A method for analyzing the confidence level of settlement prediction by integrating observed data during the filling and loading process

Abstract

The present invention discloses a method for analyzing the confidence level of settlement prediction by integrating observed data during the filling and loading process, including: S1, using the Weibull model to correct and deduce the traditional settlement difference method, so as to obtain a settlement prediction model of the corrected settlement difference method; S2, merging the settlement prediction model of the corrected settlement difference method with the method for analyzing the confidence level of subgrade settlement prediction to construct a subgrade settlement prediction confidence level analysis model that can integrate observed data during the filling and loading process; S3, based on the subgrade settlement prediction confidence level analysis model that can integrate observed data during the filling and loading process, comparing the confidence levels of various settlement prediction models that integrate observed data during the filling and loading process, analyzing the influence law of the filling and loading method on the statistical mean and standard deviation of the predicted settlement, and obtaining the correlation relationship corresponding to the influence law. A corresponding analysis system, electronic device, and computer-readable storage medium are also disclosed.

Description

Technical Field

[0001] The present invention relates to the technical fields of railways and subgrade settlement prediction and analysis, and particularly relates to a method and system for analyzing the confidence level of settlement prediction by integrating observation data during the filling and loading process. Background Art

[0002] Railway transportation occupies a backbone position in China's overall transportation network. In recent years, due to the rapid development of China's high-speed railways, railway construction and operation and maintenance have become an important driving force for boosting China's domestic economic growth and a beautiful calling card for demonstrating the international image. China has made plans for the medium- and long-term development of railways. However, since China is a country with extensive distribution of soft soil, railway lines are bound to pass through a large number of soft soil areas. An important problem faced in the construction of high-speed railways in soft soil areas is the problem of foundation settlement and post-construction settlement control. For railways of different grades, different settlement control standards have been proposed. The high smoothness requirements of the ballastless track of high-speed railways impose more stringent requirements on the post-construction settlement of the subgrade. Generally, it is required that the post-construction settlement should not be greater than 15 mm. This makes it necessary to accurately control the final settlement of the subgrade during the construction process.

[0003] However, there are still the following technical defects in the prior art:

[0004] First, in current engineering design and construction, settlement calculation is mainly carried out in the design stage, and subgrade settlement observation, prediction and evaluation are carried out in the construction stage to control the post-construction settlement. However, due to factors such as the nonlinear stress-strain relationship of soil, the variability and anisotropy of geotechnical parameters, the theoretical calculation method, especially the theoretical calculation method based on the layer-wise summation method in the design stage, has a large calculation deviation for the settlement development process and the final settlement of the foundation. The subgrade settlement observation, prediction and evaluation in the construction stage extrapolate the settlement development process and the final settlement based on the observation data. Since the settlement observation process is affected by many factors such as the foundation treatment form, geological conditions, and observation conditions, and the influence law of the above factors on the settlement prediction result is unclear, it is not easy to control the subgrade settlement of high-speed railways by using settlement observation prediction.

[0005] Second, there is no mature technical solution in the prior art for integrating the confidence level of settlement prediction with observation data during the filling and loading process. In particular, there is no technical solution for establishing a settlement prediction model that integrates observation data during the filling and loading process, comprehensively using the data during the filling period and the constant load period to predict the settlement of natural foundation and sand drain foundation, and studying the influence of subgrade filling duration, subgrade filling levels and observation conditions on the mean and standard deviation of settlement prediction.

[0006] Third, the method commonly used in engineering to predict the post-construction settlement by using the data during the constant load period after the subgrade filling is completed is difficult to meet the actual needs of the increasingly shortened construction period. Summary of the Invention

[0007] The object of the present invention is to provide a method and system for analyzing the confidence level of settlement prediction by integrating observed data during the filling and loading process. By improving the traditional settlement difference method, the modified settlement difference method is derived, and this model is combined with the method for analyzing the confidence level of subgrade settlement prediction adopted above to construct (unify: a subgrade settlement prediction confidence level analysis model that can integrate observed data during the filling and loading process). Compare the confidence levels of various settlement prediction models that integrate observed data during the filling and loading process, analyze the influence law of the filling and loading method on the statistical mean and standard deviation of the predicted settlement, and obtain their correlation.

[0008] The first aspect of the present invention lies in providing a method for analyzing the confidence level of settlement prediction by integrating observed data during the filling and loading process, including:

[0009] S1. Use the Weibull model to modify and deduce the traditional settlement difference method to obtain a settlement prediction model of the modified settlement difference method;

[0010] S2. Combine the settlement prediction model of the modified settlement difference method with the method for analyzing the confidence level of subgrade settlement prediction to construct a subgrade settlement prediction confidence level analysis model that can integrate observed data during the filling and loading process;

[0011] S3. Based on the subgrade settlement prediction confidence level analysis model that can integrate observed data during the filling and loading process, compare the confidence levels of various settlement prediction models that integrate observed data during the filling and loading process, analyze the influence law of the filling and loading method on the statistical mean and standard deviation of the predicted settlement, and obtain the correlation corresponding to the influence law.

[0012] Preferably, the S1 includes:

[0013] S11. Determine that under the multi-stage loading condition of soft soil subgrade settlement, the average degree of consolidation of the foundation is modified according to the Weibull model, and determine the basic assumptions of the settlement prediction model of the modified settlement difference method;

[0014] S12. Based on the basic assumptions of the settlement prediction model of the Weibull model, use the Weibull model to modify and deduce the traditional settlement difference method to obtain a settlement prediction model of the modified settlement difference method.

[0015] Preferably, the basic assumptions include:

[0016] (1) The consolidation process caused by each load increment occurs independently and is independent of the degree of consolidation caused by the previous load increment;

[0017] (2) The total degree of consolidation is equal to the superposition of the degrees of consolidation under the action of each load increment;

[0018] (3) The surcharge period is 0 to ti The load increment of equal-speed loading is equivalent to the degree of consolidation caused by instantaneously applying this load increment at t i / 2 in one go;

[0019] (4) The average degree of consolidation U t is defined according to the settlement deformation, that is where s t represents the current settlement deformation, and s ∞ represents the maximum settlement deformation;

[0020] (5) The degree of consolidation under each level of load, after being corrected according to the proportion of the load and superimposed, the average degree of consolidation under the total load is obtained, as shown in Equation (1):

[0021]

[0022] In the formula: U ti is the degree of consolidation under the i-th level of load;

[0023] p i is the i-th load increment;

[0024] p is the total load;

[0025] n is the total number of levels of staged loading;

[0026] From basic assumptions (4) and (5), it can be deduced that: the ratio of the consolidation settlement s cpi caused by a certain level of load to the total consolidation settlement s cp caused by the total load is equal to the ratio of this level of load increment p i to the total load p, as shown in Equation (2):

[0027]

[0028] Preferably, the S12 includes:

[0029] According to the Weibull model, under the action of the i-th load increment instantaneously applied at time t 0pi , the degrees of consolidation of the foundation equivalent to the i-th load increment at times t1 and t2 (t2 > t1 > t opi ) are respectively:

[0030]

[0031] Then the subgrade settlements caused by the i-th load increment at times t1 and t2 are respectively:

[0032]

[0033] Substituting Equation (2) into Equations (5) and (6) gives:

[0034]

[0035] If it is under constant load during the time period from t1 to t2, the settlement difference during the time period from t1 to t2 caused by the incremental loads at each level applied during the surcharge period is:

[0036]

[0037] In the formula: t op1 —— The application time of the first-stage load increment, which is corrected according to the basic assumption (3);

[0038] t1, t2 —— Any time during the constant load period after the load filling is completed, and t2 > t1;

[0039] —— The cumulative settlement amounts at the times of t1 and t2 respectively;

[0040] m —— The number of levels of the graded loads applied during the surcharge period;

[0041] p i —— The i-th level load;

[0042] p —— The total load of the subgrade filling;

[0043] S cp , α, b, r —— Model fitting parameters, which are optimized and solved by using the least square method principle.

[0044] Preferably, the merger of the modified settlement difference method settlement prediction model and the subgrade settlement prediction confidence analysis method in S2 includes merging the settlement prediction model that can fuse the observed data during the filling and loading process with the subgrade settlement prediction confidence analysis method, and obtaining the subgrade settlement prediction confidence analysis model that can fuse the observed data during the filling and loading process after selecting the optimal model based on the confidence level; wherein the settlement prediction model that can fuse the observed data during the filling and loading process includes the improved hyperbola method, the traditional settlement difference method, and the modified settlement difference method.

[0045] Preferably, the subgrade settlement prediction confidence analysis method in S2 includes:

[0046] Generating simulated settlement observation data, wherein the simulated settlement observation data is generated based on the discrete observation data with observation errors;

[0047] Conducting subgrade settlement prediction confidence analysis based on the simulated settlement observation data;

[0048] Among them, the generation of the simulated settlement observation data includes:

[0049] Regarding the settlement problems of soft soil subgrades under different foundation treatment forms and different soil property conditions, based on the homogeneous natural foundation calculation model and the sand drain foundation calculation model, the corresponding theoretical continuous data under different conditions are calculated and obtained;

[0050] The theoretical continuous data are divided into observation period data and post-settlement period data according to different first settlement observation conditions, and the observation period data are discretized according to the second settlement observation conditions to obtain discrete data;

[0051] The discrete observation data of the observation period are superimposed with observation errors according to different third settlement observation conditions to generate discrete observation data with the observation errors, and the simulated settlement observation data are generated based on the discrete observation data with the observation errors;

[0052] Among them, the first settlement observation condition is the observation duration; the second settlement observation condition is the observation frequency; the third settlement observation condition is the observation accuracy;

[0053] The analysis of the confidence level of subgrade settlement prediction based on the simulated settlement observation data includes:

[0054] Based on the discrete observation data with the observation errors, the curve fitting method is used for prediction to obtain the predicted value corresponding to each group of observation data; repeating the above process to obtain multiple predicted values to form a prediction result;

[0055] Statistical analysis is carried out on the prediction result to obtain the predicted settlement statistical mean and the predicted settlement standard deviation;

[0056] Preferably, in S3, based on the subgrade settlement prediction confidence level analysis model that can fuse the observation data of the filling and loading process, the confidence levels of multiple settlement prediction models that fuse the observation data of the filling and loading process are compared, the influence law of the filling and loading method on the predicted settlement statistical mean and standard deviation is analyzed, and the relevant relationships corresponding to the influence law include:

[0057] The calculation formulas for the predicted settlement statistical mean of the homogeneous natural foundation and the homogeneous sand drain foundation obtained by observing the observation data of the filling and loading process by the modified settlement difference method are shown in Formulas (16) and (17):

[0058] Natural foundation:

[0059]

[0060] Sand drain foundation:

[0061]

[0062] The change curve of the predicted settlement standard deviation with the observation duration is described by Formula (18):

[0063] σ = A1exp(-x1 / t1) + A2exp(-x1 / t2) (18);

[0064] Where: σ —— Standard deviation of predicted settlement (mm);

[0065] x1 —— Observation duration (days);

[0066] A1, t1, A2, t2 —— Model fitting parameters;

[0067] The variation relationships of the model fitting parameters A1, t1, A2, and t2 of the fitting curves obtained from Equation (18) for the natural foundation and the sand drain foundation with the subgrade filling duration. The fitting curves can be determined by Equations (19) and (20):

[0068] A1 = a1·x4 + b1 (19);

[0069] A2 = a2·x4 + b2 (20);

[0070] Where: x4 —— Subgrade filling duration (days);

[0071] The calculation formulas for the predicted settlement standard deviation of the homogeneous natural foundation and the homogeneous sand drain foundation obtained from the observation of the observation data of the fusion filling loading process by the modified settlement difference method are shown in Equations (21) and (22):

[0072] Natural foundation:

[0073]

[0074] Sand drain foundation:

[0075]

[0076] Moreover, determine the influence law of the subgrade filling level on the statistical mean and standard deviation of the predicted settlement.

[0077] The second aspect of the present invention also provides a system for analyzing the confidence level of settlement prediction for fusing the observation data of the filling loading process, which is used to implement the method of the first aspect, including:

[0078] The first model establishment module (101), which is used to modify and deduce the traditional settlement difference method by using the Weibull model, so as to obtain the settlement prediction model of the modified settlement difference method;

[0079] The second model establishment module (102), which is used to merge the settlement prediction model of the modified settlement difference method with the method for analyzing the confidence level of subgrade settlement prediction to construct a subgrade settlement prediction confidence level analysis model that can fuse the observation data of the filling loading process;

[0080] The confidence analysis module (103) is configured to compare the confidence levels of multiple settlement prediction models for the subgrade settlement that fuse the observed data during the filling and loading process based on the subgrade settlement prediction confidence analysis model that can fuse the observed data during the filling and loading process, analyze the influence law of the filling and loading method on the statistical mean and standard deviation of the predicted settlement, and obtain the correlation corresponding to the influence law.

[0081] A third aspect of the present invention provides an electronic device, including a processor and a memory. The memory stores multiple instructions, and the processor is configured to read the instructions and execute the method as described in the first aspect.

[0082] A fourth aspect of the present invention provides a computer-readable storage medium. The computer-readable storage medium stores multiple instructions, and the multiple instructions can be read and executed by the processor to execute the method as described in the first aspect.

[0083] Advantages of the method and system of the present invention:

[0084] (1) Based on the Weibull model and the traditional settlement difference method, a settlement difference method prediction model based on the Weibull model is derived, and the subgrade settlement prediction confidence analysis method is merged with this settlement prediction model to obtain a subgrade settlement prediction confidence analysis model that can fuse the observed data during the filling and loading process.

[0085] (2) Compare the settlement difference method based on the Weibull model derived in this paper with the original improved hyperbola method and the traditional settlement difference method: for the same observation duration, the magnitude relationship of the predicted settlement statistical mean and standard deviation of the three prediction models is: the modified settlement difference method < the traditional settlement difference method < the improved hyperbola method, which illustrates the superiority of the settlement difference method based on the Weibull model.

[0086] (3) Analyze the variation law of the predicted settlement statistical mean with the subgrade filling duration and the observation duration, and obtain an empirical formula for the influence of the predicted statistical mean by the observation duration and the subgrade filling duration. Compare the predicted settlement statistical mean of the settlement difference based on the Weibull model with the conventional curve fitting method for the same observation duration: when the subgrade filling duration is 30 days, 40 days, 50 days, and 60 days, compared with only using the dead load data, the relative errors of the predicted statistical mean of the data fusing the filling period are reduced by 9.83%, 9.87%, 10.06%, and 10.3% respectively.

[0087] (4) Analyze and predict the variation law of the settlement standard deviation with the subgrade filling duration and the observation duration, and obtain an empirical formula for the influence of the prediction standard deviation by the observation duration and the subgrade filling duration. Compare the settlement standard deviation predicted by the settlement difference based on the Weibull model and the conventional curve fitting method under the same observation duration. When the subgrade filling durations are 30 days, 40 days, 50 days, and 60 days, compared with only using the dead load data, the predicted standard deviations of the data fusion during the filling period are reduced by 9.96 mm, 9.94 mm, 9.90 mm, and 9.87 mm respectively.

[0088] (5) The allocation of the subgrade filling levels should be mainly controlled by the foundation stability during the soft soil subgrade filling process. The allocation of the subgrade filling levels has basically no influence on the predicted settlement statistical mean and standard deviation. Description of the Drawings

[0089] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the related art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0090] Figure 1 (a) is a flowchart of the settlement prediction confidence analysis method that integrates the observation data during the filling and loading process according to the embodiment of the present invention;

[0091] Figure 1 (b) is a schematic diagram of the settlement prediction confidence analysis method that integrates the observation data during the filling and loading process according to the embodiment of the present invention;

[0092] Figure 2 is a bar chart of the predicted settlement statistical mean of different settlement prediction models according to the embodiment of the present invention;

[0093] Figure 3 is a bar chart of the predicted settlement standard deviation of different settlement prediction models according to the embodiment of the present invention;

[0094] Figure 4 is a prediction distribution diagram of different settlement prediction models with an observation duration of 50 days according to the embodiment of the present invention;

[0095] Figure 5 is a prediction distribution diagram of different settlement prediction models with an observation duration of 60 days according to the embodiment of the present invention;

[0096] Figure 6 is a prediction distribution diagram of different settlement prediction models with an observation duration of 70 days according to the embodiment of the present invention;

[0097] Figure 7It is a predicted distribution map of different settlement prediction models with an observation duration of 90 days provided according to an embodiment of the present invention;

[0098] Figure 8 It is a bar chart of the statistical mean of predicted settlements under different subgrade filling durations of natural foundation provided according to an embodiment of the present invention;

[0099] Figure 9 It is a bar chart of the statistical mean of predicted settlements under different subgrade filling durations of sand-drained foundation provided according to an embodiment of the present invention;

[0100] Figure 10 It is a time history diagram of the statistical mean of predicted settlements of natural foundation with different observation durations provided according to an embodiment of the present invention;

[0101] Figure 11 It is a time history diagram of the statistical mean of predicted settlements of sand-drained foundation with different observation durations provided according to an embodiment of the present invention;

[0102] Figure 12 It is a time history diagram of the standard deviation of predicted settlements of natural foundation under different subgrade filling durations provided according to an embodiment of the present invention;

[0103] Figure 13 It is a time history diagram of the standard deviation of predicted settlements of sand-drained foundation under different subgrade filling durations provided according to an embodiment of the present invention;

[0104] Figure 14 It is a predicted result distribution map of natural foundation under different subgrade filling durations with an observation duration of 30 days provided according to an embodiment of the present invention;

[0105] Figure 15 It is a predicted result distribution map of sand-drained foundation under different subgrade filling durations with an observation duration of 30 days provided according to an embodiment of the present invention;

[0106] Figure 16 It is a predicted result distribution map of natural foundation under different subgrade filling durations with an observation duration of 60 days provided according to an embodiment of the present invention;

[0107] Figure 17 It is a predicted result distribution map of sand-drained foundation under different subgrade filling durations with an observation duration of 60 days provided according to an embodiment of the present invention;

[0108] Figure 18 It is a time history diagram of the standard deviation of predicted settlements of natural foundation with different observation durations provided according to an embodiment of the present invention;

[0109] Figure 19 It is a time history diagram of the standard deviation of predicted settlements of sand-drained foundation with different observation durations provided according to an embodiment of the present invention

[0110] Figure 20It is a histogram of the predicted settlement statistical mean under different subgrade filling levels of natural foundation provided by an embodiment of the present invention;

[0111] Figure 21 It is a histogram of the predicted settlement statistical mean under different subgrade filling levels of sand-drained foundation provided by an embodiment of the present invention;

[0112] Figure 22 It is a time history diagram of the predicted settlement standard deviation under different subgrade filling levels of natural foundation provided by an embodiment of the present invention;

[0113] Figure 23 It is a time history diagram of the predicted settlement standard deviation under different subgrade filling levels of sand-drained foundation provided by an embodiment of the present invention;

[0114] Figure 24 It is a distribution diagram of predicted results under different subgrade filling levels of natural foundation when the observation duration is 30 days provided by an embodiment of the present invention;

[0115] Figure 25 It is a distribution diagram of predicted results under different subgrade filling levels of sand-drained foundation when the observation duration is 30 days provided by an embodiment of the present invention;

[0116] Figure 26 It is a distribution diagram of predicted results under different subgrade filling levels of natural foundation when the observation duration is 60 days provided by an embodiment of the present invention;

[0117] Figure 27 It is a distribution diagram of predicted results under different subgrade filling levels of sand-drained foundation when the observation duration is 60 days provided by an embodiment of the present invention;

[0118] Figure 28 It is a structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0119] Next, the technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0120] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is 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 should not be construed as a limitation to the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0121] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "installed", "connected", "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 directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0122] Embodiment 1

[0123] As Figure 1 (a) and Figure 1 (b) shown, this embodiment provides a method for analyzing the confidence level of settlement prediction by integrating the observed data during the filling and loading process, including:

[0124] S1. Use the Weibull model to modify and deduce the traditional settlement difference method, so as to obtain the settlement prediction model of the modified settlement difference method;

[0125] S2. Merge the settlement prediction model of the modified settlement difference method with the method for analyzing the confidence level of subgrade settlement prediction to construct a subgrade settlement prediction confidence level analysis model that can integrate the observed data during the filling and loading process;

[0126] S3. Based on the subgrade settlement prediction confidence level analysis model that can integrate the observed data during the filling and loading process, compare the confidence levels of various settlement prediction models that integrate the observed data during the filling and loading process, analyze the influence law of the filling and loading method on the statistical mean and standard deviation of the predicted settlement, and obtain the relevant relationship corresponding to the influence law.

[0127] As a preferred embodiment, the S1 includes:

[0128] S11. Determine that under the multi-stage loading condition of the soft soil subgrade settlement, the average degree of consolidation of the foundation is modified according to the Weibull model, and determine the basic assumptions of the settlement prediction model of the modified settlement difference method;

[0129] In this embodiment, under the condition of multi-stage loading of soft soil subgrade settlement, the average degree of consolidation of the foundation is corrected according to the Weibull model, and the following basic assumptions are determined:

[0130] (1) The consolidation process caused by each load increment occurs independently and is independent of the degree of consolidation caused by the previous load increment;

[0131] (2) The total degree of consolidation is equal to the superposition of the degrees of consolidation under the action of each load increment;

[0132] (3) The load increment of equal-speed loading during the surcharge period of 0 to t i is equivalent to the degree of consolidation caused by instantaneously applying this load increment at t i / 2;

[0133] (4) The average degree of consolidation U t is defined according to the settlement deformation, that is

[0134] (5) After the degrees of consolidation under the action of each load are corrected and superimposed according to the proportion of the loads, the average degree of consolidation under the action of the total load can be obtained, as shown in Equation (1):

[0135]

[0136] In the formula: U ti is the degree of consolidation under the action of the i-th load;

[0137] p i is the i-th load increment;

[0138] p is the total load;

[0139] n is the total number of stages of staged loading;

[0140] From the inferences of basic assumptions (4) and (5): The ratio of the consolidation settlement s cpi caused by the action of a certain load to the total consolidation settlement s cp caused by the action of the total load is equal to the ratio of this load increment p i to the total load p, as shown in Equation (2):

[0141]

[0142] S12, the traditional settlement difference method is corrected and deduced using the Weibull model to obtain a settlement prediction model of the corrected settlement difference method, and the settlement prediction model of the corrected settlement difference method corresponds to the settlement difference method based on the Weibull model;

[0143] In this embodiment, the S12 includes:

[0144] According to the Weibull model, under the action of the i-th load increment instantaneously applied at time t 0pi , the degree of foundation consolidation equivalent to the i-th load increment at times t1 and t2 (t2 > t1 > t opi ) is respectively:

[0145]

[0146] Then the subgrade settlements caused by the i-th load increment at times t1 and t2 are respectively:

[0147]

[0148] Substituting Equation (2) into Equations (5) and (6) gives:

[0149]

[0150] If the load is constant during the time period from t1 to t2, then the settlement difference during the time period from t1 to t2 caused by the action of each load increment applied during the surcharge period is:

[0151]

[0152] Where: t op1 —— The application time of the first load increment, corrected according to the basic assumption (3);

[0153] t1, t2 —— Any time during the constant load period after the load filling is completed, and t2 > t1;

[0154] —— The cumulative settlement amounts at times t1 and t2 respectively;

[0155] m —— The number of levels of the graded loads applied during the surcharge period;

[0156] p i —— The i-th load;

[0157] p —— The total load of the subgrade filling;

[0158] S cp , α, b, r —— Model fitting parameters, optimized and solved using artificial intelligence algorithms.

[0159] In this embodiment, in order to further verify the effectiveness of the settlement prediction model based on the Weibull model, the influence of different prediction models on the prediction confidence level is calculated and determined.

[0160] Figure 2It is a histogram of the statistical mean of the predicted settlements obtained by three prediction models, namely the improved hyperbola method, the traditional settlement difference method, and the settlement difference method based on the Weibull model, after observation and prediction for different observation durations. As can be seen from the figure, at the same observation duration, the statistical means of the predicted settlements obtained by the three prediction models, that is, the systematic errors, have obvious differences. The statistical mean of the predicted settlement by the improved hyperbola method is larger than that of the traditional settlement difference method and the settlement difference method based on the Weibull model, indicating that the improved hyperbola method is not suitable for predicting the settlement of the observation data during the fusion filling and loading process. The traditional settlement difference method is slightly inferior to the settlement difference method based on the Weibull model, indicating that the settlement difference method based on the Weibull model is most suitable for predicting the settlement of the observation data during the fusion filling and loading process. Figure 3 It is a histogram of the standard deviation of the predicted settlements obtained by three prediction models, namely the improved hyperbola method, the traditional settlement difference method, and the settlement difference method based on the Weibull model, after observation and prediction for different observation durations. From Figure 3 it can be seen that at the same observation duration, the standard deviations of the predicted settlements obtained by the three prediction models, that is, the accidental errors, have obvious differences. The standard deviation of the predicted settlement by the improved hyperbola method is larger than that of the traditional settlement difference method and the settlement difference method based on the Weibull model, indicating that the improved hyperbola method is not suitable for predicting the settlement of the observation data during the fusion filling and loading process. The traditional settlement difference method is slightly inferior to the settlement difference method based on the Weibull model, indicating that the settlement difference method based on the Weibull model is most suitable for predicting the settlement of the observation data during the fusion filling and loading process.

[0161] Figures 4 - 7 The probability distributions of the prediction results of the three prediction models for the observation data during the fusion filling and loading process are plotted respectively when the number of observations is the same. Comparing the above four probability distribution diagrams, whether it is the improved hyperbola method, the traditional settlement difference method or the settlement difference method based on the Weibull model, with the extension of the observation duration, the probability density of the settlement prediction at the predicted mean value increases, indicating that with the increase of the observation duration, the prediction results converge during the observation and prediction process. The prediction results of the three prediction models for the fusion filling and loading process gradually approach zero, and the prediction results are more stable. Under different observation duration conditions, the probability distribution of the settlement difference method based on the Weibull prediction model is more concentrated, the traditional settlement difference method is similar to it, but the results are more discrete, and the prediction result of the improved hyperbola is the worst, and its results are also more discrete. This shows that by comparing the three prediction models for the observation data during the fusion filling and loading process, the settlement difference method based on the Weibull prediction model has more stable prediction results and the prediction results are more concentrated during the prediction process. The prediction results of the improved hyperbola method for railways with different post-construction settlement requirements are relatively unstable and the prediction results are more discrete during the prediction process. The prediction stability of the traditional settlement difference method is slightly worse than that of the settlement difference method based on the Weibull prediction model and better than that of the improved hyperbola method.

[0162] As a preferred embodiment, the method for analyzing the confidence level of subgrade settlement prediction in S2 includes:

[0163] Generate simulated settlement observation data, where the simulated settlement observation data is generated based on discrete observation data with observation errors. The method of this embodiment is particularly applicable to predicting using data for 3 months during the subgrade surcharge period and the constant load period, so as to achieve the purpose of early prediction;

[0164] Conduct an analysis of the confidence level of subgrade settlement prediction based on the simulated settlement observation data.

[0165] As a preferred embodiment, the generation of the simulated settlement observation data includes:

[0166] For the settlement problems of soft soil subgrades under different foundation treatment forms and different soil property conditions, calculate and obtain the corresponding theoretical continuous data under different conditions based on the homogeneous natural foundation calculation model and the sand drain foundation calculation model;

[0167] Divide the theoretical continuous data into observation period data and post-settlement period data according to different first settlement observation conditions, and discretize the observation period data according to the second settlement observation conditions to obtain discrete data;

[0168] Superimpose observation errors on the discrete observation period data according to different third settlement observation conditions to generate discrete observation data with the observation errors, and generate the simulated settlement observation data based on the discrete observation data with the observation errors;

[0169] Among them, the first settlement observation condition is the observation duration; the second settlement observation condition is the observation frequency; the third settlement observation condition is the observation accuracy.

[0170] In this embodiment, the observation accuracy refers to the influence of the observation accuracy conditions of Class I leveling, Class II leveling, Class III leveling, Class IV leveling, and leveling with larger observation errors than Class IV leveling in engineering leveling measurement on the confidence level of settlement prediction, and the elevation mean square error of the deformation point is used as the control index to distinguish the accuracy of each level of leveling measurement.

[0171] The observation frequency refers to the number or frequency of observations using the leveling observation method within the observation duration; when analyzing the influence of the observation frequency on the confidence level of settlement prediction, different constant load periods after the subgrade filling are divided into three observation periods: within 0 - 3 months after the filling is completed, 4 - 6 months after the filling is completed, and 6 months after the filling is completed, and the influence law of increasing or decreasing the observation frequency within the corresponding observation periods compared with the current relevant regulations on the prediction confidence level is determined respectively.

[0172] As a preferred embodiment, the analysis of the confidence level of subgrade settlement prediction based on the simulated settlement observation data includes:

[0173] Based on the discrete observation data with the observation errors, the curve fitting method is used for prediction to obtain the predicted values corresponding to each group of observation data; the above process is repeated to obtain a prediction result composed of multiple predicted values.

[0174] Perform statistical analysis on the prediction result to obtain the predicted settlement statistical mean and the predicted settlement standard deviation.

[0175] Respectively, corresponding influence laws are obtained based on the predicted settlement statistical mean and the predicted settlement standard deviation; the influence laws include the influence law of the observation duration on the predicted settlement statistical mean, the influence law of the observation accuracy on the predicted settlement statistical mean, the influence law of the observation frequency on the predicted settlement statistical mean, the influence law of the soil property conditions on the predicted settlement statistical mean, the influence law of the observation duration on the predicted settlement standard deviation, the influence law of the observation accuracy on the predicted settlement standard deviation, the influence law of the observation frequency on the predicted settlement standard deviation, and the influence law of the soil property conditions on the predicted settlement standard deviation.

[0176] As a preferred implementation manner, the calculation formulas for the predicted settlement statistical mean and standard deviation are shown in the following formulas (11) and (12):

[0177]

[0178] In the formula, μ is the settlement prediction mean; σ is the settlement prediction standard deviation; N s is the number of calculation samples; S k is the k-th settlement predicted value.

[0179] The merger of the modified settlement difference method settlement prediction model and the subgrade settlement prediction confidence analysis method for S2 includes the merger of the settlement prediction model that can fuse the observation data during the filling and loading process and the subgrade settlement prediction confidence analysis method, and after selecting the optimal model based on the confidence level, a subgrade settlement prediction confidence analysis model that can fuse the observation data during the filling and loading process is obtained; among them, the settlement prediction model that can fuse the observation data during the filling and loading process includes the improved hyperbola method, the traditional settlement difference method, and the modified settlement difference method.

[0180] As a preferred implementation manner, for S3, based on the subgrade settlement prediction confidence analysis model that can fuse the observation data during the filling and loading process, compare the confidence levels of multiple settlement prediction models that fuse the observation data during the filling and loading process, analyze the influence laws of the filling and loading methods on the predicted settlement statistical mean and standard deviation, and obtain the relevant relationships corresponding to the influence laws, including:

[0181] 1. The influence laws of the subgrade filling duration on the predicted settlement statistical mean and standard deviation

[0182] Figure 8It is a histogram of the statistical mean of predicted settlement for different subgrade filling durations of natural foundation. The variation of the statistical mean of predicted settlement with the observation duration under different subgrade filling durations is consistent with that described above. That is, as the observation duration gradually extends, the statistical mean of predicted settlement gradually decreases. Shortening the subgrade filling duration plays an important role in effectively controlling the statistical mean of predicted settlement. Figure 9 It is a histogram of the statistical mean of predicted settlement for different subgrade filling durations of sand-drained foundation. The variation of the statistical mean of predicted settlement with the observation duration under different subgrade filling durations is consistent with that described above. That is, as the observation duration gradually extends, the statistical mean of predicted settlement gradually decreases. Shortening the subgrade filling duration plays an important role in effectively controlling the statistical mean of predicted settlement.

[0183] Figure 10 It is a time-history curve of the statistical mean of predicted settlement for different observation durations of natural foundation. Figure 11 It is a time-history curve of the statistical mean of predicted settlement for different observation durations of sand-drained foundation. The statistical mean of predicted settlement gradually approaches zero as the observation duration gradually extends, indicating that as the observation duration extends, the difference between the mean value of the predicted value obtained by the curve fitting method through observation and the theoretical value obtained by the subgrade settlement calculation method gradually decreases, and the settlement prediction result gradually approaches the true value. At the same time, as the observation duration extends, the change rate of the statistical mean of predicted settlement gradually decreases. When the observation duration is short, the difference between the statistical mean of predicted settlement for adjacent two days is large. As the observation duration extends, the difference between the statistical mean of predicted settlement for adjacent two days significantly decreases, indicating that as the observation duration extends, the statistical mean of predicted settlement also gradually stabilizes.

[0184] Figure 10 and Figure 11 The variation curve of the statistical mean of predicted settlement with the observation duration in

[0185] δ = A1exp(-x1 / t1) + A2exp(-x1 / t2) (13)

[0186] In the formula: δ —— Statistical mean of predicted settlement;

[0187] x1 —— Observation duration (days).

[0188] A1, t1, A2, t2 —— Model fitting parameters.

[0189] The variation relationship of the model fitting parameters A1, t1, A2, t2 of the fitting curve obtained by formula (13) for natural foundation and sand-drained foundation with the subgrade filling duration. Its fitting curve can be determined by formula (14) and formula (15).

[0190] A1 = a1·x4 + b1 (14)

[0191] A2 = a2·x4 + b2 (15)

[0192] Where: x4—the duration of subgrade filling (days).

[0193] From the above analysis, the statistical mean calculation formulas for the predicted settlement of the natural foundation and sand-drained ground obtained by observing the data during the fusion filling and loading process using the settlement difference method based on the Weibull model are shown in Equations (16) and (17).

[0194] Natural foundation:

[0195]

[0196] Sand-drained ground:

[0197]

[0198] Figure 12 and Figure 13They are the time history diagrams of the standard deviation of predicted settlement under different subgrade filling durations for natural foundation and sand-drained foundation respectively. It can be seen from the two diagrams that under different subgrade filling durations, the standard deviation of predicted settlement gradually decreases with the increase of the number of observations, indicating that with the increase of the number of observations, the discreteness of the settlement prediction results gradually decreases, and the prediction results gradually concentrate near the statistical mean of the predicted settlement. Under logarithmic coordinates, the curves of the standard deviation of predicted settlement varying with the number of observations under different subgrade filling durations are approximately parallel, that is, the ratio of the standard deviation of predicted settlement under different subgrade filling durations does not increase with the extension of the observation duration. When the observation duration is 30 days, the standard deviation of predicted settlement with a subgrade filling duration of 50 days is 0.845 times that with a subgrade filling duration of 66 days, the standard deviation of predicted settlement with a subgrade filling duration of 40 days is 0.498 times that with a subgrade filling duration of 66 days, and the standard deviation of predicted settlement with a subgrade filling duration of 33 days is 0.318 times that with a subgrade filling duration of 66 days. When the observation duration is 60 days, the standard deviation of predicted settlement with a subgrade filling duration of 50 days is 0.845 times that with a subgrade filling duration of 66 days, the standard deviation of predicted settlement with a subgrade filling duration of 40 days is 0.498 times that with a subgrade filling duration of 66 days, and the standard deviation of predicted settlement with a subgrade filling duration of 33 days is 0.318 times that with a subgrade filling duration of 66 days. This shows that the influence of the subgrade filling duration on the discreteness of the settlement prediction results is consistent at different numbers of observations. The standard deviation of predicted settlement obtained under the same number of observations and subgrade filling duration with different foundation treatment forms is different, and the influence range of different subgrade filling durations on the discreteness of the settlement prediction results is also different. For the natural foundation, when the observation duration is 30 days, the standard deviations of predicted settlement with subgrade filling durations of 66 days, 50 days, 40 days, and 33 days are 7.57, 6.40, 3.77, and 2.41 respectively. The ratios of the standard deviations of predicted settlement with subgrade filling durations of 50 days, 40 days, and 33 days to that with a subgrade filling duration of 66 days are as described above. When the observation duration is 60 days, the standard deviations of predicted settlement with subgrade filling durations of 66 days, 50 days, 40 days, and 33 days are 0.32, 0.27, 0.16, and 0.10 respectively. The ratios of the standard deviations of predicted settlement with subgrade filling durations of 50 days, 40 days, and 33 days to that with a subgrade filling duration of 66 days are as described above. For the sand-drained foundation, when the observation duration is 30 days, the standard deviations of predicted settlement with subgrade filling durations of 66 days, 50 days, 40 days, and 33 days are 38.05, 34.09, 25.88, and 21.14 respectively. The ratios of the standard deviations of predicted settlement with subgrade filling durations of 50 days, 40 days, and 33 days to that with a subgrade filling duration of 66 days are 0.896, 0.680, and 0.556 respectively. When the observation duration is 60 days, the standard deviations of predicted settlement with subgrade filling durations of 66 days, 50 days, 40 days, and 33 days are 1.61, 1.45, 1.10, and 0.90 respectively. The ratios of the standard deviations of predicted settlement with subgrade filling durations of 50 days, 40 days, and 33 days to that with a subgrade filling duration of 66 days are 0.896, 0.680, and 0.556 respectively.

[0199] Figure 14 、 Figure 16 They are the distribution diagrams of the predicted results of the natural foundation when the observation durations are 30 days and 60 days respectively. Figure 15 、 Figure 17 They are the distribution diagrams of the predicted results of the sand-drained ground when the observation durations are 30 days and 60 days respectively. By comparison Figure 14 、 Figure 15 and Figure 16 、 Figure 17 it can be seen that the faster the subgrade filling duration, the smaller the discreteness of the settlement prediction results, and the settlement prediction results are more concentrated near the statistical mean of the predicted settlement. The slower the subgrade filling duration, the greater the discreteness of the settlement prediction results, and the settlement prediction results are distributed in a wider range on both sides of the mean value of the settlement prediction results. Moreover, the distribution range of the settlement prediction results obtained from the natural foundation is narrower than that obtained from the sand-drained ground. By comparison Figure 14 、 Figure 16 and Figure 15 、 Figure 17 it can be seen that as the number of observations increases, the statistical mean of the predicted settlement gradually approaches zero, indicating that as the number of observations increases, the difference between the theoretical value and the predicted value becomes smaller. For the same subgrade filling duration, as the number of observations increases, the discreteness of the settlement prediction results becomes smaller, and the settlement prediction results are more concentrated near the statistical mean of the predicted settlement, indicating that as the number of observations increases, the settlement prediction results become more accurate.

[0200] Figure 18 is the time history curve of the predicted settlement standard deviation of the natural foundation with different observation durations. Figure 19 is the time history curve of the predicted settlement standard deviation of the sand-drained ground with different observation durations. The predicted settlement standard deviation gradually approaches zero as the observation duration gradually extends, indicating that as the observation duration extends, the discreteness of the predicted values obtained by the curve fitting method through observation gradually decreases, and the settlement prediction results gradually approach the statistical mean of the predicted settlement, and the prediction results gradually become stable. At the same time, as the observation duration extends, the change rate of the predicted settlement standard deviation gradually decreases. When the observation duration is short, the difference in the predicted settlement standard deviation between adjacent two days is large. As the observation duration extends, the difference in the predicted settlement standard deviation between adjacent two days decreases significantly, indicating that as the observation duration extends, the predicted settlement standard deviation also gradually becomes stable.

[0201] Figure 18 and Figure 19 The curves of the change of the predicted settlement standard deviation with the observation duration in

[0202] σ = A1exp(-x1 / t1) + A2exp(-x1 / t2) (18)

[0203] Where: σ——Standard deviation of predicted settlement (mm);

[0204] x1——Observation duration (days).

[0205] A1, t1, A2, t2——Model fitting parameters.

[0206] The variation relationships of the model fitting parameters A1, t1, A2, t2 of the fitting curves obtained for the natural foundation and the sand-drained foundation from Equation (18) with the subgrade filling duration. The fitting curves can be determined by Equations (19) and (20).

[0207] A1 = a1·x4 + b1 (19)

[0208] A2 = a2·x4 + b2 (20)

[0209] Where: x4——Subgrade filling duration (days).

[0210] From the above analysis, the calculation formulas for the standard deviation of the predicted settlement of the natural foundation and the sand-drained foundation obtained by observing the data of the fusion filling and loading process using the settlement difference method based on the Weibull model are shown in Equations (21) and (22).

[0211] Natural foundation:

[0212]

[0213] Sand-drained foundation:

[0214]

[0215] Influence law of the subgrade filling level on the statistical mean and standard deviation of the predicted settlement

[0216] Figure 20 It is the histogram of the statistical mean of the predicted settlement for different subgrade filling levels of the natural foundation. Under different subgrade filling level conditions, the variation of the statistical mean of the predicted settlement with the observation duration is consistent with that described above. That is, as the observation duration gradually extends, the statistical mean of the predicted settlement gradually decreases, and changing the subgrade filling level basically has no effect on effectively controlling the statistical mean of the predicted settlement. Figure 21 It is the histogram of the statistical mean of the predicted settlement for different subgrade filling levels of the sand-drained foundation. Under different subgrade filling level conditions, the variation of the statistical mean of the predicted settlement with the observation duration is consistent with that described above. That is, as the observation duration gradually extends, the statistical mean of the predicted settlement gradually decreases, and changing the subgrade filling level basically has no effect on effectively controlling the statistical mean of the predicted settlement.

[0217] Figure 22 and Figure 23They are the time history diagrams of the standard deviation of predicted settlement under different subgrade filling levels for natural foundation and sand-drained foundation respectively. It can be seen from the two diagrams that under different subgrade filling levels, the standard deviation of predicted settlement gradually decreases with the increase of the number of observations, indicating that with the increase of the number of observations, the discreteness of the settlement prediction results gradually decreases, and the prediction results gradually concentrate near the statistical mean of the predicted settlement. Under logarithmic coordinates, the curves of the standard deviation of predicted settlement varying with the number of observations under different subgrade filling levels basically coincide, which shows that the influence of subgrade filling levels on the discreteness of settlement prediction results is the same at different numbers of observations. The standard deviations of predicted settlement under the same number of observations and subgrade filling levels obtained by different foundation treatment forms are different. For the natural foundation, when the number of observations is 30 times, the standard deviations of predicted settlement for subgrade filling levels of 2, 4, 6, and 8 are 2.41, 2.40, 2.39, and 2.37 respectively; when the number of observations is 60 times, the standard deviations of predicted settlement for subgrade filling levels of 2, 4, 6, and 8 are 0.10, 0.10, 0.10, and 0.10 respectively. For the sand-drained foundation, when the number of observations is 30 times, the standard deviations of predicted settlement for subgrade filling levels of 2, 4, 6, and 8 are 21.22, 21.14, 21.03, and 20.82 respectively; when the number of observations is 60 times, the standard deviations of predicted settlement for subgrade filling levels of 2, 4, 6, and 8 are 0.90, 0.90, 0.89, and 0.88 respectively.

[0218] Figure 24 、 Figure 26 They are the distribution diagrams of the predicted results of the natural foundation when the number of observations is 30 times and 60 times respectively. Figure 25 、 Figure 27 They are the distribution diagrams of the predicted results of the sand-drained foundation when the number of observations is 30 times and 60 times respectively. Comparing Figure 24 、 Figure 25 and Figure 26 、 Figure 27 it can be seen that the correlation between the subgrade filling level and the discreteness of the settlement prediction results is not significant. Increasing the subgrade filling level has basically no effect on effectively reducing the distribution range of the subgrade settlement prediction results. The distribution range of the settlement prediction results obtained from the natural foundation is narrower than that obtained from the sand-drained foundation. Comparing Figure 24 、 Figure 26 and Figure 25 、 Figure 27 it can be seen that with the increase of the number of observations, the statistical mean of the predicted settlement gradually approaches zero, indicating that with the increase of the number of observations, the difference between the theoretical value and the predicted value becomes smaller. For the same subgrade filling level, with the increase of the number of observations, the discreteness of the settlement prediction results becomes smaller, and the settlement prediction results are more concentrated near the statistical mean of the predicted settlement, indicating that with the increase of the number of observations, the settlement prediction results are more accurate.

[0219] Example 2

[0220] This embodiment also provides a system for analyzing the confidence level of settlement prediction by integrating the observed data during the filling and loading process, including:

[0221] A first model establishment module 101, which is used to correct and deduce the traditional settlement difference method by using the Weibull model, so as to obtain a settlement prediction model of the corrected settlement difference method;

[0222] A second model establishment module 102, which is used to merge the settlement prediction model of the corrected settlement difference method with the method for analyzing the confidence level of subgrade settlement prediction to construct a subgrade settlement prediction confidence level analysis model that can integrate the observed data during the filling and loading process;

[0223] A confidence level analysis module 103, which is used to compare the confidence levels of multiple settlement prediction models that integrate the observed data during the filling and loading process based on the subgrade settlement prediction confidence level analysis model that can integrate the observed data during the filling and loading process, analyze the influence law of the filling and loading method on the statistical mean and standard deviation of the predicted settlement, and obtain the correlation corresponding to the influence law.

[0224] The present invention also provides a memory storing multiple instructions for implementing the method as in Embodiment 1.

[0225] As Figure 28 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 multiple instructions that can be loaded and executed by the processor, so that the processor can execute the method as in Embodiment 1.

[0226] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A settlement prediction confidence analysis method integrating observation data of filling and loading process, characterized in that: include: S1, the traditional settlement difference method is modified and derived using the Weibull model, thereby obtaining the settlement prediction model of the modified settlement difference method; S2, combining the modified settlement difference method settlement prediction model with the roadbed settlement prediction confidence analysis method to construct a roadbed settlement prediction confidence analysis model that can integrate observation data of the filling and loading process; S3, based on the roadbed settlement prediction confidence analysis model that can integrate the observation data of the filling and loading process, compare the confidence of multiple settlement prediction models that integrate the observation data of the filling and loading process, analyze the influence of the filling and loading method on the statistical mean and standard deviation of the predicted settlement, and obtain the correlation corresponding to the influence law; The S1 includes: S11, determine the average consolidation degree of the foundation under multi-level loading conditions of soft soil roadbed settlement, modify it according to the Weibull model, and determine the basic assumptions of the settlement prediction model of the modified settlement difference method; S12, using Weibull model to modify and derive the traditional settlement difference method, so as to obtain the settlement prediction model of modified settlement difference method; The S12 includes: According to the Weibull model, The instantaneous application of Under the action of the first load increment, it is equivalent to The degree of consolidation of the foundation for the load increment is , ( ) are: （3）； （4）； In , The moment is The roadbed settlement caused by the load increment is: （5）； （6）； Substituting formula (2) into formula (5) and formula (6), we can obtain: （7）； （8）； （9）； in, It represents the consolidation settlement caused by a certain level of load. It represents the total consolidation settlement caused by the total load; If in ~ If the load is constant during the time period, the load increments at all levels applied during the loading period will cause ~ The settlement difference during the time period is: （10）； Where: ——The moment of application of the first load increment; , ——Any time during the dead load period after the load filling is completed, and ; ——Respectively , The cumulative amount of settlement at the time; — the number of graded loads applied during the loading period; ——No. Level load; ——Total load of roadbed filling; - total consolidation settlement due to total load action; ——Model fitting parameters are optimized and solved based on intelligent algorithms.

2. A settlement prediction confidence analysis method integrating observation data of filling and loading process according to claim 1, characterized in that: The basic assumptions include: (1) The consolidation process caused by each load increment occurs independently and has nothing to do with the consolidation degree caused by the previous load increment. (2) The total degree of consolidation is equal to the sum of the degrees of consolidation under each level of load increment; (3) Loading period: The load increment of the uniform loading is The degree of consolidation caused by applying the load increment instantaneously is equivalent; (4) Average degree of consolidation It is defined according to the settlement deformation, that is, ;in, Indicates the current settlement deformation, Indicates the maximum settlement deformation; (5) The degree of consolidation under each level of load is corrected according to the load ratio and superimposed to obtain the average degree of consolidation under the total load, as shown in formula (1): （1）； Where: For the Degree of consolidation under graded load; For the Level load; is the total load of the roadbed filling; is the total number of levels loaded for the classification; Based on the basic assumptions (4) and (5), it can be deduced that the consolidation settlement caused by a certain level of load is Total consolidation settlement caused by total load The ratio is equal to the load increment of this level. With total load The ratio is as shown in formula (2): （2）。 3. A settlement prediction confidence analysis method integrating observation data of filling and loading process according to claim 2, characterized in that: The S2 of merging the settlement prediction model of the modified settlement difference method with the roadbed settlement prediction confidence analysis method includes merging the settlement prediction model that can integrate the observation data of the filling and loading process with the roadbed settlement prediction confidence analysis method, and obtaining the roadbed settlement prediction confidence analysis model that can integrate the observation data of the filling and loading process after selecting the optimal model based on the confidence; wherein the settlement prediction model that can integrate the observation data of the filling and loading process includes the improved hyperbolic method, the traditional settlement difference method and the modified settlement difference method.

4. A settlement prediction confidence analysis method integrating observation data of filling and loading process according to claim 3, characterized in that: The roadbed settlement prediction confidence analysis method of S2 includes: generating simulated settlement observation data, wherein the simulated settlement observation data is generated based on discrete observation data with observation errors; Performing a confidence analysis on roadbed settlement prediction based on the simulated settlement observation data; Wherein, generating simulated settlement observation data comprises: Aiming at the settlement problem of soft soil roadbed under different foundation treatment forms and different soil conditions, the corresponding theoretical continuous data under different conditions are calculated based on the homogeneous natural foundation calculation model and the sand well foundation calculation model; The theoretical continuous data is divided into observation period data and late settlement data according to different first settlement observation conditions, and the observation period data is discretized according to the second settlement observation condition to obtain discrete data; superimposing observation errors on discrete observation period data according to different third settlement observation conditions to generate discrete observation data with the observation errors, and generating the simulated settlement observation data based on the discrete observation data with the observation errors; Among them, the first settlement observation condition is the observation duration; the second settlement observation condition is the observation frequency; the third settlement observation condition is the observation accuracy; The confidence analysis of roadbed settlement prediction based on the simulated settlement observation data includes: Based on the discrete observation data with the observation error, a curve fitting method is used to predict and obtain the prediction value corresponding to each group of observation data; the above process is repeated to obtain multiple prediction values ​​to form a prediction result; The prediction results are statistically analyzed to obtain the predicted settlement statistical mean and predicted settlement standard deviation.

5. A settlement prediction confidence analysis method integrating observation data of filling and loading process according to any one of claims 1 to 4, characterized in that: The S3, based on the roadbed settlement prediction confidence analysis model that can integrate the observation data of the filling and loading process, compares the confidence of multiple settlement prediction models that integrate the observation data of the filling and loading process, analyzes the influence of the filling and loading method on the statistical mean and standard deviation of the predicted settlement, and obtains the correlation corresponding to the influence law, including: The statistical mean calculation formulas for predicted settlement of homogeneous natural foundation and homogeneous sand well foundation obtained by integrating the observation data of filling and loading process by the modified settlement difference method are shown in equations (16) and (17): Natural foundation: （16）； Sand well foundation: （17）； The curve of the change of the predicted settlement standard deviation with the observation time is described by formula (18): （18）； Where: ——standard deviation of predicted settlement (mm); —— observation time (days); ——Model fitting parameters; The model fitting parameters of the fitting curve obtained by formula (18) for natural foundation and sand well foundation are The relationship between the change of the roadbed filling time and the fitting curve can be determined by formula (19) and formula (20): （19）； （20）； Where: ——Duration of roadbed filling (days); The calculation formulas for the predicted settlement standard deviation of homogeneous natural foundation and homogeneous sand well foundation obtained by integrating the observation data of the filling and loading process with the modified settlement difference method are shown in formulas (21) and (22): Natural foundation: （21）； Sand well foundation: （22）； And, determine the influence of roadbed filling levels on the statistical mean and standard deviation of predicted settlement.

6. A settlement prediction confidence analysis system integrating observation data of filling and loading process, used to implement the method described in any one of claims 1 to 5, characterized in that: include: A first model building module (101) is used to modify and derive the traditional differential settlement method using a Weibull model, thereby obtaining a modified differential settlement method settlement prediction model; A second model building module (102) is used to combine the modified settlement difference method settlement prediction model with the roadbed settlement prediction confidence analysis method to construct a roadbed settlement prediction confidence analysis model that can integrate observation data of the filling and loading process; The confidence analysis module (103) is used to compare the confidences of multiple settlement prediction models integrating the observation data of the filling and loading process based on the roadbed settlement prediction confidence analysis model that can integrate the observation data of the filling and loading process, analyze the influence of the filling and loading method on the statistical mean and standard deviation of the predicted settlement, and obtain the correlation corresponding to the influence law.

7. 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 5.

8. 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 5.

Images