Loess high fill compaction degree nondestructive detection method

Abstract

The invention discloses a loess high fill compaction degree nondestructive detection method, relates to the technical field of civil engineering and geological detection, and is used for solving the problems that the detection precision is insufficient, the adaptability to loess characteristics is poor, and the requirement of loess compaction degree nondestructive evaluation cannot be met. An earth surface electromagnetic wave reflection signal and soil particle vibration frequency data are collected to analyze a compaction degree deviation index, when the deviation index is higher than a judgment threshold value, a spectrum analysis algorithm is adopted to calculate compaction degree spectrum characteristics of different depths and positions, and if the spectrum characteristics are consistent, soil particle distribution characteristics and humidity change frequencies are further collected; the compaction uniformity is comprehensively analyzed, the compaction non-uniformity rate is calculated when the uniformity is low, a proper detection algorithm is selected according to the compaction non-uniformity rate to judge whether the compaction non-uniformity problem exists or not, the calculation complexity is reduced, and the judgment accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of civil engineering and geological exploration. More specifically, the present invention relates to a method for non-destructive detection of the compaction degree of high fill in loess. Background Art

[0002] The non-destructive detection technology for the compaction degree of high fill in loess is of great significance in the field of civil engineering, especially in the construction of infrastructure such as roads, railways, and water conservancy projects. Traditional compaction degree detection methods mostly rely on means such as drilling sampling or nuclear density meters. These methods have characteristics such as destructiveness, complex operation, and high cost, and it is difficult to meet the requirements of modern engineering for efficient and accurate detection. Therefore, the development of a non-destructive detection method suitable for the compaction degree of high fill in loess has become a research hotspot.

[0003] The existing technologies have the following deficiencies: At present, when the compaction degree detection methods relying on mechanical sampling or radioactive source detection face the high fill in loess environment, the following problems exist: the loess structure is loose and the pore distribution is uneven, resulting in detection errors at different layers by traditional methods and insufficient accuracy; some methods rely on radioactive devices, which have an impact on construction safety and the environment; traditional methods are generally destructive and difficult to meet the engineering requirements for the evaluation of the compaction state of a large range, high frequency, and rapid compaction, resulting in insufficient detection accuracy, poor adaptability to loess characteristics, and the inability to solve the need for non-destructive evaluation of soil compaction degree. Therefore, a method for non-destructive detection of the compaction degree of high fill in loess is proposed.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for non-destructive detection of the compaction degree of high fill in loess. By combining the electromagnetic wave reflection characteristics and the soil particle structure distribution characteristics, the analysis method of the detection signal is optimized and a multi-dimensional data analysis model is introduced to solve the problems of insufficient detection accuracy, poor adaptability to loess characteristics, and the failure to specifically solve the need for non-destructive evaluation of soil compaction degree mentioned in the background art.

[0006] To achieve the above object, the present invention provides the following technical solution, a method for non-destructive detection of the compaction degree of high fill in loess, including the following steps: Step S1: Collect the surface electromagnetic wave reflection signals and soil particle vibration frequency data of multiple detection areas to obtain the reflection signal coefficient and the vibration frequency coefficient; Step S2: Combine the reflection signal coefficients and vibration frequency coefficients into a reflection signal data set and a vibration frequency data set respectively. Set a compaction degree determination threshold and calculate the compaction degree deviation index. When the compaction degree deviation index exceeds the compaction degree determination threshold, preliminarily judge whether the compaction degree is abnormal using the spectrum analysis algorithm; Step S3: When it is preliminarily judged that the compaction degree is not abnormal, collect the soil particle distribution characteristics and humidity change frequency, and analyze the compaction uniformity by integrating the particle distribution characteristics and humidity change frequency. When the compaction uniformity is low, calculate the compaction non-uniformity rate; Step S4: Select a dynamic filtering algorithm according to the compaction uniformity or apply the compaction non-uniformity rate to an improved wavelet transform algorithm to detect whether there is a compaction non-uniformity problem.

[0007] In a preferred embodiment, in Step S1, the surface electromagnetic wave reflection signal is obtained by transmitting an electromagnetic wave with a specific frequency and receiving its reflection signal, and performing a hierarchical integration operation by combining the reflection signal intensity and the phase difference to obtain the surface electromagnetic wave reflection signal; The soil particle vibration frequency data is obtained by a vibration sensor installed on the ground surface to record the natural vibration frequency of the soil particles.

[0008] In a preferred embodiment, in Step S2, the processed reflection signal coefficients are combined into a reflection signal data set, and the vibration frequency coefficients are combined into a vibration frequency data set. The specific steps for setting the compaction degree determination threshold are as follows: Perform the same processing on the two data sets. Use the data set as the analysis data set, divide the analysis data set into two parts with the same number, and calculate the standard deviation of the two parts of the data set respectively for comparison; Select the part with the larger standard deviation as the new round of analysis data set, and then divide the new round of analysis data set into two parts with the same number to calculate the standard deviation; Select the part with the smaller standard deviation as the new round of analysis data set, and alternately select the larger and smaller parts to repeat the operation on the data set until only the last data remains; Calculate the geometric mean of the data obtained by processing the two data sets and set it as the compaction degree determination threshold.

[0009] In a preferred embodiment, in Step S2, the system calculates the reflection signal coefficients and vibration frequency coefficients of the area to be measured and takes the weighted average as the compaction degree deviation index; When the compaction degree deviation index exceeds the compaction degree determination threshold, the system judges that the compaction degree of the area to be measured may be abnormal and uses the spectrum analysis algorithm to make a preliminary judgment on the compaction degree.

[0010] In a preferred embodiment, in Step S3, the soil particle distribution characteristics are obtained by analyzing the particle size distribution curve of the soil sample, and the number of peaks of the particle size distribution curve is recorded; When obtaining the humidity change frequency, the change frequency of soil humidity is reflected by recording the number of changes in soil humidity within a set time period.

[0011] In a preferred embodiment, in step S3, when analyzing the compaction uniformity using fuzzy logic, the specific steps are as follows: Define the number of peaks in the particle size distribution curve and the number of humidity changes as input variables, and divide them into fuzzy sets; Define the compaction uniformity as the output variable, and divide it into fuzzy sets; Formulate fuzzy rules to describe the influence of the number of peaks in the particle size distribution curve and the number of humidity changes on the compaction uniformity; Perform fuzzy inference according to the fuzzy rules to determine whether the compaction uniformity is high or low.

[0012] In a preferred embodiment, in step S3, when calculating the compaction non-uniformity rate, the product of the number of peaks in the soil particle distribution characteristics and the number of humidity changes is used to obtain a normalization parameter, and the logarithmic normalization formula is used for calculation and acquisition according to the normalization parameter.

[0013] In a preferred embodiment, in step S4, when the compaction uniformity is high, the system uses a dynamic filtering algorithm to detect the compaction degree; The dynamic filtering algorithm, based on the surface electromagnetic wave reflection signal and the soil particle vibration frequency data, combines the time series signal to perform noise suppression and outlier smoothing processing on the continuous data sequence, sets a dynamically updated filtering window, automatically adjusts the filtering coefficient according to the signal change rate, and performs weighted averaging on the sampled data to achieve stable determination and trend tracking of the compaction degree; When the compaction uniformity is low, the system uses an improved wavelet transform algorithm to detect the compaction degree; The improved wavelet transform algorithm, aiming at the surface electromagnetic wave reflection signal and the vibration frequency signal, uses multi-scale wavelet decomposition and wavelet reconstruction techniques to perform multi-layer decomposition, local feature enhancement, and edge detail extraction on the signal in the time domain and frequency domain; Combined with the specific characteristics of the compaction signal, an adaptive threshold denoising strategy, a feature interval selection rule, and a multi-scale residual overlap correction algorithm are introduced. By analyzing whether there is an uneven phenomenon in the compaction degree, if there is, the specific compaction non-uniformity rate is given; If no uneven phenomenon is detected, the compaction degree data is recorded.

[0014] In a preferred embodiment, the system respectively obtains the surface electromagnetic wave reflection signal and the soil particle vibration frequency data through an electromagnetic wave transmitting and receiving device and a vibration sensor; The electromagnetic wave transmitting and receiving device and the vibration sensor are arranged on the ground surface and maintain a certain distance between them to avoid signal interference; The electromagnetic wave transmitting and receiving device is connected to the data processing unit through a cable or a wireless communication module to transmit the data of the reflected signal intensity and the phase difference; The vibration sensor is connected to the data processing unit in a wired or wireless manner to transmit the vibration frequency data.

[0015] The technical effects and advantages of the present invention: The present invention analyzes the compaction degree deviation index by collecting the surface electromagnetic wave reflection signal and the soil particle vibration frequency data. When the compaction degree deviation index is higher than the determination threshold, the spectral analysis algorithm is used to calculate the spectral characteristics of the compaction degree at different depths and positions respectively. By calculating the compaction degree deviation index and the spectral analysis algorithm, the destructive operation of the traditional detection method is reduced, and the detection efficiency is improved at the same time. Whether the compaction degree is abnormal is judged by the spectral characteristics. When the spectral characteristics are consistent, the soil particle distribution characteristics and the humidity change frequency are collected, and the compaction uniformity is analyzed by integrating the particle distribution characteristics and the humidity change frequency. When the compaction uniformity is low, the compaction non-uniformity rate is calculated, and different detection algorithms are selected according to the compaction uniformity for the user to use. Finally, the result of the detection algorithm is used to judge whether there is a compaction non-uniformity problem. By providing a suitable detection algorithm, the calculation complexity is reduced, the judgment accuracy is improved, and the compaction abnormal type is also clarified, which is convenient for subsequent precise correction of the abnormality. Description of the Drawings

[0016] Figure 1 It is a flowchart for implementing a method for non-destructive detection of the compaction degree of high fills in loess of the present invention.

[0017] Figure 2 It is a schematic diagram for analyzing the compaction uniformity by fuzzy logic in a method for non-destructive detection of the compaction degree of high fills in loess of the present invention. Specific Embodiments

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1 The present invention provides a method for non-destructive detection of the compaction degree of high fills in loess, and its specific implementation is described in detail in conjunction with Figure 1 Appendix Figure 2 and

[0020] Appendix Figure 1 shows the overall steps from signal acquisition to compaction degree judgment, including the processing process of the surface electromagnetic wave reflection signal and the soil particle vibration frequency data; Appendix Figure 2 Figure 2 shows the process of obtaining the compaction uniformity through fuzzy logic rule inference with the number of peaks of the particle size distribution curve and the humidity change frequency as input variables.

[0021] A non-destructive detection method for the compaction degree of high fill in loess includes the following methods: Step S1: Collect the surface electromagnetic wave reflection signals and soil particle vibration frequency data of multiple detection areas to obtain the reflection signal coefficient and the vibration frequency coefficient; Step S2: Combine the reflection signal coefficient and the vibration frequency coefficient into a reflection signal data set and a vibration frequency data set respectively, set the compaction degree determination threshold and calculate the compaction degree deviation index, and initially judge whether the compaction degree is abnormal by using the spectrum analysis algorithm when the compaction degree deviation index exceeds the compaction degree determination threshold; Step S3: When it is initially judged that the compaction degree is not abnormal, collect the soil particle distribution characteristics and the humidity change frequency, analyze the compaction uniformity by integrating the particle distribution characteristics and the humidity change frequency, and calculate the compaction non-uniformity rate when the compaction uniformity is low; Step S4: Select the dynamic filtering algorithm according to the compaction uniformity or apply the compaction non-uniformity rate to the improved wavelet transform algorithm to detect whether there is a compaction non-uniformity problem.

[0022] In practical applications, the present invention first collects signals from the target area through multiple detection devices.

[0023] These detection devices include an electromagnetic wave transmitting and receiving device and a vibration sensor, which are respectively used to obtain the surface electromagnetic wave reflection signal and the soil particle vibration frequency data.

[0024] The electromagnetic wave transmitting and receiving device emits electromagnetic waves with a specific frequency to the surface and receives its reflection signal, and performs hierarchical integration operations by recording the reflection signal intensity and phase difference to extract characteristic parameters, thereby obtaining the surface electromagnetic wave reflection signal.

[0025] Among them, the multiple detection areas are obtained by the experimenters based on the geological exploration data and the analysis results of the compaction working conditions, which will not be elaborated here; It should be noted that the electromagnetic wave frequency range, emission polarization form and receiving sensitivity parameters adopted are all calibrated through pre-experiments to match the dielectric characteristics and compaction response characteristics of typical loess fill areas, so as to ensure that the reflection signal can truly reflect the compactness of the surface structure. The intensity change of the reflection signal is mainly affected by the soil dielectric constant, moisture content and compaction degree, while the phase difference reflects the difference in the propagation path and speed of electromagnetic waves in different compaction layers; therefore, this method extracts features by combining intensity and phase information, and can realize high-sensitivity non-contact identification of the compaction state, which will not be elaborated here.

[0026] Vibration sensors are installed on the ground surface to record the natural vibration frequency data of soil particles.

[0027] It should be noted that when soil is subjected to environmental excitation (such as mechanical disturbance, acoustic impact or microseismic source), there is a weak but measurable vibration response between its internal particles. There are significant differences in the contact stiffness and coupling degree between soil particles under different compaction states, which in turn leads to characteristic changes in the natural vibration frequency.

[0028] The data collected by the sensor is usually manifested as a vibration signal in the low-frequency band (generally in the range of 10-300 Hz). The main frequency peak position, amplitude and spectral density characteristics can be used to evaluate the correlation between the particle contact structure and compaction degree, which will not be elaborated here.

[0029] To improve the reliability of the data, the system sets up multiple groups of detection equipment, each group of equipment is responsible for detecting an independent detection area, and finally the data of all areas are normalized to obtain multiple groups of reflection signal coefficients and vibration frequency coefficients.

[0030] After signal acquisition is complete, the system enters the data preprocessing stage. The main task of this stage is to further process the reflection signal coefficient and vibration frequency coefficient for subsequent analysis.

[0031] The specific operation is to merge the reflection signal coefficients into a reflection signal data set, and merge the vibration frequency coefficients into a vibration frequency data set.

[0032] The system then performs alternating screening operations on the two data sets to determine the compaction determination threshold.

[0033] The specific screening operation is to divide each data set into two parts of equal size, calculate the standard deviation of the two parts and compare them, and select the part with the larger standard deviation as the new round of analysis data set; Then divide the new round of analysis data set into two parts of equal size and calculate the standard deviation. At this time, the part with the smaller standard deviation is selected as the new round of analysis data set.

[0034] The above operations are repeated alternately until the last data point is left, and the geometric mean of the data obtained from the two data sets after the above processing is taken as the compaction determination threshold.

[0035] Next, the system calculates the compaction deviation index of the measured area based on the processed reflection signal coefficient and vibration frequency coefficient.

[0036] The compaction deviation index is obtained by taking the weighted average of the reflection signal coefficient and the vibration frequency coefficient.

[0037] When the compaction degree deviation index exceeds the compaction degree determination threshold, the system determines that there may be abnormal compaction in this area and uses the spectrum analysis algorithm to conduct a preliminary evaluation of this area.

[0038] The spectrum analysis algorithm extracts the spectrum characteristics of the reflection signals at different depths and positions to determine whether the compaction degree is abnormal.

[0039] If the spectrum characteristics are consistent, the subsequent steps are continued; if the spectrum characteristics show abnormalities, this area is directly marked as an abnormal area.

[0040] For the areas where the compaction degree is initially judged to be normal, the system further collects the soil particle distribution characteristics and the humidity change frequency to analyze the compaction uniformity.

[0041] The soil particle distribution characteristics are obtained by analyzing the particle size distribution curve of the soil sample; Among them, the number of peaks of the particle size distribution curve reflects the complexity of the soil particle structure. Its acquisition logic is in the sample particle size distribution frequency diagram. Based on the local maximum points of the number of particles in the particle size interval, statistics are carried out. Each significant peak corresponds to a relatively dominant particle size group. The more peaks there are, the more complex the particle composition in the soil body, the larger the particle size span, and the more uneven the pore distribution and structural stability between particles, thereby affecting the compaction uniformity.

[0042] It should be noted that the particle size distribution curve can be expressed by methods such as sieve analysis method, laser particle size analyzer measurement method, and image particle recognition algorithm analysis method. In this invention, only the laser particle size analyzer measurement method combined with the particle size - frequency histogram form is used for example, and details are not elaborated here; Furthermore, the setting of the particle size interval is set by the experimenters through the investigation of the particle gradation of soil samples in typical loess areas and the evaluation of the particle size response range of on - site compaction machinery, and details are not elaborated here; The humidity change frequency reflects the fluctuation of soil humidity by recording the number of changes in soil humidity within a set time period. Its acquisition logic is within the preset monitoring time window. By continuously collecting the reading sequence of the soil humidity sensor, the number of changes exceeding the set fluctuation threshold within the unit time is statistically counted, and the ratio of the number of changes to the monitoring time window is calculated to obtain the humidity change frequency.

[0043] It should be noted that the monitoring time window is set by the experimenters through the analysis of the characteristics of the target compaction operation cycle and the evaluation of the timeliness of soil moisture migration response. The unit time is a fixed sampling cycle in seconds, and details are not elaborated here; In order to comprehensively evaluate the compaction uniformity, the system uses fuzzy logic rules to analyze the number of peaks of the particle size distribution curve and the humidity change frequency.

[0044] The specific operation of the fuzzy logic rules is to define the number of peaks in the particle size distribution curve and the humidity change frequency as input variables and divide them into multiple fuzzy sets; Define the compaction uniformity as the output variable and also divide it into multiple fuzzy sets; For example, "many" and "few" for the number of peaks in the particle size distribution curve, "large" and "small" for the humidity change frequency, and "high" and "low" for the compaction uniformity.

[0045] Formulate fuzzy rules to describe the influence of the number of peaks in the particle size distribution curve and the humidity change frequency on the compaction uniformity. The definition of the rules can be based on professional knowledge or obtained through data analysis and experiments. For example: Rule 1: If the number of peaks in the particle size distribution curve is many and the humidity change frequency is large, then the compaction uniformity is high; Rule 2: If the number of peaks in the particle size distribution curve is few and the humidity change frequency is small, then the compaction uniformity is low; Finally, perform fuzzy reasoning according to the fuzzy rules to determine whether the compaction uniformity is high or low.

[0046] It should be noted that the division of the fuzzy sets can be adjusted according to the actual situation. For example, in this example, two fuzzy sets are used as an example. In fact, the number of peaks in the particle size distribution curve, the humidity change frequency, and the compaction uniformity can be divided into three or more sets to facilitate more accurate adjustment according to different conditions; in addition, the judgment of the number of peaks in the particle size distribution curve and the size of the humidity change frequency can be set with thresholds according to the actual situation.

[0047] Based on the analysis of the compaction uniformity, the system further calculates the compaction non-uniformity rate.

[0048] The calculation method of the compaction non-uniformity rate is to multiply the number of peaks in the particle size distribution curve by the humidity change frequency to obtain a normalized parameter, and then use the logarithmic normalization formula to process the normalized parameter to obtain the final compaction non-uniformity rate.

[0049] When the compaction uniformity is high, the system selects the dynamic filtering algorithm to detect the compaction degree; when the compaction uniformity is low, the system selects the improved wavelet transform algorithm to detect the compaction degree.

[0050] The improved wavelet transform algorithm analyzes whether there is non-uniformity in the compaction degree. If there is, it gives the specific compaction non-uniformity rate; if not, it records the compaction degree data.

[0051] During the entire detection process, the connection relationship and position relationship between components are particularly important.

[0052] Both the electromagnetic wave transmitting and receiving device and the vibration sensor are arranged on the ground surface, and a certain distance is maintained between them to avoid signal interference.

[0053] The electromagnetic wave transmitting and receiving device is connected to the data processing unit through a cable or a wireless communication module, and is used to transmit the reflected signal intensity and phase difference data. The vibration sensor is also connected to the data processing unit in a wired or wireless manner, and is used to transmit the vibration frequency data.

[0054] The data processing unit integrates various algorithm modules internally, including spectrum analysis algorithms, fuzzy logic rules, dynamic filtering algorithms, and improved wavelet transform algorithms. These algorithm modules are implemented by software and run on a hardware platform.

[0055] The data processing unit is also equipped with a storage module for saving the processing results and intermediate data.

[0056] It can be seen from the above specific implementation manners that the present invention realizes the non-destructive detection of the compaction degree of high earth-rock fills in the Loess Plateau by collecting the electromagnetic wave reflection signals on the ground surface and the vibration frequency data of soil particles, and combining the compaction degree determination threshold and the spectrum analysis algorithm.

[0057] On this basis, by introducing the particle size distribution curve and the humidity change frequency, and combining the fuzzy logic rules and the improved wavelet transform algorithm, the detection accuracy and efficiency are further improved.

[0058] The whole system is reasonably designed, and the components cooperate closely with each other, which can effectively solve the problems of insufficient detection accuracy and poor adaptability existing in the prior art, and at the same time meet the needs of non-destructive evaluation of soil compaction degree.

[0059] In order to better enable the relevant personnel in the technical field to fully understand and implement the present invention, the following further supplements and explains the specific implementation principle of the present invention in combination with a specific application scenario.

[0060] In a certain high earth-rock fill project in the Loess Plateau, it is necessary to conduct non-destructive detection of the compaction degree of a newly filled roadbed section.

[0061] First of all, a plurality of electromagnetic wave transmitting and receiving devices and vibration sensors are arranged on the ground surface of the target area. These devices are distributed at a certain distance to ensure that the entire area to be measured is covered and signal interference is avoided.

[0062] The electromagnetic wave transmitting and receiving device emits electromagnetic waves with a specific frequency to the ground surface. After the electromagnetic waves penetrate the soil, they are reflected back to the receiving device, and the system records the intensity and phase difference of the reflected signals.

[0063] At the same time, the vibration sensor is installed on the ground surface to capture the natural vibration frequency data of soil particles.

[0064] To improve data reliability, each group of devices is responsible for detecting an independent detection area, and finally normalizes the data of all areas to obtain multiple groups of reflection signal coefficients and vibration frequency coefficients.

[0065] After signal acquisition is completed, the system enters the data preprocessing stage. The main task of this stage is to further process the reflection signal coefficients and vibration frequency coefficients for subsequent analysis.

[0066] The specific operation is to merge the reflection signal coefficients into a reflection signal data set and the vibration frequency coefficients into a vibration frequency data set.

[0067] Subsequently, the system performs an alternating screening operation on these two data sets to determine the compaction degree judgment threshold. The specific screening operation is to divide each data set into two parts with the same number, calculate the standard deviation of the two parts respectively and compare their sizes, and select the part with the larger standard deviation as the new round of analysis data set; Then divide the new round of analysis data set into two parts with the same number and calculate the standard deviation. At this time, select the part with the smaller standard deviation as the new round of analysis data set.

[0068] The above operations are performed alternately until the last data point remains. Take the geometric mean of the data obtained by processing the two data sets as the compaction degree judgment threshold.

[0069] This process ensures the accuracy of the judgment threshold by gradually screening out outliers and noise in the data set.

[0070] Next, the system calculates the compaction degree deviation index of the area to be measured according to the processed reflection signal coefficients and vibration frequency coefficients.

[0071] The compaction degree deviation index is obtained by taking the weighted average of the reflection signal coefficients and vibration frequency coefficients. When the compaction degree deviation index exceeds the compaction degree judgment threshold, the system determines that there may be compaction anomalies in this area and uses the spectrum analysis algorithm to conduct a preliminary evaluation of this area.

[0072] The spectrum analysis algorithm extracts the spectrum characteristics of the reflection signals at different depths and positions to determine whether the compaction degree is abnormal.

[0073] If the spectrum characteristics are consistent, continue to execute the subsequent steps; if the spectrum characteristics show anomalies, directly mark this area as an abnormal area.

[0074] This process can quickly identify the compaction degree abnormal area by using the change law of the spectrum characteristics, reducing unnecessary subsequent detection work.

[0075] For the areas where the compaction degree is initially judged not to be abnormal, the system further collects the soil particle distribution characteristics and humidity change frequency to analyze the compaction uniformity.

[0076] The soil particle size distribution characteristics are obtained by analyzing the particle size distribution curve of the soil sample, where the number of peaks of the particle size distribution curve reflects the complexity of the soil particle structure.

[0077] The humidity change frequency reflects the fluctuation of soil humidity by recording the number of humidity changes in a set time period.

[0078] To comprehensively evaluate the compaction uniformity, the system uses fuzzy logic rules to analyze the number of peaks of the particle size distribution curve and the humidity change frequency.

[0079] The specific operation of the fuzzy logic rules is to define the number of peaks of the particle size distribution curve and the humidity change frequency as input variables and divide them into multiple fuzzy sets; Define the compaction uniformity as the output variable and also divide it into multiple fuzzy sets; Formulate fuzzy rules to describe the influence of the number of peaks of the particle size distribution curve and the humidity change frequency on the compaction uniformity; Finally, perform fuzzy inference according to the fuzzy rules to determine the output value of the compaction uniformity.

[0080] Through the flexibility of fuzzy logic, this process can effectively address the detection challenges brought by the unique non-uniformity and collapsibility of loess.

[0081] Based on the analysis of the compaction uniformity, the system further calculates the compaction non-uniformity rate.

[0082] The calculation method of the compaction non-uniformity rate is to multiply the number of peaks of the particle size distribution curve by the humidity change frequency to obtain a normalized parameter, and then use the logarithmic normalization formula to process the normalized parameter to obtain the final compaction non-uniformity rate.

[0083] When the compaction uniformity is high, the system selects a dynamic filtering algorithm to detect the compaction degree; The dynamic filtering algorithm refers to a method for the detection area with high compaction uniformity. The system performs noise suppression and outlier smoothing processing on the continuous data sequence according to the acquired surface electromagnetic wave reflection signal and soil particle vibration frequency data, combined with the time-series signal collected in real-time or at different times. By setting a dynamically updated filtering window for the time-series signal, automatically adjusting the filtering coefficient according to the signal change rate, and performing weighted average or Kalman filtering on the sampled data, etc., random interference and local anomalies are removed, and the main characteristic components reflecting the compaction state are retained, realizing the stable determination and trend tracking of the compaction degree.

[0084] When the compaction uniformity is low, the system selects an improved wavelet transform algorithm to detect the compaction degree; The improved wavelet transform algorithm refers to a processing method that, when the system detects low compaction uniformity, uses multi-scale wavelet decomposition and wavelet reconstruction techniques for the surface electromagnetic wave reflection signal and vibration frequency signal to perform multi-layer decomposition, local feature enhancement, and edge detail extraction on the signal in the time domain and frequency domain; Combined with the specific characteristics of the compaction signal, an adaptive threshold denoising strategy, feature interval selection rules, and multi-scale residual overlap correction algorithm are introduced, making the wavelet decomposition and reconstruction process more sensitive to weak anomaly regions, capable of accurately separating local compaction anomalies from background noise, and outputting the non-uniformity rate of the compaction degree, that is, by analyzing whether there is non-uniformity in the compaction degree, and if so, giving the specific compaction non-uniformity rate, If not, the compaction degree data is recorded. This process provides more accurate detection results for different compaction uniformity situations by introducing different detection algorithms.

[0085] During the entire detection process, the connection relationship and positional relationship between components are particularly important.

[0086] The electromagnetic wave transmitting and receiving device and the vibration sensor are both arranged on the surface, and a certain distance is maintained between them to avoid signal interference.

[0087] The electromagnetic wave transmitting and receiving device is connected to the data processing unit through a cable or wireless communication module for transmitting the reflection signal intensity and phase difference data; The vibration sensor is also connected to the data processing unit by wired or wireless means for transmitting vibration frequency data.

[0088] The data processing unit internally integrates various algorithm modules, including spectrum analysis algorithms, fuzzy logic rules, dynamic filtering algorithms, and improved wavelet transform algorithms. These algorithm modules are implemented by software and run on a hardware platform.

[0089] The data processing unit is also equipped with a storage module for saving processing results and intermediate data. This system design ensures the efficiency and reliability of data acquisition, transmission, and processing.

[0090] From the above specific application scenarios, it can be seen that the present invention realizes non-destructive detection of the compaction degree of high fill in the Loess Plateau by collecting surface electromagnetic wave reflection signals and soil particle vibration frequency data, combined with the compaction degree determination threshold and spectrum analysis algorithm.

[0091] On this basis, by introducing the particle size distribution curve and humidity change frequency, combined with fuzzy logic rules and improved wavelet transform algorithms, the detection accuracy and efficiency are further improved.

[0092] The entire system is reasonably designed, and the components cooperate closely with each other, which can effectively solve the problems of insufficient detection accuracy and poor adaptability existing in the prior art, and at the same time meet the needs of non-destructive evaluation of soil compaction degree.

[0093] The above formulas are all dimensionless and take their numerical values for calculation. The formula is a formula obtained by collecting a large amount of data for software simulation to get the closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0094] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center by wire or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0095] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0096] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but this implementation should not be considered to exceed the scope of the present application.

[0097] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0098] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0099] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0101] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or this part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0102] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described above.

Claims

1. A non-destructive detection method for the compaction degree of high fills in the loess area, characterized in that: including the following methods: Step S1: Collect the surface electromagnetic wave reflection signals and soil particle vibration frequency data of multiple detection areas to obtain the reflection signal coefficient and the vibration frequency coefficient; Step S2: Combine the reflection signal coefficient and the vibration frequency coefficient into a reflection signal data set and a vibration frequency data set respectively, set the compaction degree determination threshold, and calculate the compaction degree deviation index. When the compaction degree deviation index exceeds the compaction degree determination threshold, use the spectrum analysis algorithm to preliminarily judge whether the compaction degree is abnormal; Step S3: When it is preliminarily judged that the compaction degree is not abnormal, collect the soil particle distribution characteristics and the humidity change frequency, analyze the compaction uniformity based on the particle distribution characteristics and the humidity change frequency, and calculate the compaction non-uniformity rate when the compaction uniformity is low; Step S4: Select the dynamic filtering algorithm according to the compaction uniformity or apply the compaction non-uniformity rate to the improved wavelet transform algorithm to detect whether there is a compaction non-uniformity problem.

2. The non-destructive detection method for the compaction degree of high fill in loess as claimed in claim 1, wherein: In step S1, the surface electromagnetic wave reflection signal is obtained by transmitting electromagnetic waves of a specific frequency, receiving the reflection signal, and performing hierarchical integration operations in combination with the reflection signal intensity and the phase difference; The soil particle vibration frequency data is obtained by a vibration sensor installed on the ground surface to record the natural vibration frequency of the soil particles.

3. The non-destructive detection method for the compaction degree of high fill in loess as claimed in claim 2, wherein: In step S2, the processed reflection signal coefficients are combined into a reflection signal data set, and the vibration frequency coefficients are combined into a vibration frequency data set. The specific steps for setting the compaction degree determination threshold are as follows: Perform the same processing on the two data sets. Take the data set as the analysis data set, divide the analysis data set into two parts with the same number, and calculate the standard deviation in the two parts of the data set respectively for comparison; Select the part with the larger standard deviation as the new round of analysis data set, and then divide the new round of analysis data set into two parts with the same number to calculate the standard deviation; Select the part with the smaller standard deviation as the new round of analysis data set, alternately select the larger and the smaller ones, and repeat the operation on the data set until the last data remains; Calculate the geometric mean of the data obtained by processing the two data sets and set it as the compaction degree determination threshold.

4. The non-destructive detection method for the compaction degree of high fill in loess as claimed in claim 3, wherein: In step S2, the system calculates the reflection signal coefficient and the vibration frequency coefficient of the area to be measured, and takes the weighted average as the compaction degree deviation index; When the compaction degree deviation index exceeds the compaction degree determination threshold, the system judges that the compaction degree of the area to be measured may be abnormal and uses the spectrum analysis algorithm to make a preliminary judgment on the compaction degree.

5. The non-destructive detection method for the compaction degree of high fill in loess as claimed in claim 1, wherein: In step S3, the soil particle distribution characteristics are obtained by analyzing the particle size distribution curve of the soil sample, and the number of peaks of the particle size distribution curve is recorded; When obtaining the humidity change frequency, the change frequency of the soil humidity is reflected by recording the number of changes in the soil humidity within a set time period.

6. A non-destructive detection method for the compaction degree of high fills in loess, according to claim 5, characterized in that: In step S3, the specific steps of using fuzzy logic to analyze the compaction uniformity are as follows: Define the number of peaks of the particle size distribution curve and the number of humidity changes as input variables and divide them into fuzzy sets; Define the compaction uniformity as the output variable and divide it into fuzzy sets; Formulate fuzzy rules to describe the influence of the number of peaks of the particle size distribution curve and the number of humidity changes on the compaction uniformity; Perform fuzzy reasoning according to the fuzzy rules to determine whether the compaction uniformity is high or low.

7. A non-destructive detection method for the compaction degree of high fills in loess, according to claim 6, characterized in that: In step S3, when calculating the compaction non-uniformity rate, multiply the number of peaks of the soil particle distribution characteristics and the number of humidity changes to obtain a normalization parameter, and calculate and obtain it using the logarithmic normalization formula.

8. A non-destructive detection method for the compaction degree of high fills in loess, according to claim 7, characterized in that: In step S4, when the compaction uniformity is high, the system uses a dynamic filtering algorithm to detect the compaction degree; The dynamic filtering algorithm performs noise suppression and outlier smoothing processing on the continuous data sequence according to the surface electromagnetic wave reflection signal and the soil particle vibration frequency data, combined with the time series signal, sets a dynamically updated filtering window, automatically adjusts the filtering coefficient according to the signal change rate, and performs weighted averaging on the sampled data to achieve stable determination and trend tracking of the compaction degree; When the compaction uniformity is low, the system uses an improved wavelet transform algorithm to detect the compaction degree; The improved wavelet transform algorithm performs multi-scale wavelet decomposition and wavelet reconstruction techniques on the surface electromagnetic wave reflection signal and the vibration frequency signal, and performs multi-layer decomposition, local feature enhancement and edge detail extraction on the signal in the time domain and the frequency domain; Introduce an adaptive threshold denoising strategy, a feature interval selection rule and a multi-scale residual overlap correction algorithm in combination with the specific characteristics of the compaction signal, and analyze whether there is an uneven phenomenon in the compaction degree. If so, give the specific compaction non-uniformity rate; If no uneven phenomenon is detected, record the compaction degree data.

9. A non-destructive detection method for the compaction degree of high fills in loess, according to claim 8, characterized in that: The system respectively obtains the surface electromagnetic wave reflection signal and the soil particle vibration frequency data through an electromagnetic wave transmitting and receiving device and a vibration sensor; The electromagnetic wave transmitting and receiving device and the vibration sensor are arranged on the ground surface and a certain distance is maintained between them to avoid signal interference; The electromagnetic wave transmitting and receiving device is connected to the data processing unit through a cable or a wireless communication module to transmit the reflection signal intensity and phase difference data; The vibration sensor is connected to the data processing unit by wire or wirelessly to transmit the vibration frequency data.

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