Method for evaluating treatment effect of collapsibility of highway loess original foundation

Abstract

The present application relates to the technical field of highway foundation treatment and monitoring, and discloses a method for evaluating the treatment effect of collapsibility of highway loess original foundation. The method comprises the following steps: establishing an initial model based on engineering data, dynamically correcting the initial model by using real-time monitoring data after construction, and generating a dynamic response model of collapsibility. By fusing the spatio-temporal superposition results of real-time data and historical exploration data, a comprehensive evaluation feature field is constructed. The feature field is divided into regions and the difference is calculated, a response difference field is generated, and a potential collapsible abnormal area is marked. Based on the characteristics of the abnormal area, the model parameters are iteratively corrected, and virtual load working conditions are introduced to simulate the development process of collapsibility under different loads. Finally, the evaluation results are output by visualizing the interactive model. The method realizes the transformation of collapsibility evaluation from static to dynamic, from current situation analysis to development prediction, and improves the timeliness, accuracy and engineering early warning ability of the evaluation.

Description

Technical Field

[0001] This invention relates to the field of highway foundation treatment and monitoring technology, specifically a method for evaluating the effectiveness of treatment for the collapsibility of original loess foundations for highways. Background Technology

[0002] In highway construction, the collapsibility of loess foundations is a key factor affecting project safety. Currently, commonly used methods for assessing collapsibility typically establish static models based on pre-construction engineering geological survey data, and conduct post-construction comparative evaluations using point-based tests such as on-site sampling or static cone penetration tests. This method relies on exploration parameters at fixed times and locations, failing to effectively incorporate continuous changes in the foundation's condition after treatment. This results in assessment conclusions lagging behind actual project progress and failing to accurately reflect the true effectiveness of treatment measures.

[0003] Existing technical solutions suffer from static and discretization limitations. Static models cannot be updated to reflect actual changes in foundation properties, resulting in assessment results that are out of sync with the dynamic development of the project. Traditional methods often rely on data comparison from a limited number of monitoring points and empirical judgment to delineate abnormal areas, lacking an objective and quantitative identification mechanism based on multi-source data fusion and spatial difference calculation. The assessment process typically stops at a description of the current state, failing to provide predictive analysis of the subsidence development process under different load conditions. This leads to inaccurate identification of potential risk areas and makes it difficult to scientifically predict the long-term stability of treatment effects.

[0004] The core problem this invention aims to solve is how to transform the evaluation of collapsibility treatment effectiveness from a static, discrete, post-hoc judgment into a dynamic, continuous, and predictive process evaluation. The key lies in constructing an analytical model that can integrate and dynamically update real-time monitoring data, and establishing a risk warning mechanism based on data difference identification and virtual load simulation. Summary of the Invention

[0005] The purpose of this invention is to provide a method for evaluating the effectiveness of treatment for the collapsibility of loess foundations in highways, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this invention provides a method for evaluating the effectiveness of treatment for the collapsibility of loess foundations along highways, the method comprising:

[0007] Based on the engineering geological data of the untreated foundation and the preset collapsibility induction parameters, an initial collapsibility model of the original loess foundation for highways was established.

[0008] The initial collapsibility model is dynamically corrected based on real-time on-site monitoring data after the treatment construction to generate a collapsibility dynamic response model.

[0009] The real-time on-site monitoring data is spatiotemporally overlaid with the historical exploration data before the processing construction to generate a dataset comparing the foundation state before and after the processing.

[0010] The collapsibility dynamic response model is fused with the foundation state comparison dataset before and after treatment to generate a comprehensive evaluation feature field of foundation collapsibility.

[0011] The characteristic field of the comprehensive evaluation of the collapsibility of the foundation is divided into regions and the differences are calculated to obtain the collapsibility response difference field.

[0012] Based on the collapsibility response difference field and preset threshold conditions, potential collapsibility anomaly areas in the original loess foundation of highways are identified;

[0013] Based on the distribution and characteristics of potential collapsible anomaly areas, the internal parameters of the collapsible dynamic response model are iteratively corrected, and virtual load conditions are introduced into the corrected model to simulate the collapsible development process of the original loess foundation of highways under different load conditions.

[0014] A visual interactive model is constructed that includes the collapsibility development process, the collapsibility response difference field, and potential collapsibility anomaly areas. The model receives evaluation parameter adjustment instructions through the visual interactive model to drive model updates and outputs updated collapsibility evaluation results.

[0015] Preferably, the establishment of the initial collapsibility model of the original loess foundation for highways includes the following steps:

[0016] Obtain the engineering geological survey report before the original foundation treatment of the highway loess, and extract the soil layer profile information, initial moisture content information and historical collapsibility coefficient information from the engineering geological survey report;

[0017] Set collapsibility inducing parameters including immersion rate, immersion range, and additional load, which are used to simulate the conditions of foundation being wetted by water and external loading;

[0018] Based on the soil profile information, the initial moisture content information, the historical collapsibility coefficient information, and the collapsibility induction parameters, the deformation and stress response of the untreated foundation under different collapsibility inductions are calculated and simulated through the constitutive relationship of the foundation soil, generating an initial collapsibility model of the original loess foundation of the highway. The output of the initial collapsibility model is a simulation state dataset.

[0019] Preferably, the generation of the collapsibility dynamic response model includes the following steps:

[0020] A sensor network was deployed to monitor the original loess foundation of the highway after the construction was completed, and real-time monitoring data including real-time moisture content, soil pressure and surface settlement were continuously collected.

[0021] The real-time on-site monitoring data is structured according to the collection time and spatial location to form a spatiotemporally continuous on-site monitoring dataset.

[0022] Input the field monitoring dataset into the initial collapsibility model and compare the differences between the simulated state dataset and the field monitoring dataset.

[0023] Based on the difference calculation model correction coefficient, and according to the model correction coefficient, the internal calculation parameters of the initial collapsibility model are dynamically adjusted to generate a collapsibility dynamic response model that can reflect the actual state of the foundation after treatment. The collapsibility dynamic response model outputs the corrected simulation state data.

[0024] Preferably, the generation of the foundation state comparison dataset before and after processing includes the following steps:

[0025] Retrieve and process historical exploration data obtained at the same spatial coordinate point before construction. The historical exploration data includes soil sample density, soil sample void ratio, and initial static penetration resistance at the exploration point.

[0026] Align and match the real-time on-site monitoring data with the historical exploration data using the same spatial coordinates;

[0027] At the same spatial point after matching, the difference between the corresponding physical quantities in the real-time on-site monitoring data and the historical exploration data is calculated to form a difference dataset including the change in water content, the change in density and the change in bearing capacity.

[0028] The difference dataset is bound to the corresponding spatial coordinates and timestamps to generate a comparison dataset of the foundation state before and after processing with spatiotemporal attributes.

[0029] Preferably, the generation of the comprehensive evaluation feature field of foundation collapsibility includes the following steps:

[0030] Key state variables are extracted from the corrected simulated state data from the collapsibility dynamic response model.

[0031] Extract the difference data of each physical quantity from the foundation state comparison dataset before and after the processing;

[0032] Spatial interpolation is performed on the key state variables and the difference data to generate continuous spatial distribution data covering the entire evaluation area;

[0033] The continuous spatial distribution data is standardized and normalized to eliminate the influence of dimensions and generate a comprehensive evaluation feature field of foundation collapsibility. The comprehensive evaluation feature field of foundation collapsibility is a digital matrix containing multi-dimensional evaluation indicators.

[0034] Preferably, obtaining the collapsibility response difference field includes the following steps:

[0035] The area covered by the comprehensive evaluation characteristics of the foundation collapsibility is divided into multiple regular evaluation grid units;

[0036] For each of the evaluation grid cells, the statistical characteristic values ​​of multiple evaluation indicators within the comprehensive evaluation characteristic field of foundation collapsibility are calculated;

[0037] The statistical characteristic value of each evaluation grid cell is compared with the statistical characteristic value of its neighboring evaluation grid cells to obtain the local difference degree of each evaluation grid cell relative to its neighborhood.

[0038] Arrange the local differences of all evaluation grid cells according to their spatial location to form a collapsibility response difference field of the original loess foundation of the highway.

[0039] Preferably, the process of identifying potential collapsible anomaly areas in the original loess foundation of highways includes the following steps:

[0040] A preset threshold condition is used to determine abnormal states, and the threshold condition includes an upper limit for local variability and a change gradient threshold;

[0041] The local difference at each location in the collapsible response difference field is compared with the threshold condition;

[0042] Mark all spatial locations where the local difference exceeds the upper limit of the local difference or the change magnitude exceeds the change gradient threshold;

[0043] Cluster analysis is performed on the marked spatial locations to aggregate consecutive or adjacent marked points into anomaly regions, thereby identifying one or more potential sinkhole anomaly regions, and recording the range and core parameters of each potential sinkhole anomaly region.

[0044] Preferably, the iterative correction of the internal parameters of the collapsibility dynamic response model based on the distribution and characteristics of potential collapsibility anomaly areas includes the following steps:

[0045] Extract abnormal feature parameters from the core parameters of the potential sinkhole anomaly region;

[0046] The abnormal feature parameters are compared with the output results of the collapsibility dynamic response model in the corresponding region to generate a parameter error vector;

[0047] Based on the parameter error vector, a reverse feedback adjustment mechanism is used to calculate the adjustment amount of the corresponding soil constitutive parameters in the collapsibility dynamic response model;

[0048] The internal parameters of the collapsibility dynamic response model are corrected once according to the adjustment amount, and one iteration process is completed.

[0049] Repeat the steps of model calculation, comparison, error vector generation, and internal parameter correction until the parameter error vector meets the preset convergence criterion to obtain the final iteratively corrected collapsible dynamic response model.

[0050] Preferably, the simulation of the collapse development process of the original loess foundation under different load conditions includes the following steps:

[0051] In the final iteratively corrected collapsible dynamic response model, virtual load cases are defined, which include different load magnitudes, load distribution patterns, and loading time histories.

[0052] The virtual load condition is input as a boundary condition into the final iteratively corrected collapsible dynamic response model.

[0053] Run the final iteratively corrected collapsible dynamic response model to calculate the stress, strain, and collapsible deformation at each point of the foundation under the virtual load condition as a function of time.

[0054] Record and output data on the collapse development process of the loess foundation of the highway throughout the entire simulation time history.

[0055] Preferably, the step of receiving evaluation parameter adjustment instructions through the visual interactive model to drive model updates and outputting updated collapsibility evaluation results includes the following steps:

[0056] The user inputs an evaluation parameter adjustment instruction through the interface provided by the visualization interaction model. The evaluation parameter adjustment instruction includes modification of the collapsibility inducing parameters or virtual load conditions.

[0057] The evaluation parameter adjustment instructions are parsed into input parameters that the collapsibility dynamic response model can recognize;

[0058] The parsed input parameters are loaded into the collapsible dynamic response model, replacing the original corresponding parameters, and the model is triggered to recalculate.

[0059] Obtain updated data on the development process of collapsibility, updated collapsibility response difference field, and updated information on potential collapsibility anomaly areas generated after model recalculation;

[0060] The visualization and interactive model dynamically refreshes and displays all updated evaluation results of the collapsibility of the original loess foundation for highways.

[0061] Compared with the prior art, the beneficial effects of the present invention are:

[0062] The initial collapsibility model is dynamically revised based on real-time on-site monitoring data after treatment, generating a dynamic response model for collapsibility. Real-time on-site monitoring data is spatiotemporally overlaid with historical exploration data prior to treatment, generating a dataset comparing the foundation state before and after treatment. Through the fusion analysis of these two types of data, a comprehensive evaluation feature field reflecting the spatiotemporal evolution of foundation properties is constructed. This transforms collapsibility assessment from a static judgment relying on fixed exploration data to a dynamic process that can be continuously updated as the project progresses. The evaluation results can reflect the actual impact of treatment measures in real time, improving the timeliness and accuracy of condition assessment and overcoming the lag problem of traditional methods.

[0063] The comprehensive evaluation characteristic field of foundation collapsibility is divided into regions and its differences are calculated to obtain the collapsibility response difference field. Based on this difference field and preset thresholds, potential collapsibility anomaly areas can be quantitatively and automatically identified. According to the distribution and characteristics of these anomaly areas, the internal parameters of the collapsibility dynamic response model are iteratively corrected, and virtual load cases are introduced into the corrected model to simulate the collapsibility development process of the foundation under different load conditions. This achieves a leap from current state analysis to predictive simulation. It is no longer limited to describing the current state, but can predict the future collapsibility development trend of the foundation under different service loads, especially accurately locating areas with weak treatment effects, providing a forward-looking decision-making basis for engineering maintenance and risk prevention. Attached Figure Description

[0064] Figure 1 This is a schematic diagram illustrating the working principle of the method for evaluating the collapsibility treatment effect of loess foundation in highways as described in this invention.

[0065] Figure 2 A flowchart for generating a dynamic response model for collapsibility;

[0066] Figure 3 A flowchart for generating a dataset comparing the foundation conditions before and after processing;

[0067] Figure 4 Thermal map of characteristic field for comprehensive evaluation of collapsibility of loess foundation for highways;

[0068] Figure 5 This diagram illustrates the development process of subsidence in the original loess foundation of a highway under virtual load conditions. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] Please see Figure 1 This invention provides a method for evaluating the effectiveness of treatment for the collapsibility of original loess foundations for highways. The method includes: establishing an initial collapsibility model of the original loess foundation based on engineering geological data of the untreated foundation and preset collapsibility inducing parameters; dynamically correcting the initial collapsibility model based on real-time monitoring data after treatment construction to generate a dynamic response model for collapsibility; spatiotemporally overlaying the real-time monitoring data with historical exploration data before treatment construction to generate a dataset comparing the foundation state before and after treatment; fusing the dynamic response model for collapsibility with the dataset comparing the foundation state before and after treatment to generate a comprehensive evaluation feature field for foundation collapsibility; and performing feature fusion on the comprehensive evaluation feature field for foundation collapsibility. The process involves regional division and difference calculation to obtain a collapsibility response difference field. Based on the collapsibility response difference field and preset threshold conditions, potential collapsibility anomaly areas in the original loess foundation of highways are identified. The internal parameters of the collapsibility dynamic response model are iteratively corrected according to the distribution and characteristics of the potential collapsibility anomaly areas. Virtual load conditions are introduced into the corrected model to simulate the collapsibility development process of the original loess foundation of highways under different load conditions. A visual interactive model is constructed that includes the collapsibility development process, the collapsibility response difference field, and the potential collapsibility anomaly areas. The model receives evaluation parameter adjustment instructions through the visual interactive model to drive model updates and outputs updated collapsibility evaluation results.

[0071] Example 1: See Figure 2 When establishing the initial collapsibility model of the original loess foundation of the highway, the engineering geological survey report before the treatment of the original loess foundation of the highway is obtained, and the soil profile information, initial moisture content information and historical collapsibility coefficient information in the engineering geological survey report are extracted; collapsibility inducing parameters including immersion rate, immersion range and additional load are set, and the collapsibility inducing parameters are used to simulate the conditions of water immersion and external loading of the foundation; based on the soil profile information, initial moisture content information, historical collapsibility coefficient information and collapsibility inducing parameters, the deformation and stress response of the untreated foundation under different collapsibility inducing factors are calculated and simulated through the constitutive relationship of the foundation soil, and the initial collapsibility model of the original loess foundation of the highway is generated. The output of the initial collapsibility model is a simulation state dataset. When generating the collapsibility dynamic response model, a sensor network is deployed to monitor the original loess foundation of the highway after treatment and construction, continuously collecting real-time monitoring data including real-time moisture content, soil pressure, and surface settlement. The real-time monitoring data is structured according to the collection time and spatial location to form a spatiotemporally continuous field monitoring dataset. The field monitoring dataset is input into the initial collapsibility model, and the differences between the simulated state dataset and the field monitoring dataset are compared. Based on the differences, the model correction coefficient is calculated, and the internal calculation parameters of the initial collapsibility model are dynamically adjusted according to the model correction coefficient to generate a collapsibility dynamic response model that reflects the actual state of the foundation after treatment. The collapsibility dynamic response model outputs the corrected simulated state data.

[0072] In practice, the engineering geological survey report prior to the treatment of the original loess foundation of the highway was obtained. Soil profile information, initial moisture content information, and historical collapsibility coefficient information were extracted from the report. This information constituted the original geological basis for establishing the numerical model. Collapsibility inducing parameters, including immersion rate, immersion range, and additional load, were set. These parameters simulate the conditions of water immersion and external loading on the foundation. The immersion rate defines the depth of water infiltration or the increase in moisture content per unit time. The immersion range is delineated in three-dimensional space using the coordinates of polygon vertices. The additional load is set based on the design traffic load and the self-weight of the pavement structure.

[0073] In some embodiments, based on soil profile information, initial moisture content information, historical collapsibility coefficient information, and collapsibility inducing parameters, the deformation and stress response of the untreated foundation under different collapsibility inducing factors are calculated and simulated through the constitutive relationship of the foundation soil, generating an initial collapsibility model of the original loess foundation of the highway. The output of the initial collapsibility model is a simulation state dataset. In a specific implementation, when generating the initial collapsibility model of the original loess foundation of the highway, the foundation soil of the evaluation area is first discretized in three-dimensional space based on the soil profile information obtained from the engineering geological survey report, forming a finite element calculation mesh. Each mesh element corresponds to the soil layer properties and spatial location. The initial moisture content information and historical collapsibility coefficient information are assigned as the initial material parameters of the mesh element, defining the initial state of the soil. The collapsibility inducing parameters, including the immersion rate, immersion range, and additional load, are applied to the model as boundary conditions for simulation. The immersion rate controls the water infiltration process, the immersion range delineates the collapsibility area, and the additional load simulates the action of external loads. By utilizing the constitutive relations of the foundation soil, such as an elastoplastic model considering the collapsibility characteristics of loess, the stress balance equations and water transport equations of the soil are solved in a numerical simulation environment. The spatiotemporal distribution of physical quantities such as stress, strain, displacement, and pore water pressure in each grid element under different collapsibility inducing conditions is calculated, thereby simulating the deformation and stress response of the untreated foundation. The final output of the generated initial collapsibility model is a simulation state dataset. The simulation state dataset contains the spatiotemporal distribution data of stress, strain, displacement, and pore pressure at each node of the model's calculated grid under the action of preset collapsibility inducing parameters. In specific implementation, the process of establishing the initial collapsibility model adopts the finite element method, discretizing the extracted soil profile information into element grids, and assigning initial water content information, historical collapsibility coefficient information, and collapsibility inducing parameters as the initial and boundary conditions of the model.

[0074] In some embodiments, a sensor network is deployed to monitor the original loess foundation of the highway after construction. The sensor network includes moisture content sensors and earth pressure sensors buried at different depths, as well as settlement monitoring points set on the ground surface, continuously collecting real-time monitoring data including real-time moisture content, earth pressure, and surface settlement. The real-time monitoring data is structured according to the acquisition time and spatial location to form a spatiotemporally continuous field monitoring dataset. For each set of data, the acquisition timestamp, sensor three-dimensional geographic coordinates, and corresponding physical quantity readings are recorded. The field monitoring dataset is input into an initial collapsibility model, and the differences between the simulated state dataset and the field monitoring dataset are compared. The comparison is usually performed at the same spatiotemporal coordinate points, that is, comparing the physical quantities predicted by the model and the physical quantities measured by the sensors at the same location and approximately the same time point.

[0075] In practice, the model correction coefficient is calculated based on the difference, and the internal calculation parameters of the initial collapsibility model are dynamically adjusted according to the model correction coefficient. One possible method for calculating the model correction coefficient is based on a statistical measure of relative error. This measure aims to quantify the overall deviation between the model predictions and the actual field measurements. A formula for calculating the normalized overall difference is as follows:

[0076]

[0077] in: It represents the normalized population variance and is a dimensionless scalar. This represents the total number of monitoring points participating in the comparison. For the dynamic response model of collapsibility in the first... The physical quantity values ​​output at each monitoring point location; For the first Real-time monitoring data values ​​corresponding to each monitoring point; symbol This indicates taking the absolute value.

[0078] It is understandable that the normalized population variance is calculated based on this. In addition to other error distribution characteristics, an optimization algorithm is used to invert the adjustment amounts of key soil parameters in the collapsibility dynamic response model, such as adjusting the collapsibility initiation pressure, compression modulus, or permeability coefficient of loess. The internal parameters of the initial collapsibility model are then corrected according to the calculated adjustment amounts, completing one dynamic adjustment and generating a collapsibility dynamic response model that reflects the actual state of the foundation after treatment. The collapsibility dynamic response model outputs corrected simulated state data, which theoretically and statistically more closely approximates the real-time monitoring data from the field.

[0079] Example 2: See Figure 3When generating the foundation state comparison dataset before and after treatment, historical exploration data obtained at the same spatial coordinate points before treatment construction are retrieved. The historical exploration data includes soil sample density, void ratio, and initial static penetration resistance at the exploration point. The real-time monitoring data and historical exploration data are aligned and matched according to the same spatial coordinates. At the same spatial point after matching, the differences between the corresponding physical quantities in the real-time monitoring data and historical exploration data are calculated to form a difference dataset including changes in water content, density, and bearing capacity. The difference dataset is bound to the corresponding spatial coordinates and timestamps to generate a foundation state comparison dataset before and after treatment with spatiotemporal attributes.

[0080] In practice, historical exploration data obtained at the same spatial coordinate points before construction is retrieved and processed. The historical exploration data includes soil sample density, void ratio, and initial static penetration resistance at the exploration point. In practice, the historical exploration data comes from a detailed geological survey report before construction, and its spatial coordinate points are recorded through a global positioning system or measuring instruments, forming a raw dataset containing location codes, depth information, and values ​​of various physical quantities.

[0081] In some embodiments, real-time on-site monitoring data and historical exploration data are aligned and matched using the same spatial coordinates. The real-time on-site monitoring data comes from a sensor network deployed in the processed foundation. The alignment and matching process is based on a unified spatial reference system. In specific implementations, coordinate transformation and interpolation algorithms are used to ensure that the processed monitoring points and the unprocessed exploration points correspond precisely in three-dimensional space. For points that do not completely overlap, spatial interpolation methods can be used to interpolate or extrapolate historical exploration data to the monitoring point locations, or to interpolate monitoring data to the exploration point locations, to achieve a one-to-one correspondence of data on spatial grid nodes.

[0082] In practical implementation, at the same spatial point after matching, the differences between the corresponding physical quantities in the real-time on-site monitoring data and the historical exploration data are calculated. It can be understood that for a given spatial point, the difference directly reflects the change in physical state caused by the foundation treatment measures. Optionally, the calculation can be expressed as a point-by-point subtraction operation on each physical quantity, thereby forming a difference dataset including changes in moisture content, density, and bearing capacity. The change in moisture content is the difference between the real-time moisture content after treatment and the initial moisture content at the exploration point before treatment; the change in density is the difference between the inferred density after treatment and the density of the soil sample at the exploration point before treatment; and the change in bearing capacity is the difference between the bearing capacity calculated based on the inversion parameters after treatment and the bearing capacity estimated based on the initial static penetration resistance.

[0083] In some embodiments, an expression for structured calculation of the difference of a single physical quantity is as follows:

[0084]

[0085] in: Represents spatial coordinates The difference of a certain physical quantity This represents the value of the physical quantity in the processed real-time monitoring data at that coordinate. This represents the value of the same physical quantity in the historical exploration data before processing at this coordinate.

[0086] In practice, the difference dataset is bound to corresponding spatial coordinates and timestamps to generate a comparative dataset of foundation conditions before and after treatment, which has spatiotemporal attributes. The timestamp information includes the collection date of historical exploration data and the collection time of real-time on-site monitoring data. Optionally, the comparative dataset of foundation conditions before and after treatment can be stored using a spatiotemporal database or a multidimensional array with a time dimension. Each record or data unit is associated with a specific spatial location and time point, completely recording the evolution of the foundation condition from before to after treatment.

[0087] Example 3: When generating the comprehensive evaluation feature field of foundation collapsibility, key state variables are extracted from the corrected simulated state data of the collapsibility dynamic response model; difference data of each physical quantity is extracted from the foundation state comparison dataset before and after processing; spatial interpolation calculation is performed on the key state variables and difference data, and fusion is used to generate continuous spatial distribution data covering the entire evaluation area; the continuous spatial distribution data is standardized and normalized to eliminate the influence of dimensions and generate the comprehensive evaluation feature field of foundation collapsibility. The comprehensive evaluation feature field of foundation collapsibility is a digital matrix containing multi-dimensional evaluation indicators. When obtaining the collapsibility response difference field, the area covered by the comprehensive evaluation feature field of foundation collapsibility is divided into multiple regular evaluation grid units; for each evaluation grid unit, the statistical characteristic values ​​of multiple-dimensional evaluation indicators in the comprehensive evaluation feature field of foundation collapsibility are calculated; the statistical characteristic values ​​of each evaluation grid unit are compared with the statistical characteristic values ​​of adjacent evaluation grid units to obtain the local difference degree of each evaluation grid unit relative to its neighborhood; the local difference degrees of all evaluation grid units are arranged according to spatial location to form the collapsibility response difference field of highway loess foundation.

[0088] In practical implementation, key state variables are extracted from the modified simulation state data of the collapsibility dynamic response model. These key state variables include, but are not limited to, vertical stress, shear stress, volumetric strain, and pore water pressure. These variables collectively describe the mechanical and deformation state of the foundation under collapsibility-induced conditions. Difference data for various physical quantities are extracted from the foundation state comparison dataset before and after treatment. This difference data includes changes in water content, density, and bearing capacity. These data directly quantify the changes in physical state caused by the foundation treatment measures.

[0089] In some embodiments, key state variables and difference data are spatially interpolated to generate continuous spatial distribution data covering the entire evaluation area. Spatial interpolation is performed independently for each data layer to be fused. This means that for discrete monitoring or exploration point data, Kriging interpolation or inverse distance weighted interpolation is used to calculate the physical quantity values ​​at the regular grid nodes of the evaluation area. Optionally, the fusion process involves superimposing and correlating the interpolated key state variable field and the difference data field on the same spatial grid to form a comprehensive data volume containing multi-dimensional attribute information.

[0090] In practice, continuous spatially distributed data undergoes standardization and normalization calculations to eliminate the influence of dimensions, generating a comprehensive evaluation feature field for foundation collapsibility. Standardization and normalization mapping maps numerical values ​​with different physical meanings to a unified numerical range. This can be understood as performing Z-score standardization or maximum / minimum value normalization on each physical quantity at each data grid point. The comprehensive evaluation feature field for foundation collapsibility is a digital matrix containing multi-dimensional evaluation indicators. The rows and columns of this matrix correspond to the geographical grid of the evaluation area, while the depth dimension corresponds to each standardized evaluation indicator, such as normalized vertical stress and normalized moisture content change.

[0091] In some embodiments, the area covered by the comprehensive evaluation feature field of foundation collapsibility is divided into multiple regular evaluation grid cells, and the division of the evaluation grid cells is consistent with the grid used in the aforementioned spatial interpolation calculation. For each evaluation grid cell, the statistical characteristic values ​​of multiple dimensions of evaluation indicators within the comprehensive evaluation feature field of foundation collapsibility are calculated. It can be understood that the statistical characteristic values ​​include the mean, standard deviation, or weighted comprehensive score of all evaluation indicator values ​​at that grid cell. An optional expression for calculating the local variability of the evaluation grid cell is:

[0092]

[0093] in: Indicates that it is located at the th line, number The evaluation of the local variability of the grid cells in the column. It is the total number of evaluation indicators. Is the evaluation grid cell in the 1st d'h? Standardized values ​​for each evaluation indicator It is the evaluation grid cell whose all adjacent evaluation grid cells are in the th order. The average of the standardized values ​​on each evaluation indicator.

[0094] In practice, the statistical characteristic value of each evaluation grid cell is compared with that of its neighboring evaluation grid cells to obtain the local dissimilarity degree of each evaluation grid cell relative to its neighborhood. The adjacency relationship can be defined as a four-connected or eight-connected neighborhood. The comparative calculation reflects the dispersion of the central grid cell and its surrounding grid cells in terms of comprehensive evaluation characteristics. The local dissimilarity degrees of all evaluation grid cells are arranged according to their spatial location to form a collapsibility response difference field for the loess foundation of the highway. This collapsibility response difference field can be understood as a two-dimensional matrix, where each element represents the local dissimilarity degree at its corresponding location. The magnitude of the value directly reflects the spatial non-uniformity of the foundation's collapsibility response.

[0095] See Figure 4 This is a heat map of the characteristic field of the comprehensive evaluation of the collapsibility of loess foundations for highways. It is a core visualization tool in the generation stage of the comprehensive evaluation characteristic field for foundation collapsibility, used to display the spatial distribution of the comprehensive score after the fusion of multi-dimensional indicators within the evaluation area. The core area (X=6.43-10.71, Y=4.29-8.57) is red, with a comprehensive score close to 0.9, indicating a high comprehensive risk of collapsibility-related indicators in this area. The area surrounding the core area is mostly yellow / orange, with a comprehensive score between 0.6 and 0.8, belonging to areas with medium collapsibility risk. The edge of the evaluation area is blue / purple, with a comprehensive score below 0.5, indicating a low collapsibility risk. This spatial classification of collapsibility risk helps to quickly locate high-risk areas and provides basic comprehensive evaluation data support for subsequent calculations of the collapsibility response difference field and the identification of potential collapsibility anomaly areas.

[0096] Example 4: When calibrating potential collapsible anomaly areas in the original loess foundation of highways, a threshold condition for judging the anomaly state is preset. The threshold condition includes an upper limit of local variability and a change gradient threshold. The local variability of each location in the collapsibility response difference field is compared with the threshold condition. All spatial location points where the local variability exceeds the upper limit of local variability or the change amplitude exceeds the change gradient threshold are marked. The marked spatial location points are clustered to aggregate spatially continuous or adjacent marked points into anomaly areas, thereby calibrating one or more potential collapsible anomaly areas, and recording the range and core parameters of each potential collapsible anomaly area. When iteratively correcting the internal parameters of the collapsibility dynamic response model based on the distribution and characteristics of potential collapsibility anomaly areas, abnormal feature parameters are extracted from the core parameters of the potential collapsibility anomaly areas. The abnormal feature parameters are compared with the output results of the collapsibility dynamic response model in the corresponding areas to generate a parameter error vector. Based on the parameter error vector, a reverse feedback adjustment mechanism is used to calculate the adjustment amount of the corresponding soil constitutive parameters in the collapsibility dynamic response model. The internal parameters of the collapsibility dynamic response model are corrected once according to the adjustment amount, completing one iteration process. The steps of model calculation, comparison, error vector generation, and internal parameter correction are repeated until the parameter error vector meets the preset convergence criterion, and the final iteratively corrected collapsibility dynamic response model is obtained.

[0097] In practical implementation, preset threshold conditions are used to determine abnormal states. These threshold conditions include an upper limit for local variability and a gradient threshold. The upper limit for local variability defines the maximum permissible value of local variability in a single grid cell within the collapsible response difference field, while the gradient threshold defines the maximum permissible rate of change of local variability between adjacent grid cells. The local variability at each location in the collapsible response difference field is compared with the threshold conditions. In practice, this comparison process iterates through each element in the collapsible response difference field matrix, determining whether its value exceeds the upper limit for local variability. Simultaneously, the absolute values ​​of the differences between this element and its four adjacent elements (east, south, west, and north) are calculated, and it is determined whether the rate of change in any direction exceeds the gradient threshold. Refer to Table 1, an example threshold condition table.

[0098] Table 1: Threshold Condition Table

[0099] ;

[0100] All spatial locations where the local variability exceeds the upper limit or the change magnitude exceeds the gradient threshold are marked. These marked spatial locations correspond to coordinates determined by the row and column indices in the collapsibility response difference field matrix. Cluster analysis is then performed on these marked spatial locations. Density-based spatial clustering algorithms can be used to aggregate spatially continuous or adjacent marked points into anomaly regions, thereby identifying one or more potential collapsibility anomaly regions. The extent and core parameters of each potential collapsibility anomaly region are recorded. These core parameters include the center coordinates of the grid cells contained within the anomaly region, the average local variability, and the region area.

[0101] In some embodiments, the internal parameters of the collapsibility dynamic response model are iteratively corrected based on the distribution and characteristics of potential collapsibility anomaly areas. Anomaly characteristic parameters are extracted from the core parameters of the potential collapsibility anomaly areas. These anomaly characteristic parameters can specifically refer to the systematic deviation between the average observed settlement values ​​and the model's predicted values ​​within the area, or the difference between the inverted values ​​of the soil modulus within the area and the initial values ​​of the model. The anomaly characteristic parameters are compared with the output results of the collapsibility dynamic response model in the corresponding areas to generate a parameter error vector. This parameter error vector quantifies the deviation between the model's predictions and the ideal state inverted based on the characteristics of the anomaly areas.

[0102] In practical implementation, an expression for calculating the parameter error vector is as follows:

[0103]

[0104] in: Represents the parameter error vector. This represents the vector of anomalous feature parameters extracted from the core parameters of a potential collapsible anomaly region. This represents the state parameter vector output by the collapsibility dynamic response model in the corresponding potential collapsibility anomaly region. Based on the parameter error vector, a reverse feedback adjustment mechanism is used to calculate the adjustment amount of the corresponding soil constitutive parameters in the collapsibility dynamic response model. The reverse feedback adjustment mechanism can be implemented based on the gradient descent method or the Levenberg-Marquardt algorithm to determine the parameter adjustment direction and step size that reduces the norm of the parameter error vector. The internal parameters of the collapsibility dynamic response model are corrected once according to the adjustment amount, completing one iteration process. The correction of the internal parameters is usually applied to the soil partitions corresponding to the potential collapsibility anomaly region in the model definition.

[0105] The steps of model calculation, comparison, error vector generation, and internal parameter correction are repeated. In each iteration, the foundation response is recalculated using the corrected model parameters and compared again with features extracted from the updated information on potential collapsible anomalies, until the parameter error vector meets a preset convergence criterion. The convergence criterion can be set as the L2 norm of the parameter error vector being less than a specified small amount ε, or the change in the error vector between two consecutive iterations being negligible. When the convergence criterion is met, the final iteratively corrected collapsible dynamic response model is obtained.

[0106] Example 5: When simulating the collapsibility development process of the original loess foundation of a highway under different load conditions, a virtual load case is defined in the final iteratively corrected collapsibility dynamic response model. The virtual load case includes different load magnitudes, load distribution forms, and loading time histories. The virtual load case is used as a boundary condition input into the final iteratively corrected collapsibility dynamic response model. The final iteratively corrected collapsibility dynamic response model is run to calculate the stress, strain, and collapsibility deformation data of each point of the foundation under the virtual load case as a function of time. The collapsibility development process data of the original loess foundation of the highway is recorded and output throughout the entire simulation time history. When receiving evaluation parameter adjustment instructions through a visual interactive model to drive model updates and output updated collapsibility evaluation results, the system receives user-input evaluation parameter adjustment instructions through the interface provided by the visual interactive model. These instructions include modifications to collapsibility inducing parameters or virtual load conditions. The system parses these instructions into input parameters recognizable by the collapsibility dynamic response model. The parsed input parameters are then loaded into the collapsibility dynamic response model, replacing the original corresponding parameters and triggering model recalculation. The system acquires updated data on the collapsibility development process, updated collapsibility response difference field, and updated information on potential collapsibility anomalies generated after model recalculation. Finally, the system dynamically refreshes and displays all updated collapsibility evaluation results for the original loess foundation of highways in the visual interactive model.

[0107] In practical implementation, virtual load cases are defined in the final iteratively corrected collapsible dynamic response model. These virtual load cases include different load magnitudes, load distribution patterns, and loading time histories. In practice, the load magnitude is determined based on vehicle loads, pavement structure layer self-weight, and their combinations as specified in highway design codes. The load distribution pattern can be a uniformly distributed load, a concentrated load, or a moving load simulating a specific traffic flow. The loading time histories define the sequence of load application, maintenance, or unloading over time. These virtual load cases are input as boundary conditions into the final iteratively corrected collapsible dynamic response model. The load conditions are applied to the corresponding foundation surface area in the form of nodal forces or surface pressures. The loading time histories are implemented by defining multiple analysis steps.

[0108] The final iteratively corrected collapsible dynamic response model is run to calculate the stress, strain, and collapsible deformation at various points of the foundation under virtual load conditions over time. This calculation is performed in a numerical simulation environment, with the model solving the governing equations based on updated soil constitutive parameters and boundary conditions. The collapsible development process data of the original loess foundation for the highway is recorded and output throughout the entire simulation time history. This data is a four-dimensional dataset, including spatial three-dimensional coordinates and time, recording the evolution history of foundation settlement, stress redistribution, and other indicators under different virtual load conditions. An optional expression describing the time-varying development of collapsible deformation at a specific location is:

[0109]

[0110] in: Indicates the time The cumulative amount of shrinkage deformation up to a given time. Indicates in Time, corresponding to the current load The rate of collapsible deformation is obtained by integrating the process, which reflects the cumulative effect of collapsible deformation over time and load history.

[0111] In some embodiments, a visual interactive model is constructed that includes the collapse development process, the collapse response difference field, and potential collapse anomaly areas. This model provides a graphical user interface that integrates 3D geographic information, dynamic simulation results, and difference analysis findings into a unified rendering. Through the interface provided by the visual interactive model, user-inputted evaluation parameter adjustment instructions are received. These instructions include modifications to collapse inducing parameters or virtual load conditions. Optionally, users can modify the immersion rate using sliders on the interface, modify the immersion range by drawing polygonal regions, or modify the virtual load conditions by adjusting the load-time curve editor.

[0112] The evaluation parameter adjustment commands are parsed into input parameters recognizable by the collapsibility dynamic response model. This parsing process transforms the user interface interaction into specific numerical parameters or function expressions. The parsed input parameters are then loaded into the collapsibility dynamic response model, replacing the original corresponding parameters and triggering a model recalculation. This process essentially drives the evaluation parameter adjustment commands to the computational kernel of the collapsibility dynamic response model. The updated data on the collapsibility development process, the updated collapsibility response difference field, and the updated information on potential collapsibility anomaly regions generated after the model recalculation are obtained. This updated data is obtained by executing a complete simulation and analysis process corresponding to the new input parameters.

[0113] In practice, the visual interactive model dynamically refreshes and displays all updated evaluation results of the collapsibility of the loess foundation for highways. Dynamic refresh means that after a user submits an adjustment command for the evaluation parameters, the 3D collapsibility cloud map, the differential field distribution map, and the anomaly area markers on the interface are updated in real time based on the new calculation results. Users can interactively compare simulation results under different evaluation parameter adjustment commands, thereby intuitively analyzing the potential impact of various collapsibility inducing factors or load conditions on the foundation treatment effect.

[0114] See Figure 5 This is a diagram illustrating the development process of subsidence in a loess foundation under a virtual load condition. The diagram reflects the structure's humidity-induced deformation response characteristics under different load conditions. Under concentrated loads, humidity-induced deformation exhibits a trend of "continuous accumulation + small fluctuations" over time, with the largest deformation. Under uniformly distributed loads, the deformation accumulation rate is slower, and overall stability is better. Under moving loads, deformation is significantly affected by changes in load location, exhibiting a dynamic characteristic of alternating "stretching-contraction," with the most severe deformation fluctuations. This type of chart is commonly used to assess the long-term deformation stability of materials / structures under humid environments and different load conditions, and is a common data visualization method in civil engineering and materials science studies of "environment-mechanical coupling effects."

[0115] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0116] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the effectiveness of treatment for the collapsibility of loess foundations along highways, characterized in that, Includes the following steps: Based on the engineering geological data of the untreated foundation and the preset collapsibility induction parameters, an initial collapsibility model of the original loess foundation for highways was established. The initial collapsibility model is dynamically corrected based on real-time on-site monitoring data after the treatment construction to generate a collapsibility dynamic response model. The real-time on-site monitoring data is spatiotemporally overlaid with the historical exploration data before the processing construction to generate a dataset comparing the foundation state before and after the processing. The collapsibility dynamic response model is fused with the foundation state comparison dataset before and after treatment to generate a comprehensive evaluation feature field of foundation collapsibility. The characteristic field of the comprehensive evaluation of the collapsibility of the foundation is divided into regions and the differences are calculated to obtain the collapsibility response difference field. Based on the collapsibility response difference field and preset threshold conditions, potential collapsibility anomaly areas in the original loess foundation of highways are identified; Based on the distribution and characteristics of potential collapsible anomaly areas, the internal parameters of the collapsible dynamic response model are iteratively corrected, and virtual load conditions are introduced into the corrected model to simulate the collapsible development process of the original loess foundation of highways under different load conditions. A visual interactive model is constructed that includes the collapsibility development process, the collapsibility response difference field, and potential collapsibility anomaly areas. The model receives evaluation parameter adjustment instructions through the visual interactive model to drive model updates and outputs updated collapsibility evaluation results.

2. The method for evaluating the effectiveness of treatment for the collapsibility of loess foundations along highways according to claim 1, characterized in that, The establishment of the initial collapsibility model of the original loess foundation for highways includes the following steps: Obtain the engineering geological survey report before the original foundation treatment of the highway loess, and extract the soil layer profile information, initial moisture content information and historical collapsibility coefficient information from the engineering geological survey report; Set collapsibility inducing parameters including immersion rate, immersion range, and additional load, which are used to simulate the conditions of foundation being wetted by water and external loading; Based on the soil profile information, the initial moisture content information, the historical collapsibility coefficient information, and the collapsibility induction parameters, the deformation and stress response of the untreated foundation under different collapsibility inductions are calculated and simulated through the constitutive relationship of the foundation soil, generating an initial collapsibility model of the original loess foundation of the highway. The output of the initial collapsibility model is a simulation state dataset.

3. The method for evaluating the effectiveness of treatment for the collapsibility of loess foundations along highways according to claim 2, characterized in that, The process of generating the collapsibility dynamic response model includes the following steps: A sensor network was deployed to monitor the original loess foundation of the highway after the construction was completed, and real-time monitoring data including real-time moisture content, soil pressure and surface settlement were continuously collected. The real-time on-site monitoring data is structured according to the collection time and spatial location to form a spatiotemporally continuous on-site monitoring dataset. Input the field monitoring dataset into the initial collapsibility model and compare the differences between the simulated state dataset and the field monitoring dataset. Based on the difference calculation model correction coefficient, and according to the model correction coefficient, the internal calculation parameters of the initial collapsibility model are dynamically adjusted to generate a collapsibility dynamic response model that can reflect the actual state of the foundation after treatment. The collapsibility dynamic response model outputs the corrected simulation state data.

4. The method for evaluating the effectiveness of treatment for the collapsibility of loess foundations along highways according to claim 1, characterized in that, The process of generating a comparison dataset of foundation conditions before and after processing includes the following steps: Retrieve and process historical exploration data obtained at the same spatial coordinate point before construction. The historical exploration data includes soil sample density, soil sample void ratio, and initial static penetration resistance at the exploration point. Align and match the real-time on-site monitoring data with the historical exploration data using the same spatial coordinates; At the same spatial point after matching, the difference between the corresponding physical quantities in the real-time on-site monitoring data and the historical exploration data is calculated to form a difference dataset including the change in water content, the change in density and the change in bearing capacity. The difference dataset is bound to the corresponding spatial coordinates and timestamps to generate a comparison dataset of the foundation state before and after processing with spatiotemporal attributes.

5. The method for evaluating the effectiveness of treatment for the collapsibility of loess foundations along highways according to claim 1, characterized in that, The process of generating the comprehensive evaluation characteristic field of foundation collapsibility includes the following steps: Key state variables are extracted from the corrected simulated state data from the collapsibility dynamic response model. Extract the difference data of each physical quantity from the foundation state comparison dataset before and after the processing; Spatial interpolation is performed on the key state variables and the difference data to generate continuous spatial distribution data covering the entire evaluation area; The continuous spatial distribution data is standardized and normalized to eliminate the influence of dimensions and generate a comprehensive evaluation feature field of foundation collapsibility. The comprehensive evaluation feature field of foundation collapsibility is a digital matrix containing multi-dimensional evaluation indicators.

6. The method for evaluating the effectiveness of treatment for the collapsibility of loess foundations along highways according to claim 1, characterized in that, The process of obtaining the collapsibility response difference field includes the following steps: The area covered by the comprehensive evaluation characteristics of the foundation collapsibility is divided into multiple regular evaluation grid units; For each of the evaluation grid cells, the statistical characteristic values ​​of multiple evaluation indicators within the comprehensive evaluation characteristic field of foundation collapsibility are calculated; The statistical characteristic value of each evaluation grid cell is compared with the statistical characteristic value of its neighboring evaluation grid cells to obtain the local difference degree of each evaluation grid cell relative to its neighborhood. Arrange the local differences of all evaluation grid cells according to their spatial location to form a collapsibility response difference field of the original loess foundation of the highway.

7. The method for evaluating the effectiveness of treatment for the collapsibility of loess foundations along highways according to claim 1, characterized in that, The process of identifying potential collapsible anomalies in the original loess foundation of highways includes the following steps: A preset threshold condition is used to determine abnormal states, and the threshold condition includes an upper limit for local variability and a change gradient threshold; The local difference at each location in the collapsible response difference field is compared with the threshold condition; Mark all spatial locations where the local difference exceeds the upper limit of the local difference or the change magnitude exceeds the change gradient threshold; Cluster analysis is performed on the marked spatial locations to aggregate consecutive or adjacent marked points into anomaly regions, thereby identifying one or more potential sinkhole anomaly regions, and recording the range and core parameters of each potential sinkhole anomaly region.

8. The method for evaluating the effectiveness of treatment for the collapsibility of loess foundations along highways according to claim 1, characterized in that, The iterative correction of the internal parameters of the collapsibility dynamic response model based on the distribution and characteristics of potential collapsibility anomaly areas includes the following steps: Extract abnormal feature parameters from the core parameters of the potential sinkhole anomaly region; The abnormal feature parameters are compared with the output results of the collapsibility dynamic response model in the corresponding region to generate a parameter error vector; Based on the parameter error vector, a reverse feedback adjustment mechanism is used to calculate the adjustment amount of the corresponding soil constitutive parameters in the collapsibility dynamic response model; The internal parameters of the collapsibility dynamic response model are corrected once according to the adjustment amount, and one iteration process is completed. Repeat the steps of model calculation, comparison, error vector generation, and internal parameter correction until the parameter error vector meets the preset convergence criterion to obtain the final iteratively corrected collapsible dynamic response model.

9. The method for evaluating the effectiveness of treatment for the collapsibility of loess foundations along highways according to claim 1, characterized in that, The simulated collapse development process of loess foundation under different load conditions includes the following steps: In the final iteratively corrected collapsible dynamic response model, virtual load cases are defined, which include different load magnitudes, load distribution patterns, and loading time histories. The virtual load condition is input as a boundary condition into the final iteratively corrected collapsible dynamic response model. Run the final iteratively corrected collapsible dynamic response model to calculate the stress, strain, and collapsible deformation at each point of the foundation under the virtual load condition as a function of time. Record and output data on the collapse development process of the loess foundation of the highway throughout the entire simulation time history.

10. The method for evaluating the effectiveness of treatment for the collapsibility of loess foundations along highways according to claim 1, characterized in that, The process of receiving evaluation parameter adjustment instructions through the visual interactive model to drive model updates and outputting updated collapsibility evaluation results includes the following steps: The user inputs an evaluation parameter adjustment instruction through the interface provided by the visualization interaction model. The evaluation parameter adjustment instruction includes modification of the collapsibility inducing parameters or virtual load conditions. The evaluation parameter adjustment instructions are parsed into input parameters that the collapsibility dynamic response model can recognize; The parsed input parameters are loaded into the collapsible dynamic response model, replacing the original corresponding parameters, and the model is triggered to recalculate. Obtain updated data on the development process of collapsibility, updated collapsibility response difference field, and updated information on potential collapsibility anomaly areas generated after model recalculation; The visualization and interactive model dynamically refreshes and displays all updated evaluation results of the collapsibility of the original loess foundation for highways.

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