Intelligent compaction-based comprehensive analysis method for compaction quality of lime-treated soft soil subgrade

Abstract

This invention proposes a comprehensive analysis method for compaction quality of lime-treated soft soil subgrade based on intelligent compaction. The method includes the following steps: S1, acquiring vibration modulus data corresponding to multiple detection units; S2, calculating the compaction degree pass rate and stability pass rate based on the vibration modulus data corresponding to multiple detection units, obtaining the uniformity pass rate using a multi-scale variation structure analysis method, and calculating the anomaly clustering index using the DBSCAN clustering algorithm; S3, determining the weights of each indicator, including the weights of the compaction degree pass rate, uniformity pass rate, and stability pass rate; S4, calculating the compaction quality value based on each indicator and its corresponding weight; S5, determining the subgrade compaction quality grade based on the compaction quality value, and performing subgrade repair based on the subgrade compaction quality grade and the anomaly clustering index. Compared with existing technologies, this invention has advantages such as improving the spatial resolution of subgrade compaction uniformity detection and the accuracy of defect identification.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of compaction quality comprehensive analysis, in particular to a lime-treated soft soil subgrade compaction quality comprehensive analysis method based on intelligent compaction. BACKGROUND

[0002] In recent years, intelligent compaction technology has the advantages of real-time monitoring and comprehensive evaluation of subgrade compaction quality, can evaluate the uniformity of subgrade compaction, and can guide local pressure compensation of the subgrade to improve the overall uniformity of the subgrade. However, existing research on intelligent compaction technology is mostly limited to improving the correlation between intelligent compaction indicators and conventional indicators or analyzing local uniformity, and lacks research on evaluation methods for lime-treated soft soil subgrade compaction quality, especially the construction of a multi-dimensional and multi-index fusion evaluation system, especially the construction of an evaluation model that integrates the three elements of "compaction degree-uniformity-stability" for lime-treated soft soil subgrade, which makes it difficult for intelligent compaction evaluation methods to fully reflect the compaction quality of lime-treated soft soil subgrade.

[0003] Existing intelligent compaction quality evaluation methods often use strict "full qualified" standards, that is, all monitoring indicators must meet the pre-set threshold to be determined as qualified. In actual engineering, there are often cases where the compaction degree indicator has not yet fully met the standard, but the uniformity has already shown good performance. The unqualified compaction degree of lime-treated soft soil subgrade may lead to a whole determination of unqualified, but it cannot reflect the uniformity change in the compaction process, nor can it perform fine classification management and targeted optimization of the compaction quality of lime-treated soft soil subgrade. In addition, the lack of defect recognition accuracy in evaluation also easily leads to misjudgment and omission due to neglect of density differences. SUMMARY

[0004] The present application provides a lime-treated soft soil subgrade compaction quality comprehensive analysis method based on intelligent compaction to improve the spatial resolution and defect recognition accuracy of subgrade compaction degree uniformity detection,

[0005] The purpose of the present application can be achieved by the following technical solutions:

[0006] A lime-treated soft soil subgrade compaction quality comprehensive analysis method based on intelligent compaction, the method comprising the following steps:

[0007] S1, obtaining the original vibration modulus data of the subgrade and performing data preprocessing to obtain vibration modulus data corresponding to each detection unit;

[0008] S2, calculating the compaction degree pass rate and the stability pass rate based on the vibration modulus data corresponding to each detection unit, and obtaining the uniformity pass rate by using a multi-scale variation structure analysis method, and calculating the abnormal aggregation index by using a DBSCAN clustering algorithm;

[0009] S3, determine the weight of each index, the weight includes the weight of compaction degree pass rate, the weight of uniformity pass rate and the weight of stability pass rate;

[0010] S4, calculate the compaction quality value based on each index and the corresponding weight;

[0011] S5, determine the subgrade compaction quality grade based on the compaction quality value, and perform subgrade repair based on the subgrade compaction quality grade and the abnormal aggregation index.

[0012] Further, the specific steps for calculating the compaction degree pass rate and the stability pass rate based on the vibration modulus data corresponding to each detection unit are as follows:

[0013] Determine the vibration compaction target value. For the vibration modulus data of the i-th detection unit, determine whether it is not less than the vibration compaction target value, and calculate the percentage of detection units not less than the vibration compaction target value among all detection units as the compaction degree pass rate.

[0014] Obtain the front and rear vibration modulus data in the same detection unit, calculate the change rate of the two passes of vibration modulus data, determine whether the change rate is less than or equal to the threshold value, and calculate the percentage of detection units less than or equal to the threshold value among all detection units as the stability pass rate.

[0015] Further, the specific steps for obtaining the uniformity pass rate by using the multi-scale variation structure analysis method are as follows:

[0016] Predefine K spatial scales , the k-th scale corresponds to a circular window with a radius , for each detection unit and each scale , let the set of units in the window be , calculate the local semivariance , and fit the scale , and calculate the uniformity pass rate.

[0017] Further, the local semivariance is:

[0018] ;

[0019] wherein is the local semivariance of the i-th detection unit, is the set of units in the window of the i-th detection unit, p and t represent elements in the set, and are the vibration modulus values corresponding to elements p and t in the set , respectively.

[0020] Further, the uniformity pass rate is:

[0021]

[0022] where N represents the total number of detection units, is the uniformity pass rate, is the uniformity indicator function of the i-th detection unit, when the unit meets the uniformity requirement =1, otherwise =0.

[0023] .

[0024] Further, the specific steps for calculating the abnormal aggregation index by the DBSCAN clustering algorithm are as follows:

[0025] The set of detection units with the uniformity indicator function being 0 and the vibration modulus data being less than the vibration compaction target value is , j represents the serial number of the detection unit with the uniformity indicator function being 0 and being less than the vibration compaction target value, and the set Apply the DBSCAN clustering method to obtain L clusters Calculate the point density of each cluster For each point in the cluster, first calculate its nearest neighbor distance , then calculate the weighted distance , and finally calculate the average weighted distance of all unqualified points to obtain the abnormal aggregation index, and the unqualified points are the elements in the set Q.

[0026] Further, the point density of each cluster is:

[0027] ;

[0028] wherein represents the convex hull area of each cluster.

[0029] Further, the weighted distance is:

[0030]

[0031] wherein represents the nearest neighbor distance, is a small constant.

[0032] Further, the abnormal aggregation index is:

[0033] ;

[0034] wherein This represents the anomalous clustering value of point q within a cluster. Each point q within a cluster and each detection unit where the uniformity indicator function is 0 and less than the vibration compaction target value are considered. One-to-one correspondence;

[0035]

[0036] ;

[0037] in, This represents the distance from q to the j-th non-compliant point. This represents the average weighted distance of all non-conforming points. Let be the weighted distance of point q, and M represent the number of non-compliant points.

[0038] Furthermore, the specific steps for roadbed repair based on roadbed compaction quality grade and abnormal aggregation index include:

[0039] The roadbed corresponding to the detection unit with a roadbed compaction quality grade of medium and an abnormal aggregation index exceeding the abnormal aggregation threshold shall be repaired;

[0040] When the subgrade compaction quality grade is poor, the subgrade corresponding to all testing units shall be repaired.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] (1) By introducing a multi-scale variation structure analysis method, the problem of local anomalies being masked by averaging in traditional global semivariogram analysis is solved. Traditional methods use a single global semivariogram function for analysis, which causes local compaction defects to be averaged in the global statistics, resulting in insufficient detection accuracy. This invention addresses the problem by using local semivariogram analysis... The formula, in a radius of Calculate local variability within the sliding window, such that The parameters can sensitively reflect different spatial scales. By analyzing local variation features, and then using multi-scale fitting, small-scale compaction uneven areas that were missed by the global analysis are identified, thus significantly improving the spatial resolution of uniformity detection and the accuracy of defect identification.

[0043] (2) An innovative weighted nearest neighbor anomaly index (WNNI) calculation mechanism is proposed, overcoming the limitation of traditional nearest neighbor analysis in failing to distinguish the influence of density differences. Traditional nearest neighbor analysis only considers spatial distance. This approach ignores the density distribution characteristics of defective points, making it impossible to accurately distinguish the degree of anomalies between high-density and low-density areas. This invention addresses this issue through a density-weighted function. Nearest neighbor distance With cluster density Combining these factors makes the weighted distance... It can simultaneously reflect the degree of spatial aggregation and density distribution characteristics, and thus through The calculation yields standardized abnormal clustering values, thus enabling the accurate identification of high-risk abnormal clustering areas that truly require close attention. This avoids misjudgments and omissions caused by neglecting density differences in traditional methods, providing accurate spatial positioning guidance for targeted pressure replenishment.

[0044] (3) The synergistic effect of the two methods has enabled the transformation from "coarse-grained global detection" to "refined local diagnosis". The MSVA method identifies potential problem areas through multi-scale variation analysis, while the WNNI mechanism further quantifies the risk level of abnormal clustering. The combination of the two methods enables the compaction quality evaluation to change from the traditional binary judgment of "qualified / unqualified" to a multi-dimensional and accurate assessment based on spatial variation characteristics and clustering risk. Therefore, it can provide the construction party with a more scientific and accurate basis for quality control. Attached Figure Description

[0045] Figure 1 The flowcharts for S1 and S2 of the present invention are shown below;

[0046] Figure 2 This is a detailed flowchart of S2 of the present invention;

[0047] Figure 3 This is a detailed flowchart of S3 of the present invention;

[0048] Figure 4 This is a flowchart of S5 of the present invention. Detailed Implementation

[0049] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0050] Figure 1 The flowcharts for S1 and S2 of the present invention are shown below; Figure 2 This is a detailed flowchart of S2 of the present invention; Figure 3 This is a detailed flowchart of S3 of the present invention; Figure 4 This is a flowchart of S5 of the present invention.

[0051] This invention relates to the field of intelligent compaction in roadbed engineering, specifically a comprehensive evaluation method for the compaction quality of lime-treated soft soil roadbeds based on intelligent compaction, and particularly to a comprehensive closed-loop system integrating data processing, quality judgment, and construction feedback. This method performs comprehensive data processing on the vibration modulus (Evib) collected in real time by the intelligent compaction system, including preprocessing, spatial allocation, and region division, to calculate the compaction degree pass rate, uniformity pass rate, and stability pass rate. Compared to existing global semivariogram fitting methods, this invention innovatively proposes a multi-scale variation structure analysis method (MSVA) based on local window sliding to capture the compaction uniformity variability of soft soil roadbeds at different spatial scales, thereby achieving multi-level identification of uniformity characteristics and automatic detection of non-uniform regions. Furthermore, based on traditional nearest neighbor spatial analysis, a density-weighted mechanism is introduced, proposing a weighted nearest neighbor anomaly index (WNNI) to measure the degree of abnormal clustering of unqualified detection units in space. To scientifically combine the three dimensions of indicators, the CRITIC method is used to determine the weight of each indicator, and a CQ compaction quality comprehensive evaluation model is proposed. Based on the calculated CQ values, a multi-level grading evaluation standard is established to achieve a comprehensive, scientific, and refined assessment of the subgrade compaction quality. Compared with existing technologies, the beneficial effects of this invention are that it introduces MSVA and WNNI algorithms, enabling the evaluation results to accurately reflect local unevenness and defect aggregation problems in subgrade compaction. Compared with traditional weighted evaluation, it improves the spatial analysis dimension and recognition resolution, helping construction units to carry out targeted optimization control. It also transforms the single, one-sided qualified / unqualified judgment in traditional intelligent compaction evaluation into a multi-level, operable quality assessment, overcoming the limitations of the traditional "fully qualified" standard in actual engineering. It can adapt to the compaction quality grading evaluation needs of intelligent compaction systems, providing a more guiding solution for intelligent compaction quality control, and improving construction efficiency, accuracy, and project quality.

[0052] The steps of this invention include:

[0053] S1: Input and preprocessing of intelligent compaction data, including the raw data collected by the intelligent compaction system. E vib Data is imported into the data processing platform, where spatial matching, data cleaning, data format standardization, and the compaction area is divided into several detection units. Vibration modulus refers to the dynamic parameter reflecting the stiffness characteristics of soil, measured in real-time by the intelligent compaction system during the vibration compaction process; its unit is MPa.

[0054] S2: Calculation of the pass rate for each individual indicator, including:

[0055] Compaction degree pass rate ( P comp );

[0056] Uniformity pass rate ( P uni ): Introducing the "Multi-Scale Variation Structure Analysis (MSVA)" algorithm, the local semivariance is calculated within a sliding window at different spatial scales, and a spatial variation rate map of compaction uniformity is generated to identify local non-uniformity of subgrade materials;

[0057] Anomaly Clustering Index (WNNI): The DBSCAN clustering algorithm is used to identify dense areas of non-compliance points, and the weighted nearest neighbor anomaly index WNNI is calculated based on point density and nearest neighbor distance to quantitatively assess the spatial clustering risk of weak compaction areas.

[0058] Stability pass rate ( P stab ).

[0059] S3: Determination of weighting coefficients: The CRITIC method is used to determine the weighting coefficients for compaction degree, uniformity and stability.

[0060] S4: Calculation of compaction quality value (CQ): The CQ value is calculated using a weighted fusion model based on the pass rate and weighting coefficient of each individual indicator.

[0061] S5: Based on the CQ value, the compaction quality is graded and judged. The calculated CQ value is compared with the preset grading standard, and the subgrade compaction quality grade information is output and displayed spatially and provided with construction feedback through GIS.

[0062] In step S1, the preprocessing information for the intelligent compaction data includes: E vib Spatial matching of data with the precise location coordinates of regular detection points, identification and processing of outliers or missing data, and all E vib The data is standardized to a standard format, and the entire compaction area is divided into several detection units.

[0063] In step S2, the compaction pass rate ( P comp The calculation method includes: determining the target value corresponding to the qualified compaction degree. E vib The value represents the detection result of intelligent compaction for the i-th detection unit. ), determine whether it is not less than the target value for vibration compaction[ E vib ]. P comp It is calculated as a percentage of the total number of detection units that passed the test.

[0064] In step S2, the uniformity pass rate ( Puni The calculation method includes: introducing the "Multiscale Variability Structure Analysis (MSVA)" method, which... E vib The data is divided into a continuous sliding window region, and the compaction area is divided into N detection units. Record the vibration modulus of each unit. E vib Value, the detection result of intelligent compaction of the i-th detection unit ( K spatial scales are predefined. The k-th scale corresponds to the radius A circular window for each detection unit and each scale After setting the cells within the window as a set, calculate the local semivariance. .Will scale A fitting process is performed to extract variability peaks and discontinuity boundaries by fitting the degree of variation at various scales. Finally, the identification results are used to determine local heterogeneous regions. The larger the size, the more severe the inequality, and the lower the score. P uni The results are obtained by normalizing the scores of all units. The calculation and fitting formulas for the Multiscale Variation Structure Analysis (MSVA) method are as follows:

[0065]

[0066]

[0067]

[0068]

[0069] In the formula: —The i-th detection unit; —The k-th scale corresponds to the radius of the circular window; —A collection of cells within a window; —Local semivariance.

[0070] Meanwhile, in the non-conforming point clustering discrimination, the WNNI calculation mechanism is introduced, specifically including: using DBSCAN to perform density clustering of non-conforming points, identifying high-incidence areas of anomalies, and based on preset thresholds (such as...). E vib M non-compliant test units were screened out (not meeting the standards), and their set is denoted as Munqualified. Applying DBSCAN to Q yields L clusters. Calculate the point density of each cluster. For each point within a cluster First calculate its nearest neighbor distance. Then define the weighted distance. Finally, the average weighted distance of all non-conforming points is calculated, and the point-level WNNI is defined. When When WNNI > 1, the clustering degree of this point is higher than the average level; if WNNI exceeds the preset threshold T, it is determined that there is significant spatial clustering, and local compression needs to be applied to the hotspot areas. The calculation formula is as follows:

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] In the formula: —The area of ​​the convex hull of the cluster; —Point density of each cluster; —Nearest neighbor distance; —Weighted distance; —A small constant to prevent division by zero; —The average weighted distance of all non-compliant points.

[0077] In step S2, the stability pass rate ( P stab The calculation method is as follows: calculate the change rate of the intelligent compaction detection value of the same rolling part in two consecutive passes, and determine whether the change rate is less than or equal to 3%. P stab The result is calculated as the percentage of the number of detection units that passed stability tests out of the total number of detection units.

[0078] In step S3, the weighting coefficients are determined using the CRITIC method, the specific steps of which are as follows:

[0079] 1. Standardize the pass rate data for each individual indicator, transforming the original data matrix... Standardization is performed to obtain the standardized matrix. The calculation formula is:

[0080]

[0081] In the formula: i—sample number; j—original value number of the index, where j=1 represents the degree of compaction, j=2 represents uniformity, and j=3 represents stability.

[0082] 2. Calculate the standard deviation of each standardized indicator. Assume there are m evaluation samples and n evaluation indicators. For each standardized column (indicator)... j ), calculate its standard deviation The calculation formula is:

[0083]

[0084] 3. Using the Pearson correlation coefficient formula, calculate the correlation coefficient between any two indicators. j and k Correlation coefficient between The calculation formula is:

[0085]

[0086] 4. Calculate the conflict information for each indicator. The calculation formula is:

[0087]

[0088] 5. Calculate the final weights of each indicator through normalization. The calculation formula is:

[0089] .

[0090] In step S4, the formula for calculating the compaction quality value (CQ) is:

[0091]

[0092] In the formula:

[0093] P comp - The compaction pass rate is calculated as the percentage of the area that passes the compaction pass rate out of the total compacted area. The calculation formula is:

[0094]

[0095] - The uniformity pass rate is calculated as the percentage of the uniform pass area to the compacted area. The calculation formula is:

[0096]

[0097] - The stability pass rate is calculated as the percentage of the stable pass area to the compacted area. The calculation formula is:

[0098]

[0099] α, β, γ – are the weighting coefficients of the corresponding indicators.

[0100] In step S5, based on the survey results of the roadbed compaction quality requirements, a grading standard for roadbed compaction quality evaluation is established. The compaction quality value (CQ) is used as the standard for grade classification. Multiple grades of CQ values ​​are determined according to preset thresholds, and the corresponding compaction grade information is output for GIS display and construction strategy generation, as determined in Table 1.

[0101] Table 1. Correspondence between CQ values, corresponding compaction quality grades, and engineering significance.

[0102]

[0103] Based on relevant research findings both domestically and internationally, this invention proposes a method for evaluating the compaction quality of lime-treated soft soil subgrades using intelligent compaction technology. A closed-loop evaluation system consisting of three stages—data processing, quality assessment, and construction feedback—is constructed. First, the vibration modulus (…) is collected and processed. E vib The data was preprocessed, spatially interpolated, and region-divided. Based on this, three pass rate indices—compaction degree, uniformity, and stability—were calculated. Secondly, a novel multi-scale structural variation analysis (MSVA) method was introduced. A multi-scale semivariogram function was constructed using a sliding window to dynamically identify compaction uniformity characteristics and quantify local variability at different spatial scales, effectively improving sensitivity and accuracy in identifying uneven regions. Simultaneously, based on traditional nearest neighbor analysis, a density-weighted mechanism was introduced, proposing the Weighted Nearest Neighbor Anomaly Index (WNNI) to identify spatially anomalous clustering characteristics of non-compliant detection units.

[0104] To scientifically integrate the three types of evaluation indicators, this invention uses the CRITIC method to objectively assign their weights, thereby constructing a comprehensive evaluation model for CQ compaction quality. Based on this model, a multi-level grading standard is formulated to classify the compaction quality of lime-treated soft soil subgrade into four levels: "excellent, good, medium, and poor". This makes the evaluation results operable and discriminative, and can guide construction units to carry out differentiated and targeted compaction optimization control.

[0105] The beneficial effects of the intelligent roadbed compaction quality evaluation method provided by this invention are as follows:

[0106] Achieving refined grading and regional assessment of compaction quality: based on E vib By analyzing the spatial distribution characteristics of the data and integrating the MSVA algorithm to perform variational structure analysis on multi-scale compaction uniformity, the system can accurately identify local compaction defect areas in soft soil subgrades. The compaction quality grade results output by the system can guide the construction team to carry out targeted recompaction operations, effectively avoiding resource waste caused by overcompaction or large-scale rework, thereby improving construction efficiency, reducing costs, and shortening the construction period.

[0107] Provide a multi-dimensional integrated quality comprehensive evaluation mechanism: This method breaks through the traditional "qualified / unqualified" judgment mode based on a single index, constructs a comprehensive evaluation system integrating three elements of compaction degree, uniformity and stability, and introduces the CRITIC method to determine the index weights, making the evaluation results more scientific, representative and stable. This fusion mechanism can avoid the situation of negating the overall compaction effect due to the unqualified single index, and improve the rationality and inclusiveness of the overall quality judgment;

[0108] Establish an operable CQ quality grade standard to support the construction feedback closed-loop: Taking the comprehensive compaction quality index CQ as the core variable, construct a multi-level classification standard system, which can meet the needs of the intelligent compaction system for quality grade division and feedback control. Each grade standard can be bound to the GIS platform to realize the spatial visualization expression of quality data and the optimization of the additional compaction path. Further, as the core link of "evaluation - prediction - control", the CQ value can be used to generate construction feedback strategies and assist in formulating personalized construction optimization plans, significantly enhancing the intelligent control ability of the compaction process.

[0109] The present invention provides a comprehensive evaluation method for the compaction quality of a lime-treated soft soil subgrade based on intelligent compaction. The core lies in the analysis and processing of the collected intelligent compaction data, rather than the data collection itself. Data collection, as a pre-step, is completed in real time by the intelligent compaction system during the compaction process and generates E vib data, and the collection and storage are completed at the construction site, which is the basis for the subsequent data processing of the present invention. The above method not only ensures the whole-process monitoring and data collection of the compaction process in the entire construction process, but also realizes the refined evaluation of the compaction quality of the lime-treated soft soil subgrade and the accurate positioning of weak areas.

[0110] The experimental section of the present invention is 110 m in length and 36 m in width. The object of the base compaction quality evaluation is a soft soil road section of silty clay treated with 7% lime, with a loose paving thickness of 30 cm and a target compaction degree of 96%. The corresponding Evib target value is calculated to be 92.85 MPa. A BW226DH-5 full-hydraulic single-drum vibratory roller is used to perform vibratory compaction, and the Evib index is collected by the BOMAP intelligent compaction system.

[0111] In the embodiment of the present invention, referring to the specification "Technical Conditions for Continuous Compaction Control System of Highway Subgrade Filling Project" (JTT1127-2017), the following three judgment criteria are adopted:

[0112] 1. Compaction degree: Taking Evib i ≥92.85 MPa as the qualified unit;

[0113] 2. Compaction uniformity: (The text abruptly ends here, likely due to an incomplete sentence or missing information.) The unit was deemed qualified.

[0114] 3. Compaction stability: Compact the same location in two adjacent passes. Evib i A unit with a change rate ≤ 3% is considered a qualified unit.

[0115] The test section underwent four consecutive passes of vibratory compaction. After each pass, full-coverage Evib data was extracted, and the pass rates of the three indicators were calculated. The results are shown in Table 2.

[0116] Table 2. Indicator Pass Rate

[0117]

[0118] The standard deviation is calculated using the CRITIC method based on three dimensions: compaction pass rate, uniformity pass rate, and stability pass rate. Correlation coefficient Conflicting information After normalization, the final weights of each factor are obtained. (Determined by the CRITIC method, this study used...) , , The results are shown in Table 3.

[0119] Table 3 Weights of Each Factor

[0120]

[0121] Based on the weights of compaction degree, uniformity, and stability, and combined with the formula for calculating compaction quality (CQ), the CQ value for each number of compaction passes is calculated. The calculation process is as follows:

[0122]

[0123]

[0124]

[0125]

[0126] Based on the survey results of the roadbed compaction quality requirements, a grading standard for roadbed compaction quality evaluation was established. The CQ value was judged in multiple levels according to the preset threshold, and the corresponding compaction level information was output for GIS display and construction strategy generation, as shown in Table 4.

[0127] Table 4. Standards for Grading Subgrade Compaction Quality Grades

[0128]

[0129] By quantitatively evaluating the compaction quality under different numbers of rolling passes, the corresponding compaction quality grades are obtained, and the results are shown in Table 5.

[0130] Table 5 CQ values and compaction quality grades corresponding to different numbers of rolling passes

[0131]

[0132] According to the CQ values and their corresponding quality grades under each number of rolling passes shown in Table 5, there is a fluctuating trend of first rising and then falling. All results are in the "poor" grade (0.3 < CQ < 0.6), indicating that there are significant defects in the compaction quality of the subgrade of the test section under vibration compaction, and further optimization is required. Further analyzing the tabular data from three aspects: data trend, index contribution degree, and engineering significance, the passing rate of compaction degree: gradually increases with the increase in the number of rolling passes, but the absolute value is extremely low (<12%), far lower than the engineering qualified threshold (usually >80%), indicating that vibration compaction does not improve the passing rate of compaction degree of the lime-treated silty clay subgrade much, and the lime-treated silty clay subgrade is not suitable for excessive vibration compaction; the passing rate of uniformity: reaches the peak value (70.1%) when rolling 2 times, and then drops sharply to 54.5% (3 times) and then rises slightly (55.9%, 4 times), which may be related to the improvement of material distribution in the initial rolling and the crushing of aggregates caused by over-rolling in the later stage; the passing rate of stability: shows an overall upward trend, but its weight is relatively low (24.19%), and its contribution to the CQ value is relatively low (an increase of 0.029).

[0133] In addition, according to the CQ model , the contributions of each index are as follows: Uniformity plays a dominant role in the overall evaluation, with a weight as high as 49.53%, and the test data shows that its highest passing rate is 70.1%, and the corresponding contribution value is about 0.347 (0.701×0.4953), which is the main reason for the relatively high overall CQ value. In contrast, the passing rate of compaction degree reaches 11.7% when vibrating and rolling 4 times, and its contribution value is about 0.031 (0.117×0.2629); the stability index has a slightly lower weight than the compaction degree, but the highest passing rate is 16.1%, and its contribution value when vibrating and rolling 4 times is about 0.039 (0.161×0.2419). Therefore, the overall influence of compaction degree and stability on the CQ value can be relatively ignored.

[0134] In summary, from the contribution of each index, under the current vibration rolling process conditions, the overall compaction quality grade standard of the subgrade is poor (0.3 < CQ < 0.6), and the core problem is that the passing rates in the two dimensions of compaction degree and stability are always at a relatively low level, thus reducing the overall evaluation result. In the follow-up, static pressure reinforcement measures should be considered to improve the compaction effect and effectively enhance the overall quality of the project.

[0135] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A comprehensive analysis method for compaction quality of lime-treated soft soil subgrade based on intelligent compaction, characterized in that, The method includes the following steps: S1. Obtain the original vibration modulus data of the roadbed and perform data preprocessing to obtain the vibration modulus data corresponding to multiple detection units respectively; S2. Based on the vibration modulus data corresponding to multiple detection units, calculate the compaction pass rate and stability pass rate, and use the multi-scale variation structure analysis method to obtain the uniformity pass rate. Calculate the abnormal clustering index using the DBSCAN clustering algorithm. S3. Determine the weight of each indicator, including the weight of the compaction pass rate, the weight of the uniformity pass rate, and the weight of the stability pass rate; S4. Calculate the compaction quality value based on each indicator and its corresponding weight; S5. Determine the subgrade compaction quality grade based on the compaction quality value, and carry out subgrade repair based on the subgrade compaction quality grade and abnormal aggregation index; The specific steps for obtaining the uniformity pass rate using the multi-scale variation structure analysis method are as follows: Predefined K spatial scales The k-th scale corresponds to the radius A circular window for each detection unit and each scale. Let the set of cells within the window be Calculate the local semivariance ,Will For scale Perform a fitting test and calculate the uniformity pass rate; Local semivariance for: ； in, Let be the local semivariance of the i-th detection unit. Let p and t be the set of cells within the window of the i-th detection unit, and p and t represent the elements in the set. and Sets The vibrational modulus values ​​corresponding to the intrinsic elements p and t; Uniformity pass rate: ； Where N represents the total number of detection units, For uniformity throughput, This represents the uniformity indicator function for the i-th detection unit, which indicates when the unit meets the uniformity requirement. =1, otherwise =0; ； The specific steps for calculating the outlier clustering index using the DBSCAN clustering algorithm are as follows: Detection units with a uniformity indicator function of 0 and vibration modulus data less than the vibration compaction target value are considered as non-conforming points. The set of non-conforming points is thus defined as follows: , j represents the serial number of the detection unit where the uniformity indicator function is 0 and less than the vibration compaction target value. This indicates that the j-th uniformity indicator function is 0 and less than the vibration compaction target value, for the set Using the DBSCAN clustering method, L clusters were obtained. Calculate the point density of each cluster. For each cluster point First calculate its nearest neighbor distance. Then calculate the weighted distance. Finally, the average weighted distance of all non-compliant points is calculated to obtain the abnormal clustering index; Point density of each cluster for: ； in, This represents the convex hull area of ​​each cluster; Weighted distance for: ； in, Indicates the nearest neighbor distance. It is a tiny constant; The abnormal aggregation index is: ； in, This represents the anomalous clustering value of point q within a cluster. Each point q within a cluster and each detection unit where the uniformity indicator function is 0 and less than the vibration compaction target value are considered. One-to-one correspondence; ； ； in, This represents the distance from q to the j-th non-compliant point. This represents the average weighted distance of all non-conforming points. Let be the weighted distance of point q, and M represent the number of non-compliant points.

2. The comprehensive analysis method for compaction quality of lime-treated soft soil subgrade based on intelligent compaction according to claim 1, characterized in that, The specific steps for calculating the compaction pass rate and stability pass rate based on the vibration modulus data corresponding to multiple detection units are as follows: Determine the vibration compaction target value. For the vibration modulus data of the i-th detection unit, determine whether it is not less than the vibration compaction target value. Calculate the percentage of detection units that are not less than the vibration compaction target value among all detection units as the compaction pass rate. Obtain vibration modulus data from two consecutive tests within the same detection unit, calculate the rate of change of the vibration modulus data from the two tests, determine whether the rate of change is less than or equal to a threshold, and calculate the percentage of detection units that are less than or equal to the threshold among all detection units as the stability pass rate.

3. The comprehensive analysis method for compaction quality of lime-treated soft soil subgrade based on intelligent compaction according to claim 2, characterized in that, The specific steps for subgrade repair based on subgrade compaction quality grade and abnormal aggregation index include: The roadbed corresponding to the detection unit with a roadbed compaction quality grade of medium and an abnormal aggregation index exceeding the abnormal aggregation threshold shall be repaired; When the subgrade compaction quality grade is poor, the subgrade corresponding to all test units shall be repaired.

Images