Identification method for automatic switching of dry and wet holes in pore-forming detection

Abstract

The invention relates to an identification method for automatic switching of dry and wet holes in pore-forming detection, which comprises the following steps of: 1, placing an equipment probe in an air section of a filling hole, starting detection, performing real-time detection when the probe moves downwards, and recording data in X, X ', Y and Y' directions; 2, maximum value points of k data points (xi, yi) in the four known directions X, X ', Y and Y' are obtained, and maximum value point data sets corresponding to the four directions are obtained; 3, fitting the data set by adopting cubic spline interpolation; 4, performing secondary fitting on the fitting data in the step 3; and 5, according to a discrimination condition, discriminating the quadratic fitting data, and judging whether the slurry interface is entered or not. According to the invention, automatic identification of the dry and wet holes can be realized from the software level.

Description

Technical Field

[0001] The present application relates to the technical field of pile foundation detection, and in particular to a method for identifying automatic switching between dry and wet holes in hole formation detection. Background Art

[0002] Cast-in-place piles are a common deep foundation construction technology, widely used in structures such as bridges, high-rise buildings, and offshore platforms to provide strong bearing capacity and stability. Semi-wet holes are a type of cast-in-place pile drilling method between dry holes and wet holes. Semi-wet holes are made by pouring mud at the bottom of the hole to form a wet hole at the bottom, and no mud is poured at the top to form a dry hole at the top. Semi-wet holes have a greater drilling depth and higher safety than dry holes; they have a faster drilling speed and save more mud than wet holes, which can effectively reduce construction costs and pollution.

[0003] Since the diameter of the hole is different at different depths, it is usually necessary to accurately detect the hole diameter at each depth. Due to the different environments of dry holes and wet holes, traditional hole detection equipment cannot perform hole diameter detection of dry and wet holes at the same time.

[0004] There are devices in the industry that can detect the aperture of dry holes and wet holes at the same time, but they do not support automatic switching and require manual control. However, it is difficult for humans to accurately grasp the interface between air and mud, which makes the collected data have certain errors in the inversion of the actual hole wall and increases labor costs.

[0005] In addition, there are also devices that use fluid density sensors fixed on the probe to detect the density value of the mud liquid to distinguish the interface between air and mud and realize automatic switching. However, there are problems with the complex process structure of the probe, sensor maintenance and high cost. Summary of the invention

[0006] Based on the above description, the present application provides a method for automatically switching between dry and wet holes in hole detection to solve the problem of automatic identification of the interface between air and mud in semi-wet holes.

[0007] The technical solution of the present application to solve the above technical problems is as follows: a method for automatically switching between dry and wet holes in hole formation detection, comprising the following steps:

[0008] Step 1: Place the device probe in the air section of the perfusion hole, start the detection, and perform real-time detection while the probe moves downward, and record the data in the X, X', Y, and Y' directions;

[0009] Step 2: For k data points (x i ,y i ) Take the maximum points respectively, and get the maximum point data sets corresponding to the four directions, which are m1, m2, m3, and m4 respectively;

[0010] Step 3: This method first uses cubic spline interpolation to fit the data set to obtain a linear fitting curve;

[0011] Step 4: Fit the X profile to the data set A x Perform steps 2 to 3 again to obtain the fitting data set B after the second cubic spline interpolation of the X section. x ;

[0012] Step 5: Take the original waveform data collected from each section as input, the single sampling waveform length is n, and process the waveform data of the four sections according to steps 1 to 4 to obtain the data set B after quadratic fitting of the original data. x ,B x' ,B y ,B y' , set the discrimination conditions, according to data set B x ,B x' ,B y ,B y' When the four sampling sections X, X', Y, and Y' all meet the judgment conditions, it is considered that the mud interface has been entered. At this time, the equipment will automatically switch to the wet hole to continue the detection.

[0013] Based on the above technical solution, the present application can also be improved as follows.

[0014] Furthermore, the method for setting the discrimination condition is as follows: setting the threshold α, the range (0,1), and taking the X-direction section B x (x i ,y i ) set as an example, count the sets that satisfy y i The number of data points ≥yMax*α is recorded as n'. When n'≥n*α, yMax is the maximum amplitude. Data set B x ,B x' ,B y ,B y' In the equation, n is the number of points in each set.

[0015] Furthermore, in step 2, y needs to be eliminated i <0 points.

[0016] Furthermore, in step three, the cubic spline interpolation principle is as follows:

[0017] Assume that the data set m1 corresponding to the X section has n+1 data nodes, divided into n intervals, and construct a cubic function S0(x)~S in each interval. n-1 (x), assuming that each cubic function is defined by the following equation:

[0018] S i\(f(x)=a_0 + b_0(x - x i ) + c_0(x - x i ) 2 + d_0(x - x i ) 3 , i = 0, 1, ..., n - 1

[0019] That is, 4n coefficients need to be solved for.

[0020] Cubic spline interpolation satisfies four conditions

[0021] Condition 1: The n cubic functions must pass through all known nodes. Given n + 1 nodes, a total of n + 1 equations can be generated.

[0022] Condition 2: Continuous at all nodes except the first and the last node. Given n + 1 nodes and n intervals, there are n - 1 connection points, generating a total of n - 1 equations.

[0023] Condition 3: Differentiable at all nodes except the first and the last node. Given n + 1 nodes and n intervals, there are n - 1 connection points, generating a total of n - 1 equations.

[0024] Condition 4: Second-order differentiable at all nodes except the first and the last node. Given n + 1 nodes and n intervals, there are n - 1 connection points, generating a total of n - 1 equations.

[0025] Condition 5: Endpoint conditions: Specify the second derivative at the endpoints to be 0, generating a total of 2 equations

[0026] From Condition 1, n + 1 equations can be obtained. From Conditions 2, 3, and 4, n - 1 equations are obtained respectively. From Condition 5, 2 equations are obtained. In total, there are 4n equations.

[0027] Thus, the function equation \(S i (x)\) for each segment can be obtained, and according to the x segmentation range \([x i , x i+1 \), where i is the current interval number, \(0\leq x < k\), \(0\leq i\leq n - 1\). Substitute into the \(S i (x)\) function corresponding to the range to calculate the fitting data set A after cubic spline interpolation of the maximum value data set m1 corresponding to the X section x .

[0028] Furthermore, in Step 4, other sections are processed in the same way as the X section in turn, and the data set B after quadratic fitting can be obtained x' , B y , B y' , and the size of the data set is the same as that of the original data set.

[0029] Compared with the prior art, the technical solution of this application has the following beneficial technical effects:

[0030] By using algorithms to identify the interface between dry and wet holes at the software level, and then realizing the automatic switching function of aperture detection at dry and wet holes, the cost of hardware and structure is greatly saved, making the hardware structure more stable and easy to maintain;

[0031] Automatic identification of dry and wet holes for switching reduces the inconvenience of manually identifying the dry and wet hole interface with the naked eye and switching manually. The switching is more accurate and timely, and the collected data can better reflect the conditions at the interface between air and mud, while also improving the efficiency of inspection personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a result diagram after a fitting of the original data collected by the probe of this application in the dry hole mode in the air;

[0033] Figure 2 This is a result diagram of secondary fitting of the original data collected by the probe of this application in the dry hole mode in the air;

[0034] Figure 3 This is a result diagram after a fitting of the original data collected when the probe of this application enters the mud in the dry hole mode;

[0035] Figure 4 This is the result diagram after quadratic fitting of the original data collected when the probe of this application enters the mud in dry hole mode. DETAILED DESCRIPTION

[0036] In order to facilitate understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. Embodiments of the present application are provided in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0038] The present application provides a method for identifying automatic switching between dry and wet holes in hole formation detection, comprising the following steps:

[0039] Step 1: The equipment probe is placed in the air section of the perfusion hole, and the detection is turned on. The probe is moved downward for real-time detection, and the ultrasonic data collected in the X, X', Y, and Y' directions are recorded. The probe is a cube, and X, X', Y, and Y' refer to the vertical side walls of the probe respectively.

[0040] Step 2: For k data points (x i ,y i ) respectively take the maximum points and remove the y i <0, we get the corresponding maximum point data sets in four directions, namely m1, m2, m3, and m4. i ,y i ) refers to any point in a series of sound wave data, starting from the time the probe emits the ultrasonic wave to the time the probe receives the ultrasonic wave. It takes the fixed-length data containing the first broadcast, which is generally 1024 points and can be changed. Among them, x is the sound time and y is the amplitude at the corresponding moment of the current sound time.

[0041] Step 3: This method first uses cubic spline interpolation to fit the data set to obtain a first-order fitting curve. The principle of cubic spline interpolation is as follows:

[0042] Assume that the data set m1 corresponding to the X section has n+1 data nodes, divided into n intervals, and construct a cubic function S0(x)~S in each interval. n-1 (x), assuming that each cubic function is defined by the following equation:

[0043] S i (x) = a0 + b0 (x i )+c0(xx i ) 2 +d0(xx i ) 3 ,i=0,1,...,n-1

[0044] That is, 4n coefficients need to be solved.

[0045] Cubic spline interpolation satisfies four conditions:

[0046] Condition 1: The n-segment cubic function must pass through all known nodes: n+1 known nodes can generate a total of n+1 equations;

[0047] Condition 2: Continuity at all nodes, except the first and last nodes: Given n+1 nodes and n intervals, there are n-1 connection points, generating a total of n-1 equations;

[0048] Condition 3: Differentiable at all nodes except the first and the last node: Given n + 1 nodes and n intervals, there are n - 1 connection points, thus generating n - 1 equations;

[0049] Condition 4: Twice differentiable at all nodes except the first and the last node: Given n + 1 nodes and n intervals, there are n - 1 connection points, thus generating n - 1 equations;

[0050] Condition 5: Endpoint conditions: Specify the second derivative at the endpoints to be 0, generating 2 equations in total.

[0051] In summary, from Condition 1, n + 1 equations can be obtained, from Condition 2, Condition 3, and Condition 4, n - 1 equations are obtained respectively, and from Condition 5, 2 equations are obtained, totaling 4n equations. Thus, the function equation S i (x) for each segment can be obtained, and according to the x segmentation range [x i , x i+1 , where i is the current interval number, 0 ≤ x < k, 0 ≤ i ≤ n - 1, substitute into the S i (x) function in the corresponding range, and the fitting dataset A after cubic spline interpolation of the maximum value dataset m1 corresponding to the X section can be calculated x .

[0052] Step 4: Process the first - fitting dataset A of the X section x again through Steps 2 to 3, that is, the fitting dataset B after the second cubic spline interpolation of the X section is obtained x . Similarly process other sections in turn, and the dataset B after quadratic fitting can be obtained x' , B y , B y' , and the size of the dataset is the same as that of the original dataset.

[0053] Step 5: When the probe enters the mud from the air, the acoustic impedance in the mud is much greater than that in the air. At the same time, when propagating in the mud, the absorption and scattering effects are lower, and the sound speed is higher, which can maintain a higher energy density and intensity, manifested as an increase in the waveform amplitude, resulting in the waveform amplitude being in an over - amplitude state.

[0054] At this time, take the original waveform data collected from each section as input respectively, with the single - sampling waveform length being n. According to Steps 1 to 4, process the waveform data of 4 sections respectively to obtain the dataset B after quadratic fitting of the original data x , B x' , B y , B y' , and the number of points in each set is n.

[0055] Set a threshold α, in the range (0, 1), for the X - direction section Bx (x i ,y i ) set as an example, count the sets that satisfy y i The number of all data points ≥yMax*α is recorded as n'. When n'≥n*α, where yMax is the maximum amplitude, it can be determined that the sampling profile meets the conditions for entering the interface. When the four sampling profiles X, X', Y, and Y' all meet the above conditions, it is considered that they have entered the mud interface. At this time, the acquisition device will automatically switch to the wet hole mode to continue the detection, thereby achieving the purpose of the acquisition device automatically detecting the dry and wet hole switching. Among them, the acquisition device includes a dry hole mode and a wet hole mode. Since the environment in the wet hole is different from the environment in the dry hole, in the wet hole mode, the sampling parameters of the acquisition device will be adjusted relative to the dry hole mode to adapt to the detection in the wet hole environment.

[0056] The design principle of the identification method for automatic switching of dry and wet holes in hole formation detection in the present application is as follows: Figure 1 As shown, Figure 1 It is the original waveform when the probe is in the air. The horizontal axis represents the distance. The first broadcast judgment position is the distance from the probe to the hole wall. The vertical axis represents the waveform amplitude. The amplitude red curve is the original waveform curve. The blue curve is the original waveform after a cubic spline interpolation. The vertical blue line segment is the first broadcast of the waveform.

[0057] Figure 2 The blue curve in Figure 1 The result obtained after the second cubic spline interpolation of the blue curve in the middle shows that when the probe is in the air, there is almost no difference in the recognition after two fittings. Furthermore, when the probe is in the air, the aperture at the corresponding depth can be calculated by the horizontal coordinate of the vertical blue line segment, that is, the sum of the hole wall distances detected by the opposite two side walls plus the distance between the two opposite side walls of the probe is the aperture at the current depth.

[0058] Figure 3 This is the original waveform when the probe just enters the mud. At this time, the probe is still in the dry hole mode. The red curve is the original waveform curve, and the blue curve is the original waveform after a cubic spline interpolation. It can be seen that the blue curve after interpolation fitting is still messy.

[0059] Figure 4 The blue curve in Figure 3 From the result obtained after the second cubic spline interpolation of the blue curve, it can be seen that the fitting data after two fittings forms an over-amplitude state. At this time, the interface can be identified by setting the threshold α in step five. After the interface between the dry hole and the wet hole is identified by the identification method of the present application, the probe is switched to the wet hole mode and enters the wet hole for detection.

[0060] For example Figure 2 In the figure, when the probe is in the air, after the original data is fitted twice, most of the blue curve is still below the threshold line, while the corresponding Figure 4 In the figure, when the probe is in the mud, after the original data are fitted twice, most of the blue curve is above the threshold line. Then, by judging the proportion of the blue curve below the ordinate, it can be determined whether the probe is in the air or in the mud, and the interface between the dry and wet holes can be identified.

[0061] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for automatically switching between dry and wet holes in hole detection, characterized in that: The following steps are involved: Step 1: Place the device probe in the air section of the perfusion hole, start the detection, and perform real-time detection while the probe moves downward, and record the data in the X, X', Y, and Y' directions; Step 2: For k data points (x i ,y i ) Take the maximum points respectively, and get the maximum point data sets corresponding to the four directions, which are m1, m2, m3, and m4 respectively; Step 3: This method first uses cubic spline interpolation to fit the data set to obtain a linear fitting curve; Step 4: Fit the X profile to the data set A once x Perform steps 2 to 3 again to obtain the fitting data set B after the second cubic spline interpolation of the X section. x ; Step 5: Take the original waveform data collected from each section as input, the single sampling waveform length is n, and process the waveform data of the four sections according to steps 1 to 4 to obtain the data set B after quadratic fitting of the original data. x ,B x ',B y ,B y ', set the discrimination conditions, according to data set B x ,B x ',B y ,B y ', when the four sampling sections X, X', Y, and Y' all meet the discrimination conditions, it is considered that the mud interface has been entered, and the equipment will automatically switch to the wet hole to continue the detection.

2. The method for automatically switching between dry and wet holes in hole formation detection according to claim 1, characterized in that: The method for setting the discrimination condition is as follows: set the threshold α, the range (0,1), and the X-direction section B x (x i ,y i ) set as an example, count the sets that satisfy y i The number of data points ≥yMax*α is recorded as n'. When n'≥n*α, yMax is the maximum amplitude. Data set B x ,B x ',B y ,B y ', n is the number of points in each set.

3. The method for automatically switching between dry and wet holes in hole detection according to claim 1 is characterized in that: In step 2, y needs to be eliminated i <0 points.

4. The method for automatically switching between dry and wet holes in hole formation detection according to claim 1, characterized in that: In step three, the cubic spline interpolation principle is as follows: Assume that the data set m1 corresponding to the X section has n+1 data nodes, divided into n intervals, and construct a cubic function S0(x)~S in each interval. n-1 (x), assuming that each cubic function is defined by the following equation: S i (x)=a0+b0(x-x i )+c0(x-x i ) 2 +d0(x-x i ) 3 ,i=0,1,...,n-1 That is, 4n coefficients need to be solved; Cubic spline interpolation satisfies four conditions Condition 1: The n-segment cubic function must pass through all known nodes: n+1 known nodes can generate a total of n+1 equations; Condition 2: Continuity at all nodes, except the first and last nodes: Given n+1 nodes and n intervals, there are n-1 connection points, generating a total of n-1 equations; Condition 3: Differentiable at all nodes except the first and last nodes: Given n+1 nodes and n intervals, there are n-1 connection points, generating a total of n-1 equations; Condition 4: Second order differentiable at all nodes except the first and last nodes: Given n+1 nodes and n intervals, there are n-1 connection points, generating a total of n-1 equations; Condition 5: Endpoint condition: Specify the endpoint second-order derivative as 0, and generate 2 equations in total; Condition 1 can produce n+1 equations, conditions 2, 3, and 4 can produce n-1 equations respectively, and condition 5 can produce 2 equations, for a total of 4n equations; Thus, the function equation S i (x) for each segment can be obtained, and based on the segmented range of x [x i , x i+1 , where i is the current interval number, 0 ≤ x < k, 0 ≤ i ≤ n - 1, substitute into the S i (x) function in the corresponding range, and the fitting dataset A after cubic spline interpolation of the maximum value dataset m1 corresponding to the X section can be calculated x .

5. The method for automatically switching between dry and wet holes in hole formation detection according to claim 1, characterized in that: In step 4, other sections are processed in the same way as section X, and the data set B after quadratic fitting can be obtained. x ',B y ,B y ', the size of the dataset remains the same as the original dataset size.

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