A continuous detection method for dam filling compaction quality based on compaction vibration asymmetry

Abstract

The present invention provides a continuous detection method for the compaction quality of dam fillers based on compaction vibration asymmetry, comprising: dividing and extracting the filtered acceleration signal in units of one vibration cycle to obtain the acceleration signal of each vibration cycle, determining the acceleration signal point set A1 of the vibration wheel above the equilibrium position and the acceleration signal point set B1 of the vibration wheel below the equilibrium position; dividing the acceleration signal point set B1 along the axis of the vibration wheel; and extracting the acceleration signal point set B1 along the axis of the vibration wheel. t The axis is flipped to a positive value and translated to obtain the acceleration signal point set B2; the acceleration signal point set A2 and the acceleration signal point set B2 are fitted with a sine function to calculate the compaction vibration asymmetry index. CVAI The present invention directly performs time-domain analysis on acceleration, which has the advantages of high precision and simple equipment requirements. It can effectively ensure the quality of dam filling and improve the efficiency of dam filling. It can also be used in the construction of earthwork filling projects such as roads, railways, and airports.

Description

Technical Field

[0001] The invention belongs to the field of intelligent construction of hydropower projects, and in particular relates to a method for continuously detecting the compaction quality of dam fillers based on compaction vibration asymmetry. Background Art

[0002] Fill compaction is a key step in dam construction, and its quality directly impacts dam safety. Substandard compaction can lead to uneven settlement, leakage, landslides, and even serious accidents like dam failure. Common methods for measuring fill compaction quality include the knife ring method, sand injection, water injection, and nuclear density meter. These methods rely on post-process sampling and are unable to provide a comprehensive, real-time assessment of fill compaction quality.

[0003] Dam fill materials are typically compacted using vibratory rollers. The combined effects of the excitation force and the fill material's reaction force cause the vibrating wheel to vibrate, and the vibration state is related to the fill material's compaction state. In 1980, Thurner conducted frequency-domain analysis of the vibrating wheel's acceleration and found that the second harmonic increment in the acceleration spectrum increases with soil stiffness. He proposed using the ratio of the second harmonic amplitude to the fundamental amplitude to evaluate soil stiffness, defining this value as the Compaction Meter Value (CMV). Building on this, several researchers have proposed continuous compaction quality measurement methods based on acceleration frequency-domain analysis. However, these metrics require intercepting a segment of the acceleration signal for calculation, which can be significantly affected by the intercepted signal and may also suffer from spectrum leakage, leading to inaccurate results. Numerous engineering practices have demonstrated that for coarse-grained soil fill materials used in dams, the frequency-domain components of the vibrating wheel are complex, making frequency-domain-based continuous compaction quality measurement methods less accurate. Summary of the Invention

[0004] In view of the defects of the existing technology, the present invention provides a continuous detection method for the compaction quality of dam fillers based on compaction vibration asymmetry, which can effectively solve the above problems.

[0005] The technical solution adopted in the present invention is as follows:

[0006] The present invention provides a method for continuously detecting the compaction quality of dam fillers based on compaction vibration asymmetry, comprising the following steps:

[0007] Step 1: During the dam filling and compaction process, the vertical acceleration raw signal of the vibrating wheel and the three-dimensional position information of the roller are collected synchronously in real time and transmitted to the on-board controller in real time;

[0008] Step 2: The vehicle-mounted controller performs low-pass filtering on the original acceleration signal to obtain a filtered acceleration signal;

[0009] Step 3: Segment and extract the filtered acceleration signal in units of one vibration cycle to obtain the acceleration signal of each vibration cycle; for the acceleration signal of each vibration cycle, determine the acceleration signal point set A1 of the vibration wheel above the equilibrium position and the acceleration signal point set B1 of the vibration wheel below the equilibrium position; wherein, the acceleration signal point set A1 of the vibration wheel above the equilibrium position is the signal portion with positive acceleration; the acceleration signal point set B1 of the vibration wheel below the equilibrium position is the signal portion with negative acceleration; flip the acceleration signal point set B1 along the t-axis to a positive value, and translate the signal starting point to the coordinate (0,0) to obtain the acceleration signal point set B2; translate the signal starting point of the acceleration signal point set A1 to the coordinate (0,0) to obtain the acceleration signal point set A2;

[0010] Step 4: Use the sine function to fit the acceleration signal point set A2 and the acceleration signal point set B2 respectively to obtain the fitting curves A(t) and B(t) respectively;

[0011] Step 5: Based on the fitting curves A(t) and B(t), calculate the compaction vibration asymmetry index CVAI:

[0012] ;

[0013] Where: k is a constant; T is the vibration period of the roller;

[0014] Step 6: Establish the relationship between the compaction vibration asymmetry index CVAI and the conventional detection index D of dam compaction quality, thereby obtaining a compaction quality prediction model;

[0015] Step 7: Generate the roller's rolling trajectory based on the roller's three-dimensional position information, assign the corresponding rolling unit with the continuous detection result of the compaction quality obtained through the compaction vibration asymmetry index CVAI, and obtain the corresponding dam compaction quality conventional detection value based on the compaction quality prediction model, generate 2D and 3D cloud maps of the dam's compaction status distribution, and display them on the display terminal.

[0016] Preferably, the real-time synchronous acquisition of the vertical acceleration original signal of the vibrating wheel and the three-dimensional position information of the roller is specifically as follows:

[0017] An acceleration sensor is installed in the vertical direction of the vibrating wheel of the vibratory roller, and the acceleration original signal of the vertical acceleration of the vibrating wheel is collected in real time through the acceleration sensor;

[0018] The satellite positioning system is used to collect the three-dimensional position information of the roller in real time.

[0019] Preferably, the sampling frequency of the acceleration sensor is more than 20 times the vibration frequency of the roller.

[0020] Preferably, the vehicle-mounted controller performs low-pass filtering on the original acceleration signal, specifically:

[0021] Performing Fourier analysis on the original acceleration signal to determine the frequency of the maximum harmonic, and filtering out high-frequency components above the maximum harmonic frequency in the original acceleration signal.

[0022] Preferably, when fitting the acceleration signal point set A2 and the acceleration signal point set B2, the fundamental frequency and higher harmonics are considered, and the least squares method is used to fit the acceleration signal point set A2 and the acceleration signal point set B2 respectively according to the following formula:

[0023]

[0024]

[0025] Where: is the acceleration fitting curve of the vibration wheel above the equilibrium position; is the acceleration fitting curve of the vibration wheel below the equilibrium position; ~ , ~ is the fitting coefficient; is the fundamental frequency; is the maximum harmonic order that occurs.

[0026] Preferably, the fundamental frequency , obtained by Fourier transforming the original acceleration signal; or, based on the sampling frequency and the number of data samples within a vibration cycle, calculated as follows:

[0027]

[0028] Where: is the sampling frequency; The number of data samples in one vibration cycle.

[0029] Preferably, the relationship between the compaction vibration asymmetry index CVAI and the conventional detection index D of the dam compaction quality is established, specifically:

[0030] On-site compaction tests of dam fill were carried out, and the relationship between CVAI and D was established using the linear regression method according to the following formula:

[0031] Model I D=a0×CVAI+b0

[0032] Where: a0, b0 are fitting parameters; D is the conventional detection index of dam compaction quality;

[0033] or

[0034] Taking into account the filler gradation, moisture content, roller speed, vibration frequency, and amplitude, the following compaction quality prediction model is established using the multivariate linear regression method of Model II or the neural network algorithm of Model III:

[0035] Model II D=a×CVAI+b×P+c×w+d×v+e×f0+g×A+h

[0036] Model III D=f(CVAI,P,w,v,f0,A)

[0037] Where: a, b, c, d, e, g, h are fitting parameters; P is the fill material gradation characteristic parameter; w is the moisture content; v is the roller speed; f0 is the fundamental frequency; A is the amplitude.

[0038] Preferably, step 7 is specifically as follows:

[0039] The dam rolling surface is divided into rolling units with a length of L and a width of B. According to the satellite positioning system and the geometric dimensions of the roller's vibrating wheel, the time period [t m ,t n ], the time period [t m ,t n ], the average value of the compaction vibration asymmetry index CVAI is obtained. , as the continuous detection result of the compaction quality of the rolling unit; and then predicting the conventional detection value of the dam compaction quality corresponding to the continuous detection result of the compaction quality according to the compaction quality prediction model.

[0040] Preferably, it also includes:

[0041] Step 8: According to the compaction state distribution of the dam, the weak compaction quality area is determined, and the onboard controller controls the roller to supplement the compaction quality of the weak compaction quality area or optimize the vibration compaction parameters;

[0042] Step 9: When the qualified rate of compaction quality of the entire rolling surface is greater than the set value, the rolling is ended.

[0043] Preferably, the compaction quality weak zone is a continuous area with an area M where the compaction quality does not meet the requirements, where the value of M is set according to specific requirements; the compaction quality qualified rate of the rolling surface is the ratio of the number of rolling units with qualified compaction quality to the total number of rolling units.

[0044] The method for continuously detecting the compaction quality of dam fillers based on compaction vibration asymmetry provided by the present invention has the following advantages:

[0045] The present invention directly fits the time-domain curve of the collected acceleration signal and analyzes the acceleration signals at different spatial positions of the vibrating wheel to obtain the compaction vibration asymmetry index (CVAI) of the compressed filler. Compared with existing continuous detection methods for filler compaction quality, the CVAI indicator has a clear physical mechanism and directly reflects the dynamic characteristics of the vibratory roller under different compaction states of the filler. The CVAI indicator avoids the influence of the intercepted signal length on the acceleration frequency domain indicator, as well as the detection errors caused by high-order or half-order harmonics in the indicator. Therefore, it has higher detection accuracy and is suitable for continuous detection of dam filler compaction quality. It can also be used for various types of fillers such as highway and railway roadbeds. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flow chart of the method for continuous detection of dam filler compaction quality based on compaction vibration asymmetry provided by the present invention;

[0047] Figure 2 This is a schematic diagram of the CVAI calculation principle provided by the present invention. DETAILED DESCRIPTION

[0048] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] The inventors discovered through research that the vibration state of a vibratory roller during the rolling process is related to the compaction level (stiffness) of the soil. When the stiffness of the filler is low, the interaction force between the vibrating wheel and the filler is low. During a vibration cycle, the vibrating wheel and the filler remain in contact. In this case, the vertical acceleration of the vibrating wheel is roughly symmetrical throughout the loading and unloading process. However, as the stiffness of the filler increases, the interaction between the vibrating wheel and the filler also increases, and the vibrating wheel may become detached from the filler, a phenomenon known as "jump vibration." The force states of the vibrating wheel differ between the contact and jump vibration states: in the contact state, the vibrating wheel is subject to both the excitation force and the reaction force of the filler, while in the jump vibration state, the vibrating wheel is subject only to the excitation force. Therefore, within a vibration cycle, the upward and downward vibration accelerations are asymmetric, and this asymmetry increases with increasing filler stiffness.

[0050] Based on the above conclusions, the present invention proposes a new indicator for continuous detection of dam filler compaction quality - compaction vibration asymmetry index CVAI, and establishes a continuous detection method for dam filler compaction quality based on CVAI.

[0051] The present invention collects the vertical vibration acceleration signal of the vibrating wheel during the rolling process in real time and filters and extracts it. It then fits the acceleration signals of the two processes, when the vibrating wheel is below and above the equilibrium position, and calculates a new indicator for continuous detection of the compaction quality of dam fillers—the compaction vibration asymmetry index (CVAI). By establishing a relationship between CVAI and conventional quality detection indicators for dam fillers, and combining the real-time acquisition of the three-dimensional spatial position of the roller via satellite positioning equipment during the rolling process, a compaction state distribution map of the entire rolling surface of the dam is formed, enabling an assessment of the compaction state of the dam fillers and guiding roller rolling construction. The method for continuous detection of the compaction quality of dam fillers based on compaction vibration asymmetry proposed by the present invention directly analyzes acceleration in the time domain. It has the advantages of high accuracy and simple equipment requirements, can effectively ensure the quality of dam filling and improve dam filling efficiency. It can also be used in the construction of earthwork filling projects such as roads, railways, and airports.

[0052] See Figure 1 The present invention provides a method for continuously detecting the compaction quality of dam filling materials based on compaction vibration asymmetry, comprising the following steps:

[0053] Step 1: During the dam filling and compaction process, the vertical acceleration raw signal of the vibrating wheel and the three-dimensional position information of the roller are collected synchronously in real time and transmitted to the on-board controller in real time;

[0054] Specifically, an acceleration sensor is installed vertically on the vibratory roller's vibrating wheel. This sensor collects the original vertical acceleration signal of the vibrating wheel in real time. To fully demonstrate the integrity of the waveform and ensure the accuracy of the acceleration fitting curve, the acceleration sensor's sampling frequency is at least 20 times the roller's vibration frequency. A satellite positioning system is used to collect the roller's three-dimensional position information in real time.

[0055] The on-board controller has the following functions: receiving and storing acceleration signals and position information; filtering, segmenting and extracting acceleration signals; calculating CVAI; establishing the relationship between CVAI and the conventional detection index D of dam compaction quality; dividing the compaction surface into compaction units, recording the coordinates of each compaction unit and numbering them; matching compaction unit numbers with CVAI; planning the compaction path; and optimizing vibration parameters.

[0056] Step 2: The vehicle-mounted controller performs low-pass filtering on the original acceleration signal to obtain a filtered acceleration signal;

[0057] Specifically, firstly, Fourier analysis is performed on the original acceleration signal to determine the frequency of the maximum harmonic, and then high-frequency components above the maximum harmonic frequency in the original acceleration signal are filtered out.

[0058] Step 3: If Figure 2As shown, the filtered acceleration signal is segmented and extracted in units of one vibration cycle to obtain the acceleration signal of each vibration cycle; the acceleration signal of each vibration cycle is divided into two parts according to whether the vibration wheel is above and below the equilibrium position, and the acceleration signal point set A1 of the vibration wheel above the equilibrium position and the acceleration signal point set B1 of the vibration wheel below the equilibrium position are determined; wherein, the acceleration signal point set A1 of the vibration wheel above the equilibrium position is the signal part with positive acceleration; the acceleration signal point set B1 of the vibration wheel below the equilibrium position is the signal part with negative acceleration; the acceleration signal point set B1 is flipped to a positive value along the t-axis, and the signal starting point is translated to the coordinate (0,0) to obtain the acceleration signal point set B2; the signal starting point of the acceleration signal point set A1 is translated to the coordinate (0,0) to obtain the acceleration signal point set A2;

[0059] Step 4: Use the sine function to fit the acceleration signal point set A2 and the acceleration signal point set B2 respectively to obtain the fitting curves A(t) and B(t) respectively;

[0060] In this step, when fitting the acceleration signal point set A2 and the acceleration signal point set B2, the fundamental frequency and higher harmonics are considered, and the least squares method is used to fit the acceleration signal point set A2 and the acceleration signal point set B2 respectively according to the following formula:

[0061]

[0062]

[0063] Where: is the acceleration fitting curve of the vibration wheel above the equilibrium position; is the acceleration fitting curve of the vibration wheel below the equilibrium position; ~ , ~ is the fitting coefficient; is the fundamental frequency; is the maximum harmonic order that occurs.

[0064] fundamental frequency , can be obtained by Fourier transforming the original acceleration signal; or, it can be calculated according to the sampling frequency and the number of data samples within a vibration cycle using the following formula:

[0065]

[0066] Where: is the sampling frequency; The number of data samples in one vibration cycle.

[0067] Step 5: Based on the fitting curves A(t) and B(t), calculate the compaction vibration asymmetry index CVAI:

[0068] ;

[0069] Where: k is a constant, which can be 100; T is the vibration period of the roller;

[0070] The vibration period T of the roller can be obtained by Fourier transforming the acceleration, or it can be calculated according to the following formula:

[0071]

[0072] Where: f0 is the fundamental frequency.

[0073] Step 6: Establish the relationship between the compaction vibration asymmetry index CVAI and the conventional detection index D of dam compaction quality, thereby obtaining a compaction quality prediction model;

[0074] This step is specifically as follows:

[0075] When conducting on-site compaction tests on dam fillers, the relationship between CVAI and D can be established using the linear regression method according to the following formula:

[0076] Model I D=a0×CVAI+b0

[0077] Where: a0 and b0 are fitting parameters; D is the conventional detection index of dam compaction quality, such as compaction, stiffness, modulus, etc.

[0078] or

[0079] Alternatively, the filler gradation, moisture content, roller speed, vibration frequency, amplitude and other characteristics can be comprehensively considered, and intelligent algorithms such as the multivariate linear regression method of Model II or the neural network algorithm of Model III can be used to establish a compaction quality prediction model as follows:

[0080] Model II D=a×CVAI+b×P+c×w+d×v+e×f0+g×A+h

[0081] Model III D=f(CVAI,P,w,v,f0,A)

[0082] Where: a, b, c, d, e, g, h are fitting parameters; P is the fill material gradation characteristic parameter; w is the moisture content; v is the roller speed; f0 is the fundamental frequency; A is the amplitude.

[0083] Step 7: Obtain the three-dimensional position information of the roller based on the satellite positioning system, generate the roller rolling trajectory, assign the corresponding rolling unit with the continuous detection result of the compaction quality obtained by the compaction vibration asymmetry index CVAI, and obtain the corresponding conventional detection value of the dam compaction quality based on the compaction quality prediction model, generate 2D and 3D cloud maps of the dam compaction status distribution, and display them on the display terminal.

[0084] This step is specifically as follows:

[0085] The dam rolling surface is divided into rolling units with a length of L and a width of B. According to the satellite positioning system and the geometric dimensions of the roller's vibrating wheel, the time period [t m ,t n ], the time period [t m ,t n ], the average value of the compaction vibration asymmetry index CVAI is obtained. , as the continuous detection result of the compaction quality of the rolling unit; and then predicting the conventional detection value of the dam compaction quality corresponding to the continuous detection result of the compaction quality according to the compaction quality prediction model.

[0086] Step 8: Based on the dam's compaction status distribution, weak compaction quality areas are identified. The onboard controller controls the roller to apply additional pressure or optimize vibration compaction parameters in these weak compaction quality areas. The weak compaction quality area is a continuous region with an area M where the compaction quality does not meet the requirements. The value of M can be set according to specific requirements.

[0087] Step 9: When the qualified rate of compaction quality of the entire rolling surface is greater than a set value, the rolling is terminated. The qualified rate of compaction quality of the rolling surface is the ratio of the number of rolling units with qualified compaction quality to the total number of rolling units.

[0088] The present invention directly fits the time-domain curve of the collected acceleration signal and analyzes the acceleration signals at different spatial positions of the vibrating wheel to obtain the compaction vibration asymmetry index (CVAI) of the compressed filler. Compared with existing continuous detection methods for filler compaction quality, the CVAI indicator has a clear physical mechanism and directly reflects the dynamic characteristics of the vibratory roller under different compaction states of the filler. The CVAI indicator avoids the influence of the intercepted signal length on the acceleration frequency domain indicator, as well as the detection errors caused by high-order or half-order harmonics in the indicator. Therefore, it has higher detection accuracy and is suitable for continuous detection of dam filler compaction quality. It can also be used for various types of fillers such as highway and railway roadbeds.

[0089] Different from other patent applications or research papers, the biggest feature of this invention is: the time domain signal of the vibration wheel acceleration is directly fitted by a sine curve that takes into account the fundamental frequency and higher harmonics. According to the asymmetry of the vibration wheel acceleration above and below the equilibrium position, a new continuous detection index for the compaction quality of dam fillers - the compaction vibration asymmetry index CVAI and its calculation method are proposed, and a continuous detection and evaluation method for the dam compaction quality based on CVAI is established, which has higher detection accuracy for coarse-grained soil fillers in dams. This method is suitable for the detection and evaluation of the compaction quality of dam fillers, and is also suitable for highway and railway roadbed filling construction. The core protection points include:

[0090] (1) The processing method of the vibration wheel acceleration signal is to extract the acceleration signal in units of one vibration cycle and fit the acceleration signal with a sine curve that considers the fundamental frequency and higher harmonics.

[0091] (2) The idea and calculation method of the new continuous detection index of compaction quality CVAI are proposed, that is, under different filler compaction quality conditions, the vibration curves of the vibrating wheel above and below the equilibrium position are different. Based on this phenomenon, the compaction vibration asymmetry index CVAI, a new continuous detection index of compaction quality, and its calculation method are proposed, which specifically include: (a) segmenting and extracting the acceleration data within a single vibration cycle; (b) using a sine curve pair considering the fundamental wave and higher harmonics to fit the acceleration signal of the vibrating wheel above and below the equilibrium position, respectively, to obtain A(t) and B(t); (c) according to the fitting curves A(t) and B(t), the compaction vibration asymmetry index CVAI is calculated. The calculation formula of CVAI is as follows:

[0092]

[0093] Where: k is a constant, which can be taken as 100; T is the vibration period of the roller.

[0094] The CVAI index is calculated by processing the acceleration to obtain the energy spectrum, which avoids the shortcoming of the current harmonic ratio index using FFT to perform spectrum analysis, which assumes that the signal is a stable signal. It truly reflects the corresponding influence of the roadbed compaction quality on the vibration wheel force.

[0095] (3) The process of using the new continuous detection index CVAI to continuously detect and evaluate the roadbed compaction quality, namely: (a) conducting a field test of continuous detection of roadbed compaction quality; (b) collecting the vertical acceleration signal of the vibrating wheel during rolling and detecting the conventional detection indicators (compaction degree K, etc.) of the corresponding rolling area; (c) collecting the driving speed, vibration frequency, filler gradation, filler moisture content and other information of the roller during the test, and using the univariate linear regression method, multivariate linear regression method, multivariate nonlinear regression method or neural network method to establish a roadbed compaction quality detection and evaluation model based on CVAI; based on the realization, the compaction quality of different parts of the roadbed surface is detected and evaluated.

[0096] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A continuous detection method for dam filling compaction quality based on compaction vibration asymmetry, characterized in that: The following steps are involved: Step 1: During the dam filling and compaction process, the vertical acceleration raw signal of the vibrating wheel and the three-dimensional position information of the roller are collected synchronously in real time and transmitted to the on-board controller in real time; Step 2: The vehicle-mounted controller performs low-pass filtering on the original acceleration signal to obtain a filtered acceleration signal; Step 3: Segment and extract the filtered acceleration signal in units of one vibration cycle to obtain the acceleration signal of each vibration cycle; for the acceleration signal of each vibration cycle, determine the acceleration signal point set A1 of the vibration wheel above the equilibrium position and the acceleration signal point set B1 of the vibration wheel below the equilibrium position; wherein, the acceleration signal point set A1 of the vibration wheel above the equilibrium position is the signal portion with positive acceleration; the acceleration signal point set B1 of the vibration wheel below the equilibrium position is the signal portion with negative acceleration; flip the acceleration signal point set B1 along the t-axis to a positive value, and translate the signal starting point to the coordinate (0,0) to obtain the acceleration signal point set B2; translate the signal starting point of the acceleration signal point set A1 to the coordinate (0,0) to obtain the acceleration signal point set A2; Step 4: Use the sine function to fit the acceleration signal point set A2 and the acceleration signal point set B2 respectively to obtain the fitting curves A(t) and B(t) respectively; Step 5: Based on the fitting curves A(t) and B(t), calculate the compaction vibration asymmetry index CVAI: ； Where: k is a constant; T is the vibration period of the roller; Step 6: Establish the relationship between the compaction vibration asymmetry index CVAI and the conventional detection index D of dam compaction quality, thereby obtaining a compaction quality prediction model; Step 7: Generate the roller's rolling trajectory based on the roller's three-dimensional position information, assign the corresponding rolling unit with the continuous detection result of the compaction quality obtained through the compaction vibration asymmetry index CVAI, and obtain the corresponding dam compaction quality conventional detection value based on the compaction quality prediction model, generate 2D and 3D cloud maps of the dam's compaction status distribution, and display them on the display terminal.

2. The method for continuous detection of dam filling compaction quality based on compaction vibration asymmetry according to claim 1, characterized in that: The real-time synchronous acquisition of the vertical acceleration original signal of the vibrating wheel and the three-dimensional position information of the roller is specifically as follows: An acceleration sensor is installed in the vertical direction of the vibrating wheel of the vibratory roller, and the acceleration original signal of the vertical acceleration of the vibrating wheel is collected in real time through the acceleration sensor; The satellite positioning system is used to collect the three-dimensional position information of the roller in real time.

3. The method for continuous detection of dam filling compaction quality based on compaction vibration asymmetry according to claim 2, characterized in that: The sampling frequency of the acceleration sensor is more than 20 times the vibration frequency of the roller.

4. The method for continuous detection of dam filling compaction quality based on compaction vibration asymmetry according to claim 1, characterized in that: The vehicle-mounted controller performs low-pass filtering on the original acceleration signal, specifically: Performing Fourier analysis on the original acceleration signal to determine the frequency of the maximum harmonic, and filtering out high-frequency components above the maximum harmonic frequency in the original acceleration signal.

5. The method for continuous detection of dam filling compaction quality based on compaction vibration asymmetry according to claim 1, characterized in that: When fitting the acceleration signal point set A2 and the acceleration signal point set B2, the fundamental frequency and higher harmonics are considered, and the least squares method is used to fit the acceleration signal point set A2 and the acceleration signal point set B2 respectively according to the following formula: ； ； Where: is the acceleration fitting curve of the vibration wheel above the equilibrium position; is the acceleration fitting curve of the vibration wheel below the equilibrium position; ~ , ~ is the fitting coefficient; is the fundamental frequency; is the maximum harmonic order that occurs.

6. The method for continuous detection of dam filling compaction quality based on compaction vibration asymmetry according to claim 5, characterized in that: fundamental frequency , obtained by Fourier transforming the original acceleration signal; or, based on the sampling frequency and the number of data samples within a vibration cycle, calculated as follows: ； Where: is the sampling frequency; The number of data samples in one vibration cycle.

7. The method for continuous detection of dam filling compaction quality based on compaction vibration asymmetry according to claim 1, characterized in that: The relationship between the compaction vibration asymmetry index CVAI and the conventional detection index D of dam compaction quality is established, specifically: On-site compaction tests of dam fill were carried out, and the relationship between CVAI and D was established using the linear regression method according to the following formula: Model I D=a0×CVAI+b0 Where: a0, b0 are fitting parameters; D is the conventional detection index of dam compaction quality; or Taking into account the filler gradation, moisture content, roller speed, vibration frequency, and amplitude, the following compaction quality prediction model is established using the multivariate linear regression method of Model II or the neural network algorithm of Model III: Model II D=a×CVAI+b×P+c×w+d×v+e×f0+g×A+h Model III D=f(CVAI,P,w,v,f0,A) Where: a, b, c, d, e, g, h are fitting parameters; P is the fill material gradation characteristic parameter; w is the moisture content; v is the roller speed; f0 is the fundamental frequency; A is the amplitude.

8. The method for continuous detection of dam filling compaction quality based on compaction vibration asymmetry according to claim 1, characterized in that: Step 7 is as follows: The dam rolling surface is divided into rolling units with a length of L and a width of B. According to the satellite positioning system and the geometric dimensions of the roller's vibrating wheel, the time period [t m ,t n ], the time period [t m ,t n ], the average value of the compaction vibration asymmetry index CVAI is obtained. , as the continuous detection result of the compaction quality of the rolling unit; and then predicting the conventional detection value of the dam compaction quality corresponding to the continuous detection result of the compaction quality according to the compaction quality prediction model.

9. The method for continuous detection of dam filling compaction quality based on compaction vibration asymmetry according to claim 1, characterized in that: Also includes: Step 8: According to the compaction state distribution of the dam, the weak compaction quality area is determined, and the onboard controller controls the roller to supplement the compaction quality of the weak compaction quality area or optimize the vibration compaction parameters; Step 9: When the qualified rate of compaction quality of the entire rolling surface is greater than the set value, the rolling is ended.

10. The method for continuous detection of dam filling compaction quality based on compaction vibration asymmetry according to claim 9, characterized in that: The compaction quality weak zone is a continuous area with an area of ​​M where the compaction quality does not meet the requirements, where the value of M is set according to specific requirements; the compaction quality qualified rate of the rolling surface is the ratio of the number of rolling units with qualified compaction quality to the total number of rolling units.

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