Foundation settlement prediction confidence coefficient analysis method based on preloading layering condition

Abstract

The invention discloses a foundation settlement prediction confidence analysis method based on a preloading stratification condition, which comprises the following steps: obtaining a settlement theoretical curve under different soil properties, stratification properties and foundation treatment conditions based on a soft soil roadbed settlement theoretical model under a preloading stratification foundation condition, the settlement theoretical curve being used for providing basic data; processing basic data provided by the settlement theoretical curve based on engineering observation conditions to obtain simulated observation data; performing predictive analysis on the simulated observation data based on the curve fitting model to obtain an optimal use condition of the curve fitting model; and under the optimal use condition of the curve fitting model, foundation settlement prediction confidence analysis is carried out. The invention further discloses a corresponding analysis system, electronic equipment and a computer readable storage medium. The optimal use conditions of the curve fitting model under different soft soil property conditions, foundation stratification conditions and preloading foundation treatment conditions are obtained by considering common observation conditions of engineering.

Description

Technical Field

[0001] The present invention relates to the technical field of railway and foundation settlement prediction and analysis, and in particular to a foundation settlement prediction confidence analysis method and system based on preloading layering conditions. Background Art

[0002] By the end of 2023, the operating mileage of my country's high-speed railways has exceeded 44,000 kilometers. At the same time, international projects such as the Jakarta-Bandung High-Speed ​​Railway, the Hungary-Serbia Railway, and the China-Thailand Railway have continued to take root. The rapid development of high-speed railways has become an important driving force for domestic economic growth and a beautiful business card for displaying an international image. Fast and safe high-speed railways have greatly met people's daily travel needs, but they have also put forward extremely strict requirements on line deformation control. As an important part of the high-speed railway line infrastructure, the post-construction settlement control of the roadbed is a key link to ensure construction quality and line smoothness.

[0003] As a strip-shaped project, high-speed railways pass through areas with extensive soft soil. Soft soil is a special soil that needs to be faced in large quantities in the construction of domestic high-speed railways and the "Belt and Road" initiative. It is a fine-grained soil with a natural porosity 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 the physical and mechanical properties of each layer. The complex engineering characteristics and stratification make it extremely difficult to control the post-construction settlement of soft soil roadbed. Soft soil roadbeds can be treated by preloading foundations and composite foundations. The preloading method combined with drainage shafts such as bagged sand wells and plastic drainage boards is widely used in the foundation reinforcement treatment of soft soil roadbeds. It can accelerate the settlement rate, ensure the long-term stability of the roadbed settlement, simplify the construction process, and ensure that the project cost is reasonable and controllable, providing strong support for reducing costs and increasing efficiency in domestic high-speed railway construction and operation and maintenance and lowering the high-speed railway market access threshold in countries along the "Belt and Road" initiative. However, the preloaded foundation has a larger settlement and a longer construction period than the composite foundation, which is not conducive to shortening the construction period, controlling post-construction settlement and estimating the track laying time. This is the main difficulty in its large-scale promotion and application in high-speed railway construction.

[0004] The settlement observation prediction and assessment 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 relatively complex, and curve fitting models are still the main ones in engineering.

[0005] Curve fitting models generally include hyperbola method, three-point method, Asaoka method, exponential curve method, Hoshino method and other methods. The hyperbola method is a commonly used method in the prediction and evaluation of China's high-speed railway subgrade settlement. It was proposed by Nizipo Rović in 1955. The hyperbola method is suitable for fitting and extrapolating observation data during the static period. In order to effectively use the data during the filling period to achieve the purpose of early prediction, the German railway department proposed to expand the hyperbola method. Many scholars believe that the prediction results of the hyperbola method are conservative. Li Guowei and Feng Wenkai proposed corresponding correction methods; in 1959, Zeng Guoxi proposed the three-point method. Faced with the characteristics of small settlement magnitude and large data fluctuations encountered in the high-speed railway subgrade settlement, Chen Shan Xiong et al. integrated the exponential curve method, the hyperbola method and the three-point method, and proposed an improved three-point method, which achieved good results. In 1978, Japanese scholar Asaoka proposed the Asaoka method based on the one-dimensional vertical consolidation theory, also known as the Asaoka method or the graphical method. Some scholars believe that the deformation characteristics of the settlement curve after the inflection point conform to the characteristics of the exponential curve, and proposed the exponential curve method. Based on Terzaghi's one-dimensional consolidation theory, it is deduced that the degree of consolidation is proportional to the square root of time. The resulting Hoshino method is also widely used in the prediction and evaluation of high-speed railway subgrade settlement. Since then, the Verhulst model, Usher model, Logistic model, Compertz model, Weibull model, settlement difference method model, etc. have been proposed one after another. These models can reflect the relationship between the settlement and time of the whole process, and are more often used for early prediction of subgrade settlement. The applicability of the curve fitting model is generally controlled by a correlation coefficient of not less than 0.92. Wang Xingyun et al., Liu Junfei et al., Zeng Juncheng et al., Liu Hong et al. compared and analyzed the applicability of various curve fitting models, and proposed indicators and methods for evaluating the applicability of curve fitting models.

[0006] Many scholars have conducted extensive research on fitting models based on neural networks and intelligent algorithms. Zhang Huimei et al. established a soft soil roadbed settlement prediction model based on BP neural network, which was an early successful case of introducing neural networks into settlement prediction and evaluation; Feng Shengyang et al. performed equidistant interpolation processing on non-equidistant observation data, established a settlement prediction model based on least squares vector machine, and compared this model with the settlement prediction model established based on the joint method of BP neural network and grey theory; Peng Lishun et al. considered the foundation treatment form, soft soil layer thickness, soft soil compression modulus, hard shell layer thickness, hard shell layer compression modulus, roadbed height, roadbed filling period and other factors, and established a soft soil roadbed settlement prediction model using BP neural network and GA genetic algorithm; Qin Yaqiong et al. proposed a joint prediction model integrating curve fitting model and GA genetic algorithm to improve the optimization efficiency of model fitting parameters; Zhang Chunhui et al., based on the traditional BP neural network settlement prediction model, The PSO particle swarm algorithm was used to intelligently optimize the fitting parameters, and the model was compared with the traditional BP neural network settlement prediction model. It was found that the new model improved the prediction accuracy, proving the superiority of the new model; Chen Weihang et al. processed the non-equidistant observation data with equidistant time intervals and established a roadbed settlement prediction model based on the Bi-LSTM neural network; Qiu Hongsheng et al. used the SA algorithm to intelligently optimize the fitting parameters of the Verhulst model based on the non-equidistant settlement observation data, established a related settlement prediction model, compared the model with the traditional Verhulst model, and found 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 the non-uniform fill and non-equal step length settlement observation data during the filling period, and compared the model with the geometric series prediction model.

[0007] In today's high-speed railway construction, post-construction settlement control is mainly achieved from two aspects. First, theoretical calculation of roadbed settlement under external load is carried out in the design stage. Due to the spatial variability of field sampling, the nonlinearity of soil stress-strain relationship, the limitations of theoretical calculation models and other issues, the calculation accuracy of roadbed settlement process and final settlement in the design stage is poor. Second, based on limited field observation data in the construction stage, the final settlement of the roadbed and post-construction settlement are reasonably extrapolated with the help of mathematical models. Since the degree of confidence of settlement prediction results is affected by soil properties, foundation treatment forms, and observation conditions, the settlement observation prediction and evaluation methods formed in the construction stage lack theoretical support. Surveys show that over-limit roadbed settlement is the main form of high-speed railway subgrade disease. At present, geotechnical engineering design and construction methods based on deterministic theory are difficult to accurately control foundation deformation, which brings great difficulties to high-speed railway construction and operation and maintenance.

[0008] Therefore, there is a technical demand for developing new technical solutions for foundation settlement prediction confidence analysis based on preloading stratification conditions. Summary of the invention

[0009] The purpose of the present invention is to provide a foundation settlement prediction confidence analysis method and system based on preloading layered conditions, and use the theoretical settlement models of layered natural foundations and drainage shaft foundations to obtain theoretical settlement curves under different soil properties, layering properties, and foundation treatment conditions. The settlement theoretical curve is processed based on the observation conditions commonly used in engineering, and simulated settlement observation data is generated, and the simulated settlement observation data is predicted using a curve fitting model. 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 coefficient of variation of the settlement prediction results, and the optimal use conditions of different curve fitting models are analyzed. The technical problem to be solved is: considering the observation conditions commonly used in engineering such as observation time, frequency, and accuracy, the optimal use conditions of the curve fitting model under different soft soil conditions, foundation layering conditions, and preloading foundation treatment conditions are obtained.

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

[0011] S1, based on the theoretical model of soft soil roadbed settlement under the condition of preloading layered foundation, obtain the theoretical settlement curve under different soil properties, layering properties and foundation treatment conditions, and the theoretical settlement curve is used to provide basic data;

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

[0013] S3, performing prediction analysis on the simulated observation data based on the curve fitting model to obtain the optimal use condition of the curve fitting model;

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

[0015] Preferably, the theoretical model for soft soil roadbed settlement under the condition of preloaded layered foundation includes a theoretical model for layered natural foundation settlement and a theoretical model for drainage shaft foundation settlement.

[0016] Preferably, the S1 comprises:

[0017] S11, based on the theoretical model of settlement of stratified natural foundation, for the soft soil roadbed under stratified natural foundation conditions, the same assumptions as those of Terzaghi's one-dimensional consolidation theory are made except for the loading conditions, and the Terzaghi one-dimensional consolidation control equation of any soil layer i of the soft soil roadbed under stratified soft soil natural foundation conditions considering the roadbed filling process is obtained, as shown in the partial differential equation (1):

[0018]

[0019] 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 soil layer; z is the soil layer depth; t is the consolidation time; q is the loading condition;

[0020] Based on the corresponding boundary conditions, continuity conditions at the interface between layers and initial conditions as solution 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 theoretical model, for the soft soil roadbed under the layered drainage shaft foundation condition, each layer in the drainage shaft setting area satisfies the Barron strain consolidation theory assumption; the surface pressure and flow rate of each layer in the drainage shaft setting area meet the corresponding assumptions; the corresponding continuity conditions are met on the permeable surface, and the one-dimensional consolidation control equation of any soil layer i of the soft soil roadbed under the layered drainage shaft foundation condition considering the roadbed filling process is obtained, as shown in the partial differential equation (2):

[0022]

[0023] In the formula, 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;

[0024] Based on the corresponding boundary conditions, continuity conditions at the interface between layers, initial conditions and other solution conditions, the finite difference method is used to obtain the numerical solution of the partial differential equation (2);

[0025] S13, using the theoretical model of soft soil roadbed settlement under preloaded layered foundation conditions, calculate the theoretical curves of soft soil roadbed settlement under different soil properties, layering and foundation treatment conditions.

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

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

[0028] Preferably, S3 includes:

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

[0030]

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

[0032] S32, based on the equivalent permeability calculation formula in the vertical plane direction, that is, formula (4), the weighted average value of the vertical permeability of the layered foundation is obtained;

[0033]

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

[0035] S33, using the equivalent permeability coefficient method of preloaded foundation, i.e., equations (5) and (6), the main soil parameters and structural parameters of the stratified drainage shaft foundation are normalized;

[0036]

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

[0038] S34, comprehensively utilizing the correlation coefficient, relative error and coefficient of variation of the settlement prediction results to determine the comprehensive evaluation index of the applicability of the settlement prediction model, obtain the applicability of the settlement prediction model under different soft soil conditions, foundation stratification conditions, and preloading foundation treatment conditions, and obtain the optimal use conditions of different curve fitting models based on the analysis of the applicability.

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

[0040] The second aspect of the present invention further provides a foundation settlement prediction confidence analysis system based on preloading layering conditions, which is used to implement the method of the first aspect, comprising:

[0041] A settlement theory curve acquisition module (101) is used to obtain settlement theory curves under different soil properties, stratification properties, and foundation treatment conditions based on a soft soil roadbed settlement theory model under preloaded layered foundation conditions, wherein the settlement theory curves are used to provide basic data;

[0042] A simulation observation data acquisition module (102) is used to process the basic data provided by the settlement theory curve based on the engineering observation conditions to obtain simulation observation data;

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

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

[0045] A third aspect of the present invention 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 execute the method described in the first aspect.

[0046] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor to execute the method described in the first aspect.

[0047] Beneficial effects of the method and system of the present invention:

[0048] (1) The technical problem of the changing law of the confidence in settlement prediction of high-speed railway soft soil roadbed under the coupling of multiple factors was solved. Specifically: considering the observation conditions commonly used in engineering projects, such as observation time, frequency, and accuracy, the optimal use conditions of the curve fitting model under different soft soil conditions, foundation stratification conditions, and preloading foundation treatment conditions were obtained; considering the changes in observation conditions, such as observation time, frequency, and accuracy, combined with intelligent algorithms, a settlement prediction confidence analysis method was formed, and the settlement prediction mean and standard deviation under different observation conditions were statistically analyzed to reveal the changing law of settlement prediction confidence caused by the randomness of observation conditions; constructing a settlement prediction confidence analysis model that integrates the observation data of the filling and loading process to reveal the changing law of the settlement prediction confidence in high-speed railway soft soil roadbed caused by the construction process.

[0049] (2) Settlement prediction is a method of extrapolating the final settlement of the roadbed and the post-construction settlement based on the field measured data and using mathematical means such as curve fitting methods. It is very difficult to use the field measured data to conduct research on the settlement prediction confidence analysis conducted by the present invention. The reasons are that, first, the field conditions are extremely complex, and it is difficult to effectively and accurately control the changes in factors such as soil properties, stratification, foundation treatment conditions, and observation conditions; second, the settlement prediction confidence research requires a large number of repeated observations for the same working condition to obtain a large amount of observation data with random errors. Based on the field measured data, a large amount of financial, material, and human resources are consumed; third, the measured roadbed settlement curve is not only affected by external loads, but also by multiple factors such as climate, rainfall, and temperature, resulting in the settlement curve obtained on site being unsuitable for scientific research. Therefore, in order to obtain a large number of reliable samples, the present invention uses a settlement prediction confidence analysis method based on simulated observation data to conduct 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 for achieving the purpose of the present invention. The present invention adopts theoretical calculation, numerical simulation, intelligent algorithm, mathematical statistics and other methods to reveal the changing law of confidence in the prediction of high-speed railway soft soil roadbed settlement caused by the randomness of observation conditions under different soil properties, stratification and foundation treatment conditions when an appropriate curve fitting model is used. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 A flow chart of a confidence analysis method for foundation settlement prediction based on preloading stratification conditions provided in accordance with an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of a confidence analysis method for foundation settlement prediction based on preloading layering conditions provided in accordance with an embodiment of the present invention;

[0053] Figure 3 A histogram of correlation coefficients of different curve fitting methods provided according to an embodiment of the present invention;

[0054] Figure 4 A scatter plot showing the relationship between comprehensive evaluation indicators of various settlement prediction models and equivalent permeability coefficients provided according to an embodiment of the present invention;

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

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

[0057] Figure 7 A structural diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0060] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0061] Embodiment 1

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

[0063] S1, based on the theoretical model of soft soil roadbed settlement under the condition of preloading layered foundation, obtain the theoretical settlement curve under different soil properties, layering properties and foundation treatment conditions, and the theoretical settlement curve is used to provide basic data;

[0064] As a preferred embodiment, the theoretical model for soft soil roadbed settlement under the condition of preloaded layered foundation includes a theoretical model for layered natural foundation settlement and a theoretical model for drainage shaft foundation settlement.

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

[0066] S11, based on the theoretical model of settlement of stratified natural foundation, for the soft soil roadbed under stratified natural foundation conditions, the same assumptions as those of Terzaghi's one-dimensional consolidation theory are made except for the loading conditions, and the Terzaghi one-dimensional consolidation control equation of any soil layer i of the soft soil roadbed under stratified soft soil natural foundation conditions considering the roadbed filling process is obtained, 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 soil layer; z is the soil layer depth; t is the consolidation time; q is the loading condition;

[0069] Based on the corresponding boundary conditions, continuity conditions at the interface between layers and initial conditions as solution 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 foundation settlement theoretical model, for the soft soil roadbed under the layered drainage shaft foundation condition, each layer in the drainage shaft setting area satisfies the Barron strain consolidation theory assumption; the surface pressure and flow rate of each layer in the drainage shaft setting area meet the corresponding assumptions; the corresponding continuity conditions are met on the permeable surface, and the one-dimensional consolidation control equation of any soil layer i of the soft soil roadbed under the layered drainage shaft foundation condition considering the roadbed filling process is obtained, as shown in the partial differential equation (2):

[0071]

[0072] In the formula, 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;

[0073] Based on the corresponding boundary conditions, continuity conditions at the interface between layers, initial conditions and other solution conditions, the finite difference method is used to obtain the numerical solution of the partial differential equation (2);

[0074] S13, using the theoretical model of soft soil roadbed settlement under preloaded layered foundation conditions, calculate the theoretical curves of soft soil roadbed settlement under different soil properties, layering and foundation treatment conditions.

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

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

[0077] As a preferred implementation, S3 includes:

[0078] S31, based on the equivalent permeability calculation formula along the horizontal direction, that is, formula (3), the weighted average value of the radial permeability of the layered foundation is obtained;

[0079]

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

[0081] S32, based on the equivalent permeability calculation formula in the vertical plane direction, that is, formula (4), the weighted average value of the vertical permeability of the layered foundation is obtained;

[0082]

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

[0084] S33, using the equivalent permeability coefficient method of preloaded foundation, i.e., formula (5) and formula (6), the main soil parameters and structural parameters of the layered drainage shaft foundation are normalized;

[0085]

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

[0087] The present invention constructs a comprehensive evaluation index by weighted processing of correlation coefficient, relative error and coefficient of variation, and can determine the optimal use range of commonly used engineering models. When the equivalent permeability coefficient of soft foundation is less than 0.02m / d or greater than 0.7m / d, the hyperbola method is optimal; when it is 0.02-0.044m / d or 0.13-0.7m / d, the Hoshino method is optimal; when it is 0.044-0.13m / d, the Asaoka method is optimal.

[0088] In this embodiment, step S34 includes:

[0089] (1) Constructing comprehensive evaluation indicators

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

[0091] The requirements for curve fitting methods in high-speed railway subgrade deformation observation and evaluation 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 is required to accurately calculate the final settlement of the foundation; third, the quality of on-site measured data during subgrade filling and static loading is not high, which affects the stability of settlement prediction results. The curve fitting method should be insensitive to random fluctuations in the observed data and the prediction results should be relatively stable.

[0092] On the one hand, curve fitting methods have their own characteristics, and on the other hand, deformation observation and evaluation have multiple requirements, which makes it difficult for a single evaluation index to scientifically evaluate the adaptability of curve fitting methods under different conditions. To this end, the present invention constructs a comprehensive evaluation index of the applicability of curve fitting methods with correlation coefficient, relative error and coefficient of variation 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 coefficient of variation; R∈(0,1) represents the correlation coefficient; δ∈(0,1) represents the relative error;

[0095] (2) Ranking the prediction quality of each curve fitting method

[0096] like Figure 3 The figure shows the correlation coefficient bar graph of different curve fitting methods. Under different permeability conditions of natural soft soil foundation, the correlation coefficient of the hyperbola method r≥0.98, indicating that the hyperbola method can better describe the time-varying characteristics of settlement; the correlation coefficient of the exponential curve method gradually decreases with the increase of the permeability coefficient. When the permeability coefficient is greater than 11.4×10-4m / d, the decrease in the correlation coefficient increases. When the permeability coefficient is greater than 13.6×10-4m / d, the correlation coefficient r<0.94, indicating that the exponential curve method has a strong ability to describe the time-varying characteristics of settlement of soft soil foundation with low permeability; the Asaoka method is more suitable for describing the time-varying characteristics of settlement of soft soil foundation with low permeability. The correlation coefficient gradually increases with the increase of permeability coefficient. When the permeability coefficient is less than 2.76×10-4m / d, the correlation coefficient r is less than 0.92, indicating that the Asaoka method has a strong ability to describe the time-varying characteristics of settlement of soft soil foundation with high permeability; the correlation coefficient of the Hoshino method gradually increases with the increase of permeability coefficient. When the permeability coefficient of natural soft soil foundation is less than 2.76×10-4m / d, the correlation coefficient r of the Hoshino method is less than 0.96, indicating that the Hoshino method has a strong ability to describe the time-varying characteristics of settlement of soft soil foundation with high permeability.

[0097] like Figure 4As shown, the optimal use range of the model can be basically divided into zones. The equivalent permeability coefficient is less than 0.02m / d, which is zone ①. In this zone, the comprehensive evaluation index of the hyperbola method is stable at about 0.8. The fluctuation range of the index of the Asaoka method and the Hoshino method is large, and the mean value of the exponential curve method is small. 0.02-0.044m / d is zone ②. In this zone, the mean values ​​of the index of the Hoshino method, Asaoka method, and hyperbola method are high, and the mean value of the exponential curve method is small. 0.044-0.13m / d is zone ③. The mean values ​​of the index of the Asaoka method, the Hoshino method, and the hyperbola method are all high, and the mean value of the index of the exponential curve method is small. 0.13-0.7m / d is zone ④. The mean values ​​of the index of the Asaoka method, the Hoshino method, and the hyperbola method are all high and the fluctuation range is small. The index of the exponential curve method gradually increases. Greater than 0.7m / d is zone ⑤. The index of Asaoka method and Hoshino method gradually decreases with the increase of equivalent permeability coefficient, while the index of hyperbola method and exponential curve method gradually increases with the increase of equivalent permeability coefficient.

[0098] Figure 5 (a)- Figure 5 (e) is the distribution range of the comprehensive evaluation indicators of commonly used models for projects in areas ① to ⑤.

[0099] In the area, the average value of comprehensive evaluation index is hyperbolic method > Xingye method > Asaoka method > exponential curve method, and the upper and lower limits are hyperbolic method < Asaoka method < Xingye method < exponential curve method. In area ①, the hyperbolic method is the best. In area ②, the average value is Xingye method > Asaoka method > hyperbolic method > exponential curve method, and the upper and lower limits are Xingye method < exponential curve method < Asaoka method < hyperbolic method. In area ②, the average value is Xingye method > Asaoka method > hyperbolic method > exponential curve method, and the upper and lower limits are Xingye method < exponential curve method < Asaoka method < hyperbolic method. In area ③, the average value is Asaoka method > Xingye method > hyperbolic method > exponential curve method, and the upper and lower limits are Asaoka method < exponential curve method < Xingye method < hyperbolic method. In area ③, the Asaoka method is the best. In area ④, the average value is Xingye method > Asaoka method > hyperbolic method > exponential curve method, and the upper and lower limits are Xingye method < Asaoka method < hyperbolic method < exponential curve method. In area ④, the average value is Xingye method > Asaoka method > hyperbolic method > exponential curve method. In area ⑤, the mean value hyperbola method > Hoshino method > exponential curve method > Asaoka method, and in the upper and lower limit range, the hyperbola method < Hoshino method < Asaoka method < exponential curve method. In area ⑤, the hyperbola method is the best.

[0100] Comprehensive analysis shows that the hyperbola method is optimal for soft foundations with an equivalent permeability coefficient of less than 0.02 m / d and greater than 0.7 m / d; the Hoshino method is optimal when the permeability coefficient is between 0.02 and 0.044 m / d and between 0.13 and 0.7 m / d; and the Asaoka method is optimal when the permeability coefficient is between 0.044 and 0.13 m / d.

[0101] In terms of trend, the comprehensive evaluation indexes of the four curve fitting methods increase with the increase of permeability coefficient. The prediction quality of the four curve fitting methods is higher for soft soil foundation with stronger drainage capacity, while the prediction quality of the four curve fitting methods is lower for soft soil foundation with weaker drainage capacity.

[0102] In terms of value, under the conditions of different permeability coefficients of natural soft soil foundation, the hyperbola method> the Hoshino method> the Asaoka method≈ the exponential curve method. The hyperbola method has high quality in predicting the settlement of natural soft soil foundation and is most suitable for natural soft soil foundation conditions. The Hoshino method is second, and the Asaoka method and the exponential curve method are poor. In this embodiment, under the observation conditions commonly used in engineering (observation time of 12 months, observation frequency of 0-3 months, 1 time / 1 week, 4-6 months, 1 time / 2 weeks, 6 months after 1 time / 1 month, observation accuracy III level), the settlement theoretical curve is intercepted, discretized and superimposed with random errors to generate simulated settlement observation data.

[0103] S3, performing prediction analysis on the simulated observation data based on the curve fitting model to obtain the optimal use condition of the curve fitting model.

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

[0105] S4, confidence analysis of foundation settlement prediction is performed under the optimal conditions of the curve fitting model.

[0106] Embodiment 2

[0107] like Figure 6 As shown, this embodiment provides a foundation settlement prediction confidence analysis system based on preloading layering conditions, which is used to implement the method of embodiment 1, including:

[0108] The settlement theory curve acquisition module 101 is used to obtain the settlement theory curve under different soil properties, stratification and foundation treatment conditions based on the soft soil roadbed settlement theory model under the condition of preloading layered foundation, and the settlement theory 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 theory curve based on the engineering observation conditions to obtain simulated observation data; wherein the engineering observation conditions include observation duration, frequency and accuracy;

[0110] The optimal use condition determination module 103 is used to perform prediction analysis on the simulated observation data based on the curve fitting model to obtain the optimal use condition of the curve fitting model;

[0111] The foundation settlement prediction confidence analysis module 104 is used to perform foundation settlement prediction confidence analysis under the optimal use conditions of the curve fitting model.

[0112] The present invention also provides a memory storing a plurality of instructions, wherein the instructions are used to implement the method as in the first embodiment.

[0113] like Figure 7 As shown, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301, the memory 302 stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the method as in the first embodiment.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A confidence analysis method for foundation settlement prediction based on preloading stratification conditions, characterized in that: include: S1, based on the theoretical model of soft soil roadbed settlement under the condition of preloading layered foundation, obtain the theoretical settlement curve under different soil properties, layering properties and foundation treatment conditions, and the theoretical settlement curve is used to provide basic data; S2, processing the basic data provided by the settlement theory curve based on the engineering observation conditions to obtain simulated observation data; S3, performing prediction analysis on the simulated observation data based on the curve fitting model to obtain the optimal use condition of the curve fitting model; S4, confidence analysis of foundation settlement prediction is performed under the optimal conditions of the curve fitting model.

2. The confidence analysis method for foundation settlement prediction based on preloading stratification conditions according to claim 1 is characterized in that: The theoretical model for soft soil roadbed settlement under the condition of preloaded layered foundation includes a theoretical model for layered natural foundation settlement and a theoretical model for drainage shaft foundation settlement.

3. The confidence analysis method for foundation settlement prediction based on preloading layering conditions according to claim 2 is characterized in that: The S1 includes: S11, based on the theoretical model of settlement of stratified natural foundation, for the soft soil roadbed under stratified natural foundation conditions, the same assumptions as those of Terzaghi's one-dimensional consolidation theory are made except for the loading conditions, and the Terzaghi one-dimensional consolidation control equation of any soil layer i of the soft soil roadbed under stratified soft soil natural foundation conditions considering the roadbed filling process is obtained, as shown in the partial differential equation (1): 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 soil layer; z is the soil layer depth; t is the consolidation time; q is the loading condition; Based on the corresponding boundary conditions, continuity conditions at the interface between layers and initial conditions as solution 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 roadbed under the layered drainage shaft foundation condition, each layer in the drainage shaft setting area satisfies the Barron strain consolidation theory assumption; the surface pressure and flow rate of each layer in the drainage shaft setting area meet the corresponding assumptions; the corresponding continuity conditions are met on the permeable surface, and the one-dimensional consolidation control equation of any soil layer i of the soft soil roadbed under the layered drainage shaft foundation condition considering the roadbed filling process is obtained, as shown in the partial differential equation (2): In the formula, 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, continuity conditions at the interface between layers, initial conditions and other solution conditions, the finite difference method is used to obtain the numerical solution of the partial differential equation (2); S13, using the theoretical model of soft soil roadbed settlement under preloaded layered foundation conditions, calculate the theoretical curves of soft soil roadbed settlement under different soil properties, layering and foundation treatment conditions.

4. The confidence analysis method for foundation settlement prediction based on preloading stratification conditions according to claim 3 is characterized in that: The processing includes one or more of truncation, discretization and superposition random error processing.

5. The confidence analysis method for foundation settlement prediction based on preloading stratification conditions according to claim 4 is characterized in that: The engineering observation conditions include observation duration, frequency and accuracy.

6. The confidence analysis method for foundation settlement prediction based on preloading layering conditions according to claim 5 is characterized in that: The S3 includes: S31, based on the equivalent permeability calculation formula along the horizontal direction, that is, formula (3), the weighted average value of the radial permeability of the layered foundation is obtained; In formula (3), k h k is the weighted average value of radial permeability of layered foundation; z H is the weighted average value of vertical permeability coefficient of layered foundation; j is the thickness of the j-th soil layer; S32, based on the equivalent permeability calculation formula in the vertical plane direction, that is, formula (4), the weighted average value of the vertical permeability of the layered foundation is obtained; In formula (4), k j is the permeability coefficient of the j-th layer of soil; k z H is the weighted average value of vertical permeability coefficient of layered foundation; j is the thickness of the jth soil layer; H is the total thickness of the soft soil layer; S33, using the equivalent permeability coefficient method of preloaded foundation, i.e., equations (5) and (6), the main soil parameters and structural parameters of the stratified drainage shaft foundation are normalized; In formula (5) and formula (6), k ve is the equivalent permeability coefficient; l is the depth of the drainage shaft; r is the influence radius of the drainage shaft; n = r / d w is the well diameter ratio; k h k is the weighted average value of radial permeability of layered foundation; v k is the weighted average value of vertical permeability coefficient of layered foundation; s is the permeability coefficient of the soil in the smear area; q w is the water permeability of the plastic drainage board; the coating 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, comprehensively utilizing the correlation coefficient, relative error and coefficient of variation of the settlement prediction results to determine the comprehensive evaluation index of the applicability of the settlement prediction model, obtain the applicability of the settlement prediction model under different soft soil conditions, foundation stratification conditions, and preloading foundation treatment conditions, and obtain the optimal use conditions of different curve fitting models based on the analysis of the applicability.

7. The confidence analysis method for foundation settlement prediction based on preloading layering conditions according to claim 6 is characterized in that: The curve fitting model includes one or more of a hyperbola method, an exponential curve method, an Asaoka method and a Hoshino method.

8. A foundation settlement prediction confidence analysis system based on preloading layering conditions, used to implement any method of claims 1-7, characterized in that: include: A settlement theory curve acquisition module (101) is used to obtain settlement theory curves under different soil properties, stratification properties, and foundation treatment conditions based on a soft soil roadbed settlement theory model under preloaded layered foundation conditions, wherein the settlement theory curves are used to provide basic data; A simulation observation data acquisition module (102) is used to process the basic data provided by the settlement theory curve based on the engineering observation conditions to obtain simulation observation data; An optimal use condition determination module (103) is used to perform prediction analysis on the simulated observation data based on a curve fitting model to obtain the optimal use condition of the curve fitting model; The foundation settlement prediction confidence analysis module (104) is used to perform foundation settlement prediction confidence analysis under the optimal use conditions of the curve fitting model.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is used to read the instructions and execute the method according to any one of claims 1 to 7.

10. 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 by a processor to execute the method according to any one of claims 1 to 7.

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