Half-excavated and half-filled roadbed and slope stability combined monitoring and evaluation method and system

Abstract

The invention relates to the field of geotechnical engineering monitoring, in particular to a half-excavated and half-filled roadbed and slope stability combined monitoring and evaluation method and system, and the method comprises the following steps: building a data combined monitoring array, and carrying out the data synchronous collection through the data combined monitoring array, so as to obtain multi-source distributed monitoring data; 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; and constructing a multi-stage stability evaluation mechanism, and combining the stability collaborative evaluation index and the multi-stage stability evaluation mechanism to obtain a stability joint monitoring evaluation result. According to the method, the comprehensiveness and the accuracy of combined monitoring and evaluation of the stability of the half-excavated and half-filled roadbed and the slope are improved, and effective data support is provided for engineering decision 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 step of constructing a joint assessment model for roadbed-slope stability based on the multi-source standardized monitoring dataset includes: constructing a displacement-settlement synergistic analysis matrix based on the multi-source standardized monitoring dataset; constructing a critical instability state model to obtain a roadbed-slope stability index; and establishing a roadbed-slope state potential energy function by combining the displacement-settlement synergistic analysis matrix and the roadbed-slope stability index, using the roadbed-slope state potential energy function as the joint assessment model for roadbed-slope stability. This invention, by constructing a joint assessment model for roadbed-slope stability, achieves synergistic analysis of roadbed and slope stability, transforms multi-source monitoring data into quantitative assessment indicators, accurately reflects the overall stability state, and overcomes the one-sidedness of traditional single-factor assessments.

[0009] Optionally, constructing the displacement-settlement synergistic analysis matrix based on the multi-source standardized monitoring dataset includes: obtaining the rate of change of monitoring parameters, including the rate of change of horizontal displacement and the rate of change of settlement, based on the multi-source standardized monitoring dataset; determining dynamic weight coefficients, including dynamic weight coefficients for displacement and dynamic weight coefficients for settlement, based on the ratio of the rate of change of horizontal displacement to the rate of change of settlement; and constructing the displacement-settlement synergistic analysis matrix by combining the multi-source standardized monitoring dataset, the rate of change of monitoring parameters, and the dynamic weight coefficients. This invention determines dynamic weight coefficients based on the rate of change of monitoring parameters, making the displacement-settlement synergistic analysis matrix more closely reflect the dynamic changes in actual engineering projects. The dynamic weights reflect the importance of different parameters at different stages, enhancing the ability to characterize the engineering state and providing a more accurate analytical basis for subsequent stability assessment.

[0010] Optionally, the step of constructing a critical instability state model to obtain a roadbed-slope stability index includes: using the ratio of the horizontal displacement change rate to the settlement change rate as a rate-of-change ratio criterion; obtaining horizontal displacement acceleration and settlement acceleration based on the change rates of the monitored parameters; establishing an acceleration trend criterion based on the horizontal displacement acceleration and the settlement acceleration; constructing the critical instability state model by combining the rate-of-change ratio criterion and the acceleration trend criterion; and obtaining the roadbed-slope stability index based on the displacement-settlement synergistic analysis matrix and the critical instability state model. This invention combines the rate-of-change ratio criterion and the acceleration trend criterion to construct a critical instability state model, comprehensively judging instability risk from both rate and trend perspectives, and obtaining a stability index based on the synergistic analysis matrix, thereby achieving a quantitative characterization of the roadbed-slope stability state and improving the timeliness and accuracy of instability assessment.

[0011] Optionally, establishing the subgrade-slope state potential energy function by combining the displacement-settlement synergistic analysis matrix and the subgrade-slope stability index includes: performing principal component analysis on the displacement-settlement synergistic analysis matrix to obtain control variables, including a first control variable related to the displacement principal component and a second control variable related to the settlement principal component; using the subgrade-slope stability index as a state variable; and establishing a mapping relationship between the control variables and the state variables to obtain the subgrade-slope state potential energy function. This invention extracts key control variables from the synergistic matrix through principal component analysis and establishes a potential energy function by combining it with the stability index, achieving a precise mapping between control variables and state variables, intuitively reflecting the changing patterns of subgrade-slope stability, and providing strong support for subsequent acquisition of evaluation indicators.

[0012] Optionally, obtaining the stability collaborative evaluation index based on the roadbed-slope stability joint evaluation model includes: obtaining multi-dimensional characteristic parameters of the roadbed-slope based on the roadbed-slope stability joint evaluation model, including potential energy curvature, barrier height, and distance between bifurcation points; obtaining a stability sensitivity coefficient based on the potential energy curvature, obtaining a stability maintenance probability based on the barrier height, and obtaining a stability safety margin based on the distance between bifurcation points; and weightedly fusing the stability sensitivity coefficient, the stability maintenance probability, and the stability safety margin to obtain the stability collaborative evaluation index. This invention extracts multi-dimensional characteristic parameters from the evaluation model and transforms them into sensitivity coefficients, maintenance probabilities, and safety margins, then weightedly fuses them to obtain the collaborative evaluation index. The multi-dimensional characteristic parameters comprehensively cover key stability information, and the weighted fusion improves the comprehensiveness and representativeness of the index, providing accurate quantitative basis for the evaluation results.

[0013] Optionally, the construction of a multi-level stability assessment mechanism, combining the stability collaborative assessment indicators and the multi-level stability assessment mechanism to obtain joint stability monitoring and assessment results, includes: obtaining multi-level dynamic threshold ranges of the stability collaborative assessment indicators; constructing the multi-level stability assessment mechanism based on the multi-level dynamic threshold ranges; comparing the stability collaborative assessment indicators in real time according to the multi-level stability assessment mechanism to obtain preliminary monitoring and assessment results; and performing collaborative verification on the preliminary monitoring and assessment results based on the multi-source standardized monitoring dataset to obtain the joint stability monitoring and assessment results. This invention constructs a multi-level dynamic threshold assessment mechanism, performs real-time comparison of collaborative assessment indicators, and combines multi-source data for collaborative verification. This adapts to different engineering scenarios, and the collaborative verification reduces assessment errors, ensuring the reliability of the assessment results.

[0014] Secondly, this invention provides a joint monitoring and evaluation system for the stability of semi-cut / semi-fill roadbeds and slopes. The system executes the joint monitoring and evaluation method for the stability of semi-cut / semi-fill roadbeds and slopes provided by this invention. The system includes input devices, output devices, a processor, and a memory, all interconnected. The memory stores a computer program, which includes program instructions, and the processor is configured to call these instructions. This invention, through the collaboration of high-performance hardware, automates and systematizes the monitoring and evaluation process, ensuring convenient data interaction and making the entire monitoring and evaluation process efficient and orderly, thus providing stable and efficient hardware support for engineering safety management. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method for joint monitoring and evaluation of the stability of a semi-cut and semi-fill roadbed and slope, according to an embodiment of the present invention. Figure 2 This is a schematic cross-sectional view of the roadbed-slope engineering structure according to an embodiment of the present invention; Figure 3 This is a framework diagram of a joint monitoring and evaluation system for the stability of a semi-cut and semi-fill roadbed and slope, according to an embodiment of the present invention. Detailed Implementation

[0016] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0017] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0018] Please see Figure 1 One embodiment of the present invention provides a method for joint monitoring and evaluation of the stability of a semi-cut and semi-fill roadbed and slope, the method comprising the following steps: S1. Establish a joint data monitoring array and use the joint data monitoring array to collect multi-source distributed monitoring data synchronously.

[0019] In this embodiment, an engineering geological survey is conducted on the target area to clarify key parameters such as the boundary between the cut slope and the embankment, the depth of the potential sliding surface, and the thickness of the embankment. Based on these key parameters, the optimized layout of monitoring points is guided. For the cut slope, since its stability is mainly controlled by the shear deformation of deep soil and rock, a deep soil horizontal displacement measuring instrument is selected as the main monitoring method. During deployment, vertical boreholes are drilled at key sections such as the slope top and graded platforms, and inclinometer tubes are buried to a certain depth below the stable bedrock. Sensor arrays are arranged inside the tubes at fixed intervals (usually 0.5 meters or 1.0 meters) to form a vertical monitoring profile that can continuously monitor the horizontal displacement vector of soil at different depths. For the embankment subgrade, its stability issue mainly manifests as uneven settlement; therefore, fiber optic displacement sensors are used for monitoring. These sensors are laid in a ring within the subgrade fill material, with particular emphasis on settlement-sensitive areas such as the cut-fill junction, shoulders, and the subgrade centerline, forming a distributed settlement monitoring network covering both the cross-section and longitudinal section of the subgrade. Finally, by spatially coordinating the deployment of deep soil horizontal displacement measuring instruments and fiber optic displacement sensors, a three-dimensional, multi-parameter joint monitoring array is formed, encompassing deep to shallow layers and from slopes to the subgrade.

[0020] Furthermore, to achieve synchronous data acquisition, a strict time synchronization mechanism is constructed: on the one hand, a high-precision clock chip is equipped for the joint data monitoring array, and time synchronization is achieved through network time protocols or precision clock synchronization protocols to ensure that the timestamp error of different instruments is controlled within milliseconds; on the other hand, a unified acquisition trigger cycle is set, and the processor simultaneously sends start commands to the joint data monitoring array to achieve quasi-synchronous data acquisition. In terms of communication architecture, wired or wireless transmission methods are used according to site conditions to transmit the data collected from each monitoring point to the processor in real time. For deep soil horizontal displacement measuring instruments, the voltage values ​​of each depth sensor are directly read and converted into displacement values ​​as the deep soil horizontal displacement of the excavated slope; for fiber optic sensors, the demodulator converts the optical signal attenuation into digital strain or displacement data as the settlement data of the embankment subgrade; all acquired data are tagged with a unified timestamp and device identifier before transmission, forming structured monitoring data.

[0021] Furthermore, spatially distributed datasets of deep soil horizontal displacement were obtained from instruments measuring deep soil horizontal displacement on cut slopes. These datasets include displacement values ​​at different depths for each monitoring section, depicting the location and deformation depth of potential slip zones within the slope. The data is temporally sequential, reflecting the cumulative change and rate of change of displacement at specific points over time. Distributed settlement datasets were obtained from fiber optic displacement sensors on embankment subgrades. By calculating the strain at each point on the fiber optic cable, continuous, high-density settlement variation information along the cable laying path was obtained, accurately revealing the distribution of settlement basins on the subgrade surface, differential settlement at the cut-fill interface, and the development trend of settlement. Finally, the deep soil horizontal displacement dataset and the settlement dataset were spatially complementary and temporally synchronized, forming multi-source distributed monitoring data. This provides a rich and synchronized data foundation for a comprehensive analysis of the interaction between the thrust effect of cut slopes and the resistance effect of embankment subgrades from a holistic perspective.

[0022] Please see Figure 2 The diagram shows a cross-sectional view of the roadbed-slope engineering structure. The left side of the diagram represents the cut slope section, which is equipped with a bored pile retaining structure. Deep soil horizontal displacement measuring instruments are arranged in the slope area. Inclined tubes are buried through vertical drilling and sensor arrays are arranged to monitor the horizontal displacement of soil at different depths. The right side represents the roadbed section, where fiber optic displacement sensors are laid in a ring inside the fill roadbed and are arranged in layers at the cut-fill junction, shoulder, and other locations to monitor the settlement data of the roadbed.

[0023] S2. Perform quality control and preprocessing on the multi-source distributed monitoring data to obtain a multi-source standardized monitoring dataset.

[0024] In this embodiment, the outlier determination index is a multi-criteria, hierarchical fusion judgment system: First, a physical probability criterion is applied: based on engineering mechanics common sense and the specific location of the monitoring points, a reasonable range of physical quantity variation is set for each type of sensor. For example, for the horizontal displacement of deep soil, its single change should not exceed the displacement threshold corresponding to the local soil ultimate shear strength; for settlement data, its 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, a statistical distribution criterion is adopted: for the data sequence of each monitoring point over a period of time (e.g., 24 hours), its moving average and standard deviation are calculated, and the Laida criterion is used for judgment. For example, if the displacement change of a certain measuring point continuously deviates from the average value by more than three times the standard deviation over multiple consecutive periods, it is considered a statistical outlier. Finally, a correlation criterion is introduced: utilizing the characteristic that the deformation of adjacent monitoring points in a semi-cut and semi-fill embankment usually has spatial correlation, a local spatial correlation model is constructed. When the data from a monitoring point deviates significantly from the monitoring values ​​of its surrounding points in terms of trend or magnitude (i.e., a spatially isolated point), and the instrument at that point itself is not faulty, this data is identified as an anomaly. Comprehensive anomaly detection through anomaly identification indicators lays the foundation for subsequent data repair and effectively avoids data anomalies caused by momentary sensor failures, environmental electromagnetic interference, or data transmission loss.

[0025] Specifically, during the normal deformation phase, the changes in physical quantities (such as displacement and settlement) at adjacent monitoring points exhibit spatial continuity, and the monitoring value at a certain point can be predicted to some extent based on the monitoring values ​​of its neighboring points. When the actual measured value at a point deviates significantly from its predicted value based on spatial relationships, the data at that point is identified as an anomaly. Based on the above principle, a local spatial correlation model is constructed, satisfying the following relationship: in, For monitoring points In time The predicted value, For monitoring point indexing, For the time point of data collection, For indexing adjacent monitoring points, This represents the number of adjacent monitoring points. These are the weighting coefficients. For monitoring points In time The observed values, 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Specifically, S3 includes the following steps: S31. Construct a joint assessment model for roadbed-slope stability based on the multi-source standardized monitoring dataset.

[0032] Specifically, S31 includes the following steps: S311. Construct a displacement-settlement collaborative analysis matrix based on the multi-source standardized monitoring dataset.

[0033] 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.

[0034] Specifically, the rate of change of horizontal displacement satisfies the following relationship: 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.

[0035] Specifically, the rate of settlement change satisfies the following relationship: 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.

[0036] 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.

[0037] 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.

[0038] 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: 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.

[0039] 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.

[0040] 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: 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.

[0041] 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.

[0042] S312. Construct a critical instability state model to obtain the roadbed-slope stability index.

[0043] 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.

[0044] 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: 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.

[0045] 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.

[0046] 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: in, The stability state level, It is a nonlinear mapping function. The weighting parameters of 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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: 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.

[0051] 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.

[0052] S32. Obtain the stability synergistic evaluation index based on the aforementioned roadbed-slope stability joint evaluation model.

[0053] 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.

[0054] 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.

[0055] The above stability sensitivity coefficients satisfy the following relationship: 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.

[0056] The above stability maintenance probability satisfies the following relationship: 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.

[0057] The above stability safety margin satisfies the following relationship: 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

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 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.

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 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.

4. 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 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 index, and the roadbed-slope state potential energy function is used as the joint evaluation model for roadbed-slope stability.

5. The method for joint monitoring and evaluation of the stability of semi-cut and semi-fill roadbed and slope according to claim 4, characterized in that, 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 collaborative 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.

6. The method for joint monitoring and evaluation of the stability of semi-cut and semi-fill roadbed and slope as described in claim 5, characterized in that, 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.

7. The method for joint monitoring and evaluation of the stability of semi-cut and semi-fill roadbed and slope as described in claim 6, characterized in that, 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.

8. 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 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.

9. 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.

10. 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 1-9.

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