Quality control method and system for roadbed dynamic compaction construction based on rammer dynamic response

Abstract

The present invention provides a method and system for controlling the quality of roadbed dynamic compaction construction based on the dynamic response of a rammer. The control method includes the following steps: obtaining the acceleration signal of the rammer during the current tamping process; performing data processing on the acceleration signal of the rammer to obtain multiple key parameters for predicting the dynamic deformation modulus, wherein the data processing includes filtering processing, time domain analysis, and frequency domain analysis performed in sequence; obtaining the dynamic deformation modulus of the tamping point that has completed the current tamping based on the multiple key parameters and a predetermined dynamic deformation modulus prediction model; comparing the dynamic deformation modulus of the tamping point with a set dynamic deformation modulus design value, and based on the comparison result, sending a prompt to stop tamping or a prompt to continue tamping. The control method provided by the present invention performs quality control on the dynamic compaction construction based on the acceleration signal and dynamic deformation modulus prediction model obtained in real time, so as to ensure uniform compaction of the roadbed and improve construction efficiency and quality.

Description

Technical Field

[0001] The invention belongs to the technical field of roadbed dynamic compaction construction quality control, and in particular relates to a roadbed dynamic compaction construction quality control method and system based on rammer power response. Background Art

[0002] Dynamic compactors are a widely used type of engineering machinery in foundation treatment. Using a lifting device, they raise the rammer to a certain height, then release it and allow it to fall freely. The powerful impact of the rammer's fall compacts the soil, thereby increasing foundation strength and reducing potential settlement. Dynamic compactors target the foundation soil, aiming to improve its strength and uniformity.

[0003] At present, the main problems in the quality control of roadbed dynamic compaction construction are as follows:

[0004] (1) Traditional dynamic compaction construction quality control relies heavily on the operator's experience and judgment of the construction site. For example, the specification requires the average of the last two strikes to be used as the criterion for stopping the compaction process. This method is significantly affected by human factors, and the manual leveling method used cannot meet the high-precision construction requirements.

[0005] (2) The quality of dynamic compaction construction is usually evaluated through post-testing methods, such as static load tests, standard penetration tests, static penetration tests, and indoor geotechnical tests. Although these methods can effectively evaluate the bearing capacity of the roadbed, the testing process is time-consuming and costly. In addition, these testing methods are mostly carried out after the construction is completed, and it is impossible to monitor and adjust the compaction effect of the soil layer in real time during the construction process, which can easily lead to problems such as uneven compaction, over-compacting or under-compacting of the soil during the construction process.

[0006] (3) Traditional quality control methods fail to fully utilize the dynamic response data of the rammer and cannot achieve accurate and real-time compaction quality assessment. Summary of the Invention

[0007] The purpose of the present invention is to provide a dynamic compaction construction quality control method that can monitor the dynamic response of the rammer in real time during the construction process, and analyze and adjust the construction parameters in real time based on the response signal, so as to ensure uniform compaction of the roadbed and improve construction efficiency and construction quality.

[0008] To achieve the above object, the present invention provides a method for controlling the quality of roadbed dynamic compaction construction based on the dynamic response of a rammer, the control method comprising the following steps:

[0009] Step S10, obtaining the acceleration signal of the rammer during the current rammering process;

[0010] Step S20: Processing the acceleration signal of the rammer to obtain multiple key parameters for predicting the dynamic deformation modulus, wherein the data processing includes filtering, time domain analysis, and frequency domain analysis performed in sequence. The multiple key parameters include acceleration peak value, rammer action time, acceleration kurtosis, and acceleration main frequency;

[0011] Step S30: obtaining the dynamic deformation modulus of the tamping point after the current tamping is completed based on the multiple key parameters and a predetermined dynamic deformation modulus prediction model, wherein the dynamic deformation modulus prediction model is a multiple linear regression model;

[0012] Step S40: Compare the dynamic deformation modulus of the tamping point with the set dynamic deformation modulus design value. When the dynamic deformation modulus of the tamping point is greater than or equal to the dynamic deformation modulus design value, send a prompt to stop tamping; when the dynamic deformation modulus of the tamping point is less than the dynamic deformation modulus design value, send a prompt to continue tamping, and repeat steps S10 to S30.

[0013] In a specific embodiment, the dynamic deformation modulus prediction model is:

[0014]

[0015] in, is the dynamic deformation modulus, is the peak acceleration, t m is the ramming time, u is the acceleration kurtosis, f m is the main frequency of acceleration, and β0~β4 are fitting coefficients.

[0016] In a specific embodiment, a method for constructing a predetermined dynamic deformation modulus prediction model includes:

[0017] Performing multiple tamping tests at multiple test tamping points, respectively, obtaining collected data for each tamping test at each test tamping point, to obtain multiple groups of collected data, each group of collected data including a dynamic deformation modulus measurement value and an acceleration signal of the tamping hammer, wherein the dynamic deformation modulus measurement value and the acceleration signal of the tamping hammer in the same group of collected data have a corresponding relationship, and the amount of collected data is the sum of the number of tamping times for all test tamping points;

[0018] Performing data processing on each rammer acceleration signal in the collected data to obtain a plurality of factor groups, each of the factor groups including acceleration peak value, rammer action time, acceleration kurtosis and acceleration main frequency;

[0019] Combining the dynamic deformation modulus measurement value and the corresponding factor group in each of the collected data into a sample data to obtain a plurality of sample data;

[0020] Randomly extracting a first preset number of sample data from all sample data as a sample set, and using the sample set to calculate coefficients of an initial multivariate regression model to determine the multivariate regression model coefficients;

[0021] Based on the multivariate regression model coefficients, obtain the target multivariate regression model.

[0022] In a specific embodiment, the method for constructing the predetermined dynamic deformation modulus prediction model further includes a model verification process:

[0023] Randomly extracting a second preset number of sample data from all the sample data as a validation set;

[0024] Substituting each factor group in the validation set into the target multivariate regression model to obtain a predicted value of the dynamic deformation modulus corresponding to each factor group;

[0025] Based on the dynamic deformation modulus measurement value and the dynamic deformation modulus prediction value corresponding to each factor group, the model prediction accuracy is calculated to obtain multiple model prediction accuracy values;

[0026] Get the average of the prediction accuracy values ​​of multiple models and use it as the prediction accuracy of the target multivariate regression model;

[0027] The prediction accuracy of the target multivariate regression model is compared with a preset threshold. When the prediction accuracy of the target multivariate regression model is less than or equal to the preset threshold, the verified target multivariate regression model meets the requirements, and the target multivariate regression model is used as the dynamic deformation modulus prediction model.

[0028] In a specific embodiment, when the prediction accuracy of the target multivariate regression model is greater than a preset threshold, the construction method further includes:

[0029] Randomly extracting a third preset number of sample data from all the sample data as a sample set, and using the sample set to calculate coefficients of the initial multivariate regression model to determine new multivariate regression model coefficients, wherein the third preset number is greater than the first preset number, and the difference between the third preset number and the first preset number is between 10 and 20;

[0030] Based on the new multivariate regression model coefficients, a new target multivariate regression model is obtained;

[0031] Model validation was performed on the new target multivariate regression model.

[0032] In a specific embodiment, the data processing includes:

[0033] Step (1), filtering and noise reduction processing of the acceleration signal of the rammer;

[0034] Step (2): based on the acceleration signal after filtering and noise reduction, obtain the calculation data interval of the current impact acceleration, wherein the calculation data interval is the data interval corresponding to time t0 to time t1, the acceleration values ​​corresponding to time t0 and time t1 are 0, and from time t0 to time t1, the acceleration value increases from 0, exceeds the peak value, and then decreases to 0;

[0035] Step (3): Perform time domain analysis and frequency domain analysis on the data set corresponding to the calculation data interval of the current impact acceleration, and obtain the acceleration peak value according to multiple calculation formulas. , ramming hammer action time , acceleration kurtosis u, acceleration main frequency f m ,in:

[0036] Get peak acceleration The calculation formula is:

[0037] in, is the acceleration time domain data, j represents the jth acceleration value in the calculation interval;

[0038] Get the tamper action time The calculation formula is:

[0039] Among them, t0 and t1 are the starting time point and the ending time point of the calculation data interval respectively;

[0040] Get acceleration kurtosis The calculation formula is: ;

[0041] Among them, n represents the amount of acceleration data in the calculation data interval, is the acceleration time domain data, Represents the mean of the calculated data interval, and s represents the variance of the calculated data interval;

[0042] Get the main frequency f m The calculation formula is: ;

[0043] in, is the acceleration time domain data The frequency domain signal obtained by Fourier transform is To calculate the power spectral density of the frequency domain signal, The frequency corresponding to the maximum value of the power spectral density is calculated.

[0044] In a specific embodiment, the calculation formulas for calculating the mean and variance of a data interval are as follows:

[0045] ;

[0046] in, Represents the mean of the calculated data interval, n represents the amount of acceleration data in the calculated interval, is the acceleration time domain data;

[0047] ;

[0048] Among them, s represents the variance of the calculated data interval, n represents the amount of acceleration data in the calculated data interval, is the acceleration time domain data, Represents the mean of the calculated data interval.

[0049] In a specific embodiment, the step of calculating coefficients of the initial multivariate regression model using the sample set to determine the coefficients of the multivariate regression model includes:

[0050] Step (a): Substitute the sample data of l in the sample set into the initial multivariate regression model, and the resulting model form is:

[0051]

[0052] Where: ε i represents the error term, β0~β4 are the corresponding fitting coefficients, and l represents the number of samples;

[0053] Step (b) is written in expanded form according to the model form:

[0054]

[0055] Where: Y is the dependent variable matrix, X is the independent variable matrix, and β is the coefficient matrix;

[0056] Step (c) calculates the residual sum of squares according to a preset formula, which is:

[0057]

[0058] Among them, Y T is the transposed matrix of the dependent variable matrix Y, X T is the transposed matrix of the independent variable matrix X, β T is the transposed matrix of the coefficient matrix β;

[0059] Step (d), deriving the residual sum of squares to obtain an equation after the derivative of the residual sum of squares, the equation being:

[0060]

[0061] Step (e): Based on the equation where the residual sum of squares is 0 and the derivative of the residual sum of squares, the coefficient matrix is ​​calculated to obtain the coefficients of the multivariate regression model.

[0062] The present invention also provides a roadbed dynamic compaction construction quality control system based on the dynamic response of the rammer, the control system comprising:

[0063] Rammer, used for strong compaction of roadbed;

[0064] A plurality of acceleration sensors, used for collecting acceleration signals of the rammer;

[0065] A controller is communicatively connected to each of the plurality of acceleration sensors, and is used in the control method described in steps S10 to S40 above.

[0066] In a specific embodiment, a plurality of acceleration sensors are mounted on the rammer and are evenly distributed around the central axis of the rammer, and the distance between each acceleration sensor and the central axis of the rammer is the same as the distance between each acceleration sensor and the contour line of the rammer.

[0067] The beneficial effects of the present invention include at least:

[0068] 1. The present invention provides a method for controlling the construction quality of roadbed dynamic compaction based on the dynamic response of a rammer, the control method comprising the following steps: step S10, obtaining the acceleration signal of the rammer during the current tamping process; step S20, performing data processing on the acceleration signal of the rammer to obtain a plurality of key parameters for predicting the dynamic deformation modulus, the data processing comprising filtering processing, time domain analysis and frequency domain analysis performed in sequence, the plurality of key parameters comprising acceleration peak value, rammer action time, acceleration kurtosis and acceleration main frequency; step S30, obtaining the dynamic deformation modulus of the tamping point that has completed the current tamping based on the plurality of key parameters and a predetermined dynamic deformation modulus prediction model, the dynamic deformation modulus prediction model being a multiple linear regression model; step S40, The dynamic deformation modulus of the tamping point is compared with the set dynamic deformation modulus design value. When the dynamic deformation modulus of the tamping point is greater than or equal to the dynamic deformation modulus design value, a prompt to stop tamping is sent; when the dynamic deformation modulus of the tamping point is less than the dynamic deformation modulus design value, a prompt to continue tamping is sent, and steps S10 to S30 are repeated. In the present invention, the dynamic deformation modulus of the tamping point after completing the tamping is obtained by real-time acquisition of the rammer acceleration signal and a dynamic deformation modulus prediction model between multiple key parameters and the dynamic deformation modulus of the soil constructed based on the test tamping data. After comparing it with the dynamic deformation modulus design value, it can be determined whether to continue tamping the tamping point based on the comparison result, thereby realizing real-time monitoring of soil tamping quality and adjustment of construction parameters during construction.

[0069] 2. The present invention sets up multiple acceleration sensors to accurately and comprehensively obtain the acceleration signal when the rammer hits, and analyzes the acceleration signal to extract the corresponding statistical characteristics of acceleration peak value, rammer action time, acceleration kurtosis and acceleration main frequency, and directly establishes a mathematical relationship between the corresponding statistical characteristics and the dynamic deformation modulus of the soil to construct a dynamic deformation modulus prediction model. In the process of constructing the dynamic deformation modulus prediction model, the prediction accuracy of the dynamic deformation modulus prediction model can be improved by increasing the number of sample sets, thereby ensuring that the prediction accuracy of the dynamic deformation modulus prediction model is high, and the reinforcement condition of the soil after each impact can be obtained more accurately.

[0070] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic flow chart of the steps of a method for controlling the quality of roadbed dynamic compaction construction based on the dynamic response of a rammer provided in one embodiment of the present invention;

[0072] Figure 2 A schematic diagram of a rammer equipped with an acceleration sensor according to an embodiment of the present invention;

[0073] Figure 3 This is a schematic diagram of the acceleration signal collected by the acceleration sensor provided by the present invention. DETAILED DESCRIPTION

[0074] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0075] See also Figure 1 and Figure 2 The present invention provides a method for controlling the quality of roadbed dynamic compaction construction based on the dynamic response of a rammer, the control method comprising the following steps:

[0076] Step S10: Acquire the acceleration signal of the rammer during the current rammering process.

[0077] In the present invention, the acceleration signal is collected by the acceleration sensor 102 installed on the rammer 101 .

[0078] In the present invention, the number of acceleration sensors installed on the rammer may be 1, 2, 3 or other numbers.

[0079] Preferably, there are multiple acceleration sensors 20 , and the multiple acceleration sensors 20 are evenly distributed around the central axis of the rammer 10 .

[0080] More preferably, the distance between each acceleration sensor 20 and the central axis of the rammer 10 is the same as the distance between each acceleration sensor 20 and the outer contour line of the rammer 10 .

[0081] Please refer to Figure 2 , Figure 2 A top view of a rammer provided by the present invention, equipped with acceleration sensors. In the present invention, the acceleration sensors are four in number and evenly distributed around the central axis of the rammer. The four acceleration sensors are spaced the same distance from the central axis of the rammer as from the outer contour of the rammer.

[0082] In the present invention, four acceleration sensors are symmetrically mounted on the rammer, and the mounting positions are located at symmetrical points of the rammer, so as to comprehensively monitor the acceleration changes of the rammer.

[0083] It's important to note that to avoid signal distortion or delay, the connection between the accelerometer and the rammer must be secure during installation, and the accelerometer placement should be effective in preventing external interference. In this example, each accelerometer is bolted to the vehicle, and a wireless data transmission module transmits the collected acceleration signal in real time to the controller in the cab.

[0084] It is understandable that there are four acceleration sensors and four acceleration signals are obtained. In the following, after the acceleration signals are processed, the data used for analysis is the average value of the four data processing results.

[0085] Preferably, the acceleration sensor has high sensitivity and a wide frequency response range, such as a sensitivity of 100 mV / g and a frequency response range of 0.1 Hz to 20,000 Hz.

[0086] Step S20: performing data processing on the acceleration signal of the rammer to obtain multiple key parameters for predicting the dynamic deformation modulus, wherein the data processing includes filtering processing, time domain analysis and frequency domain analysis performed in sequence, and the multiple key parameters include acceleration peak value, rammer action time, acceleration kurtosis and acceleration main frequency.

[0087] In the present invention, the data processing includes the following steps:

[0088] Step (1): filtering and noise reduction processing of the acceleration signal of the rammer.

[0089] In the present invention, the filtering and noise reduction processing is specifically: using a low-pass filtering method to filter and reduce the noise of the acceleration signal, and the filter cutoff frequency is set to 500 Hz to remove high-frequency noise and external interference signals.

[0090] Step (2): Based on the acceleration signal after filtering and noise reduction, obtain the calculation data interval of the current impact acceleration, the calculation data interval is the data interval corresponding to the time t0 to the time t1, the acceleration values ​​corresponding to the time t0 and the time t1 are 0, and from the time t0 to the time t1, the acceleration value increases from 0, exceeds the peak value, and then decreases to 0.

[0091] See also Figure 3 , Figure 3 The figure shows the acceleration signal of the tamping hammer corresponding to multiple tamping points (after filtering and noise reduction). Figure 3 The upper gray filled area corresponds to the calculated data interval.

[0092] When a plurality of acceleration signals are obtained, the method further includes obtaining an average acceleration signal of the plurality of acceleration signals and using the average acceleration signal as basic data for obtaining the calculation data interval in step (b).

[0093] Take the present invention as an example with four acceleration sensors, and calculate the average value of the four signals. As the final rammer acceleration value, step (2) obtains the calculation data interval based on the final rammer acceleration value, that is, the acceleration signal after filtering and noise reduction is the average acceleration signal of the four acceleration signals after filtering and noise reduction.

[0094] Preferably, there are multiple acceleration sensors, and the acceleration signal after filtering and noise reduction processing is an average acceleration signal.

[0095] Step (3): Perform time domain analysis and frequency domain analysis on the data set corresponding to the calculation data interval of the current impact acceleration, and obtain the acceleration peak value according to multiple calculation formulas. , ramming hammer action time , acceleration kurtosis u, acceleration main frequency f m ,in,

[0096] Get peak acceleration The calculation formula is:

[0097] in, is the acceleration time domain data, and j represents the jth acceleration value in the calculated data interval.

[0098] In the present invention, Real-time data representing the average signal of multiple accelerometers.

[0099] Get the tamper action time The calculation formula is:

[0100] Among them, t0 and t1 are the starting time point and the ending time point of the calculation data interval respectively.

[0101] Get acceleration kurtosis The calculation formula is: .

[0102] Among them, n represents the amount of acceleration data in the calculation interval, is the acceleration time domain data, represents the mean of the calculation interval, and s represents the variance of the calculation interval.

[0103] In the present invention, the calculation formulas for the mean of the calculation interval and the variance of the calculation interval are as follows:

[0104] .

[0105] .

[0106] Get the main frequency f m The calculation formula is: .

[0107] in, is the acceleration time domain data The frequency domain signal obtained by Fourier transform is To calculate the power spectral density of the frequency domain signal, The frequency corresponding to the maximum value of the power spectral density is calculated.

[0108] Step S30: Based on the multiple key parameters and a predetermined dynamic deformation modulus prediction model, the dynamic deformation modulus of the tamping point after the current tamping is completed is obtained, wherein the dynamic deformation modulus prediction model is a multiple linear regression model.

[0109] Preferably, the dynamic deformation modulus prediction model is:

[0110]

[0111] in, is the dynamic deformation modulus, is the peak acceleration, is the ramming time, u is the kurtosis, f m is the main frequency, and β0~β4 are fitting coefficients.

[0112] It should be noted that, in the dynamic deformation modulus prediction model, β0 to β4 are specific numerical values.

[0113] The present invention can obtain the dynamic deformation modulus of the tamping point after each tamping in real time through a dynamic deformation modulus prediction model, thereby reducing the cost of later quality inspection, improving construction quality and efficiency, and ensuring the overall uniformity of roadbed dynamic tamping reinforcement.

[0114] The multiple key parameters obtained in step S20 are substituted into the dynamic deformation modulus prediction model to calculate the dynamic deformation modulus of the tamping point after the current tamping. The dynamic deformation model can be used to judge the compaction quality of the tamping point after the current tamping. If it does not meet the requirements, the tamping point needs to be tamped. If it meets the requirements, the construction of the tamping point is completed.

[0115] The method for constructing the predetermined dynamic deformation modulus prediction model includes:

[0116] Step 1) Perform multiple tamping tests at multiple test tamping points, obtain collected data for each tamping test at each test tamping point, and obtain multiple groups of collected data. Each group of collected data includes a dynamic deformation modulus measurement value and an acceleration signal of the tamping hammer, and the dynamic deformation modulus measurement value and the acceleration signal of the tamping hammer in the same group of collected data have a corresponding relationship. The number of collected data is the sum of the number of tamping times for all test tamping points.

[0117] The number of test points is determined by the number of samples. In principle, the more test points, the better. This way, the more groups of data are collected, and the higher the accuracy of the model determined subsequently.

[0118] In the present invention, the number of test tamping points is 10 to 20.

[0119] In the present invention, each test tamping point is subjected to 10 to 15 tamping tests.

[0120] In this way, the number of groups of collected data is 100~300 groups.

[0121] It can be understood that when there are multiple acceleration sensors on the rammer, there are also multiple acceleration signals of the rammer in each set of collected data.

[0122] In the present invention, 10 tamping points were selected for tamping test, 15 tamping times were performed on each tamping point, the tamping hammer weighed 20 tons, and the drop distance of each tamping was 1.5 m; the number of acceleration sensors was 4, evenly distributed, and the frequency was set to 20,000 Hz.

[0123] In the present invention, after each tamping point is completed, E vd The dynamic modulus tester measures the dynamic deformation modulus of soil. The acceleration signal of each tamping point corresponds to the dynamic deformation modulus value measured after the tamping is completed, providing a basis for the subsequent prediction model construction.

[0124] Step 2) performing data processing on each acceleration signal of the rammer in the collected data to obtain multiple factor groups, each of which includes acceleration peak value, rammer action time, acceleration kurtosis and acceleration main frequency.

[0125] The data processing in this step is exactly the same as that in step S20 and will not be described again here.

[0126] Step 3) combining the dynamic deformation modulus measurement value and the corresponding factor group in each of the collected data into a sample data to obtain a plurality of sample data.

[0127] In the present invention, each sample data includes a dynamic deformation modulus measurement value, and an acceleration peak value, a ram action time, an acceleration kurtosis, and an acceleration main frequency corresponding to the dynamic deformation modulus measurement value.

[0128] For ease of understanding, let's take an example. Assume that the dynamic deformation modulus measured after the first tamping point is completed for the first time is M. After data processing of the acceleration signal obtained by the first tamping point for the first time, the obtained acceleration peak value, tamping hammer action time, acceleration kurtosis and acceleration main frequency are N1, N2, N3 and N4 respectively. Then, M and N1, N2, N3 and N4 together constitute a sample data.

[0129] Step 4) randomly extracting a first preset number of sample data from all the sample data as a sample set, and using the sample set to calculate the coefficients of the initial multivariate regression model to determine the multivariate regression model coefficients.

[0130] Preferably, the first preset quantity is 1 / 2 to 4 / 5 of the total amount of sample data.

[0131] In this embodiment, the total number of sample data is 150, and the first preset number is 75-120.

[0132] The step of calculating the coefficients of the initial multivariate regression model using the sample set to determine the coefficients of the multivariate regression model includes:

[0133] Step (a): Substitute the sample data of l in the sample set into the initial multivariate regression model, and the resulting model form is:

[0134]

[0135] Where: ε i represents the error term, β0~β4 are the corresponding fitting coefficients, and l represents the number of samples.

[0136] It should be noted that in the model form, β0~β4 are independent variables that need to be solved.

[0137] In the present invention, the characteristic value acceleration peak is selected , ramming hammer action time , acceleration kurtosis u and acceleration main frequency f m As an independent variable , dynamic deformation modulus E vd As dependent variable , build the initial multiple linear regression model.

[0138] Step (b) is written in expanded form according to the model form:

[0139]

[0140] Where: Y is the dependent variable matrix, X is the independent variable matrix, and β is the coefficient matrix.

[0141] It should be noted that, in this expanded form, Y and X are known numbers, and β is the unknown number to be solved.

[0142] Step (c) calculates the residual sum of squares according to a preset formula, which is:

[0143]

[0144] Among them, Y T is the transposed matrix of the dependent variable matrix Y, X T is the transposed matrix of the independent variable matrix X, β T is the transposed matrix of the coefficient matrix β.

[0145] Step (d), deriving the residual sum of squares to obtain an equation after the derivative of the residual sum of squares, the equation being:

[0146]

[0147] Step (e): Based on the residual sum of squares being 0 and the equation after the derivative of the residual sum of squares, the coefficient matrix is ​​calculated to obtain the coefficients of the multivariate regression model.

[0148]

[0149] Step 5) Based on the multivariate regression model coefficients, obtain the target multivariate regression model.

[0150] Substitute the multivariate regression model coefficients determined in the previous step into the multivariate linear regression equation to obtain the target multivariate regression model, specifically:

[0151]

[0152] in, is the dynamic deformation modulus, is the peak acceleration, is the ramming hammer action time, is the acceleration kurtosis, f m is the main frequency, and β0~β4 are fitting coefficients.

[0153] It should be noted that β0~β4 in the target multivariate regression model are four specific values, which may be the same as β0~β4 in the dynamic deformation modulus prediction model, or they may be different, depending on the verification results of the model prediction accuracy. When the prediction accuracy of the model meets the requirements, β0~β4 in the target multivariate regression model is the same as β0~β4 in the dynamic deformation modulus prediction model. When the prediction accuracy of the model does not meet the requirements, β0~β4 in the target multivariate regression model is the same as β0~β4 in the dynamic deformation modulus prediction model.

[0154] Of course, in the present invention, the fitting coefficients can also be calculated based on experience using a sample set with sufficient data to ensure that the prediction accuracy of the target multivariate regression model meets the requirements. Therefore, the verification step of the model prediction accuracy is an optional step.

[0155] Step 6) Verify the prediction accuracy of the target multivariate regression model.

[0156] This step specifically includes:

[0157] Step 6-1: Randomly select a second preset number of sample data from all sample data as a validation set.

[0158] In the present invention, the second preset number is 1 to 10, and preferably, the second preset number is 5.

[0159] Step 6-2: Substitute each factor group in the validation set into the target multivariate regression model to obtain a plurality of dynamic deformation modulus prediction values ​​corresponding to each factor group.

[0160] Step 6-3: Calculate the prediction accuracy based on the dynamic deformation modulus measurement value and the dynamic deformation modulus prediction value corresponding to each factor group to obtain multiple prediction accuracy values.

[0161] In the present invention, the calculation formula for the prediction accuracy is: the absolute value of the difference between the dynamic deformation modulus measured value and the dynamic deformation modulus predicted value / dynamic deformation modulus measured value*100%.

[0162] Step 6-4: Obtain the average of multiple prediction accuracy values ​​and use it as the prediction accuracy of the target multivariate regression model.

[0163] Assuming that the second preset number is 5, a total of five prediction accuracy values ​​are obtained in the previous step, namely A1, A2, A3, A4 and A5. The sum of A1, A2, A3, A4 and A5 is divided by 5 to obtain the average of the multiple prediction accuracy values.

[0164] Step 6-5: Compare the prediction accuracy of the target multivariate regression model with a preset threshold. If the prediction accuracy of the target multivariate regression model is less than or equal to the preset threshold, the verified target multivariate regression model meets the requirements and is used as the dynamic deformation modulus prediction model.

[0165] When the result of the model validation process is that the prediction accuracy of the target multivariate regression model is greater than a preset threshold, the construction method further includes:

[0166] A third preset number of sample data is randomly selected from all the sample data as a sample set, and the coefficients of the initial multivariate regression model are calculated using the sample set to determine new multivariate regression model coefficients, where the third preset number is greater than the first preset number, and the difference between the third preset number and the first preset number is between 10 and 20.

[0167] Based on the new multivariate regression model coefficients, a new target multivariate regression model is obtained.

[0168] Model validation was performed on the new target multivariate regression model.

[0169] For ease of understanding, for example, assuming that the first preset number is 75 and the second preset number is 5, first randomly select 75 sample data from all the sample data as the sample set, and use the dynamic deformation modulus measurement value in the sample data as the Y value, and the acceleration peak value, rammer action time, acceleration kurtosis and acceleration main frequency as the X value to substitute into the initial multivariate regression model to obtain 75 equations, and determine the four coefficients in the initial multivariate regression model by calculating the residual sum of squares and taking the derivative of the residual sum of squares. Substitute the four coefficients into the initial multivariate regression model to obtain the target multivariate regression model; then randomly select 5 sample data from all the sample data as the validation set, and use the acceleration peak value, rammer action time, acceleration kurtosis and acceleration main frequency in the sample data of the validation set as the X value to substitute into the target multivariate regression model to obtain the dynamic deformation modulus prediction value, and then calculate the predicted value according to the formula. The dynamic deformation modulus measurement values ​​and dynamic deformation modulus prediction values ​​in the sample data of the validation set are used to calculate the model prediction accuracy value, and the average of the 5 model prediction accuracy values ​​is obtained and used as the prediction accuracy of the target multivariate regression model. The prediction accuracy of the target multivariate regression model is compared with the preset threshold. If it is less than or equal to the preset threshold, the requirement is met and the target multivariate regression model is used as the dynamic deformation modulus prediction model. If it is greater than the preset threshold, 85 sample data are randomly selected from all the sample data as a new sample set, and the new sample set is used to determine the four coefficients in the initial multivariate regression model, and a new target multivariate regression model is obtained. The new target multivariate regression model is then verified to determine whether it meets the requirements. If it still does not meet the requirements, the number of new sample sets is increased to 95, ... until the verified target multivariate regression model meets the requirements.

[0170] Step S40: Compare the dynamic deformation modulus of the tamping point with the set dynamic deformation modulus design value. When the dynamic deformation modulus of the tamping point is greater than or equal to the dynamic deformation modulus design value, send a prompt to stop tamping; when the dynamic deformation modulus of the tamping point is less than the dynamic deformation modulus design value, send a prompt to continue tamping, and repeat steps S10 to S30.

[0171] In this example, during the construction process, the design value of the dynamic deformation modulus is set is 80 MPa, if the predicted value of the dynamic deformation modulus of the tamping point is higher than or equal to , it is considered that the compaction effect of the tamping point meets the design requirements. The construction personnel receive the prompt to stop tamping and can stop the construction of the tamping point to proceed to the next tamping point. If the predicted value of the dynamic deformation modulus of the tamping point is lower than When the driver receives a prompt to continue tamping, he needs to control the tamping hammer to continue tamping until the design requirements are met.

[0172] See also Figure 2The present invention also provides a roadbed dynamic compaction construction quality control system based on the dynamic response of the rammer, the control system comprising:

[0173] Rammer 101, used for compacting the roadbed;

[0174] A plurality of acceleration sensors 102 for collecting acceleration signals of the rammer;

[0175] A controller is communicatively connected to the plurality of acceleration sensors 102 , and is configured to execute the control method described in steps S10 to S40 above.

[0176] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the controller described above can refer to the content of the aforementioned method embodiment and will not be repeated here.

[0177] Preferably, a plurality of acceleration sensors 102 are mounted on the rammer 101 and are evenly distributed around the central axis of the rammer 101 , and the distance between each acceleration sensor 102 and the central axis of the rammer 101 and the distance between each acceleration sensor 102 and the contour line of the rammer 101 are the same.

[0178] In the present invention, the arrangement point of the acceleration sensor 102 can effectively avoid external interference, thereby avoiding signal distortion or delay.

[0179] In the present invention, the number of acceleration sensors 102 is four.

[0180] In the present invention, the acceleration sensor 102 and the rammer 101 are fixed by bolts.

[0181] Preferably, the controller is installed in the cab.

[0182] The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, several simple deductions and substitutions can be made without departing from the concept of the present invention, and all of these should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for controlling the quality of roadbed compaction construction based on the dynamic response of the rammer, characterized in that: The control method comprises the following steps: Step S10, obtaining the acceleration signal of the rammer during the current rammering process; Step S20: Processing the acceleration signal of the rammer to obtain multiple key parameters for predicting the dynamic deformation modulus, wherein the data processing includes filtering, time domain analysis, and frequency domain analysis performed in sequence. The multiple key parameters include acceleration peak value, rammer action time, acceleration kurtosis, and acceleration main frequency; Step S30: Based on the multiple key parameters and a predetermined dynamic deformation modulus prediction model, the dynamic deformation modulus of the tamping point after the current tamping is obtained. The dynamic deformation modulus prediction model is a multiple linear regression model, wherein the dynamic deformation modulus prediction model is: in, is the dynamic deformation modulus, is the peak acceleration, t m is the ramming time, u is the acceleration kurtosis, f m is the main frequency of acceleration, β0~β4 are fitting coefficients; Step S40: Compare the dynamic deformation modulus of the tamping point with the set dynamic deformation modulus design value. When the dynamic deformation modulus of the tamping point is greater than or equal to the dynamic deformation modulus design value, send a prompt to stop tamping; when the dynamic deformation modulus of the tamping point is less than the dynamic deformation modulus design value, send a prompt to continue tamping, and repeat steps S10 to S30.

2. The method for controlling the quality of roadbed dynamic compaction construction based on the dynamic response of the rammer according to claim 1, characterized in that: A method for constructing a predetermined dynamic deformation modulus prediction model, comprising: Performing multiple tamping tests at multiple test tamping points, respectively, obtaining collected data for each tamping test at each test tamping point, to obtain multiple groups of collected data, each group of collected data including a dynamic deformation modulus measurement value and an acceleration signal of the tamping hammer, wherein the dynamic deformation modulus measurement value and the acceleration signal of the tamping hammer in the same group of collected data have a corresponding relationship, and the amount of collected data is the sum of the number of tamping times for all test tamping points; Performing data processing on each rammer acceleration signal in the collected data to obtain a plurality of factor groups, each of the factor groups including acceleration peak value, rammer action time, acceleration kurtosis and acceleration main frequency; Combining the dynamic deformation modulus measurement value and the corresponding factor group in each of the collected data into a sample data to obtain a plurality of sample data; Randomly extracting a first preset number of sample data from all sample data as a sample set, and using the sample set to calculate coefficients of an initial multivariate regression model to determine the multivariate regression model coefficients; Based on the multivariate regression model coefficients, obtain the target multivariate regression model.

3. The method for controlling the quality of roadbed dynamic compaction construction based on the dynamic response of the rammer according to claim 2, wherein: The method for constructing the predetermined dynamic deformation modulus prediction model further includes a model verification process: Randomly extracting a second preset number of sample data from all the sample data as a validation set; Substituting each factor group in the validation set into the target multivariate regression model to obtain a plurality of dynamic deformation modulus prediction values ​​corresponding to each factor group; Based on the dynamic deformation modulus measurement value and the dynamic deformation modulus prediction value corresponding to each factor group, the model prediction accuracy is calculated to obtain multiple model prediction accuracy values; Get the average of the prediction accuracy values ​​of multiple models and use it as the prediction accuracy of the target multivariate regression model; The prediction accuracy of the target multivariate regression model is compared with a preset threshold. When the prediction accuracy of the target multivariate regression model is less than or equal to the preset threshold, the verified target multivariate regression model meets the requirements, and the target multivariate regression model is used as the dynamic deformation modulus prediction model.

4. The method for controlling the quality of roadbed dynamic compaction construction based on the dynamic response of the rammer according to claim 3, characterized in that: When the prediction accuracy of the target multivariate regression model is greater than a preset threshold, the construction method further includes: Randomly extracting a third preset number of sample data from all the sample data as a sample set, and using the sample set to calculate coefficients of the initial multivariate regression model to determine new multivariate regression model coefficients, wherein the third preset number is greater than the first preset number, and the difference between the third preset number and the first preset number is between 10 and 20; Based on the new multivariate regression model coefficients, a new target multivariate regression model is obtained; Model validation was performed on the new target multivariate regression model.

5. The method for controlling the quality of roadbed dynamic compaction construction based on the dynamic response of the rammer according to claim 2 or 4, characterized in that: The data processing includes: Step (1), filtering and noise reduction processing of the acceleration signal of the rammer; Step (2): based on the acceleration signal after filtering and noise reduction, obtain the calculation data interval of the current impact acceleration, wherein the calculation data interval is the data interval corresponding to time t0 to time t1, the acceleration values ​​corresponding to time t0 and time t1 are 0, and from time t0 to time t1, the acceleration value increases from 0, exceeds the peak value, and then decreases to 0; Step (3): Perform time domain analysis and frequency domain analysis on the data set corresponding to the calculation data interval of the current impact acceleration, and obtain the acceleration peak value according to multiple calculation formulas. , tamper action time t m , acceleration kurtosis u, acceleration main frequency f m ,in: Get peak acceleration The calculation formula is: in, is the acceleration time domain data, j represents the jth acceleration value in the calculation interval; Get the rammer action time t m The calculation formula is: Among them, t0 and t1 are the starting time point and the ending time point of the calculation data interval respectively; The calculation formula for obtaining the acceleration kurtosis u is: ; Among them, n represents the amount of acceleration data in the calculation data interval, is the acceleration time domain data, Represents the mean of the calculated data interval, and s represents the variance of the calculated data interval; Get the main frequency f m The calculation formula is: ; in, is the acceleration time domain data The frequency domain signal obtained by Fourier transform is To calculate the power spectral density of the frequency domain signal, The frequency corresponding to the maximum value of the power spectral density is calculated.

6. The method for controlling the quality of roadbed dynamic compaction construction based on the dynamic response of the rammer according to claim 5, characterized in that: The calculation formulas for calculating the mean and variance of a data interval are as follows: ； in, Represents the mean of the calculated data interval, n represents the amount of acceleration data in the calculated interval, is the acceleration time domain data; ； Among them, s represents the variance of the calculated data interval, n represents the amount of acceleration data in the calculated data interval, is the acceleration time domain data, Represents the mean of the calculated data interval.

7. The method for controlling the quality of roadbed dynamic compaction construction based on the dynamic response of the rammer according to claim 5, characterized in that: The step of calculating the coefficients of the initial multivariate regression model using the sample set to determine the coefficients of the multivariate regression model includes: Step (a): Substitute the sample data of l in the sample set into the initial multivariate regression model, and the resulting model form is: Where: i represents the error term, β0~β4 are the corresponding fitting coefficients, and l represents the number of samples; Step (b) is written in expanded form according to the model form: Where: Y is the dependent variable matrix, X is the independent variable matrix, and β is the coefficient matrix; Step (c) calculates the residual sum of squares according to a preset formula, which is: Among them, Y T is the transposed matrix of the dependent variable matrix Y, X T is the transposed matrix of the independent variable matrix X, β T is the transposed matrix of the coefficient matrix β; Step (d), deriving the residual sum of squares to obtain an equation after the derivative of the residual sum of squares, the equation being: Step (e): Based on the equation where the residual sum of squares is 0 and the derivative of the residual sum of squares, the coefficient matrix is ​​calculated to obtain the coefficients of the multivariate regression model.

8. A roadbed dynamic compaction construction quality control system based on the dynamic response of the rammer, characterized in that: The control system includes: Rammer, used for strong compaction of roadbed; A plurality of acceleration sensors, used for collecting acceleration signals of the rammer; A controller is communicatively connected to each of the plurality of acceleration sensors, and is configured to execute the control method of steps S10 to S40 according to claim 1.

9. The roadbed dynamic compaction construction quality control system based on the rammer dynamic response according to claim 8, characterized in that: A plurality of acceleration sensors are mounted on the rammer and are evenly distributed around the central axis of the rammer. The distance between each acceleration sensor and the central axis of the rammer is the same as the distance between each acceleration sensor and the contour line of the rammer.

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