments, determining the formation label corresponding to the detection depth based on the vibration drag reduction rate includes: The change in the vibration drag reduction rate within the preset time period is obtained; Based on the change in the vibration drag reduction rate, the formation label corresponding to the detection depth is determined.

[0010] In some embodiments, determining whether the detection depth is the key feature depth based on the drag-depth time series data includes: Based on the aforementioned resistance-depth time series data, the resistance change rate and resistance change acceleration are obtained respectively; The depth of the key feature is determined based on the rate of change of resistance and the acceleration of the change of resistance.

[0011] In some embodiments, the key feature depth includes the candidate contact point depth and the compression limit point depth. Determining the virtual rigid base depth corresponding to the target pile hole based on the key feature depth and its corresponding soil type includes: Based on the resistance-depth time series data, determine the soil type and its corresponding soil compensation coefficient for each of the key feature depths; The virtual rigid base depth corresponding to the target pile hole is determined based on the soil compensation coefficient, the candidate contact point depth, and the compression limit point depth.

[0012] It should be noted that the virtual rigid substrate is not an actual physical interface within the sediment layer, but rather an equivalent rigid reference surface constructed within the transition zone between the sediment and the bearing layer, based on the mechanical property profile reflected by the resistance-depth time series data. This reference surface characterizes the depth position corresponding to the sediment reaching its ultimate compression state under the uniform penetration of the probe, and its depth value is determined by automatically optimizing and fitting the stiffness variation curve within the transition zone.

[0013] Secondly, embodiments of this application provide a rigid substrate adaptive sediment thickness measurement system based on a virtual simulation system, comprising: The first acquisition module is used to control the lowering of the detection equipment and acquire the top surface depth of the sediment layer in the target pile hole; The second acquisition module is used to control the probe in the detection device to penetrate the sediment layer at a constant speed under the assistance of dynamic high-frequency vibration, and to acquire the resistance-depth time series data corresponding to the target pile hole. The third acquisition module is used to acquire the key feature depth corresponding to the target pile hole based on the resistance-depth time series data; The first determining module is used to determine the virtual rigid base depth corresponding to the target pile hole based on the key feature depth and its corresponding soil type. The second determining module is used to determine the sediment thickness corresponding to the target pile hole based on the depth of the virtual rigid base and the depth of the top surface.

[0014] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in embodiments of this application.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in embodiments of this application.

[0016] Fifthly, embodiments of this application provide a computer program product, including a computer program, characterized in that, when the computer program is executed by a processor, it implements the method described in embodiments of this application.

[0017] The rigid base adaptive sediment thickness measurement method and system based on a virtual simulation system provided in this application control the lowering of the detection equipment and obtain the top surface depth of the sediment layer of the target pile hole; under the assistance of dynamic high-frequency vibration, the probe in the detection equipment is controlled to penetrate the sediment layer at a uniform speed and obtain the resistance-depth time series data corresponding to the target pile hole; based on the resistance-depth time series data, the key feature depth corresponding to the target pile hole is obtained; based on the key feature depth and its corresponding soil type, the virtual rigid base depth corresponding to the target pile hole is determined through automatic optimization; based on the virtual rigid base depth and the top surface depth, the sediment thickness corresponding to the target pile hole is determined, which can effectively improve the accuracy and reliability of sediment thickness measurement.

[0018] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0019] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 A flowchart illustrating an embodiment of the adaptive sediment thickness measurement method for a rigid substrate based on a virtual simulation system provided in this application is shown. Figure 2 A schematic diagram of a rigid substrate adaptive sediment thickness measurement system based on a virtual simulation system provided in an embodiment of this application is shown. Figure 3 A schematic diagram of the structure of a computer system suitable for implementing an electronic device or server according to embodiments of this application is shown. Detailed Implementation

[0020] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] During the construction of drilled piles with mud-walled construction, the presence of sediment at the bottom of the hole directly affects the bearing capacity of the pile tip. The sediment layer is essentially a loose-to-dense transitional medium composed of drill cuttings, mud cake, rock powder, etc., exhibiting significant heterogeneity and nonlinearity in its physical and mechanical properties. The core technical challenges in sediment thickness measurement can be summarized in the following four aspects: (1) Ambiguity of the sediment-bearing layer interface. There is no clear geometric boundary between the sediment and the bearing layer, but a continuous transition zone from loose sediment to dense undisturbed soil. In this transition zone, the density, particle interlocking degree and shear strength of the material show gradual characteristics. Traditional methods attempt to find an abrupt change point as the interface location, which is inherently a cognitive bias in physical terms.

[0023] (2) Interference from local hard points. Larger-sized gravel, rock fragments or cemented clumps are often mixed in the sediment layer. The peak penetration resistance generated by these local hard points is highly similar to the resistance characteristics of the bearing layer in terms of value, which can easily lead to misjudgment.

[0024] (3) Differences in mechanical response under different soil conditions. The penetration resistance characteristics of clay sediment, sandy sediment and gravelly sediment are quite different. The resistance curve of clay sediment is relatively smooth, sandy sediment has fluctuations caused by particle rearrangement, and gravelly sediment exhibits violent sawtooth jumping.

[0025] (4) The influence of operator subjectivity. The traditional hammer test method relies on the operator's sense of touch to judge the bottoming time, and the judgment of different people varies significantly; although the existing penetration probe method has a certain degree of quantification, the determination of the inflection point still requires human experience.

[0026] Based on this, this application proposes an adaptive sediment thickness measurement method for rigid substrates based on a virtual simulation system, which can effectively improve the accuracy and reliability of sediment thickness measurement without human intervention.

[0027] To further illustrate the technical solutions provided in the embodiments of this application, a detailed description is provided below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of this application provide method operation instruction steps as shown in the following embodiments or drawings, the method may include more or fewer operation instruction steps based on conventional or non-creative effort. In steps where there is no logically necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiments of this application. In actual processing or when the device executes the method, it may be executed sequentially or in parallel according to the method shown in the embodiments or drawings.

[0028] Please refer to Figure 1 , Figure 1 A schematic flowchart of an embodiment of the adaptive sediment thickness measurement method for a rigid substrate based on a virtual simulation system provided in this application is shown. Figure 1 As shown, the method includes: Step 101: Control the lowering of the detection equipment and obtain the top surface depth of the sediment layer in the target pile hole.

[0029] Specifically, the detection equipment is slowly lowered along the centerline of the target pile hole using a winch, with the lowering speed controlled between 0.2 m / s and 0.5 m / s. When it reaches approximately 1 m from the designed bottom of the hole, the speed is reduced to 0.05 m / s to 0.1 m / s to accurately locate the top surface of the sediment layer. Specifically, when the floating pressure plate at the bottom of the detection equipment contacts the top surface of the sediment, the pressure sensor experiences its first signal change, such as rising from 0 to ≥5 N. The system automatically records this moment as the depth H of the top surface of the sediment layer in the target pile hole. top It should be understood that the floating pressure plate (ring-shaped measuring cake) has a relatively large area, about 150 mm in diameter, and can span the uneven surface of the sediment. The first jump in its contact pressure represents the average position of the top surface of the sediment, rather than a local high or low point.

[0030] Step 102: Under the assistance of dynamic high-frequency vibration, control the probe in the detection equipment to penetrate the sediment layer at a uniform speed and obtain the resistance-depth time series data corresponding to the target pile hole.

[0031] It should be noted that the essence of high-frequency vibration is to reduce the effective stress between sediment particles through periodic disturbance, causing localized damage to the particle interlocking structure, thereby reducing penetration resistance. The function of dynamic high-frequency vibration is to dynamically adjust the vibration parameters of the high-frequency vibrator to meet the penetration requirements of hard sediment.

[0032] In one feasible embodiment, controlling the probe in the detection device to penetrate the sediment layer at a uniform speed under the assistance of dynamic high-frequency vibration includes: obtaining the penetration resistance corresponding to each detection depth; determining the adaptive vibration parameter corresponding to the detection depth based on the penetration resistance; and controlling the probe to run for a preset time based on the adaptive vibration parameter when the penetration resistance is greater than or equal to the resistance threshold.

[0033] In other words, the vibration parameters of a high-frequency vibrator are related to the penetration resistance encountered when the probe is inserted. For example, the vibration parameters are positively correlated with the penetration resistance, meaning that the greater the penetration resistance, the greater the vibration frequency and amplitude of the high-frequency vibrator.

[0034] In one specific embodiment, a micro-push rod can be controlled to drive the probe downwards at a speed of 10-30 mm / s. Simultaneously, a high-frequency vibrator is activated, with initial parameters set to a frequency of 200 Hz and an amplitude of 0.5 mm. The probe tip penetrates the sediment layer, and a pressure sensor collects penetration resistance data in real time at a frequency ≥200 Hz, while a displacement sensor simultaneously records the penetration depth. For example, if F < Then maintain f=200Hz and A=0.5mm; if ≤F< Then the vibration parameters will be increased to f = 300 Hz and A = 1.0 mm; if F ≥ Then the vibration parameters will be increased to f=400-500Hz and A=1.5-2.0mm, and maintained for 3-5s. Among these, it can be... 0.5MPa It can be 2.0 MPa, but this application does not specify a particular limit.

[0035] Where, if F< This indicates that the sediment at the current detection depth is soft sediment. ≤F< This indicates that the sediment at the current detection depth is of medium hardness. It should be understood that neither soft nor medium-hard sediment constitutes a rigid substrate for the sediment layer; therefore, it requires little attention. If F ≥ If the current depth of sediment is a suspected hard point or bearing layer, it is necessary to assess the rigid substrate by examining the current depth of sediment and other depths of sediment penetrated at a constant speed.

[0036] The resistance-depth time series data corresponding to the target pile hole can be {F(i), D(i)}, i=1,2,...,N. Here, i represents time.

[0037] Therefore, this application achieves parameter adaptation driven by soil type through dynamic high-frequency vibration assistance. Specifically, the mechanical response characteristics of sediment vary significantly under different soil conditions. The penetration resistance of clay sediment is generally 0.2-0.8 MPa, sandy sediment is 0.5-2.0 MPa, and gravelly sediment can reach 1.5-5.0 MPa. If fixed parameters are used, the bearing layer may be disturbed due to excessive vibration in clay, while in gravelly soil, it may not be able to penetrate effectively due to insufficient energy. The adaptive mechanism enables the system to automatically match the optimal parameter combination according to the real-time resistance characteristics.

[0038] Step 103: Based on the resistance-depth time series data, obtain the key feature depth corresponding to the target pile hole.

[0039] It should be noted that the key feature depth refers to the depth corresponding to the key feature point. In the embodiments of this application, the key feature points include candidate contact points and compressibility limit points. The candidate release point is the initial boundary where the probe tip formally enters the transition zone between the sediment and the bearing layer from the uniformly compacted zone of the sediment. The compressibility limit point is the initial stable position where the compressible space of the sediment particle skeleton has been exhausted and the probe has completely entered the original bearing layer.

[0040] In one feasible embodiment, the key feature depth corresponding to the target pile hole is obtained based on resistance-depth time series data, including: when the penetration resistance is greater than or equal to the resistance threshold, obtaining the instantaneous resistance after the probe operates with adaptive vibration parameters; determining the vibration drag reduction rate based on the instantaneous resistance and the penetration resistance; determining the stratum label corresponding to the detection depth based on the vibration drag reduction rate; and determining whether the detection depth is a key feature depth based on the resistance-depth time series data when the stratum label corresponding to the detection depth is a preset label.

[0041] In other words, when At that time, it was impossible to distinguish between localized large-diameter gravel hard spots and the actual bearing layer. Therefore, this application achieves precise decoupling of physical properties by applying time-domain controllable strong vibration excitation and observing the attenuation response characteristics of the drag.

[0042] It should be noted that the vibration drag reduction rate is used to characterize the liquefaction or weakening effect of high-frequency vibration on the liquefaction or weakening effect of sediment particle interlocking structures at the current depth. It is the gold standard for quantitatively distinguishing between localized frictional hard points and the overall rigid bearing layer. In the embodiments of this application, the vibration drag reduction rate is the percentage decrease in probe penetration resistance relative to the reference resistance before strong vibration intervention, after the system switches from the ground-state low-vibration monitoring mode to the strong vibration excitation test mode while the probe maintains a constant penetration speed.

[0043] In other words, when the vibration parameters are increased to And continue (After 3-5 seconds), the instantaneous resistance after the intervention of adaptive vibration parameters is collected in real time. According to the viscoelastic-plastic theory, if it is a local hard point (loose interlocking structure), the vibration energy will quickly break its interlocking, and the resistance will decrease rapidly; if it is a load-bearing layer (overall dense structure), the vibration energy is absorbed by elasticity, and the resistance remains basically constant.

[0044] For example, the vibration drag reduction rate can be expressed as follows:

[0045] in, The vibration drag reduction rate at time t after the adaptive vibration parameters are introduced. The instantaneous resistance after the adaptive vibration parameters are introduced. The penetration resistance before the intervention of adaptive vibration parameters.

[0046] Furthermore, based on the vibration drag reduction rate, the formation label corresponding to the detection depth is determined, including: obtaining the change of vibration drag reduction rate within a preset time period; and determining the formation label corresponding to the detection depth based on the change of vibration drag reduction rate.

[0047] It should be noted that, according to the saturation characteristics in the vibration-drag coupling model, if the drag reduction rate reaches a significant saturation level, it indicates that the vibration energy is sufficient to overcome the structural strength of the medium, classifying it as a non-bearing layer; if the drag reduction rate is constrained to a low level, it indicates that the medium has extremely high overall stiffness (bearing layer). Therefore, based on the changes in vibration drag reduction rate, the formation label corresponding to the detection depth can be determined as follows:

[0048] in, The vibration drag reduction rate at the end of the preset time window, The drag reduction threshold, It is the first derivative of the drag reduction ratio.

[0049] The vibration-resistance coupling model can be expressed as:

[0050] in, The decrease in drag caused by vibration. For penetration resistance, The vibration frequency, The amplitude of the vibration. For reference frequency, For reference amplitude, This is the vibration efficiency coefficient.

[0051] In a preferred embodiment, the drag reduction threshold It can be 30%, which can be achieved through the vibration efficiency coefficient. It is derived from the ratio of the maximum input energy.

[0052] Furthermore, when the stratum label indicates localized hard point interference, the probe continues to penetrate at a uniform speed, and the vibration parameters automatically revert to the pre-high vibration mapping value (or maintain high frequency and low amplitude to aid rock breaking). This data segment is marked as an abnormal fluctuation, and when determining the critical feature depth later, the second derivative peaks within this interval are removed. This significantly reduces abnormal jagged noise in the input data stream used to determine the critical feature depth, greatly improving the confidence and smoothness of the initial contact with the bearing stratum label. When the stratum label indicates a bearing stratum transition zone, the current depth is identified as a candidate point for the initial contact with the bearing stratum, i.e., marked as a candidate key point, serving as a reference for subsequent critical feature depth determination.

[0053] Therefore, this application can effectively distinguish between local hard points and the load-bearing layer. It should be understood that the instantaneous peak resistance generated by a local hard point is difficult to distinguish from the sustained high resistance of the load-bearing layer in static measurements. This application applies high-frequency vibration (400-500Hz) to suspected hard points using adaptive vibration parameters and observes the resistance response. If the resistance decreases by ≥30%, it is considered a local hard point disturbance; if it remains unchanged, it is considered a load-bearing layer. This adaptive excitation-response closed-loop strategy effectively reduces the false positive rate.

[0054] In one feasible embodiment, determining whether the detection depth is a key feature depth based on drag-depth time series data includes: obtaining the drag change rate and drag change acceleration respectively based on the drag-depth time series data; and determining the key feature depth based on the drag change rate and drag change acceleration.

[0055] It should be noted that the rate of change of drag and the acceleration of the drag change are the first and second derivatives of the penetration resistance with respect to displacement, respectively. Specifically, the rate of change of drag can be expressed as:

[0056] The acceleration due to the change in drag can be expressed as:

[0057] Where {F(i), D(i)} represents the resistance-depth time series data, F(i) is the penetration resistance at time i, D(i) is the depth at time i, and i is the time, i=1,2,...,N.

[0058] It should be understood that the physical meaning of the drag change rate is the increase in drag per unit depth, reflecting the rate of change of stiffness of the sediment layer along the depth direction. The physical meaning of the drag change acceleration is the depth gradient of the drag change rate, and its positive peak value marks the turning point where the stiffness of the sediment layer begins to increase sharply, that is, the starting position where the probe begins to enter the transition zone between the sediment and the bearing layer.

[0059] Based on the rate of change of resistance and the acceleration of the change of resistance, the depth of the key feature is determined, and thus: >5N / mm and If the initial value is >0.5 N / mm², it indicates that the probe has entered the starting point of the transition zone of the bearing layer, and this point is marked as a candidate contact point. When F'' approaches 0 and F'' < 0.05 N / mm² for 5 consecutive points, it indicates that the sediment layer has reached a fully compressible state, and this is marked as the compression limit point.

[0060] Step 104: Determine the virtual rigid base depth corresponding to the target pile hole based on the key feature depth and its corresponding soil type.

[0061] It should be noted that the virtual rigid substrate is not a physical interface that actually exists in the sediment layer, but a virtual reference surface constructed through mechanical property profile inversion, that is, a rigid reference surface obtained through virtual system simulation. This reference surface represents the equivalent rigid boundary position corresponding to the sediment reaching a fully compressed state during the penetration process.

[0062] It should be noted that the soil type in the embodiments of this application can be determined based on the actual environment of the pile hole or the actual sediment type of the bearing layer, or it can be determined by calculating the penetration resistance. This application does not make specific limitations in this regard. In the embodiments of this application, the soil type includes, but is not limited to, clay, sand, and gravelly soil.

[0063] In one feasible embodiment, the key feature depth includes the candidate contact point depth and the compression limit point depth. Based on the key feature depth and its corresponding soil type, the virtual rigid base depth corresponding to the target pile hole is determined, including: determining the soil type and its corresponding soil compensation coefficient corresponding to each key feature depth based on resistance-depth time series data; and determining the virtual rigid base depth corresponding to the target pile hole based on the soil compensation coefficient, the candidate contact point depth, and the compression limit point depth.

[0064] Specifically, the depth of the virtual rigid base corresponding to the target pile hole can be determined using the following expression:

[0065] in, For the depth of the virtual rigid base, For candidate contact point depth, To compress the depth of the limit point, This is the soil compensation coefficient corresponding to soil type T.

[0066] Optional, The value range is 0.3-0.7, for example, 0.5 for clay, 0.4 for sand, and 0.6 for gravelly soil. It should be understood that the lower limit of 0.3 is used to ensure that the virtual base does not exceed the hardening starting point, avoiding selection within insufficiently compressed sediment; the upper limit of 0.7 is used to ensure that it does not approach the compression limit point too closely, avoiding misjudgment due to slight disturbances in the bearing layer. This range guarantees... It always falls within the latter half of the transition zone, where the stiffness change is most significant.

[0067] In a preferred embodiment, to improve the reliability and accuracy of the virtual rigid base depth, a fixed soil compensation coefficient can be used instead of directly employing it, and instead dynamically determined by solving an unconstrained optimization problem. For example, the dynamic soil compensation coefficient can be determined using the following expression. :

[0068] in, This is the dynamic soil compensation coefficient. This is a function for fitting the resistance curve based on resistance-depth time series data. The horizontal asymptotic drag value under full compression. This is the regularization weight coefficient.

[0069] It should be noted that, yes The mean resistance of all sampling points represents the inherent impedance of the undisturbed bearing layer. In soil mechanics, the resistance only approaches this value when the probe fully enters the plastic flow zone and the sediment is compacted to its ultimate density. Therefore, the optimization logic of the first term in the dynamic soil compensation coefficient model is to find a depth point within the transition zone where the sediment has been compressed to almost the same shear strength as the bearing layer, thus defining a rigid boundary in a mechanically equivalent sense.

[0070] and It is the first derivative of the drag curve at that point (i.e., stiffness). If this value is too large, it means that the sediment at that location is still undergoing intense compression, and designating it as the base will result in an underestimation of thickness (leaving excess sediment). Therefore, the second term in the dynamic soil compensation coefficient model combines the ideas of least squares smoothing and total variation regularization to ensure that the selected point is located after the inflection point where the curvature of the curve decays the fastest.

[0071] Therefore, this application achieves this through dynamic optimization of the transition band compensation coefficient. It can automatically optimize based on the shape of the resistance curve collected in real time, rather than relying on the subjective experience and judgment of the operator.

[0072] Step 105: Determine the sediment thickness corresponding to the target pile hole based on the depth of the virtual rigid base and the depth of the top surface.

[0073] Specifically, the thickness of the sediment corresponding to the target pile hole is determined by using the difference between the depth of the virtual rigid base and the depth of the top surface. For example, the sediment thickness can be expressed as:

[0074] in, For the thickness of the sediment, For the depth of the virtual rigid base, This represents the depth of the top surface.

[0075] In one feasible embodiment, after obtaining the sediment thickness, backtracking verification is performed. Does the resistance curve after the position satisfy the physical constraint of the horizontal asymptote? If the verification deviation is less than 5%, the judgment result is valid, and the final sediment thickness value is output. If the verification deviation is ≥5%, for example, if the curve still shows an upward trend at the tail, it indicates that the verification has failed. The vibration parameters or penetration speed will be automatically adjusted and the measurement will be repeated.

[0076] In one specific embodiment, clayey sediment, sandy sediment, and gravelly sediment samples were prepared in a simulated test tank, with preset thicknesses of 30 mm, 60 mm, 100 mm, and 150 mm, respectively. Measurements were performed using the system of this application, and the results are as follows: clay 30 30.2 +0.2 0.7% clay 60 59.7 -0.3 0.5% clay 100 100.4 +0.4 0.4% sandy 30 29.6 -0.4 1.3% sandy 60 60.3 +0.3 0.5% sandy 100 99.5 -0.5 0.5% gravelly 30 31.0 +1.0 3.3% gravelly 60 59.2 -0.8 1.3% gravelly 100 101.5 +1.5 1.5% gravelly 150 149.0 -1.0 0.7% Test conclusion: The measurement error of the system proposed in this application is controlled within ±0.5mm in clay and sandy sediments and within ±1.5mm in gravelly sediments, which is far superior to the error range of 0.01-0.09cm (i.e. 0.1-0.9mm) of the existing penetration sediment thickness detection system.

[0077] In another specific embodiment, 10 piles were selected at the same construction site (a bridge project, with a pile diameter of 1.5m, a hole depth of 45m, and clay strata), and the sediment thickness was detected using three different methods: Z1 35 42 38 37 Z2 50 55 46 47 Z3 25 32 28 29 Z4 65 58 52 53 Z5 40 48 43 42 Z6 30 38 33 32 Z7 55 50 47 48 Z8 20 28 22 23 Z9 70 62 56 57 Z10 45 52 44 43 As can be seen, the measurement error of this application is controlled within ±1.5mm, which is about 5 times better than the traditional hammer test method (±8.2mm) and about 4 times better than the existing penetration probe method (±5.6mm). Moreover, in complex geological conditions such as gravelly soil layers, traditional methods are almost unusable; the existing penetration probe method has a misjudgment rate of about 15%-20%. This application, through high-frequency vibration active excitation, reduces the misjudgment rate to below 2%. The standard deviation of repeated measurements is reduced from ±6.5mm in the traditional method to ±0.8mm, significantly improving the consistency of measurement results and eliminating the uncertainty caused by differences in operator feel. Each measurement outputs a complete resistance-depth curve and mechanical property profile, providing a scientific basis for quality acceptance, while the traditional method only outputs a thickness value, which cannot be traced or verified.

[0078] In summary, the rigid base adaptive sediment thickness measurement method based on a virtual simulation system provided in this application controls the lowering of the detection equipment and obtains the top surface depth of the sediment layer in the target pile hole; under the assistance of dynamic high-frequency vibration, the probe in the detection equipment is controlled to penetrate the sediment layer at a uniform speed and obtains the resistance-depth time series data corresponding to the target pile hole; based on the resistance-depth time series data, the key feature depth corresponding to the target pile hole is obtained; based on the key feature depth and its corresponding soil type, the virtual rigid base depth corresponding to the target pile hole is determined through automatic optimization; based on the virtual rigid base depth and the top surface depth, the sediment thickness corresponding to the target pile hole is determined, which can effectively improve the accuracy and reliability of sediment thickness measurement.

[0079] It should be noted that although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed in order to achieve the desired result.

[0080] Figure 2 A schematic diagram of a rigid substrate adaptive sediment thickness measurement system based on a virtual simulation system provided in an embodiment of this application is shown.

[0081] like Figure 2 As shown, the rigid substrate adaptive sediment thickness measurement system 10 based on a virtual simulation system includes: The first acquisition module 11 is used to control the lowering of the detection equipment and acquire the top surface depth of the sediment layer in the target pile hole; The second acquisition module 12 is used to control the probe in the detection device to penetrate the sediment layer at a constant speed under the assistance of dynamic high-frequency vibration, and to acquire the resistance-depth time series data corresponding to the target pile hole. The third acquisition module 13 is used to acquire the key feature depth corresponding to the target pile hole based on the resistance-depth time series data; The first determining module 14 is used to determine the virtual rigid base depth corresponding to the target pile hole based on the key feature depth and its corresponding soil type. The second determining module 15 is used to determine the sediment thickness corresponding to the target pile hole based on the depth of the virtual rigid base and the depth of the top surface.

[0082] In some embodiments, the second acquisition module 12 is specifically used for: For each detection depth, obtain the penetration resistance corresponding to that detection depth; Based on the penetration resistance, determine the adaptive vibration parameters corresponding to the detection depth; and When the penetration resistance is greater than or equal to the resistance threshold, the probe is controlled to run for a preset duration according to the adaptive vibration parameters.

[0083] In some embodiments, the third acquisition module 13 is specifically used for: When the penetration resistance is greater than or equal to the resistance threshold, the instantaneous resistance of the probe after it has been running with the adaptive vibration parameters is obtained. The vibration drag reduction rate is determined based on the instantaneous resistance and the penetration resistance. Based on the vibration drag reduction rate, determine the formation label corresponding to the detection depth; When the formation label corresponding to the detection depth is a preset label, the detection depth is determined to be the key feature depth based on the resistance-depth time series data.

[0084] In some embodiments, the third acquisition module 13 is specifically used for: The change in the vibration drag reduction rate within the preset time period is obtained; Based on the change in the vibration drag reduction rate, the formation label corresponding to the detection depth is determined.

[0085] In some embodiments, the third acquisition module 13 is specifically used for: Based on the aforementioned resistance-depth time series data, the resistance change rate and resistance change acceleration are obtained respectively; The depth of the key feature is determined based on the rate of change of resistance and the acceleration of the change of resistance.

[0086] In some embodiments, the first determining module 14 is specifically used for: Based on the resistance-depth time series data, determine the soil type and its corresponding soil compensation coefficient for each of the key feature depths; The virtual rigid base depth corresponding to the target pile hole is determined based on the soil compensation coefficient, the candidate contact point depth, and the compression limit point depth.

[0087] It should be understood that the modules or modules described in the rigid substrate adaptive sediment thickness measurement system 10 based on the virtual simulation system are similar to those in the reference system. Figure 1 The steps in the described method correspond accordingly. Therefore, the operations and features described above for the method are also applicable to the rigid substrate adaptive sediment thickness measurement system 10 based on a virtual simulation system and its included modules, and will not be repeated here. The rigid substrate adaptive sediment thickness measurement system 10 based on a virtual simulation system can be pre-implemented in the browser or other secure applications of an electronic device, or it can be loaded into the browser or other secure applications of an electronic device through download or other means. The corresponding modules in the rigid substrate adaptive sediment thickness measurement system 10 based on a virtual simulation system can cooperate with the modules in the electronic device to implement the solution of the embodiments of this application.

[0088] The division of modules or units mentioned in the detailed description above is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0089] The following is for reference. Figure 3 , Figure 3 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of this application is shown. like Figure 3As shown, the computer system 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 302 or programs loaded from storage section 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the system's operating instructions. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0090] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0091] Specifically, according to embodiments of this application, the flowchart above refers to... Figure 2 The described process can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program contains program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the system of this application.

[0092] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operational instructions of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two connected blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified functions or operational instructions, or using a combination of dedicated hardware and computer instructions.

[0094] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be housed in a processor; for example, a processor can be described as including a first acquisition module, a second acquisition module, a third acquisition module, a first determination module, and a second determination module. The names of these units or modules do not necessarily limit the specific unit or module itself. For example, the first acquisition module can also be described as controlling the lowering of a detection device and acquiring the top surface depth of the sediment layer in the target pile hole.

[0095] In another aspect, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not assembled into the electronic device. The computer-readable storage medium stores one or more programs that, when used by one or more processors, execute the rigid substrate adaptive sediment thickness measurement method based on a virtual simulation system described in this application.

[0096] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for adaptive sediment thickness measurement on a rigid substrate based on a virtual simulation system, characterized in that, include: The detection equipment was lowered and the top depth of the sediment layer in the target pile hole was obtained. With the assistance of dynamic high-frequency vibration, the probe in the detection device is controlled to penetrate the sediment layer at a uniform speed and acquire the resistance-depth time series data corresponding to the target pile hole. Based on the resistance-depth time series data, the key feature depth corresponding to the target pile hole is obtained; The virtual rigid base depth corresponding to the target pile hole is determined based on the key feature depth and its corresponding soil type. The sediment thickness corresponding to the target pile hole is determined based on the depth of the virtual rigid base and the depth of the top surface.

2. The adaptive sediment thickness measurement method for a rigid substrate based on a virtual simulation system according to claim 1, characterized in that, The method of controlling the probe in the detection device to penetrate the sediment layer at a uniform speed under the assistance of dynamic high-frequency vibration includes: For each detection depth, obtain the penetration resistance corresponding to that detection depth; Based on the penetration resistance, determine the adaptive vibration parameters corresponding to the detection depth; and When the penetration resistance is greater than or equal to the resistance threshold, the probe is controlled to run for a preset duration according to the adaptive vibration parameters.

3. The adaptive sediment thickness measurement method for a rigid substrate based on a virtual simulation system according to claim 1, characterized in that, The step of obtaining the key feature depth corresponding to the target pile hole based on the resistance-depth time series data includes: When the penetration resistance is greater than or equal to the resistance threshold, the instantaneous resistance of the probe after it has been running with the adaptive vibration parameters is obtained. The vibration drag reduction rate is determined based on the instantaneous resistance and the penetration resistance. Based on the vibration drag reduction rate, determine the formation label corresponding to the detection depth; When the formation label corresponding to the detection depth is a preset label, the detection depth is determined to be the key feature depth based on the resistance-depth time series data.

4. The adaptive sediment thickness measurement method for a rigid substrate based on a virtual simulation system according to claim 3, characterized in that, The step of determining the formation label corresponding to the detection depth based on the vibration drag reduction rate includes: The change in the vibration drag reduction rate within the preset time period is obtained; Based on the change in the vibration drag reduction rate, the formation label corresponding to the detection depth is determined.

5. The adaptive sediment thickness measurement method for a rigid substrate based on a virtual simulation system according to claim 3, characterized in that, The step of determining whether the detection depth is the key feature depth based on the resistance-depth time series data includes: Based on the aforementioned resistance-depth time series data, the resistance change rate and resistance change acceleration are obtained respectively; The depth of the key feature is determined based on the rate of change of resistance and the acceleration of the change of resistance.

6. The adaptive sediment thickness measurement method for a rigid substrate based on a virtual simulation system according to claim 1, characterized in that, The key feature depth includes the candidate contact point depth and the compression limit point depth. Determining the virtual rigid base depth corresponding to the target pile hole based on the key feature depth and its corresponding soil type includes: Based on the resistance-depth time series data, determine the soil type and its corresponding soil compensation coefficient for each of the key feature depths; The virtual rigid base depth corresponding to the target pile hole is determined based on the soil compensation coefficient, the candidate contact point depth, and the compression limit point depth.

7. A rigid substrate adaptive sediment thickness measurement system based on a virtual simulation system, characterized in that, include: The first acquisition module is used to control the lowering of the detection equipment and acquire the top surface depth of the sediment layer in the target pile hole; The second acquisition module is used to control the probe in the detection device to penetrate the sediment layer at a constant speed under the assistance of dynamic high-frequency vibration, and to acquire the resistance-depth time series data corresponding to the target pile hole. The third acquisition module is used to acquire the key feature depth corresponding to the target pile hole based on the resistance-depth time series data; The first determining module is used to determine the virtual rigid base depth corresponding to the target pile hole based on the key feature depth and its corresponding soil type. The second determining module is used to determine the sediment thickness corresponding to the target pile hole based on the depth of the virtual rigid base and the depth of the top surface.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the adaptive sediment thickness measurement method for a rigid substrate based on a virtual simulation system as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the adaptive sediment thickness measurement method for rigid substrates based on a virtual simulation system as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the adaptive sediment thickness measurement method for a rigid substrate based on a virtual simulation system, as described in any one of claims 1-6.

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