Method for predicting confidence of ground settlement based on preloading and layering conditions

Abstract

The application discloses a kind of based on heaped load precompression stratification condition foundation settlement prediction confidence analysis method, comprising: based on heaped load precompression stratification ground condition soft soil subgrade settlement theoretical model obtains the settlement theoretical curve under different soil, stratification, foundation treatment condition, and the settlement theoretical curve is used to provide basic data;Based on engineering observation condition, the basic data provided by settlement theoretical curve is processed to obtain simulated observation data;Based on curve fitting model, simulated observation data is predicted and analyzed to obtain the best use condition of curve fitting model;Under the best use condition of curve fitting model, foundation settlement prediction confidence analysis is carried out.The corresponding analysis system, electronic equipment and computer readable storage medium are also disclosed, and the best use condition of curve fitting model under different soft soil soil condition, stratification condition, heaped load precompression foundation treatment condition is obtained by considering the observation condition commonly used in engineering.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of railway and foundation settlement prediction analysis, and particularly relates to a foundation settlement prediction confidence analysis method and system based on heaped preloading layered conditions. BACKGROUND

[0002] By the end of 2023, the operating mileage of high-speed railways in China has exceeded 44,000 kilometers. At the same time, international projects such as the Yawan high-speed railway, the Hungary-Serbia railway, and the China-Thailand railway are constantly taking root and growing. The rapidly developing high-speed railway has become an important driving force for domestic economic growth and a beautiful business card for displaying international image. The rapid and safe high-speed railway greatly meets the daily travel needs of the people, but also puts forward extremely strict requirements for line deformation control. The roadbed, as an important part of high-speed railway infrastructure, its post-construction settlement control is the key link to ensure construction quality and line smoothness.

[0003] As a strip-shaped project, high-speed railways are widely developed in soft soil areas. Soft soil is a special soil that needs to be dealt with a lot in domestic high-speed railway construction and "Belt and Road" construction. It is a fine-grained soil with a natural void ratio greater than or equal to 1, a natural water content greater than the liquid limit, and an undrained shear strength less than 30 kPa. Soft soil has low strength, poor permeability, high compressibility, poor uniformity, strong rheology, high thixotropy, complex layered distribution, and large differences in physical and mechanical properties of each layer. The complex engineering characteristics and layering make it extremely difficult to control the post-construction settlement of soft soil roadbeds. Soft soil roadbeds can be treated with preloading foundation and composite foundation. The preloading method combined with bagged sand wells, plastic drainage piles, and other drainage shafts is widely used in the foundation reinforcement treatment of soft soil roadbeds. It can accelerate the settlement rate, ensure long-term stability of the roadbed settlement, simplify the construction process, and ensure reasonable and controllable project cost, providing strong support for domestic high-speed railway construction and operation cost reduction and efficiency improvement, and reducing the market access threshold of high-speed railways along the "Belt and Road". However, the preloading foundation has a larger settlement than the composite foundation, and the construction period is longer, which is not conducive to shortening the construction period, controlling the post-construction settlement, and calculating the track laying time, which is the main problem that prevents its widespread application in high-speed railway construction.

[0004] The settlement observation prediction and evaluation method is based on limited observation data and uses mathematical models to reasonably extrapolate the final settlement and post-construction settlement. Mathematical models generally include curve fitting models and fitting models based on neural networks and intelligent algorithms. The modeling and calculation process of fitting models based on neural networks and intelligent algorithms is more complex, and curve fitting models are still the main method in engineering.

[0005] The curve fitting model generally includes hyperbolic method, three-point method, Asaoka method, exponential curve method, Hoshino method and other methods. Hyperbolic method is a commonly used method in China for high-speed railway subgrade settlement prediction and evaluation. It was proposed by Nizibo Love in 1955. Hyperbolic method is suitable for fitting and extrapolation of observation data during static period. In order to effectively use the data during filling period to achieve the purpose of early prediction, the German railway department proposed to expand the hyperbolic method. Many scholars believe that the prediction result of hyperbolic method is conservative, and Li Guowei and Feng Wenkai et al. proposed the corresponding correction method. In 1959, Zeng Guoxi proposed three-point method. In the face of the characteristics of small settlement order and large data fluctuation of high-speed railway subgrade settlement, Chen Shanxiong et al. fused the exponential curve method, hyperbolic method and three-point method, proposed the improved three-point method, and achieved good results. In 1978, Japanese scholar Asaoka proposed Asaoka method based on one-dimensional vertical consolidation theory, also known as shallow hill method or graphical method. Some scholars believe that the deformation characteristics of the settlement curve after the inflection point meet the characteristics of the exponential curve, and propose the exponential curve method. Based on Terzaghi's one-dimensional consolidation theory, it is deduced that the consolidation degree is proportional to the square root of time, and the Hoshino method obtained therefrom is also widely used in high-speed railway subgrade settlement prediction and evaluation. Since then, Verhulst model, Usher model, Logistic model, Compertz model, Weibull model, settlement difference method model and other models have been proposed. These models can reflect the relationship between the whole process settlement and time, and are more used for early prediction of subgrade settlement. The applicability of the curve fitting model is generally controlled by the correlation coefficient not less than 0.92. Wang Xingyun, Liu Junfei, Zeng Junkang and Liu Hong respectively compared and analyzed the applicability of various curve fitting models, and proposed the index and method for evaluating the applicability of curve fitting model.

[0006] Many scholars have carried out rich research on fitting models based on neural networks and intelligent algorithms. Zhang Huimei et al. established a soft soil subgrade settlement prediction model based on BP neural network, which is an early successful case of introducing neural network into settlement prediction and evaluation; Feng Shengyang et al. carried out isochronal interpolation processing on non-isochronal observation data, established a settlement prediction model based on least squares vector machine, and compared it with the settlement prediction model based on the joint method of BP neural network and grey theory; Peng Lishun et al. comprehensively considered the factors such as foundation treatment form, soft soil layer thickness, soft soil compression modulus, hard shell layer thickness, hard shell layer compression modulus, subgrade height, and subgrade filling period, and established a soft soil subgrade settlement prediction model using BP neural network and GA genetic algorithm; Qin Yajiong et al. proposed a joint prediction model combining curve fitting model and GA genetic algorithm to improve the optimization efficiency of model fitting parameters; Zhang Chunhui et al. used PSO particle swarm algorithm to intelligently optimize the fitting parameters of the traditional BP neural network settlement prediction model, and compared the new model with the traditional BP neural network settlement prediction model, finding that the new model improved the prediction accuracy and proved the superiority of the new model; Chen Weihang et al. processed non-isochronal observation data into isochronal data, established a subgrade settlement prediction model based on Bi-LSTM neural network; Qiu Hongsheng et al. used SA algorithm to intelligently optimize the fitting parameters of Verhulst model based on non-isochronal settlement observation data, established a related settlement prediction model, and compared it with the traditional Verhulst model, finding that the average relative error of the new model was greatly reduced compared with the original model; Jing Hongjun et al. established an improved GM(1,1) grey theory prediction model based on non-uniform filling during the filling period and non-equidistant settlement observation data, and compared it with the equi-proportion prediction model.

[0007] In today's high-speed rail construction, post-construction settlement control is mainly achieved in two ways. First, in the design stage, the subgrade settlement under external load is calculated theoretically. Due to the spatial variability of field sampling, the nonlinearity of soil stress-strain relationship, and the limitations of theoretical calculation models, the calculation accuracy of subgrade settlement process and final settlement in the design stage is poor. Second, in the construction stage, based on limited field observation data, mathematical models are used to reasonably extrapolate the final settlement and post-construction settlement of the subgrade. Since the confidence level of settlement prediction results is influenced by soil properties, foundation treatment form, and observation conditions, the settlement observation prediction and evaluation method formed in the construction stage lacks theoretical support. Investigations show that subgrade settlement exceeding the limit is the main disease form of high-speed railway subgrade. Currently, the deterministic theory-based geotechnical engineering design and construction methods cannot accurately control the foundation deformation, causing great difficulties in high-speed rail construction and operation and maintenance.

[0008] Therefore, there is a technical need to develop a new confidence analysis technology scheme for subgrade settlement prediction based on the conditions of preloading and layering. SUMMARY

[0009] The application aims to provide a foundation settlement prediction confidence analysis method and system based on the stacking preloading layering conditions, use the stacking natural foundation and drainage shaft foundation settlement theoretical model to obtain the settlement theoretical curve under different soil properties, layering, and foundation treatment conditions. Based on the commonly used observation conditions in engineering, the settlement theoretical curve is processed to generate simulated settlement observation data, and the curve fitting model is used to predict the simulated settlement observation data. Based on the equivalent permeability coefficient method, the main soil properties and structural parameters of the layered drainage shaft foundation are normalized, and the settlement prediction model applicability evaluation index is proposed by comprehensively utilizing the correlation coefficient, relative error, and variation coefficient of the settlement prediction results, and the best use conditions of different curve fitting models are analyzed. The technical problem to be solved is to consider the commonly used observation conditions such as observation time, frequency, and accuracy in engineering, and obtain the best use conditions of the curve fitting model under different soft soil properties, foundation layering conditions, and stacking preloading foundation treatment conditions.

[0010] The first aspect of the application is to provide a foundation settlement prediction confidence analysis method based on the stacking preloading layering conditions, comprising:

[0011] S1, obtaining the settlement theoretical curve under different soil properties, layering, and foundation treatment conditions based on the soft soil subgrade settlement theoretical model under the stacking preloading layering foundation conditions, and the settlement theoretical curve is used to provide basic data;

[0012] S2, processing the basic data provided by the settlement theoretical curve based on the engineering observation conditions to obtain simulated observation data;

[0013] S3, predicting and analyzing the simulated observation data based on the curve fitting model to obtain the best use conditions of the curve fitting model;

[0014] S4, performing foundation settlement prediction confidence analysis based on the best use conditions.

[0015] Preferably, the soft soil subgrade settlement theoretical model under the stacking preloading layering foundation conditions comprises a stacking natural foundation settlement theoretical model and a drainage shaft foundation settlement theoretical model.

[0016] Preferably, S1 comprises:

[0017] S11, based on the stacking natural foundation settlement theoretical model, for the soft soil subgrade under the stacking natural foundation conditions, making the same assumptions as the one-dimensional consolidation theory of Terzaghi except for the loading conditions to obtain the Terzaghi one-dimensional consolidation control equation of any soil layer i of the soft soil subgrade under the stacking soft soil natural foundation conditions considering the subgrade filling process, as shown in partial differential equation (1):

[0018]

[0019] wherein c vi is the vertical consolidation coefficient of the i-th soil layer; u i is the vertical excess pore water pressure of the i-th soil layer; z is the depth of the soil layer; t is the consolidation time; and q is the loading condition.

[0020] Based on the corresponding boundary conditions, the continuity conditions at the interfaces between layers, and the initial conditions as the solving conditions, the numerical solution of the partial differential equation (1) is obtained by using the finite difference method.

[0021] S12, based on the drainage shaft foundation settlement theory model, for the soft soil subgrade under the condition of layered drainage shaft foundation, the layers in the drainage shaft setting area satisfy the Barron strain consolidation theory assumption; the pore water pressure and the flow rate of the contact surface of each layer in the drainage shaft setting area satisfy the corresponding assumptions; the seepage surface satisfies the corresponding continuity conditions, and the one-dimensional consolidation control equation of any soil layer i of the soft soil subgrade under the condition of layered drainage shaft foundation considering the roadbed filling process is obtained, as shown in the partial differential equation (2):

[0022]

[0023] wherein c si is the radial consolidation coefficient of the i-th soil layer; is the radial excess pore water pressure of the i-th soil layer; and r is the influence radius of the drainage shaft.

[0024] Based on the corresponding boundary conditions, the continuity conditions at the interfaces between layers, the initial conditions and other solving conditions, the numerical solution of the partial differential equation (2) is obtained by using the finite difference method.

[0025] S13, the soft soil subgrade settlement theory model under the condition of heaped preloading layered foundation is used to calculate the soft soil subgrade settlement theory curve under different soil properties, layering and foundation treatment conditions.

[0026] Preferably, the processing includes one or more of truncation, discretization and superposition of random error processing.

[0027] Preferably, the engineering observation conditions include observation time length, frequency and accuracy.

[0028] Preferably, the S3 includes:

[0029] S31, based on the equivalent permeability coefficient calculation formula along the horizontal direction, i.e. formula (3), the weighted average value of the radial permeability coefficient of the layered foundation is obtained.

[0030]

[0031] In formula (3), k his the weighted average value of the radial permeability coefficient of the layered foundation; k z is the weighted average value of the vertical permeability coefficient of the layered foundation; H j is the thickness of the jth layer of soil;

[0032] S32, the weighted average value of the vertical permeability coefficient of the layered foundation is obtained based on the equivalent permeability coefficient calculation formula in the vertical layer direction, i.e. formula (4);

[0033]

[0034] In formula (4), k j is the permeability coefficient of the jth layer of soil; k z is the weighted average value of the vertical permeability coefficient of the layered foundation; H j is the thickness of the jth layer of soil; H is the total thickness of the soft soil layer;

[0035] S33, the main soil property parameters and structure parameters of the layered drainage shaft foundation are normalized by using the equivalent permeability coefficient method of the preloading foundation, i.e. formula (5) and formula (6);

[0036]

[0037] In formula (5) and formula (6), k ve is the equivalent permeability coefficient; l is the drainage shaft construction depth; r is the drainage shaft influence radius; n=r / d w is the well diameter ratio; k h is the weighted average value of the radial permeability coefficient of the layered foundation; k v is the weighted average value of the vertical permeability coefficient of the layered foundation; k s is the permeability coefficient of the smearing area soil; q w is the water permeability of the plastic drainage board; the smearing ratio is s=d s / d w , d s is the smearing area diameter; d w is the equivalent diameter of the drainage shaft;

[0038] S34, the applicability comprehensive evaluation index of the settlement prediction model is determined by comprehensively utilizing the correlation coefficient, the relative error and the variation coefficient of the settlement prediction result, the applicability of the settlement prediction model under different soft soil property conditions, foundation layering conditions and preloading foundation treatment conditions is obtained, and the best use conditions of different curve fitting models are obtained based on the analysis of the applicability.

[0039] Preferably, the curve fitting model includes one or more of the hyperbolic method, the exponential curve method, the Asaoka method and the Hoshino method.

[0040] The second aspect of the present application also provides a foundation settlement prediction confidence analysis system based on the preloading and precompaction layering conditions, for implementing the method of the first aspect, comprising:

[0041] A settlement theory curve acquisition module (101) is configured to obtain settlement theory curves under different soil properties, layering properties and foundation treatment conditions based on a soft soil subgrade settlement theory model under the preloading and precompaction layering conditions, and the settlement theory curves are used to provide basic data.

[0042] A simulated observation data acquisition module (102) is configured to process the basic data provided by the settlement theory curves to obtain simulated observation data based on engineering observation conditions.

[0043] An optimal use condition determination module (103) is configured to perform prediction analysis on the simulated observation data based on a curve fitting model to obtain optimal use conditions of the curve fitting model.

[0044] A foundation settlement prediction confidence analysis module (104) is configured to perform foundation settlement prediction confidence analysis based on the optimal use conditions.

[0045] The third aspect of the present application provides an electronic device comprising a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and perform the method of the first aspect.

[0046] The fourth aspect of the present application provides a computer readable storage medium storing a plurality of instructions, wherein the instructions can be read and executed by a processor to perform the method of the first aspect.

[0047] The method and system of the present application have the following advantages:

[0048] (1) The technical problem of the change rule of the high-speed rail soft soil subgrade settlement prediction confidence under the coupling action of multiple factors is solved, specifically: considering the observation conditions such as the commonly used observation time, frequency and accuracy in engineering, the optimal use conditions of the curve fitting model under different soft soil properties, foundation layering conditions and preloading and precompaction foundation treatment conditions are obtained; considering the changes of the observation conditions such as the observation time, frequency and accuracy, combining with intelligent algorithms, a settlement prediction confidence analysis method is formed, the settlement prediction mean and standard deviation under different observation conditions are statistically analyzed, and the change rule of the settlement prediction confidence caused by the randomness of the observation conditions is revealed; a settlement prediction confidence analysis model integrating the observation data in the filling and loading process is constructed, and the change rule of the high-speed rail soft soil subgrade settlement prediction confidence caused by the construction technology is revealed.

[0049] (2) Settlement prediction is based on field measured data, using curve fitting method and other mathematical means to extrapolate the final settlement of the roadbed and post-construction settlement. It is very difficult to study the settlement prediction confidence analysis of the present application using field measured data. The reasons are as follows: first, the field conditions are extremely complex, and it is difficult to effectively and accurately control the changes of soil properties, stratification, foundation treatment conditions, observation conditions and other factors; second, settlement prediction confidence research needs to carry out a large number of repeated observations for the same working conditions to obtain a large number of observation data with random errors. Based on field measured data, a large amount of financial resources, material resources and human resources are needed; third, the measured roadbed settlement curve is not only affected by external load, but also affected by climate, rainfall, temperature and other factors, resulting in that the settlement curve obtained in the field is not suitable for scientific research. Therefore, in order to obtain a large number of reliable samples, the present application uses a settlement prediction confidence analysis method based on simulated observation data to carry out research. The settlement prediction confidence analysis method which includes the generation of simulated settlement observation data, settlement prediction based on settlement observation data and statistical analysis of prediction results is one of the key technologies to achieve the purpose of the present application. The present application uses theoretical calculation, numerical simulation, intelligent algorithm, mathematical statistics and other methods to reveal the variation law of high-speed railway soft soil roadbed settlement prediction confidence caused by the randomness of observation conditions when using the appropriate curve fitting model under different soil properties, stratification and foundation treatment conditions. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the specific embodiments or related art, the following will briefly introduce the drawings needed to be used in the specific embodiments or related art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings without creative labor based on these drawings.

[0051] Figure 1 The flow chart of the foundation settlement prediction confidence analysis method based on the preloading and preloading stratification conditions according to the embodiment of the present application is provided;

[0052] Figure 2 The principle diagram of the foundation settlement prediction confidence analysis method based on the preloading and preloading stratification conditions according to the embodiment of the present application is provided;

[0053] Figure 3 The correlation coefficient column chart of different curve fitting methods according to the embodiment of the present application is provided;

[0054] Figure 4 The relationship scatter plot of the comprehensive evaluation index of a plurality of settlement prediction models and the equivalent permeability coefficient according to the embodiment of the present application is provided;

[0055] Figure 5 (a) -Figure 5 (e) is the distribution range of the comprehensive evaluation index of commonly used engineering models in zones ① to ⑤ provided according to the embodiments of the present invention;

[0056] Figure 6 This is a system architecture diagram for confidence analysis of foundation settlement prediction based on surcharge preloading stratification conditions, provided according to an embodiment of the present invention.

[0057] Figure 7 This is a structural diagram of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0058] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0059] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0060] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0061] Example 1

[0062] like Figure 1 and Figure 2 As shown, this embodiment provides a confidence analysis method for foundation settlement prediction based on surcharge preloading stratification conditions, including:

[0063] S1, Based on the settlement theory model of soft soil subgrade under surcharge preloading and stratified foundation conditions, the settlement theory curves under different soil properties, stratification and foundation treatment conditions are obtained. The settlement theory curves are used to provide basic data.

[0064] As a preferred embodiment, the soft soil subgrade settlement theory model under the condition of the preloading and surcharge embankment on the layered ground comprises a layered natural ground settlement theory model and a drainage shaft ground settlement theory model.

[0065] As a preferred embodiment, the S1 comprises:

[0066] S11, based on the layered natural ground settlement theory model, for the soft soil subgrade under the condition of the layered natural ground, making the same assumptions as the Terzaghi one-dimensional consolidation theory except for the loading condition, obtaining the Terzaghi one-dimensional consolidation control equation of any soil layer i of the soft soil subgrade under the condition of the layered soft soil natural ground considering the subgrade filling process, as shown in the partial differential equation (1):

[0067]

[0068] In the formula, c vi is the vertical consolidation coefficient of the i-th layer of soil; u i is the vertical excess pore water pressure of the i-th layer of soil; z is the soil layer depth; t is the consolidation time; q is the loading condition;

[0069] Based on the corresponding boundary conditions, the continuity conditions at the layer and layer interface, and the initial conditions as the solving conditions, the numerical solution of the partial differential equation (1) is obtained by using the finite difference method;

[0070] S12, based on the drainage shaft ground settlement theory model, for the soft soil subgrade under the condition of the layered drainage shaft ground, the layers in the drainage shaft setting area satisfy the Barron strain consolidation theory assumption; the pore pressure and flow of the contact surface of the layers in the drainage shaft setting area satisfy the corresponding assumptions; the seepage surface satisfies the corresponding continuity conditions, obtaining the one-dimensional consolidation control equation of any soil layer i of the soft soil subgrade under the condition of the layered drainage shaft ground considering the subgrade filling process, as shown in the partial differential equation (2):

[0071]

[0072] In the formula, c si is the radial consolidation coefficient of the i-th layer of soil; is the radial excess pore water pressure of the i-th layer of soil; r is the influence radius of the drainage shaft;

[0073] Based on the corresponding boundary conditions, the continuity conditions at the layer and layer interface, the initial conditions and the like as the solving conditions, the numerical solution of the partial differential equation (2) is obtained by using the finite difference method;

[0074] S13, using the soft soil subgrade settlement theory model under the condition of the preloading and surcharge embankment on the layered ground to calculate the soft soil subgrade settlement theory curve under different soil properties, layering and ground treatment conditions.

[0075] S2, processing the basic data provided by the settlement theory curve to obtain simulated observation data based on engineering observation conditions; wherein the engineering observation conditions include observation time length, frequency and accuracy;

[0076] As a preferred embodiment, the processing includes one or more of truncation, discretization and superposition of random error processing.

[0077] As a preferred embodiment, S3 includes:

[0078] S31, obtaining a weighted average value of radial permeability coefficient of the layered foundation based on an equivalent permeability coefficient calculation formula in the horizontal direction, i.e. formula (3);

[0079]

[0080] In formula (3), k h is the weighted average value of radial permeability coefficient of the layered foundation; k z is the weighted average value of vertical permeability coefficient of the layered foundation; H j is the thickness of the jth layer of soil;

[0081] S32, obtaining a weighted average value of vertical permeability coefficient of the layered foundation based on an equivalent permeability coefficient calculation formula in the vertical layer direction, i.e. formula (4);

[0082]

[0083] In formula (4), k j is the permeability coefficient of the jth layer of soil; k z is the weighted average value of vertical permeability coefficient of the layered foundation; H j is the thickness of the jth layer of soil; H is the total thickness of the soft soil layer;

[0084] S33, normalizing the main soil property parameters and structure parameters of the layered drainage shaft foundation by using the equivalent permeability coefficient method of the preloading foundation, i.e. formula (5) and formula (6);

[0085]

[0086] In formula (5) and formula (6), k ve is the equivalent permeability coefficient; l is the drainage shaft setting depth; r is the influence radius of the drainage shaft; n=r / d w is the well diameter ratio; k h is the weighted average value of radial permeability coefficient of the layered foundation; k v is the weighted average value of vertical permeability coefficient of the layered foundation; k s is the permeability coefficient of the smear zone soil; q w is the water passing performance of the plastic drainage board; the smear ratio is s=d s / dw , d s is the diameter of the application area; d w is the equivalent diameter of the drainage shaft; S34, the correlation coefficient, the relative error and the coefficient of variation of the settlement prediction results are used to determine the comprehensive evaluation index of the applicability of the settlement prediction model, the applicability of the settlement prediction model under different soft soil conditions, foundation layering conditions and preloading foundation treatment conditions is obtained, and the best use conditions of different curve fitting models are obtained based on the analysis of the applicability.

[0087] The application can determine the best use range of the commonly used model of the project by constructing the comprehensive evaluation index by weighting the correlation coefficient, the relative error and the coefficient of variation. When the equivalent permeability coefficient of the soft foundation is less than 0.02 m / d or greater than 0.7 m / d, the hyperbolic method is optimal; when the equivalent permeability coefficient is 0.02-0.044 m / d or 0.13-0.7 m / d, the Hoshino method is optimal; and when the equivalent permeability coefficient is 0.044-0.13 m / d, the Asaoka method is optimal.

[0088] In this embodiment, step S34 includes:

[0089] (1) constructing a comprehensive evaluation index

[0090] Based on the obtained variation law of the curve regression correlation coefficient, the systematic error and the accidental error of the curve obtained by using each curve fitting method with the soft foundation permeability coefficient and the characteristics of each curve fitting method, it is determined that: the hyperbolic method has strong fitting ability for the time-varying characteristics of the soft soil foundation settlement curve and high prediction accuracy, but poor stability; the exponential curve method has weak fitting ability for the time-varying characteristics of the soft soil foundation settlement curve with high permeability, and the prediction accuracy is poor, but the stability is good; the Asaoka method is not suitable for describing the time-varying characteristics of the soft soil foundation settlement curve with poor permeability, but the prediction accuracy and stability are guaranteed; the fitting ability of the Hoshino method for the time-varying characteristics of the soft soil foundation settlement curve is only inferior to the hyperbolic method and improves with the increase of the permeability coefficient, the prediction accuracy is guaranteed, and the stability is optimal.

[0091] The requirements of the curve fitting method for the deformation observation and evaluation of the high-speed railway subgrade include the following aspects: first, the curve fitting method should be able to describe the time-varying characteristics of the settlement curve; second, the high-speed railway subgrade designed based on the deformation control concept has strict post-construction settlement control, and the curve fitting method should be able to accurately calculate the final settlement of the foundation; third, the quality of the field measured data during the subgrade filling and static loading process is not high, which affects the stability of the settlement prediction results, and the curve fitting method should not be sensitive to the random fluctuations of the observation data, and the prediction results are relatively stable.

[0092] On the one hand, the curve fitting methods have their own characteristics, on the other hand, the deformation observation and evaluation have many aspects of demand, which makes it difficult for a single evaluation index to scientifically evaluate the adaptability of the curve fitting method under different conditions. Therefore, the application constructs a comprehensive evaluation index of the applicability of the curve fitting method with the correlation coefficient, the relative error and the variation coefficient as the core, as shown in formula (7).

[0093] ω = 0.3 * R + 0.4 * (1-CV) + 0.3 * (1-10 * δ) (7) ;

[0094] In the formula, ω ∈ (0, 1) is a comprehensive evaluation index; CV ∈ (0, 1) represents the variation coefficient; R ∈ (0, 1) represents the correlation coefficient; δ ∈ (0, 1) represents the relative error;

[0095] (2) The prediction quality of each curve fitting method is sorted

[0096] As Figure 3 shown in the figure is a correlation coefficient column chart of different curve fitting methods. Under the condition of different permeability of natural soft soil foundation, the correlation coefficient r of the hyperbolic curve method is greater than or equal to 0.98, indicating that the hyperbolic curve method can well describe the settlement time-varying characteristics; the correlation coefficient of the exponential curve method gradually decreases with the increase of the permeability coefficient, when the permeability coefficient is greater than 11.4*10-4 m / d, the decrease amplitude of the correlation coefficient increases, when the permeability coefficient is greater than 13.6*10-4 m / d, the correlation coefficient r is less than 0.94, indicating that the exponential curve method has strong description ability for the settlement time-varying characteristics of the soft soil foundation with small permeability; the correlation coefficient of the Asaoka method gradually increases with the increase of the permeability coefficient, when the permeability coefficient is less than 2.76*10-4 m / d, the correlation coefficient r is less than 0.92, indicating that the Asaoka method has strong description ability for the settlement time-varying characteristics of the soft soil foundation with large permeability; the correlation coefficient of the Hoshino method gradually increases with the increase of the permeability coefficient, when the permeability coefficient of the natural soft soil foundation is less than 2.76*10-4 m / d, the correlation coefficient r of the Hoshino method is less than 0.96, indicating that the Hoshino method has strong description ability for the settlement time-varying characteristics of the soft soil foundation with large permeability.

[0097] As Figure 4The model can be basically partitioned according to the best use range. When the equivalent permeability coefficient is less than 0.02 m / d, the hyperbolic curve method has a stable comprehensive evaluation index of about 0.8, the Asaoka method and the Hoshino method have a larger fluctuation range of the index, and the exponential curve method has a smaller average value. When the equivalent permeability coefficient is 0.02-0.044 m / d, the Hoshino method, the Asaoka method and the hyperbolic curve method have a higher average value of the index, and the exponential curve method has a smaller average value. When the equivalent permeability coefficient is 0.044-0.13 m / d, the Asaoka method, the Hoshino method and the hyperbolic curve method have a higher average value of the index, and the exponential curve method has a smaller average value of the index. When the equivalent permeability coefficient is 0.13-0.7 m / d, the Asaoka method, the Hoshino method and the hyperbolic curve method have a higher average value of the index and a smaller fluctuation range, and the exponential curve method gradually increases the index. When the equivalent permeability coefficient is greater than 0.7 m / d, the Asaoka method and the Hoshino method gradually decrease the index with the increase of the equivalent permeability coefficient, and the hyperbolic curve method and the exponential curve method gradually increase the index with the increase of the equivalent permeability coefficient

[0098] Figure 5 (a)- Figure 5 (e) is the distribution range of the comprehensive evaluation index of each project commonly used model in the ①-⑤ zones.

[0099] In the zones, the average value of the comprehensive evaluation index is hyperbolic curve > Hoshino method > Asaoka method > exponential curve, the upper and lower limit range is hyperbolic curve < Asaoka method < Hoshino method < exponential curve, and the hyperbolic curve is optimal in the ① zone. In the ② zone, the average value is Hoshino method > Asaoka method > hyperbolic curve > exponential curve, the upper and lower limit range is Hoshino method < exponential curve < Asaoka method < hyperbolic curve, and the Hoshino method is optimal. In the ③ zone, the average value is Asaoka method > Hoshino method > hyperbolic curve > exponential curve, the upper and lower limit range is Asaoka method < exponential curve < Hoshino method < hyperbolic curve, and the Asaoka method is optimal. In the ④ zone, the average value is Hoshino method > Asaoka method > hyperbolic curve > exponential curve, the upper and lower limit range is Hoshino method < Asaoka method < hyperbolic curve < exponential curve, and the Hoshino method is optimal. In the ⑤ zone, the average value is hyperbolic curve > Hoshino method > exponential curve > Asaoka method, the upper and lower limit range is hyperbolic curve < Hoshino method < Asaoka method < exponential curve, and the hyperbolic curve is optimal.

[0100] According to the comprehensive analysis, when the equivalent permeability coefficient is less than 0.02 m / d or greater than 0.7 m / d, the hyperbolic curve method is optimal; when the equivalent permeability coefficient is 0.02-0.044 m / d or 0.13-0.7 m / d, the Hoshino method is optimal; and when the equivalent permeability coefficient is 0.044-0.13 m / d, the Asaoka method is optimal.

[0101] In terms of trend, the comprehensive evaluation index of the four curve fitting methods increases with the increase of the permeability coefficient, the prediction quality of the four curve fitting methods is higher for soft soil foundation with strong drainage capacity, and the prediction quality of the four curve fitting methods is lower for soft soil foundation with weak drainage capacity.

[0102] In terms of magnitude, under different permeability coefficients of natural soft soil foundation, the hyperbolic method > the Hoshino method > the Asaoka method ≈ the exponential curve method, the hyperbolic method has high prediction quality for natural soft soil foundation, is most suitable for natural soft soil foundation working conditions, the Hoshino method is second, and the Asaoka method and the exponential curve method are poor. In this embodiment, under the commonly used observation conditions in engineering (observation time 12 months, observation frequency 0-3 months 1 time / 1 week, 4-6 months 1 time / 2 weeks, 6 months later 1 time / 1 month, observation accuracy Ⅲ level), the simulated settlement observation data is generated by intercepting, dispersing and superimposing random errors on the settlement theoretical curve.

[0103] S3, based on the curve fitting model, the simulated observation data is predicted and analyzed to obtain the best use condition of the curve fitting model.

[0104] As a preferred embodiment, the curve fitting model includes one or more of the hyperbolic method, the exponential curve method, the Asaoka method and the Hoshino method.

[0105] S4, under the best use condition of the curve fitting model, the foundation settlement prediction confidence analysis is carried out.

[0106] Embodiment two

[0107] As shown in Figure 6 , the present embodiment provides a foundation settlement prediction confidence analysis system based on the stacking preloading layered condition, which is used to implement the method of embodiment one, comprising:

[0108] The settlement theoretical curve acquisition module 101 is used to obtain the settlement theoretical curve under different soil properties, layering properties and foundation treatment conditions based on the soft soil subgrade settlement theoretical model under the stacking preloading layered foundation condition, and the settlement theoretical curve is used to provide basic data;

[0109] The simulated observation data acquisition module 102 is used to process the basic data provided by the settlement theoretical curve to obtain simulated observation data based on engineering observation conditions; wherein the engineering observation conditions include observation time, frequency and accuracy;

[0110] The best use condition determination module 103 is used to predict and analyze the simulated observation data based on the curve fitting model to obtain the best use condition of the curve fitting model;

[0111] The foundation settlement prediction confidence analysis module 104 is used for performing foundation settlement prediction confidence analysis under the best use condition of the curve fitting model.

[0112] The application further provides a memory which stores a plurality of instructions for implementing the method of embodiment one.

[0113] As shown in Figure 7 The application further provides an electronic device which comprises a processor 301 and a memory 302 connected with the processor 301, and the memory 302 stores a plurality of instructions which can be loaded and executed by the processor to enable the processor to perform the method of embodiment one.

[0114] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the application, and not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the application.

Claims

1. A method for predicting the confidence of ground settlement based on the preloading and precompression conditions of the layers, characterized by, The method comprises the following steps: S1, obtaining the settlement theoretical curves of soft soil subgrade under different soil properties, layering properties and foundation treatment conditions based on the settlement theoretical model of soft soil subgrade under preloading and surcharge layering foundation, the settlement theoretical curves being used to provide basic data; S2, processing the basic data provided by the settlement theoretical curves based on engineering observation conditions to obtain simulated observation data; S3, performing prediction analysis on the simulated observation data based on a curve fitting model to obtain the best use conditions of the curve fitting model; The S3 comprises: S31, calculating the weighted average value of the radial permeability coefficient of the layering foundation based on the equivalent permeability coefficient calculation formula along the horizontal direction, i.e. formula (3); （3）； In formula (3), k h is a weighted average value of the radial permeability coefficient of the layered foundation; k z is a weighted average value of the vertical permeability coefficient of the layered foundation; H j is the thickness of the jth layer of soil; S32, calculating the weighted average value of the vertical permeability coefficient of the layering foundation based on the equivalent permeability coefficient calculation formula along the vertical layer direction, i.e. formula (4); （4）； In formula (4), k j is the permeability coefficient of the jth layer of soil; k z is the weighted average of the vertical permeability coefficient of the layered foundation; H j is the thickness of the jth layer of soil; H is the total thickness of the soft soil layer; S33, performing normalization processing on the main soil property parameters and structural parameters of the layering drainage shaft foundation by using the equivalent permeability coefficient method of the preloading and surcharge foundation, i.e. formula (5) and formula (6); （5）； （6）； In formula (5) and formula (6), k ve is the equivalent permeability coefficient; l is the drainage shaft setting depth; r is the drainage shaft influence radius; n=r / d w is the well diameter ratio; k h is the weighted average value of the radial permeability coefficient of the layered foundation; k v is the weighted average value of the vertical permeability coefficient of the layered foundation; k s is the permeability coefficient of the soil in the smearing area; q w is the water passing performance of the plastic drainage plate; the smearing ratio is s=d s / d w , d s is the smearing area diameter; d w is the equivalent diameter of the drainage shaft; S34, comprehensively utilizing the correlation coefficient, relative error and variation coefficient of the settlement prediction result to determine the applicability comprehensive evaluation index of the settlement prediction model, obtaining the applicability of the settlement prediction model under different soft soil property conditions, layering conditions of the foundation and preloading and surcharge foundation treatment conditions, and obtaining the best use conditions of different curve fitting models based on the analysis of the applicability; S4, performing foundation settlement prediction confidence analysis under the best use conditions of the curve fitting model.

2. The method according to claim 1, wherein, The settlement theoretical model of soft soil subgrade under preloading and surcharge layering foundation comprises a layering natural foundation settlement theoretical model and a drainage shaft foundation settlement theoretical model.

3. The method according to claim 2, wherein, The S1 comprises: S11, based on the layering natural foundation settlement theoretical model, for the soft soil subgrade under the layering natural foundation condition, making the same assumptions as the one-dimensional consolidation theory of Terzaghi except the loading conditions to obtain the Terzaghi one-dimensional consolidation control equation of any soil layer i of the soft soil subgrade under the layering soft soil natural foundation condition considering the subgrade filling process, as shown in the partial differential equation (1): （1）； where c vi is the vertical consolidation coefficient of the ith soil layer; u i is the vertical excess pore water pressure of the ith soil layer; z is the depth of the soil layer; t is the consolidation time; q is the loading condition; Based on the corresponding boundary conditions, the continuity conditions at the layer interface and the initial conditions as the solving conditions, the numerical solution of the partial differential equation (1) is obtained by using the finite difference method; S12, based on the drainage shaft foundation settlement theoretical model, for the soft soil subgrade under the layering drainage shaft foundation condition, the layers in the drainage shaft setting area satisfy the assumptions of the Barron strain consolidation theory; the contact surface pore pressure and flow of the layers in the drainage shaft setting area satisfy the corresponding assumptions; the seepage surface satisfies the corresponding continuity conditions to obtain the one-dimensional consolidation control equation of any soil layer i of the soft soil subgrade under the layering drainage shaft foundation condition considering the subgrade filling process, as shown in the partial differential equation (2): （2）； wherein c si is the radial consolidation coefficient of the i-th soil layer; is the radial excess pore water pressure of the i-th soil layer; r is the influence radius of the drainage shaft; Based on the corresponding boundary conditions, the continuity conditions at the layer interface, the initial conditions and the like as the solving conditions, the numerical solution of the partial differential equation (2) is obtained by using the finite difference method; S13, calculating the settlement theoretical curves of soft soil subgrade under different soil properties, layering properties and foundation treatment conditions by using the settlement theoretical model of soft soil subgrade under preloading and surcharge layering foundation.

4. The method according to claim 3, wherein, The processing comprises one or more of intercepting, discretizing and superimposing random error processing.

5. The method according to claim 4, wherein, The engineering observation conditions include observation duration, frequency and precision.

6. The method according to claim 5, wherein, The curve fitting model includes one or more of hyperbolic curve method, exponential curve method, Asaoka method and Hoshino method.

7. A system for analyzing the confidence of the settlement prediction of a ground based on the preloading and layering conditions of a heap, for implementing the method according to any one of claims 1 to 6, characterized in that, The method comprises the following steps: The settlement theory curve acquisition module (101) is configured to obtain settlement theory curves for different soil properties, layering properties and foundation treatment conditions based on a soft soil subgrade settlement theory model under the condition of preloading embankment, and the settlement theory curves are used to provide basic data; The simulated observation data acquisition module (102) is configured to process the basic data provided by the settlement theory curves to obtain simulated observation data based on engineering observation conditions; The optimal use condition determination module (103) is configured to perform predictive analysis on the simulated observation data based on a curve fitting model to obtain optimal use conditions of the curve fitting model; the predictive analysis on the simulated observation data based on the curve fitting model to obtain the optimal use conditions of the curve fitting model comprises: S31, obtaining a weighted average value of the radial permeability coefficient of the layered foundation based on an equivalent permeability coefficient calculation formula along the horizontal direction, i.e., formula (3); （3）； In formula (3), k h is a weighted average value of the radial permeability coefficient of the layered foundation; k z is a weighted average value of the vertical permeability coefficient of the layered foundation; H j is the thickness of the jth layer of soil; S32, obtaining a weighted average value of the vertical permeability coefficient of the layered foundation based on an equivalent permeability coefficient calculation formula along the vertical layer direction, i.e., formula (4); （4）； In formula (4), k j is the permeability coefficient of the jth layer of soil; k z is the weighted average of the vertical permeability coefficient of the layered foundation; H j is the thickness of the jth layer of soil; H is the total thickness of the soft soil layer; S33, performing normalized processing on the main soil property parameters and structural parameters of the layered drainage shaft foundation by using the equivalent permeability coefficient method of the preloading embankment, i.e., formula (5) and formula (6); （5）； （6）； In formula (5) and formula (6), k ve is the equivalent permeability coefficient; l is the drainage shaft setting depth; r is the drainage shaft influence radius; n=r / d w is the well diameter ratio; k h is the weighted average value of the radial permeability coefficient of the layered foundation; k v is the weighted average value of the vertical permeability coefficient of the layered foundation; k s is the permeability coefficient of the soil in the smearing area; q w is the water passing performance of the plastic drainage plate; the smearing ratio is s=d s / d w , d s is the diameter of the smearing area; d w is the equivalent diameter of the drainage shaft; S34, determining a comprehensive evaluation index of the applicability of the settlement prediction model by comprehensively utilizing the correlation coefficient, relative error and variation coefficient of the settlement prediction result, obtaining the applicability of the settlement prediction model under different soft soil property conditions, foundation layering conditions and preloading embankment treatment conditions, and obtaining the optimal use conditions of different curve fitting models based on the analysis of the applicability; The foundation settlement prediction confidence analysis module (104) is configured to perform confidence analysis on the foundation settlement prediction under the optimal use conditions of the curve fitting model.

8. An electronic device, comprising: The computer readable storage medium stores a plurality of instructions, and the plurality of instructions can be read and executed by the processor to perform the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a plurality of instructions, and the plurality of instructions can be read and executed by the processor to perform the method of any one of claims 1-6.

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