A high-speed railway subgrade settlement assessment method and system based on confidence analysis

Abstract

The present invention discloses a high-speed railway subgrade settlement assessment method based on confidence analysis, including: under the conditions of different observation accuracy and observation frequency, taking the settlement prediction target standard deviation of different track structure forms of high-speed railway as the core, proposing corresponding basic technical requirements such as observation period, settlement prediction stability, settlement completion rate, etc.; forming a high-speed railway subgrade settlement prediction assessment method including the selection of curve fitting model, correction of system error, and settlement prediction structure assessment. Also disclosed are corresponding assessment systems, electronic devices, and computer-readable storage media, by analyzing the change rules of the values ​​of observation period, settlement prediction stability requirements, and settlement completion rate requirements under the conditions of different observation accuracy and observation frequency, clarifying the basic technical requirements of settlement observation and prediction analysis associated with target confidence, and proposing a settlement assessment scheme based on confidence.

Description

Technical Field

[0001] The present invention relates to the technical field of railway and roadbed settlement prediction and analysis, and in particular to a high-speed railway roadbed settlement assessment method and system based on confidence analysis. Background Art

[0002] Railway transportation occupies a backbone position in my country's overall transportation network. In recent years, based on the rapid development of my country's high-speed railways, railway construction and operation and maintenance have become an important driving force for my country's domestic economic growth and a beautiful business card for displaying its international image. my country has made plans for the medium- and long-term development of railways. However, since my country is a country with widespread soft soil, railway lines are bound to pass through a large number of soft soil areas. An important problem faced by the construction of high-speed railways in soft soil areas is the control of foundation settlement and post-construction settlement. Different settlement control standards are proposed for railways of different grades. The high smoothness requirements of high-speed railway ballastless tracks put forward more stringent requirements for post-construction settlement of the roadbed. Generally, the post-construction settlement is required to be no more than 15mm, which requires the final settlement of the roadbed to be accurately controlled during the construction process.

[0003] However, the prior art still has the following technical defects:

[0004] First, in current engineering design and construction, settlement calculation is mainly performed in the design stage and roadbed settlement observation, prediction and evaluation are performed in the construction stage to control post-construction settlement. However, due to the nonlinearity of soil stress-strain relationship, variability and anisotropy of geotechnical parameters, etc., the theoretical calculation method, especially the theoretical calculation method based on the layered summation method in the design stage, has large calculation deviations for the settlement development process and final settlement of the foundation. The roadbed settlement observation, prediction and evaluation in the construction stage is to extrapolate the settlement development process and final settlement based on the observation data. Since the settlement observation process is affected by many factors such as foundation treatment form, geological conditions, observation conditions, etc., and the influence of the above factors on the settlement prediction results is unclear, it is not easy to control the settlement of high-speed railway roadbed using settlement observation prediction.

[0005] Second, the evaluation indicators of the current settlement observation and prediction methods are based on experience and have no theoretical basis. This evaluation method based on confidence has found a theoretical basis for the formulation of relevant indicators and values.

[0006] Third, there is no technical solution for high-speed railway subgrade settlement assessment based on confidence analysis in the prior art. Summary of the invention

[0007] The purpose of the present invention is to provide a high-speed railway roadbed settlement assessment method and system based on confidence analysis, by analyzing the change law of the observation period, settlement prediction stability requirements, and settlement completion rate requirements under different observation accuracy and observation frequency conditions, clarifying the basic technical requirements of settlement observation and prediction analysis associated with the target confidence, proposing a settlement assessment method based on confidence, and determining the implementation method of settlement prediction technology based on error derivation.

[0008] A first aspect of the present invention is to provide a high-speed railway subgrade settlement assessment method based on confidence analysis, comprising:

[0009] S1, determining basic technical requirements associated with the target standard deviation, the basic technical requirements include observation condition requirements, settlement prediction stability requirements, and settlement completion rate requirements, wherein the observation condition requirements include roadbed settlement observation period, observation accuracy, and observation frequency; the basic technical requirements associated with the target standard deviation include: under conditions of different observation accuracy and observation frequency, based on the change law of the values ​​of the roadbed settlement observation period, settlement prediction stability requirements, and settlement completion rate requirements, determining basic technical requirements for settlement observation and prediction analysis associated with the target confidence;

[0010] S2, based on the basic technical requirements of settlement observation and prediction analysis associated with the target confidence, high-speed railway subgrade settlement assessment is carried out, including: selection of observation condition requirements, correction of system errors and evaluation of settlement prediction results. The evaluation indicators of the assessment include settlement prediction stability, settlement completion rate and target standard deviation.

[0011] Preferably, the core and basis of S1 is to determine that the target standard deviation of the ballastless track subgrade is 2 mm, and the target standard deviation of the ballasted track subgrade is 4 mm.

[0012] Preferably, S2 includes:

[0013] S21, selecting a curve fitting model, including: analyzing the correlation coefficient, relative error, and coefficient of variation of different curve fitting models for soft soil foundation settlement curve fitting with different soil properties and foundation treatment forms, determining the optimal prediction model under corresponding conditions, and correcting the system error based on the statistical mean calculation formula of the predicted settlement under the optimal prediction model;

[0014] S22, conduct settlement deformation observation with the observation accuracy and observation frequency determined by the settlement assessment method based on confidence;

[0015] S23, when the roadbed settlement observation period is reached, settlement prediction is performed on the settlement deformation observation data to predict the final settlement of the roadbed, and the settlement prediction stability, settlement completion rate and target standard deviation are used as indicators to evaluate the prediction result of the settlement prediction.

[0016] Preferably, the S21 includes correcting the system error by using an empirical formula for predicting the statistical mean of settlement.

[0017] Preferably, the different curve fitting methods in S21 include:

[0018] For sand well foundation, the applicability of the curve fitting method is classified and selected according to the sand well spacing and permeability coefficient. The limit of the sand well spacing is dw = 2m, and the limit of the permeability coefficient is K v =8.0E -4 m / d; the naturally deposited soft soil foundation is composed of multiple layers of soil with different permeability coefficients, and has macroscopic heterogeneity. Several soil layers are equivalent to a single soil layer with a thickness equal to the sum of the soil layers and a permeability coefficient equal to the equivalent permeability coefficient. The equivalent permeability coefficient is determined according to formula (21) and formula (22):

[0019]

[0020] Where: K j ——Permeability coefficient of each soil layer (m / s);

[0021] a——Compression coefficient (1 / MPa);

[0022] e——porosity ratio;

[0023] r w ——Gravity of water (kn / m 3 );

[0024] K v ——Equivalent permeability coefficient of multi-layer soil (m / s);

[0025] H——thickness of each soil layer (m);

[0026] The relationship between the diameter of the effective influence range of the plastic drain board arranged in an equilateral triangle and the spacing between the plastic drain boards is as follows:

[0027] d w =1.05·s (23)

[0028] Where: d w ——Diameter of the effective influence range of the plastic drainage board (m);

[0029] s——spacing between plastic drain boards (m).

[0030] Preferably, the stability of the settlement prediction is determined based on the difference between two predicted final settlement amounts at a certain time interval;

[0031] The settlement completion rate is affected by different observation frequencies and observation accuracies of natural foundation and sand well foundation;

[0032] The target standard deviation is affected by the observation accuracy under the same observation frequency condition.

[0033] Preferably, the calculation formula for the stability of the settlement prediction is as shown in formula (0):

[0034]

[0035] Where: Δ2 – the difference between two predicted final settlements at an interval of 3 months (mm);

[0036] μ i , μ i+3 ——Statistical mean of predicted settlement (mm);

[0037] σ i , σ i+3 ——Predicted settlement standard deviation (mm).

[0038] The second aspect of the present invention is to provide a high-speed railway subgrade settlement assessment system based on confidence analysis, which is used to implement the method of the first aspect, comprising:

[0039] A technical requirement determination module (101) is used to determine basic technical requirements associated with a target standard deviation, wherein the basic technical requirements include observation condition requirements, settlement prediction stability requirements, settlement completion rate requirement values, and selection of settlement prediction fitting curves, wherein the observation condition requirements include roadbed settlement observation period, observation accuracy, and observation frequency; the determination of the basic technical requirements associated with the target standard deviation includes: under conditions of different observation accuracy and observation frequency, based on the change rules of the roadbed settlement observation period, settlement prediction stability requirements, and settlement completion rate requirement values, determining basic technical requirements for settlement observation and prediction analysis associated with a target confidence level;

[0040] The roadbed settlement assessment (102) is used to conduct a high-speed railway roadbed settlement assessment with settlement prediction stability, settlement completion rate and target standard deviation as the core based on the basic technical requirements of settlement observation and prediction analysis associated with the target confidence.

[0041] 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.

[0042] 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 and execute the method described in the first aspect.

[0043] Beneficial effects of the method and system of the present invention:

[0044] (1) The basic technical requirements for settlement observation and prediction analysis associated with the target confidence level are proposed. For high-speed railway ballastless track subgrade, the target standard deviation of σ = 2 mm is used to control the accidental error of settlement prediction. For high-speed railway ballasted track subgrade, the target standard deviation of σ = 4 mm is used to control the accidental error of settlement prediction. The observation conditions such as observation period, observation accuracy, and observation frequency that meet the target standard deviation requirements are determined.

[0045] (2) A settlement assessment method and analysis process based on confidence is proposed. Different curve fitting methods and statistical mean calculation formulas for predicted settlement under different soft soil foundation conditions are used to correct the system error. The change law of settlement prediction stability index and settlement completion rate under different soft soil foundation forms, observation accuracy, and observation frequency conditions is analyzed. A roadbed settlement assessment method with settlement prediction stability, settlement completion rate, and target standard deviation as the core is proposed. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] 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.

[0047] Figure 1 A graph showing the predicted standard deviation of natural foundation settlement and the duration of the observation period provided according to an embodiment of the present invention;

[0048] Figure 2 A graph showing the predicted standard deviation of sand well foundation settlement and the duration of the observation period according to an embodiment of the present invention;

[0049] Figure 3 A graph showing the difference between the predicted standard deviation of natural foundation settlement of a high-speed railway ballastless track and the final settlement values ​​predicted twice at intervals of 3 months according to an embodiment of the present invention;

[0050] Figure 4 A graph showing the difference between the standard deviation of the natural foundation settlement prediction of the ballasted track of a high-speed railway and the final settlement values ​​predicted twice at intervals of 3 months according to an embodiment of the present invention;

[0051] Figure 5 A graph showing the difference between the predicted standard deviation of the sand well foundation settlement of the ballastless track of a high-speed railway and the predicted final settlement twice at an interval of 3 months according to an embodiment of the present invention;

[0052] Figure 6 A graph showing the difference between the predicted standard deviation of the sand well foundation settlement of the ballasted track of a high-speed railway and the predicted final settlement twice at an interval of 3 months according to an embodiment of the present invention;

[0053] Figure 7 A graph showing a standard deviation of natural foundation settlement prediction and a settlement completion rate according to an embodiment of the present invention;

[0054] Figure 8 A graph showing the predicted standard deviation of sand well foundation settlement and the settlement completion rate according to an embodiment of the present invention;

[0055] Fig. 9 A flow chart of a high-speed railway subgrade settlement assessment method based on confidence analysis provided according to an embodiment of the present invention;

[0056] Fig.10 A schematic cross-sectional diagram of a test work point design according to an embodiment of the present invention;

[0057] Fig.11 A longitudinal section diagram of engineering geology and soft soil foundation reinforcement provided according to an embodiment of the present invention;

[0058] Fig.12 A roadbed center settlement-load-time curve provided according to an embodiment of the present invention;

[0059] Fig.13 This is an architecture diagram of a high-speed railway subgrade settlement assessment system based on confidence analysis provided according to an embodiment of the present invention;

[0060] Fig.14 A structural diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] 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.

[0062] 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.

[0063] 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.

[0064] The following embodiments analyze the changing patterns of the values ​​of observation period, settlement prediction stability requirements, and settlement completion rate requirements under different observation accuracy and observation frequency conditions, clarify the basic technical requirements for settlement observation and prediction analysis associated with target confidence, propose a settlement assessment method based on confidence, and give an implementation method for settlement prediction technology based on error deduction with examples.

[0065] Before implementing the corresponding embodiment, it is necessary to analyze the technical requirements for settlement observation, including:

[0066] 1. Determine the observation conditions

[0067] like Figure 1 As shown in the figure, for high-speed railway ballastless track, since its observation accuracy must meet the accuracy requirements of not less than Class III leveling, the mean error of Class III leveling observation is ±1mm, and the probability of more than 95% of the accidental error of leveling observation appears between (-2,2)mm. Since the settlement observation prediction is based on the leveling observation data, the curve fitting method is used to extrapolate the existing data to obtain the final settlement of the roadbed. If the accidental error of the settlement prediction result can reach the error of the leveling observation, it is better. The predicted settlement standard deviation σ≤2mm is used as the target standard deviation of the accidental error of the settlement prediction result of the ballastless track. For high-speed railway ballasted track, since its observation accuracy must meet the accuracy requirements of not less than Class IV leveling, the mean error of Class IV leveling observation is ±2mm, and the probability of more than 95% of the accidental error of leveling observation appears between (-4,4)mm. Since the settlement observation prediction is based on the leveling observation data, the curve fitting method is used to extrapolate the existing data to obtain the final settlement of the roadbed. If the accidental error of the settlement prediction result can reach the error of the leveling observation, it is already better. The predicted settlement standard deviation σ≤4mm is used as the target standard deviation of the accidental error of the ballasted track settlement prediction result.

[0068] As shown in Tables 1 and 2, for both natural foundations and sand well foundations, the observation period that meets the target standard deviation of σ≤2mm is longer than the observation period that meets the target standard deviation of σ≤4mm, indicating that the observation period required for the high-speed railway ballastless track subgrade with stricter post-construction settlement control should be longer than that for the high-speed railway ballasted track subgrade with a design speed of 200km / h with less strict post-construction settlement control. Under the same observation frequency, the observation accuracy has a significant impact on the length of the observation period. Taking the observation frequency of 1 time / 1 week from the 0th to the 3rd month and 1 time / 2 weeks from the 4th to the 6th month as an example, under the condition of meeting the target standard deviation of σ≤2mm, the observation period required for the observation accuracy of Class I, Class II, Class III, and Class IV is 10 months, 11 months, 14 months, and 19 months, respectively, indicating that the higher the leveling observation accuracy, the shorter the observation period required to reach the corresponding standard. The influence of changing the observation frequency of 0-3 months and changing the observation frequency of 4-6 months on the observation period is inconsistent. For changing the observation frequency of 0-3 months, the more frequent the observation frequency, the longer the observation period required to reach the corresponding target standard deviation. Taking the observation accuracy of Class III leveling as an example, under the condition of σ≤2mm target standard deviation, the observation frequency of 0-3 months is 1 time / 2 days, 1 time / 5 days, 1 time / 1 week, and 1 time / 10 days. The required observation period is 17 months, 15 months, 14 months, and 10 months respectively; for changing the observation frequency of 4-6 months, the more frequent the observation frequency, the shorter the observation period required to reach the corresponding target standard deviation. Taking the observation accuracy of Class III leveling as an example, under the condition of σ≤2mm target standard deviation, the observation frequency of 4-6 months is 1 time / 1 week, 1 time / 10 days, 1 time / 2 weeks, and 1 time / 20 days. The required observation period is 4 months, 12 months, 14 months, and 15 months respectively. By comparing Table 6-1 and Table 6-2, it can be seen that under the same target standard deviation conditions, the observation period required for the natural foundation is significantly longer than that required for the sand well foundation.

[0069] Comprehensively analyzing the influence of observation accuracy, observation frequency, and target standard deviation on the observation period in Table 1 and Table 2, taking the observation frequency of 1 time / 1 week in the 0th to 3rd month, 1 time / 2 weeks in the 4th to 6th month, and observation accuracy III as the benchmark and considering the actual needs of controlling the construction period, it is recommended that for natural foundations, the observation period under the condition of σ≤2mm target standard deviation is 14 months, and the observation period under the condition of σ≤4mm target standard deviation is 7 months; for sand well foundations, the observation period under the condition of σ≤2mm target standard deviation is 7 months, and the observation period under the condition of σ≤4mm target standard deviation is 4 months. It can be seen that the observation accuracy and observation frequency that meet the observation period requirements are marked in the highlighted parts of Table 1 and Table 2.

[0070] It can be seen from Tables 1 and 2 that increasing the frequency of observations from 0 to 3 months is not helpful in shortening the observation period. For the observation frequency from 0 to 3 months, it is recommended to use 1 time / week for the observation frequency from 0 to 3 months; changing the observation frequency from 4 to 6 months is helpful in shortening the observation period. It is recommended to use 1 time / week, 1 time / 10 days, and 1 time / 2 weeks for the observation frequency from 4 to 6 months. Increasing the observation frequency after 6 months is helpful in shortening the observation period, but the degree of shortening the observation period is very limited. Therefore, it is recommended to follow the relevant provisions of the Technical Code for Observation and Evaluation of Settlement and Deformation of Railway Engineering (Q / CR 9230-2016) for the observation frequency after 6 months, that is, it is recommended to use 1 time / month for the observation frequency after 6 months.

[0071] The commonly used leveling observation accuracy in engineering is II level, III level, and IV level. It can be seen from Table 1 and Table 2 that when IV level observation accuracy is selected, the observation frequency should reach once per week in 4 to 6 months. The optimal leveling observation level selected by the present invention is II level, III level, and IV level, and when IV level is adopted, the observation frequency should reach once per week in 4 to 6 months.

[0072] Table 1 Calculation table of natural foundation observation period

[0073]

[0074]

[0075] Table 2 Calculation table of sand well foundation observation period

[0076]

[0077] 2. Determine the stability requirements for settlement prediction

[0078] Since the statistical values ​​generated by the two settlement predictions from 0 to i months and from 0 to i+3 months are independent of each other, a new statistic is constructed based on the calculation formula of the statistical mean and standard deviation of the predicted settlement, that is, the difference between the final settlements predicted twice with an interval of 3 months. The calculation formula is shown in formula (0).

[0079]

[0080] Where: Δ2 – the difference between two predicted final settlements at an interval of 3 months (mm);

[0081] μ i , μ i+3 ——Statistical mean of predicted settlement (mm);

[0082] σ i , σ i+3 ——Predicted settlement standard deviation (mm).

[0083] Figure 3 , Figure 4 , Figure 5 and Figure 6 The following are the difference diagrams of the final settlement predicted twice at an interval of 3 months for natural foundation and sand well foundation under the conditions of observation frequency of 1 time / 1 week from 1 to 3 months and 1 time / 2 weeks from 4 to 6 months, selected by the target standard deviation of σ≤2mm and σ≤4mm. Therefore, the difference table of the final settlement predicted twice at an interval of 3 months for natural foundation and sand well foundation affected by different observation frequencies and observation accuracy is shown in Table 3 and Table 4.

[0084] It can be seen from Tables 3 and 4 that, for both natural foundation and sand well foundation, the difference in the final settlement predicted twice at an interval of 3 months that meets the target standard deviation of σ≤2mm is smaller than the difference in the final settlement predicted twice at an interval of 3 months that meets the target standard deviation of σ≤4mm, indicating that the difference in the final settlement predicted twice at an interval of 3 months for the high-speed railway ballastless track subgrade with stricter post-construction settlement control should be smaller than the ballasted track subgrade with a design speed of 200 km / h for the high-speed railway with less strict post-construction settlement control. Under the same observation frequency, the observation accuracy has a significant impact on the difference in the final settlement predicted twice at an interval of 3 months. Taking the observation frequency of 1 time / week in the 0th to 3rd month and 1 time / 2 weeks in the 4th to 6th month as an example, under the condition of σ≤2mm target standard deviation, the difference of the final settlement predicted twice at intervals of 3 months achieved by the observation accuracy of level I, level II, level III, and level IV is 8.73mm, 7.71mm, 5.81mm, and 4.04mm, respectively, indicating that the higher the leveling observation accuracy, the greater the difference of the final settlement predicted twice at intervals of 3 months when the corresponding standard is reached. For the change of observation frequency of 4 to 6 months, the denser the observation frequency, the greater the difference of the final settlement predicted twice at intervals of 3 months when the corresponding target standard deviation is reached. Taking the accuracy of leveling observations of Class III as an example, under the condition of σ≤2mm target standard deviation, the observation frequency of 1 time / 10 days, 1 time / 2 weeks, and 1 time / 20 days in the 4th to 6th months can achieve the difference of the final settlement predicted twice at an interval of 3 months of 6.08mm, 5.81mm, and 7.27mm respectively. By comparing Table 6-3 and Table 6-4, it can be seen that under the same target standard deviation conditions, the difference of the final settlement predicted twice at an interval of 3 months that can be achieved by the natural foundation is significantly smaller than the difference of the final settlement predicted twice at an interval of 3 months required by the sand well foundation.

[0085] Table 3 Calculation table of the difference between the final settlements predicted twice at an interval of 3 months for natural foundation

[0086]

[0087]

[0088] Table 4 Calculation table of the difference in final settlement of sand well foundation predicted twice with an interval of 3 months

[0089]

[0090] 3. Determine the settlement completion rate requirements

[0091] Figure 7 and Figure 8 The following are the settlement completion rates S(t) / S(t=∞) of natural foundation and sand well foundation under the conditions of observation frequency of 1 time / week from 1 to 3 months and 1 time / 2 weeks from 4 to 6 months, respectively, selected by the target standard deviation of σ≤2mm and σ≤4mm. Therefore, the settlement completion rates S(t) / S(t=∞) of natural foundation and sand well foundation affected by different observation frequencies and observation accuracy are shown in Tables 5 and 6.

[0092] It can be seen from Tables 5 and 6 that, for both natural foundation and sand well foundation, S(t) / S(t=∞) that meets the target standard deviation of σ≤2mm is greater than S(t) / S(t=∞) that meets the target standard deviation of σ≤4mm, indicating that the S(t) / S(t=∞) required for the high-speed railway ballastless track subgrade with stricter post-construction settlement control should be greater than the high-speed railway ballasted track subgrade with a design speed of 200km / h and less strict post-construction settlement control. Under the same observation frequency, the observation accuracy has a significant impact on the size of S(t) / S(t=∞). Taking the observation frequency of 1 time / week in the 0th to 3rd month and 1 time / 2 weeks in the 4th to 6th month as an example, under the condition of σ≤2mm target standard deviation, the S(t) / S(t=∞) required for the observation accuracy of Class I leveling, Class II leveling, Class III leveling, and Class IV leveling are 74%, 75.3%, 78.6%, and 83%, respectively, indicating that the higher the leveling observation accuracy, the smaller the S(t) / S(t=∞) required to meet the corresponding standard. For the change of observation frequency from 4 to 6 months, the more frequent the observation frequency, the smaller the S(t) / S(t=∞) required to achieve the corresponding target standard deviation. Taking the III-level leveling observation accuracy as an example, under the condition of σ≤2mm target standard deviation, the S(t) / S(t=∞) required for the observation frequency of 1 time / week, 1 time / 10 days, 1 time / 2 weeks, and 1 time / 20 days in the 4th to 6th months are 63.6%, 76.5%, 78.6%, and 79.6%, respectively. By comparing Table 6-5 and Table 6-6, it can be seen that under the same target standard deviation conditions, the S(t) / S(t=∞) required for the natural foundation is significantly longer than the S(t) / S(t=∞) required for the sand well foundation.

[0093] Table 5 Calculation table of natural foundation S(t) / S(t=∞)

[0094]

[0095] Table 6 Calculation table of sand well foundation S(t) / S(t=∞)

[0096]

[0097] 4. Determine the settlement prediction fitting curve

[0098] Table 7 draws on the selection evaluation index and value of the optimal curve fitting method in the "Technical Code for Observation and Evaluation of Settlement and Deformation of Railway Engineering" (Q / CR 9230-2016), and combines the relative error between the settlement prediction result and the measured value proposed by other scholars as the evaluation index, comprehensively analyzes the differences in correlation coefficient and relative error of various curve fitting methods under different foundation treatment conditions and soil conditions, and selects at least two better curve fitting methods under different soft soil foundation conditions. After that, the observation and prediction are carried out, and the advantages and disadvantages of the better curve fitting methods in terms of the discreteness of the prediction results are compared and analyzed, so as to select the optimal curve fitting method that meets the settlement prediction confidence requirements under different soft soil foundation conditions, as shown in Table 7.

[0099] Table 7 Applicability of curve fitting method under different soft soil foundation conditions

[0100]

[0101] Note: (vertical) permeability coefficient K v The value range is 6.05E-5m / d~1.36E-3m / d;

[0102] Sand well spacing d w The value range is 0.5m~3.5m;

[0103] The value range of compression index Cc is 0.1 to 0.6;

[0104] The value range of the secondary consolidation coefficient Ca is 0.005~0.05.

[0105] Embodiment 1

[0106] like Fig. 9 As shown, this embodiment provides a high-speed railway roadbed settlement assessment method based on confidence analysis, including:

[0107] S1, determining basic technical requirements associated with the target standard deviation, the basic technical requirements include observation condition requirements, settlement prediction stability requirements, settlement completion rate requirement values ​​and selection of settlement prediction fitting curves, wherein the observation condition requirements include roadbed settlement observation period, observation accuracy and observation frequency; the basic technical requirements associated with the target standard deviation include: under conditions of different observation accuracy and observation frequency, based on the change law of the roadbed settlement observation period, settlement prediction stability requirements and settlement completion rate requirement values, determining basic technical requirements for settlement observation and prediction analysis associated with target confidence;

[0108] As a preferred implementation method, S1 is based on the need to determine the systematic error and the accidental error in the actual situation, and based on the theoretical basis of determining the homogeneous soil layer, the permeability coefficient of multiple layers of soil is converted into one layer of soil. The specific implementation process includes:

[0109] According to the geological survey data, the permeability coefficient of multi-layer soil foundation is converted into the equivalent permeability coefficient of a single soil layer;

[0110] According to the structural parameters of foundation treatment and the equivalent permeability coefficient of soft soil foundation, the curve fitting method suitable for the soft soil foundation condition is determined;

[0111] According to the track structure type of high-speed railway, the target standard deviation of the accidental error of settlement prediction results is determined;

[0112] The roadbed settlement observation period, observation accuracy and observation frequency are determined according to the soft soil foundation conditions and the target standard deviation of the accidental error of the settlement prediction result.

[0113] In this embodiment,

[0114] (1) Observation period requirements

[0115] ① Under natural foundation conditions, the observation period for high-speed railway ballastless track subgrade is 14 months, and the observation period for high-speed railway ballasted track subgrade with a design speed of 200 km / h is 7 months.

[0116] ② Under the condition of sand well foundation, the observation period for high-speed railway ballastless track subgrade is 7 months, and the observation period for high-speed railway ballasted track subgrade with a design speed of 200km / h is 4 months.

[0117] ③ Improving observation accuracy, maintaining the observation frequency of 0 to 3 months unchanged and increasing the observation frequency of 4 to 6 months can effectively shorten the observation period.

[0118] (2) Selection of observation frequency

[0119] ① The observation frequency for 0 to 3 months should be once a week.

[0120] ② The observation frequency for 4 to 6 months can be once a week, once every 10 days, or once every 2 weeks.

[0121] ③ After 6 months, the observation frequency should be once a month. The observation frequency selection table is shown in Table 8.

[0122] Table 8 Observation frequency selection table

[0123]

[0124] (3) Selection of observation accuracy

[0125] ① The observation accuracy can adopt level II, level III, and level IV.

[0126] ② When using level IV observation accuracy, the observation frequency should be once per week for 4 to 6 months.

[0127] S2, based on the basic technical requirements of settlement observation and prediction analysis associated with the target confidence, high-speed railway subgrade settlement assessment is carried out, including: selection of observation condition requirements, correction of system errors and evaluation of settlement prediction results. The evaluation indicators of the assessment include settlement prediction stability, settlement completion rate and target standard deviation.

[0128] As a preferred embodiment, S2 includes:

[0129] S21, selecting a curve fitting model, including: analyzing the correlation coefficient, relative error, and coefficient of variation of different curve fitting models for soft soil foundation settlement curve fitting with different soil properties and foundation treatment forms, determining the optimal prediction model under corresponding conditions, and correcting the system error based on the statistical mean calculation formula of the predicted settlement under the optimal prediction model;

[0130] In this embodiment, the correction of the system error includes:

[0131] The systematic error of the settlement prediction results can be corrected using the empirical formula for the statistical mean of the predicted settlement. The selection table of the systematic error correction formula is shown in Table 9.

[0132] Table 9 System error correction formula selection table

[0133]

[0134]

[0135]

[0136] S22, conduct settlement deformation observation with the observation accuracy and observation frequency determined by the settlement assessment method based on confidence;

[0137] S23, when the specified roadbed settlement observation period is reached, settlement prediction is performed on the settlement deformation observation data to predict the final settlement of the roadbed, and the settlement prediction stability, settlement completion rate and target standard deviation are used as indicators to evaluate the prediction result of the settlement prediction.

[0138] In this embodiment, the evaluation of the prediction result of the settlement prediction includes:

[0139] (1) Stability of settlement prediction

[0140] ① Under natural foundation conditions and high-speed railway ballastless track conditions, using Class III leveling observation accuracy, the observation frequency is once a week from 0 to 3 months and once a week from 4 to 6 months. When the observation frequency is twice a week from 3 months to 6 months, the difference in the final settlement predicted twice with an interval of 6 mm is 6 mm.

[0141] ② Under natural foundation conditions and high-speed railway ballasted track subgrade conditions, using Class III leveling observation accuracy, the observation frequency is once a week for 0 to 3 months and once a week for 4 to 6 months. When the observation frequency is twice a week for 4 to 6 months, the difference in the final settlement predicted twice with an interval of 3 months is 16 mm.

[0142] ③ Under the conditions of sand well foundation and ballastless track of high-speed railway, using Class III leveling observation accuracy, the observation frequency is 1 time per week from 0 to 3 months and 1 time per week from 4 to 6 months. The difference between the final settlement predicted twice with an interval of 3 months is 8 mm.

[0143] ④ Under the conditions of sand well foundation and high-speed railway ballasted track subgrade, using Class III leveling observation accuracy, the observation frequency is 1 time per week from 0 to 3 months and 1 time per week from 4 to 6 months, and the difference in the final settlement predicted twice with an interval of 3 months is 40 mm.

[0144] The standards for the difference between the final settlement predicted twice with an interval of 3 months under other observation conditions are shown in Tables 10 and 11.

[0145] Table 10 Standard for the difference between two predicted final settlements of natural foundations at intervals of 3 months

[0146]

[0147] Table 11 Standard for the difference between the final settlement of sand well foundation predicted twice with an interval of 3 months

[0148]

[0149] (2) Settlement completion rate

[0150] ① Under the conditions of natural foundation and high-speed railway ballastless track subgrade, using Class III leveling observation accuracy, the observation frequency is once a week from 0 to 3 months and once a week from 4 to 6 months, and the settlement completion rate S(t) / S(t=∞) is 79%.

[0151] ② Under natural foundation conditions and high-speed railway ballasted track subgrade conditions, using Class III leveling observation accuracy, the observation frequency is once a week from 0 to 3 months and once a week from 4 to 6 months, and the settlement completion rate S(t) / S(t=∞) is 70%.

[0152] ③ Under the conditions of sand well foundation and high-speed railway ballastless track subgrade, using Class III leveling observation accuracy, the observation frequency is 1 week / 1 time from 0 to 3 months and 1 week / 2 times from 4 to 6 months, the settlement completion rate S(t) / S(t=∞) is 90%.

[0153] ④ Under the conditions of sand well foundation and high-speed railway ballasted track subgrade, using Class III leveling observation accuracy, the observation frequency is once a week from 0 to 3 months and once a week from 4 to 6 months, and the settlement completion rate S(t) / S(t=∞) is 83%.

[0154] The settlement completion rate S(t) / S(t=∞) under other observation conditions is shown in Tables 12 and 13.

[0155] Table 12 Natural foundation S(t) / S(t=∞) standard

[0156]

[0157] Table 13 Sand well foundation S(t) / S(t=∞) standard

[0158]

[0159] (3) Target standard deviation

[0160] The accidental error of the settlement prediction result can be calculated using the established empirical formula for the predicted settlement standard deviation. For the ballastless track subgrade of a high-speed railway, the accidental error should not be greater than 2 mm. For the ballasted track subgrade of a high-speed railway with a design speed of 200 km / h, the accidental error should not be greater than 4 mm. The selection table of the accidental error calculation formula is shown in Table 14.

[0161] Table 14 Random error calculation formula selection table

[0162]

[0163]

[0164]

[0165]

[0166]

[0167] Application Case Studies

[0168] (I) Design overview and engineering geological conditions

[0169] (1) Design Overview

[0170] The test site of a ballasted track subgrade of a high-speed railway is 65m long and 4.5m high. The slope of the embankment of the entire section is 1:1.5, and a berm with a height of 1.0m and a width of 1.0m is set at the lame end.

[0171] The foundation is reinforced with plastic drainage boards, with equal load preloading, 1.2m spacing, arranged in a regular triangle, 1.04m longitudinal spacing, 15.2m deep, and a 0.6m thick medium-coarse sand cushion layer is laid on the base. To meet the needs of test embankment stability, two layers of geogrids are laid at 0.3m, with an interval of 0.2m. The design section is as follows Fig.10 shown.

[0172] (2) Overview of embankment filling at the test site

[0173] The filling period of the sand cushion layer and the embankment body is 293 days, the preloading soil construction period is 30 days, the preloading period is 325 days, and the base surface construction period is 39 days.

[0174] (3) Division of foundation soil layers and distribution of each layer

[0175] Geological drilling shows that the strata within 15m can be divided into five engineering geological layers:

[0176] ① Artificial miscellaneous fill layer (Qm1): There is widespread construction waste on the site, which is loose and extremely uneven, with a thickness of 0.9 to 2.3m. Some parts are artificial fill, and the latter is 0 to 1.55m. The southern surface of the site is paved with concrete flooring, which is about 15cm thick.

[0177] ② Clay layer (m lQ4); Quaternary Holocene marine deposits, brown-yellow to brown-gray clay, intercalated with very thin layers (about 1 mm thick) of clayey soil or silt, soft and plastic, 0.9 to 2.9 m.

[0178] ③ Sandy soil layer (m lQ4): Quaternary system marine deposits, brown-gray, gray sandy clay, with a thin layer (about 1mm thick) of silt, soft to fluid, 0.6 to 3.0m thick.

[0179] ④ Muddy clay with silt sand layer (m lQ4): Quaternary system marine sediments, light gray, gray muddy clay with silt sand, generally 10-15mm thick muddy clay with 0.5mm thick silt sand, humus and small white shells can be seen locally, plastic flow. Thickness is about 8.8-10.1m.

[0180] ⑤Silt and clay layer (m lQ4): Quaternary system marine deposits, blue-gray silt and clay, slightly densely saturated, with developed stratification, generally 1 to 6 mm silt and 2 to 3 mm thick gray-brown clay or sandy clay, mainly silt, 4.6 to 10.5 m.

[0181] The engineering geological longitudinal section of the site is as follows: Fig.11 shown.

[0182] (4) Physical and mechanical properties of each soil layer

[0183] The statistical table of physical and mechanical properties of each soil layer is shown in Table 15.

[0184] Table 15 Statistics of physical and mechanical properties of soil samples in each layer

[0185]

[0186] (II) Analysis of test results

[0187] 1. Selection of curve fitting method

[0188] For sand well foundation, the applicability of the curve fitting method is classified and selected according to the sand well spacing and permeability coefficient. The limit of the sand well spacing is dw = 2m, and the limit of the permeability coefficient is K v =8.0E -4 m / d. Most naturally deposited soft soil foundations are composed of multiple layers of soil with different permeability coefficients, and are macroscopically heterogeneous. For simplicity, several soil layers can be equivalent to a single soil layer with a thickness equal to the sum of the soil layers and a permeability coefficient equal to the equivalent permeability coefficient. The equivalent permeability coefficient can be determined according to formulas (21) and (22).

[0189]

[0190] Where: K j ——Permeability coefficient of each soil layer (m / s);

[0191] a——Compression coefficient (1 / MPa);

[0192] e——porosity ratio;

[0193] r w ——Gravity of water (kn / m 3 );

[0194] Kv ——Equivalent permeability coefficient of multi-layer soil (m / s);

[0195] H——Thickness of each soil layer (m).

[0196] According to Terzaghi's one-way consolidation theory, the relationship between the diameter of the effective influence range of the plastic drain board arranged in an equilateral triangle and the spacing between the plastic drain boards is as follows:

[0197] d w =1.05·s (23)

[0198] Where: d w ——Diameter of the effective influence range of the plastic drainage board (m);

[0199] s——spacing between plastic drain boards (m).

[0200] According to the foundation treatment design parameters and geological survey data, the spacing d of the plastic drainage board at the test site is w =1.26m<2m, equivalent permeability coefficient K v =4.75E -3 m / d>8E -4 m / d. Therefore, the shallow method can be used for prediction according to Table 7.

[0201] 2. Determination of observation conditions

[0202] For the ballasted track subgrade of the high-speed railway, the post-construction settlement limit is ≤150 mm, and the target standard deviation σ of the accidental error of the prediction result determined by the embodiment of the present invention is 4 mm; the foundation is reinforced with plastic drainage boards and equal load preloading, and the observation period can be determined according to Table 2, which is 4 months.

[0203] For sand well foundations, the observation accuracy and frequency are determined according to Table 8. The observation accuracy is Class III leveling accuracy, and the observation frequency is once a week for 0 to 3 months and once every 2 weeks for 4 to 6 months.

[0204] 3. Settlement deformation observation

[0205] The settlement deformation observation data are as follows Fig.12 shown.

[0206] 4. Evaluation of settlement prediction results

[0207] The shallow hill method was used to predict the settlement deformation data of the high-speed railway ballasted track subgrade observed for 4 months at the test site, and the settlement assessment results are shown in Table 16. The difference in the final settlement of the test site predicted twice with an interval of 3 months was 23.9mm<39mm, the settlement completion rate S(t) / S(t=∞) was 88.7%>83%, and the target standard deviation was 2.98mm<4mm. The settlement prediction results meet the technical requirements of settlement assessment, proving the correctness and feasibility of the settlement prediction technology based on error derivation proposed in the present invention.

[0208] Table 16 Settlement evaluation results of test work points

[0209]

[0210] Embodiment 2

[0211] like Fig.13 As shown, this embodiment also provides a high-speed railway roadbed settlement assessment system based on confidence analysis, including:

[0212] The technical requirement determination module 101 is used to determine the basic technical requirements associated with the target standard deviation, wherein the basic technical requirements include observation condition requirements, settlement prediction stability requirements, settlement completion rate requirement values, and selection of settlement prediction fitting curves, wherein the observation condition requirements include roadbed settlement observation period, observation accuracy, and observation frequency; the basic technical requirements associated with the target standard deviation include: under conditions of different observation accuracy and observation frequency, based on the change law of the roadbed settlement observation period, settlement prediction stability requirements, and settlement completion rate requirement values, determining the basic technical requirements for settlement observation and prediction analysis associated with the target confidence;

[0213] The roadbed settlement assessment module 102 is used to perform high-speed railway roadbed settlement assessment based on the basic technical requirements of settlement observation and prediction analysis associated with the target confidence, including: selection of observation condition requirements, correction of system errors and evaluation of settlement prediction results. The evaluation indicators include settlement prediction stability, settlement completion rate and target standard deviation.

[0214] 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.

[0215] like Fig.14 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.

[0216] 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 high-speed railway subgrade settlement assessment method based on confidence analysis, characterized in that: include: S1, determining basic technical requirements associated with the target standard deviation, wherein the basic technical requirements include observation condition requirements, settlement prediction stability requirements, and settlement completion rate requirements, wherein the observation condition requirements include roadbed settlement observation period, observation accuracy, and observation frequency; The basic technical requirements for determining the association with the target standard deviation include: determining the basic technical requirements for settlement observation and prediction analysis associated with the target confidence level based on the change law of the roadbed settlement observation period, settlement prediction stability requirements, and settlement completion rate requirements under conditions of different observation accuracy and observation frequency; S2, based on the basic technical requirements of settlement observation and prediction analysis associated with the target confidence, high-speed railway subgrade settlement assessment is performed, including: selection of observation condition requirements, correction of system errors and assessment of settlement prediction results, wherein the evaluation indicators of the assessment include settlement prediction stability, settlement completion rate and target standard deviation; The core and basis of S1 is to determine that the target standard deviation of the ballastless track subgrade is 2 mm, and the target standard deviation of the ballasted track subgrade is 4 mm; The S2 includes: S21, selecting a curve fitting model, including: analyzing the correlation coefficient, relative error, and coefficient of variation of different curve fitting models for soft soil foundation settlement curve fitting with different soil properties and foundation treatment forms, determining the optimal prediction model under corresponding conditions, and correcting the system error based on the statistical mean calculation formula of the predicted settlement under the optimal prediction model; S22, conduct settlement deformation observation with the observation accuracy and observation frequency determined by the settlement assessment method based on confidence; S23, when the roadbed settlement observation period is reached, settlement prediction is performed on the settlement deformation observation data to predict the final settlement of the roadbed, and the stability of the settlement prediction, the settlement completion rate and the target standard deviation are used as indicators to evaluate the prediction result of the settlement prediction; The different curve fitting methods in S21 include: For sand well foundation, the applicability of the curve fitting method is classified and selected according to the sand well spacing and permeability coefficient. The limit of the sand well spacing is dw=2m, and the limit of the permeability coefficient is K v =8.0E -4 m / d; The soft soil foundation of natural sedimentation is composed of multiple layers of soil with different permeability coefficients, which is macroscopically heterogeneous. Several soil layers are equivalent to a single soil layer with a thickness equal to the sum of the soil layers and a permeability coefficient equal to the equivalent permeability coefficient. The equivalent permeability coefficient is determined according to formula (21) and formula (22): （21） （22） Where: ——Permeability coefficient of each soil layer, m / s; ——Compression coefficient, 1 / MPa; ——porosity ratio; ——weight of water, kN / m 3 ; ——Equivalent permeability coefficient of multi-layer soil, m / s; ——Thickness of each soil layer, m; The relationship between the diameter of the effective influence range of the plastic drain board arranged in an equilateral triangle and the spacing between the plastic drain boards is as follows: （23） Where: ——Diameter of the effective influence range of the plastic drainage board, m; —— Spacing between plastic drain boards, m.

2. The high-speed railway subgrade settlement assessment method based on confidence analysis according to claim 1 is characterized in that: The S21 includes correcting the system error by using an empirical formula for predicting the statistical mean of settlement.

3. The high-speed railway subgrade settlement assessment method based on confidence analysis according to claim 2 is characterized in that: The stability of the settlement prediction is determined based on the difference between two predicted final settlement amounts at a certain time interval; The settlement completion rate is affected by different observation frequencies and observation accuracy of natural foundation and sand well foundation; The target standard deviation is affected by the observation accuracy under the same observation frequency condition.

4. The high-speed railway subgrade settlement assessment method based on confidence analysis according to claim 3 is characterized in that: The calculation formula for the stability of the settlement prediction is shown in formula (0): （0） Where: ——The difference between the final settlement predicted twice at an interval of 3 months, mm; , ——Statistical mean of predicted settlement, mm; , ——Predicted settlement standard deviation, mm.

5. A high-speed railway subgrade settlement assessment system based on confidence analysis, used to implement any of the methods described in claims 1-4, characterized in that: include: A technical requirement determination module (101) is used to determine basic technical requirements associated with a target standard deviation, wherein the basic technical requirements include observation condition requirements, settlement prediction stability requirements, settlement completion rate requirement values, and selection of settlement prediction fitting curves, wherein the observation condition requirements include roadbed settlement observation period, observation accuracy, and observation frequency; The basic technical requirements for determining the association with the target standard deviation include: determining the basic technical requirements for settlement observation and prediction analysis associated with the target confidence level based on the change law of the roadbed settlement observation period, settlement prediction stability requirements, and settlement completion rate requirements under conditions of different observation accuracy and observation frequency; The roadbed settlement assessment (102) is used to carry out a high-speed railway roadbed settlement assessment with settlement prediction stability, settlement completion rate and target standard deviation as the core based on the basic technical requirements of settlement observation and prediction analysis associated with the target confidence.

6. 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 4.

7. 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 4.

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