A method and system for monitoring and evaluating the stability of a semi-filled embankment and slope

Abstract

The present application relates to the field of geotechnical engineering monitoring, especially to a method and system for monitoring and evaluating the stability of semi-excavated semi-filled roadbed and slope, the method comprising the following steps: establishing a data joint monitoring array, using the data joint monitoring array to obtain multi-source distributed monitoring data through synchronous data acquisition; performing quality control and preprocessing on the multi-source distributed monitoring data to obtain a multi-source standardized monitoring data set; constructing a roadbed-slope stability joint evaluation model based on the multi-source standardized monitoring data set, and obtaining a stability collaborative evaluation index according to the roadbed-slope stability joint evaluation model; constructing a multi-level stability evaluation mechanism, and obtaining a stability joint monitoring evaluation result in combination with the stability collaborative evaluation index and the multi-level stability evaluation mechanism. The present application improves the comprehensiveness and accuracy of the joint monitoring and evaluation of the stability of semi-excavated semi-filled roadbed and slope, and provides effective data support for engineering decision-making and engineering safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geotechnical engineering monitoring, and in particular to a method and system for combined monitoring and evaluation of semi-excavation and semi-filling subgrade and slope stability. BACKGROUND

[0002] In current road engineering construction, especially in mountainous or hilly areas, semi-excavation and semi-filling subgrade is a common form of subgrade. For the monitoring of such subgrade and its slope stability, the existing technology usually adopts independent and separate monitoring methods. For the stability of the excavated slope, the main method is to bury inclinometer tubes and use portable inclinometers to measure the horizontal displacement of deep soil at regular intervals and points. For the settlement monitoring of the filled subgrade, settlement plates, settlement rods are mainly used in combination with leveling instruments for periodic manual measurement, or single-point settlement sensors are used. The above monitoring methods are fragmented in terms of data: the monitoring equipment is independently laid out, the data collection times are not synchronized, and the coordinate system references are not unified. The evaluation method usually sets fixed thresholds for horizontal displacement or settlement data respectively to alarm when the thresholds are exceeded, or uses simple mechanical empirical formulas for stability calculation, without fundamentally considering the mutual influence and synergistic mechanism of the excavated area and the filled area in the deformation process.

[0003] The existing technology has some defects: first, the separate monitoring method cannot capture the spatio-temporal correlation and interaction between the deformation of the excavated slope and the settlement of the filled subgrade, making it difficult to accurately judge the overall stability of the subgrade; second, the alarm method based on fixed thresholds cannot reflect the dynamic process of accelerated development of subgrade deformation, making it difficult to depict the complex mechanical behavior of semi-excavation and semi-filling, and the false alarm and missed alarm rates are high; finally, from data collection and processing to risk evaluation, the degree of automation and intelligence is insufficient, relying on manual experience for judgment, which cannot meet the real-time, accuracy and intelligence requirements of safety monitoring in modern engineering construction and operation. SUMMARY

[0004] In view of the defects in the prior art, the present application provides a method and system for combined monitoring and evaluation of semi-excavation and semi-filling subgrade and slope stability.

[0005] In order to achieve the above object, the first aspect, the application provides a semi-excavation and semi-filling roadbed and slope stability joint monitoring and evaluation method, the method comprises the following steps: establishing a data joint monitoring array, using the data joint monitoring array to obtain multi-source distributed monitoring data through synchronous data acquisition; quality control and preprocessing the multi-source distributed monitoring data to obtain a multi-source standardized monitoring data set; constructing a roadbed-slope stability joint evaluation model based on the multi-source standardized monitoring data set, and obtaining a stability collaborative evaluation index according to the roadbed-slope stability joint evaluation model; constructing a multi-level stability evaluation mechanism, and obtaining a stability joint monitoring and evaluation result in combination with the stability collaborative evaluation index and the multi-level stability evaluation mechanism. The application realizes multi-source data synchronous acquisition by establishing a data joint monitoring array, breaks the single monitoring limitation, comprehensively obtains key parameters of roadbed and slope, realizes stability collaborative evaluation through quality control, model construction and multi-level evaluation, improves the comprehensiveness and accuracy of monitoring and evaluation, and provides reliable data support for engineering safety.

[0006] Optionally, the establishment of the data joint monitoring array and the synchronous data acquisition by using the data joint monitoring array to obtain multi-source distributed monitoring data comprises: establishing the data joint monitoring array in combination with a deep soil body horizontal displacement measuring instrument and a fiber optic displacement sensor; collecting the deep soil body horizontal displacement amount of the excavated slope according to the deep soil body horizontal displacement measuring instrument, and collecting the settlement data of the filled roadbed according to the fiber optic displacement sensor; and taking the deep soil body horizontal displacement amount and the settlement data as the multi-source distributed monitoring data. The application constructs a monitoring array in combination with a deep soil body horizontal displacement measuring instrument and a fiber optic displacement sensor, collects core data of the excavated slope and the filled roadbed, ensures the accurate matching of monitoring data and key positions of the project, provides high-quality basic data for subsequent evaluation, and improves the pertinence and effectiveness of data acquisition.

[0007] Optionally, the quality control and preprocessing of the multi-source distributed monitoring data to obtain a multi-source standardized monitoring data set comprises: obtaining an abnormal value judgment index of the multi-source distributed monitoring data, and obtaining an abnormal monitoring value based on the abnormal value judgment index; constructing a time series prediction model based on a historical data change trend, repairing the abnormal monitoring value to obtain a multi-source quality optimized monitoring data set; and standardizing and unifying coordinates of the multi-source quality optimized monitoring data set to obtain the multi-source standardized monitoring data set. The application solves the problems of uneven quality and inconsistent format of multi-source data through abnormal value judgment and repair and standardization processing, repairs abnormal values to ensure data integrity, and standardizes and unifies coordinates to make the data comparable, thereby providing a standardized and reliable data set for subsequent model construction.

[0008] Optionally, the constructing the embankment-slope stability joint evaluation model based on the multi-source standardized monitoring data set comprises: constructing a displacement-settlement collaborative analysis matrix based on the multi-source standardized monitoring data set; constructing a critical instability state model to obtain an embankment-slope stability state index; combining the displacement-settlement collaborative analysis matrix and the embankment-slope stability state index to establish an embankment-slope state potential energy function, and taking the embankment-slope state potential energy function as the embankment-slope stability joint evaluation model. The embankment-slope stability joint evaluation model is constructed, collaborative analysis of embankment and slope stability is realized, multi-source monitoring data is converted into a quantitative evaluation index, the overall stability state is accurately reflected, and the one-sidedness of traditional single-factor evaluation is overcome.

[0009] Optionally, the constructing the displacement-settlement collaborative analysis matrix based on the multi-source standardized monitoring data set comprises: obtaining monitoring parameter change rates including horizontal displacement change rates and settlement change rates based on the multi-source standardized monitoring data set; determining dynamic weight coefficients including displacement dynamic weight coefficients and settlement dynamic weight coefficients according to a ratio of the horizontal displacement change rates and the settlement change rates; and constructing the displacement-settlement collaborative analysis matrix in combination with the multi-source standardized monitoring data set, the monitoring parameter change rates and the dynamic weight coefficients. The dynamic weight coefficients are determined based on the monitoring parameter change rates, so that the displacement-settlement collaborative analysis matrix is more suitable for actual engineering dynamic changes, the dynamic weight reflects the importance of different parameters at different stages, the ability to characterize the engineering state is enhanced, and a more accurate analysis basis is provided for subsequent stability state evaluation.

[0010] Optionally, the constructing the critical instability state model to obtain the embankment-slope stability state index comprises: taking the ratio of the horizontal displacement change rates and the settlement change rates as a change rate ratio criterion; obtaining horizontal displacement acceleration and settlement acceleration according to the monitoring parameter change rates, and establishing an acceleration trend criterion according to the horizontal displacement acceleration and the settlement acceleration; constructing the critical instability state model in combination with the change rate ratio criterion and the acceleration trend criterion; and obtaining the embankment-slope stability state index based on the displacement-settlement collaborative analysis matrix and the critical instability state model. The critical instability state model is constructed in combination with the change rate ratio criterion and the acceleration trend criterion, instability risks are comprehensively judged from the rate and trend, the stability state index is obtained based on the collaborative analysis matrix, quantitative characterization of the embankment-slope stability state is realized, and the timeliness and accuracy of instability evaluation are improved.

[0011] Optionally, the combination of the displacement-settlement synergistic analysis matrix and the roadbed-slope stability state index establishes a roadbed-slope state potential energy function, comprising: performing principal component analysis on the displacement-settlement synergistic analysis matrix to obtain control variables, including first control variables related to displacement principal components and second control variables related to settlement principal components; taking the roadbed-slope stability state index as a state variable; and establishing a mapping relationship between the control variables and the state variable to obtain the roadbed-slope state potential energy function. The application extracts key control variables from the synergistic matrix through principal component analysis, establishes a potential energy function in combination with the stability state index, realizes accurate mapping of control variables and state variables, intuitively reflects the stability state change law of the roadbed-slope, and provides strong support for subsequent evaluation indexes.

[0012] Optionally, the stability synergistic evaluation index is obtained according to the roadbed-slope stability joint evaluation model, comprising: obtaining roadbed-slope multi-dimensional feature parameters including potential energy curvature, potential barrier height and bifurcation point set distance based on the roadbed-slope stability joint evaluation model; obtaining a stability sensitivity coefficient according to the potential energy curvature, a stability maintenance probability based on the potential barrier height, and a stability safety margin according to the bifurcation point set distance; and weighting and fusing the stability sensitivity coefficient, the stability maintenance probability and the stability safety margin to obtain the stability synergistic evaluation index. The application extracts multi-dimensional feature parameters from the evaluation model and converts them into sensitivity coefficients, maintenance probabilities and safety margins, and obtains a synergistic evaluation index through weighted fusion. The multi-dimensional feature parameters comprehensively cover key stability information, and weighted fusion improves the comprehensiveness and representativeness of the index, providing accurate quantitative basis for the evaluation result.

[0013] Optionally, the multi-level stability evaluation mechanism is constructed, and a stability joint monitoring evaluation result is obtained in combination with the stability synergistic evaluation index and the multi-level stability evaluation mechanism, comprising: obtaining a multi-level dynamic threshold interval of the stability synergistic evaluation index, and constructing the multi-level stability evaluation mechanism based on the multi-level dynamic threshold interval; obtaining a preliminary monitoring evaluation result by real-time comparison of the stability synergistic evaluation index according to the multi-level stability evaluation mechanism; and obtaining the stability joint monitoring evaluation result by synergistic verification of the preliminary monitoring evaluation result based on the multi-source standardized monitoring data set. The application constructs an evaluation mechanism with multi-level dynamic thresholds, real-time compares the synergistic evaluation index, and combines multi-source data for synergistic verification, which is suitable for different engineering scenarios, reduces evaluation errors through synergistic verification, and ensures the reliability of the evaluation result.

[0014] In a second aspect, the present application provides a semi-excavation and semi-filling roadbed and slope stability joint monitoring and evaluation system, which implements the semi-excavation and semi-filling roadbed and slope stability joint monitoring and evaluation method provided by the present application, and is characterized in that the system comprises an input device, an output device, a processor and a memory, which are connected with each other, wherein the memory is used for storing a computer program, the computer program comprises program instructions, and the processor is configured to invoke the program instructions. The present application realizes the automation and systematization of monitoring and evaluation through the cooperation of high-performance hardware facilities, guarantees the convenience of data interaction, makes the whole monitoring and evaluation process efficient and orderly, and provides stable and efficient hardware support for engineering safety management. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A semi-excavation and semi-filling roadbed and slope stability joint monitoring and evaluation method flow chart for an embodiment of the present application;

[0016] Figure 2 A roadbed-slope engineering structure profile schematic diagram for an embodiment of the present application;

[0017] Figure 3 A semi-excavation and semi-filling roadbed and slope stability joint monitoring and evaluation system framework diagram for an embodiment of the present application. DETAILED DESCRIPTION

[0018] The specific embodiments of the present application will be described in detail below, and it should be noted that the embodiments described herein are only used for illustration and do not limit the present application. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present application. However, it is obvious to those skilled in the art that the specific details need not be used to implement the present application. In other instances, well-known circuits, software or methods have not been specifically described in order to avoid obscuring the present application.

[0019] Throughout the specification, the references to "one embodiment", "an embodiment", "one example" or "an example" mean that a particular feature, structure or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present application. Therefore, the phrases "in one embodiment", "in an embodiment", "one example" or "an example" appearing throughout the specification do not necessarily all refer to the same embodiment or example. In addition, specific features, structures or characteristics can be combined in one or more embodiments or examples in any appropriate combination and / or sub-combination. In addition, those skilled in the art should understand that the drawings provided herein are for illustrative purposes only and the drawings are not necessarily drawn to scale.

[0020] Please refer to Figure 1An embodiment of the present application provides a semi-excavation and semi-filling roadbed and slope stability combined monitoring and evaluation method, which comprises the following steps:

[0021] S1, a data joint monitoring array is established, and multi-source distributed monitoring data is obtained by using the data joint monitoring array for data synchronous acquisition.

[0022] In the embodiment, engineering geological survey is performed on the target area, key parameters such as the junction position of the excavated slope and the filled roadbed, the depth of the potential sliding surface, and the thickness of the filled body are determined, and the optimization layout of the monitoring points is guided based on the key parameters. For the excavated slope part, since the stability is mainly controlled by the shear deformation of the deep soil body, a deep soil body horizontal displacement measuring instrument is selected as the main monitoring means; when laying, a vertical borehole is drilled at the key section such as the slope top and the stepped platform, the inclinometer tube is buried to a certain depth below the stable bedrock, and a sensor array is arranged in the tube at a fixed interval (usually 0.5 meters or 1.0 meter), thereby forming a vertical monitoring profile capable of continuously monitoring the horizontal displacement vector of the soil body at different depths. For the filled roadbed part, the stability problem mainly manifests as uneven settlement, and therefore a fiber displacement sensor is used for monitoring; the fiber displacement sensor is laid in a ring-shaped manner in the roadbed filler, and is particularly laid in a key manner in the settlement sensitive areas such as the filled-excavated junction, the road shoulder, and the roadbed center line, thereby forming a distributed settlement monitoring network covering the roadbed cross section and longitudinal section. Finally, the deep soil body horizontal displacement measuring instrument and the fiber displacement sensor are laid in a correlated manner in space, thereby forming a three-dimensional, multi-parameter data joint monitoring array from the deep layer to the shallow layer and from the slope to the roadbed.

[0023] Further, in order to realize synchronous acquisition of data, a strict time synchronization mechanism is constructed: on the one hand, a high-precision clock chip is provided for the data joint monitoring array, and time synchronization is performed through a network time protocol or a precise clock synchronization protocol, so as to ensure that the time stamp error of different instruments is controlled within milliseconds; on the other hand, a unified acquisition trigger cycle is set, and the processor simultaneously sends a start instruction to the data joint monitoring array, thereby realizing quasi-synchronous acquisition of data. On the communication architecture, according to the field conditions, wired or wireless transmission is adopted, and the data collected by each monitoring point is transmitted to the processor in real time. For the deep soil body horizontal displacement measuring instrument, the voltage value of each depth sensor is directly read and converted into a displacement amount, which is used as the deep soil body horizontal displacement amount of the excavated slope; for the fiber sensor, the light signal attenuation amount is converted into digitalized strain or displacement data by a demodulator, which is used as the settlement data of the filled roadbed; all collected data are marked with a unified time stamp and a device identifier before transmission, thereby forming structured monitoring data.

[0024] Further, the spatially distributed deep soil horizontal displacement data set is obtained from the deep soil horizontal displacement measuring instrument of the excavated slope, which contains the displacement values of each monitoring section and different depth points, and can depict the potential sliding zone position and deformation depth inside the slope. The data is sequential in time, reflecting the cumulative change amount and change rate of the displacement of a specific point over time. The distributed settlement data set is obtained from the fiber displacement sensor of the filled embankment, and the continuous and high-density settlement change information along the cable laying path is obtained by calculating the strain of each point of the fiber, which accurately reveals the settlement basin distribution of the embankment surface, the differential settlement at the intersection of excavation and filling, and the development trend of settlement. Finally, the deep soil horizontal displacement data set and the settlement data set are complementary in space and synchronized in time to form multi-source distributed monitoring data, which provides a rich and synchronous data basis for comprehensively analyzing the interaction between the thrust effect of the excavated slope and the resistance effect of the filled embankment from a holistic perspective.

[0025] See Figure 2 , which is a schematic diagram of the cross-section of the embankment-slope engineering structure. The left side of the diagram is the excavated slope part, which is provided with bored pile retaining structure. Deep soil horizontal displacement measuring instruments are arranged in the slope area, and inclinometer tubes are buried vertically and sensor arrays are arranged for monitoring the horizontal displacement of soil at different depths. The right side is the embankment part, and fiber displacement sensors are laid in a ring inside the filled embankment, which are layered and arranged at the intersection of excavation and filling, road shoulders and other positions for monitoring the settlement data of the embankment.

[0026] S2, quality control and preprocessing of the multi-source distributed monitoring data to obtain a multi-source standardized monitoring data set.

[0027] In this embodiment, the outlier determination index is a multi-criteria, hierarchical fusion judgment system: first, the physical possibility criterion is applied: according to the engineering mechanics common sense and the specific location of the monitoring point, a reasonable physical quantity change range is set for each type of sensor. For example, for the horizontal displacement of deep soil, the single change quantity should not exceed the displacement threshold corresponding to the local soil ultimate shear strength; for the settlement data, the cumulative settlement should not be greater than the total thickness of the embankment; any data point exceeding this physical limit will be marked as an outlier. Second, the statistical distribution criterion is used: for a data sequence of a monitoring point within a period of time (such as 24 hours), the moving average and standard deviation are calculated, and the Laiyida criterion is used for judgment, for example, if the displacement change of a monitoring point continuously deviates from the average value by more than three times the standard deviation for multiple periods, it is considered statistically abnormal. Finally, the correlation criterion is introduced: by using the characteristic that the deformation of adjacent monitoring points in semi-excavation and semi-filling embankment usually has spatial correlation, a local spatial correlation model is constructed. When the data of a monitoring point deviates significantly from the monitoring values of its surrounding points in terms of change trend or magnitude (i.e. spatial isolated point), and the instrument of this point is not malfunctioning, this data is determined to be a behavior outlier. Through the comprehensive outlier determination index, the abnormal monitoring data is determined, which lays a foundation for subsequent data repair and effectively avoids data anomalies caused by sensor transient failure, environmental electromagnetic interference or data transmission loss.

[0028] Specifically, in the normal deformation stage, the physical quantity (such as displacement, settlement) of adjacent monitoring points changes with spatial continuity, and the monitoring value of a certain point can be predicted to a certain extent based on the monitoring values of its adjacent points. When the actual measurement value of a certain point deviates significantly from its predicted value based on spatial relationship, the data of this point is determined to be a behavior outlier. Based on the above principle, a local spatial correlation model is constructed, which satisfies the following relationship:

[0029]

[0030]

[0031]

[0032] wherein, is the monitoring point at time is the predicted value, is the monitoring point index, is the time point of data acquisition, is the adjacent monitoring point index, is the number of adjacent monitoring points, is the weight coefficient, is the monitoring point at time is the observed value, For monitoring points With adjacent monitoring points The three-dimensional Euclidean distance between them The distance decay is a power of 1. To standardize the residuals, For monitoring points The standard deviation of the residuals of historical normal monitoring data in the local area This is the threshold for spatial anomaly detection.

[0033] In this embodiment, different time series prediction models are used to repair data and obtain a multi-source quality optimization monitoring dataset for different types of monitoring data and anomaly patterns. For data sequences with relatively stable changes and a certain degree of autocorrelation, an autoregressive integral moving average model is constructed: First, stationarity tests and differencing are performed on a sufficiently long period of normal historical data before the marked outlier position to determine the model parameters; then, the determined autoregressive integral moving average model is used to predict the normal value that should exist at the abnormal time point, and this predicted value is used to replace the original outlier value. For data sequences with obvious nonlinear characteristics and affected by multiple factors, a long short-term memory network model is used for repair. The long short-term memory network model can better capture long-term dependencies and complex patterns in the time series; using historical data (including displacement, settlement, and environmental data) as the training set, the long short-term memory network model is trained to learn the dynamic change patterns of the data, and then predict and repair outliers.

[0034] It should be noted that the repair process is dynamic: for a single isolated outlier, the predicted value is used for replacement; for multiple consecutive outliers, bidirectional predictive interpolation is performed using the normal data segments before and after them. The multi-source quality optimization monitoring dataset obtained through the time series prediction model not only optimizes data quality but also restores the possible trajectory of changes in the monitoring data to the greatest extent possible, ensuring the continuity and consistency of the data sequence over time and providing a guarantee for accurate trend analysis.

[0035] In this embodiment, standardization aims to eliminate the influence of dimensions and orders of magnitude, making monitoring data of different natures comparable. The multi-source quality optimization monitoring dataset is standardized using the Standard Score Normalization (Z-Score) method. After Z-Score standardization, all monitoring data are converted into dimensionless values ​​with a mean of 0 and a standard deviation of 1. The sign and magnitude of the value indicate the degree of deviation of the monitoring value from its historical normal fluctuation range, facilitating direct comparison of the relative severity of slope displacement and roadbed settlement.

[0036] Furthermore, coordinate unification was performed on the standardized dataset to achieve joint spatial analysis of roadbed and slope data. First, a unified spatial coordinate system was established for the entire monitoring area. Then, the location of each monitoring sensor was precisely determined in the spatial coordinate system through field measurements. This ensured that displacement vectors (including magnitude and direction) from deep soil horizontal displacement measuring instruments and settlement data from fiber optic displacement sensors were assigned precise three-dimensional spatial coordinates. Finally, spatial correlation was established through coordinate unification, integrating the data into a multi-source standardized monitoring dataset suitable for collaborative modeling. Each data point in the multi-source standardized monitoring dataset is a standardized observation with comparable significance under a unified timestamp and spatial framework, creating the necessary conditions for subsequently constructing a joint evaluation model integrating roadbed and slope information.

[0037] S3. Construct a joint evaluation model for roadbed-slope stability based on the multi-source standardized monitoring dataset, and obtain stability collaborative evaluation indicators based on the joint evaluation model for roadbed-slope stability.

[0038] Specifically, S3 includes the following steps:

[0039] S31. Construct a joint assessment model for roadbed-slope stability based on the multi-source standardized monitoring dataset.

[0040] Specifically, S31 includes the following steps:

[0041] S311. Construct a displacement-settlement collaborative analysis matrix based on the multi-source standardized monitoring dataset.

[0042] In this embodiment, the central difference method is used to obtain the rate of change of monitoring parameters for the horizontal displacement sequence and settlement data sequence in the multi-source standardized monitoring dataset, thereby obtaining the rate of change of horizontal displacement and the rate of change of settlement.

[0043] Specifically, the rate of change of horizontal displacement satisfies the following relationship:

[0044]

[0045] in, The rate of change of horizontal displacement. For a moment The sequence of horizontal displacements, For a moment The sequence of horizontal displacements, This represents the time interval for data collection.

[0046] Specifically, the rate of settlement change satisfies the following relationship:

[0047]

[0048] in, The rate of change of settlement, For a moment Settlement data sequence, For a moment Settlement data sequence, This represents the time interval for data collection.

[0049] It should be noted that the rate of change of horizontal displacement directly reflects the shear deformation trend of the deep soil in the cut slope, and its accelerated increase often indicates the formation and development of a potential sliding surface. The rate of change of settlement characterizes the compression and consolidation process of the embankment subgrade, and whether its value tends to stabilize is the key to determining whether the subgrade has entered a stable stage. By transforming the multi-source standardized monitoring dataset into the rate of change of monitoring parameters, the focus of the analysis is shifted to the deformation development trend, laying a data foundation for subsequent assessment of dynamic stability.

[0050] In this embodiment, the stability state of the roadbed-slope is jointly determined by the shoving action of the slope and the settlement of the roadbed, and the dominance of the two changes dynamically over time. When the slope deformation is severe, displacement is given a higher weight; when the roadbed settlement is dominant, settlement is given a higher weight.

[0051] Specifically, the instantaneous rate ratio is obtained by calculating the ratio of the horizontal displacement rate to the settlement rate. This instantaneous rate ratio is then mapped to a dynamic weighting coefficient between 0 and 1 using a sigmoid function (Sigmoid for short) to achieve a smooth transition. The dynamic weighting coefficient satisfies the following relationship:

[0052]

[0053]

[0054]

[0055] in, For displacement dynamic weighting coefficients, It is a natural constant. As a scale factor, The instantaneous rate ratio As the benchmark ratio, This is the settlement dynamic weighting coefficient. The rate of change of horizontal displacement. The sedimentation rate is the rate of change.

[0056] It should be noted that the scale factor is set in conjunction with historical data to control the steepness of the weight changes; furthermore, the baseline ratio represents the equilibrium point where displacement and settlement are of equal importance, determined based on numerical simulations, for example, when... (i.e., when the rate of change of horizontal displacement is significantly greater than the rate of change of settlement) Approaching 1, A value approaching 0 indicates that the current state is primarily controlled by slope displacement, and vice versa. This adaptive weight allocation mechanism ensures that the collaborative analysis matrix can accurately capture the shift in dominant risk sources between the roadbed and slope, enabling the joint roadbed-slope stability assessment model to possess dynamic focusing capabilities.

[0057] In this embodiment, a displacement-settlement collaborative analysis matrix is ​​constructed by combining a multi-source standardized monitoring dataset, the rate of change of monitoring parameters, and dynamic weighting coefficients. The displacement-settlement collaborative analysis matrix is ​​a data structure that integrates multi-dimensional information to comprehensively characterize the collaborative deformation state of the roadbed-slope system, and satisfies the following relationship:

[0058]

[0059] in, This is the displacement-settlement synergistic analysis matrix. For displacement dynamic weighting coefficients, For a moment The sequence of horizontal displacements, The rate of change of horizontal displacement. This is the settlement dynamic weighting coefficient. For a moment Settlement data sequence, The sedimentation rate is the rate of change.

[0060] It should be noted that the first row of the displacement-settlement co-analysis matrix represents the weighted slope displacement information, and the second row represents the weighted roadbed settlement information; the first column reflects the weighted deformation scale, and the second column reflects the weighted deformation rate.

[0061] S312. Construct a critical instability state model to obtain the roadbed-slope stability index.

[0062] In this embodiment, the ratio of the rate of change of horizontal displacement to the rate of change of settlement is used as the rate of change ratio criterion; the horizontal displacement acceleration and settlement acceleration are obtained based on the rate of change of the monitoring parameters, and an acceleration trend criterion is established based on the horizontal displacement acceleration and settlement acceleration; a critical instability state model is constructed by combining the rate of change ratio criterion and the acceleration trend criterion; and the roadbed-slope stability index is obtained based on the displacement-settlement synergistic analysis matrix and the critical instability state model.

[0063] Specifically, the central difference method is used to numerically differentiate the calculated rates of change of horizontal displacement and settlement to accurately calculate the horizontal displacement acceleration and settlement acceleration, satisfying the following relationship:

[0064]

[0065]

[0066] in, For horizontal displacement acceleration, For a moment The rate of change of horizontal displacement, For a moment The rate of change of horizontal displacement, The time interval for data collection. For settlement acceleration, For a moment The rate of settlement change, For a moment The rate of change in sedimentation.

[0067] Furthermore, an acceleration trend criterion is established based on acceleration. This acceleration trend criterion is a multi-dimensional, rule-based logical judgment system, including: First, sign determination, i.e. and Whether both are positive indicates that the slope displacement and roadbed settlement have entered an accelerated state; second, amplitude determination, that is, whether the absolute value of the acceleration exceeds the threshold preset based on historical data or theoretical analysis. Significantly exceeding the threshold indicates that the acceleration process is violent; third, persistence determination, that is, whether the positive acceleration or the state exceeding the threshold continues to appear within a certain time window (such as three consecutive monitoring cycles) to exclude accidental interference.

[0068] In this embodiment, a critical instability state model is constructed by combining the rate of change ratio criterion and the acceleration trend criterion. First, a criterion score is defined for the output result of each criterion. For the rate of change ratio criterion, the score is determined based on the real-time ratio. The first criterion score is assigned based on the degree of deviation from and the trend of the stable interval. For the acceleration trend criterion, a second criterion score is assigned based on a comprehensive judgment of its sign, magnitude, and duration. Given that acceleration is a more direct precursor to instability, it is given higher weight during fusion. Then, by combining a nonlinear mapping function (such as the Sigmoid function) and a weighting method, a clear stability state level is obtained, such as "stable," "basically stable," "warning," "dangerous," and "critical instability." This constitutes a critical instability state model that reflects the subgrade-slope state, satisfying the following relationship:

[0069]

[0070]

[0071]

[0072] in, The stability state level, It is a nonlinear mapping function. The weighting parameters for the criterion, As the first criterion for scoring, As the second criterion score, This indicates taking the maximum value. This indicates taking the minimum value. The instantaneous rate ratio As the benchmark ratio, This represents the maximum permissible deviation limit of the instantaneous rate ratio. Indicates within the time window Take the average value within the range. For amplitude scoring function, For horizontal displacement acceleration, For a continuous weighted function, This is the settlement acceleration.

[0073] It should be noted that the amplitude scoring function calculates the risk score based on the magnitude of the acceleration, while the duration weighting function weights the amplitude score based on the duration or frequency of positive acceleration.

[0074] In this embodiment, the stability state level output by the critical instability model is the core basis for generating the roadbed-slope stability index. The levels "Stable," "Basically Stable," "Warning," "Dangerous," and "Critical Instability" are mapped to the median values ​​of the numerical ranges 0-0.2, 0.2-0.4, 0.4-0.6, 0.6-0.8, and 0.8-1.0, respectively, to obtain the basic index value. Then, the spatial distribution information provided by the displacement-settlement co-analysis matrix is ​​used to correct the basic index value. For example, it is analyzed whether the monitoring point with the largest acceleration in the matrix is ​​located at a critical location of a potential slip surface or a sensitive area such as the cut-fill junction; and whether the acceleration areas of displacement and settlement show spatial correlation and consistency. If such a high-risk spatial distribution pattern appears, the basic index value is adjusted by adjusting the adjustment coefficient to obtain the roadbed-slope stability index.

[0075] S313. Combine the displacement-settlement synergistic analysis matrix and the roadbed-slope stability index to establish the roadbed-slope state potential energy function, and use the roadbed-slope state potential energy function as the joint evaluation model for roadbed-slope stability.

[0076] In this embodiment, firstly, principal component analysis is performed on the displacement-settlement co-analysis matrix. The matrix containing multi-dimensional deformation information at each time point is expanded into an eigenvector, and two principal components are extracted as control variables, including a first control variable related to the displacement principal component and a second control variable related to the settlement principal component, thereby achieving feature decoupling of multi-source monitoring data. Next, the roadbed-slope stability index output by the critical instability state model is used as the state variable. This index is a comprehensive indicator that quantifies the macroscopic stability of the system. Finally, based on the cusp catastrophe theory framework, a nonlinear mapping relationship between the control variables and the state variables is established, and the roadbed-slope state potential energy function is constructed, satisfying the following relationship:

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] in, Let be the potential energy function of the roadbed-slope state. As the first control variable, As the second control variable, For state variables, For coefficient functions, The stability constraint coefficient is... The ideal steady-state index, These are the parameters of the coefficient function.

[0083] It should be noted that the ideal stability index is the stability index when the roadbed-slope system is in its most ideal stable state; the parameters of the coefficient function are constants determined by regression of historical monitoring data using the least squares method.

[0084] S32. Obtain the stability synergistic evaluation index based on the aforementioned roadbed-slope stability joint evaluation model.

[0085] In this embodiment, multidimensional characteristic parameters of the roadbed-slope are obtained based on the joint evaluation model of roadbed-slope stability. These parameters include: potential energy curvature, obtained by calculating the eigenvalues ​​of the second-order partial derivative matrix of the roadbed-slope state potential energy function at the current state point. The magnitude and sign of the eigenvalues ​​directly reflect the restoring force of the roadbed-slope system in the current state. A deep and steep potential energy curvature indicates good stability and strong resistance to disturbances, while a shallow and flat potential energy curvature indicates weak stability. The potential barrier height is quantified by calculating the vertical energy difference from the potential energy minimum point at the current state to the nearest potential energy maximum point. The potential barrier height represents the energy barrier that the roadbed-slope system needs to overcome to transition from the current stable state to another state (including the unstable state). The larger the potential barrier height, the higher the probability that the roadbed-slope system will maintain its current stable state and the less likely it is to experience sudden instability. The distance between the bifurcation points is the set of all critical instability points of the subgrade-slope state potential energy function with respect to the control variables. The critical instability point is the set of simultaneous solutions to the subgrade-slope state potential energy function where both the first and second partial derivatives are zero.

[0086] Furthermore, firstly, the system's sensitivity to external disturbances is assessed by the potential energy curvature at the current state point to obtain the stability sensitivity coefficient; secondly, based on the potential barrier height, the probability of overcoming the potential barrier height is transformed into the probability of maintaining system stability over a future period of time through the Boltzmann distribution principle in statistical physics; finally, the shortest distance from the current state's position in the control variables to the critical instability boundary (branching point set) is calculated as the stability safety margin.

[0087] The above stability sensitivity coefficients satisfy the following relationship:

[0088]

[0089] in, This is the stability sensitivity coefficient. It is a natural constant. To adjust parameters, Let be the potential energy curvature at the current state point. This is the critical threshold for curvature.

[0090] The above stability maintenance probability satisfies the following relationship:

[0091]

[0092] in, To maintain stability probability, It is a natural constant. An empirical parameter characterizing the intensity of external disturbances. Boltzmann's constant, The height of the barrier.

[0093] The above stability safety margin satisfies the following relationship:

[0094]

[0095] in, For stability safety margin, The shortest Euclidean distance from the projection of the current state point onto the control variables to the set of branch points. This represents the theoretically maximum possible distance.

[0096] In this embodiment, the weights of each sub-indicator are determined by a combination of subjective and objective methods: the stability sensitivity coefficient is given the highest weight because it directly represents the immediate vulnerability of the system; the stability maintenance probability is given a medium weight because it is associated with medium- and long-term risks; and the stability safety margin, as a basic buffer space indicator, has a relatively low weight. The fusion process uses the weighted geometric average method to calculate the stability collaborative evaluation index.

[0097] S4. Construct a multi-level stability assessment mechanism, and combine the stability collaborative assessment index and the multi-level stability assessment mechanism to obtain the joint stability monitoring and assessment results.

[0098] In this embodiment, a multi-level dynamic threshold range of stability collaborative evaluation indicators is obtained, and a multi-level stability evaluation mechanism is constructed based on the multi-level dynamic threshold range. The stability collaborative evaluation indicators are compared in real time according to the multi-level stability evaluation mechanism to obtain preliminary monitoring and evaluation results. The preliminary monitoring and evaluation results are collaboratively verified based on a multi-source standardized monitoring dataset to obtain joint stability monitoring and evaluation results.

[0099] Specifically, firstly, based on historical long-term monitoring data, cluster analysis is performed on the stability co-assessment indicators. Combined with numerical simulation and theoretical calculations, three static threshold intervals—"stable," "alert," and "dangerous"—are initially defined. Then, dynamic adjustment factors are introduced, including: a time factor, considering the long-term consolidation effect of the roadbed fill, the lower threshold of the stable zone can be appropriately relaxed over time; and an environmental factor, such as during the rainy season or periods of high seismic activity, due to increased external disturbances, the lower thresholds of the "alert" and "dangerous" zones should be adaptively lowered to improve early warning sensitivity. Finally, based on these static benchmarks and dynamic adjustment factors, a multi-level dynamic threshold interval that changes with time and environment is generated.

[0100] Furthermore, a multi-level stability assessment mechanism is constructed based on the multi-level dynamic threshold range to obtain preliminary monitoring and assessment results: when the stability co-assessment index falls into the "stable" range (stability co-assessment index ≥ 0.7), the system is assessed as safe; when it falls into the "alert" range (e.g., 0.4 ≤ stability co-assessment index < 0.7), it is assessed as requiring attention, and encrypted monitoring is initiated; when it falls into the "dangerous" range (e.g., stability co-assessment index < 0.4), it is assessed as high-risk, an early warning is issued, and engineering measures are taken.

[0101] In this embodiment, after the preliminary monitoring and assessment results are generated, the multi-source standardized monitoring data within the same time period are retrospectively analyzed. The verification includes: consistency verification, checking whether key parameters in the displacement-settlement co-analysis matrix also show a synchronous deterioration trend, and whether this coincides with the spatiotemporal decrease in the stability co-assessment indicators; spatial distribution verification, analyzing whether deformation anomalies are concentrated at key locations such as cut-fill junctions and potential slip surfaces, confirming the rationality of the spatial location of the risk; and causal logic verification: performing correlation analysis between deformation data and environmental records such as rainfall and vibration during the same period to determine whether there are reasonable external factors leading to instability. The preliminary monitoring and assessment results verified by the multi-source standardized monitoring dataset are used as the joint stability monitoring and assessment results.

[0102] Please see Figure 3 In an optional embodiment, the present invention provides a joint monitoring and evaluation system for the stability of a semi-cut and semi-fill roadbed and slope. The system includes an input device, an output device, a processor, and a memory, all interconnected. The memory stores a computer program comprising program instructions, and the processor is configured to invoke the program instructions to execute specific steps as described in the embodiments of the joint monitoring and evaluation method for the stability of a semi-cut and semi-fill roadbed and slope provided by the present invention. The joint monitoring and evaluation system for the stability of a semi-cut and semi-fill roadbed and slope provided by the present invention has a complete structure, is objective and stable, and enhances the overall applicability and practical application capability of the present invention.

[0103] In summary, the present invention provides a method and system for joint monitoring and evaluation of the stability of semi-cut and semi-fill roadbeds and slopes. By integrating a joint monitoring array of deep soil horizontal displacement measurement and fiber optic settlement sensing, it achieves synchronous acquisition of multi-source data and unified spatiotemporal benchmarks. The data undergoes quality control and preprocessing to construct a displacement-settlement synergistic analysis matrix, and a roadbed-slope state potential energy function is constructed as a joint evaluation model. By quantifying the mapping relationship between control variables and state variables, multi-dimensional feature parameters are extracted and fused to generate stability synergistic evaluation indicators. A dynamic multi-level evaluation mechanism is established, and the evaluation results are verified through the synergy of displacement and settlement deformation, outputting reliable stability joint monitoring and evaluation results, thus realizing a closed-loop chain from data perception to evaluation. The method of this invention is easy to understand, computationally simple, requires less workload, and is convenient for engineering applications, providing a theoretical foundation and technical support for the further development of geotechnical engineering monitoring.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for joint monitoring and evaluation of the stability of semi-cut / semi-fill roadbed and slope, characterized in that, Includes the following steps: A joint data monitoring array is established, and multi-source distributed monitoring data is obtained by synchronously collecting data using the joint data monitoring array. The multi-source distributed monitoring data is subjected to quality control and preprocessing to obtain a multi-source standardized monitoring dataset; A joint assessment model for roadbed-slope stability is constructed based on the multi-source standardized monitoring dataset, and a collaborative stability assessment index is obtained based on the joint assessment model for roadbed-slope stability. A multi-level stability assessment mechanism is constructed, and the joint stability monitoring and assessment results are obtained by combining the stability collaborative assessment index and the multi-level stability assessment mechanism. The establishment of a joint data monitoring array, and the use of the joint data monitoring array to synchronously collect multi-source distributed monitoring data, includes: A joint data monitoring array is established by combining deep soil horizontal displacement measuring instruments and fiber optic displacement sensors; The horizontal displacement of deep soil in the excavated slope is collected by the deep soil horizontal displacement measuring instrument, and the settlement data of the embankment is collected by the fiber optic displacement sensor. The horizontal displacement of the deep soil and the settlement data are used as the multi-source distributed monitoring data. The construction of the joint assessment model for roadbed-slope stability based on the multi-source standardized monitoring dataset includes: A displacement-settlement synergistic analysis matrix was constructed based on the aforementioned multi-source standardized monitoring dataset; Construct a critical instability state model to obtain the roadbed-slope stability index; A roadbed-slope state potential energy function is established by combining the displacement-settlement synergistic analysis matrix and the roadbed-slope stability state index, and the roadbed-slope state potential energy function is used as the joint evaluation model for roadbed-slope stability. The construction of the displacement-settlement collaborative analysis matrix based on the multi-source standardized monitoring dataset includes: The rate of change of monitoring parameters, including the rate of change of horizontal displacement and the rate of change of settlement, are obtained based on the multi-source standardized monitoring dataset. The dynamic weighting coefficients, including the displacement dynamic weighting coefficient and the settlement dynamic weighting coefficient, are determined based on the ratio of the horizontal displacement change rate to the settlement change rate. The displacement-settlement synergistic analysis matrix is ​​constructed by combining the multi-source standardized monitoring dataset, the rate of change of the monitoring parameters, and the dynamic weighting coefficients. The construction of the critical instability state model to obtain the roadbed-slope stability index includes: The ratio of the horizontal displacement rate to the settlement rate is used as the rate of change criterion. The horizontal displacement acceleration and settlement acceleration are obtained based on the rate of change of the monitored parameters, and an acceleration trend criterion is established based on the horizontal displacement acceleration and the settlement acceleration. The critical instability state model is constructed by combining the rate of change ratio criterion and the acceleration trend criterion. The roadbed-slope stability index is obtained based on the displacement-settlement synergistic analysis matrix and the critical instability state model. The establishment of the subgrade-slope state potential energy function by combining the displacement-settlement synergistic analysis matrix and the subgrade-slope stability index includes: Principal component analysis was performed on the displacement-settlement synergistic analysis matrix to obtain control variables, including a first control variable related to the principal component of displacement and a second control variable related to the principal component of settlement. The roadbed-slope stability index is used as a state variable; Establish the mapping relationship between the control variables and the state variables to obtain the roadbed-slope state potential energy function; The stability synergistic assessment index obtained based on the roadbed-slope stability joint assessment model includes: Based on the aforementioned joint evaluation model for roadbed-slope stability, multidimensional characteristic parameters of the roadbed-slope are obtained, including potential energy curvature, potential barrier height, and distance between the set of bifurcation points. The stability sensitivity coefficient is obtained based on the potential energy curvature, the stability maintenance probability is obtained based on the potential barrier height, and the stability safety margin is obtained based on the distance of the bifurcation point set. The stability collaborative evaluation index is obtained by weighting and fusing the stability sensitivity coefficient, the stability maintenance probability, and the stability safety margin.

2. The method for joint monitoring and evaluation of the stability of semi-cut and semi-fill roadbed and slope as described in claim 1, characterized in that, The process of quality control and preprocessing the multi-source distributed monitoring data to obtain a multi-source standardized monitoring dataset includes: Obtain the outlier determination index of the multi-source distributed monitoring data, and obtain the outlier monitoring value based on the outlier determination index; A time series prediction model is constructed based on the historical data change trend, and the abnormal monitoring values ​​are repaired to obtain a multi-source quality optimization monitoring dataset. The multi-source quality optimization monitoring dataset is standardized and its coordinates are unified to obtain the multi-source standardized monitoring dataset.

3. The method for joint monitoring and evaluation of the stability of semi-cut and semi-fill roadbed and slope as described in claim 1, characterized in that, The construction of a multi-level stability assessment mechanism, combining the stability collaborative assessment index and the multi-level stability assessment mechanism to obtain joint stability monitoring and assessment results, includes: Obtain the multi-level dynamic threshold range of the stability collaborative evaluation index, and construct the multi-level stability evaluation mechanism based on the multi-level dynamic threshold range; Preliminary monitoring and evaluation results are obtained by comparing the stability collaborative evaluation indicators in real time based on the multi-level stability evaluation mechanism. The stability joint monitoring and evaluation results are obtained by co-validating the preliminary monitoring and evaluation results based on the multi-source standardized monitoring dataset.

4. A joint monitoring and evaluation system for the stability of semi-cut / semi-fill roadbeds and slopes, characterized in that, The system includes an input device, an output device, a processor, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to call the program instructions to execute the joint monitoring and evaluation method for the stability of a semi-cut and semi-fill roadbed and slope as described in any one of claims 1-3.

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