A compaction parameter optimization method suitable for expressway subgrade in seasonal frozen soil area

Abstract

The present application belongs to the technical field of subgrade compaction data processing, and relates to a method suitable for optimizing subgrade compaction parameters of expressway in seasonal frozen soil area. The present application obtains grid analysis values of compaction grids, generates a contradictory grid set based on stratified water content values and compaction response values, then generates an abnormal candidate area through spatial matching results, coverage gap results, BIM risk values and deep water content trend values of adjacent compaction grids, and confirms abnormal compaction grids through temporary review points to generate reliable water content state values and compaction parameter optimization results. The present application solves the problem that in the prior art, when stratified water content values are normal on the surface but local soil bodies are actually too wet, the system is prone to misjudging compaction response abnormalities as insufficient compaction and continuing to enhance compaction, improves the identification accuracy of hidden moisture abnormalities in the sensing coverage gap, and ensures the reliability of subgrade compaction parameter optimization of expressway in seasonal frozen soil area.

Description

Technical Field

[0001] This invention relates to the field of roadbed compaction data processing technology, and in particular to a method for optimizing roadbed compaction parameters for highways in seasonally frozen soil regions. Background Technology

[0002] In the compaction process of highway subgrade in seasonally frozen soil areas, soil moisture content has a significant impact on compaction quality and subsequent frost resistance. Existing intelligent compaction methods typically collect surface, middle, and deep soil moisture content data using multi-depth moisture sensors, and combine this data with compaction force, vibration frequency, operating speed, number of compaction passes, and location data to adjust compaction parameters for the current work area. When the layered moisture content collected by the sensors is within the optimal moisture content control range, the system generally considers the area suitable for continued or enhanced compaction.

[0003] However, in seasonally frozen soil areas, snowmelt, groundwater, or moisture from construction layers can seep into the deeper layers of the roadbed through hidden channels such as freeze-thaw cracks, permeable interlayers, and construction joints. Since multi-depth moisture content sensors can only detect the soil moisture content near their installation location, when moisture enters a location not covered by the sensor, the layered moisture content values ​​collected by the sensor may still appear normal, even though the local soil is actually overly wet. In this case, if the system continues to increase the compaction force, vibration frequency, or number of compaction passes based on the normal moisture content, it can easily lead to ineffective compaction and even induce localized subsidence, frost heave, thaw settlement, and frost heave risks.

[0004] This issue directly impacts the accuracy and safety of optimizing subgrade compaction parameters for highways in seasonally frozen soil regions. On one hand, the system may incorrectly determine that the current grid has the conditions for continued compaction based on normal surface moisture content values, thus issuing commands for enhanced compaction or additional compaction. On the other hand, locally overly wet areas, if not identified in time, may form internal weak zones or hidden sinkholes after compaction, which can subsequently develop into thaw settlement, frost heave, frost-induced cracking, or uneven pavement deformation under the combined effects of freezing, thawing, and traffic loads. These problems not only reduce compaction efficiency and increase rework and maintenance costs but also affect the structural stability and driving safety of the highway during its operational period.

[0005] The above-mentioned treatment methods have significant shortcomings: when the slow increase in compaction degree, the soft vibration response, or the enhanced rebound after compaction are actually caused by localized hidden excessive moisture, continuing to increase compaction not only fails to improve compaction quality but may also lead to springy soil, localized subsidence, frost heave, thaw settlement, and the risk of subsequent frost heave. Although existing technologies can use BIM models to record compaction areas, construction layers, and compaction quality data, they typically lack a mechanism for jointly analyzing compaction response anomalies, BIM spatial risks, moisture change trends in adjacent grids, and results from temporary verification points, making it difficult to promptly identify hidden moisture anomalies within sensor network blind spots. Summary of the Invention

[0006] The purpose of this invention is to address the problem that, during the compaction of highway subgrade in seasonally frozen soil areas, when the layered moisture content is within the target moisture range but the compaction response value meets the compaction response deviation condition, existing technologies struggle to promptly identify hidden moisture anomalies within the sensor coverage gaps, easily misjudging localized over-wetness as insufficient compaction and continuing to intensify compaction. Therefore, this invention proposes a method for optimizing compaction parameters of highway subgrade in seasonally frozen soil areas.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for optimizing the compaction parameters of highway subgrade in seasonally frozen soil areas, comprising the following steps: obtaining the grid analysis values ​​of the compaction grid, including the layered moisture content values, compaction response values, spatial reference values, BIM risk values, and deep moisture content trend values ​​of adjacent compaction grids.

[0008] Based on the layered moisture content and compaction response values ​​of the compacted grid, determine whether there are conflicting grids; if so, generate a set of conflicting grids.

[0009] Based on the contradictory grid set and spatial benchmark value, spatial matching results are generated; based on the spatial matching results, coverage gap results are generated.

[0010] Based on the cover gap results, BIM risk values, and deep moisture content trend values ​​of adjacent compacted grids, anomaly candidate areas are generated.

[0011] Based on the candidate abnormal regions, the result of the verification point layout is generated. Based on the layered moisture content verification value corresponding to the verification point layout result, the abnormal compaction grid is confirmed and the reliable moisture content state value is generated.

[0012] Based on the reliable moisture content state value, the compaction parameter optimization results of the abnormal compaction grid are generated.

[0013] In summary, by employing the aforementioned method for optimizing compaction parameters of highway subgrades in seasonally frozen soil regions, the beneficial effects of this invention are as follows: This invention obtains the grid analysis values ​​of the compaction grid and generates a contradictory grid set based on the layered moisture content values ​​and compaction response values. This allows for the identification of compaction grids where the layered moisture content values ​​are within the target moisture range but the compaction response values ​​meet the compaction response deviation conditions, thus avoiding directly classifying such compaction grids as insufficiently compacted areas. Furthermore, by generating spatial matching results based on the contradictory grid set and spatial benchmark values, and further generating coverage gap results, it is possible to determine whether the abnormal compaction response locations belong to areas not effectively covered by multi-depth moisture content sensors, reducing the risk of missed detection due to insufficient sensor coverage.

[0014] This invention generates anomaly candidate areas based on cover gap results, BIM risk values, and deep moisture content trend values ​​of adjacent compacted grids. This enables the correlation between sensor cover gaps, risk locations, and deep moisture content changes, improving the ability to identify snowmelt water, groundwater, or interlayer moisture entering the deep subgrade along construction joints, permeable interlayers, and freeze-thaw cracks. By generating verification point layout results based on anomaly candidate areas and confirming abnormal compacted grids based on layered moisture content verification values, a reliable moisture content state value is generated. This verified moisture content state can replace the normal surface state collected by multi-depth moisture content sensors at the deployment locations, improving the reliability of compaction parameter optimization results.

[0015] This invention generates optimized compaction parameters based on reliable moisture content values. When the layered moisture content verification value exceeds the preset target moisture content upper limit, it generates an enhanced compaction pause marker and adjusts the compaction strength, vibration frequency, operating speed, number of compaction passes, and supplementary compaction path. This avoids further enhanced compaction in locally overly wet areas, reducing the risks of springy soil, localized collapse, frost heave, thaw settlement, and subsequent frost heave. By obtaining the layered moisture content verification value and compaction response value again after anomaly handling, and regenerating the optimized compaction parameters after meeting the recovery conditions, this invention forms a process for confirming, pausing, handling, retesting, and restoring compaction of abnormal compaction grids, preventing abnormal compaction grids from being out of the compaction process for extended periods. Attached Figure Description

[0016] Figure 1 A schematic diagram of the process of the present invention is shown;

[0017] Figure 2 A schematic diagram showing the compaction quality display results of the present invention is shown;

[0018] Figure 3 A schematic diagram showing the relationship between the layered moisture content verification value, compaction degree, and frost heave rate in this invention is presented. Detailed Implementation

[0019] The following will describe, with reference to the accompanying drawings of the embodiments of the present invention, a method for optimizing the compaction parameters of highway subgrade in seasonally frozen soil regions. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] To more clearly and intuitively demonstrate the practical application effects and advantages of the proposed method for optimizing highway subgrade compaction parameters in seasonally frozen soil regions, and to verify its feasibility and effectiveness, the invention is further described below with reference to embodiments. Through specific scenario simulations and data calculations, the method is explained in detail how it plays a role in optimizing actual highway subgrade compaction parameters, helping readers better understand the technical details and practical value of the invention. The invention is further described below with reference to embodiments.

[0021] Example 1:

[0022] In this embodiment, a compaction grid for the highway subgrade in seasonally frozen soil areas is first constructed based on construction drawings, geological survey data, geotechnical test results, and a BIM model. Then, according to soil type, frost heave sensitivity level, shallow groundwater level, and construction layer, the target moisture content range, target compaction degree, compaction operation level, and initial compaction parameters for each compaction grid are determined. During the compaction machinery operation, layered moisture content values, compaction response values, operating speed, and compaction grid location values ​​are collected using multi-depth moisture content sensors, vibration sensors, compaction strength sensors, velocity sensors, and a positioning module. These data are then uploaded to a cloud-based management platform for data fusion processing.

[0023] See Figure 1 A method for optimizing subgrade compaction parameters for highways in seasonally frozen soil regions is proposed, comprising: S1: obtaining the grid analysis values ​​of the compaction grid, including layered moisture content values, compaction response values, spatial benchmark values, BIM risk values, and deep moisture content trend values ​​of adjacent compaction grids; by simultaneously obtaining layered moisture content values, compaction response values, spatial benchmark values, BIM risk values, and deep moisture content trend values ​​of adjacent compaction grids, the basis for judging the compaction grid is no longer limited to the layered moisture content values ​​collected by multi-depth moisture content sensors at the deployment location, but rather incorporates the current moisture content state, compaction performance, spatial location, risk location, and deep moisture content changes of adjacent compaction grids into the data processing process. This solves the problem in existing technologies where the compaction grid is judged to be ready for continued compaction based solely on the layered moisture content value being within the target moisture content range, and avoids making normal compaction judgments when local moisture ingress locations are not covered by multi-depth moisture content sensors.

[0024] Specifically, the method for generating BIM risk values ​​includes: obtaining the location values ​​of construction joints, permeable interlayers, freeze-thaw cracks, and shallow groundwater based on the location values ​​of the compacted grid.

[0025] Based on the location values ​​of construction joints, permeable interlayers, freeze-thaw fissures, and shallow groundwater, risk values ​​for construction joints, permeable interlayers, freeze-thaw fissures, and shallow groundwater are generated respectively.

[0026] Based on the risk values ​​of construction joints, permeable interlayers, freeze-thaw fissures, and shallow groundwater, BIM risk values ​​corresponding to the compacted grid are generated. By combining these risk values ​​to generate BIM risk values ​​for the compacted grid, the impact of the locations of construction joints, permeable interlayers, freeze-thaw fissures, and shallow groundwater on the infiltration of concealed water can be unified into the risk assessment of the same compacted grid. This solves the problem in existing technologies where BIM models only record spatial locations and lack risk value representation for identifying concealed water anomalies.

[0027] The location values ​​that correspond to the BIM risk values ​​among the construction joint location values, permeable interlayer location values, freeze-thaw crack location values, and shallow groundwater location values ​​are determined as risk location values.

[0028] The BIM risk value is generated based on the spatial correspondence results of the risk source, the direction correspondence results of the risk, and the layer correspondence results of the risk location. The spatial correspondence results of the risk source are used to indicate whether the location value of the construction joint, the location value of the permeable interlayer, the location value of the freeze-thaw crack, or the location value of the shallow groundwater corresponds to the same compaction grid or an adjacent compaction grid as the location value of the compaction grid; the direction correspondence results of the risk are used to indicate whether the extension direction of the risk source points to the compaction grid; and the layer correspondence results of the layer correspondence results of the layer correspondence results of the layer correspondence to the middle layer or the deep layer of the compaction grid for moisture content detection.

[0029] When a risk source and a compacted grid correspond to the same compacted grid and are at the same layer, a co-location layer matching result is generated; when a risk source and a compacted grid correspond to an adjacent compacted grid and the direction of the risk source points to that compacted grid, a directional adjacency matching result is generated. Based on the co-location layer matching result or the directional adjacency matching result, the BIM risk value corresponding to the compacted grid is generated, and the corresponding risk source location value is determined as the risk location value.

[0030] Specifically, the method for generating the deep water content trend value of adjacent compacted grids includes: determining the adjacent compacted grids that are adjacent to the contradictory grids based on the contradictory grid set.

[0031] Obtain the deep moisture content values ​​of adjacent compacted grids during a continuous detection period, and generate the time series values ​​of deep moisture content of adjacent compacted grids.

[0032] Based on the time-series values ​​of deep moisture content, it is determined whether the deep moisture content values ​​of adjacent compacted grids increase with continuous detection periods, generating a deep moisture content increase judgment result. By judging the increase of deep moisture content values ​​within continuous detection periods, it is possible to identify whether there is a continuous increase in deep moisture content among adjacent compacted grids over time. This solves the problem in existing technologies where the deep moisture content value at a single detection moment is insufficient to determine whether moisture is migrating, allowing the deep moisture content trend values ​​of adjacent compacted grids to more reliably participate in the generation of anomaly candidate regions.

[0033] Based on the determination of increased deep water content, the deep water content trend value of adjacent compacted grids is generated.

[0034] S2: Based on the stratified moisture content and compaction response values ​​of the compacted grid, determine whether contradictory grids exist. If so, generate a set of contradictory grids. By generating the set of contradictory grids, identify compacted grids whose stratified moisture content is within the target moisture range but whose compaction response values ​​indicate slow compaction degree growth, soft vibration response, or enhanced post-compaction rebound. This solves the problem in existing technologies of directly equating normal stratified moisture content values ​​with satisfactory compaction conditions, and also avoids simply attributing abnormal compaction responses to insufficient compaction strength, insufficient vibration frequency, or insufficient compaction passes, thus providing clear data objects for subsequent identification of coverage gaps.

[0035] It should be noted that the method for determining the existence of contradictory grids includes: based on the stratified moisture content values ​​of the compacted grid, determining whether the stratified moisture content values ​​are within the target moisture content range, and generating a moisture content range determination result; by generating the moisture content range determination result, it can be confirmed whether the compacted grid exhibits a normal moisture content state in the multi-depth moisture content sensor data. This processing can retain the basic judgment function of existing intelligent compaction methods on stratified moisture content values, while providing a basis for subsequent comparison with the compaction response deviation results, avoiding the direct discarding of the multi-depth moisture content sensor detection results.

[0036] Based on the compaction response values ​​of the same compaction grid, it is determined whether the compaction response values ​​meet the compaction response deviation conditions, and a compaction response deviation result is generated. By generating the compaction response deviation result, abnormal behavior during the compaction process can be identified even when the surface moisture content of the stratified layers is normal. This process solves the problem in existing technologies that anomaly detection is only triggered when the moisture content of the stratified layers exceeds the target moisture content range. It enables the system to identify suspected anomalies through the compaction response deviation conditions even when localized overwetting is not directly detected by multi-depth moisture content sensors.

[0037] When the moisture content range determination result of the same compaction grid indicates that the stratified moisture content value is within the target moisture content range, and the compaction response deviation result indicates that the compaction response value meets the compaction response deviation condition, the compaction grid is identified as a contradictory grid. By identifying compaction grids that simultaneously have both moisture content range determination results and compaction response deviation results as contradictory grids, it is possible to form a contradictory judgment where the stratified moisture content value is normal but the compaction response value is abnormal. This solves the problem in existing technologies of lacking the ability to identify inconsistencies between stratified moisture content values ​​and compaction response values, allowing subsequent processing to move beyond directly performing enhanced compaction and instead proceed to the identification process of coverage gap results and abnormal candidate regions.

[0038] Collect contradictory grids to generate a set of contradictory grids.

[0039] Specifically, the compaction response values ​​include the degree of compaction increase, the change in vibratory wheel acceleration, the compaction gauge value, the post-compaction rebound, and the wheel track settlement. The degree of compaction increase characterizes the increase in compaction degree of the same compaction grid between adjacent compaction passes; the change in vibratory wheel acceleration characterizes the change in vibration response after the vibratory wheel of the compaction machinery comes into contact with the soil; the compaction gauge value characterizes the compaction state obtained by the compaction machinery based on the vibration response; the post-compaction rebound characterizes the degree of rebound of the soil surface after the compaction machinery has passed; and the wheel track settlement characterizes the degree of subsidence of the soil surface after the compaction machinery has passed.

[0040] The compaction response deviation conditions include: the compaction degree increase value is lower than the compaction degree increase benchmark value of the same type of compaction grid, or the vibratory wheel acceleration change value is lower than the vibratory wheel acceleration change benchmark value of the same type of compaction grid, or the compactor value is lower than the compactor benchmark value of the same type of compaction grid, or the post-compaction rebound amount is higher than the post-compaction rebound benchmark value of the same type of compaction grid, or the wheel track settlement amount is higher than the wheel track settlement benchmark value of the same type of compaction grid. When the compaction response value meets the compaction response deviation conditions, a compaction response deviation result is generated.

[0041] Similar compaction grids refer to compaction grids with the same soil type, the same compaction operation level, and the same compaction pass range as the current compaction grid; the benchmark values ​​for compaction degree growth, vibratory wheel acceleration change, compactor benchmark value, post-compaction rebound benchmark value, and wheel track settlement benchmark value are determined based on the historical data collection results of similar compaction grids when the layered moisture content is within the target moisture content range and they have not been identified as abnormal compaction grids.

[0042] S3: Based on the contradictory grid set and spatial reference values, generate spatial matching results; based on the spatial matching results, generate coverage gap results; through the spatial matching results, correlate the contradictory grid with the compaction grid location values, sensor deployment location values, and risk location values, thereby generating coverage gap results. This solves the problem in existing technologies of being unable to determine whether anomaly locations in compaction response belong to locations not effectively covered by multi-depth moisture content sensors, avoiding the interpretation of all compaction response anomalies as insufficient compaction, and also avoiding the treatment of all sensor-covered locations as hidden moisture anomalies.

[0043] It should be noted that the method for generating spatial matching results based on the contradictory grid set and spatial reference values ​​includes: the spatial reference values ​​include compaction grid location values, sensor deployment location values, and risk location values ​​corresponding to BIM risk values; by using the compaction grid location values, sensor deployment location values, and risk location values ​​corresponding to BIM risk values ​​as the spatial reference values, the spatial matching results can simultaneously reflect the correspondence between the compaction grid and multi-depth moisture content sensors, as well as the correspondence between the compaction grid and risk locations. This solves the problem in existing technologies where BIM models are only used to record compaction areas, construction layers, and compaction quality data, without further participation in sensor coverage gap identification.

[0044] Based on the compacted grid location value and the sensor deployment location value, it is determined whether the contradictory grids in the contradictory grid set meet the sensing coverage determination conditions, and a sensing coverage matching result is generated. The sensing coverage determination conditions include: when the planar position of the contradictory grid is within the effective detection radius of the multi-depth moisture content sensor, and the target detection layer of the contradictory grid is within the detection depth range of the multi-depth moisture content sensor, it is determined that the sensing coverage determination conditions are met.

[0045] Based on the compaction grid location value and the risk location value, it is determined whether the contradictory grids in the contradictory grid set meet the risk location matching conditions, and a risk location matching result is generated. The risk location matching conditions include: the contradictory grid and the risk location value correspond to the same compaction grid, or the contradictory grid and the compaction grid corresponding to the risk location value are adjacent.

[0046] When the sensor coverage matching result of the same contradictory grid does not meet the sensor coverage judgment condition, but the risk location matching result meets the risk location matching condition, a spatial matching result is generated. By simultaneously satisfying both the sensor coverage judgment condition and the risk location matching condition, the spatial matching result can filter out contradictory grids that have both insufficient sensor coverage and are related to risk locations. This solves the problem in existing technologies where simply judging blind spots based on sensor deployment locations easily expands the anomaly range, and also solves the problem that simply judging risk areas based on BIM risk values ​​cannot confirm whether there are sensor coverage gaps.

[0047] It should be noted that the method for generating coverage gap results based on spatial matching results includes: identifying conflicting grids that do not meet the sensor coverage determination criteria based on the spatial matching results, and designating them as gap grids to be confirmed; by identifying conflicting grids that do not meet the sensor coverage determination criteria as gap grids to be confirmed, it is possible to further filter out compacted grids from the conflicting grid set that may be misjudged due to the lack of coverage by multi-depth moisture content sensors. This solves the problem in existing technologies of difficulty in distinguishing between soil response anomalies despite sensor coverage and missed detections due to localized over-wetting caused by sensor non-coverage.

[0048] The location of the gap to be confirmed is generated based on the compacted grid location value and the sensor deployment location value of the gap grid to be confirmed.

[0049] Based on the risk location matching results, determine whether the gap grid to be confirmed meets the risk location matching conditions.

[0050] When the gap grid to be confirmed meets the risk location matching condition, the gap grid to be confirmed, the gap location to be confirmed, and the risk location matching result are generated as a coverage gap result. By generating a coverage gap result from the gap grid to be confirmed, the gap location to be confirmed, and the risk location matching result together, the coverage gap result can not only represent the detection blank, but also indicate that the detection blank has a corresponding relationship with the risk location. Thus, it solves the problem in the prior art that there are a large number of sensor-uncovered locations but lack risk screening, and makes the subsequent generation of abnormal candidate areas more focused on locations where water bypass infiltration may occur.

[0051] S4: Based on the coverage gap results, BIM risk values, and deep moisture content trend values ​​of adjacent compacted grids, anomaly candidate areas are generated. By using the coverage gap results, BIM risk values, and deep moisture content trend values ​​of adjacent compacted grids together to generate anomaly candidate areas, it is possible to link insufficient sensor coverage, the risk of hidden water infiltration, and changes in deep moisture content of adjacent compacted grids. This solves the problem in existing technologies of lacking joint analysis of compaction response anomalies, BIM spatial risks, and moisture change trends of adjacent grids, avoiding the judgment of hidden water anomalies based solely on a single moisture content value or a single compaction response value.

[0052] It should be noted that the method for generating anomaly candidate areas based on the cover gap results, BIM risk values, and deep moisture content trend values ​​of adjacent compacted grids includes: based on the cover gap results, identifying compacted grids with sensing cover gaps in the contradictory grid set, and generating gap compacted grids.

[0053] Based on the BIM risk values ​​corresponding to the notched compaction grid, the direction of concealed water infiltration corresponding to the notched compaction grid is determined, generating risk direction matching results. By determining the direction of concealed water infiltration based on the BIM risk values ​​corresponding to the notched compaction grid, the water channel characteristics reflected by the location values ​​of construction joints, permeable interlayers, freeze-thaw cracks, and shallow groundwater can be transformed into direction judgments that can be used for compaction grid analysis. This solves the problem in existing technologies where BIM risk values ​​only serve as static risk records and cannot guide the generation of anomaly candidate areas.

[0054] The direction of concealed water infiltration is determined by a combination of factors: the direction from the risk location value corresponding to the BIM risk value to the notched compacted grid; the direction of the risk source extension corresponding to the risk location value, construction joint location value, permeable interlayer location value, freeze-thaw crack location value, or shallow groundwater location value; and the order of increase in deep moisture content trend values ​​of adjacent compacted grids during continuous monitoring. When the risk location value, the direction of the risk source extension, and the order of increase all point to the notched compacted grid, this direction is determined as the direction of concealed water infiltration. This avoids relying solely on static BIM risk values ​​to determine the infiltration direction, improving the consistency between the risk direction matching results and the actual direction of concealed water migration.

[0055] Based on the risk direction matching results, adjacent compacted grids located in the direction of hidden water infiltration are selected, and the deep water content trend values ​​of the adjacent compacted grids are called.

[0056] To avoid misjudging moisture migration along the hidden water infiltration direction based solely on the increasing deep moisture content trend value of a single adjacent compacted grid, adjacent compacted grids along the hidden water infiltration direction include at least two adjacent compacted grids arranged sequentially from the risk location value corresponding to the BIM risk value to the gap compacted grid. If at least two adjacent compacted grids generate a deep moisture content increase determination result within a continuous monitoring period, and the order of the deep moisture content increase is consistent with the hidden water infiltration direction, then it is determined that the deep moisture content increases along the hidden water infiltration direction. Furthermore, adjacent compacted grids along non-hidden water infiltration directions are used as directional comparison objects. If adjacent compacted grids along non-hidden water infiltration directions do not generate a deep moisture content increase determination result within the same continuous monitoring period, it indicates that the deep moisture content increase has directional concentration, which can reduce the risk of misjudgment caused by simultaneous increases in multiple directions due to overall precipitation, widespread melting, or construction disturbance.

[0057] When the deep moisture content trend value of adjacent compacted grids indicates an increase in deep moisture content along the direction of concealed water infiltration, the notched compacted grid and its corresponding adjacent compacted grid are jointly identified as anomaly candidate areas. By determining whether the deep moisture content trend value of adjacent compacted grids increases along the direction of concealed water infiltration, it is possible to verify whether there is a trend of water migration from adjacent compacted grids to the notched compacted grid near the notched compacted grid. This solves the problem in existing technologies where a single moment's moisture content value cannot reflect the direction of water migration, and improves the correspondence between anomaly candidate areas and actual locally over-wet areas.

[0058] S5: Generate verification point layout results based on the abnormal candidate areas, and confirm the abnormal compaction grid based on the layered moisture content verification values ​​corresponding to the verification point layout results, and generate reliable moisture content state values; wherein, the layered moisture content verification values ​​are obtained through temporary insertion moisture content probes, borehole sampling moisture content detection, or on-site rapid moisture content detection devices.

[0059] It should be noted that the method for generating the layout of verification points based on the anomaly candidate area includes: determining the layout order of temporary verification points based on the gap compacted grid in the anomaly candidate area, the corresponding adjacent compacted grid, and the direction of concealed water infiltration; by determining the layout order of temporary verification points based on the gap compacted grid, the corresponding adjacent compacted grid, and the direction of concealed water infiltration, the layout order of temporary verification points can be kept consistent with the formation cause of the anomaly candidate area. This solves the problems in existing technologies where the layout of temporary verification points lacks a basis, and the verification location easily deviates from the location of the sensor coverage gap and the direction of concealed water infiltration.

[0060] Based on the arrangement sequence of temporary verification points, a first temporary verification point is generated within the notched compacted grid, a second temporary verification point is generated within the corresponding adjacent compacted grid, and a third temporary verification point is generated between the notched compacted grid and the corresponding adjacent compacted grid. By generating the first, second, and third temporary verification points within the notched compacted grid, the corresponding adjacent compacted grid, and between the two, respectively, it is possible to simultaneously verify suspected missed detection locations, the source location of deep moisture content trends, and the location of moisture transfer between them. This solves the problem in existing technologies where retesting only a single anomaly point makes it impossible to determine whether localized overwetting is being transmitted along the direction of concealed moisture infiltration.

[0061] The first, second, and third temporary verification points are used to generate verification point layout results, which are then used to collect stratified moisture content verification values.

[0062] The first temporary verification point is used to verify the mid-layer and deep-layer moisture content verification values ​​within the notched compacted grid. The second temporary verification point is used to verify the mid-layer and deep-layer moisture content verification values ​​within the corresponding adjacent compacted grid. The third temporary verification point is used to verify the mid-layer and deep-layer moisture content verification values ​​between the notched compacted grid and the corresponding adjacent compacted grid. Through the above-mentioned stratigraphic verification, it is possible to simultaneously confirm the suspected missed detection locations, the source of the increased deep-layer moisture content, and whether there is excessive moisture in the mid-layer or deep layer in the areas between the two, avoiding the omission of hidden deep-layer moisture anomalies due to only collecting surface moisture content verification values.

[0063] It should be noted that the method for confirming the abnormal compaction grid and generating a reliable moisture content state value based on the layered moisture content verification value corresponding to the verification point layout results includes: obtaining the layered moisture content verification value corresponding to each temporary verification point in the abnormal candidate area based on the verification point layout results.

[0064] Based on the stratified moisture content verification value corresponding to each temporary verification point, determine whether the stratified moisture content verification value is within the target moisture content range.

[0065] If a layered moisture content verification value exceeding the preset target moisture content upper limit exists within the same candidate region for anomalies, the compacted grid corresponding to that candidate region is confirmed as an anomalous compacted grid. By comparing the layered moisture content verification value with the preset target moisture content upper limit, the candidate region for anomalies can be transformed from a suspected anomalous state to a confirmed anomalous state. This solves the problem in existing technologies where localized over-moisture is ignored simply because the original data collected by multi-depth moisture sensors is normal, and also avoids directly assuming localized over-moisture based solely on abnormal compaction response values.

[0066] Specifically, when the mid-layer moisture content verification value corresponding to any one of the first, second, or third temporary verification points exceeds the preset target upper limit, or the deep layer moisture content verification value exceeds the preset target upper limit, it is determined that there is excessive moisture in the mid-layer or deep layer within the candidate area of ​​the anomaly, and the compaction grid corresponding to the candidate area of ​​the anomaly is confirmed as an abnormal compaction grid. This avoids continuing to increase compaction simply because the original data collected by the multi-depth moisture content sensor is normal, and also avoids directly confirming an abnormal compaction grid based on a single abnormal compaction response.

[0067] Based on the layered moisture content verification values ​​corresponding to the abnormal compaction grid, a reliable moisture content state value is generated. By generating this reliable moisture content state value based on the layered moisture content verification values ​​corresponding to the abnormal compaction grid, the moisture content state confirmed by temporary verification points can replace the normal surface state collected by multi-depth moisture content sensors at the deployment location. This solves the problem in existing technologies where compaction parameter optimization results overly rely on fixed sensor data, allowing subsequent compaction parameter optimization results to be generated based on judgments that more closely approximate the true moisture content state of the abnormal compaction grid.

[0068] Specifically, the preset target water content upper limit is determined based on the soil type corresponding to the compacted grid, the target compaction degree, the compaction operation level, the BIM risk value, and the results of pre-construction geotechnical tests. Specifically, the target water content range for the corresponding soil type is first determined through compaction tests, and then the maximum value of this target water content range is used as the initial target water content upper limit. When the BIM risk value corresponding to the compacted grid indicates that the compacted grid is located in a freeze-thaw fissure, permeable interlayer, construction joint, or shallowly buried groundwater area, the initial target water content upper limit is lowered to obtain the preset target water content upper limit. By lowering the initial target water content upper limit when the BIM risk value indicates that the compacted grid is located in a freeze-thaw fissure, permeable interlayer, construction joint, or shallowly buried groundwater area, the preset target water content upper limit can be tightened according to the risk of hidden water infiltration into the compacted grid. This solves the problem in existing technologies where using the same target water content upper limit for different risk locations leads to a lag in the identification of overly wet compacted grids at high risks. When the compacted grid does not belong to the above-mentioned risk areas, the initial target water content upper limit is used as the preset target water content upper limit.

[0069] For example, for compacted grids of silty clay, if the target moisture content determined by the pre-construction compaction test is between 14% and 17%, the initial target moisture content upper limit is 17%. If the BIM risk value corresponding to the compacted grid indicates that it is located in a construction joint or a shallowly buried groundwater area, then the preset target moisture content upper limit is set to 16.5%. If the compacted grid does not belong to a construction joint, a shallowly buried groundwater area, a freeze-thaw crack, or a permeable interlayer area, then the preset target moisture content upper limit is set to 17%. For compacted grids of silty sand, if the target moisture content determined by the pre-construction compaction test is between 10% and 13%, the preset target moisture content upper limit can be set to 13%. If the BIM risk value corresponding to the compacted grid is generated by the result of co-location matching or directional adjacency matching, then the preset target moisture content upper limit can be set to 12.5%. For compacted clay grids, if the target moisture content range determined by the compaction test before construction is 16% to 20%, the upper limit of the preset target moisture content can be set to 20%. If the BIM risk value corresponding to the compacted grid is generated by the matching results of the same layer or the matching results of the directional adjacency, the upper limit of the preset target moisture content can be set to 19%.

[0070] When confirming abnormal compaction grids based on the layered moisture content verification values ​​corresponding to the verification point layout results, if the mid-layer moisture content verification value or deep-layer moisture content verification value of any temporary verification point in the same abnormal candidate area is higher than the preset target moisture content upper limit of the corresponding compaction grid, then the compaction grid corresponding to the abnormal candidate area is confirmed as an abnormal compaction grid.

[0071] S6: Based on the reliable moisture content state value, generate the compaction parameter optimization results of the abnormal compaction grid. The compaction parameter optimization results include enhanced compaction pause marker, compaction strength, vibration frequency, working speed, number of compaction passes, and recompaction path.

[0072] It should be noted that the method for generating compaction parameter optimization results of abnormal compaction grids based on reliable moisture content state values ​​includes: determining whether the layered moisture content verification value corresponding to the abnormal compaction grid is greater than the preset target moisture content upper limit based on the reliable moisture content state value.

[0073] If the layer moisture content verification value corresponding to the abnormally compacted grid is greater than the preset target moisture content upper limit, an enhanced compaction pause flag is generated in the compaction parameter optimization results. By generating an enhanced compaction pause flag when the layer moisture content verification value is greater than the preset target moisture content upper limit, the system can prevent the abnormally compacted grid from continuing to be treated as a compaction grid requiring enhanced compaction. This solves the problem in the prior art where locally excessively wet areas are misjudged as insufficiently compacted due to abnormal compaction response, leading to further increases in compaction intensity, vibration frequency, or number of compaction passes.

[0074] Based on the enhanced compaction pause flag, enhanced compaction control is paused on abnormally compacted grids, and the compaction strength, vibration frequency, and number of compaction passes are set to remain unchanged. The operating speed is not used as an enhanced compaction parameter to further reduce it, or the original supplementary compaction control strategy is maintained. By pausing enhanced compaction control on abnormally compacted grids, setting the compaction strength, vibration frequency, and number of compaction passes to remain unchanged, and not using the operating speed as an enhanced compaction parameter to further reduce it, or maintaining the original supplementary compaction control strategy, the compaction parameter optimization result can be transformed from enhanced compaction to protective control. This solves the problem in existing technologies where applying greater compaction to locally overly wet areas increases the risk of springy soil, localized subsidence, frost heave, thaw settlement, and subsequent frost heave.

[0075] Based on the enhanced compaction pause indicator, the supplementary compaction path is set to avoid abnormal compaction grids. By setting the supplementary compaction path to avoid abnormal compaction grids, it is possible to prevent compaction machinery from repeatedly compacting within compaction grids where hidden moisture anomalies have been confirmed. This solves the problem in existing technologies where the supplementary compaction path is generated solely based on the result of insufficient compaction, failing to distinguish between insufficient compaction and hidden moisture anomalies, thus leading to repeated compaction of locally overly wet areas.

[0076] When an enhanced compaction pause flag is generated in the compaction parameter optimization results, the abnormal compaction grid is not excluded from the compaction process indefinitely. Instead, an abnormal treatment flag is generated based on the stratified moisture content verification value corresponding to the abnormal compaction grid. If the stratified moisture content verification value is greater than the preset target moisture content upper limit but does not exceed 0.5 percentage points, a static treatment flag or a drying treatment flag is generated. If the stratified moisture content verification value exceeds the preset target moisture content upper limit by more than 0.5 percentage points, a drainage treatment flag is generated. If the abnormal compaction grid still cannot bring the stratified moisture content verification value back to the target moisture content range after drainage treatment, drying treatment, or static treatment, a replacement treatment flag is generated. After completing the treatment corresponding to the abnormal treatment flag, the stratified moisture content verification value and compaction response value of the abnormal compaction grid are obtained again. When the re-acquired stratified moisture content verification value is within the target moisture content range, and the re-acquired compaction response value does not generate a compaction response deviation result, a recovery compaction indicator is generated. Based on the recovery compaction indicator, the compaction path is reset to pass through the abnormal compaction grid, and the compaction strength, vibration frequency, working speed, and number of compaction passes are regenerated. This enables the abnormal compaction grid to form a closed loop from abnormality confirmation, suspension of enhanced compaction, abnormality handling, re-measurement, to recovery compaction, avoiding simply suspending enhanced compaction without subsequent recovery judgment.

[0077] Based on the enhanced compaction pause indicator, the set compaction strength, vibration frequency, working speed, number of compaction passes, and recompaction path, the optimized compaction parameters are generated.

[0078] See Figure 2 Taking the roadbed construction area as the display object, the compaction operation area is divided into multiple compaction grids, and grid numbers are set in the horizontal and vertical directions of the compaction grids, so that each compaction grid can correspond to the compaction grid location value, layer moisture content value, compaction response value, reliable moisture content state value and compaction parameter optimization result. Figure 2 In the display, different display states are used to represent the compaction quality status of each compaction grid. The excellent state indicates that the layered moisture content of the compaction grid is within the target moisture content range and the compaction response value does not meet the compaction response deviation condition. The qualified state indicates that the compaction grid meets the current construction and acceptance requirements, but the compaction response change still needs to be tracked according to the compaction parameter optimization results. The low state indicates that the compaction grid has a compaction response deviation result, or the corresponding compaction parameter optimization result needs to be adjusted.

[0079] Furthermore, Figure 2The "low" state in the compaction grid does not directly indicate the need for enhanced compaction, but is distinguished based on the reliable moisture content state value generated by this invention. When the layered moisture content verification value of the low-state compaction grid is not greater than the preset target moisture content upper limit, the cloud management platform can treat it as a regular supplementary compaction grid and generate a supplementary compaction path based on compaction strength, vibration frequency, operating speed, and number of compaction passes. When the low-state compaction grid is identified as an abnormal compaction grid, and the reliable moisture content state value indicates that the layered moisture content verification value is greater than the preset target moisture content upper limit, the cloud management platform displays an enhanced compaction pause indicator in the corresponding compaction grid details of the compaction quality display results and sets the supplementary compaction path to avoid the abnormal compaction grid. Thus, Figure 2 It is used not only to display the compaction quality grade, but also to distinguish between low compaction caused by insufficient compaction and low compaction caused by hidden moisture abnormalities, so as to prevent the system from misjudging local over-wet areas as areas that need further compaction.

[0080] See Figure 3 , Figure 3 The horizontal axis represents the verified moisture content of the strata corresponding to the abnormal compaction grid, and the vertical axis represents the frost heave rate. Curves A, B, and C respectively represent the influence of changes in the verified moisture content of the strata on the frost heave rate under different compaction conditions. Specifically, curve A represents the change in frost heave rate when the compaction degree is 85%, curve B represents the change in frost heave rate when the compaction degree is 90%, and curve C represents the change in frost heave rate when the compaction degree is 95%.

[0081] Depend on Figure 3 It can be seen that, under the same compaction conditions, the frost heave rate increases with the increase of the layer moisture content verification value. When the layer moisture content verification value is in a low range, the frost heave rate increases slowly. However, as the layer moisture content verification value further increases, the frost heave rate increases significantly, indicating that increased moisture content increases the risk of frost heave deformation in roadbeds in seasonally frozen soil areas. The frost heave rate characterizes the degree of frost heave deformation of the compacted grid under freezing conditions. A higher frost heave rate indicates a higher risk of frost heave deformation for the compacted grid. The risks of thaw settlement, frost heave, or uneven pavement deformation can be assessed by combining the layer moisture content verification value, compaction response value, and BIM risk value.

[0082] Furthermore, the cloud-based management platform can compare the layered moisture content verification values ​​of abnormally compacted grids with the preset target moisture content upper limit, and combine this with... Figure 3The relationship of frost heave rate changes shown generates a reference display result for the frost heave rate corresponding to the abnormal compaction grid. The reference display result is used to help display the frost heave deformation trend of the abnormal compaction grid under the corresponding layer moisture content verification value and compaction degree conditions. When the layer moisture content verification value of the abnormal compaction grid is greater than the preset target moisture content upper limit, and the abnormal compaction grid also has the following conditions: compaction response deviation, BIM risk value meets the preset risk concern conditions, and the deep moisture content of adjacent compaction grids increases along the direction of hidden water infiltration, the cloud management platform generates an enhanced compaction pause mark and sets the compaction path to avoid the abnormal compaction grid, so as to avoid continuing to enhance compaction under local excessive moisture conditions and thus expanding the risk of frost heave, frost heave, or thaw settlement.

[0083] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the method for optimizing the compaction parameters of highway subgrade in seasonally frozen soil areas and the inventive concept, should be covered within the scope of protection of the present invention.

Claims

1. A method for optimizing subgrade compaction parameters for highways in seasonally frozen soil regions, characterized in that, Includes the following steps: Obtain the grid analysis values ​​of the compacted grid, including the layered moisture content value, compaction response value, spatial benchmark value, BIM risk value, and deep moisture content trend value of adjacent compacted grids; Based on the layered moisture content and compaction response values ​​of the compacted grid, determine whether there are conflicting grids; If it exists, generate a set of contradictory meshes; Based on the contradictory grid set and spatial benchmark value, spatial matching results are generated; Based on the spatial matching results, generate the coverage gap results; Based on the cover gap results, BIM risk values, and deep moisture content trend values ​​of adjacent compacted grids, anomaly candidate areas are generated; Based on the candidate abnormal regions, the result of the verification point layout is generated. Based on the layered moisture content verification value corresponding to the verification point layout result, the abnormal compaction grid is confirmed and the reliable moisture content state value is generated. Based on the reliable moisture content state value, the compaction parameter optimization results of the abnormal compaction grid are generated; Methods for generating spatial matching results include: Spatial reference values ​​include compacted grid location values, sensor deployment location values, and risk location values; Based on the compacted grid location value and the sensor deployment location value, determine whether the contradictory grids in the contradictory grid set meet the sensor coverage determination condition, and generate sensor coverage matching results. Based on the compaction grid location value and the risk location value, determine whether the contradictory grids in the contradictory grid set meet the risk location matching condition, and generate the risk location matching result; When the sensor coverage matching result representation of the same conflicting grid does not meet the sensor coverage determination condition, but the risk location matching result representation meets the risk location matching condition, a spatial matching result is generated. Methods for generating coverage gap results include: Based on the spatial matching results, contradictory grids that do not meet the sensing coverage determination conditions are identified and treated as gap grids to be confirmed. Based on the compacted grid location value and sensor deployment location value of the notch grid to be confirmed, the notch location to be confirmed is generated; Based on the risk location matching results, determine whether the gap grid to be confirmed meets the risk location matching conditions; When the gap mesh to be confirmed meets the risk location matching condition, the gap mesh to be confirmed, the gap location to be confirmed, and the risk location matching result are generated as a coverage gap result; Methods for generating BIM risk values ​​include: Based on the compaction grid location value, obtain the construction joint location value, permeable interlayer location value, freeze-thaw crack location value and shallow groundwater location value corresponding to the compaction grid location value; Based on the location values ​​of construction joints, permeable interlayers, freeze-thaw cracks, and shallow groundwater, risk values ​​for construction joints, permeable interlayers, freeze-thaw cracks, and shallow groundwater are generated respectively. Based on the risk values ​​of construction joints, permeable interlayers, freeze-thaw cracks, and shallow groundwater, BIM risk values ​​corresponding to the compaction grid are generated. The location values ​​that correspond to the BIM risk values ​​among the construction joint location values, permeable interlayer location values, freeze-thaw crack location values, and shallow groundwater location values ​​are determined as risk location values.

2. The method for optimizing subgrade compaction parameters for highways in seasonally frozen soil regions according to claim 1, characterized in that, Based on the cover gap results, BIM risk values, and deep moisture content trend values ​​of adjacent compacted grids, methods for generating anomaly candidate regions include: Based on the coverage gap results, the compacted grids with sensor coverage gaps are identified, and the gap compacted grids are generated. Based on the BIM risk values ​​corresponding to the gap compaction grid, the direction of hidden water infiltration is determined, and risk direction matching results are generated. Based on the risk direction matching results, adjacent compacted grids are selected, and the deep water content trend values ​​of adjacent compacted grids are called. When the deep water content trend value of adjacent compacted grids indicates that the deep water content is increasing along the direction of hidden water infiltration, the gap compacted grid and the corresponding adjacent compacted grid are jointly identified as anomaly candidate areas.

3. The method for optimizing subgrade compaction parameters for highways in seasonally frozen soil regions according to claim 1, characterized in that, Methods for determining the existence of contradictory grids include: Based on the layered moisture content values ​​of the compacted grid, it is determined whether the layered moisture content values ​​are within the target moisture content range, and a moisture content range determination result is generated. Based on the compaction response values ​​of the same compaction grid, determine whether the compaction response values ​​meet the compaction response deviation conditions, and generate compaction response deviation results; When the moisture content range determination result of the same compaction grid indicates that the stratified moisture content value is within the target moisture content range, and the compaction response deviation result indicates that the compaction response value meets the compaction response deviation condition, the compaction grid is determined as a conflict grid. Collect contradictory grids to generate a set of contradictory grids.

4. The method for optimizing subgrade compaction parameters for highways in seasonally frozen soil regions according to claim 2, characterized in that, Methods for generating verification point deployment results based on abnormal candidate regions include: Based on the gap compacted grid in the abnormal candidate area, the corresponding adjacent compacted grid, and the direction of hidden water infiltration, the order of temporary verification point layout is determined. Based on the arrangement order of temporary verification points, the first temporary verification point is generated in the gap compaction grid, the second temporary verification point is generated in the corresponding adjacent compaction grid, and the third temporary verification point is generated between the gap compaction grid and the corresponding adjacent compaction grid. The first, second, and third temporary verification points are used to generate verification point layout results, which are then used to collect stratified moisture content verification values.

5. The method for optimizing subgrade compaction parameters for highways in seasonally frozen soil regions according to claim 1, characterized in that, Methods for generating reliable moisture content state values ​​include: Based on the results of the verification point layout, the layered moisture content verification value corresponding to each temporary verification point in the abnormal candidate area is obtained; Based on the stratified moisture content verification value corresponding to each temporary verification point, determine whether the stratified moisture content verification value is within the target moisture content range; If there is a stratified moisture content verification value that is greater than the preset target moisture content upper limit within the same abnormal candidate area, the compaction grid corresponding to the abnormal candidate area is confirmed as an abnormal compaction grid. Based on the layered moisture content verification values ​​corresponding to the abnormally compacted grid, a reliable moisture content state value is generated.

6. The method for optimizing subgrade compaction parameters for highways in seasonally frozen soil regions according to claim 5, characterized in that, Methods for generating compaction parameter optimization results for abnormal compaction grids based on reliable moisture content state values ​​include: Based on the reliable moisture content state value, determine whether the layered moisture content verification value corresponding to the abnormal compaction grid is greater than the preset target moisture content upper limit; If the layered moisture content verification value corresponding to the abnormal compaction grid is greater than the preset target moisture content upper limit, an enhanced compaction pause marker will be generated in the compaction parameter optimization results. Based on the enhanced compaction pause flag, the enhanced compaction control for abnormal compaction grids is paused, and the compaction strength, vibration frequency and number of compaction passes are set to not increase, while the operation speed is maintained by the original supplementary compaction control strategy. Based on the enhanced compaction pause indicator, the compaction path is set to avoid abnormal compaction grids; Based on the enhanced compaction pause indicator, the set compaction strength, vibration frequency, working speed, number of compaction passes, and recompaction path, the optimized compaction parameters are generated.

7. The method for optimizing subgrade compaction parameters for highways in seasonally frozen soil regions according to claim 1, characterized in that, Methods for generating deep water content trend values ​​of adjacent compacted grids include: Based on the set of contradictory grids, determine the adjacent compacted grids that are adjacent to the contradictory grids; Obtain the deep water content values ​​of adjacent compacted grids during a continuous detection period, and generate the time series values ​​of deep water content of adjacent compacted grids; Based on the time series value of deep moisture content, it is determined whether the deep moisture content value of adjacent compacted grids increases with continuous detection period, and a deep moisture content increase determination result is generated. Based on the determination of increased deep water content, the deep water content trend value of adjacent compacted grids is generated.

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