A method, system and device for monitoring the settlement of a road pavement and subgrade

Abstract

This invention relates to the field of highway maintenance and structural health monitoring technology, specifically to a method, system, and device for monitoring pavement and subgrade settlement. The invention analyzes the settlement distribution weight based on the deviation of the collected elevation values ​​of the monitoring points from a preset elevation benchmark. Based on the settlement distribution weights of all monitoring points on the current monitoring day and historical adjacent monitoring days, as well as the distance distribution between every two monitoring points, a settlement linkage influence matrix is ​​constructed. The influence of adjacent monitoring points on the evolution of the monitoring point under analysis is analyzed. Combined with the moisture content increase of the monitoring point under analysis on the current monitoring day, the degree of subgrade erosion is obtained. Then, combined with the cumulative subgrade erosion degree on historical adjacent monitoring days, the cumulative degree of subgrade erosion at the monitoring point under analysis on the current monitoring day is determined. Based on the cumulative degree of subgrade erosion at the monitoring point under analysis on the current and historical monitoring days, the subgrade erosion change rate is analyzed, the subgrade erosion state is assessed, and the subgrade settlement monitoring results for the corresponding road section are obtained.

Description

Technical Field

[0001] This invention relates to the field of highway maintenance and structural health monitoring technology, specifically to a method, system, and device for monitoring pavement and subgrade settlement. Background Technology

[0002] In highway maintenance projects, displacement sensors and temperature and humidity sensors are usually installed under the pavement to monitor the loss of structural support and settlement at the bottom of the roadbed by calculating the elevation changes of each measuring point.

[0003] In the actual operation of heavy-load trunk highways (especially in loess geological areas), there are two objective factors that interfere with the measurement of actual settlement. First, the compaction of the road surface by heavy vehicles generates short-term high-frequency vibrations and elastic fluctuations, and directly calculating the mean of a continuous elevation sequence will introduce significant dynamic load fluctuation errors. Second, after absorbing natural precipitation, the expansion of the loess soil volume will cause the entire road section to rise. This large-scale physical uplift will mask the actual downward settlement caused by local material loss in the roadbed. Existing technologies usually directly compare the absolute difference in elevation of a single measuring point, or rely on prior geological parameters (such as porosity, compaction degree, etc.) to construct a soil theoretical formula to deduct the expansion. However, the elevation difference of a single measuring point cannot measure the cascading subsidence of adjacent road surfaces caused by deep local collapse; and models relying on bottom-layer parameters are extremely difficult to implement in field environments lacking geological data and are prone to failure. Therefore, how to accurately extract the uneven settlement characteristics that characterize the loss of local support of the roadbed from an environment of strong vibration and strong hydrological disturbance without relying on the prior parameters of the underlying geology is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0004] To address the technical problem of inaccurate roadbed settlement monitoring caused by the masking of true roadbed settlement characteristics due to heavy traffic fluctuations and loess water absorption and expansion, this invention aims to provide a method, system, and device for monitoring roadbed settlement. The specific technical solution adopted is as follows: This invention proposes a method for monitoring roadbed settlement, the method comprising: Acquire the elevation and moisture content increments of the monitoring point to be analyzed and other monitoring points within the current monitoring day; Based on the degree of deviation between the elevation acquisition value of the measurement point to be analyzed and the corresponding preset elevation benchmark value, and combined with the degree of deviation of all measurement points, the settlement distribution weight of the measurement point to be analyzed is obtained. Based on the settlement distribution weights of all monitoring points on the current monitoring day and historical adjacent monitoring days, and combined with the distance distribution between each pair of monitoring points, a settlement linkage influence matrix is ​​constructed; based on the elements corresponding to the monitoring point to be analyzed and adjacent monitoring points in the settlement linkage influence matrix, the degree of influence of adjacent monitoring points on the evolution of the monitoring point to be analyzed is obtained. Based on the degree of evolutionary influence and the increase in moisture content, the degree of subgrade loss at the monitoring point to be analyzed on the current monitoring day is obtained. Combined with the cumulative subgrade loss on historical adjacent monitoring days, the cumulative degree of subgrade loss at the monitoring point to be analyzed on the current monitoring day is obtained. Based on the cumulative degree of subgrade loss at the monitoring points under analysis on the current monitoring day and historical monitoring days, the subgrade loss change rate is analyzed; the subgrade loss change rate is used to analyze the subgrade loss status of the road section, and the subgrade settlement monitoring results of the corresponding road section of the monitoring points under analysis are obtained.

[0005] Furthermore, based on the deviation between the elevation acquisition value of the measurement point to be analyzed and the corresponding preset elevation benchmark value, and combined with the deviation of all measurement points, the settlement distribution weight of the measurement point to be analyzed is obtained, including: Extract all elevation data collected by the monitoring point to be analyzed within the current monitoring day, and take the elevation data collected at the medium level as the median elevation value of the monitoring point on a single day; The degree of deviation of the measurement point to be analyzed is obtained based on the difference between the median value of the daily elevation and the corresponding preset elevation benchmark value. The settlement distribution weight of the measurement point to be analyzed is obtained based on the proportion of the deviation degree of the measurement point to all the measurement points.

[0006] Furthermore, based on the settlement distribution weights of all monitoring points on the current monitoring day and historical adjacent monitoring days, and combined with the distance distribution between every two monitoring points, a settlement linkage influence matrix is ​​constructed, including: The settlement distribution weight sequence for the current monitoring day is constructed using the settlement distribution weights of all monitoring points on the current monitoring day, and the settlement distribution weight sequence for historical adjacent monitoring days is constructed using the settlement distribution weights of all monitoring points on historical adjacent monitoring days. Based on the distribution characteristics of the road segment distance between every two measuring points, a settlement correlation matrix is ​​constructed; The settlement distribution weight sequence of the current monitoring day is used as the target distribution boundary condition, and the settlement distribution weight sequence of adjacent historical monitoring days is used as the source distribution boundary condition. The settlement correlation matrix is ​​processed by the scaling iteration algorithm to obtain the settlement linkage influence matrix.

[0007] Furthermore, based on the distribution characteristics of the road segment distances between every two measuring points, a settlement correlation matrix is ​​constructed, including: Any two different measuring points are combined into a measuring point pair. The ratio of the road segment distance between the two measuring points in the measuring point pair to the total length of the entire road segment is taken as the distance value of the measuring point pair. Construct a spatial distance matrix based on the distance values ​​of all measurement point pairs; The spatial distance matrix is ​​subjected to nonlinear mapping to obtain the settlement correlation matrix.

[0008] Furthermore, based on the elements corresponding to the measurement point to be analyzed and adjacent measurement points in the settlement linkage influence matrix, the degree of influence of adjacent measurement points on the evolution of the measurement point to be analyzed is obtained, including: The elements of the settlement linkage effect matrix are the linkage settlement percentages between every two measuring points; The maximum value of the joint settlement ratio between the adjacent measuring points and the measuring point to be analyzed is calculated by comparing it with the joint settlement ratio of the measuring point to be analyzed on itself, thus obtaining the degree of influence of the adjacent measuring points on the evolution of the measuring point to be analyzed.

[0009] Furthermore, based on the degree of evolutionary influence and the increase in moisture content, the degree of subgrade erosion at the monitoring point to be analyzed on the current monitoring day is obtained. Combined with the cumulative subgrade erosion on historical adjacent monitoring days, the cumulative degree of subgrade erosion at the monitoring point to be analyzed on the current monitoring day is obtained, including: Based on the inner product of the settlement-related influence matrix and the spatial distance matrix, the evolution characteristics of the entire road segment are obtained. Based on the product of the evolution characteristics of the entire road segment and the degree of influence of the evolution, and the increase in the moisture content of the monitoring point to be analyzed on the current monitoring day, the degree of subgrade loss of the monitoring point to be analyzed on the current monitoring day is obtained. The cumulative degree of roadbed loss on the current monitoring day is obtained by adding the degree of roadbed loss of the monitoring point to the cumulative degree of roadbed loss on the historical adjacent monitoring days.

[0010] Furthermore, based on the cumulative degree of subgrade loss at the monitoring points under analysis on the current monitoring day and historical monitoring days, the subgrade loss change rate is analyzed, including: The cumulative degree of roadbed loss of the monitoring point to be analyzed is obtained for each monitoring day within the preset backtracking window of the current monitoring day. Linear analysis is then performed to obtain the roadbed loss change rate of the monitoring point to be analyzed.

[0011] Furthermore, the subgrade erosion change rate is used to analyze the subgrade erosion state of the road segment, and the subgrade settlement monitoring results of the road segment corresponding to the analysis point are obtained, including: The difference between the rate of change of roadbed loss at the measurement point to be analyzed and the preset benchmark rate of change is taken as the rate of change increment; If the increment of the rate of change is less than the preset allowable safety threshold, the settlement monitoring result of the road section corresponding to the monitoring point to be analyzed is normal; if the increment of the rate of change is greater than or equal to the preset allowable safety threshold, the settlement monitoring result of the road section corresponding to the monitoring point to be analyzed indicates a tendency to collapse.

[0012] The present invention also proposes a roadbed settlement monitoring system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any of the roadbed settlement monitoring methods described above.

[0013] The present invention also proposes a roadbed settlement monitoring device, the device comprising: The data extraction module is used to obtain the elevation and moisture content increments of the monitoring point to be analyzed and other monitoring points within the current monitoring day. The settlement distribution weight analysis module is used to obtain the settlement distribution weight of the measurement point to be analyzed based on the degree of deviation between the elevation acquisition value of the measurement point to be analyzed and the corresponding preset elevation benchmark value, combined with the degree of deviation of all measurement points. The evolution impact analysis module is used to construct a settlement linkage impact matrix based on the settlement distribution weights of all monitoring points on the current monitoring day and historical adjacent monitoring days, combined with the distance distribution between each pair of monitoring points; and to obtain the degree of evolution impact of adjacent monitoring points on the monitoring point to be analyzed based on the elements corresponding to the monitoring point to be analyzed and adjacent monitoring points in the settlement linkage impact matrix. The roadbed loss analysis module is used to obtain the degree of roadbed loss of the monitoring point to be analyzed on the current monitoring day based on the degree of evolution influence and the increase in moisture content, and to obtain the cumulative degree of roadbed loss of the monitoring point to be analyzed on the current monitoring day by combining the cumulative degree of roadbed loss of adjacent historical monitoring days. The monitoring result acquisition module is used to analyze the roadbed loss change rate based on the cumulative degree of roadbed loss at the monitoring point to be analyzed on the current monitoring day and historical monitoring days; and to analyze the roadbed loss status of the road section using the roadbed loss change rate to obtain the roadbed settlement monitoring results of the road section corresponding to the monitoring point to be analyzed.

[0014] The present invention has the following beneficial effects: This invention, based on the deviation between the elevation data collected at the point to be analyzed and the corresponding preset elevation benchmark, and combining the deviation values ​​of all measuring points, obtains the settlement distribution weight of the point to be analyzed, upgrading point-to-point settlement comparison to an evolutionary analysis of spatial collapse structures. Based on the settlement distribution weights of all measuring points on the current monitoring day and historical adjacent monitoring days, and combined with the distance distribution between every two measuring points, a settlement linkage influence matrix is ​​constructed. This matrix reflects the proportion of linkage settlement at each measuring point caused by the settlement and collapse of surrounding measuring points, reflecting the traction force transmitted from local collapsed road sections to surrounding road sections. Based on the elements corresponding to the point to be analyzed and adjacent measuring points in the settlement linkage influence matrix, the degree of influence of adjacent measuring points on the evolution of the point to be analyzed is obtained, thus analyzing the stability of the roadbed structure of the point to be analyzed.

[0015] Based on the degree of evolutionary influence and the increase in moisture content, the degree of subgrade loss at the monitoring point to be analyzed on the current monitoring day is obtained, thus reflecting the actual subgrade settlement and collapse characteristics occurring at the monitoring point on the current monitoring day. Combined with the cumulative subgrade loss on historical adjacent monitoring days, the cumulative degree of subgrade loss at the monitoring point to be analyzed on the current monitoring day is obtained, and the collapse risk of the corresponding road section at the monitoring point to be analyzed is assessed.

[0016] Based on the cumulative degree of subgrade loss at the monitoring points under analysis on the current monitoring day and historical monitoring days, the rate of change of subgrade loss is analyzed to determine the trend of subgrade loss. The subgrade loss status of the road section is analyzed using the rate of change of subgrade loss, and the subgrade settlement monitoring results of the corresponding road section are obtained.

[0017] This invention eliminates the masking effect of heavy traffic fluctuations and loess water absorption and expansion on the true settlement characteristics of the roadbed. The obtained roadbed settlement monitoring results can accurately determine the collapse risk of road sections before macroscopic collapse occurs, providing a highly forward-looking dynamic early warning. Attached Figure Description

[0018] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a method for monitoring road and subgrade settlement according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for obtaining a settlement-related influence matrix according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating a method for obtaining the cumulative degree of roadbed erosion according to an embodiment of the present invention. Detailed Implementation

[0020] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a roadbed settlement monitoring method, system, and apparatus according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0022] The following description, in conjunction with the accompanying drawings, details the specific scheme of a roadbed and subgrade settlement monitoring method, system, and device provided by the present invention.

[0023] Please see Figure 1 The diagram illustrates a flowchart of a roadbed settlement monitoring method according to an embodiment of the present invention, the method comprising: Step S101: Obtain the elevation and moisture content increments of the measurement point to be analyzed and other measurement points within the current monitoring day.

[0024] In order to achieve accurate assessment of road conditions and precise location of road sections at risk of collapse, it is essential to analyze the roadbed elevation and soil moisture content of each road section. Therefore, the steps in this embodiment provide the data foundation for subsequent steps.

[0025] Throughout the entire road section, multiple measuring points are divided at preset equidistant intervals. At each measuring point, a displacement sensor and a temperature and humidity sensor are deployed. Along the longitudinal direction of the highway, all measuring points are sorted according to their physical location, and each point is assigned a positive integer starting from 1 as a spatial index number. The displacement sensor and temperature and humidity sensor sample the instantaneous elevation and instantaneous soil moisture content of each measuring point throughout the day at a preset sampling frequency. This embodiment uses a calendar day as the monitoring cycle, collecting the instantaneous elevation and instantaneous soil moisture content of all measuring points in real time each monitoring day.

[0026] In this embodiment, considering that dense vehicle traffic can cause short-term high-frequency fluctuations in the road surface, if the collected instantaneous elevation values ​​and instantaneous soil moisture content are directly used to analyze the road condition, the actual roadbed settlement data will be masked by the elevation fluctuation errors caused by vehicles. Therefore, in order to ensure an accurate assessment of the road condition, it is necessary to analyze the instantaneous elevation values ​​and instantaneous soil moisture content of the road section when no vehicles are passing by.

[0027] Specifically, taking a monitoring point to be analyzed on the current monitoring day as an example, a time sliding window is preset. The variance of all instantaneous elevation values ​​collected in real time within the time sliding window is calculated and compared with a preset dynamic threshold. This dynamic threshold represents the maximum tolerance range for road surface elevation fluctuations caused by background environmental factors other than vehicle traffic. If the data variance within the time sliding window is greater than the preset dynamic threshold, it means that the road segment corresponding to the monitoring point is experiencing high-frequency fluctuations caused by passing vehicles within the time sliding window, and all instantaneous elevation values ​​within the time sliding window are removed. If the data variance within the time sliding window is less than or equal to the preset dynamic threshold, it means that the road segment corresponding to the monitoring point is in a state of no vehicle traffic within the time sliding window, and all instantaneous elevation values ​​within the time sliding window are retained.

[0028] Simply relying on the fact that the variance of the data within the time sliding window is within the maximum tolerance range is not enough to completely confirm whether the elevation fluctuations of the road surface have completely stopped (the road surface may be in the process of extremely slow position recovery). Therefore, it is necessary to further determine the rate of change of the aforementioned retained instantaneous elevation values.

[0029] Examine the three consecutive time sliding windows and determine whether the data variance within each of the three time sliding windows meets the condition of being less than or equal to the preset dynamic threshold. If it does, calculate the set of absolute values ​​of the first time derivatives of adjacent instantaneous elevation values ​​within the three time sliding windows; if it does not, shift the current time sliding window forward and continue to perform the above judgment on newly flowing time sliding windows.

[0030] The maximum value in the set of absolute values ​​of the first-order time derivative is extracted and compared with a preset undulation stopping threshold, which represents the maximum allowable deformation rate at which the road surface returns to rest. If the maximum value is less than the preset undulation stopping threshold, it indicates that the road surface elevation change of the road segment corresponding to the analysis point has completely stopped, reaching a vehicle-free and stationary state; otherwise, it proves that the road segment corresponding to the analysis point has not reached a vehicle-free and stationary state.

[0031] The moment when the third time sliding window of the three time sliding windows that achieve the vehicle-free stationary state ends is established as the exact moment when the vehicle-free stationary state is achieved. The instantaneous elevation value corresponding to this exact moment is extracted as the elevation acquisition value of the measurement point to be analyzed, and the instantaneous soil moisture content at the moment corresponding to the elevation acquisition value is taken as the moisture content acquisition value. There may be several elevation acquisition values ​​and several corresponding moisture content acquisition values ​​for the measurement point to be analyzed on the current monitoring day. The elevation acquisition value is used to subsequently obtain the daily median elevation value of the measurement point to be analyzed on the current monitoring day, and the moisture content acquisition value is used to obtain the moisture content increment of the measurement point to be analyzed on the current monitoring day.

[0032] Specifically, the arithmetic mean of all moisture content collected at the monitoring point to be analyzed on the current monitoring day is calculated to obtain the daily moisture content of the monitoring point to be analyzed on the current monitoring day. The difference between the daily moisture content of the current monitoring day and the daily moisture content of adjacent historical monitoring days is taken as the moisture content increment of the monitoring point to be analyzed on the current monitoring day.

[0033] Since there is no historical daily moisture content of adjacent monitoring days as a reference for the daily moisture content of the first monitoring day, the moisture content increment of all measuring points within the first monitoring day is assigned to 0.

[0034] In one implementation of this invention, the preset equidistant spacing is set to 30m, the preset sampling frequency is set to 100Hz, the preset time sliding window is set to 1 minute, and the preset fluctuation stopping threshold is set to 0.01mm / s. The preset parameters of this invention are empirical values ​​obtained through experiments using a large amount of historical data.

[0035] In one implementation of this invention, the preset dynamic threshold acquisition method is as follows: For any given monitoring day, extract the variance of the instantaneous elevation values ​​of each measuring point within the above-mentioned time sliding window during the period from 0:00 to 4:00 AM, take the minimum value as the benchmark value of the vehicle-free environment elevation, multiply it by the preset environmental tolerance coefficient, and obtain the preset dynamic threshold for the corresponding monitoring day.

[0036] The period from 00:00 to 04:00 was chosen as the baseline data extraction window because heavy traffic flow is at its lowest point during this time, minimizing external human-induced vibration interference. The data variance calculated within this period accurately represents the minimum value of inherent vibrations caused by environmental factors such as sensor noise and ground pulsation. Establishing this minimum value as the baseline threshold is equivalent to setting a clean noise background. Using this as a reference, high-frequency fluctuation interference caused by passing vehicles can be accurately identified and eliminated, ensuring the purity and effectiveness of the monitoring data.

[0037] Due to the influence of temperature changes, wind loads, and surrounding activities during the daytime, the natural background noise of the road surface is usually slightly higher than that during the early morning hours. To prevent the judgment conditions from being too stringent and thus failing to obtain effective data during the day, this implementation introduces a preset environmental tolerance coefficient, which is a floating-point number greater than 1. In this embodiment, it is set to 1.5.

[0038] Step S102: Based on the degree of deviation between the elevation acquisition value of the measurement point to be analyzed and the corresponding preset elevation benchmark value, and combined with the degree of deviation of all measurement points, obtain the settlement distribution weight of the measurement point to be analyzed.

[0039] Since the obtained elevation data are absolute values ​​of altitude or relative to the surface of the equipment, and subsequent analysis requires extracting the spatial transfer features of elevation undulations between adjacent measuring points to locate road sections with collapse risk, in order to eliminate the differences in the absolute elevation values ​​of each measuring point, the isolated absolute elevation values ​​must be uniformly converted into feature weights that reflect the relative collapse risk.

[0040] The deviation between the elevation data collected at each monitoring point on the current monitoring day and the corresponding preset elevation benchmark value reflects the absolute vertical settlement displacement of the roadbed relative to its initial state for each monitoring point, and is used to identify structural collapses caused by the loss of roadbed support materials. The preset elevation benchmark value represents the initial stable elevation of the roadbed in its initial state. For any given monitoring point, the greater the deviation between the elevation data collected and the preset elevation benchmark value, the greater the settlement of the road section corresponding to that monitoring point, and the greater the risk of roadbed collapse.

[0041] By analyzing the deviation between the collected elevation values ​​and the preset elevation benchmark, the settlement distribution weight of each measuring point is analyzed. The absolute settlement displacement of each measuring point is converted into a weighted proportion relative to the sum of the absolute settlement displacements of all measuring points. This eliminates the interference of different road sections' base elevations, differentiates normal road sections from collapsed sections, and directly identifies the road section with the highest relative collapse risk. This upgrades the analysis from point-to-point displacement comparison to the evolutionary analysis of spatial collapse structures. A larger settlement distribution weight indicates a more severe degree of pavement settlement relative to other road sections, and a greater relative risk of collapse.

[0042] Step S103: Based on the settlement distribution weights of all measuring points on the current monitoring day and historical adjacent monitoring days, and combined with the distance distribution between each pair of measuring points, construct a settlement linkage influence matrix; based on the elements corresponding to the measuring point to be analyzed and adjacent measuring points in the settlement linkage influence matrix, obtain the degree of influence of adjacent measuring points on the evolution of the measuring point to be analyzed.

[0043] By utilizing the distance distribution between every two monitoring points and the settlement distribution weight reflecting the relative collapse risk of each monitoring point on the current monitoring day and historical adjacent monitoring days, the spatial and temporal evolution of the settlement degree at each monitoring point is defined for the settlement linkage effect matrix. The settlement linkage effect matrix quantifies the proportion of the settlement degree at each monitoring point caused by the drag of settlement and collapse from surrounding monitoring points within two adjacent monitoring days, i.e., the linkage settlement proportion, which reflects the traction force transmitted from the local collapsed road section to the surrounding road sections.

[0044] In this embodiment, the settling linkage effect matrix is ​​composed of all monitoring points within the current monitoring day arranged in spatial index order; the columns of the settling linkage effect matrix are all monitoring points within historical adjacent monitoring days arranged in spatial index order. The element corresponding to the i-th row and j-th column of the settling linkage effect matrix represents the proportion of the linked settlement of the i-th monitoring point within the current monitoring day caused by the j-th monitoring point within the historical adjacent monitoring days. The element corresponding to the i-th row and i-th column on the main diagonal represents the proportion of the linked settlement of the i-th monitoring point within the current monitoring day caused by the i-th monitoring point within the historical adjacent monitoring days.

[0045] Therefore, based on the settlement linkage effect matrix, we can analyze the proportion of linkage settlement experienced by each measuring point due to the settlement of adjacent measuring points, and its impact on its own settlement, i.e., the degree of evolutionary influence. The smaller the degree of evolutionary influence, the less the corresponding measuring point is affected by the linkage settlement of adjacent measuring points, the more stable the roadbed structure of the corresponding road segment, and the less affected it is by the settlement of adjacent road segments; the greater the degree of evolutionary influence, the greater the linkage settlement of the corresponding measuring point, the more unstable the roadbed structure of the corresponding road segment, and the more easily it is affected by the settlement of adjacent road segments.

[0046] Preferably, such as Figure 2 As shown, in some implementations of this invention, the step of constructing a settlement linkage influence matrix based on the settlement distribution weights of all measuring points on the current monitoring day and historical adjacent monitoring days, combined with the distance distribution between every two measuring points, includes: Step S201: Construct a settlement distribution weight sequence for the current monitoring day using the settlement distribution weights of all measuring points on the current monitoring day, and construct a settlement distribution weight sequence for historical adjacent monitoring days using the settlement distribution weights of all measuring points on historical adjacent monitoring days.

[0047] Since the settlement distribution weight of a single measuring point on a single monitoring day can only reflect the static settlement degree distribution of a single measuring point, it lacks the dynamic transmission characteristics that reflect the impact of the settlement degree of a single measuring point on the associated settlement of other measuring points. Therefore, it is necessary to construct a settlement distribution weight sequence by combining the settlement distribution weights of all measuring points on the current monitoring day and the historical adjacent monitoring days, so as to inject the dynamic transmission characteristics of the associated settlement impact into the settlement associated impact matrix.

[0048] Step S202: Construct a settlement correlation matrix based on the distribution characteristics of the road segment distance between every two measuring points.

[0049] Because the cascading settlement effects of roadbed settlement have significant distance attenuation effects and transmission resistance in physical space, the settlement distribution weight sequence without spatial constraints cannot reflect the real physical path of risk diffusion across regions. Therefore, it is necessary to combine the distribution characteristics of the road segment distance between every two measuring points to analyze the cascading settlement effects of roadbed settlement between all measuring points, construct a settlement correlation matrix, and provide physical space and transmission resistance constraints for the dynamic transmission characteristics of cascading settlement effects. This enables the obtained settlement cascading effect matrix to accurately map the proportion of the settlement degree of each measuring point affected by the cascading settlement of surrounding measuring points over time, greatly improving the ability and accuracy of the settlement cascading effect matrix to characterize the evolution mechanism of structural collapse.

[0050] Specifically, the process of constructing the settlement correlation matrix is ​​as follows: Any two different measuring points along the entire road segment are combined into a measuring point pair, and the distance between the two measuring points in the pair is obtained as the distance length of the measuring point pair. Since the distance between any two measuring points is tens or hundreds of meters, while the settlement distribution weight of each measuring point ranges from 0 to 1, the dimensions of the two are severely imbalanced. Excessive physical spatial distance will obscure subtle settlement evolution characteristics. Therefore, in this embodiment, the ratio of the distance length of each measuring point pair to the total length of the entire road segment is used as the distance value of the measuring point pair, mapping the excessive physical distance to the [0,1] interval, so that it perfectly matches the magnitude of the settlement distribution weight.

[0051] Construct an N x N two-dimensional matrix, where N represents the total number of measuring points along the entire road segment. Fill in the matrix with the distance values ​​for all pairs of measuring points to obtain a spatial distance matrix. For example, assign the distance value between the i-th and j-th measuring points directly to the element in the i-th row and j-th column of the two-dimensional matrix.

[0052] Because physical distance is linear, but the cascading settlement impact of a locally collapsed road section on surrounding road sections typically decreases exponentially with distance, directly using the distance values ​​between measuring points cannot accurately determine the intensity of the cascading settlement impact between them. Therefore, after constructing a spatial distance matrix, a nonlinear mapping process is applied to it to obtain a settlement correlation matrix, representing the intensity of the cascading settlement impact between measuring points. This ensures that the closer the measuring points are physically, the greater the intensity of the cascading settlement impact; and the farther the measuring points are physically, the smaller the intensity of the cascading settlement impact. This better reflects the drag characteristics of a locally collapsed road section on surrounding road sections.

[0053] Specifically, the formula for nonlinear mapping of the spatial distance matrix is ​​as follows: Where i represents the i-th measurement point; j represents the j-th measurement point; This represents the intensity of the associated settlement influence between the i-th and j-th measuring points in the settlement correlation matrix; Represents an exponential function with base e; This represents the distance between the i-th measuring point and the j-th measuring point in the spatial distance matrix; This represents the smoothing parameter.

[0054] Smoothing parameters This parameter is used to balance the spatial distance cost with distribution smoothness. The value is typically matched to the order of magnitude of the distance, with an exemplary range of 0.01 to 0.5. Smaller smoothing parameters improve the accuracy of the transmission path, while larger parameters enhance the matrix's tolerance to sensor noise and ensure the continuity of the settlement correlation matrix.

[0055] In this embodiment of the invention, the smoothing parameter is set to 0.2.

[0056] Step S203: Use the settlement distribution weight sequence of the current monitoring day as the target distribution boundary condition, and the settlement distribution weight sequence of historical adjacent monitoring days as the source distribution boundary condition. Use the scaling iteration algorithm to process the settlement correlation matrix to obtain the settlement linkage influence matrix.

[0057] The element in the i-th row and j-th column of the settlement linkage influence matrix represents the degree of settlement of the i-th measuring point on the current monitoring day, and the proportion of linkage settlement affected by the settlement of the j-th measuring point on historical adjacent monitoring days.

[0058] Specifically, in this embodiment, the process of processing the settlement correlation matrix using the Sinkhorn algorithm is as follows: 1. Initialize scaling vectors: The system initializes two column vectors u and v of length N and assigns all their elements the value 1.

[0059] 2. Alternating Update Operation: The system enters an iterative loop. In each iteration, the following update operation is performed: (1) Multiply the preset settlement correlation matrix by the column vector v to obtain the column vector a; then, divide each element of the source distribution boundary condition vector by the element at the corresponding position of the column vector a, and assign the quotient to update the vector u.

[0060] (2) Multiply the transpose of the preset settlement correlation matrix by the updated vector u to obtain the column vector b; then, divide each element of the target distribution boundary condition vector by the element at the corresponding position of the column vector b, and assign the quotient to the updated vector v.

[0061] 3. Convergence criterion and stopping: At the end of each iteration, calculate the Euclidean distance (or the sum of absolute differences) between the currently generated matrix rows and vectors and the source distribution boundary conditions. If the distance is less than the preset convergence threshold, stop the iteration.

[0062] 4. Final matrix generation: After stopping the iteration, the system constructs the final vectors u and v into diagonal matrices Diag(u) and Diag(v) respectively, and multiplies them with the settlement correlation matrix to obtain the settlement-related influence matrix.

[0063] It should be understood that the method of scaling and iterating a matrix using the Sinkhorn algorithm is a well-known technique, and will only be briefly introduced here without further elaboration.

[0064] In this embodiment, considering that there are no historical adjacent dates on the first day of monitoring, the settlement distribution weights in the source distribution boundary conditions of the settlement linkage influence matrix calculated on the first day of monitoring are all set to... , where N is the total number of measuring points along the entire road section.

[0065] Step S104: Based on the degree of evolutionary influence and the increase in moisture content, obtain the degree of roadbed loss at the monitoring point to be analyzed on the current monitoring day. Combined with the cumulative degree of roadbed loss on historical adjacent monitoring days, obtain the cumulative degree of roadbed loss at the monitoring point to be analyzed on the current monitoring day.

[0066] Because loess is highly water-sensitive, its volume expands rapidly after absorbing rainfall. This causes the overall upward uplift data of a road section to mask and confuse the true subsidence characteristics. Directly assessing the collapse risk of each measuring point based on the degree of this evolutionary impact will lead to widespread misjudgments after rainfall.

[0067] The increase in moisture content at each measuring point reflects the trend of roadbed uplift caused by the expansion of loess due to water absorption in the corresponding road section. By analyzing the increase in moisture content for each measuring point, the influence of meteorological rainfall on the upward uplift of the road section can be eliminated, and the degree of roadbed loss reflecting the actual roadbed settlement and collapse characteristics of each measuring point within a single natural day can be obtained.

[0068] Because soil support loss in roadbeds is a very slow internal structural change process, the degree of roadbed loss at each monitoring point can only reflect the anomaly state on a single natural day, and is insufficient to fully present the cumulative roadbed loss of the corresponding road section. Therefore, obtaining the cumulative degree of roadbed loss at each monitoring point from the first day of monitoring to the current monitoring day can accurately assess the collapse risk of each road section at each monitoring point.

[0069] Preferably, such as Figure 3 As shown, in some implementations of this invention, obtaining the degree of subgrade erosion at the monitoring point to be analyzed on the current monitoring day based on the degree of evolutionary influence and the moisture content increment, and combining this with the cumulative degree of subgrade erosion on historical adjacent monitoring days to obtain the cumulative degree of subgrade erosion at the monitoring point to be analyzed on the current monitoring day, includes: Step S301: Based on the inner product of the settlement-related influence matrix and the spatial distance matrix, the evolution characteristics of the entire road segment are obtained.

[0070] Since local subgrade loss is often not isolated but is subject to the macroscopic constraints of the mutual transmission and spatial diffusion of settlement across the entire road section, the evolutionary impact of a single measuring point cannot reflect the superimposed impact of overall settlement on local measuring points. Therefore, this embodiment uses the inner product between the settlement-related impact matrix and the spatial distance matrix as the evolutionary feature of the entire road section, characterizing the risk transmission trend of global collapse constrained by physical distance, and uses it as a macroscopic background to correct the degree of local evolutionary impact.

[0071] It should be understood that the method of calculating the inner product of two matrices to obtain an eigenvalue is a well-known technique and will not be elaborated further.

[0072] Step S302: Based on the product of the evolution characteristics of the entire road segment and the degree of evolution influence, and the moisture content increment of the monitoring point to be analyzed on the current monitoring day, obtain the degree of subgrade loss of the monitoring point to be analyzed on the current monitoring day.

[0073] By multiplying the evolution characteristics of the entire road segment by the degree of evolution impact of the measurement point to be analyzed, the corrected degree of evolution impact is obtained. This achieves the physical superposition of the macro-environmental background and the degree of local evolution impact, thereby accurately quantifying the comprehensive structural collapse risk faced by the measurement point to be analyzed.

[0074] By processing the degree of influence of the modified evolution based on the moisture content increment, the influence of road section uplift data caused by meteorological rainfall can be eliminated, thereby separating the degree of roadbed loss, which is dominated by internal material loss and reflects the actual roadbed settlement and collapse characteristics within a single natural day, from the mixed monitoring data.

[0075] In this embodiment of the invention, considering that the moisture content increment is the difference between the moisture content of the current monitoring day and the adjacent monitoring day, a positive moisture content increment corresponds to soil volume expansion, resulting in upward displacement, which may mask the actual downward settlement; a negative moisture content increment corresponds to the soil being in a dry and shrinking state, and there is no phenomenon of upward lifting masking downward settlement. Therefore, to ensure the rigor of the early warning logic, when the moisture content increment is negative, the moisture content increment is forcibly set to 0.

[0076] In subsequent calculations of subgrade erosion, to eliminate the influence of road section uplift data caused by meteorological rainfall, the increase in moisture content is used as the denominator to suppress its weight. When the increase in moisture content rises significantly, a large part of the subgrade erosion at the analyzed monitoring point on the current monitoring day is caused by water expansion rather than actual structural damage. By using the increase in moisture content as the denominator, the subgrade erosion during the rainfall period can be forcibly compressed. Only when the degree of subgrade collapse far exceeds the impact of water expansion can the subgrade erosion exceed the suppression of the denominator and output a high value.

[0077] Therefore, to prevent the denominator from being zero, the increase in moisture content is compared with a very small positive number (such as 10). -6 The sum of these factors is used as an environmental disturbance removal factor. This environmental disturbance removal factor is then used to adjust the degree of evolutionary impact after correction.

[0078] Specifically, the product of the evolution characteristics of the entire road segment and the degree of evolution impact of the measurement point to be analyzed is used as the numerator, and the environmental interference elimination factor is used as the denominator. The resulting ratio is the degree of roadbed loss at the measurement point to be analyzed, reflecting the actual roadbed settlement and collapse characteristics that occurred at the measurement point to be analyzed within the current monitoring day.

[0079] Step S303: Add the degree of roadbed loss of the monitoring point to be analyzed on the current monitoring day to the cumulative degree of roadbed loss on historical adjacent monitoring days to obtain the cumulative degree of roadbed loss on the current monitoring day.

[0080] Since the structural collapse of the roadbed is essentially a progressive damage accumulation process caused by the continuous loss of internal materials, the degree of roadbed loss on a single day is easily affected by instantaneous environmental interference and is difficult to reflect the long-term internal deterioration trajectory. Therefore, it is necessary to obtain the degree of roadbed loss accumulated at the monitoring point from the first day of monitoring to the current monitoring day, and effectively superimpose the long-term minor internal damage to accurately capture the early risk signals of roadbed collapse.

[0081] Step S105: Analyze the subgrade loss change rate based on the subgrade loss accumulation degree of the monitoring point to be analyzed on the current monitoring day and historical monitoring days; use the subgrade loss change rate to analyze the subgrade loss state of the road section, and obtain the subgrade settlement monitoring results of the road section corresponding to the monitoring point to be analyzed.

[0082] Before a clearly visible large-scale surface subsidence occurs, the cumulative loss of roadbed support material exhibits a characteristic of accelerating growth over time, typically following a physical law of transitioning from linear, low-speed growth to exponential, accelerating deterioration. Therefore, to assess the subsidence risk of each road segment before a clearly visible large-scale surface subsidence occurs, this embodiment calculates the roadbed loss trend (i.e., the roadbed loss rate) of each road segment by measuring the cumulative roadbed loss at each monitoring point on the current and historical monitoring days. This analysis determines whether the roadbed settlement trend of each segment is normal and assesses the subsidence risk of each segment.

[0083] In summary, this invention, based on the deviation between the elevation data of the measurement point to be analyzed and the corresponding preset elevation benchmark, and combining the deviation of all measurement points, obtains the settlement distribution weight of the measurement point to be analyzed, upgrading the point-to-point settlement comparison to the evolution analysis of spatial collapse structures. Based on the settlement distribution weights of all measurement points on the current monitoring day and historical adjacent monitoring days, and combined with the distance distribution between every two measurement points, a settlement linkage influence matrix is ​​constructed, reflecting the proportion of linkage settlement dragged by the settlement and collapse of surrounding measurement points at each measurement point. Based on the elements corresponding to the measurement point to be analyzed and adjacent measurement points in the settlement linkage influence matrix, the degree of influence of adjacent measurement points on the evolution of the measurement point to be analyzed is obtained, thus analyzing the stability of the roadbed structure of the measurement point to be analyzed.

[0084] Based on the degree of evolutionary influence and the increase in moisture content, the degree of subgrade loss at the monitoring point to be analyzed on the current monitoring day is obtained, reflecting the actual subgrade settlement and collapse characteristics occurring at the monitoring point on the current monitoring day. Combining the cumulative subgrade loss on historical adjacent monitoring days, the cumulative subgrade loss at the monitoring point to be analyzed on the current monitoring day is obtained. Based on the cumulative subgrade loss at the monitoring point to be analyzed on the current monitoring day and historical monitoring days, the rate of change in subgrade loss is analyzed to determine the trend of subgrade loss, assess the subgrade loss status, and obtain the subgrade settlement monitoring results for the corresponding road section of the monitoring point to be analyzed.

[0085] Preferably, in some implementations of the present invention, the step of obtaining the settlement distribution weight of the measurement point to be analyzed based on the deviation between the elevation acquisition value of the measurement point to be analyzed and the corresponding preset elevation benchmark value, combined with the deviation of all measurement points, includes: Extract all elevation data collected by the monitoring point to be analyzed within the current monitoring day, and take the elevation data collected at the medium level as the median elevation value of the monitoring point on a single day; The degree of deviation of the measurement point to be analyzed is obtained based on the difference between the median value of the daily elevation and the corresponding preset elevation benchmark value. The settlement distribution weight of the measurement point to be analyzed is obtained based on the proportion of the deviation degree of the measurement point to all the measurement points.

[0086] Due to the complex monitoring environment of road sections, sensors are inevitably affected by sudden vibrations, electromagnetic interference, or instantaneous drift, resulting in outliers that significantly deviate from the true elevation data. If the arithmetic mean of the obtained elevation data is used as the elevation value of the point to be analyzed on the current monitoring day, these extreme data will skew the result, leading to inaccurate information. The median, however, depends only on the middle position of all elevation data, naturally filtering out these occasional abnormal fluctuations and accurately reflecting the true stable height of the road surface most of the time, providing a solid data foundation for subsequent settlement analysis. Therefore, this embodiment extracts all elevation data of the point to be analyzed within the current monitoring day, and uses the median of all values ​​as the daily median elevation of the point to be analyzed. This reflects the moderate level of roadbed settlement at the point to be analyzed on the current monitoring day, representing the degree of roadbed settlement of the corresponding road section on the current monitoring day.

[0087] The preset elevation benchmark value for each measuring point represents the initial stable elevation of the roadbed in the corresponding road segment under initial conditions. Subtracting the median daily elevation value of the measuring point from its preset elevation benchmark value yields the settlement increment of the roadbed relative to its initial state on the current monitoring day, i.e., the degree of deviation. This deviation is used to identify structural collapses caused by the loss of roadbed support materials. Since roadbed settlement is a downward displacement, the obtained degree of deviation is a non-negative number. A larger degree of deviation indicates a larger settlement increment of the roadbed relative to its initial state, and a greater risk of structural collapse due to the loss of roadbed support materials.

[0088] Because roadbed settlement is often non-uniformly distributed, and severe settlement in localized areas is usually a direct precursor to the loss of supporting materials or structural collapse, simply relying on the deviation of each measuring point is insufficient to accurately quantify the contribution of each measuring point to the overall road section collapse risk. Therefore, it is necessary to obtain a settlement distribution weight based on the proportion of each measuring point's deviation to the total deviation of all measuring points across the entire road section. This quantifies the contribution of each measuring point to the overall road section collapse risk, thereby effectively identifying the risk center that dominates the overall road section collapse risk. Furthermore, this method ensures that the settlement distribution weight of all measuring points is a decimal between 0 and 1, and that the sum of the settlement distribution weights of all measuring points is strictly equal to 1.

[0089] In this embodiment of the invention, to amplify the data characteristics of road sections experiencing severe subsidence, after obtaining the deviation degree of all measuring points, exponential normalization is performed on the deviation degree of all measuring points. Considering that excessively large extreme values ​​could lead to extremely small normalized deviation values ​​and a potential overflow risk, this embodiment introduces a maximum value shift and a suppression coefficient for exponential normalization. The deviation degree of all measuring points within the current monitoring day is extracted, and the deviation degree with the largest value is taken as the maximum deviation degree of the current monitoring day, which is then used as the maximum value for normalization. The suppression coefficient is a preset positive floating-point number used to suppress the steepness of exponential growth.

[0090] Specifically, the normalization formula is as follows: in, Representing the One measurement point; Representing the The normalized deviation of each measuring point on the current monitoring day; Represents an exponential function with base e; Representing the The degree of deviation of each monitoring point on the current monitoring day; This represents the maximum deviation on the current monitoring day; This represents the inhibition coefficient.

[0091] Representing the The subtraction operation calculates the negative deviation of each measurement point from its maximum deviation on the current monitoring day. This subtraction operation ensures that all difference results are non-positive numbers less than or equal to zero, thus safely limiting the final calculation result to the safe constant range of (0, 1], fundamentally eliminating program crashes caused by computer floating-point overflow.

[0092] In this embodiment of the invention, the inhibition coefficient is set to 0.1.

[0093] In one implementation of this invention, to prevent permanent initial measurement errors caused by extreme rainstorms or accidental equipment drift on the first day of monitoring, this embodiment employs a multi-day smoothing mechanism to determine the preset elevation benchmark value. The first M natural days of monitoring are defined as the initial calibration period; in this embodiment, M is exemplarily set to 7. Within the calibration period, for any given measuring point, all daily median elevation values ​​for that point from day 1 to day 7 are collected. After collection, the arithmetic mean of these 7 daily median elevation values ​​is calculated to obtain the preset elevation benchmark value for the corresponding measuring point. Since the settlement of the roadbed is extremely small (typically reaching millimeter or even sub-millimeter levels), the preset elevation benchmark value obtained through the initial calibration period can represent the true steady-state elevation of the measuring point after eliminating accidental noise and extreme anomalies within the initial calibration period.

[0094] Preferably, in some implementations of the present invention, obtaining the degree of influence of adjacent measuring points on the evolution of the measuring point to be analyzed based on the elements corresponding to the measuring point to be analyzed and adjacent measuring points in the settlement linkage influence matrix includes: In the settlement linkage effect matrix, for any element on the main diagonal, the element represents the proportion of the linkage settlement of the corresponding monitoring point on the current monitoring day due to the influence of the monitoring point itself on historical adjacent monitoring days. This indicates the tendency of the monitoring point to maintain independent settlement in situ, unaffected by surrounding monitoring points. The larger the proportion of linkage settlement to itself, the stronger the tendency of the monitoring point to maintain independent settlement in situ, unaffected by surrounding monitoring points.

[0095] Since structural collapses of roadbeds are often dominated by extreme risks in surrounding local areas, and internal stress transmission and material loss exhibit significant directional breakdown characteristics, it is necessary to analyze the degree of influence of adjacent measuring points on the evolution of the measured point under analysis based on the maximum percentage of associated settlement among all measuring points adjacent to the measured point under analysis. This can significantly improve the sensitivity and accuracy of identifying critical states of local structural collapses.

[0096] In this embodiment, the maximum value of the proportion of associated settlement of all adjacent measuring points to the measuring point to be analyzed is extracted and compared with the proportion of associated settlement of the measuring point to itself. This ratio is used to calculate the degree of influence of adjacent measuring points on the measuring point to be analyzed, representing the stability of the roadbed structure of the corresponding road segment. A smaller degree of influence indicates less impact of associated settlement on the measuring point to be analyzed from adjacent measuring points, resulting in a more stable roadbed structure and less susceptibility to settlement from adjacent road segments. Conversely, a larger degree of influence indicates greater impact from associated settlement on the measuring point to be analyzed from adjacent measuring points, resulting in a more unstable roadbed structure and greater susceptibility to settlement from adjacent road segments.

[0097] Specifically, the formula for calculating the degree of evolutionary influence is: in, Representing the One measurement point; Representing the The adjacent measuring points of the measuring point of the first measuring point are paired with the measuring point of the second measuring point. The degree of influence of the evolution of each measuring point; Representing the The measuring point is affected by the first The proportion of cascading settlement affected by each measuring point; Representing the The measuring point is affected by the first The proportion of cascading settlement affected by each measuring point; Representing the The proportion of the associated settlement of each measuring point; This represents the prevention and control of zero constant.

[0098] It is a maximum value filtering function. It is a very small positive number (such as 10) -6 ).

[0099] As can be seen from step S101 above, multiple measuring points are divided into sections along the entire road segment according to a preset equidistant interval. Therefore, except for the last and first measuring points of the entire road segment which have only one adjacent measuring point, all measuring points in the middle of the entire road segment have two adjacent measuring points. When calculating... hour, It does not exist, use it directly. The degree of influence of adjacent measuring points on the first measuring point is obtained; similarly, the degree of influence of the last measuring point in the entire road segment is obtained. When obtaining the degree of influence of a measuring point located in the middle of the entire road segment, the above formula is used for calculation.

[0100] Preferably, in some implementations of the present invention, the step of analyzing the rate of change of roadbed loss based on the cumulative degree of roadbed loss at the monitoring point under analysis on the current monitoring day and historical monitoring days includes: The cumulative degree of subgrade loss at the monitoring point under analysis is obtained for each monitoring day within a preset backtracking window of the current monitoring day. Linear analysis is then performed to obtain the subgrade loss change rate at the monitoring point under analysis. The subgrade loss change rate represents the accelerating trend of subgrade loss. The larger the subgrade loss change rate, the more it proves that the subgrade is in a state of accelerated loss and deterioration, indicating a greater risk of collapse for the corresponding road section of the monitoring point under analysis.

[0101] In one specific implementation of this invention, the preset backtracking window is set to 10 days.

[0102] In one specific implementation of this invention, the least squares method is used to linearly fit the degree of roadbed loss accumulation of the test point to be analyzed within a preset backtracking window on the current monitoring day and the historical monitoring day, and the slope of the straight line obtained after fitting is used as the roadbed loss change rate of the test point to be analyzed.

[0103] It should be understood that the methods for linear fitting and calculating the slope of a line using the least squares method are well-known techniques and will not be elaborated here.

[0104] Preferably, in some implementations of the present invention, the step of analyzing the subgrade erosion state of a road segment using the subgrade erosion change rate to obtain the subgrade settlement monitoring results of the road segment corresponding to the analysis point includes: The difference between the rate of change of roadbed erosion at the measurement point to be analyzed and the preset baseline rate of change is used as the increment of the rate of change. Since the supporting material of the roadbed itself has natural wear, it is necessary to subtract the rate of change of roadbed erosion caused by natural pavement wear due to normal driving of heavy vehicles, i.e., the preset baseline rate of change, from the current accelerating trend of roadbed erosion. This allows for the accurate extraction of the abnormal accelerating trend of roadbed erosion caused by the loss of local support in the underlying layer, thereby keenly capturing early signs of collapse risk caused by the loss of roadbed support material before a collapse occurs.

[0105] The preset allowable safety threshold represents the maximum boundary of the abnormal acceleration trend of roadbed loss that the road section corresponding to the analysis point can tolerate under normal operating conditions. If the increment of the rate of change is less than the preset allowable safety threshold, the settlement monitoring result of the road section corresponding to the analysis point is normal; if the increment of the rate of change is greater than or equal to the preset allowable safety threshold, the settlement monitoring result of the road section corresponding to the analysis point indicates a tendency to collapse, the foundation support has seriously failed, and large-scale surface collapse may occur at any time.

[0106] In this embodiment of the invention, after obtaining the monitoring results that the road section corresponding to the monitoring point to be analyzed has a tendency to collapse, the spatial index number and the rate of change increment of the monitoring point to be analyzed are recorded, the two are packaged to generate a structured settlement anomaly early warning instruction, and sent to the on-site business receiving terminal of the highway maintenance unit.

[0107] In one implementation of this invention, the preset baseline change rate is obtained as follows: the first thirty natural days of monitoring are defined as the baseline accumulation period. For each measuring point, the fitting and recording of the aforementioned roadbed loss change rate are performed only each day. At the end of the thirtieth natural day, the arithmetic mean of all roadbed loss change rates recorded within these thirty days is calculated to obtain the preset baseline change rate for each measuring point.

[0108] Specifically, if the road section is in an extremely stable state or experiences slight soil rebound after rain within the 30 days prior to monitoring, the preset baseline rate of change obtained through the above operations will be less than or equal to zero. This will cause any minor normal positive fluctuations during subsequent monitoring to trigger an alarm, resulting in the complete failure of the monitoring judgment logic. Therefore, after obtaining the preset baseline rate of change, it is determined whether this value is less than the preset engineering loss limit. If it is less, the preset engineering loss limit is assigned to the preset baseline rate of change; otherwise, the original value is retained.

[0109] In one implementation of this invention, the preset engineering loss limit is set to 0.01, and three times the preset benchmark change rate for each measuring point is used as the preset allowable safety threshold for each measuring point.

[0110] Based on the same inventive concept, the present invention also proposes a roadbed settlement monitoring system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any of the roadbed settlement monitoring methods described above.

[0111] The present invention also proposes a roadbed settlement monitoring device, the device comprising: The data extraction module is used to obtain the elevation and moisture content increments of the monitoring point to be analyzed and other monitoring points within the current monitoring day. The settlement distribution weight analysis module is used to obtain the settlement distribution weight of the measurement point to be analyzed based on the degree of deviation between the elevation acquisition value of the measurement point to be analyzed and the corresponding preset elevation benchmark value, combined with the degree of deviation of all measurement points. The evolution impact analysis module is used to construct a settlement linkage impact matrix based on the settlement distribution weights of all monitoring points on the current monitoring day and historical adjacent monitoring days, combined with the distance distribution between each pair of monitoring points; and to obtain the degree of evolution impact of adjacent monitoring points on the monitoring point to be analyzed based on the elements corresponding to the monitoring point to be analyzed and adjacent monitoring points in the settlement linkage impact matrix. The roadbed loss analysis module is used to obtain the degree of roadbed loss of the monitoring point to be analyzed on the current monitoring day based on the degree of evolution influence and the increase in moisture content, and to obtain the cumulative degree of roadbed loss of the monitoring point to be analyzed on the current monitoring day by combining the cumulative degree of roadbed loss of adjacent historical monitoring days. The monitoring result acquisition module is used to analyze the roadbed loss change rate based on the cumulative degree of roadbed loss at the monitoring point to be analyzed on the current monitoring day and historical monitoring days; and to analyze the roadbed loss status of the road section using the roadbed loss change rate to obtain the roadbed settlement monitoring results of the road section corresponding to the monitoring point to be analyzed.

[0112] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0113] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for monitoring road surface and subgrade settlement, characterized in that, The method includes: Acquire the elevation and moisture content increments of the monitoring point to be analyzed and other monitoring points within the current monitoring day; Based on the degree of deviation between the elevation acquisition value of the measurement point to be analyzed and the corresponding preset elevation benchmark value, and combined with the degree of deviation of all measurement points, the settlement distribution weight of the measurement point to be analyzed is obtained. Based on the settlement distribution weights of all monitoring points on the current monitoring day and historical adjacent monitoring days, and combined with the distance distribution between each pair of monitoring points, a settlement linkage influence matrix is ​​constructed; based on the elements corresponding to the monitoring point to be analyzed and adjacent monitoring points in the settlement linkage influence matrix, the degree of influence of adjacent monitoring points on the evolution of the monitoring point to be analyzed is obtained. Based on the degree of evolutionary influence and the increase in moisture content, the degree of subgrade loss at the monitoring point to be analyzed on the current monitoring day is obtained. Combined with the cumulative subgrade loss on historical adjacent monitoring days, the cumulative degree of subgrade loss at the monitoring point to be analyzed on the current monitoring day is obtained. Based on the cumulative degree of subgrade loss at the monitoring points under analysis on the current monitoring day and historical monitoring days, the subgrade loss change rate is analyzed; the subgrade loss change rate is used to analyze the subgrade loss status of the road section, and the subgrade settlement monitoring results of the corresponding road section of the monitoring points under analysis are obtained.

2. The method for monitoring roadbed settlement according to claim 1, characterized in that, The settlement distribution weight of the measurement point to be analyzed is obtained by combining the deviation between the collected elevation value and the corresponding preset elevation benchmark value, and the deviation of all measurement points. This includes: Extract all elevation data collected by the monitoring point to be analyzed within the current monitoring day, and take the elevation data collected at the medium level as the median elevation value of the monitoring point on a single day; The degree of deviation of the measurement point to be analyzed is obtained based on the difference between the median value of the daily elevation and the corresponding preset elevation benchmark value. The settlement distribution weight of the measurement point to be analyzed is obtained based on the proportion of the deviation degree of the measurement point to all the measurement points.

3. The method for monitoring roadbed settlement according to claim 1, characterized in that, The settlement linkage influence matrix is ​​constructed based on the settlement distribution weights of all monitoring points on the current monitoring day and historical adjacent monitoring days, combined with the distance distribution between every two monitoring points, including: The settlement distribution weight sequence for the current monitoring day is constructed using the settlement distribution weights of all monitoring points on the current monitoring day, and the settlement distribution weight sequence for historical adjacent monitoring days is constructed using the settlement distribution weights of all monitoring points on historical adjacent monitoring days. Based on the distribution characteristics of the road segment distance between every two measuring points, a settlement correlation matrix is ​​constructed; The settlement distribution weight sequence of the current monitoring day is used as the target distribution boundary condition, and the settlement distribution weight sequence of adjacent historical monitoring days is used as the source distribution boundary condition. The settlement correlation matrix is ​​processed by the scaling iteration algorithm to obtain the settlement linkage influence matrix.

4. The method for monitoring roadbed settlement according to claim 3, characterized in that, The settlement correlation matrix is ​​constructed based on the distribution characteristics of the road segment distance between every two measuring points, including: Any two different measuring points are combined into a measuring point pair. The ratio of the road segment distance between the two measuring points in the measuring point pair to the total length of the entire road segment is taken as the distance value of the measuring point pair. Construct a spatial distance matrix based on the distance values ​​of all measurement point pairs; The spatial distance matrix is ​​subjected to nonlinear mapping to obtain the settlement correlation matrix.

5. The method for monitoring roadbed settlement according to claim 1, characterized in that, The step of obtaining the degree of influence of adjacent measuring points on the evolution of the measuring point to be analyzed based on the elements corresponding to the measuring point to be analyzed and adjacent measuring points in the settlement linkage influence matrix includes: The elements of the settlement linkage effect matrix are the linkage settlement percentages between every two measuring points; The maximum value of the joint settlement ratio between the adjacent measuring points and the measuring point to be analyzed is calculated by comparing it with the joint settlement ratio of the measuring point to be analyzed on itself, thus obtaining the degree of influence of the adjacent measuring points on the evolution of the measuring point to be analyzed.

6. The method for monitoring road surface and subgrade settlement according to claim 4, characterized in that, The process of obtaining the degree of subgrade erosion at the monitoring point to be analyzed on the current monitoring day based on the degree of evolutionary influence and the increase in moisture content, and combining this with the cumulative degree of subgrade erosion on historical adjacent monitoring days to obtain the cumulative degree of subgrade erosion at the monitoring point to be analyzed on the current monitoring day, includes: Based on the inner product of the settlement-related influence matrix and the spatial distance matrix, the evolution characteristics of the entire road segment are obtained. Based on the product of the evolution characteristics of the entire road segment and the degree of influence of the evolution, and the increase in the moisture content of the monitoring point to be analyzed on the current monitoring day, the degree of subgrade loss of the monitoring point to be analyzed on the current monitoring day is obtained. The cumulative degree of roadbed loss on the current monitoring day is obtained by adding the degree of roadbed loss of the monitoring point to the cumulative degree of roadbed loss on the historical adjacent monitoring days.

7. The method for monitoring roadbed settlement according to claim 1, characterized in that, The analysis of the roadbed loss rate based on the cumulative degree of roadbed loss at the monitoring points on the current monitoring day and historical monitoring days includes: The cumulative degree of roadbed loss of the monitoring point to be analyzed is obtained for each monitoring day within the preset backtracking window of the current monitoring day. Linear analysis is then performed to obtain the roadbed loss change rate of the monitoring point to be analyzed.

8. The method for monitoring roadbed settlement according to claim 1, characterized in that, The analysis of the subgrade erosion state of the road segment using the subgrade erosion change rate yields the subgrade settlement monitoring results for the corresponding road segment at the analysis point, including: The difference between the rate of change of roadbed loss at the measurement point to be analyzed and the preset benchmark rate of change is taken as the rate of change increment; If the increment of the rate of change is less than the preset allowable safety threshold, the settlement monitoring result of the road section corresponding to the monitoring point to be analyzed is normal; if the increment of the rate of change is greater than or equal to the preset allowable safety threshold, the settlement monitoring result of the road section corresponding to the monitoring point to be analyzed indicates a tendency to collapse.

9. A roadbed settlement monitoring system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the roadbed settlement monitoring method as described in any one of claims 1 to 8.

10. A road surface and subgrade settlement monitoring device, characterized in that, The device includes: The data extraction module is used to obtain the elevation and moisture content increments of the monitoring point to be analyzed and other monitoring points within the current monitoring day. The settlement distribution weight analysis module is used to obtain the settlement distribution weight of the measurement point to be analyzed based on the degree of deviation between the elevation acquisition value of the measurement point to be analyzed and the corresponding preset elevation benchmark value, combined with the degree of deviation of all measurement points. The evolution impact analysis module is used to construct a settlement linkage impact matrix based on the settlement distribution weights of all monitoring points on the current monitoring day and historical adjacent monitoring days, combined with the distance distribution between each pair of monitoring points; and to obtain the degree of evolution impact of adjacent monitoring points on the monitoring point to be analyzed based on the elements corresponding to the monitoring point to be analyzed and adjacent monitoring points in the settlement linkage impact matrix. The roadbed loss analysis module is used to obtain the degree of roadbed loss of the monitoring point to be analyzed on the current monitoring day based on the degree of evolution influence and the increase in moisture content, and to obtain the cumulative degree of roadbed loss of the monitoring point to be analyzed on the current monitoring day by combining the cumulative degree of roadbed loss of adjacent historical monitoring days. The monitoring result acquisition module is used to analyze the roadbed loss change rate based on the cumulative degree of roadbed loss at the monitoring point to be analyzed on the current monitoring day and historical monitoring days; and to analyze the roadbed loss status of the road section using the roadbed loss change rate to obtain the roadbed settlement monitoring results of the road section corresponding to the monitoring point to be analyzed.

Images